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Record W2109259380 · doi:10.1002/pmj.20213

A Fresh Look at the Contribution of Project Management to Organizational Performance

2010· article· en· W2109259380 on OpenAlexaff
Monique Aubry, Brian Hobbs

Bibliographic record

VenueProject Management Journal · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsOPM3Project managementOrganizational performanceKnowledge managementProcess managementProject management triangleOrganizational behavior managementPerformance managementEmpirical researchSet (abstract data type)BusinessManagement scienceComputer scienceOrganizational behavior and human resourcesEngineeringSystems engineeringMarketing

Abstract

fetched live from OpenAlex

A better understanding of organizational performance and the contribution that project management can make is the aim. The article adopts the “Competing Values Framework,” a rich framework that is well established both theoretically and empirically but is not well known in the field of project management. The framework is summarized and applied in an empirical investigation of the contribution of project management in general and project management offices (PMOs) in particular to organizational performance. The examination of 11 case studies revealed multiple concurrent and sometimes paradoxical perspectives. The criteria proposed by the framework have been further developed through the identification of a preliminary set of empirically grounded performance indicators. The empirical results contribute to a better understanding of the role of project management generally and PMOs specifically. They also demonstrate the usefulness of this framework for the study of project management's contribution to organizational performance.

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)0.007
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.007
Scholarly communication0.0130.013
Open science0.0010.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.033
GPT teacher head0.330
Teacher spread0.298 · 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 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

Citations123
Published2010
Admission routes1
Has abstractyes

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