In Cloud We Trust? Co-opting Occupational Gatekeepers to Produce Normalized Trust in Platform-Mediated Interorganizational Relationships
Bibliographic record
Abstract
Interorganizational trust plays an important role in facilitating business relationships, especially for the organizational adoption of new services. Prior research suggests that interorganizational trust develops when the trustor has adequate confidence in the reliability of the trustee’s services. Nevertheless, reliability breakdowns are also an inevitable part of service provisioning. Such breakdowns are especially prominent and visible in the context of platform-based services. Yet platform-based services continue to be adopted and used by organizational customers. This increased adoption and use of such services despite their inconsistent reliability pose the following question. How is trust produced in platform-mediated interorganizational relationships? To examine this question, I conducted a 20-month field study of a cloud computing platform provider and its customers, focusing on the practices of trust production in the wake of reliability breakdowns. Here, I describe customer concerns about the platform’s inconsistent reliability that hampered the development of interorganizational trust. I then identify four practices of trust work enacted by the platform provider to address some of these concerns and to co-opt the occupational gatekeepers in customer organizations who are responsible for technology adoption decisions. Following this, I describe how and why these occupational gatekeepers performed justification work to rationalize the continued use of the platform despite its inconsistent reliability. Together, trust work and justification work facilitate the coproduction of interorganizational trust through normalizing reliability breakdowns as “business-as-usual” events. Synthesizing these findings, I developed a normalization model of trust production, and discuss the implications of normalized trust for platform-mediated interorganizational relationships in the digital economy.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".