A fuzzy-based competitiveness assessment tool for construction SMEs
Bibliographic record
Abstract
Purpose In highly competitive industries such as the construction sector, companies with limited capabilities struggle to maintain their current standing, let alone acquire more market share. Before they are able to address their shortcomings, these companies need to pinpoint where their performance stands when it comes to market demand. Furthermore, competitiveness is strongly linked with companies' ability to win tenders and deliver the associated construction projects. Tenders are, therefore, a mechanism that reflects the strengths and weaknesses of construction firms and can be deemed an indicator of competitiveness. This paper aims to help small and medium-sized enterprises (SMEs) increase their presence in the construction sector by suggesting a systematic approach to evaluate their competitiveness. Design/methodology/approach Participation requirements were extracted from 11 calls for tenders and organized into categories using a qualitative content analysis. These requirements along with winning assets deduced from the literature constitute the basis of the tool. The qualitative evaluation of the difficulty in satisfying requirements or acquiring assets was transformed into unified, quantifiable scores by means of fuzzy numbers. Findings A total of 233 requirements were found and classified in 3 main categories. In addition, a list of 54 assets organized into five categories was compiled. The entire methodology led to a five-step assessment tool whose output can be depicted on the proposed competitiveness readiness matrix (CRM). Research limitations/implications This study contributes to the limited number of articles discussing the contractor's side in the tendering process. Furthermore, it combines three theoretical perspectives (i.e. resource-based view, relational view, and industry structure perspective), which are scarcely applied in the construction management field. Consideration of the calls for tenders when developing solutions is also a unique aspect of this research when compared to previous studies. Practical implications This tool may help practitioners navigate the rather elusive tendering process by outlining the necessary elements to participate in and win tenders. It may also allow construction firms to better position themselves in the market with respect to customers' requirements and competitors' performances. Originality/value This study provides an approach of both self-assessment and market benchmarking. It assists companies in formulating strategies to become more competitive in general and make better bidding decisions. This is especially interesting because of three aspects: the study is based on a fundamental element of the construction competitiveness concept, i.e. calls for tenders; it offers a mechanism to transform systematically qualitative attributes into quantifiable scores; and it provides a practical and reliable display of the assessment results.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".