A new pathway to job crafting: gratitude, perceived responsiveness, and relational job crafting
Bibliographic record
Abstract
Purpose Since jobs are dynamically embedded in a social context, job crafting is often driven by emotional experiences that prompt individuals to reshape relationships with others at work. However, previous studies have overlooked the question of why employees are motivated to engage in relational job crafting. Using the find-remind-and-bind theory, this research elucidates the mechanism through which gratitude functions as a critical emotion that activates relational crafting. Design/methodology/approach In a two-week daily diary study with 138 full-time employees from various industries (n = 1,121 observations), we measured momentary feelings of gratitude and tested its indirect effect on relational crafting through perceived responsiveness. Findings The results consistently support a mediation model in which gratitude enhances perceived responsiveness from coworkers, which, in turn, increases relational crafting. These findings remain robust even after controlling for positive affect. Originality/value This paper represents an original effort to examine how gratitude motivates relational crafting. Exploring perceived coworkers’ responsiveness as the underlying mechanism, the study offers a novel affect-driven perspective. Additionally, it offers practical value by demonstrating that gratitude-based interventions can more precisely and effectively foster relational crafting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".