Digital insurance acceptance among older adults in the context of AI
-
DOIhttp://dx.doi.org/10.21511/ins.16(1).2025.11
-
Article InfoVolume 16 2025, Issue #1, pp. 131-145
- 11 Views
-
4 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
AI technology integration into the Indian insurance industry promises many benefits, but its acceptance among older adults remains a challenge. Previous studies have paid insufficient attention to older adults’ unique needs and concerns in the context of AI-driven insurance service acceptance in India. The purpose of the study is to evaluate the acceptance of AI-powered digital insurance services among older adults in Kerala, India, from the perspective of customer satisfaction. This exploratory study employed the insights of the Technology Acceptance Model (TAM), the Information System Continuance Model (ISCM), and the Customer Satisfaction (C-SAT) method. Data were collected by conducting interviews in September 2024 with 20 older adults using AI-powered insurance services. Findings indicate a positive trend in adopting digital insurance services among older adults. However, the mean C-SAT scores for Perceived Usefulness and Perceived Ease of Use were 58% and 56%, respectively. Customer satisfaction scores for chatbot services and automated claims processing stood at 55% and 50%, respectively. These calculated scores are below the American Customer Satisfaction Index (ACSI) benchmark of 77.9% for Q2 2024. The participants of the study also expressed concerns regarding the use of AI-powered digital insurance services, citing inadequate user training facilities, fears of financial loss, privacy issues, and security and safety concerns. These results suggest the need for enhancements in AI interface design, user training, and customer support to better meet the unique needs and concerns of older adults and improve overall satisfaction.
- Keywords
-
JEL Classification (Paper profile tab)G22, O33, J14, M31
-
References52
-
Tables6
-
Figures0
-
- Table 1. Demographic profile of participants
- Table 2. Experience of older adults using AI-powered digital insurance services
- Table 3. Assessment of ‘Perceived Usefulness’ and customer satisfaction in AI-powered digital insurance services
- Table 4. Assessment of ‘Perceived Ease of Use’ and customer satisfaction in AI-powered digital insurance services
- Table 5. Customer satisfaction regarding chatbots in insurance services
- Table 6. Customer satisfaction with automated claims processing services
-
- Alrawad, M., Lutfi, A., Alyatama, S., Al Khattab, A., Alsoboa, S. S., Almaiah, M. A., Ramadan, M. H., Arafa, H. M., Ahmed, N. A., Alsyouf, A., & Al-Khasawneh, A. L. (2023). Assessing customers’ perception of online shopping risks: A structural equation modeling-based multigroup analysis. Journal of Retailing and Consumer Services, 71, 103188.
- American Customer Satisfaction Index. (2024, August 13). U.S. customer satisfaction growth stagnates, ACSI data show [Press release].
- Anirvinna, C., Jha, D., Goodwin, R. D., & Pokhriyal, D. (2025). Consumer willingness to adopt digital coupons in post-demonetization and COVID-19 in India. Innovative Marketing, 21(1), 157-169.
- Aulia, F., Adviorika, C. U., Yuniarty, Y., Fahlevi, M., Prabowo, H., & Muchardie, B. G. (2021). Analysis of chatbot program features towards customer satisfaction in the era of digitalization. In 2021 International Conference on Information Management and Technology (ICIMTech) (pp. 604-607). IEEE.
- Bhat, A. K., Das, S., & Quazi, F. (2025). How AI in life insurance can revolutionize public health: A proactive approach to wellness and disease prevention. In Proceedings of the 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS) (pp. 1061-1067). Institute of Electrical and Electronics Engineers.
- Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370.
- Crolic, C., Thomaz, F., Hadi, R., & Stephen, A. T. (2022). Blame the bot: Anthropomorphism and anger in customer–chatbot interactions. Journal of Marketing, 86(1), 132-148.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
- Deloitte. (2024). How artificial intelligence is transforming the financial services industry.
- DiCicco-Bloom, B., & Crabtree, B. F. (2006). The qualitative research interview. Medical Education, 40(4), 314-321.
- Eckert, C., & Osterrieder, K. (2020). How digitalization affects insurance companies: Overview and use cases of digital technologies. Zeitschrift für die gesamte Versicherungswissenschaft, 109(5), 333-360.
- Finlay, S. M. (2017). Artificial intelligence and machine learning for business: A no-nonsense guide to data driven technologies (2nd ed.). Relativistic.
- Fokina, M. (2022, May 20). AI in customer service: How to enrich your customer experience. G2.
- Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American Customer Satisfaction Index: Nature, purpose, and findings. Journal of Marketing, 60(4), 7-18.
- Godbole, S., & Roy, S. (2008). Text classification, business intelligence, and interactivity: Automating C-Sat analysis for services industry. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 911-919).
- Gonzales, J. T. (2023). Implications of AI innovation on economic growth: A panel data study. Journal of Economic Structures, 12(1), Article 13.
- Goodman, L. A. (1961). Snowball sampling. The Annals of Mathematical Statistics, 32(1), 148-170.
- Hou, L., Hsueh, S. C., & Zhang, S. (2021). Digital payments and households’ consumption: A mental accounting interpretation. Emerging Markets Finance and Trade, 57(7), 2079-2093.
- Insurance Regulatory and Development Authority of India (IRDAI). (2024). Duties and responsibilities.
- International Institute for Population Sciences, & United Nations Population Fund (IIPS & UNPF). (2023). India ageing report 2023: Caring for our elders – Institutional responses. United Nations Population Fund.
- Jin, X. (2024). Empowering autonomous digital learning for older adults. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-6).
- Jingzu, G., Siyu, L., Mengling, W., Yang, Q., Mamun, A. A., & Hayat, N. (2024). Sustainable entrepreneurship through customer satisfaction and reuse intention of online food delivery applications: Insights from China. Journal of Innovation and Entrepreneurship, 13(41), 1-22.
- Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1(2), 112-133.
- Kaur, P., & Singh, M. (2025). Exploring the impact of InsurTech adoption in Indian life insurance industry: A customer satisfaction perspective. The TQM Journal, 37(2), 457-483.
- Kiwanuka, A., & Sibindi, A. B. (2025). Insurance and risk management solutions for assisting adaptation to climate change. In A. B. Sibindi (Ed.), Sustainable finance and insurance in Africa (pp. 133–154). Springer.
- Kothari, C. R. (2004). Research methodology: Methods and techniques (2nd ed.). New Age International Publishers.
- Koul, S., Jasrotia, S. S., & Mishra, H. G. (2021). Acceptance of digital payments among rural retailers in India. Journal of Payments Strategy & Systems, 15(2), 201-213.
- Kshetri, N. (2021). The role of artificial intelligence in promoting financial inclusion in developing countries. Journal of Global Information Technology Management, 24(1), 1-6.
- Lau, M. M., Cheung, R., Lam, A. Y., & Chu, Y. T. (2013). Measuring service quality in the banking industry: A Hong Kong based study. Contemporary Management Research, 9(3), 263-282.
- Lee, J., Tang, Y., & Jiang, S. (2023). Understanding continuance intention of artificial intelligence (AI)-enabled mobile banking applications: An extension of AI characteristics to an expectation confirmation model. Humanities and Social Sciences Communications, 10(1), Article 18.
- Lu, Z., Wu, J., Li, H., & Nguyen, D. K. (2022). Local bank, digital financial inclusion and SME financing constraints: Empirical evidence from China. Emerging Markets Finance and Trade, 58(6), 1712-1725.
- Mhlanga, D. (2020). Industry 4.0 in finance: The impact of artificial intelligence (AI) on digital financial inclusion. International Journal of Financial Studies, 8(3), 45.
- Ministry of Electronics and Information Technology (MeitY). (2024). Digital India.
- Muskat, M., Blackman, D., & Muskat, B. (2012). Mixed methods: Combining expert interviews, cross-impact analysis and scenario development. The Electronic Journal of Business Research Methods, 10(1), 9-21.
- Nicolescu, L., & Tudorache, M. T. (2022). Human-computer interaction in customer service: The experience with AI chatbots – A systematic literature review. Electronics, 11(10), 1579.
- Ou, P., Thet, S., Soem, V., Hor, P., Ean, L., & Theng, D. (2024). Determinants of customer satisfaction of mobile network providers in Cambodia: A study of hybrid model of structural equation modeling (SEM) and artificial neural network (ANN). SN Business & Economics, 4(85), 1-31.
- Parker, C., & Mathews, B. P. (2001). Customer satisfaction: Contrasting academic and consumers’ interpretations. Marketing Intelligence & Planning, 19(1), 38-44.
- Rao, M. R. (2022, September). Inclusive credit: The next milestone. RBI Bulletin, 13-18.
- Reis, T., Kreibich, A., Bruchhaus, S., Krause, T., Freund, F., Bornschlegl, M. X., & Hemmje, M. L. (2023). An information system supporting insurance use cases by automated anomaly detection. Big Data and Cognitive Computing, 7(1), 4.
- Restrepo-Morales, J. A., Valencia-Cárdenas, M., & López-Cadavid, D. A. (2024). Interplay of customer satisfaction, innovation, and product quality: Key determinants of company performance. Journal of Technology Management & Innovation, 19(2), 28-42.
- Roumbanis, L. (2025). On the present-future impact of AI technologies on personnel selection and the exponential increase in meta-algorithmic judgments. Futures, 166, Article 103538.
- Sahi, A. M., Khalid, H., & Abbas, A. F. (2021). Digital payment adoption: A review (2015–2020). Journal of Management Information and Decision Sciences, 24(7), 1-9.
- Salles, A., Evers, K., & Farisco, M. (2020). Anthropomorphism in AI. AJOB Neuroscience, 11(2), 88-95.
- Shandilya, E., & Fan, M. (2022). Understanding older adults’ perceptions and challenges in using AI-enabled everyday technologies. In Proceedings of the Tenth International Symposium of Chinese CHI (pp. 105-116). Association for Computing Machinery.
- Tang, Y. M., Chau, K. Y., Hong, L., Ip, Y. K., & Yan, W. (2021). Financial innovation in digital payment with WeChat towards electronic business success. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1844-1861.
- Technical Group on Population Projections. (2019). Population projections for India and states 2011–2036. Ministry of Health and Family Welfare, Government of India.
- Thakur, J., & Kansra, P. (2024). A theoretical perspective on AI’s transformative role in the health insurance sector: Unveiling the future. In AI Innovations in Service and Tourism Marketing (pp. 192–210). IGI Global.
- Tirado, D. M., Vidal-Meliá, L., Cardiff, J., & Quille, K. (2024). Vulnerable customers’ perception of corporate social responsibility in the banking sector in a post-crisis context. International Journal of Bank Marketing, 42(6), 1148-1177.
- Toucinho, A. (2020). How payments drive digital transformation. Journal of Payments Strategy & Systems, 14(1), 10-13.
- UK Statistics Authority. (2022, May 25). Ethical considerations associated with qualitative research methods. Centre for Applied Data Ethics.
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
- Wong, A. K. C., Lee, J. H. T., Zhao, Y., Lu, Q., Yang, S., & Hui, V. C. C. (2025). Exploring older adults’ perspectives and acceptance of AI-driven health technologies: Qualitative study. JMIR Aging, 8(1), e66778.