Social Media Stimuli and Retail Investor Behaviour: An Integrated Stimulus–Organism–Response Framework of FOMO, Herding, Overconfidence, and Financial Literacy

Authors

Alen Shepherd Thomas

Assistant Professor, Department of Commerce, Kuriakose Elias College, Mannanam (India)

Renjith R

Assistant Professor & Head, Department of Commerce Prajyoti Niketan College, Pudukad (India)

George Jose

Assistant Professor, Department of Commerce, Prajyoti Niketan College, Pudukad (India)

Bobby Simon

Associate Professor & Head, Department of Commerce, St. Thomas College Autonomous Palai, Arunapuram (India)

Alan Lukose

Assistant Professor, Department of Commerce, Prajyoti Niketan College, Pudukad (India)

Article Information

DOI: 10.47772/IJRISS.2026.100601329

Subject Category: Finance and Management

Volume/Issue: 10/6 | Page No: 19473-19499

Publication Timeline

Submitted: 2026-07-05

Accepted: 2026-07-10

Published: 2026-07-18

Abstract

The rapid proliferation of social media has fundamentally transformed the manner in which retail investors access, interpret, and act upon financial information. Digital platforms such as YouTube, X (formerly Twitter), Reddit, Telegram, Instagram, and other social networking sites have evolved beyond communication tools to become influential sources of investment-related information, where financial influencers ("finfluencers"), online communities, and algorithmically curated content increasingly shape investor perceptions and market participation. While existing studies have independently examined Behavioral biases, financial literacy, and social media usage, the theoretical mechanisms through which social media stimuli influence investor Behavior remain fragmented. In particular, limited attention has been devoted to understanding how digitally mediated information environments activate psychological biases and how financial literacy may mitigate these effects.
This paper develops a conceptual framework grounded in the Stimulus–Organism–Response (S–O–R) paradigm to explain the Behavioral processes linking social media exposure to retail investors' decision-making. The proposed model conceptualises social media stimuli—including finfluencer recommendations, algorithm-driven content, and online investor communities—as external stimuli that activate internal psychological states represented by fear of missing out (FOMO), herding bias, and overconfidence. These organismic states subsequently influence investment Behavior, resulting in increased speculative trading, impulsive investment decisions, and reduced reliance on fundamental analysis. The framework further proposes financial literacy as a moderating mechanism capable of attenuating the influence of Behavioral biases, although its effectiveness is expected to vary across different bias dimensions. By integrating insights from Behavioral finance, financial literacy research, and digital consumer Behavior within a unified theoretical perspective, the paper addresses an important gap in the existing literature. The proposed framework offers a foundation for future empirical research and provides practical implications for financial educators, market regulators, policymakers, and investor protection agencies seeking to promote informed investment decisions in increasingly digital financial markets.

Keywords

Behavioral Finance, Retail Investor Behavior, Social Media, Financial Literacy, Stimulus–Organism–Response (S–O–R)

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References

1. Adil, M., Singh, Y., & Ansari, M. S. (2022). How financial literacy moderates the association between Behavioral biases and investment decision? Asian Journal of Accounting Research, 7(1), 17–30. https://doi.org/10.1108/AJAR-09-2020-0086 [Google Scholar] [Crossref]

2. Agarwal, A., Rao, N. M., & Nogueira, M. C. (2025). Financially savvy or swayed by biases? The impact of financial literacy on investment decisions: A study on Indian retail investors. Journal of Risk and Financial Management, 18(6), 322. [Google Scholar] [Crossref]

3. Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, 116(1), 261–292. https://doi.org/10.1162/003355301556400 [Google Scholar] [Crossref]

4. Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21(2), 785–818. https://doi.org/10.1093/rfs/hhm079 [Google Scholar] [Crossref]

5. Barberis, N., & Thaler, R. H. (2003). A survey of behavioral finance. In G. M. Constantinides, M. Harris, & R. M. Stulz (Eds.), Handbook of the Economics of Finance (Vol. 1, pp. 1053–1128). Elsevier. https://doi.org/10.1016/S1574-0102(03)01027-6 [Google Scholar] [Crossref]

6. Bharti, N. S., & Kumar, A. (2025). Thematic review and discussion of research on herd behavior in capital markets: Highlighting the gaps and proposing future research avenues. SAGE Open. [Google Scholar] [Crossref]

7. Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026. https://doi.org/10.1086/261849 [Google Scholar] [Crossref]

8. CFA Institute. (2024). The finfluencer appeal: Investing in the age of social media. [Google Scholar] [Crossref]

9. Chen, H., De, P., Hu, Y. J., & Hwang, B. H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. Review of Financial Studies, 27(5), 1367–1403. https://doi.org/10.1093/rfs/hhu001 [Google Scholar] [Crossref]

10. Cookson, J. Anthony and Engelberg, Joseph and Mullins, William, Echo Chambers (February 14, 2022). The Review of Financial Studies, volume 36, issue 2, 2023. http://dx.doi.org/10.1093/rfs/hhac058 [Google Scholar] [Crossref]

11. Dalvi-Esfahani, M., Barati Ahmadabadi, H., Ramayah, T., & Turner, J. J. (2025). Stimulus–organism–response framework of decision-makers' intention to adopt generative AI to replace entry-level jobs: The moderating impact of personality traits. Technological Forecasting and Social Change. [Google Scholar] [Crossref]

12. Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7(2), 117–140. https://doi.org/10.1177/001872675400700202 [Google Scholar] [Crossref]

13. FINRA Investor Education Foundation. (2026). Finfluencer followers and social media scrollers: The profile, patterns, and pitfalls of social-media-informed retail investors. [Google Scholar] [Crossref]

14. Goyal, P., Gupta, P., & Yadav, V. (2023). Antecedents to heuristics: Decoding the role of herding and prospect theory for Indian millennial investors. Review of Behavioral Finance, 15(1), 79–102. https://doi.org/10.1108/RBF-07-2021-0128 [Google Scholar] [Crossref]

15. Gupta, S., & Shrivastava, M. (2022). Herding and loss aversion in stock markets: Mediating role of fear of missing out (FOMO) in retail investors. International Journal of Emerging Markets. [Google Scholar] [Crossref]

16. Haase, F., Rath, O., Krauß, J., & Schoder, D. (2025). The role of finfluencers in shaping crowd sentiment. Business & Information Systems Engineering. [Google Scholar] [Crossref]

17. International Organization of Securities Commissions (IOSCO). (2025). Finfluencers. [Google Scholar] [Crossref]

18. IOSCO. (2025). Finfluencers. International Organization of Securities Commissions. https://www.iosco.org [Google Scholar] [Crossref]

19. Jacoby, J. (2002). Stimulus–organism–response reconsidered: An evolutionary step in modeling consumer behavior. Journal of Consumer Psychology, 12(1), 51–57. https://doi.org/10.1207/S15327663JCP1201_05 [Google Scholar] [Crossref]

20. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185 [Google Scholar] [Crossref]

21. Long, W., Guo, M. Social media and capital markets: an interdisciplinary bibliometric analysis. Financ Innov 11, 77 (2025). https://doi.org/10.1186/s40854-024-00731-2 [Google Scholar] [Crossref]

22. Lusardi, A., & Mitchell, O. S. (2011). Financial literacy around the world: An overview. Journal of Pension Economics and Finance, 10(4), 497–508. [Google Scholar] [Crossref]

23. Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5 [Google Scholar] [Crossref]

24. Marfo, E., & Ofei, E. F. (2026). Does financial literacy influence investment decisions among retail investors? The moderating role of behavioral biases. Thunderbird International Business Review. [Google Scholar] [Crossref]

25. Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press. [Google Scholar] [Crossref]

26. Nirun, A. B., & Asgarli, M. (2024). FoMO in investment: A critical literature review of fear of missing out in investment. [Google Scholar] [Crossref]

27. OECD. (2023). OECD/INFE international survey of adult financial literacy. [Google Scholar] [Crossref]

28. Ontario Securities Commission. (2026). Social media and retail investing: The rise of finfluencers. [Google Scholar] [Crossref]

29. Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848. https://doi.org/10.1016/j.chb.2013.02.014 [Google Scholar] [Crossref]

30. Shiller, R. J. (2019). Narrative economics: How stories go viral and drive major economic events. Princeton University Press. [Google Scholar] [Crossref]

31. Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852 [Google Scholar] [Crossref]

32. Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3), 1139–1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x [Google Scholar] [Crossref]

33. Thaler, R. H. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214. https://doi.org/10.1287/mksc.4.3.199 [Google Scholar] [Crossref]

34. Van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449–472. https://doi.org/10.1016/j.jfineco.2011.03.006 [Google Scholar] [Crossref]

35. Vieira, V. A. (2013). Stimuli–organism–response framework: A meta-analytic review in the store environment. Journal of Business Research, 66(9), 1420–1426. https://doi.org/10.1016/j.jbusres.2012.05.009 [Google Scholar] [Crossref]

36. Yang, W., & Loang, O. K. (2024). Systematic literature review: Behavioral biases as the determinants of herding. In R. El Khoury (Ed.), Technology-driven business innovation. https://doi.org/10.1007/978-3-031-51997-0_7 [Google Scholar] [Crossref]

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