NEURAL networks and consumer behavior: NEURAL models, logistic regression, and the behavioral perspective model

Greene, Max, Morgan, Peter and Foxall, Gordon 2017. NEURAL networks and consumer behavior: NEURAL models, logistic regression, and the behavioral perspective model. The Behavior Analyst 40 (2) , pp. 393-418. 10.1007/s40614-017-0105-x

Preview

PDF - Accepted Post-Print Version Download (921kB) | Preview

Abstract

This paper investigates the ability of connectionist models to explain consumer behavior, focusing on the feedforward neural network model, and explores the possibility of expanding the theoretical framework of the Behavioral Perspective Model to incorporate connectionist constructs. Numerous neural network models of varying complexity are developed to predict consumer loyalty as a crucial aspect of consumer behavior. Their performance is compared with the more traditional logistic regression model and it is found that neural networks offer consistent advantage over logistic regression in the prediction of consumer loyalty. Independently determined Utilitarian and Informational Reinforcement variables are shown tomake a noticeable contribution to the explanation of consumer choice. The potential of connectionist models for predicting and explaining consumer behavior is discussed and routes for future research are suggested to investigate the predictive and explanatory capacity of connectionist models, such as neural network models, and for the integration of these into consumer behavior analysis within the theoretical framework of the Behavioral Perspective Model

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Springer Verlag
ISSN:	0738-6729
Date of First Compliant Deposit:	27 February 2018
Date of Acceptance:	1 December 2016
Last Modified:	27 Jun 2019 11:58
URI:	http://orca.cf.ac.uk/id/eprint/109608

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item