What Systems Thinking Means to Different Networks of Researchers

Abstract

There is a growing body of literature on the applications of Systems Thinking (ST); yet, a consensus on what constitutes ST remains elusive. Various studies have demonstrated the necessity to revise the definition of ST, especially for the purpose of ST assessment. Classification of the available definitions in the literature is an essential step to reach a common language among the scholars in the field. By using Social Network Analysis (SNA), this paper identifies the main authorship networks around ST. The analysis of 1462 papers with ST in their title leads to the emergence of three large components. Looking at the main concepts, definitions and applications in each component reveals systems thinking functions differently for each network of researchers. In the first component, ST is used as the backbone of other frameworks such as Critical Systems Thinking (CST) or Open System Thinking; while, in the second component, the applicability of ST concepts and the gap from knowledge to action are highlighted. Finally, the third component points to the potential application of ST to transform different sectors.

Full text

What Systems Thinking Means to Different Networks of
Researchers
Amin Dehdarian1,*, Kiumars Dorani2, Hesam Mahmoudi3, Masoomeh Khandan4
1The Learning Lab Future Transport Systems, ETH Zürich, 2Faculty of Business Administration, Memorial University of
Newfoundland, 3Department of Industrial and Systems Engineering, Virginia Tech, 4Labor Mobility Partnerships,
Washington DC. *the corresponding author
Extended Abstract
There is a growing body of literature on the applications of Systems Thinking (ST); yet, a
consensus on what constitutes ST remains elusive. Various studies have demonstrated the
necessity to revise the definition of ST, especially for the purpose of ST assessment.
Classification of the available definitions in the literature is an essential step to reach a common
language among the scholars in the field. By using Social Network Analysis (SNA), this paper
identifies the main authorship networks around ST. The analysis of 1462 papers with ST in their
title leads to the emergence of three large components. Looking at the main concepts, definitions
and applications in each component reveals systems thinking functions differently for each
network of researchers. In the first component, ST is used as the backbone of other frameworks
such as Critical Systems Thinking (CST) or Open System Thinking; while, in the second
component, the applicability of ST concepts and the gap from knowledge to action are highlighted.
Finally, the third component points to the potential application of ST to transform different sectors.
Introduction
Considerable research has been conducted on the definitions, approaches, methodologies, and
tools of Systems Thinking (ST). A search in Scopus alone, returns more than 1500 papers that
contain the term “systems thinking” in their titles. However, ST still suffers from the lack of
common definitions and approaches. That is one of the reasons why a comprehensive revision
of ST definition is needed especially in order to improve ST assessment methods. Many papers
do not provide any specific definition or framework. A clear definition and understanding of ST
can help researchers to use ST in many contexts effectively. Since 1994, considerable efforts
have been made to form a common understanding and definition of ST. All such efforts have been
unsuccessful. A serious challenge to address this issue is that ST has been employed in different
and sometimes contradictory approaches and methods. The first step in having the ability to
create a common definition of ST and improve current approaches is to cluster current definitions
and approaches. However, ST definitions and approaches can be clustered in a variety of ways.
This paper proposes a new method for the classification of ST research based on network
analysis.
Method and data
The proposed method to classify definitions and applications of Systems Thinking, is to use a list
of the most active researchers, and the definitions and applications in their publications. In this
respect, by using social network analysis, a network of authorship is created. Each node
represents a paper, and the link between two nodes highlights there is at least one author shared
between the two papers. As a result, a number of isolated sub-networks (the so-called
components) and isolated nodes emerge. In large components, there is a possibility of the
emergence of clusters, where nodes within each cluster have a stronger bond with each other
than the rest of the same component (Dehdarian & Tucci, 2021).To find clusters, here an
algorithm called modularization is used, which results in the identification of modules (Clauset et
al., 2004).

The data used in this network includes all papers with the term “systems thinking” in the titles in
Scopus, which results in a total number of 1502 papers. The biggest components with at least
two modules are considered here, which leads to a total of three biggest components.
Results
The biggest component is composed of six modules. The main focus in this component is on
theories and approaches that emerge from systems thinking characteristics and its practical
applications. They include soft systems methodology, open systems thinking, critical systems
thinking, the Vanguard method, lean management and total system intervention. There are also
comparisons between Systems thinking and other methods or approaches such as Operations
Research (OR) and System Dynamics (SD).
The second component is composed of four modules that can be divided into two groups. The
first group includes frameworks to transform Systems thinking theories and concepts into learning
and action. Then, systems thinking potentials and functionalities are explored in the case of the
health system. There is the possibility of looking at the health system as an example of a complex
adaptive system that provides the opportunity for a systems thinking approach to grasp the
inherent complexity of such systems. However, in reality there is a gap between knowledge and
action. Furthermore, there are potential applications for systems thinking to improve functions in
the health system as a practical need, which highlights the importance of learning and experience
in using systems thinking principles.
Finally, the third component focuses on interventions and practical solutions facilitated by systems
thinking to address practical challenges in different sectors. They range from health system issues
to different aspects of systems engineering such as planning or education, as well as examples
of large infrastructural systems such as energy and maritime systems.
Discussion
Looking at the results of network analysis shows that in the largest networks of authorship and
co-authorship, which can be interpreted as the network of the most active ST researchers, ST
and some other concepts are being used interchangeably. These concepts range from
frameworks and methods such as critical systems thinking and open systems thinking, to
definitions that highlight one aspect of ST such as connection between parts, or a holistic
approach. In addition, ST is understood as a required mindset to resolve issues in complex
adaptive systems or large engineering systems. In this respect, our results show that instead of
providing a clear definition of ST and the requirements to make the best use of ST principles and
mindset in different contexts, practical issues arising in these systems are attributed to the lack of
ST, which can be resolved by filling the gap from knowledge to action or apply ST for system
transformation.
This research is a first step to analyze the structure of the research community around ST
definitions, concepts and application, and different patterns emerging from different types of
collaboration. This research can be complemented by looking at other modes of collaboration and
knowledge exchange through the network of co-authorships (authors as nodes), main path
analysis of the ST research strands and their knowledge trajectories, and citation networks. Each
of these networks can shed light on the evolution and dissemination of knowledge about ST and
help us understand why the ST community, even though equipped with ST as a critical asset for
analyzing complex issues, has not reached consensus about its associated concepts, skills and
definitions, that can differentiate ST from other relevant theories and frameworks.

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