Complementary Log-Log Regression Model on Categorical Data for Analysis of Customer Interest in Purchasing Travel Insurance.
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| 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] |
| Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 189724906 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Complementary Log-Log Regression Model on Categorical Data for Analysis of Customer Interest in Purchasing Travel Insurance. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cahyandari%2C+Rini%22">Cahyandari, Rini</searchLink><relatesTo>1</relatesTo><i> rini_cahyandari@uinsgd.ac.id</i><br /><searchLink fieldCode="AR" term="%22Martina%2C+Annisa%22">Martina, Annisa</searchLink><relatesTo>1</relatesTo><i> annisamartina@uinsgd.ac.id</i><br /><searchLink fieldCode="AR" term="%22Rahmawati%2C+Neng+Hani%22">Rahmawati, Neng Hani</searchLink><relatesTo>2</relatesTo><i> haniiiiii.r@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Sukono%22">Sukono</searchLink><relatesTo>3</relatesTo><i> sukono@unpad.ac.id</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. Dec2025, Vol. 55 Issue 12, p4032-4044. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Travel+insurance%22">Travel insurance</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+variables%22">Independent variables</searchLink><br /><searchLink fieldCode="DE" term="%22Insurance+companies%22">Insurance companies</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+variables%22">Mathematical variables</searchLink><br /><searchLink fieldCode="DE" term="%22Customer+relations%22">Customer relations</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Investment+risk%22">Investment risk</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 4032 Subjects: – SubjectFull: Travel insurance Type: general – SubjectFull: Independent variables Type: general – SubjectFull: Insurance companies Type: general – SubjectFull: Mathematical variables Type: general – SubjectFull: Customer relations Type: general – SubjectFull: Maximum likelihood statistics Type: general – SubjectFull: Investment risk Type: general Titles: – TitleFull: Complementary Log-Log Regression Model on Categorical Data for Analysis of Customer Interest in Purchasing Travel Insurance. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cahyandari, Rini – PersonEntity: Name: NameFull: Martina, Annisa – PersonEntity: Name: NameFull: Rahmawati, Neng Hani – PersonEntity: Name: NameFull: Sukono IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19929978 Numbering: – Type: volume Value: 55 – Type: issue Value: 12 Titles: – TitleFull: IAENG International Journal of Applied Mathematics Type: main |
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