ERP implementation through critical success factors' management
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify practical activities that are essential for managing enterprise resource planning (ERP) implementation projects and that answer to the expectations of the widely recognized critical success factors (CSFs). Design/methodology/approach This work is based on an extensive literature review on CSF, which has been followed by a Delphi survey with a panel of ERP experts. For each CSF, it obtained a range, validated by experts, of practical actions to perform, supported by the resolution of the problems usually encountered in these areas. Findings The work carried out has a practical scope: the principles of the proposed method directly affect all actors in ERP projects and gives them practical results that they can apply immediately. When applied in the framework of the methodology the paper suggests, these actions will result in better oversight over the requirements of each area of expertise. In this way, overall grasp of the project is facilitated, reducing the inherent uncertainties. Research limitations/implications Findings may be limited by the small number of respondents, but each one had participated in several implementations. Moreover, no industry sector was specifically targeted; thus, the results apply a priori to most implementations. Originality/value This research helps to draw the academic and professional domains together by proposing, for the first time, a way for theoretical findings to be translated into practical actions. These results will allow all actors in an ERP implementation to understand the project imperatives faster and more accurately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".