Hyper-Personalization in Motor Insurance: Understanding Telematics Insurance Adoption Using the Extended Technology Acceptance Model

Authors

  •   Rohit Joshi Assistant Professor, Department of Management Studies, Medi-Caps University, A. B. Road, Pigdamber, Rau, Indore - 453 331, Madhya Pradesh
  •   Sunil Mishra Professor (Corresponding Author), Department of Management Studies, Medi-Caps University, A. B. Road, Pigdamber, Rau, Indore - 453 331, Madhya Pradesh
  •   Anupama Pardeshi Assistant Professor, Department of Management Studies, Medi-Caps University, A. B. Road, Pigdamber, Rau, Indore - 453 331, Madhya Pradesh
  •   Manpreet Kaur Bhatia Assistant Professor, Department of Management Studies, Medi-Caps University, A. B. Road, Pigdamber, Rau, Indore - 453 331, Madhya Pradesh

DOI:

https://doi.org/10.17010/ijom/2024/v54/i6/173946

Keywords:

telematics

, motor insurance, behavioral intention, technology adoption, usage-based insurance.

Paper Submission Date

, August 30, 2023, Paper sent back for Revision, April 1, 2024, Paper Acceptance Date, April 30, Paper Published Online, June 15, 2024

Abstract

Purpose : The analysis of driving behavior and vehicle dynamics using telematics technology paved the way for current insurance underwriting and disruptions in automobile insurance. This study aimed to understand the behavioral intentions of users toward adopting telematics-based insurance products.

Design/Methodology/Approach : Relevant constructs from extant literature were used to extend the technology acceptance model (TAM) to improve its explanatory power and identify meaningful linkages among the variables. A cross-sectional design with a quantitative survey examined how individuals perceived insurance telematics technology.

Findings : Perceived enjoyment had the greatest impact, while perceived trust had the least impact on behavioral intentions. Perceived privacy risk lowered intentions to use telematics insurance. The extended TAM model proved valid, explaining 59% of the variance in behavioral intentions. Additionally, two-thirds of the respondents were open to adjusting their driving habits for safer driving if it meant lowering insurance premiums.

Practical Implications : Telematics insurers and marketers were advised to prioritize ensuring a smooth transition for users to attain scalability and profitability. Marketers should emphasize the enjoyable aspects of telematics insurance while also addressing privacy concerns. Additionally, aligning users’ discount expectations with actual offerings was suggested to be crucial to bridge gaps.

Originality/Value : This research provided valuable insights into a recent advancement in motor insurance in India, i.e., telematics-based insurance. As far as we know, it is among the pioneering studies conducted on this subject within the Indian subcontinent.

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Published

2024-06-29

How to Cite

Joshi, R., Mishra, S., Pardeshi, A., & Bhatia, M. K. (2024). Hyper-Personalization in Motor Insurance: Understanding Telematics Insurance Adoption Using the Extended Technology Acceptance Model. Indian Journal of Marketing, 54(6), 47–64. https://doi.org/10.17010/ijom/2024/v54/i6/173946

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Articles

References

Ajzen, I., & Fishbein, M. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley.

Alturas, B. (2021). Models of acceptance and use of technology research trends: Literature review and exploratory bibliometric study. In M. Al-Emran & K. Shaalan (eds.), Recent advances in technology acceptance models and theories. Studies in systems, decision and control (Vol. 335, pp. 13–28). Springer. https://doi.org/10.1007/978-3-030-64987-6_2

Awang, Z. (2012). Research methodology and data analysis (2nd ed.). UiTM Press.

Ben-Shahar, O. (2023). Privacy protection, at what cost? Exploring the regulatory resistance to data technology in auto insurance. Journal of Legal Analysis, 15(1), 129–157. https://doi.org/10.1093/jla/laad008

Bhatia, J., & Breaux, T. D. (2018). Empirical measurement of perceived privacy risk. ACM Transactions on Computer-Human Interaction, 25(6), 1–47. https://doi.org/10.1145/3267808

Chauhan, V., Choudhary, V., & Mathur, S. (2016). Demographic influences on technology adoption behavior: A study of e-banking services in India. Prabandhan: Indian Journal of Management, 9(5), 45–59. https://doi.org/10.17010/pijom/2016/v9i5/92571

Chauhan, V., Joshi, R., & Choudhary, V. (2023). Understanding intention to adopt telematicsbased automobile insurance in an emerging economy: A mixed-method approach. Journal of Financial Services Marketing. https://doi.org/10.1057/s41264-023-00253-5

Chen, C.-F., & Chen, P.-C. (2011). Applying the TAM to travelers' usage intentions of GPS devices. Expert Systems with Applications, 38(5), 6217–6221. https://doi.org/10.1016/j.eswa.2010.11.047

Cheng, J., Feng, F. Y., & Zeng, X. (2023). Pay-as-you-drive insurance: Modeling and implications. North American Actuarial Journal, 27(2), 303–321. https://doi.org/10.1080/10920277.2022.2077220

Derikx, S., de Reuver, M., & Kroesen, M. (2016). Can privacy concerns for insurance of connected cars be compensated? Electronic Markets, 26(1), 73–81. https://doi.org/10.1007/s12525-015-0211-0

Doecke, S. D., Baldock, M. R., Kloeden, C. N., & Dutschke, J. K. (2020). Impact speed and the risk of serious injury in vehicle crashes. Accident Analysis & Prevention, 144, 105629. https://doi.org/10.1016/j.aap.2020.105629

Eling, M., & Kraft, M. (2020). The impact of telematics on the insurability of risks. Journal of Risk Finance, 21(2), 77–109. https://doi.org/10.1108/JRF-07-2019-0129

Global Market Insights. (2022). Usage-based insurance market. Gminsights. https://www.gminsights.com/industry-analysis/usage-based-insurance-ubi-market

Gupta, P., & Barkathunissa. (2022). Exploring the plausibility of pre-purchase decision process in user acceptance of smart wearable technology devices. Indian Journal of Marketing, 52(4), 44–62. https://doi.org/10.17010/ijom/2022/v52/i4/169109

Gupta, S., & Pande, S. (2017). Emerging trends in insurance sector in India: The case study of telematics. International Journal of Trend in Research and Development, 4(6), 42–45.

Gupta, S., Yadav, A., & Singla, D. (2020). Factors affecting adoption of telematics in the Indian insurance sector. International Journal of Advanced Science and Technology, 29(6s), 1005–1012.

Hair Jr., J. F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

Henckaerts, R., & Antonio, K. (2022). The added value of dynamically updating motor insurance prices with telematics collected driving behavior data. Insurance: Mathematics and Economics, 105, 79–95. https://doi.org/10.1016/j.insmatheco.2022.03.011

Holzapfel, J., Peter, R., & Richter, A. (2023). Mitigating moral hazard with usage-based insurance. Journal of Risk and Insurance. https://doi.org/10.1111/jori.12433

Hu, L. T., & 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

Insurance Regulatory and Development Authority of India. (2020, January 14). Granting approval to proposals from health, motor, and intermediaries department under the Regulatory Sandbox [Press release]. https://www.irdai.gov.in/ADMINCMS/cms/frmGeneral_Layout.aspx?page=PageNo4010&flag=1

Jain, S., & Singla, D. (2019). Influence of social networks on the adoption of telematics insurance: A study on Indian consumers. International Journal of Business Analytics and Intelligence, 7(2), 132–144.

Joshi, R. (2024). A mixed methods UTAUT2-based approach to understanding unified payments interface adoption among low-income users. Banks and Bank Systems, 19(1), 58–73. https://doi.org/10.21511/bbs.19(1).2024.06

Kongmuang, P., & Thawesaengskulthai, N. (2019). Improvement of telematics solution for motor insurance in Thailand by 5D innovation development process. In, 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA) (pp. 6–11). https://doi.org/10.1109/IEA.2019.8715152

Litman, T. (2011). Distance-based vehicle insurance feasibility, costs and benefits (Comprehensive Technical Report). Victoria Transport Policy Institute. https://www.vtpi.org/dbvi_com.pdf

Milanović, N., Milosavljević, M., Benković, S., StarÄević, D., & Spasenić, Ž. (2020). An acceptance approach for novel technologies in car insurance. Sustainability, 12(24), 10331. https://doi.org/10.3390/su122410331

Mishra, P., & Mishra, P. (2017). Vital stats: Overview of road accidents in India. PRS Legislative Research. https://prsindia.org/files/policy/policy_vital_state/Overview%20of%20Road%20Accidents.pdf

Mukhopadhyay, I., & Chakraborty, A. (2018). An empirical study on the trustworthiness of telematics technology: Evidence from India. Journal of Insurance and Risk Management, 4(2), 78–88.

Murthy, D. N., & Kumar, B. V. (2015). Internet of Things (IoT): Is IoT a disruptive technology or a disruptive business model? Indian Journal of Marketing, 45(8), 18–27. https://doi.org/10.17010/ijom/2015/v45/i8/79915

Rahman, M. M., Lesch, M. F., Horrey, W. J., & Strawderman, L. (2017). Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accident Analysis & Prevention, 108, 361–373. https://doi.org/10.1016/j.aap.2017.09.011

Rejikumar, G. (2013). A pre-launch exploration of customer acceptance of usage based vehicle insurance policy. IIMB Management Review, 25(1), 19–27. https://doi.org/10.1016/j.iimb.2012.11.002

Rekha, I. S., Basri, S., & Kavitha, T. C. (2020). Acceptance of internet banking: Comparing six theoretical models. Indian Journal of Finance, 14(3), 7–21. https://doi.org/10.17010/ijf/2020/v14i3/151073

Sardana, V., Mohapatra, A. K., Singh, A. K., & Singhania, S. (2023). Unveiling insurance and risk management insights through bibliometric and cluster analysis. Prabandhan: Indian Journal of Management, 16(11), 8–26. https://doi.org/10.17010/pijom/2023/v16i11/173213

Senyo, P. K., & Osabutey, E. L. (2020). Unearthing antecedents to financial inclusion through FinTech innovations. Technovation, 98, 102155. https://doi.org/10.1016/j.technovation.2020.102155

Shetty, A., & Basri, S. (2021). Mediating effect of attitude on the determinants of financial misselling of life insurance products in India. Prabandhan: Indian Journal of Management, 14(11), 41–57. https://doi.org/10.17010/pijom/2021/v14i11/166980

Shokeen, A., Kumar, A., & Khanna, S. (2023). Understanding students' behavioral intention to adopt blended learning: Modified UTAUT model. Prabandhan: Indian Journal of Management, 16(11), 43–64. https://doi.org/10.17010/pijom/2023/v16i11/170915

Singh, A., & Kumar, A. (2019). An empirical investigation of the impact of cultural dimensions on consumer acceptance of telematics-based insurance in India. Journal of Insurance and Financial Planning, 3(2), 112–124.

Stevenson, M., Harris, A., Wijnands, J. S., & Mortimer, D. (2021). The effect of telematic based feedback and financial incentives on driving behaviour: A randomised trial. Accident Analysis & Prevention, 159, 106278. https://doi.org/10.1016/j.aap.2021.106278

Tabeck, P. S., & Singh, A. B. (2022). Acceptance of mobile apps among bottom of pyramid customers of urban areas. Indian Journal of Marketing, 52(9), 43–58. https://doi.org/10.17010/ijom/2022/v52/i9/171984

The World Bank. (2021). Traffic crash injuries and disabilities: The burden on Indian society (Report No. 156336). https://documents.worldbank.org/en/publication/documentsreports/documentdetail/761181612392067411/main-report

Tian, X., Prybutok, V., Mirzaei, F., & Dinulescu, C. C. (2020). Millennials acceptance of insurance telematics: An integrative empirical study. American Business Review, 23(1), 156–181. https://doi.org/10.37625/abr.23.1.156-181

Tseng, H. C., Tu, P. P., Lee, Y. C., & Wang, T. S. (2013). A study of satellite navigation fleet management system usage in Taiwan with application of C-TAM-TPB model. Information Technology Journal, 12(1), 15–27. https://doi.org/10.3923/itj.2013.15.27

Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x

Vickrey, W. (1968). Automobile accidents, tort law, externalities, and insurance: An economist's critique. Law and Contemporary Problems, 33(3), 464–487. https://doi.org/10.2307/1190938

Wu, L.-H., Wu, L.-C., & Chang, S.-C. (2016). Exploring consumers' intention to accept smartwatch. Computers in Human Behavior, 64, 383–392. https://doi.org/10.1016/j.chb.2016.07.005

Zhang, T., Tao, D., Qu, X., Zhang, X., Zeng, J., Zhu, H., & Zhu, H. (2020). Automated vehicle acceptance in China: Social influence and initial trust are key determinants. Transportation Research Part C: Emerging Technologies, 112, 220–233. https://doi.org/10.1016/j.trc.2020.01.027