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Customer satisfaction measurement: comparing four methods of attribute categorisations

Author

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  • Boris Bartikowski
  • Sylvie Llosa

Abstract

The issue of how to weight and categorise service attributes has attracted great attention from academics as well as practitioners. The application of an inappropriate method could lead to misleading interpretations and useless or costly actions. We first review several streams of literature concerning the theoretical background of attribute categorisations in relation to customer satisfaction. We then identify four methods that have been developed to categorise attributes into four classes. In the next step we apply these methods in an empirical study. Criteria for distinguishing the considered approaches conceptually and methodologically are proposed, and implications for future research are discussed.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:servic:v:24:y:2004:i:4:p:67-82
    DOI: 10.1080/0264206042000275190
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    Citations

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    Cited by:

    1. Finn, Adam, 2011. "Investigating the non-linear effects of e-service quality dimensions on customer satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 18(1), pages 27-37.
    2. Flavia Dana OLTEAN & Manuela Rozalia GABOR, 2017. "Quality Management and Firm Performance in the Hotel Industry: Evidence from Mures County," North Economic Review, Technical University of Cluj Napoca, Department of Economics and Physics, vol. 1(1), pages 95-103, October.
    3. Albayrak, Tahir & Caber, Meltem, 2015. "Prioritisation of the hotel attributes according to their influence on satisfaction: A comparison of two techniques," Tourism Management, Elsevier, vol. 46(C), pages 43-50.
    4. Xian Ji & Long Shao & Yu Du, 2020. "Collaborating with Local Communities to Identify Improvement Priorities for Historic Urban Landscape Based on Residents’ Satisfaction: An Application of Asymmetric Impact-Performance Analysis in Dando," Sustainability, MDPI, Open Access Journal, vol. 12(4), pages 1-16, February.
    5. Lai, Ivan Ka Wai & Hitchcock, Michael, 2017. "Sources of satisfaction with luxury hotels for new, repeat, and frequent travelers: A PLS impact-asymmetry analysis," Tourism Management, Elsevier, vol. 60(C), pages 107-129.
    6. Henner Gimpel & Dominikus Kleindienst & Niclas Nüske & Daniel Rau & Fabian Schmied, 2018. "The upside of data privacy – delighting customers by implementing data privacy measures," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 437-452, November.
    7. Zielke, Stephan, 2008. "Exploring asymmetric effects in the formation of retail price satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 15(5), pages 335-347.
    8. 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.
    9. Chen, Pin-Zheng & Liu, Wan-Yu, 2019. "Assessing management performance of the national forest park using impact range-performance analysis and impact-asymmetry analysis," Forest Policy and Economics, Elsevier, vol. 104(C), pages 121-138.
    10. Darko Prebežac & Josip Mikulić, 2008. "Destination image and key drivers of perceived destination attractiveness," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 20(2), pages 163-178.
    11. Mikulić, Josip & Prebežac, Darko, 2012. "Using dummy regression to explore asymmetric effects in tourist satisfaction: A cautionary note," Tourism Management, Elsevier, vol. 33(3), pages 713-716.

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