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Record W4399686298 · doi:10.5267/j.jpm.2024.4.004

Investigating the impact of lean construction principles on contractors’ project performance in Ethiopia using PLS-SEM

2024· article· en· W4399686298 on OpenAlexvenueno aff
Achamyelew Maru, Wubshet Jekale, Belachew Asteray

Bibliographic record

VenueJournal of Project Management · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEngineeringConstruction engineeringProcess managementOperations management

Abstract

fetched live from OpenAlex

The construction industry faces challenges, such as schedule overruns, cost overruns, poor quality, and safety issues. Lean construction is a valuable concept for waste reduction and improving project performance. This study explored the impact of lean construction principles on contractors' project performance in Ethiopia. Using a quantitative method and simple random sampling technique, 159 respondents from construction companies were selected. This study introduced partial least squares structural equation modeling (PLS-SEM) in the study area. The results showed that process/technology lean principles, people/culture lean principles, and integrated project delivery variables had a direct positive impact on contractor performance. There was also a significant indirect relationship between process/technology-lean construction principles and project performance with a complementary partial mediation effect. However, no significant indirect associations were found between people/culture-lean construction principles and project performance through mediation of onsite construction waste management. The study used FIMIX-PLS to test robustness and detect hidden heterogeneity at non-critical levels. The findings provide researchers and practitioners to identify the influences that are critical for contractors’ projects performance improvement, and that results in the best possible outcome.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.186
GPT teacher head0.426
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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