Comparative Analysis of Customer Satisfaction in Digital vs. Traditional Insurance Policies

Thesis Abstract

The rapid digital transformation within the insurance industry has significantly altered customer engagement paradigms, prompting an urgent need to assess comparative customer satisfaction levels between digital and traditional insurance policies. This study aims to evaluate the differences in customer satisfaction, identify driving factors influencing satisfaction in both contexts, and determine the implications for policy providers and stakeholders. The specific objectives include examining customer perceptions of service quality, accessibility, transparency, and trust in both insurance delivery channels; analyzing the influence of demographic variables on satisfaction; and assessing the overall impact of digital versus traditional policy delivery on customer loyalty and retention. Employing a quantitative, cross-sectional research design, the study investigates a population of 500 policyholders across financial institutions offering both digital and traditional insurance products within the country. The sample was selected through stratified random sampling to ensure proportional representation of demographics such as age, gender, income level, and insurance type. Data collection involved structured questionnaires comprising Likert-scale items, pre-tested for validity and reliability using Cronbach’s alpha (? > 0.85). The primary data analysis employed descriptive statistics to summarize respondent characteristics, followed by inferential techniques, notably independent samples t-tests and ANOVA, to compare satisfaction levels across different groups. Multiple regression analysis was conducted to identify key determinants influencing customer satisfaction. Furthermore, Path analysis within Structural Equation Modeling (SEM) framework was utilized to test the hypothesized relationships derived from the Expectation-Confirmation Theory and Customer Satisfaction Theory. The anticipated findings are expected to reveal statistically significant differences in customer satisfaction, with digital insurance policies possibly demonstrating higher scores in aspects such as accessibility and transparency, owing to technological advances and convenience. Conversely, traditional policies may attain higher satisfaction in areas related to personalized service and trust. The analysis is also projected to identify demographic factors such as age and income level as moderators affecting satisfaction levels. These insights are set to contribute novel empirical evidence, elucidating the nuanced preferences and perceptions of policyholders in an evolving industry landscape, thus addressing existing gaps in literature concerning comparative satisfaction analysis in insurance channels. This research advances knowledge by integrating the theoretical frameworks of the Expectation-Confirmation Theory and Service Quality Dimensions within the context of digital and traditional insurance. It emphasizes the importance of aligning technological innovations with customer expectations and highlights the role of demographic variables in shaping satisfaction. The findings are expected to inform insurance providers on optimizing service delivery models and tailoring marketing strategies to enhance customer retention, loyalty, and competitive advantage. The study concludes that strategic investments in digital platforms can bolster customer satisfaction, provided that issues related to ease of use, data security, and personalized engagement are adequately addressed. Recommendations include adopting hybrid service models that blend digital efficiency with personalized service elements, implementing targeted communication based on demographic insights, and fostering trust through transparent policies and security assurances. The research also underscores the necessity for ongoing monitoring and adaptation of customer service strategies in response to shifting preferences and technological advancements. Future investigations could explore longitudinal designs to assess satisfaction trends over time and incorporate qualitative approaches to deepen understanding of customer experiences and expectations in the digital insurance environment.