A Hybrid Multiple Attribute Decision Making Model for Measuring Image Scores of a Set of Stores

Abstract

Evaluating store image is a challenging task as it incorporates with multiple attributes. Earlier quantitative studies paid minimal attention on assessing the stores based on their image scores and overlooked the interaction aspects between attributes in the process of identifying the optimal strategies for image enhancement. This paper proposes a hybrid multiple attribute decision making model for quantitatively performing image evaluation involving a set of stores. The model uses factor analysis to extract the large set of interacted attributes into fewer independent factors, Sugeno measure to characterize the interactions between attributes, Choquet integral to aggregate the interactive performance scores within each extracted factor, Mikhailov