A fuzzy-based competitiveness assessment tool for construction SMEs
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,009 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».