Workaholism, work and extra‐work satisfactions and psychological well‐being among professors in Turkey
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine potential antecedents of workaholism components identified in previous research and the relationship of these components to work and extra‐work satisfactions and psychological well‐being among professors in Turkey. It attempts to replicate previous research conducted in North America. Design/methodology/approach Data were collected from 406 professors using a web‐based questionnaire. Three workaholism components were considered: work involvement, feeling driven to work because of inner needs, and work enjoyment. Findings It was found that the three workaholism components were unrelated to three blocks of antecedent predictor variables. Both feeling driven to work and work enjoyment generally predicted validating job behaviors while work enjoyment predicted work and extra‐work satisfactions and psychological well‐being. These findings provide a partial replication of previous North American results, suggesting the need to consider both country and cultural factors in future workaholism research. Research limitations/implications All data were collected using self‐report questionnaires, raising the possibility of response set tendencies. In addition, all data were collected at one point in time, making it difficult to determine causality. Practical implications Work enjoyment emerged as a strong and consistent predictor of most work and well‐being outcomes. Organizations are encouraged to increase satisfaction levels in efforts to attain productive and healthy people. Originality/value This paper replicates previous workaholism research carried out in North America in Turkey, a secular Muslim country. The importance of considering country culture and values is highlighted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".