The structure and evolution of online rating biases in the sharing economy

Angela Lu, Bruno Abrahao

    Research output: Contribution to conferencePaperpeer-review

    Abstract

    A wave of sharing economy companies are profoundly changing the market landscape, disrupting traditional businesses alongside the social fabrics of exchange. A critical challenge to their growth, however, is that how to generate trust from online to offline transactions. Users in many online platforms rely on reputational systems such as ratings to infer quality and make decisions. However, ratings are biased by behavioral tendencies, such as homophily and power dependence. Our project examines the structure and evolution of rating biases by analyzing massive amount of platform data. Using big data techniques on leading sharing economy platforms, we identify the structure and evolution of biases, attempting to correct the tendencies in system design. We examine rating biases and their relationships to social distance among heterogeneous user populations. The coevolution of reputational systems and trust further implies long-term behavioral trends, which are critical to investigate for business growth.

    Original languageEnglish (US)
    StatePublished - 2019
    Event23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019 - Xi'an, China
    Duration: Jul 8 2019Jul 12 2019

    Conference

    Conference23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019
    CountryChina
    CityXi'an
    Period7/8/197/12/19

    Keywords

    • Rating bias
    • Sharing economy
    • Social network
    • Trust

    ASJC Scopus subject areas

    • Information Systems

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