A Bayesian Network-based customer satisfaction model: a tool for management decisions in railway transport

Chakraborty, Subrata and Mengersen, Kerrie and Fidge, Colin and Ma, Lin and Lassen, David (2016) A Bayesian Network-based customer satisfaction model: a tool for management decisions in railway transport. Decision Analytics, 3 (4). pp. 1-24. ISSN 2193-8636

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Abstract

We formalise and present an innovative general approach for developing complex system models from survey data by applying Bayesian Networks. The challenges and approaches to converting survey data into usable probability forms are explained and a general approach for integrating expert knowledge (judgements) into Bayesian complex system models is presented. The structural complexities of the Bayesian complex system modelling process, based on various decision contexts, are also explained along with a solution. A novel application of Bayesian complex system models as a management tool for decision making is demonstrated using a railway transport case study. Customer satisfaction, which is a Key Performance Indicator in public transport
management, is modelled using data from customer surveys conducted by Queensland Rail, Australia.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 19 Sep 2016 00:42
Last Modified: 19 Sep 2016 00:42
Uncontrolled Keywords: Decision making, Bayesian modelling, Management tools, Customer satisfaction, Transportation
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1186/s40165-016-0021-2
URI: http://eprints.usq.edu.au/id/eprint/29725

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