Behavioral Biases, Risk Perceptions, and Investment Framing in Blockchain Asset Exposure
Authors
-
Piyusa Das
Associate Professor, School of Management, KIIT Deemed to be University, Bhubaneswar - 751 024, Odisha
https://orcid.org/0000-0002-7027-9452
DOI:
https://doi.org/10.17010/ijf/2026/v20i8/175250Keywords:
blockchain investment, behavioral finance, risk perception, investment framing, Indian investors.JEL Classification Codes : G11, G41, O16
Publishing Chronology: Paper Submission Date : September 25, 2025 ; Paper sent back for Revision : May 25, 2026 ; Paper Acceptance Date : July 15, 2026 ; Paper Published Online : August 17, 2026.
Abstract
Purpose : The study examined whether behavioral biases, risk perceptions, and investment framing differentiated Indian investors across levels of portfolio exposure to blockchain-based digital assets.
Design/Methodology/Approach : Data were collected from 1,139 Indian investors through an online survey. A partial least squares structural equation modeling approach was used to assess the measurement properties of five behavioral-bias constructs. Multinomial logistic regression was then employed to examine differences across three exposure categories: less than 5%, 5–10%, and more than 10%.
Findings : Overconfidence was positively associated with higher exposure, whereas reliance on experts was negatively associated with the highest exposure category. Perceived risk of government shutdown was negatively associated with exposure. Investors who did not view blockchain-based digital assets as lottery-like were more likely to report higher exposure.
Practical Implications : The findings suggested that financial advisors, platforms, and regulators should account for behavioral biases, perceived regulatory risks, and investment framing when designing investor education and risk communication.
Originality/value : The study extended research on digital-asset investment by examining behavioral and perceptual factors across portfolio-exposure levels rather than treating investment as a binary outcome or general intention.
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References
1) Almeida, J., & Gonçalves, T. C. (2023). A systematic literature review of investor behavior in the cryptocurrency markets. Journal of Behavioral and Experimental Finance, 37, Article ID 100785. https://doi.org/10.1016/j.jbef.2022.100785
2) Al-Omoush, K. S., Gomez-Olmedo, A. M., & Funes, A. G. (2024). Why do people choose to continue using cryptocurrencies? Technological Forecasting and Social Change, 200, Article ID 123151. https://doi.org/10.1016/j.techfore.2023.123151
3) Angerer, M., Hoffmann, C. H., Neitzert, F., & Kraus, S. (2021). Objective and subjective risks of investing into cryptocurrencies. Finance Research Letters, 40, Article ID 101737. https://doi.org/10.1016/j.frl.2020.101737
4) Annapurna, R., & Basri, S. (2024). Role of emotions in stock investment decisions: A critical review of the literature. Indian Journal of Finance, 18(5), 50–65. https://doi.org/10.17010/ijf/2024/v18i5/173842
5) Atif Sattar, M., Toseef, M., & Fahad Sattar, M. (2020). Behavioral finance biases in investment decision making. International Journal of Accounting, Finance and Risk Management, 5(2), 69–75. https://doi.org/10.11648/j.ijafrm.20200502.11
6) Bahari, S., Al Zarliani, W. O., Suriadi, & Hasddin. (2025). Examining the role of financial literacy, cognitive bias, and emotional bias in shaping investment decisions: A study on the Indonesian Stock Exchange. Indian Journal of Finance, 19(11), 65–81. https://doi.org/10.17010/ijf/2025/v19i11/175842
7) Bartlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research: Determining appropriate sample size in survey research. Information Technology, Learning, and Performance Journal, 19(1), 43–50. https://www.opalco.com/wp-content/uploads/2014/10/Reading-Sample-Size1.pdf
8) Bikas, E., Jurevičienė, D., Dubinskas, P., & Novickytė, L. (2013). Behavioural finance: The emergence and development trends. Procedia - Social and Behavioral Sciences, 82, 870–876. https://doi.org/10.1016/j.sbspro.2013.06.363
9) Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters, 29, 216–221. https://doi.org/10.1016/j.frl.2018.07.008
10) Breakwell, G. M. (2014). The psychology of risk (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139061933
11) Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (Vol. 2, pp. 655–690). Springer. https://doi.org/10.1007/978-3-540-32827-8_29
12) Gambetti, E., & Giusberti, F. (2019). Personality, decision-making styles and investments. Journal of Behavioral and Experimental Economics, 80, 14–24. https://doi.org/10.1016/j.socec.2019.03.002
13) Gathergood, J. (2012). Self-control, financial literacy and consumer over-indebtedness. Journal of Economic Psychology, 33(3), 590–602. https://doi.org/10.1016/j.joep.2011.11.006
14) Gautam, S., Kumar, P., & Dahiya, P. (2024). The influence of neurotransmitters on cryptocurrency investment decision-making: The mediating role of risk tolerance and moderating role of investment experience. Indian Journal of Finance, 18(10), 40–55. https://doi.org/10.17010/ijf/2024/v18i10/174613
15) Gefen, D., Rigdon, E. E., & Straub, D. (2011). Editor's comments: An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), 3–14. https://doi.org/10.2307/23044042
16) Grobys, K., & Junttila, J. (2021). Speculation and lottery-like demand in cryptocurrency markets. Journal of International Financial Markets, Institutions and Money, 71, Article ID 101289. https://doi.org/10.1016/j.intfin.2021.101289
17) Gupta, N., Rana, R., & Tandon, D. (2025). Financial literacy as a moderator in behavioral biases and investor decisions. Indian Journal of Finance, 19(5), 79–94. https://doi.org/10.17010/ijf/2025/v19i5/175045
18) Gupta, P., & Kohli, B. (2021). Herding behavior in the Indian stock market: An empirical study. Indian Journal of Finance, 15(5–7), 86–99. https://doi.org/10.17010/ijf/2021/v15i5-7/164495
19) Gupta, S., Gupta, S., Mathew, M., & Sama, H. R. (2020). Prioritizing intentions behind investment in cryptocurrency: A fuzzy analytical framework. Journal of Economic Studies, 48(8), 1442–1459. https://doi.org/10.1108/JES-06-2020-0285
20) Hair, J. F., Black, W., Babin, B. J., & Anderson, R. E. (2019). Structural equation modeling: An introduction. In Multivariate data analysis (India ed., pp. 642–643). Cengage.
21) Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
22) Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning, 45(5–6), 320–340. https://doi.org/10.1016/j.lrp.2012.09.008
23) Ionescu, R., & Rădulescu, I. D. (2019). Behavioral finance and the fast evolving world of fintech. Economic Insights – Trends and Challenges, 8(4), 47–57. https://upg-bulletin-se.ro/wp-content/uploads/2020/05/5.Ionescu_Radulescu-4-2019.pdf
24) Jain, J., Walia, N., Kaur, M., & Singh, S. (2022). Behavioural biases affecting investors' decision-making process: A scale development approach. Management Research Review, 45(8), 1079–1098. https://doi.org/10.1108/MRR-02-2021-0139
25) Jain, P. A., & Ashalatha, K. (2026). Closing the confidence gap: A behavioral finance perspective on women's equity-market participation in India. Indian Journal of Finance, 20(2), 50–60. https://doi.org/10.17010/ijf/2026/v20i2/175225
26) Johnson, B., Stjepanović, D., Leung, J., Sun, T., & Chan, G. C. K. (2023). Cryptocurrency trading, mental health and addiction: A qualitative analysis of Reddit discussions. Addiction Research & Theory, 31(5), 345–351. https://doi.org/10.1080/16066359.2023.2174259
27) Jones, M., Luu, T. (Jack), & Samuel, B. (2024). The interdependence of financial literacy and crypto literacy. Economics Letters, 239, Article ID 111737. https://doi.org/10.1016/j.econlet.2024.111737
28) Kallinterakis, V., & Wang, Y. (2019). Do investors herd in cryptocurrencies – and why? Research in International Business and Finance, 50, 240–245. https://doi.org/10.1016/j.ribaf.2019.05.005
29) Katper, N. K., Azam, M., Karim, N. A., & Zia, S. Z. (2019). Behavioral biases and investors' decision-making: The moderating role of socio-demographic variables. International Journal of Financial Engineering, 6(3), Article ID 1950020. https://doi.org/10.1142/S2424786319500208
30) KuCoin Into the cryptoverse India report reveals optimistic prospects for market growth. (2022, August 23). KuCoin. https://www.kucoin.com/blog/kucoin-into-the-cryptoverse-india-report-reveals-optimistic-prospects-for-market-growth
31) Kumar, S., & Goyal, N. (2015). Behavioural biases in investment decision making – A systematic literature review. Qualitative Research in Financial Markets, 7(1), 88–108. https://doi.org/10.1108/QRFM-07-2014-0022
32) Madaan, G., & Singh, S. (2019). An analysis of behavioral biases in investment decision-making. International Journal of Financial Research, 10(4), 55–67. https://doi.org/10.5430/ijfr.v10n4p55
33) Mills, D. J., & Nower, L. (2019). Preliminary findings on cryptocurrency trading among regular gamblers: A new risk for problem gambling? Addictive Behaviors, 92, 136–140. https://doi.org/10.1016/j.addbeh.2019.01.005
34) Mitroi, A. (2014). Behavioral finance: Biased individual investment decision making; like the company but dislike the investment. Theoretical and Applied Economics, 21(1), 63–74. https://store.ectap.ro/articole/943.pdf
35) Mittal, S. K. (2022). Behavior biases and investment decision: Theoretical and research framework. Qualitative Research in Financial Markets, 14(2), 213–228. https://doi.org/10.1108/QRFM-09-2017-0085
36) Mushinada, V. N. C. (2020). Are individual investors irrational or adaptive to market dynamics? Journal of Behavioral and Experimental Finance, 25, Article ID 100243. https://doi.org/10.1016/j.jbef.2019.100243
37) Naveen, L., Singh, S., & Rout, P. K. (2026). From algorithms to action: The role of trust and engagement in adopting robo-advisors. Indian Journal of Finance, 20(3), 39–59. https://doi.org/10.17010/ijf/2026/v20i3/175428
38) Peres, R., Schreier, M., Schweidel, D. A., & Sorescu, A. (2023). Blockchain meets marketing: Opportunities, threats, and avenues for future research. International Journal of Research in Marketing, 40(1), 1–11. https://doi.org/10.1016/j.ijresmar.2022.08.001
39) Pikulina, E., Renneboog, L., & Tobler, P. N. (2017). Overconfidence and investment: An experimental approach. Journal of Corporate Finance, 43, 175–192. https://doi.org/10.1016/j.jcorpfin.2017.01.002
40) Poyser, O. (2018). Herding behavior in cryptocurrency markets. arXiv. http://arxiv.org/abs/1806.11348
41) Rahman, M., & Gan, S. S. (2020). Generation Y investment decision: An analysis using behavioural factors. Managerial Finance, 46(8), 1023–1041. https://doi.org/10.1108/MF-10-2018-0534
42) Rieger, M. O., Nguyen, T. M., Schnur, B., & Wang, M. (2021). Taking more risk tomorrow: Time horizons and investment decisions. Applied Economics Letters, 28(6), 459–463. https://doi.org/10.1080/13504851.2020.1761522
43) Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4. Bönningstedt: SmartPLS. https://www.smartpls.com
44) Rzeszutek, M., Szyszka, A., & Czerwonka, M. (2015). Investors' expertise, personality traits and susceptibility to behavioral biases in the decision making process. Contemporary Economics, 9(3), 337–352. https://doi.org/10.5709/ce.1897-9254.173
45) Safeeda, K. A., & Ganesh, R. (2024). Unmasking the grey swan: Investor biases in times of turbulence. Indian Journal of Finance, 18(10), 56–72. https://doi.org/10.17010/ijf/2024/v18i10/174614
46) Sahi, S. K., & Arora, A. P. (2012). Individual investor biases: A segmentation analysis. Qualitative Research in Financial Markets, 4(1), 6–25. https://doi.org/10.1108/17554171211213522
47) Sahi, S. K. (2017). Psychological biases of individual investors and financial satisfaction. Journal of Consumer Behaviour, 16(6), 511–535. https://doi.org/10.1002/cb.1644
48) Schatzmann, J. E., & Haslhofer, B. (2023). Exploring investor behavior in Bitcoin: A study of the disposition effect. Digital Finance, 5(3), 581–612. https://doi.org/10.1007/s42521-023-00086-w
49) Siddiqui, T. A., Ali, M. S., Sunayana, Alam, N., & Ranjan, P. (2025). Understanding financial inclusion through social and behavioural lenses. Prabandhan: Indian Journal of Management, 18(4), 52–71. https://doi.org/10.17010/pijom/2025/v18i4/174316
50) Singh, S., & Nag, A. (2016). The role of behavioral finance in modern age investment. Journal of Management and Science, 6(1), 135–149. https://doi.org/10.26524/jms.2016.12
51) Singh, S., Bharti, I., & Maurya, P. (2025). A comprehensive review of prominent biases and other factors influencing retail investors' decision-making. Indian Journal of Finance, 19(7), 47–74. https://doi.org/10.17010/ijf/2025/v19i7/175196
52) Sokolowska, J., & Makowiec, P. (2017). Risk preferences of individual investors: The role of dispositional tendencies and market trends. Journal of Behavioral and Experimental Economics, 71, 67–78. https://doi.org/10.1016/j.socec.2017.09.003
53) Sood, K., Pathak, P., Jain, J., & Gupta, S. (2025). Gauging investors' investment decisions in the crypto market through the PRISM of behavioral biases: A fuzzy AHP approach. International Journal of Emerging Markets, 20(4), 1465–1486. https://doi.org/10.1108/IJOEM-02-2022-0263
54) Steinmetz, F. (2023). The interrelations of cryptocurrency and gambling: Results from a representative survey. Computers in Human Behavior, 138, Article ID 107437. https://doi.org/10.1016/j.chb.2022.107437
55) Sunil, N., & Kumar, B. S. (2025). Financial literacy and fintech: A double-edged sword for millennial investors' behavioral biases. Indian Journal of Finance, 19(3), 44–63. https://doi.org/10.17010/ijf/2025/v19i3/174849
56) Suresh, G., & Loang, O. K. (2024). The rationality conundrum: Exploring herd mentality among individual investors in the Indian stock market. Indian Journal of Finance, 18(6), 26–45. https://doi.org/10.17010/ijf/2024/v18i6/173967
57) Syarkani, Y., & Tristanto, T. A. (2022). Examining the predictors of crypto investor decision. International Journal of Research in Business and Social Science, 11(6), 324–333. https://doi.org/10.20525/ijrbs.v11i6.1940
58) Tjondro, E., Hatane, S. E., Widuri, R., & Tarigan, J. (2023). Rational versus irrational behavior of Indonesian cryptocurrency owners in making investment decision. Risks, 11(1), 17. https://doi.org/10.3390/risks11010017
59) Ul Abdin, S. Z., Qureshi, F., Iqbal, J., & Sultana, S. (2022). Overconfidence bias and investment performance: A mediating effect of risk propensity. Borsa Istanbul Review, 22(4), 780–793. https://doi.org/10.1016/j.bir.2022.03.001
60) Zahera, S. A., & Bansal, R. (2018). Do investors exhibit behavioral biases in investment decision making? A systematic review. Qualitative Research in Financial Markets, 10(2), 210–251. https://doi.org/10.1108/QRFM-04-2017-0028
61) Zhang, C. Y., & Sussman, A. B. (2018). The role of mental accounting in household spending and investing decisions. In C. Chaffin (Ed.), Client psychology (pp. 65–96). Wiley. https://doi.org/10.1002/9781119440895.ch6
62) Zhang, S., Zhou, X., Pan, H., & Jia, J. (2019). Cryptocurrency, confirmatory bias and news readability –Evidence from the largest Chinese cryptocurrency exchange. Accounting and Finance, 58(5), 1445–1468. https://doi.org/10.1111/acfi.12454
