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Record W6998826555

Barriers to Knowledge sharing : An investigation of Practice Area Networks at WSP Sweden

2023· other· en· W6998826555 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge sharingEnthusiasmStatus quoQuarter (Canadian coin)Bridge (graph theory)Preference
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.222
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0030.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.040
GPT teacher head0.314
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueKTH Publication Database DiVA (KTH Royal Institute of Technology)French-language works237,207