A Compilation and Analysis of Critical Success Factors for the ERP Implementation
Bibliographic record
Abstract
Nowadays, the adoption of a new enterprise resource planning system is a highly complex process, and it is not as easy as people imagine. It is a challenging task that requires rigorous efforts, careful thinking, and proper planning. Likewise, it demands a detailed analysis of such factors that are critical to the implementation. The field has sparked an immense interest in the research community, and hence several previous studies have tried to assess the current status of these systems and address some issues in the literature reviews. First, the research aims to conduct a comprehensive literature survey, in order to address some issues related to the implementation and management of ERP, and point out overall trends. Afterwards, we tried to provide a contribution to the research field of the critical success factors (CSFs) of ERP projects based on a systematic approach to review a large number of refereed papers published between 2006 and 2018 on ERP from which a large number of documents relating to CSFs on ERP were extracted, and selected for analysis. From that review, we led a survey through which we tried to investigate and examine the different critical success factors that need to be considered to ensure the success of ERP systems.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.045 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".