Developing KPIs and Measurement Processes to Assess the Impact of Innovation in the Construction Industry: Current Practices and Challenges
Bibliographic record
Abstract
The construction industry's role as a key economic driver underscores the need for robust mechanisms to measure and manage innovation.While conventional metrics often emphasize cost, schedule, and immediate outputs, emerging trends highlight broader, long-term impacts-particularly those related to sustainability, social well-being, and organizational growth.This study examines current practices and challenges in performance measurement among five major public project owners in Quebec, selected due to their involvement in a digital transformation roadmap, drawing on semi-structured interviews with senior managers and technical teams.Our findings reveal a strong reliance on traditional Key Performance Indicators (KPIs), such as cost and schedule, complemented in some cases by environmental, safety, or innovation-oriented indicators.However, limited standardization, disparate data-collection methods, and fragmented systems impede consistent, real-time reporting.Moreover, long-term outcomes and intangible benefits frequently remain under-assessed, reflecting a gap between immediate project control and strategic, impact-focused goals.Despite these challenges, a gradual shift is evident: several public owners express intentions to adopt more holistic KPI sets that address sustainability, resource productivity, and innovation.Efforts to streamline data gathering-through integrated software platforms and stakeholder collaboration-indicate a growing recognition of the need for accurate, forward-looking measurements.By pinpointing the limitations and emerging directions in current practices, this research contributes new insights into how performance management can evolve to capture both immediate project results and broader societal impacts in the construction industry.
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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 | 0.144 | 0.216 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".