Understanding enterprise systems-enabled integration
Bibliographic record
Abstract
A key touted benefit of enterprise systems (ES) is organizational integration of both business processes and data, which is expected to reduce processing time and increase control over operations. In our 3-year longitudinal case study of a phased ES implementation, we employed a grounded theory methodology to discover organizational effects of ES. As we coded and analyzed our field data, we observed many integration effects. Further analysis revealed underlying dimensions of ES-enabled integration. ES-enabled integration varied depending on the relationship between the integrated business units (similar plants, stages in a business process, or dissimilar functional areas) and on whether processes or data were integrated. Turning to the literature, we realized that Thompson's three types of interdependence, pooled, sequential, and reciprocal, captured the business relationships revealed in our data. Thus, we describe the salient characteristics of ES-enabled integration using Thompson's interdependence types applied to process and data integration. We also identify dimensions of differentiation between business units that contribute to integration problems. Viewing our field data through the lens of these salient characteristics and dimensions of differentiation provided theoretical explanations for observed integration problems. These findings also help managers understand and anticipate ES-enabled integration opportunities and problems.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".