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Bayesian networks of customer satisfaction survey data


  • Silvia Salini
  • Ron Kenett


A Bayesian network (BN) is a probabilistic graphical model that represents a set of variables and their probabilistic dependencies. Formally, BNs are directed acyclic graphs whose nodes represent variables, and whose arcs encode the conditional dependencies among the variables. Nodes can represent any kind of variable, be it a measured parameter, a latent variable, or a hypothesis. They are not restricted to represent random variables, which form the “Bayesian” aspect of a BN. Efficient algorithms exist that perform inference and learning in BNs. BNs that model sequences of variables are called dynamic BNs. In this context, [A. Harel, R. Kenett, and F. Ruggeri, Modeling web usability diagnostics on the basis of usage statistics, in Statistical Methods in eCommerce Research, W. Jank and G. Shmueli, eds., Wiley, 2008] provide a comparison between Markov Chains and BNs in the analysis of web usability from e-commerce data. A comparison of regression models, structural equation models, and BNs is presented in Anderson et al. [R.D. Anderson, R.D. Mackoy, V.B. Thompson, and G. Harrell, A bayesian network estimation of the service-profit Chain for transport service satisfaction, Decision Sciences 35(4), (2004), pp. 665-689]. In this article we apply BNs to the analysis of customer satisfaction surveys and demonstrate the potential of the approach. In particular, BNs offer advantages in implementing models of cause and effect over other statistical techniques designed primarily for testing hypotheses. Other advantages include the ability to conduct probabilistic inference for prediction and diagnostic purposes with an output that can be intuitively understood by managers.

Suggested Citation

  • Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
  • Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1177-1189 DOI: 10.1080/02664760802587982

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    References listed on IDEAS

    1. Ron S. Kenett, 2011. "On the planning and design of sample surveys," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2681-2681, November.
    2. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
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    1. repec:spr:qualqt:v:51:y:2017:i:4:d:10.1007_s11135-016-0349-7 is not listed on IDEAS
    2. repec:eee:transa:v:106:y:2017:i:c:p:235-247 is not listed on IDEAS
    3. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
    4. Zhang, Ya & Zhao, Hai & He, Xuan & Pei, Fan-Dong & Li, Guang-Guang, 2016. "Bayesian prediction of earthquake network based on space–time influence domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 138-149.
    5. Claudia Tarantola & Paola Vicard & Ioannis Ntzoufras, 2012. "Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data," Quaderni di Dipartimento 160, University of Pavia, Department of Economics and Quantitative Methods.
    6. Anna Giunta & Massimo Florio & Francesco Giffoni & Emanuela Sirtori, 2017. "Big Science, Learning And Innovation: Evidence From Cern Procurement," Departmental Working Papers of Economics - University 'Roma Tre' 0225, Department of Economics - University Roma Tre.
    7. Pier Ferrari & Silvia Salini, 2011. "Complementary Use of Rasch Models and Nonlinear Principal Components Analysis in the Assessment of the Opinion of Europeans About Utilities," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 53-69, April.
    8. Giancarlo MANZI & Pier Alda FERRARI, 2014. "Statistical methods for evaluating satisfaction with public services," CIRIEC Working Papers 1404, CIRIEC - Université de Liège.
    9. Flaminia Musella & Paola Vicard, 2015. "Object-oriented Bayesian networks for complex quality management problems," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 115-133, January.
    10. Lidia Ceriani & Chiara Gigliarano, 2016. "Multidimensional well-being: A Bayesian Networks approach," Working Papers 399, ECINEQ, Society for the Study of Economic Inequality.

    More about this item


    Bayesian networks; customer satisfaction; Eurobarometer; service quality;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments


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