Development and validation of a commitment to organizational career scale: At the crossroads of individuals’ career aspirations and organizations’ needs
Bibliographic record
Abstract
This paper introduces the construct of commitment to organizational career ( COC ). Conceptualized as a specific form of goal commitment, COC reflects an individual's commitment to the goal of pursuing a long and successful career in an organization. We developed a 5‐item measure of COC and examined its validity and reliability in four studies involving employees from diverse organizations and occupations ( N s = 312, 187, 199, 309). We explore COC 's distinctiveness from related constructs, including organizational commitment components (i.e., affective, normative, and continuance subdimensions) and career commitment, as well as its ability to predict turnover intention and voluntary turnover. Finally, we examine COC 's antecedents and specify boundary conditions to its relationship to turnover. Overall, results support the reliability and validity of the COC measure. We discuss how COC contributes to generate promising research avenues for the career and commitment literatures. Practitioner points We introduce the commitment to organizational career ( COC ) construct. Four studies provide reliability and validity evidence for a COC measure that can be used in future research. COC adds to the career and commitment literatures and directs attention to organizational career goals as a common ground linking individuals’ and organizations’ interests. This common ground may provide a basis for both parties to build mutually beneficial relationships.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".