Factors influencing multifactor productivity of equipment-intensive activities
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Bibliographic record
Abstract
Purpose Due to its key role in the successful delivery of construction projects, construction productivity is one of the most researched topics in construction domain. While the majority of previous research is focused on the productivity of labor-intensive activities, there is a lack of research on the productivity of equipment-intensive activities. The purpose of this paper is to address this research gap by developing a comprehensive list of factors influencing the productivity of equipment-intensive activities and determining the most influential factors through interview surveys. Design/methodology/approach A list of 201 factors influencing the productivity of equipment-intensive activities was developed through the review of 287 articles, selected from the ten top-ranked construction journals, by searching for construction productivity in the articles’ titles, abstracts or keywords. Next, the most influential factors were determined by conducting interview surveys with 35 construction experts. To ensure that the interviewees were aware of the research objectives and the distinction between labor- and equipment-intensive activities, an information session was held prior to conducting the surveys, and the surveys were conducted in interview format to allow for clarification and discussion throughout the process. Findings Project management respondents identified foreman-, safety- and crew-related factors as the categories with the most influence on productivity; tradespeople respondents identified foreman-, equipment- and crew-related factors as the most influential categories. In total, 14 factors were identified, for which there was a significant difference between the perspectives of project management and tradespeople regarding the factors’ influence on productivity. Originality/value This paper provides a comprehensive list of factors influencing the productivity of equipment-intensive activities. It identifies the most influential factors through an interview survey of 35 construction experts, who are familiar with the challenges of equipment-intensive activities based on their experience with such activities in the industrial construction sector of Alberta, Canada. Additionally, the differences between the factors that influence the productivity of labor- and equipment-intensive activities are discussed by comparing the findings of this paper with previous research focused on labor intensive activities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it