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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.

As traditionally measured, services, which include everything from transportation to retail to healthcare to entertainment to hospitality and more, account for most economic activity. Taking a more modern view, we define service as value creation that occurs within systems of interacting economic actors. Service systems have been getting smarter over time, as big data analytics have been used to generate information and automate operations that create ever more value for people in the service systems. In this short letter, we describe some of our perspective on the use of big data analytics in smart service systems, suggesting one framework for thinking about big data in this context and outlining a set of research issues.

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