Hybrid Feature-Based Critical Success Factors in Cloud Enterprise Resource Planning Through Artificial Neural Networks and Random Forest
Bibliographic record
Abstract
Cloud enterprise resource planning (CERP) systems are widely adopted to enhance operational efficiency.However, in a global context, ERP implementation failure rates remain notably high, ranging from 67% to 90%.If left unaddressed, they will hinder sustainable economic growth and industrial transformation.To address this issue, this study adopts a structured approach by identifying critical success factors (CSFs) based on key performance indicators (KPIs) using the Delphi method and Dempster-Shafer combination method.The resulting dataset integrates multi-stage CSFs and their associated KPI performance and weight values, forming a hybrid feature set that captures interrelated implementation factors.The effectiveness of each implementation stage is assessed through user feedback scores categorized as satisfactory (>4), below satisfactory (3.0-3.9), and failure (<3.0).To evaluate the predictive capability of this hybrid dataset, both artificial neural network (ANN) and random forest (RF) models were applied separately.Each model was trained and tested independently to identify which algorithm achieves higher prediction accuracy for implementation outcomes.The findings indicate that RF significantly outperforms ANN, with an accuracy of 0.849 compared to 0.765.Additionally, confusion matrix, ROC, and AUC analyses further confirm RF's superior predictive capability.Through this research, the identification of CSFs through qualitative analysis or statistical modelling, combined with the integration of machine learning techniques, ultimately improving assessment classification for CERP implementation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".