Dealing with the “Grumpy Boomers”: re-engaging the disengaged and retaining talent
Bibliographic record
Abstract
Purpose – The aging of the workforce and the impending labour force shortage at the skilled end of the labour market increases the need for organizations to understand how to “re-engage” older workers with low commitment and reduce the turnover intentions of committed older knowledge workers. The current study addresses this issue by using employee commitment and intent to turnover scores to classify older knowledge workers into four groups: Disengaged-Exiters, Engaged-High-Performers, Retired-on-the-Job and Exiting-Performers. The purpose of this paper is to identify a set of work factors and practices that predispose older knowledge workers to fall into one or another of the four groups and offer suggestions on how organizations can increase commitment and decrease intent to turnover of their older workers. Design/methodology/approach – The paper used survey data (n=5,588) from a Canadian national study on work, family and caregiving to test the framework. Data analysis was performed using a MANCOVA with one independent variable (Boomer group), four dependent variables (job satisfaction, non-supportive culture, supportive manager, work-role overload) and one covariate (gender). Findings – The results support the framework. The findings suggest organizations that wish to retain committed Baby Boomers need to address issues with respect to workload. Alternatively, organizations who wish to increase the commitment levels of Boomers who have “Retired-on-the-Job” need to focus on supportive management, organizational culture and career development. Originality/value – This paper contributes to the literature on organizational commitment and intent to turnover by re-conceptualizing the relationship between these traditional concepts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".