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Strategic Alliances for Complex Environmental Issues

2012· article· en· W2901969692 on OpenAlexaff
Haiying Lin, Nicole Darnall

Bibliographic record

VenueAcademy of Management Proceedings · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAllianceLegitimacyContext (archaeology)Corporate governanceBusinessStrategic alliancePerspective (graphical)Diversity (politics)Resource (disambiguation)Knowledge managementResource dependence theoryInstitutional theoryIndustrial organizationPolitical scienceManagementEconomicsPoliticsComputer science

Abstract

fetched live from OpenAlex

While previous research has recognized the importance of strategic alliances in the general business context, little is known about how they relate to complex environmental settings, in spite of their increased business use in this setting. Moreover, studies that have considered alliance formation more generally view their emergence through a single theoretical lens – drawing on either the resource-based view or institutional theory – even though both views are likely to be relevant. This research conceptually integrates both theories to assess variations in firm-level motivations to form strategic alliances that address complex environmental issues. It proposes that strategic alliances typically are either competency- or legitimacy-oriented, and that four structural dimensions characterize both types of alliances—organization learning, partner diversity, governance structure, and partner relations. It presents research propositions that describe how alliances differ along these dimensions, and offers an important broader perspective on alliance formation.

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)0.004
metaresearch head score (Gemma)0.014
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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0040.004
Scholarly communication0.0050.009
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.051
GPT teacher head0.276
Teacher spread0.225 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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