Understanding Organization—Enterprise System Fit: A Path to Theorizing the Information Technology Artifact1
Bibliographic record
Abstract
Packaged software applications such as enterprise systems are designed to support generic rather than specific requirements, and hence are likely to be an imperfect fit in any particular instance. Using critical realism as our philosophical perspective, we conducted a three-year qualitative study of misfits that arose from an enterprise system (ES) implementation. A detailed analysis of the observed misfits resulted in a richer understanding of the concept of fit and of the ES artifact itself. Specifically, we found six misfit domains (functionality, data, usability, role, control and organizational culture) and within each, two types of misfit (deficiencies and impositions). These misfit types correspond to two newly defined types of fit: fit as coverage and fit as enablement. Our analysis of fit also revealed a new conceptualization of the ES artifact, with implications for IT artifacts in general.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".