Complementary Log-Log Regression Model on Categorical Data for Analysis of Customer Interest in Purchasing Travel Insurance.

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Bibliographic Details
Title:	Complementary Log-Log Regression Model on Categorical Data for Analysis of Customer Interest in Purchasing Travel Insurance.
Authors:	Cahyandari, Rini¹ rini_cahyandari@uinsgd.ac.id, Martina, Annisa¹ annisamartina@uinsgd.ac.id, Rahmawati, Neng Hani² haniiiiii.r@gmail.com, Sukono³ sukono@unpad.ac.id
Source:	IAENG International Journal of Applied Mathematics. Dec2025, Vol. 55 Issue 12, p4032-4044. 13p.
Subjects:	Travel insurance, Independent variables, Insurance companies, Mathematical variables, Customer relations, Maximum likelihood statistics, Investment risk
Abstract:	Traveling involves risks that may arise unexpectedly, and travel insurance serves as an important mechanism to mitigate and transfer the associated financial risks. However, customer decisions to purchase travel insurance are influenced by multiple factors. This study aims to identify the determinants of customer interest in purchasing travel insurance using the Complementary Log-Log regression model, which is suitable for binary and asymmetrical response variables, such as interest or no interest. The analysis considers eight predictor variables: age, type of job, undergraduate education level, annual income, number of family members, presence of chronic diseases, flight history, and experience of traveling abroad. The Maximum Likelihood Estimation (MLE) method was used to estimate the parameters, while the Wald test and Likelihood Ratio test were used to test significance. The model was selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The results show that age, type of job, annual income, number of family members, flight history, and experience of traveling abroad significantly influence customer interest. These findings suggest that factors directly related to travel risk play a crucial role in shaping customer interest. This study provides practical insights for insurance providers. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Traveling involves risks that may arise unexpectedly, and travel insurance serves as an important mechanism to mitigate and transfer the associated financial risks. However, customer decisions to purchase travel insurance are influenced by multiple factors. This study aims to identify the determinants of customer interest in purchasing travel insurance using the Complementary Log-Log regression model, which is suitable for binary and asymmetrical response variables, such as interest or no interest. The analysis considers eight predictor variables: age, type of job, undergraduate education level, annual income, number of family members, presence of chronic diseases, flight history, and experience of traveling abroad. The Maximum Likelihood Estimation (MLE) method was used to estimate the parameters, while the Wald test and Likelihood Ratio test were used to test significance. The model was selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The results show that age, type of job, annual income, number of family members, flight history, and experience of traveling abroad significantly influence customer interest. These findings suggest that factors directly related to travel risk play a crucial role in shaping customer interest. This study provides practical insights for insurance providers. [ABSTRACT FROM AUTHOR]
ISSN:	19929978