Investors’ intention to use mobile investment: an extended mobile technology acceptance model with personal factors and perceived reputation

References

• Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). Developing a general extended UTAUT model for M-payment adoption. Technology in Society, 62, 1. https://doi.org/10.1016/j.techsoc.2020.101293
   Web of Science ®Google Scholar
• Anwar, R.H., Zaki, S., Thurasamy, R., & Memon, N. (2021). Trait emotional intelligence and ESL teacher effectiveness: Assessing the moderating effect of demographic variables using PLS-MGA. Journal of Legal, Ethical and Regulatory Issues, 24(6), 1–17. https://www.abacademies.org/articles/trait-emotional-intelligence-and-esl-teacher-effectiveness-assessing-the-moderating-effect-of-demographic-variables-using-plsmga-12943.html
   Google Scholar
• Ariffin, S.K., Rahman, M.F.R.A., Muhammad, A.M., & Zhang, Q. (2021). Understanding the consumer’s intention to use the e-wallet services. Spanish Journal of Marketing ESIC, 25(3), 446–461. https://doi.org/10.1108/SJME-07-2021-0138
   Google Scholar
• Bailey, A.A., Pentina, I., Mishra, A.S., Mimoun, M.S.B. (2020). Exploring factors influencing US millennial consumers’ use of tap-and-go payment technology. International Review of Retail, Distribution and Consumer Research, 30(2), 143–163. https://doi.org/10.1080/09593969.2019.1667854
   Web of Science ®Google Scholar
• Boateng, H., Okoe, A.F., & Omane, A.B. (2016). Does personal innovativeness moderate the effect of irritation on consumers’ attitudes towards mobile advertising? Journal of Direct, Data and Digital Marketing Practice, 17, 201–210. https://doi.org/10.1057/dddmp.2015.53
   Google Scholar
• Byrne, B.M. (2013). Structural equation modelling with AMOS: Basic concepts, applications and programming. Routledge.
   Google Scholar
• Chandra, S., Srivastava, S.C., & Theng, Y.L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis. Communications of the Association for Information System, 27(1), 561–588. https://doi.org/10.17705/1CAIS.02729
   Google Scholar
• Chong, L-L., Ong, H-B., & Tan, S-H. (2021). Acceptability of mobile stock trading application: A study of young investors in Malaysia. Technology in Society, 64, 101497. https://doi.org/10.1016/j.techsoc.2020.101497
   Web of Science ®Google Scholar
• Cohen, E. (1988). Authenticity and commoditisation in tourism. Annals of Tourism Research, 15(3), 371–386. https://doi.org/10.1016/0160-7383(88)90028-X
   Web of Science ®Google Scholar
• Dahlberg, T., Mallat, N., & Oorni, A. (2003). Trust enhanced technology acceptance model consumer acceptance of mobile payment solutions: Tentative evidence. Stockholm Mobility Roundtable, 22(1), 145.
   Google Scholar
• Dhiman, N., Arora, N., Dogra, N., & Gupta, A. (2020). Consumer adoption of smartphone fitness apps” an extended UTAUT2 perspective. Journal of Indian Business Research, 12(3), 363–388. https://doi.org/10.1108/JIBR-05-2018-0158
   Web of Science ®Google Scholar
• Elche, D., Ruiz-Palomino, P., & Linuesa-Langreo, J. (2020). Servant leadership and organizational citizenship behavior: The mediating effect of empathy and service climate. International Journal of Contemporary Hospitality Management, 32(6), 2035–2053. https://doi.org/10.1108/IJCHM-05-2019-0501
   Web of Science ®Google Scholar
• Fan, L. (2022). Mobile investment technology adoption among investors. International Journal of Bank Marketing, 40(1), 50–67. https://doi.org/10.1108/IJBM-11-2020-0551
   Web of Science ®Google Scholar
• Flavian, C., Guinaliu, M., & Lu, Y. (2020). Mobile payments adoption – Introducing mindfulness to better understand consumer behavior. International Journal of Bank Marketing, 38(7), 1575–1599. https://doi.org/10.1108/IJBM-01-2020-0039
   Web of Science ®Google Scholar
• Hair, J.F., Hult, G.T.M., Ringle, C.M. & Sarstedt, M. (2017). A primer on partial least squares structural equation modelling (PLS-SEM) (2nd ed.). Sage.
   Google Scholar
• Hair, J.F., Risher, J.J., Sarstedt, M., & Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
   Web of Science ®Google Scholar
• Handarkho, Y.D., & Harjoseputro, Y. (2020). Intention to adopt mobile payment in physical stores: Individual switching behaviour perspective based on Push-Pull-Mooring (PPM) theory. Journal of Enterprise Information Management, 33(2): 285–308. https://doi.org/10.1108/JEIM-06-2019-0179
   Web of Science ®Google Scholar
• Henseler, J., Ringle, C.M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modelling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Hikida, R., & Perry, J. (2020). FinTech trends in the United States: Implications for household finance. Public Policy Review, Policy Research Institute, Ministry of Finance Japan, 16(4), 1–32. https://www.mof.go.jp/english/pri/publication/pp_review/ppr16_04_03.pdf
   Google Scholar
• Ho, J.C., Wu, C-G., Lee, C-S., & Pham, T-T. (2020). Factors affecting the behavioural intention to adopt mobile banking: An international comparison. Technology in Society, 63, 101360. https://doi.org/10.1016/j.techsoc.2020.101360
   Web of Science ®Google Scholar
• Hsiao, C-H., & Tang, K-Y. (2015). Investigating factors affecting the acceptance of self-service technology in libraries: The moderating effect of gender. Library Hi Tech, 33(1), 114–133. https://doi.org/10.1108/LHT-09-2014-0087
   Web of Science ®Google Scholar
• Kline, R.B. (2011). Principles and practice of structural equation modeling (2nd ed.). Guilford Press.
   Google Scholar
• Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
   Web of Science ®Google Scholar
• Lau. A.J., Tan, G. W-H., Loh, X-M., Leong, L-Y., Lee, V-H., & Ooi, K-B. (2021). On the way: Hailing a taxi with a smartphone? A hybrid SEM-neural network approach. Machine Learning with Applications, 4, 100034. https://doi.org/10.1016/j.mlwa.2021.100034
   Google Scholar
• Lew, S., Tan, G.W-H., Loh, X-M., Hew, J-J., & Ooi, K-B. (2021). The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model. Technology in Society, 63, 101430. https://doi.org/10.1016/j.techsoc.2020.101430
   Web of Science ®Google Scholar
• Lin, C-P., & Huang, H-Y. (2021). Modeling investment intention in online P2P lending: An elaboration likelihood perspective. International Journal of Bank Marketing, 39(7), 1134–1149. https://doi.org/10.1108/IJBM-12-2020-0594
   Web of Science ®Google Scholar
• Ling, P-S, Chin, C-H, Yi, J., & Wong, W.P.M. (2023). Green consumption behaviour among Generation Z college students in China: The moderating role of government support. Young Consumers. Advance online publication. https://doi.org/10.1108/YC-01-2022-1443
   PubMed Web of Science ®Google Scholar
• Loh, X-M., Lee, V-H., Tan, G.W-H., Hew, J-J., & Ooi, K-B. (2022). Towards a cashless society: The imminent role of wearable technology. Journal of Computer Information Systems, 62(1), 39–49. https://doi.org/10.1080/08874417.2019.1688733
   Web of Science ®Google Scholar
• Lui, T.K., Zainuldin, M.H., Yii, K-Y., Lau, L-S., & Go, Y-H. (2021). Consumer adoption of Alipay in Malaysia: The mediation effect of perceived ease of use and perceived usefulness. Pertanika Journal of Social Sciences & Humanities, 29(1), 389–418. https://doi.org/10.47836/pjssh.29.1.22
   Web of Science ®Google Scholar
• Martinez-Canas, R., Ruiz-Palomino, P., Linuesa-Langreo, J., & Blazquez-Resino, J. (2016). Consumer participation in co-creation: An enlightening model of causes and effects based on ethical values and transcendent motives. Frontiers in Psychology, 7(793). https://doi.org/10.3389/fpsyg.2016.00793
   PubMedGoogle Scholar
• Nair, P.S., Shiva, A., Yadav, N., Tandon, P. (2023). Determinants of mobile apps adoption by retail investors for online trading in emerging financial markets. Benchmarking: An International Journal, 30(5), 1623–1648. https://doi.org/10.1108/BIJ-01-2022-0019
   Web of Science ®Google Scholar
• Ng, F. Z-X., Yap, H-Y., Tan, G. W-H., Lo, P-S., & Ooi, K-B. (2022). Fashion shopping on the go: A dual-stage predictive-analytics SEM-ANN analysis on usage behaviour, experience response and cross-category usage. Journal of Retailing and Consumer Services, 65, 102851. https://doi.org/10.1016/j.jretconser.2021.102851
   Web of Science ®Google Scholar
• Nguyen, Y.T.H., Tapanainen, T., & Nguyen, H.T.T. (2022). Reputation and its consequences in Fintech services: The case of mobile banking. International Journal of Bank Marketing, 40(7), 1364–1397. https://doi.org/10.1108/IJBM-08-2021-0371
   Google Scholar
• 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. https://doi.org/10.1016/j.eswa.2016.04.015
   Web of Science ®Google Scholar
• Patil, P., Tamilmani, K., Rana, N.P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust and grievance redressal. International Journal of Information Management, 54, 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144
   Web of Science ®Google Scholar
• Purohit, S., & Arora, R. (2023). Adoption of mobile banking at the bottom of the pyramid: An emerging market perspective. International Journal of Emerging Market, 18(1), 200–222. https://doi.org/10.1108/IJOEM-07-2020-0821
   Web of Science ®Google Scholar
• Sarmah, R., Dhiman, N., & Kanojia, H. (2021). Understanding intentions and actual use of mobile wallets by millennials: An extended TAM model perspective. Journal of Indian Business Research, 13(3), 361–381. https://doi.org/10.1108/JIBR-06-2020-0214
   Web of Science ®Google Scholar
• Singh, S., & Srivastava, R.K. (2020). Understanding the intention to use mobile banking by existing online banking customers: an empirical study. Journal of Financial Services Marketing, 25, 86–96. https://doi.org/10.1057/s41264-020-00074-w
   Web of Science ®Google Scholar
• Singh, S., Singh, N., Kalinic, Z., Liebana-Cabanillas, F.J. (2021). Assessing determinants influencing continued use of live streaming services: An extended perceived value theory of streaming addiction. Expert Systems with Applications, 168, 114241. https://doi.org/10.1016/j.eswa.2020.114241
   Web of Science ®Google Scholar
• Sinha, N., & Singh, N. (2023). Moderating and mediating effect of perceived experience on merchant’s behavioral intention to use mobile payments services. Journal of Financial Services Marketing, 28, 448–465. https://doi.org/10.1057/s41264-022-00163-y
   Web of Science ®Google Scholar
• Tarhini, A., Alalwan, A.A., Shammout, A.B., & Al-Badi, A. (2019). An analysis of the factors affecting mobile commerce adoption in developing countries: Towards an integrated model. Review of International Business and Strategy, 29(3), 157–179. https://doi.org/10.1108/RIBS-10-2018-0092
   Web of Science ®Google Scholar
• Teng, P.K., Heng, B.L.J., Abdullah, S.I.N.W., Ping, W.T., & Yao, X.J. (2020). Consumer adoption of mobile payments: A distinctive analysis between China and Malaysia. International Journal of Business Continuity and Risk Management, 10(2/3), 207–223. https://doi.org/10.1504/IJBCRM.2020.108505
   Google Scholar
• Wan, S-M., Cham, L-N., Tan, G.W-H., Lo, P-S., Ooi, K-B., & Chatterjee, R-S. (2022). What’s stopping you from migrating to mobile tourism shopping? Journal of Computer Information Systems, 62(6), 1223–1238. https://doi.org/10.1080/08874417.2021.2004564
   Web of Science ®Google Scholar
• Warsame, M.G., & Ireri, E.M. (2018). Moderation effect on Islamic banking preferences in UAE. International Journal of Bank Marketing, 36(1), 41–67. https://doi.org/10.1108/IJBM-08-2016-0121
   Web of Science ®Google Scholar
• Xin, H., Techatassanasoontorn, A.A., & Tan, F.B. (2015). Antecedents of consumer trust in mobile payment adoption. Journal of Computer Information Systems, 55(4), 1–10. https://doi.org/10.1080/08874417.2015.11645781
   Web of Science ®Google Scholar
• Yan, C., Siddik, A.B., Akter, N., & Dong, Q. (2023). Factors influencing the adoption intention of using mobile financial service during the COVID-19 pandemic: The role of FinTech. Environmental Science and Pollution Research, 30, 61271–61289. https://doi.org/10.1007/s11356-021-17437-y
   PubMed Web of Science ®Google Scholar
• Yan, L-Y., Tan, G. W-H., Loh, X-M., Hew, J-J-., & Ooi, K-B. (2021). QR code and mobile payment: The disruptive forces in retail. Journal of Retailing and Consumer Services, 58, 102300. https://doi.org/10.1016/j.jretconser.2020.102300
   Web of Science ®Google Scholar
• Zhu, G., Sangwan, S., & Lu, T-J. (2010). A new theoretical framework of technology acceptance and empirical investigation on self-efficacy-based value adoption model. Nankai Business Review, 1(4), 345–372. https://doi.org/10.1108/20408741011082543
   Google Scholar