Exploring the challenges of implementing risk management maturity models for megaprojects: a study of the aerospace industry in Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".