E-procurement Facilitates Adversariality – Trustworthiness Signaled in Procurement in an Industrial High-Tech Cluster
Bibliographic record
Abstract
Technologically advanced environments normally use electronic procurement systems, and this paper explores their role in customer-supplier relationships in a Norwegian high-tech industrial cluster. This paper focuses on “trustworthiness”, about which research has suggested some identifiable characteristics. Using how trustworthiness is signaled as a sensitizing lens, procurement practices and the utilization of ERP systems and other ICT artifacts are explored in a Norwegian high-tech industrial cluster. The findings show that in dealing with strategic suppliers, personal and informal ways of negotiating terms and requirements are dominant, while the procurement of less strategic parts and commodities is conducted via electronic procurement systems. The study finds trustworthiness-building characteristics in the ways in which buyers and strategic suppliers interact. At the same time, signals sent using e-procurement in the case of less critical procurements are generally more suited to building adversariality than trust.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".