Influencing factors of the extent, timing, and pattern of online insurance adoption
DOI:
https://doi.org/10.7903/ijecs.2078Keywords:
online insurance, consumer heterogeneity, adoption timing, adoption pattern, ordered-logit modelAbstract
This study investigates the influence of consumer traits, product attributes, marketing activities, and word of mouth (WOM) on three aspects of online insurance adoption: the number of products adopted, adoption timing, and adoption pattern. We empirically examine the relationships between these variables using data from 509 consumers collected through an online survey. The results from three statistical analyses suggest that consumers with low social needs, are price-conscious, and perceive fewer risks when buying on the Internet tend to purchase more online insurance products. Furthermore, products advertised on television and the Internet or perceived as having high premium variability are likely to have shorter adoption timing than other products. Moreover, the adoption of an insurance product appears contingent upon the type of insurance previously adopted and the attribute discrepancy between products. Finally, we discuss the managerial implications of our findings to increase the effectiveness of marketing efforts.References
Dasgupta, P., & Sengupta, K. (2002). E-commerce in the Indian insurance industry: Prospects and future. Electronic Commerce Research, 2(1), 43-60.
Gebert-Persson, S., Gidhagen, M., Sallis, J. E., & Lundberg, H. (2019). Online insurance claims: when more than trust matters. International Journal of Bank Marketing, 37(2), 579-594.
Mordor Intelligence (2019). The Europe online insurance market is segmented by geography (Germany, France, United Kingdom, and Others) and type (life and non- life insurance) and market dynamics - growth, trends, and forecast (2019 - 2024). Available at: https://www.mordorintelligence.com/industry-reports/europe-online-insurance-market.
Mordor Intelligence (2020). China online insurance market - Growth, trends, COVID-19 impact, and forecast (2021 - 2026). Available at: https://www.mordorintelligence.com/industry-reports/china-online-insurance-market.
Stoeckli, E., Dremel, C., & Uebernickel, F. (2018). Exploring characteristics and transformational capabilities of InsurTech innovations to understand insurance value creation in a digital world. Electronic Markets, 28(3), 287-305.
Toukabri, M. T., & Ettis, S. A. (2021). The Acceptance and Behavior Towards E-Insurance. International Journal of E-Business Research, 17(2), 24-39.
Yao, J. (2004). Ecommerce Adoption of Insurance Companies in New Zealand. Journal Electronic Commerce Research, 5(1), 54-61.
Cata, T., & Lee, S. M. (2006). Adoption of web-based applications in the financial sector: The case of online insurance. Journal of Internet Commerce, 5(2), 41-61.
Lim, S. H., Hur, Y., Lee, S., & Koh, C. E. (2009). Role of trust in adoption of online auto insurance. Journal of Computer Information Systems, 50(2), 151-159.
Featherman, M. S., Miyazaki, A. D., & Sprott, D. E. (2010). Reducing online privacy risk to facilitate e-service adoption: the influence of perceived ease of use and corporate credibility. Journal of Services Marketing, 24(3), 219-229.
Maheswari, V. U., & Chandrasekaran, U. (2018). Buying Insurance Online: Are we there yet?. Indian Journal of Commerce and Management Studies, 9(2), 30-41.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Khare, A., Dixit, S., Chaudhary, R., Kochhar, P., & Mishra, S. (2012). Customer behavior toward online insurance services in India. Journal of Database Marketing & Customer Strategy Management, 19(2), 120-133.
Yu, T. W., & Chen, T. J. (2018). Online travel insurance purchase intention: A transaction cost perspective. Journal of Travel & Tourism Marketing, 35(9), 1175-1186.
Luo, C., Chen, Q., Zhang, Y., & Xu, Y. (2019). The effects of trust on policyholders' purchase intentions in an online insurance platform. Emerging Markets Finance and Trade, 1-18.
Ettis, S. A., & Haddad, M. M. (2019). Utilitarian and hedonic customer benefits of e-insurance: A look at the role of gender differences. International Journal of E-Business Research, 15(1), 109-126.
Rogers, E. M. (2003). Diffusion of Innovations, 5th edition, Simon and Schuster.
Bemmaor A.C. (1992). Modeling the Diffusion of New Durable Goods: Word-of-Mouth Effect Versus Consumer Heterogeneity. In: Laurent G., Lilien G.L., Pras B. (eds) Research traditions in marketing. International Series in Quantitative Marketing, vol 5. Springer, Dordrecht. 201-229.
Shabanpour, R., Shamshiripour, A., & Mohammadian, A. (2018). Modeling adoption timing of autonomous vehicles: innovation diffusion approach. Transportation, 45(6), 1607-1621.
Bilgicer, T., Jedidi, K., Lehmann, D. R., & Neslin, S. A. (2015). Social contagion and customer adoption of new sales channels. Journal of Retailing, 91(2), 254-271.
Vilpponen, A., Winter, S., & Sundqvist, S. (2006). Electronic word-of-mouth in online environments: Exploring referral networks structure and adoption behavior. Journal of Interactive Advertising, 6(2), 8-77.
Islam, T., & Meade, N. (2013). The impact of attribute preferences on adoption timing: The case of photo-voltaic (PV) solar cells for household electricity generation. Energy Policy, 55, 521-530.
Noppers, E. H., Keizer, K., Bockarjova, M., & Steg, L. (2015). The adoption of sustainable innovations: The role of instrumental, environmental, and symbolic attributes for earlier and later adopters. Journal of Environmental Psychology, 44(December), 74-84.
Sachdeva, N., Kapur, P. K., & Singh, O. (2016). An innovation diffusion model for consumer durables with three parameters. Journal of Management Analytics, 3(3), 240-265.
Prins, R., & Verhoef, P. C. (2007). Marketing communication drivers of adoption timing of a new e-service among existing customers. Journal of Marketing, 71(2), 169-183.
Foubert, B., & Gijsbrechts, E. (2016). Try it, you'll like it—or will you? The perils of early free-trial promotions for high-tech service adoption. Marketing Science, 35(5), 810-826.
Kamakura, W. A., Ramaswami, S. N., & Srivastava, R. K. (1991). Applying latent trait analysis in the evaluation of prospects for cross-selling of financial services. international Journal of Research in Marketing, 8(4), 329-349.
Knott, A., Hayes, A., & Neslin, S. A. (2002). Next‐product‐to‐buy models for cross‐selling applications. Journal of interactive Marketing, 16(3), 59-75.
Liu, T. C., & Wu, L. W. (2008). Relationship quality and cross-buying in varying levels of category similarity and complexity. Total Quality Management & Business Excellence, 19(5), 493–511.
Shapoval, K., & Setzer, T. (2018). Next-purchase prediction using projections of discounted purchasing sequences. Business & Information Systems Engineering, 60(2), 151-166.
Li, S., Sun, B., & Wilcox, R. T. (2005). Cross-selling sequentially ordered products: An application to consumer banking services. Journal of Marketing Research, 42(2), 233-239.
Maslow, A. H. (1970). Motivation and personality (2nd ed.). Harper and Row.
Bruggencate, T., Luijkx, K. G., & Sturm, J. (2018). Social needs of older people: a systematic literature review. Ageing and Society, 38(9), 1745-1770.
John, O. P., & Srivastava, S. (1999). The Big-Five trait taxonomy: History, measurement, and theoretical perspectives (Vol. 2, pp. 102-138). Berkeley: University of California.
Itani, O. S., El Haddad, R., & Kalra, A. (2020). Exploring the role of extrovert-introvert customers' personality prototype as a driver of customer engagement: Does relationship duration matter?. Journal of Retailing and Consumer Services, 53, 101980.
McCrae, R. R., & Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52(1), 81-90.
Mottram, A. J., & Fleming, M. J. (2009). Extraversion, impulsivity, and online group membership as predictors of problematic Internet use. CyberPsychology & Behavior, 12(3), 319-321.
Mooradian, T. A., & Olver, J. M. (1997). "I can't get no satisfaction:" The impact of personality and emotion on postpurchase processes. Psychology & Marketing, 14(4), 379-393.
Kabadayi, S., & Price, K. (2014). Consumer–brand engagement on Facebook: liking and commenting behaviors. Journal of Research in Interactive Marketing, 8(3), 203-223.
Bibby, P. A. (2008). Dispositional factors in the use of social networking sites: Findings and implications for social computing research. In International Conference on Intelligence and Security Informatics (pp. 392-400). Springer, Berlin, Heidelberg.
Lichtenstein, D. R., Ridgway, N. M., & Netemeyer, R. G. (1993). Price perceptions and consumer shopping behavior: a field study. Journal of Marketing Research, 30(2), 234-245.
Sinha, I., & Batra, R. (1999). The effect of consumer price consciousness on private label purchase. International Journal of Research in Marketing, 16(3), 237-251.
Lichtenstein, D. R., Bloch, P. H., & Black, W. C. (1988). Correlates of price acceptability. Journal of Consumer Research, 15(2), 243–252.
Dahana, W. D., Shin, H., & Katsumata, S. (2018). Influence of individual characteristics on whether and how much consumers engage in showrooming behavior. Electronic Commerce Research, 18(4), 665-692.
Kukar-Kinney, M. (2006). The role of price-matching characteristics in influencing store loyalty. Journal of Business Research, 59(4), 475-482.
Garven, J. R. (2002). On the implications of the internet for insurance markets and institutions. Risk Management and Insurance Review, 5(2), 105-116.
Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk perceptions in Internet shopping. Journal of Business Research, 56(11), 867-875.
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474.
Donthu, N., & Garcia, A. (1999). The Internet shopper. Journal of Advertising Research, 39(3), 52–58.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.
Luszczynska, A., Scholz, U., & Schwarzer, R. (2005). The general self-efficacy scale: multicultural validation studies. The Journal of Psychology, 139(5), 439-457.
Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of Internet banking: an empirical study. International Journal of Service Industry Management, 14(5), 501-519.
Kesharwani, A., & Tripathy, T. (2012). Dimensionality of perceived risk and its impact on Internet banking adoption: An empirical investigation. Services Marketing Quarterly, 33(2), 177-193.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
Alan, A. K., & Kabadayı, E. T. (2016). The effect of personal factors on social media usage of young consumers. Procedia-Social and Behavioral Sciences, 235(November), 595-602.
San-Martín, S., Jimenez, N., Camarero, C., & San-José, R. (2020). The Path between Personality, Self-Efficacy, and Shopping Regarding Games Apps. Journal of Theoretical and Applied Electronic Commerce Research, 15(2), 59-75.
Kline, P. (2000). The handbook of psychological testing (2nd ed.). London: Routledge.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (fifth ed.), Prentice Hall, New Jersey.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Cameron, A. C., & Trivedi, P. K. (1990). Regression-based tests for overdispersion in the Poisson model. Journal of Econometrics, 46(3), 347-364.
Yang, F. X. (2017). Effects of restaurant satisfaction and knowledge sharing motivation on eWOM intentions: the moderating role of technology acceptance factors. Journal of Hospitality & Tourism Research, 41(1), 93-127.
Dahana, W. D., Morisada, M., & Miwa, Y. (2018b). Cross-selling across stores or within a store? Impacts of cross-buying behavior in online shopping malls. Journal of Marketing Channels, 25(1-2), 47-72.
