DEPARTMENT OF MANAGEMENT PONDICHERRY ...14.139.183.117/jspui/bitstream/1/2173/1/T5744.pdfother...

Healthcare Service Quality, Patient Satisfaction and

Behavioural Intentions in Selected Corporate Hospitals

in India

Ph.D. THESIS

Submitted in partial fulfilment of the requirements for the award of the degree of

DOCTOR OF PHILOSOPHY

in MANAGEMENT

By

RAMA KRISHNA NAIK JANDAVATH

Doctoral Research Scholar

Under the Supervision of

Dr. BYRAM ANAND

B.Tech., M.B.A., Ph.D.

Assistant Professor

DEPARTMENT OF MANAGEMENT

PONDICHERRY UNIVERSITY

KARAIKAL CAMPUS, KARAIKAL – 609605

OCTOBER - 2014

CERTIFICATE

This is to certify that the thesis entitled, Healthcare Service Quality, Patient

Satisfaction and Behavioural Intentions in Selected Corporate Hospitals in India,

submitted to the Pondicherry University, in partial fulfilment of the requirements for the

award of the Degree of Doctor of Philosophy in Management, is a record of original

research work done by Mr. Rama Krishna Naik Jandavath during the period 2011 -

2014 (Full-Time) at the Department of Management, School of Management of

Pondicherry University - Karaikal Campus, under my supervision and guidance. The

thesis has not formed the basis for the award of any Degree/Associateship/Fellowship or

other similar title of any candidate of any University.

Dr. BYRAM ANAND, B.Tech., MBA., Ph.D. Assistant Professor & Research Supervisor Department of Management Pondicherry University Karaikal Campus, KARAIKAL – 609 605

(O) 04368-230209 Cell: +91 9443610064

E-mail: [email protected]

Res: 3/3, Annaisivamai pathy Illam, 5th cross street, Nehrunagar extension, Nehrunagar, Karaikal, Pondicherry (U.T)

Dr. BYRAM ANAND

Research Supervisor

DECLARATION

I, hereby, declare that the thesis entitled, Healthcare Service Quality, Patient

Satisfaction and Behavioural Intentions in Selected Corporate Hospitals in India,

submitted in partial fulfilment for the award of the degree of Doctor of Philosophy in

Management to Pondicherry University is the original work carried out by me under the

supervision of Dr. Byram Anand, Assistant Professor, Department of Management,

Pondicherry University - Karaikal Campus, and the same has not previously formed the

basis for the award of any degree, diploma, associateship, fellowship or any similar title

of recognition.

I do, further, declare that the text, figures or any other material taken from other sources

(including but not limited to books, journals and web) have been acknowledged, referred

and cited to the best of my knowledge and understanding.

Place: Karaikal

Date: (RAMA KRISHNA NAIK. JANDAVATH)

ABSTRACT

Efficient functioning of service providing organisations highly depends on quality of

their services as it contributes to companies‟ competitiveness and customer‟s satisfaction.

Thus, service quality management should be an integral part of service organisations

performance. The service industry accounts for an ever-growing share of the global

economy, and service aspects have become increasingly important for service delivery.

Since service expectations play a key role in the quality perceptions that consumers

ultimately develop, it is important for service marketers to understand the nature of

consumer expectations and the influences upon these expectations.

Owing to the scarcity of research on analysing the attempt of service quality in

corporate hospitals, and also need for a greater conceptual understanding the effect of

healthcare service quality and satisfaction on their patients intentions. The study aims to

examine the healthcare service quality by using SERVQUAL, which is customised for

healthcare services and to investigate the key determinants of patient satisfaction.

Furthermore, this research proposes to test the relationships between healthcare service

quality, patient satisfaction and behavioural intentions in corporate hospitals in India.

This research contributes to increasing academic understanding and improving corporate

healthcare marketer‟s ability to manage the patient‟s expectation.

Extending research on service quality, this study developed and tested a model of

healthcare service quality, patient satisfaction and behavioural intentions. The proposed

research model integrated three key constructs from the service quality research stream in

to the theoretical frame work of the SERVQUAL (Parasuraman et al., 1988),

Determinants and Components theory of Patient Satisfaction (Ware et al., 1983) and

other theories from social psychology, such as the theory of reasoned action (TRA;

Fishbein & Ajzen, 1975), theory of planned behaviour (TPB; Ajzen, 1991).

The data was collected from the patients who were admitted in the four corporate

hospitals (Apollo Hospitals, Care Hospitals, Fortis Healthcare Limited and Manipal

Group of Hospitals) selected from different metro-cities in India, which provide super-

specialty services such as surgical care for cardiovascular, neurological, urinary,

respiratory and orthopedic diseases. A total of 493 usable questionnaires were analysed

using SERVQUAL gaps model and structural equation modelling with Analysis of

Moment Structures (AMOS) software.

The results showed considerable support for the hypothesised research model.

The results confirm that the five dimensions of expected and perceived healthcare service

quality - tangibility, reliability, empathy, assurance and responsiveness are distinct

construct for corporate hospital service quality. Healthcare service quality has a patient

satisfaction and behavioural intentions. In the same line, results also confirm that six key

determinants of patient satisfaction, namely; admission process, medical services, nursing

services, housekeeping services, food services and overall service experience. Each

dimension has a significant relationship with patient satisfaction. The findings of this

study indicate that the establishment of higher levels of healthcare service quality will

lead patients to have a high level of satisfaction and behavioural intention.

The results of this study indicate that an understanding of the effect of healthcare

service quality dimensions in satisfaction and behavioural intentions is important to

hospital marketing managers because it offers them the opportunity to take certain actions

for improving patient‟s satisfaction and increase their intention to use positive word-of-

mouth and revisit or recommend to others.

*******

ACKNOWLEDGEMENTS

I would like to take this opportunity to thank all those people who contributed to and

made it possible for me to complete this dissertation.

First, I express my deep sense of gratitude and profound respect to my supervisor

Dr. Byram Anand, Assistant Professor, Department of Management, Pondicherry

University -Karaikal Campus, Karaikal, for the effort, great patience and immense care

he has taken during the entire process of my research work. I acknowledge his valuable

help and significant contribution from the very beginning of shaping the title of the study,

to completing the procedural formalities of the dissertation submission. He has been

continuously helping and encouraging me at all the stages of this study for ensuring

quality and perfection. I am indebted to his guidance, support and encouragement

throughout this study period.

I also extend my sincere appreciation to Doctoral Committee members, Dr. R.

Venkatesa Kumar and Dr. S.A. Senthil Kumar, for their constant encouragement at

every stage of the research and their valuable feedback in several areas leading to the

improvement of the quality of the dissertation.

My sincere thanks to Prof. R. Prabhakara Raya, Dean, School of Management

Pondicherry University and Prof. Lalitha Ramakrishnan, Head, Department of

Management, Pondicherry University - Karaikal Campus, for their support and help in

completing my research work successfully.

I thank Dr. C. Madhavaiah, Assistant Professor, Department of Management,

Pondicherry University - Karaikal Campus, for his unstinted encouragement, professional

and insightful contribution whenever needed throughout the period of this research. He

willingly gave a lot of his time for discussing all aspects of the study. Further, I wish to

thank Dr. D.H. Malini, Dr. M. Dharmalingam, Dr. K. Lavanya Latha and Dr. R.

Vishnu Vardhan, for their continuous support and encouragement throughout my

educational career at the Pondicherry University, Karaikal Campus.

I wish to express my gratitude to Prof. C.S.G. Krishnamacharyulu, Director,

RVS Institute of Management Studies and Computer Application, Karaikal and Dr. P.

Varalaxmi, Associate Professor, Kakatiya University, Warangal, for their expertise and

views during the various phases of this study.

I offer my sincere thanks to all the teaching and non-teaching staff of Pondicherry

University- Karaikal Campus for their immense help and cooperation meted out to me

during the last three years.

I also express my gratitude to University Grants Commission (UGC) - New

Delhi, for granting me the Rajiv Gandhi National Fellowship (RGNF), as this doctoral

thesis has been produced during my scholarship period at Pondicherry University.

I am very fortunate to have great loving parents. I am deeply indebted to my

affectionate mother Tirupathi Bai and father Balu Naik, for fostering the flame of

learning; ultimately, their efforts culminated in this dissertation. I thank my parents for

their love and support throughout my life. I express my special gratitude to them for

encouraging me to join Ph.D, without them this dissertation would have remained

unwritten. I must express the deepest appreciation to my brother Shankar, sisters

Saraswathi, Bharathi, Rama Devi, fiancée Savithri and brothers-in-law Ravi, Srinu Naik,

for their continuous encouragement, understanding and unlimited support throughout my

studies.

I am very much indebted to my dearest friend Irfan Bashir for his encouragement,

timely help and valuable support during the course of my Ph.D work. I unfailingly

remember him for his friendship, affectionate feeling and keen interest in my work. I am

equally thankful to my friends Kishore Kumar, Satyanarayana Rentala, Shashi Kiran,

Majid Shaban, Irfan Shafi, Rama Devi, Hezekiah, Prabhakar, Precy Raju and all other my

fellow scholars, for their company and wonderful time we shared together in the Karaikal

Campus of Pondicherry University. I express my gratitude to my dear friends Narasimha

Rao, Daniel and Priyanka Misra for their valuable help in the collection of data.

Place: Karaikal (RAMA KRISHNA NAIK. JANDAVATH)

LIST OF TABLES

Table No. Content Page

Table 1.1

India healthcare statistics vs. world (per 10,000 population)

9

Table 1.2 Cost comparison among leading destinations 13

Table 1.3 Health indicators 15

Table 2.1 Service quality definitions 26

Table 2.2 Service quality dimensions 27

Table 2.3 Criticisms on SERVQUAL 31

Table 2.4 Application of SERVQUAL 32

Table 2.5 Summary of the healthcare service quality 41

Table 2.6 Summary of studies using SERVQUAL scale for measure

healthcare service quality

46

Table 2.7 Summary of patient satisfaction studies 53

Table 2.8 Summary of studies on determinants of patient satisfaction 58

Table 2.9 Literature linking service quality, value, satisfaction and intentions

to various service encounter outcomes

66

Table 3.1 Secondary data collection sources 72

Table 3.2 Construct items of reliability 76

Table 3.3 Construct items of responsiveness 77

Table 3.4 Construct items of assurance 78

Table 3.5 Construct items of empathy 79

Table 3.6 Construct items of tangibles 80

Table 3.7 Construct items of healthcare service quality (HSQ) 81

Table 3.8 Construct items of admission process 83

Table 3.9 Construct items of medical services 83

Table 3.10 Construct items of nursing care services 84

Table 3.11 Construct items of housekeeping services 85

Table 3.12 Construct items of food services 86

Table 3.13 Construct items of overall service experience 87

Table 3.14 Construct items of patient satisfaction 88

Table 3.15 Construct items of behavioural intentions 89

Table 3.16 Population characteristics 99

Table 3.18 Sample selection of respondents 100

Table 3.19 Goodness-of-fit statistics in SEM 109

Table 3.20 Measurement model estimates 109

Table 3.21 Summary of statistics 110

Table 4.1 Questionnaire distribution and response rate 113

Table 4.2 Demographic characteristic of participants 114

Table 4.3 Construct total descriptive statistics for perceived service quality 117

Table 4.4 Construct total descriptive statistics for expected service quality 119

Table 4.5 Construct total descriptive statistics for patient satisfaction 121

Table 4.6 Construct total descriptive statistics for behavioural intentions 122

Table No. Content Page

Table 4.7

Means of expectations, perceptions, and gap scores

124

Table 4.8 Standard deviation of expectations, perceptions, and gap scores 126

Table 4.9 Survey items most or least contribution to tertiary care service

delivery (patient level of importance based on mean scores).

128

Table 4.10 Survey items most or least contribution to tertiary care service

delivery (patient level of importance based on S.D. scores).

129

Table 4.11 Construct KMO and Bartlett's Test of Sphericity values 133

Table 4.12 Healthcare service quality communalities 135

Table 4.13 Patient satisfaction and behavioural intention communalities 136

Table 4.14 Exploratory factor analysis of expected healthcare service quality 139

Table 4.15 Exploratory factor analysis of perceived healthcare service quality 140

Table 4.16 Exploratory factor analysis of patient satisfaction 142

Table 4.17 Exploratory factor analysis of behavioural intentions 144

Table 4.18 Total number of factors extracted and total variance explained in

EFA model

145

Table 4.19 Pearson‟s bivariate correlations between latent factors/Constructs 147

Table 4.20 Tests of normality 148

Table 4.21 Test of homogeneity of variances 149

Table 4.22 Multi-Collinearity Coefficients for latent factors 150

Table 4.23 Goodness of fit statistics for the Initial CFA of SQ, PS and BI model 154

Table 4.24 Goodness of fit statistics of revised CFA model of SQ, PS and BI 156

Table 4.25 Construct reliability statistics of SQ, PS and BI model 158

Table 4.26 Convergent validity of SQ, PS and BI model 159

Table 4.27 Inter-construct correlations of SQ, PS and BI model 161

Table 4.28 Discriminant validity of SQ, PS and BI model 162

Table 4.29 Structural model fit measure assessment 164

Table 4.30 Hypotheses testing / paths causal relationships 164

Table 4.31 Regression estimates of latent constructs 165

Table 4.32 Hypotheses testing 166

Table 4.33 Goodness of fit statistics for the Initial CFA of determinants of

patient satisfaction

174

Table 4.34 Revised measurement model of determinants of patient satisfaction

fit analysis

176

Table 4.35 Construct reliability statistics of determinants of patient satisfaction 177

Table 4.36 Inter-construct correlations of determinants of patient satisfaction 178

Table 4.37 Discriminant validity of determinants of patient satisfaction 178

Table 4.38 Convergent validity of determinants of patient satisfaction 179

Table 4.39 Structural model fit assessment of determinants of patient

satisfaction

181

Table 4.40 Hypotheses testing / paths causal relationships 182

Table 4.41 Regression estimates of latent constructs 182

Table 4.42 Hypotheses testing of determinants of patient satisfaction 183

LIST OF FIGURES

Figure No. Content Page

Figure 1.1 Indian healthcare industry growth rate 5

Figure 1.2 Spending as a % of GDP 6

Figure 1.3 Healthcare industry composition 7

Figure 1.4 Comparison of healthcare spend 8

Figure 3.1 Research design 70

Figure 3.2 Research model 74

Figure 4.1 SERVQUAL dimension weights 130

Figure 4.2 SERVQUAL dimension weights 131

Figure 4.3 Standard deviation SERVQUAL score for corporate hospital

services

131

Figure 4.4 Mean SERVQUAL score for corporate hospital services 132

Figure 4.5 Scree plot 154

Figure 4.6 Hypothesised CFA model derived from EFA of SQ, PS and BI 153

Figure 4.7 Final CFA model of SQ, PS and BI 155

Figure 4.8 Structural model 163

Figure 4.9 Hypothesised CFA model derived from EFA of determinants of

patient satisfaction

173

Figure 4.10 Final CFA model of determinants of patient satisfaction 175

Figure 4.11 Determinants of patient satisfaction structural equation model 180

ABBREVIATIONS

Abbreviation Expansion

AGFI Adjusted Goodness of Fit Index

AMOS Analysis of Moment Structures

ANM Axillary Nurse Midwifery

ASSOCHAM The Associated Chambers of Commerce and Industry of India

AVE Average Variance Extracted

CARE Credit Analysis & Research Limited

CBHI Central Bureau of Health Intelligence – India

CCI Corporate Catalyst India

CFA Confirmatory Factor Analysis

CFI Confirmatory Fit Index

CHC Community Health Centre

CI Census of India

CII Confederation of Indian Industry

CR Critical Ratio

GDP Gross Domestic Product

GFI Goodness of Fit Index

GNM General Nurse Midwifery

GTI Grant Thornton-India

IBEF India Brand Equity Foundation

ICC Indian Chamber of Commerce

IIHFW Indian Institute of Health and Family Welfare

ILO India Law Offices

ISO International Organisation for Standardization

JACHO The Joint Commission on Accreditation of Healthcare Organisations

JCI Joint Council of India

MBNQA Malcolm Baldrige National Quality Award

MCI Medical Council of India

MoHFW Ministry of Health and Family Welfare

MSPI Ministry of Statistic and Programme Implementation

NABH National Accreditation Board for Hospitals and Healthcare

Organisations

NFI Normative Fit Index

NHP National Health Policy

NIHFW National Institute of Health and Family Welfare

NRHM National Rural Health Mission

NSDC National Skill Development Corporation

PHC Primary Health Centre

PHPI Public Health Foundation of India

Abbreviation Expansion

SEM

Structural Equation Modelling

SERVPERF Service Performance

SERVQUAL Service Quality

SMC State Medical Council

SPSS Statistical Packages for the Social Sciences

SRS Sample Registration System

TPB Theory of Planned Behavior

TRA Theory of Reasoned Action

TQM Total Quality Management

WHO World Health Organisation

CONTENTS

Declaration i

Abstract ii

Acknowledgements iv

List of Tables vi

List of Figures viii

Abbreviations ix

Chapter - 1: Introduction

1.1. Background of the Study 1

1.1.1. Indian Healthcare Industry Overview 4

1.1.2. Composition of the Indian Healthcare Sector 6

1.1.3. Healthcare Industry Trends, Challenges and Opportunities 9

1.1.4. Healthcare Indicators 14

1.2. Statement of the Problem 16

1.3. Objectives of the Study 19

1.4. Significance and Research Contribution 19

1.5. Research Methodology Used in the Study 21

1.6. Organisation of the Thesis 22

Chapter - 2: Literature Review

2.1. Service Quality 24

2.1.1. Defining Quality 24

2.1.2. Service Quality 25

2.1.3. Dimensions of Service Quality 26

2.1.4. SERVQUAL: Development, Applications and Criticism 28

2.1.5. SERVQUAL Applications in Healthcare 32

2.2. Healthcare Service Quality 36

2.2.1. Defining Healthcare Service Quality 36

2.2.2. Dimensions of Healthcare Service Quality 38

2.2.3. Measurement of Healthcare Service Quality 42

2.3. Patient Satisfaction 48

2.3.1. Definition of Patient Satisfaction 48

2.3.2. Satisfaction in Healthcare Industry 49

2.3.3. Determinants Patient Satisfaction 55

2.4. Relationship between Healthcare Service quality and Patient Satisfaction 59

2.5. Behavioral Intentions 61

2.6. Relationship between Service quality, Patient Satisfaction and Behavioral

Intentions

64

2.7. Problem Statement and Research Gap 67

Chapter - 3: Research Methodology

3.1. Research Design 69

3.2. Data Source 71

3.2.1. Primary Data

3.2.2. Secondary Data

71

71

3.3. Research Objectives 72

3.4. Research Model 73

3.5. Operationalisation of Variables and Hypotheses Setting 75

3.6. Development of Research Instrument 92

3.6.1. Reasons for Choosing a Questionnaire 92

3.6.2. Questionnaire Format 93

3.6.3. Scaling Technique 95

3.6.4. Questionnaire Pre-test 96

3.7. Sampling Design 98

3.7.1. Population 98

3.7.2. Sampling Frame 99

3.7.3. Sampling Method 99

3.7.4. Sampling Size 100

3.8. Data Collection 101

3.9. Reliability and Validity of Research Instrument 101

3.9.1. Reliability 102

3.9.2. Validity 102

3.10. Data Analysis Method 104

3.10.1. Preliminary Data Analysis 105

3.10.2. Normality 106

3.10.3. Factor Analysis 106

3.10.4. Structural Equation Modelling 107

3.11. Conclusion 110

Chapter - 4: Data Analysis and Results

4.1. Response rate and Demographic Characteristics of Respondents 112

4.1.1. Response Rate 112

4.1.2. Demographic Characteristics of Respondents 113

4.2. Descriptive Statistics of Construct Items 116

4.2.1. Perceived Healthcare Service Quality 116

4.2.2. Expected Healthcare Service Quality 118

4.2.3. Patient Satisfaction 119

4.2.4. Behavioural Intentions 122

4.3. SERVQUAL Analysis 123

4.3.1. Gap Scores of SERVQUAL Dimensions 124

4.3.2. Relative Importance of SERVQUAL Dimensions 128

4.4. Exploratory Factor Analysis 133

4.4.1. KMO and Bartlett‟s test of Sphericity 133

4.4.2. Communalities 134

4.4.3. Exploratory Factor Extraction Model 137

4.5. Pearson‟s Correlations between Latent Factors 146

4.6. Normality of Latent Factors 148

4.7. Homogeneity of Variance in the Data 149

4.8. Multi-Collinearity Coefficients for latent factors 150

4.9. Structural Equation Modelling (SEM) for HCSQ, PS and BI 151

4.9.1. SEM for HCSQ, PS and BI 152

4.9.2. Assessment of Reliability and Validity of Constructs 157

4.9.3. Structural Model Evaluation and Hypotheses Testing 163

4.10. Structural Equation Modelling (SEM) for Determinants of Patient

Satisfaction

172

4.10.1. SEM for Determinants of Patient Satisfaction 172

4.10.2. Assessment of Reliability and Validity of Constructs 176

4.10.3. Structural Model Evaluation and Hypotheses Testing 180

4.11. Conclusion 185

Chapter - 5: Discussion and Implications

5.1. Overview of the Study 188

5.2. Discussion of the Major Findings 190

5.2.1. Response Rate 190

5.2.2. Respondents Demographic Characteristics 191

5.2.3. Discussion of Research Constructs 192

5.2.4. Hypothesis Testing 201

5.3. Research Implications 213

5.3.1. Theoretical Implications 213

5.3.2. Implications for Practicing Doctors and Supportive Staff 214

5.3.3. Implications for Management and Providers 216

5.4. Future Research Directions and Limitation of the Study 217

5.5. Conclusion 218

Bibliography

Appendix

1

This research aims to measure the healthcare service quality and investigates the

relationship between healthcare service quality, patient satisfaction and behavioural

intentions in Indian corporate hospitals. This chapter presents an overview of the research

that is to be presented over the remaining four chapters. This chapter is divided into six

sub-sections. Section 1.1 provides research background of the study. This section

describes the promising factors responsible for the growth of healthcare sector,

opportunities and challenges of Indian private healthcare industry. It also depicts nature

and indicators of Indian healthcare sector. Section 1.2 describes research problem of the

study. Section 1.3 presents objectives of the study. Section 1.4 provides a justification

and significance of the research work. Section 1.5 presents methodology used in the

research work. Section 1.6 provides structure of the thesis.

1.1. Background of the Study

According to the data available from the World Bank statistics (The World Bank Group,

2013), the service industry presents a significant part of the World Economy that

accounted for around 70 per cent of GDP in the World in 2012 (The World Bank Group,

2013). Hence, current studies could be directed to investigate the main issues in terms of

service industries. One of the main dimensions in terms of an efficient service

organizations performance is considered to be service quality as quality is vital for

market competition, brand name and consumer‟s satisfaction (Gill, 2009).

Fisk et al., (1993) stated that, in early 1970‟s that services were identified as

having adequately different characteristics to the physical products to require separate

approach to marketing. There are four characteristics mainly cited as the factors that

distinguish from services to goods; intangibility, inseparability of production and

INTRODUCTION

CHAPTER–1

2

consumption, heterogeneity and perishability (Berry, 1990; Lovelock, 1992; Bateson,

1995).

The importance of quality management in manufacturing companies has been

identified since 1930s (Fynes, 1998). Quality orientation is one of the main priorities of

any progressive organization to improve profitability (Phillips et al., 1983), market share

(Buzzell and Gale, 1987), investment returns and cost reduction (Deming, 1986;

Anderson and Zeithaml, 1984 and Parasuraman et al., 1985, 1988). Parallel with the

profound understanding the concept of quality management in manufacturing industry,

quality control activities were spread to other industries such as education, public

administration and hospitals.

Over the past few decades, there has been an increasing interest in healthcare

services, as standards and lifestyle of living have changed and there is a demand for

better medical care and eagerness to take responsibility for their own health. Providing

the high quality medical care services have become major challenge for hospitals in

respect of satisfying and retaining patients (Oliver, 1980; Parasuraman et al., 1985, 1988;

Zeithaml et al., 1996; Padma et al., 2010, and Amin et al., 2013) Thus, in order to

provide better service to patients, measuring service quality and its determinants has

become increasingly. Although service quality and patient satisfaction are related, there

are other antecedents to patient satisfaction, namely, price, condition, and availability of

the services (Natalisa and Subroto, 1998), Service quality receives special attention from

the healthcare service marketers because it is within the control of the healthcare

provider, and by improving service quality, its consequent satisfaction could be

improved, which create favourable patient intentions to revisit and recommend the

service to others. Thus, delivering quality service is pivotal to drive satisfaction.

Quality of health care is one of the most important topics in the health service

sector today. Improving and even maintaining the quality of care while reducing costs is

a critical dilemma that all healthcare administrators face. The definition, measurement,

and improvement of quality in health care have been issues of primary importance. With

pressure to increase access while curtailing costs, competitive healthcare institutions try

hard to achieve goals without letting the quality suffer. In this context, Donabedian

(1996) remarked that healthcare organisations should focus on multifaceted dimensions

3

and satisfy the needs, interests, and demands of three principal groups: those who provide

the services (i.e., the healthcare professionals), those who manage the services (i.e.,

management), and those who use the services i.e., patients (Camilleri and Callaghan,

1998). Positive patient perceptions of service quality result in patient satisfaction, patient

loyalty, and hospital profitability and the relationship between patient loyalty and

frequency of patient visits (Ladhari and Riadh, 2009) leads to profitability as it propels

patients to choose the same hospital again (Sardana, 2003). However, sometimes there

may exist a situation where the patient seeks treatment at a specific hospital by healthcare

staff even when they have not been satisfied such dissatisfied consumers (i.e., spurious

loyal) may remain attached with the hospital primarily because of higher switching costs.

These switching costs may be financial (such as extra charges to use private or

specialized hospital services) or emotional (such as relationships with doctors) in nature.

However, such experiences reduce their overall satisfaction.

Healthcare is one of the fastest growing service sub-sectors in India. Hospitals

like their counterparts have to deal with several service product characteristics such as

intangibility, heterogeneity, inseparability and perishability. Moreover, high risks exist

for the hospitals whilst offering their services in a highly competitive environment

dealing with human health, which involves sensitive decision making and extensive

service provision in comparison to other services. Competitive environment pushes

service providers to understand in-patient needs and expectations and to provide a value

added service quality, far superior to other organizations. Service quality, therefore, has

become the focus of considerable attention in respect of satisfying and retaining

customers in the service industry (Zeithaml et al., 1996; Caruana, 2002). Recent studies

says that service quality measurement can be used to understand how well a healthcare

service organization, i.e. a hospital, has functioned in terms of outcomes like service

quality over several years (Parasuraman et al., 1985, 1988; Youssef et al., 1996; Zeithaml

et al., 1996; Carman et al., 2000; Padma et al., 2009; Amin et al., 2013).

It is acknowledged that statistically significant link exists between service quality,

inpatient satisfaction and loyalty (Pakdil and Harwood, 2005; Padma et al., 2009). It has

also been claimed that if hospital service quality improves, the number of satisfied

inpatients also increases and consequently, loyalty increases in such a way that these

4

inpatients may play an active role in the positive “word of mouth” (Chahal and Kumari,

2010; Gaur et al., 2011; Kessler and Mylod, 2011; Amin et al., 2013) business and may

exert re-purchase intention and thus reduce organizational costs. Therefore, it has become

ubiquitous for service providers to seek out competitive advantages by providing superior

service. Thus, in order to better understand patient satisfaction and their intentions, this

study intends to extend the research on “healthcare service quality, patient satisfaction

and behavioural intentions in selected corporate hospitals in India” in the context of a

developing economy like India.

1.1.1. Indian Healthcare Industry Overview

Healthcare is one of the fastest growing service sectors in India. The rapid growth in

Indian economy and population has brought about a „health transition‟ in terms of

shifting demographics, socio-economic transformations and changes in disease patterns.

Despite the population growth and consistently developing economy, the expenditure in

Indian healthcare is still amongst the lowest globally and there are significant challenges

to be addressed both in terms of accessibility of healthcare service and quality of patient

care. Indian healthcare industry, which comprises hospital and allied sectors, is projected

to grow 23 per cent per annum to touch US$ 155 billion by 2017 from the current

estimated size of US $ 65 billion, according to a Yes Bank and ASSOCHAM report. The

sector has registered a growth of 9.3 per cent between 2000-2012, comparing to growth

rate of other emerging economies such as China, Brazil and Mexico. According to the

report, the growth in the sector would be driven by healthcare facilities, private and

public sector, medical diagnostic and path-labs and the medical insurance sector.

Healthcare facilities, inclusive of public and private hospitals, the core sector,

around which the healthcare sector is centered, contributed over 70 per cent of the total

sector and touch a figure of US$ 54.7 billion in 2012. A FICCI-Ernst & Young report

adds that India needs an investment of US$ 14.4 billion in the healthcare sector by 2025,

to increase its bed density to at least two per thousand population. According to a latest

report by McKinsey (2013), Indian healthcare market is expected to reach previously

projected rates of 10 to 12 per cent. With average household consumption expected to

increase by more than seven per cent per annum, the annual healthcare expenditure is

5

projected to grow at 10 per cent and also the number of insured is likely to jump from

100 million to 220 million.

Source: MEGStrat Research Consulting Firm, Gurgaon, India.

Figure 1.1 Indian Healthcare Industry Growth Rate

The Indian healthcare industry, valued at $55.0 billion in 2011, is highly

fragmented and dominated by private players. The sector is expected to grow at 24.1 per

cent per anum by 2020, driven by large investments from existing corporate hospital

chains and new entrants backed by private equity investors. Healthcare expenditure in

India being lowest among the global countries, offers tremendous scope and opportunity

to industry. From the Figure 1.1, it is obvious that industry has shown robust growth and

witnessed a phenomenal expansion in the last few years growing at over 30 per cent per

annum in 2012. It also shows that healthcare industry has estimated revenue of around

$155 billion by 2017 and $280 billion constituting of 6-7 per cent of GDP by 2020.

Despite significant growth Indian healthcare industry has considerable challenges

that exist in terms of service accessibility and patient care quality; Government support

would inherently play a significant role in the overall development and growth of the

sector. Demand for sophisticated healthcare services is poised to grow exponentially

owing to the incidence of lifestyle diseases, rising incomes, affordability, and increased

penetration of health insurance. There exist huge and enormous demands of Indian

healthcare, opportunities to invest, regulatory support for R&D, low cost and affordable

quality services as a favoured destination to neighbouring countries.

23 34 38 41 46 50 65

155

280

0

50

100

150

200

250

300

2005 2006 2007 2008 2009 2010 2012 2017E 2020E

USD Billion

6

Source: WHO World Health Statistics 2010

Figure 1.2 Spending as a per cent of GDP

The Indian healthcare is spending less than half the global average in percentage

terms when compared on a “per cent of GDP” basis. As per the world health statistics,

India spends only 4.1 per cent of its total GDP, and occupies a bottom position in

comparison to other countries; on the other hand USA occupies top position in terms of

health expenditure.

1.1.2. Composition of Indian Healthcare Sector

The Indian healthcare sector is highly fragmented and dominated by private players. The

industry has witnessed tremendous growth over the last few decades across the entire

value chain as demonstrated by strong growth in its various sub-segments that include:

hospital industry, pharmaceutical industry, diagnostic industry, medical equipment

industry and medical insurance industry. Indian hospitals are exploring various

innovative models to improve their performance and profitability, viz. introducing

telemedicine, focusing on specialty centres and day care centres. The hospital sector has

attracted several private equity players, who have been playing a significant role in

various strategies of Indian hospitals, including organic & inorganic growth. At present,

chains of diagnostic centres, chains of single-specialty hospitals (such as eye or dental

clinics), and chains of multi-specialty hospitals (Apollo Hospital Group) are all

witnessing significant growth opportunities in Indian Healthcare.

4.3

8.4

4.1

15.7

8.4 9.7

0

5

10

15

20

Chaina Brazil India USA UK Global

%

7

Source: Indian Brand Equity Foundation (IBEF) – Industry Report on Healthcare (2013)

Figure 1.3 Healthcare Sector Compositions

There has been a steady growth in the corporate hospitals throughout India since

the 1970s, which are significant for the world to recognise its dominant role in providing

the best corporate hospital services to meet the demand for healthcare in India. The

driving forces behind this emergence of corporate hospitals are plenty, namely the lack of

financial and physical resources in the public healthcare sector, the rising demand for

healthcare from domestic patients, the demand of the international patients and finally,

the economic growth of India. These driving forces are briefly discussed below.

a. Spending on Healthcare

The healthcare spend, when compared on the basis of public-private contribution, also

depicts a skewed picture. As is noted from the comparison below, Private Sector

contribution to the healthcare sector at 75 per cent is amongst the highest in the world in

percentage terms. Public spending, on the other hand, is amongst the lowest in the world

and is 23 percentage points lower than the global average. In other hand UK shows quite

opposite to Indian healthcare spending, 82 per cent of total expenditure is spent by public

sector and on the other hand, is amongst the lowest in the in the world in terms of private

sector spending and is 18 per cent of lower than global average.

Hospitals Diagnostics Medical Equipment

& Supplies

Pharmaceutical Medical

Infrastructure

Government

Hospitals include

healthcare

centres,

dispensaries,

district hospitals

and general

hospitals

Private Hospitals

Include nursing

homes, mid-tier,

and top-tier

private hospitals

Manufacture,

extraction,

processing,

purification, and

packaging of

chemical

materials

Businesses and

laboratories that

offer analytic or

diagnostic

services

including body

fluid analysis

Manufacturing medical

equipment and

supplies, such as surgical, dental,

orthopaedic,

ophthalmologic, and laboratory

instruments

It covers an

individual‟s

hospitalization

expenses and

medical

reimbursement

facility incurred

due to sickness

8

Figure 1.4 Comparisons on Spending Healthcare

Healthcare is emerging as one of the fast-growing service sectors in India,

contributing 3.9per cent to the country‟s growth domestic product (GDP). Two-thirds of

the expenditure on healthcare is contributed by the private sector, it offers huge growth

opportunity for corporate hospitals and healthcare providers.

The government is also treating healthcare as a priority sector. To encourage the

private sector to establish hospitals in tier-2 and tier-3 cities, the government has relaxed

the taxes on these hospitals for the first five years. The increased penetration of medical

insurance is also helping for the growth of the private sector in healthcare. The insured

population can avail of the high-priced better quality treatment provided by the players in

the sector. The stocks of healthcare companies have performed varyingly on the bourses.

While Fortis Malar Hospital and Fortis Healthcare have out-performed the Sensex over

the last year, Apollo Hospitals has been an under performer till now. Given the growth

potential of the private healthcare sector, it is beneficial for long-term investors to have

exposure in it.

47 44 32

64 61

53 56 68

36 39

0

20

40

60

80

100

120

Chaina Brazil India Russia Global

Private Sector Spending

Public Sector Spending

Source: World Health Statistics, 2011

%

9

1.1.3. Healthcare Sector Trends, Challenges and Opportunities

The Indian private healthcare sector is poised growth in this decade; it is still plagued by

various issues, opportunities and challenges. The major trends in Indian corporate

healthcare are follows:

a. Lack of resources in public healthcare sector

The advantage of the private sector is further established with the massive resource

crunch in the public sector, which led to the underproduction of the sector. The resource

crunch is firstly due to financial constraints, since India has not met the financial

allocation of 5 per cent of the GDP on healthcare as recommended by the Bhore

committee until now (2013). The GDP spent on healthcare dropped to an appalling

proportion of only 4.2 per cent, making it completely insufficient for any adequate

governmental infrastructural support for the public health sector (Berman, 1998). The

financial burden caused the human and physical resources to be overextended, shrinking

the public healthcare system massively. Both constraints led to the erosion of

employment opportunities in the public healthcare sector and the supply of employment

opportunities was not able to meet the uncurbed supply from the production of newly

graduated doctors in the 1980s, forcing them to go into private practice. This led to an

enormous inadequacy of doctors in the public health sector and in addition, the shift of

the doctors allowed the corporate hospitals to boost their own technical expertise, which

further boosted the position of the private sector. The private sector now has the capacity

to build their own specialised hospitals with high quality and costlier services, which

expedited the emergence of the corporate hospitals in the metropolises.

Table 1.1 India Healthcare Statistics vs. World (Per 10,000 Population)

Physicians Nursing Personnel Dentistry Personnel Hospital Beds

India 6 13 0.7 9

Brazil 17 65 12 24

China 14 14 0.4 41

Russia 43 85 3 97

Global 12 28 2 24

Source: World Health Statistics 2011, WHO

10

Thus, the resource crunch in the public sector has created a supply gap, which

multiplied the opportunities for the private sector to step in and become the default

service provider or substitute for the public sector. This explains why almost 85 per cent

of the services are being paid out of the pocket and about 20 per cent of the patients in the

OPD nationwide have indicated that they prefer go to the private hospitals despite higher

out of pocket payments (Bhat, 1999). Therefore, this emergence of corporate hospitals is

really the result of the failings of the public healthcare system, pushing patients away

from the public healthcare system into the corporate hospitals to seek medical treatment

instead.

Therefore, in order to cover up the supply gap, the government consequently has

responded by wooing the private investors to the healthcare sector particularly in the

1980s and 1990s through various mechanisms. The government also supported their

effort with the implementation of the National Health Policy in 1982, stating the need to

open up medical care to „for profit‟ and „non-profit‟ institutions (Bhat, 1999). All these

government efforts complemented the opportunities that are extended to the private

sector from the failings of the public healthcare system, which demonstrates that the

emergence of the corporate hospitals is very dependent on the edge that the government

gives to the corporate hospitals. Thus government lean for the private sector due to the

failings of the public healthcare resource crunch has allowed the corporate hospitals to

contribute to more than 70 per cent of India‟s urban healthcare service market (Mudur,

2003).

b. Rising demand for healthcare from domestic patients:

There is definitely an increase in the supply of corporate hospitals. This must be matched

with the increase in the demand for them and the greater demand stemmed from two

sources: the growing middle class with rising affluence and the changes in the morbidity

patterns.

11

i. Growing middle class:

The rapid economic development in India has brought the most benefits to the middle

class in India, increasing the population size to 120 million people (Bhat, 1999). The

middle class population is becoming more affluent, giving them higher purchasing

power. Consequently, they are able to demand and lobby for corporate hospitals, which

conformed to their perceptions of international standards (Mathiyazhagan, 2003a). Thus,

the middle-income population is significant in the emergence of the corporate hospitals,

especially with the corporate hospitals being fully capable of fulfilling their demands. It

is no wonder that the corporate hospitals find it lucrative to do business in India.

Therefore, the emergence of the corporate hospitals has been proven to be

strongly correlated to the overall socio economic growth of India, which signals that the

rapid economic boom will only serve to fuel its emergence even more by increasing the

pool of middle income families that can afford to pay and invest in these hospitals. This

results in the continued and strong growth of the corporate hospitals in the metropolitan

cities in India.

ii. Changes in morbidity pattern:

With the rapid industrialisation in India, the population continues to boom

uncontrollably, causing more people to demand for quality healthcare. This mounts even

more pressure on the public healthcare sector, worsening the resource crunch and

widening the supply gap for the private sector to step in. This further fuels the growth of

the corporate hospitals in India.

In addition, with the massive urban bias in India‟s economic growth, the urban

centers are expected to flourish. This increases the pull factor to draw out more human

resources from the rural areas to the urban areas, causing the urban population to rise

dramatically. Thus, the urban population will see a more complex epidemiological

pattern and more advanced medical technology would be required to treat those diseases,

especially since the public healthcare sector is expectedly unable to meet their demands.

Thus, the population begins to search for alternative solution in the private hospitals for

better quality healthcare services. Besides the complex epidemiological pattern and

booming urban population, the rise in the occurrence of lifestyle diseases is another factor

12

pushing the demand for the corporate hospitals. The disease like cancer, cardiovascular

diseases and diabetes, are beginning to rise rapidly because of the increasingly latent

lifestyles of the urban middle class, which came with the conveniences that

modernisation and wealth brought for the middle class. Therefore, lifestyle diseases

occupy the center for the agenda of health service provision, placing further emphasis on

individual curative services for the set of diseases that required the promotion of drug and

equipment industry (Qadeer, 2000). This vertical and technical intensive approach to

treatment causes more people to demand for individualised and highly technical services

which can only be available at the corporate hospitals, multiplying the domestic demand

for the corporate hospitals tremendously (Mathiyazhagan, 2007).

iii. Demands from international patients:

Other than the domestic patients, the corporate hospitals also gather the attention of the

international healthcare market for their high quality and low cost healthcare services.

Consequently, the international patients have been flocking to India for treatment for

various reasons and they have been contributing significantly to the emergence of the

corporate hospitals in the metropolises in India. These patients originate from two

sources. The first one is due to the generally weak public and private healthcare systems

in neighbouring Asian countries like Bangladesh, Nepal, Sri Lanka, Pakistan and Bhutan.

These weak healthcare systems are unable to meet the demands of the population in their

countries and this made them look to India for affordable and quality oriented healthcare

services. The second crowd of international patients hails from the Western and

industrialised countries like the US who look towards India to attain affordable medical

care as well as to avoid long waiting queue like the overstretched National Health System

in Britain. Therefore, the corporate hospitals have been able to fill up the supply gap,

providing high quality, low cost medical services which are easily accessible. Therefore,

both groups generate massive demands for the corporate hospitals in India, contributing

to the emergence of the corporate hospitals in the metropolitan cities in India.

13

Table 1.2 Cost comparison among leading destinations

Type of Procedure Treatment Costs ($)

USA UK India Thailand

Bone Marrow Transplant 2, 50 000 1, 50,000 30,000 62000

Open Heart Procedure 50,000 35,000 4400 14250

Knee Surgery 25,000 14,000 4500 7000

Eye Surgery 3100 2700 7000 7300 Source: Indian Brand Equity Foundation (IBEF) – Industry Report on Healthcare (2013)

iv. Economic growth in India:

India is becoming more interconnected with the world through globalisation, it is

inevitable that private players are beginning to gain a stronghold in the medical industry.

The US is one of the pioneers in propagating their interests into India through various

ways like using returning NRI doctors and pharmaceutical and medical industries (Baru,

1998). The first form of influence is the US based NRI doctors and its origins begin from

the relaxed immigration procedures in the 1960s, which led an influx of foreign Indian

doctors into the UK and US looking for better career opportunities (Baru, 1998). This led

to brain drain but as India starts to develop as an economic powerhouse, these NRI

doctors recognise the tremendous opportunities in the private healthcare sector. These

doctors brought back their expertise and knowledge to invest in specialty hospitals in

India, modelled along the lines of the American ones. This meets the demand of the

middle class and helps to strengthen the role of the private sector in this healthcare field.

The Apollo Hospital Group pave the wave of government and private support, seeing that

the group has expanded its network with 41 specialty hospitals and clinics and made

multiple collaborations with other medical institutes worldwide (Balakrishna, 2007).

Therefore the globalisation of healthcare has definitely allowed the corporate hospitals to

strengthen its position, pushing the emergence of these hospitals even more.

Thus, in response to the globalisation of healthcare, both the private sector and the

government have reacted to provide more lean for the private healthcare sector. This is

evident in the way the practitioners can work around the two sectors freely. Therefore,

with the globalisation of healthcare, the government finds it even harder to regulate this

mechanism because of the need to create the most conducive environment for private

investors to invest. This gives the doctors even more power in the healthcare sector,

14

boosting the demands for the corporate hospitals even more. Therefore, it is right to

conclude that the competitive globalised healthcare system of today has placed the Indian

government in a non-negotiable position to open up the healthcare sector to stay

competitive, which inevitably provides even more support for the emergence of the

corporate hospitals in the metropolises in India.

1.3.4. Healthcare Indicators

Despite the improving health status of the Indian population, healthcare infrastructure in

India has a long way to go towards achieving 100 per cent quality, technology and

superior healthcare delivery systems. While the Central Government is limited to family

welfare and disease control programs, the state governments are responsible for primary

and secondary medical care with a limited role in specialty care. Looking at the

healthcare indicators and the growing prevalence of non-communicable lifestyle related

diseases, both the government and private sector, realize the need to meet this basic

demand. Today, the private sector provides 68 per cent of the healthcare service. As per

the Ministry of Health and healthcare research reports, the key indicators related to

economic, demographic, diseases, vital rates, health manpower, infrastructure and market

size of Indian healthcare is provided here in the table below to have an understanding of

the existing healthcare situation in India.

15

Table1.3 Health Indicators

Economic Indicators GDP (in $ billion, 2012) 1872.9

Per Capita (in $, 2012) 1,491

Real Growth (in per cent, 2012–13) 3.986per cent

Health expenditure (in $ billion, 2012) 129.8

Health expenditure as per cent of GDP 4.1per cent

Public expenditure as per cent total 32

Private expenditure as per cent of total 68

Demographic Indicators Population (2011 census) 1210 million

Growth per year (2011 census) 18 million

Average annual growth rate (NHP-2011) 1.76per cent

Sex ratio (2011 census) 940 F per 1000 M

Literates (2011 census)

Males 82per cent

Females 65per cent

Total 74per cent

Demographic profile

(2011 census) 0-4 years 5-14 years 15-44 years 45-59 years 60 years & above

10per cent 23per cent 48per cent 12per cent 7per cent

Vital Rates Birth rate (2010) (SRS Bull. Dec 2011) 22.1/1000 popn; R=23.7, U=18.0

Crude death rate (2010) (SRS Bull. Dec 2011) 7.2/1000 popn; R=7.7, U=5.8

Infant mortality rate (2010) (SRS Bull. Dec 2011) 47/1000 Popn; R=51, U=31

Maternal mortality ratio (2007-09) (NHP-2011) 212/100,000 live births

Expectation of life at birth (2002-06) (NHP-2011) Male 62.6 years

Female 64.2 years

Health manpower and health services Total number of medical colleges (2010-11) (NHP - 2011) 355

Number of students admitted (2009-10) (NHP-2011) 39474

Number of allopathic doctors registered (MCI + SMC)(NHP-2011) 921877

Number of Dental Surgeon registered (NHP-2011) 117825

Number of Nurses registered (ANM,GNM and LHV)(NHP-2011) 18,94,968

Doctor-population ratio (2010) (Provisional) 69:1/1,00,000

Bed population ratio (2010) (Govt. Hospitals including CHCs) 1:2012

Total Number of Hospitals (2011) Public (PHC, CHC & SUB-Centres ) 176820

Number of PHC‟s functioning (NHP- 2011) 23887

Number of CHC‟s functioning (NHP-2011) 4809

Number of Subcentres functioning (NHP-2011) 148124

Sources: Census of India, 2011; Sample Registration System Bulletin – Dec 2011; National Health Profile India

(NHP) – 2010, 2011

In summary, while the Indian private healthcare sector is poised for growth in the next

decade, it is still plagued by various issues, opportunities and challenges. The major

reasons for growth in private healthcare sector in India are: increased in patient‟s

population, increased lifestyle related health issues; faster diagnosis leading to early

treatment; awareness on preventive healthcare disorders; affordable treatment costs;

16

thrust on medical tourism; improved health insurance penetration; medical insurance and

mandatory wellness checks by corporate houses and government initiatives and focus on

Public Private Partnership (PPP) models.

However, India‟s thriving economy is driving urbanisation and creating an

expanding middle class, with more disposable income to spend on healthcare. Healthcare

financing by the public sector is dwarfed by private sector spending, contributing 3.9 per

cent to the country‟s growth domestic product (GDP). As two-third of the expenditure on

healthcare is contributed by the private sector, it offers huge growth opportunity for

corporate hospitals and private healthcare providers. The emergence of India as a

destination for medical tourism leverages the country‟s state-of-the-art private hospitals

and diagnostic facilities, and relatively low cost to address the spiralling healthcare costs

of the western world. India provides best-in-class treatment, in some cases at less than

one-tenth the cost incurred in the western countries. India‟s private hospitals excel in

fields of life style diseases such as cardiology, joint replacement, orthopaedic surgery,

gastroenterology, ophthalmology, transplants and urology. Moreover, this increased

insistence on greater quality of service forces to remain Indian corporate hospitals

competitive.

In view of the above mentioned reasons, it is important for corporate hospitals to

develop full understanding of service quality, patient satisfaction and behavioural

intentions in order to provide high quality services to their patients attract international

patients and improve financial stability in global health market.

1.2. Statement of the Problem

Healthcare is one the fastest growing service sectors in India. Healthcare sector alone has

been growing massively, accounting for almost 5.2 per cent of India's GDP, Today

medical care is a prominent business segment with the private sector being the most

dominant in this segment, accounting for more than 70 per cent of India's urban

healthcare service market. Thus, the resource crunch in the public sector has created a

supply gap, which multiplied the opportunities for the private sector to step in and

become an alternative for the public sector. Therefore, the emergence of corporate

17

hospitals is really the result of the poor performance of the public healthcare system,

pushing patients away from the public healthcare system into the corporate hospitals to

seek medical treatment instead. This effective, efficient and affordable demand of private

healthcare services and their quality dependency factor, which distinguishes corporate

healthcare service provider, is thus a critical reason for its use in this study.

A healthy Indian population, characterized by balanced birth and death rates, and

a low incidence of disease is fundamental to the growth and prosperity of a nation. This

can be achieved if the quality of health care provided to the people is successful in

appropriate management of the disease, and is available to the large majority of the

population at an affordable cost. Thus, quality of patient-care is the basic principle of a

nation‟s health system.

The emergence of India as a destination for medical tourism leverages the

country‟s state-of-the-art private hospitals and diagnostic facilities, and relatively low

cost to address the spiralling healthcare costs of the western world. Moreover, Indian

corporate hospitals provide best-in-class treatment, in some cases at less than one-tenth

the cost incurred in the western counties Due to the emergence opportunities and

demands from international patients, there is a need to increase insistence on greater

quality of healthcare service in order to remain competitive.

Another factor driving the growth of India‟s healthcare sector is a rise in both

infectious and chronic degenerative diseases. The country is experiencing a rise in

lifestyle diseases such as cardiology, joint replacement, orthopaedic surgery,

gastroenterology, ophthalmology, transplants and urology. Over the next 5-10 years,

lifestyle diseases are expected to grow at a faster rate than infectious diseases in India,

and to result in an increase in cost per treatment. Wellness programs, as well as

emergence of new treatments, technologies and quality of health services, could help to

reduce the rising incidence of lifestyle diseases.

Patient expectations and perceptions of healthcare service quality are critical to a

service provider‟s long‐term success because of the significant influence perceptions

have on patient satisfaction and consequently organization financial performance. Patient

18

satisfaction affects not only the outcome of the healthcare process such as patient

compliance with physician advice and treatment, reduced incidence of patient complaints,

service utilization, and survivor of the medical service provider, but also patient retention

and favourable word‐of‐mouth. It therefore follows that patients would be more able to

articulate their expectations in high-involvement services than otherwise.

Today, people are choosing a new approach to healthcare services and are well

informed and eager to take responsibility for their own health. Patients are becoming

more conscious about the quality of healthcare services provided by hospitals. Therefore,

the consumers of healthcare services have exceptionally higher expectations and demand

a high level of accuracy, reliability, responsiveness and empathy from service providers.

An examination of the quality of healthcare services provided in corporate hospitals

could be a good start for an effective management of the patient admission system and

patient-oriented service.

Healthcare quality deficiencies have been highlighted by Hwang et al., (2003) as

a lack of standardized approaches to satisfying patients, lack of an accepted conceptual

model of the patient process and lack of consensus within the medical profession on the

role that patient satisfaction should play in healthcare quality assessment. Health care

organization quality evaluation is a multi-level effort. However, the rapid pace of change

in the healthcare system present challenges for healthcare managers charged with

delivering health services (Rad, 2005). Moreover, there is scarcity of research on

analysing service quality in corporate hospitals, and also need for a greater conceptual

understanding the effect of healthcare service quality and satisfaction on their patients

intentions.

The present research addresses following problems:

1. What are the important dimensions of healthcare service quality in corporate

hospitals according to expectations and perceptions of patients?

2. What is the relationship between patients‟ perceived quality and satisfaction or

behavioural intentions in healthcare services of corporate hospitals?

19

1.3. Objectives of the Study

The purpose of the present study is to measure the corporate hospital service quality and

investigates the relationship between healthcare service quality, patient satisfaction and

behavioural intentions. Based on the above mentioned research question and the gaps in

the literature, this research is guided by four objectives:

1. To measure healthcare service quality in Indian corporate hospitals.

2. To identify key determinants of patient satisfaction in Indian corporate hospitals.

3. To examine the effect of healthcare service quality on patient satisfaction and

behavioural intentions.

4. To investigate the effect of patient satisfaction on behavioural intentions.

1.4. Significance and Research Contribution

The pragmatic context of healthcare marketing research is that it is potentially of interest

to both an academic and managerial leadership and so health management and healthcare

marketing researchers must tackle the double hurdle of scholarly quality and relevance

(Zeithaml et al., 2006). According to Zeithaml et al., (2006), “there is only one criterion

by which we can judge health management studies: its effectiveness in informing the

activities of any individual or group who involves themselves in managerial situations”.

Implicit in this statement is the double hurdle: healthcare marketing researchers need to

contribute academic theory about management and provide information to management.

The present study makes several contributions to the understanding of patients, both

theoretically and practically.

First, in its own right, measuring corporate perceived and expected healthcare

service quality is making an important contribution to this area, as perceptions and

expectations (especially healthcare perceptions and expectation formation) continues to

be one of the least researched areas of the service experiences research (Zhang et al.,

2008). Although recent studies appearing in the literature indicate increasing interest in

the global healthcare scenario, However, the extant body of literature in the area of Indian

healthcare formation is limited. This research contributes to the process of consolidating

20

and extending the theoretical understanding of patient‟s perception, expectation and their

satisfaction formation. Similarly, the empirical investigation into the formation of patient

satisfaction of a healthcare service encounter has been ad-hoc and so marketing theory

would benefit from a more comprehensive exploration of the determinants of patient

satisfaction.

Second, this study provides insights into the impact of patient satisfaction and

behavioural intention formation, which is particularly important because the healthcare

marketing literature relating to this issue is limited. This study contributes to this area by

undertaking systematic research into this topic, which is important because patients are

more eager to take care of their own health. Indeed, few researchers have developed

rigorous conceptual models specifying relationship between the satisfaction and intention

context.

Third, empirically, this study answers the call of several researchers in the

healthcare marketing and healthcare management disciplines to further explore the

influence healthcare service quality on satisfaction and intentions. Moreover, developing

a research model and testing it empirically is a step in the direction of developing a

framework of healthcare service quality and patient satisfaction in service industries in

general.

This research also provides information to management. First, for those wishing

to manage better healthcare service quality, it is essential to have some understanding of

patients expectations, and their significance in relation to service quality since “knowing

what customers expect is the first, and possibly most critical, step in delivering service

quality” (Zeithaml et al., 2006). The antecedents of patient satisfaction are important to

healthcare service provider because these are the elements which will have an impact on

intentions to revisit and recommendation. Thus providing quality services to patients

contributes to their ability to influence organisational financial performance.

21

Additionally, from a strategic perspective, an understanding of the patients overall

health conditions and differences in the formation of service expectations can give

healthcare service providers the competitive advantage they need to grow in the global

market-place. This empirical research indicates that this is not likely to be the case, and

so this research gives managers a better understanding of patient satisfaction and

intentions.

1.5. Research Methodology used in this Study

The study population consisted of the patients who were admitted in the corporate tertiary

care hospitals functioning in different regions of India. A total four (Apollo Hospitals,

Care Hospitals, Fortis Healthcare Ltd and Manipal Group of Hospitals) hospitals are

selected, which provide super specialty services such as surgical care for cardiovascular,

neurological, urinary, respiratory and orthopaedic diseases. To establish the sample

frame, the hospitalised patients with minimum 3 days stay were considered for the

inpatients‟ sample. The patients were contacted on the basis of direct contact approach.

The structured questionnaire was used for collecting responses from respondents.

First section of the questionnaire was used to gather basic information about respondent

characteristics such as gender, age, occupation, education, marital status, income and area

of residence. Nominal scale was used to gather the demographic information. The second

section of questionnaire contained 60 measurement items of four variables (expected and

perceived healthcare service quality, patient satisfaction and behavioural intentions).

Five-point Likert scale, with anchors ranging from “strongly agree” to “strongly

disagree” was used to measure respondents agreement and disagreement with the

statement. All items were adopted from previously validated studies and modified

properly for the context of Indian healthcare to ensure the content validity of the

instrument.

Sample size was calculated using Hair‟s criterion (Hair et al., 2013), which

suggests that a sample size should be at least five times the number of estimated

parameters. A total of 500 respondents are chosen from selected hospitals and according

to Hair‟s criterion (Hair et al., 2013) this sample size for current study was considered

22

adequate. A total of 125 patients were selected proportionately from each hospital to get

the required sample size. A total of 493 suitable responses were found and a satisfactory

response rate of 98.6 per cent was achieved and 7 patients were declined due to partial

response. The anonymity of all respondents was preserved, in accordance with the

standard research protocol, necessary permission was obtained from the concerned

authorities for data collection.

All of these valid responses were coded into Statistical Package for the Social

Sciences (SPSS) version 20.0 for statistical analysis. Two types of data analysis were

performed on the data: SERVQUAL analysis and inferential analysis. The latter included

exploratory factor analysis and structural equation modelling analysis including

confirmatory factor analysis and hypotheses testing. SERVQUAL analysis and

exploratory factor analysis were performed using SPSS while structural equation

modelling (SEM) analysis was performed using Analysis of Moment Structures (AMOS)

software version 20.0. A two-stage approach was adapted to conduct SEM analysis as

recommended by Anderson and Gerbing (1988). In the first stage measurement model

was tested using confirmatory factor analysis (CFA) to assess the reliability and validity

of latent constructs. In the second stage, hypotheses related to influential factors were

tested. The SEM model fit was determined using goodness-of-fit indices and coefficient

parameter estimates, as suggested by Hair et al., (2013).

1.6. Organisation of the Thesis

This section briefly explains the structure of this thesis.

Chapter 1: This chapter introduces the issues related to the topic under investigation i.e.,

background of the study, overview of Indian healthcare, objectives of the study,

contribution of the study and methodology used in this study.

Chapter 2: This chapter is concerned with the extant service quality literature derived

primarily from marketing discipline and its application in healthcare service quality. This

chapter begins with a discussion of nature of the service, service delivery, service quality

and healthcare service environment. Next healthcare service quality is defined and

23

research orientation for healthcare service quality was discussed, factors affecting

healthcare service quality and approach to quality measurement in healthcare were also

discussed. This chapter also discusses the nature and concept of patient satisfaction, key

determinants of patient satisfaction, approach to satisfaction measurement in healthcare

and behavioural intentions.

Chapter 3: This chapter presents the methodology applied in this study. This chapter

discusses research paradigms, and research strategy. It also provides the justification of

the methodology, discusses the steps taken to collect the data, discusses the sampling

issues, explains scale items selected to measure the underlying latent factors, describes

development and operationalization of the instrument used to collect the data, reports the

pre-testing of survey instrument, discusses the data analysis techniques, presents

reliability and validity of the latent factors.

Chapter 4: This chapter reports the results of data analysis undertaken in this study using

different data analysis tools, which are explained and justified in Chapter three. Results

reported include descriptive analysis and inferential statistics including structural

equation modelling analysis. This chapter also reports the reliability and the validity of

constructs along with hypotheses testing.

Chapter 5: This chapter presents discussion and conclusions of the present study. This

chapter provides an overview of the research and discusses findings related to the results

drawn from testing of hypotheses in this study. This chapter also presents theoretical and

managerial implications drawn from the results reported in Chapter four. Finally, it

presents limitations and directions for future research followed by the conclusions.

24

The aim of the literature review is an exploration of healthcare service quality, patient

satisfaction and behavioural intention dimensions of patients and health service providers

with in the existing literature. Healthcare service quality and patient satisfaction

dimensions that will be covered in this chapter will be further utilised for empirical study

in order to construct an aligned or combined research model of healthcare service quality,

patient satisfaction and behavioural intentions.

This chapter reviews the three related constructs, healthcare service quality,

patient satisfaction and behavioural intentions, in the literature of marketing and

healthcare management. It provides a detailed description for the theoretical and

empirical development of the conceptual framework of these three constructs.

Additionally, this chapter also discusses the relationships among these three related

constructs: healthcare service quality, patient satisfaction, and behavioural intentions.

2.1. Concept of Service Quality

2.1.1. Defining Quality

The word “quality” is derived from the Latin “qualis”, it means “what kind of” (Glare,

1983). The Merriam-Webster Dictionary (2010) defines quality as “The degree of

excellence; superiority of kind; and a distinguishing attribute”. Thus, defining “quality” is

not only important from a semantic point of view but, more importantly, it is required to

direct employee‟s efforts towards a particular common cause. The common vision of

quality is arguably more important in service organizations.

LITERATURE REVIEW

CHAPTER–2

25

2.1.2. Service Quality

The role of service quality is recognised as a critical determinant for the success of the

organisation in a competitive environment. Starting from 1980s a new business trend

toward service quality was started. Zeithaml (2000) suggested that, expansions in service

quality have been linked to increase profit margin of organisations, lower cost and

positive attitude towards the service by the customers and willingness of customers to

pay price premiums. Cronin (2003) pointed out; customer perception of service quality

for organisations is directly linked to internal service quality. As customers became more

informed and demanding companies realised that product quality was not a single key for

a competitive advantage and that should be combined with service quality (Gupta et al.,

2005).

Service quality is defined in the marketing literature as a customer‟s post-

consumption evaluation of service that compares expectations with perceptions of

performance (Carman, 1990; Cronin & Taylor, 1994; Parasuraman et al., 1985, 1988,

1991b; Zeithaml & Bitner, 1996). The evaluation of service quality is based on the

manner in which the service was delivered and the outcomes that resulted from that

service (Grönroos, 1993). Customer generally evaluates service quality through a limited

number of studies and explicit indications, surrogates and features on higher abstracts and

quality dimensions (Parasuraman et al., 1985, 1988).

There is no single universal definition for the service quality in the literature

(Zineldin, 2006); however, many researchers have defined the service quality in their

own point of view. Several definitions on service quality are shown in table 2.1.

26

Table 2.1 Service Quality Definitions

S.No Author Year Definition

1 Lewis and Booms 1983

1999

A measure of how well the service level meets

customer‟s expectations.

Delivering quality service means conforming to

customer expectations on a consistent basis.

2 Grönroos

1984 A result of what consumers receive and how they receive

it.

3 Parasuraman et al., 1985

1988

A gap between patient„s expectation and perception of

service along the quality dimensions.

4 Webster 1989 A measure of how well the service level delivered

matches customer‟s expectations on a consistent basis.

5 Bojanic 1991 The ability of a service in providing customer

satisfaction related to other alternatives

6 Evans and Lindsay 1996 The total characteristics of service related to its ability to

satisfy given needs of customer.

7 Lee 2006 The ability to meet or exceed customer expectations.

8 Zineldin 2006 The art of doing the right thing, at the right time, in the

right way, for the right person and having the best

possible results.

Source: Compiled for this Study

2.1.3. Dimensions of Service Quality:

The literature authenticates multidimensionality of the service quality construct however;

no general agreement about the nature or content of the service quality dimensions exists.

A wide variety of service quality dimensions are presented in Table - 2.2. Most targets of

quality evaluation have emphasised three different categories: a) the physical context

such as facilities; b) the interpersonal interactions between either the client/employee or

between two clients; and c) the core service.

Lehtinen and Lehtinen (1983) set forth a two dimensional approach to service

quality consisting of process quality and outcome quality. Grönroos (1984) proposed the

Nordic model in the early 1980s that defines dimensions of service quality as technical

quality, functional quality and image, which affect expected service and perceived

service, and ultimately service quality. Later, in 1985, the most popular measurement

tool, SERVQUAL, was developed by Parasuraman et al., (1985). It initially included ten

dimensions, namely: tangibility, reliability, responsiveness, communication, credibility,

security, competence, courtesy, understanding and access dimensions, which were

redefined and converted into five useful dimensions, namely: tangibles, reliability,

responsiveness, assurance and empathy in 1988.

27

Cronin and Taylor (1992) developed another important tool known as

SERVPERF, which focuses on measuring customer perceptions about service

performance. Rust and Oliver (1994) developed another model known as the three-

component model comprising service product (i.e. technical quality), service delivery

(i.e. functional quality) and service environment.

Dabholkar et al., (1996) proposed a multilevel model based on three different

levels: the first level belongs to customer‟s overall perceptions of service quality; the

second level focuses on five primary dimensions (physical aspects, reliability, personal

interaction, policy and problem solving) and the third level consists of seven sub-

dimensions (appearance, convenience, promises, doing it right, inspiring confidence,

courteous and helpful). Despite such development across service quality measurement,

little effort has been made to standardise attributes that define the sub-dimensions.

Brady and Cronin (2001) developed the service quality model, based on a

hierarchical approach. They define service quality in terms of three primary dimensions,

namely: interaction quality, physical environment quality and outcome quality - each

having three secondary sub-dimensions, namely; attitude, behaviour and expertise

(interaction quality); ambient condition, design and social factors (physical environment

quality) and waiting time, tangibles and valence (outcome quality) and three tertiary sub-

dimensions, namely: reliability, responsiveness and empathy, under each secondary

dimension. Among the existing models, the hierarchical model is more comprehensive

and extensive.

Table 2.2 Service Quality Dimensions

Model Physical Environment Human Interaction Core Product

Lehtinen & Lehtinen, 1983 --- Process Quality Outcome Quality

Grönroos, 1984 --- Functional Quality Technical Quality

Parasuraman et al., 1988 Tangibles Reliability

Responsiveness

Assurance

Empathy

---

Rust & Oliver, 1994 Service Environment Service Delivery Service Product

Dabohlkar et al., 1996 Physical Aspects Reliability

Personal Interactions

---

Brady & Cronin, 2001 Physical Environment Quality Interaction Quality Outcome Quality

Source: Compiled for this study

28

Though, different scales for measuring service quality have been put forward.

SERVQUAL (Parasuraman et al., 1988) constitutes a major service quality measurement

instrument. The consensus, however, continues to elude till date as to which one is

superior. At the outset, this study focuses on a conceptual framework of SERVQUAL

(Parasuraman et al., 1988) based on modified five dimension model. Following this, scale

development along with criticism of the scale, and applications of instrument, are

discussed.

2.1.4. SERVQUAL

a. Development of SERVQUAL:

Grönroos‟ (1984) model of service quality has been recognised as a seminal work in

service quality research. The SERVQUAL instrument formulated by Parasuraman et al.,

(1985, 1988) is the most widely cited framework in the services marketing literature.

According to Grönroos (1984), service quality has two components, namely, technical

quality and functional quality. The technical quality refers to the primary care attributes

like treatment provided, infrastructure, etc. whereas functional quality indicates

secondary care attributes or how the service is delivered like friendliness of service

personnel, timely delivery, etc. Grönroos (1990) included “image” of the service provider

as the third dimension, in addition to technical and functional quality in service

evaluation. It is like a filter in consumers‟ perception of quality. Parasuraman et al.,

(1985) supported the notion that perceived service quality is an overall evaluation similar

to attitude. They proposed that service quality is a function of the differences or gaps

between customers‟ expectation and performance along the quality dimensions.

Hence, this model is called “Gaps Model”. Gaps Model depicts five gaps in a

service delivery process, which may lead to unfulfilled needs of the customers. The

SERVQUAL instrument is based on Gap-5. On the basis of information from 12 focus-

group interviews with consumers, Parasuraman et al., (1985) concluded that consumers

evaluated service quality by comparing expectations with perceptions on ten dimensions:

Tangibles, Reliability, Responsiveness, Communication, Credibility, Security,

Competence, Courtesy, Understanding/knowing customers and Access.

29

Parasuraman et al., (1988) refined their existing model and came up with a scale

to measure service quality and this scale is named SERVQUAL. This scale consisted of

five dimensions, viz., reliability, responsiveness, assurance, empathy and tangibles. The

description of these dimensions is as follows:

Reliability - Ability to provide services accurately and dependably.

Responsiveness - Readiness or quickness in responding to customers‟ needs.

Assurance - Courtesy and knowledge of the employees and their ability to convey

trust and confidence.

Empathy - Caring and individualized attention provided to customers.

Tangibles - Physical evidence in a service facility.

b. Criticisms on SERVQUAL:

The SERVQUAL scale is a milestone in service quality research and though popular, was

severely criticized by numerous researchers. Babakus and Boller (1991) performed

confirmatory factor analysis on SERVQUAL dimensions and arrived at a poor model fit.

They suggested a two-dimensional structure, one with positively worded items and the

other with negatively worded items. Parasuraman et al., (1991) addressed the issues

raised by justifying the use of gap scores for measuring service quality. They modified

the negatively worded items in their instrument to improve the overall reliability values

of the scale.

Cronin and Taylor (1992) disagreed with the gaps-score measurement, and

proposed that measuring service quality in terms of performance alone would suffice;

they developed performance-only measurement scale, which they termed “SERVPERF”.

Parasuraman et al., (1994) responded to these concerns and revised their original

instrument but disagreed on replacing their model entirely with the ones proposed by

these authors. Further criticism pertaining to SERVQUAL is that it fails to capture the

dynamics of changing expectations. Parasuraman et al., (1985, 1988) stressed that

SERVQUAL had five sound and psychometrically strong dimensions. They also claimed

that the structure and dimensionality was consistent across the chosen five independent

samples from different industries.

30

However, Carman (1990) arrived at a different dimensional structure while using

SERVQUAL scale in a study pertaining to hospitals. Nine dimensions were found:

admission service, tangible accommodations, tangible food, tangible privacy, nursing

care, explanation of treatment, access and courtesy afforded visitors, discharge planning

and patient accounting, and these dimensions explained 71per cent of variation in service

quality.

According to Babakus et al., (1993), service quality was a single-factor model

explaining 66.3 per cent of overall service-quality variance, and they concluded that

empirical evidence did not support a five-dimensional concept. SERVQUAL scale was

also criticized for not considering the technical aspect of a service and its outcomes. Even

though the developers of SERVQUAL scale claimed that it consisted of both the process

(functional) and the outcome (technical) dimensions, it lacked of any measure of

technical quality (Grönroos, 1990).

Teas (1993) believed that expectations battery of SERVQUAL lacked

discriminant validity. The use of seven-point Likert scales has been criticized on several

grounds. Rust et al., (1995) supported the notion of using gap score but they asserted

measuring the gap directly by asking the respondents to provide a score for each

performance item in relation to their expectations. This could make the scale more

reliable and reduce the length of the instrument. Some authors (Caruana et al., 2000)

demonstrated that prior items could influence the respondents‟ evaluation of subsequent

items. For SERVQUAL, in which respondents complete the expectations- and

perceptions-battery on the same Likert scale, such effects are more likely to occur.

Further, the variance extracted by SERVQUAL scale accounted for very low proportion

of item variances (Buttle, 1996).

Table 2.3 provides a summary of critique on SERVQUAL. The varied comments

on SERVQUAL mandated further investigation of dimensions of service quality and led

some researchers to develop their own scale for measuring service quality. A number of

authors (Lee et al., 2000) demonstrated that performance-only model of Cronin and

Taylor (SERVPERF) to be better than SERVQUAL. Despite these developments,

SERVQUAL is still the most widely used model in the field of service quality

(Coulthard, 2004).

31

Table 2.3 Criticisms on SERVQUAL

Criticism Literature 1. Use of attitudinal model in place of disconfirmation

model.

Cronin & Taylor (1992, 1994) and

Oliver (1993)

2. Conceptualization of service quality as gap between

perceptions and expectations.

Cronin & Taylor (1992) and Bouldinget

al., (1993)

3. Psychometric validity of gap scores. Teas (1993)

4. Focus only on functional quality rather than technical

quality.

Cronin & Taylor (1992) and Richard &

Allaway (1993)

5. Use of Likert scale for measuring service quality and

failure of the model to draw from theories of statistics,

psychology and economics.

Babakus and Mangold (1992)

6. Exclusion of crucial factors such as core service, image,

value, physical ambience, service encounter, etc.

Sureshchandar et al., (2001)

7. Number and structure of dimensions. Babakus & Boller (1991) and Carman

(1990)

8. Ambiguity and usage of expectations battery. Carman (1990) and Teas (1993)

9. Item compositions. Carman (1990)

10. Moments of truth. Carman (1990)

11. Polarity of scale.

Babakus & Boller (1991) and Babakus

& Mangold (1992)

12. Two administrations, one each for performance and

expectation

Babakus et al., (1993)

13. Order effects of expectations and perceptions Caruana et al., (2000)

14. Variance extracted in explaining service quality Babakus and Boller (1991)


c. Applications of SERVQUAL

According to Parasuraman et al., (1991), SERVQUAL is a generic instrument with good

reliability and validity and broad applicability. The purpose of SERVQUAL is to serve as

a diagnostic methodology for uncovering broad areas of a company‟s service quality

shortfalls and strengths. SERVQUAL‟s dimensions and items represent core evaluation

criteria that transcend specific companies and industries.

In accordance with this view, SERVQUAL has been used to measure service

quality in a variety of service industries, including healthcare.

32

Table 2.4 Studies on Application of SERVQUAL in different service industries

Industry Studies 1. Airline Service Natalisa and Subroto, 1998

2. Banking Tamimi and Amiri, 2003; Gan et al., 2006; Sureshchandar et al.,

2002a; Mels et al., 1997; Lam, 2002; Zhou et al., 2002

3. Dormitory Services Chen and Lee, 2006

4. Fast Foods Lee and Ulgado, 1997

5. Healthcare Carman, 1990; Babakus and Boller, 1992; Cronin and Taylor,

1992; Brown et al., 1993; Dabholkar et al., 1996; Rohini and

Mahadevappa, 2006; Ramsaran-Fowdar, 2008; Butt and de Run,

2010

6. Higher Education Mai, 2005

7. Hospitality and Tourism Akan, 1995; Parasuraman et al., 1985; Alexandris et al., 2002;

Akama and Kieti, 2003; Lau et al., 2005; Nadiri and Hussein,

2005

8. Information System Jiang et al., 2000

9. Insurance Industry Tsoukatos and Rand, 2006

10. Retail Chains Parasuraman et al., 1994

11. Telecommunications Van der Wal et al., 2002


2.1.5. The applicability of SERVQUAL in Healthcare:

Academic testing of SERVQUAL instrument was liable to occur in for-profit services.

However, a number of studies have evaluated the tool in health care service context;

Reidenbach and Sandifer-Smallwood, (1990), Babakus and Mangold (1992) and Taylor

and Cronin (1994), have tested SERVQUAL in the healthcare services, although Taylor

and Cronin (1994) commented that healthcare service managers should be encouraged to

test the dimensions in their own business environments rather than adopt SERVQUAL

factors. Youssef et al., (1996), who empirically tested the methodology in UK NHS-

Hospitals, also concurred that the survey instrument and the five dimensions were

broadly transferable to health services (Silvestro, 2005; Ramsaran-Fowdar, 2005). Other

studies, however, have resulted in the identification of further quality factors relevant to

health services which are not adequately embraced by Parasuraman‟s conceptualisation

(Silvestro, 2005).

Bowers et al., (1994) applied the SERVQUAL methodology in an army hospital

in Southeast USA. Using focus groups to identify any factors not embraced by

Parasuraman et al., (1988) five dimensions, they identified two further determinants of

health service quality, namely “caring” and “patient outcomes”. Silvestro, (2005) and

33

Ramsaran-Fowdar, (2005), further survey based on quantitative testing of Parasuraman et

al., (1988) dimensions and these additional dimensions revealed empathy,

responsiveness, reliability, communication and caring to be strongly correlated with

overall patient satisfaction.

According to Silvestro and Johnston (1992) “care” was again found out to be a

quality factor in their research and critical incident technique was used. Johnston (1995)

carried out quantitative study on hospitals and investigated the following quality factors:

such as cleanliness, aesthetics, comfort, functionality, reliability, responsiveness,

flexibility, communication, integrity, commitment, security, competence, courtesy,

friendliness, attentiveness, care, access and availability.

Gabbolt and Hogg (1995) also identified the notion of care as critical to patient

evaluations of the healthcare service quality: but they considered notion of care to be

incorporated into Parasuraman et al., (1985, 1988) five generic five dimensions, rather

than being a separate factor. Lam (1997) employed SERVQUAL in health care services.

It was also discovered that patients treated physical facilities to be the least important.

Nursing care, outcome and physician care constituted technical care whereas, food, noise,

room temperature, privacy, cleanliness and parking were parts of interpersonal care.

Dean (1999) empirically tested the transferability of SERVQUAL to health

service settings in Australia. Her research highlighted the importance of understanding

differences in patient expectations in different types of health service, thus demonstrating

that quality factors may vary not only by industry but also within industry and that the

managers of health service targeting multiple markets should distinguish between

different patient types in their analysis of patient expectations.

Lim and Tang (2000) attempted to determine the expectations and perceptions of

patients in Singapore hospitals through the use of modified SERVQUAL that included 25

items representing seven dimensions, namely, tangibles, reliability, assurance,

responsiveness, empathy, and accessibility and affordability. In their study revealed the

existence of an overall service quality gap between patients‟ perceptions and

34

expectations. In a similar study in Sweden, Øvretveit (2000) identified three factors

whereas Kilbourne et al., (2004) validated four factors in a study conducted in the USA.

Lee et al., (2000) demonstrated that almost no approach that is used is justified in

the view of prevalent understanding that healthcare recipients are often unable to evaluate

key dimensions of healthcare service and thus may not have as much to contribute to the

design of an effective healthcare systems as providers and also added that in terms of the

discriminant validity of the seven healthcare service quality dimensions, their results

were not supportive of the validity, considering that similar finding has been reported

before (Dabholkar, 1996).

Moustofa (2005) identified the three factor solution for the SERVQUAL

instrument with 67 per cent of variance explained. The result does not support the five

components of the original SERVQUAL model. A discriminant function was estimated

for patients who selected public hospitals and those who selected private hospitals.

Andaleeb (2000) empirically investigated that the patient perceptions were sought

on five aspects of service quality including responsiveness, assurance, communication,

discipline and baksheesh. Because private hospitals are not subsidised, it was felt that the

incentive structure would induce them to provide better services than public hospitals on

the measure of service quality. Roshnee and Fowdar (2004) identified additional service

quality dimensions, namely “core medical” and “professionalism/skill/competence” and a

few additional items within each of the generic quality dimensions. The core service was

found to be the most important quality attribute for patients and is not represented in the

SERVQUAL instrument.

Wisnievski and Wisnievski (2005) empirically investigated five dimensions of

service quality and they found statistically significant gap scores for reliability and

responsiveness. Comparison of these gap scores suggests that the priority gaps as far as

patients‟ assessments of service quality is concerned was that of reliability. Given the

importance of service quality, it does not appear that the SERVQUAL instrument has a

useful diagnostic role to play in assessing and monitoring service quality in nursing. It

35

enables nursing staff to identify where improvements are needed from the patients‟

perspective.

Rohini and Mahadevappa (2006) applied SERVQUAL framework to find factors

in Bangalore (India) hospitals. They obtained the perceptions of both the patients and the

hospital management. The study concluded that there existed an overall gap between

patient‟s perceptions and expectations and also between management‟s perception of

patients‟ expectations and the actual patient‟s expectations. The authors provided

recommendations to fill those gaps.

Rao et al., (2006) developed a reliable scale to measure in-patient and out-patient

perceptions in India. Their study included medicine availability, medical information,

staff behaviour, doctor behaviour and clinic infrastructure as dimensions of perceived

quality in healthcare services. Das and Hammer (2007) studied the differences in doctors‟

competencies in government and private hospitals located in rich and poor localities in

Delhi (India). The study justified the notion that public sector was performing worse than

private sector by comparing the distributions of MBBS qualified public doctors with

MBBS qualified private doctors. They also found that both government and private

hospitals in poor areas were performing worse than the hospitals located in rich areas.

Duggirala et al., (2008) proposed that healthcare SQ consisted of seven

dimensions, namely, infrastructure, personnel quality, process of clinical care,

administrative processes, safety indicators, overall experience of medical care and social

responsibility.

In summing up, this section reviews the pertinent service quality literature focus of

quality definitions, dimensions, measuring service quality, SERVQUAL dimensions,

criticism and applicability of SERVQUAL instrument. Though numerous researchers

criticised SERVQUAL instrument, this scale is a milestone in service quality research

including healthcare. Having discovered categories and dimensions and the focus of

quality within Service Quality, next section provides a discussion of Healthcare service

quality. This section is aimed to discover a point of quality focus in terms of Health

Service and study similarities and any differential characteristics of Healthcare service

36

quality categories and its dimensions comparing. This section starts from elaborating

definition of service quality in the healthcare. Then various dimensions of healthcare

service quality that exist in the literature will be presented.

2.2. Healthcare Service Quality

Quality orientation is one of the main priorities of any progressive organization. Evidence

from both production and service organizations indicate that quality is the key

determinant for market share, investment return and cost reduction (Anderson and

Zeithaml, 1984). In the health sector the importance of services and their relation with

human life, quality assurance and quality promotion has increasingly caught the attention

of researchers with patients having high expectations from hospitals and other health

providing organizations. In order to assure that medical procedures are effective not only

from the experts‟ viewpoint (technical quality) but also having the ability to satisfy the

functional quality, patient‟s expectations must be considered in health service delivery.

Hence, it is essential to evaluate services explicitly and implicitly based on consumer‟s

viewpoints (Hamidi, 1998).

2.2.1. Defining Healthcare Service Quality

Donabedian (1980) defined healthcare quality as “the application of medical science and

technology in a manner that maximises its benefit to health without correspondingly

increasing the risk”. He distinguishes three components: technical quality - the

effectiveness of care in producing achievable health gain; interpersonal quality -

accommodating patient needs and preferences; and amenities - such as physical

surroundings and organisation attributes.

Øvretveit (1992) defines quality care as the “provision of care that exceeds patient

expectations and achieves the highest possible clinical outcomes with the resources

available”. He developed a system for improving healthcare quality based on three

dimensions: professional; client and management quality. Professional quality is based on

their views of whether professionally assessed consumer needs have been met using

correct techniques and procedures. Client quality is whether or not direct beneficiaries

37

feel they get what they want from the services. Management quality is ensuring that

services are delivered in a resource-efficient way.

According to Schuster et al., (1998), good healthcare quality means “providing

patients with appropriate services in a technically competent manner, with good

communication, shared decision making and cultural sensitivity”. These healthcare

services must meet professional standards. On the other hand, they believe that poor

quality means too much care (e.g. providing unnecessary tests and medications with

associated risks and side effects), too little care (e.g. not providing an indicated diagnostic

test or a lifesaving surgical procedure), or the wrong care (e.g. prescribing medicines that

should not be given together).

Leebov et al., (2003) believe that quality healthcare is the right and ethical thing.

They argue that healthcare quality means “doing the right things right and making

continuous improvements, obtaining the best possible clinical outcome, satisfying all

customers, retaining talented staff and maintaining sound financial performance”.

Lohr (1991), quality healthcare is “the degree to which healthcare services for

individuals and population increases the likelihood of desired health outcomes and is

consistent with the current professional knowledge”. Accordingly, the quality healthcare

service goal is to increase the likelihood of achieving desired health outcomes for the

patient.

Healthcare service quality definitions indicated in the literature can be placed into

two groups: 1. Healthcare services whose characteristics and features meet predetermined

specifications and standards. In this approach, quality is defined as “conformance to

specifications, requirements or standards” and “satisfying provider‟s expectations”. The

focus is internal (i.e. supply-side quality). Terms such as accuracy, reliability and efficacy

compose quality in this category. 2. Healthcare services whose characteristics and

features meet or exceed customer needs and expectations. In this approach, “quality” is

defined as “satisfying customer expectations and needs”. Hence, the focus is external (i.e.

demand-side quality). Terms such as effectiveness, empathy, safety and affordability are

quality attributes in this category.

38

2.2.2. Dimensions of Healthcare Service Quality

Healthcare service quality is recognised as a multidimensional construct. The

identification of service quality dimensions is becoming increasingly important in

healthcare, as service providers seek to meet the challenges inherent in a more

competitive healthcare environment. In order to survive in the new environment, public

and private healthcare organizations must strategically prepare for the increased emphasis

that is being placed on increasing patient satisfaction through improved service quality.

The most widely accepted measurement scale for service quality is SERVQUAL

(Parasuraman et al., 1988), which consists of five essential service quality dimensions:

tangibles; reliability; responsiveness; assurance; and empathy. Bitner (1992) identified

three dimensions of physical environment (termed as servicescape) - ambient conditions,

spatial layout and functionality and signs, symbols and artifacts. Researchers have also

identified and measured certain factors, like delay in service delivery affecting

customers‟ perceptions of service quality (Taylor and Claxton, 1994).

Bowers et al., (1994) reported two major additional dimensions not captured by

SERVQUAL: caring and patient outcomes. The “caring dimension” implied a “personal,

human involvement, with emotions approaching love for the patient” and an “outcomes”

dimension that included “pain relief, lifesaving, anger or disappointment with life after

medical intervention”.

Brady and Cronin (2001) conducted a multi-industry study and concluded that

service quality consists of the dimensions namely outcome (waiting time and tangibles),

employee interactions and environmental quality (ambient and social conditions and

facility design). Brown and Swartz (1989) empirically investigated and identified key

dimensions: “professional credibility”, “professional competence” and “communications”

as factors significant for both physicians and patients in healthcare service quality

evaluation.

Cammilleri and O‟Callaghan (1998) reported indicators of healthcare service

quality for public and private hospitals: professional and technical care, service

personalization, environment, accessibility, patient amenities, catering and price.

39

D‟Souza (2009) developed a conceptual model of healthcare service quality

dimensions and performance in healthcare organizations: leadership, strategic planning,

customer focus, measurement, analysis, knowledge management, workforce focus, and

process management.

Dean (1999) identified four stable dimensions using SERVQUAL to compare

service quality dimensions in two different healthcare settings (medical centre, maternal

and child health centres): assurance; tangibles; empathy; reliability and responsiveness.

Ganguli and Roy (2010) identified nine service quality dimensions in the hybrid services:

customer service, staff competence, reputation, price, tangibles, ease of subscription,

technology security and information quality, technology convenience, and technology

usage easiness and reliability.

Haywood-Farmer and Stuart (1988) suggested that SERVQUAL was

inappropriate for measuring professional service quality since it excluded “core service”,

“service customisation” and “knowledge of the professional” dimensions. Kilbourne et

al., (2004) study also showed that SERVQUAL captures service quality

multidimensionality: tangibles; responsiveness; reliability and empathy; as well as an

overall (second order) service quality factor.

McDougall and Levesque‟s (1994) revealed that only three underlying elements:

tangibles, contractual performance (outcome) and customer-employee relationships

(process) are more significant dimensions of service quality. Moreover, their research

indicates the possibility of two public utility sector dimensions (Babakus and Boller,

1992) and up to nine dimensions (Carman, 1990) in a dental school patient clinic,

business school placement centre, motor care tire centre and acute care hospital, which

underpin service quality.

Morrison et al., (2003) identified five main service quality attributes that explain

patient‟s General Practitioners (GP) service preferences: communication; doctor-patient

relationship; same gender as the patient; advising; and empowering patients to make

decisions. Raja et al., (2007) reported quality awards dimensions and the selection of

criteria for assessing healthcare processes quality status, in private sector health care

40

institutions in India. The study identified six key dimensions for Indian healthcare system

includes that: service quality, leadership, resource measurement, people management,

process management and customer satisfaction.

The Joint Commission on Accreditation of Healthcare Organisations (JCAHO-

1996) identified nine key dimensions for hospitals: efficacy, appropriateness, efficiency,

respect & caring, safety, continuity, effectiveness, timelines and availability. These

dimensions are more closely related to SERQUAL five dimensions, but this scale is more

comprehensive. The JCAHO dimensions emphasises SERVQUAL dimensions, are

developed specially for hospital accreditation process.

Zeithaml et al., (1990) reported service reliability as the most critical dimension

perceived by customers, followed by responsiveness, assurance, empathy and tangibles.

Turner and Pol (1995) also reported that environment, customer‟s physical or emotional

status and other non-medical characteristics can influence healthcare service quality.

Though several researchers pointed different dimensions of service quality,

Parasuraman et al., (1988) SERVQUAL dimensions are mostly used in service quality

measurement, including healthcare. Rohini and Mahadevappa (2006) listed the

advantages of SERVQUAL as follows:

It is accepted as a standard for assessing different dimensions of service quality.

It has been shown to be valid for a number of service situations.

It has been known to be reliable.

The instrument is parsimonious because it has a limited number of items. This

means that customers and employers can fill it out quickly.

It has a standardized analysis procedure to aid interpretation and results.

In summing up, this section discussed the points of healthcare service quality focus in

terms of service quality categories and its dimensions comparing to Service quality.

Table 2.5 provides summary of healthcare service quality studies and afterwards, next

section provides measuring service quality and importance quality measurement

instrument in healthcare context.

41

S.No Author &

Year

Country Respondents Data Collection

Method

Scale Used Measurement of service quality addressed through

1. Kang and James

(2004)

USA 464 Patients

Structured

Questionnaire

Seven-point

Likert

Through technical and functional quality with Five

constructs, (functional quality, technical quality, image,

overall service quality, and Customer satisfaction).

2. Choi et al.,

(2004)

South Korea 557 Patients of General

Hospital located in

Sungnam, South Korea.

Structured

Questionnaire

Seven-point

Likert scale

Through four dimensions of quality of medical services:

(1) Convenience (2) health care providers.

(3) Physician‟s concern and (4) Tangibles

3. Mun (2004) Singapore 400 Patients Personnel

interview

Five Factor

scale

Ten Factors (Reliability, Knowledge, Promptness,

Communication, Attitude, Availability, Safety,

Consistency , Trustworthiness, Facilities)

4. Dilber et al.,

(2005)

Canada 150 Chief administrative

officers of healthcare

institutions in Turkey

Structured

Questionnaire

five-point

Likert scale

Eight critical factors

5. Raja et al., (2006) India 319 Patients Structured

Questionnaire

Five-point

Likert scale

Five dimensions

6. Zineldin (2006). Sweden 224 inpatients from three

different hospitals in Jordan

Multi-step direct

interview method

Five-point

Likert scale

Five dimensions of the total quality

7. Badri et al.,

(2007)

United Arab

Emirates

354 inpatients discharged

from public hospitals in

UAE

Structured

Questionnaire

Ten-point

scale

Based on three constructs (quality of care, process and

administration, and information)

8. Dagger et al.,

(2007)

Australia Review collected from past

research

-

-

Four dimensions (interpersonal quality, technical

quality, environment quality and administrative quality)

and nine sub-dimensions

9. Chaniotakis et al.,

(2009)

Greece 1,000 mothers from Greece

Public Hospital

Personal

interviews

five-point

Likert scale

Five service quality variables (tangibles, reliability,

responsiveness, assurance and Empathy)

10. Gill and White

(2009)

Australia 70 Managers Direct interview Seven-point

scale

Six dimensions, (Client Orientation, Provider

Empowerment, Client Involvement, Client

Empowerment, Perceived Quality, Outcomes)

11. Hadwich et al.,

(2010)

Germany In-depth interviews were

conducted 215 patients in

Switzerland

Structured

Questionnaire

C-OAR-SE

scale

13 items (accessibility, competence, information,

usability/user Friendliness, security, system integration,

trust, individualization, empathy, ethical conduct, degree

of performance, reliability, and ability to respond)

12. Chahal and

Kumari (2010)

India 400 indoor patients of five

departments in North Indian

Govt Hospitals

Structured

Questionnaire

Five-point

Likert scale

service quality dimensions (physical environment

quality), interaction quality and outcome quality

13. Alrubaiee and

Alkaa'ida (2011)

Jordan 290 respondents from 4

different hospitals in Amman

Structured

Questionnaire

Five-point

Likert scale

Five dimensions of service quality attributes (tangible,

reliability, responsiveness, empathy and assurance), with

patient satisfaction and trust as the dependent Variables.

14. D‟souza and

Sequeira (2011)

India 1330 Respondents from 76

medical college hospitals in

India.

Mail survey Five-point

Likert scale

Through nine independent variables and two dependent

variables

Table 2.5 Summary of studies used different dimensions to measure Healthcare Service Quality

42

2.2.3. Measuring Healthcare Service Quality

The service quality literature offers different models for establishing service quality

determinants as well as appropriate quality measurement techniques. However, the debate

about choosing the right and credible measurement method is on-going. Robinson (1999)

contends, as far as service quality measurement is concerned, there is little agreement

beyond the need for measurement. A detailed service quality measurement framework

review is beyond scope. Nevertheless, while most methods developed over the last few

decades belong to the user-based paradigm and employ questionnaires to collect data,

some approaches draw information from parties other than service users and employ data

collection methods other than questionnaires. Parasuraman (1995), points out that the

dominant mode of thinking in measurement of quality in services rest on disconfirmation

view, which links the expectations of consumer with their experience of service. Silvestro

(2005) pointed out that, the healthcare service management literature has focused on the

conceptualisation and modelling of healthcare service quality and has offered several

tools for its measurement which can be applicable to healthcare services. Several

researchers mentioned the necessity and importance of measuring quality of healthcare

services and indicated that the quality of healthcare doesn„t improve unless it is

measured. It has to be measured to effectively manage healthcare services (Mejabi &

Olujide, 2008).

However, the quality of healthcare service is difficult to evaluate due to its

abstractness, the high degree of intangibility and high professionalism demanded. On the

other hand, patients are quite unique as customers compared to other customers in

different services. They are worried about the outcome of the treatment and the process

of being treated. These characteristics make the measurement of the quality of healthcare

service more complex (Taner and Antony, 2006).

Moreover, in recent years the patient perceptions are increasingly used to measure

the quality of healthcare services. In reality, the healthcare sector has been slow to move

from a provider-based approach to user-based approach to assess the quality of healthcare

services. As a consequence, service providers and researchers are trying to implement

meaningful customer-oriented quality assessment measures (Michael et al., 2001).

43

Concerning the criteria to evaluate healthcare quality, there is no universal criteria and

many researchers are struggling to establish criteria to evaluate healthcare quality. The

major measurement of service quality instruments are discussed below;

Aagaja and Garg (2010) developed an instrument called, PubHosQual, to measure

the perceived service quality for public hospitals in Indian context. The purpose of their

study was to develop a scale for measuring perceived service quality for public hospitals

from the user‟s (patient‟s) perspective. PubHosQual has been tested in India. The study

results were found that reliable and valid scale called, PubHosQual (public hospital

service quality) was developed to measure the five dimensions of hospital service quality:

admission, medical service, overall service, discharge process, and social responsibility.

Chahal and Kumari (2010) developed an instrument called, multidimensional

HCSQ, to measure healthcare service quality for a tertiary care public hospital. The main

purpose of their study was to develop and empirically validate a multidimensional scale

for measuring healthcare service quality (HCSQ), based on modified Brady and Cronin‟s

hierarchical service quality model. The study also investigated HCSQ and its ability to

predict important service outcomes through two different models. In the first model,

direct effects of service quality dimensions were assumed and in the second model, direct

effects of physical environment quality were measured.

Grönroos (1984) in the early 1980‟s proposed the Nordic model in the early 1980s

that defines dimensions of service quality as technical quality, functional quality and

image, which affect expected service and perceived service, and ultimately service

quality.

Parasuraman et al., (1985) developed the most popular service quality

measurement tool called, SERVQUAL. Measurement instrument had initially included

ten dimensions, namely: tangibility, reliability, responsiveness, communication,

credibility, security, competence, courtesy, understanding and access dimensions, which

were redefined and converted into five useful dimensions, namely: tangibles, reliability,

responsiveness, assurance and empathy in 1988. The SERVQUAL model assesses

service quality in terms of difference between customer expectations and customer

44

perceptions. The instrument has been used extensively in a variety of service settings

such as banking, credit card services, retail, hospitality, logistics, higher education,

airlines, hospitals, repair & maintenance and long distance telephone services in

developed nations.

Cronin and Taylor (1992) developed another important service quality

measurement tool known as SERVPERF, which focuses on measuring customer

perceptions about service performance. Rust and Oliver (1994) developed an instrument

to measure service quality, called the three-component model comprising service product

(i.e. technical quality), service delivery (i.e. functional quality) and service environment.

Brady and Cronin (2001) developed the service quality measuring tool, based on a

hierarchical approach. The study defines service quality in terms of three primary

dimensions, namely: interaction quality, physical environment quality and outcome

quality - each having three secondary sub-dimensions, namely; attitude, behaviour and

expertise (interaction quality); ambient condition, design and social factors (physical

environment quality) and waiting time, tangibles and valence (outcome quality) and three

tertiary sub-dimensions, namely: reliability, responsiveness and empathy, under each

secondary dimension.

Edvardsson and Mattsson (1993) proposed a method for measuring service

quality, called the Experience-based method, which focuses on measuring customer

experience and their satisfaction about service performance. Tomes and Nag (1995)

measured service quality in NHS trust hospital by using expectation scores only. The

study defines service quality in terms of patient‟s expectations regarding healthcare

service provider.

Haddad et al., (1998b) developed an instrument for quality assessment of

healthcare centers. The 20-item scale proposed by Haddad et al., (1998b) seeks to

understand the user‟s opinion relating to healthcare services. The scale contents have

been identified by using the inductive process. Healthcare delivery, personnel and

facilities are the three subscales constituting the instrument.

45

Over the years, several models of service quality have evolved in health care

setting. SERVQUAL has been widely applied and frequently reported in the literature.

The development of the SERVQUAL scale by Parasuraman et al., (1988) provided an

instrument for measuring functional service quality applicable across a broad range of

services.

Rohini and Mahadevappa (2006), in his working paper included some advantages

of SERVQUAL scale using for measuring quality in service industry, as follows,

It is accepted as a standard for accessing different dimension of service quality;

It has been shown to be valid for a number of service situations;

It has been known to be reliable;

The instrument is parsimonious because it has a limited number of items. This

means that customers and employers can fill it out quickly; and

It has a standardized analysis procedure to aid interpretation and results.

Even though SERVQUAL and SERVPERF are the most commonly used scales of

service quality measurement (Gilmore and McMullan, 2009), among these two the most

commonly used measure is SERVQUAL (Ladhari, 2009). It also has a wide range of

applications in service quality measurement which includes: health care applications

(Woodside et al., 1989; Reidenbach and Sandifer-Smallwood, 1990; Babakus and Boller,

1992; Headley and Miller, 1993; Bowers et al., 1994; Bebko and Garg, 1995), Dental

clinic and acute care hospital (Carman, 1990), AIDS service agencies (Fusilier and

Simpson, 1995), Physicians (Walbridge and Delene, 1993) and nurses (Uzun, 2001);

hospitals (Babakus and Mangold, 1992; Vandamme and Leunis, 1993; Youssef et al.,

1995; Sewell, 1997; Camilleri and O‟Callaghan, 1998; Tengilimoglu et al., 1999; Lim

and Tang, 2000; Taner and Antony, 2006). The summary of SERVQUAL scale that has

been used to measure healthcare service quality in different countries is given below

Table 2.6.

46

Table 2.6 Summary of Studies using SERVQUAL scale for Measuring Healthcare Service Quality

S.No Author & Year Country Scale Area of Research Sampling size &

Instrument

Measurement of Service

Quality Addressed through

1. Zineldin (1996) Jordanian 5Q - Model 3 Jordanian & Egypt hospitals 224 Patients

Structured Questionnaire

Quality of object, processes,

infrastructure, interaction, and

atmosphere.

2. Carman (1990) USA SERVQUAL Public and Private hospitals 298 Patients

Parkside survey

SERVQUAL – Dimensions

3. Anderson (1995) Texas SERVQUAL University of Houston Health

Center

431 Patients



4. Babakus & Mangold (1992) USA SERVQUAL USA hospitals 2036 Patients

Mail Survey

SERVQUAL - Dimensions

5. Vandamme & Leunis (1993) Belgium SERVQUAL General Hospital in Belgium 200 Patients



6. Youssef et al., (1996) Singapore SERVQUAL NHS hospitals 174 Patients



7. Camilleri & O‟Callaghan

(1998)

Maltese SERVQUAL Maltese Public and Private

Hospitals

625 Patents



8. Lim & Tang (2000) Singapore SERVQUAL Singapore hospitals 224 Patients



9. Martinez Fuentes (1999) Spain SERVQUAL Selected hospitals in Spain 170 Patients

Personal interview


10. Jabnoun & Chaker (2003) UEA SERVQUAL Private hospitals in UAE 300 Patients



11. Sohail (2003) Malaysia SERVQUAL Private hospitals in Malaysia 1000 Patients

Mail Survey


12. Mostafa (2005) Egypt SERVQUAL 12 hospitals in Egypt 224 Patients




Table Continued……

47

Table 2.6 Summary of Studies using SERVQUAL scale for Measuring Healthcare Service Quality

S.No Author & Year Country Scale Area of Research Sampling size &

Instrument

Measurement of Service

Quality Addressed through 13. Narang (2010) India SERVQUAL Two missionary hospitals in

Lucknow, India.

500 Patients



14. Simbar et al (2012) Iran Observation

Checklists

16 pre-natal clinics in Iran 600 Patients


Communication, Hand washing,

History taking, Clinical

examination, laboratory tests

prescribing vaccination

15. Wisniewski & Wisniewski

(2005)

UK SERVQUAL Scottish Colposcopy Clinic 99 Patients



16. Lee et al (2000)

USA SERVQUAL Physicians from American Medical

Association

1428 Patients

Mail Survey


17. Aagja & Garg (2010) India PubHosQual Public hospitals in Gujarat, India 200 Patients


Admission, Medical service,

Overall service, Discharge, Social

responsibility

18. Koornneef (2006) Ireland SERVQUAL Children with intellectual disability

in 2 organisations

81 Patients



19. Kilbourne et al., (2004) USA & UK SERVQUAL Selected Nursing Homes in USA &

UK

195 (USA) & 99 (UK)



20. Chahal & Kumari (2010) India HCSQ –

Scale

Tertiary public hospital of North

India

400 Patients


Physical environment, Interaction

quality, Outcome quality, image

21. Al-Borie & Damanhouri

(2013)

Saudi

Arabia

SERVQUAL 5 Saudi Arabian public and 5

private hospitals

1000 Patients



22. Duggirala et al., (2008) India TQM Government and private hospitals in

Tamil Nadu and Gujarat in India

300 Patients


Infrastructure, Personnel quality,

Process of clinical care,

Administrative procedures, Safety

indicators, Overall, experience and

Social responsibility

23. Chaniotakis &

Lymperopoulos (2009)

Greece SERVQUAL Maternity Care services in Greece 1000 Patients



24. Manjunath (2007) India MBNQA 300-bed hospital in South, India 1000 Patients

In-depth Interviews

MBNQA criteria no. 4, i.e.

measure, analysis and knowledge

management

25. Padma et al., (2010) India SERVPERF Government and Private hospitals

in South India

204 Patients & 204

Attendants.


Infrastructure, Personnel quality,

Process of clinical care,

Administrative procedures, Safety

measures, Hospital image, Social

responsibility, Trustworthiness.


48

2.3. Patient Satisfaction

Patient satisfaction is considered as the center of business strategy for healthcare service

organisations. Patient satisfaction is not only the intrinsically worthy goal of hospitals,

but also it has important influences on patient retentions and hospital financial ability

(Zineldine, 2006). Patients with higher satisfaction level are more likely to compliant

with physician advice and to recommend the healthcare providers to their friends and

relatives. This section provides distinct literature related to patient satisfaction,

determinants of patient satisfaction and relationship with healthcare service quality.

2.3.1. Definition of “Satisfaction”

Dictionary definitions attribute the term “satisfaction” to the Latin root satis, meaning

“enough”. Something that satisfies will adequately fulfil expectations, needs or desires,

and, by giving what is required, leaves no room for complaint. Two points arise from

these definitions. First, a feeling of satisfaction with a service does not imply superior

service, rather than an adequate or acceptable standard was achieved. Dissatisfaction is

defined as discontent, or a failure to satisfy. It is possible that consumers are satisfied

unless something untoward happens, and that dissatisfaction is triggered by a critical

event (Avis et al., 1995). Secondly, satisfaction can be measured only against

individuals‟ expectations, needs or desires. It is a relative concept: something that makes

one person satisfied (adequately meets their expectations) may make another dis-satisfied

(falls short of their expectations).

To demonstrate the unresolved conceptual difficulties with the satisfaction

construct, in the services literature it is depicted as: both a summary psychological state

and encounter specific (Oliver, 1981); the discrepancy between prior expectations and

actual performance (Yi, 1990); comprised of both affective and cognitive components; an

outcome state (Oliver, 1989); the fulfilment response and an experiential construct

(Oliver, 1997); a response to both process and outcome (Hill, 2003). Given the range of

definitions, there has been contention in the marketing literature on how to conceptualise

and measure the service recipient satisfaction concept. The study of customer satisfaction

has largely been driven by the desire to understand the behavioural intentions of

49

customers (Cronin et al., 2000); however its measurement varies depending on the

assumptions that are made as to what satisfaction means (Gilbert et al., 2004).

2.3.2. Satisfaction in Healthcare Industry

Understanding satisfaction and service quality have, for some considerable time, been

recognised as critical to developing service improvement strategies. The inaugural quality

assurance work of Donabedian (1980) identified the importance of patient satisfaction as

well as providing much of the basis for research in the area of quality assurance in

healthcare. Oliver (1980) proposed that satisfaction is a function of the disconfirmation of

performance from expectation. Oliver (1989) defined satisfaction as an evaluative,

affective, or emotional response. So customers can evaluate the object only after they

interpret the object. Hence, satisfaction is the post-purchase evaluation of products or

services given the expectations before purchase. Satisfaction is dependent on the ability

of the supplier to meet the customer‟s norms and expectations and no matter how good

the services are, customers will continually expect better services (Fornell, 1992).

Choi et al., (2004) developed an integrative model of health care consumer

satisfaction based on three constructs: service quality and value, patient satisfaction and

behavioural intention. Service quality emerged more important than value in patient

satisfaction. Zineldine (2006) affirmed that patient satisfaction is a cumulative construct:

technical, functional, infrastructure, interaction and atmosphere. Elleuch (2008) evaluated

patient satisfaction using process characteristics (patient-provider interaction) and

physical attributes (setting and appearance). Process quality attributes were antecedents

to patient satisfaction, which in turn predicted patient‟s behaviour to return to the hospital

or recommend to others.

According to Hood (1995), the anticipated need for the measurement of patient

satisfaction has been largely driven by the underlying politics of “new public

management” and the advent of the patient rights movement (Williams, 1994), with

patient satisfaction being one of the articulated goals of healthcare delivery. Bhattacharya

et al., (2003) studied inpatients in a public tertiary hospital and concluded that the

patients expressed high levels of satisfaction with the technical quality of the doctors and

the nurses. However, the nurses and the paramedical staff fell short on behavioural issues.

50

Boyer and Francois (2006) in their study on “assessing patients satisfaction in

health services” pointed out an example, assessing patient satisfaction has been

mandatory for French hospitals since 1998, which is used to improve the hospital

environment, patient amenities and facilities in a consumerist sense, but not necessarily to

improve care. Therefore, it seems to be possible that understanding how the patient views

the experience at their hospital stays paramount to understanding the basis for future

satisfaction with health care services.

Chahal and Kumari (2012) evaluated the service quality of a tertiary public

hospital in North India by surveying inpatients and confirmed a significant relationship

between service quality dimensions, namely, physical environment quality (ambient

condition, social factor tangibles), interaction quality (attitude and behaviour, expertise

and process quality) and four performance measures: waiting time, patient satisfaction,

patient loyalty and image of public hospitals.

Chahal and Mehta (2013) collected empirical data on the three hospitals in Jammu

city to examine patient satisfaction and identified physical maintenance, physician care,

nursing care and internal facilities as key variables. Crowe et al., (2002) and Urden

(2002) separately point out that patient satisfaction is a cognitive evaluation of the service

that is emotionally affected, and it is therefore an individual subjective perception. Their

study also highlights that there is consistent evidence across settings that the most

important determinants of satisfaction are the interpersonal relationships and their related

aspects of care.

Hawthorne (2006) and Crowe et al., (2002) indicated that there is agreement that

the definitive conceptualisation of satisfaction with healthcare has still not been achieved

and that understanding the process by which a patient becomes satisfied or dissatisfied

remains unanswered. They suggest that satisfaction is a relative concept and that it only

implies adequate service.

Kumari et al., (2009) examined patients attending the outpatient department

(OPD) of government allopathic health facilities of Lucknow district, capital city of Uttar

Pradesh. Although the overall satisfaction of the patient was satisfactory, there were

deficiencies in certain areas such as short OPD hours, availability of drinking water,

availability of clean toilets and doctor-patient communication.

51

Mekoth et al., (2012) studied the OPD of a public hospital in Goa and observed

quality of physicians and clinical support staff as key determinants of patient satisfaction.

However, the quality of non-clinical staff was not found to affect patient satisfaction. Rao

et al., (2006) surveyed inpatients and outpatients who visited primary health centres,

community health centres, district hospitals and female district hospitals in the state of

Uttar Pradesh, the most populous state of India. They identified five dimensions of

service quality - medicine availability, medical information, staff behaviour, doctor

behaviour and hospital infrastructure. Patient‟s perception of service quality was found to

be marginally better than average. For outpatients, doctor behaviour was the key

determinant of patient satisfaction. For inpatients, staff behaviour was adjudged the key

determinant of patient satisfaction followed by doctor behaviour, medicine availability,

medical information and hospital infrastructure.

Senarath et al., (2013) evaluated patient satisfaction using eight dimensions:

interpersonal care, efficiency, competency, comfort, physical environment, cleanliness,

personalized information and general instructions. Sharma et al., (2011) assessed the

patient satisfaction level visiting the OPD in a premier multi-specialty hospital of North

India and concluded that the patients were satisfied with the doctor, nurses and

paramedical staff. However, certain per cent of patients opined that doctors had shown

little interest to listen to patients‟ problems and often used technical terms to explain their

illness or consequences. The majority of the patients in the survey were satisfied with the

basic amenities, but the services were found costly.

Sodani et al., (2010) measured the satisfaction of patients visiting the outpatient

department (OPDs) of district hospital, civil hospital, community health center and

primary health center of eight selected districts of Madhya Pradesh, India. They observed

an increased level of patient satisfaction with the amenities offered at higher-level

facilities compared to lower-level facilities. In contrast, patients were more satisfied with

the behaviour of doctors and staff at lower-level facilities compared to higher-level

facilities.

52

Cronin and Taylor (1992) empirically investigated and their study has suggested

that the quality of specific health care services can have a significant effect on patient

satisfaction and that satisfaction, in turn, has a positive relationship to purchase

intentions.

Smith et al., (2006) patient satisfaction, a critical indicator of health care quality,

has been defined as “the health care recipient‟s reaction to salient aspect of his or her

service experience”.

In view of the above discussion, several studies revealed that patient satisfaction is

important and key antecedent of service quality. Measuring and improving satisfaction is

not only the intrinsically worthy goal of hospitals financial performance, but also it has

important influences on quality of service provider and patient retentions. Subsequently,

table - 2.7 provides summary of patient satisfaction studies. Later, next sub-section

provides brief literature support of key determinants of patient satisfaction.

53

Table 2.7 Summary of patient satisfaction studies

S.No Author & Year Country Setting Context & Design Sample & Data collection

1. Christel et al., (2000) USA Ontario‟s 59 IHF‟S Empirical

Observational Study

200 diagnostics of IFH‟s


2. Sharma et al., (2011) India PGIMER, Chandigarh, India Empirical

Cross-sectional study

1420 OPD patients


3. Al-Doghaither et al.,

(2000)

Kuwait Selected PHC‟s from Kuwait Empirical

Observational Study

400 Patients


4. Tateke et al., (2012) Ethiopia 5 Public and 5 Private Hospitals

Ethiopia

Empirical


254 Patients (Public & Private

Hospital)


5. Mehta (2011) India Public & Private hospitals at Gwalior & New

Delhi, India.

Empirical

Observational Study

400 Patients

Self-designed Questionnaires

6. Thiele & Bennett (2010) Australia General Practitioner (GP) Empirical

Observational Study

190 General Practitioners


7. Chahal & Mehta (2013) India 2 Government Medical College Hospitals,

Jammu, India.

Empirical


528 indoor patients


8. Puri et al., (2012) India Tertiary care Public Hospitals in India Empirical


120 In-patients 120 Out-

patients


9. Otani et al., (2012) Missouri 1-Academic hospital, 6-Community hospitals,

and 3-Rural hospitals in Missouri

Empirical

Observational Study

18, 755 Patients


10. Deitrick et al., (2007) USA Lehigh Valley Hospital and Health Network

(LVHHN), Muhlenberg

Empirical

Observational Study

23 inpatients; 9 family

members; 17 staff members

Personal Interview

11. Lin & Guan (2003) USA Trained college students

North-eastern USA.

Empirical

Observational Study

139 respondents


12. Kaldenberg (2001) USA 3 Selected hospitals in USA Empirical

Observational Study

463 patients



Table Continued…

54

Table 2.7 Summary of patient satisfaction studies

S.No Author & Year Country Setting Context & Design Sample & Data collection

13. Moscato et al., (2007) USA Nursing care Services

Southern California

Empirical

Observational Study

150 Nurses

Telephonic Interview

14. Ashrafun & Uddin (2011) Bangladesh Dhaka Government Medical College Hospital,

Bangladesh.

Empirical

Observational Study

190 inpatients


15. Zia et al., (2011) Iran Ali-Ebne-Abitaleb Hospital, Iran Empirical

Observational Study

392 patients & 608 of their

relatives


16. Zhang et al., (2008) USA National Treatment Improvement Evaluation

Study (NTIES), USA

Empirical

Observational Study

4939 Admitted Clients

In-person, structured computer-

assisted interview

17. Chahal et al., (2004) India Selected Out-patients of government health

care centres in India

Empirical

Observational Study

675 respondents


18. Qu et al., (2011) USA Primary care clinics affiliated with a major

university health system, USA.

Empirical

Observational Study

479 Patients


19. Grøndahl (2012) Norway 5 Norwegian hospitals Empirical

Cross-sectional design

373 Patients

Emotional Stress Reaction

Questionnaire

20. Alaloola & Albedaiwi

(2008)

Saudi Arabia King Abdulaziz Medical City, Riyadh. Empirical


1983 Patients from inpatient,

outpatient and emergency care;


21. Avortri et al., (2011) Ghana Selected 2 public hospitals in Ghana.

Empirical

Cross-sectional analytical

approach

885 women who delivered

vaginally in two public hospitals


22. Naidu (2009) India 24 articles from international journals Empirical -

23. Alhashem et al., (2011) Kuwait Primary healthcare clinics in Kuwait Empirical

Observational Study

426 Patients


24. Elleuch (2008) Japan National cultures Centres in Japan Empirical

Observational Study

159 Japanese outpatients



55

2.3.3. Determinants of Patient Satisfaction

Satisfaction is the state of pleasure or contentment with an action, event or service and is

determined significantly by clients‟ expectations and experiences (Sixam et al., 1998).

According to Edgman-Levitan and Cleary (1996) the aspects of care that patients‟ value

include; access to care (i.e. geographical, social and financial), respect for patient‟s

values, preferences and expressed needs, provision of information and education,

provision of emotional support, involvement of family and friends, continuity of care,

physical comfort, and effective coordination of care. Satisfaction may be considered as

one of the desired outcomes of care and so patient satisfaction information should be

indispensable to quality assessments for designing and managing healthcare (Turner and

Pol, 1995). Patient satisfaction enhances hospital image, which in turn translates into

increased service use and market share (Andaleeb, 1988). Some researchers supported the

notion that factors causes customer satisfaction, including healthcare industry are

discussed below.

Al-Mandhari (2004) found that the quality of communication and the general

condition of the facilities were significant to patient satisfaction. Besides, a clean and

organised appearance of a hospital, its staff, its premises, restrooms, equipment, wards

and beds can influence patient‟s impressions about the hospital. Andaleeb (1988) tested a

five-factor model that explained considerable variation in customer satisfaction with

hospitals. These factors include communication with patients, competence of the staff,

their demeanour, quality of the facilities, and perceived costs.

Ashrafun and Uddin (2011) identified factors associated with satisfaction among

inpatients received medical and surgical care for urinary, cardiovascular, respiratory, and

ophthalmology diseases at Dhaka Government Medical College Hospital, Bangladesh.

The study revealed that ten dimensions of patient satisfaction (appointment waiting time

for doctor after admission, doctor‟s treatment and behaviour, behaviour and services of

nurses, boys and ayas, toilet and bath room condition, quality of food, number of days in

the hospital, cost for treatment, and gift/tips culture in the hospital).

56

Avortri et al., (2011) empirically investigated through cross-sectional analytical

approach and their study had predicted key dimensions of satisfaction with childbirth

services in Ghana. The study reported that continuity of care is very important in

enhancing the experiences of women who use maternity services. In conclusion, study

found that continuity of care is key factor to influence mother‟s satisfaction.

Chahal and Mehta (2013) established the structure of patient satisfaction construct

in Indian healthcare settings. Their study proposed that physician care, nursing care,

supportive staff, operational activities, physical maintenance, are the major factors that

affect patient satisfaction. Chahal and Sharma (2004) proposed that doctors, nurses,

management, facilities and cleanliness are the major factors that affect satisfaction.

Raftopoulous (2005) considered food, room characteristics and treatment to be significant

in explaining patient satisfaction.

Fowdar (2005) identified dimensions affecting patient evaluations, including core

services, customization, professional credibility, competence and communications. Mehta

(2011) yielded three factors of patient satisfaction namely promptness, medical aid and

patient interest for service quality and amenities, clinical services and physical services.

The study explained that service quality and patient satisfaction are more strongly

associated with adherence and continuity of visit.

Moscato et al., (2007) predicted factors of patient satisfaction with nursing care

services and found key factors include clinical outcomes, healthcare quality, and patient

follow-through. Muhondwa et al., (2008) conceptualized that perceived waiting time is a

strong predictor of patient satisfaction. If waiting time is longer than what is expected or

considered inappropriate, dissatisfaction will arise no matter how long the actual waiting

time.

Naik et al., (2013) revealed through an empirical study on factors of patient

satisfaction at Indian tertiary care hospitals. Their study proposed that first impression,

clinical care, nursing care, housekeeping service, food service, and overall service

experience are the major factors that affect patient satisfaction.

57

Sardana (2003) conceptualized patient satisfaction with five dimensions:

physician care, nursing care, supportive staff behaviour, convenient visiting hours and

availability of emergency aid. Tateke et al., (2012) determined the level and determinants

of patient satisfaction with outpatient healthcare services provided at public and private

hospitals in Central Ethiopia. The study found six factors of patient satisfaction that

include, self-judged health status, expectation about the services, perceived adequacy of

consultation duration, perceived provider‟s technical competency, perceived welcoming

approach and perceived body signalling which were determinants of satisfaction at both

public and private hospitals.

Tucker and Adams (2001) revealed through an empirical study that patient

satisfaction is predicted by factors relating to caring, empathy, reliability and

responsiveness. Ware et al., (1978) identified dimensions affecting patient evaluations,

including physician conduct, service availability, continuity, confidence, efficiency and

outcomes. Woodside et al., (1989) identified through an empirical study on primary

patient satisfaction determinants. Their study listed six (admission, discharge, nursing,

food, housekeeping and overall service experience) key determinates of patient

satisfaction and found a relation between service quality, satisfaction and intentions.

Zineldin (2011) examined the major factors affecting patients‟ perception of

cumulative summation. The factors included in this summation include: technical;

functional; infrastructure; interaction and atmosphere. Their study contributed to previous

academic healthcare sector studies and quality management in two ways: Zineldin‟s

(2006) model, including patient-physician relationship behavioural dimensions and

patient satisfaction.

In summing up, several studies provides and discussed different factors, determinants and

antecedents of patient satisfaction with respect to their study designs, based on the above

discussion, the potential antecedents of patient satisfaction in healthcare service quality in

this study include: admission process, medical care service, nursing care services,

housekeeping services, food services and overall service experience of service provider.

Apart from this, table - 2.8 provides summary of studies on determinants of patient

satisfaction.

Table-2.5: Summary of the Healthcare Service Quality

Dimensions

58

Table 2.8 Summary of studies on determinants of patient satisfaction

S.No Author & Year Country Sample & Data Collection Factors Contributing to Satisfaction

1. Jakobsson et al., (1994) Sweden 242 Patients

Mail Survey

Satisfaction with information, Decision-making, Ward facilities, and

Medical treatment

2. Moscato et al., (2007) USA 1,939 respondents

Telephonic Interview

Clinical outcomes, Healthcare quality, and Patient follow through

3. Naidu (2009) India Meta-analysis

Systematically Review

Healthcare output, Access, Caring, Communication and Tangibles

4. Avortri et al., (2009) Ghana 885 women patients


Friendliness of staff, Prior information, Friendliness of staff, proper

explanation of health condition, privacy and respect.

5. Narang (2010) India 500 respondents


Health care delivery system, Interpersonal and Diagnostic aspect of

care, Facility, Health personnel conduct and Drug availability

6. Ashrafun & Uddin (2011) Bangladesh 190 Inpatients


Doctors‟ treatment, Behaviour of nurses, Behaviour of boys/ayas, Gifts

or tips culture

7. Mehta (2011) India 400 Patients


Amenities, Clinical care, and Physical facilities

8. Tateke (2012) Ethiopia 204 Patients


Health status, Expectations, Perceived adequacy, Perceived provider‟s

competency and welcome Approach

9. Grøndahl et al., (2013) Norway 373 respondents


Patient-related conditions, external objective-care and patients‟

perception of actual care received

10. Tucker and Adams (2001) USA 49,478 military patients


caring, empathy, reliability and responsiveness

11. Parasuraman et al., (1988) USA 200 respondents


Reliability (competence); Responsiveness (communication); Tangibles

(physical facilities); and Empathy (staff demeanour)

12. Naik et al., (2013) India 500 Inpatients


First impression, Clinical care, Nursing care, Housekeeping service,

Food service, and Overall service experience

13. Al-Ahmadi (2009) Saudi

Arabia

1,834 Nurses


Employee demographics, Job satisfaction, and Organizational

commitment, Influenced performance

14. Alhashem et al., (2011) Kuwait 500 patients


Communication, Physician care and Nursing care

15. Adams (2001) UK 625 patients


Caring, Empathy, Reliability, Responsiveness, Access, Communication

and Outcome dimensions

16. Ghosh (2014) India 225 patients


Clinical Care, Internal Environment, Communication and

Administrative procedures


59

2.4. Relationship between Healthcare Service Quality and Patient

Satisfaction

Service quality and satisfaction have been considered as two sides of same coin.

According to Oliver (1980) satisfaction is a function of the disconfirmation of

performance from expectation. In another study Oliver (1989) defined satisfaction as an

evaluative, affective, or emotional response. So customers can evaluate the object only

after they interpret the object. Hence, satisfaction is the post-purchase evaluation of

products or services given the expectations before purchase. The effects of service quality

on customer satisfaction have been studied in many fields (Amin and Zaidi, 2008;

Caruana, 2002), and have become a controversial issue in marketing literature. Some

researchers and academics viewed that service quality is an antecedent of customer

satisfaction (Parasuraman et al., 1991; McDougall and Levesque, 2000). Cronin and

Taylor (1994) have argued that consumers might not distinguish between service quality

and satisfaction, since both constructs were based on attitude formations. Despite these

notions, a general agreement in the services literature has been that service quality and

customer satisfaction have been two distinct but closely related constructs (Dabholkar,

1996).

In all the sectors, including healthcare, service quality has been established as an

antecedent of satisfaction. Baalbaki et al., (2008) found that nursing care services of

healthcare service quality was the most influential dimension in both emergency room

and in-patient encounters with respect to patient satisfaction in Lebanon hospitals.

Chahal & Kumari (2010) found that patients are satisfied when hospital service

quality matches with their expectations and requirements, consequently, the greater the

patient satisfaction. Duggirala et al., (2008) revealed that all the seven dimensions of

healthcare service quality (infrastructure, personnel quality, process of clinical care,

administrative processes, safety indicators, overall experience of medical care and social

responsibility) were significant predictors of patient satisfaction.

Gotlieb et al., (1994) investigated through an empirical study on patient

discharge; hospital perceived service quality and satisfaction offered evidence of a clear

60

distinction between perceived service quality and patient satisfaction. They found that

patient satisfaction mediated the effect of perceived service quality on behavioural

intentions, which included adherence to treatment regimens and following provider

advice.

Kessler and Mylod (2011) revealed through an empirical study that patients have

their rights and choice, and if they are not satisfied with their hospital, they have the

opportunity to switch to another hospital. Naidu (2009) found that the relationship

between health care quality and patient satisfaction is significant. Healthcare quality

affects patient satisfaction, which in turn influences positive patient behaviours such as

loyalty.

Naik et al., (2013) in their study on Indian hospitals, empirically investigated that

all the six healthcare service quality dimensions namely, first impression, clinical care,

nursing care, communication, food & housekeeping services and overall experience of

medical care, were significant factors of patient satisfaction. They had used modified

SERVQUAL scale for this purpose.

Padma et al., (2010) conceptualized through an empirical study hospital service

quality into its component dimensions from the perspectives of patients and their

attendants; and analysed the relationship between service quality and customer

satisfaction in government and private hospitals in India. Pakdil and Harwood (2005)

studied patient satisfaction in a pre-operative assessment clinic. They showed that

patients were most dissatisfied with the waiting time and positive physician-patient

interaction increased patient satisfaction more than any other provider-customer

relationship.

Ramsaran-Fowdar (2008), empirically investigated that “reliability, fair and

equitable treatment” was the most important SQ dimension influencing patient

satisfaction in Mauritius healthcare services. Rao et al., (2006) concluded that medicine

availability, medical information, staff behaviour and doctor behaviour had significant

positive influence on patient satisfaction while waiting time had negative impact on


61

Williams et al., (1998) determined that patient satisfaction did not improve after

renovation of the emergency department of a hospital under study. They further

hypothesized that satisfaction scores might improve if the goals of renovation, efficiency

and privacy were met. Zeithaml et al., (1996) revealed through an empirical study that

high-service performance increased favourable behavioural intentions and decreased

unfavourable behavioural intention. Hence, understanding not only the dimensions of

healthcare services but also the extent of their influence of patient satisfaction gives

insights to hospital managers and administrators.

In summing up, though there seems to be a consensus in the literature that patient

satisfaction and healthcare service quality are separate and unique constructs (Cronin and

Taylor, 1992), their distinctions in definitions, characteristics and dimensions still exist

(Choi et al., 2004). Much of the confusion arises from the similarities between these two

constructs. First, both are viewed as attitudinal constructs. Second, the measurement of

both constructs is based on the comparison between expectations and perceptions of

underlying dimensions (Vinagre and Neves, 2008). Finally, quality is defined as the

perceived satisfaction by some studies (Smith and Swinehart, 2001). Even though, there

are different notions related to linking of quality and satisfaction, a general agreement in

the services literature has been that service quality and customer satisfaction have been

two distinct but closely related constructs. Next section provides literature related to

behavioural intentions and linking of intentions with healthcare service quality and


2.5. Behavioural Intentions

Numerous researchers have investigated and given various definitions of behavioural

intentions. Behavioural Intention (BI) is defined as a person‟s perceived likelihood or

“subjective probability that he/she will engage in a given behaviour” (Committee on

Communication for Behaviour Change in the 21st Century, 2002). Behavioural intention

is defined as the customer‟s subjective profitability of performing a certain behavioural

act (Fishbein and Ajzen, 1975). Ajzen (1991) argued that Behavioural intention (BI)

reflects how hard a person is willing to try, and how motivated he or she is, to perform

62

the behaviour. Zeithaml et al., (1996) defined behavioural intention as a signal of whether

customers will remain or exit the relationship with the service provider.

Behavioural Intention (BI) is the most proximate predictor of behaviour (Ajzen,

1991), and behaviour is ultimately the variable that most health communication

interventions aim to influence. Two main theories used in health communication that

include Behavioural Intention are the Theory of Reasoned Action (TRA) (Fishbein &

Ajzen, 1975) and the Theory of Planned Behaviour (TPB) (Ajzen, 1991). In addition to

Behavioural intention (BI), these two theories share the variables of attitude toward

performing the behaviour and subjective norms, which are perceptions of what important

others think about the behaviour. TPB also includes perceived behavioural control over

successful performance of the behaviour. Although Behavioural intention (BI) is most

proximate to behaviour, for some behaviour it must be considered in conjunction with

behavioural control as immediately antecedent to the behaviour (Ajzen, 2006). In this

regard, three behaviours in particular have been associated with profitability and the

market share of a firm; these customer behaviours are

1. Word-of-mouth;

2. Repurchase intention; and

3. Feedback to the service provider.

Word-of-mouth refers to a flow of information about products, services, or

companies from one customer to another. As such, word-of-mouth represents a trusted

external source of information by which customers can evaluate a product or service.

Zeithaml et al., (1996) identified two dimensions to measure behavioural intention

(Word-of-mouth) - favourable and unfavourable. Favourable intentions means the

customers will convey a positive word of mouth, repurchase intention, and loyalty

(Ladhari, 2009; Zeithaml et al., 1996), while, unfavourable behavioural intention tends to

spread a negative word of mouth and conveys their negative experiences to other

customers (Caruana, 2002; Lewis, 1991; Newman, 2001), and intention to switch to

competitors (Anthanassopoulos et al., 2001). In this sense, the relationship focuses on the

63

average customer who comes back to buy, and continues to buy until it creates a positive

attitude on the company products and services.

Many researchers have found a positive association between satisfactions and

repurchase intention (Bitner et al., 1990; Jones and Suh, 2000; Cronin and Taylor, 1992).

Rust and Zahorik (1993) suggested that a satisfied customer might switch to an

alternative supplier with a view to increasing the present satisfaction level whereas a dis-

satisfied customer might remain with the existing supplier because no better alternatives

are available.

Customer feedback - refers to the transmission of negative information

(complaints) or positive information (compliments) to providers about the services used.

Such information can be useful for providers in identifying areas in which adjustments of

performance are required. Very few researchers have examined the relationship between

feedback and satisfaction (Söderlund, 1998).

Kessler and Mylod (2011) pointed out that patient satisfaction significantly

influenced end-of-life patients‟ intention to return to a hospital. If a patient is highly

satisfied with admissions, discharge and other processes it will lead to patients returning

to the hospital. Ndubusi and Ling (2005) demonstrated that friends, neighbours and

family members have great influence on prospective customers when it comes to making

decisions to patronize a services institution, and patients really depend on the personal

recommendation from family and friends (Owusu-Frimpong et al., 2010).

Suhartanto (2000) research found that there was a positive correlation between

service quality, loyalty and paying more, but negative correlation with switching. Results

found that tangible dimensions had the strongest influenced on behavioural intentions.

Zeithaml and Bitner‟s (2000) research suggested more specific behavioural intentions to

medical care such as; following instructions from the doctors, taking medications and

returning for follow-up.

Zeithaml et al., (1996) proposed a model that when healthcare service quality

assessments are high, then the patient‟s intentions are favourable; this strengthens his/her

64

relation with the organisation. The study asserts that there is positive affect between

service quality perceptions and behavioural intentions.

2.6. Relationship between Healthcare Service Quality, Patient

Satisfaction and Behavioural Intentions

The relationship among service quality, satisfaction and behavioural intensions has

received considerable attention in the marketing literature (Brady et al., 2001; Cronin and

Taylor, 1992; Zeithaml et al., 1996). Within this research area, numerous empirical

studies have reported the positive relationship between customer satisfaction and

behavioural intentions (Woodside et al., 1989; Taylor & Baker, 1994; Cronin et al.,

2000). The evidence from health care industry also indicates that there is a significant

effect of health care service perceptions on patient behavioural intentions (Headley &

Miller, 1993; Reidenbach & Sandifer‐Smallwood, 1990). As a result, perceived service

quality is viewed as one of the antecedents of behavioural intentions. Patient satisfaction

is viewed as not only influencing the outcome of health care process, such as patient

compliance with physician advice and treatment, but also patient retention and positive

word‐of‐mouth (Calnan, 1988; Zeithaml, 2000). Its effects on patient behavioural

intentions have been empirically validated in health care industry (Anderson & Sullivan,

1993; Bitner, 1990; Reichheld, 1996).

Amin et al., (2014) found that there exist a significant relationship between

healthcare service quality, patient satisfaction and behavioural intentions. The study

findings suggest that when a patient enhances their confidence it will improve the

relationship service quality, satisfaction with their doctors, and, simultaneously, increase

patient loyalty. Arasli et al., (2005) proved, with their study, the impact of service quality

perceptions of Greek Cypriot bank customers, to overall satisfaction from their bank and

to positive word of mouth. The study suggested that reliability items were the ones that

had the highest effect on satisfaction, which in turn had a significant impact on the

positive word of mouth.

65

Bitner (1990) found a significant relation between perceived service quality and

behavioural intentions in terms of word-of-mouth and repurchase intention. Similarly,

Dabholkar et al., (1996) reported a positive association between perceptions of service

quality and the likelihood of recommending a product or service. Carpenter and Fairhurst

(2005) studied the effect of utilitarian and hedonic shopping benefits on customer

satisfaction, loyalty, and word of mouth communication in a retail branded context. They

found that “utilitarian shopping benefits” that derived from the consumer‟s belief that

specific goals for a shopping trip were satisfied and “hedonic shopping benefits” that

reflect the emotional or psychological worth of the purchase, affected satisfaction which

in turn had an indirect effect word of mouth, through loyalty.

Cronin and Taylor (1992) have evaluated the impact of both quality and

satisfaction on behavioural intentions. They reported that satisfaction had a stronger and

more consistent effect on purchase intentions than did service quality. Eleuch (2011)

highlighted that in the healthcare industry, loyalty is affected by technical attributes and

the patient‟s first impression of the staff and services setting. Indeed, the most commonly

applied model for behavioural intention starts from the well-established notion that when

patients are highly satisfied with a hospital, they continue dealing with the hospital, and

send positive messages to other people.

Garman et al., (2004) point out that the relationship between patient satisfaction

and doctors significantly increases the likelihood of the patient returning to the hospital

for treatment. In this sense, patients often develop an attitude towards purchasing

behaviour based on past experience and which leads to loyalty. Gaur et al., (2011) found

a significant relationship between patient satisfaction and loyalty. These findings suggest

that when a patient enhances their confidence it will improve the relationship satisfaction

with their doctors, and, simultaneously, increase patient loyalty.

Kessler and Mylod (2011) investigated how patient satisfaction affects the

propensity to return to hospital. The results showed that there is a statistically significant

link between satisfaction and loyalty. Although, overall, the satisfaction effect is

relatively small, contentment with a certain hospitalization experience is important.

Parasuraman et al., (1988) have reported that a positive relationship exists between

66

perceived service quality and behavioural intentions. In particular, positive word-of-

mouth has been clearly associated with superior service quality.

Taylor and Baker (1994) tested the moderating role of customer satisfaction on

the relationship between service quality and behavioural intentions. They found that the

moderating influence is supported in communication, transportation, recreation industry,

but not in the health care industry. Zeithaml et al., (1996) have reported that a positive

relationship exists between perceived service quality and behavioural intentions. In

particular, positive word-of-mouth has been clearly associated with superior service

quality.

Table 2.9 Literature Linking Service Quality, Value, Satisfaction and Intentions to

Various Service Encounter Outcomes

S.No Source Relevant Constructs Link(s) to

Outcomes

1. Amin and Nasharuddin (2013) SQ, SAT, BI SQ, SAT

2. Anderson and Sullivan (1993) SQ, SAT, BI SQ, SAT

3. Andreassen (1998) SQ, SAT, SV, BI SAT

4. Athanassopoulos (2000) SAC, SQ, SAT, BI SQ

5. Baker and Crompton (2000) SQ, SAT, BI SQ, SAT

6. Bolton and Drew (1991) SQ, SAT, SV, BI SV

7. Chang and Wildt (1994) SAC, SQ, SV, BI SV

8. Chaniotakis and Lymperopoulos (2009) SQ, SAT, WOM SQ, SAT

9. Cronin and Taylor (1992) SQ, SAT, BI SAT

10. Cronin, Brady, Brand, and Shemwell (1997) SAC, SQ, VAL, BI SV

11. Fornell et al. (1996) SQ, SAT, SV, BI SAT

12. Ostrom and Iacobucci (1995) SAC, SQ, SAT, VAL, BI SAT

13. Parasuraman, Berry, and Zeithaml (1991) SQ, BI SQ

14. Parasuraman, Zeithaml, and Berry (1988) SQ, BI SQ

15. Qin and Prybutok (2009) SQ, SAT, BI SQ, SAT

16. Saha and Theingi (2009) SQ, SAT, BI SQ, SAT

17. Sweeney, Soutar, and Johnson (1999) SAC, SQ, SV, BI SV

18. Taylor (1997) SQ, SAT, BI SQ, SAT

19. Taylor and Baker (1994) SQ, SAT, BI SQ

20. Woodside, Frey and Daly (1989) SQ, SAT, BI SQ, SAT

21. Zeithaml (1988) SAC, SQ, SV, BI SV

22. Zeithaml, Berry, and Parasuraman (1996) SQ, BI SQ


In summary, this chapter has discussed the nature of service quality, dimensions used in

assessment of service quality, criticism and applicability of service quality and

assessment of service quality has been explored. Secondly, this chapter has also discussed

67

healthcare service quality definition, dimensions and the application of assessment

approach to healthcare. Next, healthcare quality, patient satisfaction key dimensions and

relation with quality were discussed. Last but not least this chapter ends with intentions

of patient‟s i.e. revisit and recommendations to others was discussed. The next chapter

describes and justifies a methodology to investigate research objectives and hypotheses

of proposed study.

2.7. Problem Statement and Research gap

For the last three decades awareness of quality issue in healthcare setting has been

increasing. However, the year 2000 has witnessed paradigm shift in healthcare service

quality by considering the patient perspective. Thus, healthcare service providers

identified various new dimensions of healthcare service quality that differ from the

traditional service quality dimensions. The healthcare service quality was found to be a

function of patient‟s self-reported experience of service or care received by service

provider as a useful and valuable service quality measurement metric

From the above extensive review of literature is inferred that few studies have

been conducted on patient‟s expectations and perceptions of healthcare service quality,

patient satisfaction and behavioural intentions in India. However, most of the studies

have been confined to the only one of the above mentioned issue. Some nationalized

studies have attempted to measure quality and satisfaction but validity and reliability of

the scales are questionable. Moreover, there is a scarcity in understanding patient‟s

expectations & perceptions and patient satisfaction with corporate healthcare service

provider in India.

Though number of research studies has been conducted on healthcare service

quality, no general agreement on the content and nature of dimensions is established in

the literature. The most widely used instrument (i.e., SERVQUAL) has been criticized by

a number of researchers (Anderson & Zwelling, 1996; Baldwin & Sohal, 2003; K-S Choi,

et al., 2005; Curry & Sinclair, 2002; Jain & Gupta, 2004; Kang & Jeffrey, 2004). Though

SERQUAL instrument is criticized by many researchers, the instrument is more

comprehensive among other instruments (Nordic model; Three-component model; The

68

multilevel model; Hierarchical approach; SERVPERF; 5Q - Model, JACHO -Model and

TQM) and it is tested in many service industries such as Healthcare; Banking; Fast

Foods; Telecommunications; Retail Chains; Information System; Hospitality and

Tourism; Airline Service; Higher Education; Dormitory Services and Insurance Industry.

To get the holistic overview there is a need to measure patient‟s expected and perceived

service quality and satisfaction by using SERVQUAL (Parasuraman et al., 1988) scale in

Indian corporate healthcare sector.

In addition, a plethora of research has been undertaken in the healthcare sector,

for instance service quality and satisfaction (Vyas and Thakkar, 2005; Chahal and

Sharma, 2004; Gross, 2003; Sardana, 2003), service quality and image (Sardana, 2003),

and satisfaction and loyalty (Corbin et al., 2001; Ruyter et al., 1998), but composite

research on healthcare service quality and its impact on patient satisfaction and

behavioural intentions is still scarce.

Lastly, healthcare service quality and patient satisfaction in the private healthcare

sector is continuously growing, and public healthcare sector is continuously deteriorating

(Chahal, 2009; Chahal et al., 2004; Sardana, 2003) resulting in the switching of patients

from public hospitals to private hospitals. In addition, prompt service, less waiting time,

service guarantees, good physical environment, and better interaction are some other

factors contributing to comparatively positive perception of patients for private healthcare

services.

Though there are numerous studies on healthcare service quality and patient

satisfaction, no study has attempted to analyse corporate hospital service quality from the

patient‟s expectation and perception perspective and the present study addresses this gap

in the literature. Furthermore, the research aims to identify the key determinants of

patient satisfaction with corporate hospital service providers. Last but not least, this

research attempts to test the relationships among healthcare service quality, patient

satisfaction and behavioural intentions in Indian corporate healthcare Services. The

results of this research will potentially contribute to corporate healthcare care

management and hospital quality improvement.

69

This chapter aims to explain appropriate methodology for achieving the research

objectives. The overall purpose of this research study was to examine as well as extend

the body of knowledge and understanding regarding patient‟s perceptions and

expectations of corporate hospital service quality. Based on the published literature

review, a conceptual model and hypotheses were developed. In order to examine the

corporate hospital service quality, patient were asked to respond to a number of survey

questions measuring the different constructs included in the proposed theoretical model.

This chapter has been divided into twelve sections. Details of the methodology used in

this research study are described in the following sections. Section 3.1 describes the

design of the study. Section 3.2 describes source of the study. Section 3.3 provides

research objectives of the study. Section 3.4 elaborates the research model. Section 3.5

outlines the operationalisation of variables and hypotheses setting. Section 3.6 describes

the development of the research instrument. Section 3.7 gives an account of sampling

design. Section 3.8 gives an account of the data collection procedure. Section 3.8

provides reliability and validity of research instrument used in this study. Section 3.10

describes the data analysis procedures and techniques.

3.1. Research Design

The research design is described as “the framework or plan for a study, used as a guide to

collect and analyse data”. The research design helps a researcher to draw boundaries for

the research, which consists of defining study settings, type of investigations that needs to

be carried out, the unit of analysis and other issues related to the research (Saunders et al,

2009). There are three types of research designs identified from the literature review

RESEARCH METHODOLOGY

CHAPTER–3

70

include namely: exploratory research design, descriptive research design and causal or

explanatory research design.

The exploratory research was employed in the first stage to obtain the background

information about the research problem and to generate hypotheses by thorough

investigation of the literature. As a result, the researcher identified constructs and

formulated hypotheses based on the literature and previous empirical studies, as reported

in Chapter 2. The research problem was identified and the purpose of the research has

been clearly stated such that this research study focuses on testing of an integrated model.

In second stage descriptive research design was used to describe the characteristics of the

respondents and to determine the descriptive statistics like, frequencies, percentage, mean

and standard deviation of the constructs used. However, descriptive research could not

explain the relationship among the variables (Zikmund, 2000); therefore, explanatory

research was used in order to explain the relationship and association between variables

of the model. Figure 3.1 depicts research design.

Figure 3.1 Research Design

Literature Review

Identify research gap and need

of the study

Theoretical framework and

Hypothesis setting

Questionnaire Development

Final Survey and Data

collection

Data analysis

Results, Discussions and

Conclusions

Measurement Model

Structural Model

Assessment of Reliability and Validity

Hypotheses Testing

Pre-testing and Pilot Study

71

In this study, a quantitative data collection method and direct contact approach

was employed to obtain response from in-patients of corporate hospitals. A cross-

sectional study employing a direct contact approach method was carried out for collecting

the data. The direct contact approach was used because it is designated to deal directly

with respondent‟s thoughts, perceptions, expectations, feelings and opinions, especially

when collecting information regarding perceptions, attitude and beliefs is concerned

(Zikmund, 2003). In addition, direct contact approach offers more accurate means of

evaluating information about the sample and enables the researcher to draw conclusions

generalising the findings from a sample to population (Hair et al., 2013 and Zikmund,

2003). Moreover, this method is considered to be quick, economical and efficient, and

can be administered to large sample (Zikmund, 2003). In addition, this study also

employed a two-step approach in the structural equation modelling (SEM) analysis to

examine the hypothesised relationships between the latent constructs in the proposed

research model.

3.2. Data Source

For the purpose of this research study both the primary and secondary data is collected.

3.2.1. Primary Data: Primary data was collected through personal contact approach by

administering a structured questionnaire to the inpatient respondents admitted into the

hospital department and stayed for minimum 3 days in the selected studied hospital

functioning in the India.

3.2.2. Secondary Data: The secondary data sources primarily included existing

literature published in the journals, magazines, newspapers, textbooks, articles,

government reports etc. Existing literature in the form of research articles on the

individual acceptance of healthcare service quality and satisfaction has been identified,

reviewed and analysed. In this process, authors found that relevant research works on

healthcare service quality and patient satisfaction were spread across various disciplines

such as world health organisation bulletin, healthcare marketing report, hospital

management portal, Indian service marketing reports, health information systems records,

and national and international healthcare organisations publications or reports. Hence, in

72

order to cover wide range of journals, convenience sampling method has been adapted for

collecting related literature. Different secondary resources have been considered as

permitted sources for data collection because of time and resource constraints. List of

sources used for collecting secondary data was tabulated below in Table 3.17;

Table 3.1 Secondary Data Collection Sources

1. Public (National and

International) Health

Organisations Reports

World Health Organisation (WHO)

Medical Council of India (MCI)

Ministry of Health and Family Welfare (MoHFW)

Central Bureau of Health Intelligence – India (CBHI)

Ministry of Statistic and Programme Implementation (MSPI)

National Institute of Health and Family Welfare (NIHFW)

Indian Institute of Health and Family Welfare (IIHFW)

Public Health Foundation of India (PHFI)

National Rural Health Mission (NRHM)

National Health Policy (NHP)

Census of India (CI)

2. Private healthcare research

reports.

Credit Analysis & Research Limited (CARE)

The Associated Chambers of Commerce and Industry of India

(ASSOCHAM)

National Skill Development Corporation (NSDC)

India Brand Equity Foundation (IBEF)

Corporate Catalyst India (CCI)

India Law Offices (ILO)

Grant Thornton-India (GTI)

Indian Chamber of Commerce (ICC)

Confederation of Indian Industry (CII)

3. News-Papers & Magazines. Magazines News Papers

Express Healthcare

Health biz India

Forbes India

Business India

eHealth

The Hindu

Indian express

Business Standard

Economic Times

Business Line

4. Online Database Emerald Group Publishing

Limited

Springer

Taylor & Francis

Elsevier/Science Direct

Sage Publications

Proquest

EBSCO HOST

Wiley Online

Palgrave Macmillan

JSTOR

Google Scholar

Inflibnet

Libgen.com

Source: Compiled for this Study

3.3. Research Objectives

This research study intends to address a research problem, i.e. what are the important

dimensions of healthcare service quality in corporate hospitals according to expectations

and perceptions of patients, and is there any positive relationship between service quality,

satisfaction and behavioural intentions in healthcare services of corporate hospitals.

73

Further it addresses what factors affect patient satisfaction at corporate hospitals. As

discussed in the earlier healthcare marketing literature, service quality is essential factor

for success of an organisation in a competitive environment. In this study, the objective

was to develop insights appropriate to achieve and maintains high standards of quality

and satisfaction in Indian Corporate Hospitals. By addressing the above stated research

problem, hence this study aims to achieve the following objectives.

1. To measure healthcare service quality in Indian corporate hospitals.

2. To identify key determinants of patient satisfaction in Indian corporate hospitals.

3. To examine the effect of healthcare service quality on patient satisfaction and


4. To investigate the effect of patient satisfaction on behavioural intentions.

3.4. Research Model

The research model for the present study consists of three integrated components. First

component measures the patient‟s expectation and perception level of healthcare service

quality provided by the hospitals. Parasuraman et al., (1988) define service quality

(SERVQUAL) in terms of five primary dimensions, namely: tangibility, reliability,

assurance, empathy and responsiveness, and all of them are retained in present study.

Second component is related to find key determinants of satisfaction. For determining

key factors of satisfaction, six major dimensions are adapted from Woodside et al.,

(1989) study, namely: admission process, nursing care services, medical care services,

food services, housekeeping services and overall services experience. The third

component in this study is Behavioural Intentions (whether a patient would revisit in

future or recommend the hospital to friends and relatives who are seeking care). This

construct was adapted from Zeithaml, (1996). In this dimension four items are mainly

related to revisit to same hospital or recommend to whom seeking care. Figure 3.2 depicts

the research model for the study.

74

Expected Tangibility

Expected Reliability

Expected Empathy

Expected Assurance

Expected

Responsiveness

Perceived

Tangibility

Perceived Reliability

Perceived Empathy

Perceived Assurance

Perceived

Responsiveness

Admission

Process

Nursing

Services

Medical

Services

Housekeeping

Services

Food

Services

Overall

Services

Health

Care

Service

Quality

Patient

Satisfaction

Behavioural

Intentions

Figure 3.2 Research Model

75

3.5. Operationalisation of Variables and Hypotheses Setting

The proposed research model depicts mainly three constructs namely, healthcare service

quality, patient satisfaction and behavioural intentions. In the proposed research model,

based on the work of Parasuraman et al., (1985, 1988) related to the SERVQUAL model

as well as of Youssef et al., (1996) and Fuentes (1999) for the measuring expected and

perceived healthcare quality, “service quality dimensions” were measured by using five

latent variables, namely tangibles, reliability, responsiveness, assurance and empathy. In

addition to these latent variables, “Patient Satisfaction” and “Behavioural Intentions”

were included in the model, as measurement variables. Each of these variables, and key

determinants of satisfaction used to measured patient satisfaction, can be operationalized

as follows;

Reliability: The indicators of this variable, which are related to the ability to perform the

promised service dependably and accurately, incorporated the “organisation” and the

“reliability of the tertiary care hospitals” as well as, the “kept promises”, and the “right

way to carry out services”. Reliability has been viewed as a dimension of healthcare

quality for many studies (Cronin and Taylor, 1992; Carman, 1990; Vandamme and

Leunis, 1993). Corporate hospitals are established to offer super-specialty surgical

procedures including advanced cardiac, joint replacement, neurological etc. Physician

reputation is also a very important factor because patients heavily rely on word‐of‐mouth

when selecting medical care providers (Ramsaran‐Fowdar, 2005). This is supported by

empirical research (Parasuraman et al., 1988; Cronin and Taylor, 1992; Carman, 1990;

Vandamme and Leunis, 1993; and Ramsaran‐Fowdar, 2005). This evidence demonstrates

that reliability is a factor of patient‟s perception and expectation of quality services with

healthcare service provider. Thus it is proposed that:

H1 a: Expected Reliability (ERAB) has a positive influence on healthcare service

quality (HSQ).

H1 b: Perceived Reliability (PRAB) has a positive influence on healthcare service

quality (HSQ).

76

In the current study, four items were adapted (Parasuraman et al., 1988; Youssef et al.,

1996; Ramsaran-Fowdar, 2008) to measure reliability effect on healthcare service quality.

The employed items depicted in Table 3.2.

Table 3.2 Construct items of Reliability

Label Item Adapted From

RAB1 When a patient has a problem; this hospital shows a

sincere interest in solving it.

Parasuraman et al., (1988)

Ramsaran-Fowdar (2008) RAB2 This hospital is competent in providing accurate

services (e.g. correct records, accurate diagnosis,

timely treatment etc.).

Youssef et al., (1996)

RAB3 The staff of this hospital is keeping patients well-

informed about the follow-up examinations.


RAB4 This hospital provides efficient, reliable and affordable

prescribed medicines.

Ramsaran-Fowdar (2008)

Responsiveness: Willing to help customers and provide prompt services (Parasuraman et

al., 1988). The indicators of this variable, incorporated the “24-hour service availability”,

the “staff willing to respond to any need”, the “staff spends time with each one in order to

answer their questions”, and the “staff responds quickly”. This dimension assesses how

reactive healthcare service providers are to patients‟ needs and requirement (Tucker and

Adams, 2001). Patient admitted in surgical and super-specialty departments (cardiology,

neurology etc.) are seeking immediate medical care. Prompt service has a key impact on

patient‟s health status, sometimes even his or her life. This is supported by empirical

research (Parasuraman et al., 1988; Youssef et al., 1996; Chaniotakis and

Lymperopoulos, 2008; and Tucker and Adams, 2001). This evidence demonstrates that

responsiveness is a factor of patient‟s perception and expectation of quality services with

healthcare service provider. Thus the hypotheses are developed as follows:

H2 a: Expected Responsiveness (ERES) has a positive influence on healthcare service

quality (HSQ).

H2 b: Perceived Responsiveness (PRES) has a positive influence on healthcare service

quality (HSQ).

77

This study, four items were adapted to measure perceived and expected responsiveness

effect on healthcare service quality. All the four items are listed below as follows.

Table 3.4 Construct items of Responsiveness


RSP1 The services are provided at the promised times

(e.g. admission, lab services, clinical care,

emergency care, casualty services etc.).


RSP2 Hospital staffs consistently follow-up sick

cases.


RSP3 The hospital consulting hours are convenient. Chaniotakis and Lymperopoulos (2008)

RSP4 Doctors and nurses are always willing to help

patients.


Assurance: Assurance is the courtesy and knowledge of staff and their ability to inspire

trust and confidence. The indicators of this variable, incorporated the “knowledgeable

and experienced staff”, the “friendly and courteous staff”, the “treatment with dignity and

respect”, and the “staff explains thoroughly medical condition”. Patients admitted in

tertiary care hospitals especially in surgical departments are always afraid of their

illnesses. They want the professionals to be friendly, showing respect for patients,

protecting patient privacy and confidentiality, and acting as advocates for the patients

(Sofaer and Firminger, 2005). Thorough explanation of patient‟s medical condition and

treatment can make patients feel safe and relaxed, which contribute to the outcome of the

medical care. The impact of assurance on healthcare service quality is supported by

empirical research (Youssef et al., 1996; Sofaer and Firminger, 2005; Chaniotakis and

Lymperopoulos, 2008; and Padma et al., 2010). This evidence demonstrates that

assurance is a factor of patient‟s perception and expectation of quality services with


H3 a: Expected Assurance (EASS) has a positive influence on healthcare service

quality (HSQ).

H3 b: Perceived Assurance (PASS) has a positive influence on healthcare service

quality (HSQ).

78

In the current study, five items were adapted (Youssef et al., 1996; Chaniotakis &

Lymperopoulos, 2008; Padma et al., 2010) to measure perceived and expected assurance

impact on healthcare service quality. Employed items are presented in Table 3.4.

Table3.4 Construct items of Assurance


ASS1 Doctors and nursing staff are consistently courteous

with their patients


ASS2 Staff of this hospital are very knowledge Youssef et al., (1996)

ASS3 Staff instils confidence in patients (e.g. convincing

and explanations etc.).

Padma et al., (2010)

ASS4 Patients feel safe while they receive services from

the personnel of this hospital.


ASS5 Staff of this hospital thoroughly explains medical

conditions of the patients.

Chaniotakis & Lymperopoulos

(2008)

Empathy - Empathy is the individualised care provided to patients (Parasuraman et al.,

1988). The indicators of this variable, which are related to the caring and individualised

attention the organisation provides to its customers, incorporated the “staff understands

specific needs of patients”, the “staff show sincere interest”, the “staff offers personalised

attention” and the “staff looks for the best for the patients interests”. Patients want

professionals in tertiary care hospitals not only friendly and courteous, but also having

personalised knowledge of the patients, and showing individualized kindness, sympathy

and attention to them (Sofaer & Firminger, 2005). Receiving individualised care can

strengthen patients‟ emotional safety and trust, which can reduce their feeling of

vulnerability and anxiety (Sofaer & Firminger, 2005). The impact of empathy on

healthcare service quality is supported by empirical research (Parasuraman et al., 1988;

Sofaer and Firminger, 2005). This evidence demonstrates that empathy is a factor of

patient‟s perception and expectation of quality services with healthcare service provider.

Thus the hypotheses are developed as follows:

H4 a: Expected Empathy (EEMT) is positive influence on healthcare service quality

(HSQ).

79

H4 b: Perceived Empathy (PEMT) has a positive influence on healthcare service

quality (HSQ).

This study were adapted five items (Parasuraman et al., 1988; Fowdar, 2005; Vandamme

& Leunis, 1993) to measure perceived and expected empathy impact on healthcare

service quality. Measured items are listed in Table 3.5.

Table 3.5 Construct items of Empathy


EMT1 Doctors keep their patients informed and listen

to them. Fowdar (2005)

EMT2 Hospital staff understand the specific needs of

their patients (recognizing the importance of the

patient, what the patient wants etc.,).

Chaniotakis & Lymperopoulos (2008)

EMT3 Clinical staff has the knowledge and skills to

respond to the patients‟ problems.


EMT4 This hospital provides individual attention to the

patient‟s problems and care. Fowdar (2005)

EMT5 This hospital provides 24 hours services Vandamme & Leunis (1992)

Tangibles: The construct “tangibles” reflects physical facilities, equipment and

appearance of personnel (Parasuraman et al., 1988). The indicators of this variable, which

is related to the facilities and the equipment of the hospital, incorporated the “comfortable

and friendly environment”, the “clean environment”, the “up-to-date equipment”, and the

“clean and comfortable rooms”. The efficient design and layout of the environment can

not only affect the pleasantness of the surroundings (Kotler, 1974), but also direct

patients to the appropriate treatment room.

The impact of tangibility on healthcare service quality is supported by empirical research

(Parasuraman et al., 1988; Sofaer and Firminger, 2005). This evidence demonstrates that

tangibility is a factor of patient‟s perception and expectation of quality services with


H5 a: Expected Tangibility (ETAN) has a positive influence on healthcare service

quality (HSQ).

80

H5 b: Perceived Tangibility (PTAN) has a positive influence on healthcare service

quality (HSQ).

This study adapted four items from extant literature (Woodside, 1989; Nicklin and

McVeety, 2002; Chang et al., 2007 and Biork et al., 2007), were used to measure

perceived and expected tangibility impact on healthcare service quality. Employed items

listed in Table 3.6.

Table3.6 Construct items of Tangibles


TAN1 The physical facilities at this hospital are visually

appealing (e.g. well maintained reception area,

billing and registration facilities, etc.).



Vandamme and Leunis, (1993)

TAN2 Staffs of this hospital are neat in appearance (e.g.

staff with uniform and appropriate name badges,

professional appearance of staff etc.).


Vandamme and Leunis, (1993)

TAN3 This hospital has Up-to-date and well maintained

medical facilities and equipment.



TAN4 This hospital provides up-dated informative

broachers about services offered.



Healthcare Service Quality (HSQ): Healthcare Service Quality means, providing

patients with appropriate services in a technically competent manner, with good

communication, shared decision making and cultural sensitivity (Schuster et al., 1998).

The degree to which healthcare services for individuals and population increases the

likelihood of desired health outcomes and is consistent with the current professional

knowledge (Lohr, 1991). Leebov et al., (2003) believe that quality healthcare is the right

and ethical thing. They argue that “doing the right things right and making continuous

improvements, obtaining the best possible clinical outcome, satisfying all customers,

retaining talented staff and maintaining sound financial performance”. In this study, two

items were used to measure overall service quality of corporate hospitals, employed items

were adapted from the previous measures extensively applied in the health care industry

(Parasuraman et al., 1988; Youssef et al., 1996; Vandamme and Leunis, 1993). Table 3.7

presents construct items:

81

Table3.7 Construct items of Healthcare Service Quality (HCSQ)


HCSQ1 The overall feelings about the quality of healthcare service

provided at this hospital are better than I expected


HCSQ2 All things considered, quality of care received from this

hospital quiet excellent

Vandamme and

Leunis, (1993)

The hypothesised relationship of healthcare service quality with patient satisfaction and

behavioural intentions described below as follows;

Healthcare Service Quality and Patient Satisfaction: The effects of service quality on

customer satisfaction have been studied in many fields (Amin and Isa, 2008; Caruana,

2002), and have become a controversial issue in marketing literature. Some researchers

and academics viewed that service quality is an antecedent of customer satisfaction

(Parasuraman et al., 1985, 1988, 1991). In the hospital industry, Naidu (2009) found that

the relationship between health care quality and patient satisfaction is significant. A

patient is satisfied when hospital service quality matches with their expectations and

requirements, consequently, the greater the patient satisfaction (Chahal and Kumari,

2010). However, patients have their rights and choice, and if they are not satisfied with

their hospital, they have the opportunity to switch to another hospital (Kessler and

Mylod, 2011). Furthermore, there is no consensus concerning the relationship between

service quality and patient satisfaction in the hospital industry, as numerous researchers

in the healthcare industry are more focussed on measuring technical and functional

quality rather than patient satisfaction (Gill and White, 2009), and patient satisfaction

continues to be measured as a proxy for the patient‟s assessment of service quality

(Turris, 2005). Thus, it is proposed that:

H6 a: Healthcare service quality has a significant relationship with patient satisfaction

Healthcare Service Quality and Behavioural Intentions: Chahal and Kumari (2010)

investigated that service quality leads to patient satisfaction and patient loyalty.

Additionally, Gaur et al., (2011) found a significant relationship between service quality

and patients behavioural intentions. These findings suggest that when a patient enhances

their confidence it will improve the relationship quality with their doctors, and,

82

simultaneously, increase patient loyalty. Consequently, Garman et al., (2004) point out

that the relationship between patient satisfaction and doctors significantly increases the

likelihood of the patient returning to the hospital for treatment. In this sense, patients

often develop an attitude towards purchasing behaviour based on past experience

(Caruana, 2002), and which leads to loyalty (Amin et al., 2011; Kessler and Mylod,

2011). Indeed, the most commonly applied for behavioural intention starts from the well-

established notion that when patients are highly satisfied with a hospital, they continue

dealing with the hospital, and send positive messages to other people. Therefore, the

interaction between patients and service provider is one of the main factors in

determining patient intention, thus the hypothesis is developed as follows:

H6 b: Healthcare service quality has a significant relationship with behavioural

intentions

Admission Process: Admission in hospital was based on patients‟ statements about

difficulties in procedure of placement in the hospital, time that passed between from

coming in the hospital to placement in the room and starting with diagnosis and

treatments, as well as, on time that passed between from admission in the hospital to first

doctor visiting (Janicic et al., 2011). Admission Process of hospital includes the

processes of admission, stay and discharge of patients. Many studies reported that

patients are not happy with the long waiting times for diagnosis, treatment, etc. in the

hospitals across countries. The ease of getting appointments, ambulance services,

simplicity of admission and discharge, etc., all are essential in ensuring a hassle-free

treatment to patients (Padma et al., 2010). Admission Process is one of the important

issues of administrative process is the delay at different stages of patients hospital stay

(Duggirala et al., 2008). So, well maintained admission procedures are required to make

patients stay in the hospital a courteous one. Thus, it is proposed that:

H7: There is a positive relation between Admission Process (AP) and Patient

Satisfaction (PS).

This study employed three items to measure relation between admission processes of

corporate hospitals with their patient satisfaction levels. Table 3.8 depicts the construct

items.

83

Table3.8 Construct items of Admission Process


AP1 Getting appointment in this hospital is easy. Woodside et al., (1989)


AP2 Admission personnel of this hospital are providing clear

information (direction, schedule etc.) to patients.

Janicic et al., (2011)

AP3 Admission personnel of this hospital are very courteous and

helpful to patients.

Woodside et al., (1989)

Duggirala et al., (2008)

Medical Services: This is the core service construct of hospital services. Medical care

explains “what” of a service including the width and depth of services. When hospital

fails in the aspect of providing friendly and quality services to their patients, they may not

perceive the service to be of high quality if the doctor lacks the necessary competence

and skills. Many authors (Lam, 1997; Sohal, 2003; Kang and James, 2004; Rose et al.,

2004; and Duggirala et al., 2008) in their study in healthcare they used medical care is the

significant determinant of patient satisfaction. Thus the hypothesis is developed as

follows;

H8: There is a positive relationship between Medical Care Services (MS) and Patient

Satisfaction (PS).

In this current study, four items were used to measure significance between medical care

services of corporate hospitals with their patient satisfaction levels. The measured items

listed in Table 3.9.

Table 3.9 Construct items of Medical Services


MS1 Doctors of this hospital are knowledgeable to answer

patients‟ questions satisfactorily.

Rose et al., (2004)


MS2 Doctors of this hospital spend enough time with patients. Rose et al., (2004)

MS3 Doctors of this hospital are very courteous and ready to

respond in emergency.

Sohal, (2003)


MS4 Doctors of this hospital are extremely careful in explaining

what patients are expected.


Kang and James, (2004)

Nursing care: Nurses perceptions lead to actions that affect patient safety, which are

critical to all hospitals and healthcare providers. Nurses‟ actions also affect service

quality, reduce mortality and morbidity, enhance care effectiveness, control costs,

84

medical and legal complications. Nurses are a vital resource that any hospital or

healthcare provider has to ensure patient safety (Aiken et al., 2002). Healthcare leaders

need to understand factors that affect patient satisfaction perceptions when creating a

patient safety culture. Few international authors are used nursing care constructs and they

measured and estimated patient satisfaction in hospital services (Lam, 1997; Woodside,

1989; Nicklin and McVeety, 2002; Chang et al., 2007 and Biork et al., 2007). Thus the

hypothesis is developed as follows;

H9: There is a positive relation between Nursing Care Services (NS) and Patient

Satisfaction (PS).

This study adapted four items from extant literature (Woodside, 1989; Nicklin and

McVeety, 2002; Chang et al., 2007 and Biork et al., 2007), were used to measure

significance between nursing care services of corporate hospitals with their patient

satisfaction levels. Employed items listed in Table 3.10.

Table3.10 Construct items of Nursing Care Services


NS1 Nursing staff of this hospital is knowledgeable to perform the

service very well.


Chang et al., (2007)

NS2 Nurses of this hospital perform the required services (tests,

procedure, medication dispensing) at exactly the right time.

Nicklin and McVeety,

(2002)

NS3 Nursing staff of this hospital is very courteous to patients. Biork et al., (2007)

NS4 Nursing staff of this hospital always responds in a reasonable

length of time.


Chang et al., (2007)

Housekeeping Services: Housekeeping can be defined as a service which deals with

cleanliness and aesthetic of hospitals and disposal of waste, using appropriate methods,

equipment and manpower, thus providing safe and comfortable environment conductive

to patient care (Chandorkar, 2009). A housekeeper may be required to increase the

number of rooms cleaned daily, potentially enhancing efficiency or lowering quality of

services depending on if the facility is over- or understaffed (Bowblis and Hyer, 2013).

The proper and safe disposal of hospital waste constitutes an extremely important aspect

of hospital operations from both a managerial and marketing standpoint. Woodside et al.,

85

(1989), find a halo effect of association between hospital housekeeping services with

patient satisfaction. Thus, it is proposed that:

H11: There is a positive relation between Housekeeping Services (HKS) and Patient

Satisfaction (PS).

In the current study, four items were used to measure significance levels between

housekeeping services of corporate hospitals with their patient satisfaction. The construct

items are listed in Table 3.11.

Table 3.11 Construct items of Housekeeping Services


HSK1 Housekeeping staffs of this hospital have knowledge in

maintaining hygiene of hospital premises.


HSK2 Bathroom facilities/Cleanliness/Decor of this hospital is

well maintained.

Bowblis and Hyer,

(2013)

HSK3 Housekeeping staff of this hospital is well trained in

procedures for the collection and handling of wastes.


HSK4 Housekeeping staff of this hospital is knowledgeable to

maintain bio-degradable contents and their segregation.


Food Services: Patient meals are an integral part of treatment hence the provision and

consumption of a balanced diet, essential to aid recovery (Stratton et al., 2006). Yet, the

relevance and importance of patient meal service, when compared with many clinical

activities, is not always appreciated and it is often seen as an area where budgetary cuts

will have least impact. The provision of a cost effective food service to the patients, then

it optimises patient food and nutrient intake whilst minimizing food waste. Many authors

like, Lam (1997), Hasin et al., (2001), Woodside et al., (2005), Duggirala et al., (2008)

and Baalbaki et al., (2008) used food services variable to find relationship between

patient satisfaction. Thus, it is proposed that:

H10: There is a positive relation between Food Services (FS) and Patient Satisfaction

(PS).

In this study, four items were used to measure significance between food services of

corporate hospitals with their patient satisfaction levels. All these items were extracted

86

from previous measures (Woodside et al., 1989; Duggirala et al., (2008) and Baalbaki et

al., (2008)); which depicts in Table 3.12:

Table 3.12 Construct items of Food Services


FS1 The food service has been as good as I expected (consider

special diet restrictions).

Woodside et al.,(1989)

FS2 The food services menu has enough variety for me to choose

meals that I want to eat.


FS3 This hospital serves hot food and beverages at the right time. Baalbaki et al., (2008)

FS4 Food serving staffs are friendly and courteous. Baalbaki et al., (2008)

Overall Services: The overall services dimension assesses the patient‟s view of the

overall experience of care; he/she received at the hospital. de Man et al. (2002) stated that

actively managing consumer perceptions of healthcare quality is important for several

reasons. First, evaluations of higher quality are related to satisfaction, intention to use a

service again in the future if necessary, compliance with advice and treatment regimens,

choice of provider or plan, decreased turnover and malpractice law suits, and possibly

better health outcomes. In addition, high levels of consumer-perceived quality have been

shown to be positively related to financial performance in healthcare organizations.

Patient evaluation of the proper queue system, quick availability of ambulatory services,

well maintaining waiting space, well equipped laboratory, blood bank services and

radiology department, and finally comfort or quick discharge services were factors which

significantly affect degree of patient satisfaction. Thus, the dimension on overall

experience with healthcare delivery encompasses different elements of the patient‟s

experience of the treatment. Many studies like, de Man et al. (2002), Woodside et al.,

(2005), Duggirala et al., (2008) and Baalbaki et al., (2008) used overall services

experience variable to find relationship between patient satisfaction. Thus the hypothesis

is developed as follows;

H12: There is a positive relation between Overall Services Experience (OS) and

Patient Satisfaction (PS).

87

This study adapted nine items from extant literature review (de Man et al. (2002),

Woodside et al., (2005), Duggirala et al., (2008) and Baalbaki et al., (2008)); were used

to measure significance between overall experiences of healthcare services at corporate

hospitals with their patient satisfaction levels. Table 3.13 presents construct items.

Table 3.13 Construct items of Overall Service Experience


OS1 This hospital maintains proper queue management system. Woodside et al., (1989)

OS2 This hospital maintains well managed ambulatory services in

emergency.


OS3 Waiting rooms of this hospital are well furnished. Baalbaki et al., (2008)

OS4 This hospital provides well equipped X-ray services. Baalbaki et al., (2008)

OS5 This hospital conducts all lab tests in prompt way. Duggirala et al., (2008)

OS6 Blood bank services of this hospital are very efficient &

effective.


OS7 Operation theatre is well equipped with up-to-date

equipments.


OS8 The pharmacy of this hospital maintains all kinds of required

drugs.


OS9 Payment procedure of this hospital is quick and simple. Duggirala et al., (2008)

Patient Satisfaction: Patient Satisfaction as a special form of consumer attitude

reflecting on how much patients are satisfied with the healthcare service after

experiencing it (Woodside et al.,1989). Patient satisfaction is one of the main exogenous

variables in this study. Healthcare services that are provided in health care organisations

need to be satisfactory so as to provide the intended effects of the services. High-quality

services require the provision of a comprehensive set of services as well as high

performance on all aspects of care. Patient‟s satisfaction, the most important impact has

employees in healthcare institutions, doctors, nurses and other medical staff. Some

studies view patient satisfaction as a function of attributes of healthcare quality and its

turn influence on patients‟ intentions to revisit (Taylor et al., 2006). Thus, it is proposed

that:

H13: Patient Satisfaction (PS) has a positive effect on Behavioural Intentions (BI).

88

In the current study, four items adapted from extant literature (Woodside et al., (1989),

were used to measure relation between patient satisfaction and their intentions,, the items

employed to measure patient satisfaction depicts in table 3.14.

Table 3.14 Construct items of Patient Satisfaction


PS1 I am very satisfied with the medical care I received Woodside et al., (1989)

PS2 Overall, I am satisfied with this healthcare provider Woodside et al., (1989)

PS3 Overall, I am satisfied with the services provided by this

hospital


PS4 I am satisfied with ensured continuity of care provided by

this hospital (e.g. regarding notification of test results,

referral back to follow-up, transfer to hospital/specialists)


Behavioural Intention: Behavioural Intention (BI) is defined as a person's perceived

likelihood or "subjective probability that he or she will engage in a given behaviour"

(Committee on Communication for Behaviour Change in the 21st Century, 2002).

Behavioural intention is defined as the customer‟s subjective profitability of performing a

certain behavioural act (Fishbein and Ajzen, 1975). A customer with favourable service

experiences would remain loyal to the service provider, recommend it to friends and

relatives, and pay price premium (Zeithaml et al., 1996). Zeithaml and Bitner (2000)

research suggested that more specific behavioural intentions to medical care such as;

following instructions from the doctors, taking medications and returning for follow-up.

The dominant view however is that behavioural intention is a multi‐facet construct,

which includes five dimensions: loyalty to company, propensity to switch, willingness to

pay more, external response to problem, and internal response to problem (Zeithaml et

al., 1996). Some studies show a direct and relationship between healthcare service quality

and behavioural intentions (Zeithaml et al., 1996). However, some other studies confirm

that this relationship is partially or completely mediated by satisfaction (Cronin et al.,

2000; Zeithaml et al., 1996). In order to measure the behavioural intentions of the

patients towards their service provider, the following hypotheses have been proposed;

89

H14 a: The better the healthcare quality of the facilities, the greater will be the level

of intentions to return to the same hospital, if hospital care is needed in the

future.

H14 b: Patient Satisfaction (PS) with care received by corporate hospital is positively

associated with behavioural intentions to return to the same hospital if

hospital care is needed in the future.

The current study, four items adapted to measure patient‟s behavioural intentions with

corporate hospitals services. Table 3.15 presents construct items of behavioural intention

variable extracted from previous literature (Zeithaml et al., 1996).

Table 3.15 Construct items of Behavioural Intentions


BI1 I am willing to recommend this hospital to others who seek

my advice. Zeithaml et al., 1996

BI2 I will encourage my friends and relatives to go to this hospital. Zeithaml et al., 1996

BI3 If I need medical service in the future, I will consider this

hospital as my first choice.

Zeithaml et al., 1996

BI4 If I need medical service in the future, I will go to this hospital

more frequently.

Zeithaml et al., 1996

Demographic Variable:

The word demographic refers to particular characteristics of population. The word is

derived from the Greek words for people (demos) and picture (graphy). Examples of

demographic characteristics include age, race, gender, ethnicity, religion, income,

education, home ownership, sexual orientation, marital status, family size, health and

disability status, and psychiatric diagnosis. Demographic information provides data

regarding research respondents and is necessary for the determination of whether the

individuals in a particular study are a representative sample of the target population for

generalization purposes. Following characteristics are used in this study, Gender; Age

group (in years); Place of residence; Marital status; Educational level; Occupational

status; Gross monthly income (in INR); and No of days stayed in hospital. These

90

characteristics are the link to patient‟s need satisfaction levels with service provider and

affect to their revisit to the same hospital or recommend to friends and relatives whom

seeking care in future. The detailed characteristics of demographic variables used in this

study are listed below, as follows;

Gender: Gender is a demographic characteristic used as a categorical variable in this

study: 1. Male 2. Female. As discussed in earlier literature chapter, gender/sex of the

respondent directly monitor or effect the variety of healthcare measures. Some times this

variable is controlled in more complex analyses in order to assess the independent impact

of other variables such as service quality, satisfaction and intention of the respondent. So

this variable is more important controllable characteristic of this study.

Age Group (in years): Age represents how old the respondents at a particular point of

time. In this study “age” of respondent was taken categorical variable, respondents are

divided into six different age groups in this study, those are: 1. 18-29 years; 2. 30-39

years; 3. 40-49 years; 4. 50-59 years; 5. 60-69 years; and 6. 70 years & older. Age was

found to have a consistent relation with dependent variables like, service quality and

satisfaction in many previous studies. It has been widely accepted categorical variable to

measure different satisfaction level of respondent‟s in particular healthcare setting. This

socio-demographic variable used in this study is to measure patient‟s perception about

healthcare quality and their satisfaction with service provider.

Place of Residence: This represents the geographical location of residence of the

respondents. Four different residence locations were used in this study: 1. Rural; 2.

Urban; 3. Semi-urban; and 4. Metropolitan city. Respondent place/location is important

categorical variable to measure patient‟s access of health service and satisfaction with

particular service.

Marital status: Marital status defined as the current marital status of the respondent. It is

identified as a core variable in different healthcare studies. Two different marital groups

are used in this study: 1. Married; and 2. Unmarried. This variable helps to measure

different perceptions of quality and satisfaction level in respondents.

91

Educational level: This variable represents the socio-economic status of the respondent.

Education level of respondent was used as continuous variable in in this study.

Respondents are divided into five groups in this study: 1. up to S.S.C; 2. higher

secondary; 3. graduate; 4. post graduate; and 5. others. Service quality perception and

satisfaction levels vary from one group to other, so it is important variable to measure

perception of service quality and satisfaction levels with healthcare service provider, and

it helps to find patients intention to revisit or recommend to others.

Occupational status: Employment status play vital role in measuring level of

satisfaction and intentions to revisit. This represents the socio-economic status of the

respondent, there are five different groups of respondents are recorded in this study: 1.

Student; 2. Government employee; 3. Private employee; 4. Self-employed; and 5. others.

There are many past empirical studies that showed the relationship between

unemployment and health (Morris et al., 1994; and Stewart 2001).

Gross monthly income (in INR): Income is one of the most important measures of

economic well-being of respondent. In this study there are six different income level of

respondents are recorded, those are: 1. below 20,000; 2. 20,001 – 40,000; 3. 40,001 –

60,000; 4. 60,001 – 80,000; 5. 80,001 – 1, 00,000; and 6. 1,00,000 & above. This variable

varies with occupational status and education levels of respondents. There are many past

studies (Woodside et al., 2005; Duggirala et al., 2008) that have shown positive

association between income with their perception of service quality and their satisfaction

levels.

No of days stayed in hospital: staying in hospital is a supportive variable used in this

study, to measure exact perception level of patient and satisfaction level this variable

plays vital role. In this study there are five different group of time period was recoded: 1.

1 -7 days; 2. 8-14 days; 3. 15 - 21 days; 4. 20 – 28 days; and 5. 29 days & above.

Previous studies are recorded length of stay was significantly influence on quality and

satisfaction (de Man et al., 2002; Woodside et al., 2005; Duggirala et al., 2008; and

92

Baalbaki et al., 2008) and some other studies are recoded that there was no significance

between hospital stay and patient evaluation quality and satisfaction levels (K-S Choi et

al., 2005), so this is key variable to know exact relation with other dependent and

independent variables of this study.

3.6. Development of Research Instrument

3.6.1. Reasons for choosing a questionnaire:

The self-administered questionnaire is chosen as tool for data collection for the present

study because of following reasons.

Questionnaire survey is a cheaper, without significant capital investment and

quick research tool. However, there is a commonly viewed that, because of these

elements (cheap, quick response, easy construct and less capital investment),

questionnaires can be easily constructed and used without training.

Another important reason questionnaire studies can be used in the systematic

collection of information and may help to define the incidence of objective, identify an

etiological factors and investigate quality of life, as well as predict some aspects of

behaviour. Another reason for choosing questionnaire is because it is the best method to

collect original data describing a large population (Eaden, et al., 1999), hence a large

number of responses from the target population could be collected and a large number of

questions can be asked (Eaden, et al., 1999). Furthermore, questionnaire is chosen

because the data entry and analysis can be easily done using computer software packages

such as SPSS and AMOS.

93

3.6.2. Questionnaire format:

Having brief idea and developed theme on the basis of research objective, a set of

questionnaire was developed. The format of the research questionnaire was developed by

the factors identified in the focus group, depth interview and literature. The questionnaire

started with a brief introduction, which explained the purpose of conducting the research

and importance of the research. The respondents were informed that data collected is

only for academic purpose and the participation is purely voluntary and that they should

be inpatients. The respondents were informed that they have right to withdraw at any

time during the survey, if they want and were ensured the confidentiality of the data

collected. In addition, the respondents were provided with the contact information of the

researcher (i.e., Mobile number and an e-mail address) and were encouraged to raise

relevant inquiries about the study, if they wished.

This was followed by general instructions provided to the respondents to fill the

questionnaire. All the four sections of the questionnaire were provided with a brief

introduction to the section which explained the type of questions and information to be

filled in. In addition, the definition for each construct used in the study was provided for

respondent‟s better understanding, and under each construct question related to that

construct were asked. A copy of research questionnaire is included in Appendix.

The research questionnaire used in the study consisted of 4-sections,

Section-A: This section deals with number of statements to measure the

patient‟s expectation and perception level of healthcare service

quality delivered by hospital. A 22-item (SERVQUAL) item list of

expectations and perceptions were measured within one single

administration of the questionnaire. Each item was measured on a

five point Likert scale ranging from strongly agree to strongly

disagree.

Section-B: This section deals with 28-item list of statements of different

service delivery process of organisation to measure patient

satisfaction levels regarding healthcare services provided by the

94

hospital. Each item was measured on a five point Likert scale

ranging from strongly agree to strongly disagree.

Section-C: This section deals with 4-item statement to understand patient

feelings about future intentions to visit or recommend the hospital

to friends and relatives. Each item was measured on a five point

Likert scale ranging from strongly agree to strongly disagree.

Section-D: This section deals with number of statements record respondents

demographic characteristic. Each item was measured on multiple

choice answers.

Section-A, of the survey is a combination of questions are taken from the

SERVQUAL (Parasuraman et al., 1988) instrument and custom designed, with 22-item

list of questions was included (expectations and perceptions were measured with one

single administration of questionnaire). More than 60per cent of items are taken from the

SERVQUAL, and remaining 40per cent of items are taken from modified SERQUAL

instrument used in healthcare setting by Vandamme and Leunis, (1993); Youssef et al.,

(1996); Chaniotakis and Lymperopoulos (2008); Ramsaran-Fowdar (2008); and Padma et

al., (2010). The purpose of this study is not to replicate SERVQUAL, and therefore it is

not used as default. SERVQUAL instrument has been extensively researched to validate

its psychometric characteristics and this instrument has attracted criticism for its

conceptualisation of measuring service quality management issues in different industries

including healthcare setting.

Section - B, addresses six key dimensions of patient satisfaction, four dimensions

(Admission Process, nursing care services, housekeeping services and food services) are

adapted from Woodside et al., (1992). Remaining two dimensions (medical care services

and overall service experience) are adapted from Duggirala et al., (2008). This section of

attributes plays a key role to evaluate patient‟s satisfaction during hospital stay. These

dimensions has been extensively researched and validated in many healthcare studies

(Woodside et al., 1989; Lam, 1997; McVeety, 2002; Sohal, 2003; Kang and James, 2004;

95

Rose et al., 2004; Biork et al., 2007; Chang et al., 2007; Baalbaki et al., 2008; and

Duggirala et al., 2008).

Section – C, addresses four key attributes of behavioural intentions, all the four

items are adapted from Zeithaml et al., (1996). Moreover, there is strong evidence that

service quality has either a direct influence on the behavioural intentions of customers

and/or an indirect influence on such intentions, mediated through customer satisfaction

(Zeithaml et al., 1996; Cronin et al., 2000). Given these established relationships, it is

imperative that service firm‟s measure and monitor service quality and satisfaction with a

view to influencing the behavioural intentions of their customers. Many empirical

studies have investigated the relationships among the constructs of service quality,

customer satisfaction, and behavioural intentions in a variety of industries and cultures. In

this study, these items are playing key role to evaluate intentions of patients during their

stay in hospital. This part also includes six more general questions (Overall healthcare

service quality and patient satisfaction) measuring patient‟s overall value judgment of the

service offered in the hospital.

Section – D, addresses complete socio-demographic characteristic of respondents.

3.6.3. Scaling Technique:

Scaling is considered an extension of measurement which involves creating a continuum

upon which characteristics of measured object are located (Malhotra and Dash, 2011).

Scale provides a representation of the groups along which respondents arrange

themselves, thus allowing description of the distribution of respondents along the scale.

The questionnaire was kept short and precise to improve response rate. The

questionnaire comprised of dichotomous questions, multiple choice single response

questions, multiple-choice, multiple response questions, besides rating questions. Hence,

nominal, ordinal and Likert scales were employed in the questionnaire development,

which is explained below;

In this study various factors of healthcare service quality, patient satisfaction and

behavioural intentions are measured with a five point Likert interval scale with all the

96

anchors at the same distance. The anchors used in the scale range from 1 Strongly Agree,

2 Agree, 3 Neutral, 4 Disagree, 5 Strongly Disagree. This study restricts to five point

Likert rating scale because it is easy for respondents to understand five point Likert scale

and respondents can readily understand how to use it (Malhotra and Dash, 2011).

For measuring demographic characteristic of respondents, a nominal scale was

used (sex, marital status and area of residence). A nominal scale is a figurative labelling

scheme in which numbers assigned only represent labels or tags for identifying and

classifying respondents (Malhotra and Dash 2011) without any order or structure. Only

limited number of statistics are permissible which are based on frequency counts. These

include percentages, mode, chi-square, and binomial tests.

In addition, an ordinal scale is also used in this study to measure respondent

characters like “age, education levels, occupation and number of days stayed in hospital”.

An ordinal scale is a ranking scale which allows researcher to assign numbers to

respondents to indicate the relative extent to which the respondents possess some

characteristic. Thus, an ordinal scale indicates relative position, not the magnitude of the

difference between the respondents. For example, when the researcher asks respondent to

rank their experience with hospital stay, patients stayed more days in hospitals are likely

more satisfied with service providing by hospital compare to others.

3.6.4. Questionnaire Pre-testing:

Pre-testing refers to the testing of questionnaire on a small sample of target population for

the purpose of improving the questionnaire by identifying and eliminating potential

problems related to all aspects of the questionnaire including question content, wording,

sequence, form, and instructions (Malhotra and Dash, 2010). Pre-testing is to ensure that

the items elicit appropriate responses, uncover ambiguous wording or errors before the

survey is launched at large (Chahal and Kumari, 2012). As stated by Malhotra and Dash,

(2010), personal interview is best method to conduct initial pre-test and once change are

made to the questionnaire, this could be followed by another pre-test conducted by mail,

telephonic or electronic means depending on which of those methods are to be used in the

actual survey.

97

In this research pre-testing of research question was done in two steps,

a) Focus group discussion with Respondents:

Prior studies have extensively used Focus Groups as an interview technique for validating

their research instrument. In this study also focus group technique was used to check face

and content validity of the item. Problems that arise from focus group include the

difficulty of identifying difference of opinion between several groups. Focus groups tend

to discuss a topic an hour with six people (two doctors, two healthcare administrators and

two researchers). Each person has equal interview time period in ensuring balanced

discussion and focus on the research questions being discussed. From this group

discussion, some of the structure, content, or vocabulary of the questions related issues is

identified. To expedite the evaluation process and to reach sound conclusions, it is

important to carefully document the final research instrument with interactions among the

group members and after that instrument was pre-tested with small group of respondents

so as through a pilot study to reach final target respondents with the instrumented.

b) Pilot-Study:

The revised questionnaire was then subjected to the next phase of pre-testing. In this

phase a pilot study was conducted to further detect the problems in the design of survey

and to assess the psychometric properties of the survey instrument. The 40 in-patients of

two big and referred tertiary care hospitals (Care hospital and Apollo hospital)

functioning in Hyderabad, India is taken as the research population for the pilot-study.

The hospitalised patients willing to participate in the survey and with minimum four days

stay form the sample. The exploratory survey was conducted on 40 in-patients. The

personal contact approach was used to collect data from patients. The patients were also

asked to look for any difficulties with wording, problems with leading questions to again

recheck on the content and face validity. The pre-test data yielded and an inter item

analysis was then conducted to know poorly or highly associated with research

objectives.

98

From the discussion with focus group and results of pilot-study, some minor

changes to wording had been made, a questionnaire for final survey was prepared with a

22-item scale, grouped under five dimensions of perceived and expected service quality

which include; Tangibility, reliability, responsiveness, empathy and assurance, a 28-item

scale grouped under six patient satisfaction dimensions which include; Admission

process, Medical care services, Nursing care services, housekeeping services, food

services and overall service experience and intention dimension adapted from Zeithaml et

al., (1996).

3.7. Sampling Design

3.7.1. Population:

The objective of this study is to measure healthcare service quality, patient satisfaction

and behavioural intention in selected corporate hospitals. Hence, in accordance to the

objective of the study, the target population includes four referred corporate hospitals

functioning in different regions in India (Apollo Group of Hospitals, Care Hospitals,

Fortis Healthcare Ltd and Manipal Group of Hospitals). Indian Metro-cities has

approximately 300 private hospitals, of which 95 per cent are nursing homes (below 100

bed strength) offering only single speciality care. Only 5 per cent of private hospitals

offering more than two multi-speciality care. There are four main reasons to choose only

four hospitals for this study, given below are;

1. Hospital having more than 1500 bed strength and multiple chains of branches

throughout the India.

2. Hospital offering more than four medical and surgical super-speciality services such

as cardiovascular, neurological, urinary, respiratory and orthopaedic diseases.

3. Hospitals those accredited with National accreditation bodies like, JCI & NABH.

4. These four hospitals are playing very crucial role by serving the healthcare

needs of about 60 per cent of people in India, especially in the important areas like

cardiology, neurology, urology, respiratory care and orthopaedics etc.,.

99

Table 3.16 Population Characteristics

Apollo

Hospitals

Care

Hospital

Fortis

Healthcare Ltd

Manipal Group

of Hospitals

Year of establishment 1979 1997 2011 1989

Total number of branches 41 12 13 11

Bed strength 8717 1912 10307 4900

Accreditation Body JCI & NABH NABH NABH NABH

Hospital type Multi-Speciality Multi-Speciality Multi-Speciality Multi-Speciality


3.7.2. Sampling Frame:

Sampling frame refers to a complete list of population elements from which a sample

may be drawn. In this study, each in-patient getting care from five speciality departments

i.e. cardiology, neurology, urology, respiratory care and orthopaedics were finally

included to became the member of the population.

3.7.3. Sampling Method:

Probability sampling in which random selection was used it enables the researcher to

predict the probability that each element of the population will be included in the sample.

The patients were contacted on the basis of systematic random sampling. Systematic

sampling is probability sampling method in which sample members from a larger

population are selected according to a random starting point and a fixed, periodic

interval. Systematic sampling has a better chance of resulting in a representative sample,

and according to Brink and Wood (1994), randomness also associated with

generalizability which implies that the degree to which the sample represents the

population, affects the degree to which the study‟s results can be generalised to the entire

population. To establish the sample frame (selected four corporate hospitals), the

hospitalised patients from five specialties i.e. cardiology, neurology, urology, respiratory

care and orthopaedics willing to participate in the survey and with minimum 3 days stay

are considered for the sample.

100

3.7.4. Sampling Size:

The final sample size for patients is determined using pre-testing results. An adequate

sample size is pre-requisite condition for statistical analysis. The required sample size

depends on factors such as the proposed data analysis techniques, financial constraints

and access to sampling frame (Malhotra, 2003). The sample size (n) for inpatients is

determined by following formula (Hair et al., 2013);

Where,

ZB, CL = Standardised Z value associated with level of confidence.

p = estimate of expected population proportion having a desired characteristics

based on situation or prior information.

q = (I – p) or the estimate of expected population proportion not holding the

characteristics of interest.

e = acceptable tolerance level of error (percentage points).

Based on above formula and calculating sample size by using Hair‟s criterion

(Hair et al., 2013), an estimated minimum sample size was at least five times the

estimated parameter A total of 500 respondents are chosen from selected hospitals and

according to Hair‟s criterion (Hair et al., 2013) this sample size for current study was

considered adequate. A total of 25 patients were selected proportionately from five

specialties i.e. cardiology, neurology, urology, respiratory care and orthopaedics and with

minimum 3-days stay are considered for the sample from each hospital to get the required

sample size.

Table 3.17 Sample selection of respondents

Category of respondent Apollo

Hospitals

Care

Hospital

Fortis

Healthcare Ltd

Manipal Group

of Hospitals

Cardiology 25 25 25 25 Neurology 25 25 25 25 Urology 25 25 25 25 Respiratory Care 25 25 25 25 Orthopaedics 25 25 25 25 Total 125 125 125 125

101


3.8. Data Collection

Data was collected from four referred corporate hospitals functioning in different regions

in India (Apollo Group of Hospitals, Care Hospitals, Fortis Healthcare Ltd and Manipal

Group of Hospitals); patients were contacted on the basis of systematic randomly through

self-administered structured questionnaire. Data was collected from selected corporate

hospitals in two phases. In 1st phase of data collection, contacted hospitalised patients

proportionately in two hospitals Apollo Hospitals (Chennai and Hyderabad) and Care

Hospitals (Hyderabad, Nagpur and Pune). A total of 250 patients were chosen in 1st

phase, 125 inpatients of five different departments (cardiology, neurology, urology,

respiratory care and orthopaedics and with minimum 3-days hospital stay) from each

hospital. A data collection period spanned from 2nd

August 2013 to 2nd

October 2013. In

2nd

phase of data collection there are 250 inpatients proportionately chosen from another

two corporate hospitals Fortis Healthcare Ltd (Bangalore, Chennai, Pune and Nagpur )

and Manipal Group of Hospitals (Vijayawada and Bangalore), 125 inpatients of five

different departments (cardiology, neurology, urology, respiratory care and orthopaedics

and with minimum 3-days hospital stay) from each hospital. The 2nd

phase data collection

period spanned from 3rd

November 2013 to 23rd

December 2013. From the above two

phases total 500 in-patients are contacted, the direct contact approach ensured complete

responses from all the 500 hospitalised patients, but after screening 7 patients are

declined due to partial response for some questions. Hence, the final suitable data

collected for the research purpose is 493.

3.9. Reliability and Validity of Research Instrument

Majority of social science research is the enumerating of human behaviour, i.e. using any

kind of measurement instrument to observe human behaviour. According to Smallbone &

Quinton (2004), the measuring instrument of human behaviour belongs to the widely

accepted, to describe reality, easy approach of empirical-analytical or positivistic view.

Needless to say, each type of measure has specific types of issues that need to be

addressed to make the measurement meaningful, accurate, and efficient. Because of these

102

reasons, more behavioural research take place within this paradigm, measurement

instrument must be valid and reliable.

3.9.1. Reliability

Reliability is concerned with the consistency, stability and reproducibility of

measurement results (Hair et al., 2013). Reliability is the most important determinant of

measurement instrument‟s quality, such that, it helps to identify the inconsistencies and

their effect on the measurement results. According to Nunnally (1978), internal reliability

is particularly important when there are multiple measurement items for each construct.

In this study, the reliability of measurement items was evaluated by examines the

consistency of the respondent‟s answers to all the question items in the measure, as

recommended (Hair et al., 2013). Cronbach‟s alpha reliability coefficients were used to

measure the internal consistency of each measure. Reliability coefficients less than 0.6

were considered poor, 0.7 were acceptable, and those greater than 0.8 were considered

good, as suggested (Hair et al., 2013). Nunnally (1978) suggested that Cronbach‟s alpha

reliability coefficients equal to 0.7 or greater show adequate reliability. While, Hair et al.,

(2013) suggested the Cronbach‟s alpha reliability coefficients of 0.7 or higher indicate

adequate internal consistency. Therefore, a minimum cut off value of 0.7 for Cronbach‟s

alpha reliability coefficients was employed in the present research to determine the

reliability of each measure in order to find out the overall reliability of the each of the

latent constructs used in the model.

3.9.2. Validity

Validity is related with the accuracy of measures. Malhotra and Dash (2010) defined

validity as “the extent to which differences in observed scale scores reflect true

differences among objects on the characteristic being measured, rather than systematic or

random error”. In other words, validity refers to the degree to which a scale measures

what it significances to measure (Hair et al., 2013). According to Hair et al., (2013), the

better the fit between theoretical latent construct and measured items, the greater

establishment of validity. Construct‟s validity can be examined by assessing convergent

validity, discriminant validity and nomological validity, which are explained as follows

103

a) Convergent Validity

Convergent validity is the extent to which observed variables of a particular construct

share a high portion of the variance in common (Hair et al., 2013). Factor loadings of

construct, average variance extracted (AVE), and construct reliability (CR) estimation are

used to assess the convergent validity of each of the constructs (Hair et al., 2013). In

addition, Hair et al., (2013) suggested that ideal standardised loading estimates should be

0.7 or higher, AVE estimation should be greater than 0.5, and reliability estimates should

be above 0.7 to show adequate convergent validity. Therefore, in this study, the minimum

cut off criteria for loadings >0.7, AVE >0.5, and reliability >0.7 were used for assessing

the convergent validity.

b) Discriminant Validity

Discriminant validity refers to the extent to which a latent construct is truly distinct from

other latent constructs (Hair et al., 2013). Discriminant validity was assessed by a

method, suggested by Hair et al., (2013), in which the average variance extracted for each

construct is compared with the corresponding squared inter construct correlations (SIC),

and the AVE estimate consistently larger than the SIC estimates indicates support for

discriminant validity of the construct. This procedure was used in this research to assess

the discriminant validity of each of the constructs.

c) Nomological validity

Nomological validity refers the degree to which a construct behaves as it should within a

system of related constructs (Hair et al., 2013). Nomological validity is tested by

examining whether or not the correlations between the constructs in the measurement

model make sense (Hair et al., 2013). This type of the validity can be supported by

demonstrating that the CFA latent constructs are related to other latent constructs in the

model in a way that supports the theoretical framework. The construct correlations

(estimates) were used to assess the nomological validity of the model.

104

3.10. Data Analysis

According to Hair et al., (2013), the main aim of the “statistical techniques” is to assist in

establishing the plausibility of the theoretical model and to estimate the extent to which

the various explanatory factors seem to be influencing the dependent variable. The

primary purpose of this research study was to measure healthcare service quality in

Indian corporate hospitals and to found relation between three proposed latent constructs

include: healthcare service quality, satisfaction and behavioural intentions. Statistical

Package for Social Sciences (SPSS, version-20.0) was used for analysing the preliminary

data. The Analysis Moment of Structures Software (AMOS, version-20.0) was used for

Structural Equation Modelling (SEM) for measurement model analysis and structural

model to test the proposed hypothesis. Following sub-sections describe and provide

justification for using these statistical software and the techniques mentioned above.

3.10.1. Preliminary Data Analysis

Statistical Package for Social Sciences (IBM-SPSS), version 20.0, was used to analyse

the quantitative data obtained from the survey questionnaire. This software package is

widely accepted and used by researchers in different disciplines including social sciences,

and business studies (Hair et al., 2013). Therefore, this tool has been used to measure an

Indian corporate hospitals quality of service along each of the five dimensions

(SERVQUAL), by SERVQUAL scores, i.e., SERVQUAL scores = Perception score -

Expectation score (SQ = P-E). Further, identification of outliers (i.e., Mahalanobis

Distance, D2) test and find out the data normality (i.e. using kurtosis and skewness

statistics). In addition, SPSS was also applied to perform descriptive statistics such as

frequencies, percentages, mean values, and standard deviations. These analyses were

performed for each variable separately and to summarise the demographic profile of the

respondents in order to get preliminary information and the feel of the data (Hair et al.,

2013). Furthermore, before applying SEM, SPSS was used to conduct exploratory factor

analysis (EFA) for the first stage of data analysis to summarise information from many

variables in the proposed research model into a smaller number of factors, which is

known as factor/dimension reduction (Hair et al., 2013)

105

3.10.2. Outliers

Hair et al., (2013) described outliers as cases with scores that are distinctively different

from rest of the observations in a dataset. Researchers have warned that problematic

outliers can have dramatic effects on the statistical analysis such as model fit estimates

and parameter estimates (West et al., 1995) and they can create a negative variance

(Dillon et al., 1987). There are two main types of outliers i.e. univariate and multivariate

outliers.

In this study, univariate outliers were not identified because the study utilized a

Likert scale with 5 categories ranging from 1-strongly disagree to 5- strongly agree.

However, if respondents answered strongly disagree or strongly agree, these response

options might become outliers, as they are the extreme points of the scale.

Presence of multivariate outliers in data can be checked by Mahalanobis distance

(D2) test, which is a measure of distance in standard deviation units between each

observation compared with the mean of all observations (Hair et al., 2013). A large D2

identifies the case as an extreme value on one or more variables. A very conservative

statistical significance test such as p < 0.001 is recommended to be used with D2 measure

(Hair et al., 2013). In this research study, researcher measured Mahalanobis distance

using SPSS version 20.0 and then compared the critical χ2 value with the degrees of

freedom (df) equal to number of independent variables and the probability of p < 0.001.

3.10.3. Normality

Normality is defined as the “shape of the data distribution or an individual metric variable

and its correspondence to the normal distribution, which is the benchmark for statistical

methods” (Hair et al., 2013). Violation of normality might affect the estimation process

or the interpretation of results especially in SEM analysis. For instance, it may increase

the chi-square value and may possibly cause underestimation of fit indices and standard

errors of parameter estimates (Hair et al., 2013). One approach to diagnose normality is

through visual check or by graphical analyses such as the histogram. Beside the shape of

distribution, normality can also be inspected by two multivariate indexes i.e., Skewness

and kurtosis. Hair et al., (2013) point out that skewness scores outside the -1 to +1 range

106

demonstrate substantially skewed distribution. In this study, the researcher set the

maximum acceptable limit of observation values up to ±1 for the skewness and up to ±3

for the kurtosis. Thereafter, the researcher used factor analyses and structural equation

modelling for inferential statistical analyses.

3.10.4. Factor Analysis

Factor analysis (FA) techniques are used to address the problem of analysing the

structure of the correlations among a large number of measurement items (also known as

variables) by defining a large set of common underlying dimensions, known as factors.

Factor analysis takes a large set of variables and summarises or reduces them using a

smaller set of variables or components (factors) (Hair et al., 2013). The main purposes of

the factor analysis therefore include: (a) understanding the structure of a set of variables,

(b) constructing a questionnaire to measure any underlying variables, and (c) reducing a

data set to a more manageable level (Field, 2006). Therefore, at first, the researcher

identifies latent dimensions of the structure of the data and then determines the degree to

which a test item (variable) is explained by each factor. This is then followed by the

primary uses of factor analysis: summarisation and data reduction (Hair et al., 2013).

This purpose can be achieved by either exploratory factor analysis or confirmatory factor

analysis techniques. However, the exploratory factor analysis technique is used for “take

what the data give you”; whereas the confirmatory factor analysis technique involves

combining variables together on a factor or the precise set of factors for testing

hypotheses (Hair et al., 2013). In this research study, the researcher first conducted

exploratory factor analysis (EFA) to examine the dimensions of each construct (herein

called as a factor) and then confirmatory factor analysis (CFA) was performed for testing

and confirming relationships between the observed variables under each hypothesised

construct (Hair et al., 2013).

107

3.10.5. Structural Equation Modelling

Structural equation modelling (SEM) is collection of statistical models that seeks to

clarify and explain relationships among multiple latent variables (constructs). In

structural equation modelling, researchers can examine interrelated relationships among

multiple dependent and independent constructs simultaneously (Hair et al., 2013).

Consequently, Structural equation modelling analytical techniques have been used in

many disciplines and have become an important method for analysis in academic

research (Hair et al., 2013). In addition, Structural equation modelling is a multivariate

statistical approach that allows researchers to examine both the measurement and

structural components of a model by testing the relationships among multiple

independent and dependent constructs simultaneously (Hair et al., 2013). Thus, structural

equation modelling techniques were most suitable for this research study involving

multiple independent-dependent relationships that were hypothesised in the proposed

research model.

Structural equation modelling software package called Analysis of Moment

Structures (AMOS), version 20.0, was used in this research study to explore statistical

relationships between the test items of each factor and among the factors of independent

variables (i.e. HCSQ and PS) and the dependent variable (i.e., Behavioural Intention).

The reasons for selecting the Structural equation modelling for data analysis were:

Firstly, it offered a systematic mechanism to validate relationships among constructs and

indicators and to test relationships between constructs in single model (Hair et al., 2013).

Secondly, it offered powerful and rigorous statistical techniques to deal with complex

models (Hair et al, 2013). In SEM, relationships among constructs and indicators are

validated by using confirmatory factor analysis (CFA), also known as measurement

model, and relationships between constructs are tested using the structural model (Hair et

al., 2013).

108

a. Measurement model

Confirmatory factor analysis is very important technique of structural equation modelling

(Hair et al., 2013) and is generally applied when there is some background knowledge of

the underlying constructs and measurement items (Hair et al., 2013). However, it is

highly recommended that confirmatory factor analysis (CFA) should be performed after

exploratory factor analysis (EFA) in order to verify and confirm the scales derived from

EFA (Hair et al., 2013). In practice, unlike EFA, CFA is technique used to confirm a

priori hypothesis about the relationship between set of indicator variables (measurement

items) and their respective latent variables (Hair et al., 2013). There are two broad

approaches used in CFA to evaluate the measurement model: (1) deciding the goodness

of fit (GOF) criteria indices, (2) and evaluating the validity and reliability of

measurement model (Hair et al., 2013). Therefore, the researcher used the measurement

model in this research for assessing the unidiminsionality, validity, and reliability of the

measures.

b. Goodness of fit indices

Structural equation modelling (SEM) has three main types of fit measure indices:

absolute fit indices, incremental fit indices, and parsimonious fit indices (Hair et al.,

2013). The absolute fit indices are used to assess the ability of the overall model fit and

these indices include the likelihood ratio statistic chi-square (χ2), in association with root

mean square error of approximation (RMSEA), and the goodness of fit index (GFI) (Hair

et al., 2013). The incremental fit indexes are used to compare the proposed model to

some baseline model and the incremental fit indices consist of normed fit index (NFI),

and comparative fit index (CFI) (Hair et al., 2013). The parsimonious fit indices are used

to investigate whether the estimated model is simpler or can be improved by specifying

fewer estimated parameter paths (Hair et al., 2013). The parsimonious fit index includes

the adjusted goodness-of-fit index (AGFI). Details of these fit measures and their

recommended level are presented in Table 3.19.

109

Table 3.19 Goodness of Fit Statistics in SEM

Index Symbol Type of measure Recommended

Criteria

Reference

Chi-square χ2 Model fit χ

2, df, p >0.05 Hair et al., 2013

Normed chi-square χ2/df Absolute fit 1.0 < χ

2/df < 3.0

Goodness-of-fit index GFI Absolute fit > 0.90

Root mean square error

of approximation

RMSEA Absolute fit < 0.05

Hair et al., 2013

Normed fit index NFI Incremental fit > 0.90 Hair et al., 2013

Comparative fit index CFI Incremental fit > 0.90

Adjusted goodness-of-

fit index

AGFI Parsimonious fit > 0.90 Hair et al., 2013


c. Model estimates

In addition to the goodness of fit criteria, other standardised estimates are also used to

evaluate the measurement model. For example, standardised regression weight (factor

loadings), and critical ratio (CR) estimates criteria. This research study used the cut-off

point suggested by researchers for these estimates as follows. According to Hair et al.,

(2011), the factor loadings value should be greater than 0.7; however, a value greater than

0.5 is also acceptable (Churchill, 1979). The critical ratio values should be above 1.96

(Hair et al., 2013). As stated in previous section, measurement model explains the

interrelationships between observed variables and unobserved (latent) variables.

Therefore, CFA (measurement model) was performed in order to identify and confirm the

pattern by which measurement items were loaded onto a particular construct (Hair et al.,

2013). The measurement model was evaluated by using the maximum likelihood

estimation technique provided in the AMOS software. Table 3.20 summarise these

criteria.

Table 3.20 Measurement Model Estimates

Estimates Recommended values References Factor loading > 0.5 acceptable

> 0.7 good

Churchill,1979

Critical ratio (t-value) > 1.96 Hair et al., 2013

Standard residuals ± 2.8 Hair et al., 2013


110

d. Structural model evaluation and hypotheses testing

This research applied a two-step approach in the structural equation modelling analysis.

In the first step, measurement model evaluation was achieved by examining reliability,

and validity of latent constructs using CFA. Hence, the structural model can be tested as a

next main stage to examine the hypothesised relationships between the latent constructs

in the proposed model (Hair et al., 2013). The structural model (hypothesised model)

depicts the relationship among the latent constructs.

Table 3.21 Summary of Statistics

Statistics Software

package

Purpose of use Reference (s)

SERVQUAL

(SQ = P-E)

SPSS 20.0 To measure healthcare service quality Parasuraman et al.,

1988, 1989

Mahalanobis Distance (D2) SPSS 20.0 To investigate the multivariate outliers Hair et al., 2013

Kurtosis& Skewness SPSS 20.0 To find out data normality Hair et al., 2013

Descriptive statistics

(frequencies, means, SD)

SPSS 20.0 To summarize demographic information

and items analysis

Hair et al., 2013

Cronbach's Alpha SPSS 20.0 To examine the internal consistency of

each measure

Nunnally, 1978

Hair et al., 2013

Pearson‟s Correlations SPSS 20.0 To obtain preliminary information about

relationships between latent factors

Hair et al., 2013

Levene‟s test SPSS 20.0 To test the homogeneity of variance in the

data

Hair et al., 2013

Exploratory factor analysis

(EFA)

SPSS 20.0 To summarise information from many

variables in the proposed research model

into a smaller number of factors

Hair et al., 2013

Confirmatory factor analysis

(CFA)

SEM using

AMOS 20.0

To assess reliability and validity of

constructs used in the model

Hair et al., 2013

Path analysis SEM using

AMOS 20.0

To examine the hypothesised relationships

between the latent constructs in the

proposed model

Hair et al., 2013


Conclusion

The main purpose of this chapter was to discuss and choose appropriate methodology and

to discuss statistical tools and techniques used in this study. This study adapted the

quantitative (positive) approach. In fact, prior research suggested that the normal process

under a positivistic approach is to study the literature to establish an appropriate theory

and construct hypotheses. In addition, a survey tool was employed to collect data. The

survey method was used because it was designed to deal more directly with the patient‟s

111

perceptions, expectations, and intention regarding corporate hospital services. Moreover,

survey approach offers more accurate means of evaluating information about the sample

and enables the researcher to draw conclusions about generalising the findings from a

sample to the population. Additionally, surveys methods are quick, economical, efficient,

and can easily be administered to a large sample.

In order to collect data for this study a structured questionnaire was developed. The

questionnaire items were adapted from prior relevant research. The adapted items were

validated, and wording changes were made to tailor the instrument for the purposes of

this study. The questionnaire was then administered to the patient‟s personally. In

addition, pre-test and a pilot study was also used to test the reliability of measurement

items used in the questionnaire, most of the items showed adequate reliability.

SPSS 20.0 was used to analyse the quantitative data collected from the questionnaire this

tool has been used to perform descriptive statistics (frequencies, percentages, mean

values and standard deviations), SERVQUAL analysis (SQ=P-E), identification of

outliers (D2) test and find out the data normality (Kurtosis and Skewness). Structural

equation modelling (SEM) software package AMOS 20.0, was used in this research to

explore statistical relationships between the test items of each factors and among the

factors of independent variables (HCSQ and PS) and the dependent variable (behavioural

intentions).

This research applied a two-step approach in the structural equation modelling analysis.

In the first step, measurement model evaluation was achieved by examining reliability,

and validity of latent constructs using CFA. Hence, the structural model can be tested as a

next main stage to examine the hypothesised relationships between the latent constructs

in the proposed model. Analysis and results of this study presented in next chapter.

112

Results of this research study are presented in this chapter, which is divided into ten

sections. The first section provides the response rate achieved and demographic

characteristics of respondents. The second section reports descriptive statistics of items of

measured constructs. The third section presents service quality measurement values. The

fourth section reports results of exploratory factor analysis. Fifth to eighth sections give

results of Pearson‟s correlation, data normality and homogeneity values. The ninth and

tenth sections present findings of confirmatory factor analysis and results of hypotheses

tested in this study. The final section describes conclusions of the chapter.

4.1. Response Rate and Demographic Characteristics of Respondents

This section presents response rate and demographic characteristics of the respondents

are as follows;

4.1.1. Response Rate

To establish the sample frame, the hospitalized patients willing to participate in the

survey and with minimum 3 days stay are considered for the in-patient‟s sample. The in-

patients were contacted on the basis of systematic random sampling with personal contact

approach. After calculating sample size by using Hair‟s criterion (Hair et al., 2013) that a

sample size should be at least five times the estimated parameter. A total of 500

respondents are chosen from selected four hospitals and according to Hair‟s criterion

(Hair et al., 2013) this sample size for current study was considered adequate. A total of

125 patients were selected proportionately from each hospital to get the required sample

size. In this study, data were collected from patients using personal contact approach;

this method ensured complete responses from all the 500 respondents.

DATA ANALYSIS AND RESULTS

CHAPTER – 4

113

Table 4.1 depicts the very small variation in response rate may have occurred due

to personal and direct contact approach. The highest response rate was achieved in

Apollo Hospital group and Fortis Healthcare Ltd., which was already expected. The

reasons lie in the fact that the largest Indian private healthcare players and well

maintained record system. The response rate from other three hospitals namely, Care

hospitals and Manipal group of hospitals were bit lower than the Apollo Hospitals. In

total, 500 inpatients were contacted out of those 493 valid respondents were found,

reaming 7 inpatients respondent were declined due to partial response of questionnaire.

Therefore, remaining 493 respondents were used for further data analysis. Consequently,

the final usable response rate in this study was 98.6 per cent.

Table 4.1 Questionnaire Distribution and Response rate

Name of Hospital Respondents

Approached

Valid

Respondents

Response rate

%

Apollo Group of Hospitals 125 124 99.2

CARE Hospitals 125 123 98.4

Fortis Healthcare Ltd 125 124 99.2 Manipal Group of Hospitals 125 122 97.6 Total 500 493 98.6

4.1.2. Demographic Characteristics of Respondents

The demographic characteristic of respondents was collected by surveying through a

questionnaire. Distributions of the demographic characteristic, i.e. age, gender, marital

status, residential area, occupation, average monthly income, numbers of days stayed in

hospital, etc., results are presented in Table 4.2.

Gender: The respondents of the group 493; from the Table 4.2 it is observed that males

are (270) and females are (138) with the ratio of 6 to 4.

Age: From the table 4.2 it is observed that majority of respondents were young, with an

age group of 18-39 years are 32.3 (139) per cent, 40-49 years comprised 28.0 per cent

and 50 to 69 years comprised only 12.1 (60) per cent and that of 70 years and older

constituted only 0.8 per cent (4) of the total respondents.

114

Table 4.2 Demographic characteristic of Respondents.

Characteristics Number (n) per cent

Gender

Male

Female

270

223

54.8

45.2

Age (in years)

18-29 years

30-39years

40-49 years

50-59 years

60-69 years

70 years & older

159

132

138

52

8

4

32.3

26.8

28.0

10.5

1.6

0.8

Place

Rural

Urban

Semi-urban

Metropolitan City

77

209

108

99

15.6

42.4

21.9

20.1

Marital Status

Married

Unmarried

342

151

69.4

30.6

Education Levels

Up to SSC

Higher secondary

Graduate

Post Graduate

Others

140

111

104

75

63

28.4

22.5

21.1

15.2

12.8

Occupation

Student

Government Employee

Private Employee

Self Employed

others

80

176

83

67

87

16.2

35.7

16.8

13.6

17.6

Monthly Income (in INR)

Below 20,000

20,001-40,000

40,001-60,000

60,001-80,000

80,001-1,00,000

1,00,000 & Above

248

113

83

33

6

10

50.3

22.9

16.8

6.7

1.2

2.0

Number of Days Stayed in Hospital

1-7 days

8-14 days

15-21 days

20-28 days

29 days & above

200

163

94

30

6

40.6

33.1

19.1

6.1

1.2

115

Place: From the Table 4.2 it is noted that place of residence of respondents, majority of

respondents are from urban area are 42.4 per cent (209) and 21.9 per cent (108) were

from semi-urban, 20.1 per cent from metropolitan areas and 15.6 per cent (77) of patients

were from rural area. The reason for the majority of patients were belongs to urban,

because all the selected tertiary care hospitals were located in capitals of the states.

Marital Status: From Table 4.2 results of respondents shows that the majority of the

respondents were married 69.4 per cent (342) and unmarried patients constituted 30.6 per

cent (151) of the total sample.

Education Levels: The results stated in Table 4.2 in terms of educational level of

respondents it was found that 28.4 per cent (140) are up to SSC (completed 10th

standard), and 22.5 per cent (111) patients had completed 12th

standard. Bachelor‟s

degree (Graduate) comprised 21.1 per cent (104) and 15.2 per cent (75) were completed

master‟s degree (PG) from the total respondents and that of 12.8 per cent (63) of patients

were found other than these categories; i.e. some of them had higher degree (doctoral

and post-doctoral) and some were illiterate.

Occupation: The result stated in Table 4.2 shows the occupational status of respondents.

The largest per cent of respondents are found that government sector employees 35.7 per

cent (176) and 17.6 per cent (87) of respondents are found others (housewives, etc.), 16.8

per cent (83) are found private employee and 16.2 per cent (80) respondents are students

of different educational levels. 13.6 per cent (63) patients are found self-employed.

Monthly Income: From Table 4.2 it is observed that the majority of the respondents 50.3

per cent monthly income is below 20,000 (in INR), and 22.9 per cent (113) income is

between 20,001- 40, 0000 (in INR). 24.7 per cent (122) of respondents income is between

40 001 to 1, 00000 (in INR), and that of the monthly income is 1, 00,000 and above 2.0

per cent (10) of the respondents.

Number of Days Stayed in Hospital: From the Table 4.2 it is observed that regarding

patient stay in hospital, majority of respondents 40.6 per cent (200) had stayed between

1-7 days, 33.1 per cent (163) of patients were stayed between 8-14 days. Followed by 15-

116

28 days stayed are 19.1 per cent, 6.1 per cent stayed for 20-28 days and 1.2 per cent of

patients were stayed more than 29 days in hospital.

4.2. Descriptive Statistics

This section presents descriptive statistics of survey constructs are analysed below;

4.2.1. Perceived Service Quality

The respondent‟s perceptions of healthcare service quality were measured by five

SERVQUAL dimensions with 22 items using a five point Likert scale ranging from

„Strongly agree‟ (scale 1) and „Strongly disagree‟ (scale 5). The table 4.3 shows that the

mean scores vary across the five SERVQUAL dimensions ranging from 1.96 to 2.03 out

of 5, indicating that perception of corporate hospital patients are high. For perceived

responsiveness, the mean was 2.01 to 2.03 out of 5, indicating that respondents had very

high perception of the responsiveness elements of the healthcare service, followed by

perceived assurance and tangibility also had relatively high means, being above the 1.96

to 2.01 level on the 5-point scale. The standard deviation results of the five SERVQUAL

dimensions ranging from 0.903 to 0.942. For perceived responsiveness, the standard

deviation results was 0.920 to 0.942 out of 5, indicating that respondents had very high

perception of the responsiveness elements of the healthcare service, followed by

perceived assurance and tangibility also had relatively high standard deviation scores.

The reliability of the latent variable was assessed by calculating the Cronbach‟s

alpha. To test the reliability of perceived healthcare service quality instruments, the

Cronbach‟s alpha coefficient was computed. The coefficient alpha exceeded the

minimum standard of 0.70 (Nunnally and Bernstein, 1994), which indicates that it

provides a good estimate of internal consistency.

117

Table 4.3 Construct total descriptive statistics for perceived service quality

To evaluate the normality of the latent variable in the study (perceived service

quality), their kurtosis and skewness statistics were examined (Tabachnick and Fidell

2006). The further the skewness and kurtosis values are away from zero, the more likely

that the data are not normally distributed (Field 2009) and skewness values falling

outside the range of 0.714 to 0.899 indicates a positively skewed distribution (Hair et al.,

2013). Table 4.3 also provides the result of this analysis. It can be seen; the kurtosis and

skewness of the five perceived SERVQUAL dimensions of service quality appear to be

acceptable. Finally, analysis showed that there was no problem with linearity and analysis

of the residuals showed no severe issues with heteroscedasticity.

Mean S.D Variance Skewness Kurtosis C.A (α)

Tangibility 0.923

TAN1 1.97 0.920 0.847 0.775 0.053

TAN2 1.98 0.931 0.868 0.855 0.248

TAN3 1.96 0.907 0.823 0.842 0.188

TAN4 1.98 0.910 0.829 0.843 0.345

Reliability 0.941

RAB1 2.00 0.917 0.841 0.833 0.367

RAB2 1.98 0.919 0.845 0.899 0.488

RAB3 1.97 0.909 0.827 0.802 0.175

RAB4 1.97 0.903 0.816 0.753 0.017

Assurance 0.938

ASS1 2.00 0.924 0.854 0.714 -0.139

ASS2 2.01 0.928 0.862 0.841 0.321

ASS3 1.98 0.920 0.847 0.806 0.131

ASS4 1.97 0.911 0.831 0.764 0.003

ASS5 2.01 0.913 0.833 0.684 -0.134

Empathy 0.944

EMT1 1.97 0.918 0.842 0.765 -0.037

EMT2 1.99 0.912 0.831 0.804 0.251

EMT3 1.97 0.917 0.840 0.875 0.367

EMT4 1.97 0.906 0.820 0.773 0.047

EMT5 1.98 0.915 0.837 0.752 -0.039

Responsiveness 0.943

RSP1 2.01 0.938 0.880 0.788 0.156

RSP2 2.03 0.942 0.887 0.840 0.314

RSP3 2.01 0.925 0.856 0.815 0.219

RSP4 1.97 0.920 0.847 0.838 0.266

Service Quality 0.826

HCSQ1 2.06 0.942 0.887 0.861 0.486

HCSQ2 2.11 0.975 0.950 0.855 0.425

118

4.2.2. Expected Service Quality

The respondents‟ expectations of healthcare service quality were measured by five

SERVQUAL dimensions with 22 items using a five point Likert scale ranging from

„Strongly agree‟ (scale 1) and „Strongly disagree‟ (scale 5). The table 4.4 shows that the

mean scores vary across the five SERVQUAL dimensions ranging from 1.93 to 2.08 out

of 5, indicating that expectation of corporate hospital patients are high compare to

perception scores. For expected assurance, the mean was 2.01 to 2.08 out of 5, indicating

that respondents had very high expectations of the assurance elements of the healthcare

service, followed by perceived reliability and responsiveness also had relatively high

means, being above the 1.93 to 2.03 level on the 5 point scale. The standard deviation

results of the five SERVQUAL dimensions ranging from 0.880 to 1.016, all the five

latent variable have high deviation score compare to patient‟s perception level, it

indicates that patients have high expectations about corporate healthcare services.




minimum standard of 0.70 (Nunnally and Bernstein, 1994), which indicates that it

provides a good estimate of internal consistency.

To evaluate the normality of the latent variable in the study (expected service





2013). Table 4.4 also provides the result of this analysis. It can be seen; the kurtosis and

skewness of the five expected SERVQUAL dimensions of service quality appear to be

acceptable. Finally, results showed that there was no problem with linearity and analysis

of the residuals showed no severe issues with heteroscedasticity.

119

Table 4.4 Construct total descriptive statistics for expected service quality

4.2.3. Patient Satisfaction

Table 4.5 shows that the descriptive statistic (mean, standard deviation, variance,

skewness and kurtosis) scores vary across the seven (including overall satisfaction)

patient satisfaction factors extracted. From the above results mean and standard deviation

scores of admission process at corporate hospitals are shows low compare to other

satisfaction variables, ranging from 1.90 to 1.92 mean values (out of 5) and 0.926 to

0.930 standard deviation values (out of 5) respectively, its indicates that patients of

corporate hospitals are more satisfied with their admission related services. Additionally,


Tangibility 0.929

TAN1 1.99 0.880 0.774 0.693 -0.061

TAN2 2.01 0.908 0.825 0.777 0.161

TAN3 1.97 0.885 0.783 0.770 0.070

TAN4 1.99 0.916 0.839 0.753 -0.027

Reliability 0.928

RAB1 2.00 0.972 0.945 0.902 0.312

RAB2 2.02 0.956 0.914 0.876 0.360

RAB3 2.03 0.980 0.960 0.850 0.226

RAB4 2.02 0.956 0.914 0.934 0.533

Assurance 0.971

ASS1 2.04 1.005 1.011 0.931 0.386

ASS2 2.02 0.996 0.991 0.956 0.493

ASS3 2.01 0.992 0.984 0.967 0.489

ASS4 2.08 1.016 1.032 0.931 0.405

ASS5 2.08 1.010 1.021 0.858 0.223

Empathy 0.946

EMT1 1.96 0.908 0.824 0.793 0.077

EMT2 2.01 0.934 0.872 0.799 0.139

EMT3 1.96 0.925 0.856 0.854 0.125

EMT4 1.93 0.903 0.815 0.848 0.200

EMT5 1.96 0.921 0.848 0.810 0.041

Responsiveness 0.918

RSP1 2.00 0.921 0.749 0.749 0.025

RSP2 2.01 0.940 0.826 0.826 0.224

RSP3 1.95 0.928 0.814 0.814 0.000

RSP4 2.01 0.905 0.764 0.764 0.147

Service Quality 0.826

HCSQ1 2.06 0.942 0.887 0.861 0.486

HCSQ2 2.11 0.975 0.950 0.855 0.425

120

this dimension of satisfaction had the lowest standard deviation, suggesting that there is

little difference in opinion among respondents on this variable.

From the above results among the all seven variables, medical care services

showing high mean and standard deviation scores, followed by overall services, food

services, housekeeping services and nursing care services. Medical care services shows

item mean values are >2 and standard deviation scores >1, results indicates that

respondents are more satisfied with medical services provided by corporate hospitals are

good, compare to other factors of satisfaction. Results shows that the mean scores vary

across nursing care, housekeeping, food and overall service satisfaction dimensions

ranging from 1.95 to 2.15 out of 5, maximum scores of all these variable items are >2,

and standard deviation scores ranging from 0.930 to 1.006 indicating that corporate

hospital patients are satisfied with service provided by particular departments.

To evaluate the normality of the latent variable in the study (perceived service





2013). Table 4.5 also provides the result of this analysis. As can be seen, the kurtosis and

skewness of the five patient satisfaction dimensions of corporate hospitals services

appear to be acceptable. Finally, analysis showed that there was no problem with linearity

and analysis of the residuals showed no severe issues with heteroscedasticity.




minimum standard of 0.70 (Nunnally& Bernstein, 1994), which indicates that it provides

a good estimate of internal consistency.

121

Table 4.5 Construct total descriptive statistics for patient satisfaction


Admission Process 0.908

AP1 1.91 0.926 0.858 0.902 0.312

AP2 1.92 0.932 0.869 0.922 0.400

AP3 1.90 0.930 0.864 0.926 0.406

Nursing care Services 0.840

NS1 2.01 0.941 0.886 0.923 0.670

NS2 2.10 0.960 0.921 0.801 0.261

NS3 2.09 0.953 0.908 0.920 0.748

NS4 2.15 0.984 0.968 0.741 0.125

Medical care Services 0.901

MS1 2.07 1.057 1.117 0.920 0.195

MS2 2.14 1.046 1.095 0.834 0.093

MS3 2.12 1.039 1.080 0.887 0.223

MS4 2.08 1.069 1.143 0.852 -0.055

Housekeeping Services 0.893

HKS1 1.95 0.965 0.930 0.934 0.393

HKS2 1.98 0.930 0.865 0.855 0.316

HKS3 2.03 0.947 0.898 0.836 0.272

HKS4 1.97 0.985 0.970 0.936 0.346

Food Services 0.888

FS1 2.03 0.940 0.883 0.803 0.121

FS2 2.05 0.940 0.884 0.802 0.254

FS3 2.08 1.003 1.006 0.791 -0.044

Overall Services 0.977

OS1 2.09 0.993 0.986 0.874 0.299

OS2 2.08 0.981 0.963 0.909 0.484

OS3 2.14 1.001 1.003 0.814 0.183

OS4 2.09 0.977 0.954 0.929 0.545

OS5 2.11 0.986 0.973 0.885 0.402

OS6 2.12 1.006 1.013 0.895 0.338

OS7 2.09 0.988 0.976 0.945 0.552

OS8 2.10 0.992 0.984 0.878 0.359

Patient Satisfaction 0.928

PS1 2.03 0.959 0.920 0.778 0.153

PS2 2.00 0.963 0.927 0.900 0.360

PS3 1.98 0.940 0.884 0.862 0.342

PS4 2.03 0.974 0.948 0.862 0.284

122

4.2.4. Behavioural Intentions

Table 4.6 shows that the descriptive statistics of behavioural intention variable, this

dimension contain four items. The respondents were first asked to indicate their future

intentions to revisit or recommend to friends and relatives whom seek care in future. All

the four items on a five point Likert scale ranging from strongly agree (scale 1) to

strongly disagree (scale 7) were used to measure this construct. The results of the

respondents‟ ratings for each item of this construct are reported in Table 4.5. The mean

scores ranged between 1.96 and 2.07. The results indicate that patients of corporate

hospitals are more satisfied with services of healthcare service provider and they are

more interest to revisit in future and recommends to others. Additionally, this dimension

of intention had the good standard deviation values, suggesting that there is little

difference in opinion among respondents on this variable.

Table 4.6 Construct total descriptive statistics for behavioural intentions

In terms of skewness, all the items fell inside the “0 to1” range, indicating

substantial positive skewness of the data. These results indicate that the data are non-

normal. Kurtosis was not found to be a significant problem in the rest of the sample. The

reliability of this dependent variable was assessed by calculating the Cronbach‟s alpha.

To test the reliability of behavioural intention, the Cronbach‟s alpha coefficient was

computed. The coefficient alpha exceeded the minimum standard of 0.70 (Nunnally&

Bernstein, 1994), which indicates that it provides a good estimate of internal consistency.


Behavioural Intentions 0.962

BI1 2.04 0.942 0.888 0.686 -0.175

BI2 1.96 0.931 0.866 0.890 0.246

BI3 2.02 0.943 0.890 0.798 0.154

BI4 2.07 0.964 0.930 0.762 0.107

123

4.3. Service Quality Measurement (SERVQUAL - Analysis)

Service quality most often been defined in terms of customer perceptions. Hence, most of

the operational definitions or conceptual frameworks that have been suggested for service

quality are based on marketing concepts. Researchers have divided service quality into

two components: technical quality and functional quality. Technical quality refers to the

quality of the service “product”, whereas functional quality refers to the manner in which

the service “product” is delivered. In the health-care environment, technical quality can

be defined by factors such as average length of stay, readmission rates, infection rates and

out-come measures. On the other hand, functional quality can be defined by factors such

as doctors‟ and nurses‟ attitudes towards patients, cleanliness of facilities, and the quality

of hospital food etc. Generally, SERVQUAL is considered to be a robust scale for

measuring service quality across service sectors. Patients evaluations of service quality

are based on perceptions of the quality of service received relative to prior expectations.

The SERVQUAL (Parasuraman et al., 1985) instrument was designed to measure the gap

between expectations and perceptions. According to SERVQUAL developers, service

quality should be measured by subtracting customer perception from expectation scores

(Q = P-E). Positive scores signify higher service quality and vice-versa (Parasuraman et

al., 1985). In this study mean/standard deviation expectations and perceptions aggregated

according to the five SERVQUAL dimensions: tangibles, reliability, responsiveness,

assurance, and empathy. The data analysed and interrupted regarding corporate hospital

service quality measurement are listed in next sub-section.

124

4.3.1. Gap scores of SERVQUAL dimensions

Table 4.7 Means of expectations, perceptions, and gap scores

S.No Statement Expectations Perceptions Gap Score

1. Reliability

RAB1 2.002 2.004 -0.002

RAB2 2.024 1.983 0.041

RAB3 2.031 1.973 0.058

RAB4 2.016 1.971 0.045

2. Responsiveness

RES1 2.002 2.014 -0.012

RES2 2.014 2.034 -0.020

RES3 1.945 2.011 -0.066

RES4 2.006 1.973 0.033

3 Assurance

ASS1 2.041 2.001 0.039

ASS2 2.025 2.008 0.017

ASS3 2.012 1.981 0.031

ASS4 2.083 1.973 0.110

ASS5 2.075 2.012 0.063

4 Empathy

EMT1 1.963 1.965 -0.003

EMT2 2.014 1.985 0.029

EMT3 1.959 1.967 0.008

EMT4 1.933 1.968 0.035

EMT5 1.955 1.975 0.020

5 Tangibles

TAN1 1.985 1.973 0.012

TAN2 2.012 1.981 0.021

TAN3 1.967 1.955 0.012

TAN4 1.989 1.983 0.006

The above Table 4.7 shows the mean scores of perception, expectation and gap score of

each item. The mean expectation scores were high when compared to the perception

scores ranging from 1.933 to 2.083. The highest corporate hospital expectation score was

related to; “Patients feel safe while they receive services from the personnel of this

hospital”. The lowest corporate hospital expectation score was obtained from question

EMT4: “This hospital provides individual attention to the patient‟s problems and care”.

This low expectation level may be the result of previous experience or negative word of

125

mouth, communication from family members or friends who, perhaps, had disappointing

experiences with the individual care providing to patients.

The mean perception scores were lower compared to the expectation scores;

ranging from 1.965 to 2.034. The lowest perception score in corporate hospitals was

obtained from statement EMT1: “Doctors keep their patients informed and listen to

them” (1.965). It seems that respondents are not satisfied with the corporate hospital

doctor‟s empathy on patient‟s interaction; because of this reason perception mean scores

were low. The highest perception score in corporate hospitals was obtained from the

statement RES2: “Hospital staffs consistently follow-up sick cases” (2.034).

At first glance, it may appear that the tertiary care hospitals were performing well

with respect to all quality attributes since these dimensions exhibited the smallest gaps.

According to these mean gap score assurance was the most important attribute to tertiary

care hospitals because all the values are high compare to other dimensions, it‟s followed

by tangible, empathy and reliability. From the above results responsiveness was least

preference given by respondents. In addition to looking at the service gap across the five

quality attributes, the largest gap (0.11) was observed in statement ASS4. It was followed

by gaps in ASS5 (0.063), RAB3 (0.057) RAB4 (0.045) and RAB1 (0.041). Majority of

these gaps comes under reliability dimension, its showing that the corporate hospitals are

suffering from a lack of reliable treatment when patient required particular treatment like,

competent in providing accurate services; keeping patients well-informed about the

follow-up examinations, and in providing efficient, reliable and affordable prescribed

medicines.

126

Table 4.8 Standard Deviation of expectations, perceptions, and gap scores

S.No Statement Expectations Perceptions Gap Score

1 Reliability

RAB1 0.972 0.917 0.052

RAB2 0.956 0.919 0.037

RAB3 0.980 0.909 0.071

RAB4 0.956 0.903 0.053

2 Responsiveness

RES1 0.921 0.938 -0.017

RES2 0.940 0.942 -0.002

RES3 0.928 0.925 0.003

RES4 0.905 0.920 -0.015

3 Assurance

ASS1 1.005 0.924 0.081

ASS2 0.996 0.928 0.068

ASS3 0.992 0.920 0.072

ASS4 1.016 0.911 0.105

ASS5 1.010 0.913 0.097

4 Empathy

EMT1 0.908 0.918 -0.010

EMT2 0.934 0.912 0.022

EMT3 0.925 0.917 0.008

EMT4 0.903 0.906 -0.003

EMT5 0.921 0.915 0.006

5 Tangibles

TAN1 0.880 0.920 -0.040

TAN2 0.908 0.931 -0.023

TAN3 0.885 0.907 -0.022

TAN4 0.916 0.910 0.006

From the above Table 4.8 shows the standard deviation scores of perception,

expectation and gap score of each item. The standard deviation expectation scores were

high when compared to the perception scores ranging from 0.908 to 1.016. The highest

corporate hospital expectation score was related to; “Patients feel safe while they receive

services from the personnel of this hospital”. The lowest corporate hospital expectation

score was obtained from question EMT1: “Doctors keep their patients informed and

listen to them”. This low expectation level may be the result of previous experience or

negative word of mouth, communication from family members or friends who, perhaps,

had disappointing experiences with the individual care providing to patients.

127

The standard deviation perception scores were lower compared to the expectation scores;

ranging from 0.910 to 0.942. The lowest perception score in corporate hospitals was

obtained from statement TAN4: “This hospital provides up-dated informative broachers

about services offered” (0.910). It seems that respondents are not satisfied with the

corporate hospital helpdesk, they do not providing up-dated information regarding

treatments; because of this reason perception mean scores were low. The highest

perception score in corporate hospitals was obtained from the statement RES2: “Hospital

staffs consistently follow-up sick cases” (0.942).

At first glance, it may appear that the corporate hospitals were performing well with

respect to all quality attributes since these dimensions exhibited the smallest gaps.

According to these standard deviation gap score assurance was the most important

attribute to corporate hospitals because all the values are high compare to other

dimensions, it‟s followed by tangible, empathy and reliability. From the above results

responsiveness was least preference given by respondents. In addition to looking at the

service gap across the five quality attributes, Majority of these gaps comes under

assurance dimension, the largest gap (S.D Gap score; 0.10) was observed in statement

ASS4 “Patients feel safe while they receive services from the personnel of this hospital”.

It was followed by gaps in ASS5 “Staff of corporate hospital thoroughly explains medical

conditions of the patients” (S.D Gap score; 0.097); ASS1 “Doctors and nursing staff are

consistently courteous with their patients” (S.D Gap score; 0.081), ASS2 “Doctors of

this hospital are very knowledge” (S.D Gap score; 0.072) and RAB3 “The Staff of this

hospital is keeping patients well-informed about the follow-up examinations.” (S.D

Gap score; 0.071).

128

4.3.2. Relative Importance of SERVQUAL dimensions

Table 4.9 Survey items most or least contribution to tertiary care service delivery (Patient

level of importance based on mean scores).

The five highest expectations The five highest perceptions Highest expectation statements Mean Highest perception statements Mean

ASS4

ASS5

ASS1

RAB3

ASS2

2.083

2.075

2.041

2.031

2.025

PRES2

PRES1

PASS5

PRES3

PASS2

2.034

2.014

2.012

2.011

2.008

The five lowest expectations The five lowest perceptions lowest expectations statements Mean lowest perceptions statements Mean

EMT4

RES3

EMT5

EMT3

EMT1

1.933

1.945

1.955

1.959

1.963

PEMT1

PEMT3

PEMT4

PRAB4

PRES4

1.965

1.967

1.968

1.971

1.973

The five largest differences

(SERVQUAL)

The five smallest differences

(SERVQUAL) Largest differences Mean Smallest differences Mean

ASS4

ASS5

RAB3

RAB4

RAB1

0.110

0.063

0.057

0.045

0.041

RAB1

EMT1

RES1

RES3

TAN4

-0.002

-0.003

-0.012

-0.066

0.006

Table 4.9 examines the mean scores of expectations; perceptions and gap between these

dimensions in inpatients treated at corporate hospitals. The mean highest

expectations/perceptions/gap scores are listed in above table and the highest differences

between expectations and perceptions identified. The patient choice clearly shows that

assurance is the most critical dimension of the services. The results contained in Table

4.8, regarding expectations, when considered collectively, imply an important message

from inpatients to hospital managers: “Be responsive, be empathetic, be reliable, have up-

to-date equipment and facilities and, most of all, ensure that we feel secure in receiving

medical treatment”. The patients‟ responses clearly show that hospital staff were

perceived to be neat and courteous in manner, promoted the feeling of security and were

responsive to patients‟ requests.

129

Table 4.10 Survey items most or least contribution to tertiary care service delivery

(Patient level of importance based on S.D. scores).

The five highest expectations The five highest perceptions Highest expectation statements S.D. Highest perception statements S.D.

ASS4

ASS5

ASS1

ASS2

ASS3

1.016

1.010

1.005

0.996

0.992

RES2

RES1

TAN2

ASS2

RES3

0.942

0.938

0.931

0.928

0.925

The five lowest expectations The five lowest perceptions lowest expectations statements S.D. lowest perceptions statements S.D.

TAN1

TAN3

EMT4

RES4

EMT1

0.880

0.885

0.903

0.905

0.908

RAB4

EMT4

TAN3

RAB3

TAN4

0.903

0.906

0.907

0.909

0.910

The five largest differences

(SERVQUAL)

The five smallest differences

(SERVQUAL) Largest differences S.D. Smallest differences S.D.

ASS3

ASS5

ASS1

ASS2

RAB3

0.105

0.097

0.081

0.072

0.071

RES2

EMT4

EMT1

RES4

RES1

-0.002

-0.004

-0.010

-0.015

-0.017

Table 4.10 examines the standard deviation scores of expectations; perceptions and gap

between these dimensions in inpatients treated at corporate hospitals. The standard

deviation highest expectations/perceptions/gap scores are listed in above table and the

highest differences between expectations and perceptions identified.

From the Table 4.9 and Table 4.10 healthcare service quality results with respect

to mean and standard deviation scores, its clearly establish that assurance is the most

serious problem faced by the Indian corporate hospital providers. Patients‟ expectations

of service providers are highest in relation to assurance, and patients give priority to

assurance as compared to other five dimensions, yet the tangibility scores have been

consistently the lowest in this survey. It is not surprising that patients were more satisfied

when they felt more assured of their health outcomes. There is also evidence that for

services with credence properties, assurance plays an important role in patient satisfaction

(Zeithaml and Bitner, 2000).

130

Figure 4.1 SERVQUAL dimension weights

From the above figure 4.1 a meaningful input to managerial decision making is the

comparison of these service gaps with the relative importance of each dimension as

determined by the weights allocated by respondents to each category (see Figure 4.1). All

the mean weights of five SERVQUAL dimensions are ranging from 0.393 to 0.929.

According to these mean weights, responsiveness (0.393) is the least important attribute,

followed by reliability (0.517), empathy (0.533), while assurance (0.929) is the most

important. As shown in Figure 4.1, empathy and responsiveness have the least gaps;

however, they are the least important dimensions. Since the health services is fairly close

to meeting patient expectations of tangibles and assurance, additional resources allocated

to these areas may be unnecessary. On the other hand, assurance is the most important

attribute to corporate hospital providers, but exhibits a larger service gap. There is an

apparent opportunity for improvement in health services operations.

0.8479

0.5173

0.929

0.5335

0.3935

00.10.20.30.40.50.60.70.80.9

1

131

Figure 4.2 SERVQUAL dimension weights

From the above figure 4.2 its revealed that assurance is the main attribute for

managerial decision making is the comparison of these service gaps with the relative

importance of each dimension as determined by the standard deviation weights allocated

by respondents to each category (see Figure 4.2). All the standard deviation weights of

five SERVQUAL dimensions are ranging from -0.0804 to 0.1319. According to these

standard deviation scores, empathy (-0.080) is the least important attribute, followed by

responsiveness (-0.169), tangibles (-0.132), while assurance (0.131) is the most important

determinant of service quality at corporate hospitals. There is understandable opportunity

for improvement in health services operations.

Figure 4.3 Standard deviation SERVQUAL score for corporate hospital services

-0.1322

-0.3117

0.1319

-0.0804

-0.1691

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

2.4582 2.2124 3.2093 3.3708

2.4693

2.5904 2.5241

3.0774 3.4512

2.6384

00.5

11.5

22.5

33.5

4

perception

expectation

132

Figure 4.3 indicates standard deviation expectations and perceptions scores, aggregated

according to the five SERVQUAL dimensions: tangibles, reliability, responsiveness,

assurance, and empathy. As shown in Figure 4.3, perception scores fell short of

expectation for every category except assurance, indicating small service gaps (i.e.

perceptions minus expectations). In analysing the distance (gap) between expectations

and perceptions, responsiveness (-0.169), reliability (-0.312), tangibility (-0.132) and

empathy (-0.081) exhibit the smallest negative gaps while assurance (0.132) has the

positive gap. Thus, corporate hospital performance with respect to assurance is more with

patient expectations than that of other dimensions.

Figure - 4.4: Mean SERVQUAL score for corporate hospital services

Figure 4.4 shows mean expectations and perceptions scores, aggregated according to the

five SERVQUAL dimensions: tangibles, reliability, responsiveness, assurance, and

empathy. As shown in Figure 4.4 expectation scores fell short of perceptions for every

category, indicating small service gaps (i.e. perceptions minus expectations). In analysing

the distance (gap) between expectations and perceptions, responsiveness (0.394) and

reliability (0.512) exhibit the smallest gaps while assurance (0.929) has the largest gap.

The gaps for reliability (0.512) and empathy (0.533) are very close in size. Thus,

corporate hospital performance with respect to assurance and tangibles is more closely in

line with patient expectations than that of empathy, reliability, and responsiveness.

8.4888 8.2576 10.7768 10.6186

8.3022

7.6409 7.7403

9.8478 10.0851

7.9087

0

2

4

6

8

10

12

Perception

Expectation

133

4.4. Exploratory Factor Analysis (EFA)

Employing the Principal components analysis (PCA) and orthogonal method with

varimax rotation, exploratory factor analysis was performed using SPSS (version 20.0).

4.4.1. The KMO and Bartlett’s Test of Sphericity

The KMO (Kaiser-Meyer-Olkin) test measures the sampling adequacy which should be

greater than 0.5 for a satisfactory factor analysis to proceed. If any pair of variables has a

value less than this, consider dropping one of them from the analysis. The off diagonal

elements should all be very small (close to zero) in a good model. Bartlett’s Test of

Sphericity is another indication of the strength of the relationship among variables. This

tests the null hypothesis that the correlation matrix is an identity matrix. An identity

matrix is matrix in which all of the diagonal elements are “1” and all off diagonal

elements are “0”. The result of KMO and Bartlett‟s Test of Sphericity for expected

healthcare service quality, perceived healthcare service quality, patient satisfaction and

behavioural intention constructs are presented in Table 4.11 which shows that the

value of Kaiser Meyer-Olkin (KMO) measure of sampling adequacy value was

greater than 0.9 and the Bartlett‟s test of sphericity was (p <.000) , which revealed the

appropriateness of sample data for conducting factor analysis.

Table 4.11 Construct KMO and Bartlett's Test of Sphericity values

Construct Description Value

Expected Service quality

KMO - Measure of Sampling Adequacy 0.919

Bartlett's Test

of Sphericity

Approx. Chi-Square 12031.469

df 676

Sig 0.000

Perceived Service quality


Bartlett's Test

of Sphericity


df 592

Sig 0.000

Patient Satisfaction


Bartlett's Test

of Sphericity


df 861

Sig 0.000

Behavioural Intentions


Bartlett's Test

of Sphericity


df 386

Sig 0.000

134

4.4.2. Communalities

Table 4.12 and Table 4.13 shows that the communalities of all three dimensions i.e.

healthcare service quality, patient satisfaction and behavioural intention. The results show

how much of the variance in the variables has been accounted for by the extracted

factors. For instance expected and perceived healthcare service quality over 70 per cent

of the variance in quality of Indian corporate hospitals is accounted, 60 per cent of the

variance in determinant of patient satisfaction, while 90 per cent of variance explained for

behavioural intentions for.

Commonalties between measured items loaded on the expected healthcare service quality

EFA model varied from 0.745 for RES3 item to 0.930 for ASS2 item (Table 4.12). The

lowest communality of item is exceeded the minimum standard loading of 0.60. Similarly

commonalties between measured items loaded on the perceived healthcare service quality

EFA model varied from 0.693 for ASS3 item to 0.938 for RAB1 item (Table 4.12). The

lowest communality of item is exceeded the minimum standard loading of 0.60. This

result indicates that the good items loading of extracted factor.

Commonalties between measured items loaded on determinants of patient satisfaction

EFA model varied from 0.674 for NS1 and NS4 items to 0.937 for OS4 item (Table

4.13). The lowest communality of item is exceeded the minimum standard loading of

0.60. This result indicates that the good items loading of patient satisfaction extracted

factor. Commonalties between measured items loaded on the behavioural intention EFA

model varied from 0.924 for BI4 item to 0.968 for BI1 item (Table 4.13). The lowest

communality of item is exceeded the minimum standard of 0.60. This result indicates that

the good items loading of behavioural intention extracted factor.

135

Table 4.12 Healthcare Service Quality Communalities

Note: Extraction Method: Principal Component Analysis

Variable Item Expected Quality Perceived Quality

Assurance Initial Extraction Initial Extraction ASS1 1.000 0.929 1.000 0.829

ASS2 1.000 0.930 1.000 0.864

ASS3 1.000 0.833 1.000 0.693

ASS4 1.000 0.865 1.000 0.780

ASS5 1.000 0.921 1.000 0.859 Empathy EMT1 1.000 0.815 1.000 0.841

EMT2 1.000 0.851 1.000 0.902

EMT3 1.000 0.761 1.000 0.748

EMT4 1.000 0.797 1.000 0.773

EMT5 1.000 0.909 1.000 0.832 Tangible TAN1 1.000 0.854 1.000 0.833

TAN2 1.000 0.865 1.000 0.874

TAN3 1.000 0.822 1.000 0.814

TAN4 1.000 0.768 1.000 0.741 Reliability RAB1 1.000 0.901 1.000 0.938

RAB2 1.000 0.895 1.000 0.796

RAB3 1.000 0.669 1.000 0.840

RAB4 1.000 0.902 1.000 0.833 Responsiveness RES1 1.000 0.859 1.000 0.884

RES2 1.000 0.814 1.000 0.874

RES3 1.000 0.745 1.000 0.819

RES4 1.000 0.801 1.000 0.839 Service Quality HCSQ1 1.000 0.871 1.000 0.868

HCSQ2 1.000 0.868 1.000 0.869

136

Table 4.13 Patient Satisfaction and Behavioural Intention Communalities

Variable Item Label Initial Extracted

Overall Service Experience

OS1 1.000 0.911

OS2 1.000 0.673

OS3 1.000 0.854

OS4 1.000 0.937

OS5 1.000 0.918

OS6 1.000 0.876

OS7 1.000 0.834

OS8 1.000 0.929


PS1 1.000 0.809

PS2 1.000 0.744

PS3 1.000 0.875

PS4 1.000 0.890

Medical care Services

MS1 1.000 0.826

MS2 1.000 0.864

MS3 1.000 0.654

MS4 1.000 0.756

Housekeeping Services

HKS1 1.000 0.858

HKS2 1.000 0.711

HKS3 1.000 0.701

HKS4 1.000 0.833

Nursing care Services

NS1 1.000 0.674

NS2 1.000 0.684

NS3 1.000 0.726

NS4 1.000 0.674

Admission Services

AP1 1.000 0.858

AP2 1.000 0.902

AP3 1.000 0.786

Food Services

FS1 1.000 0.820

FS2 1.000 0.835

FS3 1.000 0.800

Behavioural Intentions

BI1 1.000 0.968

BI2 1.000 0.958

BI3 1.000 0.937

BI4 1.000 0.924

137

4.4.3. Exploratory Factor Extraction Model

The expectation and perception of SERVQUAL scale was used in this study to measure

healthcare service quality in Indian corporate hospitals. As previously stated (Section

4.3), the scale development procedures employed by Parasuraman et al., (1985, 1988)

appears to support the face validity of the original scale items. This section describes the

preliminary tests undertaken and the exploratory factor analyses performed. This section

concludes with a discussion of the outcome of the analysis that outlines the factor

structure selected for use in the analysis of the hypotheses.

Before proceeding with the factor analysis, it was important to determine whether the

data were appropriate for factor analysis. In this research, the coefficient alpha exceeded

the minimum standard of 0.70 (Nunnally and Bernstein, 1994), which indicates that it

provides a good estimate of internal consistency. Additionally, the Bartlett‟s Test of

Sphericity was significant, which shows that there are significant correlations among at

least some of the variables (Hair et al., 2013). The results of both these tests indicate that

the variables were correlated enough to provide a reasonable basis for factor analysis

(Tabachnick and Fidell 2006), and so the analysis proceeded.

The next step was to determine the factor method to be used. Hair et al., (2011) suggests

the use of principal components analysis when data reduction is the primary concern, as

the case this research. For this reason, principal components analysis was chosen over

common factor analysis, although SERVQUAL models are widely used in the literature

and empirical research demonstrates similar results from this model. Rotation of the

factor matrix allows the researcher to achieve simpler and theoretically more meaningful

factor solutions (Hair et al., 2013) and so the rotational method to be used also had to be

chosen. This research followed the analysis procedure detailed by Parasuraman et al.,

(1998) and factor analysed by the expected and perceived scale using the principal axis

rotation technique. VARIMAX orthogonal rotation procedure was used because this

method minimises the number of variables with high factor loadings, thereby enhancing

the interpretability of factors (Hair et al., 2013).

138

The factors retained were those with eigenvalues greater than one as factors with

eigenvalues below this number could not be interpreted (Carman 1990). In terms of factor

loadings, those less than 0.30 were excluded from the analysis, this criterion being chosen

on the basis of guidelines for assessing the levels at which factor loadings are considered

to be significant given the sample size of more than 200 respondents (Hair et al., 2013).

In the case of cross-loading, which is where a variable is found to have more than one

significant loading, it is generally recommended that each variable with a high cross-

loading be evaluated for possible deletion (Hair et al., 2013) and so variables with similar

loadings on more than one factor were deleted, as were items that did not conceptually

belong to the factor. Coefficient alphas and item-to-total correlations were computed each

time items were deleted (K-S Choi et al., 2004). In the analysis of the expected and

perceived quality data, healthcare service quality item 2&3 consistently loaded on two

factors and so it was deleted. The remaining 24 items were retained on one of the three

final factors extracted.

Summarised results are presented in Table 4.14 which shows that the factor analysis of

expected healthcare service quality. Overall, five dimensions emerged from the expected

healthcare quality data (Tangibles, Reliability, Assurance, Empathy, and Responsiveness)

explaining 84.35per cent of the variance in the data. This means that the solution is

satisfactory from a practical perspective. The first factor extracted was assurance, which

explained 19.11per cent of the variance in the data and all items had significant loadings

of >0.80. The remaining four factors extracted explained roughly the same percentage of

the variance in the data (Empathy = 18.77 per cent; Tangibles = 14.59 per cent;

Reliability = 12.66 per cent and Responsiveness = 12.18 per cent) and all items had

significant loadings of >.70. Scale reliabilities of the healthcare service quality

expectation measure were assessed with coefficient alpha and this is presented in Table

4.14. The coefficient alpha exceeded the minimum standard of 0.70 (Hair et al., 2013),

which indicates that it provides a good estimate of internal consistency.

The table 4.13 reported the results of a series of exploratory factor analyses undertaken

on the data collected in this research using the expectations portion of the SERVQUAL

measure. In common with many other empirical studies in the literature, evidence has

139

been found in support of the five factor solution presented by SERVQUAL‟s developers

(Arasli et al., 2005; Carman 1990; Cronin and Taylor 1992) and thus this factor structure

was employed to analyse the relevant hypotheses.

Table 4.14 Exploratory factor analysis of expected healthcare service quality

S.No Factor

Extracted

Item

Label

Item

Loading

Eigenvalue per cent of

Variance

Cumulative

Variance

1 Assurance 4.587 19.113 19.113

ASS1 0.929

ASS2 0.930

ASS3 0.833

ASS4 0.865

ASS5 0.921

2 Empathy 4.506 18.774 37.887

EMT1 0.815

EMT2 0.851

EMT3 0.761

EMT4 0.797

EMT5 0.909

3 Tangible 3.502 14.590 52.477

TAN1 0.854

TAN2 0.865

TAN3 0.822

TAN4 0.768

4 Reliability 3.039 12.663 65.140

RAB1 0.901

RAB2 0.895

RAB3 0.669

RAB4 0.902

5 Responsiveness 2.924 12.185 77.325

RES1 0.859

RES2 0.814

RES3 0.745

RES4 0.801

6 Service Quality 1.687 7.028 84.353

HCSQ1 0.871

HCSQ2 0.868

140

Table 4.15 Exploratory Factor Analysis of Perceived Healthcare Service Quality

Table 4.15 shows the summarised factor results of perceived healthcare service

quality. Overall, five dimensions emerged from the perceived healthcare quality data

(Tangibles, Reliability, Assurance, Empathy, and Responsiveness) explaining 83.09 per

cent of the variance in the data. This means that the solution is satisfactory from a

practical perspective. The first factor extracted was empathy, which explained 19.11 per

cent of the variance in the data and all items had significant loadings of >0.70. The

remaining four factors extracted explained roughly the same percentage of the variance in

the data (Assurance = 17.39 per cent; Responsiveness = 13.96 per cent; Reliability =

S.No Factor

Extracted

Item

Label

Item

Loading

Eigenvalue % of

Variance

Cumulative

Variance

1 Empathy 4.602 19.117 19.117

EMTP1 0.841

EMTP2 0.902

EMTP3 0.748

EMTP4 0.773

EMTP5 0.832

2 Assurance 4.176 17.399 36.576

ASSP1 0.829

ASSP2 0.864

ASSP3 0.693

ASSP4 0.780

ASSP5 0.859

3 Responsiveness 3.351 13.962 50.538

RESP1 0.884

RESP2 0.874

RESP3 0.819

RESP4 0.839

4 Reliability 3.219 13.412 63.950

RABP1 0.938

RABP2 0.796

RABP3 0.840

RABP4 0.833

5 Tangible 2.892 12.048 75.998

TANP1 0.833

TANP2 0.874

TANP3 0.814

TANP4 0.741

6 Service Quality 1.703 7.097 83.095

HCSQ1 0.868

HCSQ2 0.869

141

13.41 per cent and Tangibles = 12.04 per cent) and all items had significant loadings of

>.70. Scale reliabilities of the healthcare service quality expectation measure were

assessed with coefficient alpha and this is presented in Table 4.16. The coefficient alpha

exceeded the minimum standard of 0.70 (Hair et al., 2013), which indicates that it

provides a good estimate of internal consistency. The summarised results of an

exploratory factor analysis undertaken on the data collected in this research using the

perception portion of the SERVQUAL measure. The results consistently same with many

other empirical studies in the literature, evidence has been found in support of the five

factor solution presented by SERVQUAL‟s developers (Arasli et al., 2005; Carman

1990; Cronin and Taylor 1992) and thus this factor structure was employed to analyse the

relevant hypotheses.

The second step exploratory factor analysis was conducted for the latent variable

patient satisfaction and its predictors proposed in research model. The result of

exploratory factor analysis for determinants of patient satisfaction is shown in Table 4.16.

The Woodside et al’s (1995) predictor of patient satisfaction scale was used in this study

to identify key determinants of Indian corporate hospitals. As previously stated (Section-

4.3), the scale development procedures employed by Woodside et al’s (1995) appears to

support the face validity of the original scale items. This section describes the preliminary

tests undertaken and the exploratory factor analyses performed. This section concludes

with a discussion of the outcome of the analysis that outlines the factor structure selected

for use in the analysis of the hypotheses.

The next step was to determine the factor method to be used. Hair et al., (2011)

suggests the use of principal components analysis when data reduction is the primary

concern, as is the case in this research. VARIMAX orthogonal rotation procedure was

used because this method minimises the number of variables with high factor loadings,

thereby enhancing the interpretability of factors (Hair et al., 2013). The factors retained

were those with eigenvalues greater than one as factors with eigenvalues below this

number could not be interpreted (Carman 1990). In terms of factor loadings, those less

than 0.30 were excluded from the analysis, this criterion being chosen on the basis of

142

guidelines for assessing the levels at which factor loadings are considered to be

significant given the sample size of more than 200 respondents (Hair et al., 2013).

Table 4.16 Exploratory Factor Analysis of Patient Satisfaction

S.No Factor

Extracted

Item

Label

Item

Loading

Eigenvalue per cent of

Variance

Cumulative

Variance

1 Overall Services 7.412 24.707 24.707

OS1 0.911

OS2 0.673

OS3 0.854

OS4 0.937

OS5 0.918

OS6 0.876

OS7 0.834

OS8 0.929

2 Patient Satisfaction 4.163 13.876 38.582

PS1 0.809

PS2 0.744

PS3 0.875

PS4 0.890

3 Medical care Services 3.226 10.754 49.336

MS1 0.826

MS2 0.864

MS3 0.654

MS4 0.756

4 Housekeeping Services 2.828 9.426 58.762

HKS1 0.858

HKS2 0.711

HKS3 0.701

HKS4 0.833

5 Nursing care Services 2.364 7.880 66.642

NS1 0.674

NS2 0.684

NS3 0.726

NS4 0.674

6 Admission Services 2.251 7.503 74.145

AP1 0.858

AP2 0.902

AP3 0.786

7 Food Services

1.968 6.559 80.704

FS1 0.820

FS2 0.835

FS3 0.800

143

In the case of cross-loading, which is where a variable is found to have more than one

significant loading, it is generally recommended that each variable with a high cross-

loading be evaluated for possible deletion (Hair et al., 2013) and so variables with similar

loadings on more than one factor were deleted, as were items that did not conceptually

belong to the factor. In the analysis of the determinants of patient satisfaction data,

admission process item number - 4, Overall service Experience item numbers - 9&10,

consistently loaded on two factors and so it was deleted. The remaining 30 items were

retained on one of the three final factors extracted.

Table 4.16 shows the summarised factor results of determinants of patient

satisfaction. All the items measuring patient satisfaction determinants loaded on 7 factors.

They are admission process, nursing care services, medical care services, housekeeping

services, food services, overall service experience and patient satisfaction explaining 80.7

per cent of the variance in the data. This means that the solution is satisfactory from a

practical perspective. The first factor extracted was overall service experience, which

explained 24.7 per cent of the variance in the data and all items had significant loadings

of >0.80. The remaining four factors extracted explained roughly the same percentage of

the variance in the data (patient satisfaction = 13.87 per cent; medical care services =

10.75 per cent; housekeeping services = 9.42 per cent; nursing care services = 7.88 per

cent; admission process = 7.5 per cent and food services = 6.55 per cent) and all items

had significant loadings of >.60. Scale reliabilities of the determinants of patient

satisfaction were assessed with coefficient alpha and this is presented in Table 4.16. The

coefficient alpha exceeded the minimum standard of 0.70 (Hair et al., 2013), which

indicates that it provides a good estimate of internal consistency. The results consistently

same with many other empirical studies in the literature, evidence has been found in

support of the seven factor solution (Woodside et al’s., 1995, and Arasli et al., 2005) and

thus this factor structure was employed to analyse the relevant hypotheses.

144

Table 4.17 Exploratory Factor Analysis of Behavioural Intentions

The same exploratory factor analysis was conducted for the dependent variable

“behavioural intentions”. All items measuring behavioural intentions loaded into single

factors as shown in Table 4.17. All the four items BI1, BI2, BI3, and BI4 measure

patient‟s intentions to revisit and recommend to friends or relatives. As shown in Table

4.16, all the loadings are greater than 0.90. The cumulative variance explained is 89.7 per

cent, much higher than 60.000 per cent. Eigenvalue is higher than 1.0. The coefficient

alpha exceeded the minimum standard of 0.70 (Hair et al., 2013), which indicates that it

provides a good estimate of internal consistency. This result provides sufficient evidence

of the reliability and the validity of the measurement instruments for behavioural

intentions.

Exploratory factors extraction model

Kaiser's criterion of Eigen values greater than one and the scree plot was applied for

factors‟ extraction. Expected and perceived SERVQUAL dimensions of healthcare

service quality from 1-10 factors explained 56.78 per cent of the total variance and

remaining variables of patient satisfaction and behavioural intention explained only 26.7

per cent of variance. Table 4.18 presents results of factors extraction on the basis of the

eigenvalues greater than one criterion, which resulted in identification of overall nineteen

factors.

Scree Plot

The scree plot is a graph of the eigenvalues against all the factors. The graph is useful for

determining how many factors to retain. The point of interest is where the curve starts to

Exploratory Factor Analysis of Behavioural Intentions

S.No Factor

Extracted

Item

Label

communalities Eigenvalue Cumulative

Variance

1 Behavioural Intentions 3.588 89.706

BI1 0.968

BI2 0.958

BI3 0.937

BI4 0.924

145

flatten. Figure 4.5 shows the scree plot test used to confirm the maximum number of

factors extracted in this model under eigenvalues greater than one criterion. The slop of

the scree plot revealed extraction of overall nineteen factors, which confirmed extraction

of the same number of factors through the eigenvalues criterion. Figure 4.5 shows that the

curve begins to flatten between the factors 7 and 8.

Table 4.18 Total number of factors extracted and total variance explained in EFA model

Initial Eigenvalues

Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance Cumulative

%

1 7.606 9.508 9.508 7.606 9.508 9.508 6.993 8.741 8.741

2 5.263 6.578 16.086 5.263 6.578 16.086 4.535 5.669 14.411

3 5.130 6.412 22.498 5.130 6.412 22.498 4.162 5.202 19.613

4 4.928 6.160 28.657 4.928 6.160 28.657 4.124 5.154 24.767

5 4.263 5.328 33.986 4.263 5.328 33.986 4.048 5.060 29.827

6 4.108 5.134 39.120 4.108 5.134 39.120 3.639 4.549 34.376

7 3.827 4.784 43.904 3.827 4.784 43.904 3.439 4.299 38.675

8 3.603 4.503 48.407 3.603 4.503 48.407 3.422 4.278 42.953

9 3.477 4.346 52.753 3.477 4.346 52.753 3.358 4.198 47.151

10 3.228 4.036 56.789 3.228 4.036 56.789 3.344 4.180 51.331

11 3.076 3.845 60.634 3.076 3.845 60.634 3.322 4.152 55.483

12 2.929 3.662 64.296 2.929 3.662 64.296 3.271 4.088 59.571

13 2.911 3.639 67.935 2.911 3.639 67.935 3.240 4.049 63.621

14 2.657 3.321 71.256 2.657 3.321 71.256 3.075 3.843 67.464

15 2.470 3.087 74.343 2.470 3.087 74.343 2.949 3.686 71.150

16 2.229 2.787 77.129 2.229 2.787 77.129 2.747 3.434 74.584

17 2.157 2.696 79.825 2.157 2.696 79.825 2.570 3.213 77.797

18 1.893 2.366 82.191 1.893 2.366 82.191 2.500 3.125 80.922

19 1.034 1.293 83.484 1.034 1.293 83.484 2.050 2.562 83.484

Note: Extraction Method: Principal Component Analysis

Figure 4.5 Scree Plot

146

4.5. Pearson’s Bivariate Correlations between latent factors

Pearson‟s bivariate correlations were used to test the linearity in data. It is essential part

of the preliminary analysis to know the level of correlation in data and to figure out if

there is any departure from the linearity that might affect the correlations (Hair et

al., 2013). Results of the Bivariate Pearson‟s correlations between all latent factors are

presented in Table 4.19. All latent factors were positively and significantly correlated

with each other (p < 0.001) except the perceived reliability (PRAB) construct, which was

not significantly correlated with the perceived assurance (PASS), healthcare service

quality (HCSQ) construct, which was not significantly correlated with the admission

process of corporate hospitals (AS), expected responsiveness (ERES) construct, which

was not significantly correlated with the nursing (NS) and medical services (MS) of

corporate hospitals, expected empathy (EEMT) construct, which was not significantly

correlated with the medical services (MS) of corporate hospitals.

147

Table 4.19 Pearson‟s Bivariate Correlations between latent factors/Constructs

Pearson’s Bivariate Correlations between latent factors/Constructs ETAN ERAB EASS EEPT ERSP PTAN PRAB PASS PEPT PRSP HCSQ AS NS MS HSK FS OS PS BI

ETAN 1

ERAB .989** 1

EASS .610** .711** 1

EEPT .209** .138** .559** 1

ERSP .058 .522** .418** .788** 1

PTAN .265** .608** .321** .322** .639** 1

PRAB .080 .091 .248** .216** .260** .934** 1

PASS .760** .527** .297** .017 .153** .822** .717** 1

PEPT .523** .063 .062 .322** .232** .578** .439** .636** 1

PRSP .182** .255** .614** .431** .261** .373** .260** .470** .628** 1

HCSQ .211** .025 .479** .640** .046 .289** .353** .347** .595** .646** 1

AS .812** .048 .559** .395** .280** .109** .232** .432** .302** .606** .674** 1

NS .228** .522** .418** .449** .717** .368** .261** .636** .035 .384** .582** .532** 1

MS .079 .229** .348** .652** .667** .274** .446** .366** .382** .486** .306** .129** .608** 1

HSK .373** .226** .624** .348** .489** .089 .514** .492** .322** .284** .135** .267** .522** .306** 1

FS .850** .318** .712** .249** .328** .389** .285** .179** .428** .372** .031 .385** .091 .435** .470** 1

OS .808** .712** .276** .049 .563** .482** .495** .067 .375** .099 .077 .076 .527** .031 .347** .666** 1

PS .312** .632** .284** .566** .551** .682** .080 .070 .192** .456** .474** .480** .363** .277** .432** .206** .741** 1

BI .493** .518** .383** .428** .558** .389** .656** .092 .295** .652** .100** .329** .422** .374** .236** .452** .524** .454** 1

ETAN: Expected Tangibility; ERAB: Expected Reliability; EASS: Expected Assurance; EEPT: Expected Empathy; ERSP: Expected Responsiveness; PTAN: Perceived

Tangibility; PRAB: Perceived Reliability; PASS: Perceived Assurance; PEPT: Perceived Empathy; PRSP: Perceived Responsiveness; HCSQ: Healthcare Service

Quality; AS: Admission Services; NS: Nursing Services; MS: Medical Services; HSK: Housekeeping Services; FS: Food Services; OS: Overall Services Experience; PS:

Patient Satisfaction and BI: Behavioural Intentions.

148

4.6. Normality of Data for Latent Factors

To assume the normality and distribution of presents study data, for all latent factors was

checked with the two normality tests i.e. Kolmogorov-Smirnov test and Shapiro-Wilk test

(Table 4.20). All statistics for the both tests were found significant, which indicated

departure from the normality of the data. However, these two tests are recognised to be

sensitive to large sample size, such as the sample size of 493 in this study; therefore, they

tend to become significant. Nevertheless, skewness and kurtosis statistics found less than

±1 (see tables 4.2 to 4.5), which indicated no deviation from data normality.

Consequently, it was assumed that there was no major problem of a lack of normality in

the data in this study.

Table 4.20 Tests of Normality

Kolmogorov-Smirnova

Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

ETAN .219 493 .000 .889 493 .000

ERAB .220 493 .000 .878 493 .000

EASS .228 493 .000 .867 493 .000

EEPT .224 493 .000 .883 493 .000

ERES .214 493 .000 .892 493 .000

PTAN .224 493 .000 .885 493 .000

PRAB .220 493 .000 .883 493 .000

PASS .199 493 .000 .893 493 .000

PEPT .207 493 .000 .886 493 .000

PRES .234 493 .000 .875 493 .000

HCSQ .250 493 .000 .878 493 .000

AS .190 493 .000 .881 493 .000

NS .195 493 .000 .919 493 .000

MS .194 493 .000 .900 493 .000

HSK .215 493 .000 .891 493 .000

FS .229 493 .000 .894 493 .000

OS .227 493 .000 .881 493 .000

PS .204 493 .000 .893 493 .000

BI .193 493 .000 .929 493 .000

149

4.7. Test of Homogeneity of Variances

In correlational designs, such as factor analysis, the equality of variance assumption

means that the variance of one variable is stable at all levels of the other variables

(Garson, 2009). The assumption that dependent variables exhibit equal levels of variance

across a range of independent variables is called homoscedasticity (Hair et al., 2013). In

presence of the Homogeneity of Variance was determined by the Levene‟s Test and the

results of this test (Table 4.21) revealed that all latent constructs were no significant

except the medical services construct, which confirmed that there was homogeneity of

variance in the data for eighteen out of nineteen latent constructs.

Table 4.21 Test of Homogeneity of Variances

S.N0 Factor Label Levene Statistic df1 df2 Sig.

1. ETAN 1.183 15 477 0.281

2. ERAB 1.444 15 477 0.122

3. EASS 0.817 15 477 0.659

4. EEPT 0.614 15 477 0.864

5. ERES 1.269 15 477 0.218

6. PTAN 1.595 15 477 0.371

7. PRAB 1.099 15 477 0.354

8. PASS 2.199 15 477 0.606

9. PEPT 1.118 15 477 0.337

10. PRES 2.426 15 477 0.072

11. HCSQ 1.620 15 477 0.065

12. AS 1.937 15 477 0.018

13. NS 0.956 15 477 0.501

14. MS 3.666 15 477 0.000**

15. HSK 1.934 15 477 0.118

16. FS 2.490 15 477 0.202

17. OS 1.691 15 477 0.189

18. PS 1.569 15 477 0.078

150

4.8. Multi–Collinearity Coefficientsa for latent Factors

Multicolinearity occurs when there is a linear relationship among one or more of the

independent variable. Presence of multicolinearity for latent factors was checked by the

Durbin-Watson test. Table 4.22 show that no auto-correlation of residual (Durbin-Watson

test) and show that the model (Table 4.22) did not have multicolinearity among

independent variables (VIF < 4).

Table 4.22 Multi-Collinearity Coefficientsa for latent factors

S.No Factor Label Tolerance VIF

1. ETAN 0.935 1.069

2. ERAB 0.955 1.047

3. EASS 0.968 1.033

4. EEPT 0.954 1.048

5. ERES 0.902 1.109

6. PTAN 0.978 1.022

7. PRAB 0.903 1.108

8. PASS 0.914 1.094

9. PEPT 0.936 1.068

10. PRES 0.963 1.039

11. HCSQ 0.659 1.518

12. AS 0.890 1.123

13. NS 0.928 1.078

14. MS 0.598 1.672

15. HSK 0.930 1.075

16. FS 0.905 1.105

17. OS 0.921 1.086

18. PS 0.893 1.120

151

4.9. Structural Equation Modelling (SEM) Analysis

Structural equation modelling (SEM) is a collection of statistical models that seeks to

explain relationships among multiple variables. It enables researchers to examine

interrelationships among multiple dependent and independent variables simultaneously

(Hair et al., 2013). The reasons for selecting SEM for data analysis were, firstly; SEM

has the ability to test causal relationships between constructs with multiple measurement

items (Hair et al., 2013). Secondly, it offers powerful and rigorous statistical procedures

to deal with complex models (Hair et al., 2013). The relationships among constructs

and indicators (measurement items) are validated by using confirmatory factor analysis

(CFA), also known as the measurement model, and relationships between constructs

are tested using the structural model (Hair et al., 2013). A two-step approach was adapted

to perform SEM analysis as recommended by Anderson and Gerbing (1988). In the first

step, the measurement model was specified using the interrelationships between indicator

(observed) and latent (unobserved) factors. For the measurement model, confirmatory

factor analysis (CFA) was performed using the SEM software AMOS Version. 20.0. In

the second step, the structural model related to dependent and independent variables was

specified in order to test the hypotheses. Results of measurement and structural model are

presented as follows. However, it is to be noted that for clarification and due to the limits

of word length only final measurement model (CFA) results will be presented.

Given the validity of individual latent variables and objectives of present study,

there are two different SEM analyses were conducted. In first section or SEM model-1

included all the perceived and expected dimensions of healthcare service quality, patient

satisfaction and behavioural intention constructs. SEM model-I examines

interrelationships among multiple dependent and independent variables simultaneously

(Healthcare Service Quality, Patient Satisfaction and Behavioural Intention). In second

section or SEM model-II included all of the determinants of patient satisfaction. The

second was mainly based on third objective of present study, i.e. to find key determinants

of patient satisfaction in corporate hospitals. Two different SEM analyses are as follows

below;

152

4.9.1. HCSQ, PS and BI: SEM model-1

SEM model 1 includes the exploratory factor analysis (EFA) results mentioned above,

the remaining 22 items measuring healthcare service quality loaded in five expected and

perceived quality factors, two items measuring overall healthcare service quality loaded

in single factor, four overall patient satisfaction items loaded in single factor and four

behavioural intention items loaded in single factor. They are expected and perceived

SERVQUAL dimensions (Tangibility, Reliability, Assurance, Empathy and

Responsiveness), Patient Satisfaction and Behavioural Intentions. All main loadings are

higher than 0.60, and cross‐loadings are less than 0.30, which indicates the validity of the

measurement instruments. The coefficient alpha exceeded the minimum standard of 0.70

(Hair et al., 2010), which indicates that it provides a good estimate of internal

consistency.

a. Measurement model specification and confirmatory factor analysis results

In present study, confirmatory factor analysis (CFA) was performed on the measurement

model to assess the unidiminsionality, reliability, and validity of measures. Two broad

approaches were used in the CFA to assess the measurement model. First, consideration

of the goodness of fit (GOF) criteria indices and second, evaluating the validity and

reliability of the measurement model.

b. Goodness of fit Indices

SEM model has three main types of fit measure indices: absolute fit indices, incremental

fit indices, and parsimonious fit indices. Results of these fit measures obtained in this

study and their recommended levels are presented in Table 4.23.

Confirmatory factor analysis was performed on the measurement model

comprising thirteen factors, which were: Expected Tangibility (ETAN); Expected

Reliability (ERAB); Expected Assurance (EASS); Expected Empathy (EEMT); Expected

Responsiveness (ERES); Perceived Tangibility (PTAN); Perceived Reliability (PRAB);

Perceived Assurance (PASS); Perceived Empathy (PEMT); Perceived Responsiveness

(PRES), Healthcare Service Quality (HCSQ), Patient Satisfaction (PS) and Behavioural

Intention (BI). Figure-4.6 depicts the initial hypothesised measurement model. These

153

factors were measured using number of items (indicators). In total, 30 items (expected

and perceived service quality measured with same items) were used which were derived

from the EFA. For instance, patient satisfaction and behavioural intention was measured

by 4 items each.

Figure 4.6 Hypothesised CFA model derived from EFA of SQ, PS and BI

The measurement model was evaluated by using the maximum likelihood (ML)

estimation techniques provided by the AMOS 20.0. Table 4.23 provides summarised

results of the initial CFA. The results revealed that chi-square statistics (χ2 = 3202.788,

df=1299) was significant at p < 0.05 indicating that fit of data to the model was

moderately good. However, it was unreasonable to rely on the chi-square statistics as a

154

sole indicator for evaluating the specification of model, as this statics is sensitive to the

sample size and is very sensitive to the violations of the assumption of normality,

especially the multivariate normality; therefore, it can be misleading. Thus, other fit

indices i.e. GFI, AGFI, CFI, NFI, and RMSEA were used to assess the specification of

the model. Results revealed that the value of GFI=0.814, AGFI=0.787, CFI=0.913, and

RMSEA=0.055 (Table 4.23). These results indicated for further refinement of model as

the results were not consistent with the recommended values of the fit indices of a priori

specified measurement model.

Table 4.23 Goodness of fit statistics for the Initial CFA of SQ, PS and BI model

Absolute fit measures Incremental fit

measures

Parsimony

fit measures

χ2

Df χ2/df GFI RMSEA NFI CFI AGFI

Criteria <3 ≥ 0.90 < 0.05 ≥ 0.90 ≥ 0.90 ≥ 0.90

Obtained 3202.78 1299 2.466 0.814 0.055 0.888 0.913 0.787

Note: χ2: Chi-square; Df: degree of freedom; GFI: Goodness of fit index; RMSEA: Root mean square

error of approximation; NFI: Normated fit index; CFI: Comparative fit index; AGFI: Adjusted

goodness of fit index

From the above results goodness of fit indices of the initial run of CFA (e.g. χ2,

GFI, AGFI) were not within the recommended level, further detailed evaluation was

conducted to refine and re-specify the model, in order to improve the discriminant

validity and achieve better fit of the model (Hair et al., 2013). The model refinement

procedure applied following criteria recommended by researchers. According to Hair et

al., (2011) factor loading (i.e. Standard regression weight in AMOS 20.0) value should be

greater than 0.7 and Squared multiple correlations (SMC) value should be greater than

the cut-off point 0.5. The standard residual values should be within the threshold (above

2.58 or below 2.58) as recommended by Hair et al., (2011). Finally, modification indices

(MI) that show high covariance and demonstrate high regression weights are candidate

for deletion (Hair et al., 2013).

155

Figure 4.7 Final CFA model of SQ, PS and BI

Following these recommended criteria, the output of the initial CFA run was

examined to see whether any items proving to be problematic. Assessment of results

indicated that the standard regression weight of all measurement items was above the

recommended level (>0.7) (Hair et al., 2013). However, evaluation of standardised

residuals indicated that the values of EEMT2 & EEMT4, PEMT4, PEMT2 and PEMT3

156

items were cross loaded on same factor and these items were not within the acceptable

level (above 2.58 or below 2.58; Hair et al., 2013). Thus, after modifying these

problematic items by using modifies indices criteria in AMOS 20.0, the measurement

model was re-run, as recommended (Hair et al., 2013). Final CFA model is depicted in

Figure 4.7.

After modifying these problematic items by using modification indices criteria,

which were EEMT4, PEMT2 and PEMT3; CFA was re-run for assessing the

measurement model fit. The results of the model revealed that goodness of fit indices

were improved and the revised model demonstrated a better fit to the data. Results of the

respective measurement model after removal of redundant items (Table 4.24) indicated

the absolute fit measures i.e. GFI and RMSEA were 0.912 and 0.5, respectively, the

incremental fit measures i.e. NFI and CFI were 0.902 and 0.926, respectively and the

parsimony fit measure i.e. AGFI was 0.901. All these measures surpassed the minimum

recommended values. In addition to these indices, the ratio of χ2/df was 2.298, which was

within the acceptable threshold level (i.e., 1.0 < χ2/df < 3.0). These goodness of fit

statistics therefore confirmed that the model adequately fitted the data.

Table 4.24 Goodness of fit statistics of revised CFA model of SQ, PS and BI


measures

Parsimony

fit measures

χ2


Criteria <3 ≥ 0.90 < 0.05 ≥ 0.90 ≥ 0.90 ≥ 0.90

Obtained 3104.7 1351 2.298 0.912 0.050 0.902 0.926 0.901




Besides, other estimation criteria show that model fit the data adequately well,

such that, standard regression weight were all greater than 0.7, standard residual were all

within the threshold level, and critical ratios values were above 1.96. In summary, the

results confirmed that model was fit to the data, indicating no further refinement in the

model was required. Thus, the unidiminsionality of the model data was established (Hair

et al., 2013).

157

4.9.2. Assessment of Reliability and Validity of Constructs

This section presents results of the validity and reliability of the all expected and

perceived healthcare service quality, satisfaction and intention constructs used in this

study.

a. Reliability of Constructs

The reliability test was done to determine how strongly the dimensions were related to

each other (Hair et al., 2013). For testing the reliability, Cronbach‟s “α” value and

composite reliability (CR) values are assessed.

Cronbach’s “α” value: The internal consistency to assess the reliability of the final scale

was examined through split half method (Malhotra, 2002; Hair et al., 2013). The internal

consistency reliability test is acceptable when the reliability coefficient exceeds

Nunnally‟s (1978) reliability criterion of 0.70 levels for basic research. The value of

0.951 for overall sample support is an acceptable reliability coefficient.

The results revealed (Table 4.25) that the reliability coefficient for the construct

behavioural intention (BI) was 0.958, which was above the criteria strictly recommended

(α>0.7), indicating the observed variables are reasonably good measurement of the

construct BI. The results also revealed that construct‟s reliability estimate for BI

indicated high internal consistency and adequate reliability of the construct. Besides, all

other estimation values were above the recommended (α >0.7) cut off point indicating

strong reliability and high internal consistency in measuring relationship in the model.

This also suggested strong construct validity (Hair et al., 2013).

158

Table 4.25 Construct reliability statistics of SQ, PS and BI model

Construct Construct Reliability

Criteria ≥0.7

ERES 0.918

EASS 0.956

EEMT 0.948

PEMT 0.943

PASS 0.939

BI 0.958

PRES 0.943

PRAB 0.943

ERAB 0.933

ETAN 0.930

PS 0.928

PTAN 0.924

HCSQ 0.904

b. Validity of Constructs

Validity of the construct can be examined by assessing content validity, construct

validity, convergent validity and discriminant validity.

Content Validity: The content validity of the scale was duly assessed through review of

literature and deliberations with the subject experts, doctors and patients for this selection

of items in the service quality and service performance constructs at the time of pre-

testing. A few items were modified to make statements conceivable to respondents. All

these steps checked the face and content validity.

Construct Validity: The KMO-MSA (Kaiser-Meyer-Olkin Measure of Sampling

Adequacy) value (greater than 0.7), communality values (greater than 0.7), factor loading

values (greater than 0.7) and variance explained (greater than 0.5) criteria are used to

examine the construct validity of the scale (Hair et al., 2013). A majority of the values

met the threshold criteria and thus checked the construct validity of the sub-scales.

Convergent Validity: Convergent validity assumes that measures of constructs that should

be theoretically related to each other are, in fact, related to each other. Factor loadings of

construct, average variance extracted (AVE), and construct reliability (CR) estimation

were used by this researcher to assess the convergent validity of each of the constructs. A

159

minimum cut off criteria for standardised regression loadings >0.7, AVE >0.7 and

reliability >0.7, were used to assess the convergent validity. Results are presented in

Table 4.26.

Table 4.26 Convergent validity of SQ, PS and BI model

S.No Construct Item Standardised

Item Loading

Critical Ratio

(t-value)

Average Variance

Extracted (AVE)

1. Expected Tangibility 0.770

ETAN1

ETAN2

ETAN3

ETAN4

0.909

0.912

0.874

0.812

35.31

-*

33.38

31.77

2. Expected Reliability 0.781

ERAB1

ERAB2

ERAB3

ERAB4

0.956

0.938

0.673

0.937

-b

22.89

21.64

26.25

3. Expected Assurance 0.846

EASS1

EASS2

EASS3

EASS4

EASS5

0.927

0.965

0.864

0.887

0.958

19.32

36.13

-b

34.50

10.12

4. Expected Empathy 0.785

EEMT1

EEMT2

EEMT3

EEMT4

EEMT5

0.871

0.933

0.807

0.864

0.948

-b

27.63

26.24

23.18

21.10

5. Expected Responsiveness 0.738

ERES1

ERES2

ERES3

ERES4

0.916

0.871

0.798

0.848

23.16

19.03

-b

36.48

6. Perceived Tangibility 0.753

PTAN1

PTAN2

PTAN3

PTAN4

0.881

0.913

0.871

0.783

-b

34.58

16.74

18.79

7. Perceived Reliability 0.805

PRAB1

PRAB2

PRAB3

PRAB4

0.988

0.846

0.864

0.884

21.14

20.23

-b

32.88

* Regression weight 1 b

t-values are unavailable because the loadings are fixed for scaling purposes

Table-Conti..,

160

S.No Construct Item Standardised

Item Loading

Critical Ratio

(t-value)

Average Variance

Extracted (AVE)

8. Perceived Assurance 0.756

PASS1

PASS2

PASS3

PASS4

PASS5

0.884

0.902

0.772

0.854

0.901

-b

23.56

23.54

24.95

21.91

9. Perceived Empathy 0.769

PEMT1

PEMT2

PEMT3

PEMT4

PEMT5

0.901

0.984

0.824

0.822

0.846

21.90

22.24

27.78

22.67

24.39

10. Perceived Responsiveness 0.805

PRES1

PRES2

PRES3

PRES4

0.927

0.917

0.862

0.882

21.69

25.20

28.86

-b

11. Healthcare Service Quality 0.833

HCSQ1

HCSQ2

0.867

0.905

36.45

38.42

12. Patient Satisfaction 0.765

PS1

PS2

PS3

PS4

0.841

0.747

0.927

0.949

21.78

22.67

24.39

25.67

13. Behavioural Intentions 0.852

BI1

BI2

BI3

BI4

0.987

0.857

0.876

0.966

-*

23.56

23.54

24.92

* Regression weight 1 b

t-values are unavailable because the loadings are fixed for scaling purposes

Results (see Table 4.26) revealed that all the standardised factor loadings (standard

regression weights) were above the minimum cut off point (>0.7), the critical ratios (t-

values) were higher than 1.96 (p < 0.001 and the average variance extracted was greater

than 0.07. The results thus demonstrated a high level of convergent validity of the latent

constructs used in the model.

Discriminant Validity: Discriminant validity is examined by comparing average variance

extracted values with squared multiple correlation values (Byrne, 2001). Average

variance extracted for all the four constructs of service quality (Table 4.26) is found to be

higher than the squared multiple correlation of the items of the respective constructs

(Table 4.26). The results thus support the discriminant validity.

161

Table 4.27 Inter-construct correlations of SQ, PS and BI model

ETAN ERAB EASS EEPT ERSP PTAN PRAB PASS PEPT PRSP HCSQ PS BI

ETAN 1.000

ERAB 0.989 1.000

EASS 0.610 0.711 1.000

EEPT 0.209 0.138 0.559 1.000

ERSP 0.058 0.522 0.418 0.788 1.000

PTAN 0.265 0.608 0.321 0.322 0.639 1.000

PRAB 0.080 0.091 0.248 0.216 0.260 0.934 1.000

PASS 0.760 0.527 0.297 0.017 0.153 0.822 0.717 1.000

PEPT 0.523 0.063 0.062 0.322 0.232 0.578 0.439 0.636 1.000

PRSP 0.182 0.255 0.614 0.431 0.261 0.373 0.260 0.470 0.628 1.000

HCSQ 0.211 0.025 0.479 0.640 0.046 0.289 0.353 0.347 0.595 0.646 1.000

PS 0.312 0.632 0.284 0.566 0.551 0.682 0.080 0.070 0.192 0.456 0.474 1.000

BI 0.493 0.518 0.383 0.428 0.558 0.389 0.656 0.092 0.295 0.652 0.100 0.454 1.000

162

Table 4.28 Discriminant validity of SQ, PS and BI model

ERES EASS EEMT PEMT PASS BI PRES PRAB ERAB ETAN PS PTAN HCSQ

ERES 0.859

EASS 0.424 0.920

EEMT 0.114 0.256 0.786

PEMT 0.214 0.307 0.537 0.779

PASS 0.320 0.115 0.316 0.606 0.761

BI 0.146 0.253 0.238 0.431 0.019 0.685

PRES 0.055 0.303 0.115 0.172 0.235 0.539 0.789

PRAB 0.100 0.280 0.353 0.221 0.196 0.257 0.303 0.768

ERAB 0.306 0.123 0.160 0.260 0.379 0.313 0.220 0.252 0.729

ETAN 0.296 0.228 0.352 0.224 0.313 -0.023 0.256 -0.094 0.333 0.828

PS 0.300 0.270 0.419 0.324 0.488 0.147 -0.017 0.098 0.259 0.194 0.802

PTAN 0.149 0.039 0.226 0.152 -0.023 -0.046 0.217 0.217 0.140 0.351 0.306 0.742

HCSQ 0.280 0.331 0.349 0.326 0.272 0.272 0.324 0.155 0.204 0.125 -0.008 0.432 0.714

Note: Diagonal values are AVE and off diagonal are inter-construct squared correlations.

Results (see table 4.27 and 4.28) reveal that, the AVE estimates of all the constructs were larger than their corresponding

squared inter-construct correlations estimates, which demonstrated a high level of discriminate validity of all the

dimensions. In addition this indicates that the measured items have more in common with latent constructs.

163

4.9.3. Structural Model Evaluation and Hypotheses Testing

The structural model (see Figure 4.8) specifies that the latent service quality dimensions

of perceived and expected service quality (SERVQUAL), contributes to healthcare

service quality (HCSQ), which in turn influence patient satisfaction and behavioural

intentions; and finally, satisfaction is said to influence patient‟s intentions to return. The

dimensions for proposed structural model were tested through structural equation

modelling.

Figure 4.8 Structural model

The fit indices (see Table 4.29) indicate that the hypothesised structural model

provided the good fit to the data. Although the likelihood ratio chi-square (χ2 =

2913.524; df = 1293; p = 0.000) was significant (p<0.001); however, other fit measures

showed that model adequately fit the observed data. The absolute fit measures i.e. GFI

and RMSEA were 0.915 and 0.046 respectively indicating good fit of model. The

incremental fit measures i.e. NFI and CFI were 0.918 and 0.956 respectively, which were

above the minimum requirement showing adequate fit and the parsimony fit measure i.e.

0.414***

ETAN

ERAB

EASS

EEPT

ERES

PTAN

PRAB

PASS

PEPT

PRES

HCSQ BI PS

0.757*

*

0.211***

0.818***

0.207***

0.147*

**

0.222***

0.336***

0.416***

0.463**

0.151***

0.928***

0.233***

R2=57 R2=48

%

R2=69

%

164

AGFI was 0.908, which also was above the cut-off point of (> 0.9). In addition to these

indices, the χ2/df = 2.253 was within the threshold level i.e. 1.0 < χ

2/df < 3.0) supporting

these findings.

Table 4.29 Structural model fit measure assessment


measures

Parsimony

fit measures

χ2


Criteria <3 ≥ 0.90 < 0.05 ≥ 0.90 ≥ 0.90 ≥ 0.90

Obtained 2913.52 1293 2.253 0.915 0.046 0.918 0.956 0.908




Table 4.30 Hypotheses testing / paths causal relationships

Construct Code Hypotheses Hypothesised Relationship

Tangibility TAN H5a ETAN → HCSQ

H5b PTAN → HCSQ Reliability RAB H1a ERAB → HCSQ

H1b PRAB → HCSQ Assurance ASS H3a EASS → HCSQ

H3b PASS → HCSQ Empathy EPT H4a EEPT → HCSQ

H4b PEPT → HCSQ Responsiveness RSP H2a ERSP → HCSQ

H2b PRSP → HCSQ Healthcare Service

Quality HCSQ H6a HCSQ → PS

H6b HCSQ → BI Patient Satisfaction PS H7 PS → BI

165

Table 4.30 indicates the results of hypotheses testing of structural model. The results

show thirteen hypotheses represented by causal paths (H1a, H1b, H2a, H2b, H3a, H3b,

H4a, H4b, H5a, H5b, H6a, H6b and H7) that were used to test the relationships between

the latent constructs. The latent constructs used in the proposed theoretical model (as

described in chapter 3) were classified in two main categories: exogenous and

endogenous constructs. Exogenous constructs were the expected and perceived service

quality dimensions (Tangibility, Reliability, Empathy, Assurance and Responsiveness)

and patient satisfaction while endogenous constructs were the behavioural intention.

Goodness of fit indices and other parameters estimates were examined to evaluate the

hypothesised structural model. Assessment of parameter estimates results suggested that

eleven out of thirteen hypothesised paths were significant. Thus, indicating support for

the eleven hypotheses. These results are presented in detail as follows.

Table 4.31 Regression estimates of latent constructs

Estimate S.E. C.R. P

PRAB → HCSQ 0.757 0.027 2.143 0.002

PTAN → HCSQ 0.222 0.024 1.92 ***

PEMT → HCSQ 0.211 0.022 3.488 ***

PASS → HCSQ 0.463 0.03 2.123 ***

PRES → HCSQ 0.818 0.024 6.757 ***

EEMT → HCSQ 0.416 0.023 4.706 ***

ETAN → HCSQ 0.147 0.047 3.107 0.002

ERAB → HCSQ 0.414 0.022 1.625 ***

ERES → HCSQ 0.207 0.024 0.284 ***

EASS → HCSQ 0.336 0.022 1.619 ***

HCSQ → PS 1.51 0.536 2.816 0.005

PS → BI 0.233 0.201 7.16 *** Note: Estimate = regression weight; S.E = standard error; C.R = critical ratio, P

= significance value

After finding causal relations between hypotheses, another most important part of

structural model assessment is coefficient parameter estimates. The parameter estimates

were used to produce the estimated population covariance matrix for the structural model.

The model was defined by 30 measurement items that identified the thirteen latent

166

constructs. The covariance matrix among the constructs was applied to test the model.

When the critical ratio (CR or t-value) is higher than 1.96 for an estimate (regression

weight), then the parameter coefficient value is statistically significant at the 0.05 levels

(Hair et. al., 2011). Critical ratio or t-value was obtained by dividing the regression

weight estimate by the estimate of its standard error (S.E). Using the path estimates and

CR values, thirteen causal paths were examined in this research study. For eleven causal

paths estimates t-values were above the 1.96 critical values at the significant level p

≤0.05. The overall structural model is depicted in figure 4.8, and parameter estimates are

presented in table 4.31 It is to be noted that the out of thirteen constructs only eleven

variable shows their significance with healthcare service quality dependent variable at the

significant level <0.005.

Table 4.32 Hypotheses testing

Construct Code Hypotheses Hypothesised

Relationship

(Positive)

Standardized

regression

weights (β)

C.R. (p - value)

Supported

Tangibility TAN H5a ETAN → HCSQ 0.757 2.143 (***)YES

H5b PTAN → HCSQ 0.222 1.92 (0.128) NO

Reliability RAB H1a ERAB → HCSQ 0.211 3.488 (***) YES

H1b PRAB → HCSQ 0.463 2.123 (0.152) NO

Assurance ASS H3a EASS → HCSQ 0.818 6.757 (***) YES

H3b PASS → HCSQ 0.416 4.706 (***) YES

Empathy EPT H4a EEPT → HCSQ 0.147 3.107 (***)YES

H4b PEPT → HCSQ 0.414 3.625 (***)YES

Responsiveness RSP H2a ERSP → HCSQ 0.207 3.284 (***) YES

H2b PRSP → HCSQ 0.336 2.619 (***)YES

Healthcare Service

Quality

HCSQ H6a HCSQ → PS 1.51 2.816 (0.004)YES

H6b HCSQ → BI 0.928 4.28 (0.002)YES

Patient Satisfaction PS H7 PS → BI 0.233 7.16 (***)YES

Note: Estimate = regression weight; S.E = standard error; C.R = critical ratio, P = significance value *** at p<0.005

As shown in Table 4.32 and Figure 4.8 the structural model estimations revealed that 11

out of 13 hypotheses were significant while 2 were not significant. The following 11

hypotheses were positively significant; hence, they were supported. Description of all

hypotheses are given below, are as follows;

167

H1 a: Expected Reliability (ERAB) has a positive influence on healthcare service

quality (HCSQ).

As shown in the figure 4.8, the standardized regression weight and critical ratio for ERAB

to HCSQ is 0.211 and 3.488 respectively, suggesting that this path is statistically

significant at the p=0.000. The results demonstrated strong support for hypothesis H1a,

which was proposed in the model (presented in chapter 3). This indicated that the

expected reliability has strong significant effect on healthcare service quality to visit

corporate hospitals, implying that if there was increase in the reliability of corporate

healthcare services then it would positively influence on quality delivery of corporate

hospitals. In summary, these results further suggest that ERAB was a major determinant

of healthcare service quality.

H1 b: Perceived Reliability (ERAB) has a positive influence on healthcare service

quality (HCSQ).

Figure 4.8 indicates the standardized regression weight and critical ratio for PRAB to

HCSQ is 0.463 and 2.123 respectively, suggesting that this path is not statistically

significant at the p<0.005. The results demonstrated weak support for hypothesis H1b,

which was proposed in the model (presented in chapter 3). Here the values of

standardized regression weights (β) and critical ration (CR) are within the limit of

acceptance but “p” values is not in recommended level, because of this hypothesis H1b

was rejected.

H2 a: Expected Responsiveness (ERES) has a positive influence on healthcare service

quality (HSQ).

From figure 4.8, the standardized regression weight and critical ratio for ERES to HCSQ

is 0.207 and 3.284 respectively, suggesting that this path is statistically significant at the

p=0.000. The results demonstrated strong support for hypothesis H2a, which was

proposed in the model (presented in chapter 3). This indicated that the expected

responsiveness of corporate hospital staff has strong significant effect on healthcare

service quality of their hospitals, implying that if there was increase in the responsiveness

of medical and supportive staff of corporate hospitals then it would positively influence

168

on quality delivery of their services it influence on their customer satisfaction and intents

to revisit. In summary, these results further suggest that ERES was a major determinant

of healthcare service quality.

H2 b: Perceived Responsiveness (PRES) has a positive influence on healthcare service

quality (HSQ).

As shown in the figure 4.8, the standardized regression weight and critical ratio for PRES

to HCSQ is 0.336 and 2.619 respectively, suggesting that this path is statistically

significant at the p=0.000. The results demonstrated strong support for hypothesis H2b,


perceived responsiveness of corporate hospital staff has strong significant effect on

healthcare service quality of their hospitals, implying that if there was increase in the

responsiveness of medical and supportive staff of corporate hospitals then it would

positively influence on quality delivery of healthcare services it influence on their

patient‟s satisfaction and intents to revisit. In summary, these results further suggest that

PRES was a major determinant of healthcare service quality.

H3 a: Expected Assurance (EASS) has a positive influence on healthcare service

quality (HSQ).

Figure 4.8, denotes the standardized regression weight and critical ratio for EASS to

HCSQ is 0.818 and 6.757 respectively, suggesting that this path is statistically significant

at the p=0.000. The results demonstrated strong support for hypothesis H3a, which was

proposed in the model (presented in chapter 3). This indicated that the expected assurance

has strong significant effect on healthcare service quality to visit corporate hospitals,

implying that if there was increase in the assurance of corporate hospital medical and

supportive staff to patients regarding their condition and treatment then it would

positively influence on healthcare service quality delivery of corporate hospitals.

169

H3 b: Perceived Assurance (PASS) has a positive influence on healthcare service

quality (HSQ).

Figure 4.8, signifies the standardized regression weight and critical ratio for PASS to


at the p=0.000. The results demonstrated strong support for hypothesis H3b, which was

proposed in the model (presented in chapter 3). This indicated that the perceived

assurance has strong significant effect on healthcare service quality to visit corporate

hospitals, implying that if there was increase in the assurance of corporate hospital

medical and supportive staff to patients regarding their condition and treatment then it

would positively influence on healthcare service quality delivery of corporate hospitals.

H4 a: Expected Empathy (EEMT) has a positive influence on healthcare service quality

(HSQ).

Figure 4.8, represents the standardized regression weight and critical ratio for EEMT to



proposed in the model (presented in chapter 3). This indicated that the expected empathy


implying that if there was increase in the empathy towards their patients then it would

positively influence on quality delivery of corporate hospitals.

H4 b: Perceived Empathy (PEMT) has a positive influence on healthcare service

quality (HSQ).

From figure 4.8, the standardized regression weight and critical ratio for PEMT to HCSQ

is 0.414 and 3.625 respectively, suggesting that this path is statistically significant at the

p=0.000. The results demonstrated strong support for hypothesis H4b, which was

proposed in the model (presented in chapter 3). This indicated that the perceived empathy


implying that if there was increase in the empathy towards their patients then it would

positively influence on quality delivery of corporate hospitals.

170

H5 a: Expected Tangibility (ETAN) has a positive influence on healthcare service

quality (HSQ).

Figure 4.8, describes the standardized regression weight and critical ratio for ETAN to



proposed in the model (presented in chapter 3). This indicated that the expected

tangibility has strong significant effect on healthcare service quality to visit corporate

hospitals, implying that if there was increase in the tangibility of corporate hospitals, i.e.

physical facilities, equipment and appearance of personnel then it would positively

influence on quality delivery of corporate hospitals. In summary, the efficient design and

layout of the environment can not only affect the pleasantness of the surroundings, but

also direct patients to the appropriate corporate hospital treatment.

H5 b: Perceived Tangibility (PTAN) has a positive influence on healthcare service

quality (HSQ).

Figure 4.8 indicates the standardized regression weight and critical ratio for PTAN to

HCSQ is 0.222 and 1.92 respectively, suggesting that this path is not statistically

significant at the p<0.005. The results demonstrated weak support for hypothesis H5b,

which was proposed in the model (presented in chapter 3). Here the values of

standardized regression weights (β) and critical ration (CR) are within the limit of

acceptance but “p” values is not in recommended level, because of this hypothesis H5b

was rejected.

H6 a: Healthcare Service Quality (HCSQ) directly and positive effect on Patient

Satisfaction (PS).

Figure 4.8, depicts the standardized regression weight and critical ratio for HCSQ to PS is

1.51 and 2.816 respectively, suggesting that this path is statistically significant at the

p=0.005. The results demonstrated strong support for hypothesis H6a, which was

proposed in the model (presented in chapter 3). This indicated that the healthcare service

quality has strong significant effect on patient satisfaction, implying that if there was

increase in the quality of corporate healthcare services then it would positively influence

171

on satisfaction of their customers and its turn influence on their intention to revisit

particular hospital. In summary, these results further suggest that HCSQ was a major

determinant of patient satisfaction.

H6 b: Healthcare Service Quality (HSQ) has a positive effect on Behavioural

Intentions (BI).

As shown in the Figure 4.8, the standardized regression weight and critical ratio for

HCSQ to BI is 0.928and 4.28 respectively, suggesting that this path is statistically

significant at the p=0.005. The results demonstrated strong support for hypothesis H6b,


healthcare service quality has strong significant effect on behavioural intentions,

implying that if there was increase in the quality of corporate healthcare services then it

would positively influence on satisfaction of their customers and its turn influence on

their intention to revisit particular hospital. In summary, these results further suggest that

HCSQ was a major determinant of patient satisfaction.

H7: Patient Satisfaction (PS) has a positive influence on Behavioural Intentions (BI).

Figure 4.8, indicates the standardized regression weight and critical ratio for PS to BI is

0.233 and 7.16 respectively, suggesting that this path is statistically significant at the

p=0.005. The results demonstrated strong support for hypothesis H7, which was

proposed in the model (presented in chapter 3). This indicated that the patient satisfaction

has strong significant effect on behavioural intention, implying that if there was increase

in the quality of corporate healthcare services then it would positively influence on

satisfaction of their customers and its turn influence on their intention to revisit particular

hospital. In summary, these results further suggest that PS was a major determinant of

behavioural intention.

172

4.10. Determinant of Patient Satisfaction (Structural Model-II)

The main purpose and objective of the SEM model-II was to find key determinants of

patient satisfaction at corporate hospitals. Analysis of Structural Model-II follows similar

procedures of Model-I analysis. The reliability and validity of the measurement

instruments were assessed, Supported by sufficient reliability and validity, the proposed

hypotheses were examined using CFA and SEM.

SEM model-II includes the exploratory factor analysis (EFA) results mentioned

above, the remaining 30 items of determinants of patient satisfaction loaded in seven

factors. They are Admission Process, Nursing Services, Medical Services, Housekeeping

Services, Food Services, Overall Service Experience and Patient Satisfaction. All main

loadings are higher than 0.60, and cross‐loadings are less than 0.30, which indicates the

validity of the measured instruments. The coefficient alpha exceeded the minimum

standard of 0.70 (Hair et al., 2013), which indicates that it provides a good estimate of

internal consistency.

4.10.1. Measurement model specification and confirmatory factor analysis results

In present study, confirmatory factor analysis (CFA) was performed on the measurement

model to assess the unidiminsionality, reliability, and validity of measures. Two broad

approaches were used in the CFA to assess the measurement model. First, consideration

of the goodness of fit (GOF) criteria indices and second, evaluating the validity and

reliability of the measurement model.

Goodness of fit Indices: SEM model has three main types of fit measure indices:

absolute fit indices, incremental fit indices, and parsimonious fit indices. Results of these

fit measures obtained in this study and their recommended levels are presented in table -

4.33.

Confirmatory factor analysis was performed on the measurement model

comprising seven factors, which were: Admission Process (AP); Nursing Services (NS);

Medical Services (MS); Housekeeping Services (HKS); Food Services(FS); Overall

Service Experience(OS) and Patient Satisfaction (PS). Figure 4.9 shows the initial

173

hypothesised measurement model. These factors were measured using number of items

(indicators). In total, 30 items were used which were derived from the EFA.

Figure 4.9 Hypothesised CFA model derived from EFA of Determinants of Patient

Satisfaction

The measurement model was evaluated by using the maximum likelihood (ML)

estimation techniques provided by the AMOS 20.0. Table 4.33 provides summarised

results of the initial CFA. The results revealed that chi-square statistics (χ2 = 1413.279,

df=478) was significant at p < 0.05 indicating that fit of data to the model was moderately

good. Other fit indices i.e. GFI, AGFI, CFI, NFI, and RMSEA were used to assess the

174

specification of the model. Results revealed that the value of GFI=0.833, AGFI=0.798,

CFI=0.927, and RMSEA=0.067 (see Table 4.33). These results indicated for further

refinement of model as the results were not consistent with the recommended values of

the fit indices of a priori specified measurement model.

Table 4.33 Goodness of fit statistics for the Initial CFA of Determinants of Patient

Satisfaction


measures

Parsimony

fit measures

χ2


Criteria <3 ≥ 0.90 < 0.05 ≥ 0.90 ≥ 0.90 ≥ 0.90

Obtained 1413.27 478 2.956 0.833 0.067 0.903 0.927 0.798




From the above results goodness of fit indices of the initial run of CFA (e.g. χ2, GFI,

AGFI) were not within the recommended level, further detailed evaluation was conducted

to refine and re-specify the model, in order to improve the discriminant validity and

achieve better fit of the model (Hair et al., 2013). The model refinement procedure

applied following criteria recommended by researchers. According to Hair et al., (2013)

factor loading (i.e. Standard regression weight in AMOS 20.0) value should be greater

than 0.7 and Squared multiple correlations (SMC) value should be greater than the cut-off

point 0.5. The standard residual values should be within the threshold (above 2.58 or

below 2.58) as recommended by Hair et al., (2013). Finally, modification indices (MI)

that show high covariance and demonstrate high regression weights conducted for

deletion (Hair et al., 2013).

175

Figure 4.10 Final CFA model of Determinants of Patient Satisfaction

Following these recommended criteria, the output of the initial CFA run was

examined to see whether any items proving to be problematic. Assessment of results

indicated that the standard regression weight of all measurement items was above the

recommended level (>0.7) (Hair et al., 2013). However, evaluation of standardised

residuals indicated that the values of OS6,OS7 and OS8; PS1, PS2 and PS3; MS1 and

MS4 and HKS1and HKS2 items were cross loaded on same factor and these items were

not within the acceptable level (above 2.58 or below 2.58; Hair et al., 2013). Thus, after

modifying these problematic items by using modifies indices criteria in AMOS 20.0, the

measurement model was re-analysed, as recommended (Hair et al., 2013). Final CFA

model is depicted in figure 4.10.

After modifying these problematic items by using modification indices criteria,

which were OS6, OS7, PS1, PS2, MS1 and HKS1; CFA was re-run for assessing the

measurement model fit. The results of the model revealed that goodness of fit indices

were improved and the revised model demonstrated a better fit to the data. Results of the

respective measurement model after removal of redundant items (Table 4.34) indicated

176

the absolute fit measures i.e. GFI and RMSEA were 0.906 and 0.049, respectively, the

incremental fit measures i.e. NFI and CFI were 0.914 and 0.938, respectively and the

parsimony fit measure i.e. AGFI was 0.916. All these measures surpassed the minimum

recommended values. In addition to these indices, the ratio of χ2/df was 2.568, which was

within the acceptable threshold level (i.e., 1.0 < χ2/df < 3.0). These goodness of fit

statistics therefore confirmed that the model adequately fitted the data.

Table 4.34 Revised Measurement model of Determinants of Patient Satisfaction - fit

analysis


measures

Parsimony

fit measures

χ2


Criteria <3 ≥ 0.90 < 0.05 ≥ 0.90 ≥ 0.90 ≥ 0.90

Obtained 968.298 377 2.568 0.906 0.049 0.914 0.938 0.916




Besides, other estimation criteria show that model fit the data adequately well,

such that, standard regression weight were all greater than 0.7, standard residual were all

within the threshold level, and critical ratios values were above 1.96. In summary, the

results confirmed that model was fit to the data, indicating no further refinement in the

model was required. Thus, the unidiminsionality of the model data was established (Hair

et al., 2013).

4.10.2. Assessment of Reliability and Validity of Constructs

This section presents results of the validity and reliability of constructs used in this study.

Reliability of Constructs

The reliability test was run to determine how strongly the dimensions were related to

each other (Hair et al., 2013). For testing the reliability, Cronbach‟s “α” value and

composite reliability (CR) values are assessed.

Cronbach’s “α” value: The internal consistency to assess the reliability of the final scale

was examined through split half method (Malhotra, 2002; Hair et al., 2013). The internal

177

consistency reliability test is acceptable when the reliability coefficient exceeds

Nunnally‟s (1988) reliability criterion of 0.70 levels for basic research. The value of

0.951 for overall sample support is an acceptable reliability coefficient.

The results revealed (see Table 4.35) that the reliability coefficient for all the

construct estimation values were above the recommended (α >0.7) cut off point

indicating strong reliability and high internal consistency in measuring relationship in the

model. This also suggested strong construct validity (Hair et. al., 2011).

Table 4.35 Construct reliability statistics of Determinants of Patient Satisfaction

Construct Construct Reliability

Criteria ≥0.7 NS 0.799

OS 0.976

MS 0.925

HKS 0.809

AP 0.852

FS 0.771

NS 0.799

Validity of Constructs

Validity of the construct can be examined by assessing content validity, construct

validity, convergent validity and discriminant validity.

Content Validity: The content validity of the scale was duly assessed through review of

literature and deliberations with the subject experts, doctors and patients for this selection

of items in the determinants of patient satisfaction constructs at the time of pre-testing. A

few items were modified to make statements conceivable to respondents. All these steps

checked the face and content validity.

Construct Validity: The KMO-MSA (Kaiser-Meyer-Olkin Measure of Sampling

Adequacy) value (greater than 0.7), communality values (greater than 0.7), factor loading

values (greater than 0.7) and variance explained (greater than 0.5) criteria are used to

examine the construct validity of the scale (Hair et al., 2013). A majority of the values

met the threshold criteria and this checked the construct validity of the sub-scales.

Discriminant Validity: Discriminant validity is examined by comparing average variance

extracted values with squared multiple correlation values (Byrne, 2001). Average

178

variance extracted for all the four constructs of service quality (Table 4.37) is found to be

higher than the squared multiple correlation of the items of the respective constructs

(Table 4.37). The results thus support the discriminant validity.

Table 4.36 Inter-construct correlations of Determinants of Patient Satisfaction

AP NS MS HKS FS OS PS

AP 1.000

NS 0.849 1.000

MS 0.356 0.273 1.000

HKS 0.621 0.419 0.455 1.000

FS 0.515 0.617 0.555 0.544 1.000

OS 0.652 0.814 0.521 0.213 0.089 1.000

PS 0.549 0.407 0.884 0.461 0.395 0.318 1.000

Table 4.37 Discriminant validity of Determinants of Patient Satisfaction

NS OS PS MS HKS AP FS

NS 0.841

OS 0.639 0.969

PS 0.584 0.724 0.935

MS 0.640 0.506 0.672 0.928

HKS 0.339 0.098 0.508 0.802 0.891

AP 0.284 0.130 0.365 0.414 0.782 0.911

FS 0.762 0.117 0.491 0.351 0.486 0.235 0.889 Note: Diagonal values are AVE and off diagonal are inter-construct squared correlations.

Results (see table 4.36 and 4.37) reveal that, the AVE estimates of all the

constructs were larger than their corresponding squared inter-construct correlations

estimates, which demonstrated a high level of discriminate validity of all the dimensions.

In addition this indicates that the measured items have more in common with latent

constructs.

Convergent Validity: Convergent validity assumes that measures of constructs

that should be theoretically related to each other are, in fact, related to each other. Factor

loadings of construct, average variance extracted (AVE), and construct reliability (CR)

estimation were used by this researcher to assess the convergent validity of each of the

constructs. A minimum cut off criteria for standardised regression loadings >0.7, AVE

179

>0.7 and reliability >0.7, were used to assess the convergent validity. Results are

presented in table 4.38.

Table 4.38 Convergent validity of Determinants of Patient Satisfaction

Construct Item Standardised

Item Loading

Critical Ratio

(t-value)

Average Variance

Extracted (AVE)

Admission Process 0.713

AP1

AP2

AP3

0.858

0.902

0.786

9.82

10.55

11.28

Nursing Services 0.569

NS1

NS2

NS3

NS4

0.674

0.684

0.726

0.674

15.41

8.25

3.70

7.60

Medical Care Services 0.814

MS1

MS2

MS3

MS4

0.826

0.864

0.654

0.756

6.51

11.64

6.82

5.115

Housekeeping Services 0.646

HKS1

HKS2

HKS3

HKS4

0.858

0.711

0.701

0.833

13.12

2.83

10.60

13.63

Food Services 0.648

FS1

FS2

FS3

0.820

0.835

0.800

8.81

9.88

11.17

Overall Services 0.837

OS1

OS2

OS3

OS4

OS5

OS6

OS7

OS8

0.911

0.673

0.854

0.937

0.918

0.876

0.834

0.929

10.55

11.28

14.26

14.97

14.71

14.84

15.41

8.25

Patient Satisfaction 0.785

PS1

PS2

PS3

PS4

0.809

0.744

0.875

0.890

9.81

12.7

12.11

11.80

180

Results (see Table-4.38) revealed that all the standardised factor loadings

(standard regression weights) were above the minimum cut off point (>0.7), the critical

ratios (t- values) were higher than 1.96 (p < 0.001 and the average variance extracted was

greater than 0.07. The results thus demonstrated a high level of convergent validity of the

latent constructs used in the model.

4.10.3. Structural Model Evaluation and Hypotheses Testing

The structural model (see Figure 4.11) specifies that the latent variables of patient

satisfaction. Structural model consists of six key determinants of patient satisfaction all

the six latent factors were consistently influence on corporate hospital patient‟s

satisfaction. The dimensions for proposed structural model were tested through structural

equation modelling.

Figure 4.11 Determinants of Patient Satisfaction Structural Equation Model

The fit indices (see Table 4.39) indicate that the hypothesised structural model

provided the good fit to the data. Although the likelihood ratio chi-square (χ2 = 891.298;

df = 377; p = 0.000) was significant (p<0.001); however, other fit measures showed that

model adequately fit the observed data. The absolute fit measures i.e. GFI and RMSEA

Patient

Satisfaction

R2=68

Admission Process

Nursing Services

Overall Services

Medical Service


Food Services

0.719

181

were 0.917 and 0.042 respectively indicating good fit of model. The incremental fit

measures i.e. NFI and CFI were 0.926 and 0.969 respectively, which were above the

minimum requirement showing adequate fit and the parsimony fit measure i.e. AGFI was

0.924, which also was above the cut-off point of (> 0.9). In addition to these indices, the

χ2/df = 2.364 was within the threshold level i.e. 1.0 < χ

2/df < 3.0) supporting these

findings.

Table 4.39 Structural Model fit assessment of Determinants of Patient Satisfaction


measures

Parsimony

fit measures

χ2


Criteria <3 ≥ 0.90 < 0.05 ≥ 0.90 ≥ 0.90 ≥ 0.90

Obtained 891.298 377 2.364 0.917 0.042 0.926 0.969 0.924




Table 4.40, indicates that the results of hypotheses testing of structural model.

The results show six hypotheses represented by causal paths (H8, H9, H10, H11, H12,

and H13) that were used to test the relationships between the latent constructs. The latent

constructs used in the proposed theoretical model (as described in chapter 3) were

classified in two main categories: exogenous and endogenous constructs. Exogenous

constructs were the key determinants of patient satisfaction (Admission Process, Nursing

Services, Medical Services, Housekeeping Services, Food Services and Overall Service

Experience) while endogenous construct were the patient satisfaction. Goodness of fit

indices and other parameters estimates were examined to evaluate the hypothesised

structural model. Assessment of parameter estimates results suggested that five out of six

hypothesised paths were significant. Thus, indicating support for the five hypotheses.

These results are presented in detail as follows.

182

Table 4.40 Hypotheses testing / paths causal relationships


Relationship

Admission Process AP H8 AP → PS Nursing Services NS H9 NS → PS

Medical Services MS H10 MS → PS Housekeeping Services HKS H11 HKS → PS Food Services FS H12 FS → PS Overall Services OS H13 OS → PS

After finding causal relations between hypotheses, another most important part of

structural model assessment is coefficient parameter estimates. The parameter estimates

were used to produce the estimated population covariance matrix for the structural model.

The model was defined by 30 measurement items that identified the seven latent

constructs. The covariance matrix among the constructs was applied to test the model.

When the critical ratio (CR or t-value) is higher than 1.96 for an estimate (regression

weight), then the parameter coefficient value is statistically significant at the 0.05 levels

(Hair et. al., 2013). Critical ratio or t-value was obtained by dividing the regression

weight estimate by the estimate of its standard error (S.E). Using the path estimates and

CR values, six causal paths were examined in this research study. For five causal paths

estimates t-values were above the 1.96 critical values at the significant level p ≤0.05. The

overall structural model is depicted in figure 4.11, and parameter estimates are presented

in table 4.41.

Table 4.41 Regression estimates of latent constructs

Estimate S.E. C.R. p

OS → PS 0.457 0.026 17.576 ***

FS → PS 0.116 0.097 1.195 0.121

MS → PS 0.414 0.038 10.894 ***

HKS → PS 0.124 0.024 5.166 ***

AP → PS 0.296 0.028 10.571 ***

NS → PS 0.245 0.031 7.903 ***

Note: Estimate = regression weight; S.E = standard error; C.R = critical ratio, p = significance

value (0.005)

183

Table 4.42 indicates that the results of hypotheses testing of structural model. The

results show six hypotheses represented by causal paths (H8, H9, H10, H11, H12, and

H13) that were used to test the relationships between the latent constructs. The structural

model estimations revealed that five out of six hypotheses were significant while one

were not significant.

Table 4.42 Hypotheses testing


Relationship

(Positive)

Standardized

regression

weights (β)

C.R. (p - value)

Supported

Admission Process AP H8 AP → PS 0.355 10.571 (***)YES

Nursing Services NS H9 NS → PS 0.243 7.903 (***) YES

Medical Services MS H10 MS → PS 0.719 10.894 (***) YES

Housekeeping Services HKS H11 HKS → PS 0.153 5.166 (0.002) YES

Food Services FS H12 FS → PS 0.093 1.195 (0.121) NO

Overall Services OS H13 OS → PS 0.386 17.576 (0.001) YES

Note: β = Standardized regression weight, C.R = critical ratio, P = significance value *** at p<0.005

The following five hypotheses were positively significant; hence, they were supported.

H8: Admission Process (AP) has a positive influence on patient satisfaction (PS).

As shown in the figure 4.11, the standardized regression weight and critical ratio

for AP to PS is 0.355 and 10.571 respectively, suggesting that this path is statistically

significant at the p=0.000. The results demonstrated strong support for hypothesis H8,


admission process of corporate hospitals has strong significant effect on patient

satisfaction, implying that the waiting time and admission process is inversely effects on

patient satisfaction, i.e. if long waiting time is disappoints to patients but if patients are

getting quick response regarding their appointment admission in to hospitals leads to

more satisfaction. In summary, these results further suggest that AP was a major

determinant of patient satisfaction.

184

H9: Nursing Services (NS) has a positive influence on patient satisfaction (PS).

Figure 4.11, depicts the standardized regression weight and critical ratio for NS to

PS is 0.243 and 7.903 respectively, suggesting that this path is statistically significant at

the p=0.000. The results demonstrated strong support for hypothesis H9, which was

proposed in the model (presented in chapter 3). This indicated that the nursing services

has strong significant effect on patient satisfaction, implying that if there was increase in

the quality of nursing-care provided during his/her stay in the hospital influence on their

satisfaction about hospital services. In summary, these results further suggest that NS was

a major determinant of healthcare service quality.

H10: Medical Services (MS) has a positive influence on patient satisfaction (PS).

Figure 4.11, represents the standardized regression weight and critical ratio for

MS to PS is 0.719 and 10.625 respectively, suggesting that this path is statistically

significant at the p=0.000. The results demonstrated strong support for hypothesis H10,

which was proposed in the model (presented in chapter 3). This indicated that the medical

services of corporate hospitals has strong significant effect on patient satisfaction,

implying that the patient‟s experience in respect of the quality of care delivered by the

doctors. In summary, these results further suggest that MS was a major determinant of

healthcare service quality.

H11: Housekeeping Services (HKS) has a positive influence on patient satisfaction (PS).

Figure 4.11, denotes the standardized regression weight and critical ratio for HKS

to PS is 0.153 and 5.166 respectively, suggesting that this path is statistically significant

at the p<0.005. The results demonstrated strong support for hypothesis H11, which was

proposed in the model (presented in chapter 3). This indicated that the better the level of

cleanliness of surroundings, toilets and bath rooms of the hospital, the greater will be the

level of patient‟s satisfaction with hospital services of corporate hospitals. In summary,

these results further suggest that HKS was a major determinant of healthcare service

quality.

185

H12: Food Services (FS) has a positive influence on patient satisfaction (PS).

From the above figure 4.11, the standardized regression weight and critical ratio

for FS to PS is 0.093 and 1.195 respectively, suggesting that this path is not statistically

significant at the p<0.005. The results demonstrated weedy support for hypothesis H12,

which was proposed in the model (presented in chapter 3). Here all the three values of

standardized regression weights, critical ration and “p” values are not in recommended

level, because of this hypothesis H12 was rejected.

H13: Overall Service Experience (OS) has a positive influence on patient satisfaction

(PS).

Figure 4.11, indicates the standardized regression weight and critical ratio for OS

to PS is 0.386 and 17.576 respectively, suggesting that this path is statistically significant

at the p<0.005. The results demonstrated strong support for hypothesis H13, which was

proposed in the model (presented in chapter 3). This indicated that the overall service

experience has strong significant effect on patient satisfaction, implying that if there was

increase in the overall services of corporate hospitals then it would positively influence

on patient satisfaction. In summary, these results further suggest that OS was a major

determinant of healthcare service quality.

Conclusion

This chapter presented the results as they relate to the hypotheses and propositions

outlined in Chapter-III. The first section presented the results of the healthcare service

quality in corporate hospitals. For measuring healthcare service quality Parasuraman et

al‟s GAP model was applied. Healthcare service quality results clearly establish that

assurance is the most serious problem facing the Indian corporate hospital providers

involved in this study.

The second section presented the results of descriptive statistics of all items and

principal components analysis and orthogonal model with Varimax rotation method were

applied to perform the EFA using SPSS version 20.0. The results suggested that an item

to be deleted, as it was highly cross loaded on another latent factor.

186

After EFA analysis, several statistical procedures were applied to screen the data

to deal with outliers, homogeneity, multicolinearity and normality issues. All these tests

were important before performing structural equation modelling (SEM) because SEM is

very sensitive to such issues. Mahalanobis distance (D2) using AMOS version 20.0 was

measured to identify outliers. Results revealed that there were very few outliers; it was,

however, decided to retain all the cases, as there was insufficient evidence that these

outliers were not part of the entire population (Hair et al., 2013). Skewness and Kurtosis

were used to investigate normality of the items presented in data these results suggested

that data were normally distributed. In presence of the Homogeneity of Variance was

determined by the Levene‟s Test and the results of this test suggested that data were

relevant to appropriate for conducting SEM analysis (Hair et al., 2013). Multicolinearity

for latent factors was checked by the Durbin-Watson test (Hair et al., 2013) and the

results of this test suggested that there are no issues related to collinearity.

A two-step approach was adapted to test the model and determine causal

relationships with SEM analysis as recommended by Anderson and Gerbing (1988). In

the first step, the measurement model was specified using the interrelationships between

indicator (observed) and latent (unobserved) factors. For the measurement model,

confirmatory factor analysis (CFA) was performed using the SEM software AMOS

version.20.0. In the second step, the structural model related to dependent and

independent variables was specified in order to test the hypotheses.

Based in the research questions and objectives of the study, a two-stage SEM

analysis was applied, in first stage specified using the interrelationships between

healthcare service quality, patient satisfaction and behavioural intentions. In second stage

analysis founds the key determinants of patient satisfaction at corporate hospitals. Both

the stages measurement model fit-indices shows moderately fit, in these steps some

problematic items are modified as using modification indices.

After modifying these problematic items, CFA was performed again for the

measurement models. The results of the models revealed that goodness of fit indices were

improved and the revised model demonstrated a better fit to the data. Each latent

construct was then assessed for the reliability and validity. The assessment of these

187

constructs indicated that all constructs were reliable. Furthermore, the convergent and

discriminant validity for each construct were also confirmed.

Thereafter, structural model was assessed to test the hypothesised relationships

between latent constructs. In SEM model-I, there are eleven hypotheses (i.e. H1a, H1b,

H2a, H2b, H3a, H3b, H4a, H4b, H5a, H5b, H6a, H6a, and H7) and in SEM model-II,

there are six hypothesis (i.e. H8, H9, H10, H11, H12 and H13) represented as causal

paths were used to test the relationships between these latent constructs. Both the

goodness of fit indices and parameter estimates coefficients were examined to check

whether the hypothesised structural models fitted the data and to test the hypotheses. The

fit indices indicated that the hypothesised structural models provided the good fits to the

data. However, three hypotheses i.e. H1b, H5a, and H12 out of nineteen were statistically

not significant and thereby they were rejected.

A full discussion of these results takes place in next chapter, along with discussion of

results and hypotheses, managerial implications of empirical evidence, future research

directions, limitations and conclusions of the study also provided.

188

Based on the conceptual framework developed and the empirical evaluation of the

present study, this exploratory research presents several insights and contributions and

these are discussed in this chapter. The chapter starts with an overview of the main

objectives of this study. It then presents discussion on the key findings of this study, the

descriptive statistical findings, SEM analysis and the hypothesised relationships. This is

followed by managerial implications and recommendations to practitioners. This is

followed by the future research directions and limitations of current study. Finally, this

chapter provides the conclusions of the research.

1.1. Overview of the Study

The primary purpose of this study was to measure healthcare service quality in Indian

corporate hospitals and to find the relation between three major constructs namely,

healthcare service quality, patient satisfaction and behavioural intentions. A secondary

purpose of the study was to find key determinants of patient satisfaction at corporate

hospitals. This research study developed and empirically tested a hypothesised model for

understanding healthcare service quality and the key factors that influence patient

satisfaction at corporate hospitals. The resultant model that was developed was multi-

dimensional and was applicable to measuring corporate hospitals service quality. The

final model comprised of three primary constructs namely, healthcare service quality,

patient satisfaction and behavioural intentions. Each primary construct is said to have

some sub-dimensions. Healthcare service quality comprises of expectations and

perceptions of SERVQUAL dimensions; Tangibility, Reliability, Assurance, Empathy

and Responsiveness. All the 22-items of Parasuraman et al‟s., (1988) SERVQUAL model

scale were retained in this study, but all the items were customized to Indian healthcare

industry. Patient satisfaction comprises of six variables namely, admission process,

DISCUSSION AND IMPLICATIONS

CHAPTER – 5

189

nursing services, medical services, housekeeping services, food services and overall

service experiences. Except overall service experiences, all these variables were adopted

from Woodside et al‟s (1989) study. Behavioural intentions was measured with four

items, all the items are adopted from Zineldin (2006) study. All the four items are related

to future intentions to revisit and recommendation to family and friends who seeks

treatment in future.

The final aspect of the study was to test the proposed model using data collected

from the 60 item questionnaire. Data was collected from hospitalised inpatients from four

different corporate hospitals functioning in different Indian metro-cities. A total of 500

inpatients were contacted on the bases of personal contact approach, from those 493 were

found complete with respect to all fields. After screening all responses, 7 responses were

declined due to partial response. Thus, resulting in 493 usable responses for the data

analysis.

The data collected was then analysed using two statistical software tools i.e. SPSS

version 20.0 and AMOS version 20.0. The SPSS version 20.0 was used for the

descriptive analysis, healthcare service quality analysis (GAPS-analysis) and exploratory

factor analysis while the AMOS version 20.0 was used for structural equation modelling

(SEM) analysis i.e. confirmatory factor analysis (CFA), testing model fit to the data and

hypotheses testing. The descriptive analysis of the survey presented demographic profile

of the sample and item analysis. The exploratory factor analysis was performed to extract

latent factors (constructs), which were then confirmed by confirmatory factor analysis.

Finally, the hypothesised relationships between the constructs were examined by

structural equation modelling. A two-step approach was adopted in structural equation

modelling (SEM). In the first step, the measurement model, using CFA method was

tested to examine and assess the reliability and validity of the constructs used in the

model. In the second step, a hypothesised structural model was assessed using the path

analysis technique for testing the hypothesized causal relationships among the constructs

proposed in the research model.

The result of this research largely supports the hypothesised relationship between

proposed research models. Healthcare service quality results clearly establish that

190

assurance is the most serious problem faced by the Indian corporate hospital providers.

Patients‟ expectations of service providers are highest in relation to assurance and

patients give priority to assurance as compared to other five dimensions, yet the

tangibility scores have been consistently the lowest in this survey. In particular, the

results suggested that except expected tangibility (ETAN), perceived reliability (PRAB)

and food services (FS), all the variables influence patient‟s intentions to revisit in future

and recommend to family and friends. The next section presents a detailed discussion

about the evaluated structural model followed by implications of the study, future

research directions and conclusions.

5.2. Discussion of the Major Findings

This section provides discussion on the response rate, respondents‟ demographic

characteristics, constructs and items used in this study, and hypotheses tested in this

study.

5.2.1. Response Rate

The patients were selected using systematic random sampling and data was collected

through personal contact approach. A total of 500 inpatients (125 patients were selected

proportionately from four corporate hospitals), were contacted on the basis of personal

contact approach, of those 493 were found complete with respect to all fields. After initial

screening of all responses, 7 respondents were declined due to partial response. which

represented a response rate of 98.6 per cent of the original sample. The response rate

achieved in present study is reasonably higher than that of the earlier studies on Indian

healthcare services. For instance, the response rate reported in the study by Duggirala et

al., (2008) had received 33 per cent of useable responses, Chaniotakis and

Lymperopoulos (2009) had received 96 per cent of useable responses, Amin and

Nasharuddin (2013) had received 61.7 per cent of useable responses, Chahal and Mehta

(2013) had 70 per cent of useable responses, Gaur et al., (2011) had received 94.1 per

cent of useable responses, Gupta et al., (2012) had 73.3 per cent of useable responses,

Padma et al., (2010) had 90 per cent of useable responses. Therefore, the final response

191

rate in the present study can be considered relatively better than the previous studies

mentioned above.

5.2.2. Respondents Demographic Characteristics

The results of respondents demographic characteristics revealed that the majority of the

respondents were male (54.8 per cent). This results was not surprising because looking at

the demographics of India, it can be seen the total number of male population exceeds the

number of females (1.12:1). This difference in the ratio between the male and female

categories therefore may explain the high percentage of male responses obtained in this

survey. In addition, these results also revealed that there are more males were admitted in

super-specialty departments of hospitals in India. This is also consistent with previous

studies that revealed that the gender difference, especially in India (Duggirala et al.,

2008; Chaniotakis and Lymperopoulos, 2009; Amin and Nasharuddin, 2013; Gaur et al.,

2011; and Padma et al., 2010).

In addition to the gender, age of 59.1 per cent of respondents in this study was

between 18-49 years. This results suggests that majority of patients were young adults.

Regarding place of residence of respondents, majority of patients (42.4 per cent) were

living in urban areas. Further, majority of the patients were married (69.4 per cent). More

than 35.7 per cent of respondents worked in the service sector. Almost all the respondents

possessed a 10+2 or intermediate level qualification. In terms of monthly income, large

percentage of respondents (50.3 per cent) had income below 20,000 (in INR).

In healthcare industry particularly, patient‟s needs differ based on age, gender,

etc. and the healthcare seeking behaviours of different patient segments could produce

experiences which influence different quality judgments, and hence influence satisfaction

positively or negatively. The main aim of this research study was to measure healthcare

service quality, to find relation between proposed constructs and to find key determinants

of patient‟s satisfaction at Indian corporate hospitals, but not to find effect of

demographics on satisfaction and intentions. Even though some of the studies measured

effect of demographics on satisfaction and intentions, they were to be found negative or

non-significant relations. Tucker and Adams (2001) determined that provider

192

performance and access both affected the satisfaction. But, the demographic variables

such as age, gender, education, race, marital status and number of visits did not have any

moderating effect on satisfaction. Baldwin and Sohal (2003) attempted to include age,

gender and location as moderating variables between quality and satisfaction, but the

effect was not significant. The next section therefore presents discussion about the study

constructs and their items.

5.2.3. Discussion of Research Constructs

According to objectives and results reported in previous chapter there are two different

groups of hypotheses. The first group of hypotheses is about the dimensions of healthcare

service quality and link between healthcare service quality, patient satisfaction and


Model-I support the use of 13 dimensions (ETAN, PTAN, ERAB, PRAB, EEMT,

PEMT, EASS, PASS, ERES, PRES, HCSQ, PS and BI) to measure relation between

healthcare service quality, patient satisfaction and behavioural intentions. However,

model-I represents relation between three constructs, healthcare service quality (Expected

and Perceived) construct consists of Parasuraman et al., (1988) primary service quality

(SERVQUAL) dimensions namely: tangibility, reliability, assurance, empathy and

responsiveness. However, Model-II supports the use of 6 dimensions of key determinants

of patient satisfaction: admission process (AP), nursing services (NS), medical services

(MS), food services (FS), housekeeping services (HKS) and overall service experience

(OS). This section provides discussion on the ratings of construct items obtained through

results reported in previous study.

Tangibles

The construct “tangibles” reflects physical facilities, equipment and appearance of

personnel (Parasuraman et al., 1988). The indicators of this variable, which include the

facilities and the equipment of the hospital, incorporated the “comfortable and friendly

environment”, the “clean environment”, the “up-to-date equipment”, and the “clean and

comfortable rooms”.

193

The results revealed that the mean and standard deviation scores for four expected

measured items for this scale were between 1.97 to 2.01 and 0.880 to 0.916, and four

perceived measured items for this scale were between 1.96 to 1.98 and 0.907 to 0.931,

which reflected patients satisfaction related to tangibility of corporate hospitals and their

intentions to recommend.

This result is not unexpected when considering the characteristics of corporate

hospital service and patients. This study focused on super-specialty departments

(cardiology, respiratory etc.) patients who seek acute episodic and immediate care for

their illnesses. If required medical care can‟t be received within 24 hours, a patient‟s

illness might worsen for this patient‟s enables to access immediate care instantly and

easily is important. Therefore, the availability of up-to-date equipments and efficient

human resources are two important items to measure tangibles. The other items,

appealing facilities and pleasant smell are comparatively less important.

Overall it is important for corporate care providers to update their treatment

machines, and to enable patients to access required medical care easily. For example,

they can build LED signs to direct patients to appropriate entrance and treatment rooms

during both daytime and nights. This is consistent with prior research where attributes

were ranked (Parasuraman et al., 1988; Youssef et al., 1996; Vandamme & Leunis, 1993).

Reliability

Reliability is the ability to perform the promised service dependably and accurately

(Parasuraman et al., 1988). The indicators of this variable were measured by four items,

which are related to the ability to perform the promised service dependably and

accurately, incorporated the “organisation” and the “reliability of the tertiary care

hospitals” as well as, the “kept promises”, and the “right way to carry out services”.

The results revealed that the mean and standard deviation scores for four expected

measured items for this scale were between 2.00 to 2.03 and 0.956 to 0.980, and four

perceived measured items for this scale were between 1.97 to 2.00 and 0.903 to 0.919,

which reflected patients satisfaction related to reliability and accuracy of corporate

hospitals service provided and their intentions to recommend.

194

These findings revealed that in a hospital environment there is an expectation that

there is adequate training and professionalism to allow dependable performance of

expected service. However, consistency of performance and accuracy were significant

factors in evaluating the performance of workers in terms of the quality of service

provided and patient outcomes. While, overall it is revealed that reliability and accuracy

of service provided were more important to achieve patient satisfaction. Satisfaction leads

to the creation of a strong relationship between the healthcare service provider and

patients, leading to relationship longevity, or patient retention. This is consistent with

prior research (Anderson and Fornell, 1994; Grönroos, 1994; Rust et al., 1995; Schneider

and Bowen, 1995; Hallowell, 1996; Zeithaml et al., 1996, and Sharma and Patterson,

1999).

Assurance

Assurance is the courtesy and knowledge of staff and their ability to inspire trust and

confidence. Expectation and perception indicators of this variable were measured by five

items, incorporated the “knowledgeable and experienced staff”, the “friendly and

courteous staff”, the “treatment with dignity and respect”, and the “staff explains

thoroughly medical condition”. The results revealed that the mean and standard deviation

scores for five expected measured items for this scale were between 2.01 to 2.08 and

0.996 to 1.016, and five perceived measured items for this scale were between 1.97 to

2.01 and 0.911 to 0.928, which reflected patients satisfaction related to assurance and

courtesy of corporate hospitals service provider and their intentions to recommend.

These findings revealed that the Patient‟s expectations of service providers are

highest in relation to assurance, and patients rank assurance as the most important of the

five dimensions. This dimension impacts interpersonal relationships and as such affects

the ability of internal service groups to effectively deliver quality services to external

customer, in this case patients. However, it was apparent in healthcare environment that

assurance and courtesy, which includes attributes of attitude, respect and interpersonal

skills, was an important dimension to internal service chains like, healthcare providers.

Overall this dimension influences social factor with in the service environment and the

195

quality of interaction with in the internal service chain and consequently perceptions of

overall outcome quality.

Empathy

Empathy is the individualised care provided to patients (Parasuraman et al., 1988).

Expectation and perception indicators of this variable were measured by five items,

which is related to the caring and individualised attention the organisation provides to its

customers, incorporated the “staff understands specific needs of patients”, the “staff show

sincere interest”, the “staff offers personalised attention” and the “staff looks for the best

for the patients interests”. The results revealed that the mean and standard deviation

scores for five expected measured items for this scale were between 1.93 to 2.01 and

0.903 to 0.934, and five perceived measured items for this scale were between 1.97 to

1.99 and 0.906 to 0.918, which reflected patients satisfaction related to empathy,

communication and interaction of corporate hospitals service provider and their

intentions to recommend.

These findings shown the clarity and effectiveness of communication in the

healthcare system is crucial to the wellbeing of patients. This in turn influence on

measurable outcomes of the services and accounts for the emphasis on its perceived

importance in the internal healthcare service chains. However, understanding the patient

involves an effort to know patients and their needs. Involvement is a dyadic process

between service provider and recipients. This is more complicated in an internal value

service chain as an internal healthcare service network future a number of relationships

between medical/paramedical staff and patients, so this interaction dimension gained

greater saliency, in terms of “empathy with the physician, nursing and auxiliary staff”.

This result is also consistent with Donabedian, 1980 & 1989; Parasuraman et al., 1988;

Vandamme & Leunis, 1993 and Fowdar, 2005.

Responsiveness

Willing to help customers and provide prompt services (Parasuraman et al., 1988).

Expectation and perception indicators of this variable were measured by four items,

which is related to the willingness to help patients and provide prompt service,

196

incorporated the “24-hour service availability”, the “staff willing to respond to any need”,

the “staff spends time with each one in order to answer their questions”, and the “staff

responds quickly”. The results revealed that the mean and standard deviation scores for

five expected measured items for this scale were between 1.95 to 2.01 and 0.905 to 0.940,

and five perceived measured items for this scale were between 1.97 to 2.03 and 0.920 to

0.942, which reflected patients satisfaction related to promptness and willingness of

corporate hospitals service provider and their intentions to recommend.

From the finding, this dimension assesses how reactive healthcare service

providers are to patients‟ needs and requirement. Patient admitted in surgical and super-

specialty departments (e.g. cardiology, neurology etc.) are seeking immediate medical

care. In addition, flexible responsiveness and prompt reaction has an important influence

on healthcare service quality. For patients with severe medical conditions, particularly

those whose lives are threatened must be referred to hospital emergency rooms or

specialists for more comprehensive medical treatment. A flexible but robust

responsiveness system is highly valued by patients, and therefore it can influence on their

perceptions of healthcare service quality.

Healthcare Service Quality (HCSQ)

Healthcare Service Quality means, providing patients with appropriate services in a

technically competent manner, with good communication, shared decision making and

cultural sensitivity (Schuster et al., 1998). The results revealed that the mean and

standard deviation scores for two measured items for this scale were between 2.06 to 2.11

and 0.942 to 0.975, which reflected overall service quality perceptions of patients, their

satisfaction and their willingness to recommend to others.

The results shown the hospital service quality has a significant relationship with

customer satisfaction. The findings of this study indicate that the establishment of higher

levels of hospital service quality will lead customers to have a high level of satisfaction.

In addition, to achieve competitive advantage, corporate hospitals must keep improving

their service from time to time to make sure the level of service quality is at the

maximum level to gain patients high satisfaction and have an impact on patient‟s future

197

behavioural intention. Therefore, healthcare service quality can be used as a benchmark

for hospitals to further improve their services compared to other hospitals (Arasli et al.,

2008; Aagja and Grag, 2010; Padma et al., 2010).

Admission Process

Admission in hospital was based on patients‟ statements about difficulties in procedure of

placement in the hospital, time that passed between from coming in the hospital to

placement in the room and starting with diagnosis and treatments, as well as, on time that

passed between from admission in the hospital to first doctor visiting (Janicic et al.,

2011). Admission Process of hospital includes the processes of admission, stay and

discharge of patients. The results revealed that the mean and standard deviation scores for

three measured items for this scale were between 1.90 to 1.92 and 0.926 to 0.932, which

reflected on patient satisfaction and it was noted that admission process is key

determinant of satisfaction and which influence on patient‟s willingness to recommend to

others. Results suggest that well maintained admission procedures are required to make

patients stay in the hospital a courteous one. This is also consistent with (Woodside et al.,

1989; Sardana, 2003; Chahal and Sharma, 2004; Duggirala et al., 2008, and Padma et al.,

2010).

Medical Services

A medical care service is the core service and integral aspect of patient satisfaction.

Medical care explains “what” of a service including the width and depth of services. The

results revealed that the mean and standard deviation scores for four measured items for

this scale were between 2.07 to 2.14 and 1.039 to 1.069. This dimension was ranging

with high mean and standard deviation scores, which reflected on patient satisfaction and

it was noted that medical care service is key determinant of satisfaction and which

influence on patient‟s willingness to recommend to others.

Results suggested that medical care is an integral aspect of patient satisfaction. It

involves the process of diagnosing the ailment and providing adequate medical treatment

to the patients. The elements leading to physician care include friendly behaviour of the

physicians, communication with nurses, communication with supportive staff,

198

availability on time, provide adequate medical treatment etc. (Sardana, 2003; Chahal and

Sharma, 2004). Caring behaviour of the physicians would help them to understand the

medical history of the patients, their demographic profile, type of diseases etc. and more

importantly will help at the time of emergency for quick medical perception.

Nursing care

Nursing care is identified as next key determinant of patient satisfaction. It is known to be

the process of providing timely and adequate medical assistance as per the instructions

given by physicians. The nursing care pertains to items such as friendliness, availability

on time, provide adequate medical treatment etc. The results revealed that the mean and

standard deviation scores for four measured items for this scale were between 2.07 to

2.14 and 1.039 to 1.069, which reflected on patient satisfaction and it was noted that

nursing care service is key determinant of satisfaction and which influence on patient‟s

willingness to recommend to others. Findings suggests that the nursing care assesses the

greater influence than other dimensions on patients overall satisfaction. This is constant

with (Lam, 1997; Woodside, 1989; Nicklin and McVeety, 2002; Chang et al., 2007 and

Biork et al., 2007).


Housekeeping can be defined as a service which deals with cleanliness and aesthetic of

hospitals and disposal of waste, using appropriate methods, equipment and manpower,

thus providing safe and comfortable environment conductive to patient care (Chandorkar,

2009). The results revealed that the mean and standard deviation scores for four measured

items for this scale were between 1.95 to 2.03 and 0.930 to 0.985, which reflected on

patient satisfaction and it was noted that housekeeping services is key determinant of

satisfaction and which influence on patient‟s willingness to recommend to others.

Findings of the study suggested that the physical maintenance primarily concerns

with the focus of the hospital on developing friendly environment, well planned bed-

layout arrangement, well-furnished waiting rooms, maintaining cleanliness in the

washrooms and toilets, placement of dustbins and spittoons in corridors, proper and safe

disposal of hospital waste which will play key role improve patient satisfaction. This

199

result was tenacious with (Woodside et al, 1989; Brady and Cronin, 2001; Kang and

Jeffrey, 2004; Bernstein et al., 2009; and Bowblis and Hyer, 2013).

Food Services

Patient meals are an integral part of treatment hence the provision and consumption of a

balanced diet is essential to aid recovery (Stratton et al., 2006). The results revealed that

the mean and standard deviation scores for four measured items for this scale were

between 2.03 to 2.08 and 0.940 to 1.005, which reflected on patient satisfaction and it

was noted that food service is key determinant of satisfaction and which influence on

patient‟s willingness to recommend to others. Yet, the relevance and importance of

patient meal service, when compared with many clinical activities, is not always

appreciated and it is often seen as an area where budgetary cuts will have least impact.

Findings suggested that provision of a cost effective food service to the patients, then it

optimises patient food and nutrient intake whilst minimizing food waste. This result was

consistent with (Woodside et al., 1989; Lam, 1997; Hasin et al., 2001; Baalbaki et al.,

2008; and Duggirala et al., 2008).

Overall Services

This dimension assesses the patient‟s view of the overall experience of care he/she

received at corporate hospital. The results revealed that the mean and standard deviation

scores for eight measured items for this scale were between 2.08 to 2.14 and 0.981 to

1.006, which reflected on patient satisfaction and it was noted that overall service

experience in terms of hospital service is key determinant of satisfaction and which

influence on patient‟s willingness to recommend to others.

This dimension was ranging with good mean and standard deviation scores and

findings suggested that patient perception of healthcare quality is important for several

reasons. First, evaluations of higher quality are related to satisfaction, intention to use a

service again in the future if necessary, compliance with advice and treatment regimens,

choice of provider or plan, decreased turnover and malpractice law suits, and possibly

better health outcomes. In addition, high levels of patient-perceived quality have been

shown to be positively related to financial performance in healthcare organizations.

200

Patient evaluation of the proper queue system, quick availability of ambulatory services,

well maintaining waiting space, well equipped laboratory, blood bank services and

radiology department, and finally comfort or quick discharge services were factors which

significantly affect degree of patient satisfaction. Thus, the dimension on overall

experience with healthcare delivery encompasses different elements of the patient‟s

experience of the treatment. This result was consistent with (Woodside et al., 1989; de

Man et al. 2002; Duggirala et al., 2008 and Baalbaki et al., 2008).


Patient satisfaction as a special form of consumer attitude reflecting on how much

patients are satisfied with the healthcare service after experiencing it (Woodside et al.,

1989). Patient satisfaction is one of the main exogenous variables in this study. The

results revealed that the mean and standard deviation scores for four measured items for

this scale were between 1.98 to 2.03 and 0.940 to 0.974, which reflected on patient‟s

willingness to recommend to others and revisit in future. High-quality services require

the provision of a comprehensive set of services as well as high performance on all

aspects of care. Patient‟s satisfaction, the most important impact has employees in

healthcare institutions, doctors, nurses and other medical staff and financial performance

of healthcare organisations. These results suggested that the services provided in

hospitals need to be satisfactory so as to provide the intended effects of the services.

Behavioural Intention

Behavioural Intention (BI) is defined as a person‟s perceived likelihood or “subjective

probability that he/she will engage in a given behaviour”. The results revealed that the

mean and standard deviation scores for four measured items for this scale were between

1.98 to 2.03 and 0.940 to 0.974, which reflected that patient‟s strong behavioural

intention towards corporate hospitals and their services. Nevertheless, the average mean

and standard deviation scores of these items were above the neutral point. The high

ratings of the items of behavioural intention construct may suggest that a patient with

favourable service experiences would remain loyal to the service provider, recommend it

to friends and relatives. In addition, the Cronbach‟s alpha (α=0.928) value was greater

201

than minimum standard level, this is suggested that strong internal consistency of the

construct.

5.2.4. Hypothesis Testing

Expected Reliability (ERAB) and Healthcare Service Quality (HCSQ)

In the proposed research model, it is hypothesized that expected reliability will have a

positive significant effect on healthcare service quality (H1a). The parameter estimate

results (H1a: ERAB → HCSQ; β = 0.211, t-value = 3.488, p = 0.001) for the above

hypothesis was found statistically significant. This suggested existence of a positive

effect of the expected reliability on healthcare service quality. As such, this hypothesis

was accepted. This hypothesis was drawn from the modified Parasuraman et al‟s., (1988)

SERVQUAL model. As implied in the SERVQUAL, ERAB was found to have a

significant direct effect on the service quality. The results of this research are consistent

with the SERVQUAL findings and with those of prior research. Several researchers have

provided empirical evidence of a significant effect of the ERAB on healthcare service

quality (Parasuraman et al., 1988; Carman, 1990; Cronin & Taylor, 1992; Vandamme &

Leunis, 1993; Youssef et al., 1996; and Ramsaran-Fowdar, 2008). The acceptance and

significance of this variable, which is related to the ability to perform the promised

service dependably and accurately, incorporated to the service organisation. In summary,

the results of this hypothesis are indicating that the reliability plays an important function

in determining outcome of the service quality.

Expected Tangibility (ETAN) and Healthcare Service Quality (HCSQ)

In the proposed research model, it is hypothesized that expected tangibility will have a


results (H5a: ETAN → HCSQ; β = 0.757, t-value = 2.143, p = 0.001) for the above

hypothesis was found both positive and statistically significant. The positive relationship

between expected tangibility and healthcare service quality obtained herein is in line with

a number of studies (Parasuraman et al., 1991; Babakus and Mangold, 1992; Cronin and

Taylor, 1992; Bowers et al., 1994; and Ramsaran-Fowdar, 2005). The positive relation

because patient‟s expectations appear to impact on up-to-date facilities, physical

202

environment appealing and modern looking equipments of corporate hospitals

(Parasuraman et al., 1988; Carman, 1990; Cronin & Taylor, 1992; Vandamme & Leunis,

1993; Youssef et al., 1996; and Ramsaran-Fowdar, 2008). Thus, these results confirm the

positive relationship between expected tangibility and healthcare service quality. It

reflects, tangibility plays an important role in determining outcome of the service quality

and it is one of the most significant factors that affect healthcare service quality.

Expected Assurance (EASS) and Healthcare Service Quality (HCSQ)

In the suggested research model, it is hypothesized that expected assurance will have a


results (H3a: EASS → HCSQ; β = 0.818, t-value = 6.757, p = 0.001) for the above

hypothesis was found both positive and statistically significant. This suggested existence

of a positive effect of the expected assurance on healthcare service quality. The positive

relationship between expected assurance and healthcare service quality obtained herein is

in line with a number of studies (Parasuraman et al., 1991; Cronin and Taylor, 1992;

Ramsaran-Fowdar, 2005; Arasli et al., 2008; Badri et al., 2009; Al-Borie et al., 2013).

Perhaps such positive relation arose because patient‟s expectations appear to the

knowledge and courtesy of corporate hospital employees and their ability to inspire trust

and confidence in patients (Ramsaran-Fowdar, 2008). Moreover, these results confirm the

positive relationship between expected assurance and healthcare service quality. Indeed,

assurance plays more significant role in determining outcome of the service quality and it

could be one of the most significant factors that affect current study results.

Expected Empathy (EEPT) and Healthcare Service Quality (HCSQ)

In the proposed research model, it is hypothesized that expected empathy will have a


results (H4a: EEPT → HCSQ; β = 0.147, t-value = 3.107, p = 0.001) for the above


of a positive effect of the expected empathy on healthcare service quality. The positive

relationship between expected empathy and healthcare service quality obtained herein is

in line with a number of studies (Parasuraman et al., 1988, 1991; Cronin and Taylor,

203

1992; Ramsaran-Fowdar, 2005; Arasli et al., 2008; Badri et al., 2009; Al-Borie et al.,

2013). Perhaps such positive relation arose because patient‟s expectations appear to

caring and understanding, which a hospital provides its patients in terms of its

individualized and personalized attention (Parasuraman et al., 1988). Besides, these

results confirm the positive relationship between expected empathy and healthcare

service quality. Indeed, empathy plays more significant role in determining outcome of

the service quality and it could be one of the most significant factors that affect current

study.

Expected Responsiveness (ERSP) and Healthcare Service Quality (HCSQ)

In the proposed research model, it is hypothesized that expected responsiveness will have

a positive significant effect on healthcare service quality (H2a). The parameter estimate

results (H2a: ERSP → HCSQ; β = 0.207, t-value = 3.284, p = 0.001) for the above


of a positive effect of the expected responsiveness on healthcare service quality. The

positive relationship between expected responsiveness and healthcare service quality

obtained herein is in line with a number of studies (Parasuraman et al., 1988, 1991;

Cronin and Taylor, 1992; Ramsaran-Fowdar, 2005; Arasli et al., 2008; Badri et al., 2009;

Al-Borie et al., 2013). Perhaps such positive relation arose because patient‟s expectations

appear to willingness to provide help when they need immediate treatment and a prompt

service to patients (Parasuraman et al., 1988). Also, these results confirm the positive

relationship between expected responsiveness and healthcare service quality. Indeed,

responsiveness plays more significant role in determining outcome of the service quality

and it could be one of the significant factors that affect current study.

Perceived Reliability (PRAB) and Healthcare Service Quality (HCSQ)

In the proposed research model, it is hypothesized that perceived reliability will have a

positive significant effect on healthcare service quality (H1b). The parameter estimate

results (β = 0.463, t-value = 2.123, p = 0.152) revealed that this hypothesis (H1b: PRAB

→ HCSQ) was statistically not significant. Here both β & t-values were within the

statistical limit but significance (p) value was ≥0.005 (p = 0.152). Therefore, this

204

hypothesis was not supported but it was rejected. This result suggested that perceived

reliability does not have a significant effect on quality of service provided by corporate

hospitals. Although previous studies have asserted a significant relationship between

PRAB and HCSQ (Parasuraman et al., 1989&1991; Carman, 1990; Gilbert et al., 1992;

Bowers et al., 1994; Ramsaran-Fowdar, 2005; Arasli et al., 2008; and Badri et al., 2009),

the results of the present research suggest that PRAB was not a significant determinant of

HCSQ which, in turn, does not significantly influence patient satisfaction and their

intentions to recommend others. One probable explanation for inconsistent results

cantering on the relationship between PRAB and HCSQ may be that the patients may not

had sufficient experience (i.e., ability to perform the promised service dependably and

accurately) with the PRAB. However, this is consistent with previous study of Nekoei-

Moghadam and Amiresmaili (2008); their study also assessed same results in context of

Iranian healthcare system. Furthermore, these results confirm the negative relationship

between perceived reliability and healthcare service quality. Indeed, perceived reliability

of these results plays comparatively less significant role in determining outcome of the

corporate hospital service quality.

Perceived Tangibility (PTAN) and Healthcare Service Quality (HCSQ)

In the proposed research model, it is hypothesized that perceived tangibility will have a


results (β = 0.222, t-value = 1.92, p = 0.128) revealed that this hypothesis (H5b: PTAN

→ HCSQ) was statistically not significant. Here β-value was within the statistical limit

but t-value (t ≤ 1.196) and significance (p ≤ 0.005) value was behind the limit. Therefore,

this hypothesis was not supported but it was rejected. This result suggested that perceived

tangibility does not have a significant effect on quality of service provided by corporate

hospitals. Although previous studies have asserted a significant relationship between

PTAN and HCSQ (Parasuraman et al., 1989&1991; Carman, 1990; Gilbert et al., 1992;

Bowers et al., 1994; Ramsaran-Fowdar, 2005; Arasli et al., 2008; and Badri et al., 2009),

the results of the present research suggest that PTAN was not a significant determinant of

HCSQ which, in turn, does not significantly influence patient satisfaction and their

intentions to recommend others. One probable explanation for inconsistent results

205

centring on the relationship between PTAN and HCSQ may be that the patients may not

had sufficient experience (i.e., Physical facilities, equipment and appearance of

personnel) with the PTAN. However, this is consistent with previous study of Nekoei-

Moghadam and Amiresmaili (2008); their study also assessed same results in context of

Iranian healthcare system. Moreover, these results confirm the negative relationship

between perceived tangibility and healthcare service quality. This negative tangibility

score indicates that a service provider needs significant infrastructure improvement.

Perceived Assurance (PASS) and Healthcare Service Quality (HCSQ)

In the proposed research model, it is hypothesized that perceived assurance will have a


results (H3b: PASS → HCSQ; β = 0.416, t-value = 4.706, p = 0.001) for the above


of a positive effect of the perceived assurance on healthcare service quality. The positive

relationship between perceived assurance and healthcare service quality obtained herein

is in line with a number of studies (Parasuraman et al., 1988, 1991; Cronin and Taylor,

1992; Ramsaran-Fowdar, 2005; Arasli et al., 2008; Badri et al., 2009; Al-Borie et al.,

2013). Perhaps such positive relation arose because patient‟s felt that the staff had the

required knowledge to assist patients and were able to convey trust and confidence and

also felt that the staff paid individual and special attention to each of them and making

time to listening any patients‟ questions or anxieties regarding treatment. Moreover, these

results confirm the positive relationship between perceived assurance and healthcare

service quality. Indeed, assurance plays more significant role in determining outcome of

the service quality and it could be one of the significant factors that affect current study.

Perceived Empathy (PEPT) and Healthcare Service Quality (HCSQ)

It is hypothesized that perceived empathy will have a positive significant effect on

healthcare service quality (H4b). The parameter estimate results (H4b: PEPT → HCSQ; β

= 0.414, t-value = 3.625, p = 0.001) for the above hypothesis was found both positive and

statistically significant. This suggested existence of a positive effect of the perceived

empathy on healthcare service quality. The positive relationship between perceived

206

empathy and healthcare service quality obtained herein is in line with a number of studies

(Parasuraman et al., 1988, 1991; Cronin and Taylor, 1992; Ramsaran-Fowdar, 2005;

Arasli et al., 2008; Badri et al., 2009; Al-Borie et al., 2013). Perhaps such positive

relation arose because patient‟s felt that the staff had the understanding the patients‟

needs and were able to convey trust and also felt that the staff paid individual and special

attention to each of them and making time to listening any patients‟ questions or anxieties

regarding treatment. Moreover, these results confirm the positive relationship between

perceived empathy and healthcare service quality. Indeed, empathy plays significant role

in determining outcome of the service quality and it could be one of the significant

factors that affect current study.

Perceived Responsiveness (PRSP) and Healthcare Service Quality (HCSQ)

In the proposed research model, it is hypothesized that perceived responsiveness will

have a positive significant effect on healthcare service quality (H2b). The parameter

estimate results (H2b: PRSP → HCSQ; β = 0.336, t-value = 2.619, p = 0.001) for the

above hypothesis was found both positive and statistically significant. This suggested

existence of a positive effect of the perceived responsiveness on healthcare service

quality. The positive relationship between perceived responsiveness and healthcare

service quality obtained herein is in line with a number of studies (Parasuraman et al.,

1988, 1991; Cronin and Taylor, 1992; Ramsaran-Fowdar, 2005; Arasli et al., 2008; Badri

et al., 2009; Al-Borie et al., 2013). Perhaps such positive relation arose because patient‟s

felt that the staff had providing services promptly and quickly, helping the patient and

being available when he or she needs help. Moreover, these results confirm the positive

relationship between perceived responsiveness and healthcare service quality. Indeed,

responsiveness plays more significant role in determining outcome of the service quality

and it could be one of the significant factors that affect current study.

Healthcare Service Quality (HCSQ) and Patient Satisfaction (PS)

It is hypothesized that healthcare service quality will have a positive significant effect on

patient satisfaction (H6a). The parameter estimate results (H6a: HCSQ → PS; β = 0.151,

t-value = 2.816, p = 0.004) for the above hypothesis was found both positive and

207

statistically significant. This suggested existence of a positive effect of the healthcare

service quality on patient satisfaction. The finding that healthcare service quality as an

important predictor of satisfaction from both patients‟ and attendants‟ perspectives agrees

with the existing literature in healthcare as well as other services (Cronin and Taylor,

1992; Oliver 1993; Parasuraman et al., 1994; Ramsaran-Fowdar, 2005; Arasli et al.,

2008; and Duggirala et al., 2008). Some researchers and academics viewed that service

quality is an antecedent of customer satisfaction (Parasuraman et al., 1985, 1988 and

1991). In the hospital industry, Naidu (2009) found that the relationship between health

care quality and patient satisfaction is significant. However, patients have their rights and

choice, and if they are not satisfied with their hospital, they have the opportunity to

switch to another hospital (Kessler and Mylod, 2011). Furthermore, patient satisfaction

continues to be measured as a proxy for the patient‟s assessment of service quality

(Turris, 2005). Indeed, service quality plays more significant role in determining outcome

of the satisfaction and it could be one of the more significant factors that affect current

study.

Healthcare Service Quality (HCSQ) and Behavioural Intentions (BI)

In the proposed research model, it is hypothesized that healthcare service quality will

have a positive significant effect on behavioural intention (H6b). The parameter estimate

results (H6b: HCSQ → BI; β = 0.928, t-value = 4.28, p = 0.002) for the above hypothesis

was found both positive and statistically significant. This suggested existence of a

positive effect of the healthcare service quality on behavioural intention. The finding that

healthcare service quality as an important predictor of behavioural intention from both

patients‟ and attendants‟ perspectives agrees with the existing literature in healthcare as

well as other services (Cronin and Taylor, 1992; Oliver 1993; Parasuraman et al., 1994;

Ramsaran-Fowdar, 2005; Arasli et al., 2008; and Duggirala et al., 2008). A patient is

satisfied when hospital service quality matches with their expectations and requirements,

consequently, the greater the patient satisfaction and its leads to patient loyalty (Chahal &

Kumari, 2010). More specific, positive comments from satisfied patients can increase

intentions to recommend, while negative comments from the patients can decrease

intentions to recommend (Ennew et al., 2000). Moreover, Gremler and Brown (1996)

208

suggest that patients who are willing to offer positive intention messages are more likely

to become loyal customers themselves. So, behavioural intention may have benefits both

in terms of retention and acquisition. Indeed, service quality plays more significant role

in determining outcome of the intention and it could be one of the more significant

factors that affect current study.

Patient Satisfaction (PS) and Behavioural Intentions (BI)

In the proposed research model, it is hypothesized that patient satisfaction will have a

positive significant effect on behavioural intention (H7). The parameter estimate results

(H7: PS → BI; β = 0.233, t-value = 7.16, p = 0.001) for the above hypothesis was found

both positive and statistically significant. This suggested existence of a positive effect of

the patient satisfaction on behavioural intention. The finding that healthcare service

quality as an important predictor of behavioural intention from both patients‟ and

attendants‟ perspectives agrees with the existing literature in healthcare as well as other

services (Cronin and Taylor, 1992; Oliver 1993; Parasuraman et al., 1994; Ramsaran-

Fowdar, 2005; Arasli et al., 2008; and Duggirala et al., 2008). Kessler and Mylod (2011)

and Gaur et al., (2011) investigated how patient satisfaction affects the propensity to

return to hospital and their results showed that there is a statistically significant link

between satisfaction and intention. These findings suggest that when a patient enhances

their confidence it will improve the relationship satisfaction with their doctors, and,

simultaneously, increase patient loyalty. Consequently, Garman et al., (2004) point out

that the relationship between patient satisfaction and doctors significantly increases the

likelihood of the patient returning to the hospital for treatment. In this sense, patients

often develop an attitude towards purchasing behaviour based on past experience

(Caruana, 2002; de Matos et al., 2009; Fornell et al.,1996), and which leads to loyalty

(Amin et al., 2011; Kessler and Mylod, 2011). Indeed, patient satisfaction plays more

significant role in determining outcome of the patient‟s intentions and it could be one of

the more significant factors that affect current study.

209

Admission Process (AP) and Patient Satisfaction (PS)

In the proposed research model, it is hypothesized that admission process will have a

positive significant effect on patient satisfaction (H8). The parameter estimate results

(H8: AP → PS; β = 0.355, t-value = 10.571, p = 0.001) for the above hypothesis was

found both positive and statistically significant. This suggested existence of a positive

effect of the admission process on patient satisfaction and also observed that the

admission process was one of the key predictor of patient satisfaction with corporate

hospital services. The positive relationship between admission process and patient

satisfaction obtained herein is in line with a number of studies (Woodside et al., 1989;

Bowers et al., 1994; Zeithaml and Bitner, 2000; Tucker and Adams, 2001; Otani and

Kurz, 2004; Ramsaran-Fowdar, 2005; Naidu, 2008; Ashrafun, 2011; Chahal and Mehta,

2013). Efficient admission process makes patients appreciate service offered better and

service delivery processes should be standardized so that customers could receive a

hassle-free service (Sureshchandar et al., 2002a). Admission process is one of the

important issues in hospitals, if ease of getting admission/appointment is delay it will

affects different stages of the patient‟s hospital stay (Duggirala et al., 2008). So, well

defined admission procedure is required to make the patient‟s stay in the hospital a

pleasant one. Moreover, these results confirm the positive relationship between admission

process and patient satisfaction. Indeed, admission process plays more significant role in

determining outcome of the satisfaction and it could be one of the key factors that affect


Medical-care Services (MS) and Patient Satisfaction (PS)

In the proposed research model, it is hypothesized that medical-care services will have a


(H9: MS → PS; β = 0.355, t-value = 10.571, p = 0.001) for the above hypothesis was


effect of the medical-care services on patient satisfaction and also observed that the

medical-care services were the key predictor of patient satisfaction with corporate

hospital services. The positive relationship medical-care services and patient satisfaction

obtained herein is in line with a number of studies (Woodside et al., 1989; Bowers et al.,

210

1994; Strasser et al., 1995; Zeithaml and Bitner, 2000; Ramsaran-Fowdar, 2005; Naidu,

2008; Duggirala et al., 2008; Arasli et al., 2008; Padma et al., 2010; Ashrafun, 2011;

Chahal and Mehta, 2013). Jain and Gupta (2004) opined: “medical care quality has come

to be recognized as a strategic tool for attaining operational efficiency and improved

healthcare provider business performance.” Specifically, patient satisfaction is a measure

of patient‟s attitude towards the physicians, the medical care (patient receives) and the

health care system (Newman et al., 1998). There is a clear relationship between medical

care satisfaction and patient compliance; when patients are dissatisfied with medical

advice they are less likely to cooperate (Ditto et al., 1995). Moreover, these results

confirm the positive relationship between medical-care services and patient satisfaction.

Indeed, medical-care services plays significant role in determining outcome of the

satisfaction and it could be one of the key factors that affect patient satisfaction.

Nursing-care Services (NS) and Patient Satisfaction (PS)

In the proposed research model, it is hypothesized that nursing services will have a


(H10: NS → PS; β = 0.243, t-value = 7.903, p = 0.001) for the above hypothesis was


effect of the nursing services on patient satisfaction and also observed that the nursing

services were the key predictor of patient satisfaction with corporate hospital services.

The positive relationship nursing services and patient satisfaction obtained herein is in

line with a number of studies (Woodside et al., 1989; Vandamme and Leunis 1993;


Kurz, 2004; Ramsaran-Fowdar, 2005; Naidu, 2008; Duggirala et al., 2008; Padma et al.,

2010; Ashrafun, 2011). Chahal and Mehta (2013), found the robust relationship between

nursing care services and patient satisfaction in Indian healthcare systems. Baalbaki et

al., (2008) found that nursing care services were the most influential dimension in both

emergency room and in-patient encounters with respect to patient satisfaction in Lebanon

hospitals. Otani and Kurz (2004) concluded that nursing care services were more

important in improving customer satisfaction and behavioural intentions than other

factors, in the US healthcare sector. Naik et al., (2014), was found to be the function of

211

nursing care items that include, availability of nurses at the time of requirement and

spending sufficient time are found to have high contribution in enhancing patient

satisfaction, in comparison to other items in Indian healthcare system. Moreover, these

results confirm the positive relationship between nursing care services and patient

satisfaction. Indeed, nursing care services plays significant role in determining outcome

of the satisfaction and it could be one of the key factors that affect patient satisfaction.

Housekeeping Services (HKS) and Patient Satisfaction (PS)

In the proposed research model, it is hypothesized that housekeeping services will have a


(H11: HKS → PS; β = 0.153, t-value = 5.166, p = 0.002) for the above hypothesis was


effect of the housekeeping services on patient satisfaction and also observed that the

housekeeping services were one of the key predictor of patient satisfaction with corporate

hospital services. The positive relationship between housekeeping services and patient

satisfaction obtained herein is in line with a number of studies (Woodside et al., 1989;


Kurz, 2004; Ramsaran-Fowdar, 2005; Naidu, 2008; Duggirala et al., 2008; Padma et al.,

2010; Ashrafun, 2011; Chahal and Mehta, 2013). Brady & Cronin (2001) and Sardana

(2003), was measured housekeeping services with well-furnished waiting rooms,

maintaining cleanliness in the washrooms and toilets, placement of dustbins and spittoons

in corridors which will improve patient satisfaction and found that the robust relation

with patient satisfaction. Kang and Jeffrey (2004), housekeeping services was measured

through six items related to internal atmosphere: overall cleanliness, natural light, clean

toilets, spacious wards and good outer appearance and their results pointed that positive

relation with patient satisfaction. Naik et al., (2014), found the positive relation

housekeeping services and they stated that physical facilities should not only be visually

appealing, but also be hygienic, particularly in healthcare service. Reidenbach &

Smallwood (1990) and Otani & Kurz (2004) used the constructs, “physical surroundings”

and “pleasantness of surroundings” in their studies, respectively, to denote the physical

facilities and ambience. JCI Accreditation (2007) has also identified “facilities

212

management” as a key function in hospitals. Moreover, these results confirm the positive

relationship between housekeeping services and patient satisfaction. Indeed,

housekeeping services plays significant role in determining outcome of the satisfaction

and it could be one of the key factors that affect patient satisfaction.

Food Services (FS) and Patient Satisfaction (PS)

In the proposed research model, it is hypothesized that food services will have a positive

significant effect on patient satisfaction (H12). The parameter estimate results (β = 0.093,

t-value = 1.195, p = 0.121) revealed that this hypothesis (H12: FS → PS) was statistically

not significant. Here β-value was within the statistical limit but t-value (t ≤ 1.196) and

significance (p ≤ 0.005) value was behind the limit. Therefore, this hypothesis was not

supported but it was rejected. This result suggested that food services do not have a

significant effect on patient satisfaction. Although previous studies have a positive

relationship between FS and PS asserted herein is in line with a number of studies

(Woodside et al., 1989; Cronin and Taylor 1992; Towers and Pang Ng 1995; Clemes et

al., 2001; Otani and Kurz, 2004; Ramsaran-Fowdar, 2005; Naidu, 2008; Duggirala et al.,

2008; Padma et al., 2010; Ashrafun, 2011; Chahal and Mehta, 2013). However, the

results of the present research suggest that FS was not a significant determinant of PS

which, in turn, does not significantly influence intentions to recommend others. One

probable explanation for inconsistent results centering on the relationship between FS

and PS may be that the patients may not had sufficient experience (i.e. timely and

hygienic food supplied; adequate selection food) with the FS. However, this is consistent

with previous study of Naik et al., (2014); their study also assessed same results in

context of Indian corporate healthcare system. Moreover, these results confirm the

negative relationship between food services and patient satisfaction.

Overall Services Experience (OS) and Patient Satisfaction (PS)

In the proposed research model, it is hypothesized that overall services experience will

have a positive significant effect on patient satisfaction (H13). The parameter estimate

results (H13: MS → PS; β = 0.386, t-value = 17.576, p = 0.001) for the above hypothesis

was found both positive and statistically significant. This suggested existence of a

213

positive effect of the overall services experience on patient satisfaction and also observed

that the overall services experience were the key predictor of patient satisfaction with

corporate hospital services. The positive relationship overall services experience and

patient satisfaction obtained herein is in line with a number of studies (Woodside et al.,

1989; Bowers et al., 1994; Tucker and Adams, 2001; Otani and Kurz, 2004; 2005; Naidu,

2008; Ashrafun, 2011; Chahal and Mehta, 2013). Polluste et al., (2000) and Naik et al.,

(2014) found that the different hospitals services, i.e. blood bank services, radio-

diagnostic services, emergency services, ambulance services, security services, pharmacy

services, billing and discharge services were factors which significantly influenced

degree of satisfaction. Owing to the nature of different hospitals service it becomes

necessary to differentiate between overall patient satisfaction and transaction specific

satisfaction; i.e. specific service encounter (Bitner and Hubbert, 1994). Thus, the items on

overall experience with healthcare delivery encompass different elements of the patient‟s

experience of the treatment. Moreover, these results confirm the positive relationship

between overall services experience and patient satisfaction. Indeed, overall services

experience plays significant role in determining outcome of the satisfaction and it could

be one of the key factors that affect patient satisfaction.

5.3. Research Implications

The implications of this research are presented under three headings i.e. theoretical

implications, implications for practicing doctors and supportive staff, and implications for

management, which are discussed below.

5.3.1. Theoretical Implications

The results of this study have a number of significant theoretical implications. First, this

research applied an expected and perceived SERVQUAL model in a new context of the

Indian corporate hospital services. The success of incorporation of the patient satisfaction

and behavioural intentions in the proposed research model is evident from the results.

The results suggest that the proposed model of the corporate hospital services

demonstrates a considerable exploratory and predictive power. Thus, the integration of

214

the patient satisfaction and behavioural intentions with SERVQUAL is both theoretically

appealing as well as empirically significant.

Second, integrated model for the corporate hospital service quality, satisfaction

and intentions developed in this study can be employed for exploring other healthcare

services such as public healthcare services, emergency care services etc. Furthermore,

this research identified key determinants of patient satisfaction and relation between three

main constructs i.e. healthcare service quality, patient satisfaction and behavioural

intentions. Therefore, the comprehensive and parsimonious model developed for this

research makes important contribution to the literature on healthcare services.

Third, many studies conducted in healthcare have focused either on measuring

service quality or patient satisfaction or behavioural intentions individually in Indian

context. However, little research had focused on linkage between quality, satisfaction and

behavioural intention in the context of healthcare. The present study measured healthcare

service quality, investigated key determinants of patient satisfaction and link between

healthcare service quality, patient satisfaction and behavioural intention of Indian

corporate healthcare system.

Fourth, the data for the current empirical study was collected using direct contact

approach from hospitalised patients of super-specialty services such as surgical care for

cardiovascular, neurological, urinary, respiratory and orthopedic diseases. This method

gives advantages of versatility, speed and cost-effectiveness. In addition, structural

equation modelling (SEM) using the AMOS statistical package was used to test the

measurement and structural models. Use of this methodology employing sophisticated

statistical tools has been limited in previous literature; thus, this study sets a new pattern

in the research on Indian healthcare service systems.

5.3.2. Implications for practicing doctors and supporting staff

First, practicing doctors should note that the traditional approach to treating patients only

with medicines will no longer suffice their patients‟ needs. Patients expect more than that

from their doctors. For example, patients want their doctors to be more humane and

exhibit more kindly behaviour in their interactions with them. Therefore, doctors should

215

broaden their approach in treating patients by incorporating the needs of patients in their

service delivery.

Second, since effective communication can greatly contribute to the creation,

development and retention of long-term relationships with their patients, doctors and

paramedical staff need to seriously consider making their communication efficient and

effective. Specifically, this involves building and retaining relationships with clients

through better-than-average interaction and explaining behaviour.

Third, since retention of customer loyalty is vital and harder to achieve than

simply attracting clients in the first place (Roberts, 2000), doctors, medical specialists,

paramedical staff etc. should endeavour to enhance the level of patient loyalty by

delivering professional and attentive customer-driven interaction behaviour. Such loyalty

can be maintained by providing high quality services to patients in terms of informative

and beneficial communication.

Fourth, doctors and paramedical staff need to respond to patients‟ confidence in

them by providing quality services based on their needs and satisfaction. For example,

many patients consider their doctors as advisors and open their hearts to them in sharing

personal issues with them in the hope of obtaining guidance in overcoming issues that are

indirectly associated with their illness. Doctors and paramedical staff should consider this

important issue while interacting with patients.

Fifth, doctors and paramedical staff need to improve their listening behaviour by

letting patients communicate what they actually want from their doctors. Doctors should

then assure their patients that the issues they raised have been heard and they will do

what is necessary. Finally, doctors and paramedical staff should be fully aware of the

service needs of patients. Their interaction strategy should be tailored to understand the

unique communication needs of the individual patient for facilitating the development of

mutual bonding.

216

5.3.3. Implications for management

First, in order to improve patients‟ satisfaction and increase their loyalty to the medical

service providers, management should evaluate their doctors‟ performance not only in

terms of their technical proficiency but also their ability and willingness to effectively

communicate with their patients during interactions.

Second, management can formally introduce in-service training programs aimed

at equipping every individual doctor with the knowledge and interaction skills needed for

professional communication with patients

Third, occasional surveys of patient satisfaction of services with special

references to the interaction and listening behaviour of doctors would enable a healthcare

service management group to be alert to any actions required to ensure that patients‟

needs are being met.

Finally, healthcare managers have to consider healthcare delivery as a network

event rather than as an isolated encounter by involving patients‟ family/friends in the

care. Managers can also focus on budget neutral approaches for the factors which have

little or no impact on satisfaction. Reducing negative word of mouth can have significant

bearing on the very business model and financials of hospitals. These above implications

are merely applicable to doctors and the healthcare providers in an emerging economy

with a large population such as India, where the doctor-population ratio is very low and

as such doctors usually have to see a large number of patients compared to medical

specialists. The reality is that in countries such as India doctors have less time to spend

on each patient and consequently patients have less healthcare-related information and

education in emerging economies. It is essential that Indian medical practices develop a

culture of fostering effective communication and explaining behaviour and this will

require communication skills training.

217

5.4. Future Research Directions and Limitations of the study

This research has developed an integrated model that provided systematic way to

measure healthcare service quality, patient satisfaction and behavioural intentions by

Indian corporate healthcare providers, several beneficial areas for future research,

however, remain to be explored. For example, results of current study are limited to

corporate healthcare run by private players; future research may apply or replicate this

study in other healthcare domains, such as public healthcare services, urgent care services

and medical tourism. This would be valuable in establishing the external validity of

model.

In addition, it will be interesting for future research to test and explore the model

developed for this study in other developing countries and developed counties in Asia.

This will be valuable in providing evidence concerning the robustness of research model

across different cultural and demographical setting.

This research examined the concept of hospital service quality, patient satisfaction

and behavioural intention from the perspective of patients. However, this study did not

explore the perspective of service providers and patient attendants. So, in future research,

service provider and patient attendant‟s perspectives is necessary to measure patient

satisfaction from triad perspective in corporate healthcare services.

The findings of this study are based on overall satisfaction of the patients, but no

comparison has been made between the satisfaction level of patients seeking treatments

from the public and private healthcare sectors. In future research, employees‟

perspectives, along with patients, is necessary to measure patient satisfaction from dyad

perspective in public and private healthcare services.

Future research could also be conducted to expand the research model by

including additional factors. For example communication, word-of-mouth, loyalty and

trust has been found as one of the significant factor influencing patients intentions, future

research may include these variables in the model to gain a comprehensive understanding

of the patient satisfaction.

218

Although this study has provided some interesting findings, there are a number of

limitations of this research.

First, this research examined the concept of healthcare service quality, patient

satisfaction and behavioural intention from the perspective of patients and corporate

hospitals run by the private players.

Second, the study is restricted to selected corporate hospitals, but its results can be

generalised for other hospitals across the country, as well as other developing countries.

Third, to assess healthcare quality and patient satisfaction from overall patients‟

perspectives, outdoor patients need also to be considered in the future study, which are

excluded from the scope of the study.

Fourth, being consumer-based research, perceptions of physicians, medical

assistant, nurses, technicians, laboratory assistants and menial staff, were not considered.

Last, the findings of the study are based on overall satisfaction of the patients, but

no comparison has been made between the satisfaction level of patients seeking

treatments from the public and private healthcare sectors. As the simple size in small and

does not represent the universe the conclusion as drawn maybe biased. In future research,

employees‟ perspectives, along with patients, is necessary to measure patient satisfaction

from dyad perspective in public and private healthcare services.

Conclusion

First, it aimed to explore patient‟s perceptions and expectations of healthcare service

quality. Healthcare service quality results clearly establish that assurance is the most

serious problem faced by the Indian corporate hospital providers. Patients‟ expectations

of service providers are highest in relation to assurance, and patients give priority to

assurance as compared to other five dimensions, yet the tangibility scores have been

consistently the lowest in this survey. It is not surprising that patients were more satisfied

when they felt more assured of their health outcomes. There is also evidence that for

services with credence properties, assurance plays an important role in patient

satisfaction.

219

The results confirmed that the five dimensions of expected and perceived service quality

namely, tangibles, reliability, empathy, assurance and responsiveness are the distinct

construct for healthcare service quality. Each dimension has a significant relationship

with healthcare service quality. Among these five dimensions, assurance, responsiveness,

empathy and reliability have the most important influence on patient‟s perceptions of

healthcare service quality. These findings suggest that it is important for corporate

healthcare providers to focus on improving both technical and functional dimensions to

enhance their healthcare service quality.

Another objective was to investigate the key determinants of patient satisfaction.

Admission process, medical care services, nursing care services, housekeeping services

and overall service experience had significant influence and determined these five

dimensions are key determinants of corporate hospital‟s patient satisfaction. Only food

services offered by corporate hospitals had negative relation with patient satisfaction.

These findings suggest that it is important for corporate healthcare providers to provide

high quality nutritious food with reasonable pricing is a fundamental requirement for the

success.

Additionally, patient behavioural intentions were also examined. Expected and perceived

healthcare service quality and patient satisfaction significantly affected behavioural

intentions. When patients were satisfied with the quality of healthcare services, they were

more likely to do more business with the same provider and to recommend it to their

friends and relatives. Therefore, narrowing the disconfirmation between patient

expectations and their perceptions and increasing the assimilation effect is critical to

patient loyalty and positive word‐of‐mouth.

Lastly, the purpose of this study was to measure healthcare service quality and its relation

with patient satisfaction and behavioural intention. The SEM approach was used to test

the constructs framework between healthcare service quality, patient satisfaction and

behavioural intention. The results show that healthcare service quality has a significant

relationship with patient satisfaction and behavioural intentions. The findings of this

study indicate that the establishment of higher levels of healthcare service quality will

lead patients to have a high level of satisfaction. The results show that patients are more

220

satisfied with the approach that hospitals use to solve problems and quality of treatment

provided by them. In this sense, behavioural intention was based on willingness to

recommend the hospital to others, willingness to inform about the advantages of the

hospital and considering the same hospital as a first choice in future medical treatment.

***********

Bibliography

Aagja, J. P. and Garg, R. (2010a), “Measuring perceived service quality for public

hospitals (PubHosQual) in the Indian context”, International Journal of

Pharmaceutical and Healthcare Marketing, Vol. 4 No.1, pp. 60-83.

Aiken, L.H., Clarke, S.P. and Sloane, D.M. (2002), “Hospital staffing, organization, and

quality of care: cross-national findings”, International Journal for Quality in

Health Care, Vol. 14 No. 1, pp. 5-13

Ajzen, I. (1991), “The Theory of planned behaviour organizational behaviour and Human

decision processes”, Vol. 50 No.2, pp. 179-211.

Ajzen, I. (2002), “Perceived behavioural control, self-efficacy, locus of control, and the

theory of planned behaviour”, Journal of Applied Social Psychology, Vol. 32, pp.

665-683.

Akama, J.S. and Kieti, D.M. (2003), “Measuring tourist satisfaction with Kenya‟s

wildlife safari: a case study of Tsavo West National Park”, Tourism Management,

Vol. 21, pp. 876-96.

Akan, P. (1995), “Dimensions of service quality: a study in Istanbul”, Managing Service

Quality, Vol. 5, pp. 39-43.

Al-ahmadi, H. (2009), “Factors affecting performance of hospital nurses in Riyadh

Region, Saudi Arabia”, International Journal of Health Care Quality Assurance,

Vol. 22 No. 1, pp. pp. 40-54.

Alaloola, N. a., and Albedaiwi, W. a. (2008), “Patient satisfaction in a Riyadh Tertiary

Care Centre”, International Journal of Health Care Quality Assurance, Vol. 21

No. 7, pp. 630–637.

Al-Doghaither, A.H., Abdelrhman, B.M. and Wahid-Saeed, A.A. (2000), “Patients‟

satisfaction with physician's services in primary healthcare centres in Kuwait

City, Kuwait”, The Journal of the Royal Society for the Promotion of Health, Vol.

120 No. 3, pp. 170-174.

Alexandris, K., N. Dimitriadis and D. Markata (2002), “Can perceptions of service

quality predict behavioural intentions? An exploratory study in hotel sector in

Greece”, Managing Service Quality, VOL. 12 No. 4, pp. 224–31.

Alhashem, A.M., Alquraini, H. and Chowdhury, R.I. (2011), “Factors influencing patient

satisfaction in primary healthcare clinics in Kuwait”, International Journal of

Health Care Quality Assurance, Vol. 24 No. 3, pp. 249-262.

Alkaa'ida, F. (2008), “Effect of marketing performance on health service quality in

Jordanian hospitals. Unpublished PhD Thesis, Omdurman Islamic University (in

Arabic).

Al-Mandhari, A.S., Hassan, A.A. and Haran, D. (2004), “Association between perceived

health status and satisfaction with quality of care: evidence from users of primary

health care in Oman”, Family Practice, Vol. 21 No.5, pp. 519–527.

Alrubaiee, L., and Alnazer, N. (2010), “Investigate the Impact of Relationship Marketing

Orientation on Customer Loyalty: The Customer's Perspective”, International

Journal of Marketing Studies, Vol. 2, No. 1, pp. 155-174.

Amin, M. and Nasharuddin, S.Z. (2013), “Hospital service quality and its effects on

patient satisfaction and behavioural intention”, Clinical Governance: An

International Journal, Vol. 18 No. 3, pp. 238-254.

Amin, M., and Nasharuddin, S. Z. (2013), “Hospital service quality and its effects on

patient satisfaction and behavioural intention”, Clinical Governance: An

International Journal, Vol. 18 No.3, pp. 238–254.

Amin, Muslim. and Isa, Zaidi. (2008), “An examination of the relationship between

perception of service quality and customer satisfaction: A SEM approach towards

Malaysian Islamic banks”, International Journal of Islamic Middle Eastern

Finance and Management, Vol. 1 No. 3, pp. 191-209.

Andaleeb, S. S. (2001), “Service quality perceptions and patient satisfaction: a study of

hospitals in a developing country”, Social Science & Medicine, Vol. 52, pp.

1359–1370.

Andaleeb, S.S. (1998), “Determinants of customer satisfaction with hospitals: a

managerial model”, International Journal of Health Care Quality Assurance, Vol.

11, 6-7, pp. 181-7.

Anderson, C and Zeithaml, C P (1984), “Stage of the Pro- duct Life Cycle, Business

Strategy, and Business Performance,” Academy of Management Journal, Vol. 27,

pp. 5-24.

Anderson, E. (1995), “Measuring service quality in a University health clinic”,

International Journal of Health Care Quality Assurance, Vol.8 No2, pp. 32-37.

Anderson, E. A., and Zwelling, L. A. (1996), “Measuring service quality at the

University of Texas M. D. Anderson Cancer Center”, International Journal of

Health Care Quality Assurance, Vol. 9 No. 7, pp. 9–22.

Anderson, E.W. and Fornell, C. (1994), “A customer satisfaction research prospectus”, in

Rust, R.T. and Oliver, R. (Eds), Service Quality: New Directions in Theory and

Practice, Sage, Thousand Oaks, CA, pp. 241-68.

Anderson, E.W. and Sullivan, M.W. (1993), “The antecedents and consequences of

customer satisfaction for firms”, Marketing Science, Vol. 12 No. 2, pp. 125-43.

Anderson, E.W. and Sullivan, M.W. (1993), “The antecedents and consequences of

customer satisfaction for firms”, Marketing Science, Vol. 12 No. 2, pp. 125-43.

Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modelling in practice: a

review and recommended two-step approach”, Psychological Bulletin, Vol. 103

No. 3, pp. 411-23.

Anthanassopoulos, A., Gounaris, S., and Sathakopoulos, V. (2001), “Behavioural

responses to customer satisfaction: an empirical study”, European Journal of

Marketing, Vol. 35 No. 5/6, pp. 687-707.

Arasli, H., Ekiz, E. H. and Katircioglu, S.T. (2008), “Gearing service quality into public

and private hospitals in small islands”, International Journal of Health Care

Quality Assurance, Vol. 21 No. 1, pp. 8-23.

Arasli, H., Mehtap-Smadi, S., and Katircioglu, S.T. (2005), “Customer service quality in

the Greek Cypriot banking industry”, Managing Service Quality, Vol. 15 No. 1,

pp. 41-56.

Ashrafun, L., and Uddin, M. J. (2011), “Factors Determining Inpatient Satisfaction with

Hospital Care in Bangladesh”, Asian Social Science, Vol. 7 No. 6, pp. 15–24.

Avis, M., Bond, M. and Arthur, A. (1997), “Questioning patient satisfaction: an empirical

investigation in two outpatient clinics”, Social Science& Medicine, Vol. 44 No. 1,

pp. 85-92.

Avortri, G.S., Beke, A. and Abekah-Nkrumah, G. (2011), “Predictors of satisfaction with

child birth services in public hospitals in Ghana”, International Journal of Health

Care Quality Assurance, Vol. 24 No. 3, pp. 223-237.

Baalbaki, I., Ahmed, Z.U., Pashtenko, V.H. and Makarem, S. (2008), “Patient satisfaction

with healthcare delivery systems”, International Journal of Pharmaceutical and

Healthcare Marketing, Vol. 2, pp. 47-62.

Babakus, E. and Boller, G.W. (1991), “An empirical assessment of the SERVQUAL

scale”, Journal of Business Research, Vol. 24, pp. 253-68.

Babakus, E. and Boller, G.W. (1992), “An empirical assessment of the SERVQUAL

scale”, Journal of Business Research, Vol. 24 No. 3, pp. 253-68.

Babakus, E. and Mangold, W.G. (1992), “Adapting the SERVQUAL scale to hospital

services: an empirical investigation”, Health Services Research, Vol. 26, pp. 767-

86.

Babakus, E., Pedrick, D.L. and Inhofe, M. (1993), “Empirical Examination of a Direct

Measure of Perceived Service Quality Using SERVQUAL Items”, Memphis State

University, Memphis, TN, Unpublished Manuscript.

Badri, M., Attia, S., and Ustadi, A. (2008), “Testing the not so obvious models in

healthcare quality”, International Journal for Quality in Healthcare, Vol. 21 No.

2, pp. 159-74.

Badri, M., Attia, S., and Ustadi, A. (2009), “Healthcare quality and moderators of patient

satisfaction: testing for causality”, International Journal of Health Care Quality

Assurance, Vol. 22 No. 4, pp. 382-410.

Badri, M., Dodeen, H., Al Khaili, M., and Abdulla, M. (2005), “Development of the

national inpatient satisfaction constructs and items for the United Arab Emirates”,

International Journal of Applied Health Studies, Vol. 1 No. 3, pp. 1-22.

Baker, D., and Crompton, J. (2000), “Quality, Satisfaction, and Behavioral Intentions”,

Annual of Tourism Research, Vol. 27, pp. 785-804.

Baker, T. L., & Taylor, S. a. (1997), “Patient satisfaction and service quality in the

formation of customer‟s future purchase intentions in competitive health service

settings”, Health Marketing Quarterly, Vol. 15 No. 1, pp. 1-15.

Balakrishna, P. (2007), “In the pink of health”, Retrieved, from Business standard:

http://www.businessstandard.com/india/storypage.php?autono=290789.

Baldwin, A., and Sohal, A. (2003), “Service quality factors and outcomes in dental care”,

Managing Service Quality, Vol. 13, pp. 207–216.

Bateson, J.E.G. (1995), “Perceived Control and the Service Encounter in the Service

Encounter,” J.A. Czepiel, M.R. Solomon, and C. Surprenant, eds., Lexington

Books, pp. 67-82.

Bebko, C.P. and Garg, R.K. (1995), “Perceptions of responsiveness in service delivery”,

Journal of Hospital Marketing, Vol. 9 No. 2, pp. 35-45.

Berman, P. (1998), “Rethinking Health Care Systems: Private Health Care Provision in

India”, World Development, Vol. 26 No. 8, pp. 16-26.

Berry, L. L., Zeithaml, Valarie A, & Parasuraman, A. (1990), “Five imperatives for

improving service quality”, MIT Sloan Management Review, Vol. 31 No. 4, pp.

29-38.

Bevan G, Hood C (2006). “Have targets improved performance in the English NHS?”,

British Medical Journal, Vol. 332, pp. 419-22.

Bhat, R. (1999), “Governance of private sector corporate hospitals and their financial

performance”, IIMA-Research and Publication, Vol. 18.

Bhattacharya, A., Menon, P., Koushal, V. and Rao, K.L.N. (2003), “Study of patient

satisfaction in a tertiary referral hospital”, Journal of the Academy of Hospital

Administration, Vol. 15 No. 1, pp. 1-3.

Biork, I.T., Samdal, G.B., Hansen, B.S., Torstad, S. and Hamilton, G.A. (2007), “Job

satisfaction in a Norwegian population of nurses: a questionnaire survey”,

International Journal of Nursing Studies, Vol. 44 No. 5, pp. 747-57.

Bitner, Mary Jo (1990), “Evaluating Service Encounters: The Effects of Physical

Surroundings and Employee Response,” Journal of Marketing, Vol. 54 No. 4, pp.

69-82.

Bitner, Mary Jo, and Amy R. Hubbert (1994), “Encounter Satisfaction versus Overall

Satisfaction versus Quality,” in Service Quality, R. Rust and R. Oliver, eds.

Thousand Oaks, CA: Sage Publications, pp. 72-94.

Bojanic, D.C. (1991), “Quality Measurement in Professional Services Firms” Journal of

Professional Services Marketing, Vol.7 No. 2, pp. 27-36.

Boltan, R.N. and Drew, J.H. (1991a), “A multistage model of customer‟s assessment of

service quality and value”, Journal of Consumer Research, Vol. 17, pp. 375-85.

Boltan, R.N. and Drew, J.H. (1991b), “A longitudinal analysis of the impact of service

changes on customer attitudes”, Journal of Marketing, Vol. 55, pp. 1-9.

Boltan, Ruth N. and James H. Drew (1991), “A longitudinal analysis of the impact of

service changes on customer attitude”, Journal of Marketing, Vol. 55 No. 1, pp.

1-19.

Boulding, W., Kalra, A., Staelin, R. and Zeithaml, V.A. (1993), “A dynamic process

model of service quality: from expectations to behavioral intentions”, Journal of

Marketing Research, Vol. 30, pp. 7-27.

Bowers, M., Swan, J. and Koehler, W. (1994), “What attributes determine quality and

satisfaction with health care delivery?”, Health Care Management Review, Vol.

19 No. 4, pp. 49-55.

Boyer, L. and Francois, P. (2006), “perceptions and use of the results of patient

satisfaction surveys by care providers in a French teaching hospital”, International

Journal of Quality in Health Care, Vol. 18 No. 5, pp. 359-364.

Brady, M.K. and Cronin, J.J Jr (2001), “Some new thoughts on conceptualizing perceived

service quality: a hierarchical approach”, Journal of Marketing, Vol. 65 No. 4, pp.

34-49.

Brady, Michael, K. and Cronin Joseph (2001), “Some New Thoughts on Conceptualizing

Perceived Service Quality: A hierarchical Approach”, Journal of Marketing, Vol.

65 No. 3, pp.34-49.

Brink P, Wood M. (1994), “Basic Steps in Planning Nursing Research: From Question to

Proposal”, 4th edition, Boston: Jones & Bartlett, New Jersey.

Brown, Stephen and Teresa Swartz (1989), “A Gap analysis of professional service

quality”, Journal of Marketing, Vol. 53 No. 2, pp. 92-98.

Brown, T.J., Churchill, G.A. and Peter, J.P. (1993), “Improving the measurement of

service quality”, Journal of Retailing, Vol. 69 No. 1, pp. 127-39.

Butt, M.M. and Cyril de Run, E. (2010), “Private healthcare quality: applying a

SERVQUAL model”, International Journal of Health Care Quality Assurance,

Vol. 23 No. 7, pp. 658-673.

Buttle, F. (1994), “What‟s wrong with SERVQUAL?”, Working Paper No. 277,

Manchester Business School, Manchester.

Buzzell, R. D. and Gale, B. T. (1987), “The PIMS Principles: Linking Strategy to

Performance”, Free Press, New York, NY.

Byrne, B.M. (2001), “Structural Equation Modelling with Amos: Basic Concepts,

Applications and Programming”, Lawrence Erlbaum, Mahwah, NJ.

Calnan, M. (1988), “Towards a conceptual framework of lay evaluation of health care”,

Social Science and Medicine, Vol. 27 No. 9, pp. 927-33.

Camilleri, D. and Callaghan, O‟,C. (1998), “Comparing public and private hospital care

service quality”, Journal of Marketing, Vol. 11 No. 4, pp. 127-33.

Carman, J.M. (2000), “Patient perceptions of service quality: combining the dimensions”,

Journal of Services Marketing, Vol. 14 No. 4, pp. 337-52.

Carman, James M. (1990), “Consumer Perceptions of Service Quality: An Assessment of

the SERVQUAL Dimensions,” Journal of Retailing, Vol. 66, pp. 33-55.

Carpenter, J.M. and Fairhurst, A. (2005), “Measuring service quality: a re-examination

and extension”, Journal of Marketing, Vol. 9 No. 3, pp. 256-69.

Caruana, A. (2002), “Service loyalty: the effects of service quality and the mediating role

of customer satisfaction”, European Journal of Marketing, Vol. 36, pp. 811-828.

Caruana, Albert, Michael T. Ewing, and B. Ramaseshan (2000), “Assessment of the

three-column format SERVQUAL: An experimental approach,” Journal of

Business Research, Vol. 49 No. 1, pp. 57-65.

Chahal, H. (2009), “Predicting patient loyalty and service quality relationship - a case

study of civil hospital. Ahmedabad”, Vision, Vol. 12 No. 4, pp. 45-55.

Chahal, H. and Kumari, N. (2010), “Development of multidimensional scale for health

care service quality (HCSQ) in Indian context”, Journal of Indian Business

Research, Vol. 2 No. 4, pp. 230-255.

Chahal, H. and Sharma, R.D. (2004), “Managing health care service quality in a primary

health care centre”, Metamorphosis, Vol. 3 No. 2, pp. 112-31.

Chahal, H., and Kumari, N. (2012), “Service quality and performance in the public

health-care sector”, Health Marketing Quarterly, Vol. 29 No. 3, pp. 181-205.

Chahal, H., and Mehta, S. (2013), “Modelling patient satisfaction construct in the Indian

health care context”, International Journal of Pharmaceutical and Healthcare

Marketing, Vol. 7 No. 1, pp. 75-92.

Chahal, H., Sharma, R. D., and Gupta, M. (2004), “Patient Satisfaction in Public

Outpatient Health Care Services”, Journal of Health Management, Vol. 6 No. 1,

pp. 23-45.

Chalip, L., Green, B. C., and Hill, B. (2003), “Effects of sport event media on destination

image and intention to visit”, Journal of Sport Management, Vol. 17, pp. 214-234.

Chandorkar, A.G. (2009), “Hospital administration and planning”, Paras Medical

Publisher, 2nd edition, Hyderabad, India.

Chang, K. and Chelladurai, P. (2003), “System-based quality dimensions in fitness

services: development of the scale of quality”, The Service Industries Journal,

Vol. 23, pp. 65-83.

Chang, Tung-Zong and Albert R. W. (1994), “Price, Product Information, and Purchase

Intention: An Empirical Study,” Journal of the Academy of Marketing Science,

Vol. 22 No. 1, pp. 16-27.

Chaniotakis, I. E., and Lymperopoulos, C. (2009), “Service quality effect on satisfaction

and word of mouth in the health care industry” Managing Service Quality, Vol.

19 No. 2, pp. 229-242.

Chen, T.L. and Lee, Y.H. (2006), “Kano two-dimensional quality model and importance-

performance analysis in the student‟s dormitory service quality evaluation in

Taiwan”, The Journal of American Academy of Business, Vol. 9 No. 2, pp. 324-

31.

Chen, T.L. and Lee, Y.H. (2006), “Kano two-dimensional quality model and importance-

performance analysis in the student‟s dormitory service quality evaluation in

Taiwan”, The Journal of American Academy of Business, Vol. 9 No. 2, pp. 324-

31.

Choi, K., Lee, H., Kim, C. and Lee, S. (2005), “The service quality dimensions and

patient satisfaction relationships in South Korea: comparisons across gender, age

and types of service”, Journal of Services Marketing, Vol. 19 No. 3, pp. 140-9.

Choi, K-S., Cho, W-H., Sunhee, L., Hanjoon, L. and Kim, C. (2004), “The relationship

among quality, value, satisfaction and behavioral intention in healthcare provider

choice: a South Korean study”, Journal of Business Research, Vol. 57, pp. 913-

21.

Churchill, G.A. Jr (1979), “A paradigm for developing better measures of marketing

constructs”, Journal of Marketing Research, Vol. 16 No. 1, p. 64-72.

Corbin, C.L., Kelley, S.W. and Schwartz, R.W. (2001), “Concepts in service marketing

for healthcare professionals”, The American Journal of Surgery, Vol. 181 No.3,

pp. 1-7.

Coulthard, L.J.M. (2004), “Measuring service quality: a review and critique of research

using SERVQUAL”, International Journal of Market Research, Vol. 46 No. 4,

pp. 479-97.

Cronin, J. J. Jr. (2003), “Looking back to see forward in services marketing: some ideas

to consider,” Managing Service Quality, Vol. 13 No. 5, pp. 332-337.

Cronin, J. Joseph and Steven A. Taylor (1992), “Measuring Service Quality: A Re-

examination and Extension,” Journal of Marketing, Vol. 56 No. 7, pp. 55-68.

Cronin, J.J. Jr, Brady, M.K. and Hult, T.M. (2000), “Assessing the effects of quality,

value, customer satisfaction on consumer behavioral intentions in service

environment”, Journal of Retailing, Vol. 76 No. 2, pp. 193-216.

Cronin, Michael K. Brady, Richard R. Brand, Roscoe Hightower, Jr., and Donald J.

Shemwell (1997), “A Cross-Sectional Test of the Effect and Conceptualization of

Service Value,” The Journal of Services Marketing, Vol. 11 No. 6, pp. 375-391.

Crowe, R., Gage, H., Hampson, S., Hart, J., Kimber, A., Storey, L. and Thomas, H.

(2002), “The measurement of satisfaction with healthcare: implications for

practice from a systematic review of the literature”, Health Technology

Assessment, Vol. 6 No. 32, pp. 1-244.

Curry, A., and Sinclair, E. (2002), “Assessing the quality of physiotherapy services using

SERVQUAL”, International Journal of Health Care Quality Assurance, Vol. 15

No. 5, pp. 197-205.

D‟souza, L. B. (2009), “Trends and approaches in lean healthcare”, Leadership in Health

Services, Vol. 22 No. 2, pp. 121-139.

Dabholkar, P.A., Thorpe, D.I. and Rentz, J.O. (1996), “A measure of service quality for

retail stores: scale development and validation”, Journal of the Academy of

Marketing Science, Vol. 24 No. 1, pp. 3-16.

Dagger, T.S., Sweeney, J.C. and Johnson, L.W. (2007), “A hierarchical model of health

service quality”, Journal of Service Research, Vol. 10 No. 2, pp. 123-42.

Das, J. and Hammer, J. (2007), “Which doctor? Combining vignettes and item-response

to measure doctor quality”, Journal of Development Economics, Vol. 78, pp. 348-

83.

de Man, S., Gemmel, P., Vlerick, P., Rijk, P.V. and Dierckx, R. (2002), “Patients‟ and

personnel‟s perceptions of service quality and patient satisfaction in nuclear

medicine”, European Journal of Nuclear Medicine and Molecular Imaging, Vol.

29, pp. 1109-17.

Dean, A.M. (1999), “The applicability of SERVQUAL in different healthcare

environments,” Health Marketing Quarterly, Vol. 16 No. 3, pp. 1-21.

Deitrick, L., Ray, D., Stern, G., Fuhrman, C., Masiado, T. and Yaich, S.L. (2005),

“Evaluation and recommendations from a study of a critical-care waiting room”,

Journal of Healthcare Quality, Vol. 27, pp. 17-25.

Deming, W.E. (1986), “Out of the Crisis, Massachusetts Institute of Technology”, Center

for Advanced Engineering Studies, Cambridge, MA.

Dilber, M. Bayyurt, N., Zaim, S. and Tarim, M. (2005), “Critical factors of total quality

management and its effect on performance in health care industry: A Turkish

experience”, Problems and Prospective management, Vol. 4, pp. 220-234.

Dillon, W. R., and Kumar, A. (1985), “Attitude organization and the attitude-behavior

relation: A critique of Bagozzi and Bumkrant‟s reanalysis of Fishbein and Ajzen”,

Journal of Personality and Social Psychology, Vol. 49, pp. 33-46.

Ditto, P.H., Moore, K.A., Hilton, J.L. and Kalish, J.R. (1995), “Beliefs about physicians:

their role in health care utilization, satisfaction and compliance”, Basic and

Applied Social Psychology, Vol. 17 No. 1&2, pp. 23-48.

Donabedian, A. (1966), “Evaluating the quality of medical care”, Milbank Memorial

Fund Quarterly, Vol. 44, pp.166‐206.

Donabedian, A. (1980), “The Definition of Quality and Approaches to its Assessment”,

Health Administration Press, Ann Arbor, MI.

Duggirala, M., Rajendran, C. and Anantharaman, R.N. (2008), “Patient perceived

dimensions of total quality service in healthcare”, Benchmarking: An

International Journal, Vol. 15 No. 5, pp. 560-583.

Eaden, J., Mayberry, M. K., and Mayberry, J. F. (1999), “Questionnaires: the use and

abuse of social survey methods in medical research”, Postgraduate Medical

Journal, Vol. 75 No. 885, pp. 397-400.

Edgman-Levitan, S. and Cleary, P.D. (1996), “What information do consumers want and

need?”, Health Affairs, Vol. 15 No. 4, pp. 42-56.

Edvardsson, B., Larsson, G. and Setterlind, S. (1997), “Internal service quality and the

psychological work environment: an empirical analysis of conceptual

interrelatedness”, Service Industries Journal, Vol. 17 No. 2, pp. 252-63.

Eleuch, A. (2011), “Healthcare service quality perception in Japan”, International

Journal of Health Care Quality Assurance, Vol. 24 No. 6, pp. 417-429.

Elizabeth A. Anderson, (1995), “Measuring service quality at a university health clinic”,

International Journal of Health Care Quality Assurance, Vol. 8 No. 2 pp. 32-37.

Ennew, C.T., Banerjee, A.K. and Li, D. (2000), “Managing word of mouth

communication: empirical evidence from India”, International Journal of Bank


Evans, J.R. and Lindsay, W.M (1996), “The management and control of quality”, West

publishing company, p. 15.

Fishbein, M. and I Ajzen (1975), “Belief, Attitude, Intention and Behaviour: An

Introduction to Theory and Research”, Addison-Wesley, Reading, MA.

Fisk R.P., Brown S.W., Bitner, M.J., (2003), “Tracks the evaluation of the service

marketing literature”, Journal of Retailing, Vol.69 No. 1, pp. 61-103.

Fornell, C. (1992), “A National Customer Satisfaction Barometer: The Swedish

Experience”, Journal of Marketing, Vol. 56 No. 1, pp. 6-21.

Fornell, Michael D. Johnson, Eugene W. Anderson, Jaesung Cha, and Barbara Everitt

Bryant. (1996). “The American Customer Satisfaction Index: Nature, Purpose,

and Findings,” Journal of Marketing, Vol. 60 No. 10, pp. 7-18.

Fowdar, R. (2005), “Identifying health care attributes”, Journal of Health and Human

Services Administration, Vol. 27 No. 4, pp. 428-43.

Fusilier, M.R. and Simpson, P.M. (1995), “AIDS patient‟s perceptions of nursing care

quality”, Journal of Health Care Marketing, Vol. 15 No. 1, pp. 49-53.

Fynes, B. (1998), “Quality management practices: A review of the literature”, Journal of

Irish Business and Administrative Research, Vol. 19/20 No. 2, pp. 113‐138.

Gabbott, M. and Hogg, G. (1994), “Grounds of discrimination establishing criteria for

evaluation”, Service Industries Journal, Vol. 15 No. 1, pp. 90-96.

Gan, C., Clemes, M., Limsobunchai, V. and Weng, A. (2006), “A legit analysis of

electronic banking in New Zealand”, International Journal of Marketing, Vol. 24

No. 6, pp. 360-83.

Ganguli, S., and Roy, S. K. (2010), “Service quality dimensions of hybrid services”,

Managing Service Quality, Vol. 20 No. 5, pp. 404-424.

Garman, A.N., Garcia, J. and Hargreaves, M. (2004), “Patient satisfaction as a predictor

of return-to-provider behavior: analysis and assessment of financial implications”,

Quality Management in Health Care, Vol. 13 No. 1, pp. 75-80.

Gaur, S. S., Xu, Y., Quazi, A. and Nandi, S. (2011), “Relational impact of service

provider‟s interaction behavior in health care”, Managing Service Quality, Vol.

21 No. 1, pp. 67-87.

Ghosh (2014), “Measuring patient satisfaction: An empirical study in India”, Leadership

in Health Services, Vol. 27 No. 3, pp. 240-254.

Gilbert, G. R., Veloutsou, C., Goode, M. M. H., and Moutinho, L. (2004), “Measuring

customer satisfaction in the fast food industry: A cross‐national approach”,

Journal of Services Marketing, Vol. 18 No. 5, pp. 371‐383.

Gill, L., & White, L. (2009), “A critical review of patient satisfaction”, Leadership in

Health Services, Vol. 22 No. 1, pp. 8–19.

Gilmore, A. and McMullan, R. (2009), “Scales in services marketing research: a critique

and way forward”, European Journal of Marketing, Vol. 43 No. 5/6, pp. 640-51.

Glare, P.G.W. (1983), Oxford Latin Dictionary, Oxford University Press, Oxford.

Gotlieb, J.B., Grewal, D. and Brown, S.W. (1994), “Consumer satisfaction and perceived

quality: complementary or divergent constructs”, Journal of Applied Psychology,

Vol. 79 No. 6, pp. 875-85.

Gremler, D.D. and Brown, S.W. (1996), “Service loyalty: its nature, importance, and

implications”, in Edvardsson, B., Brown, S.W. and Johnston, R. (Eds), Advancing

Service Quality: A Global Perspective, International Service Quality Association,

Jamaica, NY, pp. 171-80.

Grøndahl, V. A., Hall-lord, M. L., Karlsson, I., Appelgren, J., and Wilde-larsson, B.

(2013), “Exploring patient satisfaction predictors in relation to a theoretical

model”, International Journal of Health Care Quality Assurance, Vol. 26 No. 1,

pp. 37-54.

Grönroos, C. (1984), “A service quality model and its marketing implications”, European

Journal of Marketing, Vol. 18 No. 4, pp. 36-44.

Grönroos, C. (1990), “Service management and marketing”, Lexington, Ma: Lexington

Books.

Grönroos, C. (1993), “Toward a third phase in service quality research: challenges and

future directions”, in Swartz, T.A., Bowen, D.E. and Brown, S.W. (Eds),

Advances in Services Marketing and Management, Vol. 2, JAI Press, Greenwich,

CT, pp. 49-64.

Gross, R. (2003), “A consumer based tool for evaluating the quality of health services in

the Israeli health care system following reform”, Health Policy, Vol. 68, pp. 143-

158.

Gupta, A., McDaniel J. C., and Herath S. K. (2005), “Quality management in service

firms: sustaining structures of total quality service”, Managing Service Quality,

Vol. 15 No. 4, pp. 389-402

Gupta, P.C., Witty, J. and Wright, N. (1993), “An approach to consumer feedback in an

outpatient specialty service”, International Journal of Health Care Quality


Haddad, S., Fournier, P. and Potvin, L. (1998), “Measuring lay people‟s perceptions of

the quality of primary health care services in developing countries Validation of a

20-item scale”, International Journal for Quality in Health Care, Vol. 10 No. 3,

pp. 93-104.

Hadwich, K., Georgi, D., Tuzovic, S., Büttner, J. and Bruhn M. (2010), “Perceived

quality of e-health services: A conceptual scale development of e-health service

quality based on the C-OAR-SE approach”, International Journal of

Pharmaceutical and Healthcare Marketing, Vol. 4 No. 2, pp.112–136.

Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2011),

“Multivariate Data Analysis (Seventh Edition)”, Pearson Education Ltd., New

Delhi-110017, India.

Hallowell, R. (1996), “The relationships of customer satisfaction, customer loyalty, and

profitability: an empirical study”, International Journal of Service Industry

Management, Vol. 7 No. 4, pp. 27-42.

Hamidi, Y. (1998), “New perspective to health care administration”, Journal of Tadbir,

No. 83, p. 46.

Hasin, M.A.A., Seeluangsawat, R. and Shareef, M.A. (2001), “Statistical measures of

customer satisfaction for health-care quality assurance: a case study,”

International Journal of Health Care Quality Assurance, Vol. 14 No. 1, pp. 6-14.

Hawthorne, G. (2006), “Review of Patient Satisfaction Measures”, Australian

Government Department of Health and Ageing, Canberra.

Haywood-Farmer, J. and Stuart, F. (1988), “Measuring the quality of professional

services: the management of service operations”, Proceedings of the 3rd Annual

International Private Conference of the UK Operations Management Association,

London, University of Warwick, Warwick.

Headley, D. E., and Miller, S. J. (1993), “Measuring service quality and its relationship to

future consumer behaviour”, Journal of Health Care Marketing, Vol. 13 No. 4,

pp. 32-41.

Hussein M Al-Borie, Amal M. Sheikh Damanhouri, (2013), “Patient‟s Satisfaction of

Service Quality in Saudi Hospitals: A SERVQUAL Analysis”, International

Journal of Health Care Quality Assurance, Vol. 26 No.1, pp. 20-30.

Hwang, L., Eves, A. and Desombre, T. (2003), “Gap analysis of patient meal service

perceptions”, International Journal of Health Care Quality Assurance, Vol. 16

No. 1, pp. 143-53.

Jabnoun, N. and Chaker, M. (2003), “Comparing the quality of private and public

hospitals”, Managing Service Quality, Vol. 13 No. 4, pp. 290-299.

Jain, S. K., and Gupta, G. (2004), “Measuring service quality: SERVQUAL vs.

SERVPERF scales”, VIKALPA, Vol. 29 No. 2, pp. 25‐37.

Jakobsson, L., Hallberg, R.I., Loven, L. and Ottoson, B. (1994), “Patient satisfaction with

nursing care evaluation before and after cutback in expenditure and intervention

at a surgical clinic”, International Journal of Quality in Health Care, Vol. 6 No.

4, pp. 361-369.

Janicic, R., Lecic-cvetkovic, D., Filipovic, V., Vukasinovic, Z., and Jovanovic, V. (2011),

“Patient‟s satisfaction as key point in healthcare services”, Journal of Society for

development in new net environment in B&H, Vol. 5 No. 6, pp. 1701-1709.

Jiang, J.J., Klein, G. and Crampton, S.M. (2000), “A note on SERVQUAL reliability and

validity in information system service quality measurement”, Decision Sciences,

Vol. 31 No. 3, pp. 725-44.

Johnston, R. (1995), “The determinants of service quality: satisfiers and dissatisfiers,”

International Journal of Service Industry Management, Vol. 6 No. 5, pp. 53-71.

Jones, M. and Suh, J. (2000), “Transaction-specific satisfaction and overall satisfaction:

an empirical analysis”, Journal of Services Marketing, Vol. 14 No. 2, pp. 147-59.

Kaldenberg, D., Becker, B.W., Browne, B.A. and W.G. Browne (1997), “Identifying

Service Quality Strengths and Weaknesses Using SERVQUAL: A Study of

Dental Services”, Health Marketing Quarterly, Vol. 15 No. 2, pp. 69-86.

Kandampully, J., and Suhartanto, D. (2000), “Customer loyalty in the hotel industry: The

role of customer satisfaction and image”, International Journal of Contemporary

Hospitality Management, Vol. 12 No. 6, pp. 346‐351.

Kang, G. D. and Jeffrey, J. (2004), “Service quality dimensions: an examination of

Grönroo‟s service quality model”, Managing Service Quality, Vol. 14 No. 4, pp.

266-77.

Kessler, D. P., and Mylod, D. (2011), “Does patient satisfaction affect patient loyalty?”,

International Journal of Health Care Quality Assurance, Vol. 24 No. 4, pp. 266-

273.

Kilbourne, W.E., Duffy, J.A., Duffy, M., and Giarchi, G. (2004), “The applicability of

SERVQUAL in cross-national measurements of health care quality”, Journal of

Services Marketing, Vol. 18 No. 7, pp. 524-33.

Koornneef, E. (2004), “NDA national standards for disability services”, Frontline, No.

61, p. 23.

Kotler, P. (1974), “Atmospherics as a marketing tool”, Journal of Retailing, Vol. 49, pp.

49-64.

Kumari, R., Idris, M.Z., Bhusan, V., Khanna, A., Agarwal, M. and Singh, S.K. (2009),

“Study on patient satisfaction in the government allopathic health facilities of

Lucknow district India”, Indian Journal of Community Medicine, Vol. 34 No. 1,

pp. 35-42.

Ladhari, R. (2009), “A review of twenty years of SERVQUAL research”, International

Journal of Quality and Service Sciences, Vol. 1 No. 2, pp.172–198.

Lam, S.S.K. (1997), “SERVQUAL: a tool for measuring patient‟s opinions of hospital

service quality in Hong Kong”, Total Quality Management, Vol. 8, pp. 145-52.

Lam, T.K.P. (2002), “Making sense of SERVQUAL‟s dimensions to the Chinese

customers in Macau”, Journal of Market-Focused Management, Vol. 5 No. 10,

pp. 43-58.

Lau, P.M., Akbar, A.K. and Fie, D.Y.G. (2005), “Service quality: a study of luxury hotels

in Malaysia”, The Journal of American Academy of Business, Vol. 7 No. 2, pp.

46-56.

Lee, H., Lee, Y. and Yoo, D. (2000), “The determinants of perceived service quality and

its relationship with satisfaction”, Journal of Services Marketing, Vol. 14 No. 3,

pp. 217-31.

Lee, M. and Ulgado, F.M. (1997), “Consumer evaluations of fast-food services: a cross-

national comparison”, Journal of Services Marketing, Vol. 11 No. 1, pp. 39-50.

Leebov, W., Jean, C. and Ersoz, C.J. (2003), “The healthcare manager‟s guide to

continuous quality improvement”, i-Universe, available at: www. iUniverse.com.

Lehtinen, U., and Lehtinen, J. R. (1983), “Service Quality: A Study of Quality

Dimensions”, Unpublished manuscript, Helsinki, Finland OY.

Lewis, B.R. (1991), “Customer care in services organizations”, Management Decisions,

Vol. 29 No. 1, pp. 31-4.

Lewis, R.C. and Booms, B.H. (1983), “The marketing aspects of quality”, in Berry, L.L.,

Shostack, G.L. and Upah, G.D. (Eds), Emerging Perspectives on Services

Marketing, American Marketing Association, Chicago, IL, pp. 99-104.

Lim, P. C., and Tang, N. K. (2000), “A study of patients' expectations and satisfaction in

Singapore hospitals”, International Journal of Health Care Quality Assurance,

Vol. 13 No. 7, pp. 290-299.

Lin, X., and Guan, J. (2013.), “Patient Satisfaction and Referral Intention”, Health

Marketing Quarterly, Vol. 20 No. 2, pp. 49-68.

Lohr, K. (1991), “Medicare: A Strategy for Quality Assurance”, National Academy

Press, Vol.1, Washington, DC.

Lovelock, C.H. (1992), “Managing Services: Marketing, Operations, and Human

Resources”, Prentice-Hall, Englewood Cliffs, New Jersey.

Mai, L.W. (2005), “A comparative study between UK and US: the student satisfaction in

higher education and its influential factors”, Journal of Marketing Management,

Vol. 21 No. 7/8, pp. 859-78.

Malhotra, N.K. (2003), “Marketing Research: An Applied Orientation”, Pearson

Education, Delhi (Singapore).

Malhotra, N.K. and Dash, S. (2011), “Marketing Research: An Applied orientation”, 6th

ed., Pearson Education, New Delhi.

Manjunath, U., Metri, B. a., and Ramachandran, S. (2007), “Quality management in a

healthcare organisation: a case of South Indian hospital”, The TQM Magazine,

Vol. 19 No. 2, pp. 129-139.

Martinez Fuentes, C. (1999), “Measuring hospital service quality: a methodological

study”, Managing Service Quality, Vol. 9 No. 4, pp. 230-40.

Mathiyazhagan, M.K (2007), “Determinants of Cost Efficiency of the Hospitals in

Karnataka State in India”, Journal of Quantitative Economics, Vol. 5 No. 2, pp.

95-115.

Mathiyazhagan, M.K. (2003a), “People‟s Choice of Health Care Provider: Policy Options

for Rural Karnataka in India”, Journal of Health Management, Vol. 5 No. 1, pp.

111-137.

McDougall, G. H., and Levesque, T. J. (2000), “Customer satisfaction with service:

Putting perceived value into the equation”, Journal of Services Marketing, Vol. 14

No. 5, pp. 392‐410.

Mehta, S. (2011), “Service Quality as Predicator of Patient Satisfaction: A Study of the

Health Care Sector”, Journal of Health Management, Vol. 13 No. 2, pp. 211-229.

Mejabi, O.V., and Olujide, J.O. (2008), “Dimensions of Hospital Service Quality in

Nigeria”, European Journal of Social Sciences, Vol. 5 No. 4, pp. 141-159.

Mekoth, N., George, B.P., Dalvi, V., Rajanala, N. and Nizomadinov, K. (2012), “Service

quality in the public sector hospitals: a study in India”, Hospital Topics, Vol. 90

No. 1, pp. 16-22.

Mels, G., Boshoff, C., and Nel, D., (1997),"The Dimensions of Service Quality: The

Original European Perspective Revisited", The Service Industries Journal, Vol.

17, No. 1, pp. 173-189.

Michael, C. L., Lucie K. O., and Walter, L. L. (2001), “Patient‟s Perceptions of Service

Quality Dimensions: An Empirical Examination of Health Care in New Zealand,

Health Marketing Quarterly, Vol. 19 No. 1, pp.3-22.

Morisi, T. L. (1996), “Commercial banking transformed by computer technology”,

Monthly Labour Review, Vol. 119 No. 8.

Morrison, P. D. and J. H. Roberts (1998), “Matching Electronic Distribution Channels to

Product Characteristics: The Role of Congruence in Consideration Set

Formation”, Journal of Business Research Vol. 41, pp. 223-229.

Moscato, S. R., Gullion, C. M., Tanner, C., Shapiro, S. E., and Izumi, S. (2007),

“Predictors of Patient Satisfaction with Telephone Nursing Services”, Clinical

Nursing Research, Vol. 16 No. 2, pp. 119-137.

Mostafa, M.M. (2005), “An empirical study of patients‟ expectations and satisfactions in

Egyptian hospitals‟”, International Journal of Healthcare Quality Assurance, Vol.

18 No. 7, pp. 516-32.

Mudur, G. (2003), “India plans to expand private sector in healthcare”, British Medical

Journal, Vol. 326 No. 520, pp. 26-35.

Muhondwa, E.P.Y., Leshabari, M.T., Mwangu, M., Mbembati, N. and Ezekiel, M.J.

(2008), “Patient satisfaction at the Muhimbili National Hospital in Dar Es Salaam,

Tanzania”, East African Journal of Public Health, Vol. 5 No. 2, pp. 67-79.

Nadiri, H. and Hussain, K. (2005), “Perceptions of SQ in North Cyprus hotels”,

International Journal of Contemporary Hospitality Management, Vol. 17, pp.

469-80.

Naidu, A. (2009), “Factors affecting patient satisfaction and healthcare quality”,


381.

Naik, J. R. K., Anand, B., and Bashir, I. (2013), “Healthcare Service Quality and word of

mouth : Key drivers to achieve Patient Satisfaction”, Pacific Business Review

International, Vol. 5 No. 12, pp. 39–44.

Naik, J.R.K., Anand, B. and Bashir, I. (2014), “Antecedents of Patient Satisfaction

at Tertiary care hospitals”, Abhigyan,Vol. 32, No. 1, pp. 64-77.

Narang, R. (2010), “Measuring perceived quality of health care services in India”,


186.

Natalisa, D. and Subroto, B. (1998), “Effects of management commitment on service

quality to increase customer satisfaction of domestic airlines in Indonesia”,

Singapore Management Review, Vol. 25 No. 1, pp. 85-105.

Ndubusi, N. O. and Ling, T. A. (2005), “Complaint behaviour of Malaysian consumers”,

Management Research News, Vol. 29 No.1/2, pp. 65-76.

Nekoei-Moghadam, M., and Amiresmaili, M. (2011), “Hospital services quality

assessment: Hospitals of Kerman University of Medical Sciences, as a tangible

example of a developing country”, International Journal of Health Care Quality

Assurance, Vol. 24 No. 1, pp. 57–66.

Newman, K. (2001), “Interrogating SERVQUAL: a critical assessment of service quality

measurement in a high street retail bank”, International Journal of Bank


Newman, R.D. (1998), “Satisfaction with outpatient health care services in Manica

Province”, Health Policy and Planning, Vol. 13 No. 2, pp. 174-80.

Nicklin, W. and McVeety, J.E. (2002), “Canadian nurse‟s perceptions on patient safety in

hospitals”, Canadian Journal of Nursing Leadership, Vol. 15 No. 3, pp. 11–21.

Nunnally, J.C. (1978), “Psychometric Theory”, McGraw-Hill, New York, NY.

Nunnally, J.C. and Bernstein, I.H. (1994), “Psychometric Theory”, 3rd ed., McGraw Hill,

New York, NY.

Oliver, R. (1997), “Satisfaction: A Behavioural Perspective on the Consumer,” New

York: McGraw Hill.

Oliver, R.L. (1980), “A cognitive model of the antecedents and consequences of

satisfaction decisions”, Journal of Marketing Research, Vol. 17, pp. 460-469.

Oliver, R.L. (1993), “A conceptual model of service quality and service satisfaction:

compatible goals, different concepts”, in Teresa, A.S., David, E.B. and Stephen,

W.B. (Eds), Advances in Marketing and Management, JAI Press, Greenwich, CT,

pp. 65-85

Ostrom, A., and Iacobucci, D. (1995), “Consumer trade‐offs and the evaluation of

services”, Journal of Marketing, Vol. 59, pp. 17‐28.

Otani, K., Kurz, R. S., and Harris, L. E. (2005), “Managing primary care using patient

satisfaction measures”, Journal of Healthcare Management, Vol. 50 No. 5, pp.

311-25.

Otani, K., Kurz, R.S., Burroughs, T.E. and Waterman, B. (2003), “Reconsidering models

of patient satisfaction and behavioral intentions”, Healthcare Management

Review, Vol. 28 No. 1, pp. 7-20.

Øvretveit, J. (1992), “Health Service Quality: An Introduction to Quality Methods for

Health Services”, Blackwell, Oxford.

Øvretveit, J. (2001), “The Norwegian approach to integrated quality development”,

Journal of Management in Medicine, Vol. 5 No. 2, pp. 125-41.

Owusu-Frimpong, N., Nwankwo, S., and Dason, B. (2010), “Measuring service quality

and patient satisfaction with access to public and private healthcare delivery”,

International Journal of Public Sector Management, Vol. 23 No. 3, pp. 203-220.

Padma, P., Rajendran, C. and Sai, L.P. (2009), “A conceptual framework of service

quality in healthcare: perspectives of Indian patients and their attendants”,

Benchmarking: An International Journal, Vol. 16, pp. 157-91

Padma, P., Rajendran, C., and Sai, L.P. (2010), “Service quality and its impact on

customer satisfaction in Indian hospitals: Perspectives of patients and their

attendants”, Benchmarking: An International Journal, Vol. 17 No. 6, pp. 807-841.

Pakdil, F., and Harwood, T. N. (2005), “Patient satisfaction in a preoperative assessment

clinic: an analysis using SERVQUAL dimensions”, Total Quality Management &

Business Excellence, Vol. 16 No. 1, pp. 15-30.

Parasuraman, A, Zeithaml, V A and Berry, L. L. (1985), “A Conceptual Model of Service

Quality and Its Implications for Future Research,” Journal of Marketing, Vol. 49,

pp. 41-50.

Parasuraman, A, Zeithaml, V A and Berry, L. L. (1988), “SERVQUAL: A Multiple Item

Scale for Measuring Consumer Perceptions of Service Quality,” Journal of

Retailing, Vol. 64 No. 1, pp. 12-40.

Parasuraman, A., Berry, L. L., and Zeithaml, V. A. (1990), “Guidelines for Conducting

Service Quality Decrease,” Marketing Research, Vol. 2 No. 2, pp. 34-44.

Parasuraman, A., Berry, L. L., and Zeithaml, V. A. (1991), “Refinement and

Reassessment of the SERVQUAL Scale,” Journal of Retailing, Vol. 67 No. 4, pp.

420-50.

Parasuraman, A., Zeithaml, V. A., and Berry, L. L. (1994), “Reassessment of

Expectations as a Comparison Standard in Measuring Service Quality:

Implications for Further Research,” Journal of Marketing, Vol. 58 No. 1, pp. 111-

124.

Phellas, C. N., Bloch, A., and Seale, C. (2011), “Structured Methods : Interviews”,

Questionnaires and Observation, pp. 181–205.

Phillips, L W, Chang, D R and Buzzell, R D (1983), “Product Quality, Cost Position and

Business Performance: A Test of Some Key Hypothesis,” Journal of Marketing,

Vol. 47 No. Spring, pp. 26-43.

Pines JM, Garson, C., Baxt W.G., Rhodes K.V., Shofer E.S., and Hol-lander J.E. (2007),

“Crowding is associated with variable perceptions of care compromise”,

Academic Emergency Medical Journal, Vol. 14, pp. 1176-1181.

Polluste, K., Kalda, R. and Lember, M. (2000), “Primary health care system in transition:

the patient‟s experience”, International Journal for Quality in Health Care, Vol.

12 No. 6, pp. 503-9.

Pui-Mun Lee., PohWah Khong., and Dhanjoo, N. G. (2006), “Impact of deficient

healthcare service quality”, The TQM Magazine, Vol. 18 No. 6, pp. 563-571.

Puri, N., Gupta, A.K., Aggarwal, A.K. and Kaushal, V. (2012), “Outpatient satisfaction

and quality of health care in north Indian medical institute”, International Journal

of Health Care Quality Assurance, Vol. 25 No. 8.

Qadeer, I. (2000), “Health Care Systems in Transtives III, India Part-I”, The Indian

Experience. Journal of Public Health Medicine, Vol. 22 No.1, pp. 8-16.

Qin, H., and Prybutok, V. R. (2008), “Service quality , customer satisfaction, and

behavioral intentions in fast-food restaurants, International Journal of Quality

and Service Sciences, Vol. 1 No. 1, 2009 pp. 78-95.

Qu, H., Platonova, E. a., Kennedy, K. N., and Shewchuk, R. M. (2011), “Primary care

patient satisfaction segmentation”, International Journal of Health Care Quality

Assurance, Vol. 24 No. 7, pp. 564–576.

Raja, M.P.N., Deshmukh, S.G. and Wadhawa, S. (2007), “Quality award dimensions: a

strategic instrument for measuring health service quality”, International Journal

of Health Care Quality Assurance, Vol. 20 No. 5, pp. 363-78.

Ramsaran-Fowdar, R. R. (2008), “The relative importance of service dimensions in a

healthcare setting”, International Journal of Health Care Quality Assurance, Vol.

21 No. 1, pp. 104–124.

Ramsaran-Fowdar, R.R., 2005, “Identifying health care quality attributes”, Journal of

Health and Human Services Administration, Winter 2004/Spring 2005, Vol. 27,

No. ¾, pp. 428-444.

Rao, K.D., Peters, D.H. and Bandeen-Roche, K. (2006), “Towards patient-centered health

services in India-a scale to measure patient perceptions of quality”, International

Journal for Quality in Health Care, Vol. 18 No. 6, pp. 414-21.

Reichheld, F. (1996), “The loyalty effects: The hidden force behind growth, profit, and

lasting value”, Boston, MA: Harvard Business School Press.

Reidenbach, E. R., and Sandifer‐Smallwood, B. (1990), “Exploring perceptions of

hospital operations by a modified SERVQUAL approach”, Journal of Health

Care Marketing, Vol. 10 No. 4, pp. 47‐55.

Richard, M.D. and Allaway, A.W. (1993), “Service quality attributes and choice

behaviour”, Journal of Services Marketing, Vol. 7 No. 1, pp. 59-68.

Roberts, J.H. (2000), “Developing new rules for new markets”, Journal of the Academy

of Marketing Science, Vol. 28 No. 1, pp. 31-44.

Robinson, S., Roth, S., Keim, J., Levenson, M., Flentje, J. and Bashor, K. (1991), “Nurse

Burnout: work related and demographic factors as culprits”, Research in Nursing

and Health, Vol. 14 No. 3, pp. 223-8.

Rohini, R. and Mahadevappa, B. (2006), “Service quality in Bangalore hospitals: an

empirical study”, Journal of Services Research, Vol. 6 No. 1, pp. 59-82.

Rose, R.C., Uli, J., Abdul, M. and Ng, K.L. (2004), “Hospital service quality: a

managerial challenge”, International Journal of Health Care Quality Assurance,

Vol. 17 No. 3, pp. 146-59.

Roshnee, R.R-Fawdar (2005), “Identifying health care quality attributes”, Journal of

Health Care and Human Services Administration, Vol. 27 No.3/4, pp. 428-444.

Rundle-Thiele, S., and Russell-Bennett, R. (2010), “Patient influences on satisfaction and

loyalty for GP services”, Health Marketing Quarterly, Vol. 27 No. 2, pp. 195-

214.

Rust, R.T. and Oliver, R.L. (1994), “Service quality: insights and managerial implications

from the frontlines”, in Rust, R.T. and Oliver, R.L. (Eds), Service Quality: New

Directions in Theory and Practice, Sage, Newbury Park, CA, pp. 1-19.

Rust, R.T., and Zahorik, A.J. (1993), “Customer satisfaction, customer retention and

market share”, Journal of Retailing, Vol. 69, pp. 193‐215.

Rust, R.T., Zahorik, A.J. and Keiningham, T.L. (1995), “Return on quality (ROQ):

making service quality financially accountable”, Journal of Marketing, Vol. 59

No. 4, pp. 58-70.

Ruyter, K., Martin, W. and Josee, B. (1998), “On the relationship between perceived

service quality, service loyalty and switching costs”, International Journal of

Service Industry Management, Vol. 9 No. 5, pp. 436-53

Saha, G.C. and Theingi, “Service quality, satisfaction, and behavioural intentions: A

study of low-cost airline carriers in Thailand”, Managing Service Quality, Vol.19

No.3, pp. 350-72.

Sample Registration System Bulletin (2011), “Morbidity and utilisation of medical

services 46th

Round, July 1997 - December 2011, Report No-468, Department of

Statistics, Government of India, New Delhi.

Sardana, G.D. (2003), “Performance grading of hospitals: a conceptual framework”,

Productivity, Vol. 44 No. 3, pp. 450-65.

Saunders, M., Lewis, P., and Thornhill, A. (2009), “Research Methods for Business

Students”, 5 Edition, Harlow: Financial Times Prentice Hall.

Schneider, B. and Bowen, D.E. (1995), Winning the Service Game, HBS Press, Boston,

MA.

Schuster, M.A., McGlynn, E.A. and Brook, R.H. (1998), “How good is the quality of

healthcare in the United States?”, The Milbank Quarterly, Vol. 76 No. 4, pp. 517-

64.

Senarath, U., Gunawardena, N.S., Sebastiampillai, B., Senanyake, A., Lekamge, S.,

Seneviratna, M.J. and Wijeratne, D. (2013), “Patient satisfaction with nursing care

and related hospital services at the National hospital of Sri Lanka”, Leadership in

Health Services, Vol. 26 No. 1, pp. 63-77.

Sewell, N. (1997), “Continuous quality improvement in acute healthcare: Creating a

holististic and integrated approach”, International Journal of Health Care Quality


Sharma, N. and Patterson, P.G. (1999), “The impact of communication effectiveness and

service quality on relationship commitment in consumer, professional services”,

Journal of Services Marketing, Vol. 13 No. 2, pp. 151-70.

Sharma, R., Sharma, M. and Sharma, R.K. (2011), “The patient satisfaction study in

multi-specialty tertiary level hospital, PGIMER, Chandigarh”, Leadership in

Health Services, Vol. 24 No. 1, pp. 64-73.

Shemwell, D.J., Yavas, U. and Bilgin, Z. (1998), “Customer-service provider

relationships: an empirical test of a model of service quality, satisfaction and

relationship-oriented outcomes”, International Journal of Service Industry


Silvestro, R. (2005), “Applying gap analysis in the health service to inform the service

improvement agenda”, International Journal of Quality and Reliability


Silvestro, R. and Johnston, R. (1990), “The determinants of service quality-enhancing

and hygiene factors”, Proceedings of the QUIS-II Symposium, St John‟s

University, New York, NY, July.

Simbar, M., Alizadeh-Dibazari, Z., Abedsaidi, J. and Alavi-Majd, H. (2005),

“Assessment of quality of care in postpartum wards of Shahid Beheshti Medical

Science University Hospitals”, International Journal of Health Care Quality


Sixam, H.J., Spreeuwenber, P.M. and Van Der Pasch, M.A. (1998), “Patient satisfaction

with the general practitioner: a two-level analysis”, Medical Care, Vol. 36, pp.

212-29.

Smallbone, T. and Quinton, S. (2004), “Increasing Business Student‟s Confidence in

Questioning the Validity and Reliability of their Research”, Electronic Journal of

Business Research Methods, Vol. 2 No. 2, pp.153-162.

Smith, M.A., Schussler-Fiorenza, C. and Rockwood, T. (2006), “Satisfaction with care”,

in Kane, R.L. (Ed.), Understanding Health Care Outcome Research, 2nd ed.,

Jones and Bartlett Publishers, Sudbury, MA, pp. 185-216.

Sodani, P.R., Kumar, R.K., Srivastava, J. and Sharma, L. (2010), “Measuring patient

satisfaction: a case study to improve quality of care at public health facilities”,

Indian Journal of Community Medicine, Vol. 35 No. 1, pp. 52-6.

Soderlund, M. (1998), “Customer satisfaction and its consequences on customer

behaviour revisited: the impact of different levels of satisfaction on word-of-

mouth, feedback to the supplier and loyalty”, International Journal of Service

Industry Management, Vol. 9 No. 2, pp. 169-88.

Sofaer, S. and Firminger, K. (2005), “Patient perceptions of the quality of health

services”, Annual Review Public Health, Vol. 26 No. 4, pp. 513-9.

Sohail, M. (2003), “Service quality in hospitals: more favourable than you might think”,

Managing Service Quality, Vol. 13 No. 3, pp. 197-206.

Stewart, D. M., (2003), “Piecing together Service Quality: A framework for robust

service”, Production and Operations Management, Vol. 12 No. 2, pp. 246-265.

Storbacka, K., Strandvik, T., and Grönroos, C. (1994), “Managing customer relationships

for profit: The dynamics of relationship quality”, International Journal of Service

Industry Management, Vol. 5 No. 5, pp. 21-38.

Stratton R..J., Bowyer G., and Elia M. (2006), “Food snacks or liquid oral nutritional

supplements as a first-line treatment for malnutrition in post-operative patients?”,

Nutritive Science, Vol. 65 No. 4A.

Sureshchandar, G.S., Rajendran, C. and Anantharaman, R.N. (2002b), “The relationship

between service quality and customer satisfaction-a factor specific approach”,

Journal of Services Marketing, Vol. 16, pp. 363-79.

Sureshchandar, G.S., Rajendran, C. and Kamalanabhan, T.J. (2001), “Customer

perceptions of service quality: a critique”, Total Quality Management, Vol. 12,

pp. 111-24.

Sweeney, Jillian C., Geoffrey N. Soutar, and Lester Johnson, W. (1999), “The Role of

Perceived Risk in the Quality-Value Relationship: A Study in a Retail

Environment,” Journal of Retailing, Vol. 75 No. 1, pp. 77–105.

Tabachnick, B.G. and Fidell, L.S. (2007), “Using multivariate statistics”, 5th ed., Pearson

Education, Boston.

Tamimi, H.A. and Amiri, A.A. (2003), “Analysing service quality in the UAE Islamic

banks”, Journal of Financial Services Marketing, Vol. 8 No. 2, pp. 119-32.

Taner, T., and Antony, J. (2006), “Comparing public and private hospital care service

quality in Turkey”, Leadership in Health Services, Vol. 19 No. 2 pp. 1-10.

Tateke, T., Woldie, M., and Ololo, S. (2012), “Determinants of patient satisfaction with

outpatient health services at public and private hospitals in Addis Ababa,

Ethiopia”, African Journal of Primary Health Care & Family Medicine, Vol. 4

No. 1, pp. 1-12.

Taylor, D., Kennedy, M. P., Virtue, E., and McDonald, G. (2006), “A multifaceted

intervention improves patient satisfaction and perceptions of emergency

department care”, International Journal of Quality in Health Care, Vol. 18 No. 4,

pp. 238‐245.

Taylor, S. A., and J. J. Cronin, Jr. (1994), “Modelling Patient Satisfaction and Service

Quality”, Journal of Health Care Marketing, Vol. 14 No. 1, pp. 34-44.

Taylor, S. and Claxton, J.D. (1994), “Delays and dynamics of service evaluations”,

Journal of the Academy of Marketing Science, Vol. 22 No. 3, pp. 254-64.

Taylor, S.A. and Baker, T.L. (1994), “An assessment of the relationship between service

quality and customer satisfaction in the formation of customer‟s purchase

intentions”, Journal of Retailing, Vol. 70 No. 2, pp. 163-78.

Taylor, S.A. and Cronin, J.J. (1994), „Modelling patient satisfaction and service quality‟,

Journal of Health Care Marketing, Vol. 14 No.1, pp. 34-44.

Teas, R.K. (1993), “Expectations, performance evaluation and consumers‟ perceptions of

quality”, Journal of Marketing, Vol. 57, pp. 18-34.

Tengilimoglu, D., Kisa, A. and Dziegielewski, S.F. (1999), “Patient satisfaction in

Turkey: differences between public and private hospitals”, Journal of Community

Health, Vol. 24 No. 1, pp. 73-91.

The Joint Commission on Accreditation of Healthcare Organisations (1996),

“Accreditation manual for hospitals”, Vol.2, Oakbrook Terrace.

The World Bank (2013), “Financing health services in developing countries: An agenda

for reform”, Washington D.C., USA.

Tomes, A. E., and Ng, S. C. P. (1995), “Service Quality in Hospital Care: The

Development of an In-patient Questionnaire,” International Journal of Health

Care Quality Assurance, Vol. 8 No. 3, pp. 25-33.

Tsoukatos, E. and Rand, G.K. (2006), “Path analysis of perceived service quality,

satisfaction and loyalty in Greek insurance”, Managing Service Quality, Vol. 16

No. 5, pp. 501-19.

Tucker III, J. L. and Adams, S. R. (2001), “Incorporating patient‟s assessments of

satisfaction and quality: An integrative model of patient‟s evaluations of their

care”, Managing Service Quality, Vol. 11No.4, pp. 272‐286.

Tucker, J.L. and Adams, S.R. (2001), “Incorporating patients‟ assessments of satisfaction

and quality: an integrative model of patients‟ evaluations of their care”, Managing

Service Quality, Vol. 11 No. 4, pp. 272-87.

Turner, P. D., and L. G. Pol. (1995), “Beyond Patient Satisfaction: Broaden the Scope of

Quality Measurement by Integrating the Marketing View with Other

Perspectives”, Journal of Health Care Marketing, Vol. 15 No. 3, pp. 45-53.

Turris, S.A. (2005), “Unpacking the concept of patient satisfaction: a feminist analysis”,

Journal of Advanced Nursing, Vol. 50 No. 3, pp. 293-298.

Urden, K.D. (2002), “Patient satisfaction measurement: current issues and implications”,

Outcomes Management, Vol. 6 No. 6, pp. 125-31.

Uzun, O. (2001), “Patient satisfaction with nursing care at a university hospital in

Turkey”, Journal of Nursing Care Quality, Vol. 16 No. 1, pp. 24-33.

van der Wal, R.W.E., Pampallis, A. and Bond, C. (2002), “Service quality in a cellular

telecommunications company: a South African experience”, Managing Service

Quality, Vol. 12 No. 5, pp. 323-35.

van der Wiele, T., Boselie, P. and Hesselink, M. (2002), “Empirical evidence for the

relationship between customer satisfaction and business performance”, Managing

Service Quality, Vol. 12 No. 3, pp. 184-93.

Vandamme, R. and Leunis, J. (1993), “Development of a multiple-item scale for

measuring hospital service quality”, International Journal of Service Industry


Vyas, P. H., & Thakkar, P. D. (2005), “Market performance analysis and measurement of

patients‟ satisfaction in health care services” Indian Journal of Commerce, Vol.

58 No. 1, pp. 151-161.

Walbridge, S.W. and Delene, L.M. (1993), “Measuring physician attitudes of service

quality”, Journal of Health Care Marketing, Vol. 13 No. 1, pp. 7-15.

Ware, J.E. Jr, Snyder, M.K. and Wright, W.R. (1977), “Some Issues in the Measurement

of Patient Satisfaction with Health Care Services”, The Rand Corporation, Santa

Monica, CA, p. 6021.

Webster, C. (1989), “Can consumers be segmented on the basis of their service quality

expectations?”, Journal of Services Marketing, Vol. 3 No. 2, pp. 35-53.

Williams, B. (1994), “Patient satisfaction: a valid concept?”, Social Science and

Medicine, Vol. 38 No. 4, pp. 509-16.

Williams, S. (1998), “Quality and care: patients‟ perceptions”, Journal of Nursing Care

Quality, Vol. 12 No. 6, pp. 18-25.

Wisnievski, M. and Wisnievski, H. (2005), “Measuring service quality in a hospital

colposcopy clinic”, International Journal of Health Care Quality, Vol. 18, No.

2/3, pp. 217-229.

Woodside, A.G., Frey, L.L. and Daly, R.T. (1989), “Linking service quality, customer

satisfaction”, Journal of Health Care Marketing, December, pp. 5-17.

Woodside, J.M., Lykousis, V., Foucher J.-P., Dupré, S., Alexandri, S., de Lange G.,

Mascle, J., Zitter, T., (2005), “The scientists of the ANAXIMANDER,

MEDIFLUX and MEDINAUT projects and NAUTINIL and MIMES shipboard

scientific parties”, Comparative Eastern Mediterranean mud volcanology, VIII

International Conference on Gas in Marine Sediments.

Woodward, C. a., Ostbye, T., Craighead, J., Gold, G., and Wenghofer, E. F. (2000),

“Patient Satisfaction as an Indicator of Quality Care in Independent Health

Facilities: Developing and Assessing a Tool to Enhance Public Accountability”,

American Journal of Medical Quality, Vol. 15 No. 3, pp. 94-105.

Yi, Y. (1990), “A critical review of consumer satisfaction”, in Zeithaml, V. (Ed.), Review

of Marketing, American Marketing Association, Chicago, IL, pp. 68-123.

Youssef, F. N., Jones, R., Hunt, A., and Hospital, O. (1996), “Health care quality in NHS

hospitals”, International Journal of Health Care Quality Assurance, Vol. 9 No. 1,

pp. 15–28.

Zeithaml, Berry, and Parasuraman, A. (1996), “The Behavioral Consequences of Service

Quality,” Journal of Marketing, Vol. 60 No. 4, pp. 31–46.

Zeithaml, V. A. (1988), “Consumer Perceptions of Price, Quality, and Value: A Means-

End Model and Synthesis of Evidence”, Journal of Marketing, Vol. 52 No. 4, pp.

2-22.

Zeithaml, V. A. (2000), “Service quality, profitability, and the economic worth of

customers: What we know and what we need to learn”, Journal of the Academy of

Marketing Science, Vol. 28 No. 1, pp. 67-85.

Zeithaml, V.A. (1981), “How Consumer Evaluation Processes Differ Between Goods and

Services”, in J.H Donnelly and W.R George, eds., Marketing of Services,

American Marketing Association, Chicago, IL, 186-90.

Zeithaml, V.A. (1988), “Consumer Perceptions of Price, Quality, and Value: A Means-

End Model and Synthesis of Evidence,” Journal of Marketing, Vol. 52 No. 7, pp.

2-22.

Zeithaml, V.A. (2000), “Service quality, profitability and economic worth of customers:

what we know and we need to learn”, Academy of Marketing Science Journal,

Vol. 28 No. 1, pp. 67-85.

Zeithaml, V.A. (2000), “Service Quality, Profitability, and the Economic Worth of

Customers: What We Know and What We Need to Learn”, Journal of the

Academy of Marketing Science, Vol. 28 No. 1, pp. 67-85.

Zeithaml, V.A. and Bitner, M.J. (1996), “Services Marketing”, McGraw-Hill, New York,

NY.

Zeithaml, V.A. and Bitner, M.J. (2000), “Services Marketing: Integrating Customer

Focus Across the Firm”, McGraw-Hill, New York, NY.

Zeithaml, V.A., Berry, L.L. and Parasuraman, A. (1996), “The behavioral consequences

of service quality”, Journal of Marketing, Vol. 60 No. 2, pp. 31-46.

Zeithaml, V.A., Berry, L.L. and Parasuraman, A. (1996), “The Behavioural

Consequences of Service Quality,” Journal of Marketing, Vol. 60 No. 4, pp. 31-

46.

Zeithaml, V.A., Berry, L.L., and Parasuraman, A. (1988), “Communication and Control

Processes in the Delivery of Service Quality”, Journal of Marketing, Vol. 52 No.

4, pp. 35-48.

Zeithaml, V.A., Berry, L.L., and Parasuraman, A. (1993), “The Nature and Determinates

of Customer Expectations of Service”, Journal of Marketing Science, Vol. 21,

pp.1-12.

Zeithaml, V.A., Parasuraman, A. and Berry, L.L. (1990), “Delivering Quality Service”,

The Free Press, New York, NY.

Zhang, J. Sharon E.B., and Gianfranco, W. (2008), “Review and future directions of

customer services research,” Journal of Business Research, Vol. 61, pp. 211-224.

Zhang, Jingyun, Sharon E. Beatty, and Gianfranco Walsh (2008), “Review and future

directions of cross-cultural customer services research,” Journal of Business

Research, Vol. 61 No. 1, pp. 211-224.

Zhou, L., Zhang, Y. and Xu, J. (2002), “A Critical Assessment of SERVQUAL‟s

Applicability in the Banking Context of China”, Asia Pacific Advances in

Consumer Research, Vol. 5, Association for Consumer Research, Valdosta, GA,

pp. 14-21.

Zia, T. S., Mohsen, R., Riji, H. M., Abbas, E., and Mostafa, L. (2011), “Patient

Satisfaction with Care Received from Emergency Department of Ali-Ebne-

Abitaleb Hospital, Iran”, Journal of Health Management, Vol. 13 No. 1, pp. 1-

14.

Zikmund, W.G. (2000), “Exploring Marketing Research”, 7th Edition, Dryden Press,

Forth Worth, TX.

Zineldin, M. (2000), “Beyond relationship marketing: Technological marketing”,

Marketing Intelligence and Planning, Vol. 18 No. 1, pp. 9-23.

Zineldin, M. (2006), “The quality of health care and patient satisfaction: An exploratory

investigation of the 5Q‟s model at some Egyptian and Jordanian medical clinics”,


92.

Zineldin, Mosad (1996), “Bank Strategic Positioning and Some Determinants of Bank

Selection,” International Journal of Bank Marketing, Vol. 14 No. 6, pp. 12-22.

*********

Appendix – 1

QUESTIONNAIRE USED FOR THE STUDY

Dear Respondent,

Greetings! I am, J. Rama Krishna Naik, doing PhD at the Department of Management,

Pondicherry University. I have undertaken this study to examine the Healthcare Service

Quality, Patient Satisfaction and Behavioural Intentions in Selected Corporate

Hospitals in India, owing to the fact that the healthcare sector in India is one of the

largest and challenging service sectors of India. Further, service quality and satisfaction

are the two main key strategies to increase business performance of healthcare

organisations.

In this regard, I request you to spend your valuable time and participate in this

research study for providing your opinion regarding service quality and satisfaction by

completing the attached surveys. The following questionnaire will require approximately

ten minutes to complete. There is no compensation for responding nor is there any known

risk. In order to ensure that all information will remain confidential, please do not include

your name. If you choose to participate in this project, please answer all questions as

honestly as possible and return the completed questionnaires promptly. Participation is

strictly voluntary and you may refuse to participate at any time.

Thank you for taking the time to assist me in my educational endeavours. The

data collected will potentially contribute to healthcare management and quality

improvement. If you wish to have a summary copy of this study, please write me through

Electronic Mail mentioned below with all your details. Completion and return of the

questionnaire will indicate your willingness to participate in this study. If you require

additional information or have questions, please feel free to contact me at the number

listed below.

Sincerely,

Rama Krishna Naik Jandavath

The following sets of statements are aimed to measure the service quality delivered by

hospital. For each statement, please show the extent to which you believe this hospital

has the feature described by the statement. Please put „√‟ mark in box that most closely

approximates

1 2 3 4 5 Strongly Agree Agree Neutral Disagree Strongly Disagree

Rank your

EXPECTATIONS of this hospital services

Rank your

PERCEPTIONS of this hospital services

Tangible 1 2 3 4 5 1 2 3 4 5

1. The physical facilities at this hospital are visually

appealing (e.g. well maintained reception area,

billing and registration facilities, etc.).

o o o o o o o o o o

2. Staffs of this hospital are neat in appearance (e.g.

staff with uniform and appropriate name badges,

professional appearance of staff etc.).

o o o o o o o o o o

3. This hospital has Up-to-date and well maintained

medical facilities and equipment. o o o o o o o o o o

4. This hospital provides up-dated informative

broachers about services offered. o o o o o o o o o o

Reliability 1 2 3 4 5 1 2 3 4 5

5. When a patient has a problem, this hospital shows

a sincere interest in solving it. o o o o o o o o o o

6. This hospital is competent in providing accurate

services (e.g. correct records, accurate diagnosis,

timely treatment etc.).

o o o o o o o o o o

7. The Staff of this hospital is keeping patients well-

informed about the follow-up examinations. o o o o o o o o o o

8. This hospital provides efficient, reliable and

affordable prescribed medicines. o o o o o o o o o o

Assurance 1 2 3 4 5 1 2 3 4 5

9. Doctors and nursing staff are consistently

courteous with their patients. o o o o o o o o o o

10. Doctors of this hospital are very knowledge. o o o o o o o o o o

11. This hospital staff instills confidence in patients

(e.g. convincing and explanations etc.). o o o o o o o o o o

12. Patients feel safe while they receive services from

the personnel of this hospital. o o o o o o o o o o

Section - A

13. Staff of this hospital thoroughly explains medical

conditions of the patients. o o o o o o o o o o

Empathy 1 2 3 4 5 1 2 3 4 5

14. Doctors keep their patients informed and listen to

them. o o o o o o o o o o

15. Hospital staff understand the specific needs of their

patients (recognizing the importance of the patient,

what the patient wants etc.,).

o o o o o o o o o o

16. Clinical staff has the knowledge and skills to

respond to the patients‟ problems. o o o o o o o o o o

17. This hospital provides individual attention to the

patient‟s problems and care. o o o o o o o o o o

18. This hospital provides 24 hours services o o o o o o o o o o

Responsiveness 1 2 3 4 5 1 2 3 4 5

19. The services are provided at the promised times

(e.g. admission, lab services, clinical care,

emergency care, casualty services etc.).

o o o o o o o o o o

20. Hospital staffs consistently follow-up sick cases. o o o o o o o o o o

21. The hospital consulting hours are convenient. o o o o o o o o o o

22. Doctors and nurses are always willing to help

patients. o o o o o o o o o o

The following sets of statements are aimed to measure your satisfaction levels regarding

healthcare services provided by the hospital you are treated in. Please put „√‟ mark in

box that most closely approximates.


Admission Process 1 2 3 4 5

1 Getting appointment in this hospital is easy. o o o o o

2 Admission personnel of this hospital are providing clear information

(direction, schedule etc.) to patients. o o o o o

3 Admission personnel of this hospital are very courteous and helpful

to patients. o o o o o

Section - B

Nursing Services 1 2 3 4 5

1 Nursing staff of this hospital are knowledgeable to perform the

service very well. o o o o o

2 Nurses of this hospital perform the required services (tests,

procedure, medication dispensing) at exactly the right time. o o o o o

3 Nursing staff of this hospital is very courteous to patients. o o o o o

4 Nursing staff of this hospital always respond in a reasonable length

of time. o o o o o

Medical Services 1 2 3 4 5

1 Doctors of this hospital are knowledgeable to answer patients‟

questions satisfactorily. o o o o o

2 Doctors of this hospital spend enough time with patients. o o o o o

3 Doctors of this hospital are very courteous and ready to respond in

emergency. o o o o o

4 Doctors of this hospital are extremely careful in explaining what

patients are expected to do in words he/she understands. o o o o o

Housekeeping Services 1 2 3 4 5

1 Housekeeping staff of this hospital have knowledge in maintaining

hygiene of hospital premises. o o o o o

2 Bathroom facilities/Cleanliness/Décor of this hospital is well

maintained. o o o o o

3 Housekeeping staff of this hospital is well trained in procedures for

the collection and handling of wastes. o o o o o

4 Housekeeping staff of this hospital is knowledgeable to maintain

bio-degradable contents and their segregation. o o o o o

Food Services 1 2 3 4 5

1 The food service has been as good as I, expected (consider special

diet restrictions). o o o o o

2 The food services menu has enough variety for me to choose meals

that, I want to eat. o o o o o

3 This hospital serves hot food and beverages at the right time. o o o o o

4 Food serving staffs are friendly and courteous. o o o o o

Overall Services 1 2 3 4 5

1 This hospital maintains proper queue management system. o o o o o

2 This hospital maintains well managed ambulatory services in

emergency. o o o o o

3 Waiting rooms of this hospital are well furnished. o o o o o

4 This hospital provides well equipped X-ray services. o o o o o

5 This hospital conducts all lab tests in prompt way. o o o o o

6 Blood bank services of this hospital are very efficient & effective. o o o o o

7 Operation theatre is well equipped with up-to-date equipments. o o o o o

8 The pharmacy of this hospital maintains all kinds of required drugs. o o o o o

9 Payment procedure of this hospital is quick and simple. o o o o o

The following sets of statements are aimed to understand your feelings about future

intentions to visit or recommend this hospital to friends and relatives. Please put „√‟ mark

in box that most closely approximates your experience.


Behavioral Intentions 1 2 3 4 5

1 I am willing to recommend this hospital to others who seek my

advice o o o o o

2 I will encourage my friends and relatives to go to this hospital o o o o o

3 If I need medical service in the future, I will consider this hospital as

my first choice o o o o o

4 If I need medical service in the future, I will go to this hospital more

frequently o o o o o

Taking everything in to account, how do you feel about following

Healthcare Service Quality 1 2 3 4 5

1 The overall feelings about the quality of healthcare service provided

at this hospital are better than I expected o o o o o

2 All things considered, quality of care received from this hospital

quiet excellent o o o o o

Patient Satisfaction 1 2 3 4 5

1 I am very satisfied with the medical care I received o o o o o

2 Overall, I am satisfied with this healthcare provider o o o o o

3 Overall, I am satisfied with the services provided by this hospital o o o o o

4 I am satisfied with ensured continuity of care provided by this

hospital (e.g. regarding notification of test results, referral back to

follow-up, transfer to hospital/specialists)

o o o o o

Section - C

Provide your demographic details in this section; put „√‟ mark in box that most closely

approximates you.

Thank you for taking time to fill this questionnaire, if you are interested to receive a copy of

this report please mention your E-mail address here ……………………………………

**********

1. Gender

1.Male o 2.Female o

2. Age Group (in years)

1. 18-29 years o 2. 30-39 years o

3. 40-49 years o 4. 50-59 years o

5. 60-69 years o 6. 70 years & older

3. Place of Residence

1. Rural o 2. Urban o

3. Semi-urban o 4. Metropolitan city o

4. Marital status

1. Married o 2. Unmarried o

5. Educational level

1.Up to S.S.C o 2.Higher secondary o

3.Graduate o 4.Post graduate o

5. Others o

6. Occupational status

1. Student o 2.Government employee o

2. Private employee o 4. Self employed o

5. Other o

7. Gross monthly income (in INR)

1. Below 20,000 o 2. 20,001 – 40,000 o

3. 40,001 – 60,000 o 4. 60,001 – 80,000 o

5. 80,001 – 1,00,000 o 6. 1,00,000 & above o

8. No of days stayed in hospital

1. 1 -7 days o 2. 8-14 days o

3. 15 - 21 days o 4. 20 – 28 days o

5. 29 days & above o

Section - D

Appendix-2

List of Publications

Publications Arising from Thesis

1. J. Rama Krishna Naik, Dr Byram Anand and Irfan Bashir (2014), “An Empirical

Investigation to Determine Patient Satisfaction Factors at Tertiary care Hospitals in

India”, International Journal of Quality and Service Sciences (IJQSS – Emerald

Group Publishing Limited), Vol. 6, No. 4, Up-coming Issue, (ISSN 1756-669X).

2. J. Rama Krishna Naik, Dr Byram Anand and Irfan Bashir (2014), “Antecedents of

Patient Satisfaction at Tertiary care hospitals”, Abhigyan, Vol. 32, No. 1, April –

June: 2014, Up-coming Issue. (ISSN 0970 - 2385).

3. J. Rama Krishna Naik, Dr Byram Anand and Irfan Bashir (2013), “Healthcare

Service Quality and word of mouth: Key drivers to achieve Patient Satisfaction”,

Pacific Business Review International, Vol. 5, No. 12, June - 2013, pp. 39-44. (ISSN

0974-438X).

4. J. Rama Krishna Naik, Irfan Bashir and Dr Byram Anand (2012), “Indian Medical

Tourism: Service Quality and Patient Satisfaction”, Management Trends: An

International Management Journal, Vol. 9, No. 2, December - 2012, pp. 67-75.

(ISSN 0973-9203).

5. J. Rama Krishna Naik and Dr. Byram Anand (2012), “Rural health Services in

India: Challenges and Opportunities”, in Management Practices in Global

Perspective (Ed. Y. Subbarayudu), Paramount Publishing House: New Delhi, pp.

285-290. (ISBN: 978-81-921579-0-0)

6. J. Rama Krishna Naik and Dr. Byram Anand (2012), “Application of ICT in Indian

Health Care”, in Management Practices in Global Perspective (Ed. Dr A.

Rajamohan and Dr. A.A Ananth), Southern Book House: Puducherry, pp. 463-467.

(ISBN: 978-81-909275-0-5)

7. J. Rama Krishna Naik and Dr. Byram Anand (2012), “IT in Health care: Moving

towards digital future”, in Business Management in India: Interventions and

challenges (Ed. Dr. B Sekhar), Tumkur University: Mysore, pp. 463-467. (ISBN:

978-81-921523-0-3)

Other Publications

1. J. Rama Krishna Naik, Dr Byram Anand and Irfan Bashir (2014), Competency

Developing through effective Human Resource Management Practices in Indian

Insurance Industry”, The Journal of Insurance Institute of India, Vol.39 No.5,

pp. 59-68, July-September, 2014.(ISSN No: 2278-6759).

2. Irfan Bashir, C. Madhavaiah and J. Rama Krishna Naik (2013), “Customer

Acceptance of Internet Banking Services: A Review of Extensions and

Replications to Technology Adoption Model (TAM)” Asia-Pacific Marketing

Review, Vol. II, No. 1, January-June 2013 pp. 55-72, (ISSN : 2277-2057).

3. Irfan Bashir, J Rama Krishna Naik & C Madhavaiah (2013),”Potential Business

Applications of Quick Response (QR) Codes” Prajnan: Journal of Social and

Management Sciences, Vol. XLI, No. 4, pp. 353-366, January – March 2013,

(ISSN No: 0970-8448).

4. Irfan Bashir, J Rama Krishna Naik & C Madhavaiah (2013), “Critical Analysis

of Traditional And Modern Insurance Distribution Channels In India” The

Journal of Insurance Institute of India, Vol.38 No.2, pp. 59-68, January-March,

2013.(ISSN No: 2278-6759).

5. Irfan Bashir, C. Madhavaiah and J. Rama Krishna Naik (2013), “Motor

Insurance Frauds in India: Detection and Control Mechanisms” Srusti

Management Review - A Journal of Management & IT, Vol. VI, No. II,

JulyDecember.2013 pp. 27-35, (ISSN: 0974 - 4274).

6. J. Rama Krishna Naik and Dr. Byram Anand (2011), “Brand image effect on

health insurance customer services”, Journal of Management and science, Vol.

3, No. 1, January-March, pp. 28-32. (Online ISSN 2250-1819 /Printed ISSN

2249-1260)

**********

Date post:	10-Apr-2021
Category:	Documents
Upload:	others
View:	2 times
Download:	0 times

DEPARTMENT OF MANAGEMENT PONDICHERRY ...14.139.183.117/jspui/bitstream/1/2173/1/T5744.pdfother...

Documents