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Dimensions of uncertainty and their moderating effect on new product development project performance

2008· article· en· W1831369538 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

VenueR and D Management · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsPolytechnique MontréalUniversité du Québec à MontréalHEC Montréal
Fundersnot available
KeywordsModerationDimension (graph theory)Reliability (semiconductor)New product developmentProduct (mathematics)Differential (mechanical device)Measure (data warehouse)Computer scienceEconometricsKnowledge managementMathematicsBusinessMarketingEngineeringData miningPower (physics)

Abstract

fetched live from OpenAlex

In this study, we measure the dimensions of uncertainty, starting from the definitions constructed for and generally used in innovation projects. We then evaluate their direct and indirect effects on the performance of product and service development projects. Four dimensions of uncertainty are delimited with satisfactory validity and reliability, suggesting a differential moderating effect of the four types of uncertainty (technical and project uncertainty, market uncertainty, fuzziness and complexity) depending on the performance dimension (effectiveness and efficiency) and co‐moderator (project methods and human resource adequacy). Of the four dimensions explored, technical and project, and market uncertainty are true moderators and have the largest interactive effect, fuzziness has a strong direct effect on both performance dimensions whereas complexity weakly directly influences effectiveness. The latter two also influence the relations between performance and the factors related to human resources and project management methods.

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)0.001
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 score0.970
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.077
GPT teacher head0.314
Teacher spread0.237 · 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