Abstract
This study applies Bayesian network technique to analyse the relationships among customer online shopping behaviours and customer requirements. This study first proposes an initial behaviour-requirement relationship model as domain knowledge. Through conducting a survey customer data is collected as evidences for inference of the relationships among the factors described in the model. After creating a graphical structure, this study calculates conditional probability distribution among these factors, and then conducts inference by using the Junction-tree algorithm. A set of useful findings has been obtained for customer online shopping behaviours and their requirements with motivations. These findings have potential to help businesses adopting more suitable online system development.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)
Gelman, A., Carlin, J., Stern, H., Rubin, D.: Bayesian Data Analysis. Chapman & Hall/CRC, Boca Raton (1995)
Hahn, J., Kauffman, R.J.: Evaluating selling web site performance from a business value perspective. In: Proceedings of international conference on e-Business, Beijing, China, May 23-26, pp. 435–443 (2002)
Heckerman, D.: A tutorial on learning Bayesian networks. Technical Report MSRTR-95-06, Microsoft Research (1996)
Lauritzen, S., Spiegelhalter, D.: Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems. J. R. Statis. Soc. B 50, 157–224 (1988)
Lin, C.: A critical appraisal of customer satisfaction and e-commerce. Managerial Auditing Journal 18, 202–212 (2003)
Lu, J., Tang, S., McCullough, G.: An assessment for internet-based electronic commerce development in businesses of New Zealand, Electronic Markets. International Journal of Electronic Commerce and Business Media 11, 107–115 (2001)
Wade, R.M., Nevo, S.: Development and Validation of a Perceptual Instrument to Measure E-Commerce Performance. International Journal of Electronic Commerce 10, 123 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, Z., Lu, J., Bai, C., Zhang, G. (2006). Customer Online Shopping Behaviours Analysis Using Bayesian Networks. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_163
Download citation
DOI: https://doi.org/10.1007/11941439_163
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
eBook Packages: Computer ScienceComputer Science (R0)