HERD BEHAVIOR IN USING MOBILE PAYMENT WITH UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT2)

Authors

  • Rohmad Fuad Armansyah STIE Perbanas Surabaya

:

https://doi.org/10.9744/jmk.23.2.111-128

Keywords:

Herd behavior, behavioral intention, usage behavior, UTAUT2, mobile payment

Abstract

The development of information technology, followed by higher bank competition, has encouraged innovation in providing various alternatives for non-cash payments. Mobile payment services are increasingly popular with the increasing use of smartphones in the last five years in Indonesia, so that financial behavior has shifted its function towards digitalization. This study aims to discuss the financial herding behavior of using mobile payments using the Unified Theory of Acceptance and Use of Technology (UTAUT2) approach. The data used is respondent data as many as 355 Indonesian students who use the mobile payment application on a mobile device. The results obtained by the variable performance expectance and price value have a significant effect on the behavioral intention of users of mobile payment applications in Indonesia. Then behavioral intention was proven to significantly influence usage behavior and herd behavior as a form of financial behavior deviation which was proven to affect performance expectations.

References

Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology , 32 (4), 665–683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x

Al-Qeisi, KI (2009). Analyzing the Use of UTAUT Model in Explaining an Online Behavior: Internet Banking Adoption .

Al-Sobhi, F., Weerakkody, V., & Al-Shafi, S. (2010). The role of intermediaries in facilitating e-government diffusion in Saudi Arabia. Proceedings of the European, Mediterranean and Middle Eastern Conference on Information Systems: Global Information Systems Challenges in Management, EMCIS 2010 , 2010 , 1–17.

Alrashed, MA, & Alotaibi, MB (2017). The Role of Trust in the Acceptance of Government Cloud. International Journal of Technology Diffusion . https://doi.org/10.4018/ijtd.2017070101

Arenas-Gaitán, J., Peral-Peral, B., & Ramón-Jerónimo, MA (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce .

Armansyah, RF (2018). Herd Behavior and Indonesian Financial Crisis. Journal of Advanced Management Science , 6 (2), 86–89. https://doi.org/10.18178/joams.6.2.86-89

Banerjee, AV (1992). A Simple Model of Herd Behavior. The Quarterly Journal of Economics , 107 (3), 797–817. https://doi.org/10.2307/2118364

Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior . https://doi.org/10.1016/j.chb.2015.04.024

Barki, H., & Benbasat, I. (2010). Quo vadis, TAM? Journal of the Association for Information Systems . https://doi.org/http://aisel.aisnet.org/jais/vol8/iss4/16

Brown, SA, & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. In MIS Quarterly: Management Information Systems . https://doi.org/10.2307/25148690

Childers, TL, Carr, CL, Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing . https://doi.org/10.1016/S0022-4359(01)00056-2

Chinn, WW (1998). The Partial Least Squares Approach to Structural Equation Modeling. Modern Methods for Business Research .

Chong, AYL, Chan, FTS, & Ooi, KB (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems . https://doi.org/10.1016/j.dss.2011.12.001

Cugola, G., Ghezzi, C., Pinto, LS, & Tamburrelli, G. (2014). SelfMotion: A declarative approach for adaptive service-oriented mobile applications. Journal of Systems and Software . https://doi.org/10.1016/j.jss.2013.10.057

Davis, FD (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems . https://doi.org/10.2307/249008

Davis, FD (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies . https://doi.org/10.1006/imms.1993.1022

Dodds, WB (1991). In search of value: How price and store name information influence buyers' product perceptions. Journal of Consumer Marketing . https://doi.org/10.1108/07363769110034974

Duan, W., Gu, B., & Whinston, AB (2009). Informational cascades and software adoption on the Internet: An empirical investigation. MIS Quarterly: Management Information Systems . https://doi.org/10.2307/20650277

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. In Philosophy Rhetoric . https://doi.org/10.1002/cncr.26402

Gaitán, JA, Peral, BP, & Ramón, JMA (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce , 20 (1), 1–23.

Gefen, D., Srinivasan Rao, V., & Tractinsky, N. (2003). The conceptualization of trust, risk and their electronic commerce: The need for clarifications. Proceedings of the 36th Annual Hawaii International Conference on System Sciences, HICSS 2003 . https://doi.org/10.1109/HICSS.2003.1174442

Ghose, A., Goldfarb, A., & Han, SP (2011). How is the mobile internet different? Search costs and local activities. International Conference on Information Systems 2011, ICIS 2011 . https://doi.org/10.1287/isre.1120.0453

Ghozali, I. (2014). Structural Equation Modeling, an Alternative Method with Partial Least Square (PLS) . Diponegoro University Publishing Agency.

Groß, M. (2015). Exploring the acceptance of technology for mobile shopping: an empirical investigation among Smartphone users. International Review of Retail, Distribution and Consumer Research . https://doi.org/10.1080/09593969.2014.988280

Hew, JJ, Lee, VH, Ooi, KB, & Lin, B. (2016). Mobile social commerce: The booster for brand loyalty? Computers in Human Behavior . https://doi.org/10.1016/j.chb.2016.01.027

Hoehle, H., Aljafari, R., & Venkatesh, V. (2016). Leveraging Microsoft's mobile usability guidelines: Conceptualizing and developing scales for mobile application usability. International Journal of Human Computer Studies . https://doi.org/10.1016/j.ijhcs.2016.02.001

Hong, H., Cao, M., & Wang, GA (2017). The effects of network externalities and herding on user satisfaction with mobile social apps. Journal of Electronic Commerce Research .

Hox, JJ, Moerbeek, M., van de Schoot, R., Hox, JJ, Moerbeek, M., & van de Schoot, R. (2018). Multilevel Factor Models. In Multilevel Analysis . https://doi.org/10.4324/9781315650982-14

Ismarmiaty, I., & Etmy, D. (2018). Modified UTAUT2 Approach Model in Analysis of Acceptance and Use of E-Government Technology in West Nusa Tenggara. MATRIX: Journal of Management, Informatics Engineering and Computer Engineering . https://doi.org/10.30812/matrik.v18i1.347

Ismiyanti, F., & Armansyah, F. (2010). Motive of Going Public, Herding, Company Size, and Underpricing in the Indonesian Capital Market. Journal of Applied and Theory Management , 1 , 20–42.

Kim, SJ, Wang, RJH, & Malthouse, EC (2015). The Effects of Adopting and Using a Brand's Mobile Application on Customers' Subsequent Purchase Behavior. Journal of Interactive Marketing , 31 (August), 28–41. https://doi.org/10.1016/j.intmar.2015.05.004

Kuisma, T., Laentuken, T., & Hiltunen, M. (2007). Mapping the reasons for resistance to Internet banking: A means-end approach. International Journal of Information Management . https://doi.org/10.1016/j.ijinfomgt.2006.08.006

Legner, C., Urbach, N., & Nolte, C. (2016). Mobile business application for service and maintenance processes: Using ex post evaluation by end-users as input for iterative design. Information and Management . https://doi.org/10.1016/j.im.2016.03.001

Loehlin, JC, McCrae, RR, Costa, PT, & John, OP (1998). Heritabilities of Common and Measure-Specific Components of the Big Five Personality Factors. Journal of Research in Personality . https://doi.org/10.1006/jrpe.1998.2225

Lu, HP, & Su, PYJ (2009). Factors affecting purchase intention on mobile shopping web sites. Internet Research . https://doi.org/10.1108/10662240910981399

Lu, Y., Deng, Z., & Wang, B. (2010). Exploring factors affecting Chinese consumers' usage of short message service for personal communication. Information Systems Journal . https://doi.org/10.1111/j.1365-2575.2008.00312.x

Luo, X., Li, H., Zhang, J., & Shim, JP (2010). Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision Support Systems . https://doi.org/10.1016/j.dss.2010.02.008

Moore, GC, & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research . https://doi.org/10.1287/isre.2.3.192

Morosan, C., & DeFranco, A. (2016). Investigating American iPhone Users' Intentions to Use NFC Mobile Payments in Hotels. In Information and Communication Technologies in Tourism 2016 (pp. 427–440). https://doi.org/10.1007/978-3-319-28231-2_31

Nikou, S., & Bouwman, H. (2014). Ubiquitous use of mobile social network services. Telematics and Informatics . https://doi.org/10.1016/j.tele.2013.11.002

Nistor, N., Lerche, T., Weinberger, A., Ceobanu, C., & Heymann, O. (2014). Towards the integration of culture into the Unified Theory of Acceptance and Use of Technology. British Journal of Educational Technology . https://doi.org/10.1111/j.1467-8535.2012.01383.x

Obstfeld, M. (1986). Speculative Attack and the External Constraint in a Maximizing Model of the Balance of Payments. The Canadian Journal of Economics , 19 (1), 1–22. https://doi.org/10.3386/w1437

Oghuma, AP, Chang, Y., Libaque-Saenz, CF, Park, MC, & Rho, JJ (2015). Benefit-confirmation model for post-adoption behavior of mobile instant messaging applications: A comparative analysis of KakaoTalk and Joyn in Korea. Telecommunications Policy . https://doi.org/10.1016/j.telpol.2015.07.009

Osborne, J., Mueller, RO, & Hancock, GR (2011). Best Practices in Structural Equation Modeling. In Best Practices in Quantitative Methods . https://doi.org/10.4135/9781412995627.d38

Oye, ND, A. Iahad, N., & Ab.Rahim, N. (2014). The history of the UTAUT model and its impact on ICT acceptance and usage by academicians. Education and Information Technologies , 19 (1), 251–270. https://doi.org/10.1007/s10639-012-9189-9

Straub, ET (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research , 79 (2), 625–649. https://doi.org/10.3102/0034654308325896

Sugiyono. (2017). Quantitative Research Methods, Qualitative and R & D . Alfabeta, CV.

Sun, H. (2013). A longitudinal study of herd behavior in the adoption and continued use of technology. MIS Quarterly: Management Information Systems . https://doi.org/10.25300/MISQ/2013/37.4.02

Taiwo, AA, & Downe, AG (2013). The theory of user acceptance and use of technology (UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical and Applied Information Technology .

Taylor, DG, & Levin, M. (2014). Predicting mobile app usage for purchasing and information-sharing. International Journal of Retail and Distribution Management . https://doi.org/10.1108/IJRDM-11-2012-0108

Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly: Management Information Systems . https://doi.org/10.2307/249633

Teo, AC, Tan, GWH, Ooi, KB, Hew, TS, & Yew, KT (2015). The effects of convenience and speed in m-payment. Industrial Management and Data Systems , 115 (2), 311–331. https://doi.org/10.1108/IMDS-08-2014-0231

Thompson, RL, Higgins, CA, & Howell, JM (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly: Management Information Systems . https://doi.org/10.2307/249443

Thong, JYL, Hong, SJ, & Tam, KY (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human Computer Studies . https://doi.org/10.1016/j.ijhcs.2006.05.001

Van Der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly: Management Information Systems . https://doi.org/10.2307/25148660

Venkatesh, V., Morris, MG, Davis, GB, & Davis, FD (2003a). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems , 27 (3), 425–478. https://doi.org/10.2307/30036540

Venkatesh, V., Morris, MG, Davis, GB, & Davis, FD (2003b). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems , 27 (3), 425–478. https://doi.org/10.2307/30036540

Venkatesh, V., Thong, JYL, & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems , 36 (1), 157–178. https://doi.org/10.2307/41410412

Veríssimo, JMC (2016). Enablers and restrictors of mobile banking app use: A fuzzy set qualitative comparative analysis (fsQCA). Journal of Business Research . https://doi.org/10.1016/j.jbusres.2016.04.155

Wang, M., Cho, S., & Denton, T. (2017). The impact of personalization and compatibility with past experience on e-banking usage. International Journal of Bank Marketing , 35 (1), 45–55. https://doi.org/10.1108/IJBM-04-2015-0046

Wong, CH, Tan, GWH, Loke, SP, & Ooi, KB (2014). Mobile TV: A new form of entertainment? Industrial Management and Data Systems . https://doi.org/10.1108/IMDS-05-2014-0146

Yadav, M., Joshi, Y., & Rahman, Z. (2015). Mobile Social Media: The New Hybrid Element of Digital Marketing Communications. Procedia - Social and Behavioral Sciences . https://doi.org/10.1016/j.sbspro.2015.03.229

Yang, K., & Forney, JC (2013). The moderating role of consumer technology anxiety in mobile shopping adoption: Differential effects of facilitating conditions and social influences. Journal of Electronic Commerce Research .

Zeithaml, VA (1988). Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence. Journal of Marketing . https://doi.org/10.2307/1251446

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

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

Zuiderwijk, A., Janssen, M., & Dwivedi, YK (2015). Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Information Quarterly . https://doi.org/10.1016/j.giq.2015.09.005

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Published

2022-01-25

How to Cite

Armansyah, R. F. (2022). HERD BEHAVIOR IN USING MOBILE PAYMENT WITH UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT2). Jurnal Manajemen Dan Kewirausahaan, 23(2), 111-128. https://doi.org/10.9744/jmk.23.2.111-128