Customers' Perceptions and Usage of Online Retailing Services in Nairobi County, Kenya

i

CUSTOMERS’ PERCEPTIONS AND USAGE OF ONLINE

RETAILING SERVICES IN NAIROBI COUNTY, KENYA

BY

PETER M. MWENCHA

D86/CTY/21719/2010

A THESIS SUBMITTED TO THE SCHOOL OF BUSINESS IN

FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF

DOCTOR OF PHILOSOPHY DEGREE IN BUSINESS

ADMINISTRATION OF KENYATTA UNIVERSITY

JULY, 2015

i

EFFECT OF CUSTOMER PERCEPTIONS ON THE USAGE OF

ONLINE RETAILING SERVICES IN NAIROBI COUNTY, KENYA

BY

PETER M. MWENCHA

BA (IR) UNITED STATES INTERNATIONAL UNIVERSITY (2008)

MBA (MARKETING) KENYA METHODIST UNIVERSITY (2010)

A DISSERTATION SUBMITTED TO THE SCHOOL OF BUSINESS

IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE

AWARD OF DOCTOR OF PHILOSOPHY DEGREE IN BUSINESS

ADMINISTRATION OF KENYATTA UNIVERSITY

JUNE, 2013

ii

DECLARATION

This thesis is my original work and has not been presented for any award in any other

university. No part of this thesis should be reproduced without the authority of the

author and/or Kenyatta University.

Signature: ________________________________ Date: __________________

Mwencha, Peter Misiani

Business Administration Department

We confirm that the work reported in this research thesis was carried out by the

candidate under our supervision as the appointed university supervisors.

Signature: ________________________________ Date: ___________________

Dr. Muathe, SMA (PhD)

Department of Business Administration,

School of Business,

Kenyatta University

Signature: _______________________________ Date: ___________________

Prof. Kuria Thuo, J. (PhD)

School of Business

Gretsa University

iii

DEDICATION

To my parents, my father Samuel Mwencha and my late mother Mary Mwencha, My

uncles Amb. Erastus Mwencha, Hon. Henry Obwocha and Stephen Mwencha, my

Aunts Mary Mwencha, Dolline Obwocha, Victoria Sagero, Getrude Sagero and

Millicent Bochere, my elder brother Mogaka Mwencha, my sister Evelyne Kerubo, my

younger brothers Charles Mokaya and Henry Orenge, friends Peter Muendo, Belinda

Maina, Sheridan Muruka, Anthony Riri, Evelyne Kamau, Davies and Milda Kinyua as

well as my cousins Mogaka and Kinya Mwencha, Patrick, Norah and Maurice Masenge

for their love, encouragement and unwavering support.

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ACKNOWLEDGEMENT

I wish to acknowledge several individuals who contributed both directly and indirectly

towards the completion of this study. It has been a long, difficult but rewarding

endeavor which would not have been possible without their encouragement and

support.

First and foremost, I am most grateful to my first supervisor, Dr. Muathe, SMA (Ph.D)

who was of great assistance to me at all stages of this study. His guidance,

encouragement as well as patience throughout the course of this study were

indispensable. I would also like to thank my second supervisor, Prof. J. Kuria Thuo, for

his insightful suggestions and meticulous supervision at each and every stage. I would

also like to express my gratitude to the faculty at Kenyatta University School of

Business, in particular, Dr. Ambrose Jagongo (Ph.D), Dr. David Nzuki (Ph.D), Dr.

James Kilika (Ph.D) and Dr. Samuel Maina (Ph.D) for being accessible and helpful

whenever I needed clarifications regarding the thesis.

Second, I also acknowledge the support of my colleagues in the PhD program, in

particular, Dr. Stanley Karanja (Ph.D), Dr. Reuben Njuguna (Ph.D), Dr. Siaw

Frimpong (Ph.D), Dr. Jedidah Muli (Ph.D) and Dr. Rebecca Mensah (Ph.D). Stanley

was always keeping tabs on my progress, Njuguna was a good sounding board for

ideas, Frimpong was welcome company during the long hours in the library, Jedidah

provided me with numerous research tips and Rebecca was an excellent motivator.

Third, I would also like to thank my family and friends for their moral and in-kind

support during the course of this study as well as for bearing the inconvenience that

comes with such a winding undertaking. I am truly indebted to them.

Fourth, there are other people who contributed towards the completion of this study but

could not be mentioned here. They know themselves. I am grateful for all their

contributions and I sincerely thank them all for the part they played.

Last but not least, I give thanks to God almighty for giving me good health and for

sustaining me throughout the entire period of working on this document.

v

TABLE OF CONTENTS

Page

Title....................................................................................................................................... i

Declaration.......................................................................................................................... ii

Dedication ..........................................................................................................................iii

Acknowledgements .......................................................................................................... iv

Table of contents ................................................................................................................ v

List of tables....................................................................................................................... ix

List of figures .................................................................................................................... xii

Operational definition of terms .....................................................................................xiii

Abbreviations and acronyms ......................................................................................... xvi

Abstract ..........................................................................................................................xviii

CHAPTER ONE: INTRODUCTION ............................................................................. 1

1.1 Background of the Study ..................................................................................... ..1

1.1.1 Usage of Online Retailing Services ........................................................................ 4

1.1.2 Customers‘ Perceptions of Online Retailing........................................................... 6

1.1.3 Online Retailing Services in Kenya ........................................................................ 8

1.2 Statement of the Problem ...................................................................................... 9

1.3 Research Objectives ............................................................................................ 11

1.4 Research Hypotheses........................................................................................... 12

1.5 Significance of the Study .................................................................................... 13

1.6 Scope of the Study .............................................................................................. 14

1.7 Limitations of the Study...................................................................................... 15

1.8 Organization of the Study ................................................................................... 16

CHAPTER TWO : LITERATURE REVIEW .............................................................. 18

2.1 Introduction ......................................................................................................... 18

vi

Page

2.2 Theoretical Review of E-Commerce Usage ........................................................ 18

2.2.1 Behavioral Model of System Usage ................................................................ 18

2.2.2 Innovation Diffusion Theory ............................................................................ 21

2.2.3 Expectation-Confirmation Theory .................................................................... 23

2.2.4 Perceived Risk Theory ...................................................................................... 25

2.2.5 Theory of Consumption Values ......................................................................... 27

2.3 Empirical Literature Review ............................................................................... 29

2.3.1 Usage of Online Retailing Services ................................................................... 29

2.3.2 Antecedent Role of Customer Perceptions ........................................................ 32

2.3.3 Mediating Role of Customer Satisfaction .......................................................... 39

2.3.4 Moderating Effect of Demographic Factors ...................................................... 44

2.4 Summary of Empirical Literature and Research Gaps ....................................... 45

2.5 Conceptual Framework ....................................................................................... 47

CHAPTER THREE : RESEARCH METHODOLOGY ............................................. 51

3.1 Introduction ......................................................................................................... 51

3.2 Research Philosophy ........................................................................................... 51

3.3 Research Design .................................................................................................. 52

3.4 The Empirical Model .......................................................................................... 53

3.4.1 The Direct Effects Model .................................................................................... 53

3.4.2 The Mediated Effects Model ............................................................................... 56

3.4.3 The Interaction Effects Model ............................................................................. 57

3.5 Operationalization and Measurement of Study Variables................................... 58

3.6 The Study Area.................................................................................................... 60

3.7 Target Population ................................................................................................ 61

3.8 Sampling Design and Procedure ......................................................................... 62

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Page

3.8.1 Sampling Technique ........................................................................................... 63

3.8.2 Sample Size Determination ............................................................................... 65

3.9 Data Collection Instrument ................................................................................. 66

3.9.1 Self Administered Questionnaire ....................................................................... 67

3.9.2 Key Informant Interview .................................................................................. 67

3.9.3 Validity of Data Collection Instruments ............................................................ 68

3.9.4 Reliability of Data Collection Instruments ........................................................ 70

3.10 Data Collection Procedures ................................................................................. 72

3.11 Data Analysis and Presentation ........................................................................... 76

3.12 Ethical Issues ....................................................................................................... 83

CHAPTER FOUR: RESEARCH FINDINGS AND DISCUSSIONS ......................... 85

4.1 Introduction ......................................................................................................... 85

4.2 Descriptive Data Analysis ................................................................................... 85

4.2.1 Response Rate.................................................................................................... 85

4.2.2 Sample Demographic Characteristics ................................................................ 86

4.2.3 Customer Perceptions of Online Retailing Users .............................................. 88

4.2.4 Usage of Online Retailing Services .................................................................... 90

4.2.5 Customer Satisfaction of Online Retailing Users .............................................. 90

4.3 Regression Analysis and Test of Hypotheses ..................................................... 91

4.3.1 Diagnostic Tests ................................................................................................ 92

4.3.2 Test of Hypotheses ............................................................................................ 95

4.3.2 Summary of Research Hypotheses .................................................................. 102

4.4 Content Analysis ............................................................................................... 103

4.4.1 Interview Participants ..................................................................................... 103

4.3.2 Key Themes ..................................................................................................... 104

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Page

CHAPTER FIVE: SUMMARY, CONCLUSIONS AND RECOMMENDATIONS110

5.1 Introduction ....................................................................................................... 110

5.2 Summary ........................................................................................................... 110

5.3 Conclusions ....................................................................................................... 113

5.4 Policy Implications ............................................................................................ 115

5.5 Contributions of the study to knowledge .......................................................... 117

5.6 Suggestions for further study ............................................................................ 119

REFERENCES ............................................................................................................... 120

APPENDICES ................................................................................................................ 145

Appendix 1: Supplementary Statistical Analyses ........................................................... 145

Appendix 2: List of Online Retailing Firms in Nairobi, Kenya ...................................... 154

Appendix 3: Data Collection Instruments........................................................................ 155

a). Cover Letter....................................................................................................... 155

b). Questionnaire .................................................................................................... 156

c). Interview Guide ................................................................................................. 159

Appendix 4: Research Authorization ............................................................................... 162

a). Clearance Letter ................................................................................................ 162

b). Research Permit................................................................................................. 163

Appendix 5: Code Book ................................................................................................. .164

a). Codebook for Quantitative Data Analysis ....................................................... 164

b). Codebook for Qualitative Data Analysis .......................................................... 165

c). Summary of Major Themes............................................................................... 166

Appendix 6: Summary of Empirical Review & Research Gaps ..................................... .168

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LIST OF TABLES

Page

Table 3.1: Operationalization and Measurement of Study Variables ................................ 59

Table 3.2: Distribution of Target Population ..................................................................... 62

Table 3.3: Sampling Frame & Sample Distribution Table ................................................ 66

Table 3.4: Reliability of Questionnaire Items ................................................................... 71

Table 3.5: Inferential Data Analysis Techniques ............................................................... 78

Table 4.1: Distribution of Responses ................................................................................. 86

Table 4.2: Demographic Characteristics of the Sample..................................................... 87

Table 4.3: Descriptive Statistics Results for Customers‘ Perceptions ............................... 88

Table 4.4: Descriptive Statistics Results for Individual Perceptual Indicators ................. 89

Table 4.5: Descriptive Statistics Results for Customer Satisfaction.................................. 90

Table 4.6: Descriptive Statistics Results for Usage of Online Retail Services .................. 90

Table 4.7: Results of Collinearity Statistics ....................................................................... 93

Table 4.8: Results of Kolmogorov-Smirnov Normality Test ........................................... 94

Table 4.9: Results of Logit Regression Analysis ............................................................... 95

Table 4.10: Results of of Simple Linear Regression Analysis ......................................... 97

Table 4.11: Summary of Hypotheses Test ...................................................................... 102

Table 4.12: Distribution of Interview Participants ......................................................... 103

Table A.1: Reliability Output - Usefulness .................................................................... 145

Table A.2: Reliability Output - Compatibility ............................................................... 145

Table A.3: Reliability Output - Ease-of-Use ................................................................. 145

Table A.4: Reliability Output - Financial Risk ............................................................... 145

Table A.5: Reliability Output - Performance Risk ........................................................ 145

Table A.6: Reliability Output - Personal Risk ............................................................... 146

Table A.7: Reliability Output - Monetary Value ............................................................ 146

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Page

Table A.8: Reliability Output - Convenience Value ...................................................... 146

Table A.9: Reliability Output - Social Value .................................................................. 146

Table A.10: Reliability Output - Emotional Value ......................................................... 146

Table A.11: Reliability Output - Level of Satisfaction ................................................... 147

Table A.12: Correlation Matrix for the Three Predictor Variables ................................. 147

Table A.13: Collinearity Output showing Tolerance and VIF ........................................ 147

Table A.14: Hosmer and Lemeshow Test for the Main Effects Model ........................... 147

Table A.15: Normality Test for Predictor Variables ....................................................... 148

Table A.16: Dependent Variable Encoding for Predictor Variables ............................... 148

Table A.17: Model Summary for Logistic Regression of Direct Effects Model ............ 148

Table A.18: Classification Table for Direct Effects Model ............................................ 149

Table A.19: Case processing summary for Direct Effects Model .................................. 149

Table A.20: Logistic Regression Results for Direct Effects Model ............................... 149

Table A.21: Model Summary for Linear Regression Results for the Relationship

between Customers‘ Perceptions and Customer Satisfaction ..................... 150

Table A.22: ANOVA (F-Test) for Linear Regression Results of the Relationship

between Customers‘ Perceptions and Customer Satisfaction .................... 150

Table A.23: Linear Regression Results of Relationship between Customers‘ Perceptions

and Customer Satisfaction ........................................................................... 150

Table A.24: Dependent Variable Encoding for Logistic Regression of Relationship

between Customer Satisfaction and Usage ................................................. 151

Table A.25: Classification Table: Logistic Regression of Relationship between

Customer Satisfaction and Usage ................................................................ 151

Table A.26: Case Processing Summary for Logistic Regression of the Relationship

between Customer Satisfaction and Usage ................................................. 151

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Page

Table A.27: Model Summary for Logistic Regression of Relationship between

Customer Satisfaction and Usage .............................................................. 152

Table A.28: Logistic Regression Results of the Relationship between Customer

Satisfaction and Usage ............................................................................... 152

Table A.29: Case Processing Summary for Moderated Effects Model .......................... 152

Table A.30: Dependent Variable Encoding for Moderated Effects Model .................... 152

Table A.31: Moderated Effects Model Summary ........................................................... 153

Table A.32: Classification Table for Moderated Effects Regression ............................. 153

Table A.33: Logistic Regression Results for Moderated Effects Model ........................ 153

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LIST OF FIGURES

Page

Figure 2.1 : Behavioral Model of System Usage .............................................................. 19

Figure 2.2 : Diffusion of Innovation Theory ..................................................................... 21

Figure 2.3 : Expectations-Confirmation theory ................................................................. 24

Figure 2.4 : Perceived Risk Theory ................................................................................... 26

Figure 2.5 : Theory of Consumption Value ....................................................................... 28

Figure 2.6 : Conceptual Framework .................................................................................. 48

xiii

OPERATIONAL DEFINITION OF TERMS

Active users Individuals that have used a particular online retailing

service at least once in the last three months.

Adoption The acceptance (initial use) of an online retailing service

B2C E-Commerce The direct activity between businesses and consumers

through which consumers fulfill their needs (e.g.

information, products or services) using information and

communications technology applications such as the

internet.

C2C E-Commerce The direct activity amongst consumers through which

they fulfill their needs (e.g. information, products or

services) using information and communications

technology applications such as the internet.

Compatibility The degree to which using a particular online retailing

service is perceived as being consistent with the existing

values, needs and past experiences of the potential user.

Constructs Abstractions (theoretical concepts) that cannot be

observed directly but are useful in interpreting empirical

data and in theory building.

Customers’ Perceptions The subjective opinions/beliefs/judgments of an

individual vis-à-vis an online retailing service based on

prior use experience.

Customer Satisfaction The customer‘s overall positive evaluation of the online

retailing service following initial usage or based on all

prior interactions/encounters and experiences with the

online retailing service.

Data triangulation The collecting of study data over different times or from

different sources.

Demographic Variables Personal characteristics/attributes of a consumer that tend

to remain static throughout an individual‘s life time, or

evolve slowly over time. This includes age, gender, race,

education, income, lifestyle, etc.

E-commerce The use of electronic communications and digital

information processing technology in business

xiv

transactions for value creation between organizations,

between organizations and individuals as well as between

individuals.

Inactive users Individuals that have not used a particular online retailing

service at least once in the last three months.

Innovation An idea, practice or object that is perceived as new by an

individual.

Market Development Possible ways of increasing and sustaining the usage of

online retailing services.

Market Prospects Economic/business potential of the online retailing sub-

sector.

Member Checking Verifying the credibility of constructions of the key

informant interview participants.

Methodical triangulation The use of a combination of methods such as document

analysis, interviews and surveys in a study.

Moderating variable A third variable that modifies the strength or direction of

a causal relationship.

Monetary value The customer‘s evaluation of the total financial cost of

the online retailing service (including the price paid)

relative to the benefits received.

Null hypothesis A statement that contradicts the assumed result/outcome

of the study.

Online retailing The selling and buying of goods/services through the

internet.

Operational definition The definition that ascribes meaning to a construct by

specifying operations that the researcher must perform to

measure or manipulate the construct.

Perception A subjective opinion/belief/judgment of an individual

vis-à-vis an online retailing service.

Perceived risk The transaction-related risks that consumers face as a

result of using online retailing services.

Perceived value The consumer‘s evaluation of the benefits of online

retailing usage.

Post-adoption behavior The various adoption outcomes, use behaviors, and

feature extension behaviors made by an individual after

xv

an online retailing service has been implemented, made

accessible to the user, and applied by the user in

accomplishing his/her online shopping activities.

Prevailing Attitudes Opinions, thoughts and feelings regarding online retailing

services.

Psychographics A customer‘s inner feelings and predisposition to behave

in a certain way.

Pure play online firms Firms that provide their services only via online/internet

channels e.g. internet retailing.

Retailer An entity that sells products and services directly to the

final consumers for their personal use, be it online,

offline or both.

Theory A set of interrelated constructs/variables that present a

systematic view of a phenomenon by specifying

relationships among the variables, with the purpose of

explaining the phenomenon.

Usage The utilization of one or more features of an online

retailing service by registered users within a certain

timeframe.

Usage Diversity The types/nature and extent of usage/utilization of online

retailing services

Usage Drivers Determinants of online retailing usage

Variable A construct that can assume different values and scores.

Variable respecification A procedure in which the existing data are modified to

create new variables, or in which a large number of

variables are collapsed into fewer variables in line with

the study‘s objectives.

xvi

ABBREVIATIONS AND ACRONYMS

ANOVA Analysis of Variance

B2B Business-to-Business

B2C Business-to-Consumer

BMSU Behavioural Model of System Usage

CA Communications Authority

CCK Communication Commission of Kenya

CS Customer Satisfaction

COFEK Consumer Federation of Kenya

CUI Continued Usage Intention

C2C Business-to-Consumer

DOI Diffusion of Innovation

DV Dependent Variable

E-COMMERCE Electronic Commerce

ECM Expectations Confirmation Model

ECT Expectations (Dis) Confirmation Theory

IS Information Systems

IT Information Technology

IV Independent Variable

ICT Information and Communication Technology

IDT Innovation Diffusion Theory

KS Kolmogorov Smirnov Test

LOGIT Logistic Regression

MIS Management Information Systems

xvii

NACOSTI National Commission for Science, Technology and Innovation

PCI Perceived Characteristics of Using an Innovation

PEOU Perceived Ease of Use

PERVAL Perceived Value Scale

PR Perceived Risk

PU Perceived Usefulness

PV Perceived Value

RR Response Rate

SERVQUAL Service Quality

SCT Social Cognitive Theory

SEM Structural Equation Modelling

SNS Social Networking Sites

SPSS Statistical Package for Social Sciences

TAM Technology Adoption Model

TRA Theory of Reasoned Action

TCV Theory of Consumption Values

US United States

UN-HABITAT United Nations Human Settlements Programme

UTAUT Unified Theory of Acceptance and Use of Technology

VIF Variance Inflation Factor

xviii

ABSTRACT

In spite of the huge increase in internet usage in Kenya over the past years, the usage of

online retailing services in Kenya is still very low, thereby posing an existential threat

to the service providers. To induce more initial users to continue using these services,

there is need to establish what affects their continued usage. Individual factors, in

particular customer perceptions, have been shown by both the information systems (IS)

as well as the marketing fields to have significant effect on sustained use of online

retailing services. This study therefore sought to establish the effect of customer

perceptions on the usage of online retailing services in Nairobi County, Kenya. Its

objectives were to establish whether there is a relationship between perceived attributes

and usage of online retailing services, to determine whether there is a relationship

between perceived risk and usage of online retailing services, to analyse whether there

is a relationship between perceived value and usage of online retailing services, to

evaluate whether customer satisfaction has a mediating effect on the relationship

between customer perceptions and usage of online retailing services and to establish

whether customer demographics have a moderating effect on the relationship between

customer perceptions and usage of online retailing services. The study employed a

descriptive, cross-sectional, survey design and explanatory design. The target

population was 6 online retailing firms and the respondents for this study were the

18,147 registered users of these six online retailing firms in Nairobi County, Kenya. A

sample of 391 respondents was selected using multi-stage sampling methods including

purposive, stratified and simple random sampling. Primary data was collected using a

self-administered structured questionnaire and an interview guide, while secondary data

was collected through document review. Questionnaire responses were analyzed using

descriptive and inferential statistics which involved both linear and logistic regression

analysis. Figures and tables were used to present the data. Data from key informant

interviews was analyzed using content analysis technique to complement the

quantitative data. The results showed that consumer perceptions have a significant

effect on the usage of online retailing services. The study also found that customer

satisfaction does have a mediating effect on the customer perception – usage

relationship. Furthermore, the research established that demographic factors do not

have a significant moderating effect on the customer perception - usage relationship.

The findings of this study underscore the importance of customer perceptions and

customer satisfaction in enhancing the likelihood of success of online retailing services.

Consequently, the study recommends that online retailers should enhance service

features/attributes as a way of ensuring success of their services by taking into

consideration customer-specific needs by personalizing the website to make it more

useful, compatible with customer requirements and easy to use for users. In addition,

online retailing service providers need to build trust amongst their users regarding

online purchasing. Further, online retailers should design and deliver a unique value

proposition that has both functional as well as hedonistic appeals. Online retailers

should also have an effective customer satisfaction strategy for purposes of customer

retention. Moreover, it is imperative for online retailing firms to have a good

understanding of their target customers, since this will not only help in determining the

appropriate customer engagement strategies but also how to enhance the long-term

usage of their services. On the government‘s part, the study recommends the tackling

the barriers to online shopping usage primarily through legislation. Since usage also

hinges on trust, the government could license a suitable entity to oversee online

consumer protection to address users‘ concerns.

1

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

The commercial use of the Internet has grown tremendously over the last two decades,

and is characterized by a proliferation of various online-based electronic commerce (e-

commerce) services. One of these services is online retailing, which has been described

using a number of different terms (Mottner, Thelen & Karande, 2002). It has been

referred to as internet retailing, e-retailing, or e-tailing (Anderson, 2000), as part of

interactive home shopping (Alba, Lynch, Weitz & Janisqewski, 1997), and by the

broader terms electronic commerce (Daniel & Klimis, 1999) and e-commerce (Boscheck,

1998).

According to the Australian Government Productivity Commission (AGPC), online

retailing can take several forms: i) as ‗pure play‘ services in which businesses provide

online-only services in particular retail categories, ii) as brick-and-click (multi-channel)

establishments where online activities are combined with bricks-and-mortar operations,

iii) as online marketplaces where buyers and sellers interact on an electronic trading

platform provided by a third-party and iv) as manufacturer-owned websites where

products are sold directly to customers, thus by-passing middlemen (2011).

The late 1990s heralded the coming of age of online retailing, with the unprecedented

growth in reported sales surpassing triple digit growth (United States Census Bureau,

2004), though it slowed considerably due the failure of e-commerce firms in 2000

(Rohm & Swaminathan, 2004). Nonetheless, the U.S. Commerce Department has been

reporting annual e-commerce statistics since 1999, signifying the importance of this sub-

sector to the world‘s largest economy (Haynes & Taylor, 2006).

2

Due to its huge popularity, online retailing has had a significant impact on several market

segments such as travel, consumer electronics, hobby goods, and media goods across the

globe (Weltevrenden & Boschma, 2008). Consequently, online retailing has evolved into

an established marketing channel in its own right within the consumer marketplace

(Doherty & Ellis-Chadwick, 2010).

In terms of size, the U.S. is the largest market, and is expected to reach $278.9 billion in

sales in 2015 (Forrester, 2011a). In Europe, the second largest market, the number of

online buyers is expected to grow from 157 million to 205 million by 2015; total sales

are forecast to reach 133.6 billion Euros (Forrester, 2011b). Africa is also gradually

embracing online retailing, with countries like South Africa and Egypt ahead of the rest.

In South Africa for instance, 51% of those with access to the internet are shopping

online, according to a 2011 MasterCard Worldwide survey (Kermeliotis, 2011).

Kenya is showing strong growth potential, as it was the fastest growing Internet market

in Africa in 2011 (yStats.com, 2012) with its internet population rising by about 19% to

stand at 14.032 million users in 2012 from 12.5 million in 2011 (Communications

Commission of Kenya (CCK), 2012). A recent survey of 1700 individuals found that

18% to 24% of the respondents purchase music, movies and e-books online, thus

signaling the growth of online shopping in Kenya (Juma, 2010). This upward trend has

been aided by the increasing number of young people who prefer to access information

via their mobile phones, coupled with the declining prices of internet connectivity costs

as well as the high uptake of mobile payment services. This has created an opportunity

for online trading platforms such as N-Soko, OLX, Jumia and Rupu, among others

(Okuttah, 2014).

3

Notwithstanding the significant growth in internet usage in Kenya over the past few

years, the online shopping market is still quite small even by regional standards. Reports

indicate that the Communications Authority (CA) estimates the value of e-commerce in

Kenya at Sh4.3 billion, in comparison to South Africa‘s Sh54 billion, Egypt‘s Sh17

billion and Morocco‘s Sh. 9.6 billion (Okuttah, 2014).

This disproportionately low online retailing usage situation is corroborated by Nakumatt,

the leading supermarket chain in Kenya and East Africa, which insists that it has felt no

impact from online retailing. The company decries the fact that despite its presence on

online retail services, it was seeing little benefit if any there from. According to the retail

chain‘s analysis, ―online shopping is yet to take root in East Africa and still commands

less than 1 percent of turnover. We therefore cannot attest to having been affected by

online shopping‖ (Consumer Federation of Kenya (COFEK), 2013).

In the long run, this low usage of online retailing services poses a problem for investors

on how to monetize and sustain their investments in these platforms (Okuttah, 2014),

since low usage may result in the service provider incurring undesirable costs of

maintaining the loss-making service. Continued lossmaking may eventually lead to

closure of the service (Cooper & Zmud, 1990; Bhattacherjee, 2001a), as was evident

during the closure of the online retail service provider Kalahari.co.ke in 2011, the

Kenyan arm of the South African based firm Naspers. According to Naspers, ―the

performance of the service had been below expectations since the launch of the service in

2009 and reaching profitability was not a reasonable near-term prospect‖

(Bizcommunity, 2011).

4

Past studies have established that the success of e-retailers hinges more profoundly on

the continued use of the system than on initial adoption (Parthasarathy & Bhattacherjee,

1998; Shih & Venkatesh, 2004; Limayem, Hirt & Cheung, 2007). In view of the current

state-of-affairs, and given the growing importance online retailing, this study sought to

understand the predictors of online retailing service usage by consumers in Kenya.

1.1.1 Usage of Online Retailing Services

Usage behavior is an important concept in both the information systems (IS) and

marketing fields, due to the necessity of service providers to increase the uptake of their

services and sustain their usage at levels that are economically viable for them to

continue providing the service. For this reason, both researchers as well as practitioners

have sought to understand system usage behavior, in particular, what influences it

(Lucas, 1975; Schewe, 1976; Swanson, 1988; Taylor & Todd, 1995b; Bhattacherjee,

2001a; Hong, Thong, & Tam, 2006).

However, a review of usage literature shows that it is a complex construct with multiple

conceptualizations (Burton-Jones & Straub, 2006). For instance, Bhattacherjee (2001a)

divided IS usage into two stages: initial adoption and continuous usage, while Liu (2007)

postulated that the use of online shopping services is likely to move from partial usage

(i.e. information search only) to full usage (i.e. completing the entire transaction process

online) during post-adoption use. As such, the context within which it is used is

important in determining its relevant measures and dimensions. In the context of this

research, the usage of online retailing services is examined at the individual level of

analysis (i.e. the consumer use context) as a post-adoption phenomenon that is also

referred to as continued use. Therefore, usage serves as the dependent variable in the

research model conceptualized for this study.

5

The usage construct in this study is derived from the Behavioral Model of System Usage

(BMSU), an early IS model by Schewe (1976) that relates user attitudes to end-user

system usage behavior as well as the Expectation-Confirmation Theory (ECT), a

marketing theory by Oliver (1980; 2010) and its related Expectation (Dis) Confirmation

Model (ECM) by Bhattacherjee (2001a) which adapted the ECT to IS context. ECT is

the dominant theoretical lens used to explain IT continuance/discontinuance behaviors

(Bhattacherjee & Barfar, 2011). Due to the similarity between re-purchasing products or

services in a consumer context and the continued use of technology, the ECM posits an

equivalent relationship in the continued technology usage context (Bhattacherjee,

2001a).

Given the empirical support for the impact of continued usage on the IT system success,

establishing what that affect customers‘ usage behavior (either to continue or to

discontinue usage of an IT) is of importance (Hong et al, 2006). Accordingly, research in

IT continuance has examined different factors and/or processes that motivate continued

usage or discontinuance of IT products or services, following their initial acceptance

(Bhattacherjee & Barfar, 2011).

In essence, continued usage of IS can be influenced by individual/psychological,

system/technical and organizational factors (Bajaja & Nidumolu, 1998). However, this

study restricts itself to examining individual psychological factors, specifically the

antecedent role of customers‘ perceptions on usage of online retailing services, coupled

with the mediating role of satisfaction on the perception-usage relationship. This is in

line with a study conducted by Lucas (1975), which ascertained that the use of an IS is

dependent on user attitudes and perceptions.

6

It is important to note that as opposed to organizational IS usage, individuals use IS such

as online retailing services not only for utilitarian purposes, but also for hedonic

purposes (Monsuwé, Dellaert & De Ruyter, 2004; Bridges & Florsheim, 2008; Ozen &

Kodaz, 2012). Therefore, the affective aspect of online shopping is just as important as

the cognitive aspect and therefore needs to be taken into consideration when seeking to

establish what affects the usage of online retailing services (Ozen & Kodaz, 2012).

1.1.2 Customers’ Perceptions of Online Retailing

Perceptions are essentially mental maps made by people to give them a meaningful

picture of the world on which they can base their decisions (Berelson & Steiner, 1964).

Perception occurs when stimuli are registered by one of the five human senses: vision,

hearing, taste, smell and touch (Hoyer & MacInni, 2008) via a process of sensing,

selecting, and interpreting stimuli in the external, physical world into the internal, mental

world (Wilkie, 1994). This perceptual process leads to a response, which is either overt

(actions) or covert (motivations, attitudes, and feelings) or both.

From a consumer behavior perspective, perceptions are an attempt by a consumer to

obtain and process information about a market situation with a purpose to make himself

aware of the market and market offerings – market events, marketers, products/services,

advertisement, physical environment of the market outlet and so on (Sahaf, 2008).

Consumers establish and continuously update their perceptions about the alternative

products/services that they are considering and based on those perceptions, they

determine their attitudes towards the products (preferences).

7

The selection and interpretation of stimuli is highly subjective and is based on what the

consumer expects to see in light of previous experience, on the number of plausible

explanations he or she can envision, on motives and interests at the time of perception,

and on the clarity of the stimulus itself. Due to the way people view, experience and

remember things, two people may have differing perceptions of the same stimulus item

(e.g. a product or service). This makes perceptions a confusing and complex

phenomenon (Schiffman & Kanuk, 2010).

Perception has strategy implications for marketers because consumers make decisions

based on what they perceive rather than on the basis of objective reality. As a result,

marketers have realised that understanding the perceptual process of consumers helps

them to design better ways to help customers perceive their products and services

favorably, especially since products and services that are perceived distinctly and

favorably have a much better chance of being purchased than products or services with

unclear or unfavorable images (Schiffman & Kanuk, 2010).

Similarly, researchers in IS have over the years sought to establish how potential users‘

perceptions of an IT innovation influences its adoption (Tornatzky & Klein, 1982; Moore

& Benbasat, 1991) and continued usage (Lucas, 1975; Schewe, 1976; Parthasarathy &

Bhattacherjee, 1998; Bhattacherjee, 2001b; Venkatesh, Morris, Davis & Davis, 2003).

For this reason, organizations interested in influencing usage of their services need to

better understand their customers‘ perceptions (Schewe, 1976; Bhattacherjee, 2001a;

Bhattacherjee & Barfar, 2011). Consequently, the customer perception construct serves

as the independent variable in this study and has theoretical foundations from three

perceptual constructs identified in literature as playing an antecedent role via-a-vis online

retailing usage. These are: - perceived attributes, perceived risk and perceived value.

8

1.1.3 Online Retailing Services in Kenya

In the last decade, Kenya has undergone a transformation in its information and

communication technology (ICT) sector which has also had significant impact on

Kenya‘s social and economic structures (World Bank (WB), 2010). The ICT sector‘s

growth has outperformed every other sector, expanding by 23 percent annually during

this time and is now six times its size at the beginning of the decade (Ibid). This

remarkable growth has been characterized by introduction of various e-commerce

products and services into the market, which target Kenya‘s rapidly growing internet

population that stood at 14.032 million users in 2012 (CCK, 2012).

One of these innovative services is online retailing (or e-tailing), a subset of e-commerce

where firms provide a platform for the purchase and sale of goods between consumers

and sellers via the Internet (AGPC, 2011). Online retailing in Kenya is a beneficiary of

the growing use of the internet by both businesses and consumers. A survey of 1700

individuals found that 18% to 24% of the respondents purchase music, movies and e-

books online (Juma, 2010), while reports indicate that the Communications Authority

(CA) estimates the value of e-commerce in Kenya at Sh4.3 billion (Okuttah, 2014).

There are currently several online retailing firms in Kenya, majority of which are located

in Nairobi. They include Ravenzo, Jumia, Mzoori.com, Rupu.com, OLX.com, amongst

others. However, the usage of these services by consumers has not been commensurate

with business projections, resulting in the closure of some of them (e.g. Kalahari.co.ke).

There is therefore need to gain an increased understanding of what affects the use of

these online retailing services at the individual context. For this reason, the online

consumer context and more specifically, their usage of online retailing services in

Nairobi, Kenya, is presented as the main context of this study.

9

1.2 Statement of the Problem

The remarkable growth in the Kenyan ICT sector in the last decade has been

characterized by a surge in e-commerce activities, with several online services and

applications being introduced into the market (World Bank, 2010). However, while the

adoption of these online services is generally high, the conversion rate of the initial

adopters to long-term users is very low, as noted by Magutu, Mwangi, Nyaoga, Ondimu,

Kagu, Mutai, Kilonzo & Nthenya (2011).

This low usage of these online services by consumers poses a financial sustainability

problem for service providers, who may incur undesirable costs of maintaining the loss-

making online service. Consequently, continued loss-making may eventually lead to

closure of the online service, resulting in waste of effort to develop the service

(Bhattacherjee & Parthasarathy, 1998). This has been the case in Kenya, where the

failure of several online retailing firms – the latest being Kalahari.co.ke - has been

largely attributed to financial losses as a result of low usage by consumers.

The poor usage of online retailing services in Kenya is also attested to by Nakumatt, the

leading supermarket chain in Kenya and East Africa, which claims that it has felt no

impact from e-commerce. The company decries the fact that despite its presence on

online retail services, it was seeing little benefit if any there from, maintaining that

online shopping is yet to take root in East Africa going by current turnover (COFEK,

2013). This low uptake of online retailing services in Kenya therefore signifies the need

to understand what affects consumers‘ sustained usage of online retail services as a way

of increasing the chances of success of these services in Kenya.

10

Past studies (Bajaj & Nidumolu, 1998; Liu & Forsythe, 2009; Bhattacherjee & Barfar,

2011) have thus sought to establish the determinants of online retailing services usage. In

these studies, individual psychological factors - in particular customer perceptions - have

been shown to have a significant effect on the usage (Whyte, Bytheway & Edwards,

1997; DeLone & McLean, 2003; Venkatesh et al., 2003).

A review of empirical literature in this area reveals the different types of customer

perceptions and their relationship with usage. For instance, a study by Smith, (2008)

showed that perceived attributes do affect online retailing usage. On the other hand,

perceived risk has been established as having a significant effect on usage of e-

commerce (Yildirim & Cengel, 2012). Likewise, perceived value has been established as

one of the key factors affecting repeat usage in the online retailing context (Chen &

Dubinsky, 2003; Hu & Chuang, 2012). However, no prior study has combined these

three perceptions in the online retailing usage context. This study therefore contributes to

knowledge in this area by doing so.

Customer satisfaction with an online retailing service also has an effect on its subsequent

usage, as customers may discontinue usage due to unsatisfactory trial outcomes or usage

experiences (Rogers, 1995; 2010; Bhattacherjee, 2001a; 2001b). Due to the similarity

between re-purchasing products/services in a consumer context and the continued use of

technology, an equivalent relationship in the continued technology usage context is

posited. Moreover, satisfaction has also been shown to be affected by customer

perception of their initial use experiences with an IT (Bhattacherjee, 2001a; 2001b). In

other words, customer satisfaction has a mediating role on the relationship between

customer perception and usage. There‘s therefore need to understand this mediating role

11

of customer satisfaction, particularly in the online retailing context in Kenya. This study

therefore sought to fill this gap in extant research.

Further, the profile of consumers has also been shown to be of importance in

understanding their continued usage of online services (Bhattacherjee & Parthasarathy,

1998). However, most IS studies have concentrated on user psychographic factors,

ignoring demographic factors. To enhance the explanatory capacity of the proposed

model, the moderating role of customer demographic factors was incorporated.

Therefore, this research empirically examined the moderating role of three demographic

characteristics - age, income and education level - on the relationship between customer

perceptions and usage of online retailing services in Kenya.

1.3 Research Objectives

1.3.1 General Objective

The main objective of this study was to investigate the relationship between consumers‘

perceptions and usage of online retailing services in Nairobi County, Kenya.

1.3.2 Specific Objectives

i. To establish the relationship between perceived attributes and usage of online

retailing services in Nairobi County, Kenya.

ii. To analyse the relationship between perceived risk and usage of online retailing

services in Nairobi County, Kenya.

iii. To determine the relationship between perceived value and usage of online


12

iv. To assess the relationship between customers‘ perceptions and customer

satisfaction with online retailing services in Nairobi County, Kenya.

v. To establish the relationship between customer satisfaction and usage of online


vi. To establish the moderating effect of customer demographics on the relationship

between customer perceptions and usage of online retailing services in Nairobi

County, Kenya.

1.4 Research Hypotheses

i. H01: There‘s no relationship between perceived attributes and usage of online


ii. H02: There‘s no relationship between perceived risk and usage of online retailing


iii. H03: There‘s no relationship between perceived value and usage of online


iv. H04: There‘s no relationship between customers‘ perceptions and customer


v. H05: There‘s no relationship between customer satisfaction and usage of online


vi. H06: Customer demographics have no moderating effect on the relationship

between customer perceptions and usage of online retailing services in

Nairobi County, Kenya.

13

1.5 Significance of the Study

This study shall be of significance in the following ways:

1.5.1 Policy Significance

The findings will assist e-commerce stakeholders including service providers and the

government in designing policies that are geared towards enhancing sustained use of

online retailing services in Kenya. This will reduce the possibility of failure arising from

low usage as it will enable online retailing companies to provide their services

sustainably.

1.5.2 Practical Significance

The outcome of this study shall also be of significance to e-commerce practitioners in

particular the managers who are responsible for developing and implementing strategies

aimed at achieving a viable customer base. It is therefore important for online retailing

firms to have a good understanding of their target customers, since this will not only help

in determining the appropriate customer engagement strategies but also how to increase

the long-term usage of their services.

1.5.3 Theoretical Significance

The academic value of this study is two-fold: First, the conceptual and empirical insights

stemming from this study can be used to develop new knowledge, thereby helping both

broaden and deepen researchers‘ understanding of consumer technology usage behavior,

in particular, with regards to the online retailing context. Second, the study provides

researchers with a rigorous and methodologically sound way of how to integrate

quantitative and qualitative methods in order to contribute to a rich and comprehensive

study.

14

1.6 Scope of the Study

In line with Mottner, Thelen and Karande (2002), this research focused on online

services that sell products and services to the end user or consumer, also known as

Business-to-Consumer (B2C) e-commerce. This differs significantly from Business-to-

Business (B2B) e-commerce, which was not addressed in this study.

Seeing that the study focused on online retailing service providers in Nairobi, Kenya, it

therefore restricted itself to online retailing services that are provided exclusively via the

online channel (i.e. pure play and e-marketplace services). Due to the unique Kenyan

context, results of this study will only be inferred to Kenyan online retailers, thus

limiting their generalizability to other sectors and countries.

Further, the study‘s sampling pool was restricted to registered users of online retailing

services, majority of who were highly educated and more conversant with internet use

for shopping than the wider population of Kenyans. Therefore, to generalize the results

for the larger population, it is suggested that in future, research should expand the scope

of the current study by using a sample of both users and non-users of online shopping

services.

The current study only considered individual aspects (i.e. customer perceptions,

satisfaction and demographics) that affect online retailing usage, despite there being

other usage determinants such as organizational and environmental factors, not to

mention other psychological factors like motivation and learning (Kotler & Armstrong,

2000). Due to the subjective nature of perceptions, the study should be repeated with a

different sample to ensure the validity of its findings.

15

1.7 Limitations of the Study

Several limitations in this mixed-methods study are worth noting: First, quantitative data

for this study was collected using a survey questionnaire and therefore suffers from

biases such as non-response inherent in most survey-based research. To address this

concern, the quantitative survey method was supplemented by qualitative key informant

interviews for integration/triangulation in line with Bryman (2006).

The second limitation had to do with the sampling frame which poses a major challenge

to internet surveys (Simsek, Veiga & Lubatkin, 2005). According to Wilson (2006), it is

unlikely that the sampling frame/list will match the population of interest exactly, which

will result in sampling frame error. This is compounded by the fact that few master

directories exist that lists individuals (and their email addresses) from a particular

population that has access to the internet, and the few that do exist may be seriously

flawed. To reduce sample frame error, Wilson (2006) recommends adding a number of

lists together to create the sample frame. For the current study, several lists of online

retailing service providers were amalgamated in order to form the study population from

which final sampling frame was derived. They include the Kenya ICT Board Tandaa

Grant Applicants list, the Kenya Postel Directories list of E-Commerce service providers

amongst others.

The third limitation regards the lack of representativeness arising from non-response.

Internet surveys studies suffer from a lack of representativeness, which has to do with the

extent to which the sample represents the population from which it was drawn.

Representativeness may prove difficult to achieve using internet surveys for some

population, particularly those where a large percentage of its members dislike the

experience of participating in electronic surveys for various reasons (Simsek et al., 2005)

16

resulting in non-response problem. One way of reducing the number of non-responses in

online surveys is to repeat the contact one or more times (Kumar, Aaker & Day, 2002).

In this study, respondents were sent two reminders via e-mail to solicit their

participation, as recommended by Nulty (2008).

The fourth limitation concerned the nature of the study‘s respondents. The respondents

of this study reside in Nairobi County, the capital city of Kenya, which is more

cosmopolitan and urbanized in comparison to the rest of the country. This makes it rather

difficult to generalize the results to the rest of the country. Overall however, these

limitations are not believed to have necessarily compromised the eventual findings.

1.8 Organization of the Study

This thesis is divided into five chapters. Chapter one, the introduction, consists of the

research problem, research questions and hypotheses. The significance, scope and

limitations were also outlined, finishing with the organization of the study.

Chapter two builds a theoretical foundation upon which the research is based by

reviewing the relevant literature. The theoretical frame of reference of the study and links

to relevant empirical discussions are presented. Also, the key concepts used in this study,

i.e. customers‘ perceptions, customer satisfaction, usage and customer characteristics are

discussed and presented as a conceptual framework. Finally, gaps within the literature

are identified and linked to the research problem of this study.

Chapter three details the research methodology employed in this study and outlines the

empirical research methods used. It covers research issues and hypothesis development,

research design, model specification, data collection, scale development, sample

17

selection and size, research timing, data analysis, research validity and reliability as well

as ethical issues.

Chapter four presents the findings of the study along with their discussion. It comprises

data collection details as captured using the research questionnaire and other sources of

secondary data as well as the analysis of those findings which are presented in the form

of tables, figures, charts and narratives.

Chapter five provides a summary and discussion of the main findings of the study. It also

outlines the conclusions, recommendations of the study, its contribution to knowledge as

well as suggestions for further study.

18

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This chapter reviews the theoretical as well as empirical literature on online retailing

usage by customers. It begins with a review of five theories and models popular in IS and

consumer behaviour, followed by an empirical review of the literature regarding the

main concepts used in this study, i.e. customers‘ perceptions, customer satisfaction,

usage and customer characteristics, which are then presented as a conceptual framework.

Finally, a summary of relevant literature is conducted and subsequently gaps within the

literature are identified and linked to the research problem of this study.

2.2 Theoretical Review

This study is underpinned by four theories and one model commonly used in explaining

technology adoption and usage behavior. These are (i) Behavioral model of system usage

(BMSU), (ii) Innovation diffusion theory (IDT), (iii) Expectation-confirmation theory

(ECT), (iv) Perceived risk theory (PRT) and (v) Theory of consumption values (TCV).

Amongst these, the BMSU and ECT are the dominant theoretical lenses used in this

study.

2.2.1 Behavioral Model of System Usage

The Behavioral model of system usage (BMSU) is an early IS theory that was advanced

by Schewe (1976) to explain end-user system usage behavior in organizational contexts.

The parsimonious model attempted to predict system usage from perceptual, attitudinal

and exogenous variables. It was developed in response to the need to explore how

individual psychological factors and other behavioral aspects of the system user affect

19

MIS usage. Till then, attention to computer usage in the literature had focused on

technical aspects of computer based information systems.

The attitudinal model can be reduced to four sets of variables: (1) perceptions of the MIS

(system dimensions), (2) exogenous variables outside the MIS such as the perceived

support and influence of IT personnel that normally may affect an individual‘s attitudes

toward the system and system usage, (3) attitudes toward the MIS and (4) system usage

(Schewe, 1976). The model was used to explore the relationships between MIS users'

perceptions of their computer system, perceived variables exogenous to the system,

attitudes, and system usage. In this study, the online retailing usage construct as well as

the customer satisfaction construct (attitudes toward the MIS) are drawn from the

BMSU.

Figure 2.1: Behavioral model of system usage Source: Schewe (1976).

Beliefs

A. About MIS

Dimensions

B. About MIS related

objects,

atmosphere, and

significant others

System

Usage

Evaluative

Process

Perceptual

Processes

Attitude

towards

use of the

MIS

Constraints

Beliefs

A. About MIS

Dimensions

B. About MIS related

objects, atmosphere

and significant others

20

The model presumed that a favorable attitude toward the use of an information system is

central to obtaining high system use. However, the study by Schewe (1976) revealed that

attitudes do not appear to determine individual usage behavior. This can be attributed to

the fact that the study was conducted in an organizational setting and it is possible that

there were constraints which intervene to override the influence of attitudes on behavior.

While it does appear to show a direct relationship between usage and perceptions,

Schewe (1976) found no significant relationship between system use and user

satisfaction (Robey, 1979). This is corroborated in a study Mawhinney (1990), which

found no relationship between user satisfaction and system use. Similarly, Lawrence and

Low (1993) did not find this relationship to be significant.

IS researchers have not employed the BMSU as widely as other models in studies of

continued use of IS by end-users. As such, empirical studies based on the BMSU have

been confined to its traditional organizational context, thus limiting its usefulness in

explaining IS usage behaviors in the consumer context. Perhaps, its low explanatory

powers and factor inconsistencies may be due to the exclusion of important moderating

variables reflecting individual differences, as argued by Sun and Zhang (2006). Its

parsimonious nature implies that other variables that may be influencing system usage

were not included in the model.

The main limitation of the model is that it was developed to explain individual MIS

usage in the organizational context, which is generally perceived as mandatory (Shewe,

1976). This study therefore sought to extend its use in an individual context. To address

its other shortcoming, the model was enhanced by incorporating additional perceptual

factors as antecedents to usage in this study. Moreover, customer demographics were

also included in this study as moderating factors to account for the interaction effect.

21

2.2.2 Innovation Diffusion Theory

Grounded in sociology, the Innovation Diffusion Theory (IDT) by Rogers (1962; 1995;

2003) is one of the first models to be employed in technology adoption research. It has

been used since the 1960s to study a variety of innovations, ranging from agricultural

tools to organizational innovation (Tornatzky & Klein, 1982). IDT describes how

innovations (ideas, practices and technology) are spread into a social system network

resulting in institutionalization of the innovation by incorporating it in routine practice/

continued usage (Murray, 2009). Based on this approach, Internet shopping is regarded

as an innovation, which like other innovations takes time to spread through the social

system (Alba, Lynch, Weitz & Janisqewski, 1997; Verhoef & Langerak, 2001).

Figure 2.2: Diffusion of innovation Theory

Source: Rogers (1995)

22

The IDT focuses on the utility of an innovation - conceptualized as its perceived

characteristics (attributes) - and posits that the rate of adoption is partially determined by

the perceived attributes (or characteristics) of the innovation, and proposes several

attributes potentially important across diverse innovation adoption domains. According

to Rogers (1962; 1995; 2003), these perceived attributes (or core constructs) of this

model include relative advantage, compatibility, complexity, trialability and

observability.

These attributes were later refined by Moore and Benbasat (1991) in their perceived

characteristics of using an innovation (PCI) model for the IS context to study individual

technology acceptance into relative advantage, compatibility, ease of use (instead of

complexity), image, result demonstrability and visibility (instead of observability), and

voluntariness of use. Another related model is the technology adoption model (TAM),

whose two constructs, perceived usefulness and perceived ease of use, are quite similar

to the IDT constructs - perceived relative advantage and perceived complexity (Davis,

1989; Al-Gahtani, 2001). Consequently, in this study, the perceived attributes construct

(perceived usefulness, perceived compatibility and perceived ease of use) is drawn from

the IDT, the related PCI model and TAM.

Empirical MIS studies based on the IDT have largely supported its predictive power

(Fichman & Kemerer, 1999; Chircu & Kaufmann, 2000). For instance, an online

shopping study of Dutch households by Verhoef and Langerak (2001) which explored

the impact of relative advantage, compatibility, and complexity on e-shopping found that

consumers‘ perceptions of relative advantage and compatibility positively influenced

their intention to adopt online grocery shopping. Also, results obtained by Hansen (2005)

23

suggest that perceived complexity, perceived compatibility, and perceived relative

advantage highly influence consumers‘ adoption of online grocery buying.

However, the theory has its limitations, the major one being that while it explains the

formation of a favorable attitude toward a particular innovation, it does not provide

further analysis of the attitude evolving into the adoption behavior (Chen, Gillenson &

Sherrell, 2002).

2.2.3 Expectation-Confirmation Theory

The Expectation-Confirmation Theory (ECT) or Expectations Disconfirmation Theory

(EDT) is a marketing theory that was first developed by Oliver (1977; 1980; 2010) and

later refined by Churchill and Suprenant (1982). The theory focuses on the post-purchase

behavior of individuals. It is a widely used in the consumer behavior literature,

particularly in explaining consumer satisfaction and repeat purchase. According to this

theory, a customer‘s initial expectations, combined with perceived product/service

performance (confirmation), lead to post-purchase satisfaction. In addition, the positive

or negative (dis)confirmation between expectations and performance mediates the effect

on satisfaction (Oliver 1977; 1980; Churchill & Suprenant, 1982; Oliver, 2010).

The ECT was adapted to the IS context by Bhattacherjee (2001a), who constructed and

empirically validated the Expectation-Confirmation Model (ECM) based on the ECT to

predict IS continued usage in a consumer context. It lays emphasis on a user‘s

psychological motivations that materialize post initial adoption of IS. According to the

model, users‘ intention to continue to use an IS are dependent on three antecedent

constructs: user satisfaction, user confirmation, and post-adoption expectations (or

perceived usefulness).

24

Bhattacherjee (2001a) describes the process by which IS users reach a continued use

decision is as follows. First, users form a conception of perceived usefulness after using

a particular IS for a period of time. Second, users compare the performance of the IS to

the perception of usefulness (so as to determine to what extent their perception of

usefulness about that IS has been confirmed). If the user finds that the product/service is

as useful as he/she perceived, confirmation is formed and the user forms a notion of

satisfaction. Finally, satisfied users are more likely to continue the usage of that IS

whereas dissatisfied users intend to discontinue the service (Bhattacherjee (2001a, as

cited in Hossain & Quaddus, 2012).

Notable use of the ECT has been made in an effort to better understand end-user

satisfaction with IS and consumer-oriented online services (Bhattacherjee, 2001a; Nevo

& Furneau, 2009). One of the key streams of IS research uses ECT to explain the

adoption and continued use of IS and relies on the premise that these behaviors are result

from users‘ satisfaction. For example, Wixom and Todd (2005) applied ECT to the study

Figure 2.3: Expectations-Confirmation Theory Source: Oliver (1977; 1980).

Expectations

Repurchase

Intention

Disconfirmation

Perceived

Performance

Satisfaction

25

of usage intentions in the context of data warehousing while Bhattacherjee (2001a) used

it to study continuance intentions among online banking users (Nevo & Furneau, 2009).

In this study, the customer satisfaction construct as well as the usage construct are drawn

from the ECT (Oliver, 1980; 2010) and the related ECM by Bhattacherjee (2001b).

Satisfaction is characterized by ECT as either an outcome state following the

consumption experience or more widely as an evaluative process encompassing the

entire consumption experience (Yi, 1990). With regards to continued usage, due to the

similarity between re-purchasing products/services in a consumer context and the

continued use of technology by consumers, ECT posits an equivalent relationship in the

continued online usage context (Bhattacherjee, 2001a; 2001b).

According to Bhattacherjee and Barfar (2011), ECT has been criticized by Ortiz de

Guinea and Marcus (2009) for one of its underlying premise that views IT continuance

as an intentional, reasoned or purposeful behavior; this ignores the role of emotions and

habit. To address this shortcoming, emotional value has been incorporated in this study

as an antecedent to usage.

2.2.4 Perceived Risk Theory

The Perceived Risk Theory was first introduced by Bauer (1960) to explain consumer

behavior. According to this theory, consumers perceive risk because they face

uncertainty and potentially undesirable consequences as a result of purchase or usage of

products/services. This means that the more risk consumers perceive, the less likely they

will purchase/use a product or service (Bhatnagar, Misra & Rao, 2000). The perceived

risk construct in this study is derived from the perceived risk theory and adapted to the

online retailing context.

26

The core constructs of the theory have been decomposed by researchers into several

perceived risk dimensions. For instance, Cunningham (1967) conceptualized six

dimensions of perceived risk: performance, financial, opportunity/time, safety, social,

and psychological risk, while Jacoby and Kaplan (1976) came-up with six components of

consumers‘ perceived purchase risk: Performance Risk, Financial Risk, Physical Risk,

Convenience Risk, Social Risk, Psychological Risk. Bhatnagar et al. (2000) have argued

that two types of risk exist when buying over the internet; product risk and financial risk.

These risks are thought to be present in every choice situation but in varying degrees,

depending upon the particular nature of the decision (Taylor, 1974). Moreover, different

individuals have different levels of risk tolerance or aversion (Bhatnagar et al., 2000).

Figure 2.4: The six dimensions of perceived risk Source: Cunningham (1976).

Performance Risk

Financial Risk

Perceived

Risk

Opportunity/Time

Risk

Safety Risk

Social Risk

Psychological Risk

27

Perceived risk has been applied in various studies of the consumer technology use

context. For instance, an early study of telephone shopping by Cox and Rich (1964)

found that consumers perceive higher risks in new innovative channels. In the e-

commerce context, perceived risk has been applied in studies such as internet banking

adoption (Tan & Teo, 2000), usage of e-commerce services (Liebermann & Stashevsky,

2002) continued usage of internet banking (El-Kasheir, Ashour & Yacout, 2009), online

consumers‘ purchasing behavior (Zhang, Tan, Xu & Tan, 2012) amongst others.

2.2.5 Theory of Consumption Value

The Theory of Consumption Values (TCV) is a consumer behavior theory that was

developed by Sheth, Newman and Gross (1991a; 1991b). Over the years, TCV has

evolved into a popular marketing theory and has been widely applied in various contexts,

including IS. The theory focuses on explaining why consumers choose to use or not to

use a specific product or service, arguing that consumer decisions are made based on

perceived value.

The TCV has five core constructs which are conceptualized as five different types of

values (functional value, social value, epistemic value, and emotional value, and

conditional value) that underlie consumer choice behavior. A particular choice may be

determined by one value or influenced by several values (Sheth et al., 1991a; 1991b). In

this study, the perceived value construct is drawn from the TCV by Sheth et al. (1991a;

1991b) and adapted to the online retailing context.

28

Kalafatis, Ledden and Mathioudakis (n.d.) re-specified three fundamental propositions

that underpin the TCV: (1) consumer choice is a function of multiple consumption

values; (2) the values make differential contributions in the choice situation, and (3) the

values are independent of each other. Thus, all or any of the consumption values can

influence a decision and can contribute additively and incrementally to choice;

consumers weight the values differently in specific buying situations, and are usually

willing to trade-off one value in order to obtain more of another.

TCV‘s strong point is its analytical strength, which helps practitioners to understand

consumer decision making. This enables them to develop practical strategies that address

real market conditions (Gimpel, 2011). TCV has been used in several IS studies on

technology adoption decisions (Kim, Lee & Kim, 2008).

Figure 2.5: The five values influencing consumer choice behavior Source: Sheth, Newman, and Gross (1991b).

Consumer Choice Behavior

Epistemic Value

Conditional Value

Functional Value

Emotional Value

Social Value

29

However, the theory‘s main limitation is due to the fact that it applies only in cases of

individual, voluntary and rational or systematic decision situations (Sheth et al., 1991a,

1991b); therefore, it cannot be used to predict the behaviour of two or more individuals

and is thus restricted to individual end-user/consumer acceptance contexts.

2.3 Empirical Literature Review

The empirical review is made up of related literature regarding the hypothesized

relationships between the various study constructs: i) customers‘ perceptions, ii) usage,

iii) customer satisfaction and iv) customer demographic characteristics. These

relationships are discussed in the following sections.

2.3.1 Usage of Online Retailing Services by Consumers

A review of usage literature shows that it is a complex construct with multiple

conceptualizations (Burton-Jones & Straub, 2006). As such, the context within which it

is used is important in determining its relevant measures and dimensions. In this

research, the usage of online retailing services is studied at the individual level (i.e. the

consumer context) as a post-adoption phenomenon that is also referred to as continued

use. Therefore, usage serves as the dependent variable in line with Bhattacherjee and

Barfar (2011), who argue that the goal of IT continuance research (or IT post-adoption

research) is to predict actual behaviors (and not intentions). In their opinion, continuance

research should operationalize and measure IT usage behavior rather than end at

intention.

According to Turner, Kitchenham, Brereton, Charters and Budgen (2010), the actual

usage of information technology can be measured using both objective and subjective

forms. Objective measures are usually generated from logs of usage generated by the

30

software itself. In comparison with objective measures of actual usage, subjective

measures of usage are based on the individual opinion that is usually established using a

questionnaire. Examples of subjective measures of technology use include self-reported

usage measures of the frequency or intensity of using the technology in question, in line

with Legris, Ingham and Collerette (2003).

Consequently, for this study, usage of online retailing services shall be measured

objectively, instead of through perceptual measures such as use intentions and self-

reported use. Usage behavior shall be obtained from system log records in line with

Venkatesh et al. (2003) who in their longitudinal study captured system data from four

organizations over a six-month period and measured actual usage behavior over a six

month period as duration of use via system logs.

While there are several factors that affect usage behaviour, this study restricts itself to

examining the antecedent role of customers‘ perceptions on usage of online retailing

services, in line with extant research that has established the significant relationship

between perceptions and system usage. Some of the earliest studies were carried out by

Lucas (1975) and Schewe (1976). For Lucas, who carried out an extensive MIS user

behavior study, favorable user attitude and perceptions of IS lead to high levels of use of

the same. On the contrary, Schewe established that user satisfaction is not associated

with IS usage.

Bhattacherjee (2001b) carried out an empirical analysis of the antecedents of continued

usage of an e-commerce service. The study developed a model of B2B e-commerce

continuance behaviour which was empirically tested in a survey of online brokerage

users. The study found that consumers' continuance intention is determined by their

31

satisfaction with initial service use as well as its perceived usefulness. However, it only

used the ECT as its basis and thus lacked moderating variables. By employing

continuance intention as the DV, the study also failed to assess actual usage.

In another study, DeLone and McLean (2004) adapted their updated model of IS success

to an e-commerce context through an extensive review of relevant literature. According

to the model, e-commerce usage is directly influenced by satisfaction with the e-

commerce system. The major weakness of their study was its conceptual nature since it

failed to undertake empirical testing of the proposed relationships between the six

dimensions. Instead, two case studies were used to demonstrate how the model can be

used to guide the identification and specification of e-commerce success metrics.

Barnett, Kellermanns, Pearson and Pearson (2006-2007) replicated and extended the

landmark study of technology acceptance and use conducted by Straub, Limayem and

Karahanna-Evaristo (1995). The study empirically examined the TAM and clarified

conceptual ambiguities that had hampered system usage research. It found that perceived

ease of use was a significant predictor of objective system use, while perceived

usefulness was a significant predictor for self-reported usage behavior. The study‘s key

weakness was its basis on the TAM, which is more suited for initial usage context.

Bhattacherjee, Perols and Sanford (2008) conceptually extended the IT continuance

model by adding continuance behavior as the DV instead of BI. Data for the study was

collected via a longitudinal survey of document management system usage among

administrators and staff personnel at a governmental agency in Ukraine. However, the

study failed to demonstrate the strong intention-behavior association in the IT

continuance context, since intention explained only 26% of the variance in continuance

http://www.citeulike.org/user/rrbarb/author/Kellermanns:FW

http://www.citeulike.org/user/rrbarb/author/Pearson:AW

http://www.citeulike.org/user/rrbarb/author/Pearson:RA

32

behavior. It therefore called for additional constructs that may be able to predict

continuance behavior better.

Petter, DeLone and McLean (2008) carried out a meta-analysis where they summarized

empirical studies that have investigated the relationship between satisfaction and usage

constructs. They classified the level of support for the relationship as strong, moderate,

or mixed in order to summarize the empirical results across all studies. According to

their analysis, 17 of 21 studies showed a positive, moderate support for the satisfaction-

use relationship, 1 showed a positive mixed, while the rest (3) showed no support at all.

However, this study failed to address the relationship between customer perceptions and

usage.

A recent study by Ramayah and Lee (2012) investigated the role of the users‘

satisfaction in influencing e-learning success by empirically establishing the impact of

satisfaction on e-learning usage among students in a public university in Malaysia. The

study employed an adaption of the DeLone and McLean‘s (2002, 2003) extended model,

and analyzed the response data using the structural equation modelling (SEM) method.

The findings of the study supported the hypothesis that user satisfaction is positively

related to usage continuance. However, since it was based on the Delone and McLean

model (2003), the study fails to address the role of moderating variables in the

perception-usage relationship.

2.3.2 Antecedent Role of Customers’ Perceptions on Online Retailing Service

Usage

To induce more initial adopters to continue using their services, service providers have to

establish what motivates consumers to return repeatedly. One of the factors found in

33

prior consumer technology adoption studies to influence continued use is the customers‘

perceptions (Venkatesh & Davis, 2000; Venkatesh et al., 2003). In general, one of the

ways in which perceptions towards e-commerce are determined is through the user‘s past

online experiences (Im, Kim & Han, 2008); these perceptions subsequently go on to

influence consumers‘ purchase decisions (Osman, Yin-Fah & Hooi-Choo, 2010). In this

study, the customer perception construct serves as the independent variable and has

theoretical foundations from three perceptual constructs identified in literature as playing

an antecedent role via-a-vis usage of online retailing services. These are perceived

attributes, perceived risk and perceived value.

2.3.2.1 Relationship between Perceived Attributes and Usage of Online Retailing

Services

Perceived attributes have been found to influence consumer behavior vis-à-vis

technology use. The antecedent role of perceived attributes/characteristics of an

innovation on its adoption was first suggested by Rogers (1962) and was later refined by

Moore and Benbasat (1991) in the context of individual IS usage. A review of literature

reveals several conceptualizations of the perceived attributes construct. In this study, it

has three dimensions (perceived usefulness, perceived compatibility and perceived ease

of use) drawn from work on the TAM (Davis, 1989), DOI (Rogers, 1995), PCI (Moore &

Benbasat, 1991).

Usefulness of an online retailing service enhances consumer shopping activities and

consists of aspects such as the relevance of information, the breadth of offerings amongst

other things. Compatibility has to do with the fit between the individual current

circumstances (e.g. experience, values, needs, and habits) and the features of the online

retailing service. A lack of compatibility hampers the adoption of innovations (Rogers,

34

1995). Ease-of-use relates to the simplicity with which consumers can operate an online

retailing system to perform shopping activities such as browsing, communicating and

carrying out actual transaction such as ordering and payment. It is the polar opposite of

complexity.

A review of prior literature reveals different usage outcomes based on the antecedent role

of the perceived attributes of an IS. For instance, Parthasarathy and Bhattacherjee (1998)

empirically examined usage in the online service context and found that the perceived

service attributes such as usefulness and compatibility determine usage behavior.

Moreover, Bhattacherjee‘s (2001b) empirical analysis of the antecedents of e-commerce

service continuance demonstrated that perceived usefulness is a key determinant of

customer‘s continued usage intention (CUI). This study‘s main weaknesses are its

employment of the ECT as its sole basis as well as its lack of moderating variables. It

also used continuance intention as the DV, which has been criticized as being weakly

correlated with actual use.

A study by Saeed and Abdinnour-Helm (2008) examined the effects of IS characteristics

and perceived usefulness on post-adoption usage of information systems. The context

was that of a web-based student information system that students use to manage their

academic work. Data was collected from 1032 respondents and used to empirically test

the model. The results showed that perceived IS usefulness is a good predictor of post-

adoption usage. However, despite testing for the moderating role of gender and

experience, it failed to examine the moderating effect of age, income and education level

on model relationships.

35

El- Kasheir et al. (2009) empirically established factors affecting continued usage of

internet banking among Egyptian customers. The study, which was based on several

intentional models and employed a sample of users of internet banking services, found

perceived ease-of-use to be the strongest predictor of intentions to continue usage of

internet banking services. This study‘s main weakness was its use of continued intention

instead of actual usage as the DV since it has been criticized as being a poor proxy for

actual use.

2.3.2.2 Relationship between Perceived Risk and Usage of Online Shopping Services

Perceived risk is a subjective consumer behavior concept that relates to the uncertainty

and consequences associated with a consumer‘s action. A perception of risk with regards

to a particular activity/transaction (e.g. purchasing or using a product or service)

dissuades a consumer from taking further action in that regard (Bhatnagar et al., 2000).

The notion of perceived risk as a key antecedent to consumer behavior has been

established in prior research. For instance, Sharma, Durand and Gur-Arie (1981) showed

that the willingness to purchase products is inversely related to the amount of perceived

risk associated with a purchase decision.

By and large, perceived risk is conceptualized as a multi-dimensional construct in several

studies (Cox & Rich, 1964; Jacoby & Kaplan, 1972; Bettman, 1973; Bhatnagar et al.,

2000, Zhang et al., 2012). In this study, the perceived risk construct has three dimensions

that have been derived from a review of relevant literature. These are i) financial risk

(Jacoby & Kaplan, 1972; Bettman, 1973, Bhatnagar et al., 2000), ii) performance risk

(Jacoby & Kaplan, 1972; Bettman, 1973) and iii) personal/privacy risks drawn from

work by Jarvenpaa and Todd (1997).

36

Financial risk has to do with the potential financial loss a consumer is likely to incur as a

result of overpaying (being overcharged) or due to fraud (Bhatnagar et al., 2000)

whereby what is paid for is not received. Consumers assume financial risk when paying

for products/services electronically e.g. in online shopping or electronic auctions.

Performance risk has to do with concerns that the online service or the desired (or even

paid for) product will not function as expected. For consumers who provide personal

information during online transactions, the risk of having this information compromised

through identity theft and credit card information going to the hands of hackers

comprises personal/privacy risk. For many shoppers, online retail raises concerns about

privacy and security. These concerns have often been cited as potential barriers to online

retail (Forsythe & Shi, 2003; Shim et al., 2001).

Moreover, in the online retailing context, the intangible nature of online transactions

poses a risk for consumers, impeding further use of online purchasing services

(Bhatnagar et al., 2000; Hansen, 2007). Previous research on its antecedent role also

suggests that perceived risk negatively impacts internet shopping. For instance,

Liebermann and Stashevsky (2002) investigated the role of perceived risks as barriers to

e-commerce usage in Israel amongst both users and non-users. The study, which

employed a cross-sectional design and had a sample of 465 employed adults, only considered

barriers to e-commerce usage, which were mapped as perceived risk components. The model

was tested empirically against field data and showed that Internet credit card stealing and

supplying personal information (i.e. privacy risk) affects both current and future e-commerce

usage.

37

However, an empirical study by El-Kasheir et al. (2009) on factors affecting continued

usage of internet banking among Egyptian bank customers established that that perceived

risk had no relationship with customer continued intention to use the service. Data was

collected from 65 respondents using mall interception technique and multiple regression

analysis was used to test the research hypothesis. This study‘s main weaknesses was its

employment of mall interception to collect data as well as its use of continued intention

instead of actual usage, since it has been criticized as being a poor proxy for actual use.

In the context of B2C e-commerce, a study by Zhang et al. (2012) on the dimensions of

consumers‘ perceived risk and their influences on online consumers‘ purchasing

behavior demonstrated five independent dimensions (perceived health risk, perceived

quality risk, perceived time risk, perceived delivery risk and perceived after-sale risk)

which affect significantly online consumers‘ purchasing behavior. The results also

showed that the other three dimensions - perceived privacy risk, perceived social risk and

perceived economic risk - are the less relevant factors.

2.3.2.3 Relationship between Perceived Value and the Usage of Online Retailing

Services

Perceived value is a broad and abstract concept comprised of various components

(Bolton & Drew, 1991) that refers to the benefits ascribed to the purchase/use of a

product or service. It‘s a widely used business concept that aggregates perceptions about

product/service benefits and tradeoffs and is thus considered as the pivot in relationship

marketing and customer loyalty (Casalo, Flavian & Guinaliu, 2008). Therefore,

understanding consumers‘ value perceptions of their online experience is crucial in

enhancing the use of internet as an alternative marketing channel (Andrews et al., 2007).

Accordingly, it has attracted a lot of attention from both marketing and IS fields as a

significant determinant of consumers‘ decision-making behavior in various contexts,

including usage of online retailing services.

38

Prior studies contend that perceived value is a complex construct that is multi-

dimensional in nature (e.g. Sheth et al., 1991b; Sánchez-Fernández & Iniesta-Bonillo,

2007). Accordingly, the perceived values construct in this study has four dimensions

drawn from relevant literature, namely i) monetary value, ii) convenience value, iii)

social value and iv) emotional value.

Monetary value derives from the customer‘s evaluation of the total financial cost of

using the online retailing service (including the price paid) relative to the benefits

received. Convenience value pertains to the perceived ease with which users are able to

find information and/or products on the online retailing service, pay and, have them

delivered as expected by the consumer (i.e. minimization of the overall shopping effort),

while social value is derived from the collective/group significance ascribed to the use of

the e-commerce by an individual. According to the concept, usage of online retailing

services should be congruent with the norms of a consumer‘s friends or associates. On

the other hand, emotional value is related to various affective states induced by usage of

online retailing services. These can be positive (e.g., confidence or excitement) or

negative (e.g., fear or anger).

Similarly, consumers in the online shopping context also have diverse perception of

value, as argued by Hu and Chuang (2012) in their study of the relationship between

value perception and loyalty (re-purchase) intention toward an e-retailer website in

Taiwan. SEM was used to test the data from 243 students and 418 workers. The findings

concluded that utilitarian value is more important than hedonic value in terms of

influencing loyalty intention for online shopping. The study thus recommended the use

39

of various website elements and attributes as a way of offering either more hedonic or

more utilitarian value to online buyers in order to attract and retain more visitors.

Yen (2011) carried out an empirical study on the impact of perceived value on continued

usage intention in social networking sites (SNS) amongst savvy Facebook users in

Taiwan. Mediated regression analysis was used to analyze data from 205 respondents.

The findings revealed that PV, including information value, sociable value, and hedonic

value, has a positive impact on CUI. This study‘s limitation is due to the fact that it only

considered three ―get‖ values for measuring PV, ignoring ―give‖ components such as

monetary value.

It would therefore be expected that if an individual perceived an e-commerce system to

have a high value, (s)he would be more willing to try (accept) and use it. Therefore,

identifying and delivering value for potential customers is crucial for e-commerce

service providers in order to induce continued use of their services by consumers.

2.3.3 Mediating Role of Customer Satisfaction on the Relationship between

Customer Perceptions and Usage of Online Retailing

Customer satisfaction is another multi-attribute concept that is originally based on a

study by Katz (1960) explaining the role of attitudes in shaping social behaviour.

According to the study, the underlying dimensions of attitude include: affect (feelings),

behaviour (actions), and cognitions (learning and beliefs). It has evolved into a widely

used business concept that aggregates customer evaluations about a product/service.

Consequently, understanding of what creates a satisfying customer experience has

become crucial, more so due to its role in influencing repeat customers behavior (e.g.

repurchase, continued usage).

40

While customer satisfaction has been a popular topic in marketing practice and IS

research, it is also one of the most controversial concepts due to its multiple

conceptualizations and meanings. Despite many attempts to measure and explain

customer satisfaction, there still does not appear to be a consensus regarding its

definition. This lack of a clear and broadly accepted conceptual and operation definition,

has resulted in the arbitrary development of satisfaction measurement instruments, and

conclusions about interactions with other constructs are problematic (Caruana, 2002).

For this study, customer satisfaction is used a post-initial usage evaluation and is treated

as a mediating variable influencing the relationship between customer perceptions and

continued usage of online retailing services. The customer satisfaction construct in this

study is drawn from the ECT (Oliver, 1977; 1980, 2010) and related ECM by

Bhattacherjee (2001a) which adapted the ECT to the IS context. IS researchers have

made notable use of ECT in an effort to better understand end-user satisfaction with

information systems and related services. In this regard, prior studies use ECT to explain

the adoption and continued use of IS and rely on the premise that these behaviors are the

result of user satisfaction (Bhattacherjee, 2001a; Nevo & Furneaux, 2009; Hossain &

Quaddus, 2012).

In the service context, customer satisfaction can be viewed from the i) transactional

and/or ii) cumulative/relational orientations/perspective. In earlier studies, satisfaction

has been defined from transactional perspective (e.g. Oliver, 1980; 1993), where it is

based on a one time, specific post-purchase evaluative judgment of a service encounter.

However the conceptualization of satisfaction as a customer‘s overall/cumulative

41

assessment/evaluation construct based on purchase and consumption experiences over a

time period (Bitner & Hubbert, 1994) has become more dominant in research.

In terms of the diagnostic and predictive value of satisfaction measurement, cumulative

satisfaction is more useful and reliable determinant of long-term use than transaction-

specific in that it is based on series of purchase and consumption occasions rather than

just one occasion of transaction. This is also the case in the IS context, where satisfaction

is often regarded as the basis of system usage continuance, while dissatisfaction may

cause users to discontinue the system use (Bhattacherjee, 2001a; Nevo & Furneaux,

2009). Therefore, this study employs the cumulative approach to conceptualize

satisfaction which is evaluated from the time of registration with the e-commerce

service.

Giese and Cote (2000) reviewed relevant satisfaction literature with the view of

identifying its conceptual domain. They defined the customer as the ultimate user of a

product/service and came to the conclusion that satisfaction was comprised of three basic

components, (i) a response (cognitive or emotional) pertaining to (ii) a particular focus

(expectations, product, consumption experience, etc.) determined at (iii) a particular time

(after consumption, after choice, based on accumulated experience, etc).

The mediating role of customer satisfaction in the service context was demonstrated by

Bolton and Lemon (1999), who developed and empirically tested a dynamic model of

customers‘ usage of services, where usage was employed as both an antecedent and

consequence of satisfaction. The aim of the study was to identify causal links among

customer‘s prior usage, satisfaction evaluations, and subsequent usage. The study was

significant in that it provided an in-depth examination of the dynamic relationship

42

between customer satisfaction and customer usage. According to its findings, customers

who have high levels of cumulative satisfaction with a continuously provided service in

the current time period will have higher usage levels of the service in a subsequent time

period.

Bhattacherjee (2001b) carried out an empirical analysis of the antecedents of electronic

commerce service continuance. The study examined the key drivers of consumers'

intention to continue using B2B electronic commerce services using multiple theoretical,

synthetic perspectives to develop a model of continuance behaviour which was

empirically tested using a field survey of online brokerage users. The study found that

consumers' continuance intention is determined by their satisfaction with initial service

use as well as their perceived usefulness of service use.

Devraj, Fan and Kohli (2002) carried out a study on the antecedents of B2C e-commerce

satisfaction where they developed and empirically tested a model for consumer

satisfaction with the e-commerce channel using constructs from TAM, TCA and

SERVQUAL. Subjects purchased similar products through conventional as well as EC

channels and reported their experiences in a survey after each transaction. The findings

of the study demonstrated the influence of perceived ease of use and perceived

usefulness on satisfaction in the B2C e-commerce context.

DeLone and McLean (2003; 2004) conceptually extended the DeLone and McLean IS

Success Model (1992) to measure e-commerce success. The mediating role of customer

satisfaction was put forward in their study, where they proposed that ease of use

influences user satisfaction which subsequently directly influences usage of e-commerce

services.

43

A study by Serenko, Turel and Yol (2006) in the mobile phone services established that

customer satisfaction was influenced by perceived value. With regards to the C2C

context of e-commerce, Jones and Leonard (2007) carried out an empirical study which

was aimed at establishing the factors that impact satisfaction in C2C e-commerce.

According to the study‘s findings, TAM, TCA, and SERVQUAL all impact satisfaction

in C2C e-commerce. On the other hand, Ortiz de Guinea and Marcus (2009) noted that

satisfaction may drive IT usage directly.

Chen, Mocker, Preston and Teubner (2010) investigated the confirmation of expectations

and satisfaction with the internet shopping context of e-commerce in Taiwan by

integrating various theories (ECT, SCT, and TAM) and testing the research hypotheses

using empirical data that was collected from a sample of 342 responses. The results

suggest that satisfaction is influenced by perceived usefulness, and that both satisfaction

and perceived usefulness determined consumer‘s repurchase intention.

The mediation effect of end-user satisfaction on the relationship between PV and CUI

was established in an empirical study by Yen (2011) on the impact of perceived value on

continued usage intention in social networking sites (SNS) amongst savvy Facebook

users in Taiwan. Mediated regression analysis was used to analyze response data from

205 users. The findings revealed that the mediation effect of end-user satisfaction is only

significant to social value and hedonic value but not information value. This study

however didn‘t consider/ignored ―give‖ components such as monetary value.

As evidenced in the literature, customer satisfaction is critical for retaining current

users/customers. As a result, a fundamental understanding of the role of customer

44

satisfaction is of great importance in e-commerce. This study therefore seeks to establish

whether customer satisfaction has a mediating effect on the customer perception-e-

commerce usage relationship among consumers in Kenya.

2.3.4 Moderating Effect of Customer Demographic Characteristics on the

Relationship between Customer Perceptions and Usage of Online Retailing

Services

Explaining repurchase intention (in this case continued usage) using satisfaction alone

may not suffice (Capraro, Broniarczyk & Srivastava, 2003) since it has been reported

that only 15–35% of satisfied customers return (Reichheld, 1996). It is therefore

important to examine the role of potential moderators in attaining a better understanding

continued system use in the online context. Of particular interest to this study is the

moderating role of demographic factors.

The employment of moderators may potentially increase the predictive validity of a

model under investigation, and explain the inconsistent findings in various disciplines

(Judge & Bono, 2001). For this reason, moderator variables have enjoyed a surge of

popularity in the marketing literature in recent years, and scholars have acknowledged

their importance for predicting consumer behaviour (Baron & Kenny, 1986; Sharma et

al., 1981). The importance of moderators arises from their ability to enhance

understanding of the relationship between relevant independent variables and dependent

variables, as well as seemingly established relationships (Walsh, Evanschitzky &

Wunderlich, 2008).

In the IS context, Sun and Zhang (2006) argue that low explanatory powers and factor

inconsistencies of IS models may be due to the exclusion of important moderating

variables reflecting individual differences. Several studies have therefore called for the

45

inclusion of individual characteristics into the study of individual technology acceptance

and use (e.g. Venkatesh et al., 2003). In view of that, customer demographic factors will

serve as the moderating variables of this study.

According to Hoffman, Novak and Schlosser (2000), key elements of a consumers‘

demographic profile that have been found to influence their online behaviour include

variables such as income, education, age. Moreover, age, income, education offer

significant information regarding the demographic characteristics of the targeted

population. For this reason, these three variables have been included in previous studies

that examined the usage of various information systems, including online retailing

services. This study shall therefore seek to establish the moderating role of 3

demographic measures (i.e. age, income and educational level) on the relationship

between customer perceptions and usage of online retailing services.

Previous online purchasing research has examined the three demographic characteristics.

For instance, Bhatanager et al. (2000) examined the moderating effect of age amongst

other demographic factors (gender, marital status and years on the internet) in a previous

study on how risk, convenience and demographics affect internet shopping behavior.

They found mixed results on the moderating effect of age on internet shopping behavior.

Other online purchasing studies report that e-commerce purchasers are younger, more

educated and have higher income than non online purchasers (Ratchford, Talukdar &

Lee, 2001).

In the context of mobile phone services, an empirical investigation into the moderating

roles of demographic variables, namely age, income, and gender, in forming perceptions

and behavioral outcomes with was carried out by Serenko et al. (2006). Structural

46

equation modeling techniques and split-sample approach for moderation analysis were

applied to a dataset of 1,253 mobile phone users in the U.S. While age and income were

found to have a significant effect on several of the model‘s relationships, the study

proposed that gender has a very limited effect on the examined relationships in the

mobile services context. However, the study didn‘t go as far as to examine the customer

perception – usage relationship.

However, a study by El-Kasheir et al. (2009) that empirically established factors

affecting continued usage of internet banking among Egyptian bank customers found that

demographic variables such as age, gender, marital status, education level and income

level had no relationship with customer continued intention to use the service. Analysis

of variance (ANOVA) was used to test the research hypothesis that differences in the

demographic characteristics of the bank customers can affect their continued intention to

use internet banking. It main weakness was its use of mall interception techniques of data

collection, which is prone to selection bias.

More recently, Hernández, Jiménez and José Martín (2011) conducted a study on

whether age, gender and income really moderate online shopping behaviour. The study‘s

aim was to establish if individuals‘ socioeconomic characteristics – age, gender and

income – influence their online shopping behaviour. The individuals sampled were

experienced e-shoppers i.e. individuals who often make purchases on the internet. The

results showed that socioeconomic variables moderate neither the influence of previous

use nor the perceptions of e-commerce; in short, they do not condition the behaviour of

the experienced e-shopper.

47

The conflicting outcomes of various studies point to the need for more research in this

important area. Therefore, this study seeks to establish the moderating influence of

customer characteristics on the perception-usage link in online retailing services.

2.4 Summary of Empirical Literature and Research Gaps

This section contains a summary of literature reviewed regarding usage of online

retailing services and research gaps. Appendix 6 presents a summary of the empirical

reviews as well as the important research gaps that have been identified in this section of

the study.

2.5 Conceptual Framework

The conceptual framework for this study is made up of various consumer behavior and

technology adoption constructs, their variables, indicators and is based on the premise

that customer perceptions have an effect on usage of online retailing services but this

effect is mediated by customer satisfaction and moderated by demographic factors as

described in the empirical literature review in the previous section. The framework is a

graphical representation of how these constructs of interest are interconnected and is

depicted in Figure 2.1.

The presumed interrelationships amongst the study‘s constructs are organized into three

sub-models. The first proposed relationship models the main effects of the predictor

variable (customer perceptions) on the criterion variable (usage of online retailing

services). Since the customer perception variable is made up of three constructs

(perceived attributes, perceived risk and perceived value), the study proposes that each of

them has a direct effect on the DV (usage of online retailing services).

48

CUSTOMER PERCEPTIONS

Figure 2.6: Schematic Diagram Source: Researcher (2013).

ONLINE RETAILING

SERVICE USAGE

- Active Use

- Inactive Use

DEMOGRAPHIC FACTORS

- Age

- Income

- Education level

CUSTOMER

SATISFACTION

- Level of Satisfaction

Perceived Attributes

- Usefulness

- Compatibility

- Ease of Use

Perceived Risk

- Financial risk

- Performance risk

- Personal (Privacy)

risk

Perceived Value

- Monetary value

- Convenience value

- Social value

- Emotional value

H3

H2

H1

H6

H5

H4

INDEPENDENT

VARIABLE

MEDIATING

VARIABLE

MODERATING

VARIABLE

DEPENDENT

VARIABLE

49

The second relationship is mediation effect model of customer satisfaction on the

relationship between the predictor and criterion variables. The main concern of

mediators is how cognitive mechanisms operate. In short, mediation ―explain[s] how

external physical events take on internal psychological significance‖ (Baron & Kenny,

1986). As argued by Wu and Zumbo (2008), mediators have dual roles — an outcome

variable (effects) of the independent variable and an independent variable (causes) that

occur before the dependent variable. In this case, it proposed that customer perception

has a direct effect on customer satisfaction, and subsequently, customer satisfaction has a

significant effect on usage of online retailing services.

The mediation effects model for this study is based on Spencer, Zanna and Fong (2005)

concept of ―experimental-causal-chain design‖, whereby the mediator typically functions

like a DV for the manipulated IV and in the same way, the mediator is subsequently

manipulated to act like an IV for the outcome variable (usage). According to this

concept, a researcher conducts two separate manipulated experiments; one aims to

establish the causal relationship of X on M, and the other aims to establish the causal

relationship of M on Y. They point out that the strength of this design is that by

manipulating both the independent variable and the mediator, one can make strong

inferences about the causal chain of events in psychological processes.

The third relationship models the interaction effect of the moderating variable (customer

demographics) on the relationship between the predictor and criterion variables.

According to Wu and Zumbo (2008) a moderator‘s function is to explain the strength

and direction of the causal effect of the IV on the DV. It thus serves as a supplementary

50

variable for improving a hypothesized bi-variate causal relationship, and less as a causal

variable responsible for the outcome effect. In regard to causal order, a moderator

variable is prior to the dependent variable and has no causal relationship with the

independent variable (figure 2.1). In short, moderation specifies various conditions under

which the direction and/or strength of the relationship varies (Baron & Kenny, 1986).

For this study, three demographic factors — age, income and education level — are

conceptualized as having a moderating effect on the customer perception – usage

relationship.

51

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Introduction

This chapter presents the research methodology. It includes the research design,

empirical model of the study, target population, sampling and sample size, data

collection instruments. In addition, the techniques that were employed in analyzing data

as well as the ethical issues arising from the study are outlined.

3.2 Research Philosophy

The philosophical perspective grounds the methodological logic and criteria for a

research study (Crotty, 2003). For this study, the philosophical foundation employed

was pragmatism, a practical substitute to positivism and anti-positivism (Goldkuhl,

2004). This is because pragmatism does not restrict one‘s choice to between positivism

and interpretivism insofar as methods, logic and epistemology are concerned (Creswell,

2003; Pansiri, 2006).

Essentially, pragmatism presupposes that objectivist and subjectivist perspectives are

not mutually exclusive. Hence, a mixture of ontology, epistemology and axiology is

acceptable to interrogating social phenomena - of importance is what works best to

address the research problem. Pragmatist researchers thus prefer to use both

quantitative and qualitative data as this allows for better grasp of social reality

(Wahyuni, 2012). Instead of laying emphasis on methods, the research problem is

considered as the most important issue; this frees researchers to choose the methods,

techniques and procedures of research that best meet their needs and purposes

(Creswell, 2003).

52

In practical terms, pragmatism embraces the two extremes:- quantitative methods

espoused by positivism/post-positivism and qualitative methods espoused by

interpretivist proponents (Pansiri, 2006). For this reason, pragmatism is regarded as the

basis of mixed-method research (Tashakkori & Teddlie, 1998; Teddlie & Tashakkori,

2003) popular in the social and behavioural sciences (Maxcy, 2003; Pansiri, 2006).

Consequently, this study employed a similar pragmatic approach. As a result of the

pragmatic approach adopted by the study, both method and data triangulation were

achieved through the use of qualitative as well as quantitative methods of data

collection. In this study, survey served as the main quantitative data collection method,

supplemented by key informant interview as the qualitative data collection method, in

line with Bryman (2006).

3.3 The Research Design

According to Sekaran and Bougie (2010), the research design addresses important

issues relating to a research study such as purpose of the study, location of the study,

type of investigation, extent of researcher interference, time horizon and the unit of

analysis. In view of this, this study adopted a mixed design made of descriptive –

specifically cross-sectional survey design – and explanatory research design (Saunders,

Lewis & Thornhill, 2009) to establish the relationship between customers‘ perceptions

and the usage of online retailing services in Nairobi, Kenya.

Descriptive design involves assessing the study phenomena without the ability to

control or manipulate variables, and thus require the researcher(s) to collect data and

determine relationships without inferring causality (Swanson & Holton, 2005).

Saunders et al. (2009) argue that descriptive studies should be regarded as a means to

53

an end and not an end in itself. Consequently, this study employed description prior to

delving into explanation of phenomena.

Explanatory research design serves to test hypotheses derived from the theory and thus

test causality between the independent and dependent variable (Saunders et al., 2009).

The explanatory approach of the study which followed tested the causal relationships

between the variables to determine their significance.

3.4 The Empirical Model

The model used in this study is based on the premise that customer perceptions have an

effect on usage of online retailing services but this effect is mediated by customer

satisfaction and moderated by demographic factors. These causal relationships between

the study‘s variables were organized into three sub-models that are explained in the

following section:

3.4.1 The Direct Effects Model

The first sub-model was the direct-effects model, whose relationship is the direct effect

of the predictor variable (customers‘ perceptions) on the criterion variable (usage of

online retailing services). Since the customer perception variable was made up of three

constructs (perceived attributes, perceived risk and perceived value), the study

presumed that each of them has a direct effect on the DV (usage of online retailing

services).

The direct effect model was empirically analyzed using the logistic regression (Logit)

analysis method, a statistical technique of modeling the non-linear relationship between

continuous IVs and dichotomous dependent variables (Liou, 2008). Logistic regression

has become an important statistical procedure employed in various social and

54

behavioral studies (Moosbrugger et al., in press) to estimate the coefficients of a

probabilistic model involving a set of independent variables that best predict the value

of the dependent variable. A positive coefficient increases the probability, while a

negative value decreases the predicted probability of the outcome being in either of the

two dependent categories (Mazzarol, 1998).

Logit analysis was deemed suitable for this study because of the binary/dichotomous

nature of the dependent variable y (usage), which can have either of two values

representing different outcomes categories: the value 1 which denotes active user with a

probability of P, or the value 0 which denotes inactive user with a probability of 1 - P.

The empirical model estimated the conditional probability that the dependent variable

is either one or the other. This is illustrated in the Equation 1, where Pi is the

conditional probability of observing whatever value of y is observed for a given

observation.

……………………………. (1)

The formula for the probability itself is equation 2 shown below

(2)

Whereby:

y = The dichotomous dependent variable

y = Estimated regression equation = B0 + B1X1 + B2X2 +…+ BkXk + ε1

P (y = 1) = The conditional probability of an individual being classified as belonging to

either of two outcome categories: 1 or 0

).....(B -1

.....B

)1(22110

22110

kk

kk

XBXBXBe

XBXBXBe

yP

55

e = Exponential, the quantity 2.1828+, the base for natural logarithms X1 - Xk

B0 = Intercept Term

B1- k = Logistic regression coefficients for predictor variables

X1 - Xk = Predictor variables

ε1= Error Term

Consequently, the logistic regression model that was used to establish the direct effects

of the predictor variables on the criterion variable is expressed as:

………. (3)

Whereby:

y = The dichotomous DV (usage of online retailing services) with 1 (active user) or 0

(inactive user).

y = Estimated regression equation = B0 + B1X1 + B2X2 + B3X3 + ε1

P (y = 1) = The conditional probability of an individual being classified as belonging to

either of two outcome categories: 1 (active user) or 0 (inactive user).

e = Exponential, the quantity 2.1828+, the base for natural logarithms X1, X2, and X3

B0 = Intercept Term

B1- 3 = Logistic regression coefficients for predictor variables

X1 = Perceived Attributes

X2 = Perceived Risk

X3 = Perceived Value

ε1= Error Term

3322110B)1(logit XBXBXByP

56

It is noted that the left hand side of the equation is not the dependent variable, y, itself;

but the so-called ‗log odds‘ or ‗logit‘ of y.

3.4.2 The Mediated Effect Model

The second relationship in the conceptual research model is the mediation effect of

customer satisfaction on the relationship between the predictor and criterion variables.

The mediation effect of customer satisfaction (M) on the relationship between the

predictor and criterion variables was obtained from two regression equations (4 & 5) in

line with Spencer et al., (2005). The first equation (Equation 4) used simple linear

regression, a data analysis technique for identifying underlying correlations among data

in research (Nimon, 2010). Equation 4 is illustrated below:

M = B0 + B1P1 + ε1 ………....................................................................………..…..... (4)

Where:

M = Customer Satisfaction (Dependent variable)

B0 = Constant

B1 = Linear regression coefficients

P1 = Customer Perceptions (Composite Value)

ε1= Error Term

In equation 5, the mediating variable, customer satisfaction (M) was re-conceptualized

as an independent variable affecting usage. The effect of customer satisfaction on usage

was established using Equation 5, a binary logistic regression shown below.

Logit [ p ( y = 1) ] = B0 + B1M + ε1 ….............…………...………..………..…..... (5)

57

Where:

Y= Online Retailing Service Usage

P (y = 1) = Probability of belonging to either 1 or 0

B1 = Logistic regression coefficient

M = Customer Satisfaction (Mediating Variable)

ε1= Error Term

Equations 4 and 5 represent simple cases of correlational and are thus useful for making

statements about the relationship between two variables. Neither is useful for

establishing evidence of causality; to do this, one must also attempt to establish proper

time order and to control for potentially confounding variables.

3.4.3 The Interaction Effect Model

The third relationship modeled the interaction effect of the moderating variable

(demographic factors) on the relationship between the independent variable (customer

perceptions) and the dependent variable (usage). For this study, three demographic

factors — age, income and education level — were conceptualized as a composite

value Z. This was obtained using a logistic regression equation. In order to do this,

three data computation procedures were performed. First, a composite value (CPER) of

the IV (customer perceptions) was derived. Second, the moderating variable Z

(demographic factors) was also computed as a continuous composite value (DEMF) of

the three demographic factors (age, income and level of education) employed in the

study. Third, the researcher computed a new variable to represent the interaction; this is

called CPERDEMF; it is obtained by forming the product CPER × DEMF.

Subsequently, a logistic regression was performed using CPER, DEMF, and

CPERDEMF as predictors of usage.

58

Accordingly, the interaction effect of the moderating variable Z (demographic factors)

on the relationship between the predictor and criterion variables was obtained using the

following logistic regression equation:

Logit [ p (y=1) ] = B0 + B1P1 + B2Z + B3(P.Z) + ε1 …….………………………. (6)

Where:

Y = Usage of online retailing services

p (y = 1) = Probability of belonging to either 1 or 0

B1- 3 = Logistic regression coefficients


Z = Moderating Variable (Customer Demographics)

PZ = Interaction Effect

ε1= Error Term

In total, three empirical models were estimated. The first model (equations 1, 2 & 3)

reported the results for the direct effects only, while the second model (equations 4 &

5) introduced the mediation effects of the mediating variable M, whereas the third

model (equation 6) captured the interaction effects of the moderating variable Z on the

IV-DV relationship.

3.5 Operationalisation and Measurement of Study Variables

This section defined the criterion, predictor, mediating and moderating variables used

to operationalize this study as well as the measures used in their assessment. Table 3.1

presents a summary of the different variables, their indicators, their operational

definitions and instruments used to assess each of the variables.

59

Table 3.1 Operationalization and Measurement of Study Variables

Predictor Variable

Variable Indicator Nature Operationalisation Measure Question

No.

Hypothesized

direction

Online

Retailing

Service

Usage

Continued

Usage

Criterion/

Dependent

Variable (DV)

The current utilization

of one or more features

of e-commerce services

by registered users of

online retailing firms.

1 item, Binary,

1 = Active Usage,

0 = Inactive use;

(Venkatesh et al.,

2003)

N/A

_

Perceived

Attributes

Usefulness

Independent

Variable (IV)

The degree to which

consumers believe that

using an e-commerce

service will enhance

their activities.

4 items; 7 point

Likert scale.

Davis 1989; Davis

et al., 1989)

B1-B4

Positive

Compatibility

Independent

Variable (IV)

The degree to which

using an e-commerce

service is perceived as

being consistent with

the values, needs, and

habits of potential user.

4 items; 7 point

Likert scale

(Moore &

Benbasat, 1991)

B5-B8

Positive

Ease-of-Use

Independent

Variable (IV)

The degree to which an

e-commerce system is

perceived as relatively

easy to understand and

use.

6 items; 7 point

Likert scale

(Davis 1989;

Davis et al., 1989;

Forsythe et al.,

2006)

B9-B14

Positive

Perceived

Risk

Financial risk Independent

Variable (IV)

The degree of anxiety

regarding the perceived

financial loss as a result

of e-commerce usage.

3 items; 7 point

Likert scale

Zhang et al.,

(2012); Forsythe

et al. (2006)

B15-B17

Negative

Performance

risk

Independent

Variable (IV)

The level of uncertainty


performance of an e-

commerce system.

5 items; 7 point

Likert scale.

Forsythe et al.

(2006)

B18-B22

Negative

Personal risk Independent

Variable (IV)

The level of anxiety


compromise/insecurity

of personal information

as a result of e-

commerce usage.

3 items; 7 point

Likert scale

Forsythe et al.

(2006); Tan

(1999)

B23-B25

Negative

Perceived

Value

Monetary

value

Independent

Variable (IV)

The financial utility

derived from e-

commerce usage

4 items; 7 point

Likert scale;

(Sweeney &

Soutar, 2001)

B26-B29

Positive

Convenience

Value

Independent

Variable (IV)

The time, place and

execution utility

derived from usage of

5 items; 7 point

Likert scale;

(Chang et al.,

2012; Mosavi &

Ghaedi, 2012)

B30-B34

Positive

60

the e-commerce system

Social value Independent

Variable (IV)

The perceived

favorability/ approval

derived from one‘s

social milieu regarding

e-commerce usage

5 items; 7 point

Likert scale

(Tan,1999;

Sweeney &

Soutar, 2001)

B35-B39

Positive

Emotional

value

Independent

Variable (IV)

The utility derived from

the feelings or affective

states that an e-

commerce service

generates

7 items; 7 point

Likert scale

(Thompson et al.,

1991; Compeau &

Higgins 1995b;

Compeau et al.

1999, Sweeney &

Soutar, 2001)

B40-B46

Positive

Customer

Satisfaction

Level of

Satisfaction

Mediating

Variable (MV)

The level of satisfaction

with the e-commerce

system

4 item, 5 point

Categorical scale

(Westbrook,

1980; Oliver &

Bearden, 1983;

Oliver &

Westbrook, 1982;

Swan et al, 1981)

Ordinal

C1- C5 Positive

Demographic

Factors

Age Moderating

Variable (MV) Age group

1 item; Ordinal

(Venkatesh et al.,

2003) A1. Negative

Income Moderating

Variable (MV) Level of Income

1 item; Ordinal

(Hernández,2011) A2. Positive

Education Moderating

Variable (MV) Level of Education 1 item; Ordinal A3. Positive

Source: Researcher (2013)

3.6 The Study Area

The area of study was Nairobi County in Kenya. Nairobi, one of the 47 counties in the

country, is unique in that it serves as the capital city of Kenya (Institute of Economic

Affairs (IEA), 2011). It is the largest city in East Africa, with a population of over three

million. Nairobi is also an international, regional, national and local hub for commerce,

transport, regional cooperation and economic development, connecting eastern, central

and southern African countries (United Nations Human Settlements Programme (UN-

HABITAT), 2006).

61

As the capital of the country and the seat of national government, the city generates

over 45% of the national gross domestic product (GDP) and is thus a major contributor

to the Kenya‘s economy. It provides employment for its residents and commuters from

its environs, employing 25% of Kenyans and 43% of the country‘s urban workers (UN-

HABITAT, 2006). At present, the city is growing faster than ever as it develops into a

regional economic center with the potential to become Africa‘s next major business hub

(IBM, 2012).

The rationale for choosing Nairobi County as the area of study was three-fold. First,

Nairobi is a cosmopolitan and urbanized area in comparison to the rest of the country.

Second, there are several local as well as international firms that offer e-commerce

services. Third, the primary target market for most of these firms is Nairobi and its

environs (i.e. the Nairobi Metropolitan Area), given that the bulk of internet users in

Kenya reside in Nairobi.

3.7 Target Population

The target population for the study was made up of online retailing firms in Nairobi,

Kenya. To arrive at the number of online retailers in Nairobi, the researcher relied on

Kenya ICT Board (2012) records and Kenya Postel Directories (2012), which show that

there are 25 registered online retailing firms in Nairobi, Kenya (see Appendix 2).

However, the accessible ones were 6, which formed the target population for this study.

Accordingly, the respondents for this study were the 18,147 registered users drawn

from these six online retailing firms in Nairobi, Kenya. This included 12 key

informants who are regarded as expert sources of information (Marshall, 1996). They

included owners/CEOs, senior managers and employees of these online retailers as well

62

as consultants who were identified with the help of the online retailing firms as being

particularly knowledgeable and accessible.

Table 3.2: Distribution of Target Population

No

Target

Population

No. of

Respondents

Usage Category

1.

Firm 1 4868

Active Users 1022

Inactive Users 3846

2.

Firm 2 931

Active Users 111

Inactive Users 820

3.

Firm 3 6470

Active Users 2076

Inactive Users 4394

4.

Firm 4 1909

Active Users 278

Inactive Users 1631

5.

Firm 5 1447

Active Users 138

Inactive Users 1309

6.

Firm 6 2522

Active Users 363

Inactive Users 2159

Total 6 18,147 Total 18,147

Source: Kenya ICT Board (2012); Kenya Postel Directories (2012)

The distribution of six online retailing firms and their respective numbers of registered

users is illustrated in Table 3.2, whereby the users are categorized into usage categories

(active and inactive users).

3.8 Sampling Design and Procedure

Sampling design and procedure for this study concerned the sampling techniques used

in the study to determine a representative sample from the general population. These

are explained in the following sections.

63

3.8.1 Sampling Technique

The study employed both probability and non-probability sampling techniques to draw

samples from the target population. To achieve this, the study used the nested sampling

approach which involves using sample members selected for one stage of the study as a

sampling frame for choosing a subset/sample for an ensuing stage of the study (Collins,

Onwuegbuzie & Jiao, 2007). In this study, sampling for the questionnaire phase

preceded that of the interview, thus providing a sampling frame for the smaller, more

focused qualitative phase. In total, the sampling frame for this study was made up of

the 18, 147 registered members (active and in-active users) of six online retailing firms

in Nairobi, Kenya. This is illustrated in Table 3.3.

Stratified random sampling, a probability sampling technique, was used to select the

sample from the 18, 147 respondents. Stratified random sampling was employed

because the sampling frame was not homogeneous since the sample contained sub-

groups thereby necessitating a fair representation of these sub-groups in the sample size

(Ahuja, 2005). This technique ensures that observations from all relevant strata are

included in the sample (Lemm, 2010). Stratified sampling also guarantees that every

possible sample matches the population distribution on strata-defining characteristics

(Mallet, 2006).

To this end, proportional stratification technique was initially employed to select the

elements from the respective strata. In proportional stratified sampling, the population

is divided into groups or strata. Samples are then selected by strata, in proportion to

strata sizes (Mallet, 2006). A sample with proportionate stratification is chosen such

that the distribution of observations in each stratum of the sample is the same as the

distribution of observations in each stratum within the population (Lemm,

64

2010). Proportionate stratification uses the same fraction (multiplier) for each subgroup

(Latham, 2007) to insure representation of all sub-groups. The sampling fraction, which

refers to the size of the sample stratum divided by the size of the population stratum

(n/N), is equivalent for all strata (Lemm, 2010). This is illustrated in Table 3.3.

Subsequently, a sample was randomly drawn from each strata and categories using

random sampling method. In simple random sampling, every possible combination of

population elements is equally likely to be selected (Mallet, 2006), thereby eliminating

possible bias. For this study, a computerized random number generator was used to

select the respondents out of the whole population. This was aimed at eliminating bias

in the sample selection.

Non-probability sampling was used to select participants for the key informant

interview using the stratified purposeful sampling scheme from among those who

participated in the questionnaire-based survey. In this technique, the sampling frame is

initially separated into strata to obtain fairly smaller homogeneous groups from which a

purposeful sample is selected from each stratum (Collins et al., 2007).

For this study, there were 6 strata which were based on the number of online retailing

firms that participated in the study. The sample of key informants was purposively

selected from each stratum and consisted of the registered users of these firms who had

expressed an interest in participating in a follow-up interview.

Potential key informants were identified with the help of representatives of the online

retailing firms and thereafter contacted by the researcher. The basis for selection was (i)

their level of activity on the website, (ii) having adequate information about online

retailing and (iii) willingness to participating in a follow-up interview. Campbell (1955)

65

recommended that key informants should (1) take up roles that make them conversant

with the researched focus and (2) be available to share their insights with the

researcher. They are however not considered as representing the sampled units

statistically (John & Reve, 2001). For that reason, selection of potential key informants

was based on their correspondence with the selection criteria enumerated by Campbell

(1955).

3.8.2 Sample Size Determination

The sample size was determined using Yamane‘s (1967) formula for calculating the

sample size since it is relevant to studies where probability sampling is used. According

to the formula, n is the sample size, N is the population size and e is the margin of

error. A 95% confidence level and e = 0.05 were assumed for the equation in this study.

n = N

1 + N (e)2

For this study, N = 18,147 and ε = 0.05. At 95% confidence level, this translated to a

sample size of 391 respondents out of a target population of 18, 147.

n = 18,147 = 391.37 ~ 391

1 + 18,147 (0.05*0.05)

Selection into the sample was based on two key parameters of interest. First, to be

considered for this study, individuals must have been registered users of the selected

online retailing firms. Second, the selected individuals comprised those who are

registered on the service for more than three months. Table 3.3 shows the sampling

frame as well as the distribution of users sampled from the respective online retailing

firms.

66

Table 3.3: Sampling Frame and Sample Distribution

Strata Population Proportionate Sample

Size Category Frequency Multiplier Category Frequency

1. Firm 1 4868

Active Users 1022

0.0215

Active Users 22

Inactive Users 3846 Inactive Users 83

2. Firm 2 931

Active Users 111

0.0215

Active Users 3


3. Firm 3 6470

Active Users 2076

0.0215

Active Users 45


4. Firm 4 1909

Active Users 278

0.0215

Active Users 6


5. Firm 5 1447

Active Users 138

0.0215

Active Users 3


6. Firm 6 2522

Active Users 363

0.0215

Active Users 8


Total 18147 Total 18,147 0.0215 Total 391

Source: Researcher (2013)

3.9 Data Collection Instruments

Both primary and secondary data was collected in this study. Primary data was

collected using a mixed-mode approach with the help of two instruments: (i) a self-

administered questionnaire and (ii) an interview guide for key informants. The

questionnaire was used to collect quantitative data while the interview guide was used

for qualitative data collection. Mixed-mode approach has several advantages.

According to De Leeuw (2005), it is an affordable option that compensates for the

flaws inherent in each individual mode, it can provide more choice and flexibility for

respondents, while improving timeliness and minimizing non-response and non-

response bias. Furthermore, it is also more efficient as compared to other alternatives.

67

3.9.1 Self-Administered Questionnaire

In essence, the questionnaire was composed of three different sections (A, B and C) and

it consisted of 54 questions that were close-ended with ordered responses. The

measures were adopted from previous studies (Swan et al., 1981; Oliver & Westbrook,

1982; Oliver & Bearden, 1983; Thompson et al., 1991; Compeau & Higgins 1995b;

Compeau et al. 1999, Sweeney & Soutar, 2001; Venkatesh et al., 2003); Hernández et

al., 2011) and reworded to suit the context of the current study. The measures were

organized based on the research questions and specific objectives. Each scale item was

modelled as a reflective indicator of its hypothesized latent construct

The survey questionnaire was marked because the respondents were divided into two

groups (active users and inactive users) depending on whether or not one was using

online retailing service at the time of the survey. Depending on the usage, the

respondent was presented with a corresponding survey questionnaire. A sample of the

final questionnaire is shown in Appendix 3B of this thesis.

3.9.2 Key Informant Interview Guide

The key informant interview was a follow-up to the questionnaire survey. Therefore,

qualitative data was collected using the key informant technique with the help of an

interview guide (Appendix 3C).

For this study, the interview guide was composed of 9 questions that were open-ended.

Some of the measures were adopted from previous studies as well as the findings of the

quantitative study, and some additional open questions to elicit more information if

possible so as to suit the research questions and specific objectives. A sample of the

final interview guide is available in Appendix 3C of this thesis.

68

The first category of questions (questions 1 – 5) was based on the two variables

(satisfaction and usage). The second category (questions 6 & 7) explored 9 constructs

namely: usefulness, convenience, ease of use, financial risk, performance risk, privacy

risk, monetary value, convenience value and social value. The questions were aimed at

ascertaining if the variables do affect the satisfaction and usage of online retailing

services. The third category (questions 8 & 9) contained provocative questions that

were meant to elicit more information that was required regarding the state of the

online retailing sector in Kenya as a whole.

In addition to the primary data, secondary data was collected from a variety of both

print and online sources including published reports, book chapters, sectoral directories

and trade publications such as industry magazines and newsletters.

3.9.3 Validity of the Data Collection Instruments

Validity of the questionnaire was assessed using both criterion-related and content

validity. Criterion-related validity was demonstrated through the questionnaire items

which were derived from measures validated in prior research (Nunnally & Bernstein,

2004; Cooper & Schindler, 2008) and standardized and adapted to the context of this

study as is indicated in Table 3.1. Content validity was achieved using a panel of five

experts in the field who were asked to give their views and suggestions on how to

improve the questionnaire (Nunnally & Bernstein, 2004; Cooper & Schindler, 2008).

The five experts evaluated the questionnaire and found that the questions were relevant

to the study variables.

69

In addition, the questionnaire was pilot-tested on 30 selected respondents who are

online retailing users and who were subsequently excluded in the main survey. Pilot-

testing of the questionnaire was conducted before the actual research so as to get an

indication of the expected responses with a view to identifying ambiguous and unclear

questions as well as to detect possible weaknesses in the design and instrumentation as

suggested by Cooper and Schindler (2008).

Some of the pre-testers voiced their concerns regarding the length of the questionnaire,

wondering whether respondents‘ attention could be maintained. Notwithstanding these

concerns regarding the number of items, all were found to be relevant and therefore

none was deleted. However, some items relating to convenience value, social value and

level of satisfaction had to be improved due to their perceived similarity. An example

of this questionnaire can be found in Appendix 3B.

For the key informant technique, the main threats to validity or credibility that were

identified were addressed through the methods recommended by Creswell and Miller

(2000). These methods include triangulation, peer review/debriefing and member

checking.

The first techniques that was employed in enhancing validity of the qualitative study

was the use of multi-method strategies for data collection and analysis (method and

data triangulation) as recommended by Easterby-Smith et al. (2002). It was realized

through the use of a combination of qualitative as well as quantitative methods of data

collection including document analysis, surveys and interviews. Gray (2004) affirms

that triangulation is a practical means of strengthening of a study since its aids in

overcoming the major flaws of the respective methods employed.

70

The other technique used was peer review or debriefing, which is described by

Creswell and Miller (2000) as ―the review of the data and research process by someone

who is familiar with the research or the phenomenon being explored.‖ For this study,

the two supervisors, two e-commerce managers as well as two IT consultants were

engaged as peer reviewers. Before the interviews were carried out, the reviews were

asked to evaluate the interview guide so as to ensure that the questions were clear,

relevant and comprehensive. Some changes regarding the logical flow and choice of

respondents were made to the initial sample based on the feedback received. During the

thematic analysis, they were also asked to assess the proposed categories, themes and

interpretations so as to improve the grasp of the study‘s findings by ensuring that it

provides a rational and practical account of the phenomena.

The final technique through which the validity of findings was established is member

checking. Member checking is a process for obtaining feedback from a few key

informants in which collected data is ‗played back‘ to the informant to check for

perceived accuracy and reactions (Cho & Trent, 2006). It occurs throughout the

research process. For this study, the researcher sent research participants a copy of the

documented interview to confirm that it reflected their perspective of the subject

matter. After the thematic analysis, the researcher prepared a brief summary of the

findings and shared it with the available key informants.

3.9.4 Reliability of the Data Collection Instruments

For this study, Cronbach‘s Alpha coefficient (α) statistical procedure was used to

assess reliability of the quantitative measures as recommended by Mugenda and

Mugenda (2003). As a rule of thumb, reliability of 0.7 and above is recommended for

most research purposes to denote the research instrument as reliable (Roberts, Priest &

71

Traynor, 2006). Using this cut-off value, all but one of the measures in the

questionnaire exhibited internal consistency by having Cronbach‘s alpha values greater

than 0.7. The single variable that did not meet this cut-off is social value, which had a

Cronbach‘s alpha value of 0.538. However, the Cronbach‘s Alpha for social value does

meet the guidelines suggested by Hair, Anderson, Tatham and Black (2006), who

recommended reliability level of 0.5 and above.

Table 3.4 Reliability of Questionnaire Items

Factor Measure Number of Items Reliability

(Cronbach‘s

alpha)


Usefulness 4 items (B1 – B4) 0.954

Compatibility 4 items (B5 – B8) 0.954

Ease-of-Use 4 items (B9 – B14) 0.950

Perceived Risk

Financial Risk 3 items (B15 – B17) 0.801

Performance Risk 5 items (B18 – B22) 0.702

Personal Risk 3 items (B23 – B25) 0.885

Perceived Value

Monetary Value 4 items (B26 – B29) 0.839

Convenience Value 5 items (B30 – B34) 0.954

Social Value 5 items (B35 – B39) 0.538

Emotional Value 7 items ( B 40 – B46) 0.975

Customer Satisfaction Level of Satisfaction 5 items (C1 – C5) 0.941

Source of data: Survey (2013)

As a result, this variable was not excluded in the data analysis in spite of its weak

reliability, meaning that all the item-to-total correlation values exceeded the 0.50 cut-

off value suggested by Hair et al. (2006). The remaining ten variables had an alpha

value of 0.7 and above indicating that they were reliable as indicated by Roberts et al.

(2006).

72

For the key informant interviews, the main threats to reliability/dependability were the

lack of standardization of the interview processs, the choice of informants/ informant

bias as well as interviewee/participant bias (Saunders et al., 2009).

One of the techniques used by the researcher to build reliability with regards to

interview standardization was usage of an interview schedule to guide the interview

process. The researcher also outlined the procedures employed in the study, especially

the data gathering procedures and processes. This will enable future researchers to

replicate the work, and perhaps arrive at similar outcomes (Shenton, 2004).

Informant bias resulting from the sort of entities that were approached and agreed to

participate in the interview was overcome through the sampling approach that was used

(Saunders et al., 2009) as well as the criteria used to select interview participants which

stipulated the nature of persons identified as informants (LeCompte & Goetz, 1982).

Interviewee or response bias may result from the interview participants saying what

they thought their superiors required them to say out of fear of losing their job. One of

the ways through which it was addressed was by ensuring that the interviews were

anonymous. Further to this, the responses were presented in a way that they could not

be linked to a specific organization or individual so as to jeopardize participants‘

anonymity (Elfving & Sundqvis, 2011).

3.10 Data Collection Procedures

To begin with, the researcher received a letter of introduction from Kenyatta University

Graduate School to facilitate the study. Afterwards, a research permit approving the

researcher to conduct the research was sought from the National Commission for

73

Science, Technology and Innovation (NACOSTI). In addition, the authorization and

consent to collect information from study respondents was sought and granted from the

management of the sampled online retailing firms before administering the

questionnaire to the respondents.

Consequently, primary data was collected using two methods: a cross-sectional, mixed-

mode survey of users of online retailing services in Nairobi, Kenya for quantitative data

that was supplemented with in-depth interviews of key informants for qualitative data;

secondary data was collected via a review of existing literature and data from relevant

print and online sources.

3.10.1 Quantitative Data Collection Procedures

Quantitative data collection began by contacting respondents via e-mail and telephone

to inform them of the nature and objective of the study as well as to solicit their

participation in the survey. To ensure maximum response, the questionnaire was

administered using a mixed-mode method, as recommended by De Leeuw (2005).

Despite the fact that there have been reservations regarding mixed-mode methods of

questionnaire administration, it seems not to have a statistically significant effect on

findings (De Leeuw & Hox 2011).

To begin with, the questionnaire was sent to respondents via e-mail as an attachment to

the selected respondents. This method was preferred as it was cheaper and allowed for

faster data collection (Cooper & Schindler, 2008). Emailing ensures significant cost

savings in terms of postage and paper materials can be made (Phellas, Bloch & Seale,

2012). It has also been shown to be useful in surveying individuals who may be

unwilling to participate in personal or phone interviews, but who might respond to an

email survey when it is convenient to them (Simsek et al., 2005).

74

Each questionnaire was sent with a covering letter (Annexure 3A) from the researcher

explaining the purpose of the study and soliciting their participation. The questionnaire

was marked so as to differentiate between active and inactive users. It included a brief

introduction that explained the purpose of the study as well as solicited the recipients‘

participation. After the questionnaire had been distributed, email reminders were sent at

a weekly interval to those who did not respond so as to try and ensure that higher

response rates were achieved. Rogelberg & Stanton (2007) found that higher response

rates result in findings which have greater credibility among key stakeholders.

In order to increase the survey response rate (RR), the e-mail questionnaires were

followed-up with a self-administered questionnaire that was delivered to non-

respondents using the drop-and-pick method as recommended in previous studies

(Rojas-Méndez & Davies, n.d., Ibeh et al., 2004), whereby respondents are contacted in

person and asked to fill in a questionnaire at their most convenient time. Picking up the

completed questionnaires was scheduled at a specified time as recommended by Paxson

(1992). This allowed for personal contact with the respondent and provided the

opportunity to explain the purpose of the survey and thereby increase the motivation to

respond. The respondent may also seek clarifications during such instances, thus

improving the quality of the data that is collected (Phellas et al., 2012).

The drop-and-pick method was preferred because it reduces non-response bias through

reduction of non-coverage, non-contact or refusal to participate (Paxson, 1992). On

average, two follow-ups were made before the questionnaires were ready to be picked

for analysis.

75

3.10.2 Qualitative Data Collection Procedures

On the other hand, qualitative data collection was conducted using key informant

interviews with the help of an interview guide that was developed by the researcher

based upon the findings from the quantitative data analysis and the literature review

that had been carried out previously. It is a useful data collection technique (Homburg,

Klarmann, Reimann & Schilke, 2012), that can either be used on its own or in

combination with other methods (Marshall, 1996). One of the advantages of using the

key informant technique is in connection with the quality of data that can be collected

in considerably short period of time (Lincoln & Guba, 1985).

The researcher conducted interviews where a set of questions based on themes arising

from the literature review as well as the quantitative data analysis were asked; it

included some previously formulated open-ended questions aimed at eliciting pertinent

information as recommended by Creswell (2003). Following Marshall‘s (1996)

suggestion, subjects who were identified with the help of the participating firms were

invited to participate through the official cover letter (Appendix 3A) that was prepared

and delivered at their offices. Those who responded positively by expressing were later

contacted via telephone to arrange a convenient date and place for the interview.

The face-to-face interviews took the researcher about 40 minutes to conduct and were

held at the respondents‘ office for the sake of their convenience. The interview

schedule was delivered prior to the actual interview to give them ample time to prepare.

The interview tried to capture respondents‘ explicit and implicit knowledge of the

subject matter and participants were free to deviate; the interviewer intervened only to

clarify issues or move on to a new theme. The sample interview schedule is in

Appendix 3C.

76

The interview guide was composed of 9 open-ended questions that allowed for

interviewees‘ comments. The questions – which were adopted from previous studies -

explored the same themes as in the questionnaire and were reworded to suit the context

of the current study. They were organized based on the research objective, specific

objectives as well as research hypotheses (H1 - H6).

The method used for recording interviews for documentation and later analysis was

note taking whereby the researcher took down detailed notes so as to ensure that

insightful comments and details are not forgotten or lost. At the end of the interview,

the interviewer reviewed the notes for legibility and coherence. Where this was not the

case, the interviewer asked the respondent to clarify his/her remarks. In took close to 10

weeks to collect the required data for the study.

3.11 Data Analysis and Presentation

Due to the mixed-method that was used to collect data, the study employed a sequential

technique to analyse and present the data. This involves sequential analysis of

quantitative and qualitative data, whereby the initial results of the analysis of the first

data set are used to inform the analysis of the second set of data (United States Agency

for International Development (USAID), 2013). The section below expounds further.

3.11.1 Quantitative Data Analysis

Quantitative data was analysed using both descriptive and inferential statistics.

Descriptive statistics that were used include frequency of the distribution, mean and

standard deviation. Descriptive data analysis prepares the data for further inferential

analysis. Inferential statistics in this study involved conducting both logistic and linear

regression analysis of the response data to test the causality of the IV and DV.

77

Prior to the analysis, the data collected during the research was coded and entered into

SPSS to create a dataset for analysis. Afterwards, variable re-specification was

undertaken by collapsing the response data into fewer variables in line with the specific

research objectives/questions. All the research variables were then defined and label in

a codebook, as recommended by O‘Neill (2009). Appendix 5A illustrates the codebook

listing all the variables included for the statistical analysis, as well as their labels and

the codes ascribed to each answer category given.

Before regression analysis, diagnostic tests comprising multi-collinearity and normality

test were carried out so as to establish if the independent variables (IVs) in the study

model are inter-related. Multi-collinearity arises when two or more independent

variables are highly correlated with each other (De Fusco, 2007). For this study, the

collinearity test was conducted using correlation analysis, tolerance and variance

inflation factor analysis. Normality was also tested for the linear regression equation

using the Kolmogorov-Smirnov (KS) test. According to Ul-Islam (2011), a key goal in

linear regression in checking for normality is to ensure that the t-statistic is giving us

the correct message that whether the independent variable is a significant explanatory

variable or not. All the above analysis were done using SPSS version 19.

The variables, tested hypotheses, regression models as well as the expected outcomes

are summarized in Table 3.5. In additionally, diagnostics tests were carried out on both

the logit and linear regression models to evaluate their (i) overall significance/fit and

(ii) significance of predictor variables. These tests included a multi-collinearity test,

goodness-of-fit test and a normality test. SPSS software was used to perform both

diagnostic tests and the regressions. Tables, figures and narratives were used to present

the data.

78

Table 3.5: Inferential Data Analysis Techniques

Hypothesis Hypothesis Test Statistical Model Expected Outcome

Hypothesis 1:

H0:Perceived attributes

does not have a significant

positive influence on usage

of online retailing services

X 0: β = 0

X 0: β ≠ 0

Reject H0 if p < 0.05,

otherwise fail to reject the H 0

Where:


P (y = 1)= Probability of belonging to either 1 or 0

β1- 3 = Logistic regression coefficients


X2 = Perceived Risk


ε1= Error Term

If the regressed B1 coefficient for

the B1X1 product term is

statistically significant (i.e. p <

0.05), this is interpreted as

evidence of X1 having a

significant effect on Y.

Hypothesis 2:

H0:Perceived risk does not

have a significant negative

influence on usage of

online retailing services

X 0: β = 0

X 0: β ≠ 0



Where:





X2 = Perceived Risk


ε1= Error Term









79

Hypothesis 3:

H0:Perceived value does

not have a significant

positive effect on usage of


X 0: β = 0

X 0: β ≠ 0



Where:





X2 = Perceived Risk


ε1= Error Term







Hypothesis 4:

H0:Customer perception


influence on customer

satisfaction with online

retailing services

X 0: β = 0

X 0: β ≠ 0



M = β0 + β1P1 + ε1

Where:

Y= M = Customer Satisfaction

β0 = Constant

β1 = Linear regression coefficient


ε1= Error Term


the B1P1 product term is



evidence of P1 having a

significant effect on Y (M).


80

Hypothesis 5:

H0:Customer satisfaction


influence on usage of


X 0: β = 0

X 0: β ≠ 0



Logit [ P (y = 1) ] = β0 + β1M + ε1

Where:

Y= Usage of online retailing services


β1 = Logistic regression coefficient M = Mediating Variable (Customer Satisfaction)

ε1= Error Term

If the B1 coefficient for the B1M

product term in the regression is



evidence of M having a significant

effect on Y.

Hypothesis 6:

H0:Demographic factors do

not have a significant

influence on the

relationship between

customer perceptions and

usage of online retailing

services

X 0: β = 0

X 0: β ≠ 0



Where:



β1-3 = Logistic regression coefficients

P1 = Customer Perceptions (Composite Value) Z = Moderating Variable (Customer Demographics)

PZ = Interaction Effect

ε1= Error Term


the PZ product term is statistically

significant (i.e. p < 0.05), this is

interpreted as a significant

interaction between P 1 & Z as

predictors of Y.

.

PZBZBPByP 3210B)1(logit

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3.11.2 Qualitative Data Analysis

To correspond to the sequential approach and to complement the quantitative findings,

directed content analysis technique was used to manually analyze qualitative data from

twelve key informant interview transcripts. Directed content analysis allows for the use

of key concepts/variables identified from prior quantitative research as initial coding

categories in a qualitative study (Potter & Levine-Donnerstein, 1999). These coding

categories are then operationalized using definitions that are based on the theoretical

review (Hsieh & Shannon, 2005).

Accordingly, pre-determined codes were adopted from seven previous ones used for

quantitative data analysis (usage, perceived attributes, perceived risk, perceived value,

customer perceptions, customer satisfaction and demographic factors). In addition,

another three new codes (industry prospects, challenges/problems and policy

recommendations) that were developed by the researcher based on the literature review

formed part of total code categories. Through the inductive coding method, these

initial/preliminary codes were used to group/cluster raw data from the transcripts for

further analysis so as to make meaning and draw insights for further analysis (Ary,

Jacobs & Sorensen, 2010).

As put forward by Hsieh and Shannon (2005), data that did not fall into these pre-

determined codes was isolated and later analyzed to ascertain if they represent a new

category or a subcategory of an existing code. Appendix 5B illustrates the codebook for

the qualitative data analysis, including the codes and their respective

description/operationalization.

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Once coding of the transcripts was completed and all items with a particular code

placed together, the sets of items were reviewed to ensure that they belonged together

since some may fall in more than one category. At this stage, the researcher also started

considering how the codes could be merged into broader categories (Ary et al., 2010).

Thematic coding methods were considered as they are often used for reducing text data

into convenient summary categories or themes used to draw conclusions about a sample

(Krippendorff, 1980; Weber, 1990; Jackson & Trochim, 2002). These methods are

mostly used with denser types of text, such as in-depth interview transcripts where

richer context can result in the identification of repeated themes (Jackson & Trochim,

2002).

In line with the constant comparative method (Ary et al., 2010), the clustered data

underwent thematic analysis whereby similar data was further labeled and analyzed so

as to create meaning and thereafter merged into broader categories or emerging themes.

Here, the researcher relied on a combination of qualitative analysis methods to

determine and classify the themes as suggested by Ryan & Bernard (2003). They entail

the use of observational techniques such as identifying and sorting of words and key

phrases, and checking for repetition, similarity and differences in expressions or

statements

After sorting the data into broad categories, the researcher carried out further analysis

of the various categories to determine whether some could be merged into themes (Ary

et al., 2010). Consequently, additional thematic analysis of the transcripts was carried

out manually in order to establish the key themes. In this study, the interview

information was sorted into five broad themes: (i) usage diversity, (ii) prevailing

attitudes, (iii) usage drivers, (iv) market development and (v) market prospects.

83

Appendix 5C shows the codebook for the thematic analysis, as well as the definitions

given to each concept and significant statements attributed to respondents regarding

each thematic area.

3.12 Ethical Issues

The main ethical issues in this study revolved around confidentiality, honesty among

respondents/participants and data collection in general. As far as possible, the

researcher addressed these key ethical concerns.

To begin with, confidentiality mainly concerned the identity of online retailing

services/companies (as well as that of their customers who served as respondents). All

six online retailing firms granted the researcher access to their customers, but preferred

not to be identified by name given the highly competitive and developing nature of the

online retailing industry, compelling the researcher to describe the firms in a general

manner. Moreover, since the respondents did not want their identities disclosed, care

was taken to guarantee anonymity of the research participants.

The second confidentiality issue is ensuring the anonymity of the interviewee in

relation to the disclosed information: Some of the information shared by the

interviewee during the interview could jeopardize his or her position. There‘s therefore

need to protect respondents by non-disclosure of their identity and from those whose

interests conflict with those of the interviewee (DiCicco-Bloom & Crabtree, 2006).

Also, honesty among the research participants was necessary for the success of this

study. For this reason, the researcher insisted on honesty on the part of all of the

respondents. This was not to be taken for granted since not all participants may have

84

been aware of what constitutes ethical behavior. According to Zickmund & Babin,

(2010), honest cooperation is the main obligation of the research participant.

In terms of data collection, the researcher sought permission from the purposively

selected online retailing companies as well as notified all potential respondents

beforehand regarding the nature and objective of the study. This was also aimed at

encouraging participation.

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CHAPTER FOUR

RESEARCH FINDINGS AND DISCUSSION

4.1 Introduction

This chapter comprises of data collection details as captured using the research

questionnaire, key informant interview guide and documentary sources of secondary

data as well as the analysis of those findings. Questionnaire feedback was analyzed

using both descriptive and inferential statistics and has been summarized and presented

in the form of tables, charts and narratives. On the other hand, interview feedback and

documentary evidence were analyzed using content analysis and presented in narrative

form.

4.2 Descriptive Data Analysis

Descriptive statistics provide a summary of the characteristics of response data

(Wilson, 2006). The descriptive statistics that were used for analysis of the key

variables in this study include frequency of the distribution (in terms of counts and

percentages) as well as measures of central tendencies (mean) and dispersion (standard

deviation).

4.2.1 Response rates

From three hundred and ninety one (391) respondents who are registered as users of 6

online retailing services in Nairobi County, Kenya, two hundred and eighty-seven (287)

were able to participate in the study by completing and returning the questionnaire.

However, a number of these questionnaires (34) were poorly/improperly filled, while

another 13 arrived too late, necessitating their exclusion from the study. Ultimately, the

final respondents amounted to 240, equivalent to a 61.38% response rate. This is

depicted in Table 4.1.

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Table 4.1: Distribution of responses

Strata

Stratified Sample Response

Proportionate

Sample Category Frequency Frequency Percentage

1. Firm 1 105

Active Users 22 23 7.69

Inactive Users 83 39 18.97

2. Firm 2 21



3. Firm 3 139



4. Firm 4 41



5. Firm 5 31



6. Firm 6 54



Total 391 Total 391 240 100

Source: Survey Data (2014)

While there is still no consensus on what percentage of response rate should be

acceptable for reporting and analysis in research, the rule of thumb is the higher the

better (Rojas-Méndez & Davies, n.d.). Accordingly, Babbie (1990) argued that ―a

response rate of at least 50 percent is generally considered adequate for analysis and

reporting, a response rate of at least 60 percent is considered good, and a response rate

of 70 percent or more is very good‖. More recently, Rubin and Babbie (2011)

suggested that a 50 percent response rate is considered adequate for reporting and

analysis. This means that the response data was more than adequate for analysis and

reporting.

4.2.2 Demographic Characteristics of Online Retail Users

Table 4.2 shows a summary of the demographic characteristics of the respondents

based on Section A of the questionnaire.

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Table 4.2: Demographic characteristics of the sample (n = 240)

Variable Category Frequency Percentage

Age 18-23 Years

24-29 Years

30-35 Years

36-41 Years

42-47 Years

48 years and above

Total

30

105

77

24

4

0

240

12.5

43.8

32.1

10.0

1.7

0

100.0

Level of Education High School Cert.

Diploma

Bachelor‘s Degree

Masters‘ Degree

Doctorate

Professional

Other

Total

1

37

139

51

8

3

1

240

0.4

15.4

57.9

21.3

3.3

1.3

0.4

100

Monthly Income Less than KSh 24,999

KSh25,000 – 49,999

KSh50,000 – 74,999

KSh75,000 – 99,999

KSh100,000 – 124,999

KSh125,000 & above

Total

31

45

54

43

32

35

240

12.9

18.8

22.5

17.9

13.3

14.6

100.0

Source: Survey data (2014)

In terms of the respondents‘ (n=240) ages, the majority (43.8 %) were between 24 – 29

years while the minority (1.7 %) were between 42 – 47 years of age. None were older

than 48 years of age. When it comes to education, a majority of the respondents (57.9

%) have a Bachelor‘s degree, followed by 51 (21.3 %) who have a Master‘s degree and

37 (15.4 %) who have a diploma. Only 1 (0.4 %) had a high school certificate, while 3

(1.3 %) had a professional qualification. With regards to the monthly income of the

respondents, the majority (22.5%) earned between KSh 50,000 – 74,999, whereas the

minority (12.9 %) had a monthly income of less than KSh 24,999 per month.

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Taken as a whole, the demographic information showed that the respondents are

predominantly young, relatively well educated and with relatively high levels of

income. These findings concur with past studies regarding online population (Bellman

et al., 1999) and e-shoppers in particular, which established that the online shoppers are

generally younger, with high level of income and more educated (Li et al., 1999;

Vrechopoulos et al., 2001; Dholakia & Uusitalo, 2002).

4.2.3 Customer Perceptions of Online Retail Users

The key customer perception variables of interest to this study were perceived attributes

(PATT), perceived risk (PRSK) and perceived value (PVAL) as well as the composite

variable customer perceptions (CPER). Table 4.3 provides a summary of the

descriptive statistics for the three predictor variables as well as that of the composite

variable.

Table 4.3: Descriptive Statistics Results for Customers’ Perceptions

Variable Measure Statistic

PATT Mean 4.3899

Std. Deviation 1.54140

PRSK Mean 4.1256


PVAL Mean 4.2589


CPER Mean 4.2585

Std. Deviation .62906


The results indicate that perceived attribute had the highest mean score (4.389), closely

followed by perceived value (4.258) and perceived risk (4.125). This high value

concurs with several studies have empirically established that the perceived attributes is

a key element in usage behavior of online services (Parthasarathy & Bhattacherjee,

1998; Bhattacherjee, 2001b). The mean score of the customer perception variables was

also reasonably high (4.26).

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On the other hand, the standard deviation for perceived attributes (1.54) is the largest,

followed by perceived risk (1.28) and perceived value (1.16). According to Rumsey

(2011) a large standard deviation isn‘t necessarily a bad thing; it just reflects a large

amount of variation in the data set that is being studied. This indicates that the

responses for perceived attributes are further away from the mean than those of

perceived value.

Descriptive statistics (mean and sum) of to the 10 individual perceptual indicators we

also carried out in order to establish the overall emphasis placed by respondents on

each. This is presented in Table 4.4.

Table 4.4: Descriptive Statistics Results for Individual Perceptual Indicators

No. Variable Mean Sum Rank

1. Usefulness (USFL) 4.25 825.25 4

2. Compatibility (CMPT) 4.19 813.00 5

3. Ease-of-Use (EOUS) 4.17 809.17 6

4. Financial Risk (FINR) 3.87 754.00 9

5. Performance Risk (PRFR) 4.09 793.60 7

6. Personal Risk (PRSR) 4.86 943.67 2

7. Monetary Value (MOVL) 4.29 832.25 3

8. Convenience Value (COVL) 5.11 992.00 1

9. Social Value (SOVL) 2.89 560.60 10

10. Emotional Value (EMVL) 4.02 780.29 8


The results reveal that convenience value (COVL) had the highest scores (sum =

992.00; mean = 5.11) for an individual indicator, implying that consumers placed the

utmost importance on the convenience of using online retailing services. This is

corroborated in a study by Robinson, Riley, Rettie and Rolls-Wilson (2007) which

established that the key motivation for online shopping is the convenience of round-the-

clock shopping and having the items delivered at one‘s door step. On the opposite end,

social value had the lowest scores (sum = 560.60; mean = 2.89).

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4.2.4 Customer Satisfaction of Online Retail Users

In addition, the study assessed the customer satisfaction of users of online retailing

services. Table 4.5 provides a summary of the results of the descriptive statistics for the

customer satisfaction variable.

Table 4.5: Descriptive Statistics Results for Customer Satisfaction

Variable Measure Statistic

CSAT N 240

Mean 3.1650



The results in Table 4.5 indicate that customer satisfaction had a mean score of 3.165.

This moderate value could imply that users are not very enthusiastic about the online

retailing services. This is a cause for concern since customers‘ overall satisfaction is an

indication of how well customers like their experience with using the website, and it is

probably the best indication of their willingness to return to the site again if they are to

make another purchase in the category (Jiang & Rosenbloom, 2005). With regards to

the standard deviation, the value was 1.087.

4.2.5 Usage of Online Retailing Services

The study also assessed the usage of online retailing services. Table 4.6 provides a

summary of the results of the descriptive statistics for the usage variable.

Table 4.6: Descriptive Statistics Results for Usage of Online Retailing Services

Variable Category Frequency Percentage

USAGE Active User 129 53.8

Inactive User 111 46.3

Total 240 100.00


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The study results in Table 4.6 reveal that the number of active users of online retailing

services who responded was 129 (53.8 %), which was moderately higher than the

number of respondents making up inactive users, who amounted to 111 respondents

(46.3 %). This points to a fairly representative number/level of respondents who

participated in the study, making it generalizable as argued in the problem statement.

4.3 Regression Analysis and Test of Hypotheses

The previous section dealt with descriptive statistics regarding the customer perceptions

affecting usage of online retailing services in Nairobi County, Kenya. However, in

order to empirically test the study‘s premise - that there is a relationship between

customers‘ perceptions and the usage of online retailing services but this relationship is

mediated by customer satisfaction and moderated by demographic factors - inferential

statistics using logistic and linear regression methods was conducted as appropriate at

95 percent confidence level ( = 0.05) on the response data.

Both linear and logistic regressions analyze the relationship between independent

variable(s) and an independent variable. On its part, linear regression analyzes linear

relationships, which require a continuous numerical dependent variable (such as

customer satisfaction in this study) that follows a normal distribution. In contrast,

logistic regressions require binary dependent variable (i.e. usage in this study), which

are coded 0/1 and indicate if a condition is or is not present, or if an event did or did not

occur (Sweet & Grace-Martin, 2010).

Therefore, the first technique - logistic regression analysis – was used in the first model

(equations 1, 2 and 3 in Chapter 3) to empirically analyze the response data for

purposes of establishing the main effects of the predictor variables on the criterion

variable as a way of estimating the conditional probability of being either an active or

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inactive user. In addition, it was used in the third model (equation 6) to establish the

moderating effect of variable Z on the IV-DV relationship. The second regression

method - binary linear regression - was carried out on the second model (equations 4)

so as to establish the effect of customer perceptions on customer satisfaction (M).

4.3.1 Diagnostic Tests

However, before subjecting the data to regression analysis, diagnostic tests were first

carried out on the collected data to establish if it conformed to the requisite

assumptions. The first diagnostic test was the multicollinearity test, which was done so

as to establish if the three IV‘s (perceived attributes, perceived risk and perceived

value) are inter-related or not. For this study, the collinearity tests were conducted

using correlation analysis, tolerance and variance inflation factor (VIF) analysis. Table

A.12 shows the correlation matrix for the three predictor variables.

According to DeFusco (2007), multi-collinearity arises when two or more independent

variables are highly correlated with each other. It is important to note that there is no

consensus in extant literature on the acceptable correlation value/level between two

variables, but Cooper and Schindler (2008) recommend a correlation value of 0.8 or

greater to denote multicollinearity between two IVs (The IV - constant value is ignored,

since collinearity among the predictors is what is under investigation). As is evident in

the correlation matrix (Table A.12), there is no correlation value of IVs that is greater

than 0.8; the highest correlation value of IVs is 0.519. We can therefore conclude that

the correlation between the predictor variables in the model was not significant to

warrant dropping any of them.

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Besides bivariate correlation, the study also employed tolerance and variance inflation

factor (VIF) analysis to determine the multicollinearity. This is illustrated in Table 4.7

and Table A.13 in the appendix.

Table 4.7: Results of Collinearity Statistics

Variable Tolerance VIF

Perceived attributes (PATT) 0.261 3.826

Perceived risk (PRSK) 0.468 2.135

Perceived value (PVAL) 0.348 2.877


Results of the study reveal no multicollinearity problem for the three IVs: perceived

attributes, perceived risk, and perceived value. This is due to the fact that the tolerance

values for the three variables are greater than 0.1, while the VIF values are all lesser

than 10, which show that there is no collinearity amongst the three predictors (Field,

2005). Consequently, all three variables were retained in current research model and

used in the regression analysis.

Another diagnostic test that was carried out is the goodness-of-fit test. In logistic

regression, goodness-of-fit tests for proposed models are commonly used to describe

how well a proposed model fits a set of observations (Wu, 2010). There are different

goodness-of-fit tests all of which have pros and cons. In this study, the Hosmer-

Lemeshow (H&L) test was used. The H&L test for goodness-of-fit is a statistical

measure that shows how good a model fits the data. According to the test, if the

significance measure of the model is less than 0.05, the model doesn‘t fit the data very

well. It is significant if it is larger than 0.05. Table 4.8 presents the significance value of

the H&L Test (1.0) for the logistic regression of the main effects model, which is

greater than the required 0.05, meaning that the model does fit the data very well.

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For the linear regression (equation 4), the overall fit of the model was assessed using

the F-test, in line with Doane and Seward (2009). Since the p-value ˂ = 0.05, the

null hypothesis that the proposed linear regression model doesn‘t fit the data very well

was rejected, implying that the model is statistically significant and therefore useful in

predicting customer satisfaction.

Also, a diagnostic test to ascertain the normality of the customer perceptions (CPER)

distribution was undertaken. Normality is an important assumption of many statistical

procedures such as t-tests, linear regression analysis, discriminant analysis and

ANOVA (Razali & Wa, 2011). This study used the formal normality test, specifically

the 1-sample Kolmogorov-Smirnov (KS), to test for evidence of the normality of the

customer perceptions distribution, in line with Razali and Wa (2011). The outcome of

the test as is shown in Table 4.8 as well as Table A.15 in the appendix. According to

the KS test, if the significant value is less than 0.05, there is a significant difference

between the population and sample, implying that the data is not normally distributed.

Table 4.8: Results of Kolmogorov-Smirnov Normality Test

Predictor Variable Significant Value

Perceived Attributes (PATT) .000

Perceived Risk (PRSK) .000

Perceived Value (PVAL) .000

Customer Perceptions (CPER) .200


The study results in Table 4.8 show that while the KS test significant-values for the

three predictors was 0.000, the overall KS test p-value (.200) for the composite

customer perceptions distribution was greater than the significant level (p = 0.05), thus

implying that the customer perception variable data is normally distributed. This

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means that the customer perception distribution satisfies the assumptions of equal

variance and normality.

Since the assumptions of the two regression models are reasonably satisfied, the

researcher proceeded to perform inference for the regression models.

4.3.2 Test of Hypotheses

After the successful conducting the preliminary diagnostic tests and confirming that the

data complied with the requisite assumptions, regression analysis was performed on the

data to test the hypotheses. The relevant hypotheses tests are presented in the sections

4.2.3.1. – 4.2.3.6 and the results are summarized in Tables 4.9 and 4.10..

Table 4.9: Results of Logit Regression Analysis

Variable β t = β/S.E Wald P-Value

Perceived Attributes (PATT) 6.006 2.903 8.429 0.004

Perceived Risk (PRSK) -1.834 -2.148 4.618 0.032

Perceived Value (PVAL) 2.329 2.149 4.621 0.032

Customer Satisfaction (CSAT) 5.171 6.488 42.056 0.000

Interaction Term (CPERDEMF) -0.196 -0.374 0.141 0.708

Observations (n) 240

Nagelkerke R Squared (Main Effects) 0.974

Classification Rate(Main Effects) 98.8%

Hosmer and Lemeshow (Main Effects) (8 df) 0.097 1.0

Nagelkerke R Squared (Equation 5) 0.874

Classification Rate(Equation 5) 95 %

Nagelkerke R Squared (Equation 6) 0.616

Classification Rate(Equation 6) 81.3%

Dependent variable is Usage (USAGE)

Note * p≤ 0.05


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To begin with, logistic regression analysis was used in the first model (see equations 1,

2 and 3 in chapter 3) for purposes of establishing the direct effects of the predictor

variables on the criterion variable as a way of estimating the conditional probability of

someone being either an active or inactive user. In addition, it was used in the third

model (equation 6) to establish the moderating effect of variable Z on the IV-DV

relationship. The results are summarized in Table 4.9

For the logistic regression model summary, the coefficient of determination (R2) was

estimated using the Nagelkerke‘s R2, a supplementary goodness-of-fit measure

recommended by Pallant (2007). Table 4.8 shows that it was 0.974 for the main effects

and 0.874 for equation 5, indicating a very strong relationship between the IVs and the

DV. This means that about 97.4% and 87.4% of the variation in the outcome variable is

explained by the independent variable.

Additionally, the Wald statistic, was used to determine the ―significance‖ of the

contribution of each variable in the model, in line with Chan (2004), whereby, the

higher the value, the more ―important‖ it is. The relevant hypotheses tests that were

conducted to assess the significance of the Wald statistic tested the null hypothesis at

95% confidence level wherein the acceptability level of the hypothesis test was =

0.05, as recommended by Burns and Burns (2009). The relevant hypotheses tests are

presented in the sections.

The second regression method - simple linear regression - was used out on the second

model (equation 4) so as to establish the effect of customer perceptions on customer

satisfaction (M). The relevant results are summarized in Table 4.10.

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Table 4.10: Results of Simple Linear Regression Analysis

Variable β S.E. t = β/S.E P-Value Customer Perceptions (CPER) 1.258 0.077 16.337 0.000

Observations (n) 240

R-Squared (Equation 4) - 0.531

Adjusted R-Squared (Equation 4) 0.529

F-Stat (Equation 4) (1 df) 269.024 0.000

Dependent variable is Customer Satisfaction (CSAT)

Note * p≤ 0.05


For the simple linear regression model summary (equation 4), the adjusted R-Squared

(R2) value was 0.531, meaning that about 53% of the variation in the DV can be

explained by the model. The adjusted R2 is important as it helps to discourage

overfitting of the model (Doane & Seward, 2011).

4.3.2.1 Hypothesis 1: Relationship between Perceived Attributes and Usage of

Online Retailing Services

For the main effects model, the first hypothesis to be tested regarded the relationship

between perceived attributes and usage of online retailing services.

H01: There‘s no relationship between perceived attributes and usage of online


As revealed in Table 4.9, the null hypothesis which proposes that there‘s no

relationship between perceived attributes and usage of online retailing services was

rejected since β ≠ 0 and p-value = 0.004. This is consistent with past research by

Adams, Nelson and Todd (1992), which empirically established perceived attributes

such as usefulness and ease-of-use are important determinants of system use and

Parthasarathy and Bhattacherjee (1998) which established that the perceived attributes

of an online service such as usefulness and compatibility determine usage behavior.

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Similarly, Bhattacherjee‘s (2001b) empirical study of the antecedents of e-commerce

service continued usage demonstrated that perceived usefulness is a key determinant of

customer‘s continued usage intention (CUI). This can be interpreted that usage depends

on cognitive beliefs (i.e. perceptions) about attributes of online retailing services.

4.3.2.2 Hypothesis 2: Relationship between Perceived Risk and Usage of Online

Retailing Services

For the second predictor variable of the main effects model, perceived risk, the

hypotheses that was tested at 95% confidence level acceptability level = 0.05 is as

follows:

H02: There‘s no relationship between perceived risk and usage of online retailing


The research findings depicted in Table 4.9 show that for perceived risk (PRSK), β = -

1.834 and p-value = 0.032. Hence, the null hypothesis for H2 is rejected since β ≠ 0 and

p-value < = 0.05. However, the study‘s findings show that perceived risk has a

significant negative effect on usage. The result concurs with the findings of previous

studies (Jarvenpaa & Tractinsky, 1999; Bhatnagar et al., 2000; Lee et al., 2000;

Forsythe, Chuanlan, Shannon & Gardner, 2006; Barnes., Bauer, Neumann & Huber,

2007) that perceived risk is negatively associated with online shopping. It also parallels

a more recent study by Liu and Forsythe (2010) who argued that risk is often a barrier

to online transactions. This simply means that the greater the perceived risk, the less

likely consumer are to use online retailing services in the future.

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4.3.2.3 Hypothesis 3: Relationship between Perceived Value and Usage of Online

Retailing Services

The relationship between perceived value and usage was tested using the following

hypothesis:

H03: There‘s no relationship between perceived value and usage of online retailing


The study results reveal in Table 4.9 that for perceived value (PVAL), β = 2.329 and p-

value = 0.032. Therefore, the null hypothesis was rejected since β ≠ 0 and p-value < .

This means that perceived value has a statistically significant effect on the usage of

online retailing services. The findings of the study are consistent with the previous

research which established that perceived customer value is a significant determinant of

online transaction behavior (Chew, Shingi & Ahmad, 2006). As pointed out by Abadi,

Hafshejani and Zadeh (2011), users will perceive online shopping to be valuable when

they see colleagues, friends and family members use it and get a recommendation of

using it from them. Accordingly, the researcher did not drop any of the three IVs from

the model since their effect was significant based on the Wald statistic.

4.3.2.4 Hypothesis 4: Relationship between Customers’ Perceptions and Customer

Satisfaction with Online Retailing Services

The fourth hypothesis to be tested regarded the relationship between customers‘

perceptions and customer satisfaction with online retailing services.

H04: There‘s no relationship between customers‘ perceptions and customer


The study results in Table 4.10 reveal that for the cumulative customer perceptions

(CPER), β = 1.258 and p-value = 0.000. Consequently, the null hypothesis was rejected

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since β ≠ 0 and p-value < , meaning that customer perceptions have a statistically

significant effect on customer satisfaction with online retailing services. This outcome

lends support to the findings of Bolton and Drew (1994) that empirically established

that customer perceptions have a significant positive relationship with customer

satisfaction in the service context. More importantly however, it corroborates studies

(Westbrook and Oliver, 1981) that advanced the notion that satisfaction may comprise

of both emotional (i.e. perceived value) and cognitive (i.e. perceived attributes,

perceived risk) determinants. This is in line with Sing (1991) who argued that customer

satisfaction can be understood as a collection of multiple satisfactions with various

objects that constitute the service system.

4.3.2.5 Hypothesis 5: Relationship between Customer Satisfaction and Usage of

Online Retailing Services

The fifth hypothesis sought to assess the relationship between customer satisfaction and

usage.

H05: There‘s no relationship between customer satisfaction and usage of online


According to Table 4.9, for customer satisfaction (CSAT), β = 5.171 and p = 0.000.

Since p = 0.000 < α = 0.05, the null hypothesis was rejected, meaning that customer

satisfaction has a statistically significant effect on usage. This outcome is in line with

an earlier studies by Baroudi, Olson and Ives (1986), who argued that user satisfaction

could lead to system usage, and Bhattacherjee (2001a), according to whom, satisfied

users tend to continue using an IS, while dissatisfied users tend to discontinue IS usage

and/or switch to an alternative. It also corroborates a study by Ortiz de Guinea and

Marcus (2009) which established that satisfaction drives IT usage directly and

relationship marketing literature which relates satisfied consumers with repeated

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purchases/usage and loyalty (Martin & Camarero, 2009). Table A.28 presents the

output for the logistic regression for customer satisfaction and usage.

4.3.2.6 Hypothesis 6: Moderating Effect of Demographic Factors on the

Relationship between Customer Perceptions and Usage of Online Retailing

Services

The sixth and final hypothesis sought to test the moderating effects of demographic

factors and is as follows:

H06: Demographic factors do not have a moderating effect on the relationship

between customer perceptions and usage of online retailing services in Nairobi

County, Kenya.

As revealed in Table 4.9, for the interaction variable (CPERDEMF): β = - 0.196 and p

= 0.708, implying that the interaction term is not statistically significant. We thus fail

to reject the null hypothesis which proposes that demographic factors have no

significant moderating effect on the usage of online retailing services in Nairobi,

Kenya. The insignificant moderating effect of demographic factors on perceptions is

not surprising, since it is in line with several past technology adoption and usage

studies (Szajna, 1996; Gefen & Straub, 1997; Gefen & Keil, 1998; Hernandez et al.,

2011). The results also confirm the findings by Bellman et al. (1999), who argue that

while demographics appear to influence initial use of the Internet, its effect seems to

disappear once people are online on a regular basis. In other words, the behaviour of

experienced users is not identical to that of an individual during their initial

employment of the IT/IS in question (Gefen, Karahanna & Straub, 2003; Yu, Ha, Choi

& Rho, 2005), since the experience acquired modifies the effect of the variables

considered. This implies that that once an individual becomes familiar with the IT/IS

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(in our case online shopping), the experience acquired may nullify the importance of

their socio-economic characteristics (Sun & Zhang, 2006). In conclusion, this can be

interpreted that demographic factors do not influence the usage behaviour of the

experienced online shopper.

4.3.3 Summary of the Test of Hypotheses

The results of the hypothesized relationships are summarized in Table 4.11.

Table 4.11: Summary of Hypotheses Tests

Hypothesis

Causal

Relationship

Outcome of

Hypothesis Test

H01: There‘s no relationship between

perceived attributes and usage of


Perceived attributes Usage

Rejected Ho


perceived risk and usage of online

retailing services

Perceived risk Usage

Rejected Ho


perceived value and usage of


Perceived value Usage

Rejected Ho


customers‘ perceptions and

customer satisfaction with online

retailing services

Customer perceptions

Customer Satisfaction

Rejected Ho


customer satisfaction and usage of


Customer satisfaction Usage

Rejected Ho

H6: Demographic factors do not have a

moderating effect on the

relationship between customer

perceptions and usage of online

retailing services

Demographic factors

(Customer perceptions

Usage)

Failed to reject Ho


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As presented in the table, all but one of causal relationships between the constructs

postulated by our model is well supported. The study found support for H1-H5.

However, the results failed to support H6, that demographic factors have a moderating

effect on the relationship between customer perceptions and usage of online shopping

services.

4.4 Content Analysis

This section presents findings for the qualitative analysis of data collected during the

key informants interviews aimed at providing an in-depth perspective of the effects of

customer perceptions on online retailing usage in Kenya. The findings are summarized

into five major themes and are presented in the form of narratives.

4.4.1 Interview Participants

From each of the six online retailing firms that agreed to participate in the study, the

researcher identified two individuals who were deemed eligible to serve as key

informants and who agreed to participate in the semi-structured interviews. In total, the

researcher conducted twelve (12) in-depth semi-structured interviews with key

decision-makers in each firm. These individuals were selected due to their influential

roles/occupations within the firms. The distribution of interviewees is depicted in Table

4.12.

Table 4.12: Distribution of interview participants

Role/Occupation Number of Interviewees

1. Online retailing entrepreneurs/owners 2

2. Consultants working for the online retailing firms 2

3. Managers of the online retailing firms 7

4. Employees of the online retailing firms 1

Total 12


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4.4.2 Key Themes

The key informant interview findings gave an in-depth perspective regarding the effect

of customer perceptions on online retailing usage in Kenya. The findings largely

complemented those found in the quantitative analysis, with no major contradictions.

They are summarized in the following section based on the five major themes that were

developed via directed content analysis as outlined in Chapter 3.

4.4.2.1 Theme No 1: Usage Diversity

The findings revealed that usage of online retailing services is quite prevalent in Kenya,

as all the respondents admitted that they have used local online retailing services before

to buy and sell goods and services. It was interesting to note the multi-faceted/diverse

usage behaviour of respondents: while some used the websites primarily for price

comparison (researching), others went further and used them for purchasing items such

as books, electronics (television sets and mobile phones) as well as tickets for events.

As one participant stated, ―I used Jumia to look at the price range of a laptop that would

fit my budget as well as look at the specifications of that laptop‖. Another interviewee

reported that ―… I was looking for products I wanted to purchase [but] I was also

selling some goods on OLX‖. Another respondent reported used Mzoori.com,

Rupu.com, Jumia and OLX ―to look for products for purchase and was also selling

some goods on OLX‖. Interestingly, a majority disclosed that they have used online

retailing services within the last three months.

However, one response that stood out was from someone who had not shopped online

for more than three months. As that participant explained, ―…I only use online

retailing stores when I am seeking to buy high cost items because I get an opportunity

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to compare prices across different e-commerce stores. I do not do this frequently that‘s

why I cannot do this (use the websites) every now and then…‖. When asked whether

there are any goods they wouldn‘t purchase online, one respondent cited electronics,

clothes and furniture (especially sofas). According to the respondent, ―I want to see and

feel and test (them) before purchasing‖. Other mentioned food as another item that they

wouldn‘t purchase online ―due to its perishability‖.

4.4.2.2 Theme No 2: Prevailing Attitudes

There were a number of interesting responses which highlighted the mixed opinions,

thoughts and feelings regarding online retailing in Kenya. For instance, when asked

―how satisfied (or dissatisfied) are you with local online retailing services in Kenya?‖,

the respondents were roughly split in the middle as some replied in the affirmative

while others were negative.

Those respondents who did not have a favorable attitude attributed their negative

attitudes to a variety of factors including product availability, delays in delivery, issue

with payment modes, mistakes during delivery and poor customer service. According

to one owner-manager, ―the inconsistency in product availability coupled with the lack

of accurate information on some websites has made online users form a negative

impression towards online retailing services‖. This point was emphasized by another

dissatisfied respondent who stated that ―quite a number of online retailers cannot

guarantee proper inventory management – you order an item online but you cannot

have it (delivered) because it is out of stock‖.

Moreover, one employee remarked that ―I‘m very dissatisfied. I have not been able to

purchase anything because what‘s available is not within my budget range‖. Yet

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another respondent who is an owner added that ―I‘m somewhat satisfied…however, it

can be quite costly to roll out‖.

On the other hand, one of the owners who responded in the affirmative asserted that ―I

am very satisfied…we have come a long way in terms of e-commerce in Kenya‖.

Another respondent added by saying ―I‘m very satisfied with online retailing in Kenya

because of how fast it is growing and the convenience it brings‖. Another like-minded

respondent remarked that ―It‘s starting to be more and more appreciated as people

become busy with fast/complicated lifestyles‖.

4.4.2.3 Theme No 3: Usage Drivers

The study also identified several determinants of online retailing usage based on the

interview responses. Overall, the interview findings indicated that convenience, product

"assortment/variety, price (i.e. deals & discounts) and credibility are the most important

drivers of online retailing usage. All respondents agreed that when these aspects are

missing, it becomes difficult to enhance or even sustain usage of a service.

Further, the participants agreed that their convenience value is very high. As one

stated, ―online retailing provides convenience as buyers get whatever they purchase

without moving…‖ while another remarked that ―online shopping experience takes

away the headache associated with traditional in-store shopping. They [online retailing

websites] are very convenient for price comparison purposes‖. As one participant

stated, ―I loved the convenience of getting the television set delivered to my doorstep

without leaving the comfort of my house!‖.

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Another important driver was usefulness. One interviewee agreed that online retailing

services ―are very useful for price comparison‖ while another remarked that ―online

retailing is quite useful as it helps one purchase products after viewing a variety without

having to waste time walking around‖. Ease of use was another driver. ―The websites

and mobile apps are generally very easy to use‖. According to the respondent, one

reason for this is ―modern payment methods such as M-pesa which make it easy to

transact though it comes with some level of risk which most Kenyans are aware of and

have found ways to overcome‖.

Indeed, the growth in electronic payment services such as credit cards and mobile

money offered reliable and flexible payment options which have also contributed the

growth of adoption and usage of online retailing. Majority of respondents confirmed

that M-pesa is the main mode used for payment when shopping online because it

reduces financial risk which has made many shy off from shopping online. One of the

consultants interviewed confirmed this by stating that ―Mpesa is easy to use unlike

credit cards which can be abused after the purchase‖. As one senior employee in one of

the online retailing firms put it: ―There is a chance of personal details getting in the

wrong hands, especially for those using visa cards to make payments‖. The respondent

also indicated the performance risk involved by saying that ―there are chances that

delivery does not occur as promised e.g. delivery within 24 hours turns to delivery after

72 hours‖.

With regards to social value, one owner remarked that ―Online retailing provides a

platform where people can be able to comment or review their experiences or

products/services. That way shopping decisions are made based on that [social

value]…‖.

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4.4.2.4 Theme No 4: Market Development

While the respondents attested to the accelerated pace of change in how consumers are

shopping online, most acknowledged that the sector was growing at a slower pace than

expected and that more needs to be done to develop the sector in Kenya. In this regard,

they suggested several possible strategies that could be employed in order to increase

uptake and sustain the usage of online retailing services in Kenya.

One respondent who works as an internal consultant for one of the online retailing

firms had three key recommendations: ―Increase the product range and ensure that

products are always in stock; delivery to homes would increase the convenience of this

value chain; ensure that the regulatory frameworks for protecting customers privacy are

enhanced and there is judicial recourse if these are compromised‖. One owner

recommended that online retailers should ―build trust; avail a variety of products; offer

good prices for products; avail all information on one page; offer return policies; offer

cash on delivery and offer warranties‖. Another added that ―there‘s need for more

regulation from government to reduce the risks associated with online shopping‖.

Moreover, one consultant cited the need for ―data privacy laws to protect consumers‘

sensitive data‖.

This example also shows that companies cannot ignore the risks that users face when

using their services as ignoring them carries significant reputational risk through word-

of-mouth as a result of negative customer experience. The consensus was that stringent

regulation of e-commerce was a necessary component for the sector‘s growth.

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4.4.2.5 Theme No 5: Market Prospects

Last but not least, the respondents gave an analysis of the economic/business potential

of the online retailing industry in Kenya. Generally, Kenya is seen as a regional leader

in e-commerce and online retailing in particular, the current challenges

notwithstanding. As it turns out, most respondents affirmed that the current online

retailing activity in Kenya is a pretty strong indicator of its potential to drive online

sales in the near future.

For the most part, the consensus was that the prospects for online retailing in Kenya are

bright and going by recent developments and trends, it may soon become a viable sales

channel. As one respondent remarked, ―it is growing at a very fast pace compared to

the rest of the region‖. This was supported by another respondent who added that

―online shopping as a trend is picking up and has a bright future. This is because more

internet users are turning to online shopping especially due to its convenience‖.

Moreover, another respondent noted that ―online retailing is gaining momentum; while

we have already achieved good progress, a lot more needs to be done to ensure security

while transacting, product variety and value added services‖. Last but not least was one

consultant who added that ―online shopping is an industry that is growing in Kenya and

people are beginning to embrace it. A few things need to be worked on and it will turn

into a lucrative industry‖.

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CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 Introduction

This chapter presents a summary and discussion of the main findings of the study with

respect to each study objective. Also, conclusions based on the findings are made,

followed by recommendations of the study as well as suggestions for further study

which are proposed at the end of this chapter. Every attempt was made to represent the

facts with completeness and clarity.

5.2 Summary

In the last decade, Kenya has undergone a transformation in its ICT sector, which

outperformed every other sector during this time. This remarkable growth has been

characterized by introduction of various e-commerce services such as online retailing

into the market, which target Kenya‘s rapidly growing internet population. However,

this huge increase in internet usage in Kenya during this period has not been matched

by a corresponding usage of online retailing services. Reports indicate that the usage of

online retailing services in Kenya is still very low, thereby posing an existential threat

to the service providers due to the financial sustainability problem of maintaining the

loss-making online services. Consequently, continued loss-making may eventually lead

to closure of the online service, resulting in waste of effort to develop the service.

Stakeholders are therefore keen to establish the reasons as to why customers use online

retailing services, especially the continued usage or post-adoption use.

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Accordingly, research in IT continuance has examined different factors and/or

processes that motivate continued usage or discontinuance of IT products or services,

following their initial acceptance. Studies show that continued usage of IS can be

influenced by individual/psychological, system/technical and organizational factors.

However, this study restricted itself to examining individual psychological factors,

specifically the antecedent role of customer perceptions on usage of online retailing

services, coupled with the mediating role of satisfaction on the perception-usage

relationship.

Accordingly, the general objective of this online consumer behaviour study was to

empirically determine the relationship between customer perceptions and the usage of

online retailing services in Nairobi County, Kenya. It proposed a research framework

which blended online consumer behavior and technology adoption constructs, their

variables, indicators and the presumed relationships among them as described in the

empirical literature.

This mixed method study made use of a descriptive, specifically cross-sectional, survey

design and explanatory, correlational design. Quantitative data was collected by use of

a self-reporting questionnaire that was administered to selected respondents, who are

individuals registered as users of 6 online retailing services in Nairobi County, Kenya.

On the other hand, qualitative data was collected using a semi-structured interview

guide and analysis of relevant records and documents.

Questionnaire responses were analyzed using descriptive and inferential statistics;

descriptive statistics that were used to summarize the data include frequencies, means

and standard deviations. Inferential statistics, which involved both linear and logistic

112

regression analysis, was employed to infer the relationship between the study variables.

The model specification was tested using multi-collinearity test, goodness-of-fit test

and normality test. Tables, charts and narratives were used to present the data.

Results of the quantitative analysis that was presented in the previous chapter reveal

that five out of the six causal relationships between the constructs postulated by the

research model are well supported by the study‘s findings. Those variables that had a

positive association with usage are perceived attributes, perceived value and customer

satisfaction, while customer perception had a positive effect on customer satisfaction.

On the other hand, perceived risk had a significant negative effect on usage. However,

the study established that demographic factors do not have a significant moderating

effect on the relationship between customer perceptions and usage of online retailing

services.

Qualitative data from twelve semi-structured key informant interview transcripts was

manually analyzed using the directed content analysis technique into initial coding

categories. Subsequently, the researcher carried out further thematic analysis of the

various categories which were then merged into themes. Its findings complemented

those found in the quantitative analysis, with no major contradictions. Based on the

content analysis, 5 key themes were identified as providing meaningful insight into the

usage of online retailing services context in the Kenyan context. These are usage

diversity, prevailing attitudes, usage drivers, market development and market prospects.

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5.3 Conclusions

Several important conclusions can be drawn from the findings of this study. These are

categorized into six main areas in line with the objectives of the study. These

conclusions are detailed in the following section.

For one, the current study has shown that perceived attributes is a key factor motivating

the usage of online retailing services in Kenya. This finding underscores the

significance of perceived website functionality vis-a-vis online customer decision-

making behavior and is consistent with earlier studies which empirically examined

post-adoption usage in the online service context.

The results have also drawn attention to the role of perceived risk as a stumbling block

to online retailing usage in Kenya. It is evident from the study that perceived risk plays

a key role in determining continued usage of online retailing services, albeit a negative

one. This is consistent with previous research which shows that customer risk

perceptions are negatively associated with usage of online retailing stores as consumers

are only willing to purchase product/service from an online vendor that is perceived as

low risk. In short, the greater the perceived risk, the less likely consumer are to use

online retailing services in the future. Therefore, reducing such risk is crucial to online

vendors‘ success.

In addition, the study conclusively established that perceived value is positively

associated with usage. This concurs with previous studies which established that

perceived customer value is an important determinant of online transactions behavior. It

is also evident that customer perceived value in online retailing is analogous to offline

context. This means that online shoppers opt for and repatronize online retailers who

offer superior customer value in the same way as in traditional stores.

114

Furthermore, the findings conclusively reveal that taken as a whole, customer

perceptions also have a significant effect on customer satisfaction with online retailing

services. This is consistent with past studies according to which people form attitudes

(e.g. satisfaction) towards products/services based on four underlying reasons:

utilitarian function (perceived attributes), value-expressive function (perceived value),

ego-defensive function (risk perceptions), and knowledge function (awareness

of/experience with the product/service).

Similarly, the findings reveal that there is a relationship between customer satisfaction

and usage of online retailing services, supporting earlier studies which argued that user

satisfaction has an influence on system usage. This implies that when a system‘s use

fulfills user need, the resultant user satisfaction with the system is expected to result in

its greater use. On the other hand, if system usage does not meet user expectations,

satisfaction will decrease thus limiting further use. Such dissatisfied users may

discontinue system usage altogether and seek other alternatives

On the contrary, demographic factors have no relationship between customer

perceptions and usage of online retailing services. This suggests that the explanatory

power of the study model is not in any way enhanced by taking into account the

moderating effect of the user demographic factors, as has been established in previous

post-adoption studies.

5.4 Policy Implications

The empirical findings of this study have implications for practitioners as well as policy

makers who want to enhance the likelihood of success of new online retailing services.

These recommendations are outlined in the following section.

115

5.4.1 Implications for Practitioners

In view of the significance of perceived attributes, online retailers should enhance their

service features/attributes as a way of ensuring success of their services. Therefore, the

design of the online retailing services should take into consideration customer-specific

needs by personalizing the website to make it more useful, compatible with customer

requirements and easy to use for users with various levels of computer skills. Further,

providers should differentiate themselves by offering attractive features that are highly

regarded by target customers in addition to developing a user-friendly website that

provides needed menu options and functionalities. Moreover, e-retailers should come

up with a strategy for raising awareness of the relative advantages of online retailing

services vis-à-vis other alternatives (e.g. offline shopping) as people are more likely to

use such services when they perceive that advantages outweigh disadvantages.

There‘s need for online retailing service providers to reduce the risk perception

amongst their users regarding online purchasing. Ignoring it could lead to significant

reputational damage as a result of negative customer experience. One effective strategy

for mitigating risk perception is reassuring users of safety of their transactions using

website elements that assure users of the security and privacy of their information such

as mandatory verification of users coupled with offering various secure methods of

payment. Another strategy is instituting strict data protection policies that guarantee the

privacy of personal information. Also, having security seal icons, offering extensive

user information and getting the endorsement of credible third parties will help in

building trust in the services.

Without a doubt, online retailers should design and deliver a unique value proposition

that has both functional as well as hedonistic appeals. Functional value can be in terms

116

of convenience which is typified by reduced response time, minimized customer effort,

and quick completion of transactions, while monetary/price value can be achieved via

price comparison and/or offering lower prices than offline channels. Hedonistic appeal

can be achieved through appealing to the user‘s emotional value chiefly through the

website design.

Further to this, online retailers should have an effective customer satisfaction strategy

for purposes of customer retention since satisfied customers will continue using the

system in the long run, while dissatisfied users may discontinue system usage

altogether and seek other alternatives. Some of the ways that retailers can deal with this

issue effectively include the establishment of customer relationship management

sections to handle complaints and service recovery, having service agents online with

whom they can chat real time when they have a question or require assistance, as well

as providing comprehensive and easily accessible self-service information that is easy-

to-read and understand.

Indeed, it is imperative for online retailing firms to have a good understanding of their

target customers‘ needs, wants and demands, since this will not only help in

determining the appropriate customer acquisition strategies but also how to enhance the

long-term usage of their services. One approach that online retailers can employ is by

establishing and growing strategic partnerships with a range of brands that are highly

sought after by their users. This will address the challenge of product variety and

availability.

5.4.2 Implications for Government

Online retailing offers a novel way to connect Kenya to global markets while creating

the much needed jobs for the economy. Owing to this, it behooves the Government to

117

embrace, nurture and facilitate e-commerce usage in Kenya in order to spur further

growth in this area.

However, for online retailing to meet the high expectations of users, investors and other

stakeholders, the government should urgently address the current bottlenecks

hampering online shopping usage in Kenya. For instance, there‘s need for legislation

that will address e-commerce users‘ security and privacy concerns with a view to

ensuring that organizations respect and protect consumers‘ privacy rights while at the

same time offering legal recourse for the victims of fraud and other crimes arising from

online activities.

Also, as part of its online consumer protection framework, the government - in

consultation with industry stakeholders - should expedite the development of online

consumer protection guidelines for e-commerce users. If need be, a quasi-independent

multi-sectorial entity could be tasked with overseeing such a program as is the case in

other countries.

5.5 Contribution of the Study to Knowledge

The results of this study contribute to new knowledge in several ways. Most

importantly, this study makes up for the dearth of empirical research on online

shopping behavior of consumers in Kenya, specifically with regards to the relationship

between customers‘ perceptions and the usage of online retailing services.

Further, the study provides a suitable consumer decision making model that is useful in

predicting why individuals continue or discontinue usage of online retailing services.

118

The results of this study have ascertained the usefulness of the non-linear logistic

regression model in explaining usage as a dichotomous dependent variable.

Moreover, this study fills the apparent methodology gap in current online consumer

behavior literature by employing a rigorous and methodologically sound mixed-method

research design in which both qualitative and quantitative approaches are integrated to

contribute to a rich and comprehensive study. It therefore provides future scholars with

a useful framework of how to incorporate both quantitative and qualitative methods in

their online consumer research projects.

Undoubtedly, the study also theoretically contributes to new knowledge with its

conceptualization of how customer satisfaction mediates the relationship between

customers‘ perceptions and usage and by subsequently empirically validating its

mediating role on the relationship between customer perception and usage in online

retailing services.

In practical terms, the study findings regarding to the dual role played by customer

satisfaction could of help to practitioners in the online retailing field when designing

their usage as well as customer retention strategies since online shoppers who are

satisfied with online retailers are likely to recommend the online retailer to someone

and consider the retailer to be their first choice for future transactions. In contrast,

online shoppers who are dissatisfied are likely to switch to competitors in case they

experienced a problem with an online retailer, or to complain to other customers.

119

5.6 Suggestions for Further Study

This study primarily sought to establish the relationship between individual perceptual

factors and usage of online retailing services, ignoring other determinants such as

organizational and environmental factors. Owing to this omission, future studies should

incorporate these missing factors as a way of enhancing the validity of the current

model. Another area for future study regards the design of the research model; the

insignificance of the moderating variable in this study underscores the need to

undertake further research to explore the efficacy of other variables in the research

model as a way of improving its statistical significance. Perhaps future studies could

incorporate additional moderating variables such as psychographic factors in analyzing

the usage of online retailing services.

120

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APPENDICES

APPENDIX 1: SUPPLEMENTARY STATISTICAL ANALYSES (SPSS

OUTPUT RESULTS)

Table A.1: Reliability Output for Usefulness (4 items: B1 – B4)

Cronbach's

Alpha

Cronbach's Alpha Based on

Standardized Items N of Items

.954 .958 4


Table A.2: Reliability Output for Compatibility (4 items: B5 – B8)

Cronbach's

Alpha



.954 .955 4


Table A.3: Reliability Output Ease-of-Use (6 items: B9 – B14)

Cronbach's

Alpha



.950 .951 6


Table A.4: Reliability Output Financial Risk (3 items: B15 – B17)

Cronbach's

Alpha



.801 .802 3


Table A.5: Reliability Output Performance Risk (5 items: B18 – B22)

Reliability Statistics

Cronbach's

Alpha



.702 .702 5


144

Table A.6: Reliability Output Personal Risk (3 items: B23 – B25)

Cronbach's

Alpha



.885 .893 3


Table A.7: Reliability Output Monetary Value (4 items: B26 – B29)

Cronbach's

Alpha



.839 .839 4


Table A.8: Reliability Output Convenience Value (5 items: B30 – B34)

Cronbach's

Alpha



.954 .955 5


Table A.9: Reliability Output Social Value (5 items: B35 – B39)

Cronbach's

Alpha



.538 .829 5


Table A.10: Reliability Output Emotional Value (7 items: B40 – B46)

Cronbach's

Alpha



.975 .975 7


145

Table A.11: Reliability Output Level of Satisfaction (5 items: C1 – C5)

Cronbach's

Alpha



.941 .943 5


Table A.12: Correlation Matrix of the three Predictor Variables

Constant PATT PRSK PVAL

Step 1 Constant 1.000 -.906 -.001 -.742

PATT -.873 1.000 -.259 .494

PRSK .178 -.519 1.000 -.134

PVAL -.767 .491 -.189 1.000


Table A.13: Collinearity Output for Tolerance and VIF

Coefficientsa

Model Collinearity Statistics

Tolerance VIF

1

PATT .261 3.826

PRSK .468 2.135

PVAL .348 2.877

a. Dependent Variable: CSAT


Table A.14: Hosmer and Lemeshow Test for the Direct Effects Model

Hosmer and Lemeshow Test

Step Chi-square df Sig.

1 .097 8 1.000


146

Table A.15: Test of Normality for Predictor Variables

Tests of Normality

Kolmogorov-Smirnov

a Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

PATT .110 240 .000 .927 240 .000

PRSK .100 240 .000 .964 240 .000

PVAL .091 240 .000 .957 240 .000

CPER .043 240 .200* .986 240 .017

a. Lilliefors Significance Correction

*. This is a lower bound of the true significance.


Table A.16: Dependent variable Encoding for Direct Effects Model

Dependent Variable Encoding

Original Value Internal Value

0 0

1 1


Table A.17: Model Summary for Logistic Regression for Direct Effects Model

Model Summary

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square

1 17.617a .729 .974

a. Estimation terminated at iteration number 11 because parameter estimates changed by less than .001.


147

Table A.18: Classification Table for the Direct Effects Model

Classification Tablea

Observed

Predicted

USAGE Percentage

Correct 0 1

Step 1 USAGE 0 110 1 99.1

1 2 127 98.4

Overall Percentage 98.8

a. The cut value is .500


Table A.19: Case processing summary for Direct Effects Logistic Regression

Case Processing Summary

Unweighted Casesa N Percent

Selected Cases

Included in Analysis 240 98.8

Missing Cases 3 1.2

Total 243 100.0

Unselected Cases 0 .0

Total 243 100.0

a. If weight is in effect, see classification table for the total number of cases.


Table A.20: Logistic Regression Results for the Direct Effects Model

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a

PATT 6.006 2.069 8.429 1 .004 405.759

PRSK -1.834 .854 4.618 1 .032 .160

PVAL 2.329 1.084 4.621 1 .032 10.269

Constant -27.845 10.471 7.071 1 .008 .000

a. Variable(s) entered on step 1: PATT, PRSK, PVAL.


148

Table A.21: Model summary for Linear Regression Results for the Relationship

between Customers’ Perceptions and Customer Satisfaction

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .728a .531 .529 .74603

a. Predictors: (Constant), CPER


Table A.22: ANOVA (F-Test) for Linear Regression Results of the Relationship

between Customers’ Perceptions and Customer Satisfaction

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 149.726 1 149.726 269.024 .000b

Residual 132.460 238 .557

Total 282.186 239


b. Predictors: (Constant), CPER

Table A.23: Linear Regression Results of the Relationship between Customers’

Perceptions and Customer Satisfaction

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) -2.193 .330 -6.642 .000

CPER 1.258 .077 .728 16.402 .000



149

Table A.24: Dependent Variable Encoding for Logistic Regression of the

Relationship between Customer Satisfaction and Usage



0 0

1 1


Table A.25: Classification table: Logistic Regression of the Relationship between

Customer Satisfaction and Usage


Observed

Predicted

USAGE Percentage

Correct 0 1

Step 1 USAGE 0 106 5 95.5

1 7 122 94.6




Table A.26: Case Processing Summary for Logistic Regression of the Relationship

between Customer Satisfaction and Usage



Selected Cases


Missing Cases 3 1.2

Total 243 100.0


Total 243 100.0



150

Table A.27: Model Summary for Logistic Regression of Relationship between

Customer Satisfaction and Usage of Online Retailing Services

Model Summary


1 76.227a .655 .874

a. Estimation terminated at iteration number 8 because parameter estimates changed by less than .001.


Table A.28: Logistic Regression Results for Relationship between Customer

Satisfaction and Usage of Online Retailing Services



Step 1a

CSAT 5.171 .797 42.056 1 .000 176.063

Constant -16.593 2.626 39.925 1 .000 .000

a. Variable(s) entered on step 1: CSAT.


Table A.29: Case Processing Summary for Moderated Effects Model



Selected Cases


Missing Cases 3 1.2

Total 243 100.0


Total 243 100.0



Table A.30: Dependent variable encoding for moderation effects model



0 0

1 1


151

Table A.31: Moderation Effects Model Summary

Model Summary


1 183.125a .461 .616

a. Estimation terminated at iteration number 6 because parameter estimates changed by less than .001. Source: Survey Data (2014)

Table A.32: Classification table for moderation effects regression


Observed

Predicted

USAGE

Percentage Correct 0 1

Step 1 USAGE 0 85 26 76.6

1 19 110 85.3




Table A.33: Logistic Regression Results for Moderation Effects Model



Step 1a

CPER 4.587 1.698 7.302 1 .007 98.235

DEMF .534 2.187 .060 1 .807 1.707

CPERDEMF -.196 .523 .141 1 .708 .822

Constant -18.356 7.070 6.742 1 .009 .000

a. Variable(s) entered on step 1: CPER, DEMF, CPERDEMF.


152

APPENDIX 2: LIST OF ONLINE RETAILING FIRMS IN NAIROBI, KENYA

NO. NAME

1. Area254.co.ke

2. BuyRentKenya.com

3. Buyandsell.co.ke

4. Cars.co.ke

5. Cheki

6. DailyShark.co.ke

7. Digitalduka

8. DukaWala.com

9. Kenyacarbazaar.com

10. Kilakitu.co.ke

11. Maduqa.com

12. MamaMikes

13. Mimi.co.ke

14. Mottiz.com

15. Mzoori.com

16. N-Soko

17. OLX (formerly Dealfish)

18. PataUza

19. PigiaMe

20. Rupu.com

21. Ravenzo

22. Sokobay.com

23. UzaNunua

24. Jumia

25. Zetu

Source: Kenya ICT Board (2012); Postel Directory (2012), others.

153

APPENDIX 3: DATA COLLECTION INSTRUMENTS

APPENDIX 3A: COVER LETTER

Peter Misiani Mwencha

P.O. Box 53553-00200 Nairobi,

Kenya.

Dear Respondent,

RE : PARTICIPATION IN ACADEMIC SURVEY/INTERVIEW:

I am a student pursuing a Doctor of Philosophy degree in Business Administration at

Kenyatta University, Nairobi Campus. Part of the requirements of this course is to

submit a research thesis on a topic of interest to me. To this end, I am currently

conducting a study on the Effect of Customer Perceptions on the Usage of Online

Retailing Services in Nairobi County, Kenya. I will therefore be most grateful if you

could take time off your busy schedule to respond to the questions in the attached

questionnaire or to make yourself available for a brief interview so as to enable me carry

out this research. This is only an academic exercise and you are assured of anonymity

and confidentiality.

Thank you in advance for your cooperation.

Kind regards,

Peter Misiani Mwencha

Admission Number: D86/CTY/21719/2010

154

APPENDIX 3B: QUESTIONNAIRE

KENYATTA UNIVERSITY

School of Business

Effects of Customer Perceptions on the Usage of Online Retailing Services in

Nairobi County, Kenya

Dear Sir/Madam

The purpose of this questionnaire is to collect information on the effects of customer

perceptions on the usage of electronic commerce services in online retailing firms in

Nairobi County, Kenya as part of a study for the award of PhD at Kenyatta University. I

will be most grateful if you could take time off your busy schedule to respond to the

questions. This is only an academic exercise and you are assured of anonymity and

confidentiality. Thank you.

SECTION A. CUSTOMER DEMOGRAPHIC FACTORS

First things first: Tell us a bit about yourself. Please respond to each item by choosing the

response that best describes you.

1.

2.

3.

Age:

18- 23 24 - 29 30 – 35

36 - 41 42 - 47 48 years & above

Highest Level of Education:

High School Certificate Diploma Bachelor‘s Degree

Masters Degree Doctorate Professional

Other

Monthly income (gross): Below KSh 24,999 KSh 25,000-49,999

KSh 50,000-74,999 KSh 75,000-99,999

Ksh 100,000-KSh 124,999 Ksh 125,000 & above

155

SECTION B. CUSTOMER PERCEPTION MEASURES

Please indicate the extent to which you disagree or agree with each of the following

statements by marking with a cross (X) in the appropriate block provided. Please use

the following seven-point rating scale ranging from 1 = “strongly disagree” to 7 =

“strongly agree”.

CUSTOMER PERCEPTIONS Value Label

Variable Label 1 2 3 4 5 6 7


1. The system enables me to accomplish what I want more

quickly

2. The system makes me more effective

3. The system makes it easier to do what I want

4. I find the system useful

5. The e-commerce service fits my image well.

6. Using the system is compatible with all aspects of my

lifestyle.

7. I think that using the system fits well with the way I like to

do things.

8. Using the system fits into my lifestyle.

9. I find the system to be clear and understandable.

10. It‘s easy to get the system to do what I want it to do

11. It‘s easy to find what is being sought

12. The system has no hassles

13. Learning to operate the system is easy for me.

14. Overall, I believe that the system is easy to use.

Perceived Risk

15. This service costs more than conventional methods

16. I might be overcharged for using this service

17. I might not receive the product/service that I paid for

18. Inability to touch and feel the item worries me

19. One can't examine the actual product

20. It‘s not easy to get what I want

21. Information takes too long to come up/load

22. The e-commerce service failed to perform to my

satisfaction

23. My credit card number may not be secure

24. My personal information may be sold to advertisers

25. My personal information may not be securely kept

Perceived Value

26. This e-commerce service is reasonably priced.

27. This e-commerce service is competitively priced

28. This e-commerce service offers value-for-money

29. Using this e-commerce service is economical

30. I can use this e-commerce service anytime

31. I can use this e-commerce service anyplace

32. This e-commerce service is convenient for me to use

156

33. I feel that the e-commerce service is convenient for me

34. I value the convenience of using this e-commerce service

35. This service would help me feel acceptable by others

36. This service would improve the way I am perceived

37. Using this service would make a good impression on others

38. My friends and relatives think more highly of me for using

this service.

39. This service would give its user social approval

40. I enjoy using the system.

41. Some aspects of the system make me want to use it

42. I feel relaxed about using the system

43. Using the system makes me feel good

44. Using the system gives me pleasure

45. Using the system is fun

46. It‘s exciting to use the e-commerce service

SECTION C. CUSTOMER SATISFACTION MEASURES

Please indicate the extent to which you are satisfied with the e-commerce system by

marking with a cross (X) on one of the five blocks provided below the position which

most closely reflects your satisfaction with the service.

Thank you very much for your time.

1.

2.

3.

4.

5.

How satisfied were you with the online retailing service initially?

Very Dissatisfied Slightly Dissatisfied Neither

Somewhat Satisfied Very Satisfied

To what extent does this online retailer meet your needs? Extremely well Pleased Satisfied

Mixed Extremely poorly

My experience with this online retailer was very satisfactory Strong Yes Yes Neutral

No Strong No

Overall, I am _ with the service?

Delighted Pleased Satisfied

Mixed Mostly Dissatisfied

If I could do it all over again, I would still use this service?

Strong Yes Yes Neutral

No Strong No

157

APPENDIX 3C: KEY INFORMANT INTERVIEW GUIDE

KENYATTA UNIVERSITY

School of Business

Effects of Customer Perceptions on the Usage of Online Retailing Services in

Nairobi County, Kenya

Dear respondent,

The purpose of this interview is to collect information from key informants on the effects

of customer perceptions on the usage of online retailing services in Nairobi County,

Kenya as part of a study for the award of PhD at Kenyatta University. I will be most

grateful if you could take time off your busy schedule to make yourself available briefly to

answer the questions in this interview guide. This is only an academic exercise and you are

assured of anonymity and confidentiality. Thank you.

Yours sincerely,

Peter M. Mwencha

D86/CTY/21719/2010

School of Business

Kenyatta University

Working Guide/Manual on Qualitative Information

Kindly answer the following questions as truthfully and comprehensively as possible.

1. Have you used any local online retailing website(s) before? [Yes]) [No]

a. If yes, give examples and explain what you used the website(s) for? __________________

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

b. If yes, what factors attracted you to use the online retailing website(s)?

_________________________________________________________________________

_________________________________________________________________________

________________________________________________________________________

158

c. If yes, have you used the website(s) within the last three months? [Yes] [No]

d. If no, explain why not? ______________________________________________________

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

2. Are there any products you wouldn‘t buy online? Give examples and please explain why?

________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

3. What issues/concerns do you have regarding online retailing services?

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

4. Which payment method(s) would you prefer (or use) when shopping online? Explain why?

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

5. How satisfied (or dissatisfied) are you with local online retailing services? Give reasons

why.

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

6. Please comment on the following aspects regarding online retailing services in Kenya:

a. Usefulness:____________________________________________________________

______________________________________________________________________

b. Convenience:___________________________________________________________

______________________________________________________________________

c. Ease of Use: _________________________________________________________

______________________________________________________________________

159

d. Financial Risk: _________________________________________________________

______________________________________________________________________

e. Performance Risk: ______________________________________________________

______________________________________________________________________

f. Privacy Risk: __________________________________________________________

______________________________________________________________________

g. Monetary Value: _______________________________________________________

______________________________________________________________________

h. Convenience Value: _____________________________________________________

______________________________________________________________________

i. Social Value: __________________________________________________________

______________________________________________________________________

7. What problems/difficulties/challenges do people encounter when using these websites?

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

8. Give practical suggestions and policy recommendations that would enhance the usage of

online retailing services in Kenya?

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

9. What is your overall assessment of online shopping in the Kenyan context?

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

_________________________________________________________________________

Thank you very much for your time and attention.

160

APPENDIX 4: RESEARCH AUTHORISATION

APPENDIX 4A: CLEARANCE LETTER

161

APPENDIX 4B: RESEARCH PERMIT

162

APPENDIX 5: CODEBOOK

APPENDIX 5A: CODE BOOK FOR QUANTITATIVE DATA ANALYSIS

Variable SPSS Variable Name

1. Usage USAGE

2. Perceived Attributes PATT

3. Perceived Risk PRSK

4. Perceived Value PVAL

5. Customer Perceptions CPER

6. Customer Satisfaction CSAT

7. Demographic Factors DEMF

8.

Interaction Term between Customer Perception

and Demographic Factors

CPERDEMF


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APPENDIX 5B: CODE BOOK FOR QUALITATIVE DATA ANALYSIS

Code/Variable Operational Definition

1. Usage

The utilization of one or more features of an online

retailing service by registered users within a certain

timeframe. It could be browsing or actual purchase.

2. Perceived Attributes Users‘ perceptions regarding the online retailing

service‘s functional features, properties and qualities.

3. Perceived Risk The transaction-related risks that consumers face as a

result of using online retailing services.

4. Perceived Value The consumer‘s evaluation of the benefits of online

retailing usage.

5. Customer Perceptions

The subjective opinions/beliefs/judgments of an

individual regarding an online retailing service based

on prior use experience.

6.


The customer‘s overall positive evaluation of the

online retailing service following initial usage or

based on all prior interactions/encounters and

experiences with the online retailing service.

7.

Demographic Factors

Are any personal characteristics/attributes of

consumers that tend to remain static throughout an

individual‘s life time, or evolve slowly over time.

This includes age, gender, race, education, income,

lifestyle, etc.

8.

Industry Prospects

Forecasts/predictions regarding future developments

of the online retailing industry. Could be poor or

bright.

9. Challenges/Problems

A hindrance/barrier to online retailing industry

growth or development

10. Policy

Recommendations

Written policy advice prepared for decision makers

regarding a specific issue.


164

APPENDIX 5C: SUMMARY OF MAJOR THEMES

Theme Description/Operational

Definition Examples of Significant Statements

1. Usage Diversity Types/nature and extent of

usage/utilization of online retailing

services

I used Jumia to look at the price range of a laptop that would fit my

budget as well as look at the specifications of that laptop.

I was looking for products I wanted to purchase [but] I was also

selling some goods on OLX.

I used Mzoori.com, Rupu.com, Jumia and OLX to look for products

for purchase and was also selling some goods on OLX.

I only use online retailing stores when I am seeking to buy high cost

items because I get an opportunity to compare prices across different

e-commerce stores. I do not do this frequently that‘s why I cannot do

this (use the websites) every now and then…

2. Prevailing Attitudes Opinions, thoughts and feelings

regarding online retailing services

The inconsistency in product availability coupled with the lack of

accurate information on some websites has made online users form a

negative impression towards online retailing services.

Quite a number of online retailers cannot guarantee proper inventory

management – you order an item online but you cannot have it

(delivered) because it is out of stock.

I‘m very dissatisfied. I have not been able to purchase anything

because what‘s available is not within my budget range.

I‘m somewhat satisfied…however, it can be quite costly to roll out.

I‘m very satisfied with online retailing in Kenya because of how fast

it is growing and the convenience it brings.

3. Usage Drivers

Determinants of online retailing usage

Online retailing is quite useful at it helps one purchase products after

viewing a variety without having to waste time walking around.

It‘s very convenient as it allows one to purchase by just clicking and

having the product delivered at one‘s doorstep.

Payment methods such as cash and m-pesa. They are both easy to

165

pay on delivery as there‘s no risk of losing money and they are fast

when transacting.

There is a chance of personal details getting in the wrong hands,

especially for those using visa cards to make payments. Moreover,

there are chances that delivery does not occur as promised e.g.

delivery within 24 hours turns to delivery after 72 hours.

4. Market

Development

Possible ways of increasing and

sustaining the usage of online

retailing services

Increase the product range and ensure that products are always in

stock; delivery to homes would increase the convenience of this

value chain; ensure that the regulatory frameworks for protecting

customers privacy are enhanced and there is judicial recourse if these

are compromised.

Build trust; avail a variety of products; offer good prices for

products; avail all information on one page; offer return policies;

offer cash on delivery and offer warranties.

There‘s need for more regulation from government to reduce the

risks associated with online shopping

Enact data privacy laws to protect consumers‘ sensitive data.

Having better user interface, user experience and e-security.

5. Market Prospects Economic/business potential of the

online retailing sub-sector (industry

category)

Online shopping as a trend is picking up and has a bright future. This

is because more internet users are turning to online shopping

especially due to its convenience.

Online retailing is gaining momentum; while we have already

achieved good progress, a lot more needs to be done to ensure

security while transacting, product variety and value added services.

Online shopping is an industry growing in Kenya and people are

beginning to embrace it. A few things need to be worked on and it

will turn into a lucrative industry.

Its heading in the right direction but we can do better.

It‘s still at a pretty early stage. There a few outstanding efforts.

However, there are many copy cats.


166

APPENDIX 6: SUMMARY OF EMPIRICAL REVIEW AND RESEARCH GAPS

Variable Thematic

Area

Author Study Key Findings Weaknesses Knowledge Gaps

Customer

Perceptions

Perceived

Attributes

Parthasarathy

&

Bhattacherjee

(1998)

Understanding Post-

adoption Behavior in the

Context of Online

Services

Perceived attributes such

as usefulness and

compatibility determine

continued usage behavior.

Employed IDT as the sole

basis to study continued

usage of online services.

There‘s need to include

more theories to study

consumer usage behavior of

e-commerce services.

El-Kasheir,

Ashour &

Yacout (2009)

Factors Affecting

Continued Usage of

Internet Banking Among

Egyptian Customers

Perceived ease-of-use is

the strongest predictor of

CUI. Perceived risk had no

relationship with CUI.

Used mall interception to

collect data; Employed CUI

as DV instead of actual

usage.

There‘s need to employ

actual usage as the DV in

future studies instead of

continuance intentions.

Perceived

Risk

Liebermann &

Stashevsky

(2002)

Perceived risks as barriers

to internet and e-

commerce usage

Internet credit card stealing

and supplying personal

information (Privacy risk)

affects both current and

future e-commerce usage.

Only privacy risk was

established as having an

influence on e-commerce

usage.

Further studied on the

influence of perceived risk

dimensions on the usage of

e-commerce services are

needed.

Zhang, Tan,

Xu and Tan

(2012)

Dimensions of

Consumers‘ Perceived

Risk and Their Influences

on Online Consumers‘

Purchasing Behavior

Perceived privacy risk,

perceived social risk and

perceived economic risk

don‘t influence online

shopping behavior

The study focused on the

online shopping segment of

e-commerce in the pre-

adoption context.

There‘s need to carry out

studies focused on other

segments of e-commerce in

the post-adoption context.

Perceived

Value

Oh (1999)

Service Quality,


and Customer Value: A

Holistic Perspective

Perceived value is an

immediate antecedent to

customer satisfaction and

repurchases intention.

This study used single-item

overall measurement for

most variables.

Each model construct or

variable should be

measured with multiple

items

Yen (2011)

The Impact of Perceived

Value on Continued

usage Intention in Social Networking Sites

Information value, social

value, and hedonic value

have a positive effect on CUI.

Only considered ―get‖

components (social value

and hedonic value) ignoring ―give‖ components.

There‘s need to integrate

both give and get

components of perceived value.

167

Customer

Satisfaction

Level of

Satisfaction

Bolton and

Lemon (1999)

A Dynamic Model Of

Customers‘ Usage of

Services: Usage as an

Antecedent and

Consequence of

Satisfaction

High level of cumulative

satisfaction with initial

usage will lead to higher

usage levels of the service

in subsequent periods.

No moderating variables

used in the study.

There‘s need to establish

the moderating effect of

customer characteristics on

the perception – usage

relationship.

DeLone &

McLean (2004)

Measuring E-Commerce

Success: Applying the

DeLone & McLean

Information Systems

Success Model

Proposed that ease of use

influences user satisfaction

which subsequently

directly influences usage

of e-commerce services.

Conceptually enhanced the

DeLone & McLean IS

Success Model (2003)

without empirical tests.

There‘s need to empirically

validate the relationship

proposed in the model.

Chen, Huang,

Hsu, Tseng &

Lee (2010)

Confirmation of

Expectations and

Satisfaction with Internet

Shopping: The Role of

Internet Self-efficacy

Satisfaction is influenced

by perceived usefulness

and both satisfaction and

perceived usefulness

determine consumer‘s

repurchase intention.

Ignored the role of

perceived value and risk as

determinants of e-

commerce repurchase

intentions.

There‘s need to investigate

the role of value and risk

perceptions as determinants

of usage of e-commerce

services in future studies

Yen (2011)

The Impact of Perceived

Value on Continued

usage Intention in Social

Networking Sites

End-user satisfaction

mediates the relationship

between PV and CUI

Only considered ―get‖

components e.g. social

value and hedonic value.

ignoring ―give‖

components.

There‘s need to integrate

both give and get

components of perceived

value.

Customer

Factors

Age

Venkatesh,

Morris, Davis

& Davis

(2003)

User Acceptance of

Information Technology:

Towards a Unified View

Age has a moderating

influence on the

relationship between

facilitating conditions and

use of IT

Was based on an

organizational context

where usage is not entirely

voluntary.


the moderating influence of

age in a voluntary context.

Income

Hernández,

Jiménez &

Martin

(2011)

Age, gender and income:

do they really moderate

online shopping

behaviour?

Age and income have no

moderating influence on

use of e-commerce

Only establish the

moderating effect of age

and income but not

education



education level

Education

Level

Riddel & Song

(2012)

Role of Education in

Technology Use and

Adoption: Evidence from Canadian Workplace and

Employee Survey

Education has an influence

on computer usage but not

on usage of computer-controlled or computer-

assisted devices.

Was based on workplace/

organizational context

where usage is not entirely voluntary.



education in a voluntary (i.e. consumer) context.

168

Usage

Continued

Usage

Bhattacherjee

(2001b)

An empirical analysis of

the antecedents of

electronic commerce

service continuance

Perceived usefulness and

user satisfaction influence

e-commerce continuance

intention.

Is based on the ECT. Also

lacked moderating

variables. Focused on B2B

E-Commerce. Used CUI as

the DV

There‘s need for an

integrated model of E-

Commerce Usage by

Consumers.

Brown &

Jayakordi

(2008)

B2C e-Commerce

Success: a Test and

Validation of a Revised

Conceptual Model

CUI of an online retail site

is directly influenced by

perceived usefulness, user

satisfaction and system

quality.

Is limited to the B2C E-

Commerce context in South

Africa. Lacks moderating

variables.

Employed the user‘s

continuance intention as the

DV. In this study, actual

usage is employed as the

DV.

Wen, Prybutok

and Xu (2011)

An Integrated Model for

Customer Online

Repurchase Intention.

In the post-purchase stage,

PU play a more important

role than hedonic factors in

predicting customer online

repurchase intention.

Respondents were college

students in the U.S., thus

limiting generalizability to

other populations &

contexts.

There‘s need for more

studies on customer e-

commerce usage that has a

wider respondent make-up

outside of the U.S.

Chen & Chou

(2012)

Exploring the

continuance intentions of

consumers for B2C online

shopping: Perspectives of

fairness and trust

Empirically tested and

validated that satisfaction

is a strong predictor of the

continuance intentions of

consumers.

Employed continuance

intentions as the DV for e-

commerce success in the

B2C E-Commerce context.

This study shall employ

actual use as the DV.

Ramayah &

Lee (2012)

System Characteristics

Satisfaction and E-

Learning Usage: A

Structural Equation

Model.

user satisfaction is

positively related to usage

continuance

Limited itself by using

DeLone & McLean‘s

Model (2003) as the sole

basis for the study

There‘s need to develop an

integrated model for use in

explaining IS continuance.


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Customers' Perceptions and Usage of Online Retailing Services in Nairobi County, Kenya

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