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Predicting Individual Systems Thinking Skills Using Bayesian Network Approach
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DOI：10.1109/ACCESS.2025.3534632.png)
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摘要
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Emergence in complex systems is often compounded by diverse information and rapid technological acceleration. The problems and behaviors of increasingly complex systems continue confining practitioners' systems engineering capabilities to maintain performance consistency. While systems engineering provides a process for integrating various engineering disciplines to deliver desired end results, systems thinking (ST) provides the mechanism for drawing broad perspectives on the configuration, patterns, and cycles of complex systems, with a view to analyzing and improving system performance. ST can, therefore, be construed as an essential skill for designing and managing complex systems that need to sustain desired specifications. Although several methods exist in the extant literature to appraise the ST skills of practitioners, none has been recommended for prediction and diagnostic purposes. To fill this void, this research study aims to develop and validate a Bayesian network tool that incorporates seven main factors and the corresponding underpinning sub-factors that influence individual ST skills, as identified by Jaradat and Keating. The study seeks to answer whether differences in systems thinking skills are evident between practitioners in two sectors, namely defense and industry/business. The results indicate that all the main ST factors are imperative to predicting overall individual ST skills for defense and industry/business practitioners. However, defense practitioners scored higher along six dimensions, resulting in a higher overall individual ST score than industry practitioners. Although industry practitioners scored higher than defense practitioners on the independence (autonomy) dimension, this dimension alone was insufficient to strengthen the overall ST skills above that of defense practitioners.
Keyword:
Systems thinking
Complex systems
Bayes methods
Instruments
Decision making
System dynamics
Probabilistic logic
Industries
Training
System performance
systems skills
systems thinking
predictive models
Bayesian network
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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