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Record W2770526023 · doi:10.1177/875697281604700405

The Impact of Residual Risk and Resultant Problems on Information Systems Development Project Performance

2016· article· en· W2770526023 on OpenAlex
Russell Purvis, Raymond M. Henry, Stefan Tams, Varun Grover, John D. McGregor, Steve Davis

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 · 2016
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsResidualMediationResidual riskTask (project management)Project managementProject risk managementProcess managementCategorical variableComputer scienceRisk analysis (engineering)Risk managementRisk assessmentProject management triangleEngineeringBusinessSystems engineeringReliability engineeringPolitical scienceComputer security

Abstract

fetched live from OpenAlex

The research presented in this article considers how residual risk impacts project performance and: (1) evaluates the impact of specific categories of residual risks (actor, technology, task, and structure) on project performance; and (2) demonstrates the mediation role of categorical problems caused by residual risk on project performance. Data from 92 projects analyzed using partial least squares found support for mediation, and not direct effects between: (1) actor project problems and the effects of actor residual risk; (2) task project problems and the effects of task residual risk; and (3) technology project problems and the effects of technology residual risk on information systems development (ISD) project 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.

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)0.000
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: none
Teacher disagreement score0.974
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.268
Teacher spread0.249 · 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