Using the Fuzzy Delphi Method to Apply a Model of Knowledge Transfer through International Strategic Alliances in Up-Stream Oil and Gas Sectors
Bibliographic record
Abstract
The upstream petroleum industry includes associated service businesses such as seismic and drilling contractors, service rig operators, engineering firms and various scientific, technical service and supply companies. These extremely high-tech activities require the continuous inflow of knowledge and technologies for reconfiguring and rebuilding capabilities that fit with the continuous changes in the marketplace for sustaining a competitive advantage. To that end, infrastructures and policy orientations are required to create a conducive environment for knowledge transfer (KT) to enhance knowledge capability. This paper aims to explore how strategic alliances lead to KT that enhances organizational capabilities. We analyze the case of the National Iranian Oil Company (NIOC) to test the fuzzy Delphi model (FDM) framework. We develop a conceptual framework establishing the link between the strategic alliances (SA) and their facilitators for developing the knowledge capabilities of upstream oil and gas companies. We tested the proposed model using FDM to show how international strategic alliances (ISA) in the upstream oil and gas sector transfer knowledge and have positive effects on developing the NIOC’s knowledge capability. Positive outcomes include knowledge acquisition from partners, developing knowledge management techniques and facilitating the implementation of knowledge-based structure, developing high-tech production and exploration methods, increasing investment in innovation, and developing human resources and information technology uses. Keywords: Knowledge Transfer, Upstream Oil and Gas Sector, International Strategic Alliances, Emerging Countries To cite this document: Salman Kimiagari, Samira Keivanpour, Md. Samim Al-Azad, and Muhammad Mohiuddin, "Using the Fuzzy Delphi Method to Apply a Model of Knowledge Transfer through International Strategic Alliances in Up-Stream Oil and Gas Sectors", Contemporary Management Research, Vol.11, No.4, pp. 409-428, 2015. Permanent link to this document: http://dx.doi.org/10.7903/cmr.13183
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".