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: |
03 May 2020 14:43 |
URI: |
http://orca.cf.ac.uk/id/eprint/109608 |
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