Jiang Bian
Prof. Jiang BIAN
Assistant Professor

3917 0027

KK 1127

Academic & Professional Qualification
  • PhD, Stanford University
  • MS, Stanford University
  • Bachelors, Tianjin University

Jiang is an Assistant Professor in Management and Strategy at HKU Business School. Jiang’s research interests lie at the intersection of organizations, innovation and firm strategy. Her work focuses on understanding how firm activities and performance are impacted by regulatory and social forces. Specifically, her dissertation examines how entrepreneurial ventures manage collaborative innovation both within and beyond their organizational boundaries.

Jiang earned her PhD from the Stanford Technology Ventures Program at Stanford University. She also holds an MS in Civil and Environmental Engineering from Stanford University, as well as a BS in Management and a BA in English from Tianjin University. Before her PhD, Jiang worked in strategic consulting for infrastructure project development, procurement, and investment.

  • Strategy in the Digital World (PMGM7022, 2021-2022)
  • Strategic Management (STRA4701, Semester 2, 2021-2022)
Research Interest
  • Innovation Management
  • Collaborative Strategy
  • Entrepreneurship
  • Regulatory Environment
  • Healthcare
  • China
Selected Publications
  • “Good to Go First? Position Effects in Expert Evaluation of Early-Stage Ventures,” (with Jason Greenberg, Jizhen Li and Yanbo Wang), Management Science, Volume 68 Issue 1, January 2022, pp. 300-315.


Awards and Honours
  • National Science Foundation of the U.S. (NSF) Doctoral Dissertation Research Grant
Recent Publications
Good to Go First? Position Effects in Expert Evaluation of Early-Stage Ventures

There is often considerable anxiety and conflicting advice concerning the benefits of presenting/being evaluated first. We thus investigate how expert evaluators vary in their evaluations of entrepreneurial proposals based upon the order in which they are evaluated. Our research setting is a premiere innovation fund competition in Beijing, China, where the prize money at stake is economically meaningful, and evaluators are quasi-randomly assigned to evaluate written grant proposals without the possibility of peer influence. This enables us to credibly recover a causal position effect. We also theorize and test how heterogeneity in evaluators’ prior (context-specific) judging experience moderates position effects. Overall, we find that a proposal evaluated first requires total assets in the top 10th percentile to merely equal the evaluation of a proposal in the bottom 10th percentile that is not evaluated first. Firm and evaluator fixed-effects models yield consistent findings. We consider evaluation design elements that may mollify these position effects in the discussion section.