MétaCan
Menu
Back to cohort
Record W4393316354 · doi:10.1111/radm.12686

The relationship among informational diversity, dynamic capabilities, and innovative performance in turbulent environments: evidence from R&D teams

2024· article· en· W4393316354 on OpenAlex

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

VenueR and D Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of TorontoConcordia University
Fundersnot available
KeywordsDiversity (politics)TurbulenceKnowledge managementBusinessComputer scienceGeographySociologyMeteorology

Abstract

fetched live from OpenAlex

The study examines the link between informational diversity and innovative performance, with a specific emphasis on the importance of a team's dynamic capabilities in turbulent environments. Utilizing 131 research and development teams, with 289 members from 87 manufacturing firms, our findings reveal that environmental turbulence not only influences the development of dynamic capabilities necessary to leverage diverse information among team members but also amplifies the positive relationship of these capabilities with innovative performance. The results demonstrate the significant roles of both environmental turbulence and dynamic capabilities in the diversity–performance relationship, providing fresh insights into this area of literature. For example, merely possessing diverse informational resources is not correlated with performance improvements. Instead, teams appear to require a catalyst to cultivate dynamic capabilities that effectively transform these resources into innovation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.020
GPT teacher head0.226
Teacher spread0.206 · 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