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소셜데이터 및 ARIMA 분석을 활용한 소비자 관점의 헬스케어 기술수요 예측 연구

A Study on the Demand Forecasting of Healthcare Technology from a Consumer Perspective : Using Social Data and ARIMA Model Approach

  • 양동원 (연세대학교 기술경영협동과정) ;
  • 이준기 (연세대학교 정보대학원)
  • 투고 : 2020.03.03
  • 심사 : 2020.05.04
  • 발행 : 2020.08.31

초록

Prior studies on technology predictions attempted to predict the emergence and spread of emerging technologies through the analysis of correlations and changes between data using objective data such as patents and research papers. Most of the previous studies predicted future technologies only from the viewpoint of technology development. Therefore, this study intends to conduct technical forecasting from the perspective of the consumer by using keyword search frequency of search portals such as NAVER before and after the introduction of emerging technologies. In this study, we analyzed healthcare technologies into three types : measurement technology, platform technology, and remote service technology. And for the keyword analysis on the healthcare, we converted the classification of technology perspective into the keyword classification of consumer perspective. (Blood pressure and blood sugar, healthcare diagnosis, appointment and prescription, and remote diagnosis and prescription) Naver Trend is used to analyze keyword trends from a consumer perspective. We also used the ARIMA model as a technology prediction model. Analyzing the search frequency (Naver trend) over 44 months, the final ARIMA models that can predict three types of healthcare technology keyword trends were estimated as "ARIMA (1,2,1) (1,0,0)", "ARIMA (0,1,0) (1,0,0)", "ARIMA (1,1,0) (0,0,0)". In addition, it was confirmed that the values predicted by the time series prediction model and the actual values for 44 months were moving in almost similar patterns in all intervals. Therefore, we can confirm that this time series prediction model for healthcare technology is very suitable.

키워드

참고문헌

  1. 김경범, 황경수, "계절 ARIMA 모형을 이용한 제주공항 여객 수요예측 및 효율적 운영에 관한 연구", 한국산학기술학회, 제13권, 제8호, 2012, 3381-3388. https://doi.org/10.5762/KAIS.2012.13.8.3381
  2. 김도성, 조성한, 이정수, 김민석, 김남현, "특허분석을 통한 한국의 디지털 헬스케어 분야 경쟁력 분석 연구", 디지털융복합연구, 제16권, 제9호, 2018, 229-237. https://doi.org/10.14400/JDC.2018.16.9.229
  3. 김종찬, 이준혁, 김갑조, 박상성, 장동식, "특허 키워드 시계열 분석을 통한 부상 기술 예측", 정보처리학회, 제3권, 제9호, 2014, 355-360. https://doi.org/10.3745/KTSDE.2014.3.9.355
  4. 이진수, "디지털 헬스케어 플랫폼과 주요기업 동향", KHIDI Brief, 2014, 1-12.
  5. 장성희, 이진영, 이창원, "UTAUT이론을 이용한 uHealthcare 이용의도에 영향을 미치는 요인", 대한경영학회지 춘계학술발표대회 발표논문집, 2011, 280-288.
  6. 전승표, 박도형, "웹검색 트래픽 정보를 활용한 지능형 브랜드 포지셔닝 시스템 : 태블릿 PC 사례를 중심으로", 지능정보시스템학회지, 제19권, 제3호, 2013, 93-111.
  7. 정기철, 김승현, 정일영, 이다은, 김가은, "수요자 중심의 헬스케어 산업 전망과 대응전략", 정책연구, 2017, 1-160.
  8. 최가영, 이정희, 유리화, "시계열분석을 통한 자연휴양림 계절별 이용수요 예측 : 계절ARIMA 모형과 지수평활 모형을 중심으로", 관광경영연구, 제21권, 제3호, 2017, 271-289.
  9. 한국과학기술연구원, "2014년도 15대 국가 융합기술 수준조사", 한국과학기술연구원 융합연구정책센터, 2014, 1-371.
  10. Butler, D., "When Google Got Flu Wrong", Nature, Vol.494, 2013, 155-156. https://doi.org/10.1038/494155a
  11. Choi, H. and H. Varian, "Predicting the Present with Google Trends", Economic Record, Vol.88, No.1, 2012, 2-9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
  12. Li, X., Q. Xie, J. Jiang, Y. Zhou, and L. Huang, "Identifying and Monitoring the Development Trends of Emerging Technologies Using Patent Analysis and Twitter Data Mining : The Case of Perovskite Solar Cell Technology", Technological Forecasting and Social Change, Vol.146, 2019, 687-705. https://doi.org/10.1016/j.techfore.2018.06.004
  13. Vosen, S. and T. Schmidt, "Forecasting Private Consumption : Survey-Based Indicators vs. Google Trends", Journal of Forecasting, Vol.30, No.6, 2011, 565-578. https://doi.org/10.1002/for.1213
  14. Zeng, Y., P. Dong, Y. Shi, and Y. Li, "On the Disruptive Innovation Strategy of Renewable Energy Technology Diffusion : An AgentBased Model", Energies, Vol.11, No.11, 2018, 1-21.