| Issue |
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
|
|
|---|---|---|
| Article Number | 01007 | |
| Number of page(s) | 12 | |
| Section | AI-Integrated Physics, Technology, and Engineering | |
| DOI | https://doi.org/10.1051/epjconf/202534401007 | |
| Published online | 22 December 2025 | |
https://doi.org/10.1051/epjconf/202534401007
Probabilistic modeling of retirement financial preparedness among higher education staff: A bayesian regression perspective
1 Department of Educational Research & Evaluation, Graduate School, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
2 Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
3 Department of Sport Science, Faculty of Sports and Health Sciences, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
4 Department of Economics Education, Faculty of Economics and Business, Universitas Negeri Yogyakarta, Indonesia
5 Department of Foreign Language and Literature, Webster University in Tashkent, Taskent, Uzbekistan
* Corresponding author: heri_retnawati@uny.ac.id
Published online: 22 December 2025
This study addresses the problem of low retirement financial preparedness among higher education staff in Indonesia, a critical issue as demographic, behavioral, and institutional uncertainties affect post-employment well-being. To respond to this challenge, the research applies a Bayesian probabilistic modeling framework to capture the stochastic nature of retirement readiness and propose data-driven solutions for financial policy and education. The research contribution is the integration of behavioral and institutional determinants into a unified Bayesian model that quantifies uncertainty and identifies probabilistic predictors of retirement preparedness among academic personnel. The study utilized survey data from 110 respondents, both civil servants (ASN) and non-civil servants, and employed Bayesian logistic regression with Markov Chain Monte Carlo (MCMC) estimation. Posterior diagnostics confirmed model convergence (̂R < 1.01; ESS > 5,000) and predictive adequacy (WAIC = 0.41; LOOIC = 0.63). The results indicate that behavioral engagement and institutional access strongly influence readiness: investment planning, pension benefit availability, and financial strategy implementation significantly raise preparedness probabilities. Income adequacy interacts positively with strategic financial behavior, while age and education show weaker effects. In conclusion, retirement readiness in Indonesian academia remains low yet improvable through behavioral interventions and expanded institutional support, providing empirical evidence for targeted financial literacy and pension reform initiatives.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

