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Top1. Introduction
Recently, technological convergence has become a prominent phenomenon in society. With the advancement of the Fourth Industrial Revolution, convergence is accelerated by blurring boundaries between areas. Technological convergence could trigger newly emerging areas across various industries, which could be interdisciplinary (Lee et al., 2015). These interdisciplinary areas seem to undergo prompt development, and some of them could be influential to academia and industries. Such new and interdisciplinary areas have attracted the interest of researchers in several ways. In particular, it is necessary to examine how research has changed, and how it is likely to evolve. Tracking the emergence of interdisciplinary areas can be difficult and biased because many interdisciplinary studies and their topics are continuously evolving.
Such convergences among various areas require deeper understanding for exploitation, and a data-driven approach could play an important role in not stuck one side or a subjective perspective. Such evolutions among emerging topics could deepen our knowledge to prepare for and exploit relevant changes in interdisciplinary areas. That is, identifying emerging topics and their evolution has become of interest. To address this necessity, this study proposes a framework for analyzing the evolution of interdisciplinary areas based on bibliometric data by applying text mining and simulation techniques. Through this framework, I expect to understand what could emerge from various opportunities and their evolutions. In particular, this study empirically applies the framework to the smart city area.
A smart city could be the frontier where industry, academia, and the daily lives of citizens are interacting. It covers diverse areas relevant to the city. Recently, the development of smart cities has accelerated with the advancement of digital transformation. It is important to carefully track its evolution to establish emerging industries and achieve continuous growth. This study attempts to provide a comprehensive and empirical approach to such convergence-triggered interdisciplinary areas.
Indeed, cities have continuously been places of innovation, and smart cities are considered an important urban platform for pursuing innovation in a sustainable economy (Chatterjee et al., 2017; Joss et al., 2019). Problem-solving with citizen participation through smart city infrastructure is expected to play a critical role in leveraging innovation. Such innovation can eventually contribute to the sustainable growth of citizens’ daily lives (Su et al., 2018). A smart city is a citizen platform for solving problems highlighted in diverse areas (Maestre-Gongora & Bernal, 2019). Therefore, many researchers and practitioners now focus on smart cities, which are regarded as interdisciplinary testbeds for pursuing innovation.
Unfortunately, previous studies might be limited to comprehensively understand and predict interdisciplinary areas, where diverse sub-areas are actively interacting with each other. Previous studies have exploited various techniques for finding emerging technologies; however, unfortunately, these studies seem to lack an integrated perspective, particularly for smart cities. This study attempts to propose a framework for identifying and predicting emerging topics and their convergence from an interdisciplinary area, and this paper attempts to show its empirical application to smart cities. More specifically, it first explores emerging topics by analyzing the abstracts of interdisciplinary areas with latent Dirichlet allocation (LDA), which is a generative probabilistic model for discovering topics from given documents. Applying topic modeling to abstracts is expected to contribute to identifying emerging topics in interdisciplinary areas. Based on the yearly investigation on topic networks, the representative features of evolutions, such as openness and diversity, of these networks are empirically measured using the network simulation technique, SIENA (Simulation Investigation for Empirical Network Analysis).