Bibliographic record
Abstract
Innovation has become not only the domain of a few progressive enterprises but the key to survival and success of the many. Innovative changes in management practices can assist in ensuring survival in an increasingly competitive world. The systems in place to manage and administer organizations are critical to exploiting technological, process, and product innovations. This paper examines the adoption and non-adoption of a particular systems innovation, Electronic Data Interchange (EDI). This empirical study of 379 companies compares adopters, adopters-in-process, and non-adopters of EDI. The focus is on the internal characteristics of firms. It is found that larger firms have a knowledge advantage which is a key factor in the adoption process. This knowledge advantage overcomes some of the misconceptions regarding EDI perceived by non-adopters. Further, critical barriers to adoption such as management support, systems cost, and implementation are important, but overcome by adopters, whereas non-adopters have difficulties in these areas. Adopters and non-adopters are compared according to their satisfaction and experience with internal systems. The exception is when implementation issues are involved. Non-adopters' perceptions are found to differ from adopters on this critical dimension. The results suggest that systems innovations may not be consistent with traditional taxonomies of innovations.
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".