Green productivity is a sustainable development approach. The purpose of this study is to identify green productivity Indicatorsand to determine the relationships between Indicatorsto present the model through fuzzy cognitive mapping. The fuzzy cognitive mapping method used in this research is based on the Rodriguez-Repcio and et al. (2007) automated FCM. The statistical population of the study included all the experts of Plastonic Company. Green productivity indicators were identified through Literature review. Fuzzy cognitive mapping was created Using four matrices, Initial Influence Matrix, fuzzified Influence Matrix, Strength of Influence Relationships Matrix, Final Matrix of Influence and the software Mental Modeler. The cognitive mapping represents the relationships between the green productivity Indicatorsand the weights among them. According to the model, four policy or scenarios were designed in software to identify the relative changes of each indicator. Finally, the fourth scenario with the most positive impact on other indicators was selected as the best scenario among the designed scenarios.
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Nikshapoori, M., Abbasnejad, T., & Ahmadi Kahnali, R. (2019). Analysis of Causal Relationships between Green Productivity Indicators with Fuzzy Cognitive Mapping Approach. Journal of Industrial Management Perspective, 8(4), 97-119.
MLA
Mehdi Nikshapoori; Tayebeh Abbasnejad; Reza Ahmadi Kahnali. "Analysis of Causal Relationships between Green Productivity Indicators with Fuzzy Cognitive Mapping Approach". Journal of Industrial Management Perspective, 8, 4, 2019, 97-119.
HARVARD
Nikshapoori, M., Abbasnejad, T., Ahmadi Kahnali, R. (2019). 'Analysis of Causal Relationships between Green Productivity Indicators with Fuzzy Cognitive Mapping Approach', Journal of Industrial Management Perspective, 8(4), pp. 97-119.
VANCOUVER
Nikshapoori, M., Abbasnejad, T., Ahmadi Kahnali, R. Analysis of Causal Relationships between Green Productivity Indicators with Fuzzy Cognitive Mapping Approach. Journal of Industrial Management Perspective, 2019; 8(4): 97-119.