Diffusion of ERP in the Construction Industry: An ERP Modules Approach: Case Study of Developing Countries
Bibliographic record
Abstract
The risk–benefit analysis of ERP implementation is worth investigating to optimize the efficiency of ERP deployment in the construction sector. This study investigates the factors affecting the dissipation of ERP through diffusion models in developing countries. Moreover, it suggests a strategy to adopt ERP modules that optimize process integration and project efficiency through the priority factors method. According to the study, the internal model best describes the studied modules, and it suggests that imitative behavior and word of mouth significantly influence ERP adoption in the Africa and Middle East regions. This research concludes with an optimized order for deploying ERP modules based on the importance, urgency, and ease of implementation of each module. It is as follows: work progress (500), budgeting (405), procurement (343), site operations (280), planning and scheduling (270), accounting (252), inventory management (126), document control (90), and tendering (6). Therefore, it can be concluded that this study fills the research gap of ERP module adoption using diffusion models and priority factors within the construction industry, specifically in the specified regions. However, considering dynamic influence factors might provide more precise predictions, while involving a greater number of companies’ owners might highlight a greater importance of external factors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".