Fear of Embrace? Employees’ Diverging Appraisals of Automation, and Consequences for Job Attitudes
Bibliographic record
Abstract
In the coming decades, technological advancements will continue to increase automation in the workplace. The impact of automation on individual workers will be varied; in some cases, automation may harm (e.g., job loss), and in other cases benefit (e.g., enhanced performance) employees. We argue that employees’ anticipation of automation will shape their current work-related attitudes, such as their job engagement and turnover intentions, and thus their likelihood of manifesting these outcomes. We present two complementary studies, a survey (Study 1) and an experiment (Study 2), showing that employees have varying appraisals of the impact of automation on their well-being, both fearful (automation-related job insecurity), and optimistic (automation-related performance optimism). Across both studies, we found that automation-related job insecurity is detrimental to job attitudes, but that this effect is mitigated for people who feel a great deal of control at work. We also found that automation-related performance optimism is positively related to job engagement, and that this effect is stronger for people who feel a great deal of control at work – however we did not replicate these findings in the Study 2. The theoretical and practical implications of this research are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".