Welcome to the new Schulich Peer-Reviewed Publication Database!
The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:
- Faculty Member’s Name;
- Area of Expertise;
- Whether the Publication is Open-Access (free for public download);
- Journal Name; and
- Date Range.
At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.
If you have any questions or input, please don’t hesitate to get in touch.
Zhang, L. Zhang, J. and Tan, J. (2017). "Network Structure, Partner Features and Performance: Evidence from Venture Capital Syndicates, forthcoming in Best Paper Proceeding", Academy of Management Annual Meeting.
AbstractAlthough many research has been done to understand the consequence of network structure on performance at the firm level, we have limited knowledge regarding how network structures influence alliance performance and how they interplay with partner attributes in this process. Focusing on the venture capital (VC) syndicate networks, we argue and show evidence for the positive influences of internal network density and external network structural hole on the performance of the investee startups. Moreover, we investigate how the features of lead and non-lead VC firms interact with the two network structural variables. The findings suggest that a big syndicate team augments the benefits of cooperation and coordination from a dense internal network, but a lead VC firm with high network status strengthens the information benefits from a high level of external network structural holes. Our cross-level research offers important implications on partner selection in strategic alliance research and the roles of lead and non- lead VC firms in VC studies.
Narayanan, M., and M. Lévesque (2014). "Venture Capital Deals: Belief and Ownership", IEEE Transactions on Engineering Management, 61(4), 570-582.
AbstractWe use a principal-agent model to examine how venture capitalists can determine the ownership division when fund-seeking entrepreneurs possess private information on their disutility of effort. This situation is especially applicable to early-stage first-time entrepreneurs seeking funding, since no history exists on their potential performance. The venture capitalist must thus consider this private information by forming a belief on the entrepreneur's effort level toward the proposed investment opportunity. Formal modeling enables us to describe how the deal process unfolds and to build a simulation. We then identify a unique investor's belief and resulting ownership sharing that maximizes the return to the entrepreneur, one that maximizes the return to the venture capitalist, as well as one that maximizes the deal welfare. We also conjecture an ordering relationship between these critical beliefs and between their resulting ownership allocations. Furthermore, we identify conditions under which the venture capitalist should choose to revise the investment offer if rejected by the entrepreneur. This paper thus moves us closer to a comprehensive theory of venture investment decisions.
Cumming, D., Henriques, I. and Sadorsky, P. (2013). "‘Cleantech’ Venture Capital Around the World", International Review of Financial Analysis, 44, 86-97.