A methodology for developing Bayesian networks: An application to information technology (IT) implementation |
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Authors: | Eitel JM Lauría Peter J Duchessi |
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Institution: | 1. School of Computer Science and Mathematics, Marist College, 3399 North Road, Poughkeepsie, NY 12601, USA;2. School of Business, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA |
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Abstract: | Bayesian Networks (BNs) are probabilistic inference engines that support reasoning under uncertainty. This article presents a methodology for building an information technology (IT) implementation BN from client–server survey data. The article also demonstrates how to use the BN to predict the attainment of IT benefits, given specific implementation characteristics (e.g., application complexity) and activities (e.g., reengineering). The BN is an outcome of a machine learning process that finds the network’s structure and its associated parameters, which best fit the data. The article will be of interest to academicians who want to learn more about building BNs from real data and practitioners who are interested in IT implementation models that make probabilistic statements about certain implementation decisions. |
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Keywords: | Decision support systems Bayesian networks IT implementation Artificial intelligence |
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