Helping as an opportunity and risk: an alternative side to gratitude in co-worker dyads
Bibliographic record
Abstract
Purpose As workplaces and relationships evolve with increasing complexity, co-worker dynamics have become a key concern for HR managers and scholars. An important yet overlooked aspect of co-worker dynamics is gratitude. This paper adopts a relationship-specific conceptualization of gratitude and explores its influence on prosocial behaviors within co-worker dyads. The proposed model also suggests structural-relational factors under which these relationships are affected. Design/methodology/approach The conceptual paper draws insights from personal relationships to consider an alternative side of gratitude’s prosocial action tendencies, thereby highlighting two: risk-oriented and opportunity-oriented. These assumptions are then situated within the affect theory of social exchange to predict gratitude’s influence on prosocial behaviors within co-worker dyads. Findings The proposed model illuminates the importance of studying relationship-specific gratitude within co-worker relations by illustrating its effects on two types of prosocial action tendencies – opportunity-oriented and risk-oriented and varying prosocial behaviors (from convergent to divergent). Structural-relational factors, such as positional and physical distance between co-workers, are considered to affect these relationships. Originality/value While the study of gratitude in the workplace is emerging, little research has examined its influence on the nature of prosocial behaviors within co-worker relations. This paper advances the notion that gratitude serves an adaptive function in co-worker dyads, thereby highlighting the risk-oriented and opportunity-oriented continuum, and its implications for the type and scope of prosocial behaviors exchanged.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".