Table III Summary of issues,... | Emerald Publishing

Table III

Summary of issues, opportunities, and resolutions building a system thinking model

	Opportunities	Issues	Resolutions	Citations
Data	Relies on qualitative data Useful for exploring and describing social systems Provides a conceptual model as a visual aid for decision making Helpful in describing and analyzing processes, behavior and complex problems Allows the visual depiction of behavioral data relationships	The longitudinal process to collect data, design and analyze the model May require some form of quantitative analysis to confirm results Quantification of data may increase uncertainty by being non-denumerably infinite Case studies depend on intuitive judgment for analysis Case study data may not apply to a general scenario	First, visualize system behavior then quantifying variables identified to simulate relationships Start with visual diagrams to incite insightful discussion to make the problem clear before a simulation Accept dynamic conclusions to increase the ability to think through the dynamic system Avoid repeating the thought process for analyzing the future model Allow model comparison with other case studies	Coyle (2000), Forrester (1994)
Time	Requires a longitudinal study to capture and evaluate the system Time and experience assist in addressing fundamental assumptions about the model	The complex nature of the system requires more time to analyze implicit and explicit issues and system behavior Time constraints prolong the completion of the model The inclination to produce an incomplete model due to time constraints will not aid in quality decisions	Allow time to work with practitioners to provide relevance to research Combine consulting and research to provide different insights before finalizing the model Reiterate findings with experts to confirm and validate findings	Größler et al. (2008), Senge and Sterman (1992)
Design	Model complexities provide ample ground for discovery Increases critical thought processes Creates a concise story to abridge complex model Computer models analyze explicit variables	Difficult and intense situations are complex Complexities increase by adding more and different variables (e.g. varied organizations, ephemeral operations, and challenges) into the model Adding more variables make it hard to streamline, understand, and capture all and each characteristic(s), behavior and variable in the model Increases uncertainties with unanswered questions, tensions and debates between practitioners and academics to reach a conclusion and conflicting terms, concepts, and variables	Think systematically and logically about the design problem and how it can be solved Identify key variables that highlight and address the identified situation Focus on the primary task to connect variables to create a concise story Try not to connect all variables to each-other Limit, prioritize, address, and connect critical variables that reoccur based on proximity: best identified and connected to the closest variable of interest Seek to capture the root cause and effect of the feedback behavior Avoid complications by having arrows run across another arrow in the visual model Avoid wandering too far from the original design problem Avoid including unnecessary variable relationships to explain the model	Dyson and Chang (2005)
Results	Highlights variable relationships to allow for simplified learning solutions Helps in selecting ideas, variables, and languages for decision making Supports systems thinking and organizational learning process Provides insights into managerial issues	Captures a broader context that may have explicit and implicit variable relationships Complex systems can be counterintuitive by having unknown factors Variable relationships may cause implicit assumptions Explicit model assumptions may cause the model to seem instinctual Variables may have two or more cause and effect relationships Models provide counterintuitive results and may increase disagreements on the conclusion Complex models with too many variables may deter insightful discourse	Counterintuitive results increase conflicting views Focus on increasing the explicit meanings of the model design Iteratively question the purpose of the model to reach an intuitive conclusion Have creative debates and dialogue to highlight intuitive and counterintuitive results of the model Aim to identify the most pressing variable relationship	Daellenbach and McNickle (2005)

	Opportunities	Issues	Resolutions	Citations
Data	Relies on qualitative dataUseful for exploring and describing social systems Provides a conceptual model as a visual aid for decision making Helpful in describing and analyzing processes, behavior and complex problems Allows the visual depiction of behavioral data relationships	The longitudinal process to collect data, design and analyze the model May require some form of quantitative analysis to confirm resultsQuantification of data may increase uncertainty by being non-denumerably infiniteCase studies depend on intuitive judgment for analysis Case study data may not apply to a general scenario	First, visualize system behavior then quantifying variables identified to simulate relationshipsStart with visual diagrams to incite insightful discussion to make the problem clear before a simulation Accept dynamic conclusions to increase the ability to think through the dynamic systemAvoid repeating the thought process for analyzing the future modelAllow model comparison with other case studies	Coyle (2000), Forrester (1994)
Time	Requires a longitudinal study to capture and evaluate the systemTime and experience assist in addressing fundamental assumptions about the model	The complex nature of the system requires more time to analyze implicit and explicit issues and system behaviorTime constraints prolong the completion of the model The inclination to produce an incomplete model due to time constraints will not aid in quality decisions	Allow time to work with practitioners to provide relevance to researchCombine consulting and research to provide different insights before finalizing the model Reiterate findings with experts to confirm and validate findings	Größler et al. (2008), Senge and Sterman (1992)
Design	Model complexities provide ample ground for discoveryIncreases critical thought processesCreates a concise story to abridge complex modelComputer models analyze explicit variables	Difficult and intense situations are complex Complexities increase by adding more and different variables (e.g. varied organizations, ephemeral operations, and challenges) into the modelAdding more variables make it hard to streamline, understand, and capture all and each characteristic(s), behavior and variable in the modelIncreases uncertainties with unanswered questions, tensions and debates between practitioners and academics to reach a conclusion and conflicting terms, concepts, and variables	Think systematically and logically about the design problem and how it can be solved Identify key variables that highlight and address the identified situation Focus on the primary task to connect variables to create a concise story Try not to connect all variables to each-otherLimit, prioritize, address, and connect critical variables that reoccur based on proximity: best identified and connected to the closest variable of interestSeek to capture the root cause and effect of the feedback behavior Avoid complications by having arrows run across another arrow in the visual model Avoid wandering too far from the original design problemAvoid including unnecessary variable relationships to explain the model	Dyson and Chang (2005)
Results	Highlights variable relationships to allow for simplified learning solutions Helps in selecting ideas, variables, and languages for decision makingSupports systems thinking and organizational learning processProvides insights into managerial issues	Captures a broader context that may have explicit and implicit variable relationships Complex systems can be counterintuitive by having unknown factorsVariable relationships may cause implicit assumptionsExplicit model assumptions may cause the model to seem instinctual Variables may have two or more cause and effect relationships Models provide counterintuitive results and may increase disagreements on the conclusion Complex models with too many variables may deter insightful discourse	Counterintuitive results increase conflicting viewsFocus on increasing the explicit meanings of the model design Iteratively question the purpose of the model to reach an intuitive conclusionHave creative debates and dialogue to highlight intuitive and counterintuitive results of the model Aim to identify the most pressing variable relationship	Daellenbach and McNickle (2005)