디지털융복합연구 (Journal of Digital Convergence)

  • 제21권2호
  • /
  • Pages.27-32
  • /
  • 2023
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

빅데이터를 활용한 시장분석 및 사업화방법론 분석시스템

Marketability analysis and commercialization methodology analysis system using big dataof Digital Policy & Management

  • 김용호 (광주대학교 AI소프트웨어학과 ) ;
  • 박형범 (호남대학교 대학원 창업학과 )
  • 투고 : 2023.08.25
  • 심사 : 2023.09.20
  • 발행 : 2023.09.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 빅데이터를 활용한 시장성 분석 및 사업화방법론 분석 시스템에 관한 것으로서, 분석대상 제품에 대해 바이럴 마케팅이 가능한 컨텐츠 채널을 토대로 해당 제품의 시장성을 분석할 수 있는 빅데이터를 활용한 시장성 분석 및 사업화방법론 분석 시스템에 관한 것이다. 본 연구에 따른 빅데이터를 활용한 시장성 분석 및 사업화방법론 분석 시스템은 컨텐츠 채널에서 제공되는 마케팅 컨텐츠를 분석하여 분석대상 제품에 대한 시장성을 분석하므로 분석대상 제품에 대한 보다 정확한 바이럴 마케팅 효과를 판별할 수 있다는 장점이 있다고 하겠다.

This study is about a marketability analysis and commercialization methodology analysis system using big data, and a marketability analysis and commercialization methodology analysis system that can analyze the marketability of the product based on a content channel capable of viral marketing. The marketability analysis and commercialization methodology analysis system using big data according to this study analyzes the marketability of the products to be analyzed by analyzing the marketing content provided on the content channel, so it has the advantage of determining more accurate viral marketing effects on the products to be analyzed.

키워드

참고문헌

  1. P. Baah-Peprah & R. Shneor. (2022). A trust-based crowdfunding campaign marketing framework: theoretical underpinnings and big-data analytics practice. International Journal of Big Data Management, 2(1), 1-24. DOI : 10.1504/IJBDM.2022.119453 
  2. J. Zhao & Q. Li. (2022). Big Data-Artificial Intelligence Fusion Technology in Education in the Context of the New Crown Epidemic. Big Data, 10(3). 262-276. DOI : 10.1089/big.2021.0245 
  3. D. Castle, P. D. Hebert, E. L. Clare, I. D. Hogg & C. Tremblay. (2021). Capturing the value of biosurveillance "big data" through natural capital accounting. Big Earth Data, 5(3), 352-367.  https://doi.org/10.1080/20964471.2021.1946290
  4. O. Hinz, B. Skiera, C. Barrot. & J. U. Becker. (2011). Seeding strategies for viral marketing: An empirical comparison. Journal of marketing, 75(6), 55-71. DOI : 10.1509/jm.10.0088 
  5. S. Malodia, A. Dhir, A. Bilgihan, P. Sinha & T. Tikoo. (2022). Meme marketing: How can marketers drive better engagement using viral memes?. Psychology & Marketing, 39(9), 1775-1801.  https://doi.org/10.1002/mar.21702
  6. A. Castiglione, G. Cozzolino, F. Moscato & V. Moscato. (2021). Cognitive Analysis in Social Networks for Viral Marketing. IEEE Transactions on Industrial Informatics, 17(9). 6162-6169  https://doi.org/10.1109/TII.2020.3026013
  7. Lu, Yang & Li, Jiguo(2022). Lightweight Public Key Authenticated Encryption With Keyword Search Against Adaptively-Chosen-Targets Adversaries for Mobile Devices. IEEE Transactions on Mobile Computing, 21(12). 4397-4409.  https://doi.org/10.1109/TMC.2021.3077508
  8. M. Kargar, L. Golab, D. Srivastava, J. Szlichta & M. Zihayat. (2020). Effective keyword search over weighted graphs. IEEE Transactions on Knowledge and Data Engineering, 34(2), 601-616.  https://doi.org/10.1109/TKDE.2020.2985376
  9. B. Zhang & S. Fu. (2023). Do market pressure and social network affect farmers' adoption of low-carbon fertilization practices? Evidence from China. Environmental Science and Pollution Research, 30(18), 51804-51815.  https://doi.org/10.1007/s11356-023-25664-8
  10. B. Wittner & S. Kauffeld. (2023). Social capital and career planning amongst first generation and non-first generation high school and college students in Germany: a social network analysis approach. International Journal for Educational and Vocational Guidance, 23(2). 295-317.  https://doi.org/10.1007/s10775-021-09513-z
  11. M. Gonzalez-Ruiz, F. A. Cervantes & R. Mondragon-Ceballos. (2022). Social networks and tolerance differences associated to grouping patterns in Ateles geoffroyi. Folia Primatologica, 94(1), 51-72.  https://doi.org/10.1163/14219980-20210301
  12. T. Negoro, H. Okura, M. Maehata, S. Hayashi, S. Yoshida, N. Takada & A. Matsuyama. (2019). Trends in clinical trials for stroke by cell therapy: data mining ClinicalTrials. gov and the ICTRP portal site. NPJ Regenerative medicine, 4(1), 20. 
  13. Li, Shiyingxue & S. Nomura. (2017). Utilization of Data at SUUMO, a Real Estate Information Portal Site. Journal Japanese Society For Artificial Intelligence, 32(4), 547-552. 
  14. J. Wilson, A. Brooke, J. Dunn, A. Goodman, C. Hamilton-Wood & B. Vaidya, (2015). Recurrent phaeochromocytoma along the laparoscopic portal sites. Internal medicine journal, 45(3), 359-361.  https://doi.org/10.1111/imj.12678
  15. J. Wu et al. (2023). Towards low polymer content transparent composites via novel integration engineering. Holzforschung, 77(4), 293-304. https://doi.org/10.1515/hf-2022-0150