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The copyright © of this thesis belongs to its rightful author and/or other copyright owner. Copies can be accessed and downloaded for non-commercial or learning purposes without any charge and permission. The thesis cannot be reproduced or quoted as a whole without the permission from its rightful owner. No alteration or changes in format is allowed without permission from its rightful owner.

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DETERMINANTS OF MOBILE SHOPPING BEHAVIOUR: A STUDY AMONG STUDENTS IN UUM

MOHAMAD NOOR FAREZ BIN ROMLI By

Thesis Submitted to School of Business Management,

Universiti Utara Malaysia,

In Partial Fulfilment of the Requirement for the Master of Sciences (Management)

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PERMISSION TO USE

In presenting this dissertation paper in partial fulfilment of the requirements for a Post Graduate degree from the Universiti Utara Malaysia (UUM), I agree that the Library of this university may make it freely available for inspection. I further agree that permission for copying this dissertation/project paper in any manner, in whole or in part, for scholarly purposes may be granted by my supervisor(s) or in their absence, by the Dean of Othman Yeop Abdullah Graduate School of Business where I did my dissertation paper. It is understood that any copying or publication or use of this dissertation paper parts of it for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the UUM in any scholarly use which may be made of any material in my dissertation paper.

Request for permission to copy or to make other use of materials in this dissertation paper in whole or in part should be addressed to:

Dean of Othman Yeop Abdullah Graduate School of Business Universiti Utara Malaysia

06010 UUM Sintok Kedah Darul Aman

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ii ABSTRACT

Rapid changing in technology have resulted in altering the ways of shopping from physical to virtual which be access through online. This technology has a significant shift in the retail industry to ensure they can sustain and continue to have the customer by fast the implementation of web-based technology or even application build in on mobile device operating system which creates the new way of shopping being called mobile shopping. Besides, mobile shopping expands the consumers' power in shopping as well as involve the behaviour of the user. The purpose of this study is to know the determinants of mobile shopping behaviour based on a study among students in UUM. The research model was adapted and modified the framework from Technology Acceptance Model (TAM) and The Theory of Planned Behaviour (TPB) to match with objective of research and added with two potential variable which is satisfaction and trust to recognise the key determinants of mobile shopping behaviour.

Overall the variables selected in this research model is perceived ease of use, perceived usefulness, satisfaction, attitude, trust, and subjective norm. The software of Statistical Package for Social Science (SPSS) is used to analyse a total of 377 of questionnaires among UUM’s students. The outcomes from analyses indicate that there is a significant relationship between the six independent variables which are perceived ease of use, perceived usefulness, satisfaction, attitude, trust, and subjective norm with the dependent variable, mobile shopping behaviour. Therefore, trust was found to be the most significant and strong variable that influential mobile shopping behaviour among students in UUM.

Keywords: mobile shopping behaviour, perceived ease of use, perceived usefulness, satisfaction, attitude, trust, subjective norm

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iii ABSTRAK

Perubahan pesat dalam teknologi telah menyebabkan perubahan cara membeli-belah dari fizikal hingga maya yang dapat diakses menerusi talian. Teknologi ini mempunyai peralihan ketara dalam industri peruncitan untuk memastikan mereka dapat mengekalkan dan terus mempunyai pelanggan dengan mencepatkan pelaksanaan teknologi berasaskan web atau membina aplikasi pada sistem operasi peranti mudah alih yang mencipta cara membeli-belah baru yang dipanggil sebagai mobile shopping.

Selain itu, mobile shopping memperluaskan kuasa pengguna dalam membeli-belah serta melibatkan tingkah laku pengguna. Tujuan kajian ini adalah untuk mengetahui penentu tingkah laku mobile shopping berdasarkan kajian di kalangan pelajar UUM.

Model penyelidikan diadaptasi dan diubahsuai dari Model Penerimaan Teknologi (TAM) dan Teori Perilaku Terancang (TPB) untuk dipadankan dengan objektif penyelidikan dan ditambah dengan dua pemboleh ubah berpotensi iaitu kepuasan dan kepercayaan untuk mengenali penentu utama dalam tingkah laku mobile shopping.

Secara keseluruhan, pembolehubah yang dipilih dalam model penyelidikan ini persepsi atas kemudahan penggunaan, persepsi atas kemanfaatan, kepuasan, sikap, kepercayaan, dan norma subjektif. Perisian Pakej Statistik untuk Sains Sosial (SPSS) digunakan untuk menganalisis sejumlah 377 soal selidik di kalangan pelajar UUM.

Hasil dari analisis menunjukkan bahawa terdapat hubungan yang signifikan antara enam pemboleh ubah bebas persepsi atas kemudahan penggunaan, persepsi atas kemanfaatan, kepuasan, sikap, kepercayaan, dan norma subjektif dengan pemboleh ubah yang bergantung, mobile shopping. Oleh itu, kepercayaan didapati sebagai pemboleh ubah yang paling penting dan kuat yang mempengaruhi tingkah laku mobile shopping di kalangan pelajar UUM.

Kata kunci: tingkah laku terhadap mobile shopping, persepsi atas kemudahan penggunaan, persepsi atas kemanfaatan, kepuasan, sikap, kepercayaan, norma subjektif

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ACKNOWLEDGEMENT

All the praises and thanks be to Allah, the Lord of all that exists. I am grateful to Allah SWT for awarding me good health, wisdom, and capability to complete this research according to the period given. I want to express my sincere gratitude to all those involved who had helped and guided me during this journey of research.

Firstly, I would like to state my most profound appreciation to my humble supervisor, Dr. Nurul Sharniza binti Husin for her valued and untiring supervisory role in this research. No words can describe enough to express how thankful and appreciate support and encouragement throughout the preparation and completion of this research.

Most remarkably, I would like to express gratitude to my parents and my wife for their continuous endless moral support as well as prayers for my success over the years.

Without love, this journey may become difficult for me to go through alone.

Special thanks to the Academic Department, Universiti Utara Malaysia, for providing me data on the total numbers of postgraduate students. Finally, thank you for all the respondents for their valuable time, kindness and support in participating in this study.

Thank you.

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TABLE OF CONTENTS

PERMISSION TO USE ... i

ABSTRACT ... ii

ABSTRAK ... iii

ACKNOWLEDGEMENT ... iv

TABLE OF CONTENTS ... v

LIST OF TABLES ... viii

LIST OF FIGURES ... ix

LIST OF APPENDICES ... x

LIST OF ABBREVIATIONS ... xi

CHAPTER 1: INTRODUCTION 1.0 Introduction ... 1

1.1 Background of the Study ... 1

1.2 Problem Statement ... 4

1.3 Research Questions ... 6

1.4 Research Objectives ... 7

1.5 Significant of Study ... 8

1.5.1 Theoretical Contributions ... 8

1.5.2 Practical Contributions ... 8

1.6 Scope of Study ... 9

1.7 Definitions of Key Terms... 10

1.8 Organisation of the Study ... 12

1.9 Summary ... 12

CHAPTER 2: LITERATURE REVIEW 2.1 Introduction ... 13

2.2 Mobile Shopping Behaviour ... 13

2.3 Perceived Ease of Use ... 15

2.4 Perceived Usefulness ... 17

2.5 Satisfaction ... 18

2.6 Attitude ... 20

2.7 Trust ... 21

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2.8 Subjective Norm... 24

2.9 Underpinning Theory ... 25

2.9.1 Technology Acceptance Model ... 25

2.9.2 Theory of Planned Behaviour ... 26

2.10 Summary ... 28

CHAPTER 3: RESEARCH METHODOLOGY 3.1 Introduction ... 29

3.2 Research Framework ... 29

3.3 Hypotheses Development... 30

3.4 Research Design ... 31

3.5 Research Sampling and Technique ... 32

3.5.1 Population ... 32

3.5.2 Sampling size ... 33

3.5.3 Sampling Procedure and Technique ... 34

3.6 Research Instrument Development ... 35

3.6.1 Section A: Demographic Information ... 38

3.6.2 Section B: Dependent and Independent Variables ... 39

3.6.3 Pilot Test ... 40

3.7 Data Collection Procedure ... 42

3.8 Techniques of Data Analysis ... 43

3.8.1 Reliability Analysis ... 44

3.8.2 Descriptive Analysis ... 44

3.8.3 Hypothesis Testing ... 44

3.8.3.1 Correlation Analysis ... 44

3.8.3.2 Multiple Regression Analysis ... 45

3.9 Summary ... 45

CHAPTER 4: FINDINGS 4.0 Introduction ... 46

4.1 Participation and Response Rate ... 46

4.2 Data Screening ... 47

4.3 Descriptive Statistic of the Demographic Profile... 47

4.3.1 Age ... 48

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4.3.2 Gender ... 49

4.3.3 Race ... 49

4.3.4 Education Level ... 49

4.3.5 Monthly Personal Income (RM) ... 50

4.4 Reliability Analysis ... 50

4.5 Descriptive Statistic for Normality Assumption ... 51

4.6 Descriptive Analysis ... 54

4.7 Correlation Analysis... 56

4.8 Regression Analysis ... 59

4.8.1 Interpretation for Unstandardized Coefficients Values Result ... 61

4.9 Summary ... 64

CHAPTER 5: DISCUSSION AND CONCLUSION 5.0 Introduction ... 65

5.1 Research Summary... 65

5.2 Discussion of Research Objectives ... 66

5.3 Contribution of the Study ... 71

5.3.1 Theoretical Contribution ... 72

5.3.2 Practical Contribution ... 72

5.4 Limitation of Study ... 73

5.5 Recommendation for Future Research ... 74

5.6 Conclusion ... 75

REFERENCES ... 76

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LIST OF TABLES

Tables Pages

Table 3.1 Total Number of Student UUM ... 33

Table 3.2 Summary of Table Sample Size of Population ... 34

Table 3.3 The population of Student UUM ... 35

Table 3.4 Operational Definition of Variables ... 35

Table 3.5 Measurement of Variables ... 36

Table 3.6 Items in Demographic Information ... 38

Table 3.7 Likert Scale ... 40

Table 3.8 Indicator Cronbach’s Alpha Coefficient Size ... 41

Table 3.9 Reliability Test for Pilot Test ... 42

Table 3.10 Summaries of Data Analysis’s Technique ... 43

Table 4.1 Summary of Demographic Profile of Respondent (N=365) ... 48

Table 4.2 Indicator Cronbach’s Alpha Coefficient Size ... 51

Table 4.3 Summary of Reliability Test Result ... 51

Table 4.4 Descriptive Statistics ... 53

Table 4.5 Pearson’s Correlation Scale ... 54

Table 4.6 Summary of Pearson Correlation ... 56

Table 4.7 Summary of Descriptive Statistics ... 57

Table 4.8 Summary of Model Summary ... 60

Table 4.9 Summary of Coefficients ... 60

Table 4.10 Estimated Equation for the Proposed Model ... 61

Table 4.11 Summary of Hypothesis Results ... 64

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LIST OF FIGURES

Figures Pages

Figure 1.1 Percentage Age Distribution of Mobile Shopping

Consumers...3 Figure 3.1 Research Framework……...30

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x

LIST OF APPENDICES

Appendices Pages

APPENDIX A1: SET OF QUESTIONNAIRES...92 APPENDIX A3: RESULT FROM IBM SPSS STATISTIC 24...97

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LIST OF ABBREVIATIONS

H1 Hypothesis 1

H2 Hypothesis 2 H3 Hypothesis 3

H4 Hypothesis 4

H5 Hypothesis 5 H6 Hypothesis 6

SPSS Statistical Package for Social Science UUM Universiti Utara Malaysia

TAM Technology Acceptance Model TPB Theory of Planned Behaviour COB College of Business

CAS College of Arts and Sciences

COLGIS College of Law, Government and International Studies

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CHAPTER 1 INTRODUCTION

1.0 Introduction

This research will start by the first chapter which the explanation of fundamentals and structure of the research based on selected topic which regarding determinants of mobile shopping behaviour. Several subchapters are listed to give a better illustration regarding this research. Moreover, this research correspondingly promotes and assists in a better understanding of the selected issue, but the discussion is still under the framework of the study.

1.1 Background of the Study

As the twenty-first century, a narrative change in the development of mobile devices, which empower consumers with innovative, interface improvements, and provide them with an ability to improve together with advances in technology to deliver a more comfortable and productive way of doing things (Groß, 2015). Moreover, since the ability as well as the advance of technology only in the mobile device, the significant shift in the retail industry by fast through the accelerated implementation of web-based technology or even application build in on mobile device operating system.

Correspondingly, this situation may be related to mobile commerce, which this term is referring to online services that can be reached through access mobile websites or applications within mobile devices (Zhang, Chen, & Lee, 2013). This statement also followed by (Nassuora, 2013) which states that mobile commerce offers enhanced

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REFERENCES

Abdulwahab, L., Dahalin, Z. M., & Galadima, M. . (2014). Data screening and Preliminary Analysis of the Determinants of User Acceptance of Telecentre.

Journal of Information Systems: New Paradigms, 1(December), 11–23.

Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16–23.

https://doi.org/10.1016/j.jretconser.2014.09.003

Ajzen, I, & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Retrieved from http://www.citeulike.org/group/38/article/235626 Ajzen, Icek. (1991). The theory of planned behavior. Organizational Behavior and

Human Decision Processes, 50(2), 179–211.

https://doi.org/https://doi.org/10.1016/0749-5978(91)90020-T

Ajzen, Icek, & Driver, B. L. (1991). Prediction of leisure participation from behavioral, normative, and control beliefs: An application of the theory of planned behavior. Leisure Sciences, 13(3), 185–204.

https://doi.org/10.1080/01490409109513137

Al-Swidi, A., Huque, S. M. R., Hafeez, M. H., & Shariff, M. N. M. (2014). The role of subjective norms in theory of planned behavior in the context of organic food consumption. British Food Journal, 116(10), 1561–1580.

https://doi.org/10.1108/BFJ-05-2013-0105

Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust.

International Journal of Information Management, 37(3), 99–110.

https://doi.org/10.1016/j.ijinfomgt.2017.01.002

(17)

77

Amin, M., Rezaei, S., & Abolghasemi, M. (2014). User satisfaction with mobile websites: the impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Business Review International, 5(3), 258–274.

https://doi.org/10.1108/NBRI-01-2014-0005

Amirtha, R., & Sivakumar, V. J. (2018). Does family life cycle stage influence e- shopping acceptance by Indian women? An examination using the technology acceptance model. Behaviour & Information Technology, 37(3), 267–294.

https://doi.org/10.1080/0144929X.2018.1434560

Ansari, M. S., Channar, Z. A., & Syed, A. (2012). Mobile phone adoption and appropriation among the young generation. Procedia - Social and Behavioral Sciences, 41, 265–272. https://doi.org/10.1016/j.sbspro.2012.04.030

Armitage, C. J., & Conner, M. (2001). Efficacy of the Theory of Planned Behaviour:

A meta-analytic review. British Journal of Social Psychology, 40(4), 471–499.

https://doi.org/10.1348/014466601164939

Beckett, C., Eriksson, L., Johansson, E., & Wikström, C. (2017). Multivariate Data Analysis (MVDA). In Pharmaceutical Quality by Design: A Practical Approach.

https://doi.org/10.1002/9781118895238.ch8

Benbasat, I., & Barki, H. (2007). Quo vadis TAM? Journal of the Association for Information Systems, 8(4), 211–218. https://doi.org/10.17705/1jais.00126 Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic

commerce service continuance. Decision Support Systems, 32(2), 201–214.

https://doi.org/10.1016/S0167-9236(01)00111-7

Burke, C. S., Sims, D. E., Lazzara, E. H., & Salas, E. (2007). Trust in leadership: A multi-level review and integration. Leadership Quarterly, 18(6), 606–632.

https://doi.org/10.1016/j.leaqua.2007.09.006

(18)

78

Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming, 2nd ed. In Structural equation modeling with AMOS: Basic concepts, applications, and programming, 2nd ed. New York, NY, US: Routledge/Taylor & Francis Group.

Cavana, R., Delahaye, B., & Sekeran, U. (2001). Applied Business research:

Qualitative and Quantitative Methods. Retrieved from https://eprints.qut.edu.au/10523/

Chen, L. da. (2008). A model of consumer acceptance of mobile payment.

International Journal of Mobile Communications, 6(1), 32.

https://doi.org/10.1504/IJMC.2008.015997

Chen, H.-J. (2018). What drives consumers’ mobile shopping? 4Ps or shopping preferences? Asia Pacific Journal of Marketing and Logistics, 30(4), 797–815.

https://doi.org/10.1108/APJML-08-2017-0167

Chen, S. C., Chen, H. H., & Chen, M. F. (2009). Determinants of satisfaction and continuance intention towards self-service technologies. Industrial Management

and Data Systems, 109(9), 1248–1263.

https://doi.org/10.1108/02635570911002306

Chen, Y.-F., & Lan, Y.-C. (2018). An Empirical Study of the Factors Affecting Mobile Shopping in Taiwan. In Mobile Commerce (pp. 1329–1340).

https://doi.org/10.4018/978-1-5225-2599-8.ch063

Chen, Y.-M., Hsu, T.-H., & Lu, Y.-J. (2018). Impact of flow on mobile shopping intention. Journal of Retailing and Consumer Services, 41, 281–287.

https://doi.org/10.1016/j.jretconser.2017.04.004

Chi, T. (2018). Understanding Chinese consumer adoption of apparel mobile commerce: An extended TAM approach. Journal of Retailing and Consumer

(19)

79

Services, 44, 274–284. https://doi.org/10.1016/j.jretconser.2018.07.019

Chong, A. Y.-L. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240–1247.

https://doi.org/10.1016/j.eswa.2012.08.067

Chong, A. Y.-L., Chan, F. T. S., & Ooi, K.-B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1), 34–43.

https://doi.org/10.1016/j.dss.2011.12.001

Coakes, S. J., & Steed, L. (2009). SPSS: Analysis without anguish using SPSS version 14.0 for Windows. John Wiley & Sons, Inc.

Connelly, L. M. (2008). Pilot studies. Medsurg Nursing : Official Journal of the Academy of Medical-Surgical Nurses, 17(6), 411–412. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/19248407

Dabholkar, P. A., & Sheng, X. (2009). The role of perceived control and gender in consumer reactions to download delays. Journal of Business Research, 62(7), 756–760. https://doi.org/10.1016/j.jbusres.2008.06.001

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008

Dulloo, R. (2018). Impact of demographic factors on consumers trust towards mobile shopping apps. Journal of Advanced Research in Dynamical and Control Systems, 10(7), 926–940.

Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. In The psychology of attitudes. Orlando, FL, US: Harcourt Brace Jovanovich College Publishers.

(20)

80

Evans, J. D. (1996). Straightforward statistics for the behavioral sciences. Pacific Grove: Brooks/Cole Pub. Co.

Faqih, K. M. S., & Jaradat, M. I. R. M. (2015). Assessing the moderating effect of gender differences and individualism-collectivism at individual-level on the adoption of mobile commerce technology: TAM3 perspective. Journal of Retailing and Consumer Services, 22(January 2019), 37–52.

https://doi.org/10.1016/j.jretconser.2014.09.006

Ferri, F., Grifoni, P., & Guzzo, T. (2013). Factors Determining Mobile Shopping. A Theoretical Model of Mobile Commerce Acceptance. International Journal of Information Processing & Management, 4(7), 89. Retrieved from https://pdfs.semanticscholar.org/ddb7/011f2a51d5db23a894e45bc6135ed6d91f ed.pdf

Fong, K. K.-K., & Wong, S. K. S. (2015). Factors Influencing the Behavior Intention of Mobile Commerce Service Users: An Exploratory Study in Hong Kong.

International Journal of Business and Management, 10(7).

https://doi.org/10.5539/ijbm.v10n7p39

Friedrich, R., Gröne, F., Hölbling, K., & Peterson, M. (2009). The March of Mobile Marketing: New Chances for Consumer Companies, New Opportunities for Mobile Operators. Journal of Advertising Research, 49(1), 54–61.

https://doi.org/10.2501/S0021849909090096

Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211–231. https://doi.org/10.1108/APJML-06-2013-0061 Gefen, D., Srinivasan Rao, V., & Tractinsky, N. (2003). The conceptualization of trust,

risk and their electronic commerce: the need for clarifications. 36th Annual

(21)

81

Hawaii International Conference on System Sciences, 2003. Proceedings of The, 10 pp. https://doi.org/10.1109/HICSS.2003.1174442

Gerpott, T. J., & Thomas, S. (2014). Empirical research on mobile Internet usage: A meta-analysis of the literature. Telecommunications Policy, 38(3), 291–310.

https://doi.org/10.1016/j.telpol.2013.10.003

Groß, M. (2015). Mobile shopping: A classification framework and literature review.

International Journal of Retail and Distribution Management, 43(3), 221–241.

https://doi.org/10.1108/IJRDM-06-2013-0119

Gupta, A., & Arora, N. (2017). Understanding determinants and barriers of mobile shopping adoption using behavioral reasoning theory. Journal of Retailing and Consumer Services, 36, 1–7. https://doi.org/10.1016/j.jretconser.2016.12.012 Guriting, P., & Ndubisi, N. O. (2006). Borneo online banking: evaluating customer

perceptions and behavioural intention. Management Research News, 29(1/2), 6–

15. https://doi.org/10.1108/01409170610645402

Hair, J. F., Page, M., & Brunsveld, N. (2019). Essentials of Business Research Methods. In Essentials of Business Research Methods.

https://doi.org/10.4324/9780429203374

Hanafizadeh, P., Behboudi, M., Koshksaray, A. A., & Tabar, M. J. S. (2014). Mobile- banking adoption by Iranian bank clients. Telematics and Informatics, 31(1), 62–

78. https://doi.org/10.1016/j.tele.2012.11.001

Hanjaya, S. T. M., Kenny, S. K., & Gunawan, S. S. S. E. F. (2019). Understanding Factors influencing Consumers Online Purchase intention Via Mobile App:

Perceived Ease of use, Perceived Usefulness, System Quality, information Quality, and Service Quality. Marketing of Scientific and Research Organizations, 32(2), 175–205. https://doi.org/10.2478/minib-2019-0035

(22)

82

Hasbullah, N. A., Osman, A., Abdullah, S., Salahuddin, S. N., Ramlee, N. F., & Soha, H. M. (2016). The Relationship of Attitude, Subjective Norm and Website Usability on Consumer Intention to Purchase Online: An Evidence of Malaysian Youth. Procedia Economics and Finance, 35, 493–502.

https://doi.org/10.1016/S2212-5671(16)00061-7

Heijden, H. van der. (2004). User Acceptance of Hedonic Systems. MIS Quarterly, 28(4), 695–704.

Hess, T. J., & Mcnab, A. L. (2014). R Eliability G Eneralization of P Erceived E Ase of U Se , P Erceived U Sefulness ,. MIS Quarterly, 38(1), 1–28.

Holmes, A., Byrne, A., & Rowley, J. (2014). Mobile shopping behaviour: Insights into attitudes, shopping process involvement and location. International Journal of Retail and Distribution Management, 42(1), 25–39.

https://doi.org/10.1108/IJRDM-10-2012-0096

Hsieh, C. (2007). Mobile Commerce: Assessing New Business Opportunities.

Communications of the IIMA, 7(1), 87–100.

Hsu, M.-H., Yen, C.-H., Chiu, C.-M., & Chang, C.-M. (2006). A longitudinal investigation of continued online shopping behavior: An extension of the theory of planned behavior. International Journal of Human-Computer Studies, 64(9), 889–904. https://doi.org/10.1016/j.ijhcs.2006.04.004

Hung, M.-C., Yang, S.-T., & Hsieh, T.-C. (2012). An Examination of The Determinants of Mobile Shopping Continuance. International Journal of Electronic Business Management, 10(1), 29–37.

Hung, M. C., Hwang, H. G., & Hsieh, T. C. (2007). An exploratory study on the continuance of mobile commerce: an extended expectation-confirmation model of information system use. International Journal of Mobile Communications,

(23)

83

5(4), 409. https://doi.org/10.1504/IJMC.2007.012788

Joubert, J., & Belle, J. Van. (2013). The Role of Trust and Risk in Mobile Commerce Adoption within South Africa. International Journal of Business, Humanities and Technology, 3(2), 27–38. Retrieved from http://www.ijbhtnet.com/journals/Vol_3_No_2_February_2013/3.pdf

Jun, G., & Jaafar, N. I. (2011). A Study on Consumers’ Attitude towards Online Shopping in China. International Journal of Business and Social Science, 2(22), 122–132. https://doi.org/10.1108/QMR-06-2013-0041

Kalinic, Z., & Marinkovic, V. (2016). Determinants of users’ intention to adopt m- commerce: an empirical analysis. Information Systems and E-Business Management, 14(2), 367–387. https://doi.org/10.1007/s10257-015-0287-2 Kaur, J., & Soch, H. (2019). Mobile Shopping Adoption: Insights into Attitude,

Intentions and Flow Experience. International Journal of Management Studies, VI(4), 20. https://doi.org/10.18843/ijms/v6si4/03

Khine, M. S. (2013). Structural equation modeling approaches in educational research and practice. In Application of Structural Equation Modeling in Educational Research and Practice (pp. 279–283). https://doi.org/10.1007/978-94-6209-332- 4

Kourouthanassis, P. E., & Giaglis, G. M. (2012). Introduction to the Special Issue Mobile Commerce: The Past, Present, and Future of Mobile Commerce Research. International Journal of Electronic Commerce, 16(4), 5–18.

https://doi.org/10.2753/JEC1086-4415160401

Krejcie, R. V, & Morgan, D. W. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30(3), 607–610.

https://doi.org/10.1177/001316447003000308

(24)

84

Kuo, Y.-F., & Yen, S.-N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103–110. https://doi.org/10.1016/j.chb.2008.07.007

Lai, J. Y., Debbarma, S., & Ulhas, K. R. (2012). An empirical study of consumer switching behaviour towards mobile shopping: a Push-Pull-Mooring model.

International Journal of Mobile Communications, 10(4), 386.

https://doi.org/10.1504/IJMC.2012.048137

Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e- learning: An extension of the expectation-confirmation model. Computers and Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002 Li, M., Dong, Z. Y., & Chen, X. (2012). Factors influencing consumption experience

of mobile commerce: A study from experiential view. Internet Research, 22(2), 120–141. https://doi.org/10.1108/10662241211214539

Lim, Y. J., Osman, A., Romle, A. R., & Othman, Y. H. (2015). Attitude towards Online Shopping Activities in Malaysia Public University. Mediterranean Journal of Social Sciences. https://doi.org/10.5901/mjss.2015.v6n2s1p456 Lu, J. (2014). Are personal innovativeness and social influence critical to continue

with mobile commerce? Internet Research, 24(2), 134–159.

https://doi.org/10.1108/IntR-05-2012-0100

Madan, K., & Yadav, R. (2018). Understanding and predicting antecedents of mobile shopping adoption. Asia Pacific Journal of Marketing and Logistics, 30(1), 139–

162. https://doi.org/10.1108/APJML-02-2017-0023

Malaquias, R. F., & Hwang, Y. (2016). An empirical study on trust in mobile banking:

A developing country perspective. Computers in Human Behavior, 54, 453–461.

https://doi.org/10.1016/j.chb.2015.08.039

(25)

85

Malaysian Communications and Multimedia Commission. (2018). E-Commerce Consumers Survey 2018. Malaysian Communications and Multimedia Commission, 1–49.

Marriott, H. R., & Williams, M. D. (2018). Exploring consumers perceived risk and trust for mobile shopping: A theoretical framework and empirical study. Journal of Retailing and Consumer Services, 42(December 2017), 133–146.

https://doi.org/10.1016/j.jretconser.2018.01.017

Marriott, H. R., Williams, M. D., & Dwivedi, Y. K. (2017). What do we know about consumer m-shopping behaviour? International Journal of Retail and Distribution Management, 45(6), 568–586. https://doi.org/10.1108/IJRDM-09- 2016-0164

Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13.

https://doi.org/10.1016/j.ijinfomgt.2013.06.002

Miller, J. (1991). Short Report: Reaction Time Analysis with Outlier Exclusion: Bias Varies with Sample Size. The Quarterly Journal of Experimental Psychology Section A, 43(4), 907–912. https://doi.org/10.1080/14640749108400962

Mukesh, K., Salim, A. T., & Ramayah, T. (2013). Business Research Methods. Kuala Lumpur: Oxford University Press.

Muniady, R., Al-Mamun, A., Yukthamarani Permarupan, P., & Binti Zainol, N. R.

(2014). Factors influencing consumer behavior: A study among university students in Malaysia. Asian Social Science, 10(9), 18–25.

https://doi.org/10.5539/ass.v10n9p18

Nassuora, A. B. (2013). Understanding Factors Affecting the Adoption of M-

(26)

86

commerce by Consumers. Journal of Applied Sciences, 13(6), 913–918.

https://doi.org/10.3923/jas.2013.913.918

Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37(March), 8–22.

https://doi.org/10.1016/j.jretconser.2017.02.010

Nor, K. M., & Pearson, J. M. (2008). An Exploratory Study Into The Adoption of Internet Banking in a Developing Country: Malaysia. Journal of Internet Commerce, 7(1), 29–73. https://doi.org/10.1080/15332860802004162

Oliver, R. L. (1981). Measurement and evaluation of satisfaction processes in retail settings. Journal of Retailing, 57(3), 25–48.

Ovčjak, B., Heričko, M., & Polančič, G. (2015). Factors impacting the acceptance of mobile data services – A systematic literature review. Computers in Human Behavior, 53, 24–47. https://doi.org/10.1016/j.chb.2015.06.013

Pallant, J. (2011). SPSS survival manual: A step by step guide to data analysis using the SPSS program (4th ed.). Berkshire: Allen & Unwin.

Pavlou, & Fygenson. (2006). Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior. MIS Quarterly, 30(1), 115. https://doi.org/10.2307/25148720

Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model. International Journal of

Electronic Commerce, 7(3), 101–134.

https://doi.org/10.1080/10864415.2003.11044275

Phong, N. D., Khoi, N. H., & Le, A. N.-H. (2018). Factors affecting mobile shopping:

a Vietnamese perspective. Journal of Asian Business and Economic Studies,

(27)

87

25(2), 186–205. https://doi.org/10.1108/JABES-05-2018-0012

Pieri, M., & Diamantinir, D. (2010). Young people, elderly and ICT. Procedia - Social

and Behavioral Sciences, 2(2), 2422–2426.

https://doi.org/10.1016/j.sbspro.2010.03.348

Priya, R., Gandhi, A. V., & Shaikh, A. (2018). Mobile banking adoption in an emerging economy. Benchmarking: An International Journal, 25(2), 743–762.

https://doi.org/10.1108/BIJ-01-2016-0009

Ramayah, T. (2006). Interface Characteristics, Perceived Ease of Use and Intention to Use an Online Library in Malaysia. Information Development, 22(2), 123–133.

https://doi.org/10.1177/0266666906065575

Renny, Guritno, S., & Siringoringo, H. (2013). Perceived Usefulness, Ease of Use, and Attitude Towards Online Shopping Usefulness Towards Online Airlines Ticket Purchase. Procedia - Social and Behavioral Sciences, 81, 212–216.

https://doi.org/10.1016/j.sbspro.2013.06.415

Revels, J., Tojib, D., & Tsarenko, Y. (2010). Understanding consumer intention to use mobile services. Australasian Marketing Journal (AMJ), 18(2), 74–80.

https://doi.org/10.1016/j.ausmj.2010.02.002

Rouibah, K., Abbas, H., & Rouibah, S. (2011). Factors affecting camera mobile phone adoption before e-shopping in the Arab world. Technology in Society, 33(3–4), 271–283. https://doi.org/10.1016/j.techsoc.2011.10.001

San-Martín, S., Prodanova, J., & Jiménez, N. (2015). The impact of age in the generation of satisfaction and WOM in mobile shopping. Journal of Retailing

and Consumer Services, 23, 1–8.

https://doi.org/10.1016/j.jretconser.2014.11.001

Sanakulov, N., & Karjaluoto, H. (2015). Consumer adoption of mobile technologies:

(28)

88

a literature review. International Journal of Mobile Communications, 13(3), 244.

https://doi.org/10.1504/IJMC.2015.069120

Sapsford, R. (2007). Survey Research (2nd ed.). 2nd ed.

https://doi.org/10.4135/9780857024664

Sekaran, U. (2003). Research methods for business A Skill-Building Approach Fourth Edition Uma. In Journal of Chemical Information and Modeling (Vol. 53).

https://doi.org/10.1017/CBO9781107415324.004

Sekaran, U., & Bougie, R. (2016). Research Methods for Business: A Skill-Building Approach. West Sussex: John Wiley & Sons Ltd.

Seock, Y.-K., & Norton, M. J. T. (2008). College Students’ Perceived Attributes of Internet Websites and Online Shopping. College Student Journal, 42(1), 186–

198. Retrieved from

http://www.eric.ed.gov/ERICWebPortal/detail?accno=EJ816880

Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review.

Telematics and Informatics, 32(1), 129–142.

https://doi.org/10.1016/j.tele.2014.05.003

Shang, D., & Wu, W. (2017). Understanding mobile shopping consumers’

continuance intention. Industrial Management and Data Systems, 117(1), 213–

227. https://doi.org/10.1108/IMDS-02-2016-0052

Shih, Y., & Fan, S. (2013). Adoption of Instant Messaging By Travel Agency Workers in Taiwan : Integrating Technology Readiness with the Theory of Planned Behavior. International Journal of Business and Information, 8(1), 120–136.

Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom: Extending UTAUT with Innovativeness, Risk, and Trust. Psychology

(29)

89

& Marketing, 32(8), 860–873. https://doi.org/10.1002/mar.20823

Susan, D., & Holmes, J. G. (1991). The dynamics of interpersonal trust: Resolving uncertainty in the face of risk. Cooperation and Prosocial Behaviour, 190.

Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144–176.

Retrieved from http://www.jstor.org/stable/23011007

Thakur, R. (2018). The role of self-efficacy and customer satisfaction in driving loyalty to the mobile shopping application. International Journal of Retail and Distribution Management, 46(3), 283–303. https://doi.org/10.1108/IJRDM-11- 2016-0214

Um, N.-H. (2019). Antecedents and Consequences of Consumers’ Attitude toward Social Commerce Sites. Journal of Promotion Management, 25(4), 500–519.

https://doi.org/10.1080/10496491.2018.1448324

Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412 Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology

Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Wei, T. T., Marthandan, G., Chong, A. Y., Ooi, K., & Arumugam, S. (2009). What drives Malaysian m‐commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370–388.

https://doi.org/10.1108/02635570910939399

Weiers, R. M. (2011). Introduction to Business Statistics. In South-Western Cengage Learning (Seventh). Ohio: Joe Sabatino.

(30)

90

Winchester, C. L., & Salji, M. (2016). Writing a literature review. Journal of Clinical Urology, 9(5), 308–312. https://doi.org/10.1177/2051415816650133

Wong, C. H., Lee, H. S., Lim, Y. H., Chua, B. H., & Tan, G. W. H. (2012). Predicting the consumers’ intention to adopt mobile-shopping: an emerging market perspective. International Journal of Network and Mobile Technologies 3, 3(April), 24–39.

Wu, W.-Y., & Ke, C.-C. (2015). An Online Shopping Behavior Model Integrating Personality Traits, Perceived Risk, and Technology Acceptance. Social Behavior and Personality: An International Journal, 43(1), 85–97.

https://doi.org/10.2224/sbp.2015.43.1.85

Yang, K. (2010). Determinants of US consumer mobile shopping services adoption:

implications for designing mobile shopping services. Journal of Consumer Marketing, 27(3), 262–270. https://doi.org/10.1108/07363761011038338

Yang, K. (2012). Journal of Retailing and Consumer Services Consumer technology traits in determining mobile shopping adoption : An application of the extended theory of planned behavior. Journal of Retailing and Consumer Services, 19(5), 484–491. https://doi.org/10.1016/j.jretconser.2012.06.003

Yang, K., & Kim, H. (2012). Mobile shopping motivation: an application of multiple discriminant analysis. International Journal of Retail & Distribution Management, 40(10), 778–789. https://doi.org/10.1108/09590551211263182 Yang, S. (2016). Role of transfer-based and performance-based cues on initial trust in

mobile shopping services: a cross-environment perspective. Information Systems and E-Business Management, 14(1), 47–70. https://doi.org/10.1007/s10257-015- 0274-7

Yen, D. C., Wu, C.-S., Cheng, F.-F., & Huang, Y.-W. (2010). Determinants of users’

(31)

91

intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906–915.

https://doi.org/10.1016/j.chb.2010.02.005

Zarmpou, T., Saprikis, V., Markos, A., & Vlachopoulou, M. (2012). Modeling users’

acceptance of mobile services. Electronic Commerce Research, 12(2), 225–248.

https://doi.org/10.1007/s10660-012-9092-x

Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902–1911. https://doi.org/10.1016/j.chb.2012.05.008

Zhang, R., Chen, J. Q., & Lee, C. J. (2013). Mobile Commerce and Consumer Privacy Concerns. Journal of Computer Information Systems, 53(4), 31–38.

https://doi.org/10.1080/08874417.2013.11645648

Zhao, L., Lu, Y., Zhang, L., & Chau, P. Y. K. (2012). Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model.

Decision Support Systems, 52(3), 645–656.

https://doi.org/10.1016/j.dss.2011.10.022

Zhou, T. (2013). An empirical examination of the determinants of mobile purchase.

Personal and Ubiquitous Computing, 17(1), 187–195.

https://doi.org/10.1007/s00779-011-0485-y

Zikmund, W., Babin, B., Carr, J., & Griffin, M. (2010). Business Research Methods Eight Edition. Cengage Learning., 668.

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APPENDIX A1: SET OF QUESTIONNAIRES

SCHOOL OF BUSINESS MANAGEMENT MASTER OF SCIENCE (MANAGEMENT) Dear Participants,

I am Mohamad Noor Farez bin Romli, a postgraduate student from University Utara Malaysia (UUM), Sintok, Kedah. I am soliciting your co-operation to participate in this research project entitled “Determinants of Mobile Shopping Behaviour: A Study Among Students in UUM”. The purpose of this study is to identify the factors that significantly influence mobile shopping behaviour in among university student.

I will be grateful if you could complete the enclosed questionnaire based on your genuine feelings. The success of this study is highly dependent on your valuable, sincere and honest response. The following questionnaire will require approximately 5 – 10 minutes to complete. For your information, your responses will be used for academic purposes only. All personal information shall be treated as strictly private and confidential.

Thank you for taking the time to assist me in my educational endeavours. The data collected will provide useful information in understanding the behaviour of this market segment. If you require additional information or have any enquiries pertaining to this study, please contact me at 017-5090 861 or email to sayafarez@gmail.com.

Thank you for your precious time and participation.

Mohamad Noor Farez bin Romli

Student,

MSc (Management), OYAGSB, UUM

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93 Section A: Demographic Question

Instruction: Please tick (✓) on the answer that you choose.

1. Age:

19 – 22 31 – 34

23 – 26 35 and above

27 – 30

2. Gender:

Male Female

3. Race:

Malay Indian

Chinese Others (Please specify):

________________

4. Education Level:

Bachelor’s degree PHD

Master’s degree

5. Monthly Personal Income (RM):

Less than 1000 3001 – 4000

1001 –2000 4001 – 5000

2001 – 3000 5001 and above

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94

Section B: Determinant of Mobile Shopping Behaviour

Instruction: Please circle the numbers that best indicate the extent of you agree or disagree with the following statements.

Strongly

Disagree Disagree Neither Agree

Nor Disagree Agree Strongly

Agree

1 2 3 4 5

Mobile Shopping Behaviour

No Statement

Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree

1. I intend to use mobile

shopping 1 2 3 4 5

2. I expect that I would use

mobile shopping 1 2 3 4 5

3. I plan to use mobile

shopping 1 2 3 4 5

4. I am ready to use mobile devices to make commercial

transactions 1 2 3 4 5

Perceived Ease of Use

No Statement

Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree

1. I find it easy to shop online via the internet using mobile

device. 1 2 3 4 5

2. I find it easy to learn to use mobile device to shop online for food and other items online.

1 2 3 4 5

3. I find it easy to shop online through mobile device and

using online payments. 1 2 3 4 5

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95 Perceived Usefulness

No Statement

Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree

1. Generally, I find mobile

shopping useful. 1 2 3 4 5

2. I find mobile shopping for food and other items useful

in my daily life. 1 2 3 4 5

3. I find online shopping via mobile device helps me in

my daily life. 1 2 3 4 5

Satisfaction

No Statement

Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree

1. My mobile shopping experience was very

pleasant. 1 2 3 4 5

2. My mobile shopping experience was absolutely delightful.

1 2 3 4 5

3. My mobile shopping experience made me feel

contented. 1 2 3 4 5

Attitude

No Statement

Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree

1. I like the idea of using

mobile shopping 1 2 3 4 5

2. Using mobile shopping is a

wise idea 1 2 3 4 5

3. Using mobile shopping is a

good idea 1 2 3 4 5

4. Using mobile shopping is a

positive idea 1 2 3 4 5

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96 Trust

No Statement

Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree

1. I believe payments made through mobile shopping channel will be processed securely

1 2 3 4 5

2. I believe transaction conducted through mobile

shopping will be secure 1 2 3 4 5

3. I believe my personal information will be kept confidential while using mobile shopping technology

1 2 3 4 5

Subjective Norm

No Statement

Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree

1. Relatives and friends

influence my decision to use

mobile shopping 1 2 3 4 5

2. Mass media influence my decision to use mobile

shopping 1 2 3 4 5

3. I would use mobile shopping more often if people in my community widely used the service

1 2 3 4 5

4. It is the current trend to use

mobile shopping 1 2 3 4 5

END OF QUESTIONNAIRE, THANK YOU

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97

APPENDIX A2: RESULT FROM IBM SPSS STATISTIC 26

1) Frequency Table for Demographic Profile

Statistics

Age Gender Race Education

Monthly Personal Income

N Valid 365 365 365 365 365

Missing 0 0 0 0 0

a) Age

Frequency Percent Valid Percent

Cumulative Percent

Valid 19-22 221 60.5 60.5 60.5

23-26 121 33.2 33.2 93.7

27-30 19 5.2 5.2 98.9

31-34 3 .8 .8 99.7

35 and above 1 .3 .3 100.0

Total 365 100.0 100.0

b) Gender

Frequency Percent Valid Percent

Cumulative Percent

Valid Male 86 23.6 23.6 23.6

Female 279 76.4 76.4 100.0

Total 365 100.0 100.0

c) Race

Frequency Percent Valid Percent

Cumulative Percent

Valid Malay 223 61.1 61.1 61.1

Chinese 96 26.3 26.3 87.4

Indian 25 6.8 6.8 94.2

Others 21 5.8 5.8 100.0

Total 365 100.0 100.0

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98

d) Education

Frequency Percent Valid Percent

Cumulative Percent

Valid Bachelor's degree 282 77.3 77.3 77.3

Master's degree 79 21.6 21.6 98.9

PHD 4 1.1 1.1 100.0

Total 365 100.0 100.0

e) Monthly Personal Income

Frequency Percent Valid Percent

Cumulative Percent

Valid less than 1000 332 91.0 91.0 91.0

1001-2000 27 7.4 7.4 98.4

2001-3000 2 .5 .5 98.9

3001-4000 1 .3 .3 99.2

4001-5000 2 .5 .5 99.7

5001 and above 1 .3 .3 100.0

Total 365 100.0 100.0

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99

2) Reliability Analysis for Each Independent and Dependent Variables

a) Mobile Shopping Behaviour

Case Processing Summary

N %

Cases Valid 365 100.0

Excludeda 0 .0

Total 365 100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics Cronbach's

Alpha N of Items

.886 4

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted I intend to use mobile

shopping

14.05 2.017 .793 .838

I expect that I would use mobile shopping

14.14 2.000 .841 .821

I plan to use mobile shopping

14.15 1.978 .732 .862

I am ready to use mobile devices to make

commercial transactions

14.26 2.156 .651 .891

(40)

100 b) Perceived Ease Of Use

Case Processing Summary

N %

Cases Valid 365 100.0

Excludeda 0 .0

Total 365 100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics Cronbach's

Alpha N of Items

.848 3

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted I find it easy to shop

online via the internet using mobile device.

8.76 1.039 .773 .732

I find it easy to learn to use mobile device to shop online for food and other items online.

8.88 1.073 .740 .764

I find it easy to shop online through mobile device and using online payments.

8.92 1.142 .638 .861

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101 c) Perceived Usefulness

Case Processing Summary

N %

Cases Valid 365 100.0

Excludeda 0 .0

Total 365 100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics Cronbach's

Alpha N of Items

.811 3

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted Generally, I find mobile

shopping useful.

8.41 1.341 .641 .762

I find mobile shopping for food and other items useful in my daily life.

8.79 1.116 .712 .686

I find online shopping via mobile device helps me in my daily life.

8.61 1.255 .635 .767

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102 d) Satisfaction

Case Processing Summary

N %

Cases Valid 365 100.0

Excludeda 0 .0

Total 365 100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics Cronbach's

Alpha N of Items

.776 3

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted My mobile shopping

experience was very pleasant.

8.71 1.002 .730 .558

My mobile shopping experience was absolutely delightful.

8.81 1.158 .655 .654

My mobile shopping experience made me feel contented.

8.77 1.238 .470 .851

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103 e) Attitude

Case Processing Summary

N %

Cases Valid 365 100.0

Excludeda 0 .0

Total 365 100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics Cronbach's

Alpha N of Items

.780 4

Item-Total Statistics Scale Mean if

Item Deleted

Scale Variance if Item Deleted

Corrected Item- Total Correlation

Cronbach's Alpha if Item Deleted I like the idea of

using mobile shopping

13.67 2.118 .522 .757

Using mobile shopping is a wise idea

13.90 1.768 .654 .689

Using mobile shopping is a good idea

13.88 1.747 .604 .717

Using mobile shopping is a positive idea

14.06 1.873 .567 .735

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104 f) Trust

Case Processing Summary

N %

Cases Valid 365 100.0

Excludeda 0 .0

Total 365 100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics Cronbach's

Alpha N of Items

.793 3

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted I believe payments made

through mobile shopping channel will be

processed securely

8.87 1.040 .763 .582

I believe transaction conducted through mobile shopping will be secure

8.91 1.086 .559 .811

I believe my personal information will be kept confidential while using mobile shopping technology

9.08 1.203 .602 .753

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105 g) Subjective Norm

Case Processing Summary

N %

Cases Valid 365 100.0

Excludeda 0 .0

Total 365 100.0

a. Listwise deletion based on all variables in the procedure.

Reliability Statistics Cronbach's

Alpha N of Items

.828 4

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item

Deleted Relatives and friends

influence my decision to use mobile shopping

13.11 2.151 .741 .742

Mass media influence my decision to use mobile shopping

13.21 2.333 .770 .735

I would use mobile shopping more often if people in my community widely used the service

13.23 2.575 .460 .875

It is the current trend to use mobile shopping

13.30 2.445 .691 .770

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106 3) Descriptive Statistic Analysis

Descriptive Statistics

N Mean Std. Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error

Mobile Shopping Behaviour 365 4.7164 .46705 -3.139 .128 15.182 .255

Perceived Ease of Use 365 4.4265 .50198 -1.158 .128 4.620 .255

Perceived Usefulness 365 4.3014 .53156 -1.248 .128 4.099 .255

Satisfaction 365 4.3826 .50456 -1.777 .128 8.518 .255

Attitude 365 4.6260 .44072 -3.153 .128 17.959 .255

Trust 365 4.4758 .50132 -1.538 .128 4.750 .255

Subjective Norm 365 4.4041 .49971 -1.575 .128 6.293 .255

Valid N (listwise) 365

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