Earticle

초록

영어

Despite the rapid growth of outbound tourism in China, the adoption of travel insurance remains limited. This study investigates the determinants of insurance purchase among 2,000 Chinese travelers using machine learning and deep learning techniques. The dataset includes demographic, socioeconomic, and behavioral variables such as age, income, family size, health status, and travel experience. Four models—logistic regression, Random Forest, a baseline deep neural network (DNN), and a dropout- enhanced DNN—were developed and compared. Results show that the dropout-enhanced DNN achieved the highest accuracy (about 82%), outperforming the baseline DNN (79%), Random Forest (79%), and logistic regression (75%). Feature importance analysis indicated that annual income, age, and family size are the most decisive predictors of adoption. The findings highlight the value of artificial intelligence in predicting consumer behavior in the insurance sector. For practitioners, the results suggest that insurers should target higher-income and family-oriented travelers while leveraging airlines and agencies as key distribution channels. Predictive analytics can thus support more effective segmentation, targeting, and personalized insurance design.

목차

Abstract
1. Introduction
2. Related Research
2.1 Travel Insurance and Consumer Behavior
2.2 Machine Learning and Deep Learning Applications
3. Research Methodology
3.1 Data Collection and Preprocessing
3.2 Classification Model Development
3.3 Evaluation Strategy
4. Experimental Results
4.1 Classification Model Performance
4.2 Key Predictors of Insurance Adoption
4.3 Summary of Findings
5. Conclusion and Business Implications
5.1 Conclusion
5.2 Business Implications
References

키워드

저자

• 이정승 [ Jung Seung Lee | Associate Professor, Department of Business Administration, Hoseo University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005