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Bayesian Count Data Modeling for Finding Technological Sustainability

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  • Sunghae Jun

    (Department of Big Data and Statistics, Cheongju University, Chungbuk 28503, Korea)

Abstract

Technology developments change society, and society demands new and innovative technology developments. We analyze technology to understand society and technology itself. Much research related to technology analysis has been introduced in various fields. Most of it has been on patent analysis. This is because detailed and accurate results of research and development are patented. In this paper, we study a new patent analysis method based on the count data model and Bayesian regression analysis. Using the count data model, we analyzed the technological keywords extracted from the collected patent documents. We used the prior distribution of Bayesian statistics to reflect the experience and knowledge of the relevant technological experts in the analysis model. Moreover, we applied the proposed model to find sustainable technologies. Finding and developing sustainable technologies is an important activity for companies and research institutes to maintain their technological competitiveness. To illustrate how our modeling could be applied to real domains, we carried out a case study using the patent documents related to artificial intelligence.

Suggested Citation

  • Sunghae Jun, 2018. "Bayesian Count Data Modeling for Finding Technological Sustainability," Sustainability, MDPI, vol. 10(9), pages 1-12, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3220-:d:168650
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    References listed on IDEAS

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    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, September.
    2. Hilbe,Joseph M., 2014. "Modeling Count Data," Cambridge Books, Cambridge University Press, number 9781107611252, January.
    3. Sangsung Park & Seung-Joo Lee & Sunghae Jun, 2015. "A Network Analysis Model for Selecting Sustainable Technology," Sustainability, MDPI, vol. 7(10), pages 1-16, September.
    4. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    5. Juhwan Kim & Sunghae Jun & Dongsik Jang & Sangsung Park, 2018. "Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models," Sustainability, MDPI, vol. 10(1), pages 1-12, January.
    6. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
    7. Sangsung Park & Sunghae Jun, 2017. "Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
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    Citations

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    Cited by:

    1. Sangsung Park & Sunghae Jun, 2020. "Sustainable Technology Analysis of Blockchain Using Generalized Additive Modeling," Sustainability, MDPI, vol. 12(24), pages 1-15, December.
    2. Arik Sadeh & Claudia Florina Radu & Cristina Feniser & Andrei Borşa, 2020. "Governmental Intervention and Its Impact on Growth, Economic Development, and Technology in OECD Countries," Sustainability, MDPI, vol. 13(1), pages 1-30, December.
    3. Sangsung Park & Seongyong Choi & Sunghae Jun, 2021. "Bayesian Structure Learning and Visualization for Technology Analysis," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    4. Jong-Min Kim & Bainwen Sun & Sunghae Jun, 2019. "Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    5. Nguyen Thao Nguyen & Tran Hoang Thanh Phuong, 2024. "The assessment of sustainable tourism: Application to Kien Giang destination in Vietnam," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 14(1), pages 104-122.
    6. Marco Vacchi & Cristina Siligardi & Davide Settembre-Blundo, 2024. "Driving Manufacturing Companies toward Industry 5.0: A Strategic Framework for Process Technological Sustainability Assessment (P-TSA)," Sustainability, MDPI, vol. 16(2), pages 1-25, January.

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