I have delayed my weekly reads due to vacation / travels abroad. Nevertheless, I collected interesting reads from the previous month.
For the next month, I’ll be at the University of Cambridge to conduct a study on how fragment-based drug discovery thrived in the area.
The Legitimacy Threshold Revisited: How Prior Successes and Failures Spill Over to Other Endeavors on Kickstarter – previous outcomes in Kickstarter affect future crowdfunding efforts by “encouraging audiences to repeatedly support other related endeavors or by discouraging them from doing so.”
The Time Efficiency Gain in Sharing and Reuse of Research Data – sharing research data can yield to efficiency gains to the scientific community
Does combining different types of collaboration always benefit firms? Collaboration, complementarity and product innovation in Norway – conventional thinking dictates that firms should collaborate as much as they can to increase the chances of innovation occurring. This study however finds that pursuing all types of collaborations (in this case, scientific and supply chain) might not be useful all the time as these might interact and may negatively impact innovation.
It’s in the Mix: How Firms Configure Resource Mobilization for New Product Success – networks are always fascinating. Here, they look at the new product development through a network perspective.
Improving the peer review process: a proposed market system – Currently, reviewers do not receive any compensation given the amount of work they have to do. This is bad for science as well because papers do not get reviewed properly/fast enough. Creating a market system for the review process for better incentivization of both authors and reviewers might improve the process.
Federal funding of doctoral recipients: What can be learned from linked data – New datasets are always exciting. Researchers in this study propose linking a huge dataset on university payrolls with another huge survey about PhD graduates. It would be interesting to see how other researchers will use data to understand innovation, basic research, career development to name a few.
Universities and open innovation: the determinants of network centrality – Universities that are located centrally in their university-industry networks are also in better position to generate spinoffs and conduct projects with external funding.