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Stream/Area:
-
Subject:
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Paper ID:
IJSREM67966
Volume:
10 |
10 |
Oct 2026
Pages:
1-16
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Bachelor of Business Administration
Koneru Lakshmaiah Educational Foundation
Bachelor of Business Administration
Koneru Lakshmaiah Educational Foundation
FinTech has changed the way people in India pay for things, but most research stops at the individual: it asks why someone decides to use a payment app and rarely asks what happens next. This study examines how young consumers in three localities of Visakhapatnam (AU Campus Area, Siripuram and Dwaraka Nagar) are adopting FinTech and whether respondents see that adoption supporting the growth of small and medium enterprises (SMEs). Primary data were collected with a structured questionnaire of 27 items, and 90 responses from persons aged 18 to 35 were analysed. Descriptive statistics, Cronbach’s alpha, Pearson correlation, chi-square tests, one-way ANOVA and linear regression were used. UPI was used by every respondent and 66.7% paid digitally every day. The chi-square tests showed that payment frequency and the share of transactions made digitally are significantly associated with age, occupation, SME linkage, internet reliability and gender (frequency only). The ANOVA showed that adoption intensity differs by age (F(2, 87) = 6.22, p = .003), occupation (p = .002) and network quality (p = .001) but not by income. In the multiple regression (R² = .206, p = .001) social influence was the only significant predictor of adoption (β = .266, p = .010); effort expectancy (p = .063) and trust (p = .078) were marginal. Personal adoption intensity did not predict perceived SME growth (β = −.083, p = .436), although respondents with an SME link rated FinTech’s SME benefits higher. The data file contained only 30 distinct response patterns, each recorded three times, so the significance levels should be treated as provisional (Section 7.9).
KEYWORDS
This study examined FinTech adoption among 90 young consumers in the AU Campus Area, Siripuram and Dwaraka Nagar of Visakhapatnam and its relation to perceived SME growth. Digital payment use is almost universal, but its intensity varies with age, occupation, SME linkage, gender and, above all, internet reliability. Social influence is the clearest driver of adoption, with effort expectancy and trust playing a smaller, marginal role. Respondents who are linked to SMEs hold the strongest belief that FinTech supports SME growth, yet they are not the heaviest users, and personal usage intensity does not predict this belief. Peer-led promotion, SME-focused digital training, stronger trust-building and better connectivity are therefore the practical routes for turning wide FinTech access into measurable gains for local SMEs. Given the small sample, the low scale reliability and the repeated responses in the data file, the results should be regarded as preliminary and validated on a larger, independent sample.
Alamoodi and Selamat (2021). [Complete reference: authors’ initials, title, journal, volume, pages and DOI.] 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. [Verify details.] Bagale and Srivastava (2023). [Complete reference: authors’ initials, title, journal, volume, pages and DOI.] Chawla, D., & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India: An empirical study. International Journal of Bank Marketing, 37(7), 1590–1618. [Verify details.] 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 Fahad and Shahid (2022). [Complete reference: authors’ initials, title, journal, volume, pages and DOI.] Gulia and Singh (2023). [Complete reference: authors’ initials, title, journal, volume, pages and DOI.] Gupta and Xia (2020). [Complete reference: authors’ initials, title, journal, volume, pages and DOI.] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. Hu et al. (2020). [Complete reference: authors, title, journal, volume, pages and DOI.]
Publication Date
10/14/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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