Are Emotionally Intelligent Employees Less Likely to Hide Their Knowledge?
Bibliographic record
Abstract
In today's knowledge‐intensive economy, organizations are constantly faced with new challenges to be more innovative (Salaman & Storey, ). Therefore, they have increasingly viewed knowledge management (KM) as an important strategy. Many have even implemented explicit knowledge sharing (KS) practices in an attempt to maintain their competitive advantage and improve performance (Hsu, ; Law & Ngai, ). However, much of the knowledge utilized by the organization is out of its control since it is held and managed at the individual level. Moreover, employees often choose to conceal this knowledge (Connellyet al., ; Peng, ; Connelly & Zweig, ; Demirkasimoglu, ) a phenomenon known as knowledge hiding (KH). This paper reviews the literature on KH and on Emotional Intelligence (EI) theory and practice, arguing that there is a potential connection between the two. Specifically, KH may be reduced, through increasedteamwork,trust, andorganizational commitment, which are all outcomes of high EI in employees. A narrative overview approach (Green et al., ) was used to find, synthesize, and review the literature. A search of the available research literature was performed across some of the major digital library sources including the Education Resources Information Center (ERIC), Emerald, Google Scholar and ProQuest databases. A meta‐synthesis was then used to integrate, evaluate, and interpret the findings. The resulting review provides a summary of the current literature and offers a rationale for conducting future research. This paper is useful for both academics and practitioners who are concerned with the incorporation of EI practices into their KM strategies. It could also provide further insight into organizational KM strategy, specifically relating to hiring, training, and promoting KM processes. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".