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)
Source: Compiled for this study
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
Source: Compiled for this study
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
Structured Questionnaire
SERVQUAL – Dimensions
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
Structured Questionnaire
SERVQUAL - Dimensions
6. Youssef et al., (1996) Singapore SERVQUAL NHS hospitals 174 Patients
Structured Questionnaire
SERVQUAL – Dimensions
7. Camilleri & O‟Callaghan
(1998)
Maltese SERVQUAL Maltese Public and Private
Hospitals
625 Patents
Structured Questionnaire
SERVQUAL - Dimensions
8. Lim & Tang (2000) Singapore SERVQUAL Singapore hospitals 224 Patients
Structured Questionnaire
SERVQUAL - Dimensions
9. Martinez Fuentes (1999) Spain SERVQUAL Selected hospitals in Spain 170 Patients
Personal interview
SERVQUAL - Dimensions
10. Jabnoun & Chaker (2003) UEA SERVQUAL Private hospitals in UAE 300 Patients
Structured Questionnaire
SERVQUAL - Dimensions
11. Sohail (2003) Malaysia SERVQUAL Private hospitals in Malaysia 1000 Patients
Mail Survey
SERVQUAL - Dimensions
12. Mostafa (2005) Egypt SERVQUAL 12 hospitals in Egypt 224 Patients
Structured Questionnaire
SERVQUAL - Dimensions
Source: Compiled for this study
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
Structured Questionnaire
SERVQUAL - Dimensions
14. Simbar et al (2012) Iran Observation
Checklists
16 pre-natal clinics in Iran 600 Patients
Structured Questionnaire
Communication, Hand washing,
History taking, Clinical
examination, laboratory tests
prescribing vaccination
15. Wisniewski & Wisniewski
(2005)
UK SERVQUAL Scottish Colposcopy Clinic 99 Patients
Structured Questionnaire
SERVQUAL - Dimensions
16. Lee et al (2000)
USA SERVQUAL Physicians from American Medical
Association
1428 Patients
Mail Survey
SERVQUAL - Dimensions
17. Aagja & Garg (2010) India PubHosQual Public hospitals in Gujarat, India 200 Patients
Structured Questionnaire
Admission, Medical service,
Overall service, Discharge, Social
responsibility
18. Koornneef (2006) Ireland SERVQUAL Children with intellectual disability
in 2 organisations
81 Patients
Structured Questionnaire
SERVQUAL – Dimensions
19. Kilbourne et al., (2004) USA & UK SERVQUAL Selected Nursing Homes in USA &
UK
195 (USA) & 99 (UK)
Structured Questionnaire
SERVQUAL – Dimensions
20. Chahal & Kumari (2010) India HCSQ –
Scale
Tertiary public hospital of North
India
400 Patients
Structured Questionnaire
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
Structured Questionnaire
SERVQUAL – Dimensions
22. Duggirala et al., (2008) India TQM Government and private hospitals in
Tamil Nadu and Gujarat in India
300 Patients
Structured Questionnaire
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
Structured Questionnaire
SERVQUAL – Dimensions
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.
Structured Questionnaire
Infrastructure, Personnel quality,
Process of clinical care,
Administrative procedures, Safety
measures, Hospital image, Social
responsibility, Trustworthiness.
Source: Compiled for this study
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
Structured Questionnaire
2. Sharma et al., (2011) India PGIMER, Chandigarh, India Empirical
Cross-sectional study
1420 OPD patients
Structured Questionnaire
3. Al-Doghaither et al.,
(2000)
Kuwait Selected PHC‟s from Kuwait Empirical
Observational Study
400 Patients
Structured Questionnaire
4. Tateke et al., (2012) Ethiopia 5 Public and 5 Private Hospitals
Ethiopia
Empirical
Cross-sectional study
254 Patients (Public & Private
Hospital)
Structured Questionnaire
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
Structured Questionnaire
7. Chahal & Mehta (2013) India 2 Government Medical College Hospitals,
Jammu, India.
Empirical
Cross-sectional study
528 indoor patients
Structured Questionnaire
8. Puri et al., (2012) India Tertiary care Public Hospitals in India Empirical
Cross-sectional study
120 In-patients 120 Out-
patients
Structured Questionnaire
9. Otani et al., (2012) Missouri 1-Academic hospital, 6-Community hospitals,
and 3-Rural hospitals in Missouri
Empirical
Observational Study
18, 755 Patients
Structured Questionnaire
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
Structured Questionnaire
12. Kaldenberg (2001) USA 3 Selected hospitals in USA Empirical
Observational Study
463 patients
Structured Questionnaire
Source: Compiled for this study
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
Structured Questionnaire
15. Zia et al., (2011) Iran Ali-Ebne-Abitaleb Hospital, Iran Empirical
Observational Study
392 patients & 608 of their
relatives
Structured Questionnaire
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
Structured Questionnaire
18. Qu et al., (2011) USA Primary care clinics affiliated with a major
university health system, USA.
Empirical
Observational Study
479 Patients
Structured Questionnaire
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
Cross-sectional study
1983 Patients from inpatient,
outpatient and emergency care;
Structured Questionnaire
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
Structured Questionnaire
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
Structured Questionnaire
24. Elleuch (2008) Japan National cultures Centres in Japan Empirical
Observational Study
159 Japanese outpatients
Structured Questionnaire
Source: Compiled for this study
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.
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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
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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
Structured Questionnaire
Friendliness of staff, Prior information, Friendliness of staff, proper
explanation of health condition, privacy and respect.
5. Narang (2010) India 500 respondents
Structured Questionnaire
Health care delivery system, Interpersonal and Diagnostic aspect of
care, Facility, Health personnel conduct and Drug availability
6. Ashrafun & Uddin (2011) Bangladesh 190 Inpatients
Structured Questionnaire
Doctors‟ treatment, Behaviour of nurses, Behaviour of boys/ayas, Gifts
or tips culture
7. Mehta (2011) India 400 Patients
Structured Questionnaire
Amenities, Clinical care, and Physical facilities
8. Tateke (2012) Ethiopia 204 Patients
Structured Questionnaire
Health status, Expectations, Perceived adequacy, Perceived provider‟s
competency and welcome Approach
9. Grøndahl et al., (2013) Norway 373 respondents
Structured Questionnaire
Patient-related conditions, external objective-care and patients‟
perception of actual care received
10. Tucker and Adams (2001) USA 49,478 military patients
Structured Questionnaire
caring, empathy, reliability and responsiveness
11. Parasuraman et al., (1988) USA 200 respondents
Structured Questionnaire
Reliability (competence); Responsiveness (communication); Tangibles
(physical facilities); and Empathy (staff demeanour)
12. Naik et al., (2013) India 500 Inpatients
Structured Questionnaire
First impression, Clinical care, Nursing care, Housekeeping service,
Food service, and Overall service experience
13. Al-Ahmadi (2009) Saudi
Arabia
1,834 Nurses
Structured Questionnaire
Employee demographics, Job satisfaction, and Organizational
commitment, Influenced performance
14. Alhashem et al., (2011) Kuwait 500 patients
Structured Questionnaire
Communication, Physician care and Nursing care
15. Adams (2001) UK 625 patients
Structured Questionnaire
Caring, Empathy, Reliability, Responsiveness, Access, Communication
and Outcome dimensions
16. Ghosh (2014) India 225 patients
Structured Questionnaire
Clinical Care, Internal Environment, Communication and
Administrative procedures
Source: Compiled for this study
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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
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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
patient satisfaction.
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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
patient satisfaction.
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
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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
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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
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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.
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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
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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
Source: Compiled for this study
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
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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.
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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
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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
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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
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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
behavioural intentions.
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.
Youssef et al., (1996)
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
Label Item Adapted From
RSP1 The services are provided at the promised times
(e.g. admission, lab services, clinical care,
emergency care, casualty services etc.).
Youssef et al., (1996)
RSP2 Hospital staffs consistently follow-up sick
cases.
Youssef et al., (1996)
RSP3 The hospital consulting hours are convenient. Chaniotakis and Lymperopoulos (2008)
RSP4 Doctors and nurses are always willing to help
patients.
Youssef et al., (1996)
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
healthcare service provider. Thus the hypotheses are developed as follows:
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
Label Item Adapted From
ASS1 Doctors and nursing staff are consistently courteous
with their patients
Youssef et al., (1996)
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.
Youssef et al., (1996)
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).
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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
Label Item Adapted From
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.
Padma et al., (2010)
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
healthcare service provider. Thus the hypotheses are developed as follows:
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
Label Item Adapted From
TAN1 The physical facilities at this hospital are visually
appealing (e.g. well maintained reception area,
billing and registration facilities, etc.).
Parasuraman et al., (1988)
Youssef et al., (1996)
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.).
Youssef et al., (1996)
Vandamme and Leunis, (1993)
TAN3 This hospital has Up-to-date and well maintained
medical facilities and equipment.
Parasuraman et al., (1988)
Youssef et al., (1996)
TAN4 This hospital provides up-dated informative
broachers about services offered.
Youssef et al., (1996)
Parasuraman et al., (1988)
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)
Label Item Adapted From
HCSQ1 The overall feelings about the quality of healthcare service
provided at this hospital are better than I expected
Youssef et al., (1996)
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
Label Item Adapted From
AP1 Getting appointment in this hospital is easy. Woodside et al., (1989)
Padma et al., (2010)
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
Label Item Adapted From
MS1 Doctors of this hospital are knowledgeable to answer
patients‟ questions satisfactorily.
Rose et al., (2004)
Duggirala et al., (2008)
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)
Duggirala et al., (2008)
MS4 Doctors of this hospital are extremely careful in explaining
what patients are expected.
Duggirala et al., (2008)
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
Label Item Adapted From
NS1 Nursing staff of this hospital is knowledgeable to perform the
service very well.
Woodside et al., (1989)
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.
Woodside et al., (1989)
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
Label Item Adapted From
HSK1 Housekeeping staffs of this hospital have knowledge in
maintaining hygiene of hospital premises.
Woodside et al., (1989)
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.
Woodside et al., (1989)
HSK4 Housekeeping staff of this hospital is knowledgeable to
maintain bio-degradable contents and their segregation.
Woodside et al., (1989)
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
Label Item Adapted From
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.
Duggirala et al., (2008)
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
Label Item Adapted From
OS1 This hospital maintains proper queue management system. Woodside et al., (1989)
OS2 This hospital maintains well managed ambulatory services in
emergency.
Duggirala et al., (2008)
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.
Duggirala et al., (2008)
OS7 Operation theatre is well equipped with up-to-date
equipments.
Woodside et al., (1989)
OS8 The pharmacy of this hospital maintains all kinds of required
drugs.
Duggirala et al., (2008)
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
Label Item Adapted From
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
Woodside et al., (1989)
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)
Woodside et al., (1989)
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;
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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
Label Item Adapted From
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
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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.
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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
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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.
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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
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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;
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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
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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.
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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.
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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.,.
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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
Source: Compiled for this study
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.
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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
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Source: Compiled for this study
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
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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
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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.
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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)
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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).
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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
Source: Compiled for this study
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
Source: Compiled for this study
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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
Source: Compiled for this study
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.
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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-
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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.
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.
To evaluate the normality of the latent variable in the study (expected 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 0.693 to 0.967 indicates a positively skewed distribution (Hair et al.,
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,
Mean S.D Variance Skewness Kurtosis C.A (α)
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
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 0.741 to 0.945 indicates a positively skewed distribution (Hair et al.,
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.
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& Bernstein, 1994), which indicates that it provides
a good estimate of internal consistency.
121
Table 4.5 Construct total descriptive statistics for patient satisfaction
Mean S.D Variance Skewness Kurtosis C.A (α)
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.
Mean S.D Variance Skewness Kurtosis C.A (α)
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
KMO - Measure of Sampling Adequacy 0.922
Bartlett's Test
of Sphericity
Approx. Chi-Square 10625.460
df 592
Sig 0.000
Patient Satisfaction
KMO - Measure of Sampling Adequacy 0.905
Bartlett's Test
of Sphericity
Approx. Chi-Square 16943.777
df 861
Sig 0.000
Behavioural Intentions
KMO - Measure of Sampling Adequacy 0.962
Bartlett's Test
of Sphericity
Approx. Chi-Square 2567.271
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
Patient Satisfaction
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
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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
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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
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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
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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
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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
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 3104.7 1351 2.298 0.912 0.050 0.902 0.926 0.901
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
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).
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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).
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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.
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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.
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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
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 2913.52 1293 2.253 0.915 0.046 0.918 0.956 0.908
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
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
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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,
which was proposed in the model (presented in chapter 3). This indicated that the
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
HCSQ is 0.416 and 4.706 respectively, suggesting that this path is statistically significant
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
HCSQ is 0.147 and 3.107 respectively, suggesting that this path is statistically significant
at the p=0.000. The results demonstrated strong support for hypothesis H4a, which was
proposed in the model (presented in chapter 3). This indicated that the expected empathy
has strong significant effect on healthcare service quality to visit corporate hospitals,
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
has strong significant effect on healthcare service quality to visit corporate hospitals,
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
HCSQ is 0.757 and 2.143 respectively, suggesting that this path is statistically significant
at the p=0.001. The results demonstrated strong support for hypothesis H5a, which was
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
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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,
which was proposed in the model (presented in chapter 3). This indicated that the
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.
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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
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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
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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
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 1413.27 478 2.956 0.833 0.067 0.903 0.927 0.798
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., (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).
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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
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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
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 968.298 377 2.568 0.906 0.049 0.914 0.938 0.916
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
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
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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
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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
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>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
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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
Housekeeping Services
Food Services
0.719
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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
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 891.298 377 2.364 0.917 0.042 0.926 0.969 0.924
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
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.
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Table 4.40 Hypotheses testing / paths causal relationships
Construct Code Hypotheses Hypothesised
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)
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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
Construct Code Hypotheses Hypothesised
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,
which was proposed in the model (presented in chapter 3). This indicated that the
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.
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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.
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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.
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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
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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.
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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
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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
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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
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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
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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
behavioural intentions.
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”.
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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.
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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
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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,
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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
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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,
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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 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). 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
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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.
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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
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
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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
positive significant effect on healthcare service quality (H5a). The parameter estimate
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
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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
positive significant effect on healthcare service quality (H3a). The parameter estimate
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
positive significant effect on healthcare service quality (H4a). The parameter estimate
results (H4a: EEPT → HCSQ; β = 0.147, t-value = 3.107, p = 0.001) for the above
hypothesis was found both positive and statistically significant. This suggested existence
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,
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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
hypothesis was found both positive and statistically significant. This suggested existence
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
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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
positive significant effect on healthcare service quality (H5b). The parameter estimate
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
positive significant effect on healthcare service quality (H3b). The parameter estimate
results (H3b: PASS → HCSQ; β = 0.416, t-value = 4.706, p = 0.001) for the above
hypothesis was found both positive and statistically significant. This suggested existence
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
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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)
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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
patient satisfaction.
Medical-care Services (MS) and Patient Satisfaction (PS)
In the proposed research model, it is hypothesized that medical-care services will have a
positive significant effect on patient satisfaction (H9). The parameter estimate results
(H9: MS → 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 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
positive significant effect on patient satisfaction (H10). The parameter estimate results
(H10: NS → PS; β = 0.243, t-value = 7.903, p = 0.001) for the above hypothesis was
found both positive and statistically significant. This suggested existence of a positive
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;
Bowers et al., 1994; Zeithaml and Bitner, 2000; Tucker and Adams, 2001; Otani and
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
positive significant effect on patient satisfaction (H11). The parameter estimate results
(H11: HKS → PS; β = 0.153, t-value = 5.166, p = 0.002) for the above hypothesis was
found both positive and statistically significant. This suggested existence of a positive
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;
Bowers et al., 1994; Zeithaml and Bitner, 2000; Tucker and Adams, 2001; Otani and
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.
***********
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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.
1 2 3 4 5 Strongly Agree Agree Neutral Disagree Strongly Disagree
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.
1 2 3 4 5 Strongly Agree Agree Neutral Disagree Strongly Disagree
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)
**********