A Role–resource Approach–avoidance Model of Job Crafting: A Multimethod Integration and Extension of Job Crafting Theory
Bibliographic record
Abstract
Job crafting refers to changes to a job that workers make with the intention of improving the job for themselves. It may include structural (i.e., physical and procedural), social, and cognitive forms. We draw on two studies to develop a role–resource approach–avoidance taxonomy that integrates and extends the dominant role- and resource-based perspectives of job crafting according to characteristics of approach and avoidance. Study 1 used both qualitative and quantitative methods to analyze job crafting activities described during employee interviews to understand the nature and outcomes of specific job crafting activities. Study 2 provides quantitative support for the specific job crafting types emerging from Study 1, and further explores job crafting outcomes. Approach role crafting includes role expansion and social expansion, while avoidance role crafting includes work-role reduction. Role crafting outcomes include: increased enrichment, increased engagement, and decreased strain through changes in work role boundaries. Approach resource crafting includes work organization, adoption, and metacognition, while avoidance resource crafting includes withdrawal crafting. Resource crafting outcomes include: increased performance, increased engagement, and reduced strain through the development, acquisition, and conservation of resources. Avoidance crafting positively relates to work withdrawal and tends to have fewer relationships with positive outcomes compared to approach 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".