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Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models

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  • Jong-Min Kim

    (Statistics Discipline, Division of Sciences and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA)

  • Bainwen Sun

    (Statistics Discipline, Division of Sciences and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA)

  • Sunghae Jun

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

Abstract

To find sustainable technology in various areas, we propose an analytical methodology based on data envelopment analysis (DEA) and the state space model (SSM). DEA is an analytical method used to compare the efficiencies and performances of several items. In DEA, for sustainable technology analysis, the items of DEA can be the technological keywords or international patent classification (IPC) codes in patent documents. In this paper, the proposed method is used to find the relative performance of different patent keywords using comparison and evaluation. We apply this methodology to compare the technological efficiencies between patent keywords for sustainable technology analysis. We apply the additive model and directional distance function of DEA to develop the proposed methodology for building the technological structure of target technology. In addition, we forecast the future trend of target technology using the SSM and find the area of sustainable technology by its result. The SSM is well suited for time series forecasting on technology analysis. We extract the IPC codes from patent documents for the SSM. In our research, we combine the results of DEA and the SSM to find the area of technological sustainability. To illustrate the validity and performance of our research, we conduct a case study using the patent documents used and registered by Apple.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3597-:d:244316
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Sangsung Park & Seongyong Choi & Sunghae Jun, 2021. "Bayesian Structure Learning and Visualization for Technology Analysis," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    3. Sangsung Park & Sunghae Jun, 2020. "Sustainable Technology Analysis of Blockchain Using Generalized Additive Modeling," Sustainability, MDPI, vol. 12(24), pages 1-15, December.
    4. Sunghae Jun, 2019. "Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management," Sustainability, MDPI, vol. 11(18), pages 1-12, September.

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