Navigating the era of disruption: How emotions can prompt job crafting behaviors
Bibliographic record
Abstract
Abstract Environmental disruptions can disturb the status quo. This can create the need for employees to navigate rapidly evolving demands in their work environment, often before formalized strategic plans can be developed and/or implemented. As such, understanding how employees experience and respond to these disruptions is critical for effective strategic human resource management. Drawing on appraisal theories of emotion, we argue that employees' appraisals of how the disruption has impacted their work can elicit discrete emotions (e.g., frustration and pride). In turn, these emotions can encourage employees to address challenges and opportunities by engaging in job crafting behaviors. Importantly, job crafting behaviors can have implications for subsequent employee outcomes (e.g., performance and well‐being). We test our predictions using a three‐wave survey (N = 402) in the context of the COVID‐19 pandemic—an unexpected environmental disruption that sparked rapid change. Theoretically, our findings provide insight into why and how employees can self‐initiate changes to their jobs in response to environmental disruptions as well as how job crafting behaviors impact employee outcomes. Practically, our findings provide insight and guidance to SHRM practitioners on how to effectively support and manage employees before, during, and after environmental disruptions.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".