When The Institution Doesn't Guarantee The Pension: A Capacity-To-Behaviour Model Of Digital Financial Tools and Retirement Readiness Among Private Higher Education Academicians

Abstract

A public-university lecturer in most systems retires into a pension the institution itself underwrites. A private-university lecturer usually does not. Retirement income depends on what that person personally saved, invested, and planned, often through the same mobile banking apps, robo-advisors, and digital pension calculators everyone else uses. This paper proposes a structural model of how digital financial tool adoption (DFTA) and financial self-efficacy (FSE) shape retirement planning readiness (RPR) among academicians at private higher-education institutions, and plans its statistical analysis in advance instead of reporting survey results. Financial behaviour (FB) is the mechanism that converts capability into readiness, and institutional financial-literacy support is a boundary condition on the DFTA-to-FB path. The model draws on the Theory of Planned Behaviour and the Capacity-Willingness-Opportunity framework. It extends two recent PLS-SEM studies, one linking digital financial literacy to retirement planning among Indian millennials, the other linking it to financial well-being among university faculty, to a population neither has tested: academicians whose employer does not guarantee their pension. Because the study has not yet been fielded, we report no survey findings. We report what can be reported at this stage: a measurement instrument adapted from validated prior scales and an a priori power analysis. A Monte Carlo simulation (400 replications per candidate sample size, 300 bootstrap resamples per mediation test), combined with the Kock and Hadaya (2018) inverse square root method, shows that 300 to 350 respondents give adequate power for the model's central mediated pathway. The direct effects of DFTA and FSE on readiness stay below 80% power at every sample size tested, and the hypothesised moderation effect remains underpowered (power below 0.50) even at 450 respondents. These findings bear directly on how the eventual study should be resourced and scoped. We present the paper as an analysis plan: the instrument, the framework, and the power budget that a private-HEI retirement-readiness survey would need, ahead of collecting data from academicians and not in place of it.