An Analysis of Neural Network Algorithms’ Accuracy for Processing Consumer Purchase Patterns
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Abstract
An emphasis on the feedforward neural network model, this study examines whether connectionist models are adequate to explain consumer behaviour. Additionally, the viability of integrating connectionist concepts into the Behavioural Perspective Model's theoretical framework is investigated. A key component of consumer behaviour, customer loyalty is predicted by a variety of neural network models with differing levels of complexity. Neural networks consistently outperform logistic regression in predicting client loyalty when compared to the more conventional method. Consumer choice is demonstrated to be mostly explained by utilitarian considerations and independently determined informational reinforcement. It is recommended that future studies explore the explanatory and predictive powers of connectionist models, such as neural network models, and how they might be included into the theoretical framework of the Behavioural Perspective Model to analyse consumer behaviour. It also looks at how well connectionist models can explain and forecast consumer behaviour.
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References
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