Benchmarking firms' operational performance according to their use of internet‐based interorganizational systems
Bibliographic record
Abstract
Purpose Drawing on the concepts of benchmarking and of fit as profile deviation, the purpose of this paper is to identify the critical dimensions of usage of internet based interorganizational systems (IOISs) of the best performing firms. Design/methodology/approach Empirical evidence is gathered through an electronic survey conducted with 228 manufacturers in the computer and electronic product manufacturing sector. Findings Data collected demonstrates that: volume of use and depth of use are the two critical dimensions of internet based IOISs usage on the supplier side; volume of use, level of integration, diversity of types and depth of use are the four critical dimensions of internet based IOISs usage on the customer side; and a deviation from these patterns of internet based IOISs usage should result in poorer operational performance. Statistical analyses also show the relative importance of each of the critical dimensions of internet based IOISs usage on both the supplier and customer sides of the supply chain. Practical implications The paper findings indicate that manufacturers in the computer and electronic product manufacturing sector should not approach their supply chain management and eBusiness strategies from a single business network perspective; rather, they must take into account the specificities of their downstream supply chain to implement an internet based IOIS strategy that will satisfy the diversified needs of their customer base. Originality/value This paper is the only one to date that draws on both a benchmarking approach and contingency theory to assess the impact of internet based IOISs usage on a firm's operational 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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".