Relationships between Employees’ Identifications and Citizenship Behavior in Work Groups: The Role of the Regularity and Intensity of Interactions
Bibliographic record
Abstract
This paper explores the relationships of various employees’ identifications (personal, interpersonal, micro-group, group and organizational) in their two components (cognitive and affective) with two dimensions of organizational citizenship behavior (OCB): offering quality ideas and suggestions, and providing help and support within small work groups. Two studies were conducted in Russia on two respective samples: (1) employees of commercial enterprises (N = 183) characterized by a relatively high regularity and intensity of within-group interactions; and (2) the academic staff of higher education institutions (N = 157), which typically have relatively less regular, low-intensity within-group interactions. The research employed four questionnaires to assess the participants’ identifications in both of their components. In addition, managers in the respective organizations filled out an organizational communicativeness questionnaire and a two-factor OCB assessment instrument. It was found that the relationships between (a) particular identifications and (b) the ratio of group identification to other identifications, on the one hand, and OCB, on the other, depend on the degree of regularity of within-group interactions, as well as on the identification components. Organizational communicativeness did not moderate the relationship between identifications and OCB, but was significantly positively correlated with both OCB dimensions. The theoretical and practical implications of the study findings are discussed.
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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 | 0.002 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".