The relationship between interorganizational information systems and operations performance
Bibliographic record
Abstract
Purpose The purpose of this study is to explore the relationship between interorganizational information system (IOIS) adoption in supplier coordination and operations performance improvements. Design/methodology/approach The paper focuses on the association between dyadic and multilateral IOISs and improvements in performance priorities associated with stable and dynamic supply networks, using data on 201 manufacturers in 13 countries from the international manufacturing strategy survey (IMSS) database. Regression models were used to test relationships between IOIS adoption and operations performance improvements. Findings Analysis indicates that dyadic IOISs appear to be more associated with the performance priorities of stable supply chains (cost, delivery, and quality), while multilateral IOISs appear to be more associated with the performance priorities of dynamic supply chains (flexibility and quality). Research limitations/implications Survey data were collected in the years 2000 and 2001. Some of the conclusions might be reassessed in light of recent developments in information technology. Data were limited to medium/large manufacturers of fabricated metal products, machinery, and equipment. Practical implications Findings suggest that the choice of IOISs must follow the company's product portfolio and supply chain configuration. Dynamic networks with innovative products may benefit from multilateral IOISs; stable networks with functional products may benefit from dyadic IOISs. Originality/value This appears to be the first study to provide empirical evidence to performance effects of IOISs in light of existing supply chain frameworks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".