A modular fuzzy inference system approach in integrating qualitative and quantitative analysis of store image
The image situation in a store includes various stimuli, such as color, sound, scent, taste, layout and space, which are important clues for buyers. This paper describes store image response and a fuzzy logic model developed by comprehensive literature studies on image measurements (including atmospheric factors) and perceptual measures. Here, a fuzzy inference system is proposed as an alternative approach to handle effectively the impreciseness and uncertainty that are normally found in store image selection processes. This paper also shows that the proposed decision-making model is application to modified stimulus–organism–response (S–O–R) framework for integrating qualitative and quantitative analysis. The result of the simulation indicates a numerical and linguistic change in the store image perception after analyzing three input parameters. This finding is able to provide a solid foundation on which retailers and decision makers can base suitable strategies for ensuring the efficiency and stability of store image management system. Copyright Springer Science+Business Media B.V. 2012
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Volume (Year): 46 (2012)
Issue (Month): 6 (October)
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References listed on IDEAS
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- Collan, Mikael & Liu, Shuhua, 2002. "Fuzzy Logic and Intelligent Agents: Towards the Next Step of Capital Budgeting Decision Support," Working Papers 398, IAMSR, Åbo Akademi.
- Samli, A. Coskun & Kelly, J. Patrick & Hunt, H. Keith, 1998. "Improving the Retail Performance by Contrasting Management- and Customer-Perceived Store Images: A Diagnostic Tool for Corrective Action," Journal of Business Research, Elsevier, vol. 43(1), pages 27-38, September.
- Belk, Russell W, 1975. " Situational Variables and Consumer Behavior," Journal of Consumer Research, University of Chicago Press, vol. 2(3), pages 157-64, December.
- Chebat, Jean-Charles & Michon, Richard, 2003. "Impact of ambient odors on mall shoppers' emotions, cognition, and spending: A test of competitive causal theories," Journal of Business Research, Elsevier, vol. 56(7), pages 529-539, July.
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