Trust, distrust, and open‐book accounting in three client‐vendor relationships
Bibliographic record
Abstract
Abstract Extant management accounting research has conceptualized the interplay between trust, distrust, and open‐book accounting (OBA) as a relationship‐level phenomenon, largely ignoring that inter‐organizational relationships are often complex entities that comprise multiple arenas of exchange where (dis)trust and OBA can interact in different—and potentially contradictory—ways. To address this theoretical oversight, we draw on relational exchange theory to propose that (dis)trust largely forms within OBA domains where different forms of data are exchanged for different purposes. Trust and distrust may thereby coexist within a relationship. We also propose that (dis)trust spills over to influence the conditions for OBA in other domains. Consequently, trust and distrust not only take shape in cumulative processes within OBA domains but also diffuse between domains in a relationship. Empirical observations from a longitudinal case study of a retail buyer's attempts to introduce OBA in three vendor relationships lead us to suggest that competence‐trust spillover is determined largely by domain similarity, while goodwill‐trust spillover relies to a greater extent on staff mobility between OBA domains. Overall, the negative effects of distrust spillover on the implementation of OBA appear greater than the positive effects of trust spillover. Our study shows that, by analyzing the domain‐specific nature of trust and distrust, future research can increase our understanding of the relationship between data characteristics and (dis)trust, as well as explain how trust and distrust interact to determine conditions for data exchange between organizations.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".