Journal of the Korean Society of Clothing and Textiles (한국의류학회지)

The Korean Society of Clothing and Textiles (한국의류학회)
권호 표지
DOI QR코드

DOI QR Code

An Investigation of a Sensibility Evaluation Method Using Big Data in the Field of Design -Focusing on Hanbok Related Design Factors, Sensibility Responses, and Evaluation Terms-

디자인 분야에서 빅데이터를 활용한 감성평가방법 모색 -한복 연관 디자인 요소, 감성적 반응, 평가어휘를 중심으로-

  • An, Hyosun (Dept. of Clothing & Textiles, Ewha Womans University) ;
  • Lee, Inseong (Dept. of Fashion Industry, Ewha Womans University)
  • Received : 2016.06.13
  • Accepted : 2016.10.06
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study seeks a method to objectively evaluate sensibility based on Big Data in the field of design. In order to do so, this study examined the sensibility responses on design factors for the public through a network analysis of texts displayed in social media. 'Hanbok', a formal clothing that represents Korea, was selected as the subject for the research methodology. We then collected 47,677 keywords related to Hanbok from 12,000 posts on Naver blogs from January $1^{st}$ to December $31^{st}$ 2015 and that analyzed using social matrix (a Big Data analysis software) rather than using previous survey methods. We also derived 56 key-words related to design elements and sensibility responses of Hanbok. Centrality analysis and CONCOR analysis were conducted using Ucinet6. The visualization of the network text analysis allowed the categorization of the main design factors of Hanbok with evaluation terms that mean positive, negative, and neutral sensibility responses. We also derived key evaluation factors for Hanbok as fitting, rationality, trend, and uniqueness. The evaluation terms extracted based on natural language processing technologies of atypical data have validity as a scale for evaluation and are expected to be suitable for utilization in an index for sensibility evaluation that supplements the limits of previous surveys and statistical analysis methods. The network text analysis method used in this study provides new guidelines for the use of Big Data involving sensibility evaluation methods in the field of design.

Keywords

References

  1. Ahn, M. S., & Oh, I. K. (2015). Analysis of attitudes on using five-star hotel packages applying network text analysis method. Korean Journal of Tourism Research, 30(5), 163-181.
  2. An, H., & Lee, I. (2015). A conceptual framework for Asian women's emotional needs in fashion design. International Journal of Fashion Design, Technology and Education, 8(3), 206-213. doi:10.1080/17543266.2015.1053421
  3. An, H., & Lee, I. (2016). Current status of Korean fashion design sensibility evaluation methods and their application overseas. Journal of the Korean Society of Clothing and Textiles, 40(4), 660-668. doi:10.5850/JKSCT.2016.40.4.660
  4. Beck, J. (2007). The sales effect of word of mouth: A model for creative goods and estimates for novels. Journal of Cultural Economics, 31(1), 5-23. doi:10.1007/s10824-006-9029-0
  5. Behrendt, S., Richter, A., & Trier, M. (2014). Mixed methods analysis of enterprise social networks. Computer Networks, 75(Part B), 560-577. doi:10.1016/j.comnet.2014.08.025
  6. Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of 'big data'? McKinsey Quarterly. Retrieved February 28, 2016, from http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/areyou-ready-for-the-era-of-big-data
  7. Caniato, M., Vaccari, M., Visvanathan, C., & Zurbrugg, C. (2014). Using social network and stakeholder analysis to help evaluate infectious waste management: A step towards a holistic assessment. Waste Management, 34(5), 938-951. doi:10.1016/j.wasman.2014.02.011
  8. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345-354. doi:10.1509/jmkr.43.3.345
  9. Choi, J., Yi, S., & Lee, K. C. (2011). Analysis of keyword networks in MIS research and implications for predicting knowledge evolution. Information & Management, 48(8), 371-381. doi:10.1016/j.im.2011.09.004
  10. Cukier, K. (2010). Data, data everywhere: A special report on managing information. London: The Economist.
  11. Davenport, T. H. (2014). Big data at work: Dispelling the myths, uncovering the opportunities. Watertown, MA: Harvard Business Review Press.
  12. Ding, H., & Wang, Y. (2010). Analyzing 'opinion leader' attributes in SNS cyberspace:an investigation of Douban.com. Journalism & Communication, 3, 82-91.
  13. Ham, Y. G., & Chea, S. B. (2012). 빅데이터, 경영을 바꾸다 [Big data, changes management]. Seoul: Samsung Economic Research Institute.
  14. Huang, Y., Basu, C., & Hsu, M. K. (2010). Exploring motivations of travel knowledge sharing on social network sites: An empirical investigation of U.S. college students. Journal of Hospitality Marketing & Management, 19(7), 717-734. doi:10.1080/19368623.2010.508002
  15. Hwang, S. G., Yu, J. S., Choi, C. I., & Jeon, Y. J. (2015). Big data. Seoul: IT Daily.
  16. Jang, Y. I. (2014, October 20). 에딧디(EDITD), 패션 빅데이터 분석 통한 트렌드 예측 [EDITD, Big data analysis for fashion trend forecasting]. FashionNet Korea. Retrieved February 28, 2016, from http://www.fashionnetkorea.com/market/market_gl_brandstory.asp?mode=view&idx=226&user_pwd=&gubun=FASHION&board_cd=MGBRAND&curpage=15&searchWord=&menu_id=&strOpt=
  17. Jung, W. J. (2013). Big data. Seoul: Cloudbooks.
  18. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59-68. doi:10.1016/j.bushor.2009.09.003
  19. Kho, J., Cho, K., & Cho, Y. (2013). A study on recent research trend in management of technology using keywords network analysis. Journal of Intelligence and Information Systems, 19(2), 101-123. doi:10.13088/jiis.2013.19.2.101
  20. Kim, B. (2012). Understanding key factors of users' intentions to repurchase and recommend digital items in social virtual worlds. Cyberpsychology, Behavior and Social Networking, 15(10), 543-550. doi:10.1089/cyber.2012.0128
  21. Kim, B. K., & Kim, M. K. (2015). Keywords network analysis of temple stay using big-data. Journal of Tourism Sciences, 39(5), 27-40. doi:10.17086/JTS.2015.39.5.27.40
  22. Kim, H. A., & Ryu, H. S. (2011). Textures and sensible image on structural properties of washable wool and normal wool knit fabrics (Part 1)-Focus on the relationship of subjective evaluation, mechanical properties and objective hand measurements and preferences-. Journal of the Korean Society of Clothing and Textiles, 35(11), 1362-1376. doi:10.5850/JKSCT.2011.35.11.1362
  23. Kim, J. Y., & Lee, K. H. (2009). Analysis of fashion sensibility on the compound fashion image of contemporary fashion. Journal of the Korean Society for Clothing Industry, 11(1), 6-13.
  24. Kim, Y., & Choi, J. (2010). Color sensibility and preference of the black color fabrics. Korean Journal of the Science of Emotion & Sensibility, 13(2), 337-346.
  25. Lee, J. M., & Rha, J. Y. (2015). Exploring consumer responses to the cross-border e-commerce using text mining. Journal of Consumer Studies, 26(5), 93-124.
  26. Lee, M., & Youn, S. (2009). Electronic word of mouth (eWOM): How eWOM platforms influence consumer product judgement. International Journal of Advertising, 28(3), 473-499. doi:10.2501/S0265048709200709
  27. Lee, S. K. (2015). A review of big data analysis based on marketing perspective. Korean Journal of Business Administration, 28(1), 21-35.
  28. Lee, S. S. (2013). 네트워크 분석 방법론 [Network analysis methods]. Seoul: Nonhyung.
  29. Lee, Y. J., & Yoon, J. H. (2014). A study on utilizing SNS big data in the tourism studies: Based on an analysis of key words for tourism information search. International Journal of Tourism Hospitality Research, 28(3), 5-14.
  30. Lienert, J., Schnetzer, F., & Ingold, K. (2013). Stakeholder analysis combined with social network analysis provides finegrained insights into water infrastructure planning processes. Journal of Environmental Management, 125, 134-148. doi:10.1016/j.jenvman.2013.03.052
  31. Luo, Q., & Zhong, D. (2015). Using social network analysis to explain communication characteristics of travel-related electronic word-of-mouth on social networking sites. Tourism Management, 46, 274-282. doi:10.1016/j.tourman.2014.07.007
  32. McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-66.
  33. Nagamachi, M. (1989). Sensory engineering approach to automobile. Journal of the Society of Automotive Engineers of Japan, 43(1), 94-100.
  34. Narayanan, V. K., & Armstrong, D. J. (Eds.). (2004). Causal mapping for research in information technology. Hershey, PA: IGI Global.
  35. Osgood, C. E. (1952). The nature and measurement of meaning. Psychological Bulletin, 49(3), 197-237. doi:10.1037/h0055737
  36. Park, Y. J. (2014). The study of 'classical music' as reflected in bigdata: The concepts, musicians, and opinions. Journal of Music Education Science, 19, 127-144.
  37. Riegner, C. (2007). Word of mouth on the web: The impact of web 2.0 on consumer purchase decisions. Journal of Advertising Research, 47(4), 436-447. doi:10.2501/S0021849907070456
  38. Shin, S. H. (2014, September 15). 패션업계의 빅데이터 활용 사례 [Application of big data in the fashion industry]. FashionNet Korea. Retrieved February 28, 2016, from http://www.fashionnetkorea.com/market/market_gl_bizreport_issue.asp?gubun=FASHION&board_cd=MGREPORT&strOpt=menu_cd:CF20101&mode=view&idx=1921
  39. Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent dirichlet allocation. Journal of Marketing Research, 51(4), 463-479. doi:10.1509/jmr.12.0106
  40. Turner, D., Schroeck, Mi., & Shockley, R. (2013). Analytics: The real-world use of big data in financial services. Somers, NY: IBM Corporation.
  41. Song, M. J. (2012). 빅 데이터가 만드는 비즈니스 미래지도 [Big data to make future business map]. Seoul: Hans Media Publisher.
  42. Sweeney, J. C., Soutar, G. N., & Mazzarol, T. (2008). Factors influencing word of mouth effectiveness: Receiver perspectives. European Journal of Marketing, 42(3/4), 344-364. doi:10.1108/03090560810852977
  43. The third wave of customer analytics. (2012). PWC. Retrieved February 28, 2016, from http://www.pwc.com/us/en/technology-forecast/2012/issue1/features/feature-customer-analytics-capabilities_affecting-enterprises.html
  44. Walsh, G., & Mitchell, V. W. (2010). The effect of consumer confusion proneness on word of mouth, trust, and customer satisfaction. European Journal of Marketing, 44(6), 838-859. doi:10.1108/03090561011032739
  45. Wang, L. C., Baker, J., Wagner, J. A., & Wakefield, K. (2007). Can a retail web site be social? Journal of Marketing, 71(3), 143-157. doi:10.1509/jmkg.71.3.143
  46. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
  47. You, H. S., & Jung, M. Y. (2013). A study on the factors that influence consumers when purchasing or renting Hanbok. Journal of the Korea Academia-Industrial Cooperation Society, 14(1), 74-79. doi:10.5762/KAIS.2013.14.1.74
  48. Zhang, Z., Ye, Q., Law, R., & Li, Y. (2010). The impact of e-word-of-mouth on the online popularity of restaurants: A comparison of consumer reviews and editor reviews. International Journal of Hospitality Management, 29(4), 694-700. doi:10.1016/j.ijhm.2010.02.002