What Can Motivate Me to Keep Working? Analysis of Older Finance Professionals’ Discourse Using Self-Determination Theory
Bibliographic record
Abstract
The twin issues of population aging and critical talent shortages induce employers to encourage older workers to prolong their professional lives. Over the past two decades, studies have mainly examined which human resources practices influence older workers’ ability, motivation, and opportunity to continue working. Our conceptual lens rest on self-determination theory (SDT). This study explores how older professionals in the financial services sector may see how three psychological needs (i.e., autonomy, competence, and relatedness) are satisfied or frustrated through various management practices such as monetary rewards, benefits, career development, and work content and context. Our interviews with older finance professionals also show the relevance of a fourth need, beneficence, to understand their decision to continue to work. Results of this study are likely to be significant at both managerial and societal levels in the perspective of sustainable development or employability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".