A hybrid multiple attribute decision making model for measuring image scores of a set of stores

• Main Authors: Krishnan, Anath Rau, Engku Abu Bakar, Engku Muhammad Nazri, Mat Kasim, Maznah
• Format: Article
• Language: English
• Published: GSSRR 2014
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/19407/1/IJSBAR%2016%20%201%202014%20%20407-431.pdf; http://repo.uum.edu.my/19407/; http://gssrr.org/index.php?journal=JournalOfBasicAndApplied&page=article&op=view&path%5B%5D=2314

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’s fuzzy analytical hierarchy process to assign the factors’ weights, and weighted average operator to aggregate the independent factor scores of each store into a single global image score.An evaluation involving three stores located at Pekan Sabak, Selangor was conducted in order to demonstrate the feasibility of the model.The ranking on three stores derived via proposed model matched with the benchmark ranking unlike the ranking yielded by a classical aggregation operator.The model will be supportive for the retailers to identify their relative positions with their competitors and to systematically implement potential strategies for image enhancement by taking into account the interactions between attributes.