Abstract
With today’s technological advances and uncertainty, interdisciplinary research (IDR) is recognized as the core element for an advancement not only in academic realms but also in non-academic worlds. Conventionally, the concept of interdisciplinarity has been measured indirectly through using historical data and estimating how frequently the knowledge is exchanged among various disciplinary fields. However, they were incapable of determining whether a certain discipline may turn out to be a successful interdisciplinary research field in the future. This paper, in response, highlights the significance of data points appearing in the future for predicting the degree of interdisciplinarity in academic fields. To this mean, the future data points are predicted through a stochastic basis, and they are further used to predict the future degree of disciplinary convergence. To demonstrate the effectiveness of this approach, we have selected the case of nanotechnology. Nanotechnology is a multidisciplinary academic field where diverse scientific and engineering disciplines merge with one another and contributes to various scientific development and social problem solving. In details, citation data of nanotechnology journals are gathered to estimate the expected citation frequency through a stochastic model, called Glänzel–Schubert–Schoepflin model, and its future potential of IDR is predicted through a diversity index. The result of the analysis predicted that the IDR of nanotechnology will be actively occurred with other disciplines such as chemistry, material science and energy in the future, and it also holds higher probability of being more frequently conducted compared to other field of studies.
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This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1A09917423).
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Jang, W., Kwon, H., Park, Y. et al. Predicting the degree of interdisciplinarity in academic fields: the case of nanotechnology. Scientometrics 116, 231–254 (2018). https://doi.org/10.1007/s11192-018-2749-z
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DOI: https://doi.org/10.1007/s11192-018-2749-z