Systems thinking (ST) is an interdisciplinary domain that offers different ways to better understand the behavior and structure of a complex system. Over the past decades, several publications can be identified in academic literature, focusing on different aspects of systems thinking. However, two critical questions are not properly addressed in the extant body of ST literature: (i) How to conduct the content analysis exclusively to derive the prominent statistics (i.e., influential journals, authors, affiliated organizations and countries) pertaining to the domain of ST? (ii) How to get better insights regarding the current and emerging trends that may evolve over time based on the existing body of ST literature? To address these gaps, the aim of this research study is to provide a comprehensive insight into the domain of systems thinking through bibliometric and network analysis. Beginning with over 6000 accumulated publications, the analysis narrowed down to 626 prominent articles with proven influence published over the past three decades. Leveraging rigorous bibliometric tools analysis, this research unveils the influential authors, leading journals and top contributing organizations and countries germane to the domain of systems thinking. In addition, citation, co-citation and page rank analysis used to rank top influential articles in the area of systems thinking. Finally, with the aid of the network analysis, key clusters in the existing literature are identified based on the research areas of systems thinking. The findings of this research will serve as a bluebook for practitioners and scholars to conduct future research within systems thinking context.

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Due to the widespread of new technologies, the modern electric power system has become much more complex and uncertain. The Integration of technologies in the electric power system has increased the exposure of cyber threats and correlative susceptibilities from malicious cyber-attacks. To better address these cyber risks and minimize the effects of the power system outage, this research identifies the potential causes and mitigation techniques for the smart grid (SG) and assesses the overall cyber resilience of smart grid systems using a Bayesian network approach. Bayesian network is a powerful analytical tool predominantly used in risk, reliability, and resilience assessment under uncertainty. The quantification of the model is examined, and the results are analyzed through different advanced techniques such as predictive inference reasoning and sensitivity analysis. Different scenarios have been developed and analyzed to identify critical variables that are susceptible to the cyber resilience of a smart grid system of systems. Insight drawn from these analyses suggests that overall cyber resilience of the SG system of systems is dependent upon the status of identified factors, and more attention should be directed towards developing the countermeasures against access domain vulnerability. The research also shows the efficacy of a Bayesian network to assess and enhance the overall cyber resilience of the smart grid system of systems.

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