Dealing with Stressful Work Events: Insights for Managers and Employees
Bibliographic record
Abstract
Stressful work events are a daily reality for workers. Given its prevalence, understanding how workers deal with stressful work events is both theoretically and practically imperative to “putting the worker front and center” (Academy of Management, 2023). We extend the extant focus on employees’ responses by exploring both employees’ and managers’ experiences of stressful events, and hence more fully capture workers’ experience. Our symposium gathers international scholars from Austria, Canada, Finland, Germany, the Netherlands, and the United States to investigate workers’ experiences of stressful work events using diverse situations, perspectives, and methodologies. Specifically, our papers investigate (a) employees’ experience of, and responses to managerial inaction as a stressful event, (b) how leadership role occupancy and autonomy influence the specific self-regulation strategies workers pursue to cope with taxing work demands, (c) how the enactment of justice is a form of identity work in managers during challenging work events, (d) how self- and other-directed assessments of identity threat emerge during the stressful situations of layoffs and denied promotions in managers and employees, and (e) how interpersonal justice assists/impedes professors in reacting to negative student feedback. Following the paper presentations, Dr. Laurie Barclay will conclude the symposium with an engaging, interactive discussion that highlights key insights and future research directions. By showcasing theoretical and practical insights into how workers (i.e., employees and managers) and organizations can more effectively manage stressful work situations, we aim to fulfill the Academy of Management’s objective to “putting the worker front and center”. Managerial Inaction and Discrete Emotions: Examining the Employee Outcomes of Managerial Inaction Author: Christine Chi Hye Hwang; U. of Guelph Do the Demands of Leader Roles Promote Better Personal Coping? Author: Anita Keller; U. of Groningen Author: Elissa El Khawli; U. of Groningen Author: John Schaubroeck; U. of Missouri Author: Susanne Scheibe; Groningen U. (RuG) Justice Enactment as Identity Work: How Being Fair Can Alter Leadership Identity Author: Marjo-RIitta Diehl; Aalto U. School of Business Author: Julia Zwank; SRH Mobile U. Dual Perspectives on Bad News Delivery: Threats and Violations in Leaders and Followers Author: Claudia Christina Kitz; U. of Groningen Author: Heiko Breitsohl; U. of Klagenfurt, Austria Author: Kai Christian Bormann; Bielefeld U. Professors in Pain: Coping with Student Evaluation of Teaching Author: Abiola Sarnecki; Wiesbaden Business School Author: Myriam N. Bechtoldt; EBS U. of Business and Law
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".