Perceived surveillance and technostress among older employees
Bibliographic record
Abstract
Workplace digital surveillance is an ongoing situation in which employees’ performance and behaviour are observed, inspected, tracked and recorded digitally by their employers. Based on a sample of 569 employees aged 60 and over from six countries, this study explored whether older employees’ Perceived Employer Surveillance (PES) is associated with their reports of technostress (stress induced by technology use). Analysis indicated that 31.5 per cent of the sample thought their employers digitally monitored them. These individuals were similar to those who did not report PES in their background characteristics. However, their internet use was more intense, diverse and sophisticated. In addition, they reported more perceived surveillance by other agents, such as commercial companies and social institutions. The level of technostress among employees who reported PES was significantly higher than among those who did not. This difference resulted from disparities in the sense of invasion and privacy concerns. Greater technostress was associated with feeling monitored by the employer, the state and individuals such as family and friends. Yet, the PES–technostress association was significant among women only. The findings highlight a paradoxical situation in which, parallel to the tremendous effort invested in supporting older workers’ activity and health, employers’ surveillance potentially puts them, and women in particular, at risk of technostress that may decrease their productivity and harm their job satisfaction and well-being. Accordingly, using digital systems to surveil older employees should be thoroughly considered.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".