The role of project management office in developing knowledge management infrastructure
Bibliographic record
Abstract
Purpose Knowledge management (KM) is regarded as an essential factor in project-based organizations (PBOs), leading to organizational learning across projects. Over recent years, most PBOs have inserted project management offices (PMOs) into their hierarchical charts to manage their projects much more coherently. These offices can correspondingly provide KM facilities in PBOs. Thus, this study aimed to analyze the relationship between PMO functions and KM infrastructure, as KM enablers in organizations, in Iranian oil and gas upstream PBOs. Design/methodology/approach A two-phase quantitative survey strategy was exercised in this research. The first phase was to investigate the relationship between PMOs and KM infrastructure and to prioritize PMO functions and KM infrastructure based on their existing implementation/establishment status in Iranian oil and gas upstream PBOs. The research participants, identified through the website of the National Iran Oil Company (NIOC), were comprised of 46 oil and gas upstream PBOs which applied for exploration and production (E&P) certificate in Iran in 2016 and 2017. Accordingly, a total number of 46 questionnaires were submitted to the aforementioned companies with a return rate of 41 cases. The second phase was fulfilled questioning 19 Iranian oil and gas industry experts to determine the one-to-one effect of PMO functions on KM infrastructure and to verify the first-phase results. Findings The results indicated a strong relationship between PMO functions and KM infrastructure. This relationship was significant with regard to “practice management” and “technical support”, having the most considerable connections with KM infrastructure. According to the first-phase results, the main functions of PMOs in Iranian oil and gas industry were “practice management” and “technical support”. Considering KM infrastructure, “structure” showed the lowest mean value while “culture”, “human resources” and “processes and procedures” obtained the highest scores. The results also demonstrated that PMO functions could lead to more improvements in “processes and procedures”, as a sub-component of KM infrastructure, compared with other sub-components. Furthermore, the oil and gas industry experts believed that “organizational culture” in KM could be shaped by most of PMO functions. Originality/value This study fulfilled the need for exploring the relationship between PMO functions and KM since academic literature lacked a thorough investigation, to the best of authors' knowledge, pertaining to the effects of PMO functions on KM development in oil and gas PBOs.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".