Scrutinizing Competitiveness of Construction Companies Based on an Integrated Multi-Criteria Decision Making Model
Why this work is in the frame
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Bibliographic record
Abstract
The construction sector continues to experience significant challenges brought by new techniques and technologies. Hence, there is a dire need for construction companies to address critical issues concerning changing environmental conditions, construction innovations, market globalization and many other aspects, thereby enhancing their competitive edge. Thus, the primary goal for this research is to develop a multi-criteria decision making model that would consider and evaluate all essential factors in determining the competitiveness index of construction companies. In the developed model, three new pillars (3P) for competitiveness are introduced: (1) non-financial internal pillar; (2) non-financial external pillar; and (3) financial pillar. The 3P includes 6 categories and 26 factors that are defined and incorporated in the developed assessment model for the purpose of measuring the companies’ competitiveness. The weights for the identified factors are computed using fuzzy analytical network process (FANP) to diminish the uncertainty inherited within the judgment of the respondents. The weight of factors and their affiliated performance scores are used as an input for the preference ranking organization method for enrichment evaluation (PROMETHEE II) technique. In this regard, PROMETHEE II is undertaken as a ranking technique to prioritize any given construction company by determining its respective competitiveness index. The developed model is validated through five cases studies that reveal its potential of illustrating detailed analysis with respect to the competitive ability of construction companies. A sensitivity analysis is carried out to determine the most influential factors that affect the competitiveness of construction companies. It is anticipated that the developed evaluation model can be used in the decision-making process by all parties involved in construction projects. For instance, contractors can leverage the evaluation model in taking better decisions pertinent to the markup values. In addition, it can benefit employers in the evaluation process of contractors.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it