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Data-Driven Understanding of Smart Service Systems Through Text Mining

Author

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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,)

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.

Suggested Citation

  • Chiehyeon Lim & Paul P. Maglio, 2018. "Data-Driven Understanding of Smart Service Systems Through Text Mining," Service Science, INFORMS, vol. 10(2), pages 154-180, June.
    • Handle: RePEc:inm:orserv:v:10:y:2018:i:2:p:154-180
    DOI: serv.2018.0208
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    2. Grybauskas, Andrius & Stefanini, Alessandro & Ghobakhloo, Morteza, 2022. "Social sustainability in the age of digitalization: A systematic literature Review on the social implications of industry 4.0," Technology in Society, Elsevier, vol. 70(C).
    3. Lim, Chiehyeon & Cho, Gi-Hyoug & Kim, Jeongseob, 2021. "Understanding the linkages of smart-city technologies and applications: Key lessons from a text mining approach and a call for future research," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
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    5. Henkens, Bieke & Verleye, Katrien & Larivière, Bart, 2021. "The smarter, the better?! Customer well-being, engagement, and perceptions in smart service systems," International Journal of Research in Marketing, Elsevier, vol. 38(2), pages 425-447.
    6. Lee, Changhun & Lim, Chiehyeon, 2021. "From technological development to social advance: A review of Industry 4.0 through machine learning," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    7. Michel Muschkiet & Tobias Wulfert & Robert Woroch & Gero Strobel & Leonardo Banh, 2023. "Unleashing the digital building bricks," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    8. Minjun Kim & Silvana Trimi, 2023. "Transforming data into information for smart services: integration of morphological analysis and text mining," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 257-280, March.
    9. Lim, Chiehyeon & Kim, Ki-Hun & Kim, Min-Jun & Heo, Jun-Yeon & Kim, Kwang-Jae & Maglio, Paul P., 2018. "From data to value: A nine-factor framework for data-based value creation in information-intensive services," International Journal of Information Management, Elsevier, vol. 39(C), pages 121-135.
    10. Berndt Jesenko & Christian Schlögl, 2021. "The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6785-6801, August.
    11. Gavurova, Beata & Kelemen, Miroslav & Polishchuk, Volodymyr, 2022. "Expert model of risk assessment for the selected components of smart city concept: From safe time to pandemics as COVID-19," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    12. Valerie Graf-Drasch & Maximilian Röglinger & Annette Wenninger & Sabiölla Hosseini, 2022. "A Contextualized Acceptance Model for Proactive Smart Services," Schmalenbach Journal of Business Research, Springer, vol. 74(3), pages 345-387, September.
    13. Kurtz, Julian & Zinke-Wehlmann, Christian & Lugmair, Nina & Schymanietz, Martin & Roth, Angela, 2023. "Characterising smart service systems – Revealing the smart value," SMR - Journal of Service Management Research, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 7(2), pages 112-128.
    14. Mingyu Park & Youngjung Geum, 2021. "On the data-driven generation of new service idea: integrated approach of morphological analysis and text mining," Service Business, Springer;Pan-Pacific Business Association, vol. 15(3), pages 539-561, September.
    15. Roberto Casado-Vara & Angel Martín del Rey & Ricardo S. Alonso & Saber Trabelsi & Juan M. Corchado, 2020. "A New Stability Criterion for IoT Systems in Smart Buildings: Temperature Case Study," Mathematics, MDPI, vol. 8(9), pages 1-13, August.
    16. Sojung Kim & Henri Christiaans & Joon Sang Baek, 2019. "Smart Homes as Product-Service Systems: Two Focal Areas for Developing Competitive Smart Home Appliances," Service Science, INFORMS, vol. 11(4), pages 292-310, December.

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