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Analysis of Factors Affecting the Value Chain Using the Three-Stage Hybrid Sem-Ann-Ism (Seanis) Approach
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| Author:
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BIJAN MEHRMANESH, SEYED HASSAN HATAMINASAB, SHANAZ NAYEBZADEH
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| Abstract:
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Introduction: this study aims at presenting a three-stage hybrid SEM-ANN-ISM (SEANIS) model. The model includes Structural Equation Modeling (SEM), Artificial Neural Network (ANN), and Interpretive Structural Modeling (ISM) for the analysis of factors affecting the customer value chain in the health industry of Iran. This study presents a modern approach for the analysis of factors affecting a model.
Methods: step 1 uses the Structural Equation Modeling (SEM) to analyze the criteria and sub-criteria of the chain value model. Factor analysis is used to analyze the correlations of criteria and sub-criteria. Finally, the equations are specified and the effect of causal factors on the value chain, the effect of interfering factors on causal factors and strategies, the effect of the value chain on strategies, and the effect of strategies on consequences are confirmed as the main hypotheses of the study. Step 2 includes an artificial neural network and analysis of the effect of sub-criteria on each other and presents the matrix of components. Step 3 is associated with the interpretive structural modeling to identify and present the final hierarchical model. For data analysis and to present the model, SMARTPLS software was used.
Results: the final analysis of the value chain model in the health industry includes causal, field, and interfering criteria, strategies, and consequences identifying and confirming the factors affecting the model.
Conclusion: final analysis led to the final confirmation of model validity and usability in the health industry.
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| Keyword:
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model, value chain, SEANIS, ISM, ANN, SEM.
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2021.13.01.747
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| Download:
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Request For Article
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