New IJM now accepts submissions exclusively via the Editorial Scholar portal — a dedicated submission & review system.
Go to Editorial Scholar →
Associated Management Consultants Pvt. Ltd. | COPE Member · ID: JM07589
Scopus Q3 UGC-CARE ABDC: C |
Indian Journal of Marketing logo

Indian Journal of Marketing

ISSN: 0973-8703 Frequency: Monthly Peer Review: Double-blind Published since: 1968 Language: English
A publication of AMCPL
amcon.co.in
New Delhi, India
Indexed in: Scopus Q3 UGC-CARE Group II ABDC: C Google Scholar J-Gate NAAS NISCAIR Crossref

Original Article

Open Access Original Article

Fostering Digital Engagement and Customer Retention in Indian Insurance : An Empirical Study

Neha Singh1Rajeshwari Panigrahi2Rashmi Shekhar3

1 Research Scholar , GITAM School of Business, GITAM (Deemed to be University), Rushikonda, Visakhapatnam - 530 045, Andhra Pradesh

2 Professor, GITAM School of Business, GITAM (Deemed to be University), Rushikonda, Visakhapatnam - 530 045, Andhra Pradesh

3 Associate Professor, Amity Institute of Information Technology, Amity University, Patna - 801 503, Bihar

Volume 54
Issue 7
Pages 68–82
Year 2024
Received: Aug. 30, 2023 Accepted: April 30, 2024 Published: July 1, 2024
Abstract

Purpose: Internet penetration in India has significantly increased over the past decade, making digital engagement accessible and affordable. This paper investigated the digital engagement of life insurance customers in India, its influence on customer retention, and the impact of perceived usefulness, perceived ease of use, and perceived value.

Methodology: A mixed method approach was adopted based on an exploratory sequential design to identify the constructs and test their relationship. The authors analyzed data from 375 respondents and used confirmatory factor analysis and path analysis to test the main hypotheses using the AMOS v24 software.

Findings: The findings showed that perceived ease of use and usefulness positively impacted consumers’ digital engagement. In the life insurance industry, digital engagement has a large and favorable impact on perceived value and customer retention, with perceived value acting as a partly mediating factor in customer retention.Practical

Implications: Promoting the digital adoption of services among consumers is imperative for service providers to ensure better customer management, irrespective of sector and service or product. The consumer decision-making process is intricate in the life insurance sector due to the product’s complexity and the long-term association between the consumer and service provider. The study findings will help service providers focus on significant touchpoints to improve customer engagement by enhancing the engagement activities’ ease of use, usefulness, and perceived value.

Originality: The paper extended our understanding of the determinants of digital engagement and its subsequent impact on consumers in the context of life insurance, which has not been explored in the existing literature.

Keywords Digital engagement customer retention insurance technology acceptance model structural equation modeling
How to Cite

Neha Singh, Rajeshwari Panigrahi, Rashmi Shekhar (2024). Fostering Digital Engagement and Customer Retention in Indian Insurance : An Empirical Study. Indian Journal of Marketing, 54(7), 68–82. https://doi.org/10.17010/ijom/2024/v54/i7/174017

References
  1. Abdullah, N., Roslan, A., Yusuf, R. Y., & Rasid, M. F. (2020). Behavior of Malaysian igeneration in purchasing life insurance policy. Universal Journal of Accounting and Finance, 8(4), 153–160. https://doi.org/10.13189/ujaf.2020.080408
  2. Al-Abdullatif, A. M., & Gameil, A. A. (2021). The effect of digital technology integration on students' academic performance through project-based learning in an e-learning environment. International Journal of Emerging Technologies in Learning (iJET), 16(11), 189–210. https://doi.org/10.3991/ijet.v16i11.19421
  3. Al-Gharaibah, O. B. (2020). Customer retention in five-star hotels in Jordan: The mediating role of hotel perceived value. Management Science Letters, 10, 3949–3956. https://doi.org/10.5267/j.msl.2020.7.015
  4. Basri, S., & Shetty, D. (2018). Predicting e-banking adoption: An evaluation of perceptions and behavioural intentions of small and medium enterprises in Karnataka. Indian Journal of Finance, 12(7), 28–41. https://doi.org/10.17010/ijf/2018/v12i7/129969
  5. Beckett, A., Hewer, P., & Howcroft, B. (2000). An exposition of consumer behaviour in the financial services industry. International Journal of Bank Marketing, 18(1), 15–26. https://doi.org/10.1108/02652320010315325
  6. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588
  7. Boksberger, P. E., & Melsen, L. (2011). Perceived value: A critical examination of definitions, concepts and measures for the service industry. Journal of Services Marketing, 25(3), 229–240. https://doi.org/10.1108/08876041111129209
  8. Bolton, R. N., Gustafsson, A., McColl-Kennedy, J., Sirianni, N. J., & Tse, D. K. (2014). Small details that make big differences: A radical approach to consumption experience as a firm's differentiating strategy. Journal of Service Management, 25(2), 253–274. https://doi.org/10.1108/JOSM-01-2014-0034
  9. Byrne, B. M. (2011). Structural equation modeling with mplus: Basic concepts, applications, and programming (1st ed.). Routledge. https://doi.org/10.4324/9780203807644
  10. Colicev, A., Malshe, A., Pauwels, K., & O'Connor, P. (2018). Improving consumer mindset metrics and shareholder value through social media: The different roles of owned and earned media. Journal of Marketing, 82(1), 37–56. https://doi.org/10.1509/jm.16.0055
  11. Creswell, J. W., & Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.
  12. 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
  13. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
  14. Drehlich, M., Naraine, M., Rowe, K., Lai, S. K., Salmon, J., Brown, H., Koorts, H., Macfarlane, S., & Ridgers, N. D. (2020). Using the technology acceptance model to explore adolescents' perspectives on combining technologies for physical activity promotion within an intervention: Usability study. Journal of Medical Internet Research, 22(3), Article ID e15552. https://doi.org/10.2196/15552
  15. Drummond, C., O'Toole, T., & McGrath, H. (2020). Digital engagement strategies and tactics in social media marketing. European Journal of Marketing, 54(6), 1247–1280. https://doi.org/10.1108/EJM-02-2019-0183
  16. Eigenraam, A. W., Eelen, J., van Lin, A., & Verlegh, P. W. (2018). A consumer-based taxonomy of digital customer engagement practices. Journal of Interactive Marketing, 44(1), 102–121. https://doi.org/10.1016/j.intmar.2018.07.002
  17. Farhat, K., Aslam, W., & Mokhtar, S. S. (2021). Beyond social media engagement: Holistic digital engagement and a social identity perspective. Journal of Internet Commerce, 20(3), 319–354. https://doi.org/10.1080/15332861.2021.1905474
  18. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  19. Givi, M. E., Keshavarz, H., & Azad, Z. K. (2023). Quality assessment of e-learning website using asymmetric impact–performance analysis and Kano's customer satisfaction model: A case study based on WebQual 4.0. Information Discovery and Delivery, 51(1), 35–46. https://doi.org/10.1108/IDD-08-2021-0083
  20. Gupta, S., & Prusty, S. (2023). Does consumer empowerment influence e-payment systems adoption? A digital consumer-centric perspective. Journal of Financial Services Marketing. https://doi.org/10.1057/s41264-023-00238-4
  21. Hair, J. F. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.
  22. 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
  23. 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
  24. Jiang, Y., & Stylos, N. (2021). Triggers of consumers' enhanced digital engagement and the role of digital technologies in transforming the retail ecosystem during COVID-19 pandemic. Technological Forecasting and Social Change, 172, 121029. https://doi.org/10.1016/j.techfore.2021.121029
  25. Khan, K. A., & Akhtar, M. A. (2021). Digital engagement as a predictor of financial capability, financial advice, and financial satisfaction. Studies in Business and Economics, 16(2), 127–141. https://doi.org/10.2478/sbe-2021-0029
  26. Leeflang, P. S., Verhoef, P. C., Dahlström, P., & Freundt, T. (2014). Challenges and solutions for marketing in a digital era. European Management Journal, 32(1), 1–12. https://doi.org/10.1016/j.emj.2013.12.001
  27. Mahadevan, K., Punjani, K. K., & Joshi, S. (2023). Examining online grocery purchase intentions through an extended TAM framework: A mediation analysis approach. Indian Journal of Marketing, 53(11), 41–57. https://doi.org/10.17010/ijom/2023/v53/i11/170597
  28. Malhotra, G. (2022). Consumer retention in two-wheeler industry: A moderated mediation model. Asia Pacific Journal of Marketing and Logistics, 34(8), 1681–1701. https://doi.org/10.1108/APJML-03-2021-0187
  29. McDonald, R. P., & Ho, M.-H. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64–82. https://doi.org/10.1037/1082-989X.7.1.64
  30. Milan, G. S., Eberle, L., & Bebber, S. (2015). Perceived value, reputation, trust, and switching costs as determinants of customer retention. Journal of Relationship Marketing, 14(2), 109–123. https://doi.org/10.1080/15332667.2015.1041353
  31. Nagdev, K., & Rajesh, A. (2018). Consumers' intention to adopt internet banking: An Indian perspective. Indian Journal of Marketing, 48(6), 42–56. https://doi.org/10.17010/ijom/2018/v48/i6/127835
  32. Nomi, M., & Sabbir, M. M. (2020). Investigating the factors of consumers' purchase intention towards life insurance in Bangladesh: An application of the theory of reasoned action. Asian Academy of Management Journal, 25(2). https://doi.org/10.21315/aamj2020.25.2.6
  33. Padival, A., Michael, L. K., & Hebbar, S. (2019). Consumer perception towards social media advertisements: A study done in a semi-urban city of South India. Indian Journal of Marketing, 49(2), 38–51. https://doi.org/10.17010/ijom/2019/v49/i2/141582
  34. Parasuraman, A. (1997). Reflections on gaining competitive advantage through customer value. Journal of the Academy of Marketing Science, 25(2), 154–161. https://doi.org/10.1007/BF02894351
  35. Patra, G., Mukhopadhyay, I., & Dash, C. K. (2019). Digital employer branding for enabling Gen Y in the ITeS sector in Eastern India. Prabandhan: Indian Journal of Management, 12(3), 38–49. https://doi.org/10.17010/pijom/2019/v12i3/142339
  36. Petrick, J. F. (2002). Development of a multi-dimensional scale for measuring the perceived value of a service. Journal of Leisure Research, 34(2), 119–134. https://doi.org/10.1080/00222216.2002.11949965
  37. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
  38. Ruggieri, R., Savastano, M., Scalingi, A., Bala, D., & D'Ascenzo, F. (2018). The impact of digital platforms on business models: An empirical investigation on innovative start-ups. Management & Marketing, 13(4), 1210–1225. https://doi.org/10.2478/mmcks-2018-0032
  39. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (eds.), Handbook of market research (pp. 1–40). Springer. https://doi.org/10.1007/978-3-319-05542-8_15-1
  40. Schultz, D. (2016). The future of advertising or whatever we're going to call it. Journal of Advertising, 45(3), 276–285. https://doi.org/10.1080/00913367.2016.1185061
  41. Shetty, B., Bhandary, R., Chandra, S. R., & Shetty, A. D. (2018). Antecedents to customer loyalty in the restaurant industry: A millennial perspective. Indian Journal of Marketing, 48(7), 23–35. https://doi.org/10.17010/ijom/2018/v48/i7/129720
  42. Shimpi, S. S. (2018). Social media as an effective marketing tool: An empirical study. Indian Journal of Marketing, 48(7), 36–50. https://doi.org/10.17010/ijom/2018/v48/i7/129725
  43. Srivastava, M. (2019). Role of customer engagement in customer loyalty for retail service brands: Customer orientation of salesperson as a mediator. Indian Journal of Marketing, 49(11), 7–19. https://doi.org/10.17010/ijom/2019/v49/i11/148273
  44. 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
  45. Wang, S., Li, J., & Zhao, D. (2017). Understanding the intention to use medical big data processing technique from the perspective of medical data analyst. Information Discovery and Delivery, 45(4), 194–201. https://doi.org/10.1108/IDD-03-2017-0017
  46. Wheaton, B., Muthén, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84–136. https://doi.org/10.2307/270754
Editorial Scholar

Submit your Research to IJM

Submit your Manuscript → Author Guidelines