Interorganizational Information Systems and Interorganizational Relationships
Bibliographic record
Abstract
As the external environment and alliance partnerships become more complex, managers should consider appropriate partners to enhance the efficiency and performance of their chain, as well as to gain potential competitive advantages (Chang, et al., 2007). Additionally, due to increasing global competition many organizations are aware of the benefits of using electronic solutions to support their Business-to-Business (B2B) environment. Thus, they opt to establish an electronic infrastructure to carry out physical chain's transactions and cover potential interorganizational relations. This would explain the prevalent use of interorganizational Information Systems (IOS) over previous years. Indeed, several well-known firms such as Wal-Mart, Dell Computer, and Carrefour have attained strategic advantages by setting IOS in their chains. In regard to their incontestable success within B2B networks, the chapter first focuses on the concept of information technology and particularly “interorganizational information systems” and its theoretical approaches. Accordingly, this chapter argues as a second step the theoretical relation between information technology (or IOS) and interorganizational contexts. Some approaches are advanced to conceptualize this interaction. The socio-technical approach is largely presented due to its relevance to research propositions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".