Prof. Henry Sauermann (Georgia Tech)
- Datum: 27.05.2009
Zeit: 10:15 - 11:45
Ort: Raum 202, Kaulbachstr. 45 / 2.OG
Complicating Merton: The Motives, Incentives, and Innovative Activities of Academic Scientists and Engineers
Using survey data from over 2000 PhD academic scientists and engineers (Survey of Doctorate Recipients 2001 and 2003), we examine to what extent academics’ research and commercial activities are related to their motives concerning intellectual challenge, money, advancement, and contribution to society as well as to financial incentives set by the institution (the share of licensing income going to the inventor). Our measures of commercial activities include patent applications and commercialized patents as well as a novel measure of consulting activity. With respect to research activities, our respondents indicated whether they were engaged primarily in basic research, applied research, or both. We find that the effects of motives vary importantly across the broadly defined fields of life sciences, engineering and physical sciences. In particular, the desire to contribute to society is the key motive predicting patenting in the life sciences, while pecuniary motives are strong predictors of commercial activities in the physical sciences. In engineering, patenting is predicted by challenge and advancement motives. The differences across fields possibly reflect the very different role patents play across academic fields as well as different characteristics of the patented inventions. Financial incentives in the form of royalty shares set by academic institutions have no systematic association with patenting activity for the average academic. However, we find some marginally significant positive relationships for individuals who care strongly about money. We also find significant relationships between pecuniary and nonpecuniary motives on the one hand and consulting as well as the choice between basic and applied research on the other. Again, we observe interesting differences across fields. Royalty shares have no significant effects on consulting activity or the nature of individuals’ research. Our findings complement the growing body of research on commercial activity in academia and have important implications for researchers as well as policy makers.
- Datum: 27.05.2009
Zeit: 17:15 - 18:45
Ort: Raum 024, Ludwigstr. 28 / RG
Knowledge Flows under the Microscope: The Transition of Science PhDs into Startups and Established Firms
Science PhDs entering industrial employment are important channels of knowledge flows (Cohen, Nelson, & Walsh, 2002; Stephan, 2006) and depending on the field, up to 50% of students eventually take industry positions (Fox & Stephan, 2001). Despite this important role of PhD scientists in industry, little is known regarding which scientists decide to work in industry versus academia, and why. We surveyed over 400 PhD students in science and engineering fields at three major U.S. research universities, asking them about their current work, perceptions of different career options, departmental norms regarding industry employment, perceived labor market conditions, their preferences for different job characteristics (e.g., publishing opportunities, freedom), as well as past performance. Using regression analysis, we relate these measures to students’ preferences for future employment in industry (startups and established firms) versus academia. We find that students perceive industry and academia as offering clearly different career paths and students also have very different expectations regarding the nature of employment in startups versus established firms. Students systematically self-select into academia, startups, and established firms depending on their preferences for job characteristics such as the ability to publish, salary, the nature of research (i.e., basic research versus applied research), and access to cutting edge equipment and technologies. Our findings have implications for academic administrators, managers, and policy makers. From a research perspective, our finding of systematic self-selection may be of particular interest to scholars relying on samples of either academic or industrial scientists.