Influencing knowledge workers: the power of top management
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the key leadership characteristics (in the form of social power) needed in a knowledge‐based firm that can influence knowledge workers (KWs) to participate actively in creating, sharing, and using knowledge. Design/methodology/approach Data measuring top leaders social power and knowledge management (KM) practices is gathered from 402 KWs representing 180 Multimedia Super Corridor status firms in Malaysia. Findings The analysis indicates that expert power has a positive influence on the extent of knowledge acquisition and dissemination practices. Legitimate power is found to impede knowledge acquisition practices. Furthermore, reliance on referent power no longer works in a knowledge‐based context. Finally, the paper found the impact of coercive, legitimate, and reward power to be contingent on the organizational size. Research limitations/implications Besides leaders potential to influence, there may be other factors that could influence the extent of KM practices in organization. Further, this paper explores the power of top management, which could not be generalized to leaders from middle or lower level management. Future research should address these limitations. Practical implications The paper implies that knowledge leaders need to enhance certain bases of power that have the potential to improve the extent of KM practices in organizations. Originality/value This paper provides useful insights about the significance of leaders' power bases with emphasis on new approaches needed in knowledge‐based organizations.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".