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

Sengupta, Ushnish and Henry M. Kim (2021). "Meeting Changing Customer Requirements in Food and Agriculture Through the Application of Blockchain Technology", Frontiers in Blockchain.

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Abstract This research summarizes the implementation of blockchain technology in the food and agriculture industry in Canada. Our research indicates that blockchain solutions are an existing and proven set of technologies. We also describe how blockchain based supply chain traceability information has many more benefits than its current use for food safety and product recalls. We recommend that costs for development of blockchain based solutions should also be distributed across stakeholders, and apportioned by the relevant industry associations. Our research indicates that adoption of blockchain technology in agriculture will achieve critical mass earlier when the industry applies a consortium approach, in a regulatory environment that is supported by government. This report also makes recommendations relevant to the integration of blockchain for end consumers of food.

Goldsby, T.J., Knemeyer, A.M., Schwieterman, M.A. and Rungtusanatham, M. (2018). "Supply Chain Portfolio Characteristics: Do They Relate to Post-IPO Financial Performance?", Transportation Journal, 57(4), 429-463.

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Abstract In the years following an initial public offering (IPO), firms have to manage portfolios of customers and suppliers in order to achieve growth goals during this particularly uncertain time in a firm's lifecycle. The current research sheds light on three key questions: (1) Do firms benefit from conducting a large portion of business with a large customer or supplier? (2) Is it beneficial if the focal firm represents a large portion of business for customers and suppliers? And, (3) is balanced portfolio dependence helpful to a focal firm? The extant literature, drawing insights from the logics of power and embeddedness, is divided on these questions. We utilize a secondary data set of focal firms (post-IPO) and their portfolios of relationships with customers and suppliers to explain where each theoretical perspective applies to the management of supply chain portfolios.

Cook, W., Guo, C., Li, W., Li, Z., Liang, L. and Zhu, J. (2017). "Efficiency Measurement of Multistage Processes: Context Dependent Numbers of Stages", Asia Pacific Journal of Operations Research, 34(6).

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Abstract An important area of research involving the benchmarking methodology data envelopment analysis (DEA), concerns the modeling of multistage situations. In the usual multistage settings, it is generally assumed that all decision-making units (DMUs) have the same number and configuration of stages. However, in many real-world examples, this assumption does not hold. Consider, for example, a supply chain setting where for some DMUs, products are shipped directly from a supplier to a retailer (single-stage), while for other DMUs, products can be transshipped through distribution centers (two or more stages). In the current paper, we investigate an efficiency measurement situation where the DMUs exhibit a mix of single and two-stage setups. The particular case examined involves a set of high technology firms that can be thought of as falling into two groups; those firms where the output of interest is the annual revenue generated, and those that not only generate revenue, but as well invest a portion of that revenue in R&D. Firms in the first group can be viewed as being single-stage DMUs while those in the other group are of the two-stage type. The modeling complication here is that the set of DMUs do not explicitly form a homogeneous set of units. We develop a DEA-style model aimed at measuring efficiency in the presence of such nonhomogeneous two-group structures.