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Developing similarity based IPA under intuitionistic fuzzy sets to assess leisure bikeways

Author

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  • Chu, Chun-Hsiao
  • Guo, Yu-Jian

Abstract

Since its introduction in 1977, importance-performance analysis (IPA) has been used widely to assess marketing and operating strategies. In previous IPA studies, three methods have been used to position the crosshairs: the mean, median, and middle positions of scale. However, as several studies have pointed out, differently positioning the crosshairs may lead to dramatically different results. To resolve this inconsistency, this study proposes a similarity-based importance-performance analysis (SBIPA) under intuitionistic fuzzy sets. The basic idea of SBIPA is to classify service attributes into the most similar quadrant of a conventional IPA grid according to the proposed similarity measure. Using SBIPA to assess the Tamsui Golden Riverside Bikeway shows that the natural environment of the bikeway is attractive enough to support tourism, but authorities should pay greater attention to improving the facilities of the bikeway.

Suggested Citation

  • Chu, Chun-Hsiao & Guo, Yu-Jian, 2015. "Developing similarity based IPA under intuitionistic fuzzy sets to assess leisure bikeways," Tourism Management, Elsevier, vol. 47(C), pages 47-57.
  • Handle: RePEc:eee:touman:v:47:y:2015:i:c:p:47-57
    DOI: 10.1016/j.tourman.2014.09.008
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    File URL: http://www.sciencedirect.com/science/article/pii/S0261517714001782
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    References listed on IDEAS

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    Cited by:

    1. Chung-Shing Chan, 2017. "The application of fuzzy sets theory in eco-city classification," Place Branding and Public Diplomacy, Palgrave Macmillan, vol. 13(1), pages 4-17, February.
    2. D'Urso, Pierpaolo & Disegna, Marta & Massari, Riccardo & Osti, Linda, 2016. "Fuzzy segmentation of postmodern tourists," Tourism Management, Elsevier, vol. 55(C), pages 297-308.
    3. Kuei-Hu Chang & Yung-Chia Chang & Kai Chain & Hsiang-Yu Chung, 2016. "Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-29, September.

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