Unveiling the Future: Decrypting Mobile Investment Resistance with Multi-Stage SEM-ANN-NCA Approach

References

• Abbasi, K., Alam, A., Du, M., & Huynh, T. L. D, (2021). FinTech, SME efficiency and national culture: Evidence from OECD countries. Technological Forecasting and Social Change, 163, 120454. https://doi.org/10.1016/j.techfore.2020.120454
   Web of Science ®Google Scholar
• Afroze, D., & Rista, F. I. (2022). Mobile financial services (MFS) and digital inclusion – A study on customers’ retention and perceptions. Qualitative Research in Financial Markets, 14(5), 768–785. https://doi.org/10.1108/QRFM-06-2021-0095
   Web of Science ®Google Scholar
• Ali, I., & Warraich, N. F. (2023). Impact of personal innovativeness, perceived smartphone ease of use and mobile self-efficacy on smartphone-based personal information management practices. The Electronic Library, 41(4), 419–437. https://doi.org/10.1108/EL-12-2022-0262
   Web of Science ®Google Scholar
• Antioco, M., & Kleijnen, M. (2010). Consumer adoption of technological innovations: Effects of psychological and functional barriers in a lack of content versus a presence of content situation. European Journal of Marketing, 44(11/12), 1700–1724. https://doi.org/10.1108/03090561011079846
   Web of Science ®Google Scholar
• Arli, D., & Bakpayev, M. (2023). Exploring the role of innovation attributes on mobile payment adoption. Journal of Consumer Marketing, 40(7), 826–841. https://doi.org/10.1108/JCM-04-2021-4630
   Web of Science ®Google Scholar
• Asnakew, Z. S. (2020). Customers’ continuance intention to use mobile banking: Development and testing of an integrated model. The Review of Socionetwork Strategies, 14(1), 123–146. https://doi.org/10.1007/s12626-020-00060-7
   Web of Science ®Google Scholar
• Baklouti, F., & Boukamcha, F. (2023). Consumer resistance to Internet banking services: Implications for the innovation resistance theory. Journal of Financial Services Marketing, 29(2), 364–376. https://doi.org/10.1057/s41264-023-00210-2
   Web of Science ®Google Scholar
• Balachandran, D., Tan, G. W.-H., Ooi, K.-B., & Wei, J. (2024). What’s stopping you from migrating to mobile payment? International Journal of Human–Computer Interaction, 40(2), 295–314. https://doi.org/10.1080/10447318.2022.2115334
   Web of Science ®Google Scholar
• Blut, M., & Wang, C. (2020). Technology readiness: A meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science, 48(4), 649–669. https://doi.org/10.1007/s11747-019-00680-8
   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(3), 201–210. https://doi.org/10.1057/dddmp.2015.53
   Google Scholar
• Breet, S., van Rhee, H., & Dul, J. (2018). Necessary condition analysis (NCA) in three steps: A demonstration. Appendix to Dul, Van Der Laan, & Kuik (2019) in Organizational Research Methods.
   Google Scholar
• Bruner, G. C., & Kumar, A. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of Business Research, 58(5), 553–558. https://doi.org/10.1016/j.jbusres.2003.08.002
   Web of Science ®Google Scholar
• Campbell, D., & Cowton, C. J. (2015). Method issues in business ethics research: Finding credible answers to questions that matter. Business Ethics, 24(S1), S3–S10. https://doi.org/10.1111/beer.12093
   Google Scholar
• Cham, T.-H., Cheah, J.-H., Cheng, B.-L., & Lim, X.-J. (2022). I Am too old for this! Barriers contributing to the non-adoption of mobile payment. International Journal of Bank Marketing, 40(5), 1017–1050. https://doi.org/10.1108/IJBM-06-2021-0283
   Web of Science ®Google Scholar
• Chan, F. T. S., & Chong, A. Y. L. (2012). A SEM–neural network approach for understanding determinants of interorganizational system standard adoption and performances. Decision Support Systems, 54(1), 621–630. https://doi.org/10.1016/j.dss.2012.08.009
   Web of Science ®Google Scholar
• Chang, J., Wang, G., & Mao, T. (2015). Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model. Journal of Hydrology, 529, 1211–1220. https://doi.org/10.1016/j.jhydrol.2015.09.038
   Web of Science ®Google Scholar
• Chawla, D., & Joshi, H. (2020). The moderating role of gender and age in the adoption of mobile wallet. foresight, 22(4), 483–504. https://doi.org/10.1108/FS-11-2019-0094
   Web of Science ®Google Scholar
• Chen, P. T., & Kuo, S. C. (2017). Innovation resistance and strategic implications of enterprise social media websites in Taiwan through knowledge sharing perspective. Technological Forecasting and Social Change, 118, 55–69. https://doi.org/10.1016/j.techfore.2017.02.002
   Web of Science ®Google Scholar
• Chen, Q., Lu, Y., Gong, Y., & Tang, Q. (2019). Why do users resist service organization’s brand mobile apps? The force of barriers versus cross-channel synergy. International Journal of Information Management, 47(C), 274–282. https://doi.org/10.1016/j.ijinfomgt.2018.07.012
   Google Scholar
• Chou, C.-P., & Bentler, P. M. (1995). Estimates and tests in structural equation modeling. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 37–55). Sage Publications, Inc.
   Google Scholar
• Das, P., Verburg, R., Verbraeck, A., & Bonebakker, L. (2018). Barriers to innovation within large financial services firms. European Journal of Innovation Management, 21(1), 96–112. https://doi.org/10.1108/EJIM-03-2017-0028
   Web of Science ®Google Scholar
• David-West, O., Iheanachor, N., & Kelikume, I. (2018). A resource-based view of digital financial services (DFS): An exploratory study of Nigerian providers. Journal of Business Research, 88(7), 513–526. https://doi.org/10.1016/j.jbusres.2018.01.034
   Web of Science ®Google Scholar
• de Oliveira Santini, F., Eberle, L., Ladeira, W. J., Milan, G. S., Graciola, A. P., & Sampaio, C. H. (2023). Trust in the financial services context: A meta-analysis. International Journal of Bank Marketing, 41(7), 1693–1714. https://doi.org/10.1108/IJBM-06-2022-0248
   Web of Science ®Google Scholar
• Dul, J. (2016). Necessary condition analysis (NCA) logic and methodology of “necessary but not sufficient” causality. Organizational Research Methods, 19(1), 10–52. https://doi.org/10.1177/1094428115584005
   Web of Science ®Google Scholar
• Dul, J. (2019). Conducting necessary condition analysis for business and management students (pp. 1–160). Sage Publications Limited.
   Google Scholar
• Ediagbonya, V., & Tioluwani, C. (2023). The role of fintech in driving financial inclusion in developing and emerging markets: Issues, challenges and prospects. Technological Sustainability, 2(1), 100–119. https://doi.org/10.1108/TECHS-10-2021-0017
   Google Scholar
• Elbadrawy, R., & Abdel Aziz, R. (2011). Resistance to mobile banking adoption in Egypt: A cultural perspective. International Journal of Managing Information Technology, 3(4), 9–21. https://doi.org/10.5121/ijmit.2011.3402
   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
• Farhan, N. M., & Setiaji, B. (2023). Indonesian journal of computer science. Indonesian Journal of Computer Science, 12(2), 284–301. http://ijcs.stmikindonesia.ac.id/ijcs/index.php/ijcs/article/view/3135
   Google Scholar
• Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
   PubMed Web of Science ®Google Scholar
• Ferreira, J. B., da Rocha, A., & da Silva, J. F. (2014). Impacts of technology readiness on emotions and cognition in Brazil. Journal of Business Research, 67(5), 865–873. https://doi.org/10.1016/j.jbusres.2013.07.005
   Web of Science ®Google Scholar
• Frishammar, J., Essén, A., Bergström, F., & Ekman, T. (2023). Digital health platforms for the elderly? Key adoption and usage barriers and ways to address them. Technological Forecasting and Social Change, 189, 122319. https://doi.org/10.1016/j.techfore.2023.122319
   Web of Science ®Google Scholar
• Ghosh, M. (2024). Empirical study on consumers’ reluctance to mobile payments in a developing economy. Journal of Science and Technology Policy Management, 15(1), 67–92. https://doi.org/10.1108/JSTPM-02-2021-0031
   Web of Science ®Google Scholar
• Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632. https://doi.org/10.1007/s11747-017-0517-x
   Web of Science ®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. https://doi.org/10.2753/MTP1069-6679190202
   Google Scholar
• Hameed, I., Akram, U., & Ashraf, A. (2023). Consumers’ usage of mobile payment systems: An application of the innovation resistance and coping theory on the tourism sector. Kybernetes. https://doi.org/10.1108/K-03-2023-0495
   Web of Science ®Google Scholar
• Handoko, B. L., & Mozes, L. A. A. (2021). Analysis of factors affecting investor intention to use mobile online mutual fund application. International Conference Proceeding Series (pp. 63–69). ACM. https://doi.org/10.1145/3457640.3457658
   Google Scholar
• Heinze, J., Thomann, M., & Fischer, P. (2017). Ladders to m-commerce resistance: A qualitative means-end approach. Computers in Human Behavior, 73, 362–374. https://doi.org/10.1016/j.chb.2017.03.059
   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 modeling. 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
• Hew, J. J., Lee, V. H., & Leong, L. Y. (2024). Deciphering the resistance behaviours towards mobile commerce applications: A Mobile Commerce Applications Resistance Theory (MOCART). Computers in Human Behavior, 151(5), 108033. https://doi.org/10.1016/j.chb.2023.108033
   Google Scholar
• Hew, J. J., Leong, L. Y., Tan, G. W. H., Lee, V. H., & Ooi, K. B. (2018). Mobile social tourism shopping: A dual-stage analysis of a multi-mediation model. Tourism Management, 66, 121–139. https://doi.org/10.1016/j.tourman.2017.10.005
   Web of Science ®Google Scholar
• Hew, J. J., Leong, L. Y., Tan, G. W. H., Ooi, K. B., & Lee, V. H. (2019). The age of mobile social commerce: An Artificial Neural Network analysis on its resistances. Technological Forecasting and Social Change, 144, 311–324. https://doi.org/10.1016/j.techfore.2017.10.007
   Web of Science ®Google Scholar
• Hew, J.-J., Lee, V.-H., & Leong, L.-Y. (2023). Why do mobile consumers resist mobile commerce applications? A hybrid fsQCA-ANN analysis. Journal of Retailing and Consumer Services, 75, 103526. https://doi.org/10.1016/j.jretconser.2023.103526
   Web of Science ®Google Scholar
• Hew, T.-S., & Syed Abdul Kadir, S. L. (2016). Predicting instructional effectiveness of cloud-based virtual learning environment. Industrial Management & Data Systems, 116(8), 1557–1584. https://doi.org/10.1108/IMDS-11-2015-0475
   Web of Science ®Google Scholar
• Hongxia, P., Xianhao, X., & Weidan, L. (2011). Drivers and barriers in the acceptance of mobile payment in China [Paper presentation]. 2011 International Conference on E-Business and E-Government (ICEE) (pp. 1–4). https://doi.org/10.1109/ICEBEG.2011.5887081
   Google Scholar
• Ifezue, A. N., Ama, N. O., & Moseki, K. K. (2016). An exploratory factor analysis of older adults’ resistance to innovation adoption: A case study of university of Botswana. Journal of Management Research, 8(4), 19. https://doi.org/10.5296/jmr.v8i4.9825
   Google Scholar
• Illia, A., Lawson-Body, A., Akalin, G., & White, L. (2022). Impact of innovation resistance, trust, and risk propensity on investment apps use. Issues in Information Systems, 23(3), 209–220. https://doi.org/10.48009/3_iis_2022_117
   Google Scholar
• Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79(3), 111–122. https://doi.org/10.1016/j.chb.2017.10.035
   Google Scholar
• Kang, H., & Jung, E. H. (2021). The smart wearables-privacy paradox: A cluster analysis of smartwatch users. Behaviour & Information Technology, 40(16), 1755–1768. https://doi.org/10.1080/0144929X.2020.1778787
   Web of Science ®Google Scholar
• Khan, K., & Hyunwoo, K. (2009). Factors affecting consumers’ resistance: A study of smartphones. Journal of Consumer Marketing, 26(5), 350–358. https://doi.org/10.1108/07363760910973717
   Google Scholar
• Khanra, S., Dhir, A., Kaur, P., & Joseph, R. P. (2021). Factors influencing the adoption postponement of mobile payment services in the hospitality sector during a pandemic. Journal of Hospitality and Tourism Management, 46(1), 26–39. https://doi.org/10.1016/j.jhtm.2020.11.004
   Web of Science ®Google Scholar
• Kim, C., Oh, E., Shin, N., & Chae, M. (2009). An empirical investigation of factors affecting ubiquitous computing use and U-business value. International Journal of Information Management, 29(6), 436–448. https://doi.org/10.1016/j.ijinfomgt.2009.06.003
   Web of Science ®Google Scholar
• Kleijnen, M., Lee, N., & Wetzels, M. (2009). An exploration of consumer resistance to innovation and its antecedents. Journal of Economic Psychology, 30(3), 344–357. https://doi.org/10.1016/j.joep.2009.02.004
   Web of Science ®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
• Kubat, M. (1999). Neural networks: A comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, 13(4), 409–412. https://doi.org/10.1017/S0269888998214044
   Google Scholar
• Lau, U. H., Lee, L. E., Kang Lew, D., Loo, L. S., Ooi, S. X., Lee, V. H., Loh, X. M., & Tan, G. H. W. (2022). Mobile payment adoption: Barriers for baby boomers in Malaysia BT. In M. Al-Emran, M. A. Al-Sharafi, M. N. Al-Kabi, & K. Shaalan (Eds.), Proceedings of international conference on emerging technologies and intelligent systems (pp. 568–579). Springer International Publishing.
   Google Scholar
• Laukkanen, P., Sinkkonen, S., & Laukkanen, T. (2008). Consumer resistance to internet banking: Postponers, opponents and rejectors. International Journal of Bank Marketing, 26(6), 440–455. https://doi.org/10.1108/02652320810902451
   Google Scholar
• Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking. Journal of Business Research, 69(7), 2432–2439. https://doi.org/10.1016/j.jbusres.2016.01.013
   Web of Science ®Google Scholar
• Laukkanen, T., Sinkkonen, S., Kivijärvi, M., & Laukkanen, P. (2007). Innovation resistance among mature consumers. Journal of Consumer Marketing, 24(7), 419–427. https://doi.org/10.1108/07363760710834834
   Google Scholar
• Laukkanen, T., Sinkkonen, S., Laukkanen, P., & Kivijarvi, M. (2008). Segmenting bank customers by resistance to mobile banking. International Journal of Mobile Communications, 6(3), 309–320. https://doi.org/10.1504/IJMC.2008.017513
   Web of Science ®Google Scholar
• Lee, H., Jeong Cho, H., Xu, W., & Fairhurst, A. (2010). The influence of consumer traits and demographics on intention to use retail self‐service checkouts. Marketing Intelligence & Planning, 28(1), 46–58. https://doi.org/10.1108/02634501011014606
   Google Scholar
• Leong, L. Y., Hew, T. S., Ooi, K. B., & Wei, J. (2020). Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach. International Journal of Information Management, 51(2), 102047. https://doi.org/10.1016/j.ijinfomgt.2019.102047
   Google Scholar
• Leong, L.-Y., Hew, T.-S., Ooi, K.-B., & Lin, B. (2019). Do electronic word-of-mouth and elaboration likelihood model influence hotel booking? Journal of Computer Information Systems, 59(2), 146–160. https://doi.org/10.1080/08874417.2017.1320953
   Web of Science ®Google Scholar
• Leong, L.-Y., Hew, T.-S., Tan, G. W.-H., & Ooi, K.-B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604–5620. https://doi.org/10.1016/j.eswa.2013.04.018
   Web of Science ®Google Scholar
• Lian, J.-W., & Yen, D. C. (2013). To buy or not to buy experience goods online: Perspective of innovation adoption barriers. Computers in Human Behavior, 29(3), 665–672. https://doi.org/10.1016/j.chb.2012.10.009
   Web of Science ®Google Scholar
• Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management, 37(2), 14–24. https://doi.org/10.1016/j.ijinfomgt.2016.10.008
   Web of Science ®Google Scholar
• Ling, P.-S., Lee, K. Y. M., Ling, L.-S., & Mohd Suhaimi, M. K. A. (2024). Investors’ intention to use mobile investment: An extended mobile technology acceptance model with personal factors and perceived reputation. Cogent Business & Management, 11(1), 2295603. https://doi.org/10.1080/23311975.2023.2295603
   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
• Low, K. H. (2016). Factor affecting consumer resistance to Paypal mobile payment adoption: A study of generation X consumers. UTAR.
   Google Scholar
• Mahatanankoon, P., & Vila-Ruiz, J. (2007). Why won’t consumers adopt M-commerce? An exploratory study. Journal of Internet Commerce, 6(4), 113–128. https://doi.org/10.1080/15332860802086367
   Google Scholar
• Malek, M. S., & Bhatt, V. (2023). Investigating the effect of risk reduction strategies on the construction of mega infrastructure project (MIP) success: A SEM-ANN approach. Engineering, Construction and Architectural Management. https://doi.org/10.1108/ECAM-12-2022-1166
   Web of Science ®Google Scholar
• Maseeh, H. I., Nahar, S., Jebarajakirthy, C., Ross, M., Arli, D., Das, M., Rehman, M., & Ashraf, H. A. (2023). Exploring the privacy concerns of smartphone app users: A qualitative approach. Marketing Intelligence & Planning, 41(7), 945–969. https://doi.org/10.1108/MIP-11-2022-0515
   Web of Science ®Google Scholar
• Migliore, G., Wagner, R., Cechella, F. S., & Liébana-Cabanillas, F. (2022). Antecedents to the adoption of mobile payment in China and Italy: An integration of UTAUT2 and innovation resistance theory. Information Systems Frontiers: a Journal of Research and Innovation, 24(6), 2099–2122. https://doi.org/10.1007/s10796-021-10237-2
   PubMed Web of Science ®Google Scholar
• Mollick, J., Cutshall, R., Changchit, C., & Pham, L. (2023). Contemporary mobile commerce: Determinants of its adoption. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 501–523. https://doi.org/10.3390/jtaer18010026
   Web of Science ®Google Scholar
• Moorthy, K., Suet Ling, C., Weng Fatt, Y., Mun Yee, C., Ket Yin, E. C., Sin Yee, K., & Kok Wei, L. (2017). Barriers of mobile commerce adoption intention: Perceptions of generation X in Malaysia. Journal of Theoretical and Applied Electronic Commerce Research, 12(2), 37–53. https://doi.org/10.4067/S0718-18762017000200004
   Web of Science ®Google 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, 30(5), 1623–1648. https://doi.org/10.1108/BIJ-01-2022-0019
   Web of Science ®Google Scholar
• Nalluri, V., & Chen, L. S. (2024). Modelling the FinTech adoption barriers in the context of emerging economies—An integrated Fuzzy hybrid approach. Technological Forecasting and Social Change, 199(2024), 123049. https://doi.org/10.1016/j.techfore.2023.123049
   Google Scholar
• Ooi, K. B., Lee, V. H., Tan, G. W. H., Hew, T. S., & Hew, J. J. (2018). Cloud computing in manufacturing: The next industrial revolution in Malaysia? Expert Systems with Applications, 93(4–5), 376–394. https://doi.org/10.1016/j.eswa.2017.10.009
   Google Scholar
• Patel, R., Bhatt, V., Thomas, S., Trivedi, T., & Pandey, S. (2024). Predicting the cause-related marketing participation intention by examining big-five personality traits and moderating role of subjective happiness. International Review on Public and Nonprofit Marketing, 21(1), 199–228. https://doi.org/10.1007/s12208-023-00371-9
   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(2), 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144
   Google Scholar
• Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623–656. https://doi.org/10.2307/25148814
   Web of Science ®Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Puppala, H., Peddinti, P. R. T., Tamvada, J. P., Ahuja, J., & Kim, B. (2023). Barriers to the adoption of new technologies in rural areas: The case of unmanned aerial vehicles for precision agriculture in India. Technology in Society, 74(C), 102335. https://doi.org/10.1016/j.techsoc.2023.102335
   Google Scholar
• Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5–14. https://doi.org/10.1108/EUM0000000002542
   Google Scholar
• Rana, N. P., Luthra, S., & Rao, H. R. (2020). Key challenges to digital financial services in emerging economies: The Indian context. Information Technology & People, 33(1), 198–229. https://doi.org/10.1108/ITP-05-2018-0243
   Web of Science ®Google Scholar
• Raut, R. K., & Kumar, S. (2024). An integrated approach of TAM and TPB with financial literacy and perceived risk for influence on online trading intention. Digital Policy, Regulation and Governance, 26(2), 135–152. https://doi.org/10.1108/DPRG-07-2023-0101
   Web of Science ®Google Scholar
• Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1617–1643. https://doi.org/10.1080/09585192.2017.1416655
   Web of Science ®Google Scholar
• Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. SmartPLS GmbH, Boenningstedt. Journal of Service Science and Management, 10(3), 32–49.
   Google Scholar
• Rocha, M. A. V., Hammond, L., & Hawkins, D. (2005). Age, gender and national factors in fashion consumption. Journal of Fashion Marketing and Management, 9(4), 380–390. https://doi.org/10.1108/13612020510620768
   Google Scholar
• Rojas-Méndez, J. I., Parasuraman, A., & Papadopoulos, N. (2017). Demographics, attitudes, and technology readiness. Marketing Intelligence & Planning, 35(1), 18–39. https://doi.org/10.1108/MIP-08-2015-0163
   Web of Science ®Google Scholar
• Rombe, E., Zahara, Z., Nuriya, I., & Desma, M. (2021). Exploring e-mobile banking implementation barriers on Indonesian millennial generation consumers. International Journal of Data and Network Science, 5(4), 579–586. https://doi.org/10.5267/j.ijdns.2021.8.010
   Google Scholar
• Sampat, B., Mogaji, E., & Nguyen, N. P. (2024). The dark side of FinTech in financial services: A qualitative enquiry into FinTech developers’ perspective. International Journal of Bank Marketing, 42(1), 38–65. https://doi.org/10.1108/IJBM-07-2022-0328
   Web of Science ®Google Scholar
• Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 39(5), 1035–1064. https://doi.org/10.1002/mar.21640
   Web of Science ®Google Scholar
• Saxena, N., Gera, N., & Taneja, M. (2023). An empirical study on facilitators and inhibitors of adoption of mobile banking in India. Electronic Commerce Research, 23(4), 2573–2604. https://doi.org/10.1007/s10660-022-09556-6
   Web of Science ®Google Scholar
• Shareef, M. A., Dwivedi, Y. K., Kumar, V., Davies, G., Rana, N., & Baabdullah, A. (2019). Purchase intention in an electronic commerce environment. Information Technology & People, 32(6), 1345–1375. https://doi.org/10.1108/ITP-05-2018-0241
   Web of Science ®Google Scholar
• Shareef, M. A., Kumar, V., Dwivedi, Y. K., & Kumar, U. (2016). Service delivery through mobile-government (mGov): Driving factors and cultural impacts. Information Systems Frontiers, 18(2), 315–332. https://doi.org/10.1007/s10796-014-9533-2
   Web of Science ®Google Scholar
• Sharma, S. K., Al-Badi, A. H., Govindaluri, S. M., & Al-Kharusi, M. H. (2016). Predicting motivators of cloud computing adoption: A developing country perspective. Computers in Human Behavior, 62, 61–69. https://doi.org/10.1016/j.chb.2016.03.073
   Web of Science ®Google Scholar
• Sim, J.-J., Tan, G. W.-H., Wong, J. C. J., Ooi, K.-B., & Hew, T.-S. (2014). Understanding and predicting the motivators of mobile music acceptance – A multistage MRA-artificial neural network approach. Telematics and Informatics, 31(4), 569–584. https://doi.org/10.1016/j.tele.2013.11.005
   Web of Science ®Google Scholar
• Soliman, D. A., & Salem, M. S. (2014). Investigating intention to use mobile instant messenger: The influence of socialibility, self-expressiveness, and enjoyment. The Journal of American Academy of Business, 19(2), 286–293.
   Google Scholar
• Statista. (2023). Smartphone users in India 2010–2040. s.l.: Shangliao Sun. https://www.statista.com/statistics/467163/forecast-of-smartphone-users-in-india/
   Google Scholar
• Statista. (2024). Mobile app usage in India – statistics & facts. s.l.: Tanushree Basuroy. https://www.statista.com/topics/5600/mobile-app-usage-in-india/#topicOverview
   Google Scholar
• Suryavanshi, A. K. S., Bhatt, V., Thomas, S., Patel, R., & Jariwala, H. (2024). Predicting cause-related marketing patronage intentions, corporate social responsibility motives and moderating role of spirituality. Social Responsibility Journal, 20(4), 682–702. https://doi.org/10.1108/SRJ-12-2022-0564
   Web of Science ®Google Scholar
• Talwar, M., Corazza, L., Bodhi, R., & Malibari, A. (2023). Why do consumers resist digital innovations? An innovation resistance theory perspective. International Journal of Emerging Markets, 18(5), 1111–1136. https://doi.org/10.1108/IJOEM-03-2022-0529
   Web of Science ®Google Scholar
• Talwar, S., Talwar, M., Kaur, P., & Dhir, A. (2020). Consumers’ resistance to digital innovations: A systematic review and framework development. Australasian Marketing Journal, 28(4), 286–299. https://doi.org/10.1016/j.ausmj.2020.06.014
   Web of Science ®Google Scholar
• Talwar, S., Talwar, M., Kaur, P., Singh, G., & Dhir, A. (2021). Why have consumers opposed, postponed, and rejected innovations during a pandemic? A study of mobile payment innovations. Australasian Journal of Information Systems, 25, 1–27. https://doi.org/10.3127/ajis.v25i0.3201
   Web of Science ®Google Scholar
• Tan, G. W. H., & Ooi, K. B. (2018). Gender and age: Do they really moderate mobile tourism shopping behavior? Telematics and Informatics, 35(6), 1617–1642. https://doi.org/10.1016/j.tele.2018.04.009
   Web of Science ®Google Scholar
• Tan, G. W.-H., Ooi, K.-B., Leong, L.-Y., & Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior, 36, 198–213. https://doi.org/10.1016/j.chb.2014.03.052
   Web of Science ®Google Scholar
• Thomas, S., Bhatt, V., & Patel, R. (2024). Impact of skepticism on CRM luxury campaign participation intention of Generation Z. International Journal of Emerging Markets, 19(4), 964–988. https://doi.org/10.1108/IJOEM-10-2021-1568
   Web of Science ®Google Scholar
• Widyanto, H. A., Kusumawardani, K. A., & Yohanes, H. (2022). Safety first: Extending UTAUT to better predict mobile payment adoption by incorporating perceived security, perceived risk and trust. Journal of Science and Technology Policy Management, 13(4), 952–973. https://doi.org/10.1108/JSTPM-03-2020-0058
   Web of Science ®Google Scholar
• Xu, C., Peak, D., & Prybutok, V. (2015). A customer value, satisfaction, and loyalty perspective of mobile application recommendations. Decision Support Systems, 79(2), 171–183. https://doi.org/10.1016/j.dss.2015.08.008
   Google Scholar