Bibliographic record
Abstract
Information sharing among firms within many industries accounts for a substantial part of the gains from information technology adoption in these industries. In this paper we examine the determinants of information sharing between retailers and their suppliers, in the food industry supply chain. Using a data set known as the Super Market Panel Data gathered by the University of Minnesota's Food Industry Center, the behavior of food retailers is examined in their adoption of information sharing technologies that facilitate vertical exchange of information with suppliers. Evidence supports the hypothesis that retail firms with larger number of suppliers are more inclined to share, rather than to withhold, information. Prior theoretical work suggests that this happens because retail firms with large number of suppliers are less concerned about opportunistic behavior of suppliers from the misuse of retailer information by the supplier. We also find that self‐distribution chains are more inclined to share information. Finally, the structure of the market plays an interesting role in the type of information sharing platforms that the retailers adopt. Dans de nombreux secteurs, l'échange d'information entre entreprises représente une part substantielle des gains issus de l'adoption des technologies de l'information. Dans le présent article, nous avons examiné les déterminants de l'échange d'information entre les détaillants de la chaîne d'approvisionnement alimentaire et leurs fournisseurs. À l'aide de l'ensemble de données Supermarket Panel Data, établi par le Food Industry Center de l'University of Minnesota, nous avons examiné le comportement des détaillants en alimentation quant à l'adoption des technologies d'échange d'information facilitant l'échange d'information vertical avec les fournisseurs. Les observations appuient l'hypothèse selon laquelle les détaillants qui comptent de nombreux fournisseurs sont plus disposés àéchanger qu’à retenir l'information. D'après des études théoriques antérieures, cette situation serait attribuable au fait que les détaillants qui comptent de nombreux fournisseurs sont moins préoccupés par le comportement opportuniste de fournisseurs qui utiliseraient de façon malveillante de l'information sur le détaillant. Nous avons également observé que les chaînes d'autodistribution étaient plus enclines àéchanger de l'information. Finalement, la structure du marché joue un rôle intéressant dans le type de plateformes d'échange d'information qu’adoptent les détaillants.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.014 |
| Open science | 0.001 | 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".