Publications Database

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.

 

Search Results

Obschonka, M. and Lévesque M. (2026). "AI, Trust, and the Market for Lemons: Rethinking the Credibility of Entrepreneurship Research", Small Business Economics, 66, 1529–1555.

Open Access Download

Keywords
Abstract Entrepreneurship research faces a growing challenge: studies using advanced AI methods are trusted less than those that do not. In a survey of 172 entrepreneurship scholars, we document a substantial trust discount—AI-based studies are perceived, as a category, to be significantly less credible than those using conventional statistical methods. To explain this outcome, we adapt Akerlof’s market-for-lemons framework and introduce the concept of “social lemons”: studies devalued due to deep epistemic opacity. This stems from a double-black-box challenge: opaque AI methods are applied to uncertain, elusive entrepreneurial phenomena. Echoing dysfunctional market dynamics in Akerlof’s market-for-lemons framework, this dual opacity can create conditions whereby flawed research goes undetected, while high-quality work is crowded out. We outline a multi-stakeholder roadmap for safeguarding research credibility, offering actionable measures to help transform today’s trust discount into a future trust dividend—positioning AI as an accelerator of insight, rather than a source of doubt.