THE ROLE OF LEADERSHIP IN TACIT KNOWLEDGE TRANSFER IN THE NIGERIAN OIL AND GAS INDUSTRY.
Bibliographic record
Abstract
The oil and gas industries are knowledge driven industry. The technology deployed in deep water exploration and production involve knowledge-intensive process by highly technical personnel. The problem was that the leadership of the oil and gas industries have not necessitated early recovery of tacit knowledge transfer from experts to employees managing the plant operations. The purpose of this qualitative multiple case study was to gain an understanding of how oil and gas industry leaders in Nigeria facilitate the transfer of tacit knowledge from experts to employees managing the plant after exploration activities. The conceptual framework was the socialization, externalization, combination, and internalization model developed by Nonaka and Takeuchi and Burns’ transformational leadership theory. A qualitative multiple case study design was used by adopting multiple sources of information including semi-structured interviews, field notes, and review of organizational documents. The unit of analysis was leaders in an oil and gas services organization. The data analysis processes involved coding of the data, categorizing the coded data, and subsequently generating themes in line with the research question using NVivo Version 12 software. Findings indicated that leaders facilitated the transfer of tacit knowledge through the creation of a safe working environment and demonstration of care for the employees. The opportunity to facilitate the transfer of tacit knowledge from expert to employees managing operations after exploration enhance the organization’s stability and promotes healthy communities. Keywords– Knowledge-Transfer, Leadership Style, Oil and Gas Industry
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".