Impact of Project Selection Criteria on Organizational Performance: A Machine Learning Approach
Publication: ASCE Inspire 2023
ABSTRACT
Recent studies in project management have indicated the importance of project selection criteria in organizational performance. This study proposes a machine learning approach to predict the impact of project selection criteria on organizational performance. This relationship was investigated using a dataset containing data from reputable organizations and applying different machine learning algorithms including multiple linear regression, k-nearest neighbor, and support vector machine. Our results demonstrated that there is a significant relationship between the parameters of project selection criteria and organizational performance. Further, it was identified that most significant relationship exists between project time, project return, technical risks, and sales, and the weak relationship exists between TechAvail and MarShare. This study contributes to the relationship between project selection criteria and organizational performance and provides insights into the application of emerging tools in data science and machine learning in operations management and project management research.
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Published In
ASCE Inspire 2023
Pages: 133 - 142
History
Published online: Nov 14, 2023
ASCE Technical Topics:
- Algorithms
- Analysis (by type)
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Engineering fundamentals
- Infrastructure
- Infrastructure resilience
- Linear functions
- Management methods
- Mathematical functions
- Mathematics
- Organizations
- Practice and Profession
- Project management
- Regression analysis
- Statistical analysis (by type)
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