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Record W2159188695 · doi:10.1109/hicss.2011.381

Software Project Risk Drivers as Project Manager Stressors and Coping Resources

2011· article· en· W2159188695 on OpenAlexaff
Suzanne Rivard, Yannik St‐James, Andrea Cameron

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsSoftware project managementStressorProject managerProject risk managementCoping (psychology)Project managementSoftware developmentKnowledge managementComputer scienceRisk managementTeam software processProcess managementSoftwareProject management triangleRisk analysis (engineering)Software development processEngineeringPsychologyBusinessSoftware constructionSystems engineeringClinical psychology

Abstract

fetched live from OpenAlex

Although the stressful nature of high risk exposure software projects and the adverse repercussions of stress on project participants and performance have long been recognized, there is still little research on the subject. This paper builds upon two foundations - the cognitive-transactional theory of stress and the concept of software project risk exposure - to propose a model of software project risk drivers as software project manager stressors and coping resources. The model posits that some software project risk drivers - core project characteristics and project objectives - play the role of stressors and that other risk drivers - project environment characteristics - play the role of coping resources. The model further suggests that software project managers are faced with both chronic stress and acute stress, which have different antecedents. This paper broadens current understanding of the role of software project risk drivers; it also contributes to knowledge on software project management by focusing on the emotional components this activity.

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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.768
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.029
GPT teacher head0.259
Teacher spread0.230 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2011
Admission routes1
Has abstractyes

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