Exploring the challenges of implementing risk management maturity models for megaprojects: a study of the aerospace industry in Canada
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,025 | 0,005 |
| Communication savante | 0,010 | 0,004 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».