References
- Abayomi, O. J., Olabode, A. C., Reyad, M. A. H., Tetteh Teye, E., Haq, M. N., & Mensah, E. T. (2019). Effects of demographic factors on customers’ mobile banking services adoption in Nigeria. International Journal of Business and Social Science, 10(1), 63–24. https://doi.org/10.30845/ijbss.v10n1p9
- Alafeef, M., Singh, D., & Ahmad, K. (2011). Influence of demographic factors on the adoption level of mobile banking applications in Jordan. Journal of Convergence Information Technology, 6(12), 107–113. https://www.researchgate.net/profile/Mohammad-Alafeef/publication/269778208_Influence_of_Demographic_Factors_on_the_Adoption_Level_of_Mobile_Banking_Applications_in_Jordan/links/5e60de0c299bf1bdb85449ac/Influence-of-Demographic-Factors-on-the-Adoption-Level-of-Mobile-Banking-Applications-in-Jordan.pdf.
- AlKailani, M. (2016). Factors affecting the adoption of internet banking in Jordan: An extended TAM model. Journal of Marketing Development and Competitiveness, 10(1), 39–52 http://www.m.www.na-businesspress.com/JMDC/AlKailaniM_Web10_1_.pdf.
- AlSamawi, A. M., Al-Fosail, B. A., Al-Hada, S. Z., & Al Dubaili, A. A. (2020). Electronic Money Service in Yemen - Challenges and Opportunities for Success. Institute of Banking Studies,Yemen. http://ibs.edu.ye/sites/default/files/Electronic%20Money%20Service%20in%20Yemen%20-%20Challenges%20and%20Opportunities%20for%20Success.pdf
- Aluri, A., & Palakurthi, R. (2011). The influence of demographic factors on consumer attitudes and intentions to use RFID technologies in the US hotel industry. Journal of Hospitality and Tourism Technology, 2(3), 188–203. https://doi.org/10.1108/17579881111173749
- Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
- Arner, D. W., Barberis, J., & Buckley, R. P. (2015). The evolution of fintech: A new post-crisis paradigm. Geo. J. Int’l L, , http://hdl.handle.net/10722/221450.
- Chawla, D., & Joshi, H. (2018). The moderating effect of demographic variables on mobile banking adoption: An empirical investigation. Global Business Review, 19(3_suppl), S90–S113. https://doi.org/10.1177/0972150918757883
- Chen, X., & Li, S. (2017). Understanding continuance intention of mobile payment services: An empirical study. Journal of Computer Information Systems, 57(4), 287–298. https://doi.org/10.1080/08874417.2016.1180649
- Chin, W. W., Thatcher, J. B., & Wright, R. T. (2012). Qfteriy problems with the ULMC technique1. MIS Quarterly, 36(3), 1003–1019. https://doi.org/10.2307/41703491
- Chong, A. Y. L., Ooi, K. B., Lin, B., & Tan, B. I. (2010). Online banking adoption: An empirical analysis. International Journal of Bank Marketing, 28(4), 267–287. https://doi.org/10.1108/02652321011054963.
- Chuang, L., Liu, C., & Kao, H. (2016). The adoption of fintech service : TAM perspective. International Journal of Management and Administrative Sciences, 3(7), 1–15. https://www.ijmas.org/3-7/IJMAS-3601-2016.pdf.
- Davies, F. D., & Venkatesh, V. (1995). Measuring user acceptance of emerging information technologies: An assessment of possible method biases. Proceedings of the twenty-eighth annual Hawaii international conference on system sciences, 4, 729–736.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
- Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
- Diana, N., & Leon, F. M. (2020). Factors affecting continuance intention of fintech payment among millennials in Jakarta. European Journal of Business and Management Research, 5(4)1–9. http://dx.doi.org/10.24018/ejbmr.2020.5.4.444.
- EY. (2017). EY fintech adoption index 2017 - the rapid emergence of fintech. EY FinTech Adoption Index, 2017, 1–44. http://www.ey.com/GL/en/Industries/Financial-Services/ey-fintech-adoption-index
- Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: An updated review. Ecological Processes, 5(1), 19. https://doi.org/10.1186/s13717-016-0063-3
- Fernando, E., Surjandy, M., & Touriano, D. (2018). Development and validation of instruments adoption fintech services in indonesia (perspective of trust and risk). 3rd international conference on sustainable information engineering and technology, SIET 2018 - proceedings, October 2019, 283–287. https://doi.org/10.1109/SIET.2018.8693192
- Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and researc. Journal of Business venturing 10(2), 177- I89. https://philarchive.org/archive/FISBAI .
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
- Frost, J. (2020). The economic forces driving fintech adoption across countries. BIS Bank for International Settlements, 838, . https://doi.org/10.2139/ssrn.3515326
- GSMA. (2019). The mobile gender gap report 2019. 52. https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2019/03/GSMA-Connected-Women-The-Mobile-Gender-Gap-Report-2019.pdf
- Gulamhuseinwala, I., Bull, T., & Lewis, S. (2015). FinTech is gaining traction and young, high-income users are the early adopters. Journal of Financial Perspectives, 3(3), 16–23 https://lampadia.com/assets/uploads_documentos/cca6f-ey_gfsi_journal_aw_lr.pdf#page=16.
- Habibi, F., & Zabardast, M. A. (2020). Digitalization, education and economic growth: A comparative analysis of middle East and OECD countries. Technology in Society, 63(9), 101370. https://doi.org/10.1016/j.techsoc.2020.101370
- Haddad, C., & Hornuf, L. (2019). The emergence of the global fintech market: Economic and technological determinants. Small Business Economics, 53(1), 81–105. https://doi.org/10.1007/s11187-018-9991-x
- Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLSSEM. Indeed a Silver Bullet Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
- Hazaea, S. A., Zhu, J., Khatib, S. F. A., Bazhair, A. H., Elamer, A. A., & Karami, C. (2021). Sustainability assurance practices: A systematic review and future research agenda. Environmental Science and Pollution Research, 28(1), 1–22. https://doi.org/10.1007/s11356-020-11060-z
- Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management and Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
- Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11(3), 340. https://doi.org/10.3390/sym11030340
- Huh, H. J., Kim, T. (Terry), & Law, R. (2009). A comparison of competing theoretical models for understanding acceptance behavior of information systems in upscale hotels. International Journal of Hospitality Management, 28(1), 121–134. https://doi.org/10.1016/j.ijhm.2008.06.004
- Humbani, M., & Wiese, M. (2018). A cashless society for all: determining consumers’ readiness to adopt mobile payment services. Journal of African Business, 19(3), 409–429. https://doi.org/10.1080/15228916.2017.1396792
- Im, S., Bayus, B. L., & Mason, C. H. (2003). An empirical study of innate consumer innovativeness, personal characteristics, and new-product adoption behavior. Journal of the Academy of Marketing Science, 31(1), 61–73. https://doi.org/10.1177/0092070302238602
- Jaradat, M. R., & Twaissi, N. M. (2010). Assessing the introduction of mobile banking in jordan using technology acceptance model. International Journal of Interactive Mobile Technologies (IJIM), 4(1), 14–21. https://core.ac.uk/download/pdf/270196463.pdf.
- Jin, C. C., Seong, L. C., & Khin, A. A. (2019). Factors affecting the consumer acceptance towards fintech products and services in Malaysia. International Journal of Asian Social Science, 9(1), 59–65. https://doi.org/10.18488/journal.1.2019.91.59.65
- Kalra, D. (2019). Overriding FINTECH. Proceeding of 2019 international conference on digitization: landscaping artificial intelligence, ICD 2019, November, 254–259. https://doi.org/10.1109/ICD47981.2019.9105915
- Karsh, A et al. (2021). Fintech in the eyes of millennials and generation Z (the financial behavior and fintech perception). Banks and Bank Systems, 15(3), 20–28. https://doi.org/10.21511/bbs.15(3).2020.03 .
- Kim, Y., Park, Y.-J., Choi, J., & Yeon, J. (2015). An empirical study on the adoption of “fintech” service: Focused on mobile payment services. Advanced Science and Technology Letters 114(26), 136–140. https://doi.org/10.14257/astl.2015.114.26
- Kock, N. (2015). Common method bias in PLS-SEM. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
- Lee, H., Cho, H. J., Xu, W., & Fairhurst, A. (2010). The influence of consumer traits and demographics on intention to use retail self‐service checkouts. Marketing Intelligence & Planning.
- Lim, S. H., Kim, D. J., Hur, Y., & Park, K. (2019). An empirical study of the impacts of perceived security and knowledge on continuous intention to use mobile fintech payment services. International Journal of Human-Computer Interaction, 35(10), 886–898. https://doi.org/10.1080/10447318.2018.1507132.
- Liu, W. (2020). Human capital accumulation, income protection insurance and poverty reduction in the least developed countries. Australian Economic Papers, 60(2), 361–372. https://doi.org/10.1111/1467-8454.12208
- Memon, A. H., & Rahman, I. A. (2014). SEM-PLS analysis of inhibiting factors of cost performance for large construction projects in Malaysia: Perspective of clients and consultants. The Scientific World Journal 2014, . https://doi.org/10.1155/2014/165158
- Meyliana, M., Fernando, E., & Surjandy, S. (2019). The influence of perceived risk and trust in adoption of fintech services in Indonesia. CommIT (Communication and Information Technology) Journal, 13(1), 31. https://doi.org/10.21512/commit.v13i1.5708
- Mkpojiogu, E. O. C., Hashim, N. L., & Adamu, R. (2016). Observed demographic differentials in user perceived satisfaction on the usability of mobile banking applications. Knowledge management international conference (kmice 2016), August, 263–268.
- Mulyana, A., Disman, D., Wibowo, L., & Hurriyati, R. (2020). Application of customer behavior in using fintech as business media based on the unified theory of acceptance and use of technology model 3rd Global Conference On Business, Management, and Entrepreneurship (GCBME 2018) 117 (Advances in Economics, Business and Management Research) . , 69–75. https://doi.org/10.2991/aebmr.k.200131.016
- Nitzl, C., & Chin, W. W. (2017). The case of partial least squares (PLS) path modeling in managerial accounting research. Journal of Management Control, 28(2), 137–156. https://doi.org/10.1007/s00187-017-0249-6
- O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6
- Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007. https://doi.org/10.1016/j.jbusres.2006.06.003
- Robertson, A., Lockett, N., Brown, D., & Crouchley, R. (2007). Entrepreneurs' attitude towards the computer and its effect on e-business adoption. Institute for Small Business & Entrepreneurship. http://eprints.lancs.ac.uk/7043/
- Ryu, H. (2017). Industrial management & data systems article information : What makes users willing or hesitant to use fintech ?: The moderating effect of user type. Industrial Management & Data Systems, 118(3), 541–569. https://doi.org/10.1108/IMDS-07-2017-0325
- Ryu, H.-S. (2018). What makes users willing or hesitant to use fintech?: The moderating effect of user type. Industrial Management & Data Systems.
- Salleh, F., & Ibrahim, M. D. (2011). Demographic characteristics differences of risk taking propensity among micro and small business owners in Malaysia. International Journal of Business and Social Science, 2(9), 149–153. https://www.researchgate.net/profile/Fauzilah-Salleh/publication/235910538_Demographic_Characteristics_Differences_of_Risk_Taking_Propensity_among_Micro_and_Small_Business_Owners_in_Malaysia/links/00b7d5140ae56723f1000000/Demographic-Characteristics-Differences-of-Risk-Taking-Propensity-among-Micro-and-Small-Business-Owners-in-Malaysia.pdf.
- Shah, C., Jain, A., Ahmed, S., Khandelwal, N., & Misra, S. (2019). Fintech and adoption model: A user perspective. Adalya Journal, 8(12), 488. http://adalyajournal.com/
- Stewart, H., & Jürjens, J. (2018). Data security and consumer trust in fintech innovation in Germany information & computer security data security and consumer trust in fintech innovation in Germany article information. Information & Computer Security, 26(1), 109–128. https://doi.org/10.1108/ICS-06-2017-0039
- Tang, K. L., Ooi, C. K., & Chong, J. B. (2020). Perceived risk factors affect intention to use fintech. Journal of Accounting and Finance in Emerging Economies, 6(2), 453–463. https://doi.org/10.26710/jafee.v6i2.1101
- Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205.
- Teo, A. C., Tan, G. W. H., Cheah, C. M., Ooi, K. B., & Yew, K. T. (2012). Can the demographic and subjective norms influence the adoption of mobile banking? International Journal of Mobile Communications, 10(6), 578–597. https://doi.org/10.1504/IJMC.2012.049757
- United Nations. (2021).THE LEAST DEVELOPED COUNTRIES REPORT 2020 productive capacities for the new decade ISSN 0257-7550 (UN, 2021)https://books.google.co.in/books/about/The_Least_Developed_Countries_Report_202.html?id=qWsnzgEACAAJ&source=kp_book_description&redir_esc=y .
- Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40. https://www.researchgate.net/profile/Nils-Urbach/publication/228467554_Structural_equation_modeling_in_information_systems_research_using_Partial_Least_Squares/links/0912f50ffa471d65f7000000/Structural-equation-modeling-in-information-systems-research-using-Partial-Least-Squares.pdf.
- Vinzi, V. E., Trinchera, L., & Amato, S. (2010). Handbook of Partial Least Squares (Springer Berlin, Heidelberg). 978-3-540-32827-8. https://doi.org/10.1007/978-3-540-32827-8
- Wausups, J. (2017). BankruptionHow Community BankingCan Survive Fintech (Wiley)9781119273868 https://books.google.co.in/books?hl=en&lr=&id=umZJDQAAQBAJ&oi=fnd&pg=PR9&dq=Bankruption,+how+community+banking+can+survive+fintech.&ots=5OrdDmKUnY&sig=SX49XWWgaGD58OVhmdo3o4B73R4&redir_esc=y#v=onepage&q=Bankruption%2C%20how%20community%20banking%20can%20survive%20fintech.&f=false .
- Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195. https://doi.org/10.2307/20650284
- Zhao, Q., Tsai, P. H., & Wang, J. L. (2019). Improving financial service innovation strategies for enhancing China’s banking industry competitive advantage during the fintech revolution: A hybrid MCDM model. Sustainability (Switzerland), 11(5), 1–29. https://doi.org/10.3390/su11051419