Exploring Cryptocurrency Investment Choices among Generation Z Muslims – A Conceptual Analysis

Authors

  • Verina Purnamasari Universitas Islam Sultan Agung, Indonesia
  • Dian Essa Nugrahini Universitas Islam Sultan Agung, Indonesia
  • Diah Ayu Kusumawati Universitas Islam Sultan Agung, Indonesia

Abstract

The emergence of cryptocurrency as a form of digital currency investment is a consequence of the global shift towards a technology-driven way of life. Despite its notable volatility, intricate price dynamics, and decentralized nature, cryptocurrencies have managed to attract a considerable number of investors for reasons that are not extensively explored. Indonesia, situated in Asia and characterized by a predominantly Islamic population and heightened awareness of cryptocurrencies, stands out in this context. The primary objective of this study is to pinpoint the factors that influence investment decisions in cryptocurrencies among potential Generation Z investors in Indonesia. The research methodology involves the dissemination of surveys to individuals who meet specific criteria, including being Indonesian citizens, at least 17 years old, practicing Islam, and possessing a National Identity Card, a prerequisite for engaging in cryptocurrency investments. The gathered data will undergo analysis using Structural Equation Modeling (SEM) equations through the SmartPLS application Keywords: Cryptocurrencies, Investments, Attitudes to risk, Subjective norms, Machine learning forecasting.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Akyildirim, E., Goncu, A., & Sensoy, A. (2021). Prediction of cryptocurrency returns using machine learning. Annals of Operations Research, 297(1–2), 3–36. https://doi.org/10.1007/s10479-020-03575-y

Alaeddin, O., & Altounjy, R. (2019). Trust, Technology Awareness and Satisfaction Effect into the Intention to Use Cryptocurrency among Generation Z in Malaysia. International Journal of Engineering & Technology, 7(4.29), 8–10. https://doi.org/10.1108/JIBR-10-2015-0112

Alessandretti, L., ElBahrawy, A., Aiello, L. M., & Baronchelli, A. (2018). Anticipating Cryptocurrency Prices Using Machine Learning. Complexity, 2018, 1–16. https://doi.org/10.1155/2018/8983590

Andrianto, Y. (2017). The Effect of Cryptocurrency on Investment Portfolio Effectiveness. Journal of Finance and Accounting, 5(6), 229. https://doi.org/10.11648/j.jfa.20170506.14

Ayedh, A., Echchabi, A., Battour, M., & Omar, M. (2021). Malaysian Muslim investors’ behaviour towards the blockchain-based Bitcoin cryptocurrency market. Journal of Islamic Marketing, 12(4), 690–704. https://doi.org/10.1108/JIMA-04-2019-0081

Bakar, N. A. (2017). High volatility detection method using statistical process control for cryptocurrency exchange rate: a case study of bitcoin. The International Journal of Engineering and Science (IJES), 6(11), 39–48.

Bananuka, J., Kasera, M., Najjemba, G. M., Musimenta, D., Ssekiziyivu, B., & Kimuli, S. N. L. (2019). Attitude: mediator of subjective norm, religiosity and intention to adopt Islamic banking. Journal of Islamic Marketing, 11(1), 81–96. https://doi.org/10.1108/JIMA-02-2018-0025

Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding Behaviour in Cryptocurrencies.

Brauneis, A., & Mestel, R. (2019). Cryptocurrency-portfolios in a mean-variance framework. Finance Research Letters, 28, 259–264. https://doi.org/10.1016/j.frl.2018.05.008

Catania, L., Grassi, S., & Ravazzolo, F. (2019). Forecasting cryptocurrencies under model and parameter instability. International Journal of Forecasting, 35(2), 485–501. https://doi.org/10.1016/j.ijforecast.2018.09.005

Chen, C. Y., Hardle, W. K., Hou, A. J., & Wang, W. (2018). Pricing cryptocurrency options: the case of Crix. IRTG 1792 [Discussion Paper 2018-004].

Chen, T.-H., Ho, R.-J., & Liu, Y.-W. (2019). Investor personality predicts investment performance? A statistics and machine learning model investigation. Computers in Human Behavior, 101, 409–416. https://doi.org/10.1016/j.chb.2018.09.027

Corter, J. E., & Chen, Y.-J. (2006). Do Investment Risk Tolerance Attitudes Predict Portfolio Risk? Journal of Business and Psychology, 20(3), 369–381. https://doi.org/10.1007/s10869-005-9010-5

Crosby, M., Nachiappan, Pattanayak, P., Verma, S., & Kalyanaraman, V. (2015). Blockchain technology: beyond bitcoin. Applied Innovation Review, 2, 8–19.

Dickason, Z., & Ferreira, S. (2018). Establishing a link between risk tolerance, investor personality and behavioural finance in South Africa. Cogent Economics & Finance, 6(1), 1519898. https://doi.org/10.1080/23322039.2018.1519898

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

Gandal, J. E., & Hałaburda, H. (2014). Competition in the cryptocurrency market. Bank of Canada [Working Paper No. 2014-33].

Goldsmith, R. E., & Goldsmith, E. B. (2006). The effects of investment education on gender differences in financial knowledge. Journal of Personal Finance, 5(2), 55–69.

Grable, J. e. (2000). Financial risk tolerance and additional factors that affect risk taking in everyday money matters. Journal of Business and Psychology, 14, 625–630.

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

Jain, D., & Mandot, N. (2012). Impact of demographic factors on investment decision of investors in Rajasthan. Journal of Arts, Science and Commerce, 3(2), 81–92.

Khan, F. (2019). Is all that glitters, gold? A behavioral aspect of cryptocurrency market. Economic and Social Development, 808–812.

Kourtidis, D., Šević, Ž., & Chatzoglou, P. (2011). Investors’ trading activity: A behavioural perspective and empirical results. The Journal of Socio-Economics, 40(5), 548–557. https://doi.org/10.1016/j.socec.2011.04.008

Krollner, B., Vanstone, B., & Finnie, G. (2010). Financial time series forecasting with machine learning techniques: a survey. Computational and Machine Learning, 25–30.

Kuo Chuen, D. L., Guo, L., & Wang, Y. (2017). Cryptocurrency: A New Investment Opportunity? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2994097

Lavere, M. (2019). Study finds 94% of endowments are investing in cryptocurrency. Ethereum World News (EWN).

Lee, M.-C. (2009). Predicting and explaining the adoption of online trading: An empirical study in Taiwan. Decision Support Systems, 47(2), 133–142. https://doi.org/10.1016/j.dss.2009.02.003

Li, X., & Wang, C. A. (2017). The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin. Decision Support Systems, 95, 49–60. https://doi.org/10.1016/j.dss.2016.12.001

Lin, I.-C., & Liao, T.-C. (2017). A survey of blockchain security issues and challenges”. International Journal of Network Security, 19(5), 653–659.

Magendans, J., Gutteling, J. M., & Zebel, S. (2017). Psychological determinants of financial buffer saving: the influence of financial risk tolerance and regulatory focus. Journal of Risk Research, 20(8), 1076–1093. https://doi.org/10.1080/13669877.2016.1147491

Mokhtarian, E., & Lindgren, A. (2017). Title Rise of the Crypto Hedge Fund: Operational Issues and Best Practices for an Emergent Investment Industry. https://ssrn.com/abstract=3055979

Pak, O., & Mahmood, M. (2015). Impact of personality on risk tolerance and investment decisions. International Journal of Commerce and Management, 25(4), 370–384. https://doi.org/10.1108/IJCoMA-01-2013-0002

Park, J. H., Gu, B., Leung, A. C. M., & Konana, P. (2014). An investigation of information sharing and seeking behaviors in online investment communities. Computers in Human Behavior, 31, 1–12. https://doi.org/10.1016/j.chb.2013.10.002

Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259–268. https://doi.org/10.1016/j.eswa.2014.07.040

Peng, Y., Albuquerque, P. H., Sa, J. C., Padula, A. J., & Montenegro, M. R. (2018). The best of two worlds: forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression. Expert Systems with Applications, 97, 177–192.

Punch, K. F. (2013). Introduction to Social Research: Quantitative and Qualitative. Sage Publications, London.

Rose, C. (2015). The Evolution Of Digital Currencies: Bitcoin, A Cryptocurrency Causing A Monetary Revolution. International Business & Economics Research Journal (IBER), 14(4), 617. https://doi.org/10.19030/iber.v14i4.9353

Schaupp, L. C., Festa, M., Knotts, K. G., & Vitullo, E. A. (2022). Regulation as a pathway to individual adoption of cryptocurrency. Digital Policy, Regulation and Governance, 24(2), 199–219. https://doi.org/10.1108/DPRG-08-2021-0101

Shehhi, A. Al, Oudah, M., & Aung, Z. (2014). Investigating factors behind choosing a cryptocurrency. 2014 IEEE International Conference on Industrial Engineering and Engineering Management, 1443–1447. https://doi.org/10.1109/IEEM.2014.7058877

Soomro, B. A., Shah, N., & Abdelwahed, N. A. A. (2022). Intention to adopt cryptocurrency: a robust contribution of trust and the theory of planned behavior. Journal of Economic and Administrative Sciences. https://doi.org/10.1108/JEAS-10-2021-0204

Sung, J., & Hanna, S. D. (1997). Factors Related to Risk Tolerance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2234

Trafimow, D., Sheeran, P., Conner, M., & Finlay, K. A. (2002). Evidence that perceived behavioural control is a multidimensional construct: Perceived control and perceived difficulty. British Journal of Social Psychology, 41(1), 101–121. https://doi.org/10.1348/014466602165081

Viscusi, W. K., Phillips, O. R., & Kroll, S. (2011). Risky investment decisions: How are individuals influenced by their groups? Journal of Risk and Uncertainty, 43(2), 81–106. https://doi.org/10.1007/s11166-011-9123-3

Vyas, C. A., & Lunagaria, M. (2014). Security concerns and issues for bitcoin. International Journal of Computer Applications. Proceedings on National Conference Cum Workshop on Bioinformatics and Computational Biology NCWBCB, 2, 10–12.

Wallentin, F. Y., Schmidt, P., Davidov, E., & Bamberg, S. (2004). s there any interaction effect between intention and perceived behavioral control? Methods of Psychological Research Online, 8(2), 127–157.

Wang, W., Guo, L., & Sun, R. (2019). Rational herd behavior in online learning: Insights from MOOC. Computers in Human Behavior, 92, 660–669. https://doi.org/10.1016/j.chb.2017.10.009

Wesley, M. (2018). An investigation into the diffusion of the cryptocurrency innovation. International Business and Entrepreneurship [Working Paper].

Yilmaz, N. K., & Hazar, H. B. (2018). Determining the factors affecting investors’ decision making process in cryptocurrency investments. PressAcademia Procedia, 8(1), 5–8.

Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350–361. https://doi.org/10.1016/j.neucom.2017.01.026

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Published

2024-03-16

How to Cite

Verina Purnamasari, Dian Essa Nugrahini, & Diah Ayu Kusumawati. (2024). Exploring Cryptocurrency Investment Choices among Generation Z Muslims – A Conceptual Analysis. Proceedings of Femfest International Conference on Economics, Management, and Business, 2, 497–506. Retrieved from https://ejournal.unida.gontor.ac.id/index.php/FICCOMSS/article/view/11823

Issue

Vol. 2 (2024): Proceedings of Femfest International Conference on Economics, Management, and Business

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Articles