Uncovering the antecedents and motivational determinants of job crafting
Bibliographic record
Abstract
Purpose The purpose of this mixed-methods study is to examine the motivational determinants and contextual antecedents of individual job crafting behaviors. Design/methodology/approach The current research uses the mixed-methods design to elucidate the relationship between career outcome expectations and different forms of job crafting through external regulation. In Study 1, surveys were collected and analyzed from 151 employees across occupations and ranks using purposeful sampling approach. In Study 2, interview data were thematically analyzed to add complementarity and completeness to the findings. Findings In Study 1 (n = 151), a direct relationship between career outcome expectations and different forms of job crafting was established. Mediation analysis indicated an indirect relationship between career outcome expectations and approach crafting through external-social regulation. The authors found support for the accentuating role of turnover intentions on career outcome expectations and external social and material regulations. In Study 2 (n = 25), a thematic analysis of semi-structured interviews confirmed that when employees experience unfulfilled career expectations, employees attempt to realign the work situations. Such expectations may be tied to various forms of work-related external regulations and may lead to job crafting behaviors. The individuals depicted these behaviors while experiencing turnover intentions. Originality/value The current study brings together literature from job design, motivation and careers to consider the role of career expectations and external regulation in predicting job crafting behaviors. Taken together, the findings unearth the cognitive and contextual antecedents of job 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".