i 

 

DETERMINANTS OF TERTIARY STUDENTS’ E-
SHOPPING ACCEPTANCE IN LAGOS STATE, 

NIGERIA 
  

 
 

A Thesis Submitted to the School of Post Graduate Studies, 

University of Lagos, in Partial Fulfillment of the Requirements 

for the Award of Doctor of Philosophy (Ph.D.) in Marketing. 

 

 

 

 

BY 

 

 

NWAGWU, KENNEDY OGBONNA 
 

B.Sc. (Hons) Business Management (Port Harcourt), M.Sc. 
Marketing (Lagos) 

Matriculation Number: 029023071 
 
 
 
 
 

October, 2016 

 
 

 



ii 

 

 

SCHOOL OF POST GRADUATE STUDIES 
UNIVERSITY OF LAGOS 

 

CERTIFICATION 
This is to certify that the thesis: 

DETERMINANTS OF TERTIARY STUDENTS’ E-SHOPPING 
ACCEPTANCE IN LAGOS STATE, NIGERIA 

 
Submitted to the 

School of Postgraduate Studies 
University of Lagos 

 
For the award of the degree of 

DOCTOR OF PHILOSOPHY (Ph.D.) 
is a record of original research carried out 

By: 
NWAGWU, KENNEDY OGBONNA 

In the Department of Business Administration 

 
…………………………      ……………..     ………………... 
AUTHOR’S NAMESIGNATURE            DATE 
 

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1ST SUPERVISOR’S NAME               SIGNATURE                       DATE 

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2ND SUPERVISOR’S NAME              SIGNATURE                       DATE 

…………………………       …………….      ……………….. 
1ST INTERNAL EXAMINER             SIGNATURE                       DATE 

…………………………       …………….       ………………. 
2ND INTERNAL EXAMINER             SIGNATURE                      DATE 
 

………………………..         …………….       ………………. 
EXTERNAL EXAMINER                   SIGNATURE                      DATE 

………………………..         …………….       ………………. 
SPGS REPRESENTATIVE                SIGNATURE                      DATE 
 



iii 

 

DECLARATION 
 

I declare that this dissertation submitted for the degree of Doctor of Philosophy in Marketing 

is my own original work and has not been previously submitted to any University or 

institution for any academic award. I further declare that all sources cited or quoted have been 

acknowledged and referenced to the best of my knowledge. 

 

 

 

………………………………. 
Nwagwu, Kennedy Ogbonna 

October, 2016 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



iv 

 

DEDICATION 
To: God Almighty 
 
WHO makes all things beautiful at HIS own time. 
 
To: Tessy, my wife and Alexander, Jefferson and Adaeze, my children 
 
For their love, patience and prayers. 
 
To: the memory of my late father, Pa Alexander Ibeananam Nwagwu 
 
Who was there when I set out for this quest but didn’t live to see me 

return with the Golden Fleece. Papa, may God grant your soul a peaceful  

rest, Amen! 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



v 

 

ACKNOWLEDGEMENTS 

I am thankful and grateful to the King of kings and Lord of lords, Almighty Jehovah, WHO is 

the greatest of all inspirations and the fountain of all knowledge. 

To my first supervisor Dr P.K.A. Ladipo, what shall I say? You were both my teacher and 

psychologist. Your words of encouragement always propped me up whenever I was at my 

low ebb. Your belief in my ability to excel as an academic and researcher is both refreshing 

and challenging. A great coach and mentor; always nudging me on to be the best I can be! 

Sir, your support was unrivaled and I make bold to say that I am the luckiest among my 

colleagues to have worked under your supervision as a doctoral student. I pray that Almighty 

God will reward you immensely. To, my second supervisor, Dr A.C. Oniku, I say thank you. 

Your contributions no doubt added to the quality of this thesis. 

I must acknowledge the immense contributions of Professor O.L. Kuye (my HOD) and 

Professor R.K. Ojikutu (my Dean of Faculty) in ensuring that I commenced the immediate 

process leading to both my internal defense and VIVA, as soon as this work was ready. May 

God bless you, sirs and grant you speed in all your endeavours, Amen. 

 
To my other lecturers, whose teaching made my quest for a Ph.D. course in the faculty of 

Business Administration worth the while such as Professor S. A. Banjoko, Professor J.N. 

Mojekwu, Professor Wilfred Iyiegbuniwe, Professor S.I. Owualah, Dr E.O. Oyatoye, 

Professor B.E.A. Oghojafor, Dr. Hamadu Dallah and Dr Peter, I. Iyiegbuniwe. I say, thank 

you. 

 
Other lecturers in the department/faculty who encouraged me one way or the other include Dr 

Sulaiman (former head, Business Administration dept), Dr. C.B.N. Uche, Dr S.A. Adebisi, 

Dr. Vincent, Dr. Olusoji George, Dr O.J. Oluwafemi, Dr J.S. Okonji, Dr O.O. Olayemi, Dr 

O.O. Dakare, Dr. O. Akintunde and Dr. Francis C. Anyim. I am grateful for all your support. 

 
Let me also extend this litany of thanks to my mates with whom I started this journey 

together: Mrs Gloria Alaneme (my close ally), A.G. Adekoya (my class governor), Mrs A.B. 

Ofuani, T.O. Olufayo, I. I. Iwuji, R.D. Bakare, C.I. Omoera, E.D. Ighodalo, K.O. Ikenwa, Dr 

S.A. Aduloju and Dr I.K. Muo. Certainly, guys, our interactions sharpened and widened my 

intellect.  

 



vi 

 

Also, I must acknowledge such friends as Dr Rahim Ganiyu, Mrs. Salome Ighomereho, Dr 

Dawodu, Dr Abass A. Shiro, Dr. Dumebi A. Ideh, Mr. O.O. Sode, Dr. Emeka Mbah, Dr E. 

Badejo, Mr. Adeleke and Mr. Otayemi Oluwaseun Olutayo (who was fervent in his prayers 

for me). My interactions with all of you helped in shaping me into a rounded scholar. 

 
Not to be forgotten in this litany of thanks are the administrative staff of Department of 

Business Administration, who were of assistance throughout the duration of this programme 

in the persons of Mrs. Eunice Adeola (Dept. Secretary), Mr Nwazue Princewill Okpala, Mrs 

Obi, Miss Jacenta O, and Mr. Raji (now with Student Affairs). I appreciate you all. 

 
I owe a lot of love to my mum, Madam Matilda Nwagwu, and my younger siblings, Ndidi, 

Chikodi, Chinemerem, Chilaka and his wife- Chinyereugo, Mary and Victor. You are the best 

family any one could ask for. May God keep us all to reap the fruits of our labour, Amen. 

 
To my cousins, Nathaniel, Friday, Godson, (and the entire Uzowuru family); Michael and his 

wife, Precious; and my bosom friends, Dr. Edwin Eke, Barrister Timothy Nwosu, Mr. Pius O. 

Uwagbai, Mr.  & Mrs. Emeka Ogubie, Mr.  & Mrs. Deinde Ogundipe, Mr.  & Mrs. Kofo 

Olayinka, Mr. Zubby Osuchukwu, Mr.  & Mrs. Chinweuba Izima,Mr. Albert Ukachi, Mr. 

Biodu Bakare, Mr. Alex Chukwu, and Pastor Banji. I say thank you for your moral and 

financial support while this programme lasted. 

 
Equally deserving of my accolades are my in-laws, Dr (Mrs.) Anyalewa A. Ajonye, Mr. Oche 

Eka, Mrs. Chii Eka, Mrs. Christy Oklobia, Mrs. Comfort Okubama, Mr. Innocent 

Ikegwuruka, Mr. Ethel Mgbike, Pastor Chinedu O. Ofili and Pastor (Mrs.) Grace Ofili. I 

appreciate all your prayers including emotional and material contributions. 

 
I am grateful to my pastors at the RCCG Zion Assembly parish Ire-Akari Estate, Isolo, who 

were fervent in their prayers. I must particularly mention Pastor Agarah, Pastor Sam 

Adekoya, Pastor (Mrs) Dammy Adekoya, Pastor (Mrs) Lola Onaadepo, Pastor Lekan 

Oyewole, Pastor Jonathan Timothy and Elder Chukwuemeka. It is only God that will reward 

you for your sacrifices and unceasing prayers. 

 
Not to be forgotten also is the support I got from my team in the church- Redeemers’ men’s 

fellowship (Zion Assembly Chapter). To bro Peter Nweke, bro Kola Ogunlade, bro Dotun, 



vii 

 

bro Duntoye and the rest of men’s executives, I am grateful as your support made it possible 

for me to function effectively as your president even while embarking on this Ph.D. journey. 

 

Equally deserving of my thanks are Professor Don Baridam (former Vice chancellor 

University of Port Harcourt) who was my lecturer and undergraduate supervisor; Professor 

I.C. Achumba and Late Professor Nnamdi Asika, both of whom opened my eyes to how 

marketing and research method/tools can be deployed to solve societies’ problems. 

 
This acknowledgement will not be complete without doffing my hat in honour of the love of 

my life, a jewel of inestimable value and a virtuous woman. Tessy, my wife, you have always 

done me good and will never do me evil all the days of your life. You have been both my life 

and prayer partner. God bless you. This quest would have been a total failure without your 

support. You always prop me up at those moments my courage faltered and doubts seemed to 

overwhelm me. God has used you to cover my shame. I am immensely grateful, my love. 

 
To all those who reviewed this thesis both at the department and APC levels, I sincerely 

appreciate your unbiased comments that have rubbed off positively on the quality of this 

work. Lastly, to the many authors whose works I consulted, I am indeed grateful; a 

comprehensive list of these works is shown as references and bibliography. 

 

Kennedy Ogbonna NWAGWU 

October, 2016 
 

 

 

 

 

 

 

 

 

  



viii 

 

TABLE OF CONTENTS 

                                                                                                                                           Page  

Title page           i 

Certification           ii 

Declaration           iii 

Dedication           iv 

Acknowledgements          v 

Table of Contents          viii 

List of Tables           xi 

List of Figures           xiii 

List of Appendices          xiv 

Abstract           xv 

 
Chapter One: Introduction 

1.1 Background to the Study        1 

1.2  Statement of the Problem        7 

1.3   Purpose of the Study         8 

1.4   Research Questions         10 

1.5  Research Hypotheses         10 

1.6   Significance of the Study        11 

1.7   Scope and Delimitations of the Study       12 

1.8  Operational Definition of Terms       13 

1.9  Summary of the Chapter        14 

 
Chapter Two: Literature Review 

2.1 Preamble           17 

2.2  Theoretical Framework        17 

2.2.1   Theory of Reasoned Action (TRA)       17 

2.2.2   The Theory of Planned Behaviour (TPB)      20 

2.2.3   Theory of Diffusion of Innovation       22 

2.3   Conceptual Framework        26 

2.4 Empirical Literature Review        51 

2.5  Summary of the Chapter        63 



ix 

 

 

Chapter Three: Methodology 

3.0   Preamble          64 

3.1   Research Design         64 

3.2   Study Population         65 

3.3   Sampling Procedure         66 

3.4   Sample Size          70 

3.5   Data and Collection Instrument       73 

3.6   Measurement and Scale Development      75 

3.7  Instrument Validation         84 

3.8   Statistical Tools:         86 

3.9   Effect Size Estimation Models       86 

3.10  Summary of the Chapter        86 

 
Chapter Four: Data Presentation and Analyses 

4.0   Preamble          87 

4.2 Profile of Respondents based on Type of Programme    88 

4.3 Profile of Respondents based on Socio-demographics    89 

4.4   Results of Hypotheses Testing       92 

4.5 Summary and Discussion of Findings      148 

4.6   Summary of the Chapter        157 

 
Chapter Five: Summary, conclusions and recommendations  

5.0   Preamble          158 

5.1   Summary           158 

5.2   Conclusions          160 

5.3   Implications          165 

5.4   Recommendations         166 

5.5  Contributions to Knowledge        168 

5.6  Limitations of Research        169 

5.7   Suggestions for Further Studies       171 

Appendix A          172 

Appendix B          173 



x 

 

Appendix C          179 

Appendix D          182 

Appendix E          183 

Appendix F          185 

Appendix G          187 

Appendix H          188

 Appendix I          190 

Appendix J          191 

Appendix K          193 

Appendix L          194 

Appendix M          198 

Appendix N          199 

References          200 

 

 

 

 

 

 

  



xi 

 

LIST OF TABLES 

Table                                                                                                                             Page 

Table 2.1: List of some TAM based researches and their acceptance constructs  50 

Table 3.1: Sample frame         71 

Table 3.2: Calculated sample size        72 

Table 3.3: Bio data of Student Respondents       76 

Table 3.4: Bio data of Lecturer Respondents       77 

Table 3.5: Students classification of products      77 

Table 3.6: Classification of products by Lecturers      78 

Table 3.7: Selected products for each product type      79 

Table 3.8: Scale Reliability: Descriptive statistics and Cronbach Alpha   84 

Table 4.1: Type of institution/programme enrolled in by respondents   88 

Table 4.2: Socio-demographic profile of respondents     89 

Table 4.3a: Group Statistics         91 

Table 4.3b: Independent Samples Test       91 

Table 4.4a: Descriptives         93 

Table4.4b: Test of Homogeneity of Variances      93 

Table4.4c: ANOVA          93 

Table 4.4d: Robust Tests of Equality of Means      93 

Table 4.4e: Multiple Comparisons        94 

Table 4.4f: 3way crosstab of age, intention to shop & gender  

for respondents of 31-35years         94 

Table 4.4g: 3way crosstab of age, intention to shop & gender  

for respondents of 25yrs&below        94 

Table 4.5a: Descriptives         95 

Table 4.5b: Multiple Comparisons        97 

Table 4.5c: ANOVA          97 

Table 4.5d: Robust Tests of Equality of Means      97 

Table 4.5e: Multiple Comparisons        97 

Table4.6a: Descriptives         99 

Table4.6b: Test of Homogeneity of Variances      99 

Table 4.6c: ANOVA          99 



xii 

 

Table 4.6d: Multiple Comparisons        100 

Table 4.7: Summary of results obtained from testing of hypothesis one   102 

Table 4.9 a: Paired Samples Statistics       103 

Table 4.9b: Paired Samples Test        104 

Table 4.10: Summary of results obtained from testing of hypothesis two   107 

Table 4.14a: Perceived risk predicted from perceived usefulness    110 

Table 4.14b: Intention predicted from both Perceived usefulness & perceived risk  110 

Table 4.14c: Total Effect Model        111 

Table 4.14d: Total, Direct and Indirect Effects      111 

Table 4.15a: Perceived risk predicted from perceived ease of use    112 

Table 4.15b: Intention predicted from both Perceived ease of use & perceived risk  112 

Table 4.15c: Total Effect Model        112 

Table 4.15d: Total, Direct, and Indirect Effects      113 

Table 4.16 Summary of Collinearity Statistics      121 

Table 4.16a: Descriptive Statistics        123 

Table 4.16b: Correlations Matrix for Variables of the study     123 

Table 4.16c: Summary of results of bootstrapped regression analysis for H05  124 

Table 4.16d: Part and partial correlations       125 

Table 4.16e: Calculation of Predictors main effects      125 

Table 4.17a: Classification Tablea,b for Block 0      130 

Table 4.17b: Omnibus Tests of Model Coefficients      130 

Table4.17c: Hosmer and Lemeshow Test       130 

Table4.17d: Model Summary         131 

Table 4.17e: Classification Tablea for Block 1      132 

Table 4.17f: Variables in the Equation       134 

Table 4.17g: Regression analysis of perceived risk with intention to use e-channels 136 

Table 4.18a: regression analysis of PU & PE with intention to use e-channels  137 

Table 4.18b: regression analysis of PU, PE, innovativeness & PR with intention  138 

Table 4.18c: Model Summary when PU&PE are the predictors    138 

Table 4.18d: Model Summary for the six predictors      138 

Table 4.19: Summary of results of Hypotheses testing     141 

 

 



xiii 

 

LIST OF FIGURES 
Figure                                                                                        Page 

Figure 2.1 Technology Acceptance Model (TAM) with the Attitude construct  15 

Figure 2.2: The parsimonious Technology Acceptance Model (TAM)   17 

Figure 2.3: Theory of reasoned action (TRA)      18 

 

Figure 2.4: Theory of planned behavior       21 

Figure 2.5 Adopter categories of innovation       24 

Figure 2.6:The interaction of the four components in the diffusion process  

leading to innovation adoption        25 

Figure 2.7: A modified Technology Acceptance Model for predicting e-shopping  

                  Acceptance         26 

Figure 4.1: Design of Basic Mediation Model      109 

Figure 4.2: Mediation Effect of Perceived Risk on the Relationship  

Between Perceived Usefulness and E-Shopping Acceptance     113 
 
Figure 4.3:Mediation Effect of Innovativeness on The Relationship  
Between Perceived Risk and E-Shopping Acceptance     117 
 

Figure 5: Scatterplot of ZRESID against ZPRED      121 

Figure 6: Normal P-P Plot of Regression Standardized Residual    122 

 

 
  

 

 

 

 

 

 
 

 

 



xiv 

 

LIST OF APPENDICES 

Appendix                                                                                         Page 

Appendix A:List of Tertiary Institutions in Lagos     165 
 
Appendix B: Main Questionnaire       166 

Appendix C: Stage 1 Questionnaire       171 

Appendix D: Cross-tabulation Analysis of Age, Gender and Intention  174 

Appendix E: Mediation Analysis for Hypothesis Three    175 

Appendix F: Mediation Analysis for Hypothesis Four    177 

Appendix G:Residual Analysis for Hypothesis Five     179 
 
Appendix H:  SPSS Output for Boothstrapped Regression for H05   180 

 

Appendix I: Testing For Linearity of the Logit     182 

Appendix J: Residual Analysis for Hypothesis Six     183 

Appendix K: Result of Multicollinearity Test for Predictors In Hypothesis H06 184 

 

Appendix L: Full Logistic Regression Analysis Result for H06   185 
 
Appendix M: SPSS OUTPUT For Regression Analysis of Perceived Risk and 
Intention to Shop Online (A Post Hoc Test)       186 

Appendix N: Full Computation of F-Statistics   

For R2 Values of 0.007, 0.028 And 0.053      190 

 

 

 

 

 

 

 

 



xv 

 

ABSTRACT 

The global resurgence of online shopping and availability of information and communication 

technology infrastructure are attracting online retail businesses to Nigeria. E-shopping is 

novel in this clime hence, the necessity for operators to understand the precursors to its 

acceptance given the cultural differences among global consumers.As part of understanding 

the Nigerian online shopper, this study investigates tertiary students’ acceptance of e-

shopping using a modified technology acceptance model. To achieve the objectives of this 

study, a descriptive research design based on cross-sectional survey was employed while a 

structured questionnaire served as instrument for data collection. Multi-stage sampling 

technique was used to select one thousand one hundred students of three tertiary institutions 

in Lagos State. These students whose responses yielded data for analyses were drawn from 

both full-time and part-time programmes of these institutions. While percentages and 

frequency tables were used to analyze and present the study’s descriptive statistics, 

parametric statistical tests such as t-tests, analysis of variance (ANOVA), multiple and 

logistic regression analyses were used as inferential statistics in testing the study’s hypotheses 

through the instrumentality of Hayes process tool and SPSS version 19. Key findings of this 

study show that: perceived usefulness, perceived ease-of-use, innovativeness, and perceived 

risk have a significant combined effect on e-shopping acceptance (R2=19.21%, F=65.09); 

among Socio-demographic variables only age has significant effect on e-shopping (Welch F= 

2.577, p< 0.05); also, the mediatory roles of perceived risk in technology acceptance model 

(TAM) were detected. Deployment of encryption technology to mitigate risk concerns and 

recognition of local consumer information in formulation of marketing programmes among 

others, are recommended. 

 

Keywords: E-shopping, E-commerce, Modified-TAM, Product-type, Socio-demographics. 
 

 

 



1 

 

CHAPTER ONE 

INTRODUCTION 

1.1 Background to the Study 

The evolution of buying and selling in Nigeria as captured by Oguntunde and Oyeyipo (2012) 

shows that from 1880’s trade was mainly conducted through barter. This method of exchange 

was discarded when the West African Currency Board was established in 1912. With the 

introduction of currency as legal tender at this period, it became possible for goods and 

services to be exchanged with currency. Buying and selling at these periods took place at 

some physical locations (market places) on certain market days, and then later civilization 

brought such store formats as kiosks, shops, supermarkets, malls, etc. where daily 

transactions are conducted. However, with the invention of Internet, customers now patronise 

online vendors in virtual offices or market spaces. This new way of shopping is relatively a 

new phenomenon (Oguntunde & Oyeyipo, 2012). 

The birthing of Internet and the World Wide Web have therefore been revolutionary (Ovia, 

2008). The cliché- “global village” has now become a popular lexicon as people can easily 

interact with one another virtually and remotely. With the aid of these two inventions coupled 

with other dozens of software/ technologies, people can easily engage in such activities as e-

mailing, browsing and online shopping. 

As to be expected, commercial business outlets are leveraging the opportunities inherent in 

these new technologies to reach out to their various publics. The Internetwith itsworldwide 

reach, instant 24/7 communications capability, ease of updating, and low cost have all 

converged to create vast new market opportunities for businesses to capitalize on. Presently, 

many ‘brick and mortar’ business organisations have transformed into ‘click and mortar’ 



2 

 

firms as they target online customers while ‘click only’ companies that exist only in virtual 

world have been established. 

Electronic commerce as defined by Akinola, Akinyede and Agbonifo (2011) consists of 

buying and selling of products or services over such electronic systems as the Internet and 

other computer networks. However, the process of e-shopping aspect of e-commerce has 

been aptly described byOguntunde and Oyeyipo (2012) as: 

an affair of scrolls, clicks, double-clicks, drags and drops into a virtual  

and possibly, animated cart. Once the delivery day is specified, means of  

payment indicated, credit card pin supplied, total purchase is calculated  

including shipment, the deal is then struck. The customers or clients wait  

patiently for delivery of their goods and services at the other end such  

as offices, accommodation or even, picnic ground or resort places (p.41) 

 

As reported by Zhou, Dai and Zhang (2007) since the late 1990s, online shopping has 

become increasingly popular as a good number of consumers buy different types of products 

from the Internet. Retail sales from US online shopping outlets were estimated to grow from 

$172 billion in 2005 to $329 billion in 2010 (Johnson & Tesch, 2005 cited in Tong, 2010).  

In their study cited in Monsuwe, Dellaert and de Ruyter (2004), the GfK Group (2002) 

reports a rise in online shopping activities in six key European markets from 27.7 percent to 

31.4 percent in 2003 which shows that 59 million patrons in Europe use the Internet regularly 

for shopping. Also, Verdict report, 2000-2006, cited in Vazquez and Xu (2009) shows that in 

the UK online spending overshot total retail spending by 1.5 percent. This growth in online 

retail activities reflects in both the number of e-shoppers and the volume of their purchases. 

Though, the record of online shopping expenditure of African countries as a whole, Nigeria 

inclusive, seems to be scarce, yet popular press continues to report rising online shopping 



3 

 

activities of Nigerians. E-commerce adoption has been, at best, sporadic in the developing 

world. In 2002, while developed countries contributed towards 95% of e-commerce, Africa 

and Latin America accounted for less than 1% (UNCTAD, 2002). This could be due to low 

literacy level and poor supporting infrastructure prevalent in these climes at that time. 

However, the recent upsurge in investment by both government and private investors in 

information and communication technology (ICT) infrastructure is making ICT services 

commonly available to the people of Nigeria. 

Plausibly, the popularity of electronic commerce is anchored on the ubiquitous Internet which 

is now available in many nations of the world and subsequently, accessible to non-technical 

people. Africa Internet usage statistics for June 2016 shows that there are only six African 

countries with the Internet penetration rate higher than 50% (Internet World Stats, 2016). 

Nigeria, with her 51.1% penetration rate, occupies the sixth position among these elite 

countries led by Kenya which has 69.6% penetration rate. When reflected in numbers, 

however, Nigeria, which boosts of over 90 million Internet users, topples Kenya, which 

harbours only about 32 million Internet subscribers, as the continent’s number one Internet 

user (Ajala, 2015; Internet World Stats, 2016). Again, Ubabukoh (2015) reports that Nigeria 

is the fastest growing market among the top 30 Internet countries, globally. 

According to Nigeria communication commission’s report for September 2015, Nigeria has 

over 150 million telephone subscribers and currently, there are about 205 licensed Internet 

Service Providers (ISPs) as well as a number of data carriers, Internet exchange and gateway 

operators.  All these have made Nigeria become one of the biggest and fastest growing 

telecom markets in Africa, attracting huge amounts of foreign investments, having overtaken 

South Africa to become the continent’s largest mobile market (Emmanuel, 2012).  

 



4 

 

As a result of the foregoing, scholars believe that there is an increasing awareness of the 

benefits and potential opportunities arising from e-commerce and consequently, e-commerce 

is slowly but surely taking off gradually in Nigeria (Folorunsho, Awe, Sharma & Jeff, 2006). 

Also, the Central bank of Nigeria cashless policy which was introduced in 2012 has 

provideda fertile ground for e-commerce activities to thrive by making available such digital 

payment instruments as credit card, online cheque/electronic fund transfer, debit card, 

micropayment, digital cash and money orders etc. As to be expected, the improvements in 

ICT and related infrastructure are attracting reasonable number of online merchants (both 

‘click and mortar’and ‘click only’ firms) that are offering diversified number of products in 

the Internet. 

 
The rise in ownership of personal computers, mobile phones, handheld devices such as 

personal digital assistants, PDAs (e.g. palm tops, Nokia Communicator, etc), tablets and 

smartphones that can access the Internet, have lead to widespread use of the Internet, an 

indication that there would be a high possibility that these Internet users would shop online 

(Sefton, 2000). Additionally, Nie and Erbring (2000) observe that 52% of the consumers use 

the Internet for product information, 42% for travel information, and 24% for buying. By 

2004, 62 percent of Internet users had bought products from the Internet at least once over the 

first six months of 2004 (Aqute Research, 2004 cited in Kamarulzaman, 2007). It is expected 

that, the figures will increase significantly over time, moving from its infancy to a market 

with significant potential, with millions of people shopping online as more and more people 

become Internet savvy (Strauss & Frost, 1999; Shim, Eastlick, Lotz, & Warrington, 2001; 

Kamarulzaman, 2007). However, despite this reported increase, there is very limited 

information on how and why certain groups of consumers shop online while others accept e-

shopping albeit, reluctantly. 



5 

 

 

The important position the consumer occupies in determining the success of any venture and 

particularly in attaining both marketing and corporate goals is incontrovertible. Hence the 

fulcrum of success of these emerging online retailers (e-tailers) hinges essentially on 

consumer patronage. As recent report shows, investment in information and communication 

technology (ICT) and related businesses has continued to rise, for instance, MTN, Rocket 

Internet and Goldman Sachs have invested about sixty-four billion naira (#64bn) equivalent 

of three hundred and twenty-seven million US dollars ($327m) on African Internet Group, 

the owners of Jumia.com as at February, 2016 (Ubabukoh, 2016). With this leap in 

investments on online merchandising, the consequences of failure become more acute 

(Venkatesh, 1999) and the need for success becomes even more critical (Zhou et al, 2007). 

 
The current deployment of Internet by a growing number of retailers as an outlet to reach 

customers have stimulated considerable researches which focus on attracting and retaining 

consumers by examining consumer acceptance of the Internet as a shopping channel 

(Jarvenpaa & Todd, 1997; Childers, Carr, Peck, & Carson, 2001; Yoh,Damhorst, Sapp & 

Laczniak, 2003; Keen, Wetzels, de Ruyter & Feinberg, 2004; Ha & Stoel, 2009; Liu & 

Forsythe, 2010). Thus, the question of why consumers prefer to engage in online shopping 

for some goods and not for others has continued to arouse the interest of scholars (Girard, 

Korgaonkar & Silverblatt, 2003).  

 
Extant literature reveals that e-shopping studies have largely focused on understanding what 

drives consumers to shop online from either a consumer- or a technology-oriented 

perspective (Jarvenpaa & Todd 1997).  Whilst scholars who adopt consumer- oriented 

approach are concerned with consumers’ salient beliefs about online shopping, those of 

technology school on the other hand, focus on how the technical specifications of an online 



6 

 

store affect an individual’s perceptions and, subsequent use of that technology (Chen, 

Gillenson & Sherrell, 2002; Tong, 2010). Though, several theories exist, the technology 

acceptance model (TAM) has been generously employed for understanding of electronic 

commerce (Tong, 2010) and has been extensively applied in the online shopping context 

(Bruner & Kumar, 2005; McKechnie, Winklhofer & Ennew, 2006). 

  
Online consumer behaviour is currently an emerging theoretical body of research (Vazquez & 

Xu, 2009), however, a holistic view of online shopping acceptance from the perspective of 

the consumers is yet to be undertaken (Zhou et al., 2007). Hence, scholars continue to search 

for answers to questions posed by this current phenomenon. Literature however shows that 

technology acceptance model continues to be extended as scholars search for better 

understanding of the online consumer. Technology acceptance model has been modified with 

the integration of such constructs as trust and perceived risk (Pavlou, 2003), personal 

characteristics, trust and perceived risk (Kamarulzaman, 2007), innovativeness and 

technology anxiety (Kim & Forsythe, 2010), personal characteristics, prior shopping 

experience, perceived enjoyment and perceived risks (Tong, 2010).  

 
While these scholarly works extending technology acceptance model (TAM) are ongoing, it 

is imperative to note that there is paucity of studies integrating the constructs of socio-

demographics and product type, to technology acceptance model in order to improve its 

predictive capability. There is equally scarce empirical study on online shopping in Africa 

generally and Nigeria particularly (Molla & Licker, 2005; Aghaunor & Fotoh, 2006) due 

mainly to the novelty of the phenomenon of online shopping aspect of electronic commerce 

in this clime. It is the challenge of this present study to fill these twin gaps by employing 

Nigerian data in the validation of these constructs. 

 



7 

 

 

 

1.2  Statement of the Problem 

Literature shows that online shopping since its early history has had mixed results as news 

stories carried the early success stories tagged “dot-com boom” between the mid 1990s to 

year 2000 and the failure stories tagged “dot-com bust” or “dot-bomb”  between year 2000 

and 2002 (Schneider, 2008). In spite of its current popularity particularly in the developed 

countries, studies still show that the acceptance of this new way of shopping has not been the 

same or certain in all markets, whether in the developed or less developed markets such as 

Nigeria. For example, in the United States of America many online firms such as e-

Toys.com, Garden.com, Pets.com, etc, are noted to have collapsed during the ‘dot.com bust’ 

era. 

 
Presently, Nigeria has become the choice destination of investments in online merchandizing 

which has resulted in the establishment of many cyber-sellers such as Jumia.com, 

Konga.com, Cheki.com, Adiba.com, Yudala.com, etc. Given that many of these online firms 

are worth billions of naira and that electronic shopping is novel in this clime, there are some 

concerns as to the acceptance of this new way of shopping in Nigeria, thus, calling the 

successful operations of these online business ventures to question. As noted by Udeji (2016), 

only about nine percent of Nigerians shop online, leaving majority as traditional shoppers. As 

a result, it has become imperative to identify the factors that drive acceptance of this new way 

of shopping or risk wastage of billions of investment money. 

Also, e-shopping concept is not only of interest to practitioners but also to scholars as both 

are confronted with the problem of unraveling the question of why some consumers prefer to 

engage in online shopping why others do not or why consumers shop certain goods online 



8 

 

and not for others. Though, an emerging area of research, literature however, reveals that a 

growing number of e-shopping studies have been undertaken in the developed countries of 

United States of America and United Kingdom while limited studies exists in the less 

developed countries of Africa in general and Nigeria in particular (Molla & Licker, 2005; 

Aghaunor & Fotoh, 2006). As a result, while information is available on the profile of 

western e-shopper, scant information exists about the profile of African and Nigerian e-

shopper. The scarce information about the Nigerian online shopper could be hazardous to the 

survival of these firms. 

 
Given that there are differences among global consumers, many scholars advocate the need 

for closer examination of online shopping intentions in specific countries, due to cultural 

differences and the prior imperfection of technology acceptance relationships of varying 

consumer markets (Bobbit & Dabholkar, 2001; Goldsmith, 2002). In support of this view, 

Boateng (2011) argue that there exist a mismatch between the realities of developing 

countries firms and assumptions of western models of enterprise. Thus, relying on western e-

shoppers profile as basis for targeting Nigerians could be costly to the survival of these new 

online retail firms and as Garcia-Murillo (2004) posits, more research is needed to redefine 

existing knowledge to be consistent and applicable with the nature of the environment. The 

foregoing, thus, brings to the fore the problems necessitating this current research. 

 
Finally, while several theories are emerging to guide research in electronic commerce studies, 

the technology acceptance model (TAM) has been extensively applied in the online shopping 

context (Bruner and Kumar, 2005; McKechnie et al., 2006). Empirical evidence exist in 

extant literature of successful linkages of TAM’s constructs of perceived ease of use and 

perceived usefulness with such constructs as trust, perceived risk, personal characteristics, 

prior shopping experience, innovativeness and perceived enjoyment in predicting e-shopping 



9 

 

behaviour (Pavlou, 2003; Kamarulzaman, 2007; Kim & Forsythe, 2010;Tong, 2010). 

However, little empirical studies have focused on integrating socio-demographics, 

innovativeness, product type and perceived risk with TAM’s constructs of perceived ease of 

use and perceived usefulness in order to broaden the robustness of TAM in predicting e-

shopping acceptance. 

1.3  Purpose of the Study 

The main purpose of this study is to determine the factors that influence students of tertiary 

institutions in Lagos State, Nigeria to shop online and to modify technology acceptance 

model (TAM) by integrating its constructs with socio-demographics, innovativeness, product 

type and perceived risk in order to improve its capability in predicting online consumer 

behaviour. In specific terms, the objectives of the study are to: 

i. determine the effects of socio-demographic variables on consumers’ e-shopping 

acceptance. 

ii. investigate the impact of product type on consumers’ Internet shopping acceptance. 

iii. investigate the mediatory role of perceived risk in the relationship between perceived 

usefulness and e-shopping acceptance. 

iv. examine the mediatory role of perceived risk in the relationship between perceived 

ease-of-use and e-shopping acceptance. 

v. determine if the combined effect of innovativeness, perceived usefulness, perceived 

ease-of-use and perceived risk do predict consumers’ acceptance of online shopping. 

vi. determine the role of socio-demographics, product type, innovativeness, perceived 

risk, perceived usefulness and perceived ease-of-use in predicting online shopping 

intention. 



10 

 

vii. to modify technology acceptance model (TAM) with infusion of socio-

demographics, innovativeness, product type and perceived risk to enhance its 

capability in predicting online shopping intention. 

 
 

 

 

1.4  Research Questions 

The following research questions are posed to provide the bearing for this study: 

i. What effect do socio-demographic variableshave on consumers’ e-shopping 

acceptance? 

ii. What role does product type play in influencing consumers’ e-shopping acceptance? 

iii. To what extent can perceived risk mediate the relationship between perceived 

usefulness and e-shopping acceptance? 

iv. To what extent can perceived risk mediate the relationship between perceived ease-of-

use and e-shopping acceptance? 

v. To what extent does the combination of innovativeness, perceived usefulness, 

perceived ease-of-use and perceived risk affect consumers’ acceptance of online 

shopping? 

vi. To what extent would intention to shop online be predicted from socio-demographics, 

product type, innovativeness, perceived risk, perceived usefulness and perceived ease-

of-use? 

vii. To what extent would the inclusion of socio-demographics, innovativeness, product 

type and perceived risk in TAM improve its capacity to predict e-shopping 

acceptance? 



11 

 

 
1.5  Research Hypotheses 

Arising from research questions, the following hypotheses are formulated: 

i. Socio-demographic variables of consumers do not significantly affect their e-

shopping acceptance.  

ii. Product types available online do not significantly influence e-shopping acceptance.  

iii. Perceived risk does not significantly mediate the relationship between perceived 

usefulness and e-shopping acceptance. 

iv. Perceived risk does not significantly mediate the relationship between perceived ease-

of-use and e-shopping acceptance. 

v. The combined effect of innovativeness, perceived usefulness, perceived ease-of-use 

and perceived risk do not significantly predict consumers’ acceptance of online 

shopping. 

vi. Socio-demographics, product type, innovativeness, perceived usefulness, perceived 

ease-of-use and perceived risk do not significantly contribute in predicting 

consumers’ online shopping intention. 

vii. The infusion of socio-demographics, product type, innovativeness and perceived risk 

to technology acceptance model (TAM) does not significantly improve its capacity to 

predict intention to shop online. 

1.6  Significance of the Study 

It is hoped that the present study will provide helpful information on the Nigerian online 

shopper. No doubt such information is necessary to guide the marketing strategies of these 

online firms if they must realize both marketing and organisational goals.  Again, as extant 

literature has shown, e-shopping aspect of electronic commerce is both an emerging and an 

evolving area of research which is yet to enjoy a common view (Zhou et al, 2007). Thus, the 



12 

 

present study enriches this area of research by exposing the online behaviour of consumers in 

developing countries such as Nigeria. 

 
By understanding the precursors to Internet shopping acceptance in Nigeria, online retailers 

will be better equipped to provide quality service which will ultimately benefit prospective 

patrons. With improvement in quality of service, the survival of these firms will be 

guaranteed by the continuous patronage of satisfied customers. The survival of these firms by 

extension will eventually translate into more revenue for the government through taxation 

and employment opportunities for citizens. Again, the exposition of factors that influence 

online shopping acceptance behaviour and the attenuating effect of perceived risk on such 

behaviours will help guide government policy decisions particularly in protecting customers 

from unwholesome practices of some recalcitrant and dubious online vendors. 

 
Additionally, this work will be of interest to the academia as it serves as a reference material 

for future research and by integrating socio-demographics, innovativeness, product type and 

perceived risk into the constructs of technology acceptance model, this work adds to the 

predictive capability of this model in both explaining and understanding of online consumer 

behaviour. The study is expected to extend the frontier of knowledge in this study area. 

 
1.7  Scope and Delimitations of the Study 

The purpose of this study is to examine the factors that influence tertiary students’ online 

shopping acceptance in Lagos state, Nigeria and the modification of technology acceptance 

model with the integration of socio-demographics, innovativeness, product type and 

perceived risk in order to enhance the capacity of this model to predict consumers’ online 

behaviour. To achieve the above, samples for this study are drawn from tertiary students, 

some of whom are part of working class Nigerians who are majorly IT users. Also, Lagos 



13 

 

being home to twentypublic and private tertiary institutions the requisite sampling method 

that ensured that representative sample is drawn in order to have a balanced view was 

employed. In this case, the study is delimited to cover both full time and part time students of 

theselected tertiary institutions domiciled in Lagos. Students are no doubt high IT users. 

Again, Lagos which is the commercial nerve centre of Nigeria where people of all ethnic and 

tribal groups converge and which enjoys the greatest investment in ICT infrastructure confers 

on those who school and work in its domain, the greatest advantage and possibility to shop 

online. 

1.8 Operational Definition of Terms: 

E-shopping acceptance: this is the consumer positive intention to engage in online product 

information search and product purchase. 

Perceived risk: this covers the extent of risk the online shopper perceives s/he is exposed to 

while engaged in online shopping activities. The risks covered here include time risk, 

psychological risk, privacy risk, financial risk, performance risk, Social risk and Overall risk. 

Innovativeness: this is the willingness and tendency of the consumer to learn about and 

adopt innovations related to Internet shopping. 

Perceived ease of use: this is the perception of the consumer that interacting with both the 

technologies and processes of online shopping will be effortless. 

Perceived usefulness: this encompasses the perception of the consumer that engaging in 

online shopping is useful. 

Socio-demographics: these are taken to mean respondents’ gender, age, income and level of 

education. 

Product Types: these are taken to include search, experiential and credence products. 

Brick and mortar firms: traditional retail firms with physical retail outlets. 



14 

 

Click and mortar firms: retail firms with physical retail outlets that have added e-shopping 

channels to their operations. 

Click only firms: whole online retail firms without any physical retail outlet. 

Drag and drop: choosing and/ selecting a product from an e-tailer’s web page into a 

shopping cart for purchase. 

Shopping Cart: this is an electronic basket where the e-shopper drops/deposits selected 

goods to facilitate billing before checking out of the e-tailer’s web page. 

E-tailer: Another name for ‘click and mortar’ and ‘click only’ firms. 

 

1.9  Summary of the Chapter 

In this chapter, the evolution of shopping from market place to ‘market space’ and the 

transformatory role of Internet and related technologies in making this new way of shopping 

possible, were discussed. These technologies which revolutionalisedbusiness processes and 

practices have provided the opportunity for the establishment of non-store enterprises that 

offer different types of products to customers online. As a business concept, online shopping 

originated from the Western world in mid-1990. Though, currently enjoying a rebirth, e-

shopping has had a low period in its historywith collapse of many online firms. Being new to 

Nigeria and Nigerians, the problem that necessitated this study was majorly to ascertain if 

this new way of shopping is acceptable to Nigerians by searching for insight into those who 

will shop online and what type of products they are willing to buy. These problems lead to 

the formation of the main objectives of this study which are to determine the factors that 

influence consumers to shop online and to modify Technology Acceptance Model (TAM) by 

integrating it with socio-demographics, innovativeness, product type and perceived risk to 

make it more predictive in the domain of e-shopping. Also, in this chapter, research questions 

and hypotheses were stated while the significance of the study was identified. The study’s 



15 

 

scope and delimitation were equally highlighted while operational definitions of terms ended 

the chapter. 

  



16 

 

CHAPTER TWO 

LITERATURE REVIEW 

 
2.1 Preamble 

Several theoretical models have been advanced as researchers continue to focus efforts on 

identifying factors that influence e-commerce acceptance behaviour. In particular, the 

technology acceptance model (TAM), introduced by Davis and his colleagues (Davis, 1989; 

Davis, Bagozzi & Warshaw, 1989), has received considerable interest and mention;  and has 

become established as a parsimonious yet powerful model for explaining and predicting 

technology usage intentions and acceptance behaviour (Yi & Hwang, 2003; Lucas & Spitler, 

1999). Although this model is specifically tailored to understand the adoption of computer-

based technologies on the job or in the workplace, it has proven to be suitable as a theoretical 

foundation for the adoption of e-commerce as well (Lederer,Maupin, Sena & Zhuang, 2000; 

Moon & Kim, 2001; Chen et al., 2002; Pavlou, 2003; Ha & Stoel, 2009). 

 

 

 

 

 

 

 

 

 

 

Figure 2.1 Technology Acceptance Model (TAM) with the Attitude-construct 

Source: Davis (1989) 

As noted by Tong (2010), the Technology Acceptance Model in its original form identified 

perceived usefulness and attitude as having significant effects on use (see figure 2.1), as a 

result, attitude as a construct was later removed from the model, thereby giving birth to the 



17 

 

present TAM which is seen as parsimonious. In this parsimonious TAM, the constructs of 

perceived usefulness and perceived ease-of-use are found to have significant effects on 

behavioural intention with perceived usefulness showing stronger effect. Davis et al., (1989) 

propose that attitude should be excluded because it did not fully mediate perceived usefulness 

and perceived ease of use. Venkatesh (2000) posits that attitude’s partial mediation of 

intention was explained as deriving from people intending to use a technology because it was 

useful even though they might not have a positive attitude toward using it. The discarding of 

attitude from the model helps to better illustrate the effect of perceived ease-of-use and 

perceived usefulness on intention which is the key dependent variable of interest. 

Though, Venkatesh, (2000) and Vijayasarathy, (2004) see TAM’s parsimony as a key 

limitation, yet literature shows that a large number of studies continues to ascertain the 

validity of TAM as a parsimonious model in a variety of technology-related contexts (Davis, 

1989; Davis et al., 1989; Rose & Straub, 1998; Porter & Donthu, 2006). The present study 

adapts and extends this parsimonious technology acceptance model. 

 
The theory of TAM proposes that a person’s actual system usage is dependent on his/her 

behavioural intention, which in turn is jointly determined by perceived usefulness and 

perceived ease-of-use. Conceptually, perceived usefulness is the degree of a person’s belief 

that using a technology will improve his or her performance in the job, and perceived ease-of- 

use is the degree to which a person is convinced that using a technology will be effortless 

(Davis, 1989). Behavioural intention is defined on the other hand, as the extent to which a 

person intends to actualize a particular behaviour (Davis et al., 1989). TAM posits that the 

impact of other external variables on behavioural intention is fully mediated by these two 

beliefs of usefulness and ease-of-use. 

 



18 

 

 

Figure 2.2 the parsimonious Technology Acceptance Model (TAM) 
Sources: Davis et al (1989), Vankatesh,Morris, Davis and Davis(2003). 

 
Having provided background information on Davis (1989) Technology Acceptance Model 

(TAM), in this section, the remaining part of this chapter will focus on the study’s theoretical 

framework, conceptual framework and review of empirical literature. 

 
2.2  Theoretical Framework 

Three theories provided the anchor upon which the precursors to tertiary students’ acceptance 

of electronic shopping are examined. These theories include: theory of reasoned action 

(TRA), theory of planned behaviour (TPB) and theory of diffusion of innovation. 

 
2.2.1  Theory of Reasoned Action (TRA) 

Theory of reasoned action (TRA) is a theory popularly used in social psychology for 

predicting or explaining cognitive and affective behaviour using the belief-attitude-intention-

behaviour relationship (Shih, 2004; Davis, 1989). This theory associates attitudes to the 

construct of behaviour in such a way that behaviours are seen as dependent on behavioural 

intentions which, in turn, are determined by attitudes to the behaviour and subjective norms. 

It is essentially a series of linked concepts which provide social psychologists the platform to 

create hypotheses that will aid understanding and predict human behaviour (McKemey & 

Sakyi-Dawson, 2000). Theory of reasoned action is seen as one of the “expectancy-value” 



19 

 

models of human behaviour with terms that are not alien to those of the well-established 

subjective expected utility model often used by economists (Lynne, 1995). 

 

 
Figure 2.3 Theory of Reasoned Action (TRA) 

Source: Ajzen and Fishbein, 1969, 1975, 1980 

 

The works of Fishbein and Ajzen (1975) and Ajzen and Fishbein (1977, 1980), the theory of 

reasoned action (TRA) is anchored on the postulation that the most important cause of a 

person’s behaviour is his or her behavioural intent. Intentions to perform a behaviour are 

viewed as being driven by both an individual’s attitudes toward the behaviour and subjective 

norms, or influences and motivations of the individual to comply with normative beliefs 

(Bagozzi, Baumgartner & Yi, 1992; Randall, 1989; Shimp & Kavas, 1984). The normative 

influence on intention is what Fishbein and Ajzen referred to as one’s subjective norm (Hale, 

Householder & Green, 2002).TRA is generally recognized as being most applicable to 

completely volitional behaviours where individuals perceive themselves as having complete 

control over their choices (Hale, Householder & Green, 2002). “According to Ajzen and 

Fishbein, the theory of reasoned action is based on the assumption that human beings are 

rational and make systematic use of available information. People consider the implications 

of their actions before they decide whether or not to perform a given behaviour” (Tlou, 2009, 

P.26). 



20 

 

 

The theory of reasoned action, thus, is positioned to explain volitional behaviours, and 

therefore, excludes such involuntary and unconscious behaviours as those that are 

spontaneous, impulsive, habitual, mindless, the result of cravings or simply scripted (Langer, 

1989; Bentler & Speckart, 1979). Additionally, Liska (1984)  notes that behaviours which 

require special skills, unique opportunities or resources or cooperation of others to be 

performed should be excluded as one may be hindered from executing a behaviour because of 

a skill deficiency, lack of opportunity, or lack of cooperation from others and not because of a 

voluntary decision not to engage in the behaviour.  

 
The theory of reasoned action is relevant to this study given the fact that online shopping is 

an activity that requires conscious and voluntary effort from rational consumers. As 

electronic shopping is novel in this part of the world, it is logical to assume that prospective 

patrons would think through the consequences of engaging in this new way of shopping prior 

to doing so. As TRA has shown, behaviour is influenced by intent through the routes of 

attitude, belief and subjective norms. Thus, the author projects that beyond the type of 

product sold online and consumers’ socio-demographics and level of innovativeness, it is 

reasonable to expect consumers’ intention to engage in this new way of shopping to be 

shaped by their belief about the usefulness, ease of use and risk associated with online 

shopping.  

 
Again, as portrayed in TRA, these beliefs act jointly with consumers’ normative belief to 

affect intention.Davis (1989) Technology Acceptance Model (which this work seeks to 

extend) is itself anchored on theory of reasoned action asthe two major constructs of TAM- 

perceived usefulness and perceived ease of use are products of users’ beliefs which could also 



21 

 

be influenced by a user’s significant others such as colleagues in the office, friends and 

family.  

 

Since its introduction to behavioural research, TRA has been applied to study a wide variety 

of situations and is now regarded as one of the most influential theories about volitional 

human behaviour (Trafimow & Finlay, 2002). Past research has tested the TRA on a variety 

of behavioural intentions, such as blood donation (Burnkrant & Page, 1982), bone marrow 

donation (Bagozzi, Lee, & Van Loo, 1996), religious donation (Chuchinprakarn, Greer, & 

Wagner, 1998), Workplace HIV/AIDS health promotion programme (Tlou, 2009), and online 

shopping intention (Chuchinprakarn, 2005). Having documented the successful application of 

theory of reasoned action (TRA) to past studies; it is considered appropriate for the present 

study for reasons already stated above. 

2.2.2  The Theory of Planned Behaviour (TPB) 

Theory of reasoned action (TRA) as proposed by Fishbein and Ajzen (1975) and Ajzen and 

Fishbein (1977, 1980), was related to voluntary behaviour. Practically, however, it was 

discovered that behaviour is not always voluntary and under control, but could sometimes be 

deliberative and planned. Consequently, TRA was modified with the addition of perceived 

behavioural control. With this addition the theory was called the theory of planned behaviour 

(TPB). The Theory of Planned Behaviour (TPB) is essentially an extension of the Theory of 

Reasoned Action (TRA) that includes measures of controlled belief and perceived 

behavioural control aimed at predicting deliberate and planned behaviour (Armitage & 

Conner, 2001). The theory states that attitude toward behaviour, subjective norms, and 

perceived behavioural control, together shape an individual's behavioural intentions and 

behaviours. 



22 

 

 
Figure 2.4: Theory of planned behaviour. 

Source: Ajzen, 1991  

 

The theory of planned behaviour (TPB) holds that individual actions are guided by beliefs 

about the likely outcomes of behaviours, beliefs about the expectations of others, and beliefs 

about the nature of control that the individual has over conditions that may facilitate or 

impede performing the behaviours (Ajzen, 1988; 1991; Ajzen & Madden 1986). In relating 

these areas, the theory suggests, for instance, that individuals’ behavioural intentions will be 

stronger when supported by favourable beliefs about the outcome and other’s expectations. 

Such individuals may then carry out their intentions to perform certain behaviours when 

appropriate opportunities arise as a result of their beliefs that they have a sufficient actual 

degree of control over the behaviour. 

 
In line with the foregoing, Perceived behavioural control (PBC) is held to influence both 

intention and behaviour. The justification behind the addition of PBC was that it would allow 

prediction of behaviours that were not under complete volitional control. Thus, while the 

TRA could adequately predict behaviours that were comparatively straightforward (i.e. under 



23 

 

volitional control), under situations where there were constraints on action, the mere 

formation of an intention was inadequate to predict behaviour. The inclusion of PBC provides 

information about the potential constraints on action as perceived by the actor, and in turn 

explains why intentions do not always predict behaviour (Armitage & Conner, 2001). 

 
With consideration to the foregoing, this theory is relevant to this work given the fact that 

consumers must not only be literate but must also know how to navigate through the e-tailers’ 

web pages to be able to shop through them. This theory therefore, suggests that when 

consumers consider this lack of ability as a potential constraint on action then their positive 

intention to shop online will be in jeopardy.  

The theory of planned behaviour (TPB) has been extensively applied to studies of the 

relations among beliefs, attitudes, behavioural intentions and behaviours in various fields 

such as leisure choice (Ajzen, 1990), media campaign (Stead,Tagg, MacKintosh& Eadie, 

2005), workplace HIV/AIDS health promotion programme (Tlou, 2009), binge-drinking 

(Johnston & White, 2003), blood donation (Giles, McClenahan, Cairns & Mallet, 2004), 

investment decisions (East, 1993), and electronic commerce adoption (Pavlou & Fygenson, 

2006). 

2.2.3  Theory of Diffusion of Innovation 

The blueprint for exploring consumer acceptance of innovative products/services is drawn 

from the area of research known as the diffusion of innovations. As a theory that deals with 

acceptance of innovations, Schiffman and Kanuk (2004) posit that the theory of diffusion of 

innovation primarily covers two related processes: the diffusion process and the adoption 

process. While the diffusion process deals with the spread of an innovation from its source to 

http://en.wikipedia.org/wiki/Beliefs
http://en.wikipedia.org/wiki/Theory_of_planned_behavior


24 

 

the consuming public, the adoption process focuses on the stages through which a consumer 

passes when deciding to accept or reject the innovation. 

Cheng, Kao and Lin (2004) observe that the theory of diffusion of innovation has been 

studied from the viewpoint of diverse disciplines using different types of products, services 

and ideas. The variants of diffusion of innovation model discernible in literature include 

Bass’ model, Moore’s model and Rogers’ model, with the latter receiving more attention. 

Bass (1969) applied mathematical methods in developing a diffusion of innovation model in 

which five adoption categories were proposed, from the earliest adoption onward: innovators, 

early adopters, the early majority, the late majority and the laggards. Bass model explains that 

the number of adopters during a period is almost identical to the number of sales throughout 

most of the diffusion process. Thus, the number of adoptions in a period serves as a good 

proxy for sales (Chang, 2010). The Bass model has been revised and implemented in 

forecasting innovation diffusion in diverse fields (Mahajan, Muller, & Bass, 1990); and has 

the potential to predict the distribution of the adoption curve (Chang, 2010). 

Moore (1995) developed a diffusion of innovation model that is focused on technological 

innovations with the same adopter categories as mentioned above and with the same terms to 

represent the five stages of innovation adoption. The major contribution of Moore’s model to 

diffusion of innovation (DOI) school of thought is the assumption of a discontinuous 

innovation process and the focus solely on organization, with a new technology adoption 

requirement (Cheng, Kao& Lin, 2004). 

Rogers’ diffusion of innovation model is the pioneer and most popular of the three traditional 

diffusion of innovation models. Rogers (1962) developed the first model of diffusion and 

defined it as, “the process by which an innovation is communicated through certain channels 



25 

 

over time among the members of a social system”. Chang (2010) posits that diffusion of 

innovation theory explicates the adoption process of an innovation by modeling its entire life 

cycle according to the aspects of communications and human information interactions. 

Rogers (2003), sees an innovation to be any “idea, practice, or object that is perceived as new 

by an individual or other unit of adoption”. Drawing from Rogers’ definition of innovation, 

online shopping web pages, can be seen as a new idea conceptualized by both ‘click only’ 

and ‘click and mortar’ firms, as a distribution channel to reach customers. This is certainly an 

innovation as it is different from the traditional way of shopping, particularly, in this clime. 

Cheng et al, (2004) note that Roger’s model classified innovation adoption framework into 

five onward stages: innovators, early adopters, the early majority, the late majority, and the 

laggards, with 2.5%, 13.5%, 34%, 34% and 16% of the population respectively (see figure 

2.5). The diffusion process is affected by four key elements: innovation, the social system 

which the innovation affects, the communication channels of that social system, and time 

(Rogers, 2003). As one of the most influential theories of communication in marketing, the 

focus of diffusion theory is on the means by which information about an innovation is 

disseminated. As opined by (Chang, 2010) “Rogers’ model serves as a comprehensive 

framework for understanding diffusion process of an innovation and the underlying factors 

driving the diffusion”. 



26 

 

 

Figure 2.5 Adopter categories of innovation 
Source: Schiffman and Kanuk (2007) 

All innovations (products, services, ideas etc) do not have equal potential for consumer 

acceptance. While some innovations enjoy instant acceptance others may take some time to 

achieve same (Schiffmam & Kanuk, 2004). Although there are no precise formulae by which 

marketers can evaluate an innovation’s likely acceptance, Rogers (2003) has identified five 

innovation characteristics that seem to influence consumer acceptance of innovative products: 

relative advantage, compatibility, complexity, trialability and observability. These 

characteristics, Chen and Crowston (n.d.) argue, account for much of the dynamic nature of 

the rate or speed of adoption. Rogers (2004) further posit that in addition to the afore-

mentioned characteristics of innovation, communication channels and social system are likely 

to have varying influences at different times during the diffusion process. How these four 

elements interact in the diffusion process for innovation adoption is succinctly captured by 

Chen, Kirkley, and Raible (2008) as shown in figure 2.6 



27 

 

 

Figure 2.6the interaction of the four components in the diffusion process leading to 

innovation adoption. 

Source: Chenet al, 2008. 

Theory of diffusion of innovation has enjoyed large support and extensive application in such 

academic disciplines as anthropology, communication, geography, sociology, marketing, 

political science, public health, economics, technology management (Moseley,2004; Rogers, 

2004; Chang, 2010) and therefore, can be eminently employed as a foundational theory for 

this study. 

2.3  Conceptual Framework 

Based on the foundational theories discussed in the preceding section, this thesis employs a 

conceptual model (see figure 2.7) which extends the parsimonious technology acceptance 

model (TAM). This conceptual model further highlights both the predictor and criterion 

variables and the relationships that exist between them. As a result, this thesis proposes that 

the constructs of socio-demographics, innovativeness, product type, and perceived risk, 

acting jointly with TAM’s original constructs of perceived usefulness and perceived ease of 

use, will be more efficient in predicting consumers’ acceptance of e-shopping. Consequently, 



28 

 

this conceptual model becomes the research framework upon which these predictor variables 

are investigated vis-à-vis their impact in determining tertiary students’ acceptance of e-

shopping in Lagos Nigeria, and therefore, guides the rest of this study. 

 
Figure 2.7: A modified Technology Acceptance Model for predicting e-shopping acceptance 

Source: Developed by researcher (2016) 

Socio-Demographics:  

Broadly, the concept of socio-demographics has been defined to include such variables as 

gender, age, education, income, marital status, occupation (Khan, 2006), and culture (Chau, 

Cole, Montoya-Weiss & O’Keefe, 2002; Zhou, et al, 2007). However, among these preceding 

socio-demographic variables, Burke (2002) found some relationship between gender, age, 

education and income, and consumers’ attitude toward e-shopping, though, the relationship is 

significantly moderated by such TAM variables as “ease of use” and “usefulness”. For the 

present study, the socio-demographic variables investigated include gender, age, education 

and income. 

 
Socio-demographic variables play vital roles in consumers’ purchase decisions, evaluation of 

products before purchase and choice of where to shop (Lancaster & Massingham, 2011). 

Hence, Oghojafor and Nwagwu (2013) argue that outlet for shopping is an integral choice set 

of today’s modern customer. As emerging retail practice reveals, shopping outlets can take 

Perceived 
Ease of use 



29 

 

both store and non-store forms (Jobber, 2009). Internet web pages for shopping have become 

one of such non-store patronage medium (Brown, Pope & Voges, 2003) whose acceptance as 

proposed in this conceptual model is influenced directly by such socio-demographic variables 

of consumers as gender, age, education level and income and indirectly through the types of 

products sold online, level of consumers’ risk perception, innovativeness, perception of ease 

of use and usefulness of Internet as a medium for shopping. 

 
Zhou et al, (2007) observe that socio-demographic variables engaged the attention of scholars 

at the early stages of their study to unravel determinants of electronic shopping. Though, 

while some of the studies that examined the relationship between socio-demographics and e-

shopping have found that socio-demographics influence customers’ attitude towards online 

shopping (Gupta, Pitkow & Recker, 1995; Haque & Khatibi, 2005; Khatibi, Haque & Karim, 

2006; Hashim, Ghani & Said, 2009), yet others have reported mixed results, particularly in 

studies relating to age and intentions to engage in e-shopping. 

 
Gender:  

This is the socio-demographic variable that deals with the sexes of consumers. Of the two 

genders, women exhibit more positive attitude toward shopping and equally obtain greater 

satisfaction from shopping than men (Alreck & Settle 2002). In fact, in some countries like 

Nigeria, wives mainly shop for their families particularly for essential goods (Oghojafor, 

Ladipo & Nwagwu, 2012).  

 
Aside the differences that exist in the attitudes of consumers due to gender, researchers are 

curious also, to understand how these differences when juxtaposed with the differences 

between an online store and their physical counterparts will influence online shopping 

acceptance. Writing on the differences between an online retail stores and the physical ones, 



30 

 

Lohse and Spiller (1998) aver to the differences that exist between these forms of retailing by 

noting that in an online store a help button on the home page of an e-tailer’s shopping 

webpage do take over brick-and-mortar store’s sales clerk’s friendly advice and service; and 

also, a physical store’s familiar layout is replaced by a maze of pull-down menus, product 

indices and search features.  These differences both in gender of consumers and even in store 

forms, no doubt, will continue to attract interest of scholars. 

 
Age: 

This socio-demographic variable refers to the chronological number in years of existence of a 

consumer. According to Zhou et al., (2007) and Girard, et al, (2003), research findings on the 

impact of this variable on Internet shopping have remained mixed and inconclusive. 

 

Education: 

Education as a socio-demographic variable refers to consumers’ level of formal education 

attainment. According to Monsuwe et al., (2004), education plays a moderating role in the 

relationship between the basic determinants of consumers’ attitude and intention to shop 

online. Higher educated consumers are more comfortable using non-store channels, such as 

the Internet to shop (Burke, 2002). Reason for this is that education is often positively 

correlated with an individual’s level of Internet literacy (Liet al., 1999).  

 
Income: 

This refers to the income of respondents. Early studies such as Donthu and Garcia (1999), 

Korgaonkar and Wolin (1999), Li et al, (1999), Bagchi and Mahmood (2004), Mahmood, 

Bagchi, and Ford (2004) and Susskind (2004) have compared the profile of online shoppers 

to those of traditional store shoppers and found that online shoppers tend to earn more 

income than traditional shoppers. Justifying these results, Zhou et al, (2007) posit that most 



31 

 

of the goods bought online such as books, CDs, holiday and leisure travel, PC hardware, and 

software, are items which shoppers demand as their income increases.Extant literature 

however shows that current focus is now on the effect of income on e-shopping behaviour of 

consumers. Analysis of these studies has shown that while some studies have found income 

as an influencing factor of online shopping behaviour, others have not (Hashim, et al, 2009; 

Chang, Cheung, & Lai, 2005). 

 
As captured in this study, Internet web pages for shopping is proposed in this conceptual 

model to be influenced directly by such socio-demographic variables of consumers as gender, 

age, education level and income.Socio-demographic variables equally affects e-shopping 

acceptance indirectly through the types of products sold online, level of consumers’ risk 

perception, innovativeness, perception of ease of use and usefulness of Internet as a medium 

for shopping. 

 
Innovativeness:  

The concept of consumer innovativeness has enjoyed generous contributions from scholars 

and as a result, has been approached from different perspectives. However, some scholars 

whose works have continued to expand and enrich the concept and measurement of consumer 

innovativeness include Venkatraman and Price (1990) whose work distinguishes ‘cognitive’ 

from ‘sensory’ innovativeness. The former refers to individuals who prefer to engage in 

activities that stimulate the mind while the latter seek sensory stimulation. 

 
Similarly, independent judgment making and novelty-seeking are two facets of consumer 

innovativeness identified by Manning, Bearden and Madden (1995). While Independent 

judgment making is the extent to which an individual shopper makes innovation decisions 

independently of others’ communicated experiences (Midgley & Dowling, 1978) novelty-



32 

 

seeking is the desire of a shopper to seek out information about new product (Hirschman, 

1980).  

Also, Price and Ridgway (1983) formulated the concept of ‘use innovativeness.’ They 

defined this concept as the use of previously adopted products in novel ways (Hirschman, 

1980). Finally, Chiu, Fang and Tseng (2010) view innovativeness as relating to a person’s 

tendency to be a technology pioneer and assume thought leadership 

As a consequence of these differing perspectives, Roehrich (2004) opines that there seem to 

be lack of consensus on the concept of innovativeness in literature and quickly observed that 

the central theme that runs through the different conceptualizations of innovativeness is that 

the term describes consumer’s early purchase of a new product (Cestre, 1996) and the 

tendency to be attracted by new products (Steenkamp, Hofstede & Wedel, 1999). Roehrich 

(2004) also referred to the works of Midgley and Dowling (1978), which made a distinction 

between actualized and innate innovativeness which has influenced many writers to think of 

innovativeness as a trait.  

 
However, Goldsmith and Foxall (2003) noted that generally, the concept of innovativeness 

refers to individual differences that are evident in the way people respond to new things. 

They further distinguish between three approaches to the conceptualization of innovativeness 

which exist in literature. These recognized approaches include behavioural, global trait, and 

domain-specific innovativeness 

In their own contribution, Citrin, Sprott and Silverman (2000), argue that in spite of the fact 

that a number of scholars have adopted diverse approaches to define and to measure 

innovativeness of consumers (see Bass, 1969; Craig & Ginter, 1975; Hirschman, 1980b; 

Goldsmith & Hofacker, 1991; Joseph & Vyas, 1984; Rogers & Shoemaker, 1971), two main 



33 

 

types of the innovativeness construct have emerged, namely open processing or general 

innovativeness and domain-specific innovativeness. 

The behavioural perspective on innovativeness according to Goldsmith and Foxall (2003) 

identifies the concept with the act of adoption. Consumers are thus designated as innovators 

or otherwise depending on whether they adopt a new product or not. Moreover, the degree of 

innovativeness they possess depends on how quickly they adopt after encountering the 

innovation. According to Foxall (1990), this behavioural view is conceived within a broader 

approach to consumer behaviour and, this depicts the behaviour of the earliest adopters of 

new products (consumer initiators) as determined by the high levels of both utilitarian 

(functional, technical, economic) and symbolic (social, psychological) rewards available to 

the consumer at this initial phase of the life cycle of the new product. 

 
The Goldsmith and Foxall (2003) global trait view of innovativeness can be equated to Citrin 

et al., (2000) open processing or general consumer innovativeness, which argues that 

innovativeness is a type of personality trait. Personality traits are thought to be relatively 

enduring patterns of behaviour or cognition that differentiate people. Innovativeness 

describes people’s reactions to new and perhaps, uncommon things. These reactions range 

from a very positive attitude toward change to a very negative attitude. Across the population, 

these attitudes are hypothesized to follow a bell-shaped normal distribution (Rogers, 1995). 

Others whose works capture these personality trait theory include Jackson (1976), Hurt, 

Joseph and Cook (1977), Goldsmith (1991), Costa and McCrae (1992), and Popkins (1998). 

 
In discussing the concept of general/open-processing innovativeness, Citrin et al., (2000) 

borrowed from the work of Joseph and Vyas, (1984) which focus on a consumer’s cognitive 

style. Cognition incorporates an individual's intellectual, perceptual, and attitudinal 



34 

 

characteristics. Cognitive style affects the ways in which an individual reacts to new 

products, sensations, experiences, and communications within their environment. This 

approach contends that a person who scores high on the trait of open-processing innovation 

cognitive style will be open to new experiences, and will, in fact, seek out these experiences. 

 
While the general consumer innovativeness (global personality traits) is an important concept 

in the explanation of behaviour, it has proved to be only weakly associated with specific 

consumer behaviours (see Foxall & Goldsmith, 1988). For this reason, efforts have been 

made to conceptualize ‘consumer innovativeness’ as the tendency to buy new products soon 

after they appear in the marketplace (Foxall, Goldsmith & Brown, 1998, pp. 40–45). Thus, 

consumer innovativeness is a more restricted or less general concept than global 

innovativeness. 

 

Domain specific innovativeness is seen as an alternative to the global view of innovativeness. 

It suggests that while it is true that people are different in their acceptance of new ideas, 

experiences, products, it is useful to think also of innovativeness as a domain-specific 

characteristic. That is, consumers are seen as being more or less innovative within specific 

product categories, such as a fashion enthusiast, a wine connoisseur, or a movie buff. 

Innovativeness does not overlap across product categories unless these are closely related 

(Goldsmith & Goldsmith, 1996). 

 

Again, Citrin et al., (2000) observe that a limitation associated with a general approach to 

innovativeness is that consumer innovation may be more domain or product specific, and less 

of an individual personality characteristic. They opine that domain- or product category-

specific innovation reflects the tendency to learn about and adopt innovations within a 

specific domain of interest and, therefore, taps a deeper construct of innovativeness more 



35 

 

specific to an area of interest. From their perspective therefore, Citrin et al, (2000) see 

innovativeness in the area of adoption of Internet shopping as being domain specific rather 

than global. This present work aligns with this view and therefore hypothesizes that 

innovativeness do affect e-shopping acceptance. As a result, in the conceptual model, the 

level of a consumer’s innovativeness is proposed to directly affect his e-shopping acceptance 

and indirectly also, through his perceptions of risk, usefulness and ease of use of these online 

shopping channels. 

 
Product Type:  

Products can basically be classified into two categories of consumer and industrial goods. 

However, since the focus of this study is on consumer online shopping rather than industrial 

online buying, the goods of interest here remain consumer goods. In classifying consumer 

goods, Copeland (1923) identifies goods in separate categories such as convenience, 

shopping, and specialty goods. In addition to these three categories, Kotler and Armstrong 

(2004) identified a fourth category which they termed unsought products.  

 
Throwing more light on how these classifications are conceptualized in conventional 

marketing research, Aspinwall (1968) and Holton (1958) propose that products classification 

should reflect shopping effort more appropriately  and should be placed along a 

continuum. While Kotler (2003) employed product characteristics as a basis for classifying 

products into three categories of durability, tangibility and use goods, other writers have used 

level of information asymmetry to classify products into three types: search goods, 

experiential goods and credence goods (Darby & Karni 1973; Nelson, 1970, 1974).  

 
In classifying products as search goods, experiential goods and credence goods, these studies 

suggest that all goods/services be placed on a continuum ranging from easy to difficult to 

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evaluate; their location on the continuum, which depends on the level of information 

asymmetry, determines whether they are regarded as search, experiential, or credence 

goods/services. Thus, Search products are those that can be evaluated from externally 

provided information. Experiential products, on the other hand, require not only information, 

but also need to be personally inspected or tried. Credence products are those that are difficult 

to assess, even after purchase and use (Laroche, Yang, McDougall, & Bergeron, 2005). 

 
In line with the above conceptualizations, Zeithaml and Bitner (2000) group such goods as 

clothing and furniture as being high in search attributes because they are easy to evaluate 

before purchase. Goods/services such as vacations, telecommunication, or restaurants rely on 

experiential attributes because their intangible nature precludes customers from evaluating 

their quality until the time of purchase and consumption (Brush & Artz 1999; Klein 1998). 

Finally, Lovelock (2001) identifies credence goods/services to include, legal services, 

financial investments, and education. The specialized knowledge needed to provide a 

credence good/service makes it difficult for the client to evaluate the service quality even 

after purchase and consumption. 

 
Though a large number of studies have adopted these models (Hsieh, Chiu, & Chiang, 2005), 

yet, other writers have argued that these models as designed may not be completely fit for 

online marketing. Alba et al., (1997) in their incisive discussion of whether search, 

experiential or credence products are more prone to online purchase, argue that quality of 

information and a consumer’s ability to predict post-purchase satisfaction with products will 

be more accurate predictors of a product’s suitability for online purchase. Although they offer 

a more complex product classification alternative, their proposition is that certain products 

are more likely to be bought online than others. 

 



37 

 

Peterson, Balasubramanian, and Bronnenberg (1997) propose that owing to the special 

characteristics of the Internet, a more relevant classification system is necessary for 

classifying products online. The lack of physical contact and assistance in shopping on the 

Internet is one factor that should influence this classification. Another factor is the need to 

feel, touch, smell or try the product, which is not possible when shopping online (Monsuwe et 

al., 2004). 

 
With regard to the foregoing therefore Peterson, Balasubramanian and Bronnenberg (1997) 

propose a classification system based on three dimensions: cost and purchase frequency, 

value proposition and degree of differentiation. The first dimension ranges from inexpensive, 

frequently purchased goods (e.g. consumable products such as milk) to expensive, 

infrequently purchased goods(e.g. durable products such as a tv set). They argue that 

individuals avoid purchasing inexpensive and frequently purchased goods online. The second 

dimension follows the product value proposition and classifies the products as either tangible 

and physical products or intangible services. The third dimension refers to the degree of 

product differentiation. Thus, Peterson et al., (1997) conclude that in general, when purchase 

fulfillment requires physical delivery, the more frequent the purchase and the smaller the cost 

(e.g. milk), the less likely there is to be a good ``fit'' between a product or service and the 

Internet-based marketing. 

 
Monsuwe et al., (2004) contend that some product categories are more suitable for online 

shopping than other categories. They argue that consumers’ decisions whether or not to shop 

online are influenced by the type of product or service under consideration. Consequently, 

Monsuwe et al., (2004) propose that clearly standardized and familiar products such as 

books, videotapes, CDs, groceries, and flowers, have a higher potential to be considered 

when shopping on the Internet, especially since quality uncertainty in such products is 



38 

 

virtually absent, and no physical assistance or pre-trial is needed (Grewal, Iyer & Levy, 2002; 

Reibstein, 1999). On the other hand, personal-care products like perfume and lotion, or 

products that require personal knowledge or experience like computers and cars, are less 

likely to be considered while shopping online (Elliot & Fowell, 2000). Thus, if personal 

interaction with a salesperson is required for the product under consideration, consumers’ 

intention to shop on the Internet is low. Furthermore, if consumers need to test the product 

under consideration, or have the necessity to feel, touch or smell the product, then their 

intention to shop online is low as well. However, in case of standardized and familiar goods, 

or certain sensitivity products that require a level of privacy and anonymity, consumers’ 

intention to shop on the Internet is high (Grewal et al., 2002). 

 
Expanding on the concept of intangibility of goods in the face of online marketing, Laroche, 

Yang, McDougall, and Bergeron (2005) observe that though intangibility is a key 

differentiating factor between goods and services as the term refers to “what cannot be seen, 

tasted, felt, heard, or smelled” (see Kotler & Bloom, 1984).  In this sense, intangibility refers 

to the total inability of human senses to access the product or service’s attribute.  

 
Selling of tangible/physical goods in the Internet has continued to extend the 

conceptualization of intangibility which has continued to evolve, first from a two dimensional 

construct (Dub´e-Rioux, Regan&Schmitt 1990; Breivik, Troye, & Olsson 1998) and most 

recently to a three dimensional one (Laroche, Bergeron, & Goutaland 2001). This 

classification of goods and services has become particularly useful with the increased 

physical intangibility of both goods and services that is mainly the result of technological 

advances. Digital information is becoming commonplace with the introduction of software 

products and music technology which are now found in varying degrees in CD, DVD, MP3 

and MP4 formats. Although these items are goods, they are physically intangible, being 



39 

 

audible only through a CD or MP3 player or visible through a computer terminal (Freiden, 

Goldsmith, Takacs, & Hofacker 1998). Intangibility has strong impact on consumer decision 

making (Laroche et al., 2001). A good/service’s intangibility is a dominant feature of the ease 

or difficulty that an individual has when making a pre-purchase evaluation of an item; as a 

result, Internet use necessitates a more complete understanding of intangibility (Laroche et 

al., 2005). 

 
The present study aligns with the point of view that online product type has an impact on 

online shopping. Given the increased physical intangibility of both goods and services on the 

Internet, this study borrows from the classification of online products based on information 

asymmetry and therefore proposes that whether or not a product is bought online is dependent 

on whether the product is a search, experiential or credence good and the level of risk a 

consumer perceives about the product and medium and, also whether s/he perceives online 

stores as both easy and a useful channel to purchase such products.       

 

Perceived Usefulness (PU): 

 It is germane to discuss the conceptualization and role of perceived usefulness in the 

technology adoption process for online shopping from the point of view of consumers 

shopping motivation. Without doubt, consumers harbour multiple shopping motivations 

(Westbrook & Black 1985), however, extant literature reveals that most of these motivations 

are grouped into utilitarian and hedonic motivations. These facets of shopping motivations 

primarily help in the study of consumer shopping behaviour (Childers et al., 2001). This is 

because these two motivations maintain a basic underlying presence across consumption 

phenomena (Babin, Darden & Griffin 1994). Childers et al., (2001) further argue that this 



40 

 

dual classification of motivations is in tandem with the acceptance of interactive shopping 

behaviour as a new form of shopping mediated by technology.  

 
The hedonic motivation for shopping covers the enjoyment part of the shopping process 

while utilitarian motivation is concerned with the functional aspect. The utilitarian motivation 

is goal-directed and views the consumer as a rational entity who carefully considers and 

evaluates information about products before purchase. Thus, from the functional perspective; 

consumers are concerned with purchasing products in a timely and an efficient manner to 

achieve their goals with a minimum of discomfort or irritation (Childers et al., 2001). While 

some consumers may be shopping only for utilitarian purposes, others may be primarily 

enjoying these interactive media, and thus both factors can ultimately affect their attitude 

toward using interactive forms of shopping. The seeming positive disposition that the 

consumer holds in the capability of the technology to lead to the achievement of his shopping 

motivation is reflected within the TAM framework as Perceived usefulness; and as Davis et 

al., (1989) posit perceived usefulness (PU) is a major influence of attitude on the use of 

technology. 

PU is conceptualized by Davis (1989) as the degree to which a user believes that the 

technology will improve the performance of an activity. In e-commerce, it refers to how 

effectively Internet shopping helps consumers to accomplish their task (Tong, 2010) and 

therefore refers to the outcome of the shopping experience (Childers et al, 2001). In e-

shopping an activity involves the ability to improve shopping performance, shopping 

productivity, and most importantly, accomplishing shopping goals. These as noted by 

McCloskey (2004), were the indices of a successful shopping activity. The findings of 

Barkhi, Belanger, and Hicks (2008) are in agreement with this as their study suggests that 

consumers will build positive attitudes toward products and services that are sufficiently 



41 

 

beneficial in terms of providing a solution and negative attitudes toward those that are not 

beneficial. 

 
Perceived usefulness is a comparable theoretical concept to the construct of relative 

advantage in the Diffusion of Innovation theory (Chen et al., 2002). Scholars conceptualize 

relative advantage to be the degree to which an innovation is seen as offering a clear 

advantage. This advantage may include economic profit, a social prestige or other benefits 

(Rogers, 1995).Zarrad and Debabi (2012) therefore assert that perceived usefulness refers to 

the advantages a person receives from the use of Internet as a medium for shopping such as 

saving time and money and having access to information. 

 
But what other factors influence the behaviour and workings of this construct? Davis (1989) 

theory of technology acceptance is anchored on such beliefs about the task-value and user-

friendliness of new information systems. Although this work has been extremely valuable in 

explaining first-order effects, Venkatesh and Davis (1994) and Karahanna and Straub (1999) 

however seek answers on how and why the beliefs of usefulness and ease of use start to form 

in the first place. What, for example, explains how a user comes to believe that a system is 

useful in his or her job? What would be the presumably different psych-sociological 

antecedents for a belief that a system is simple or difficult to use? 

 
Literature reveals that social contexts can act as precedence to PU by creating perceptions of 

usefulness and ease-of-use (Karahanna & Straub, 1999). According to Yi, Jackson, Park, and 

Probst (2006) subjective norms and image are additional factors identified in literature to 

have positive impact on perceived usefulness.  In the area of electronic commerce, perceived 

ease of use, as a predictor of perceived usefulness, has been suggested as affecting 

consumers’ perception of usefulness. However, both are construed to be closely linked as 



42 

 

Ramayah and Ignatius (2005) argue that consumers who see online shopping as effortless 

would in turn develop a tendency to perceive it as useful. The reason behind such a 

phenomenon is due to the fact that a consumer would naturally try to mould his or her view 

of online shopping based on past experiences in engaging in online shopping activities and 

the ease in which the shopping activity is executed (Lim & Ting, 2012).  Consistent with this 

line of argument this thesis proposes that ease of use of online shopping platforms will lead to 

its perception as useful for achieving shopping motivations which will subsequently result in 

its acceptance. 

 
Perceived Ease of Use (PE): 

The second crucial determinant of technology adoption as identified by Davis et al., (1989) is 

“perceived ease of use”, referring to the degree to which a person believes that using the new 

technology will be effortless. While “perceived usefulness” refers to consumers’ views 

regarding the outcome of the experience, “perceived ease of use” conceptualizes consumers’ 

perceptions regarding the process leading to the final outcome. Hence, Lim and Ting (2012) 

define perceived ease of use as the concentration of physical and mental efforts that a user 

hopes to expend when using the technology. Other theoretical perspectives studying user 

acceptance have equally used similar constructs-Thompson, Higgins and Howell (1994) 

employed a construct called "complexity," and Moore and Benbasat (1991) also tagged the 

construct, "ease of use." 

 
In spite of the prominent role that perceived ease of use is adjudged to play in TAM research 

in particular and user technology acceptance research generally, Vankatesh and Davis (1996) 

recognize the importance of understanding the antecedents of key TAM constructs of 

perceived usefulness and perceived ease of use in order to appreciate TAM’s explanation of 

acceptance and use of technology. The role of the construct of perceived ease of use is better 



43 

 

understood by scrutinizing the two paths through which it impacts intention. On the one hand, 

a user’s perceived ease of use affects his intentions directly and also indirectly through 

perceived usefulness, and on the other hand, it is a prime obstacle that is in the way a user 

would need to surmount in order to aid his acceptance and subsequent use of the technology 

(Vankatesh, 2000). This line of thought is equally highlighted in literature as immense 

collection of research in behavioural decision making (e.g., Payne, Bettman, & Johnson 

1993) and Information System (e.g., Todd & Benbasat 1991, 1992,1993,1994) show that 

users attempt to minimize effort in their behaviours, thus supporting a relationship between 

perceived ease of use and usage behaviour. 

In his work titled “Determinants of perceived ease of use: Integrating control, intrinsic 

motivation, and emotion into the technology acceptance model” Vankatesh (2000) argues that 

understanding the determinant structure of this key driver of user acceptance and usage is 

critical as it will  engender favourable perceptions which will lead to technology acceptance 

and usage. 

 
In the area of e-commerce, Buton-Jones and Hubona (2005), note that the ease of learning 

and user skillfulness at using prevalent systems such as web technologies and interfaces on 

online shopping sites, are valid determinants of users’ opinion to a technology being easy to 

use. Also, Selamat, Jaffar, and Ong (2009) argue that a technology which is rated to be easier 

to use than another is more likely to be accepted by users whereas the more complicated a 

technology is seen to be, the slower the adoption rate will be. This aligns with the proposition 

of Teo (2001) that a technology which is easy to use usually involve less user effort and 

thereby increases the likelihood of adoption and usage of such technology.  

 



44 

 

Other studies like Bisdee, (2007) and Yulihasri and Daud, (2011) have also found that 

perceived ease of use had a positive influence on consumers’ attitude in using the Internet to 

shop online. This is consistent with the work of Childers et al., (2001) which argued that 

online merchants who are able to provide online shopping sites which are clear and 

understandable, with less mental effort requirement, and allow consumers to shop without 

encumbrances results in ease of use perceptions in users’ minds with favourable attitudinal 

association to online retailers who can do so. 

 
It is note-worthy however, that while results of several studies have been consistent that 

users’ perceived ease of use is mediated by perceived usefulness in its impact on users’ 

acceptance and use of technology, studies on the direct effect of perceived ease of use on 

adoption of technology have continued to produce mixed results. Thus, prompting Gefen and 

Straub (2000) to look at the role of perceived ease of use (PE) in TAM as contentious while 

Keil, Beranek and Konsynski (1995) have questioned the overall essence of PE in IT 

adoption. 

In a study titled “The relative importance of perceived ease of use in IS adoption: a study of 

e-commerce adoption” Gefen and Straub (2000) provided a theoretical elucidation of the 

mixed effects of perceived ease of use (PE) on IT adoption by distinguishing between tasks 

that are intrinsic from those that are extrinsic to the IT. They explain that tasks that are 

intrinsic to the IT are the ones where the IT itself is primarily the “ends,” for which the IT is 

ultimately being adopted. On the other hand, tasks that are extrinsic to the IT, are those in 

which the IT is merely a “means” to attaining the primary objective in which case the IT not 

only acts as the central component of the process, but also serves as the interface through 

which a goal is achieved. 

 



45 

 

Applying the above proposition to e-commerce, Gefen and Straub (2000) posit that when a 

Web site is used as a medium to purchase products, perceived ease of use would not 

influence IT adoption because in this case, IT ease-of-use is not an inherent quality of the 

purchased product. On the other hand, when the Web site is used only to make product 

inquiry, perceived ease of use affects IT adoption because the information sought is attached 

to the IT and thus its quality