Grace Gu - Faculty & Research - Harvard Business School
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Grace Gu

Doctoral Student

Yuan (Grace) Gu is a Doctoral Candidate in the Technology and Operations Management Unit at Harvard Business School. Her research interests lie in the intersection of technological innovation and strategic management, especially in high-technology industries such as IT and online platforms.
Working Papers
  1. Trust and Disintermediation: Evidence from an Online Freelance Marketplace

    Grace Gu and Feng Zhu

    As an intermediary improves trust between two sides of its market to facilitate matching and transactions, it faces an increased risk of disintermediation—with sufficient trust, the two sides may circumvent the intermediary to avoid the intermediary’s fees. We investigate the relationship between increased trust and disintermediation by leveraging a randomized control trial on a major online freelance marketplace. Our results show that enhanced trust increases the chance for hiring high-quality freelancers. When the trust level is sufficiently high, however, it also increases disintermediation, which offsets the revenue gains from the increase in hiring high-quality freelancers. We also identify heterogeneity across clients and freelancers in their tendencies to disintermediate.

    Keywords: disintermediation; intermediaries; trust; online marketplace; Trust; Marketplace Matching;


    Gu, Grace, and Feng Zhu. "Trust and Disintermediation: Evidence from an Online Freelance Marketplace." Harvard Business School Working Paper, No. 18-103, May 2018.  View Details
  2. Ideological Segregation among Online Collaborators: Evidence from Wikipedians

    Shane Greenstein, Grace Gu and Feng Zhu

    Do online communities segregate into separate conversations about “contestable knowledge”? We analyze the contributors of biased and slanted content in Wikipedia articles about U.S. politics and focus on two research questions: (1) Do contributors display tendencies to contribute to topics with similar or opposing bias and slant? (2) Do contributors learn from experience with extreme or neutral content, and does that experience change the slant and bias of their contributions over time? Despite heterogeneity in contributors and their contributions, we find an overall trend towards less segregated conversations. Contributors tend to edit articles with slants that are the opposite of their own views, and the slant from experienced contributors becomes less extreme over time. The experienced contributors with the most extreme biases decline the most. We also find some significant differences between Republicans and Democrats.

    Keywords: Information; Prejudice and Bias; Online Technology; Web Sites;


    Greenstein, Shane, Grace Gu, and Feng Zhu. "Ideological Segregation among Online Collaborators: Evidence from Wikipedians." Working Paper. (Revised March 2017.)  View Details