Version 1
: Received: 6 November 2024 / Approved: 7 November 2024 / Online: 7 November 2024 (08:50:52 CET)
How to cite:
Świecka, B.; Kowalczyk-Rólczyńska, P.; Pieńkowska-Kamieniecka, S.; Śledziowski, J.; Terefenko, P. Identifying Key Factors in Consumers’ Pension Decisions Using Data Mining. Evidence from Poland. Preprints2024, 2024110497. https://doi.org/10.20944/preprints202411.0497.v1
Świecka, B.; Kowalczyk-Rólczyńska, P.; Pieńkowska-Kamieniecka, S.; Śledziowski, J.; Terefenko, P. Identifying Key Factors in Consumers’ Pension Decisions Using Data Mining. Evidence from Poland. Preprints 2024, 2024110497. https://doi.org/10.20944/preprints202411.0497.v1
Świecka, B.; Kowalczyk-Rólczyńska, P.; Pieńkowska-Kamieniecka, S.; Śledziowski, J.; Terefenko, P. Identifying Key Factors in Consumers’ Pension Decisions Using Data Mining. Evidence from Poland. Preprints2024, 2024110497. https://doi.org/10.20944/preprints202411.0497.v1
APA Style
Świecka, B., Kowalczyk-Rólczyńska, P., Pieńkowska-Kamieniecka, S., Śledziowski, J., & Terefenko, P. (2024). Identifying Key Factors in Consumers’ Pension Decisions Using Data Mining. Evidence from Poland. Preprints. https://doi.org/10.20944/preprints202411.0497.v1
Chicago/Turabian Style
Świecka, B., Jakub Śledziowski and Paweł Terefenko. 2024 "Identifying Key Factors in Consumers’ Pension Decisions Using Data Mining. Evidence from Poland" Preprints. https://doi.org/10.20944/preprints202411.0497.v1
Abstract
As pension benefits from statutory public schemes become less generous and many countries face pension-savings crises, the willingness to participate in supplementary retirement-saving instruments becomes crucial for sustainable financial well-being. The study highlighted factors influencing participation in auto-enrollment and private supplementary pension savings. The study focuses mainly on financial literacy and trust. We used the CAWI method with 867 interviews in Poland - the first country in Central and Eastern Europe to introduce an auto-enrolment pension system. Our study uses multivariable data-mining tools, and several regression models were applied. We used Logistic Regression (LR), Multivariate Linear Regression (MLR), and Factor Analysis of Mixed Data (FAMD) to support the LR analysis. We propose four regression models. Our findings present that: 1. The lower the consumer’s knowledge level, the more their decisions are based on trust. 2. Trust in the state, rather than trust in financial institutions, plays a crucial role for people with low financial literacy, which is a critical factor in choosing the auto-enrolment option for pension savings. 3. Men had higher odds of auto-enrolment pension saving than women. 4. Employees of economic universities and academics had higher odds of participating in capital pension plans than those of general universities and non-academics. Our findings can signal to governments and policymakers about factors influencing the choice of auto-enrolment supplementary retirement savings. These findings strengthen the role of sustainable economic education.
Keywords
pension; trust; financial literacy; consumer savings; employee capital plans; long-term financial security; survey; data mining; auto-enrollment pensions
Subject
Business, Economics and Management, Economics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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