Leader knowledge hiding and employee organizational identification in the Egyptian service industry
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the relationship between leader-signaled knowledge-hiding behavior (LSKH) and employee organizational identification (OI) with self-interest climate perceptions (SIC) as a mediator. This study also takes into consideration the impact of individual differences (i.e. employee trait of agreeableness) in shaping these relationships. Design/methodology/approach Two-wave data were collected from a sample of employees working in service industry companies in Egypt (N = 305). The mediation model (model 4) and the moderated mediation model (model 14) were tested using the statistical package for the social sciences PROCESS macro. The indirect effect of LSKH behavior on employee OI was examined using the bootstrapping approach (n = 5,000) with 95% confidence intervals (CI) for the indices. Findings Findings show that LSKH behavior has a negative impact on employee OI through SIC perceptions. Additionally, a moderation analysis indicates that the employee trait agreeableness strengthens the negative relationship between SIC and OI as well as the indirect relationship between LSKH behavior and employee OI. Originality/value While previous studies mainly focused on employee knowledge-hiding behavior, this study extends this nascent stream of literature by investigating the impact of this behavior at the leader’s level in the Egyptian cultural context. The results provide insights into the consequences of this type of behavior on important outcomes, namely, SIC and OI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".