Off the beaten path: what drives scientists’ entry into new fields?

Abstract

Given that venturing into unknown territory carries substantial risk, scientists do not take the decision to enter a new field lightly. This paper analyzes a broad set of factors associated with the risks and rewards from entry into new-to-the-researcher scientific fields, including individual capacities and preferences as well as incentives stemming from career progression and access to funding. Using a panel of researchers in biomedical sciences and science and engineering from a large European research university, we find that productivity affects new field entry as such but is not associated with entry into fields that are very distant to one’s current expertise. Scientists in more senior ranks, with larger co-author networks and collaborating with PhD students, are more likely to enter new fields, but these factors do not represent an additional push to enter very remote fields. Such “long jumps” are more likely to be made by above-average talented rather than merely productive researchers. Finally, accounting for its endogeneity, we find that funding does not make new field entry more likely.

Getting off to a good start: emerging academic fields and early-stage equity financing

“…Governments tend to direct funds to "future technologies" to generate positive externalities resulting from innovative activity (e.g., increase in diversity, competition, and economic growth) (Cantner & Kösters, 2012, p. 924). A further benefit of such increasing interest is that an emerging academic field will also attract a growing number of young high-skilled workers (e.g., Ph.D. students or recent graduates) trained in that field (Kelchtermans et al, 2022), and as a consequence, the new venture associated with such a field will be more likely to be able to find highly skilled employees. The presence of highly skilled employees trained in the field allows the venture to access tacit field-specific academic knowledge, ensuring competitive advantage (Knockaert et al, 2011).…”

“…First, our results may be subject to endogeneity problem coming from reverse causality, namely, the funding may spur the involvement of scientists in emerging academic fields. Recent studies, however, have found that academics enter emerging academic fields because of their career age and level of talent (Kelchtermans et al, 2022), but not as a response to funding.…”

“…Finally, we respond to the call to use computer science advances in management research using NLP to categorize the academic knowledge (Hannigan et al, 2019;Kaplan & Vakili, 2015). When studying academic knowledge, management studies often rely on pre-existing classification schemes (e.g., Web of Science subject categories) (Kelchtermans et al, 2022), which may fail to account for innovativeness. NLP is ideal to study the emergence of new categories that occur outside established classification schemes (Durand & Khaire, 2017), because NLPbased clustering is not restricted to established classifications (Hannigan et al, 2019).…”