Planned retirement age: do attachment to work and expectations relating to workplace adjustments matter?
Bibliographic record
Abstract
Purpose In the context of population aging, retirement has become a central issue in academic, professional and government discourse. A consensus can be seen to be emerging around the idea of postponing retirement in favor of promoting active aging. From this perspective, the purpose of this study, using work-role attachment theory and met expectations theory, is to focus on the pre-retirement period and aims to better understand how certain individual factors and expectations explain the planned age of retirement. Design/methodology/approach An online survey was administered in 2015 to employees aged 45 and over working at a Canadian firm in the high-technology sector. Findings The results show that career commitment, attachment to work and expectations relating to workplace adjustments prior to retirement were positively associated with planned retirement age, whereas expectations relating to professional development showed a negative association with this variable. Practical implications This study fits into a line of research focusing on the end-of-career period and sheds light on the decision to retire by looking closely at the impact of employment conditions and human resource management practices on this decision. In a labor market context marked by high numbers of workers aged 55 and older, combined with the increasingly critical need for skilled labor and considering the expectations of workers leading up to their retirement could help to better plan these workers' end-of-career period. Originality/value Many studies have examined the characteristics of retirees after retirement. The authors’ study is one of the few that examines the aspirations of workers between the ages of 45 and 55 who are still employed but are beginning to consider their retirement plans, including the decision to continue working longer. Its originality also lies in combining work-role attachment theory and met expectations theory.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".