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Identifying opportunities for improvement in online shopping sites


  • Tontini, Gerson


The aim of this study is to show how different methods may provide online shopping managers with information regarding which attributes affect customer satisfaction, and how to identify what to improve or offer in the market. For this purpose, 409 Brazilian users of online shopping answered questionnaires, evaluating 26 attributes. These attributes are grouped on five dimensions: Accessibility, Fault recovery, Security, Flexibility, and Interaction/feedback. The present study evaluates different actions suggested by Importance Performance Analysis (Martilla and James, 1977; Slack, 1994) and Improvement Gap Analysis (Tontini and Picolo, 2010), exploring the limitations and strengths of each method. The results show that Improvement Gap Analysis overcomes the limitations of Importance Performance Analysis, related to the nonlinear relationship between attribute performance and customer satisfaction.

Suggested Citation

  • Tontini, Gerson, 2016. "Identifying opportunities for improvement in online shopping sites," Journal of Retailing and Consumer Services, Elsevier, vol. 31(C), pages 228-238.
  • Handle: RePEc:eee:joreco:v:31:y:2016:i:c:p:228-238
    DOI: 10.1016/j.jretconser.2016.02.012

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    References listed on IDEAS

    1. Abalo, Javier & Varela, Jesus & Manzano, Vicente, 2007. "Importance values for Importance-Performance Analysis: A formula for spreading out values derived from preference rankings," Journal of Business Research, Elsevier, vol. 60(2), pages 115-121, February.
    2. Azzopardi, Ernest & Nash, Robert, 2013. "A critical evaluation of importance–performance analysis," Tourism Management, Elsevier, vol. 35(C), pages 222-233.
    3. Wei-Jaw Deng & Ying-Feng Kuo & Wen-Chin Chen, 2008. "Revised importance--performance analysis: three-factor theory and benchmarking," The Service Industries Journal, Taylor & Francis Journals, vol. 28(1), pages 37-51, January.
    4. Abbie Griffin & John R. Hauser, 1993. "The Voice of the Customer," Marketing Science, INFORMS, vol. 12(1), pages 1-27.
    5. 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.
    6. repec:dau:papers:123456789/4623 is not listed on IDEAS
    7. S. Rolland & Ina Freeman, 2010. "A new measure of e-service quality in France," Post-Print hal-00576616, HAL.
    8. Robert Johnston & Adrian Fern, 1999. "Service Recovery Strategies for Single and Double Deviation Scenarios," The Service Industries Journal, Taylor & Francis Journals, vol. 19(2), pages 69-82, April.
    9. Elena Pokryshevskaya & Evgeny Antipov, 2013. "Importance-performance analysis for internet stores: a system based on publicly available panel data," HSE Working papers WP BRP 08/MAN/2013, National Research University Higher School of Economics.
    10. Bauer, Hans H. & Falk, Tomas & Hammerschmidt, Maik, 2006. "eTransQual: A transaction process-based approach for capturing service quality in online shopping," Journal of Business Research, Elsevier, vol. 59(7), pages 866-875, July.
    11. Dickinger, Astrid & Stangl, Brigitte, 2013. "Website performance and behavioral consequences: A formative measurement approach," Journal of Business Research, Elsevier, vol. 66(6), pages 771-777.
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    Cited by:

    1. Thaichon, Park & Quach, Sara, 2016. "Dark motives-counterfeit purchase framework: Internal and external motives behind counterfeit purchase via digital platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 33(C), pages 82-91.


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