Technological change and an aging workforce: investigating the career experiences of older workers
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».