Defining KPIs to assess the impact of innovation in construction: Insights from Public Project Owners
Notice bibliographique
Résumé
This thesis aims to define key performance indicators (KPIs) for evaluating the impact of innovations in the AECO industry. The work began by clarifying core concepts - innovation, performance measurement, and impact assessment - to build a shared vocabulary and an analytical baseline. With these foundations, an investigation of current practice was carried out with public project owners (PPOs) in Québec to understand what is measured, how it is measured, and why. A Design Science Research (DSR) approach was used. First, a literature review assembled 42 KPIs grouped into nine categories: Cost, Time, Productivity, Quality, Safety, Scope (changes/claims/RFIs), Risk, Sustainability, and Innovation. Second, semi-structured interviews with five Québec PPOs documented current KPI practice, data workflows, and difficulties. Third, prioritization workshops with the same four PPOs plus one additional owner positioned the 42 KPIs on a usefulness–measurability matrix. Finally, a post-workshop validation questionnaire (seven responses from six PPOs) confirmed priorities for implementation. Findings show a narrow core in current dashboards - cost and time - together with implementation barriers: data-quality issues, fragmented systems, and cultural factors affecting adoption. At the same time, owners expressed a clear interest in moving beyond the “iron triangle” toward scope discipline, risk management, productivity, quality and stakeholder outcomes, safety, sustainability, and innovation. Workshop inputs were ordered by combining the average scores for usefulness and ease of measurement with agreement weights based on the interquartile range (IQR). Usefulness was emphasized (80%), while ease of measurement contributed (20%) to reflect feasibility without letting it dominate. This procedure produced an owner-endorsed shortlist of 17 KPIs that balances decision value, consensus, and practicality. The prioritized set comprises the following metrics: cost predictability (design/construction), cost per unit, time predictability (design/construction), time per unit, labor productivity expressed as dollars per unit, stakeholder satisfaction, lost-time incidents, change order (cost) – client-initiated, change order (cost) – contractor-initiated, number of change orders – clientinitiated, number of change orders – contractor-initiated, requests for information (RFI), waste generation, carbon footprint, energy consumption, scope of risks (risk-exposure score), and cost of risk mitigation. Together, these cover the main control needs of public owners while opening space for sustainability and risk-informed decisions as data pipelines mature. The main contribution is an owner-validated KPI set spanning nine categories and a replicable prioritization procedure that integrates perceived value, stakeholder agreement, and measurability. Practically, owners can begin routine reporting with the validated shortlist, while planning staged adoption of sustainability and future innovation-specific indicators as data governance improves. Limitations include a small, Québec-based sample and a crosssectional snapshot of the current practices of public project owners. Future work will pilot the prioritized KPIs on live projects, strengthen data governance and integration, and run longitudinal studies linking project-phase indicators to post-handover outcomes, enabling a more complete evaluation of innovation impacts across the asset life cycle. With these results, the impact of innovation can be identified across a wider set of dimensions than time and cost, and assessed beyond immediate outputs to include longer-term outcomes.
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 | 0,027 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,010 | 0,006 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».