The impact of enterprise systems on corporate performance: A study of ERP, SCM, and CRM system implementations
Bibliographic record
Abstract
Abstract This paper documents the effect of investments in Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Customer Relationship Management (CRM) systems on a firm's long‐term stock price performance and profitability measures such as return on assets and return on sales. The results are based on a sample of 186 announcements of ERP implementations, 140 SCM implementations, and 80 CRM implementations. Our analysis of the financial benefits of these implementations yields mixed results. In the case of ERP systems, we observe some evidence of improvements in profitability but not in stock returns. The results for improvements in profitability are stronger in the case of early adopters of ERP systems. On average, adopters of SCM system experience positive stock returns as well as improvements in profitability. There is no evidence of improvements in stock returns or profitability for firms that have invested in CRM. Although our results are not uniformly positive across the different enterprise systems (ES), they are encouraging in the sense that despite the high implementation costs, we do not find persistent evidence of negative performance associated with ES investments. This should help alleviate the concerns that some have expressed about the viability of ES given the highly publicized implementation problems at some firms.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".