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How to Improve Interface Management Behaviors in EPC Projects: Roles of Formal Practices and Social Norms

2018· article· en· W2883714063 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management in Engineering · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsProcurementInterface (matter)Knowledge managementAffect (linguistics)Project managementPsychologyBusinessComputer scienceEngineeringMarketing

Abstract

fetched live from OpenAlex

Interface management (IM) has emerged as an effective strategy to reduce interface-related issues and risks by facilitating communication and coordination among diverse parties, particularly in engineering, procurement, and construction (EPC) projects. This study developed and tested a theoretical model to investigate how formal IM practices, social norms (i.e., management norms and project norms regarding IM), and personal attitudes interactively affect individuals’ IM behaviors. The results show that an individual’s IM behaviors are directly driven not only by formal IM practices but also by management and project norms regarding IM. Additionally, formal IM practices have significantly positive effects on management norms, project norms, and personal attitudes toward IM. The findings of this research contribute to the IM body of knowledge by offering insights into the relationships among interface participants’ IM behaviors, formal IM practices, social norms, and personal attitudes in EPC projects. Understanding these in-depth underlying relationships can help to develop effective strategies (e.g., developing and maintaining favorable management and project norms) for motivating and supporting IM behaviors.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
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 head0.039
GPT teacher head0.340
Teacher spread0.301 · 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