Interpersonal Influences of Emotions in the Workplace: New Directions in Theory and Research
Bibliographic record
Abstract
In recent years, scholarly interest in the role of emotions in the workplace has been rapidly increasing. Whereas early research focused on individuals’ emotions as outcomes of workplace events, more recent studies have demonstrated that the emotions expressed by organizational members can also impact the perceptions, attitudes, and behaviors of others. However, the literature on the interpersonal effects of emotions is still in its infancy; we lack a precise understanding of when and how such interpersonal influence occurs. Drawing upon multiple theoretical perspectives and methodologies, this symposium brings together leading experts to provide new insights that can contribute to a richer and more precise understanding of the interpersonal effects of emotions in the workplace. Specifically, the presentations in this symposium (a) examine individual and personality differences (e.g., implicit theories of personality, cultural background) that influence how an observer responds to another person’s emotions, (b) identify important emotion characteristics (e.g., intensity, target, complexity) that moderate the effects of emotions on employees’ attitudes and behaviors, (c) highlight cognitive and affective mechanisms (e.g., inferences, attitude formation) linking antecedents to outcomes, and (d) identify a range of organizationally-relevant outcomes of expressing and observing emotions in the workplace (e.g., cooperation, relationship satisfaction, leadership effectiveness). In addition, the symposium will include an interactive discussion and question period aimed at shedding further light on these issues and identifying future research avenues. The Interpersonal Effects of Incidental Emotions in Negotiation Presenter: Annika Hillebrandt; Wilfrid Laurier U. Presenter: Laurie J. Barclay; Wilfrid Laurier U. Presenter: Russell Cropanzano; U. of Colorado, Boulder Everything in Moderation: The Social Effects of Anger Depend on Its Perceived Intensity Presenter: Hajo Adam; Northwestern U. Presenter: Jeanne M Brett; Northwestern U. Benefiting from Complexity: The Social Function of Emotional Complexity for Leaders Presenter: Naomi B. Rothman; Lehigh U. Presenter: Shimul Melwani; U. of North Carolina, Chapel Hill How Implicit Theories of Personality Shape Interpretations and Outcomes of Leader Anger Expression Presenter: Bo Shao; U. of New South Wales Presenter: Lu Wang; U. of New South Wales
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".