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Collaboration and opportunism in megaproject alliance contracts: The interplay between governance, trust and culture

2021· article· en· W3145228674 on OpenAlexaff
Peter Galvin, Stéphane Tywoniak, Janet Sutherland

Bibliographic record

VenueInternational Journal of Project Management · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversity of Ottawa
FundersCooperative Research Centres, Australian Government Department of IndustryAustralian Government
KeywordsOpportunismMegaprojectAllianceCorporate governanceBusinessOrder (exchange)Industrial organizationPublic relationsKnowledge managementManagementEconomicsPolitical scienceMarket economyFinanceComputer science

Abstract

fetched live from OpenAlex

Alliance contracts have been introduced in megaprojects to improve the alignment of objectives, risk and reward between client and contractor. However, the relational norms of alliances are not sufficient on their own to eliminate opportunistic behaviors. This study shows that, investing in mechanisms supportive of governance, culture, and trust provides a platform upon which firms may foster collaboration and limit self-interest oriented behavior amongst alliance partners. Our qualitative case study of a major project-based organization reveals the impact of these mechanisms, and more pointedly, how they interact and often reinforce each other. Governance, culture and trust are interlinked and complementary, and managers need to reflect holistically on their interactions in order to establish collaborative, rather than opportunistic 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0070.005
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.400
Teacher spread0.351 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations126
Published2021
Admission routes1
Has abstractyes

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