Cost-benefit factor analysis in e-services using bayesian networks

  • Authors:

  • Jie Lu;Chenggang Bai;Guangquan Zhang

  • Affiliations:

  • Faculty of Information Technology, University of Technology, Sydney P.O. Box 123, Broadway, NSW 2007, Australia;Department of Automatic Control, Beijing University of Aeronautics and Astronautics, China;Faculty of Information Technology, University of Technology, Sydney P.O. Box 123, Broadway, NSW 2007, Australia

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

This study applies Bayesian network techniques to analyze and verify the relationships among cost factors and benefit factors in e-service systems. This study first establishes a Bayesian network for e-service cost-benefit factor relationships based on our previous study [Lu, J. & Zhang, G. Q. (2003). Cost benefit factor analysis in e-services. International Journal of Service Industry Management (IJSIM), 14(5), 570-595]. It then calculates conditional probability distributions among these factors shown in the Bayesian network. Finally it runs a Junction-tree algorithm to conduct inference for verifying these cost-benefit factor relationships, and the data collected through a survey is as evidences in the inference process. Through the above application of Bayesian network techniques a set of useful findings is obtained for the costs involved in e-service developments against the benefits received by adopting these e-service systems. The case of 'increased investments in maintaining e-services' would significantly contribute to 'enhancing perceived company image', and the case of 'increased investments in security of e-service systems' would bring high benefits in 'building customer relationships' and 'improving cooperation between companies'. These findings have great potential to improve the strategic planning of businesses by determining more effective investments items and adopting more suitable development activities in e-service systems and applications.