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Interorganizational Trust: Revisiting Core Assumptions

2013· article· en· W2322008926 on OpenAlexaboutno aff
Deepak Malhotra

Bibliographic record

VenueAcademy of Management Proceedings · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsnot available
Fundersnot available
KeywordsDistrustReciprocity (cultural anthropology)EnablingPublic relationsInterpersonal communicationCorporate governanceCore (optical fiber)SociologyPolitical scienceOrder (exchange)BusinessPsychologyComputer scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

In recent decades, the topic of interorganizational trust has attracted attention from many researchers. Thanks to their cumulative efforts, trust has been established as an important governance mechanism and as an enabler of collaboration between organizations. However, this literature has relied on strong assumptions. While these assumptions have proved useful, an important question is to what extent they should be revisited in order to advance our understanding of interorganizational trust. This symposium brings together four papers that revisit core assumptions underlying the literature on interorganizational trust. From interpersonal to interorganizational trust: The role of indirect reciprocity Presenter: Bart Vanneste; INSEAD How contracts influence both trust and distrust: An information-processing view Presenter: Fabrice Lumineau; Purdue U. Trust in the balance: Asymmetric antecedents of interorganizational trust Presenter: Bill McEvily; U. of Toronto Presenter: Akbar Zaheer; U. of Minnesota Presenter: Darcy Kathryn Fudge Kamal; Chapman U.

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)0.019
metaresearch head score (Gemma)0.041
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 score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.024
Scholarly communication0.0090.026
Open science0.0030.005
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.234
Teacher spread0.212 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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