Competitiveness assessment model for construction companies
Bibliographic record
Abstract
The Construction industry is facing an enormous number of challenges due to continuous advancements in construction technologies and techniques. Consequently construction management theories have to confront critical newly issues concerning market globalization and construction innovations. The key factor to address these challenges is to improve the competitive abilities of the competing construction firms. Literature reports that existing competitive practices for construction companies do not fully address the recent industry problems or consider the available technologies and challenges. Therefore, this research proposes a competitive assessment model with a wide range of essential factors and attributes that covers broad aspects of the present competitive market. Four new pillars (4P) for competitiveness assessment are proposed for construction firms: (1) Organization Performance, (2) Project Performance, (3) Environment and Client, and (4) Innovation and Development. These pillars have the potential to assist construction firms manage not only short term strategic plans but also long term ones. Based on the 4P concept, 21 factors and 80 criteria are defined and incorporated in the proposed company competitiveness assessment model. A questionnaire survey is conducted in the Canadian and Vietnamese market in order to collect 2 main sets of information. The first set includes pair wise comparison feedback regarding the relative importance of each factor, while the other set collects utility scores concerning their impacts. Based on the collected data, the AHP technique is used to calculate the factors' relative weight. In addition, the utility functions are developed in order to build the competitiveness assessment model using multi-attribute utility theory (MAUT). The developed MAUT model is tested via three case studies in which the results show that the model has a potential to demonstrate a detailed performance analysis pertaining to the competitive ability of any given construction firm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".