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Research Article

Multi-Dimensional Herding Behavior of Mobile Payment Users

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References

  • Nguyen L-T, Dwivedi YK, Tan G-H, Aw EC, Lo PS, Ooi KB. Unlocking pathways to mobile payment satisfaction and commitment. J Comput Inf Syst. 2023;63(4):998–1015. doi:10.1080/08874417.2022.2119444.
  • Oh O, Gupta P, Agarwal M, Rao H. ICT mediated rumor beliefs and resulting user actions during a community crisis. Gov Inf Quartely. 2018;35(2):243–58. doi:10.1016/j.giq.2018.03.006.
  • Dalton PS, Pamuk H, Ramrattan R, Uras B, Soest DV. Electronic payment technology and business finance: a randomized, controlled trial with mobile money. Manage Sci. 2023;70(4):2590–625. doi:10.1287/mnsc.2023.4821.
  • Hsieh P-J. Understanding medical consumers’ intentions to switch from cash payment to medical mobile payment: a perspective of technology migration. Technol Forecast Soc. 2021;173:121074. doi:10.1016/j.techfore.2021.121074.
  • The Hindu. The cyber threat to mobile banking. Tratto da The Hindu; 2022 Aug 28. https://www.thehindu.com/sci-tech/technology/the-cyber-threat-to-mobile-banking/article65821978.ece.
  • KPMG. Impact of COVID-19 on digital payments in India. Tratto da. 2020. https://assets.kpmg.com/content/dam/kpmg/in/pdf/2020/08/impacting-digital-payments-in-india.pdf.
  • Bhattacherjee A. Understanding information systems continuance: an expectation-confirmation model. Manag Inf Syst Res Cent. 2001;25(3):351–70. doi:10.2307/3250921.
  • Chen X, Li S. Understanding continuance intention of mobile payment services: an empirical study. J Comput Inf Syst. 2017;57(4):287–98. doi:10.1080/08874417.2016.1180649.
  • Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quartely. 1989;13(3):319–40. doi:10.2307/249008.
  • Venkatesh V, Morris MG, Davis G, Davis FD. User acceptance of information technology: toward a unified view. MIS Quartely. 2003;27(3):425–75. doi:10.2307/30036540.
  • Gao L, Waechter KA. Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation. Inf Syst Front. 2017;19(3):525–48. doi:10.1007/s10796-015-9611-0.
  • Yang S, Lu Y, Gupta S, Cao Y, Zhang R. Mobile payment services adoption across time: an empirical study of the effects of behavioral beliefs, social influences, and personal traits. Comput Human Behav. 2012;28(1):129–42. doi:10.1016/j.chb.2011.08.019.
  • Banerjee AV. A simple model of herd behavior. Q J Econ. 1992;107(3):797–817. doi:10.2307/2118364.
  • Sun H. A longitudinal study of herd behavior in the adoption and continued use of technology. MIS Quartely. 2013;37(4):1013–41. doi:10.25300/MISQ/2013/37.4.02.
  • Bikchandani S, Sharma S. Herd behavior in financial markets. IMF Staff Papers. 2000;47(3):279–310. doi:10.2307/3867650.
  • Ariely D, Norton MI. How actions create – not just reveal – preferences. Trends Cogn Sci. 2008;12(1):13–16. doi:10.1016/j.tics.2007.10.008.
  • Pal A, Herath T, De’ R, Rao H. Why do people use mobile payment technologies and why would they continue? An examination and implications from India. Res Policy. 2021;50(6):104228. doi:10.1016/j.respol.2021.104228.
  • Zhou T, Lu Y, Wang B. Integrating TTF and UTAUT to explain mobile banking user adoption. Comput Human Behav. 2010;26(4):760–67. doi:10.1016/j.chb.2010.01.013.
  • de Luna IR, Liébana-Cabanillas F, Sánchez-Fernández J, Muñoz-Leiva F. Mobile payment is not all the same: the adoption of mobile payment systems depending on the technology applied. Technol Forecast Soc. 2019;146:931–44. doi:10.1016/j.techfore.2018.09.018.
  • Deloitte. Survey of global investment and innovation incentives- India. Tratto da. 2020. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/Tax/us-tax-india-2020-survey-of-giii.pdf.
  • Türker C, Altay BC, Okumuş A. Understanding user acceptance of QR code mobile payment systems in Turkey: an extended TAM. Technol Forecast Soc. 2022;184:121968. doi:10.1016/j.techfore.2022.121968.
  • Tang J-W, Tsai P-H. Exploring critical determinants influencing businesses’ continuous usage of mobile payment in post-pandemic era: based on the UTAUT2 perspective. Technol Soc. 2024;77:102554. doi:10.1016/j.techsoc.2024.102554.
  • Darban M, Polites GL. Why is it hard to fight herding? The roles of user and technology attributes. ACM SIGMIS Database Database Adv Inf Syst. 2020;51(4):93–122. doi:10.1145/3433148.3433154.
  • Hung S-W, Cheng M-J, Tung Y-J. Following the herd? An empirical investigation into the adoption of mobile payment systems. Int J Bank Mark. 2024; doi:10.1108/IJBM-03-2023-0195.
  • Spyrou S. Herding in financial markets: a review of the literature. Rev Behavioral Finance. 2014;5(2):175–94. doi:10.1108/RBF-02-2013-0009.
  • Darban M, Kim M, Koksal A. When the technology abandonment intentions remitted: the case of herd behavior. Inf Technol Manag. 2021;22(3):163–78. doi:10.1007/s10799-021-00329-5.
  • Duan W, Gu B, Whinston AB. Informational cascades and software adoption on the internet: an empirical investigation. Mis Q. 2009;33(1):23–48. doi:10.2307/20650277.
  • Erjavec J, Manfreda A. Online shopping adoption during COVID-19 and social isolation: extending the UTAUT model with herd behavior. J Retail Consum Serv. 2022;65:102867. doi:10.1016/j.jretconser.2021.102867.
  • Yang Q, Lee YC. The effect of live streaming commerce quality on customers’ purchase intention: extending the elaboration likelihood model with herd behaviour. Behav Inf Technol. 2023; 1–22. doi:10.1080/0144929X.2023.2295032.
  • Mattke J, Maier C, Reis L, Weitzel T. Herd behavior in social media: the role of Facebook likes, strength of ties, and expertise. Inf Manag. 2020;57(8):103370. doi:10.1016/j.im.2020.103370.
  • Vedadi A, Warkentin M. Can secure behaviors Be contagious? A two-stage investigation of the influence of herd behavior on security decisions. J Assoc Inf Syst. 2020;21(2):428–59. doi:10.17705/1jais.00607.
  • Duane A, O’Reilly P, Andreev P. Realising M-Payments: modelling consumers’ willingness to M-pay using smart phones. Behav Inf Technol. 2014;33(4):318–34. doi:10.1080/0144929X.2012.745608.
  • Shin DH. Towards an understanding of the consumer acceptance of mobile wallet. Comput Human Behav. 2009;25(6):1343–54. doi:10.1016/j.chb.2009.06.001.
  • Marakas GM, Yi MY, Johnson RD. The multilevel and multifaceted character of computer self-efficacy: toward clarification of the construct and an integrative framework for research. Inf System Res. 1998;9(2):126–63. doi:10.1287/isre.9.2.126.
  • Zhou T, Lu Y, Wang B. Examining online consumers’initial trust building from an elaboration likelihood model perspective. Inf Syst Front. 2016;18(2):265–75. doi:10.1007/s10796-014-9530-5.
  • Simon HA. A behavioral model of rational choice. Q J Econ. 1955;69(1):99–118. doi:10.2307/1884852.
  • Hong H, Cao M, Wang GA. The effects of network externalities and herding on user satisfaction with mobile social apps. J Electron Commerce Res. 2017;18(1):18.
  • Li X, Kauffman RJ, Feifei Y, Zhang Y. Externalities, incentives and strategic complementarities: understanding herd behavior in it adoption. Inf Syst E-Bus Manag. 2014;12(3):443–64. doi:10.1007/s10257-013-0231-2.
  • Hertwig R, Pachur T. Heuristics, history of. Int Encycl Soc Behav Sci. 2015;10:829–35.
  • Bikhchandani S, Hirshleifer D, Welch I. A theory of fads, fashion, custom, and cultural change as informational cascades. J Polit Econ. 1992;100(5):992–1026. doi:10.1086/261849.
  • Rahayu S, Rohman A, Harto P. Herding behavior model in investment decision on emerging markets: experimental in Indonesia. J Asian Finance Econ Bus. 2021;8(1):053–059.
  • Galariotis EC, Krokida SI, Spyrou SI. Bond market investor herding: evidence from the European financial crisis. Int Rev Financ Anal. 2016;48:367–75. doi:10.1016/j.irfa.2015.01.001.
  • Walden EA, Browne GJ. Sequential adoption theory: a theory for understanding herding behavior in early adoption of novel technologies. J Assoc Inf System. 2009;10(1):1. doi:10.17705/1jais.00181.
  • Lu N, Guo X, Zhang J, Chen G, Zhang N. Understanding the continued use of intra-organizational blogs: an adaptive habituation model. Comput Human Behav. 2015;50:57–65. doi:10.1016/j.chb.2015.03.070.
  • Kang W, Shao B, Chen H. What influences users’ continuance intention of internet wealth management services? A perspective from network externalities and herding. Electron Commer Res. 2022;24(1):1–34. doi:10.1007/s10660-022-09580-6.
  • Zhang CB, Li YN, Wu B, Li DJ. How WeChat can retain users: roles of network externalities, social interaction ties, and perceived values in building continuance intention. Comput Human Behav. 2017;69(1):284–93. doi:10.1016/j.chb.2016.11.069.
  • Zhou T, Lu Y. Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Comput Human Behav. 2011;27(2):883–89. doi:10.1016/j.chb.2010.11.013.
  • Lu HP, Lin LY. Factors influencing online auction sellers’ intention to pay: an empirical study integrating network externalities with perceived value. J Electron Com. 2012;13(3):238–54.
  • Chiu CM, Cheng HL, Huang HY, Chen C-F. Exploring individuals’ subjective well-being and loyalty towards social network sites from the perspective of network externalities: the Facebook case. Int J Inf Manag. 2013;33(3):539–52. doi:10.1016/j.ijinfomgt.2013.01.007.
  • Kahneman D, Tversky A, Slovic P. Judgment under uncertainty: heuristics and biases. Cambridge University Press; 1982. doi:10.1017/CBO9780511809477.
  • Raafat RM, Chater N, Frith C. Herding in humans. Trends Cogn Sci. 2009;13(10):420–28. doi:10.1016/j.tics.2009.08.002.
  • Goyal P, Gupta P, Yadav V. Antecedents to heuristics: decoding the role of herding and prospect theory for Indian millennial investors. Rev Behav Finance. 2023;15(1):79–102. doi:10.1108/RBF-04-2021-0073.
  • Mussweiler, T Strack, F. Comparing Is Believing: A Selective Accessibility Model of Judgmental Anchoring. Eur Rev Social Psychol. 1999;10(1):135–167. doi:10.1080/14792779943000044.
  • Gurdgiev C, O’ Loughlin D. Herding and anchoring in cryptocurrency markets: investor reaction to fear and uncertainty. J Behav Exp Finance. 2020;25:100271. doi:10.1016/j.jbef.2020.100271.
  • Kauffman RJ, Li X. Payoff externalities, informational cascades and managerial incentives: a theoretical framework for it adoption herding. Atlanta (GA): INFORMS Conference on IS and Technology; 2003.
  • Yang HL, Lin SL. The reasons why elderly mobile users adopt ubiquitous mobile social service. Comput Human Behav. 2019;93:62–75. doi:10.1016/j.chb.2018.12.005.
  • ET Now Digital. UPI ‘tap and pay’ from January? Cool feature to tap your phone and make payments! How does it work? 2023 Dec 31. https://www.etnownews.com/personal-finance/upi-tap-and-pay-from-january-cool-feature-to-tap-your-phone-and-make-payments-how-does-it-work-article-106421663.
  • Singh N, Sinha N, Liébana-Cabanillas FJ. Determining factors in the adoption and recommendation of mobile wallet services in India: analysis of the effect of innovativeness, stress to use and social influence. Int J Inf Manag. 2020;50:191–205. doi:10.1016/j.ijinfomgt.2019.05.022.
  • Chan FK, Thong JY, Venkatesh V, Brown SA, Hu P-H, Tam KY. Modeling citizen satisfaction with mandatory adoption of an e-government technology. J Assoc Inf Syst. 2010;11(10):519–49. doi:10.17705/1jais.00239.
  • Parker WD, Prechter RR. Herding: an interdisciplinary integrative review from a socionomic perspective. SSRN Electron J. 2005; Available at SSRN, 2009898. doi:10.2139/ssrn.2009898.
  • Rafdinal W, Senalasari W. Predicting the adoption of mobile payment applications during the COVID-19 pandemic. Int J Bank Mark. 2021;39(6):984–1002. doi:10.1108/IJBM-10-2020-0532.
  • Talwar S, Talwar M, Kaur P, Singh G, Dhir A. Why have consumers opposed, postponed, and rejected innovations during a pandemic? A study of mobile payment innovations. Australas J Inf Syst. 2021;25. doi:10.3127/ajis.v25i0.3201.
  • Hirshleifer D, Lourie B, Teoh SH. Decision fatigue and heuristic analyst forecasts. J Financ Econ. 2019;133(1):83–98. doi:10.1016/j.jfineco.2019.01.005.
  • Gong X, Cheung CM, Liu S, Zhang KZ, Lee MK. Battles of mobile payment networks: the impacts of network structures, technology complementarities and institutional mechanisms on consumer loyalty. Inf System J. 2021;32(4):696–728. doi:10.1111/isj.12366.
  • Gong X, Zhang KZ, Chen C, Cheung CM, Lee MK. What drives self-disclosure in mobile payment applications? The effect of privacy assurance approaches, network externality, and technology complementarity. Inform Technol Peopl. 2019;33(4):1174–213. doi:10.1108/ITP-03-2018-0132.
  • Qasim A, Abu-Shanab E. Drivers of mobile payment acceptance: the impact of network externalities. Inf Syst Front. 2016;18(5):1021–34. doi:10.1007/s10796-015-9598-6.
  • Pal A, Herath T, De’ R, Rao H. Is the convenience worth the risk? An investigation of mobile payment usage. Inf Syst Front. 2021;23(4):941–61. doi:10.1007/s10796-020-10070-z.
  • Srivastava SC, Chandra S, Theng YL. Evaluating the role of trust in consumer adoption of mobile payment systems: an empirical analysis. Commun Assoc For Inf Syst. 2010;27:561–88. doi:10.17705/1CAIS.02729.
  • Tew H-T, Tan G-H, Loh X-M, Lee V-H, Lim W-L, Ooi K-B. Tapping the next purchase: embracing the wave of mobile payment. J Comput Inf Syst. 2022;62(3):527–35. doi:10.1080/08874417.2020.1858731.
  • Bandura A, Freeman W, Lightsey R. Self-efficacy: the exercise of control. J Cognit Psychotheraphy. 1999;13(2):158–66. doi:10.1891/0889-8391.13.2.158.
  • Ajzen I, Fishbein M. Theory of reasoned action-theory of planned behavior. Univ South Fla. 1998;2007:67–98.
  • Bhattacherjee A, Perols J, Sanford C. Information technology continuance: a theoretic extension and empirical test. J Comput Inf Syst. 2008;49(1):17–26. doi:10.1080/08874417.2008.11645302.
  • Al-Debei MM, Al-Lozi E, Papazafeiropoulou A. Why people keep coming back to Facebook: explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decis Support Syst. 2013;49(1):43–54. doi:10.1016/j.dss.2012.12.032.
  • Franque FB, Oliveira T, Tam C, Santini FD. A meta-analysis of the quantitative studies in continuance intention to use an information system. Internet Res. 2021;31(1):123–58. doi:10.1108/INTR-03-2019-0103.
  • Chen YL, Hao Kuo M, Yi Wu S, Tang K. Discovering Recency, Frequency, and Monetary (RFM) sequential patterns from customers’ purchasing data. Electron Commer Res Appl. 2009;8(5):241–51. doi:10.1016/j.elerap.2009.03.002.
  • Pandey V, Ansari S. Impact of game design elements on actual usage vs future use intentions of mobile payment app users: a motivation based approach. Inf Syst Front. 2023; 1–27. doi:10.1007/s10796-023-10433-2.
  • Fishbein M. A theory of reasoned action: some applications and implications. Nebr Symp Motiv. 1979;27:65–116.
  • Triandis H. Values, attitudes, and interpersonal behavior. Nebr Symp Motiv. 1979;27:195–259.
  • Limayem M, Hirt SG, Cheung CM. How habit limits the predictive power of intention: the case of information systems continuance. MIS Q. 2007;31(4):705–37. doi:10.2307/25148817.
  • Kim C, Mirusmonov M, Lee I. An empirical examination of factors influencing the intention to use mobile payment. Comput Human Behav. 2010;26(3):310–22. doi:10.1016/j.chb.2009.10.013.
  • Hair JF, Ringle CM, Sarstedt M. PLS SEM: indeed a silver bullet. J Mark Theory Pract. 2011;19(2):139–52. doi:10.2753/MTP1069-6679190202.
  • Minhas A. Number of e-payment users in India as of October 2022, by gender. Tratto il giorno. 2023 [accessed 2023 Jul 5]. da. https://www.statista.com/statistics/1105601/india-number-of-e-payment-users-by-gender/.
  • Government of India. C-08: educational level by age and sex for population age 7 and above (total), India - 2011. 2023 [accessed 2023 Jul 5]. https://censusindia.gov.in/nada/index.php/catalog/44790.
  • bankbazar. Income tax slabs for FY 2023-24. 2023 [accessed 2023 Jul 5]. https://www.bankbazaar.com/tax/income-tax-slabs.html.
  • Ringle C, Wende S, Becker J. SmartPLS 3. Bönningstedt: SmartPLS; 2015 [accessed 2016 Jul 15].
  • Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur Bus Rev. 2019;31(1):2–24. doi:10.1108/EBR-11-2018-0203.
  • Fronell C, Larcker DF. Structural equation models with unobservable variables and measurement error: algebra and statistics. Am Mark Assoc. 1981;18(3):382–88. doi:10.1177/002224378101800313.
  • Cenfetelli RT, Bassellier G. Interpretation of formative measurement in information systems research. Mis Q. 2009;33(4):689–707. doi:10.2307/20650323.
  • Diamantopoulos A, Winklhofer H. Index construction with formative indicators: an alternative to scale development. J Mark Res. 2001;38(2):269–77. doi:10.1509/jmkr.38.2.269.18845.
  • Podsakoff PM, Mackenzie SB, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879–903. doi:10.1037/0021-9010.88.5.879.
  • Knock N. Common method bias in PLS-SEM: a full collinearity assessment approach. Int J E-Collab. 2015;11(4):10. doi:10.4018/ijec.2015100101.
  • Simmering MJ, Fuller CM, Richardson HA, Ocal Y, Atinc GM. Marker variable choice, reporting, and interpretation in the detection of common method variance: a review and demonstration. Organ Res Method. 2015;18(3):473–511. doi:10.1177/1094428114560023.
  • Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. 1988. p. 567. doi:10.4324/9780203771587.
  • Liébana-Cabanillas F, Sánchez-Fernández J, Muñoz-Leiva F. Antecedents of the adoption of the new mobile payment systems: the moderating effect of age. Comput Human Behav. 2014;35:464–78. doi:10.1016/j.chb.2014.03.022.
  • Kalinić Z, Liébana-Cabanillas FJ, Muñoz-Leiva F, Marinković V. The moderating impact of gender on the acceptance of peer-to-peer mobile payment systems. Int J Bank Mark. 2020;38(1):138–58. doi:10.1108/IJBM-01-2019-0012.
  • Jia L, Song X, Hall D. Influence of habits on mobile payment acceptance: an ecosystem perspective. Inf Syst Front. 2022;24:1–20.
  • de Guinea AO, Markus M. Why break the habit of a lifetime? Rethinking the roles of intention, habit, and emotion in continuing information technology use. MIS Q. 2009;33(3):433–44. doi:10.2307/20650303.
  • Upadhyay N, Upadhyay S, Abed SS, Dwivedi YK. Consumer adoption of mobile payment services during COVID-19: extending meta-UTAUT with perceived severity and self-efficacy. Int J Bank Mark. 2022;40(5):960–91. doi:10.1108/IJBM-06-2021-0262.
  • Patil P, Tamilmani K, Rana NP, Raghavan V. Understanding consumer adoption of mobile payment in India: extending meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. Int J Inf Manag. 2020;54:102144. doi:10.1016/j.ijinfomgt.2020.102144.
  • Novianggie V, Asandimitra N. The influence of behavioral bias, cognitive bias, and emotional bias on investment decision for college students with financial literacy as the moderating variable. Int J Academic Res Acc Finance Manag Sci. 2019;9(2):92–107.
  • Aryan A, Gera I. MeitY calls for survey to gauge popularity of UPI. The Economics Times. Trattoda. 2023. https://economictimes.indiatimes.com/industry/banking/finance/banking/meity-calls-for-survey-to-gauge-popularity-of-upi/articleshow/104293478.cms?from=mdr.
  • Das Gupta S. 5G speeds up, gender gap in mobile money awareness still loading: report. New Delhi: Business Standard. Tratto da; 2024 https://www.business-standard.com/industry/news/5g-speeds-up-gender-gap-in-mobile-money-awareness-still-loading-report-124032601028_1.html.
  • Mundada M. India’s digital transformation: a tale of transformation, tenacity and technology. FirstPost. Tratto da; 2024. https://www.firstpost.com/business/indias-digital-transformation-a-tale-of-transformation-tenacity-and-technology-13708052.html.

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