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

Bai, X., Fang, X., Li, Z. and Sheng, O. (2017). "Utility-Based Link Recommendation for Online Social Networks", Management Science, 63(6), 1938-1952.

Open Access Download

Abstract Link recommendation, which suggests links to connect currently unlinked users, is a key functionality offered by major online social networks. Salient examples of link recommendation include “People You May Know” on Facebook and LinkedIn as well as “You May Know” on Google+. The main stakeholders of an online social network include users (e.g., Facebook users) who use the network to socialize with other users and an operator (e.g., Facebook Inc.) that establishes and operates the network for its own benefit (e.g., revenue). Existing link recommendation methods recommend links that are likely to be established by users but overlook the benefit a recommended link could bring to an operator. To address this gap, we define the utility of recommending a link and formulate a new research problem—the utility-based link recommendation problem. We then propose a novel utility-based link recommendation method that recommends links based on the value, cost, and linkage likelihood of a link, in contrast to existing link recommendation methods that focus solely on linkage likelihood. Specifically, our method models the dependency relationship between the value, cost, linkage likelihood, and utility-based link recommendation decision using a Bayesian network; predicts the probability of recommending a link with the Bayesian network; and recommends links with the highest probabilities. Using data obtained from a major U.S. online social network, we demonstrate significant performance improvement achieved by our method compared with prevalent link recommendation methods from representative prior research.

Johnston, D., Knoppen, D. and Sáenz, M. (2015). "Supply Chain Relationships as a Context for Learning Leading to Innovation", International Journal of Logistics Management, 26(3), 543-567.

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Abstract Purpose The purpose of this paper is to integrate the literature on learning in the context of boundary spanning innovation in supply chains. A two-dimensional framework is proposed: the learning stage (exploration, assimilation, exploitation) and the learning facet (structural, cultural, psychological and policy). Supply chain management (SCM) practices are examined in light of this framework and propositions for further empirical research are developed. Design/methodology/approach In total, 60 empirical papers from the major journals on supply chain relationships published over an 11-year time span (2000-2010) were systematically analyzed. Findings The paper reveals a comprehensive set of best practices and identifies four gaps for future research. First, assimilation and exploitation are largely ignored as mediating learning stages between exploration and performance. Second, knowledge brokers and reputation management are key mechanisms that foster assimilation. Third, the iteration from exploitation back to exploration is critical though underdeveloped in efficiency seeking supply chains. Fourth, the literature stresses structural mechanisms of learning, at the expense of a more holistic view of structural, cultural, psychological and policy mechanisms. Research limitations/implications The search could be extended to other journals that report on joint learning and innovation. Practical implications The framework provides guidelines for practitioners to develop learning capabilities and leverage the knowledge from supply chain partners in order to continuously or radically improve boundary spanning processes and products. Originality/value The study is multi-disciplinary; it applies a model developed by learning scholars to the field of SCM.