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Evaluating Rank-Coherence of Crowd Rating in Customer Satisfaction

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  • Venera Tomaselli

    (University of Catania)

  • Giulio Giacomo Cantone

    (University of Catania)

Abstract

Crowd rating is a continuous and public process of data gathering that allows the display of general quantitative opinions on a topic from online anonymous networks as they are crowds. Online platforms leveraged these technologies to improve predictive tasks in marketing. However, we argue for a different employment of crowd rating as a tool of public utility to support social contexts suffering to adverse selection, like tourism. This aim needs to deal with issues in both method of measurement and analysis of data, and with common biases associated to public disclosure of rating information. We propose an evaluative method to investigate fairness of common measures of rating procedures with the peculiar perspective of assessing linearity of the ranked outcomes. This is tested on a longitudinal observational case of 7 years of customer satisfaction ratings, for a total amount of 26.888 reviews. According to the results obtained from the sampled dataset, analysed with the proposed evaluative method, there is a trade-off between loss of (potentially) biased information on ratings and fairness of the resulting rankings. However, computing an ad hoc unbiased ranking case, the ranking outcome through the time-weighted measure is not significantly different from the ad hoc unbiased case.

Suggested Citation

  • Venera Tomaselli & Giulio Giacomo Cantone, 2024. "Evaluating Rank-Coherence of Crowd Rating in Customer Satisfaction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 173(1), pages 283-303, May.
    • Handle: RePEc:spr:soinre:v:173:y:2024:i:1:d:10.1007_s11205-020-02581-8
    DOI: 10.1007/s11205-020-02581-8
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    References listed on IDEAS

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    1. Johannes Müller-Trede & Shoham Choshen-Hillel & Meir Barneron & Ilan Yaniv, 2018. "The Wisdom of Crowds in Matters of Taste," Management Science, INFORMS, vol. 64(4), pages 1779-1803, April.
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    3. Joseph K. Goodman & Gabriele Paolacci, 2017. "Crowdsourcing Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(1), pages 196-210.
    4. Jeacle, Ingrid & Carter, Chris, 2011. "In TripAdvisor we trust: Rankings, calculative regimes and abstract systems," Accounting, Organizations and Society, Elsevier, vol. 36(4), pages 293-309.
    5. Domenico Piccolo & Rosaria Simone, 2019. "Rejoinder to the discussion of “The class of cub models: statistical foundations, inferential issues and empirical evidence”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 477-493, September.
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