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Record W2403321770 · doi:10.1002/cjas.1384

Environment, competitive strategy, and organizational characteristics: A path analytic model of construction organizations’ performance in South Africa

2016· article· en· W2403321770 on OpenAlexvenueno aff
Luqman Oyekunle Oyewobi, Abimbola Windapo, James Olabode Bamidele Rotimi

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamBusinessOrganizational performanceCompetitive advantageOrder (exchange)Sample (material)Construction industryStructural equation modelingEmpirical researchPartial least squares regressionKnowledge managementIndustrial organizationMarketingProcess managementComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

Abstract While mainstream strategic management researchers have paid attention to the causes of performance differential among organizations, there is a dearth of empirical research within the construction industry on the subject. We examine the relationship between environment, organizational characteristics, competitive strategies, and performance of construction organizations in the South African construction industry. In order to develop a model for improving organizations’ performance, partial least squares was employed using quantitative data collected from a sample of 72 large construction firms listed on the Construction Industry Development Board contractors’ register in South Africa. The results reveal that organizational characteristics have a direct influence on organizational performance, while the relationship between the business environment and organizational performance is mediated by competitive strategies. Copyright © 2016 ASAC. Published by John Wiley & Sons, Ltd.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.006
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.107
GPT teacher head0.302
Teacher spread0.195 · 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.

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

Citations18
Published2016
Admission routes1
Has abstractyes

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