Optimism-Bias-Oriented Fraud-Risk Appraisal in Mobile Banking: Measurement Evidence and Demographic Patterns among Users in Chennai

S. Jayaprakash Narayanan1 and V. Shanmugasundaram2*
1Ph.D Scholar, Department of Commerce, Madras Christian College, Chennai, Tamil Nadu, India
2Assistant Professor, Department of Commerce, Madras Christian College, Chennai, Tamil Nadu, India
*Corresponding Author E-mail: shanmugasundaram@mcc.edu.in
Keywords: optimism bias; mobile banking; behavioral cybersecurity; fraud-risk appraisal; Protection Motivation Theory; threat appraisal
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Abstract

Purpose: This study evaluates an optimism-bias-oriented fraud-risk appraisal measure and examines whether demographic, socioeconomic, and usage characteristics differentiate such appraisal among mobile-banking users in Chennai.

Design/methodology/approach: A bilingual English-Tamil questionnaire was completed by 114 users in May 2026. Reliability, minimum-residual factor analysis, 5,000-replication parallel analysis, Welch tests, effect sizes, Spearman correlation, hierarchical regression, HC3 robust standard errors, and response-pattern sensitivity analysis were used.

Findings: The six-item measure showed good internal consistency (α = .856; ω = .860) and one-factor evidence (Kaiser-Meyer-Olkin = .821; loadings = .591–.822). The mean score was 3.10 (SD = 0.94). Age, reported gender, education, occupation, income, application type, and usage-frequency groups were not reliably differentiated (all p > .10). Usage intensity showed a weak positive tendency, ρ = .166, 95% bootstrap CI [−.031, .359], p = .078. Demographic and socioeconomic predictors explained 2.3% of variance; adding usage intensity increased R² to .041, while application type added little (final R² = .043).

Originality: The study distinguishes classical comparative optimism from a broader fraud-risk appraisal composite and shows that direct threat-appraisal assessment is more defensible than demographic profiling for mobile-banking risk communication.

Citation

S. Jayaprakash Narayanan and V. Shanmugasundaram, Optimism-Bias-Oriented Fraud-Risk Appraisal in Mobile Banking: Measurement Evidence and Demographic Patterns among Users in Chennai, International Journal of Business and Economics Research (IJBER) e-ISSN: 2455-3921; 12.3 (2026): 7-20.

DOI: https://doi.org/10.5281/zenodo.22937855

References

  1. Almansoori, A., Al-Emran, M., & Shaalan, K. (2023). Exploring the frontiers of cybersecurity behavior: A systematic review of studies and theories. Applied Sciences, 13(9), 5700.
  2. Cho, H. L., Miller, D. G., & Kim, S. Y. H. (2020). Understanding people’s unrealistic optimism about clinical research participation. Journal of Medical Ethics, 46(3), 172–176.
  3. Comerford, D. A. (2021). Apparent age and gender differences in survival optimism: To what extent are they a bias in the translation of beliefs onto a percentage scale? Judgment and Decision Making, 16(4), 1072–1096.
  4. Dricu, M., Moser, D. A., & Aue, T. (2022). Optimism bias and its relation to scenario valence, gender, sociality, and insecure attachment. Scientific Reports, 12, 18534.
  5. Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185.
  6. Joshi, M. S., & Carter, W. (2013). Unrealistic optimism: East and West? Frontiers in Psychology, 4, 6.
  7. Kumar, R., Singh, R., Kumar, K., Khan, S., & Corvello, V. (2023). How does perceived risk and trust affect mobile banking adoption? Empirical evidence from India. Sustainability, 15(5), 4053.
  8. Mou, J., Cohen, J. F., Bhattacherjee, A., & Kim, J. (2022). A test of Protection Motivation Theory in the information security literature: A meta-analytic structural equation modeling approach. Journal of the Association for Information Systems, 23(1), 196–236.
  9. Owen, M., Flowerday, S. V., & van der Schyff, K. (2024). Optimism bias in susceptibility to phishing attacks: An empirical study. Information & Computer Security, 32(5), 656–675.
  10. Reserve Bank of India. (2025a, January 27). Payment System Report, December 2024.
  11. Reserve Bank of India. (2025b, October 23). Payment Systems Report, June 2025.
  12. Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. The Journal of Psychology, 91(1), 93–114.
  13. Rogers, R. W. (1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In J. T. Cacioppo & J. T. Petty (Eds.), Social psychophysiology: A sourcebook (pp. 153–176). Guilford Press.
  14. Shepperd, J. A., Waters, E. A., Weinstein, N. D., & Klein, W. M. P. (2015). A primer on unrealistic optimism. Current Directions in Psychological Science, 24(3), 232–237.
  15. Sommestad, T., Karlzén, H., & Hallberg, J. (2015). A meta-analysis of studies on Protection Motivation Theory and information security behaviour. International Journal of Information Security and Privacy, 9(1), 26–46.
  16. Weinstein, N. D. (1980). Unrealistic optimism about future life events. Journal of Personality and Social Psychology, 39(5), 806–820.
  17. You, J., Fung, H. H., & Isaacowitz, D. M. (2009). Age differences in dispositional optimism: A cross-cultural study. European Journal of Ageing, 6(4), 247–252.

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Published by: Dr. BGR Publications | India

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

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