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
Tan, J. and Zhao, X.Y. (Forthcoming). "Technological Strategies in an Innovation Ecosystem: A Dynamic View of Interaction between Leaders and Followers Based on Evolutionary Game Theory and Multiagent Simulation", Journal of Management Science (管理科学学报).
Abstract
The knowledge-based competition between agents in an innovation ecosystem not only impacts the survival and development of individuals but also affects the evolution of the innovation ecosystem. From the evolutionary game perspective, this paper explores agents’ choice of appropriability strategies and following strategies in an innovation ecosystem under different environments. An asymmetric evolutionary game model and a multi-agent simulation model are constructed successively to analyze the influence of institutional, ecological, and technological factors on the evolutionary equilibrium stability and evolutionary stable strategies of an innovation ecosystem. It is found that the key factors affecting the equilibrium of system evolution are costs of patent operation and protection, the degree of relief on patent disputes, government subsidies, and the relative technical difficulty between substitution and imitation. Based on different situations formed by the above factors, there are four evolutionary stable states: (patenting, imitation), (patenting, substitution), (secret, imitation), and continuous fluctuation of strategies. The outcomes of simulation models which show random fluctuation are consistent with the results of evolutionary stability analysis in the trend. When a cooperation network is dense or a search scope is wide, the long-term evolution of each group tends to the strategies with large average income expectation due to the increase of interaction, the decrease of decision uncertainty, and the improvement of decision effectiveness. When information fuzziness is high, the uncertainty of decision-making increases, and the system fluctuates more violently. Moreover, the strategic interaction between groups is weakened. The long-term evolution of the system deviates from the results of evolutionary stability analysis.Belk, R. (2020). "Resurrecting Marketing", Academy of Marketing Science Review, 10, 168-171.
