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The dynamics of online ratings with heterogeneous preferences in online review platform

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  • Zhang, Yuhan
  • Feng, Xin
  • Wu, Ye
  • Xiao, Jinghua

Abstract

Nowadays online consumer reviews (OCR) has increasingly received scholars' attention as an important form of word-of-mouth. Recent study shows that online reviews of a product, such as a book or a restaurant, have effect on long-term consuming behavior and the future rating of the product, it mainly reflects that the early high rating of a product will lead the decrease trend of rating over time. To confirm the existence of the effect and explore how it works, over 180,000 reviews on Dianping.com were collected to investigate the behavior patterns and intrinsic dynamics. In this paper, four temporal evolution patterns were observed via evaluating the cumulative average rating series for each restaurant. Moreover, a conceptual model considering the influence of heterogeneous preferences and the self-selection mechanism was introduced, and the numerical results coincided with the empirical analysis well enough to support the hypotheses. We find special preferences result in tendentious consumption and unrepresentative reviews, these reviews lead the potential consumers to over- or under-estimate the products and directly affect the subsequent ratings. The conclusions of this paper can contribute to the specific policies to adjust the initial rating effect for the specific marketing strategies.

Suggested Citation

  • Zhang, Yuhan & Feng, Xin & Wu, Ye & Xiao, Jinghua, 2018. "The dynamics of online ratings with heterogeneous preferences in online review platform," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 26-30.
    • Handle: RePEc:eee:chsofr:v:109:y:2018:i:c:p:26-30
    DOI: 10.1016/j.chaos.2018.02.003
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    References listed on IDEAS

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    1. Nilashi, Mehrbakhsh & Ahmadi, Hossein & Arji, Goli & Alsalem, Khalaf Okab & Samad, Sarminah & Ghabban, Fahad & Alzahrani, Ahmed Omar & Ahani, Ali & Alarood, Ala Abdulsalam, 2021. "Big social data and customer decision making in vegetarian restaurants: A combined machine learning method," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).

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