An empirical study on non-physical waste factors in the construction industry
Bibliographic record
Abstract
Purpose This study highlights the findings of an empirical study to investigate waste factors (WFs) affecting the performance and delivery of construction projects in developing countries. The objectives of this study are to identify non-physical WFs in developing nations and rank the identified factors based on their degree of influence on the key performance indicators (KPIs) of cost, quality and time. Design/methodology/approach In total, 34 WFs were identified through a detailed literature review and consolidated using semi-structured interviews with construction practitioners. The statistical analysis involved a normality test using the Shapiro–Wilk test to determine if sample data have been drawn from a normally distributed population, ranking the WFs using the Frequency Index (FI), Severity Index (SI) and Importance Index (IMPI), ranking the WFs based on their effect on the project KPIs of cost, quality and time, and identify clustering structures for the identified WFs to using factor analysis (FA). Findings The results revealed ineffective planning and scheduling, rework/repair of defective work and resource quality problems (human, material and equipment) as the three most important WFs affecting construction projects. The factor analyses showed that WFs can be grouped into five interrelated components, suggesting the need for integrated and holistic strategies to overcome the identified WF. Practical implications Understanding the effects of WFs on construction projects is a first step towards designing holistic solutions to ensuring projects deliver value to the clients and other stakeholders. The findings of this study provide direction to construction practitioners on where to focus appropriate strategies to manage the identified WFs effectively and, therefore, improve the productivity of construction projects. Originality/value This study provides the first holistic analysis of WFs affecting the productivity of construction projects in developing countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".