Utilizing e‐business technologies in supply chains: The impact of firm characteristics and teams
Bibliographic record
Abstract
Abstract This paper presents findings from an exploratory study that analyzes the drivers and outcomes of e‐business technology use in the supply chain. Using a combination of case studies and survey data from a diverse sample of industries, the research examines how industry context, firm characteristics and firm‐level strategic resources, such as purchasing teams, influence the exploitation of e‐business technologies and the relationship between e‐business technology use and firm performance. Based on a synthesis of related literatures from transaction cost economics and the relational view of the supply chain, a two‐dimensional framework for e‐business technology is proposed with transactional and relational dimensions. However, empirical analysis indicated that transactional technologies can be further subdivided into two factors: dyadic cooperation and price determination. Significant differences were found between the two dimensions in terms of their overall levels of adoption, with dyadic coordination being the most widely adopted. In addition, the development of strategic resources expanded, in particular internal and customer teams, the use of e‐business technologies expanded. Purchasing organizational structure and firm size also were positively related to the adoption of transactional e‐business technologies. Finally, of particular importance to practitioners, e‐business technologies targeted at reducing dyadic coordination costs lead to improved financial performance.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".