Uncovering the Bitcoin investment behavior: An emerging market study
-
DOIhttp://dx.doi.org/10.21511/imfi.21(4).2024.04
-
Article InfoVolume 21 2024, Issue #4, pp. 35-48
- 163 Views
-
41 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Bitcoin remains a popular investment choice despite the regulatory obstacles and failures of many crypto firms. This intriguing behavior of investors necessitates calls for more in-depth research. This study explores the underlying motivations behind the intention to invest in Bitcoin by considering inaction regret aversion, overconfidence bias, herding, risk affinity, profit expectancy, perceived ease of investing, and social media influence in shaping the investors’ attitude towards investing in Bitcoin and consequently on behavioral intention to invest in Bitcoin. The study employs PLS-SEM and mediation analysis on a sample of 439 individuals from India with no history of cryptocurrency trading or investment. Path analysis demonstrates that inaction regret aversion, risk affinity, profit expectancy of Bitcoin, perceived ease of investing in Bitcoin, and social media influence are significant positive predictors of attitude toward investing in Bitcoin. Notably, profit expectancy remains the most relevant variable in the stated context. Attitude toward investing in Bitcoin positively and significantly influences the behavioral intention to invest in Bitcoin. The current study also indicates the significance of attitude as a mediator in the mentioned context.
- Keywords
-
JEL Classification (Paper profile tab)D91, G11, G40, G41
-
References50
-
Tables6
-
Figures1
-
- Figure 1. Hypothesized model
-
- Table 1. Demographic profile of respondents
- Table 2. Path estimates
- Table 3. Mediation analysis
- Table 4. Hypotheses testing summary
- Table A1. Constructs
- Table B1. Validity and reliability statistics
-
- Abramova, S., & Böhme, R. (2016). Perceived benefit and risk as multidimensional determinants of bitcoin use: a quantitative exploratory study (Paper presented at Thirty Seventh International Conference on Information Systems, 2016, Dublin).
- Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50(2), 179-211.
- Ali, A. (2011). Predicting Individual Investors-Intention to Invest: An Experimental Analysis of Attitude as a Mediator. International Journal of Economics and Management Engineering, 5(2), 157-164.
- Allen, D. G., Weeks, K. P., & Moffitt, K. R. (2005). Turnover intentions and voluntary turnover. The moderating roles of self-monitoring, locus of control, proactive personality, and risk aversion. Journal of Applied Psychology, 90(5), 980-990.
- Baker, H. K., Kumar, S., Goyal, N., & Gaur, V. (2019). How financial literacy and demographic variables relate to behavioral biases. Managerial Finance, 45(1), 124-146.
- Bannier, C., Meyll, T., Röder, F., & Walter, A. (2019). The gender gap in ‘Bitcoin literacy’. Journal of Behavioral and Experimental Finance, 22, 129-134.
- Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, 116(1), 261-292.
- Bizzi, L., & Labban, A. (2019). The double-edged impact of social media on online trading: Opportunities, threats, and recommendations for organizations. Business Horizons, 62(4), 509-519.
- Böyükaslan, A., &Ecer, F. (2021). Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework. Technology in Society, 67.
- Bui, L. (2022). Investor Behavior in the cryptocurrency market: A quantitative study investigating individual investors’ adoption intention to Bitcoin in the cryptocurrency market.
- Chen, Y. (2018). Blockchain tokens and the potential democratization of entrepreneurship and innovation. Business Horizons, 61(4), 567-575.
- Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least square latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217.
- Choudhary, S., Jain, A., & Biswal, P. C. (2024). Dynamic Linkages among Bitcoin, Equity, Gold and Oil: An Implied Volatility Perspective. Finance Research Letters, 62.
- da Gama Silva, P. V. J., Klotzle, M. C., Pinto, A. C. F., & Gomes, L. L. (2019). Herding behavior and contagion in the cryptocurrency market. Journal of Behavioral and Experimental Finance, 22, 41-50.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
- Delfabbro, P., King, D. L., & Williams, J. (2021). The psychology of cryptocurrency trading: Risk and protective factors. Journal of Behavioral Addictions, 10(2), 201-207.
- Ehm, C., Kaufmann, C., & Weber, M. (2014). Volatility inadaptability: Investors care about risk, but cannot cope with volatility. Review of Finance, 18(4), 1387-1423.
- Fischhoff, B., Slovic, P., & Lichtenstein, S. (1982). Lay foibles and expert fables in judgments about risk. The American Statistician, 36(3b), 240-255.
- Folkinshteyn, D., & Lennon, M. (2016). Braving Bitcoin: A technology acceptance model (TAM) analysis. Journal of Information Technology Case and Application Research, 18(4), 220-249.
- Glaser, M., & Weber, M. (2007). Overconfidence and trading volume. The Geneva Risk and Insurance Review, 32(1), 1-36.
- Gupta, S., Gupta, S., Mathew, M., & Sama, H. R. (2021). Prioritizing intentions behind investment in cryptocurrency: a fuzzy analytical framework. Journal of Economic Studies, 48(8), 1442-1459.
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall.
- Inman, J. J., & Zeelenberg, M. (2002). Regret in repeat purchase versus switching decisions: The attenuating role of decision justifiability. Journal of Consumer Research, 29(1), 116-128.
- Kim, S. B., Sun, K. A., & Kim, D. Y. (2013). The influence of consumer value-based factors on attitude-behavioral intention in social commerce: The differences between high-and low-technology experience groups. Journal of Travel & Tourism Marketing, 30(1-2), 108-125.
- Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. The Economic Journal, 92(368), 805-824.
- Malhotra, Y., & Galletta, D. (2005). A multidimensional commitment model of volitional systems adoption and usage behavior. Journal of Management Information Systems, 22(1), 117-151.
- Mattke, J., Maier, C., Reis, L., & Weitzel, T. (2021). Bitcoin investment: a mixed methods study of investment motivations. European Journal of Information Systems, 30(3), 261-285.
- 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.
- Mudholkar, G. P., & Uttarwar, V. R. (2015). The impact of social networking sites on investment decisions.
- Nadler, P., & Guo, Y. (2020). The fair value of a token: How do markets price cryptocurrencies? Research in International Business and Finance, 52
- Nagy, R. A., & Obenberger, R. W. (1994). Factors influencing individual investor behavior. Financial Analysts Journal, 50(4), 63-68.
- Nosić, A., & Weber, M. (2010). How riskily do I invest? The role of risk attitudes, risk perceptions, and overconfidence. Decision Analysis, 7(3), 282-301.
- Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150-162.
- Pelster, M., & Gonzalez, G. R. (2016). Social media interactions and biases in investment decisions. Centre for Economic Policy Research.
- Pham, Q. T., Phan, H. H., Cristofaro, M., Misra, S., & Giardino, P. L. (2021). Examining the intention to invest in cryptocurrencies: An extended application of the theory of planned behavior on Italian independent investors. International Journal of Applied Behavioral Economics, 10(3), 59-79.
- Podsakoff, P. M., & Todor, W. D. (1985). Relationships between leader reward and punishment behavior and group processes and productivity. Journal of Management, 11(1), 55-73.
- Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531-544.
- Prosad, J. M., Kapoor, S., & Sengupta, J. (2015). Behavioral biases of Indian investors: a survey of Delhi-NCR region. Qualitative Research in Financial Markets, 7(3), 230-263.
- 39. Sattar, M. A., Toseef, M., & Sattar, M. F. (2020). Behavioral finance biases in investment decision making. International Journal of Accounting, Finance and Risk Management, 5(2), 69-75.
- Schwartz, B., Ward, A., Monteross, J., Lyubomirsky, S., White, K., & Lehman, D. (2002). Maximizing vertus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83, 1178-1197.
- Shalev, E., & Morwitz, V. G. (2012). Influence via comparison-driven self-evaluation and restoration: The case of the low-status influencer. Journal of Consumer Research, 38(5), 964-980.
- 42. Spyrou, S. (2013). Herding in financial markets: a review of the literature. Review of Behavioral Finance, 5(2), 175-194.
- Sudzina, F., Dobes, M., & Pavlicek, A. (2023). Toward the psychological profile of cryptocurrency early adopters: Overconfidence and self-control as predictors of cryptocurrency use. Current Psychology, 42, 8713-8717.
- Syarkani, Y., & Tristanto, T. A. (2022). Examining the predictors of crypto investor decision: The relationship between overconfidence, financial literacy, and attitude. International Journal of Research in Business and Social Science, 11(6), 324-333.
- Tsiros, M. (2008). Releasing the regret lock. Consumer response to new alternatives after a sale. Journal of Consumer Research, 35(6), 1039-1059.
- Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
- Wokke, J., & Rodenrijs, N. (2018). Will social media make or break the acceptance in new technology? A quantitative study of consumer acceptance in cryptocurrency.
- Yoo, K., Bae, K., Park, E., & Yang, T. (2020). Understanding the diffusion and adoption of Bitcoin transaction services: The integrated approach. Telematics and Informatics, 53.
- Younus, A. M., Tarazi, R., Younis, H., & Abumandil, M. (2022). The role of behavioural intentions in implementation of bitcoin digital currency factors in terms of usage and acceptance in New Zealand: cyber security and social influence. ECS Transactions, 107(1).