Korean Journal of Psychology : General

理쒓렐샇 寃깋

Korean Journal of Psychology : General - Vol. 39 , No. 3

[ Regular issue ]
The Korean Journal of Psychology: General - Vol. 39, No. 3, pp.445-480
ISSN: 1229-067X (Print)
Print publication date 25 Sep 2020
Received 22 Sep 2020 Accepted 25 Sep 2020
DOI: https://doi.org/10.22257/kjp.2020.9.39.3.445

국내 ICT 업종 종사자들의 직장에 대한불만 요인 분석 및 전/현직자 간 차이 분석: 토픽 모델링 적용
장재윤 ; 최연재1) ; 강지연2)
1)서강대학교 심리학과, 석사 (cyj12323@naver.com)
2)서강대학교 심리학과, 석사과정 (jofo0222@gmail.com)

An Exploratory Analysis of Domestic ICT Workers’ Dissatisfaction with their Jobs and Differences between Former and Incumbent Employees: Application of Topical Modeling
Jae Yoon Chang ; Yeon Jae Choi1) ; Ji-Yeon Kang2)
1)Sogang University
2)Sogang University
Correspondence to : 장재윤, 서강대학교 심리학과, 교수, 서울시 마포구 신수동 1 Tel: 02-705-7956, E-mail: jych@sogang.ac.kr


초록

본 연구는 대표적 텍스트 마이닝 기법인 토픽 모델링(topic modeling) 방법을 적용하여, ICT 업종 종사자들이 채용 플랫폼의 기업 리뷰에 회사의 단점(불만 요인)으로 기술한 텍스트를 분석하고, 현직자와 전직자 간에 기술 내용에 의미 있는 차이가 있는지를 분석하였다. 구체적으로 Schmidel 등(2019)이 제시한 조직 연구에서의 토픽 모델링 절차를 따라, ICT 업종에 근무하는 전, 현직 종업원들이 국내 채용 관련 플랫폼인 잡플래닛에 회사의 단점으로 기술한 텍스트 데이터(128,464개)를 분석하였으며, 도출된 토픽들을 직무태도 및 이직의 예측변인들에 대한 국내외 연구결과들을 참조하여 해석하였다. 분석 결과, 가장 적절한 것으로 판단된 50개의 토픽 중 44개가 명명되었고, 그중 가장 높은 비율로 나타난 토픽은 ‘낮은 연봉’이었다. 44개의 토픽들 중 현직자와 이직자 간에 유의하게 차이가 나게 언급되는 토픽들이 확인되었고, 추출된 토픽들은 기존의 직무만족 및 이직 연구들에서 중요하게 다루어진 변인들과 유사한 것들도 있지만, 새롭게 도출된 것들도 있었다. 마지막으로, 새로운 접근법으로서의 조직심리 분야의 텍스트 마이닝 방법의 가능성에 대해 논의하였다.

Abstract

This study applied the Topic Modeling method, one of the text mining techniques, to analyze the text describing the shortcomings(unsatisfactory factors) of the company in the corporate review of the recruitment platform and to analyze whether there are meaningful differences in text contents bettween the stayers and the leavers. Specifically, following the procedures proposed by Schmidel et al.(2019), we analyzed the texts describing the company's shortcomings by former and incumbent employees in the ICT sector in South Korea and derived 50 topics through Topic Modeling. 44 of the 50 topics judged to be most appropriate were labeded by referring to domestic and international research results on job attitude and predictors of turnover, and the highest percentage of topic among them was 'low salary'. Among the 44 topics, significant differences were identified between stayers and leavers. In addition, the extracted topics were similar to the variables that were important in the existing job satisfaction and turnover studies, but there were also new ones. Finally, the potential of text mining methods in the field of organizational psychology as a new approach was discussed.


Keywords: job satisfaction, turnover, text-mining, topic modeling, employer review website, big data
키워드: 직무만족, 이직, 텍스트 마이닝, 토픽 모델링, 기업리뷰 웹사이트, 빅데이터

Acknowledgments

세 저자는 논문 완성에 균등하게 기여하였으며, 저자 순서는 나이로 결정하였다. 논문 작성 과정에서 석혜원 교수(서강대)와 이태헌 교수(중앙대)의 지원과 권준범 학부생(서강대)의 도움이 있었다.


References
1. Abbasi, S. M., & Hollman, K. W. (2000). Turnover: The real bottom line. Public Personnel Management, 29(3), 333-342.
2. Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1-7.
3. Baek, Y. (2017). Text mining using R. Hanul Academy.
4. Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of science. The Annals of Applied Statistics, 1(1), 17-35. https://projecteuclid.org/euclid.aoas/1183143727
5. Brayfield, A. H., & Crockett, W. H. (1955). Employee attitudes and employee performance. Psychological Bulletin, 52(5), 396-424.
6. Briscoe, J. P., & Hall, D. T. (2006). The interplay of boundaryless and protean careers: Combinations and implications. Journal of Vocational Behavior, 69, 4-18.
7. Cavanaugh, M. A., Boswell, W. R., Roehling, M. V., & Boudreau, J. W. (2000). An empirical examination of self-reported work stress among US managers. Journal of Applied Psychology, 85(1), 65-74.
8. Choi, N-E. (2013). Job hopping is on the rise. LG Business Insight, 1242, 18-23. http://www.lgeri.com/report/view.do?idx=18024
9. DiMaggio, P., Nag, M., & Blei, D. (2013). Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of US government arts funding. Poetics, 41(6), 570-606.
10. Griffeth, R. W., Hom, P. W., & Gaertner, S. (2000). A meta-analysis of antecedents and correlates of employee turnover: Update, moderator tests, and research implications for the next millennium. Journal of Management, 26(3), 463-488.
11. Hall, D. T. (2002). Careers in and out of organizations. Sage.
12. Herzberg, F., Mausnes, B., Peterson, R. O., & Capwell, D. F. (1957). Job attitudes; review of research and opinion. Psychological Service of Pittsburgh. https://psycnet.apa.org/record/1958-02165-000
13. Holtom, B. C., Mitchell, T. R., Lee, T. W., & Eberly, M. B. (2008). 5 turnover and retention research: a glance at the past, a closer review of the present, and a venture into the future. Academy of Management Annals, 2(1), 231-274.
14. Hom, P. W., Lee, T. W., Shaw, J. D., & Hausknecht, J. P. (2017). One hundred years of employee turnover theory and research. Journal of Applied Psychology, 102(3), 530-545.
15. Ilies, R., Wilson, K. S., & Wagner, D. T. (2009). The spillover of daily job satisfaction onto employees' family lives: The facilitating role of work-family integration. Academy of Management Journal, 52(1), 87-102.
16. Joshi, A., Dencker, J. C., & Franz, G. (2011). Generations in organizations. Research in Organizational Behavior, 31, 177-205.
17. Jung, K. (2010). A study of foresight method based on text mining and complexity network analysis. KISTEP. https://www.kistep.re.kr/c3/sub2_2.jsp
18. Jung, Y., & Suh, Y. (2019). Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems, 123, 113074.
19. Kang, M., Kim, S., & Park, S. (2012). Analysis and utilization of big data. Communications of the Korean Institute of Information Scientists and Engineers, 30(6), 25-32. http://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE01879803
20. Kil, H. (2018). The study of Korean stopwords list for text mining. URIMALGEUL: The Korean Language and Literature, 78, 1-25. http://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE07540294
21. Kim, D., Kang, J., & Lim, J. (2016). Comparative analysis of job satisfaction factors, using LDA topic modeling by industries : The case study of job planet reviews. Journal of Information Technology Services, 15, 157-171.
22. Kim, S. (2005). A meta analysis of content analysis research in Korea: Focusing on methodological elements for better content analysis research. Communication Theory, 1(2), 39-67. http://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE01009312
23. Kwon, M., & Park, S. (2010). Examination of the determinants of SW manpower’ turnover intention: Testing the mediating role of job satisfaction, Journal of Information Technology Services. 9(1), 73-90. https://www.koreascience.or.kr/article/JAKO201023557666456.page
24. Lee, J., & Chae, C. (2019). Differences and multi-dimensionality of the perception of career success among Korean employees: A topic modeling approach. Journal of the Korea Contents Association, 19(6), 58-71.
25. Lee, J., Kim, S., & Kang, J. (2017) A study on job satisfaction/retention factors and job unsatisfaction/turnover factors by industries using job reviews. Journal of Information Technology Services, 16(1), 1-26.
26. Lee, T. W., Hom, P. W., Eberly, M. B., Li, J., & Mitchell, T. R. (2017). On the next decade of research in voluntary employee turnover. Academy of Management Perspectives, 31(3), 201-221.
27. Lee, T. W., & Mitchell, T. R. (1994). An alternative approach: The unfolding model of voluntary employee turnover. Academy of Management Review, 19(1), 51-89.
28. Lee, W., & Choi, S. (2011). Determinants of IT industry employees’ intent to leave. The Journal of the Korea Contents Association, 11(5), 369-383.
29. Lee, Y. (2020. June 20). As the ideal boss of the millennial workers, ‘considerate boss’ took first place. Financial Today. http://www.ftoday.co.kr/news/articleView.html?idxno=203044
30. Lee, Y., & Kang. J. (2018). Related factors of turnover intention among Korean hopital nurses: A systematic review and meta-analysis. Korean Journal of Adult Nursing, 30(1), 1-17.
31. March, J. G., & Simon, H. A. (1958). Organizations. John Wiley & Sons.
32. Mobley, W. H., Griffeth, R. W., Hand, H. H., & Meglino, B. M. (1979). Review and conceptual analysis of the employee turnover process. Psychological Bulletin, 86(3), 493-522.
33. Moro, S., Ramos, R. F., & Rita, P. (2020). What drives job satisfaction in IT companies? International Journal of Productivity and Performance Management, in press.
34. Nahm, C. (2016). An illustrative application of topic modeling method to a farmer’s diary. Cross-Cultural Studies, 22(1), 89-135. http://hdl.handle.net/10371/95582
35. O'Reilly III, C. A., Caldwell, D. F., & Barnett, W. P. (1989). Work group demography, social integration, and turnover. Administrative Science Quarterly, 34(1), 21-37. https://www.jstor.org/stable/2392984
36. Park, H., Kim, D., & Chang, S. (2019). Research trend analysis on smart city based on structural topic modeling(STM). Journal of Digital Contents Society, 20(9), 1839-1846.
37. Park, J., & Song, M. (2013). A study on the research trends in library & information science in Korea using topic modeling. Journal of the Korean Society for Information Management, 30(1), 7-32.
38. Pinder, C. C. (2008). Work motivation in organizational behavior (2nd edition). Psychology Press.
39. Podsakoff, N. P., LePine, J. A., & LePine, M. A. (2007). Differential challenge stressor-hindrance stressor relationships with job attitudes, turnover intentions, turnover, and withdrawal behavior: A meta analysis. Journal of Applied Psychology, 92(2), 438-454.
40. Price, J. L. (1977). The study of turnover. Iowa State University Press.
41. Price, J. L., & Mueller, C. W. (1986). Absenteeism and turnover of hospital employees. JAI press.
42. Reisenbichler, M., & Reutterer, T. (2019). Topic modeling in marketing: Recent advances and research opportunities. Journal of Business Economics, 89, 327-356.
43. Roberts, M. E., Stewart, B. M., & Tingley, D. (2014). stm: R package for structural topic models. Journal of Statistical Software, 10(2), 1-40.
44. Rubenstein, A. L., Eberly, M. B., Lee, T. W., & Mitchell, T. R. (2018). Surveying the forest: A meta‐analysis, moderator investigation, and future‐oriented discussion of the antecedents of voluntary employee turnover. Personnel Psychology, 71(1), 23-65.
45. Schmiedel, T., Müller, O., & vom Brocke, J. (2019). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22(4), 941-968.
46. Schmitt, N. (1994). Method bias: The importance of theory and measurement. Journal of Organizational Behavior, 15(5), 393-398. https://www.jstor.org/stable/2488211
47. Siegrist J. (2012). Effort-Reward Imbalance at work: Theory, measurement and evidence. University Düsseldorf.
48. Smith, P. C., Kendall, L., & Hulin, C. L. (1969). The measurement of satisfaction in work and retirement: A strategy for the study of attitudes. Rand McNally.
49. Stamolampros, P., Korfiatis, N., Chalvatzis, K., & Buhalis, D. (2019). Job satisfaction and employee turnover determinants in high contact services: Insights from Employees’ Online reviews. Tourism Management, 75, 130-147.
50. Weiss, D. J., Dawis, R. V., England, G. W., & Lofquist, L. H. (1967). Manual for the Minnesota Satisfaction Questionnaire. University of Minnesota, Industrial Relations Center.
51. Woo, K., & Jung, S. (2019). Comparison of korean morphology analyzers according to the types of sentence. The Korean Institute of Information Scientists and Engineers, 1388-1390. http://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE09301942
52. Zhou, L, Wang, M, Chang, CH, Liu, S, Zhan, Y, Shi, J. (2017). Commuting stress process and self‐regulation at work: Moderating roles of daily task significance, family interference with work, and commuting means efficacy. Personnel Psychology, 70, 891-922.