Interorganizational Information Systems Adoption in Supply Chains
Bibliographic record
Abstract
Supply chain management (SCM) enabled by advances in technology, aims to develop a technical infrastructure linking technology and people, in an effort to align the technology with the capabilities of the organization and among its trading partners. This has led to the importance of the interorganizational information system (IOS) which has been increasingly recognized by organizations. There are several IOS types, including B2B electronic commerce (EC), customer-oriented strategic systems, EDI and electronic markets. The factors influencing the adoption of these systems are presented in the literature, but the IOS adoption in supply chains with supply chain context specific antecedents is very limited. To fill the gap in the literature, in this study a comprehensive model is built on the foundations of technology adoption at the organizational levels and by examining the supply chain context specific antecedents behind the motivations of adoption of technology in supply chains. The developed TOESCM research framework considers the TOE (technological-organizational-environmental) framework and SCM context specific antecedents such as information sharing, interorganizational relationships, and collaboration among trading partners to determine the adoption of IOS in supply chains.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".