DeFi era: the behavioral intentions toward cryptocurrency in Lebanon
DOI:
https://doi.org/10.1108/inmr-02-2023-0022Palavras-chave:
Perceived benefits, Cryptocurrency, Herd behavior, Usage intentions, Regulatory support, Trust in cryptocurrencyResumo
PurposeThis paper examines the factors which impact the behavioral intentions toward cryptocurrency based on signaling theory.
Design/methodology/approachData were collected through online questionnaire, and responses from 223 individuals in Lebanon were analyzed through SEM technique using Amos 24.
FindingsThe outcomes portrayed the positive effect of perceived benefits and trust in cryptocurrency on behavioral intentions toward cryptocurrency; while not supporting the hypothesized influence of herd behavior and regulatory support.
Originality/valueThis paper is among the first studies to adopt Signaling Theory (ST) in the cryptocurrency behavioral intentions research. Moreover, it is of the initial efforts in Lebanon and Middle East in evaluating behavioral intentions to use cryptocurrency, and it provide insights for future researchers, crypto project owners, crypto investors and crypto trading platforms.
Downloads
Referências
Abbasi, G. A., Tiew, L. Y., Tang, J., Goh, Y.-N., & Thurasamy, R. (2021). The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis. PLOS ONE, 16(3), e0247582. doi: 10.1371/journal.pone.0247582.
Abramova, S., & Böhme, R. (2016). Perceived benefit and risk as multidimensional determinants of bitcoin use: A quantitative exploratory study. In International Conference on Information Systems (pp. 1–20). doi: 10.17705/4icis.00001.
Ajaz, T., & Kumar, A. S. (2018). Herding in crypto-currency markets. Annals of Financial Economics, 13(02), 1850006. doi: 10.1142/S2010495218500069.
Ajzen, I. (2019). Theory of planned behavior. Theory of Planned Behavior. Available from: https://people.umass.edu/aizen/tpb.diag.html#null-link.
Alqaryouti, O., Siyam, N., Alkashri, Z., & Shaalan, K. (2020a). Cryptocurrency usage impact on perceived benefits and users’ behaviour. In M. Themistocleous, & M. Papadaki (Eds.), Information Systems (Vol. 381, pp. 123–136). Springer International Publishing. doi: 10.1007/978-3-030-44322-1_10.
Alqaryouti, O., Siyam, N., Alkashri, Z., & Shaalan, K. (2020b). Users’ knowledge and motivation on using cryptocurrency. In M. Themistocleous, & M. Papadaki (Eds.), Information Systems (Vol. 381, pp. 113–122). Springer International Publishing. doi: 10.1007/978-3-030-44322-1_9.
Alzahrani, S., & Daim, T. U. (2019). Evaluation of the cryptocurrency adoption decision using hierarchical decision modeling (HDM). In 2019 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 1–7). doi: 10.23919/PICMET.2019.8893897.
Amini, M. (2014). The factors that influence on adoption of cloud computing for small and medium enterprises, [Universiti Teknologi Malaysia]. Available from: http://rgdoi.net/10.13140/RG.2.1.5007.0889.
Arias-Oliva, M., Pelegrín-Borondo, J., & Matías-Clavero, G. (2019). Variables influencing cryptocurrency use: A technology acceptance model in Spain. Frontiers in Psychology, 10, 475. doi: 10.3389/fpsyg.2019.00475.
Aydın, Ü., Agan, B., & Aydın, Ö. (2022). Herd behavior in crypto asset market and effect of financial information on herd behavior. SSRN Electronic Journal. doi: 10.2139/ssrn.4171315.
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. doi: 10.1108/JIMA-04-2019-0081.
Barroso, C., Carrión, G. C., & Roldán, J. L. (2010). Applying Maximum likelihood and PLS on different sample sizes: Studies on SERVQUAL model and employee behavior model. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares (pp. 427–447). Springer Berlin Heidelberg. doi: 10.1007/978-3-540-32827-8_20.
Boateng, S. L. (2019). Online relationship marketing and customer loyalty: A signaling theory perspective. International Journal of Bank Marketing, 37(1), 226–240. doi: 10.1108/IJBM-01-2018-0009.
Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters, 29, 216–221. doi: 10.1016/j.frl.2018.07.008.
Boxer, M., & Thompson, N. (2020). Herd behaviour in cryptocurrency markets. 1–12.
Chong, O.-P., Bany-Ariffin, A. N., Nassir, A., & Muhammad, J. (2019). An empirical study of herding behaviour in China’s A-share and B-share markets: Evidence of bidirectional herding activities. Capital Markets Review, 27(2), 22.
Civelek, M. E. (2018). Essentials of structural equation modeling. Lincoln, NE: Zea Books, available at: https://digitalcommons.unl.edu/zeabook/64/
Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39–67. doi: 10.1177/0149206310388419.
Connolly, A., & Kick, A. (2015). What differentiates early organization Adopters of bitcoin from non-adopters? 6.
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. doi: 10.1016/j.jbef.2019.01.006.
Daryaei, M., Jassbi, J., Radfar, R., & Khamseh, A. (2020). Bitcoin adoption as a new technology for payment mechanism in a tourism collaborative network. In L. M. Camarinha-Matos, H. Afsarmanesh, & A. Ortiz (Eds), Boosting Collaborative Networks 4.0 (Vol. 598, pp. 167–176). Springer International Publishing. doi: 10.1007/978-3-030-62412-5_14.
De Filippi, P., Mannan, M., & Reijers, W. (2020). Blockchain as a confidence machine: The problem of trust & challenges of governance. Technology in Society, 62, 101284. doi: 10.1016/j.techsoc.2020.101284.
Dewan, P., & Dharni, K. (2019). Herding behaviour in investment decision Making.pdf. British Journal of Economics, Management & Trade, 24(2), 1–12, doi: 10.9734/jemt/2019/v24i230160.
Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 453. doi: 10.2307/249524.
D’Alfonso, A., Langer, P., & Vandelis, Z. (2016). The future of cryptocurrency an investor’s comparison of Bitcoin and ethereum. Toronto: Ryerson University.
Esmaeilzadeh, P., Cousins, K., & Subramanian, H. (2020). A utility theory model for individual adoption of bitcoin. In CONF-IRM 2020 Proceedings (Vol. 14).
Etemadi, N., Strozzi, F., Van Gelder, P., & Etemadi, T. (2021). An ISM modelling of success factors for blockchain adoption in a cyber secure supply chain. In 2021 The 4th International Conference on Computers in Management and Business (pp. 65–72). doi: 10.1145/3450588.3450600.
Gao, X., Clark, G. D., & Lindqvist, J. (2016). Of two minds, multiple addresses, and one ledger: Characterizing opinions, knowledge, and perceptions of bitcoin across users and non-users. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 1656–1668). doi: 10.1145/2858036.2858049.
Gazali, H. M. (2019). Bitcoin investment behaviour: A pilot study. International Journal on Perceptive and Cognitive Computing (IJPCC), 5(2), 6.
Hair, J. F., Black, W., Babin, B., & Anderson, R. (2014). Multivariate data analysis (7th ed.), Pearson new internat. Pearson.
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. doi: 10.1007/s11747-014-0403-8.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. doi: 10.1080/10705519909540118.
IMF. (2021). The crypto ecosystem and financial stability challenges. Washington, DC: International Monetary Fund.
Jalal, R. N.-U.-D., Sargiacomo, M., Sahar, N. U., & Fayyaz, U.-E.-R. (2020). Herding behavior and cryptocurrency: Market asymmetries, inter-dependency and intra-dependency. The Journal of Asian Finance, Economics and Business, 7(7), 27–34. doi: 10.13106/JAFEB.2020.VOL7.NO7.027.
Jalan, A., Matkovskyy, R., Urquhart, A., & Yarovaya, L. (2022). The role of interpersonal trust in cryptocurrency adoption. SSRN Electronic Journal. doi: 10.2139/ssrn.4099518.
Kjærland, F., Khazal, A., Krogstad, E., Nordstrøm, F., & Oust, A. (2019). An analysis of bitcoin’s price dynamics. Journal of Risk and Financial Management, 11(4), 63, doi: 10.3390/jrfm11040063.
Kyriazis, N. A. (2020). Herding behaviour in digital currency markets: An integrated survey and empirical estimation. Heliyon, 6(8), e04752. doi: 10.1016/j.heliyon.2020.e04752.
Lau, Y. (2021). Cryptocurrencies hit market cap of $3 trillion for the first time as Bitcoin and Ether reach record highs. Fortune.Com. Available from: https://fortune.com/2021/11/09/cryptocurrency-market-cap-3-trillion-bitcion-ether-shiba-inu/
Lou, A. T. F., & Li, E. Y. (2017). Integrating innovation diffusion theory and the technology acceptance model: The adoption of blockchain technology from business managers’ perspective. 4.
Mazikana, A. T. (2018). The impact of cryptocurrencies in Zimbabwe. An analysis of bitcoins. SSRN Electronic Journal. doi: 10.2139/ssrn.3376307.
McMorrow, J., & Esfahani, M. S. (2021). An exploration into people’s perception and intention on using cryptocurrencies. HOLISTICA – Journal of Business and Public Administration, 12(2), 109–144. doi: 10.2478/hjbpa-2021-0018.
Mendoza-Tello, J. C., Mora, H., Pujol-López, F. A., & Lytras, M. D. (2019). Disruptive innovation of cryptocurrencies in consumer acceptance and trust. Information Systems and E-Business Management, 17(2-4), 195–222. doi: 10.1007/s10257-019-00415-w.
Mikhaylov, A., Danish, M. S. S., & Senjyu, T. (2021). A new stage in the evolution of cryptocurrency markets: Analysis by hurst method. In H. Dinçer, & S. Yüksel (Eds.), Strategic Outlook in Business and Finance Innovation: Multidimensional Policies for Emerging Economies (pp. 35–45). Emerald Publishing. doi: 10.1108/978-1-80043-444-820211004.
Muchlis Gazali, H., Hafiz Bin Che Ismail, C. M., & Amboala, T. (2018). Exploring the intention to invest in cryptocurrency: The case of bitcoin. In 2018 International Conference on Information and Communication Technology for the Muslim World (ICT4M) (pp. 64–68). doi: 10.1109/ICT4M.2018.00021.
Rao, S., Lee, K. B., Connelly, B., & Iyengar, D. (2018). Return time leniency in online retail: A signaling theory perspective on buying outcomes. Decision Sciences, 49(2), 275–305. doi: 10.1111/deci.12275.
Rejikumar, G., Asokan-Ajitha, A., Dinesh, S., & Jose, A. (2022). The role of cognitive complexity and risk aversion in online herd behavior. Electronic Commerce Research, 22(2), 585–621. doi: 10.1007/s10660-020-09451-y.
Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355. doi: 10.2307/1882010.
Start.io (2022). Crypto investors in Lebanon. Available from: https://www.start.io/audience/crypto-investors-in-lebanon.
TripleA.io (2022). Cryptocurrency across the world. Available from: https://triple-a.io/crypto-ownership-data/.
Voskobojnikov, V., Abramova, S., Beznosov, K., & Böhme, R. (2021). NON-ADOPTION of crypto-assets: Exploring the role of trust, self-efficacy, and risk. In Twenty-Ninth European Conference on Information Systems (ECIS 2021).
World Economic Forum. (2021). Navigating cryptocurrency regulation: An industry perspective on the insights and tools needed to shape balanced crypto regulation. Geneva, Switzerland. available at: https://www3.weforum.org/docs/WEF_Navigating_Cryptocurrency_Regulation_2021.pdf
Xiong, J., & Tang, Y. (2020). Mobile cryptocurrency for development in asia-moderating effects of advantage, complexity, and compatibility. In AMCIS 2020 Proceedings (Vol. 9, pp. 1–6).
Downloads
Publicado
Edição
Seção
Licença
Copyright (c) 2025 Ali Abdel Karim Abou Ali
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
• O(s) autor(es) autoriza(m) a publicação do artigo na revista;
• O(s) autor(es) garante(m) que a contribuição é original e inédita e que não está em processo de avaliação em outra(s) revista(s);
• A revista não se responsabiliza pelas opiniões, ideias e conceitos emitidos nos textos, por serem de inteira responsabilidade de seu(s) autor(es);
• É reservado aos editores o direito de proceder ajustes textuais e de adequação do artigos às normas da publicação.
