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Insights at UBC Sauder

UBC Sauder faculty awarded over $360k in SSHRC Insight Development Grants for 2022

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Posted 2022-11-17
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UBC Sauder faculty have been awarded over $360,000 in 2022 Insight Development Grants for their research, made possible by the Government of Canada’s Social Sciences and Humanities Research Council (SSHRC).

The grants were part of an investment of more than $1 billion in funding to support scientists, researchers and students, announced this week by the Honourable François-Philippe Champagne, Minister of Innovation, Science and Industry. The investment, made through a number of funding programs, will support researchers across Canada in different fields, disciplines and stages of their careers.

Social Sciences and Humanities Research Council (SSHRC) Insight Grants SSHRC Insight Grants support research excellence in the social sciences and humanities. Funding is available to both emerging and established scholars for research initiatives of two to five years. Stable support for long-term research initiatives is central to advancing knowledge. It enables scholars to address complex issues about individuals and societies, and to further our collective understanding. In total, SSHRC awarded over $35 million to fund 585 research projects from 71 Canadian institutions through the 2022 Insight Development Grants competition.

UBC Sauder recipients are:

  • Jenna D'Adduzio and co-applicant Yifang Xie for:
    Retail Investor Activity and Managers' Social Media Disclosures
  • Baek Jung Kim and co-applicants Yanwen Wang and Chunhua Wu for:
    Teach for Aptitude: The Impact of Learning Apps
  • Gene Lee for:
    Robots Serve Humans? Understanding the Economic and Societal Impacts of AI Robots in the Service Industry
  • Ning Nan and co-applicant Carson Woo for:
    Gamification for Pro-Environmental Social Cooperation
  • Scott Orr for:
    Scale, Scope and Market Power
  • Lingtao Yu for:
    Rethinking the Nature of Abusive Supervision
  • Mi Zhou for:
    Digital education analytics leveraging interpretable features with machine learning