Data-Driven Understanding of Smart Service Systems Through Text Mining

Chiehyeon Lim () and Paul P. Maglio ()
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Chiehyeon Lim: School of Management Engineering and School of Business Administration, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
Paul P. Maglio: Ernest and Julio Gallo Management Program, School of Engineering, University of California, Merced,

Service Science, 2018, vol. 10, issue 2, 154-180

Abstract: Smart service systems are everywhere, in homes and in the transportation, energy, and healthcare sectors. However, such systems have yet to be fully understood in the literature. Given the widespread applications of and research on smart service systems, we used text mining to develop a unified understanding of such systems in a data-driven way. Specifically, we used a combination of metrics and machine learning algorithms to preprocess and analyze text data related to smart service systems, including text from the scientific literature and news articles. By analyzing 5,378 scientific articles and 1,234 news articles, we identify important keywords, 16 research topics, 4 technology factors, and 13 application areas. We define “smart service system” based on the analytics results. Furthermore, we discuss the theoretical and methodological implications of our work, such as the 5Cs (connection, collection, computation, and communications for co-creation) of smart service systems and the text mining approach to understand service research topics. We believe this work, which aims to establish common ground for understanding these systems across multiple disciplinary perspectives, will encourage further research and development of modern service systems.

Keywords: smart service; smart system; smart service system; text mining; data-driven understanding (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:10:y:2018:i:2:p:154-180

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