Fig. 1. The network among the concepts of THR
Fig. 2. Dynamics in the employment of skill field
Fig. 3. Dynamics in the employment of task field
Table 1. Concepts extracted from keyword analysis
Table 2. Basic statistical analysis results
Table 3. Analysis of association between concept of THR
Table 4. ANOVA on personality triat
Table 5. ANOVA on communication triat
Table 6. ANOVA on innovation triat
References
- Statistics Korea. (2018). July 2018 Employment Trends.
- Ministry of Employment and Labor. (2018). Labor Force Survey by Business Type in the first half of 2018.
- K. Lee. (2015). Seeking direction of youth employment policy. Labor Review, 124, 15-30.
- S. Hong. (2018). Private information protection method and countermeasures in Big-data environment : Survey. Journal of the Korea Convergence Society, 9(10), 55-59. https://doi.org/10.15207/JKCS.2018.9.10.055
- H. Joo & H. Joo. (2017). A Study on the Types and Characteristics of Unemployed Youth in Korea: Focusing on the Youth Panel Survey. Journal of Korea Human Resources Administration, 16(2), 51-73.
- S. Kim, H. Yoo & G. S. Han. (2013). Study For the Effectiveness of University's Recruitment Support System. Journal of the Korean Data Analysis Society, 15(6), 3077-3086.
- J. Keum. (2007). The Youth Unemployment in Korea: Facts and Policy Implications. Journal of Social Science, 9, 27-54.
- H. Kwon & H. Yoo. (2011). School to Work Transition of Youth in Korea: the Characteristics and Policy Implications. Social Welfare Policy, 38(1), 1-31.
- S. Park & J. Ban. (2007). The Cause and Labor Market Effects of Overeducation in Korea . The Korean Social Security Association, 23(4), 1-28.
- J. Nam, J. Seong & B. Kim. (2014). Recent employment trend analysis. Labor Review, 110, 55-66.
- B. Sung & Y. You. (2018). Analysis of Vocational Training Needs Using Big Data Technique. Journal of the Korea Convergence Society, 9(5), 21-26. https://doi.org/10.15207/JKCS.2018.9.5.021
- J. J. Castillod. (2016). Teamwork in the Automobile Industry: Radical Change or Passing Fashion?. Springer.
- Y. Jae. (2017). 4th Industrial Revolution. ICCC International Digital Design Invitation Exhibition, 112-112.
- T. D. Jick. (1979). Mixing qualitative and quantitative methods: Triangulation in action. Administrative science quarterly, 24(4), 602-611. https://doi.org/10.2307/2392366
- S. P. Borgatti & M. G. Everett. (1997). Network analysis of 2-mode data. Social networks, 19(3), 243-269. https://doi.org/10.1016/S0378-8733(96)00301-2
- P. N. Krivitsky, M. S. Handcock, A. E. Raftery & P. D. Hoff. (2009). Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. Social networks, 31(3), 204-213. https://doi.org/10.1016/j.socnet.2009.04.001
- M., Brusco & D. Steinley (2011). A tabu-search heuristic for deterministic two-mode blockmodeling of binary network matrices. Psychometrika, 76(4), 612-633. https://doi.org/10.1007/s11336-011-9221-9
- A. Gandomi & M. Haider. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
- M. Kantardzic. (2011). Data mining: concepts, models, methods, and algorithms. John Wiley & Sons.
- R. J. Roiger. (2017). Data mining: a tutorial-based primer. Chapman and Hall/CRC.
- H. C. Park. (2017). A Study on the Relative Mutual Information Measures in a Viewpoint of Association Rule. Journal of the Korean Data Analysis Society, 19(3), 1327-1336. https://doi.org/10.37727/jkdas.2017.19.3.1327
- J. Scott. (2017). Social network analysis. Sage.
- M. Hermann, T. Pentek & B. Otto. (2016). Design principles for industrie 4.0 scenarios. In System Sciences (HICSS), 2016 49th Hawaii International Conference on (pp. 3928-3937). IEEE.
- C. C. Clogg & J. W. Shockey. (1984). Mismatch between occupation and schooling: A prevalence measure, recent trends and demographic analysis. Demography, 21(2), 235-257. https://doi.org/10.2307/2061042
- G. S. Linoff & M. J. Berry. (2011). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.
- X. Wu, X. Zhu, G. Q. Wu & W. Ding. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107. https://doi.org/10.1109/TKDE.2013.109
- B. H. Erickson. (2017). Good networks and good jobs: The value of social capital to employers and employees. In Social capital (pp. 127-158). Routledge.
- P. B. Cragg & M. King. (1993). Small-firm computing: motivators and inhibitors. MIS quarterly, 17(1), 47-60. https://doi.org/10.2307/249509
- C. Wallace & G. Chen. (2006). A multilevel integration of personality, climate, self-regulation, and performance. Personnel Psychology, 59(3), 529-557. https://doi.org/10.1111/j.1744-6570.2006.00046.x
- K. Sisson & J. Storey. (2000). Realities of Human Resource Management: Managing the Employment Relationship. McGraw-Hill Education (UK).
- R. W. Lee, S. Hu, J. Y. Park & H. S. Lee. (2017). Analysis of the Effect of Lifelong Learning Motivation on Lifelong Learning Competency: Focusing on the Mediation Effect of Empowerment. Journal of the Korean Data Analysis Society, 19(2), 931-943. https://doi.org/10.37727/jkdas.2017.19.2.931
- K. L. Choi & B. Kim. (2013). A Study on the Relationship between Job Transition and Personal Attributes using Multiple Logit Model. Journal of the Korean Data Analysis Society, 15(2), 799-812.
- S. Hu, T. Y. Jung & Y. R. Cha. (2009). An Analysis on The Effects of Corporate Image, Harmful Items Relevance And Sponsorship Purpose on Sponsorship Effectiveness. Journal of the Korean Data Analysis Society, 11(4), 2147-2164.