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A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development

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  • Hong Joo Lee

    (Department of Industrial and Management, Kyonggi University, Suwon 443760, Gyeonggi, Korea)

  • Hoyeon Oh

    (Department of Industrial and Management, Kyonggi University, Suwon 443760, Gyeonggi, Korea)

Abstract

Based on the rapid development of Information and Communication Technology (ICT), all industries are preparing for a paradigm shift as a result of the Fourth Industrial Revolution. Therefore, it is necessary to study the importance and diffusion of technology and, through this, the development and direction of core technologies. Leading countries such as the United States and China are focusing on artificial intelligence (AI)’s great potential and are working to establish a strategy to preempt the continued superiority of national competitiveness through AI technology. This is because artificial intelligence technology can be applied to all industries, and it is expected to change the industrial structure and create various business models. This study analyzed the leading artificial intelligence technology to strengthen the market’s environment and industry competitiveness. We then analyzed the lifecycle of the technology and evaluated the direction of sustainable development in industry. This study collected and studied patents in the field of artificial intelligence from the US Patent Office, where technology-related patents are concentrated. All patents registered as artificial intelligence technology were analyzed by text mining, using the abstracts of each patent. The topic was extracted through topic modeling and defined as a detailed technique. Promising/mature skills were analyzed through a regression analysis of the extracted topics. In addition, the Bass model was applied to the promising technologies, and each technology was studied in terms of the technology lifecycle. Eleven topics were extracted via topic modeling. A regression analysis was conducted to identify the most promising/mature technology, and the results were analyzed with three promising technologies and five mature technologies. Promising technologies include Augmented Reality (AR)/Virtual Reality (VR), Image Recognition and Identification Technology. Mature technologies include pattern recognition, machine learning platforms, natural language processing, knowledge representation, optimization, and solving. This study conducts a quantitative analysis using patent data to derive promising technologies and then presents the objective results. In addition, this work then applies the Bass model to the promising artificial intelligence technology to evaluate the development potential and technology diffusion of each technology in terms of its growth cycle. Through this, the growth cycle of AI technology is analyzed in a complex manner, and this study then predicts the replacement timing between competing technologies.

Suggested Citation

  • Hong Joo Lee & Hoyeon Oh, 2020. "A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development," Sustainability, MDPI, vol. 12(14), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5609-:d:383592
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    References listed on IDEAS

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    Cited by:

    1. Menger Tu & Sandy Dall'erba & Mingque Ye, 2022. "Spatial and Temporal Evolution of the Chinese Artificial Intelligence Innovation Network," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    2. Hansu Hwang & SeJin An & Eunchang Lee & Suhyeon Han & Cheon-hwan Lee, 2021. "Cross-Societal Analysis of Climate Change Awareness and Its Relation to SDG 13: A Knowledge Synthesis from Text Mining," Sustainability, MDPI, vol. 13(10), pages 1-21, May.
    3. Bernardo Nicoletti & Andrea Appolloni, 2023. "Artificial Intelligence for the Management of Servitization 5.0," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
    4. Soyoung Kim & Boyoung Kim, 2020. "A Decision-Making Model for Adopting Al-Generated News Articles: Preliminary Results," Sustainability, MDPI, vol. 12(18), pages 1-14, September.
    5. Xin Du & Hengming Zhang & Yawen Han, 2022. "How Does New Infrastructure Investment Affect Economic Growth Quality? Empirical Evidence from China," Sustainability, MDPI, vol. 14(6), pages 1-30, March.
    6. Carmen Isensee & Kai-Michael Griese & Frank Teuteberg, 2021. "Sustainable artificial intelligence: A corporate culture perspective [Sustainable artificial intelligence: Eine unternehmenskulturelle Perspektive]," NachhaltigkeitsManagementForum | Sustainability Management Forum, Springer, vol. 29(3), pages 217-230, December.

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