Do Demographics Shape Perceived Usefulness of Financial Technology? Evidence from an Indian Context

Abstract

Purpose: This research aims to identify the significant demographic determinants and to examine the demographic differences that shape people’s perception of the usefulness of financial technology.

Methods: Data were collected via an online survey questionnaire on perceived usefulness and demographic variables such as age, gender, marital status, place of living, education, academic field, tax-paying status, family type, family income, and occupation. After editing and outlier deletion, 412 usable responses were retained. Analysis was completed through descriptive statistics, correlation, regression, one-way ANOVA, and factor analysis.

Results: Findings indicated perceived usefulness as a unidimensional, valid, and reliable construct. It was found that demographic factors such as age, marital status, place of living, education, tax-paying status, family income, and occupation significantly influence perceived usefulness. However, gender, academic field, and family type came out as insignificant determinants. Also, older respondents, married individuals, those living in urban and semi-urban areas, highly educated individuals, those who pay taxes, those belonging to high-income families, business professionals, and corporate and bank employees reported high levels of perception regarding the usefulness of financial technology.

Implications: The study implicates building marketing and inclusion strategies according to key demographic characteristics to enhance adoption and accessibility.

Originality: It contributes to the literature by adding marital status, academic field, tax-paying status, family type, and occupation as unique demographic variables that have not or have been minimally explored earlier. This provides unique value to the research for financial technology firms and policymakers and adds to the literature on financial technology.

  • Page Number : 33-46

  • Published Date : 2026-02-28

  • Keywords
    Demography, Determinants, Perceived usefulness, Financial technology, FinTech

  • DOI Number
    10.15415/jtmge/2025.162003

  • Authors
    Rajesh Gupta and Karnika Gupta

References

  • Alshari, H. A., & Lokhande, M. A. (2022). The impact of demographic factors of clients’ attitudes and their intentions to use FinTech services on the banking sector in the least developed countries. Cogent Business & Management, 9(1), 2114305. https://doi.org/10.1080/23311975.2022.2114305
  • Ambalov, I. A. (2021). Decomposition of perceived usefulness: A theoretical perspective and empirical test. Technology in Society, 64, 101520. https://doi.org/10.1016/j.techsoc.2020.101520
  • Arner, D. W., Barberis, J. N., & Buckley, R. P. (2015). The evolution of FinTech: A new post-crisis paradigm. University of Hong Kong Faculty of Law Research Paper No. 2015/047; UNSW Law Research Paper No. 2016-62. https://dx.doi.org/10.2139/ssrn.2676553
  • 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. https://doi.org/10.1016/j.procs.2024.03.122
  • Bhat, K. P., Kumar, R. G., Nandini, A. S., Balaji, K. R. A., & Kulsum, S. (2025). FinTech: A convenience revolution or a privacy compromise. Economic Sciences, 21(2S), 70–85. https://doi.org/10.69889/hh2ewh39
  • Cahyadi, H., Tarigan, R. P., Masman, R. R., Trisnawati, E., & Wijaya, H. (2024). Exploring the dynamics of FinTech usage behavior moderated by customer characteristics in Indonesia. International Journal of Innovative Research and Scientific Studies, 7(3), 997–1008. https://doi.org/10.53894/ijirss.v7i3.2993
  • Choi, Y., Han, S., & Lee, C. (2024). Exploring drivers of FinTech adoption among elderly consumers. Technology in Society, 78, 102669. https://doi.org/10.1016/j.techsoc.2024.102669
  • Cochran, W. G. (1977). Sampling techniques. John Wiley and Sons. https://books.google.co.in/books?id=xbNn41DUrNwC&lpg=PP1&pg=PR13#v=onepage&q&f=false
  • 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
  • Department of Economic and Statistical Affairs, Haryana. (2025). Economic survey of Haryana 2024–25. Government of Haryana. https://cdnbbsr.s3waas.gov.in/s32b0f658cbffd284984fb11d90254081f/uploads/2025/03/20250317378593833.pdf
  • Fu, J., & Mishra, M. (2022). FinTech in the time of COVID-19: Technological adoption during crises. Journal of Financial Intermediation, 50, 100945. https://doi.org/10.1016/j.jfi.2021.100945
  • Gupta, K., Wajid, A., & Gaur, D. (2024). Determinants of continuous intention to use FinTech services: The moderating role of COVID-19. Journal of Financial Services Marketing, 29, 536–552. https://doi.org/10.1057/s41264-023-00221-z
  • Gupta, R. K. (2024). Demographic dynamics of FinTech adoption: Exploring patterns and preferences in an emerging economy. IITM Journal of Business Studies (Special issue), 263–283. https://doi.org/10.48165/iitmjbs.2024.SI.17
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. https://pesquisa.bvsalud.org/portal/resource/pt/biblio-1074274#
  • Kalinga, M. L., & Senarathna, E. C. K. (2023). The determinants of financial technology (FinTech) usage acceptance among undergraduates. Journal of Business Studies, 10(2), 40–58. https://doi.org/10.4038/jbs.v10i2.97
  • Krupa, D., & Buszko, M. (2023). Age dependent differences in using FinTech products and services – Young customers versus other adults. PLoS ONE, 18(10), e0293470. https://doi.org/10.1371/journal.pone.0293470
  • Kumar, J., & Rani, V. (2025). Financial innovation and gender dynamics: A comparative study of male and female FinTech adoption in emerging economies. International Journal of Accounting and Information Management, 33(2), 334–353. https://doi.org/10.1108/IJAIM-03-2024-0098
  • Lee, I., & Shin, Y. J. (2018). FinTech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35–46. https://doi.org/10.1016/j.bushor.2017.09.003
  • Mahmud, K., Joarder, M. M. A., & Muheymin-Us-Sakib, K. (2023). Adoption factors of FinTech: Evidence from an emerging economy country-wide representative sample. International Journal of Financial Studies, 11(1), 9. https://doi.org/10.3390/ijfs11010009
  • Mittal, K., Kumar, S., & Prasad, N. R. (2025). Breaking barriers to FinTech adoption: A multivariate analysis of the role of demographic traits. European Economic Letters (EEL), 15(1), 34–41. https://doi.org/10.52783/eel.v15i1.2362
  • Mundfrom, D. J., Shaw, D. G., & Lu Ke, T. (2005). Minimum sample size recommendations for conducting factor analyses. International Journal of Testing, 5(2), 159–168. https://doi.org/10.1207/s15327574ijt0502_4
  • National Capital Region Planning Board. (2018, April). National Capital Region: Constituent areas. Ministry of Housing and Urban Affairs, Government of India. https://ncrpb.nic.in/ncrconstituent.html
  • Phuong, N. T. H., Thuy, N. D., Giang, T. L., Han, B. T. N., Hieu, T. H., & Long, N. T. (2022). Determinants of intention to use FinTech payment services: Evidence from Vietnam’ generation Z. International Journal of Business, Economics and Law, 26(1), 354–366. https://www.ijbel.com/wp-content/uploads/2022/06/IJBEL26.ISU1_301.pdf
  • Pradhan, K. C., Kumar, S., & Sharma, R. (2025). Adopting digital financial technology in Madhyapradesh, Central India: Opportunities, challenges, and determinants. Journal of the Knowledge Economy, 16, 10599–10638. https://doi.org/10.1007/s13132-024-02190-7
  • Rani, V., & Kumar, J. (2024). Gender differences in FinTech adoption: What do we know, and what do we need to know? Journal of Modelling in Management, 19(4), 1215–1236. https://doi.org/10.1108/JM2-06-2023-0121
  • Setyanti, A. M., Khoiruddin, M. A., & Finuliyah, F. (2025). Rethinking financial inclusion in the digital age: Determinants of FinTech adoption in Indonesian households. Neo Journal of Economy and Social Humanities, 4(2), 209–224. https://doi.org/10.56403/nejesh.v4i2.261
  • Sharma, D., & Munjal, P. (2024). Determining the key drivers of FinTech adoption in India. International Journal of Process Management and Benchmarking, 16(4), 533–554. https://doi.org/10.1504/IJPMB.2024.137146
  • Singla, M., Jain, N., & Rani, P. (2025). FinTech adoption in emerging economies: Exploring demographic patterns and preferences. Journal of Economic and Administrative Sciences. Advance online publication. https://doi.org/10.1108/JEAS-10-2024-0402
  • Solarz, M., & Swacha-Lech, M. (2021). Determinants of the adoption of innovative FinTech services by millennials. E&M Economics and Management, 24(3), 149–166. https://doi.org/10.15240/tul/001/2021-3-009
  • Ting, H., Memon, M. A., Thurasamy, R., & Cheah, J. H. (2025). Snowball sampling: A review and guidelines for survey research. Asian Journal of Business Research, 15(1), 1–15. https://doi.org/10.14707/ajbr.250186
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  • Wu, G., & Peng, Q. (2024). Bridging the digital divide: Unraveling the determinants of FinTech adoption in rural communities. SAGE Open, 14(1), 1–16. https://doi.org/10.1177/21582440241227770