Can employees perform well if they fear for their lives? Yes – if they have a passion for work
Bibliographic record
Abstract
Purpose With a basis in conservation of resources theory, the purpose of this paper is to investigate the mediating role of championing behaviour in the relationship between employees’ fear of terror and their job performance, as well as the buffering role of their passion for work, as a personal resource, in this process. Design/methodology/approach The tests of the hypotheses rely on three-wave, time-lagged data collected from employees and their supervisors in Pakistan. Findings An important reason that concerns about terrorist attacks diminish performance is that employees refrain from championing their own entrepreneurial ideas. This mediating role of idea championing is less salient, however, to the extent that employees feel a strong passion for their work. Practical implications For human resource managers, this study pinpoints a key mechanism – a reluctance to mobilize active support for entrepreneurial ideas – by which fears about terrorism attacks can spill over into the workplace and undermine employees’ ability to meet their performance requirements. It also reveals how this mechanism can be better contained by the presence of adequate personal resources. Originality/value This study adds to burgeoning research on the interplay between terrorism and organizational life by specifying how and when employees’ ruminations about terrorism threats might escalate into diminished performance outcomes at work.
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".