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Model-based approach for importance–performance analysis

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  • Federica Cugnata
  • Silvia Salini

Abstract

The importance that users or customers attach to various services and products is an essential part of customer satisfaction surveys. Some proposals for linking satisfaction and importance can be found in available literature. The objective is to identify and understand the dimensions with high importance but low perceived quality. These dimensions are primary candidates for focused improvement initiatives. In this study, we propose to apply a class of statistical models, denoted as CUB models, generally used to estimate the feeling and the uncertainty, to measure the importance of items on observed overall satisfaction. A questionnaire with explicit variables of importance for each dimension is considered to compare the obtained ranks with the observed ones. Then the estimated importance and the perceived quality, both obtained with the CUB models, will be jointly analyzed in different datasets coming from various fields. This approach will be compared with some others reported in the literature. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • 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.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:6:p:3053-3064
    DOI: 10.1007/s11135-013-9940-3
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    References listed on IDEAS

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    1. Ron S. Kenett & Galit Shmueli, 2014. "On information quality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(1), pages 3-38, January.
    2. Federico ANDREIS & Pier Alda FERRARI, 2015. "Customer Satisfaction Evaluation Using Multidimensional Item Response Theory Models," Departmental Working Papers 2015-25, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    3. Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2010. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fair Trade Coffee," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 1(1), pages 1-12.
    4. Maria Iannario, 2010. "On the identifiability of a mixture model for ordinal data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 87-94.
    5. 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.
    6. 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.
    7. Jiunn-I Shieh & Hsin-Hung Wu, 2011. "Applying information-based methods in importance–performance analysis when the information of importance is unavailable," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(3), pages 545-557, April.
    8. Ron S. Kenett & Silvia Salini, 2011. "Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 465-475, September.
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    Cited by:

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    2. Stefania Capecchi & Domenico Piccolo, 2017. "Dealing with heterogeneity in ordinal responses," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 2375-2393, September.
    3. Zhou Lu & Zhuyao Zhuo, 2021. "Modelling of Chinese corporate bond default – A machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 6147-6191, December.

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