Antecedents and consequences of trust in supply chain: the role of information technology
Bibliographic record
Abstract
Trust has been a central construct in studies of inter-firm relationships. Many operational, organizational, social, and cultural factors have been identified to have significant impact on inter-firm trust. In this study, we investigate the role of information technology in generating inter-firm trust and the consequences of this trust in the context of supply networks. Using structural equation modeling techniques, our data show that the level of information systems integration among the partner firms in a supply network significantly impacts the trust among the firms which, together with the integrated information systems, explains more than half of the variances in information sharing and business process coupling in the network. Given the substantial evidence in the literature on the impact of information sharing and process coupling on supply chain performance, we conclude that information systems integration among the partners is critical to supply network performance. We also confirm that information systems flexibility and use of standards in information systems significantly contribute to the level of systems integration among the partners in supply networks as suggested in prior studies. Our findings extend the current literature on inter-firm trust by considering the role of information technology in addition to other important factors already identified.
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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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".