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Influence of Habits on Mobile Payment Acceptance: An Ecosystem Perspective

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Abstract

With the increase in the use of various mobile devices, mobile payments have become a crucial driver for commerce success. However, the percentage of consumers who use or continue using mobile payments in the US is low. This study adopts information technology (IT) ecosystem view and transfer of learning theory and explores the effects of five types of technology use habits on consumers’ intention to continue using mobile payments. Results indicate that consumers’ online shopping, mobile service use, and cell phone use habits have a positive relationship with their mobile payment use habit, positively affecting their intention to continue using mobile payments. Theoretical and practical implications of the findings are presented.

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References

  • Aarts, H., & Dijksterhuis, A. (2000). Habits as knowledge structures: Automaticity in goal-directed behavior. Journal of Personality and Social Psychology, 78(1), 53–63.

    Google Scholar 

  • Adomavicius, G., Bockstedt, J. C., & Gupta, A. (2012). Modeling supply-side dynamics of IT components, products, and infrastructure: An empirical analysis using vector autoregression. Information Systems Research, 23(2), 397–417.

    Google Scholar 

  • Adomavicius, G., Bockstedt, J. C., Gupta, A., & Kauffman, R. J. (2007). Technology roles and paths of influence in an ecosystem model of technology evolution. Information Technology Management, 8(2), 185–202.

    Google Scholar 

  • Adomavicius, G., Bockstedt, J. C., Gupta, A., & Kauffman, R. J. (2008a). Making sense of technology trends in the information technology landscape: A decision science approach. MIS Quarterly, 32(4), 779–809.

    Google Scholar 

  • Adomavicius, G., Bockstedt, J. C., Gupta, A., & Kauffman, R. J. (2008b). Understanding evolution in technology ecosystems. Communications of the ACM, 51(10), 117–122.

    Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Organization Behavior & Human Decision Processes, 50(2), 179–211.

    Google Scholar 

  • Ajzen, I. (2002). Residual effects of past on later behavior: Habituation and reasoned action perspectives. Personality Soc Psych. Rev, 6(2), 107–122.

    Google Scholar 

  • Aldrich, H. E., & Yang, T. (2014). How do entrepreneurs know what to do? Learning and organizing in new ventures. Journal of Evolutionary Economics, 24(1), 59–82.

    Google Scholar 

  • Andreev, P., Duane, A., O’Reilly, P. (2011). Conceptualizing consumer perceptions of contactless M-payments through smart phones. International Federation for Information Processing: IFIP WG8.2.

  • Au, Y. A., & Kauffman, R. J. (2008). The economics of Mobile payments: Understanding stakeholder issues for an emerging financial technology application. Electronic Commerce Research and Application, 7(2), 141–164.

    Google Scholar 

  • Baldwin, T. T., & Ford, J. K. (1988). Transfer of training: A review and directions for future research. Personnel Psychology, 41(1), 63–105.

    Google Scholar 

  • Basole, R. C. (2009). Visualization of Interfirm relations in a converging Mobile ecosystem. Journal of Information Technology, 24(2), 144–159.

    Google Scholar 

  • Beauchamp, M. B., & Ponder, N. (2010). Perceptions of retail convenience for in-store and online shoppers. Marketing Management Journal, 20(1), 49–65.

    Google Scholar 

  • Bem, D. (1972). Self-perception theory. New York: Academic Press.

    Google Scholar 

  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). Learning and transfer. In J. D. Bransford, A. L. Brown, & R. R. Cocking (Eds.), How people learn: Brain, mind, experience, and school (pp. 31–78). Washington, DC: National Academy Press.

    Google Scholar 

  • Butterfield, E. C., & Nelson, G. D. (1989). Theory and practice of teaching for transfer. Educational Technology, Research and Development, 37(3), 5–38.

    Google Scholar 

  • Byrnes, J. P. (1996). Cognitive development and learning in instructional contexts. Boston: Allyn & Bacon.

    Google Scholar 

  • Carter, J. (2019). More people browse on mobile but buy via desktop. Smart Insights. Retrieved from https://www.smartinsights.com/ecommerce/more-people-browse-on-mobile-but-buy-via-desktop/ on October 30, 2019.

  • Chandra, S., Srivastava, S. C., & Theng, Y. (2010). Evaluating the role of Trust in Consumer Adoption of Mobile payment systems: An empirical analysis. Communications of the Association for Information Systems, 27(1), 561–588.

    Google Scholar 

  • Cheung, C. M. K., & Limayen, M. (2005). The role of habit in information systems continuance: Examining the evolving relationship between intention and usage, Proceedings of the 26th International Conference on Information Systems, Las Vegas.

  • Chen, L., Baird, A., & Straub, D. (2019). Fostering participant health knowledge and attitudes: An econometric study of a chronic disease-focused online health community. Journal of Management Information Systems, 36(1), 194–229.

    Google Scholar 

  • Chen, X., & Li, S. (2016). Understanding continuance intention of Mobile payment services: An empirical study. Journal of Computer Information Systems, 57(4), 1–12.

    Google Scholar 

  • Chin, W. W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), 7–16.

    Google Scholar 

  • China business industry research institute. (2018). Three charts to understand China's ride-hailing market and urban penetration. Retrieved from https://www.askci.com/news/chanye/20180626/0925411125151.shtml/ on March 23, 2020.

  • Chiu, C., Hsu, M., Lai, H., & Chang, C. (2010). Exploring Online Repeat Purchase Intentions: The Role of Habit. Taipei: Proceeding of Pacific Asia Conference on Information Systems.

    Google Scholar 

  • Cronbach, L. J. (1971). Test Validation. In R. L. Thorndike (Ed.), Education measurement (pp. 443–507). Washington, D.C.: American Council on Education.

    Google Scholar 

  • Dabholkar, P. A., & Bagozzi, R. P. (2002). An Attitudinal Model of Technology-Based Self-Service: Moderating Effects of Consumer Traits and Situational Factors. Journal of the Academy of Marketing Science, 30(3), 184–201.

    Google Scholar 

  • Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14, 265–284.

    Google Scholar 

  • Dahlberg, T., Mallat, N., Ondrus, J., & Zmijewska, A. (2008). Past, present and future of Mobile payments research: A literature review. Electronic Commerce Research and Applications, 7(2), 165–181.

    Google Scholar 

  • Desse, J. (1958). Transfer of training: The psychology of learning. New York: McGraw-Hill.

    Google Scholar 

  • Eagly, A. H., & Chaiken, S. (1983). The psychology of attitudes. Fort Worth: Harcourt Brace Jovanovich.

    Google Scholar 

  • Eisend, M. (2019). Explaining Digital Piracy: A Meta-Analysis. Information Systems Research, 30(2), 636–664.

    Google Scholar 

  • Flinders, K.. (2015). PayPal buys mobile payments startup Paydiant. http://www.computerweekly.com/news/2240241647/PayPal-buys-mobile-payments-startup-Paydiant.

  • Ford, J. K., & Weissbein, D. A. (1997). Transfer of training: An updated review and analysis. Performance Improvement Quarterly, 10(2), 22–41.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Google Scholar 

  • Francisco, L. C., Francisco, M. L., & Juan, S. F. (2015). Payment systems in new electronic environments: Consumer behavior in payment systems via SMS. International Journal of Information Technology & Decision Making, 14(2), 421–449.

    Google Scholar 

  • Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of End User Computing, 15(3), 1–13.

    Google Scholar 

  • Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-graph: Tutorial and annotated example. Communications of the Association for Information Systems, 16(1), 91–109.

    Google Scholar 

  • Giovanis, A. N., Binioris, S., & Polychronopoulos, G. (2012). An Extension of TAM Model with IDT and Security/Privacy Risk in the 43 Adoption of Internet Banking Services in Greece. EuroMed Journal of Business, 7(1), 24–53.

    Google Scholar 

  • Gupta, S., & Kim, H. W. (2007). The moderating effect of transaction experience on the decision Calculus in online repurchase. International Journal of Electronic Commerce, 12(1), 127–158.

    Google Scholar 

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.

    Google Scholar 

  • Haskell, R. E. (2001). Transfer of learning: Cognition, instruction and reasoning. San Diego: Academic Press.

    Google Scholar 

  • Hoffman, D. L., & Novak, T. P. (1996). Marketing in Hypermedia Computer-Mediated Environments: Conceptual foundations. Journal of Marketing, 60(7), 50–68.

    Google Scholar 

  • Holton, E. F. (1996). The flawed four-level evaluation model. Human Resource Development Quarterly, 7(1), 5–21.

    Google Scholar 

  • Holton, E. F., Bates, R. A., Bookter, A. I., & Yamkovenko, B. V. (2007). Convergent and divergent validity of the learning transfer system inventory. Human Resource Development Quarterly, 18(3), 385–419.

    Google Scholar 

  • Holton, E. F., & Baldwin, T. T. (2003). Making transfer happen: An action perspective on learning transfer systems. Advances in Developing Human Resources, 8, 1–6.

    Google Scholar 

  • Holton, E. F., Bates, R. A., & Ruona, W. E. A. (2000). Development of a generalized learning transfer system inventory. Human Resource Development Quarterly, 11(4), 333–360.

    Google Scholar 

  • Hong, W., Thong, J. Y. L., Chasalow, L. C., & Dhillon, G. (2011). User acceptance of agile information systems: A model and empirical test. Journal of Management Information Systems, 28(1), 235–272.

    Google Scholar 

  • Hull, C. L. (1943). Principles of behavior: An introduction to behavior theory. New York: Appleton-Century-Crofts.

    Google Scholar 

  • Insight & Info Consulting Ltd. (2019). Analysis report of China's ride-hailing industry in 2019 - market status survey and development prospect forecast. Retrieved from https://wenku.baidu.com/view/5278d6b20d22590102020740be1e650e52eacfbb.html/ on March 23, 2020.

  • Jarvenpaa, S. L., & Lang, K. R. (2005). Managing the paradoxes of Mobile technology. Information Systems Management, 22(4), 7–23.

    Google Scholar 

  • Jia, L., Xue, G., Fu, Y., & Xu, L. (2018). Factors affecting consumers’ acceptance of e-commerce consumer credit service. International Journal of Information Management, 40, 103–110.

    Google Scholar 

  • Jiang, L., Yang, Z., & Jun, M. (2013). Measuring consumer perceptions of online shopping convenience. Journal of Service Management, 24(2), 191–214.

    Google Scholar 

  • Jin, C. (2013). The perspective of a revised TRAM on social capital building: The case of Facebook usage. Information & Management, 50, 162–168.

    Google Scholar 

  • Kalakota, R., & Robinson, M. (2001). M-business: The race to mobility. Boston: McGraw-Hill Trade.

    Google Scholar 

  • Khansa, L., Ma, X., Liginlal, D., & Kim, S. S. (2015). Understanding members’ active participation in online question-and-answer communities: A theory and empirical analysis. Journal of Management Information Systems, 32(2), 162–203.

    Google Scholar 

  • 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.

    Google Scholar 

  • Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying Postadoption phenomena. Management Science, 51(5), 741–755.

    Google Scholar 

  • Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Two competing perspectives on automatic use: A theoretical and empirical comparison. Information Systems Research, 16(4), 418–432.

    Google Scholar 

  • Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford Press.

    Google Scholar 

  • Kraut, R. E., Mukhopadhyay, T., Szczypula, J., Kiesler, S., & Scherlis, B. (1999). Information and communication: Alternative uses of the internet in households. Information Systems Research, 10(4), 287–303.

    Google Scholar 

  • Labrecque, J. S., & Wood, W. (2015). What measures of habit strength to use? Comment on Gardner (2015). Health Psychology Review, 9(3), 303–310.

    Google Scholar 

  • Labrecque, J., Wood, W., Neal, D., & Harrington, N. (2017). Habit slips: When consumers unintentionally resist new products. Journal of the Academy of Marketing Science, 45(1), 119–133.

    Google Scholar 

  • Lakshmanan, A., & Krishnan, H. S. (2018). The Aha! Experience: Insight and discontinuous learning in product usage. Journal of Marketing, 75(6), 105–123.

    Google Scholar 

  • Lankton, N. K., Wilson, E. V., & Mao, E. (2010). Antecedents and determinants of information technology habit. Information & Management, 47(5–6), 300–307.

    Google Scholar 

  • Lee, K., & Joshi, K. (2016). Examining the use of status quo Bias perspective in IS research: Need for Reconceptualizing and incorporating biases. Information Systems Journal, 27(6), 733–752.

    Google Scholar 

  • Lee, Y. E., & Benbasat, I. (2004). A framework for the study of customer Interface design for mobile commerce. International Journal of Electronic Commerce, 8(3), 79–102.

    Google Scholar 

  • Lim, D. H., & Johnson, S. D. (2002). Trainee perceptions of factors that influence learning transfer. International Journal of Training and Development, 6(1), 36–48.

    Google Scholar 

  • Limayem, M., Cheung, C. M. K., & Chan, G. W. W. (2003). Explaining information systems adoption and post-adoption. Seattle: Proceedings of the 24th International Conference on Information Systems.

    Google Scholar 

  • Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention. MIS Quarterly, 31(4), 705–737.

    Google Scholar 

  • Lin, C., Shih, H., & Sher, P. J. (2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Marketing, 24(7), 641–657.

    Google Scholar 

  • Lin, J., Lu, Y., Wang, B., & Wei, K. K. (2011). The role of Inter-Channel trust transfer in establishing Mobile commerce trust. Electronic Commerce Research and Applications, 10(6), 615–625.

    Google Scholar 

  • Liljander, V., Gillberg, F., Gummerus, J., & Riel, A. V. (2006). Technology readiness and the evaluation and adoption of self-service technologies. Journal of Retailing and Consumer Services, 13, 177–191.

    Google Scholar 

  • Lindbladh, E., & Lyttkens, C. H. (2002). Habit versus choice: The process of decision-making in health-related behavior. Social Science & Medicine, 55(3), 451–465.

    Google Scholar 

  • Lobato, J. (2006). Alternative perspectives on the transfer of learning: History, issues, and challenges for future research. Journal of the Learning Sciences, 15(4), 431–449.

    Google Scholar 

  • Lu, J., Wang, L., & Hayes, L. A. (2012). How do technology readiness, platform functionality and trust influence C2C user satisfaction? Journal of Electronic Commerce Research, 13(1), 50–69.

    Google Scholar 

  • Lu, Y., Yang, S., Chau, P. Y. K., & Cao, Y. (2011). Dynamics between the trust transfer process and intention to use Mobile payment services: A cross-environment perspective. Information & Management, 48(8), 393–403.

    Google Scholar 

  • Macaulay, C., & Cree, V. E. (1999). Transfer of learning: Concept and process. Social Work Education, 18(2), 183–193.

    Google Scholar 

  • 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.

    Google Scholar 

  • McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for E-commerce: An integrative typology. Information Systems Research, 13(3), 334–359.

    Google Scholar 

  • McKnight, D. H., Cummings, L. L., & Chervany, N. L. (1998). Initial trust formation in new organizational relationships. Academy of Management Review, 23(3), 473–490.

    Google Scholar 

  • Murray, K. B., Liang, J., & Haubl, G. (2010). ACT2.0: The next generation of assistive consumer technology research. Internet Research, 20(3), 232–254.

    Google Scholar 

  • Nambisan, S. (2013). Information technology and product/service innovation: A brief assessment and some suggestions for future research. Journal of the Association for Information Systems, 14(4), 215–226.

    Google Scholar 

  • Newell, A., & Simon, H. A. (1972). Human Problem Solving. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Novak, T. P., Hoffman, D. L., & Ynng, Y. F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22–42.

    Google Scholar 

  • Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124(1), 54–74.

    Google Scholar 

  • Pavlou, P. A., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31(1), 105–136.

    Google Scholar 

  • Perkins, D. N., & Salomon, G. (1992). Transfer of learning. In T.N., Postlethwaite, & T., Husen (Eds.), International Encyclopedia of Education (2nd ed.). Oxford: Pergamon Press.

    Google Scholar 

  • Podsakoff, P., MacKenzie, S., Lee, J., & Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Google Scholar 

  • Polites, G. L., & Karahanna, E. (2012). Shackled to the status quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Quarterly, 36(1), 21–42.

    Google Scholar 

  • Polites, G. L., & Karahanna, E. (2013). The Embeddedness of information systems habits in organizational and individual level routines: Development and disruption. MIS Quarterly, 37(1), 221–246.

    Google Scholar 

  • Pousttchi, K., Schiessler, M., & Wiedemann, D. G. (2009). Proposing a comprehensive framework for analysis and engineering of Mobile payment business models. Information Systems and e-Business Management, 7(3), 363–393.

    Google Scholar 

  • Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 M3 (Beta). Hamburg: SmartPLS.

    Google Scholar 

  • Rogers, E. M. (2003). Diffusion of Innovations (5th). New York: Free Press.

    Google Scholar 

  • Royer, J. M. (1979). Theories of the transfer of learning. Educational Psychologist, 14(1), 53–69.

    Google Scholar 

  • Setterstrom, A. J., Pearson, J. M., & Orwig, R. A. (2013). Web-enabled wireless technology: An exploratory study of adoption and continued use intention. Behavior & Information Technology, 32(11), 1139–1154.

    Google Scholar 

  • Shah, D., Kumar, V., & Kim, K. H. (2014). Managing customer profits: The power of habits. Journal of Marketing Research, 51(6), 726–741.

    Google Scholar 

  • Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142.

    Google Scholar 

  • Sripalawat, J., Thongmak, M., & Ngramyarn, A. (2011). M-banking in metropolitan Bangkok and a comparison with other countries. Journal of Computer Information Systems, 51(3), 67–76.

    Google Scholar 

  • Swanson, B. E. (1994). Information systems innovation among organizations. Management Science, 40(9), 1069–1092.

    Google Scholar 

  • Thorndike, E. L. (1924). Mental discipline in high school studies. Journal of Educational Psychology, 15(1), 83–98.

    Google Scholar 

  • Thorndike, E. L., & Woodworth, R. S. (1901). The influence of improvement in one mental function upon the efficiency of other functions. Psychological Review, 8(6), 247–261.

    Google Scholar 

  • Trafimow, D. (2000). Habit as both a direct cause of intention to use a condom and as a moderator of the attitude-intention and subjective norm-intention relations. Psychology and Health, 15(3), 383–393.

    Google Scholar 

  • Venkatesh, V., Ramesh, V., & Massey, A. P. (2003). Understanding usability in Mobile commerce. Communications of the ACM, 46(12), 53–56.

    Google Scholar 

  • Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

    Google Scholar 

  • Verplanken, B., Aarts, H., & van Knippenberg, A. D. (1997). Habit, information acquisition, and the process of making travel mode choices. European Journal of Social Psychology, 27(5), 539–560.

    Google Scholar 

  • Verplanken, B., & Orbell, S. (2003). Reflections on past behaviour: A self-report index of habit strength. Journal of Applied Social Psychology, 33(6), 1313–1330.

    Google Scholar 

  • Vitak, J., Crouse, J., & LaRose, R. (2011). Personal internet use at work: Understanding cyberslacking. Computers in Human Behavior, 27(5), 1751–1759.

    Google Scholar 

  • Wilson, E. V., Mao, E., & Lankton, N. K. (2010). The distinct roles of prior IT use and habit strength in predicting continued sporadic use of IT. Communications of the Association for Information Systems, 27(1), 185–206.

    Google Scholar 

  • Wood, W., Quinn, J. M., & Kashy, D. A. (2002). Habits in everyday life: Thoughts, emotions, and action. Journal of Personal Social Psychology, 83(6), 1281–1297.

    Google Scholar 

  • Wu, F., & Yen, Y. (2014). Factors influencing the use of Mobile financial services: Evidence from Taiwan. Modern Economy, 5(13), 1221–1228.

    Google Scholar 

  • Xu, J., Benbasat, I., & Cenfetelli, R. T. (2014). The nature and consequences of trade-off transparency in the context of recommendation agents. MIS Quarterly, 38(2), 379–406.

    Google Scholar 

  • Yang, K. (2010). The effects of technology self-efficacy and innovativeness on consumer Mobile data service adoption between American and Korean consumers. Journal of International Consumer Marketing, 22(2), 117–127.

    Google Scholar 

  • Yorks, L., O’Neil, J., Marsick, V. J., Lamm, S., Kolodny, R., & Nilson, G. (1998). Transfer of learning from an action reflection learning program. Performance Improvement Quarterly, 11(1), 59–73.

    Google Scholar 

  • 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.

    Google Scholar 

  • Zhou, T., & Lu, Y. (2011). Examining Postadoption usage of Mobile services from a dual perspective of enablers and inhibitors. International Journal of Human-Computer Interaction, 27(12), 1177–1191.

    Google Scholar 

  • Zhou, T. (2014). An empirical examination of initial Trust in Mobile Payment. Wireless Personal Communication, 77(2), 1519–1531.

    Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant ID: NSFC 71602009); Beijing Institute of Technology Basic Research Fund Program (Grant ID: 20172142005); Special Fund for Joint Development Program of Beijing Municipal Commission of Education; Beijing Institute of Technology Research Fund Program for Young Scholars.

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Appendix

Appendix

1.1 Items

Online Shopping Habit: Adopted from Setterstrom et al. (2013)

  1. 1.

    Shopping online has become automatic to me.

  2. 2.

    Shopping online is natural to me.

  3. 3.

    When faced with a particular need, shopping online is an obvious choice to me.

Mobile Service Use habit: Adopted from Setterstrom et al. (2013)

  1. 1.

    Using mobile services other than mobile payments has become automatic to me.

  2. 2.

    Using mobile services other than mobile payments is natural to me.

  3. 3.

    When faced with a particular need, using mobile services other than mobile payments is an obvious choice to me.

Cell Phone Use habit: Adopted from Setterstrom et al. (2013)

  1. 1.

    Using cellphones has become automatic to me.

  2. 2.

    Using cellphones is natural to me.

  3. 3.

    When faced with a particular need, using a cellphone is an obvious choice to me.

Computer Use habit: Adopted from Setterstrom et al. (2013)

  1. 1.

    Using computers has become automatic to me.

  2. 2.

    Using computers is natural to me.

  3. 3.

    When faced with a particular need, using a computer is an obvious choice to me.

Mobile Payment Use habit: Adopted from Setterstrom et al. (2013)

  1. 1.

    Using mobile payments has become automatic to me.

  2. 2.

    Using mobile payments is natural to me.

  3. 3.

    When faced with a particular need, using mobile payments is an obvious choice to me.

Intention to continued use: Adopted from Venkatesh et al. (2012)

  1. 1.

    I intend to continue using mobile payments in the future.

  2. 2.

    I predict that I will continue to use mobile payments frequently in the future.

  3. 3.

    I will strongly recommend that others use mobile payments.

Perceived Ease of Use: Adopted from Lin et al. (2011)

  1. 1.

    Learning to use mobile payments is easy for me.

  2. 2.

    Becoming skillful at using mobile payments is easy for me.

  3. 3.

    Overall, I find mobile payments easy to use.

Perceived Usefulness: Adopted from Kim et al. (2010)

  1. 1.

    Using mobile payments enables me to pay quickly.

  2. 2.

    Using mobile payments makes it easy for me to conduct transactions.

  3. 3.

    I find mobile payments a useful possibility for making payments.

Technology Readiness—discomfort: Adopted from Jin (2013)

  1. 1.

    I sometimes think that mobile payments are not designed for use by ordinary people.

  2. 2.

    Mobile payments have health risks that are not discovered until after people have used them.

  3. 3.

    Mobile payments have safety risks that are not discovered until after people have used them.

  4. 4.

    Mobile payments consistently appear to fail at the worst possible time.

Technology Readiness—insecurity: Adopted from Lu et al. (2012)

  1. 1.

    I can never be sure that the financial information I provided with my cellphone actually reaches the right place.

  2. 2.

    I consider it unsafe to perform any kind of payments with my cellphone.

  3. 3.

    I am concern that financial information I send with my cellphone will be seen by other people.

Technology Readiness—optimism: Adopted from Liljander et al. (2006)

  1. 1.

    Using mobile payments allows me to have better control on my daily life.

  2. 2.

    Using mobile payments gives me freedom of mobility.

  3. 3.

    Products and services that use mobile payment technology are more convenient to use than those without mobile payment technology.

Technology Readiness—innovativeness: Adopted from Liljander et al. (2006)

  1. 1.

    Other people seek advice from me on new information technologies.

  2. 2.

    In general, I am among the first in my circle of friends to acquire new IT when it is available.

  3. 3.

    I can usually determine new information technologies without help from others.

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Jia, L., Song, X. & Hall, D. Influence of Habits on Mobile Payment Acceptance: An Ecosystem Perspective. Inf Syst Front 24, 247–266 (2022). https://doi.org/10.1007/s10796-020-10077-6

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