.jpg){kind=logo}
Adoption of Financial Technology (FinTech) Services for Iraqi Bank Users: an extension of technology acceptance model
Alaa Mahdi Sahi
Abstract
The study aims to explore the determinants of Financial Technology adoption among Iraqi bank users. Building on the Technology Acceptance Model, the study integrates Perceived Economic Wellbeing and Digital Literacy with traditional TAM constructs including Perceived Ease of Use, Perceived Usefulness, and Subjective Norms. The data were collected through a survey methodology which included banking clients in Iraq. A total of 280 bank customers contributed to the research, yielding a response rate of 93.33%. Hypotheses were tested using Structural Equation Modelling (SEM). The findings suggest that perceived usefulness, ease of use, subjective norms, and digital literacy are instrumental in adoption FinTech services. On the other hand, economic well-being has no significant impact. This implies that digital literacy and social influence exert a significant effect on FinTech acceptance by users in developing countries. The study will be useful for policy makers and the management of financial institutions aiming to enhance FinTech adoption through policies aimed to improve users’ perceptions of usefulness and ease-of-use as well as to develop users’ digital literacy. The study contributes to the existing literature limited literature on FinTech adoption in the Arab world, particularly Iraq
Keywords
Adoption, FinTech, Iraq, TAM
References
Addai, I., & Pokimica, J. (2010). Ethnicity and economic well-being: The case of Ghana. Social Indicators Research, 99, 487–510.
Albayati, H., Kim, S. K., & Rho, J. J. (2020). Accepting financial transactions using blockchain technology and cryptocurrency: A customer perspective approach. Technology in Society, 62, 101320.
Alfadda, H. A., & Mahdi, H. S. (2021). Measuring students’ use of zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50(4), 883–900.
Amin, H., Abdul‐Rahman, A., Ramayah, T., Supinah, R., & Mohd‐Aris, M. (2014). Determinants of online waqf acceptance: An empirical investigation. The Electronic Journal of Information Systems in Developing Countries, 60(1), 1–18.
Amnas, M. B., Selvam, M., Raja, M., Santhoshkumar, S., & Parayitam, S. (2023). Understanding the Determinants of FinTech Adoption: Integrating UTAUT2 with Trust Theoretic Model. Journal of Risk and Financial Management, 16(12), 505.
Apanasevic, T., Markendahl, J., & Arvidsson, N. (2016). Stakeholders’ expectations of mobile payment in retail: lessons from Sweden. International Journal of Bank Marketing, 34(1), 37–61.
Arvidsson, N. (2014). Consumer attitudes on mobile payment services–results from a proof of concept test. International Journal of Bank Marketing, 32(2), 150–170.
Ashoer, M., Jebarajakirthy, C., Lim, X.-J., Mas’ ud, M., & Sahabuddin, Z. A. (2024). Mobile fintech, digital financial inclusion, and gender gap at the bottom of the pyramid: An extension of mobile technology acceptance model. Procedia Computer Science, 234, 1253–1260.
Bawden, D. (2008). Origins and concepts of digital literacy. Digital Literacies: Concepts, Policies and Practices, 30(2008), 17–32.
Belanche, D., Casaló, L. V, & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411–1430.
Carranza, R., Díaz, E., Sánchez-Camacho, C., & Martín-Consuegra, D. (2021). e-Banking adoption: an opportunity for customer value co-creation. Frontiers in Psychology, 11, 621248.
Chen, W.-C., Chen, C.-W., & Chen, W.-K. (2019). Drivers of mobile payment acceptance in China: An empirical investigation. Information, 10(12), 384.
Contreras Pinochet, L. H., Diogo, G. T., Lopes, E. L., Herrero, E., & Bueno, R. L. P. (2019). Propensity of contracting loans services from FinTech’s in Brazil. International Journal of Bank Marketing, 37(5), 1190–1214.
Davis, F. D. (1989a). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, F. D. (1989b). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, F. D. (1989c). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, F. D. (1989d). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340.
Davis, F. D. (1989e). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, F. D. (1989f). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205, 219.
Duane, A., O’Reilly, P., & Andreev, P. (2014). Realising M-Payments: modelling consumers’ willingness to M-pay using Smart Phones. Behaviour & Information Technology, 33(4), 318–334.
Dzogbenuku, R. K., Amoako, G. K., Kumi, D. K., & Bonsu, G. A. (2022). Digital payments and financial wellbeing of the rural poor: The moderating role of age and gender. Journal of International Consumer Marketing, 34(2), 113–136.
Eshet, Y. (2004). Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia, 13(1), 93–106.
Firmansyah, E. A., Masri, M., Anshari, M., & Besar, M. H. A. (2022). Factors affecting fintech adoption: a systematic literature review. FinTech, 2(1), 21–33.
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Sage publications Sage CA: Los Angeles, CA.
Gasiorowska, A. (2014). The relationship between objective and subjective wealth is moderated by financial control and mediated by money anxiety. Journal of Economic Psychology, 43, 64–74.
Gbongli, K., Xu, Y., & Amedjonekou, K. M. (2019a). Extended technology acceptance model to predict mobile-based money acceptance and sustainability: A multi-analytical structural equation modeling and neural network approach. Sustainability, 11(13), 3639.
Gbongli, K., Xu, Y., & Amedjonekou, K. M. (2019b). Extended technology acceptance model to predict mobile-based money acceptance and sustainability: A multi-analytical structural equation modeling and neural network approach. Sustainability, 11(13), 3639.
Gopi, M., & Ramayah, T. (2007). Applicability of theory of planned behavior in predicting intention to trade online: Some evidence from a developing country. International Journal of Emerging Markets, 2(4), 348–360.
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.
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322.
Long, T. Q., Morgan, P. J., & Yoshino, N. (2023). Financial literacy, behavioral traits, and ePayment adoption and usage in Japan. Financial Innovation, 9(1), 101.
Lukonga, I. (2021). Fintech and the real economy: Lessons from the Middle East, North Africa, Afghanistan, and Pakistan (MENAP) region. The Palgrave Handbook of Fintech and Blockchain, 187–214.
Marakarkandy, B., Yajnik, N., & Dasgupta, C. (2017). Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM). Journal of Enterprise Information Management, 30(2), 263–294.
Milian, E. Z., Spinola, M. de M., & de Carvalho, M. M. (2019). Fintechs: A literature review and research agenda. Electronic Commerce Research and Applications, 34, 100833.
Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078.
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414.
Patel, K. J., & Patel, H. J. (2018). Adoption of internet banking services in Gujarat: An extension of TAM with perceived security and social influence. International Journal of Bank Marketing, 36(1), 147–169.
Pebley, A. R. (2002). Ethnicity, Language, and Economic Well-Being in Rural Guatemala.
Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732.
Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6–30.
Roy, S. K., Balaji, M. S., Kesharwani, A., & Sekhon, H. (2017). Predicting Internet banking adoption in India: A perceived risk perspective. Journal of Strategic Marketing, 25(5–6), 418–438.
Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142.
Sinha, N., & Verma, P. (2020). Adoption of ict enabled agricultural extension services through perceived economic wellbeing: Ict and pewb. International Journal of Information and Communication Technology Education (IJICTE), 16(3), 30–41.
Stewart, H., & Jürjens, J. (2018). Data security and consumer trust in FinTech innovation in Germany. Information & Computer Security, 26(1), 109–128.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.
Teo, A., Tan, G. W., Cheah, C., Ooi, K., & Yew, K. (2012). Can the demographic and subjective norms influence the adoption of mobile banking? International Journal of Mobile Communications, 10(6), 578–597.
Ullah, S., Kiani, U. S., Raza, B., & Mustafa, A. (2022). Consumers’ intention to adopt m-payment/m-banking: the role of their financial skills and digital literacy. Frontiers in Psychology, 13, 873708.
Van Deursen, A. J. A. M., & Van Dijk, J. A. G. M. (2014). The digital divide shifts to differences in usage. New Media & Society, 16(3), 507–526.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003a). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003b). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003c). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126, 207–216.
Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206–215.
Wang, Z., Guan, Z., Hou, F., Li, B., & Zhou, W. (2019). What determines customers’ continuance intention of FinTech? Evidence from YuEbao. Industrial Management & Data Systems, 119(8), 1625–1637.
DOI: https://doi.org/10.24967/ekombis.v9i1.3324
Article Metrics
Abstract view : 995 times
PDF : 434 times
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Alaa Mahdi Sahi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Alaa Mahdi SahiAbstract
Keywords
References
Addai, I., & Pokimica, J. (2010). Ethnicity and economic well-being: The case of Ghana. Social Indicators Research, 99, 487–510.
Albayati, H., Kim, S. K., & Rho, J. J. (2020). Accepting financial transactions using blockchain technology and cryptocurrency: A customer perspective approach. Technology in Society, 62, 101320.
Alfadda, H. A., & Mahdi, H. S. (2021). Measuring students’ use of zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50(4), 883–900.
Amin, H., Abdul‐Rahman, A., Ramayah, T., Supinah, R., & Mohd‐Aris, M. (2014). Determinants of online waqf acceptance: An empirical investigation. The Electronic Journal of Information Systems in Developing Countries, 60(1), 1–18.
Amnas, M. B., Selvam, M., Raja, M., Santhoshkumar, S., & Parayitam, S. (2023). Understanding the Determinants of FinTech Adoption: Integrating UTAUT2 with Trust Theoretic Model. Journal of Risk and Financial Management, 16(12), 505.
Apanasevic, T., Markendahl, J., & Arvidsson, N. (2016). Stakeholders’ expectations of mobile payment in retail: lessons from Sweden. International Journal of Bank Marketing, 34(1), 37–61.
Arvidsson, N. (2014). Consumer attitudes on mobile payment services–results from a proof of concept test. International Journal of Bank Marketing, 32(2), 150–170.
Ashoer, M., Jebarajakirthy, C., Lim, X.-J., Mas’ ud, M., & Sahabuddin, Z. A. (2024). Mobile fintech, digital financial inclusion, and gender gap at the bottom of the pyramid: An extension of mobile technology acceptance model. Procedia Computer Science, 234, 1253–1260.
Bawden, D. (2008). Origins and concepts of digital literacy. Digital Literacies: Concepts, Policies and Practices, 30(2008), 17–32.
Belanche, D., Casaló, L. V, & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411–1430.
Carranza, R., Díaz, E., Sánchez-Camacho, C., & Martín-Consuegra, D. (2021). e-Banking adoption: an opportunity for customer value co-creation. Frontiers in Psychology, 11, 621248.
Chen, W.-C., Chen, C.-W., & Chen, W.-K. (2019). Drivers of mobile payment acceptance in China: An empirical investigation. Information, 10(12), 384.
Contreras Pinochet, L. H., Diogo, G. T., Lopes, E. L., Herrero, E., & Bueno, R. L. P. (2019). Propensity of contracting loans services from FinTech’s in Brazil. International Journal of Bank Marketing, 37(5), 1190–1214.
Davis, F. D. (1989a). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, F. D. (1989b). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, F. D. (1989c). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, F. D. (1989d). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340.
Davis, F. D. (1989e). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, F. D. (1989f). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205, 219.
Duane, A., O’Reilly, P., & Andreev, P. (2014). Realising M-Payments: modelling consumers’ willingness to M-pay using Smart Phones. Behaviour & Information Technology, 33(4), 318–334.
Dzogbenuku, R. K., Amoako, G. K., Kumi, D. K., & Bonsu, G. A. (2022). Digital payments and financial wellbeing of the rural poor: The moderating role of age and gender. Journal of International Consumer Marketing, 34(2), 113–136.
Eshet, Y. (2004). Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia, 13(1), 93–106.
Firmansyah, E. A., Masri, M., Anshari, M., & Besar, M. H. A. (2022). Factors affecting fintech adoption: a systematic literature review. FinTech, 2(1), 21–33.
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Sage publications Sage CA: Los Angeles, CA.
Gasiorowska, A. (2014). The relationship between objective and subjective wealth is moderated by financial control and mediated by money anxiety. Journal of Economic Psychology, 43, 64–74.
Gbongli, K., Xu, Y., & Amedjonekou, K. M. (2019a). Extended technology acceptance model to predict mobile-based money acceptance and sustainability: A multi-analytical structural equation modeling and neural network approach. Sustainability, 11(13), 3639.
Gbongli, K., Xu, Y., & Amedjonekou, K. M. (2019b). Extended technology acceptance model to predict mobile-based money acceptance and sustainability: A multi-analytical structural equation modeling and neural network approach. Sustainability, 11(13), 3639.
Gopi, M., & Ramayah, T. (2007). Applicability of theory of planned behavior in predicting intention to trade online: Some evidence from a developing country. International Journal of Emerging Markets, 2(4), 348–360.
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.
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322.
Long, T. Q., Morgan, P. J., & Yoshino, N. (2023). Financial literacy, behavioral traits, and ePayment adoption and usage in Japan. Financial Innovation, 9(1), 101.
Lukonga, I. (2021). Fintech and the real economy: Lessons from the Middle East, North Africa, Afghanistan, and Pakistan (MENAP) region. The Palgrave Handbook of Fintech and Blockchain, 187–214.
Marakarkandy, B., Yajnik, N., & Dasgupta, C. (2017). Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM). Journal of Enterprise Information Management, 30(2), 263–294.
Milian, E. Z., Spinola, M. de M., & de Carvalho, M. M. (2019). Fintechs: A literature review and research agenda. Electronic Commerce Research and Applications, 34, 100833.
Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078.
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414.
Patel, K. J., & Patel, H. J. (2018). Adoption of internet banking services in Gujarat: An extension of TAM with perceived security and social influence. International Journal of Bank Marketing, 36(1), 147–169.
Pebley, A. R. (2002). Ethnicity, Language, and Economic Well-Being in Rural Guatemala.
Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732.
Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: an empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6–30.
Roy, S. K., Balaji, M. S., Kesharwani, A., & Sekhon, H. (2017). Predicting Internet banking adoption in India: A perceived risk perspective. Journal of Strategic Marketing, 25(5–6), 418–438.
Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142.
Sinha, N., & Verma, P. (2020). Adoption of ict enabled agricultural extension services through perceived economic wellbeing: Ict and pewb. International Journal of Information and Communication Technology Education (IJICTE), 16(3), 30–41.
Stewart, H., & Jürjens, J. (2018). Data security and consumer trust in FinTech innovation in Germany. Information & Computer Security, 26(1), 109–128.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.
Teo, A., Tan, G. W., Cheah, C., Ooi, K., & Yew, K. (2012). Can the demographic and subjective norms influence the adoption of mobile banking? International Journal of Mobile Communications, 10(6), 578–597.
Ullah, S., Kiani, U. S., Raza, B., & Mustafa, A. (2022). Consumers’ intention to adopt m-payment/m-banking: the role of their financial skills and digital literacy. Frontiers in Psychology, 13, 873708.
Van Deursen, A. J. A. M., & Van Dijk, J. A. G. M. (2014). The digital divide shifts to differences in usage. New Media & Society, 16(3), 507–526.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003a). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003b). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003c). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126, 207–216.
Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206–215.
Wang, Z., Guan, Z., Hou, F., Li, B., & Zhou, W. (2019). What determines customers’ continuance intention of FinTech? Evidence from YuEbao. Industrial Management & Data Systems, 119(8), 1625–1637.
Ekombis Sains: Jurnal Ekonomi, Keuangan dan Bisnis is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
