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Record W2080731462 · doi:10.4236/ti.2014.51006

Collaborative Meeting as an Integrative Mechanism in a Multinational Investment Project

2014· article· en· W2080731462 on OpenAlexvenueno aff
Leena Pekkinen, Jaakko Kujala

Bibliographic record

VenueTechnology and Investment · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationContext (archaeology)Mechanism (biology)Knowledge managementProject managementInvestment (military)Process managementWork (physics)BusinessComputer sciencePolitical scienceEngineeringSystems engineering

Abstract

fetched live from OpenAlex

In multinational and complex projects that are often implemented by multiple organizations, the entire projects need to be divided into manageable subprojects. At the same time, all subprojects are needed to be kept aligned with the project goals and targets by integration and coordination. The purpose of this article is to study the role of a particular, work-shop type, collaborative meeting by utilizing the characteristics of an integrative information processing framework. A single case study method was used to observe the practices of collaborative meetings. This study contributes to the project management research by analysing how collaborative meeting practice can be used as a mechanism to reduce uncertainty and equivocality in a large investment project. The results of this study are two folds: Firstly, the case project’s collaborative meetings are described in detail; secondly, the perceived features and procedures of the collaborative meetings in the case project are illustrated showing the role of the collaborative meetings as an integrative tool. Moreover, the perceived integrative characteristics of the collaborative meetings reducing uncertainty and equivocality are presented. This study indicates that collaborative meeting is an integrative mechanism reducing uncertainty and equivocality in a large investment project context.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.345
Teacher spread0.317 · 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 designTheoretical or conceptual
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

Citations7
Published2014
Admission routes1
Has abstractyes

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