Multi-disciplinarity breeds diversity: the influence of innovation project characteristics on diversity creation in nanotechnology

Abstract

Nanotechnology is an emerging and promising field of research. Creating sufficient technological diversity among its alternatives is important for the long-term success of nanotechnologies, as well as for other emerging technologies. Diversity prevents early lock-in, facilitates recombinant innovation, increases resilience, and allows market growth. Creation of new technological alternatives usually takes place in innovation projects in which public and private partners often collaborate. Currently, there is little empirical evidence about which characteristics of innovation projects influence diversity. In this paper we study the influence of characteristics of EU-funded nanotechnology projects on the creation of technological diversity. In addition to actor diversity and the network of the project, we also include novel variables that have a plausible influence on diversity creation: the degree of multi-disciplinarity of the project and the size of the joint knowledge base of project partners. We apply topic modelling (Latent Dirichlet allocation) as a novel method to categorize technological alternatives. Using an ordinal logistic regression model, our results show that the largest contribution to diversity comes from the multidisciplinary nature of a project. The joint knowledge base of project partners and the geographical distance between them were positively associated with technological diversity creation. In contrast, the number and diversity of actors and the degree of clustering showed a negative association with technological diversity creation. These results extend current micro-level explanations of how the diversity of an emerging technology is created. The contribution of this study could also be helpful for policy makers to influence the level of diversity in a technological field, and hence to contribute to survival of emerging technologies.

Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)

“…The presented research has several limitations. First, the analysis uses two high-quality, high-coverage data sources (Web of Science and NSF Awards database) but other data (e.g., publication data from the arXiv preprint repository [25] or patents to capture technology evolution [7]) could be added in future studies to capture science and technology developments. Note that an inclusion of additional data sources would require disambiguation and cleaning of author names and geographical locations.…”

“…Secondly, we used a variety of metrics such as the number of publications and citations, publication sources (journals), disciplines and subdisciplines, authors and their co-authorship relations, authors' geographical locations, keywords, extracted terms (NLP MaxMatch feature engineering method), award $ amounts, type of organization and funding agency. Future work might also like to consider additional metrics such as authors' diversity proposed by [7] or topic diversity recently suggested by [25]. Finally, we focused on a small, static subset of keywords and their alternatives (IoT vs Internet of Things) found in abstracts, titles, or keywords and did not examine contextual or semantic changes of terms over time.…”

“…Other recent studies introduced several novel NLP methods to measure research diversity and interdisciplinarity. For example, topic modeling has been used to measure the degree of topic diversity [6], Shannon's entropy measure was applied to compute technological diversity using EU-funded nanotechnology projects data [7]. This paper uses robust and widely used topic identification methods and focuses on visualizing the emergence, co-evolution, and convergence of science and technology areas.…”

The sustainable start‐up paradox: Predicting the business and climate performance of start‐ups

“…Previous studies have shown that text mining is a particularly well‐suited approach to map technological systems because it can be used to accurately assess a technology's complex features and identify patterns between different technologies (Aharonson & Schilling, 2016; Arts, Appio, & Van Looy, 2013; Blei, 2011; Páez‐Avilés, van Rijnsoever, Juanola‐Feliu, & Samitier, 2018). The technology description sections of the application forms are well suited for text mining because they are similar in length to the abstracts that are often used as the input in text‐mining models (Grün & Hornik, 2011; Páez‐Avilés et al, 2018; Zhao et al, 2015).…”

How academic researchers select collaborative research projects: a choice experiment

“…If the various disciplines are integrated during the project, then collaboration becomes interdisciplinary (ibid). Projects that combine multiple disciplines are more likely to lead to innovations (Fleming 2001;Páez-Avilés et al 2018;Yegros-Yegros et al 2015) and often are viewed as necessary for solving complex societal problems. However, such collaborations are not as rewarding in terms of career development (Van Rijnsoever and Hessels 2011).…”