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Applying LSA text mining technique in envisioning social impacts of emerging technologies: The case of drone technology

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  • Kwon, Heeyeul
  • Kim, Jieun
  • Park, Yongtae

Abstract

This research proposes a novel method of identifying and understanding the holistic overview of emerging technologies’ unintended consequences. Latent Semantic Analysis (LSA) text mining technique is employed to yield multiple groups of contextually similar terms from future-oriented data sources, comprising both experts’ and the public's concerns regarding future technologies. Resulting term clusters are considered as new abstractions, or so-called scenarios, of future social impacts. Furthermore, the study acquires greater depth and breadth in conceptualizing social impacts through considering condition- and value-related terms as key linking factors to previous social impact-related literature. Our proposed methodology seeks to gain insights into the utilization of future-oriented data sources for the foresight activity, hoping to mitigate public skepticism and pursue a better social acceptance of emerging technologies.

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  • Kwon, Heeyeul & Kim, Jieun & Park, Yongtae, 2017. "Applying LSA text mining technique in envisioning social impacts of emerging technologies: The case of drone technology," Technovation, Elsevier, vol. 60, pages 15-28.
    • Handle: RePEc:eee:techno:v:60-61:y:2017:i::p:15-28
    DOI: 10.1016/j.technovation.2017.01.001
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    1. Carlsen, Henrik & Johansson, Linda & Wikman-Svahn, Per & Dreborg, Karl Henrik, 2014. "Co-evolutionary scenarios for creative prototyping of future robot systems for civil protection," Technological Forecasting and Social Change, Elsevier, vol. 84(C), pages 93-100.
    2. Kerstin Cuhls, 2003. "From forecasting to foresight processes-new participative foresight activities in Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(2-3), pages 93-111.
    3. Ribeiro, Barbara E. & Quintanilla, Miguel A., 2015. "Transitions in biofuel technologies: An appraisal of the social impacts of cellulosic ethanol using the Delphi method," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 53-68.
    4. Roger Blake & Ramakrishna Ayyagari, 2012. "Analyzing Information Systems Security Research to Find Key Topics, Trends, and Opportunities," Journal of Information Privacy and Security, Taylor & Francis Journals, vol. 8(3), pages 37-67, July.
    5. Keller, Jonas & von der Gracht, Heiko A., 2014. "The influence of information and communication technology (ICT) on future foresight processes — Results from a Delphi survey," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 81-92.
    6. Carlsen, H. & Dreborg, K.H. & Godman, M. & Hansson, S.O. & Johansson, L. & Wikman-Svahn, P., 2010. "Assessing socially disruptive technological change," Technology in Society, Elsevier, vol. 32(3), pages 209-218.
    7. Featherston, Charles R. & Ho, Jae-Yun & Brévignon-Dodin, Laure & O'Sullivan, Eoin, 2016. "Mediating and catalysing innovation: A framework for anticipating the standardisation needs of emerging technologies," Technovation, Elsevier, vol. 48, pages 25-40.
    8. Franks, Daniel M. & Cohen, Tamar, 2012. "Social Licence in Design: Constructive technology assessment within a mineral research and development institution," Technological Forecasting and Social Change, Elsevier, vol. 79(7), pages 1229-1240.
    9. K. Coussement & D. van den Poel, 2008. "Integrating the voice of customers through call center emails into a decision support system for churn prediction," Post-Print hal-00788086, HAL.
    10. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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