Interpersonal Interactions and Employee Well-Being: Exploring Coworker, Leader, and Follower Roles
Bibliographic record
Abstract
Interpersonal interactions are ubiquitous in daily work life, shaping relevant employee outcomes. This symposium focuses on the unique role of interpersonal processes in determining employees’ optimal functioning at work as captured by well-being while also accounting for individual performance. The presentations differentiate the diverse roles employees can occupy in interpersonal interactions, such as being a leader, a follower, and a coworker. Additionally, the presentations capture the full spectrum of interaction valence, ranging from clearly positive or negative interactions to ambiguous interpersonal processes. Thereby, the studies included in this symposium pursue novel theoretical and methodological approaches to move interpersonal research forward. The Impact of Gratitude Variability on Employee Well-Being Author: Meghan Kane; University of Central Florida Author: Lauren Rachel Locklear; Texas Tech University Author: Mark G. Ehrhart; University of Central Florida Putting Leaders Down: The Consequences of Feeling Underappreciated by Followers Author: Daniel Kim; Author: Klodiana Lanaj; University of Florida Author: Remy E. Jennings; Florida State University Author: Yejoo Lee; University of Florida Author: Alex Settles; University of Florida Inconsistent Leadership: Scale Development and Validation Author: Duygu Biricik Gulseren; York University Author: Zhanna Lyubykh; Simon Fraser University Author: Langxi Wang; Saint Mary's University Author: E Kevin Kelloway; Saint Mary's University The Double-Edged Sword of Leader Performance Expectations: A Diary Study Author: Julia Iser-Potempa; University of Mannheim Author: Jette Völker; University of Mannheim Relational Boundary Management at Work: A Boundary-Theory Perspective on Coworker Interactions Author: Jette Völker; University of Mannheim Author: Julia Iser-Potempa; University of Mannheim Author: Anna Neumer; University of Mannheim
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".