Open Access
| 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 | |
- Firdausia, Y.K., Samsiyah, S, The Influence of Financial Literacy, Financial Inclusion and Financial Behavior on Intention in Investing Lecturers And Education Personnel Pgri Adibuana University Surabaya in The Capital Market. International Journal of Economics, Business and Accounting Research (IJEBAR). 7, 576–581 (2023). https://doi.org/10.29040/ijebar.v7i2.8421 [Google Scholar]
- Pujiastuti, S.L., Maesaroh, I., Andriyansah, A, Financial Literacy and Inclusion as Determinants of Investment Interest Among Indonesian Migrant Workers. Society. 13, 670–686 (2025). https://doi.org/10.33019/society.v13i1.886 [Google Scholar]
- Satria, A., Hutabarat, S., Wijaya, C, Analysis the Effect of Financial Literacy on Financial Planning for Retirement (Case Study Lecturers and Administrative Staffs in Universitas Indonesia). International Journal of Management (IJM). 11, 741–750 (2020). https://doi.org/10.34218/IJM.11.5.2020.066 [Google Scholar]
- Banks, J., Crawford, R, Managing Retirement Incomes. Annu Rev Econom. 14, 181–204 (2022). https://doi.org/10.1146/ANNUREV-ECONOMICS-051420-014808/CITE/REFWORKS [Google Scholar]
- Benartzi, S., Thaler, R. H, Heuristics and Biases in Retirement Savings Behavior. Journal of Economic Perspectives. 21, 81–104 (2007). https://doi.org/10.1257/JEP.21.3.81 [Google Scholar]
- Chen, A., Hentschel, F., Steffensen, M, On retirement time decision making. Insur Math Econ. 100, 107–129 (2021). https://doi.org/10.1016/J.INSMATHECO.2021.05. 002 [Google Scholar]
- Sulka, T, Planning and saving for retirement. Eur Econ Rev. 160, 104609 (2023). https://doi.org/10.1016/J.EUROECOREV.2023.10 4609 [Google Scholar]
- Buehler, H., Horvath, B., Limmer, Y., Schmidt, T, Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling. (2025) [Google Scholar]
- Hansen, L.P., Sargent, T. J, Risk, ambiguity, and misspecification: Decision theory, robust control, and statistics. Journal of Applied Econometrics. 39, 969–999 (2024). https://doi.org/10.1002/JAE.3010 [Google Scholar]
- Bornmann, L., Stefaner, M., de Moya Anegón, F., Mutz, R, Excellence networks in science: A Web- based application based on Bayesian multilevel logistic regression (BMLR) for the identification of institutions collaborating successfully. J Informetr. 10, 312–327 (2016). https://doi.org/10.1016/J.JOI.2016.01.005 [Google Scholar]
- Goligher, E.C., Heath, A., Harhay, M. O, Bayesian statistics for clinical research. The Lancet. 404, 1067–1076 (2024). https://doi.org/10.1016/S0140-6736(24)01295-9 [Google Scholar]
- van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M.G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., Yau, C, Bayesian statistics and modelling. Nature Reviews Methods Primers. 1, 1–26 (2021). https://doi.org/10.1038/S43586-020-00001- 2;SUBJMETA [Google Scholar]
- Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D. B, Bayesian Data Analysis. Bayesian Data Analysis. (2013). https://doi.org/10.1201/B16018 [Google Scholar]
- Vehtari, A., Gelman, A., Gabry, J, Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 27, 1413–1432 (2017). https://doi.org/10.1007/S11222-016-9696-4/METRICS [Google Scholar]
- Retnawati, H, The comparison of accuracy scores on the paper and pencil testing vs. Computer-based testing. TOJET: The Turkish Online Journal of Educational Technology. 14 (2015) [Google Scholar]
- Hershey, D.A., Mowen, J. C, Psychological Determinants of Financial Preparedness for Retirement. Gerontologist. 40, 687–697 (2000). https://doi.org/10.1093/GERONT/40.6.687 [Google Scholar]
- Hershey, D.A., Jacobs-Lawson, J.M., Austin, J. T, Effective Financial Planning for Retirement. (2012). https://doi.org/10.1093/OXFORDHB/9780199746 521.013.0133 [Google Scholar]
- Noone, J.H., Stephens, C., Alpass, F, The Process of Retirement Planning Scale (PRePS): Development and validation. Psychol Assess. 22, 520–531 (2010). https://doi.org/10.1037/A0019512 [Google Scholar]
- Lee, S.R., Jung, E., Jin, S., Wang, Z.A., Brown, P., Polotsky, E, The association between subjective and objective financial knowledge: Path analysis to savings behavior by age. Social Sciences & Humanities Open. 11, 101232 (2025). https://doi.org/10.1016/J.SSAHO.2024.101232 [Google Scholar]
- Shefrin, H.M., Thaler, R. H, THE BEHAVIORAL LIFE-CYCLE HYPOTHESIS. Econ Inq. 26, 609–643 (1988). https://doi.org/10.1111/J.1465-7295.1988.TB01520.X [Google Scholar]
- Ajzen, I, The theory of planned behavior. Organ Behav Hum Decis Process. 50, 179–211 (1991). https://doi.org/10.1016/0749-5978(91)90020-T [CrossRef] [Google Scholar]
- Lusardi, A., Mitchell, O. S, Financial Literacy and Planning: Implications For Retirement Wellbeing Financial Literacy and Planning: Implications for Retirement Wellbeing. (2011) [Google Scholar]
- Lusardi, A., Mitchell, O. S, How Ordinary Consumers Make Complex Economic Decisions: Financial Literacy and Retirement Readiness. [Google Scholar]
- Beránek, M, Financial Literacy and Retirement Planning: A Meta-Analysis. (2025) [Google Scholar]
- Akben-Selcuk, E., Aydin, A. E, Ready or Not, Here It Comes: A Model of Perceived Financial Preparedness for Retirement. J Adult Dev. 28, 346–357 (2021). https://doi.org/10.1007/S10804-021- 09387-Z/METRICS [Google Scholar]
- Asebedo, S.D., Seay, M.C., Archuleta, K., Brase, G, The Psychological Predictors of Older Preretirees’ Financial Self-Efficacy. Journal of Behavioral Finance. 20, 127–138 (2019). https://doi.org/10.1080/15427560.2018.1492580 [Google Scholar]
- Lamarche, V.M., Rolison, J. J, Hand-in-hand in the golden years: Cognitive interdependence, partner involvement in retirement planning, and the transition into retirement. PLoS One. 16, e0261251 (2021). https://doi.org/10.1371/JOURNAL.PONE.026125 1 [Google Scholar]
- Tomar, S., Kent Baker, H., Kumar, S., Hoffmann, A.O. I, Psychological determinants of retirement financial planning behavior. J Bus Res. 133, 432–449 (2021). https://doi.org/10.1016/J.JBUSRES.2021.05.007 [Google Scholar]
- Widjaja, I., Arifin, Z., Setini, M, The effects of financial literacy and subjective norms on saving behavior. (2020). https://doi.org/10.5267/j.msl.2020.6.030 [Google Scholar]
- Setiawan, M., Effendi, N., Santoso, T., Dewi, V.I., Sapulette, M. S, Digital financial literacy, current behavior of saving and spending and its future foresight. Economics of Innovation and New Technology. 31, 320–338 (2022). https://doi.org/10.1080/10438599.2020.1799142 [CrossRef] [Google Scholar]
- Clare, A.D., Seaton, J., Smith, P.N., Thomas, S. H, The Science of Flexible Retirement Choices: Switching Retirement Savings into an Annuity. SSRN Electronic Journal. (2023). https://doi.org/10.2139/SSRN.4422579 [Google Scholar]
- Nguyen, M.H., Khuc, Q. Van, La, V.P., Le, T.T., Nguyen, Q.L., Jin, R., Nguyen, P.T., Vuong, Q. H, Mindsponge-Based Reasoning of Households’ Financial Resilience during the COVID-19 Crisis. Journal of Risk and Financial Management, 15, 542 (2022). https://doi.org/10.3390/JRFM15110542 [Google Scholar]
- Blake, D, Nudges and Networks: How to Use Behavioural Economics to Improve the Life Cycle Savings-Consumption Balance. Journal of Risk and Financial Management, 15, 217 (2022). https://doi.org/10.3390/JRFM15050217 [Google Scholar]
- Smyrnis, G., Bateman, H., Dobrescu, L., Newell, B.R., Thorp, S, Motivated saving: The impact of projections on retirement contributions. [Google Scholar]
- Fan, L., Henager, R, A Structural Determinants Framework for Financial Well-Being. J Fam Econ Issues. 43, 415–428 (2022). https://doi.org/10.1007/S10834-021-09798-W [Google Scholar]
- Hong, P.Y.P., Wathen, M. V., Shin, A.J., Yoon, I., Park, J. H, Psychological Self-Sufficiency and Financial Literacy among Low-Income Participants: An Empowerment-Based Approach to Financial Capability. J Fam Econ Issues. 43, 690–702 (2022). https://doi.org/10.1007/S10834-022- 09865-W [Google Scholar]
- Greig, F., Tan, F., Clarke, A., Khang, K., McKinnon, K., Zhang, V, The Vanguard Retirement Outlook: A National Perspective on Retirement Readiness. SSRN Electronic Journal. (2023). https://doi.org/10.2139/SSRN.4756986 [Google Scholar]
- Heo, W., Kim, E., Kwak, E.J., Grable, J. E, Identifying Hidden Factors Associated with Household Emergency Fund Holdings: A Machine Learning Application. Mathematics, 12, 182 (2024). https://doi.org/10.3390/MATH12020182 [Google Scholar]
- Candra, C., Retnawati, H, A Meta-Analysis of Constructivism Learning Implementation towards the Learning Outcomes on Civic Education Lesson. International Journal of Instruction. 13, 835 (2020). https://doi.org/10.29333/iji.2020.13256a [Google Scholar]
- Zurqoni, Z., Retnawati, H., Rahmatullah, S., Djidu, H., Apino, E, Has Arabic Language Learning Been Successfully Implemented?. International Journal of Instruction. 13, 715 (2020). https://doi.org/10.29333/iji.2020.13444a [Google Scholar]
- Santiago, P.H.R., Quintero, A., Haag, D., Roberts, R., Smithers, L., Jamieson, L, Drop-the-p: Bayesian CFA of the Multidimensional Scale of Perceived Social Support in Australia. Front Psychol. 12, 542257 (2021). https://doi.org/10.3389/FPSYG.2021.542257/BIBTEX [Google Scholar]
- Ananta, A., Moeis, A.I.A., Widianto, H.T., Yulianto, H., Arifin, E. N, Pension and Active Ageing: Lessons Learned from Civil Servants in Indonesia. Social Sciences, 10, 436 (2021). https://doi.org/10.3390/SOCSCI10110436 [Google Scholar]
- Murguia, A., Pfau, W. D, Retirement Income Beliefs and Financial Advice Seeking Behaviors. SSRN Electronic Journal. (2021). https://doi.org/10.2139/SSRN.3788425 [Google Scholar]
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.

