Integrating the end-user’s voice in the target value setting process during the project definition phase: challenges and potential solutions
Bibliographic record
Abstract
The construction industry, one of the largest sectors in the global economy, continues to face challenges such as inefficiency, fragmentation, and lack of transparency, leading to cost overruns, delays, and low productivity. To address these issues, methodologies from manufacturing, particularly Target Value Delivery (TVD), have been adapted to promote collaboration and value creation for stakeholders. However, despite its potential, TVD adoption remains limited due to challenges such as the clear definition of value in complex projects. This thesis explores the application of TVD in the construction sector, focusing on value definition through user participation during the initial project phases. The chosen constructive research methodology is based on two case studies in Quebec: a campus expansion project and a high school modernization project, both incorporating Lean approaches and tools to foster collaboration and value creation. The researcher collected data through document analysis, workshop observations, and semistructured interviews with project stakeholders, including architects, engineers, users, and government representatives. Qualitative techniques helped identify how participatory design and Lean methodologies supported decision-making and value definition. The case studies were supplemented by expert interviews to enrich the analysis of the practical challenges and benefits of TVD implementation. The findings highlight the importance of early stakeholder engagement, particularly end-user participation, to align project outcomes with functional requirements and stakeholder values. However, challenges persist, such as difficulty in defining value, managing expectations, and coordinating multidisciplinary teams. This thesis proposes recommendations to strengthen the integration of participatory workshops and user inclusion tools, enabling more effective and value-aligned project delivery. This thesis contributes to value-focused construction practices by proposing strategies for enhancing the use of participatory design and Lean methodologies in construction projects. Recommendations include the structured use of participatory design workshops and tools that support improved user inclusion in decision-making processes, leading to more effective and value-aligned project delivery.
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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 | 0.165 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.032 | 0.023 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".