Unveiling the aftermath of choice: exploring health insurance buyer’s regret using mixed method approach
Neha Kumari et al.
What the paper says
Purpose This paper aims to examine the relationship between the purchase of health insurance products and post-purchase regret (PPR). It investigates the impact of various factors, namely, forgone alternatives, under consideration, over consideration, premium paid and product word-of-mouth (WOM), which have been identified in existing literature as contributors to PPR. In addition, it assesses the influence of PPR on dissatisfaction, rumination and brand switching within the realm of Health Insurance. Design/methodology/approach In this research, a mixed-methods approach was used, combining qualitative and quantitative research methods to provide a thorough exploration of the research question or phenomenon under study. The quantitative aspect of the study relied on self-reported data obtained through an online survey while qualitative data was collected through interviews of health insurance customers. Partial least squares structural equation modeling and artificial neural network were used for quantitative data analysis, whereas ATLAS.ti software was used for the qualitative study. Findings The findings of the study suggest that PPR significantly influences customer behavior, including rumination, brand switching intention and dissatisfaction. From the statistical testing of the conceptual model, premium paid and over consideration were identified as the two most significant factors, affecting PPR. Originality/value Most of the studies have been conducted on PPR in consumption of products/services and there is a lack of adequate research on PPR in health insurance products. The study has tested a conceptual model of PPR in health insurance products and has added value to the existing literature on PPR.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.