How failures in interorganizational knowledge transfer impact process safety: Insights from the case study of an ageing offshore oil & gas facility acquisition
Bibliographic record
Abstract
An increasingly common form of organizational change in high-hazard industries is the acquisition of ageing facilities. While the safety implications of other types of organizational change have been widely studied, the risks associated with ageing facility acquisitions remain under-investigated. This acquisition process is often accompanied by significant challenges in interorganizational knowledge transfer (IKT). Although knowledge is widely recognized as essential to process safety, the impacts of IKT failures require further exploration, especially when original operational teams are not retained. This study aims to identify the process safety impacts of unsuccessful IKT during the acquisition of an ageing facility in which no personnel were transferred. Drawing on a qualitative case study of an offshore oil and gas platform acquisition, this study offers an in-depth analysis of the organizational and operational discontinuities that emerged during the asset handover. Safety incident data from the case study platform revealed an increase in safety incidents following the acquisition. Interviews with process safety experts and practitioners were conducted to map IKT challenges to Risk-Based Process Safety (RBPS). Failures in IKT were found to vary in their impact on process safety. Governance-related aspects of the IKT process, such as the availability of personnel from both companies during the transfer, and access to legacy databases, were found to be the most critical. These failures not only directly impacted RBPS elements such as process knowledge management but also contributed to safety impacts. These findings support the development of improved IKT frameworks for managers and regulators to avoid operational safety risks following an ageing facility acquisition.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".