Detached but not deviant: the impact of career expectations and job crafting on the dysfunctional effects of amotivation
Bibliographic record
Abstract
Purpose This study aims to investigate the link between amotivation and workplace deviance. The authors further outlined how the relationship between amotivation and deviant behavior can be mitigated via proactive work strategies such as job crafting and career outcome expectations. Design/methodology/approach The authors conducted a convergent design, mixed-method study to investigate workplace deviance as an outcome of amotivation or the lack of motivation towards an activity. The quantitative data from cross-sectional surveys entailed 127 respondents. The qualitative data comprised of 25 in-depth interviews. The authors sought insights from individuals' lived experiences to understand how amotivated individuals behave at work. Findings The quantitative findings contended a significant relationship between amotivation and organizational deviance. The authors also found evidence for the buffering role of career outcome expectations on amotivation and deviance. Finally, avoidance job crafting has been shown to significantly attenuate the aforementioned relationship. The qualitative study identified three broader themes about amotivated individuals' work outcomes. Practical implications Amotivation can arise among individuals who feel trapped in a job they want to exit and can result in a range of dysfunctional outcomes including workplace deviance. While amotivated employees may be hard to flag, employers can keep such individuals from demonstrating workplace deviance through placing interventions such as job crafting and career development programs. Originality/value The existing literature on work motivation has predominantly overlooked the role of amotivation in determining employee outcomes. The current research generates a new line of inquiry by identifying workplace deviance as an outcome of amotivation. The authors further highlighted that such dysfunctional outcomes of amotivation can be mitigated by job crafting and career outcomes expectancies.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".