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Expanding Impacts of Technology-Assisted Service Systems Through Generalization: Case Study of the Japanese Service Engineering Research Project

Author

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  • Kentaro Watanabe

    (Service Intelligence Research Team, Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo 135-0064, Japan)

  • Masaaki Mochimaru

    (Human Informatics Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8560, Japan)

Abstract

The importance of technologies for services has been remarkably emphasized recently with the terms “digitalization” and “smart service systems.” Technologies, especially information and communications technologies, have been regarded as an important driver of innovating services, and various researchers of services and service systems have investigated the development of technology-assisted service systems and their methods. However, few studies report how to generalize and disseminate developed service systems and technologies for other service systems. This type of study is essential to expand the impact of the study on technology-assisted service systems on the industrial and societal levels. As our research question, we examined the necessary processes and deliverables to develop and disseminate technology-assisted service systems. We analyzed five cases of a service engineering research project in Japan that aimed at improving service industry productivity through disseminating developed technologies. We conducted semistructured interviews with the researchers responsible in each case. As a result, we clarified the generalization process through continuous codevelopment with stakeholders in the same or different service systems. We also specified a set of research outputs: technology, activity guideline, and application process, designated as a technological support model for a service system. The features of these outputs can be emphasized by categorizing them into three types: knowledge-worker support model, organization management support model, and association management support model. These processes and outputs provide a practical guideline for developing and disseminating technology-assisted service systems effectively.

Suggested Citation

  • Kentaro Watanabe & Masaaki Mochimaru, 2017. "Expanding Impacts of Technology-Assisted Service Systems Through Generalization: Case Study of the Japanese Service Engineering Research Project," Service Science, INFORMS, vol. 9(3), pages 250-262, September.
    • Handle: RePEc:inm:orserv:v:9:y:2017:i:3:p:250-262
    DOI: 10.1287/serv.2017.0183
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. Lu Kong & Hessam Sadatsafavi & Rohit Verma, 2019. "Usage and Impact of Information and Communication Technologies in Healthcare Delivery," Service Science, INFORMS, vol. 11(3), pages 172-188, October.

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