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Record W4410942771 · doi:10.1016/j.ijme.2025.101215

Developing innovation capabilities through Programme Communities of Practice: Evidence from Wales

2025· article· en· W4410942771 on OpenAlexfundno aff
Gary Walpole, Zheng Liu, Nick Clifton, Songdi Li

Bibliographic record

VenueThe International Journal of Management Education · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
FundersLlywodraeth CymruCardiff Metropolitan UniversityUK Research and InnovationNational Search and Rescue Secretariat
KeywordsBusinessSociologyEnvironmental planningPublic relationsKnowledge managementEconomic growthEngineering ethicsRegional sciencePolitical scienceGeographyEngineeringComputer scienceEconomics

Abstract

fetched live from OpenAlex

This paper presents empirical evidence on how publicly funded programme communities of practice (CoPs) enhance learning and innovation activities at the organisational and inter-organisational level, which in turn develop organisation's innovation capabilities. It draws upon studies on two programme CoPs in Wales and presents eight case studies. Findings reveal, firstly, that CoP can promote the learning of new tools, knowledge, and technologies to encourage product and service innovation. Secondly, CoPs can promote innovative solutions to common challenges, such as implementing circular economy principles. Thirdly, CoP can facilitate collaborative inter-organisation innovation. Our paper answers the recent call for empirical research on the role of publicly funded collaborative projects that support business to innovate. It also expands the understanding of adopting CoP for management education. Practically the framework we developed can guide policy makers and practitioners on how universities can share risk, knowledge, and support organisations to develop their innovation capabilities and achieve sustainable development.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.347
Teacher spread0.279 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations3
Published2025
Admission routes1
Has abstractyes

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Same venueThe International Journal of Management EducationSame topicInnovative Approaches in Technology and Social DevelopmentFrench-language works237,207