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Record W2574564457 · doi:10.1287/orsc.2016.1102

Mutual and Exclusive: Dyadic Sources of Trust in Interorganizational Exchange

2017· article· en· W2574564457 on OpenAlex
Bill McEvily, Akbar Zaheer, Darcy Fudge Kamal

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

VenueOrganization Science · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDyadConceptualizationPosition (finance)Information exchangePower (physics)Social exchange theoryBusinessPsychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Trust in interfirm exchange has traditionally been treated as mutually held and jointly determined by the two parties in a relationship. Yet, the expectations of exchange partners can, and routinely do, differ with respect to the goals, preferences, and vulnerabilities in their shared relationship. To account for such differences in expectations, we propose a broadened conceptualization of the sources of interorganizational trust as dyadic. Viewing the sources of trust as dyadic expands the conventional focus on mutual elements to further emphasize exclusive features of an exchange relationship. To substantiate our theory, we examine a key source of interorganizational trust, exchange hazards, and assess the extent to which its effects vary as a function of (1) the locus of exchange hazards (own versus other) in the dyad, (2) the degree of power imbalance in the dyad, and (3) each party’s power position in the dyad. To assess the validity of our claims, we devise a matched dyad research design and collect identical information from both buyers and suppliers in a given exchange relationship. Based on our results, we make three unique observations consistent with the notion of dyadic sources of trust. First, the same exchange hazards have contrasting effects on trust (enhancing versus diminishing) across the dyad. Second, the degree of power imbalance has opposing effects across the dyad. Third, the relative significance of partners’ exchange hazards varies based on their respective power positions. The online appendix is available at https://doi.org/10.1287/orsc.2016.1102

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.021
GPT teacher head0.266
Teacher spread0.245 · 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