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  • A multi-methodology approach to creating a causal loop diagram

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    Dhirasasna506649-Published.pdf (2.633Mb)
    File version
    Version of Record (VoR)
    Author(s)
    Dhirasasna, N
    Sahin, O
    Griffith University Author(s)
    Sahin, Oz
    Dhirasasna, Nina
    Year published
    2019
    Metadata
    Show full item record
    Abstract
    Developing causal loop diagrams (CLDs) involves identifying stakeholders and endogenous variables and formulating variable causal relationships. Traditionally, the CLDs are developed mainly using a qualitative approach such as literature review, observations and interviews with stakeholders. However, modellers may question which stakeholders should be approached, whether the relevant variables are selected, and what to do when stakeholders perceive different variable relationships in the CLDs differently. Applying in a case study, this research proposes a multi-method approach by combining both quantitative and qualitative ...
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    Developing causal loop diagrams (CLDs) involves identifying stakeholders and endogenous variables and formulating variable causal relationships. Traditionally, the CLDs are developed mainly using a qualitative approach such as literature review, observations and interviews with stakeholders. However, modellers may question which stakeholders should be approached, whether the relevant variables are selected, and what to do when stakeholders perceive different variable relationships in the CLDs differently. Applying in a case study, this research proposes a multi-method approach by combining both quantitative and qualitative methods to select stakeholders, identify endogenous/exogenous variables, and develop the CLDs. The proposed quantitative method is expected to provide modellers with a justifiable stakeholder and variable selection process. The method also highlights possible hidden variables and relationships, which were further explored with a traditional qualitative approach.
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    Journal Title
    Systems
    Volume
    7
    Issue
    3
    DOI
    https://doi.org/10.3390/systems7030042
    Copyright Statement
    © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Artificial intelligence
    Software engineering
    Information systems
    Publication URI
    http://hdl.handle.net/10072/406708
    Collection
    • Journal articles

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