MétaCan
Menu
Back to cohort
Record W3210431679 · doi:10.3390/buildings11110502

Collaborative Interorganizational Relationships in a Project-Based Industry

2021· article· en· W3210431679 on OpenAlexafffund
Ahmed Khouja, Nadia Lehoux, Yan Cimon, Caroline Cloutier

Bibliographic record

VenueBuildings · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConceptualizationKnowledge managementBusinessIntegrated project deliveryIdentification (biology)MultitudeCategorizationResource (disambiguation)Process managementEnterprise resource planningComputer scienceProject managementEngineeringSystems engineeringPolitical science

Abstract

fetched live from OpenAlex

The project-based construction industry finds itself in a paradoxical situation: while it weighs heavily in the world economy, it does have a history of low productivity. One important issue that plagues the industry is related to the challenges that stem from collaborative efforts (or lack thereof) between actors. The objective of this paper is to explore how actors of the construction industry organize their inter-firm relationships while examining the characteristics of such interactions and the elements affecting them (drivers, barriers, facilitators, outcomes). These interactions and elements were uncovered using a systematic literature review. A qualitative content analysis was carried out to categorize these elements and to generate dimensions describing the forms. The 139 articles retrieved depicted 12 relational forms established between construction companies (in descending order of citation): partnering, alliancing, project delivery methods, supply chain integration, joint ventures, integrated project delivery, joint risk management, collaborative design, contingent collaboration, quasi-fixed network, resource sharing, and collaborative planning. A multitude of drivers, barriers, facilitators, and outcomes were found. An analysis of the results led to the conceptualization of a multidimensional profile, which allows for a practical and flexible identification of the relationship form potential partners in the construction sector intend to establish. To provide guidelines for the implementation of this profile, a three-step framework was developed.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.370
Teacher spread0.280 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueBuildingsSame topicConstruction Project Management and PerformanceFrench-language works237,207