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Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing

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  • Sangsung Park

    (Graduate School of Management of Technology, Korea University, Seoul 136-701, Korea)

  • Sunghae Jun

    (Department of Statistics, Cheongju University, Chungbuk 360-764, Korea)

Abstract

The technology of three-dimensional (3D) printing was commercialized in the late 1980s. Since then, the development of this technology has been dramatically increasing. Moreover, 3D printing technology has been used in many different fields, such as electronics and medical appliances, because 3D printing is a technological convergence based on precision instruments, chemical materials, and electrical equipment. The technological impact of 3D printing is so powerful that we need to analyze 3D printing technology to understand the 3D printing industry. In addition, we want more analytical results for understanding the sustainability of 3D printing technology. Thus, we compare the technologies between 3D printing competitors to find their technological innovations and evolution from a technological sustainability. To analyze the 3D printing technology, we propose a new methodology of statistical technology analysis combing social network analysis with time series clustering. In our case study, we make a comparison between “3D Systems” and “Stratasys”, two major 3D printing companies, because they have been leading the sustainable technologies of 3D printing in the market. We illustrate how the proposed methodology can be applied to practical problems from the case study. This paper contributes to the sustainable technology management, and our research can expand to other competitors with diverse technological fields as well as 3D printing.

Suggested Citation

  • Sangsung Park & Sunghae Jun, 2017. "Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:7:p:1142-:d:103090
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    References listed on IDEAS

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    1. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    2. Sternitzke, Christian & Bartkowski, Adam & Schramm, Reinhard, 2008. "Visualizing patent statistics by means of social network analysis tools," World Patent Information, Elsevier, vol. 30(2), pages 115-131, June.
    3. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    4. 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.
    5. Jaehyun Choi & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Predictive Model of Technology Transfer Using Patent Analysis," Sustainability, MDPI, vol. 7(12), pages 1-21, December.
    6. Dar-Zen Chen & Wen-Yau Cathy Lin & Mu-Hsuan Huang, 2007. "Using Essential Patent Index and Essential Technological Strength to evaluate industrial technological innovation competitiveness," Scientometrics, Springer;Akadémiai Kiadó, vol. 71(1), pages 101-116, April.
    7. Jongchan Kim & Joonhyuck Lee & Gabjo Kim & Sangsung Park & Dongsik Jang, 2016. "A Hybrid Method of Analyzing Patents for Sustainable Technology Management in Humanoid Robot Industry," Sustainability, MDPI, vol. 8(5), pages 1-14, May.
    8. Butts, Carter T., 2008. "Social Network Analysis with sna," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i06).
    9. 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.
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    Cited by:

    1. Sang Ho Kook & Ki Hong Kim & Chulung Lee, 2017. "Dynamic Technological Diversification and Its Impact on Firms’ Performance: An Empirical Analysis of Korean IT Firms," Sustainability, MDPI, vol. 9(7), pages 1-16, July.
    2. 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.
    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. 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.
    5. Silvia H. Bonilla & Helton R. O. Silva & Marcia Terra da Silva & Rodrigo Franco Gonçalves & José B. Sacomano, 2018. "Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges," Sustainability, MDPI, vol. 10(10), pages 1-24, October.
    6. Nicholas Igbudu & Zanete Garanti & Temitope Popoola, 2018. "Enhancing Bank Loyalty through Sustainable Banking Practices: The Mediating Effect of Corporate Image," Sustainability, MDPI, vol. 10(11), pages 1-11, November.
    7. 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.
    8. Sunghae Jun, 2018. "Bayesian Count Data Modeling for Finding Technological Sustainability," Sustainability, MDPI, vol. 10(9), pages 1-12, September.
    9. 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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