Paper Info
Title
A 2-tuple integrated DEA-based approach for neuromarketing technology evaluation
Abstract
Purpose Neuromarketing, which is an interdisciplinary area, concentrates on evaluating consumers' cognitive and emotional reactions to different marketing stimuli. In spite of advantages, neuromarketing still requires development and lacks a strong theoretical framework. Techniques that are used in neuromarketing studies have different superiorities and limitations, and thus, there is a need for the evaluation of the relevance of these techniques. The purpose of this study is to introduce a novel integrated approach for the neuromarketing research area. Design/methodology/approach The proposed approach combines 2-tuple linguistic representation model and data envelopment analysis to obtain the most efficient neuromarketing technique. It is apt to handle information provided by using both linguistic and numerical scales with multiple information sources. Furthermore, it allows managers to deal with heterogeneous information, without loss of information. Findings The proposed approach indicates that functional magnetic resonance imaging (fMRI) is the best performing neuromarketing technology. Recently, fMRI has been widely used in neuromarketing research. In spite of its high cost, its main superiorities are improved spatial and temporal resolutions. On the other hand, transcranial magnetic stimulation (TMS) and positron emission tomography (PET) are ranked at the bottom because of their poor resolutions and lower willingness of participants. Originality/value This paper proposes a common weight data envelopment analysis (DEA)-based decision model to cope with heterogeneous information collected by the experts to determine the best performing neuromarketing technology. The decision procedure enables the decision-makers to handle the problems of loss of information and multi-granularity by using the fusion of 2-tuple linguistic representation model and fuzzy information. Moreover, a DEA-based common weight model does not require subjective experts' opinions to weight the evaluation criteria.
Year
DOI
Venue
2019
10.1108/K-01-2018-0014
KYBERNETES
Keywords
Field
DocType
Data envelopment analysis,Neuromarketing,Fuzzy decision-making,2-Tuple linguistic representation,Fusion of fuzzy information,Technology selection
Neuromarketing,Ranking,Tuple,Computer science,Fuzzy logic,Originality,Data envelopment analysis,Artificial intelligence,Decision model,Cognition,Machine learning,Management science
Journal
Volume
Issue
ISSN
48.0
5.0
0368-492X
Citations 
PageRank 
References 
0
0.34
17
Authors
2
Name
Order
Citations
PageRank
Mehtap Dursun11215.93
Nazli Göker241.78