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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".