Factors Influencing Customer Acceptance Towards Electronic Wallets (E-Wallets) in Malaysia: Perceived Security as Focus Variable
Abstract
An electronic wallet is a form of online service that facilitates the electronic transfer of funds through the utilization of Internet connectivity. The utilization of electronic wallets has been expanded to encompass a wide range of online activities, including the facilitation of purchasing transactions through e-commerce platforms. From the data collected by the Statistica Research Department, from 2020 until May 2023, the number of e-commerce scams in Malaysia is 19, 252 cases. In addition, Malaysia saw a drastic increase in online scams over the last two years during the COVID-19 pandemic. According to the Royal Malaysia Police (PDRM) Commercial Crimes Investigation Department (CCID), a total of 71,833 scams, amounting to more than RM5.2 billion in losses, were reported from 2020 until May 2022. This situation creates a need to perform research that addresses this problem. Therefore, this study explored the perception of security factors linked to the acceptance of e-wallet usage among Malaysian customers. This study uses a quantitative research method with a questionnaire. The underpinning theory is the Technology Acceptance Model (TAM), which consists of two variables namely perceived usefulness (PU) and Perceived Ease of Use (PEOU). This theory is widening by including one additional variable, Perceived Security (PS). Then collected data were analysed using structural equation modeling. The population is Malaysian consumers who use e-wallet. The number of samples is 150 respondents. The finding shows all three variables are significant in increasing the adoption of e-wallets among Malaysian consumers. The findings of this study help e-wallet providers to develop better facilities to meet the customers’ expectations. At the same time, this study provides insight to the central bank to develop policy on the security aspects of e-wallet services. This study also exposes the public to financial literacy of security for e-wallet usage to prevent deceived by scam activities
References
Abdul Sathar, M.B., Rajagopalan, M., Naina, S.M. & Parayitam, S. (2023). A moderated-mediation model of perceived enjoyment, security and trust on customer satisfaction: evidence from banking industry in India. Journal of Asia Business Studies, 17(3), 656-679.
Abu Bakar, N., Rosbi, S. & Uzaki, K. (2020). E-Wallet Transactional Framework for Digital Economy: A Perspective from Islamic Financial Engineering. International Journal of Management Science and Business Administration, 6(3), 50-57.
Ajzen, I. and Fishbein, M. (1980) Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Amankwa, E., Amissah, G. & Okoampa-Larbi, R. (2023). Cashless economy – the nexus of COVID-19 and E-wallet usage intentions: a multi-group analysis between formal and informal sector workers in Ghana. Journal of Science and Technology Policy Management, 14(6), 1155-1183.
Anderson, J. C., Gerbing, D. W., & Hunter, J. E. (1987). On the Assessment of Unidimensional Measurement: Internal and External Consistency, and Overall Consistency Criteria. Journal of Marketing Research, 24(4), 432.
Andrew, J. V., Ambad, N. A., & Tan, K. E. (2019). A Model of Factors Influencing Consumers’
Intention to Use e-Wallet System in Malaysia: A Systematic Review. Malaysian
Journal of Business and Economics, 6(2).
Apiors, E.K. & Suzuki, A. (2018). Mobile money, individuals’ payments, remittances, and investments: evidence from the Ashanti region, Ghana. Sustainability, 10(5), 1-26.
Bandura, A. (1982). Self-Efficacy Mechanism in Human Agency. American Psychologist, 37, 122-147.
Bolodeoku, P. B., Igbinoba, E. E., Salau, P. O., Chukwudi, K. C., & Idia, S. E. (2022). Perceived usefulness of technology and multiple salient outcomes: the improbable case of oil and gas workers. Heliyon, 8(4), e09322.
Cameron, A. C., & Windmeijer, F. (1997). An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Econometrics, 77(2), 329–342.
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.
Cangur, S., & Ercan, I. (2015). Comparison of model fit indices used in structural equation modeling under multivariate normality. Journal of Modern Applied Statistical Methods, 14(1), 152–167.
Chen, C., & Tsai, J. (2019). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628–638.
Chen, C. (2023). Extending the Technology Acceptance Model: A new perspective on the adoption of blockchain technology. Human Behavior and Emerging Technologies, 2023, 1–14.
Chen, S., Li, S., & Li, C. (2012). Recent Related Research In Technology Acceptance Model: A Literature Review. Australian Journal of Business and Management Research, 01(09), 124–127.
Cheung, G. W., Cooper–Thomas, H. D., Lau, R. S., & Wang, L. C. (2023). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management.
Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ, 7, e623.
Choi, Y., & Sun, L. (2016). Reuse Intention of Third-Party Online Payments: A Focus on the
Sustainable Factors of Alipay. Sustainability, 8, 147.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly.
Fang, X., Chan, S., Brzezinski, J., & Xu, S. (2006). Moderating effects of task type on wireless technology acceptance. Journal of Management Information Systems, 22(3), 123–157.
Felix, & Wella. (2019). Does Generation X Intend to Use E-Wallet in Daily Transactions?
2019 4th Technology Innovation Management and Engineering Science International
Conference (TIMES-ICON).
Fishbein, M. and Ajzen, I. (1975) Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research. Addison-Wesley Publishing Co, Inc., Boston.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study - A case of China. Computers in Human Behavior, 53(December 2015), 249–262.
Hair, J. F., Black, W. C., Balin, B. j., & Anderson, R. E. (2010). Multivariate data analysis. Maxwell Macmillan International Editions.
Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121
Hair, J. F., Babin, B. J., & Krey, N. (2017). Covariance-Based Structural Equation Modeling in theJournal of Advertising: Review and Recommendations. Journal of Advertising, 46(1), 163–177.
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110.
Han, Y. S. (2020). Factors Influencing the Intention to Use the Media Platform with Blockchain Technology : Focus on ‘Technology Acceptance Model (TAM).’ Mi’dieo Jendeo & Munhwa, 35(2), 139–188.
Harris, J. E., & Gleason, P. (2022). Application of path analysis and structural equation modeling in nutrition and dietetics. Journal of the Academy of Nutrition and Dietetics, 122(11), 2023–2035.
Hayes, A. F., Montoya, A. K., & Rockwood, N. (2017). The Analysis of Mechanisms and Their Contingencies: PROCESS versus Structural Equation Modeling. Australasian Marketing Journal, 25(1), 76–81.
Hill, C. R., & Hughes, J. N. (2007). An examination of the convergent and discriminant validity of the Strengths and Difficulties Questionnaire. School Psychology Quarterly, 22(3), 380–406.
Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications. Sage Publications, Inc..
Hoyle, R. H. (2011). Structural equation modeling for social and personality psychology. Sage Publications Ltd.
Kaabachi, S., Mrad, S. & Petrescu, M. (2017). Consumer initial trust toward internet-only banks in France. International Journal of Bank Marketing, 35(6), 903-924.
Kaabachi, S., Ben Mrad, S. & Fiedler, A. (2020). The moderating effect of e-bank structure on French consumers’ trust. International Journal of Bank Marketing, 38(2), 501-528.
Kaur, S. & Arora, S. (2020). Role of perceived risk in online banking and its impact on behavioral intention: trust as a moderator. Journal of Asia Business Studies, 15(1), 1-30.
Khoon S. W. (2023). Malaysia's Digital Payments Revolution: The E-Wallet Impact on Consumer Behavior, available at: https://www.linkedin.com/pulse/malaysias-digital-payments-revolution-e-wallet-impact-wei-khoon-shoo retrieved on 24 November 2023
Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322.
Kim, M. J., Chung, N., & Lee, C. K. (2011). The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea. Tourism Management, 32(2), 256–265.
Kline, R.B. (2010) Principles and Practice of Structural Equation Modeling. The Guilford Press, New York.
Legris, P., Ingham, J. & Collerette, P. (2003), Why do people use information technology? A critical review of the technology acceptance model. Information & Management, Vol. 40, No.3, pp.191-204.
Linck, K., Pousttchi, K., & Wiedemann, D. G. (2006). Security Issues in Mobile Payment from the Customer Viewpoint. ECIS 2006 Proceedings
Lindsay, R., Jackson, T.W. & Cooke, L. (2011). Adapted technology acceptance model for mobile policing. Journal of Systems and Information Technology, 13(4), 389-407.
Maditinos, D., Chatzoudes, D. & Sarigiannidis, L. (2013). An examination of the critical factors affecting consumer acceptance of online banking: A focus on the dimensions of risk. Journal of Systems and Information Technology, 15(1), 97-116.
Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: a comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883.
Mansour, K. (2016). An analysis of business’ acceptance of internet banking: an integration of e-trust to the TAM. Journal of Business & Industrial Marketing, 31(8), 982-994.
Mohd Razif, N. N., Misiran, M., Sapiri, H., & Md Yusof, Z. (2020). Perceived risk for
acceptance of E-wallet platform in Malaysia among youth: Sem approach.
Management Research Journal, 9, 1- 24.
Mohsin Butt, M. & Aftab, M. (2013). Incorporating attitude towards Halal banking in an integrated service quality, satisfaction, trust and loyalty model in online Islamic banking context. International Journal of Bank Marketing, 31(1), 6-23.
Morgan, G. B., Hodge, K. J., Wells, K. E., & Watkins, M. W. (2015). Are fit indices biased in favor of Bi-Factor models in Cognitive Ability research?: A comparison of fit in correlated factors, Higher-Order, and Bi-Factor models via Monte Carlo Simulations. Journal of Intelligence, 3(1), 2–20.
Mueller, R. O., & Hancock, G. R. (2018). Structural equation modeling. In The reviewer’s guide to quantitative methods in the social sciences (pp. 445-456). Routledge.
Ooi, K. B., & Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation
using mobile users to explore smartphone credit card. Expert Systems with
Applications, 59, 33-46
Patel, A., Qi, W. & Wills, C. (2010). A review and future research directions of secure and trustworthy mobile agent‐based e‐marketplace systems. Information Management & Computer Security, 18(3), 144-161.
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H. & Pahnila, S. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research, 14(3), 224-235.
Punwatkar, S., & Verghese, M. (2018). Adaptation of e-Wallet Payment: An Empirical Study
on Consumers’ Adoption Behavior in Central India. International Journal of
Advanced in Management, Technology and Engineering Sciences, 1147–1156.
Rantung, H.M., Tumbuan, A.J.F.A. & Gunawan, E.M. (2020). The Determinants Influencing Behaviorial Intention to Use E-Wallet during Covid-19 Pandemic in Manado. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis Dan Akuntansi, 8(4), 352-360.
Revathy, C. & Balaji, P. (2020). Determinants of behavioural intention on E-wallet usage: an empirical examination in amid of Covid-19 lockdown period. International Journal of Management, 11(6), 92-104.
Robey, D. (1979). User Attitudes and Management Information System use. Academy of Management Journal, 22(3), 527–538.
Rod, M., Ashill, N.J., Shao, J. & Carruthers, J. (2009). An examination of the relationship between service quality dimensions, overall internet banking service quality and customer satisfaction: A New Zealand study. Marketing Intelligence & Planning, 27(1), 103-126.
Salem, M.Z., Baidoun, S. & Walsh, G. (2019). Factors affecting Palestinian customers’ use of online banking services. International Journal of Bank Marketing, 37(2), 426-451.
Segars, A. H. (1997). Assessing the unidimensionality of measurement: a paradigm and illustration within the context of information systems research. Omega, 25(1), 107–121.
Senali, M.G., Iranmanesh, M., Ismail, F.N., Abdul Rahim, N.F., Khoshkam, M. & Mirzaei, M. (2022). Determinants of intention to use e-wallet: personal innovativeness and propensity to trust as moderators. International Journal of Human–Computer Interaction, 1-13.
Serenko, A. (2008). A model of user adoption of interface agents for email notification. Interacting with Computers, Vol. 20, No. 4-5, 461-472.
Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4–11.
Sikdar, P. & Makkad, M. (2015). Online banking adoption: A factor validation and satisfaction causation study in the context of Indian banking customers. International Journal of Bank Marketing, 33(6), 760-785.
Su, Y., & Li, M. (2021). Applying Technology Acceptance Model in Online Entrepreneurship Education for new entrepreneurs. Frontiers in Psychology, 12.
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55.
Taylor, S. and Todd, P. (1995) Decomposition and Crossover Effects in the Theory of Planned Behavior: A Study of Consumer Adoption Intentions. International Journal of Research in Marketing, 12, 137-155.
Tsai, J. Y., Egelman, S., Cranor, L., & Acquisti, A. (2011). The effect of online privacy information on purchasing behavior: An experimental study. Information Systems Research, 22(2), 254–268.
Undale, S., Kulkarni, A. & Patil, H. (2021). Perceived e-wallet security: impact of COVID-19 pandemic. Vilakshan - XIMB Journal of Management, 18(1), 89-104.
Yadama, G. N., & Pandey, S. (1995). Effect of sample size on Goodness-Fit of-Fit indices in structural equation models. Journal of Social Service Research, 20(3–4), 49–70.
Yee‐Loong Chong, A., Ooi, K., Lin, B. and Tan, B. (2010). Online banking adoption: an empirical analysis. International Journal of Bank Marketing, 28(4), 267-287.
Abu Bakar, N., Rosbi, S. & Uzaki, K. (2020). E-Wallet Transactional Framework for Digital Economy: A Perspective from Islamic Financial Engineering. International Journal of Management Science and Business Administration, 6(3), 50-57.
Ajzen, I. and Fishbein, M. (1980) Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Amankwa, E., Amissah, G. & Okoampa-Larbi, R. (2023). Cashless economy – the nexus of COVID-19 and E-wallet usage intentions: a multi-group analysis between formal and informal sector workers in Ghana. Journal of Science and Technology Policy Management, 14(6), 1155-1183.
Anderson, J. C., Gerbing, D. W., & Hunter, J. E. (1987). On the Assessment of Unidimensional Measurement: Internal and External Consistency, and Overall Consistency Criteria. Journal of Marketing Research, 24(4), 432.
Andrew, J. V., Ambad, N. A., & Tan, K. E. (2019). A Model of Factors Influencing Consumers’
Intention to Use e-Wallet System in Malaysia: A Systematic Review. Malaysian
Journal of Business and Economics, 6(2).
Apiors, E.K. & Suzuki, A. (2018). Mobile money, individuals’ payments, remittances, and investments: evidence from the Ashanti region, Ghana. Sustainability, 10(5), 1-26.
Bandura, A. (1982). Self-Efficacy Mechanism in Human Agency. American Psychologist, 37, 122-147.
Bolodeoku, P. B., Igbinoba, E. E., Salau, P. O., Chukwudi, K. C., & Idia, S. E. (2022). Perceived usefulness of technology and multiple salient outcomes: the improbable case of oil and gas workers. Heliyon, 8(4), e09322.
Cameron, A. C., & Windmeijer, F. (1997). An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Econometrics, 77(2), 329–342.
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.
Cangur, S., & Ercan, I. (2015). Comparison of model fit indices used in structural equation modeling under multivariate normality. Journal of Modern Applied Statistical Methods, 14(1), 152–167.
Chen, C., & Tsai, J. (2019). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628–638.
Chen, C. (2023). Extending the Technology Acceptance Model: A new perspective on the adoption of blockchain technology. Human Behavior and Emerging Technologies, 2023, 1–14.
Chen, S., Li, S., & Li, C. (2012). Recent Related Research In Technology Acceptance Model: A Literature Review. Australian Journal of Business and Management Research, 01(09), 124–127.
Cheung, G. W., Cooper–Thomas, H. D., Lau, R. S., & Wang, L. C. (2023). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management.
Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ, 7, e623.
Choi, Y., & Sun, L. (2016). Reuse Intention of Third-Party Online Payments: A Focus on the
Sustainable Factors of Alipay. Sustainability, 8, 147.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly.
Fang, X., Chan, S., Brzezinski, J., & Xu, S. (2006). Moderating effects of task type on wireless technology acceptance. Journal of Management Information Systems, 22(3), 123–157.
Felix, & Wella. (2019). Does Generation X Intend to Use E-Wallet in Daily Transactions?
2019 4th Technology Innovation Management and Engineering Science International
Conference (TIMES-ICON).
Fishbein, M. and Ajzen, I. (1975) Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research. Addison-Wesley Publishing Co, Inc., Boston.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study - A case of China. Computers in Human Behavior, 53(December 2015), 249–262.
Hair, J. F., Black, W. C., Balin, B. j., & Anderson, R. E. (2010). Multivariate data analysis. Maxwell Macmillan International Editions.
Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121
Hair, J. F., Babin, B. J., & Krey, N. (2017). Covariance-Based Structural Equation Modeling in theJournal of Advertising: Review and Recommendations. Journal of Advertising, 46(1), 163–177.
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110.
Han, Y. S. (2020). Factors Influencing the Intention to Use the Media Platform with Blockchain Technology : Focus on ‘Technology Acceptance Model (TAM).’ Mi’dieo Jendeo & Munhwa, 35(2), 139–188.
Harris, J. E., & Gleason, P. (2022). Application of path analysis and structural equation modeling in nutrition and dietetics. Journal of the Academy of Nutrition and Dietetics, 122(11), 2023–2035.
Hayes, A. F., Montoya, A. K., & Rockwood, N. (2017). The Analysis of Mechanisms and Their Contingencies: PROCESS versus Structural Equation Modeling. Australasian Marketing Journal, 25(1), 76–81.
Hill, C. R., & Hughes, J. N. (2007). An examination of the convergent and discriminant validity of the Strengths and Difficulties Questionnaire. School Psychology Quarterly, 22(3), 380–406.
Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications. Sage Publications, Inc..
Hoyle, R. H. (2011). Structural equation modeling for social and personality psychology. Sage Publications Ltd.
Kaabachi, S., Mrad, S. & Petrescu, M. (2017). Consumer initial trust toward internet-only banks in France. International Journal of Bank Marketing, 35(6), 903-924.
Kaabachi, S., Ben Mrad, S. & Fiedler, A. (2020). The moderating effect of e-bank structure on French consumers’ trust. International Journal of Bank Marketing, 38(2), 501-528.
Kaur, S. & Arora, S. (2020). Role of perceived risk in online banking and its impact on behavioral intention: trust as a moderator. Journal of Asia Business Studies, 15(1), 1-30.
Khoon S. W. (2023). Malaysia's Digital Payments Revolution: The E-Wallet Impact on Consumer Behavior, available at: https://www.linkedin.com/pulse/malaysias-digital-payments-revolution-e-wallet-impact-wei-khoon-shoo retrieved on 24 November 2023
Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322.
Kim, M. J., Chung, N., & Lee, C. K. (2011). The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea. Tourism Management, 32(2), 256–265.
Kline, R.B. (2010) Principles and Practice of Structural Equation Modeling. The Guilford Press, New York.
Legris, P., Ingham, J. & Collerette, P. (2003), Why do people use information technology? A critical review of the technology acceptance model. Information & Management, Vol. 40, No.3, pp.191-204.
Linck, K., Pousttchi, K., & Wiedemann, D. G. (2006). Security Issues in Mobile Payment from the Customer Viewpoint. ECIS 2006 Proceedings
Lindsay, R., Jackson, T.W. & Cooke, L. (2011). Adapted technology acceptance model for mobile policing. Journal of Systems and Information Technology, 13(4), 389-407.
Maditinos, D., Chatzoudes, D. & Sarigiannidis, L. (2013). An examination of the critical factors affecting consumer acceptance of online banking: A focus on the dimensions of risk. Journal of Systems and Information Technology, 15(1), 97-116.
Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: a comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883.
Mansour, K. (2016). An analysis of business’ acceptance of internet banking: an integration of e-trust to the TAM. Journal of Business & Industrial Marketing, 31(8), 982-994.
Mohd Razif, N. N., Misiran, M., Sapiri, H., & Md Yusof, Z. (2020). Perceived risk for
acceptance of E-wallet platform in Malaysia among youth: Sem approach.
Management Research Journal, 9, 1- 24.
Mohsin Butt, M. & Aftab, M. (2013). Incorporating attitude towards Halal banking in an integrated service quality, satisfaction, trust and loyalty model in online Islamic banking context. International Journal of Bank Marketing, 31(1), 6-23.
Morgan, G. B., Hodge, K. J., Wells, K. E., & Watkins, M. W. (2015). Are fit indices biased in favor of Bi-Factor models in Cognitive Ability research?: A comparison of fit in correlated factors, Higher-Order, and Bi-Factor models via Monte Carlo Simulations. Journal of Intelligence, 3(1), 2–20.
Mueller, R. O., & Hancock, G. R. (2018). Structural equation modeling. In The reviewer’s guide to quantitative methods in the social sciences (pp. 445-456). Routledge.
Ooi, K. B., & Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation
using mobile users to explore smartphone credit card. Expert Systems with
Applications, 59, 33-46
Patel, A., Qi, W. & Wills, C. (2010). A review and future research directions of secure and trustworthy mobile agent‐based e‐marketplace systems. Information Management & Computer Security, 18(3), 144-161.
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H. & Pahnila, S. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research, 14(3), 224-235.
Punwatkar, S., & Verghese, M. (2018). Adaptation of e-Wallet Payment: An Empirical Study
on Consumers’ Adoption Behavior in Central India. International Journal of
Advanced in Management, Technology and Engineering Sciences, 1147–1156.
Rantung, H.M., Tumbuan, A.J.F.A. & Gunawan, E.M. (2020). The Determinants Influencing Behaviorial Intention to Use E-Wallet during Covid-19 Pandemic in Manado. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis Dan Akuntansi, 8(4), 352-360.
Revathy, C. & Balaji, P. (2020). Determinants of behavioural intention on E-wallet usage: an empirical examination in amid of Covid-19 lockdown period. International Journal of Management, 11(6), 92-104.
Robey, D. (1979). User Attitudes and Management Information System use. Academy of Management Journal, 22(3), 527–538.
Rod, M., Ashill, N.J., Shao, J. & Carruthers, J. (2009). An examination of the relationship between service quality dimensions, overall internet banking service quality and customer satisfaction: A New Zealand study. Marketing Intelligence & Planning, 27(1), 103-126.
Salem, M.Z., Baidoun, S. & Walsh, G. (2019). Factors affecting Palestinian customers’ use of online banking services. International Journal of Bank Marketing, 37(2), 426-451.
Segars, A. H. (1997). Assessing the unidimensionality of measurement: a paradigm and illustration within the context of information systems research. Omega, 25(1), 107–121.
Senali, M.G., Iranmanesh, M., Ismail, F.N., Abdul Rahim, N.F., Khoshkam, M. & Mirzaei, M. (2022). Determinants of intention to use e-wallet: personal innovativeness and propensity to trust as moderators. International Journal of Human–Computer Interaction, 1-13.
Serenko, A. (2008). A model of user adoption of interface agents for email notification. Interacting with Computers, Vol. 20, No. 4-5, 461-472.
Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4–11.
Sikdar, P. & Makkad, M. (2015). Online banking adoption: A factor validation and satisfaction causation study in the context of Indian banking customers. International Journal of Bank Marketing, 33(6), 760-785.
Su, Y., & Li, M. (2021). Applying Technology Acceptance Model in Online Entrepreneurship Education for new entrepreneurs. Frontiers in Psychology, 12.
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55.
Taylor, S. and Todd, P. (1995) Decomposition and Crossover Effects in the Theory of Planned Behavior: A Study of Consumer Adoption Intentions. International Journal of Research in Marketing, 12, 137-155.
Tsai, J. Y., Egelman, S., Cranor, L., & Acquisti, A. (2011). The effect of online privacy information on purchasing behavior: An experimental study. Information Systems Research, 22(2), 254–268.
Undale, S., Kulkarni, A. & Patil, H. (2021). Perceived e-wallet security: impact of COVID-19 pandemic. Vilakshan - XIMB Journal of Management, 18(1), 89-104.
Yadama, G. N., & Pandey, S. (1995). Effect of sample size on Goodness-Fit of-Fit indices in structural equation models. Journal of Social Service Research, 20(3–4), 49–70.
Yee‐Loong Chong, A., Ooi, K., Lin, B. and Tan, B. (2010). Online banking adoption: an empirical analysis. International Journal of Bank Marketing, 28(4), 267-287.
Published
2024-03-31
How to Cite
ABU BAKAR, Nashirah et al.
Factors Influencing Customer Acceptance Towards Electronic Wallets (E-Wallets) in Malaysia: Perceived Security as Focus Variable.
International Journal of Business and Technology Management, [S.l.], v. 6, n. 1, p. 399-412, mar. 2024.
ISSN 2682-7646.
Available at: <https://myjms.mohe.gov.my/index.php/ijbtm/article/view/25630>. Date accessed: 16 sep. 2024.
Section
English Section
