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Record W4367368846 · doi:10.1177/23409444231166717

Understanding the effect of boundary spanning activities on team identification in new product development teams

2023· article· en· W4367368846 on OpenAlexaff
Pilar Carbonell, Ana Isabel Rodríguez Escudero

Bibliographic record

VenueBRQ Business Research Quarterly · 2023
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsYork University
Fundersnot available
KeywordsBoundary spanningTeam compositionTeam effectivenessIdentification (biology)New product developmentRelevance (law)Affect (linguistics)Knowledge managementBusinessBoundary (topology)PsychologyProduct (mathematics)MarketingComputer scienceMathematicsPolitical science

Abstract

fetched live from OpenAlex

Despite the relevance of team boundary spanning in new product development (NPD), there is great deal we still do not know about these activities. For example, little is known about how these activities affect the development of team identification, an important emergent state with critical implications for new product performance. This study extends our understanding of this topic by exploring the relationship between team boundary spanning and team identification. This relationship is examined via the intervening mechanisms of team potency and team boundedness and the moderating effect of intra-team communication. Data from a time-lagged survey study of 140 NPD projects revealed that team potency and team boundedness, respectively, positively and negatively mediate the relationship between team boundary spanning and team identification. Intra-team communication was found to reduce the negative effect of team boundary spanning on team boundedness. JEL CLASSIFICATION 031, 032, 036

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.124
GPT teacher head0.400
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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