Factors Influencing Customer Satisfaction in Health Insurance: A comprehensive study in Bengaluru, India
Objective: This paper focuses on the multifaceted dimension of customer satisfaction in health insurance within Bengaluru city. Bengaluru city is a metropolis in India with a diversified population with diverse insurance preferences, and healthcare requirements. Through this comprehensive study, we...
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| Format: | Article |
| Language: | English |
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ACHSM
2025-05-01
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| Series: | Asia Pacific Journal of Health Management |
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| Online Access: | https://journal.achsm.org.au/index.php/achsm/article/view/3747 |
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| author | Chyavana Kondapally M Vikram Reddy B Mohan Kumar |
| author_facet | Chyavana Kondapally M Vikram Reddy B Mohan Kumar |
| author_sort | Chyavana Kondapally |
| collection | DOAJ |
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Objective: This paper focuses on the multifaceted dimension of customer satisfaction in health insurance within Bengaluru city. Bengaluru city is a metropolis in India with a diversified population with diverse insurance preferences, and healthcare requirements. Through this comprehensive study, we strove to analyze the impact of demographic features and health insurance benefits on customer satisfaction.
Methods: We have gathered data using a structured questionnaire. Chi–square and logistic regression tests were conducted using R – Program to understand which factors influence consumer satisfaction in Health Insurance (HI).
Summary: This research investigates the factors influencing customer satisfaction in health insurance (HI) in Bengaluru, India, a city with diverse healthcare needs and insurance preferences. Using statistical tools like Chi-square and Logistic Regression in R, the study identifies key demographic and policy-related factors affecting satisfaction.
Results: Occupation (p<0.01), family income (p<0.001), health insurance type (p<0.05), and ‘Is affected by COVID-19 previously’ (p<0.05) features are statistically significant. Claim settlement ratio (p=0.004), co-payment percentage (p=0.034), network hospitals coverage (p=0.018), tax benefits (p=0.015), and waiting periods (p=0.020) were found to be influencing factors of HI customer satisfaction.
Conclusion: This customer satisfaction study enables policymakers and insurance service providers to understand the dynamics of the customer. The government, HI providers and researchers can use these results to plan new HI policies and improve customer satisfaction in HI.
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| format | Article |
| id | doaj-art-e23df15b6ad64c2b87fef01b069d1bee |
| institution | DOAJ |
| issn | 1833-3818 2204-3136 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | ACHSM |
| record_format | Article |
| series | Asia Pacific Journal of Health Management |
| spelling | doaj-art-e23df15b6ad64c2b87fef01b069d1bee2025-08-20T03:13:43ZengACHSMAsia Pacific Journal of Health Management1833-38182204-31362025-05-01201Factors Influencing Customer Satisfaction in Health Insurance: A comprehensive study in Bengaluru, IndiaChyavana Kondapally0M Vikram Reddy1B Mohan Kumar2School of Management Studies (SMS), GIET University, Odisha, IndiaSchool of Management Studies (SMS), GIET University, Odisha, IndiaBadruka college of commerce & Arts, Hyderabad, Telangana, India Objective: This paper focuses on the multifaceted dimension of customer satisfaction in health insurance within Bengaluru city. Bengaluru city is a metropolis in India with a diversified population with diverse insurance preferences, and healthcare requirements. Through this comprehensive study, we strove to analyze the impact of demographic features and health insurance benefits on customer satisfaction. Methods: We have gathered data using a structured questionnaire. Chi–square and logistic regression tests were conducted using R – Program to understand which factors influence consumer satisfaction in Health Insurance (HI). Summary: This research investigates the factors influencing customer satisfaction in health insurance (HI) in Bengaluru, India, a city with diverse healthcare needs and insurance preferences. Using statistical tools like Chi-square and Logistic Regression in R, the study identifies key demographic and policy-related factors affecting satisfaction. Results: Occupation (p<0.01), family income (p<0.001), health insurance type (p<0.05), and ‘Is affected by COVID-19 previously’ (p<0.05) features are statistically significant. Claim settlement ratio (p=0.004), co-payment percentage (p=0.034), network hospitals coverage (p=0.018), tax benefits (p=0.015), and waiting periods (p=0.020) were found to be influencing factors of HI customer satisfaction. Conclusion: This customer satisfaction study enables policymakers and insurance service providers to understand the dynamics of the customer. The government, HI providers and researchers can use these results to plan new HI policies and improve customer satisfaction in HI. https://journal.achsm.org.au/index.php/achsm/article/view/3747Customer satisfactionpurchase decisionHealth Insurancelogistic regression |
| spellingShingle | Chyavana Kondapally M Vikram Reddy B Mohan Kumar Factors Influencing Customer Satisfaction in Health Insurance: A comprehensive study in Bengaluru, India Asia Pacific Journal of Health Management Customer satisfaction purchase decision Health Insurance logistic regression |
| title | Factors Influencing Customer Satisfaction in Health Insurance: A comprehensive study in Bengaluru, India |
| title_full | Factors Influencing Customer Satisfaction in Health Insurance: A comprehensive study in Bengaluru, India |
| title_fullStr | Factors Influencing Customer Satisfaction in Health Insurance: A comprehensive study in Bengaluru, India |
| title_full_unstemmed | Factors Influencing Customer Satisfaction in Health Insurance: A comprehensive study in Bengaluru, India |
| title_short | Factors Influencing Customer Satisfaction in Health Insurance: A comprehensive study in Bengaluru, India |
| title_sort | factors influencing customer satisfaction in health insurance a comprehensive study in bengaluru india |
| topic | Customer satisfaction purchase decision Health Insurance logistic regression |
| url | https://journal.achsm.org.au/index.php/achsm/article/view/3747 |
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