Understanding Cooperative Membership through Big Data Analysis: Evidence from Philippine Consumer Data Using Double-Selection Lasso Logistic Regression

• 발행기관: The International Academy of Global Business and Trade
• 저자명: Liezel Cruz Leila Rahnema
• 간행물 정보: 『Journal of Global Business and Trade』Vol. 21, No. 2, 31~43쪽, 전체 13쪽
• 주제분류: 사회과학 > 무역학
• 파일형태: PDF
• 발행일자: 2025.08.30

국문 초록

Purpose – This study identifies key factors influencing cooperative membership in the Philippines using largescale consumer data. By applying advanced statistical techniques to big data, it offers a nuanced understanding of how financial behaviors—rather than demographic traits—drive cooperative participation, providing actionable insights for policy and cooperative development. Design/Methodology/Approach – Using data from the 2014 Consumer Finance Survey of 15,501 respondents nationwide, the study employs a Double-Selection Lasso Logistic Regression (DSlogit) model to isolate significant socioeconomic and financial behavioral predictors of cooperative membership. Findings – Financial engagement—such as savings accounts, long-term banking relationships, and credit card ownership—along with formal employment status, significantly increases cooperative membership. In contrast, sole proprietorship and informal borrowing are negatively associated. Research Implications – The results underscore the importance of financial inclusion strategies and suggest that cooperatives should innovate to better serve entrepreneurs and underserved populations. Future research should explore digital financial platforms and conduct comparative studies across cooperative types and regions to deepen understanding of membership dynamics.

목차

Ⅰ. Introduction

Ⅱ. Review of Related Literature

Ⅲ. Methodology

Ⅳ. Results and Discussion

Ⅴ. Conclusion

Ⅵ. Policy Recommendations

References

키워드

big data analysis consumer finance survey cooperative membership double selection lasso logistic regression philippines