IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i18p4945-d265945.html
   My bibliography  Save this article

Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management

Author

Listed:
  • Sunghae Jun

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

Abstract

Many companies take the sustainability of their technologies very seriously, because companies with sustainable technologies are better able to survive in the market. Thus, sustainable technology analysis is important issue in management of technology (MOT). In this paper, we study the management of sustainable technology (MOST). This focuses on the sustainable technology in various MOT fields. In the MOST, sustainable technology analysis is dependent on time periods. We propose a method of sustainable technology analysis using a Bayesian structural time series (BSTS) model based on time series data. In addition, we use the Bayesian regression to find the relational structure between technologies. To show the performance of our method and how the method can be applied to practical works, we carry out a case study using the patent data related to artificial intelligence technologies.

Suggested Citation

  • Sunghae Jun, 2019. "Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management," Sustainability, MDPI, vol. 11(18), pages 1-12, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:4945-:d:265945
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/18/4945/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/18/4945/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Junseok Lee & Ji-Ho Kang & Sunghae Jun & Hyunwoong Lim & Dongsik Jang & Sangsung Park, 2018. "Ensemble Modeling for Sustainable Technology Transfer," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    2. 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.
    3. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, 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. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    6. 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.
    7. 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.
    8. Sangsung Park & Sunghae Jun, 2017. "Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shengbin Hao & Haili Zhang & Michael Song, 2019. "Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance," Sustainability, MDPI, vol. 11(24), pages 1-15, December.
    2. Chanamart Intapan & Chukiat Chaiboonsri & Pairach Piboonrungroj, 2021. "Forecasting for the Optimal Numbers of COVID-19 Infection to Maintain Economic Circular Flows of Thailand," Economies, MDPI, vol. 9(4), pages 1-22, October.
    3. Zhu Xiangyu & Yang Yang, 2021. "Big Data Analytics for Improving Financial Performance and Sustainability," Journal of Systems Science and Information, De Gruyter, vol. 9(2), pages 175-191, April.
    4. Pairach Piboonrungroj & Satawat Wannapan & Chukiat Chaiboonsri, 2023. "The Impact of Gastronomic Tourism on Thailand Economy: Under the Situation of COVID-19 Pandemic," SAGE Open, , vol. 13(1), pages 21582440231, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Sunghae Jun, 2018. "Bayesian Count Data Modeling for Finding Technological Sustainability," Sustainability, MDPI, vol. 10(9), pages 1-12, September.
    3. 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.
    4. Sangsung Park & Sunghae Jun, 2020. "Sustainable Technology Analysis of Blockchain Using Generalized Additive Modeling," Sustainability, MDPI, vol. 12(24), pages 1-15, December.
    5. Sangsung Park & Sunghae Jun, 2020. "Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis," Sustainability, MDPI, vol. 12(2), pages 1-11, January.
    6. Sangsung Park & Sunghae Jun, 2017. "Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
    7. Vasile Gherheș & Ciprian Obrad, 2018. "Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI)," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    8. Sangsung Park & Sunghae Jun, 2017. "Technology Analysis of Global Smart Light Emitting Diode (LED) Development Using Patent Data," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    9. Daiho Uhm & Jea-Bok Ryu & Sunghae Jun, 2017. "An Interval Estimation Method of Patent Keyword Data for Sustainable Technology Forecasting," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    10. Alptekin Durmuşoğlu, 2017. "Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences," Sustainability, MDPI, vol. 9(5), pages 1-10, May.
    11. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    12. Jongchan Kim & Jaehyun Choi & Sangsung Park & Dongsik Jang, 2018. "Patent Keyword Extraction for Sustainable Technology Management," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    13. Gyula Dörgő & Viktor Sebestyén & János Abonyi, 2018. "Evaluating the Interconnectedness of the Sustainable Development Goals Based on the Causality Analysis of Sustainability Indicators," Sustainability, MDPI, vol. 10(10), pages 1-26, October.
    14. Qazi, Abroon & Dickson, Alex & Quigley, John & Gaudenzi, Barbara, 2018. "Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 196(C), pages 24-42.
    15. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    16. Fangzhong Qi & Leilei Zhang & Kexiang Zhuo & Xiuyan Ma, 2022. "Early Warning for Manufacturing Supply Chain Resilience Based on Improved Grey Prediction Model," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    17. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    18. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    19. De Iuliis, Melissa & Kammouh, Omar & Cimellaro, Gian Paolo & Tesfamariam, Solomon, 2021. "Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    20. Amin Mahmoudi & Saad Ahmed Javed, 2022. "Probabilistic Approach to Multi-Stage Supplier Evaluation: Confidence Level Measurement in Ordinal Priority Approach," Group Decision and Negotiation, Springer, vol. 31(5), pages 1051-1096, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:4945-:d:265945. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.