Research Strategies in Science-based Start-ups - Effects on performance in Danish and Swedish biotechnology

Although biotech start-ups fail or succeed based on their research few attempts have been made to examine if and how they strategize in this core of their activity. Popular views on Dedicated Biotech Firms (DBFs) see the inherent uncertainty of research as defying notions of strategizing, directing instead the attention to the quality of their science, or the roles of boards, management, and collaborative networks etc. Using a unique comprehensive dataset on Danish and Swedish biotech start-ups in drug discovery this paper analyzes their research strategies. Adopting a Simonean point of departure we develop a contingency view on complex problem solving which structures the argument into three steps: 1) Characterising the problem architectures addressed by different types of DBFs; 2) Testing and confirming that DBFs form requisite research strategies, by which we refer to problem solving approaches developed as congruent responses to problem architectures; 3) Testing and confirming that financial valuation of firms is driven by achievements conforming to requisite research strategies. These strategies, in turn, require careful combination of multiple dimensions of research. Findings demonstrate that Shonhoovens classical argument that “strategy matters” is valid not only for the larger high-tech firms covered by her study, but also for small research-based start-ups operating at the very well springs of knowledge where science directly interacts with technologies. Even though a lot more research is needed along these lines, these findings offer new implications for the understanding, management, and financing of these firms.

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations