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Customer satisfaction study via a latent segment model

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  • Fonseca, Jaime R.S.

Abstract

The aim of this study is to apply a new conceptual model, and a new technique as an approach to the modelling of customers’ satisfaction, and to develop an overall satisfaction index (OSI). This study evaluates customers’ satisfaction of a certain public organization service, and argues that in order to estimate the global customers’ satisfaction measure we must appeal to methodologies recognizing that satisfaction must be understood as a latent variable, quantified through multiple indicators. Thus, it is natural that we consider the latent segment models (LSM) approach to proceed to the evaluation of customer's service satisfaction. As a result of these models estimation, we selected a three latent segment model, that is to say, the latent variable customer satisfaction has three classes: segment 1, with 50.4 percent of the customers, that represents “The Very Satisfied†, for those to whom everything is very well in the organization service; a segment 2, with 33.4 percent of the customers, representative of the “The Well Satisfied†, not totally satisfied with the quality of the organization, and a segment 3, with 16.2 percent of the customers, “Satisfaction Demanders†, thinking that organizational quality can be improved. Finally, we developed an overall satisfaction index which is important to show how the company as a whole is performing.

Suggested Citation

  • Fonseca, Jaime R.S., 2009. "Customer satisfaction study via a latent segment model," Journal of Retailing and Consumer Services, Elsevier, vol. 16(5), pages 352-359.
    • Handle: RePEc:eee:joreco:v:16:y:2009:i:5:p:352-359
    DOI: 10.1016/j.jretconser.2009.04.001
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    References listed on IDEAS

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    1. Bhatnagar, Amit & Ghose, Sanjoy, 2004. "A latent class segmentation analysis of e-shoppers," Journal of Business Research, Elsevier, vol. 57(7), pages 758-767, July.
    2. Boris Bartikowski & Sylvie Llosa, 2004. "Customer satisfaction measurement: comparing four methods of attribute categorisations," The Service Industries Journal, Taylor & Francis Journals, vol. 24(4), pages 67-82, July.
    3. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    4. Jianan Wu & Wayne S. DeSarbo, 2005. "Rejoinder for market segmentation for customer satisfaction studies via a new latent structure multidimensional scaling model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(4‐5), pages 317-318, July.
    5. Jianan Wu & Wayne S. DeSarbo, 2005. "Market segmentation for customer satisfaction studies via a new latent structure multidimensional scaling model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(4‐5), pages 303-309, July.
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    Cited by:

    1. Janez Dolšak & Nevenka Hrovatin & Jelena Zorić, 2020. "Analysing Consumer Preferences, Characteristics, and Behaviour to Identify Energy-Efficient Consumers," Sustainability, MDPI, vol. 12(23), pages 1-19, November.
    2. Qaisar Ali, 2018. "Service Quality from Customer Perception: Evidence from Carter Model on Bank Islam Brunei Darussalam (BIBD)," International Journal of Business and Management, Canadian Center of Science and Education, vol. 13(2), pages 138-138, January.
    3. Fonseca, Jaime R.S., 2014. "e-banking culture: A comparison of EU 27 countries and Portuguese case in the EU 27 retail banking context," Journal of Retailing and Consumer Services, Elsevier, vol. 21(5), pages 708-716.
    4. Joachim Büschken & Ulf Böckenholt & Thomas Otter & Daniel Stengel, 2022. "Better Information From Survey Data: Filtering Out State Dependence Using Eye-Tracking Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 620-665, June.

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