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Record W7028451805

Exploring the challenges of implementing risk management maturity models for megaprojects: a study of the aerospace industry in Canada

2022· other· en· W7028451805 on OpenAlexaboutno aff

Bibliographic record

VenueEspace École de technologie supérieure (École de technologie supérieure) · 2022
Typeother
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRisk managementMaturity (psychological)IT risk managementCapability Maturity ModelProject risk managementProject managementRisk management toolsBest practiceRisk assessment
DOInot available

Abstract

fetched live from OpenAlex

The inherent complexity of control and evaluation of the risk management processes in megaprojects across sectors can be a major challenge with the potential to result in project failure and impairment. Thus, there is a need for a measurable progressive and effective approach for risk management processes, which deal with megaprojects complexity and unique characteristics. In particular, risk management maturity models enable firms to understand and identify potential challenges and opportunities that arise in megaprojects concerning risk management processes. Doing so enables firms to properly manage risks and unforeseen issues in megaprojects. Risk management maturity models can also lead to success by managing the complexity and challenges of risks in product development and high-tech engineering projects such as aerospace. Although prior literature has identified and supported best practices for risk management maturity, their reliability is not always supported empirically. In fact, extant literature has not explored different challenges, opportunities, and potential solutions that can drive risk management maturity models, particularly megaprojects in the aerospace industry. Thus, several gaps remain in the literature. \n \nFirst, there remains difficulty assessing the risk management processes, interpreting the results, and identifying the right set of challenges that can affect the success of risk management in megaprojects. Second, managers who aim to implement the risk management processes, often fail to identify and benefit from risk management maturity models because they are unfamiliar with the benefits of such models. It is essential for project leaders to identify the challenges, and potential solutions, of risk management maturity besides the other factors such as schedules, costs, and deadlines by implementing comprehensive strategies, networking, and having a broad vision of risk management maturity models, particularly for those in megaprojects. \n \nTherefore, this study aims to investigate key barriers, challenges, and potential solutions that need to be considered to effectively implement the risk management maturity models for megaprojects in the aerospace industry. It answers the following research question: What are the challenges and solutions that impact the implementation of risk management maturity models for megaprojects in the aerospace industry? The research follows an inductive research approach and benefits from semi-structured interviews alongside secondary sources of data. It identifies seven key challenges, which, can be potential areas for improvement if managed properly. These include systematic assessment model for risk management processes, safety depth of project design and planning, level of communications, lessons learned, degree of knowledge and expertise, organizational capability, and supplier analysis. The current study also provides discussion by comparing its findings back to the relevant literature while discussing similarities and differences. It then discusses the implications and contributions of this research for its extant literature. It also discusses new theoretical insights that have been generated through this study. By the end, managerial implications for managers and policymakers are highlighted. Personal reflections on limitations of the scope and quality of the analysis undertaken are presented. Finally, recommendations and directions for future research are offered.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.977

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0250.005
Scholarly communication0.0100.004
Open science0.0040.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.298
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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