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Systemic Functions Evaluation based Technological Innovation System for the Sustainability of IoT in the Manufacturing Industry

Author

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  • Yu-Sheng Kao

    (Department of Technology Management for Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan)

  • Kazumitsu Nawata

    (Department of Technology Management for Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan)

  • Chi-Yo Huang

    (Department of Industrial Education, National Taiwan Normal University, Taipei 106, Taiwan)

Abstract

Technological innovations are regarded as the tools that can stimulate economic growth and the sustainable development of technology. In recent years, as technologies based on the internet of things (IoT) have rapidly developed, a number of applications based on IoT innovations have emerged and have been widely adopted by various public and private sectors. Applications of IoT in the manufacturing industry, such as manufacturing intelligence, not only play a significant role in the enhancement of industrial competitiveness and sustainability, but also influence the diffusion of innovative applications that are based on IoT innovations. It is crucial for policy makers to understand these potential reasons for stimulating IoT industrial sustainability, as they can facilitate industrial competitiveness and technological innovations using supportive means, such as government procurement and financial incentives. Therefore, there is a need to ascertain different factors that may affect IoT industrial sustainability and further explore the relationship between these factors. However, finding a set of factors that affects IoT industrial sustainability is not easy. Recently, the robustness of a theoretical framework, termed the technological innovation system (TIS), has been verified and has been used to explore and analyze technological and industrial development. Thus, it is suitable for this research to use this theoretical model. In order to find out appropriate factors and accurately analyze the causality among factors that influence IoT industrial sustainability, this research presents a Bayesian rough Multiple Criteria Decision Making (MCDM) model based on TIS functions by integrating random forest (RF), decision making trial and evaluation (DEMATEL), Bayesian theory, and rough interval numbers. The proposed analytical framework is validated by an empirical case of defining the causality between TIS functions to enable the industrial sustainability of IoT in the Taiwanese smart manufacturing industry. Based on the empirical study results, the cause group consists of entrepreneurial activities, knowledge development, market formation, and resource mobilization. The effect group is composed of knowledge diffusion through networks’ guidance of the search, and creation of legitimacy. Moreover, the analytical results also provide several policy suggestions promoting IoT industrial sustainability that can serve as the basis for defining innovation policy tools for Taiwan and late coming economies.

Suggested Citation

  • Yu-Sheng Kao & Kazumitsu Nawata & Chi-Yo Huang, 2019. "Systemic Functions Evaluation based Technological Innovation System for the Sustainability of IoT in the Manufacturing Industry," Sustainability, MDPI, vol. 11(8), pages 1-34, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2342-:d:224164
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    2. Chi-Yo Huang & Min-Jen Yang & Jeen-Fong Li & Hueiling Chen, 2021. "A DANP-Based NDEA-MOP Approach to Evaluating the Patent Commercialization Performance of Industry–Academic Collaborations," Mathematics, MDPI, vol. 9(18), pages 1-26, September.
    3. Yu-Sheng Kao & Kazumitsu Nawata & Chi-Yo Huang, 2019. "Evaluating the Performance of Systemic Innovation Problems of the IoT in Manufacturing Industries by Novel MCDM Methods," Sustainability, MDPI, vol. 11(18), pages 1-33, September.
    4. Chi-Yo Huang & Liang-Chieh Wang & Ying-Ting Kuo & Wei-Ti Huang, 2021. "A Novel Analytic Framework of Technology Mining Using the Main Path Analysis and the Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process," Mathematics, MDPI, vol. 9(19), pages 1-24, October.
    5. Skare, Marinko & PORADA-ROCHON, Małgorzata, 2022. "The role of innovation in sustainable growth: A dynamic panel study on micro and macro levels 1990–2019," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Parra-López, Carlos & Reina-Usuga, Liliana & Garcia-Garcia, Guillermo & Carmona-Torres, Carmen, 2024. "Functional analysis of technological innovation systems enabling digital transformation: A semi-quantitative multicriteria framework applied in the olive sector," Agricultural Systems, Elsevier, vol. 214(C).
    7. Jhon Wilder Zartha Sossa & Juan Fernando Gaviria Suárez & Natalia María López Suárez & José Luis Solleiro Rebolledo & Gina Lía Orozco Mendoza & Valentina Vélez Suárez, 2022. "Innovation Systems and Sustainability. Development of a Methodology on Innovation Systems for the Measurement of Sustainability Indicators in Regions Based on a Colombian Case Study," Sustainability, MDPI, vol. 14(23), pages 1-24, November.
    8. Marco Vacchi & Cristina Siligardi & Fabio Demaria & Erika Iveth Cedillo-González & Rocío González-Sánchez & Davide Settembre-Blundo, 2021. "Technological Sustainability or Sustainable Technology? A Multidimensional Vision of Sustainability in Manufacturing," Sustainability, MDPI, vol. 13(17), pages 1-18, September.
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