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., Lévesque, M., Ooms, F., Pollack, J.M., Grégoire, D., Behrend, T.S. and B. Nikolaev. (Forthcoming). "Entrepreneurship ex Machina: Transformative Artificial Intelligence for Theory and Practice", Entrepreneurship Theory & Practice.
Folta, T.B., Lévesque, M., Schulz, M., Schwens, C, and K. Wennberg. (Forthcoming). "Mapping the Landscape of Hybrid Entrepreneurship and Understanding its Implications for Entrepreneurship Research", Entrepreneurship Theory & Practice.
Asgari, N., Subramanian, A., Lévesque, M. and P.-H. Soh. (Forthcoming). "It’s Time To Break Up: Dynamics Surrounding Young-Established Firm Alliance Duration", Production and Operations Management.
Bryan, C., & Lyons, B.J. (Forthcoming). "Beyond Backlash: Advancing Dominant-Group Employees’ Learning, Allyship, and Growth Through Social Identity Threat", Academy of Management Review.
Aaron Babier, Timothy Chan, Adam Diamant, Rafid Mahmood (Forthcoming). "Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation", Management Science.
Abstract
We consider a data-driven framework for learning to generate decisions to instances of continuous optimization problems where the feasible set varies with an instance-specific auxiliary input. We use a data set of inputs and feasible solutions, as well as an oracle of feasibility, to iteratively train two machine learning models. The first model is a binary classifier for feasibility, which then serves as a barrier function to train the second model via an interior point method. We develop theory and optimality guarantees for interior point methods when given a barrier that relaxes the feasible set, and extend these results to obtain probabilistic out-of-sample guarantees for our learning framework. Finally, we implement our method on a radiation therapy treatment planning problem to predict personalized treatments for head-and-neck cancer patients.Kautonen, T., Lévesque, M., Stephan, U. and R. Bakker (Forthcoming). "Entrepreneurship Across the Lifespan: Unpacking the Age-Entrepreneurship Relationship", Journal of Business Venturing.
Slade Shantz, A., Zietsma, C., Kistruck, G., & Barin-Cruz, L. (Forthcoming). "Exploring the Relative Efficacy of ‘Within-Logic Contrasting’ and ‘Cross-Logic Analogizing’ Framing Tactics for Adopting New Entrepreneurial Practices in Contexts of Poverty", Journal of Business Venturing.
Weber, L., Slade Shantz, A., Kistruck, G., & Lount, R. (Forthcoming). "Give Peace a Chance? Addressing Organizational Conflict Events in Intractable Conflict Environments", Journal of Management.
Adam Diamant (Forthcoming). "Introducing Prescriptive and Predictive Analytics to MBA Students with Microsoft Excel", INFORMS Transactions on Education.
Adam Diamant, Anton Schevchenko, David Johnston, Fayez Quereshy (Forthcoming). "Consecutive Surgeries With Complications: The Impact of Scheduling Decisions", International Journal of Operations & Production Management.