Chrystina Valentine Kwesnady
ABSTRACT
This research has the aim of knowing the role of Access to Technology (AT, Firm Innovative (FI), and Herd Behavior (HB) towards individual Intention to Invest in Cryptocurrency (IC)., which are further moderated by Overconfidence (OC) and Past Behavior (PB) to find out their relationship to the wealth creation obtained. The data was collected from individual cryptocurrency investors in Indonesia using non-probability sampling questionnaire to verify respondents have an experience in cryptocurrency investments and valid to be references in measuring the impact of intention to invest in cryptocurrency on the wealth creation. Statistical testing through the SEM-PLS method shows that AT, FI, and HB have a positive and significant influence on IC. Moderation of OC on IC shows a significant positive relationship to WC because it helps individuals think more rationally by considering other people's opinions and own analysis. Moderation of PB on IC also shows a significant positive relationship to WC because it helps individuals set strategies in achieving the desired return from previous experiences.
- L. Ante, “The non-fungible token (NFT) market and its relationship with Bitcoin and Ethereum,” SSRN Electron. J., no. 20, pp. 1–9, 2021, doi: 10.2139/ssrn.3861106.Google Scholar
Cross Ref
- G. A. Abbasi, L. Y. Tiew, J. Tang, Y. N. Goh, and R. Thurasamy, “The adoption of cryptocurrency as a disruptive force: Deep learning- based dual stage structural equation modelling and artificial neural network analysis,” PLoS One, vol. 16, no. 3 March 2021, pp. 1–26, 2021, doi: 10.1371/journal.pone.0247582..Google ScholarCross Ref
- V. Ferrari, “The regulation of crypto-assets in the EU – investment and payment tokens under the radar,” Maastrich. J. Eur. Comp. Law, vol. 27, no. 3, pp. 325–342, 2020, doi: 10.1177/1023263X20911538.Google ScholarCross Ref
- A. Alharbi and O. Sohaib, “Technology Readiness and Cryptocurrency Adoption: PLS-SEM and Deep Learning Neural Network Analysis,”Google Scholar
- IEEE Access, vol. 9, pp. 21388–21394, 2021, doi: 10.1109/ACCESS.2021.3055785.Google ScholarCross Ref
- A. Mikhaylov, “Cryptocurrency market analysis from the open innovation perspective,” J. Open Innov. Technol. Mark. Complex., vol. 6, no. 4, pp. 1–19, 2020, doi: 10.3390/joitmc6040197.Google ScholarCross Ref
- W. A. Kaal, “Initial Coin Offerings: The top 25 jurisdictions and their comparative regulatory responses,” Stan. J. Blockchain L. Pol'y, vol. 41, 2018.Google Scholar
- C. Bellavitis, C. Fisch, and J. Wiklund, “A comprehensive review of the global development of initial coin offerings (ICOs) and their regulation,” J. Bus. Ventur. Insights, vol. 15, 2021.Google ScholarCross Ref
- R. G. Saleksa and A. Firmansyah, “Pengaruh Innovation Terhadap Firm Performance Pada Umkm,” J. Manaj. Teor. dan Terap., no. 3, 2014.Google Scholar
- P. V. J. da Gama Silva, M. C. Klotzle, A. C. F. Pinto, and L. L. Gomes, “Herding behavior and contagion in the cryptocurrency market,” J. Behav. Exp. Financ., vol. 22, pp. 41–50, 2019, doi: 10.1016/j.jbef.2019.01.006.Google ScholarCross Ref
- W. Hung, C. C. Lu, and C. F. Lee, “Mutual fund herding its impact on stock returns: Evidence from the Taiwan stock market,” Pacific Basin Financ. J., vol. 18, no. 5, pp. 477–493, 2010, doi: 10.1016/j.pacfin.2010.06.001.Google ScholarCross Ref
- J. M. Palma-Ruiz, J. Castillo-Apraiz, and R. Gómez-Martínez, “Socially responsible investing as a competitive strategy for trading companies in times of upheaval amid covid-19: Evidence from spain,” Int. J. Financ. Stud., vol. 8, no. 3, pp. 1–13, 2020, doi: 10.3390/ijfs8030041.Google ScholarCross Ref
- M. Qasim, R. Y. Hussain, I. Mehboob, and M. Arshad, “Impact of herding behavior and oQasim, M., R.Y. Hussain, I. Mehboob and M. Arshad. 2019. Impact of herding behavior and overconfidence bias on investors’ decision-making in Pakistan. Accounting 5:81– 90.verconfidence bias on investors’ decision-making inP,” Accounting, vol. 5, no. 2, pp. 81–90, 2019, doi: 10.5267/j.ac.2018.07.001.Google ScholarCross Ref
- V. Novianggie and N. Asandimitra, “The influence of behavioral bias, cognitive bias, and emotional bias on investment decision for college students with financial literacy as the moderating variable,” Int. J. Acad. Res. Accounting, Financ. Manag. Sci., vol. 9, no. 2, pp. 92–107, 2019.Google Scholar
- R. K. Raut, N. Das, and R. Kumar, “Extending the theory of planned behaviour: Impact of past behavioural biases on the investment decision of Indian investors,” Asian J. Bus. Account., vol. 11, no. 1, pp. 265–292, 2018, doi: 10.22452/ajba.vol11no1.9.Google ScholarCross Ref
- M. Rahman and S. S. Gan, “Generation Y investment decision: an analysis using behavioural factors,” Manag. Financ., vol. 46, no. 8, pp. 1023–1041, 2020, doi: 10.1108/MF-10-2018-0534.Google ScholarCross Ref
- M. M. Queiroz, S. Fosso Wamba, M. De Bourmont, and R. Telles, “Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy,” Int. J. Prod. Res., vol. 59, no. 20, pp. 6087–6103, 2021, doi: 10.1080/00207543.2020.1803511.Google ScholarCross Ref
- A. Chokor and E. Alfieri, “Long and short-term impacts of regulation in the cryptocurrency market,” Q. Rev. Econ. Financ., vol. 81, pp. 157–173, 2021, doi: 10.1016/j.qref.2021.05.005.Google ScholarCross Ref
- H. Jokar and V. Daneshi, “The impact of investors’ behavior and managers’ overconfidence on stock return: Evidence from Iran,” Cogent Bus. Manag., vol. 5, no. 1, pp. 1–11, 2018, doi: 10.1080/23311975.2018.1559716.Google ScholarCross Ref
- T. Reyes, R. S. Vassolo, E. E. Kausel, D. P. Torres, and S. Zhang, “Does overconfidence pay off when things go well? CEO overconfidence, firm performance, and the business cycle,” Strateg. Organ., no. 1995, 2020, doi: 10.1177/1476127020930659.Google ScholarCross Ref
- I. Ajzen, “Attitudes, personality and behavior,” Dorsey Press, 1988.Google Scholar
- M. Ahmad and S. Z. A. Shah, “Overconfidence heuristic-driven bias in investment decision-making and performance: mediating effectsGoogle Scholar
- of risk perception and moderating effects of financial literacy,” J. Econ. Adm. Sci., vol. 38, no. 1, pp. 60–90, 2022, doi: 10.1108/jeas-07- 2020-0116.Google ScholarCross Ref
- I. Acharjamayum, R. Patgiri, and D. Devi, “Blockchain: a tale of peer to peer security,” IEEE Symp. Ser. Comput. Intell., pp. 609–617, 2018.Google Scholar
- M. Ahmad, I. Mehboob, and S. Z. ul Abdin, “How Behavioral Factors Influence Investment Performance of Individual Investors in Pakistan Stock Market: A Moderated Mediation Approach,” Rev. Econ. Dev. Stud., vol. 7, no. 3, pp. 395–405, 2021, doi: 10.47067/reads.v7i3.385.Google ScholarCross Ref
- R. W. Pahlevi and I. I. Oktaviani, “Determinants of Individual Investor Behaviour in Stock Investment Decisions,” AFRE (Accounting Financ. Rev., vol. 1, no. 2, pp. 53–61, 2018, doi: 10.26905/afr.v1i2.2427.Google ScholarCross Ref
- W. Sun, A. T. Dedahanov, H. Y. Shin, and K. S. Kim, “Switching intention to crypto-currency market: Factors predisposing some individuals to risky investment,” PLoS One, vol. 15, no. 6, pp. 1–16, 2020, doi: 10.1371/journal.pone.0234155.Google ScholarCross Ref
- C. Pang, Y. Wang, Q., Li, and G. Duan, “Integrative capability, business model innovation and performance: Contingent effect of business strategy,” J. Innov. Manag., 2019.Google ScholarCross Ref
- M. C. Tsai and C. Wang, “Linking service innovation to firm performance: The roles of ambidextrous innovation and market orientation capability,” Chinese Manag. Stud., vol. 11, no. 4, pp. 730–750, 2017, doi: 10.1108/CMS-03-2017-0045.Google ScholarCross Ref
- S. Zhu, M. Song, M. K. Lim, J. Wang, and J. Zhao, “The development of energy blockchain and its implications for China's energy sector,”Google Scholar
- Resour. Policy, vol. 66, no. January, p. 101595, 2020, doi: 0.1016/j.resourpol.2020.101595.Google Scholar
- L. Mazambani and E. Mutambara, “Predicting FinTech innovation adoption in South Africa: the case of cryptocurrency,” African J. Econ. Manag. Stud., vol. 11, no. 1, pp. 30–50, 2020, doi: 10.1108/AJEMS-04-2019-0152.Google ScholarCross Ref
- K. A. Wambach, “Breastfeeding intention and outcome: A test of the theory of planned behavior,” Res. Nurs. Health, vol. 20, no. 1, pp. 51–59, 1997.Google ScholarCross Ref
- M. V. Lakshman, S. Basu, and R. Vaidyanathan, “Market-wide herding and the impact of institutional investors in the Indian capital market,” J. Emerg. Mark. Financ., vol. 12, no. 2, pp. 197–237, 2013.Google ScholarCross Ref
- T. C. Chiang, T. Li, J., L. An, and E. Nelling, “Dynamic herding behavior in Pacific-Basin markets: Evidence and implications,” Multinatl. Financ. J., vol. 3, no. 4, pp. 165–200, 2013.Google ScholarCross Ref
- L. Kengatharan and N. Kengatharan, “The Influence of Behavioral Factors in Making Investment Decisions and Performance: Study on Investors of Colombo Stock Exchange, Sri Lanka,” Asian J. Financ. Account., vol. 6, no. 1, p. 1, 2014, doi: 10.5296/ajfa.v6i1.4893.Google ScholarCross Ref
- M. Waweru, N., E. Munyoki, and E. Uliana, “The effects of behavioral factors in investment decision-making: a survey of institutional investors operating at the Nairobi Stock Exchange,” Int. J. Bus. Emerg. Mark., vol. 1, no. 1, pp. 24–41, 2008.Google ScholarCross Ref
- H. Yi and Y. Xiugang, “Managers’ Overconfidence, Risk Preference, Herd Behavior and Non-efficient Investment,” Indep. J. Manag. Prod., vol. 10, no. 1, p. 056, 2019, doi: 10.14807/ijmp.v10i1.845.Google ScholarCross Ref
- M. Siraji, “Heuristics Bias and Investment Performance: Does Age Matter? Evidence from Colombo Stock Exchange,” Asian J. Econ. Bus. Account., vol. 12, no. 4, pp. 1–14, 2019, doi: 10.9734/ajeba/2019/v12i430156.Google ScholarCross Ref
- J. F. Hair Jr, M. Sarstedt, L. Hopkins, and V. G. Kuppelwieser, “Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research,” Eur. Bus. Rev., 2006..Google Scholar
- R. Ahmed, S. Riaz, R. Aqdas, S. Ibn, and U. L. Hassan, “The Relationship among Overconfidence, Economic Expectation, Social Factors and Investment Decision Making Behavior with the Mediating and Moderating Effects,” J. Contemp. Issues Bus. Gov., vol. 27, no. 02, 2021, doi: 10.47750/cibg.2021.27.02.127.Google ScholarCross Ref
- W. Abdillah and J. Hartono, Partial Least Square (PLS): alternatif structural equation modeling (SEM) dalam penelitian bisnis. Yogyakarta: Andi Publisher, 2015.Google Scholar
- W. Zhang, “Application of crowd thought and herd behavior in economic investment,” Rev. Argentina Clin. Psicol., vol. 29, no. 1, pp. 328– 333, 2020, doi: 10.24205/03276716.2020.44.Google ScholarCross Ref
- A. T. Okoth, “The Effect Of Individual Behaviour On Investment Decisions Of Investors At The Nairobi Security Exchange,” 2019.Google Scholar
Recommendations
Investor Overconfidence, Firm Valuation, and Corporate Decisions
Behavioral theory predicts that investor overconfidence leads to overpricing because overconfident investors overestimate the quality of their information and underestimate risk. We test this prediction by using a measure of investor overconfidence ...
Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT)
For over a decade, researchers have devoted much effort to construct theoretical models, such as the Technology Acceptance Model (TAM) and the Expectation Confirmation Model (ECM) for explaining and predicting user behavior in IS acceptance and ...
How Riskily Do I Invest? The Role of Risk Attitudes, Risk Perceptions, and Overconfidence
Our study analyzes the determinants of investors' risk-taking behavior. We find that investors' risk-taking behavior is affected by their subjective risk attitude in the financial domain and by the risk and return of an investment alternative. Our ...
Comments