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Record W3086177894 · doi:10.12821/ijispm070104

Project resilience: a conceptual framework

2022· article· en· W3086177894 on OpenAlexaff
Khalil Rahi

Bibliographic record

VenueInternational journal of information systems and project management · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsResilience (materials science)Conceptual frameworkProcess managementComputer scienceKnowledge managementBusinessSystems engineeringSociologyEngineeringSocial scienceMaterials science

Abstract

fetched live from OpenAlex

Resilience is a novel but promising concept in project management studies. Resilience thinking can help projects maintain their performance through flexible, systemic and context-specific approaches once faced with disruptive events. That said, the main goal of this paper is to advance an interdisciplinary understanding of project resilience by proposing a definition and a conceptual framework of this concept. To achieve this article's objectives, the literature on project risk management is first reviewed to identify current research effort and limitations of dealing with disruptions. Consecutively, the concept of resilience in its broader applicability is explored, where two dimensions are sieved; awareness and adaptive capacity. The literature on the new concept of project resilience is also scrutinized, where its novel nature, the lack of scientific studies to conceptualize it, and its significance to project management are demonstrated. These facts helped propose a definition and a conceptual framework of project resilience, where a set of relationships are instigated, which constitute a base line to perform further disquisitions to assess their validity. Implications for future contributions advocate conceptual exchanges with more advanced research fields (e.g. organizational resilience). These exchanges can assist in the development of indicators to evaluate the ability of projects to deal with disruptive events and enhance their resilience.

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)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.051
GPT teacher head0.365
Teacher spread0.314 · 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
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

Citations1
Published2022
Admission routes1
Has abstractyes

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