Mutual and Exclusive: Dyadic Sources of Trust in Interorganizational Exchange
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".