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Record W2773746053 · doi:10.1142/s1363919618500482

EVALUATING BARRIERS TO KNOWLEDGE SHARING AFFECTING NEW PRODUCT DEVELOPMENT TEAM PERFORMANCE

2017· article· en· W2773746053 on OpenAlex
Anirban Ganguly, Debdeep Chatterjee, John V. Farr

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Innovation Management · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsConcordia University
Fundersnot available
KeywordsNew product developmentBusinessKnowledge managementProcess managementKnowledge sharingProduct (mathematics)Set (abstract data type)Affect (linguistics)Computer scienceMarketingPsychology

Abstract

fetched live from OpenAlex

Manufacturing and service organisations have repeatedly stressed the importance of knowledge management and sharing as an integral part of their growth and business strategy. Unfortunately, knowledge sharing (KS) barriers or factors can have a negative influence on a new product development (NPD) project team performance can make it difficult for the organisation to achieve sustained superior performance. The purpose of this research is to identify and explore a set of important KS barriers that might negatively affect the performance of a NPD project team. Specifically, this research focussed on identifying and evaluating the barriers to development and to offer guidelines to decision makers to improve KS to foster effective processes. This research can be utilised by decision-makers to design and develop effective processes and mitigation strategies to ensure effective KS.

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.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.107
GPT teacher head0.429
Teacher spread0.321 · 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