Analysis of Factors Influencing the Intention to Use Insurance Claim Information Systems: A Case Study of an Insurance Company Using a Modified TAM Approach

Authors

  • Dicky Sopandi Universitas Bina Nusantara
  • Rudy Universitas Bina Nusantara

DOI:

https://doi.org/10.59261/jequi.v8i2.332

Keywords:

Digital Transformation, Business Rules Management System (BRMS), User Trust, Training, Transformational Leadership, Insurance Industry

Abstract

Background: This research is motivated by the low adoption rate of Artificial Intelligence (AI)-based information systems in the insurance claim process, particularly Business Rules Management Systems (BRMS), even though these systems are designed to improve efficiency and consistency in decision-making. The main problems lie in the lack of user trust, limited training, and the role of leadership in supporting digital transformation.

Objective: Therefore, this study aims to analyze the factors influencing the intention to use insurance claim information systems using a modified Technology Acceptance Model (TAM) approach, expanded with the variables of Trust, Transformational Leadership, and Training.

Methods: This research employs a quantitative approach with a survey method targeting Claim Adjusters at the insurance company under study. Data were collected through questionnaires and analyzed using a structural model to test the relationships between variables, including Perceived Ease of Use, Perceived Usefulness, Trust, Transformational Leadership, Training, Intention to Use, and Actual System Usage.

Results: The results show that Transformational Leadership positively affects Trust, Trust significantly affects Intention to Use, and Training affects Perceived Ease of Use. Furthermore, Perceived Ease of Use influences Perceived Usefulness, which ultimately impacts Intention to Use and the actual usage of the system. These findings confirm that human and organizational factors play a crucial role in the successful implementation of technology, in addition to the technical factors of the system itself.

Conclusion: In conclusion, the successful adoption of AI-based information systems in the insurance industry is determined not only by technological sophistication but also by the level of user trust, the effectiveness of training, and the support of transformational leadership.

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References

Alathamneh, F. F., & Shelash Al-Hawary, S. I. (2023). Impact Of Digital Transformation On Sustainable Performance. International Journal Of Data & Network Science, 7(2).

Aulia, N. S., & Marsasi, E. G. (2024). The Role Of Perceived Usefulness, Perceived Ease Of Use, And Task Technology Fit To Increase Perceived Impact On Learning. Sentralisasi, 13(1), 163–181. https://doi.org/10.33506/Sl.V13i1.3031

Bass, B. M., & Avolio, B. J. (1994). Improving Organizational Effectiveness Through Transformational Leadership. Sage.

Bellundagi, M. (2023). Integrating Machine Learning With Business Rule Management Systems For Adaptive Enterprise. International Journal Of Research Publications In Engineering, Technology And Management (IJRPETM), 6(1), 8023–8039.

Chatterjee, S., Chaudhuri, R., Vrontis, D., & Basile, G. (2022). Digital Transformation And Entrepreneurship Process In SMES Of India: A Moderating Role Of Adoption Of AI-CRM Capability And Strategic Planning. Journal Of Strategy And Management, 15(3), 416–433. https://doi.org/10.1108/Jsma-02-2021-0049

Chatterjee, S., Chaudhuri, R., Vrontis, D., & Thrassou, A. (2024). Workforce Service Quality In The Post-COVID-19 Era: From The Perspective Of Organisation Data-Driven Competency. Production Planning & Control, 35(13), 1579–1592.

Chen, C., Hu, W., & Wei, X. (2025). From Anxiety To Action: Exploring The Impact Of Artificial Intelligence Anxiety And Artificial Intelligence Self-Efficacy On Motivated Learning Of Undergraduate Students. Interactive Learning Environments, 33(4), 3162–3177.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease Of Use, And User Acceptance Of Information Technology. Mis Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Demir, S., & Uşak, M. (2025). Analyzing The Implementation Of Pls-Sem In Educational Technology Research: A Review Of The Past 10 Years. Sage Open, 15(2). https://doi.org/10.1177/21582440251345950

Fareed, M. Z., Su, Q., Almutairi, M., Munir, K., & Fareed, M. M. S. (2022). Transformational Leadership And Project Success: The Mediating Role Of Trust And Job Satisfaction. Frontiers In Psychology, 13. https://doi.org/10.3389/Fpsyg.2022.954052

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude. Intention And Behavior: An Introduction To Theory And Research, 1–52.

Guzmán Ortiz, C. V., & Navarro Acosta, N. G. (2020). Impact Of Digital Transformation On The Individual Job Performance Of Insurance Companies In Peru.

Hair, 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

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. https://doi.org/10.1080/10705519909540118

Islam, M. A., Somu, S., & Aldaihani, F. M. F. (2026). The Rise Of Agentic AI: Synthesis Of Current Knowledge And Future Research Agenda. Global Business And Organizational Excellence, 45(3), 402–416. https://doi.org/10.1002/Joe.70019

Kassem, G. (2024). A Standardized Framework For The Discovery Of Potential Tasks For Robotic Process Automation: A Process Mining Approach. The German University In Cairo.

Kusumastuti, R., Haryanto, R., Hidayati, U., Setiawan Wibowo, T., Jambi, U., Negeri Banjarmasin, P., Pgri Nganjuk, S., & Mahardhika Surabaya, S. (2023). Security As A Moderation Of Interest In Using A Digital Wallet With A Technology Acceptance Model (TAM). Jurnal Ekonomi, 12(03)

Mishra, A., Basumallick, S., Lu, A., Chiu, H., Shah, M. A., Shukla, Y., & Tiwari, A. (2021). The Healthier Healthcare Management Models For Covid-19. Journal Of Infection And Public Health, 14(7), 927–937.

Partsch, M. V., & Goretzko, D. (2026). Detecting Model Misfit In Structural Equation Modeling With Machine Learning—A Proof Of Concept. Multivariate Behavioral Research, 61(1), 1–24. https://doi.org/10.1080/00273171.2025.2552304

Pedersen, C. L., & Ritter, T. (2024). Digital Authenticity: Towards A Research Agenda For The AI-Driven Fifth Phase Of Digitalization In Business-To-Business Marketing. Industrial Marketing Management, 123, 162–172. https://doi.org/10.1016/J.Indmarman.2024.10.005

Rezvani, S., Heidari, S., Roustapisheh, N., & Dokhanian, S. (2026). The Effectiveness Of System Quality, Habit, And Effort Expectation On Library Application Use Intention: The Mediating Role Of Perceived Usefulness, Perceived Ease Of Use, And User Satisfaction. International Journal Of Business Information Systems, 51(4), 503–520. https://doi.org/10.1504/Ijbis.2026.152777

Ritter, T., & Pedersen, C. L. (2020). Digitization Capability And The Digitalization Of Business Models In Business-To-Business Firms: Past, Present, And Future. Industrial Marketing Management, 86, 180–190. https://doi.org/10.1016/J.Indmarman.2019.11.019

Slovin, E. (1960). Slovin’s Formula For Sampling Technique. Retrieved On February, 13(1960), 2013.

Syamillah, F., Sadat, A. M., & Berutu, M. B. (2025). The The Influence Of Perceived Usefulness, Perceived Easy Of Use, And Perceived Value On Intention To Use E-Commerce. Jrmsi-Jurnal Riset Manajemen Sains Indonesia, 16(2), 88–99.

Tseng, S. M. (2025). Determinants Of The Intention To Use Digital Technology. Information, 16(3), 170. https://doi.org/10.3390/Info16030170

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance Of Information Technology: Toward A Unified View1. Mis Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital Transformation: A Multidisciplinary Reflection And Research Agenda. Journal Of Business Research, 122, 889–901. https://doi.org/10.1016/J.Jbusres.2019.09.022

Wilson, N., Keni, K., & Tan, P. H. P. (2021). The Role Of Perceived Usefulness And Perceived Ease-Of-Use Toward Satisfaction And Trust Which Influence Computer Consumers’ Loyalty In China. Gadjah Mada International Journal Of Business, 23(3), 262–294.

Xue, L., Rashid, A. M., & Ouyang, S. (2024). The Unified Theory Of Acceptance And Use Of Technology (UTAUT) In Higher Education: A Systematic Review. Sage Open, 14(1). https://doi.org/10.1177/21582440241229570

Yana, H., Hidayat, K., Sukesi, K., Yuliati, Y., & Sofiana, E. (2022). The Effect Of Agricultural Modernization On Work Preferences In Batu, East Java, Indonesia. Anuário Do Instituto De Geociências, 45, 1–8.

Yang, M., Al Mamun, A., Gao, J., Rahman, M. K., Salameh, A. A., & Alam, S. S. (2024). Predicting M-Health Acceptance From The Perspective Of Unified Theory Of Acceptance And Use Of Technology. Scientific Reports, 14(1), 339.

Yousefi, M., Zhang, J., & Jiang, Z. (2025). Bolstering Teaching Performance In Chinese Universities Through Transformational Leadership And Perceived Organizational Support. International Journal Of Chinese Education, 14(1). https://doi.org/10.1177/2212585x251321181

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Published

2026-06-18