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.
Lévesque, M., Obschonka, M. and S. Nambisan (2022). "Pursuing Impactful Entrepreneurship Research Using Artificial Intelligence", Entrepreneurship Theory and Practice, 46(4).
AbstractIt is time for the entrepreneurship field to come to terms with leading-edge artificial intelligence (AI). AI holds great promise to transform entrepreneurship into a more relevant and impactful field, but it must overcome conflicts between the AI-driven research approach and that of the traditional, theory-based research process. We explore these opportunities and challenges and suggest concrete approaches that entrepreneurship researchers can use to harness the power of AI with rigor and enhance research relevance. We conclude that incorporating the power of AI in entrepreneurship research and managing the associated risks offer a new and “grand challenge” for the field.
Dolbec, P., Fischer, E. and Canniford, R. (2021). "Something Old, Something New: Enabled Theory Building in Qualitative Marketing Research", Marketing Theory, 21(4).