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

A. Myers, K. Albats, M. Kozlova, J.S. Yeomans (2026). "Initial Opportunity Selection for Deep Technologies: Deep Technology Opportunity Navigator (DTON)", Technovation, 151, 103464.

Open Access Download

Keywords
Abstract Deep technology ventures (DTVs) are new firms that aim to commercialize fundamental scientific or engineering breakthroughs and are usually located in the high-cost economies that fund such research. Deep technologies can potentially provide value to many industries or sectors but doing so requires making significant investments amid uncertainty. That situation complicates DTVs’ choice of the opportunity to focus on. We employed an iterative design science approach to develop a practical tool that can help DTVs navigate that complex decision. In doing so, we first reviewed the innovation management literature to identify the important factors in opportunity selection by a DTV. We subsequently interviewed a panel of deep technology entrepreneurship experts to supplement that information. These factors were translated into tool requirements. We analyzed existing opportunity selection tools against these requirements, finding only partial support for the DTV context. The same requirements were used to build an add-on to the widely used market opportunity navigator (MON) tool specifically to inform decision-making in DTVs. We label that advancement the deep technology opportunity navigator (DTON). The DTON was deployed as part of a facilitated workshop with two DTVs to collect feedback and iterate the design of the workshop and tool. The results of those workshops are supplemented by a thought experiment to imagine how it could help DTVs, providing illustrative examples of the potential impact of the new tool. The resulting DTON design is a practical tool that DTVs and other stakeholders can use to guide strategic decision-making, and its validation contributes to the literature on science-based entrepreneurship and opportunity selection.