CHAI Sen

Sen's research examines the entire developmental course of creative innovations from idea conception to commercialization, with the goal of helping managers and policymakers better support innovation and increase organizations' chances of creating commercially successful ideas. Her current projects focus on understanding why breakthoughs are missed, the process by which researchers or teams of researchers in firms and universities conceive of commercially successful ideas, and the effect of twitter as well as the role of physical and virtual temporary colocation as vehicles for collaboration. In the context of innovation policy, she is studying innovative performance and productivity effects of academic-industry collaboration grants in Denmark, as well as understanding which managerial and structural factors of these funding schemes make them more effective.

Prior to Harvard, Sen worked in the San Francisco and Seattle offices of Deloitte Consulting LLP as a consultant helping clients optimize their business processes. She received a B.Eng. in Electrical Engineering from McGill University and a M.S. in Management Science and Engineering from Stanford University. She has also passed all three levels of the CFA curriculum.

Sen grew up in Shanghai, Paris, New York City and Montreal, and is fluent in Mandarin Chinese and French. She enjoys traveling and learning about new cultures during her free time.

• 2013 : DBA in Technology and Operations Management (Harvard Business School, United States of America)
• 2006 : MS in Management Science and Engineering (Stanford University, United States of America)
• 2005 : BEng in Electrical Engineering (Magna Cum Laude), Minor in Management (McGill University, Canada)

• 2011 : Passed all 3 levels of CFA ® curriculum (CFA Institute, United States of America)

Full-time academic appointments

• 2018 - Present : Associate Professor (ESSEC Business School, France)
• 2015 - 2018 : Assistant Professor (ESSEC Business School, France)

Other Academic Appointments

• 2013 - 2015 : Wertheim Post - Doctoral Fellow ; Advisor: Richard Freeman (Harvard University, United States of America)
• 2012 : Visiting Scholar at the Coleman Fung Institute for Engineering Leadership (University of California, United States of America)

Professional appointments

• 2006 - 2008 : Consultant (Deloitte, United States of America)

• 2022 : Nominated, SMS London Annual Conference Responsible Research Paper Prize
• 2019 : Outstanding Conference Paper (Interdisciplinary Network for Group Research)
• 2018 : ESSEC Foundation Research Award (ESSEC Foundation)
• 2017 : Honorable Mention, Best Conference Paper Award, for the article: Framing Catastrophic Failure as a Learning Opportunity: Lessons From Virgin Galactic and SpaceX (SILVESTRI L., DOSHI A., CHAI S.), 2017 Strategic Management Society Annual Meeting.
• 2017 : Nominated, Best Conference Paper Proceedings for the article Framing Catastrophic Failure as a Learning Opportunity: Lessons FromVirgin Galactic and SpaceX (SILVESTRI L., DOSHI A., CHAI S.), 2017 Academy of Management Annual Meeting.
• 2014 : Finalist, INFORMS/Technology Innovation Management and Entrepreneurship Section Best Dissertation Competition
• 2014 : Nominated, REER Conference Kauffman Foundation Best Student Paper
• 2004 : McKinsey & Company Case Competition Award
• 2004 : National Science and Engineering Research Council of Canada Student Research Award
• 2003 : Canadian Institutes of Health Research Award

• 2019 : ESSEC Business School Research Grant (ESSEC Business School, France)
• 2017 : ESSEC Business School Research Grant (ESSEC Business School, France)
• 2016 - 2018 : Marie Sklodowska - Curie Actions Individual Fellowship - European Commission Horizon 2020 Research and Innovation Framework Programme
• 2015 : ESSEC Business School Research Grant (ESSEC Business School, France)
• 2014 : Economics of Knowledge Contribution and Distribution Research Grant
• 2014 : Mack Institute Research Fellowship ( with Anoop Menon )
• 2014 : Q.A. Shaw McKean Jr. Fellowship
• 2013 : Economics of Knowledge Contribution and Distribution Research Grant
• 2008 - 2013 : Harvard Business School Wyss Doctoral Fellowship (full tuition & stipend)
• 2003 : Federation of Chinese Canadian Professionals Quebec Scholarship
• 2001 - 2005 : McGill University McConnell Undergraduate Scholarship (full tuition)

• CHAI, S., DOSHI, A. and SILVESTRI, L. (2022). How Catastrophic Innovation Failure Affects Organizational and Industry Legitimacy: The 2014 Virgin Galactic Test Flight Crash. Organization Science, 33(3), pp. 1068-1093.
• MELL, J.N., JANG, S. and CHAI, S. (2021). Bridging Temporal Divides: Temporal Brokerage in Global Teams and Its Impact on Individual Performance. Organization Science, 32(3), pp. 731-751.
• CHAI, S., D'AMOUR, A. and FLEMING, L. (2020). Explaining and Predicting the Impact of Authors within a Community: an Assessment of the Bibliometric Literature and Application of Machine Learning. Industrial and Corporate Change, 29(1), pp. 61-80.
• CHAI, S. and MENON, A. (2019). Breakthrough Recognition: Bias Against Novelty and Competition for Attention. Research Policy, 48(3), pp. 733-747.
• CHAI, S. and FREEMAN, R. (2019). Temporary Colocation and Collaborative Discovery: Who Confers at Conferences. Strategic Management Journal, 40(13), pp. 2138-2164.
• CHAI, S. (2017). Near Misses in the Breakthrough Discovery Process. Organization Science, 28(3), pp. 411-428.
• CHAI, S. and SHIH, W. (2016). Bridging Science and Technology through Academic-Industry Partnerships. Research Policy, 45(1), pp. 148-158.
• CHAI, S. and SHIH, W. (2016). Why Big Data Isn’t Enough. MIT Sloan Management Review, 58(2), pp. 57-61.
• SHIH, W. and CHAI, S. (2015). What to Know about Locating in a Cluster. MIT Sloan Management Review, 57(1), pp. 104-107.

• CHAI, S., MENON, A., TABAKOVIC, H. and LEE, C. (2021). Using Machine Learning to Predict Technology Generality. In: 2021 Academy of Management Proceedings. Academy of Management, pp. 10.5465/AMBPP.2021.11418abstract.

• CHAI, S., MENON, A., TABAKOVIC, H. and LEE, C. (2021). Using Machine Learning to Predict Technology Generality. In: 41st SMS Annual Conference 2021. Virtual.