There is a lack of understanding regarding the optimal conditions for interdisciplinary research. This study investigates what characteristics of researchers are associated with disciplinary and interdisciplinary research collaborations and what collaborations are most rewarding in different scientific disciplines. Our results confirm that female scientists are more engaged in interdisciplinary research collaborations. Further, a scientist's years of research experience are positively related with both types of collaboration. Work experience in firms or governmental organizations increases the propensity of interdisciplinary collaborations, but decreases that of disciplinary collaborations. Disciplinary collaborations occur more frequent in basic disciplines; interdisciplinary collaborations more in strategic disciplines. We also found that in both types of disciplines, disciplinary collaborations contribute more to career development than interdisciplinary collaborations. We conclude with three recommendations for science and innovation policy, while emphasising the need to distinguish between different scientific disciplines.

I explore the sample size in qualitative research that is required to reach theoretical saturation. I conceptualize a population as consisting of sub-populations that contain different types of information sources that hold a number of codes. Theoretical saturation is reached after all the codes in the population have been observed once in the sample. I delineate three different scenarios to sample information sources: “random chance,” which is based on probability sampling, “minimal information,” which yields at least one new code per sampling step, and “maximum information,” which yields the largest number of new codes per sampling step. Next, I use simulations to assess the minimum sample size for each scenario for systematically varying hypothetical populations. I show that theoretical saturation is more dependent on the mean probability of observing codes than on the number of codes in a population. Moreover, the minimal and maximal information scenarios are significantly more efficient than random chance, but yield fewer repetitions per code to validate the findings. I formulate guidelines for purposive sampling and recommend that researchers follow a minimum information scenario.

The high value of collaboration among scientists and of interactions of university researchers with industry is generally acknowledged. In this study we explain the use of different knowledge networks at the individual level from a resource-based perspective. This involves viewing networks as a resource that offers competitive advantages to an individual university researcher in terms of career development. Our results show that networking and career development are strongly related, but it is important to distinguish between different types of networks. Although networks on various levels (faculty, university, scientific, industrial) show strong correlations, we found three significant differences. First, networking within one's own faculty and with researchers from other universities stimulates careers, while interactions with industry do not. Second, during the course of an academic career a researcher's scientific network activity first rises, but then declines after about 20 years. Science-industry collaboration, however, continuously increases. Third, the personality trait 'global innovativeness' positively influences science-science interactions, but not science-industry interactions.