NEURAL Networks and Consumer Behavior: NEURAL Models, Logistic Regression, and the Behavioral Perspective Model

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 to make 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.


Thu, 15 Jun 2017, 5:00 pm


The Modulated Contingency

Thu, 15 Jun 2017, 5:00 pm


The Fuzzy Outline of an Operant

Wed, 31 May 2017, 5:00 pm




Beyond Basic or Applied

Wed, 31 May 2017, 5:00 pm





Reinforcing Rilkean Memories

Wed, 31 May 2017, 5:00 pm





Killeen and Jacobs (2016) Are Not Wrong

Wed, 31 May 2017, 5:00 pm


Simply Too Many Notes

Wed, 31 May 2017, 5:00 pm



Driving With the Rear View Mirror

Wed, 31 May 2017, 5:00 pm



The Future of Behavior Analysis: Foxes and Hedgehogs Revisited

Abstract

Some twenty-five years ago The Behavior Analyst published a paper by David Rider (The Behavior Analyst, 14, 171–181, 1991) titled “The speciation of behavior analysis.” Rider’s thesis was that basic and applied behavior analysis, for a variety of reasons, are destined to become independent species. In a commentary on this paper I pointed out, for example, that scientists and engineers are interdependent, especially at the frontiers of application. I was sanguine about a continuing analogous relationship between basic and applied behavior analysis. However, especially in the last decade, indications are that basic and applied behavior analysis may indeed be emerging as distinct species. I discuss several themes in a review of the “literature of survival” addressing the evolving complex relations between basic and applied behavior analysis, including constraints on training leading to narrow foci of application, our often self-imposed isolation from those with whom we could productively collaborate, and the difficulties of obtaining sufficient support for our science. All these challenges reflect a briar-patch of interlocking contingencies; each one depends crucially on the others and we cannot effectively address any in isolation. Thus solutions will not be easy, but our long-term survival as a coherent discipline absolutely depends on finding some.


Wed, 31 May 2017, 5:00 pm


Description and Validation of a Computerized Behavioral Data Program: “BDataPro”

Abstract

BDataPro is a Microsoft Windows®-based program that allows for real-time data collection of multiple frequency- and duration-based behaviors, summary of behavioral data (in terms of average responses per min, percentage of 10-s intervals, and cumulative responses within 10-s bins), and calculation of reliability coefficients. The current article describes the functionality of the program. BDataPro is freely available for download from the authors’ institution websites.


Wed, 31 May 2017, 5:00 pm