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.

Open Access Download

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.

View Paper

Keywords
Abstract Purpose The authors determine how the scheduling and sequencing of surgeries by surgeons impacts the rate of post-surgical complications and patient length-of-stay in the hospital. Design/methodology/approach Leveraging a dataset of 29,169 surgeries performed by 111 surgeons from a large hospital network in Ontario, Canada, the authors perform a matched case-control regression analysis. The empirical findings are contextualized by interviews with surgeons from the authors’ dataset. Findings Surgical complications and longer hospital stays are more likely to occur in technically complex surgeries that follow a similarly complex surgery. The increased complication risk and length-of-hospital-stay is not mitigated by scheduling greater slack time between surgeries nor is it isolated to a few problematic surgery types, surgeons, surgical team configurations or temporal factors such as the timing of surgery within an operating day. Research limitations/implications There are four major limitations: (1) the inability to access data that reveals the cognition behind the behavior of the task performer and then directly links this behavior to quality outcomes; (2) the authors’ definition of task complexity may be too simplistic; (3) the authors’ analysis is predicated on the fact that surgeons in the study are independent contractors with hospital privileges and are responsible for scheduling the patients they operate on rather than outsourcing this responsibility to a scheduler (i.e. either a software system or an administrative professional); (4) although the empirical strategy attempts to control for confounding factors and selection bias in the estimate of the treatment effects, the authors cannot rule out that an unobserved confounder may be driving the results. Practical implications The study demonstrates that the scheduling and sequencing of patients can affect service quality outcomes (i.e. post-surgical complications) and investigates the effect that two operational levers have on performance. In particular, the authors find that introducing additional slack time between surgeries does not reduce the odds of back-to-back complications. This result runs counter to the traditional operations management perspective, which suggests scheduling more slack time between tasks may prevent or mitigate issues as they arise. However, the authors do find evidence suggesting that the risk of back-to-back complications may be reduced when surgical pairings are less complex and when the method involved in performing consecutive surgeries varies. Thus, interspersing procedures of different complexity levels may help to prevent poor quality outcomes. Originality/value The authors empirically connect choices made in scheduling work that varies in task complexity and to patient-centric health outcomes. The results have implications for achieving high-quality outcomes in settings where professionals deliver a variety of technically complex services.