Enterprise Resource Planning and the Price of Efficiency: The Trade Off Between Business Efficiency and the Innovative Capability of Firms
Bibliographic record
Abstract
Enterprise Resource Planning (ERP) business software offers the integration of business functions and can reduce data collection and processing duplication efforts. It has become one of the most successful products in the world. For many firms such as Microsoft, Owens-Corning, ICI, UBS and Procter & Gamble, it has changed the way they work (see Gartner, How Procter & Gamble runs its global business on SAP, CS-15-3473, Research Note, 25 February 2002). The market leaders in this highly lucrative business-to-business market are SAP, Oracle, Baan and PeopleSoft. This paper reviews the ERP and innovation management literature in order to shed light on the potential problems that may exist in rigid ERP systems. It seems there is increasing evidence that firms fail to obtain the benefits of these investments within the anticipated timeframes (B. dos Santos and L. Sussman, Improving the return on IT investment: the productivity paradox, International Journal of Information Management, vol. 20, No. 6, 2000, pp. 429-440). Moreover, and possibly of greater concern is the affect on the firm's innovative ability. Especially in some creative working environments where previously autonomous and creative individuals are now being restricted to what's on offer via 'pull-down' menus.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".