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Record W1903127205 · doi:10.1002/smj.2245

Reconceptualizing competitive dynamics: A multidimensional framework

2014· article· en· W1903127205 on OpenAlexafffund
Ming‐Jer Chen, Danny Miller

Bibliographic record

VenueStrategic Management Journal · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsUniversity of AlbertaHEC Montréal
FundersSocial Sciences and Humanities Research Council of CanadaBatten Institute for Innovation and Entrepreneurship, Darden School of Business, University of VirginiaDarden School Foundation
KeywordsConceptualizationCompetition (biology)Transaction costCompetitive advantageIndustrial organizationField (mathematics)EconomicsStakeholderConceptual frameworkDynamics (music)Time horizonDatabase transactionMicroeconomicsComputer scienceSociologyManagementMathematics

Abstract

fetched live from OpenAlex

Competitive dynamics research, despite progress, lacks a conceptual framework that can extend the field's reach to address today's environment. Increasing stakeholder power and globalization are but two of the organizational and economic forces compelling a broader conceptualization of competition. Our framework expands competitive dynamics along five dimensions—aims of competition, mode of competing, roster of actors, action toolkit, and time horizon of interaction—that prove useful for contrasting the rivalrous and competitive‐cooperative modes and a new approach we call relational competition. We draw conjectures about the moderators, such as industry and culture, that determine the appropriateness of these forms of interaction, and conclude by relating our method to three discrete perspectives: the configurational, transaction cost, and stakeholder views . Copyright © 2014 John Wiley & Sons, Ltd.

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)0.023
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.025
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0140.010
Science and technology studies0.0050.042
Scholarly communication0.0250.034
Open science0.0040.016
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.243
Teacher spread0.217 · 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 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

Citations350
Published2014
Admission routes2
Has abstractyes

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