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Record W3112329866 · doi:10.1177/8756972820973082

Agile, Traditional, and Hybrid Approaches to Project Success: Is Hybrid a Poor Second Choice?

2020· article· en· W3112329866 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

VenueProject Management Journal · 2020
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsAgile software developmentScope (computer science)StakeholderProcess managementQuality (philosophy)Management scienceProject managementQuality managementExplanatory powerEngineeringComputer scienceOperations managementSystems engineeringManagementManagement systemEconomicsSoftware engineering

Abstract

fetched live from OpenAlex

Three project management approaches—traditional, agile, and hybrid—were considered in this study. Results from an international study, including 477 cross-industry projects, indicated that 52% of projects could be categorized as hybrid approaches. A regression analysis using multiple outcome measures indicated substantial explanatory power (0.21 < R 2 <0.41). Analysis suggested that hybrid and agile approaches significantly increase stakeholder success over traditional approaches while achieving the same budget, time, scope, and quality outcomes. Hybrid approaches were found to be similar in effectiveness to fully agile approaches. Results validate decisions by practitioners to combine agile and traditional practices and suggest that hybrid is a leading project management approach.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.477
Threshold uncertainty score0.987

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 communication0.0010.001
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 head0.145
GPT teacher head0.277
Teacher spread0.133 · 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