System Dynamics was originally created by Forrester in the mid-1950s to help corporations improve their industrial processes (Forrester, 1961). This approach obliges a shifting in the way of thinking about things. System dynamics focuses on describing the endogenous feedback structure of a system. The word endogenous means arising from within, and thus an endogenous structure generates the dynamics of a system through the interaction of variables and actors represented in the model. This modeling approach differs from theories relying on exogenous variables (variables arising from without), which explain dynamics in terms of other variables whose behavior is assumed (Sterman, 2000).
By modeling feedback structure one can better understand dynamic complexity that can be found in situations where the same action has different effects in the short and the long run, and where an action has one set of consequences locally and a different set of consequences in another part of the system (Senge, 1990 and Sterman, 2000). It has been argued that real leverage in many management situations lies in understanding dynamic complexity (Senge, 1990). In general, research shows that endogenous structure is difficult to understand and that feedback, time delays, and nonlinearity are counterintuitive and poorly understood (Sterman, 1989 and Sterman, 2002).
Sterman (2000) claims that except the theory of nonlinear dynamics and feedback control developed in mathematics, physics, and engineering, SD also draws on cognitive and social psychology, economic, and other social
sciences. Therefore, System Dynamic is fundamentally interdisciplinary and suitable to support managers to learn and understand complex systems. We must take into consideration of the organization as a system constituted by interacting components and avoid merely consider events and their causality in isolated manners (Forrester, 1961). Another meaning of the “system”
terminology is presentation of an interactively interdependent group of entities. According to Kirkwood (1998), one of the common ways that people explicate business performance is presenting how one group of events causes another and this orientation is not a powerful way of thinking to alter unsuitable performance. The reason for this inefficiency is that the process of efforts to find a set of main causes could lead to be almost forever due to the fact that there are always existed another set of events that causes the set of events was found are the main cause. These long time taking lead to a difficulty for determining what should to do for improving the business performance. The scholar also argued that one way of thinking could help to significantly improve the current performance is shifting from the even oriented thinking to a system thinking that concentrates on the internal system structure. When the system structure deficiencies are still exist, any correcting effort by event replacements could lead to even more difficult problems.
Usually, the system structure is the major underlying source of the difficulty that make decision maker stuck on solving business problems.(Tu and Chang, 2010)
The dynamics of a system through the interaction of variables and actors displayed in the model which is different from theories relying on exogenous variables, which explain dynamics in terms of other variables whose behavior
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is assumed (Sterman, 2000). It can also effectively examine the models in the context that different policies within the model change the nature of ongoing behavior.
By modeling feedback structure one can better understand dynamic complexity that can be found in situations where the same action has different effects in the short and the long run, and where an action has one set of consequences locally and a different set of consequences in another part of the system (Senge, 1990; Sterman, 2000). It has been argued that real leverage in many management situations lies in understanding dynamic complexity (Senge, 1990). In general, research shows that endogenous structure is difficult to understand and that feedback, time delays, and nonlinearity are counterintuitive and poorly understood (Sterman, 1989, 2002).
System dynamics has been widely utilized to study dynamic behavior of various social systems and has been applied to policy analysis and design both in the public and private sectors. By applying the computer simulation, SD can show how structures, policies, decisions and time delays within organizations and business systems are interrelated and influence growth and stability (Lee, 2006). With a foundation of decision making, dynamic relationships, feedback analysis, and simulation, systems can be defined and modeled that will allow experimentation in a laboratory setting (Chasey, de la Garza, and Drew, 2002).
Therefore, System Dynamic is fundamentally interdisciplinary and suitable to support managers to learn and understand complex systems.
Coyle refers to that managers can use System Dynamic to deal with the
time-dependent behavior of managed systems, and understand interrelationships among variables influencing the behavior of the system over time (Coyle, 1996). Unlike other theories such as the economics model and input-output model, the Systems Dynamics emerged and currently provides a strong theoretical basis for analyzing such systems, in terms of non-order, non-linear, long-term, quantitative analyses (Lee and Von Tunzelmann, 2005). It can also effectively examine the models in the context that different policies within the model change the nature of ongoing behavior.
System dynamics has been widely utilized to study dynamic behavior of various social systems and has been applied to policy analysis and design both in the public and private sectors. It has for example been used to model the dynamics of industries such as the structural changes in the commercial jet aircraft industry (Lyneis, 2000). System dynamics has also been applied in the telecommunications context. Tookey, Whalley, and Howick (2006) used causal loop diagrams to model broadband diffusion in remote and rural Scotland. Davies, Howell, and Mabin (2009) examined the case of telecommunications sector regulation in New Zealand. Pagani and Fine (2008) developed a map to analyze the dynamic forces that influence the structure and development of third generation wireless communications value networks and services. Similar thinking has also been promoted for example by Fransman who argues that the new ICT ecosystem works through symbiotic relationships between different group of actors and by Andrew and Petkov who highlight the need for a systems thinking approach to the planning of rural telecommunications infrastructure (Andrew and Petkov, 2003; Fransman, 2010)
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Chapter Three Research Methodology
This study explores the change process of consumer behavior in C2C platform by using system thinking, and the effectiveness of “promotion”