Employee commitment before and after an economic crisis: A stringent test of profile similarity
Bibliographic record
Abstract
Researchers have recently begun to take a person-centered (profile) approach to investigate how the affective, normative and continuance commitment mindsets combine within the three-component model of organizational commitment. The meaningfulness of the profiles identified in this research depends, in part, on evidence that similar profiles emerge across samples, particularly those drawn from a common population. We conducted a particularly stringent test of similarity by comparing profiles for samples of employees drawn from a large Turkish conglomerate prior to ( N = 346) and following ( N = 797) a major economic crisis. Using procedures recently introduced by Morin et al., (2016) we found similarity in the number (seven) and structure of the profiles before and after the crisis; only the distribution of individuals across profiles (i.e. the relative size of the profiles) differed. We also found similarity in the patterns of relations with theoretical antecedent, correlate, and outcome variables, suggesting that a common set of principles might be operating regardless of major differences in the work environment. In addition to providing strong evidence for the meaningfulness of commitment profiles, this study is one of the first to investigate the impact of an economic crisis on employee commitment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".