Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Analysis of the Differences in Recognition of Talented Human Resources Between Enterprises and Job Seekers

구인기업과 구직자 간에 인식하는 인재상의 차이 분석

  • Hu, Sung-Ho (Department of Psychology, ChungAng University)
  • Received : 2020.05.15
  • Accepted : 2020.07.20
  • Published : 2020.07.28
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Abstract

This study comparatively analyzed the differences in the talented human resources perceived by enterprises and job seekers in terms of recruitment trends of companies related to the 4th Industrial Revolution, focusing on 16 factors. The analysis data was collected from enterprises and job seekers related to the 4th Industrial Revolution, and the analysis method was applied to a convergence research methodology that mixes social network analysis and variance analysis using big data type. As a result, several things were verified. First, large enterprises emphasized communication, and small enterprises emphasized competency and confidence. Second, in the manufacturing industry, enterprises emphasized confidence and competence, and job seekers emphasized spec and passion. Third, in the service industry, enterprises emphasized personality and competence, and job seekers emphasized spec and global. Fourth, there was a big difference in talented human resources between enterprises and job seekers according to manufacturing and service industries. Based on these results, we discussed the opening of employment information for enterprises to reduce the recognition mismatch in the talented human resources.

본 연구는 4차 산업혁명 관련기업의 채용경향에 있어서 구인기업과 구직자가 인식하는 인재상의 차이를 16개로 구성된 요소를 중심으로 비교분석하였다. 분석자료는 4차 산업혁명 관련 기업과 구직자들을 대상으로 자료를 수집하였으며, 분석방법은 빅데이터 유형의 자료를 활용하여 사회연결망 분석과 변량분석을 혼합한 융합연구방법론을 적용하였다. 결과적으로 다음의 몇 가지를 검증하였다. 첫째, 대기업은 소통을 강조하고, 중소기업은 역량과 자신감을 강조하는 것으로 나타났다. 둘째, 제조업에서 구인기업은 자신감과 역량을 강조하고, 구직자는 스펙과 열정을 강조하는 것으로 나타났다. 셋째, 서비스업에서 구인기업은 인성과 역량을 강조하고, 구직자는 스펙과 글로벌을 강조하는 것으로 나타났다. 넷째, 구직자들은 제조업과 서비스업에 따른 인재상이 큰 차이가 있었다. 이러한 결과를 토대로 인재상에 대한 인식 불일치 현상을 줄이기 위한 기업의 채용 정보개방에 대해 논의하였다.

Keywords

References

  1. J. Flynn, S. Dance & D. Schaefer. (2017). Industry 4.0 and its Potential Impact on Employment Demographics in the UK. Advances in Transdisciplinary Engineering, 6, 239-244.
  2. M. Skilton & F. Hovsepian. (2017). The 4th industrial revolution: Responding to the impact of artificial intelligence on business. Cham: Springer.
  3. A. M. French, J. Shim, M. Risius & H. Jain. (2019). The 4th Industrial Revolution Powered by the Integration of 5G, AI, and Blockchain. AMCIS in Cancun.
  4. T. Schroder. (2019). A regional approach for the development of TVET systems in the light of the 4th industrial revolution: the regional association of vocational and technical education in Asia. International Journal of Training Research, 17(1), 83-95. https://doi.org/10.1080/14480220.2019.1629728
  5. U. Meyer. (2019). The emergence of an envisioned future. Sensemaking in the case of "Industrie 4.0" in Germany. Futures, 109, 130-141. https://doi.org/10.1016/j.futures.2019.03.001
  6. S. Hu & H. Chang. (2019). Employment Trends in the Fourth industrial Revolution Era: Analysis of Hiring Trends of Autonomous Automobile Industry Related Companies. Journal of Digital Convergence, 17(1), 1-8. DOI:https://doi.org/10.14400/JDC.2019.17.1.001
  7. B. H. Shin & H. K. Jeon. (2018). A Study on Disaster Information Support using Big Data. Journal of the Korea Convergence Society, 9(8), 25-32. DOI : https://doi.org/10.15207/JKCS.2018.9.8.025
  8. H. Lee, Y. W. Kim & K. Y. Kim. (2018). Implement of MapReduce-based Big Data Processing Scheme for Reducing Big Data Processing Delay Time and Store Data. Journal of the Korea Convergence Society, 9(10), 13-19. DOI : https://doi.org/10.15207/JKCS.2018.9.10.013
  9. L. C. Freeman. (2017). Research methods in social network analysis. New York: Routledge.
  10. E. Giuliani & C. Pietrobelli. (2016). Social Network Analysis for Evaluating Cluster Development Programs. The Impact Evaluation of Cluster Development Programs, 37, 117-150.
  11. J. E. Mote. (2005). R&D ecology: using 2-mode network analysis to explore complexity in R&D environments. Journal of Engineering and Technology Management, 22(1-2), 93-111. https://doi.org/10.1016/j.jengtecman.2004.11.004
  12. F. Ciarapica, M. Bevilacqua & S. Antomarioni. (2019). An approach based on association rules and social network analysis for managing environmental risk: A case study from a process industry. Process Safety and Environmental Protection, 128, 50-64. https://doi.org/10.1016/j.psep.2019.05.037
  13. J. Song, Q. Feng, X. Wang, H. Fu, W. Jiang & B. Chen. (2019). Spatial association and effect evaluation of CO2 emission in the Chengdu-Chongqing urban agglomeration: quantitative evidence from social network analysis. Sustainability, 11(1), 1. https://doi.org/10.3390/su11010001
  14. B. K. Sung & Y. Y. You. (2018). Analysis of Vocational Training Needs Using Big Data Technique. Journal of the Korea Convergence Society, 9(5), 21-26. DOI : https://doi.org/10.15207/JKCS.2018.9.5.021
  15. M. A. Somers, S. J. Cabus, W. Groot and H. M. van den Brink. (2019). Horizontal mismatch between employment and field of education: Evidence from a systematic literature review. Journal of Economic Surveys, 33(2), 567-603. https://doi.org/10.1111/joes.12271
  16. J. P. Martin & R. Torres. (2000). Korean Labor Market and Social Safety-Net Reforms: Challenges and Policy Requirements. Journal of the Korean Economy, 1(2), 267-300.
  17. L. Barth et al. (2014). Semantic word cloud representations: Hardness and approximation algorithms. Heidelberg: Springer.