Teleworkers' job performance: a study examining the role of age as an important diversity component of companies' workforce
Bibliographic record
Abstract
Purpose Teleworking seems to be the new future of the workplace. It has been widely adopted during the COVID-19 crisis, which has greatly influenced work organization conditions. This pandemic and its accompanying changes represent significant challenges for employees' performance, depending on their age if the study considers the physical and psychological vulnerabilities of older employees and their assumed or expected difficulties to cope with the new information and communication technologies (ICTs). This study aims at examining the direct effects of teleworking, and age on job performance (in-role). As well as analyzing the moderating effect of age on the relationship between teleworking and in-role job performance in times of crisis. Design/methodology/approach Data were collected in Canada from 18 companies, with a sample of 272 employees. Multivariate regression and moderation regression analyses were performed using Stata 13. Findings Results revealed that when teleworking, older age is associated with lower job performance and younger age is associated with higher job performance. Conversely, when working on-site, older age is associated with higher job performance, whereas younger age is associated with lower job performance. Practical implications From a practical perspective, these results highlight the importance of decision authority and recognition. As well as the presence of age disparities related to work arrangements. Managers need to adopt an inclusive approach and develop work arrangements that take into consideration employees' needs and ages. Some insights and practical recommendations are presented in this paper to support managers and human resource practitioners. Originality/value Studies examining the in-role job performance of teleworkers and the effects of age are sparse. This study helps to expand research on human resources management, job performance and age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".