Blockchain-Based Smart Contracts in Insurance Service Delivery: A Conceptual Analysis
Abstract
Blockchain is a revolutionary technology that offers a new kind of inventive service. It can handle a variety of sophisticated issues associated with the secrecy, integrity, and availability of fast and secure distributed systems. This concept paper begins by addressing the shift in people's attitudes towards the insurance industry, particularly in Malaysia, and then goes on to understand how the Unified Theory of Acceptance and Use of Technology (UTAUT), Task Technology Fit (TTF), and Initial Trust Model (ITM), influence behavioural intentions in using blockchain smart contracts. A digital insurance platform must be redefined after the increase in online insurance sales transactions prompted by COVID-19 to satisfy the market's expectations. Whereas traditional paper contracts rely on middlemen for execution, blockchain smart contracts are now based on blockchains, which include an immutable record of data and the ability to remove single points of failure. Despite the growing popularity of blockchain research in recent years, research on blockchain smart contract adoption behaviour at the individual level concerning insurance services remains limited. Hence, this study utilises the three models to characterise how performance expectancy, technological context, and initial trust interact to forecast behavioural intention. Furthermore, we stressed the need for additional research to demonstrate the intention to employ blockchain smart contracts is impacted by performance anticipation, technical environment, and personal initial trust. Based on the review, we will design realistic research that will incorporate prospects for theoretical progress as well as empirical discoveries in blockchain smart contract studies. The findings are intended to assist policymakers in developing suitable and improved strategies for capturing interest in blockchain smart contract insurance services in the Malaysian market. We also believe that the evolution of blockchain technology in tandem with smart contracts will enable the creation of new sorts of innovative services, such as insurance.
References
Aljaafreh, A., Al-Hujran, O., Al-Ani, A., Al-Debei, M. M., & Al-Dmour, N. (2021). Investigating the role of online initial trust in explaining the adoption intention of internet banking services. International Journal of Business Information Systems, 36(4), 474-505.
Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., ... & Peacock, A. (2019). Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and Sustainable Energy Reviews, 100, 143-174.
Chang, V., Baudier, P., Zhang, H., Xu, Q., Zhang, J., & Arami, M. (2020). How Blockchain can impact financial services–The overview, challenges and recommendations from expert interviewees. Technological forecasting and social change, 158, 120166.
Chen, H. S., Jarrell, J. T., Carpenter, K. A., Cohen, D. S., & Huang, X. (2019). Blockchain in healthcare: a patient-centered model. Biomedical journal of scientific & technical research, 20(3), 15017.
Farooq, A., Dubinina, A., Virtanen, S., & Isoaho, J. (2021). Understanding Dynamics of Initial Trust and its Antecedents in Password Managers Adoption Intention among Young Adults. Procedia Computer Science, 184, 266-274.
Fuller, R. M., & Dennis, A. R. (2009). Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks. Information Systems Research, 20(1), 2-17.
Gangwar, H., Date, H., & Raoot, A. D. (2014). Review on IT adoption: insights from recent technologies. Journal of Enterprise Information Management.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236.
Geebren, A., Jabbar, A., & Luo, M. (2021). Examining the role of consumer satisfaction within mobile eco-systems: Evidence from mobile banking services. Computers in Human Behavior, 114, 106584.
Hans, R., Zuber, H., Rizk, A., & Steinmetz, R. (2017). Blockchain and smart contracts: Disruptive technologies for the insurance market.
Han, M., Li, Z., He, J., Wu, D., Xie, Y., & Baba, A. (2018). A novel blockchain-based education records verification solution. In Proceedings of the 19th Annual SIG Conference on Information Technology Education (pp. 178-183).
Howard, M. C., & Rose, J. C. (2019). Refining and extending task–technology fit theory: Creation of two task–technology fit scales and empirical clarification of the construct. Information & Management, 56(6), 103134.
Hu, Y., Liyanage, M., Mansoor, A., Thilakarathna, K., Jourjon, G., & Seneviratne, A. (2019). Blockchain-based smart contracts-applications and challenges. arXiv preprint arXiv:1810.04699.
Isaac, O., Abdullah, Z., Ramayah, T., & Mutahar, A. M. (2017). Internet usage, user satisfaction, task-technology fit, and performance impact among public sector employees in Yemen. The International Journal of Information and Learning Technology.
Isaac, O., Aldholay, A., Abdullah, Z., & Ramayah, T. (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model. Computers & Education, 136, 113-129.
Jiang, Y., & Lau, A. K. (2021). Roles of consumer trust and risks on continuance intention in the sharing economy: An empirical investigation. Electronic Commerce Research and Applications, 47, 101050.
Kim, G., Shin, B., & Lee, H. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19(3), 283-311.
Kim, K. K., & Prabhakar, B. (2004). Initial trust and the adoption of B2C e-commerce: The case of internet banking. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 35(2), 50-64.
Lee, C. C., Cheng, H. K., & Cheng, H. H. (2007). An empirical study of mobile commerce in insurance industry: Task–technology fit and individual differences. Decision support systems, 43(1), 95-110.
Lin, H. C., Han, X., Lyu, T., Ho, W. H., Xu, Y., Hsieh, T. C., ... & Zhang, L. (2020). Task-technology fit analysis of social media use for marketing in the tourism and hospitality industry: a systematic literature review. International Journal of Contemporary Hospitality Management.
Liu, D., & Tu, W. (2021). Factors influencing consumers' adoptions of biometric recognition payment devices: combination of initial trust and UTAUT model. International Journal of Mobile Communications, 19(3), 345-363.
Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on management information systems (TMIS), 2(2), 1-25.
Miltgen, C. L., Popovič, A., & Oliveira, T. (2013). Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context. Decision support systems, 56, 103-114.
Morabito, V. (2017). Business innovation through blockchain. Cham: Springer International Publishing.
Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International journal of information management, 34(5), 689-703.
Pärssinen, M., Kotila, M., Rumin, R. C., Phansalkar, A., & Manner, J. (2018). Is blockchain ready to revolutionize online advertising?. IEEE Access, 6, 54884-54899.
Pournader, M., Shi, Y., Seuring, S., & Koh, S. L. (2020). Blockchain applications in supply chains, transport and logistics: a systematic review of the literature. International Journal of Production Research, 58(7), 2063-2081.
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117-2135.
Schuetz, S., & Venkatesh, V. (2020). Blockchain, adoption, and financial inclusion in India: Research opportunities. International journal of information management, 52, 101936.
Tran, A. Q., Nguyen, L. H., Nguyen, H. S. A., Nguyen, C. T., Vu, L. G., Zhang, M., & Ho, C. S. (2021). Determinants of Intention to Use Artificial Intelligence-Based Diagnosis Support System Among Prospective Physicians. Frontiers in public health, 9.
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.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of enterprise information management.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.
Wu, C., Kao, S. C., & Shih, C. H. (2018). Task-technology fit in knowledge creation: the moderating role of cognitive style. VINE Journal of Information and Knowledge Management Systems.
Xie, H., David, A., Mamun, M. R. A., Prybutok, V. R., & Sidorova, A. (2022). The formation of initial trust by potential passengers of self-driving taxis. Journal of Decision Systems, 1-30.
Yang, L., Yang, S. H., & Plotnick, L. (2013). How the internet of things technology enhances emergency response operations. Technological Forecasting and Social Change, 80(9), 1854-1867.
Yoo, S., Li, H., & Xu, Z. (2021). Can I Talk To An Online Doctor? Understanding The Mediating Effect Of Trust On Patients’online Health Consultation. Journal of Organizational Computing and Electronic Commerce