Exploring managerial factors affecting ERP implementation: an investigation of the Klein‐Sorra model using regression splines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Predicting successful implementation of enterprise resource planning (ERP) systems is still an elusive problem. The cost of ERP implementation failures is exceedingly high in terms of quantifiable financial resources and organizational disruption. The lack of good explanatory and predictive models makes it difficult for managers to develop and plan ERP implementation projects with any assurance of success. In this paper we investigate the Klein & Sorra theoretical model of implementation effectiveness. To test this model we develop and validate a data collection instrument to capture the appropriate data, and then use multivariate adaptive regression splines to examine the assertions of the model and suggest additional significant relationships among the factors of their model. Our research offers new dimensions for studying managerial interventions in IT implementation and insights into factors that can be managed to improve the effectiveness of ERP implementation projects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.018 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it