디지털융복합연구 (Journal of Digital Convergence)

  • 제17권1호
  • /
  • Pages.1-8
  • /
  • 2019
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

4차 산업혁명 시대의 채용경향: 자율주행자동차산업 관련 기업의 채용경향성 분석

Employment Trends in the Fourth industrial Revolution Era : Analysis of Hiring Trends of Autonomous Automobile Industry Related Companies

  • 허성호 (중앙대학교 중앙철학연구소) ;
  • 장혜영 (중앙대학교 정치국제학과)
  • Hu, Sungho (Chung-Ang Philosophical Studies, Chung-Ang University) ;
  • Chang, Hyeyoung (Department of Political Science and International Relations, Chung-Ang University)
  • 투고 : 2018.10.10
  • 심사 : 2019.01.20
  • 발행 : 2019.01.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 4차 산업혁명 시대의 주요 직종군에 주목하여 기업의 채용경향성을 파악하는 것을 목표로 4차 산업혁명 관련 신직업 군 중 하나인 자율주행자동차 산업을 중심으로 기업의 채용 경향을 분석하였다. 기업의 채용공고 정보를 빅데이터로 분석하여 다음의 결과를 도출하였다. 우선, 채용경향성을 기술 분야와 업무분야로 나누어 확인한 결과, 기술분야가 하드웨어분야의 기업이라면 인성특질과 혁신특질이 두드러지는 인재상을 요구하였다. 다음으로 업무분야가 생산직이라면 인성특질이 두드러진 인재상을 원하는 것으로 나타났다. 또한 업무분야가 관리직이라면 소통특질이 두드러진 인재상을 요구하고 있는 것으로 확인되었다. 본 연구결과는 채용준비를 하는 구직자의 입장에서 자신의 인재상 특성을 확인하고 채용경향의 적합도를 고려하여 지원하면 효율적인 취업전략을 도모하는데 기초자료로 사용할 수 있다는 의의가 있다.

The purpose of this study is to analyze the employment trends of autonomous automobile industry which is related to the 4th Industrial Revolution. Previously, big data of the employment trends were divided into skill field and task field. As a result, if a company was employed in the field of skill field, it was required to have talent in which personality traits and innovation traits were prominent. Second, if the task field is a production worker, it is desirable to have talented person with outstanding personality traits. In addition, if the task field is management, it has been confirmed that communication qualities require outstanding talent. The results of this study suggest that it is possible to use the data as a basic data for finding an effective employment strategy by identifying the characteristics of the talented person and considering the suitability of the tendency of hiring.

키워드

DJTJBT_2019_v17n1_1_f0001.png 이미지

Fig. 1. The network among the concepts of THR

DJTJBT_2019_v17n1_1_f0002.png 이미지

Fig. 2. Dynamics in the employment of skill field

DJTJBT_2019_v17n1_1_f0003.png 이미지

Fig. 3. Dynamics in the employment of task field

Table 1. Concepts extracted from keyword analysis

DJTJBT_2019_v17n1_1_t0001.png 이미지

Table 2. Basic statistical analysis results

DJTJBT_2019_v17n1_1_t0002.png 이미지

Table 3. Analysis of association between concept of THR

DJTJBT_2019_v17n1_1_t0003.png 이미지

Table 4. ANOVA on personality triat

DJTJBT_2019_v17n1_1_t0004.png 이미지

Table 5. ANOVA on communication triat

DJTJBT_2019_v17n1_1_t0005.png 이미지

Table 6. ANOVA on innovation triat

DJTJBT_2019_v17n1_1_t0006.png 이미지

참고문헌

  1. Statistics Korea. (2018). July 2018 Employment Trends.
  2. Ministry of Employment and Labor. (2018). Labor Force Survey by Business Type in the first half of 2018.
  3. K. Lee. (2015). Seeking direction of youth employment policy. Labor Review, 124, 15-30.
  4. 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
  5. 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.
  6. 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.
  7. J. Keum. (2007). The Youth Unemployment in Korea: Facts and Policy Implications. Journal of Social Science, 9, 27-54.
  8. 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.
  9. S. Park & J. Ban. (2007). The Cause and Labor Market Effects of Overeducation in Korea . The Korean Social Security Association, 23(4), 1-28.
  10. J. Nam, J. Seong & B. Kim. (2014). Recent employment trend analysis. Labor Review, 110, 55-66.
  11. 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
  12. J. J. Castillod. (2016). Teamwork in the Automobile Industry: Radical Change or Passing Fashion?. Springer.
  13. Y. Jae. (2017). 4th Industrial Revolution. ICCC International Digital Design Invitation Exhibition, 112-112.
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. M. Kantardzic. (2011). Data mining: concepts, models, methods, and algorithms. John Wiley & Sons.
  20. R. J. Roiger. (2017). Data mining: a tutorial-based primer. Chapman and Hall/CRC.
  21. 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
  22. J. Scott. (2017). Social network analysis. Sage.
  23. 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.
  24. 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
  25. G. S. Linoff & M. J. Berry. (2011). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.
  26. 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
  27. 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.
  28. P. B. Cragg & M. King. (1993). Small-firm computing: motivators and inhibitors. MIS quarterly, 17(1), 47-60. https://doi.org/10.2307/249509
  29. 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
  30. K. Sisson & J. Storey. (2000). Realities of Human Resource Management: Managing the Employment Relationship. McGraw-Hill Education (UK).
  31. 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
  32. 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.
  33. 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.