Disappointed but still dedicated: when and why career dissatisfied employees might still go beyond the call of duty
Bibliographic record
Abstract
Purpose The purpose of this research is to examine how employees' experience of career dissatisfaction might curtail their organizational citizenship behavior, as well as how this detrimental effect might be mitigated by employees' access to valuable peer-, supervisor- and organizational-level resources. The frustrations stemming from a dissatisfactory career might be better contained in the presence of these resources, such that employees are less likely to respond to this resource-depleting work circumstance by staying away from extra-role activities. Design/methodology/approach The research hypotheses were tested with survey data collected from employees who work in the retail sector. Findings Career dissatisfaction relates negatively to organizational citizenship behaviors, and this relationship is weaker at higher levels of peer goal congruence, supervisor communication efficiency and organization-level informational justice. Practical implications For organizations that cannot completely eradicate their employees' career-related disappointment, this study shows that they can still maintain a certain level of work-related voluntarism, to the extent that they develop and hone valuable resources internally. Originality/value This study adds to extant research by detailing the contingent effects of a hitherto understudied determinant of employees' extra-role work behavior, namely, perceptions of limited career progress.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".