Barriers to Knowledge sharing : An investigation of Practice Area Networks at WSP Sweden
Bibliographic record
Abstract
This investigative case study of how barriers to knowledge sharing manifest at the Swedish subsidiary of the global consulting firm WSP, seeks to bridge the theory-practice gap in the knowledge management literature. The investigation focused on Practice Area Networks, or PANs, which are global information and communication networks at WSP, and the extent to which these are utilized by junior and senior consultants. The method mainly relies on semi-structured interviews, where 20 interviews were held with different consultants from all business areas. Additionally, a survey with 375 respondents from across all Swedish WSP offices was conducted as well as an on-site investigation at the Stockholm office which included several discussions with different managers. The results from the investigation reveal that despite a prolific enthusiasm for knowledge sharing across WSP Sweden, barriers nevertheless manifest in both a preference for the status quo and through employees’ internal communication problems. Consequently, PANs are not readily adopted but instead an approximate quarter of consultants are both aware of PAN and active users. Since employee knowledge is amongst the most valuable assets of a consulting firm, three propositions for future research were formulated concerning the barriers to knowledge sharing.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".