The differential impact of interactions outside the organization on employee well‐being
Bibliographic record
Abstract
We examine two different perspectives of interactions outside the organization: the relational work design perspective and the emotional labour perspective. The relational work design perspective suggests that interactions outside the organization have favourable outcomes for employees, whereas the emotional labour perspective suggests that such interactions have adverse outcomes for employees. Our goal is to reconcile findings from these two research streams. In Study 1, using data from employees working in diverse occupations, we find that interactions outside the organization have a positive indirect effect on employee well‐being via task significance, and a negative indirect effect on employee well‐being via surface acting. In Study 2, using data collected across two time points, we replicate these findings. In Study 3, we further extend these results and illustrate that interactional autonomy and interactional complexity are influential moderators that shape the strength of the mediated relationships. Our results aid in reconciling and extending findings from two different research streams, and enhance our understanding of the role of interactions outside the organization. Practitioner points Managers should consider that employees’ interactions outside the organization have the potential to improve their well‐being. Organizations could redesign jobs to enable employees in customer‐facing roles to have greater discretion in how they interact with their customers and also increase the variety of these interactions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".