Purpose: The current study examined the influence of neurotransmitters on cryptocurrency investment choices. Moreover, the research assessed the mediator risk tolerance (RT) and moderator investment experience on the connection between neurotransmitters and investment choices.Research Design/

Methodology: The analysis of the data involved 504 responses from individuals in India’s Western and Northern regions who either invested in cryptocurrencies or had knowledge of such investments. The proposed theoretical model of cryptocurrency investment choices was examined in the study using “variance-based partial least square structural equation modeling” (PLS-SEM).

Findings: The outcomes of this research specified that neurotransmitters play a substantial role in cryptocurrency investment choices, and they had a substantial impact on making investment choices. It was also noted that a significant moderator between neurotransmitters and Bitcoin investment decisions is RT. However, it was determined that investment experience had no moderating effect.Practical

Implications: This study revealed that, in order to make better-informed investment decisions, businesses, governments, and investors should consider the impact of neurotransmitters.Value/

Originality: The study was innovative since it is one of the first to examine how neurotransmitters, together with mediator RT and moderator investment experience, affected Bitcoin investment decisions. Additionally, the conceptual framework could be very helpful to cryptocurrency portfolio managers and investors in understanding how the brain functions during the decision-making process. They would then be better equipped to allocate their assets with knowledge and efficiency.

1. Ahmad, M. (2018). Impact of neurotransmitters, emotional intelligence, and personality on investor's behavior and investment decisions. Pakistan Journal of Commerce and Social Science, 12(1), 330–362. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049638765&partnerID=40&md5=20fd9ccad0cc12621d42a937d0939ea4
2. Al-Mansour, B. Y. (2020). Cryptocurrency market: Behavioral finance perspective. The Journal of Asian Finance, Economics and Business, 7(12), 159–168. https://doi.org/10.13106/JAFEB.2020.VOL7.NO12.159
3. 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
4. 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. https://doi.org/10.1108/MF-01-2018-0003
5. Cheah, J.-H., Ting, H., Ramayah, T., Memon, M. A., Cham, T.-H., & Ciavolino, E. (2019). A comparison of five reflective–formative estimation approaches: Reconsideration and recommendations for tourism research. Quality & Quantity, 53(3), 1421–1458. https://doi.org/10.1007/s11135-018-0821-7
6. Christoffersen, J., & Stæhr, S. (2019). Individual risk tolerance and herding behaviors in financial forecasts. European Financial Management, 25(5), 1348–1377. https://doi.org/10.1111/eufm.12231
7. Chuang, W.-I., Lee, B.-S., & Wang, K.-L. (2014). US and domestic market gains and Asian investors' overconfident trading behavior. Financial Management, 43(1), 113–148. https://doi.org/10.1111/fima.12030
8. Conway, C. C., & Slavich, G. M. (2017). Behavior genetics of prosocial behavior. In Compassion: Concepts, research and applications (1st ed.). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781315564296-9/behavior-genetics-prosocial-behavior-christopher-conway-george-slavich
9. Dangi, M., & Kohli, B. (2018). Role of behavioral biases in investment decisions: A factor analysis. Indian Journal of Finance, 12(3), 43–57. https://doi.org/10.17010/ijf/2018/v12i3/121997
10. Gautam, S., & Kumar, P. (2023a). Behavioral biases of investors in the cryptocurrency market. In AIP Conference Proceedings (Vol. 2782, Iss. 1), 020105. AIP Publishing. https://doi.org/10.1063/5.0154194
11. Gautam, S., & Kumar, P. (2023b). Prominent behavioral biases affecting investment decisions: A systematic literature review with TCCM and bibliometric analysis. International Journal of Indian Culture and Business Management. https://doi.org/10.1504/IJICBM.2023.10059339
12. Gautam, S., & Kumar, P. (2024). A study of the evolutionary trends in blockchain and cryptocurrency: A bibliometric approach. In S. Taneja, P. Kumar, K. Sood, E. Özen, & S. Grima (eds.), Finance analytics in business (pp. 251–266). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83753-572-920241013
13. Gautam, S., Kumar, P., Dahiya, P., & Sandhu, S. (2024). Emotional intelligence and cryptocurrency investments: Unraveling the mediating role of risk tolerance and moderating role of investment experience. International Journal of Business Innovation and Research. https://doi.org/10.1504/IJBIR.2024.10065382
14. Ghasemy, M., Teeroovengadum, V., Becker, J.-M., & Ringle, C. M. (2020). This fast car can move faster: A review of PLS-SEM application in higher education research. Higher Education, 80(6), 1121–1152. https://doi.org/10.1007/s10734-020-00534-1
15. Ghosh, S. K. (2021, June 22). Rising retail participation in stock market: Is it the beginning of a long-term behavioural change? (Issue 23). SBI Ecowrap. https://sbi.co.in/documents/13958/10990811/220621-Ecowrap_20210622.pdf/98268407-3a8a-bacf-085b-3daef4984292?t=1624355750351
16. Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214. https://doi.org/10.1080/07421222.2001.11045669
17. Gomes, A., Gorton, G., & Madureira, L. (2007). SEC regulation fair disclosure, information, and the cost of capital. Journal of Corporate Finance, 13(2–3), 300–334. https://doi.org/10.1016/j.jcorpfin.2006.11.001
18. Hair Jr., J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
19. Hair Jr., J. F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications Inc. https://us.sagepub.com/en-us/nam/a-primer-on-partial-least-squares-structural-equation-modeling-pls-sem/book270548
20. Hair Jr., J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
21. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
22. Hiser, J., & Koenigs, M. (2018). The multifaceted role of the ventromedial prefrontal cortex in emotion, decision making, social cognition, and psychopathology. Biological Psychiatry, 83(8), 638–647. https://doi.org/10.1016/j.biopsych.2017.10.030
23. Khan, A., & Mubarik, M. S. (2020). Measuring the role of neurotransmitters in investment decision: A proposed constructs. International Journal of Finance & Economics, 27(1), 258–274. https://doi.org/10.1002/ijfe.2150
24. Kuhnen, C. M., & Chiao, J. Y. (2009). Genetic determinants of financial risk taking. PLoS ONE, 4(2), e4362. https://doi.org/10.1371/journal.pone.0004362
25. Kuhnen, C. M., & Knutson, B. (2005). The neural basis of financial risk taking. Neuron, 47(5), 763–770. https://doi.org/10.1016/j.neuron.2005.08.008
26. Lang, M., Bahna, V., Shaver, J. H., Reddish, P., & Xygalatas, D. (2017). Sync to link: Endorphin-mediated synchrony effects on cooperation. Biological Psychology, 127, 191–197. https://doi.org/10.1016/j.biopsycho.2017.06.001
27. Lemmers-Jansen, I. L., Krabbendam, L., Veltman, D. J., & Fett, A.-K. (2017). Boys vs. girls: Gender differences in the neural development of trust and reciprocity depend on social context. Developmental Cognitive Neuroscience, 25, 235–245. https://doi.org/10.1016/j.dcn.2017.02.001
28. Linge, A. A., Jiwani, A., & Kakde, B. B. (2024). Factors affecting risk attitude and investors' happiness of newly employed individuals. Indian Journal of Finance, 18(5), 66–80. https://doi.org/10.17010/ijf/2024/v18i5/173843
29. Lippi, A., & Rossi, S. (2020). Run for the hills: Italian investors' risk appetite before and during the financial crisis. International Journal of Bank Marketing, 38(5), 1195–1213. https://doi.org/10.1108/IJBM-02-2020-0058
30. Malik, M. S., Hanif, M. I., & Azhar, M. Z. (2019). The impact of overconfidence bias on investment decisions : Mediating role of risk tolerance. International Journal of Research and Innovation in Social Science, 3(8), 154–160. https://www.rsisinternational.org/virtual-library/papers/the-impact-of-overconfidence-bias-on-investment-decisions-mediating-role-of-risk-tolerance/
31. Manzoor, A., Jan, A., & Shafi, M. (2023). Do personality and demographic variances of individual investors challenge the assumption of rationality? A two-staged regression modeling-artificial neural network approach. Indian Journal of Finance, 17(10), 64–78. https://doi.org/10.17010/ijf/2023/v17i10/168549
32. Miendlarzewska, E. A., Kometer, M., & Preuschoff, K. (2019). Neurofinance. Organizational Research Methods, 22(1), 196–222. https://doi.org/10.1177/1094428117730891
33. Nag, A. K., & Shah, J. (2022). An empirical study on the impact of gen Z investors' financial literacy to invest in the Indian stock market. Indian Journal of Finance, 16(10), 43–59. https://doi.org/10.17010/ijf/2022/v16i10/172387
34. Padoa-Schioppa, C., & Conen, K. E. (2017). Orbitofrontal Cortex: A neural circuit for economic decisions. Neuron, 96(4), 736–754. https://doi.org/10.1016/j.neuron.2017.09.031
35. Patel, R. J. (2017). Co-movement and integration among stock markets : A study of 14 countries. Indian Journal of Finance, 11(9), 53–66. https://doi.org/10.17010/ijf/2017/v11i9/118089
36. Pertl, M.-T., Benke, T., Zamarian, L., & Delazer, M. (2017). Effects of healthy aging and mild cognitive impairment on a real-life decision-making task. Journal of Alzheimer's Disease, 58(4), 1077–1087. https://doi.org/10.3233/JAD-170119
37. Raghuraman, A. P., & Padoa-Schioppa, C. (2014). Integration of multiple determinants in the neuronal computation of economic values. Journal of Neuroscience, 34(35), 11583–11603. https://doi.org/10.1523/JNEUROSCI.1235-14.2014
38. Raheja, S., & Dhiman, B. (2019). Relationship between behavioral biases and investment decisions: The mediating role of risk tolerance. DLSU Business and Economics Review, 29(1), 31–39.
39. Ringle, C. M., Sven Wende, & Becker, J.-M. (2022). SmartPLS 4. Boenningstedt: SmartPLS. https://www.smartpls.com/
40. Ritika, & Kishor, N. (2022). Risk preferences for financial decisions: Do emotional biases matter? Journal of Public Affairs, 22(2), e2360. https://doi.org/10.1002/pa.2360
41. Rodrigues, C. G., & Gopalakrishna, B. V. (2023). Optimism and self-control : Complementary predictors of financial risk-taking propensity among working adults. Indian Journal of Finance, 17(7), 8–24. https://doi.org/10.17010/ijf/2023/v17i7/170966
42. Saari, U. A., Damberg, S., Frömbling, L., & Ringle, C. M. (2021). Sustainable consumption behavior of Europeans: The influence of environmental knowledge and risk perception on environmental concern and behavioral intention. Ecological Economics, 189, 107155. https://doi.org/10.1016/j.ecolecon.2021.107155
43. Sarstedt, M., Hair Jr., J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003
44. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (eds.), Handbook of market research (pp. 1–40). Springer. https://doi.org/10.1007/978-3-319-05542-8_15-1
45. Sen, S., & Banerjee, S. (2024). Exploring the relationship between psychological maturity and leadership personality traits – A discourse analysis. Prabandhan: Indian Journal of Management, 17(5), 60–70. https://doi.org/10.17010/pijom/2024/v17i5/173482
46. Singh, H. P., Goyal, N., & Kumar, S. (2016). Behavioural biases in investment decisions: An exploration of the role of gender. Indian Journal of Finance, 10(6), 51–62. https://doi.org/10.17010/ijf/2016/v10i6/94879
47. Singh, S., Mallaram, G. K., & Sarkar, S. (2017). Pathological gambling: An overview. Medical Journal of Dr. D.Y. Patil University, 10(2), 120–135. https://doi.org/10.4103/0975-2870.202105
48. Tseng, K. C. (2006). Behavioral finance, bounded rationality, neuro-finance, and traditional finance. Investment Management and Financial Innovations, 3(4), 7–18. https://www.researchgate.net/publication/265357711
49. Xi, D., O'Brien, T. I., & Irannezhad, E. (2020). Investigating the investment behaviors in cryptocurrency. The Journal of Alternative Investments, 23(2), 141–160. https://doi.org/10.3905/JAI.2020.1.108
50. Yalcin, K. C., Tatoglu, E., & Zaim, S. (2016). Developing an instrument for measuring the effects of heuristics on investment decisions. Kybernetes, 45(7), 1052–1071. https://doi.org/10.1108/K-05-2015-0130
51. Yokoyama, R., Nozawa, T., Sugiura, M., Yomogida, Y., Takeuchi, H., Akimoto, Y., Shibuya, S., & Kawashima, R. (2014). The neural bases underlying social risk perception in purchase decisions. NeuroImage, 91(1), 120–128. https://doi.org/10.1016/j.neuroimage.2014.01.036
52. Zhao, H., & Zhang, L. (2021). Financial literacy or investment experience: Which is more influential in cryptocurrency investment? International Journal of Bank Marketing, 39(7), 1208–1226. https://doi.org/10.1108/IJBM-11-2020-0552