Technological change and an aging workforce: investigating the career experiences of older workers
Bibliographic record
Abstract
This dissertation examines the intersection between two significant economic and societal challenges: an aging workforce and rapid technological change. The aging workforce is a growing concern, particularly in Canada, where the population of older workers (55 years and older) surpasses that of younger entrants (15 to 24 years). This demographic shift, already contributing to labour shortages in key sectors like manufacturing and healthcare, poses risks to labour participation rates and the stability of healthcare and pension systems (Acemoglu & Restrepo, 2017; Maestas et al., 2016). Given the projected exodus of older workers and limited incoming replacements, scholars and practitioners advocate for delayed or phased retirements to mitigate talent shortages. Simultaneously, technological change reshapes work, presenting opportunities and challenges, especially for older workers who may find adapting to new technologies daunting. This environment makes it critical to understand how technology affects older workers' experiences, including their retirement intentions. I conducted two studies to better understand the impact of technology and technological changes on older workers' work experiences. In Study One, I conducted a systematic literature review to synthesize existing research on technology's impact on older workers, with a comprehensive analysis of 121 articles, including both peer-reviewed (n=82) and grey literature sources (n=39). Thematic analysis revealed key areas in the current literature, such as socio-demographic factors, training and development, and retirement planning. The results of this study also included descriptive insights on journals, methodologies, regions, and publication dates, highlighting 14 important research gaps. These gaps guided recommendations for future studies, which aim to address the implications of technological innovations on an aging workforce. In the second study, I empirically examined the relationship between technological change and older workers' retirement intentions using a sample of 361 participants. Testing a moderated mediation model grounded in the Job Demand-Resources (JD-R) theory, I analyzed burnout and perceived work ability as serial mediators alongside moderating factors of computer self-efficacy, technological training, and organizational justice. Findings accentuate the complex interplay of burnout, work ability, and retirement intentions, emphasizing that burnout negatively impacts work ability, which in turn influences retirement intentions. Notably, technological training significantly moderated the relationship between burnout and work ability, reinforcing its role as an important factor shaping older workers' capacity to adapt within technologically evolving work environments. Ultimately, this dissertation provides valuable implications for both theory and practice. The findings from both studies provide important directions for the successful integration and retention of older employees in the rapidly changing technological work environment, as well as for creating a supportive work environment for them.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".