Exploring An Alternative Method of Evaluating the Effects of Erp: A Multiple Case Study
Bibliographic record
Abstract
Previous research has already established that compared to other types of investments, information technology (IT) investments are insufficiently or not at all evaluated. This can be partly explained by the lack of adequate IT evaluation methods and tools. In the case of enterprise resource planning (ERP) systems whose effects on organizational processes and performance are intrinsically profound and wide-ranging compared to those of traditional IT limited to some spheres of organization, evaluation activities may be an issue of great concern. This study thus aims to propose and test an alternative evaluation method adaptable to the organizational context, making it possible to measure the contribution of an ERP system to organizational performance in all its aspects. Combining a process-based model and a scorecard model, the proposed method was first designed from a review of information systems evaluation literature. It has then been validated and refined through a multi-case study of manufacturing firms: an in-depth pilot case study was conducted, and thereafter the study was replicated on two other cases. Results show that the method proposed here enables organizations to determine the extent to which the firm's operational and overall performance has been impacted by the adoption and use of ERP systems, through the automational, informational, and transformational effects of ERP on their business processes. From a practical point of view, three contributions must be mentioned: the proposed method allows for a strong contextualization of its application, it is action-oriented, and it allows comparison across organizations even though organizational contexts may totally differ.
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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 | 0.035 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".