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Statistical ERGM analysis for consulting company network data

직장 네트워크 데이터에 대한 통계적 ERGM 분석

  • Park, Yejin (Department of Statistics, Duksung Women's University) ;
  • Um, Jungmin (Department of Statistics, Duksung Women's University) ;
  • Hong, Subeen (Department of Statistics, Duksung Women's University) ;
  • Han, Yujin (Department of Statistics, Duksung Women's University) ;
  • Kim, Jaehee (Department of Statistics, Duksung Women's University)
  • 박예진 (덕성여자대학교 정보통계학과) ;
  • 엄정민 (덕성여자대학교 정보통계학과) ;
  • 홍수빈 (덕성여자대학교 정보통계학과) ;
  • 한유진 (덕성여자대학교 정보통계학과) ;
  • 김재희 (덕성여자대학교 정보통계학과)
  • Received : 2022.04.09
  • Accepted : 2022.05.16
  • Published : 2022.08.31

Abstract

A company is a social group of many individuals that work together to obtain better results, and it is an organization that pursues common goals such as profit. As a result, forming networks among members, as well as individual communication abilities, is critical. The purpose of this research was to determine what factors influence the creation of employee advice relationships. Using the ERGM(Exponential Random Graph Model) approach, we looked at the network data of 44 individuals from consulting firms with offices in the United States and Europe. The significance of structural network factors like connectivity was first discovered. Second, the gender factor had the most significant main influence on the likelihood of adopting each other's advice. Third, geographical homogeneity resulted in higher link probabilities than major impacts of gender. This research looked at ways to make a company's network more efficient and active.

회사는 영리 등의 공동 목표를 달성하는 조직으로, 더 나은 성과를 도출해내기 위해 함께 노력하는 수많은 개인으로 구성된 사회 집단이다. 이에 따라 개인의 의사소통 능력을 비롯한 구성원 간의 네트워크 형성이 중요해지고 있다. 이러한 배경으로부터 본 연구는 직원 간 조언 관계 형성에 어떠한 요인이 영향을 미치는지 알아보고자 수행되었다. 이를 위해 미국과 유럽에 지사를 둔 컨설팅 회사 내 직원 44명의 네트워크 데이터를 ERGM(Exponential Random Graph Model) 방법으로 분석하였다. 분석 결과로 첫째, 연결을 비롯해 네트워크의 구조와 관련한 변수들이 유의하였다. 둘째, 서로 조언을 구할 확률에 성별 속성이 가장 큰 주효과로 나타났다. 셋째, 지역별 동질성은 성별 주효과보다 더 큰 연결 확률을 유도하였다. 이러한 결과로부터 직장 내 네트워크가 조금 더 효율적으로 활발하게 이루어질 수 있는 방법을 제시하였다.

Keywords

Acknowledgement

이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 연구 기초연구실 (No. 2021R1A4A5028907) 지원과 기본연구 (No. 2021R1F1A1054968) 지원을 받아 수행한 연구 과제입니다.

References

  1. Akaike H (1973). 2nd International Symposium of Information Theory, Akademiai Kiado, Budapest.
  2. Brin S and Page L (1998). The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems Journal, 30(1), 107-117. https://doi.org/10.1016/S0169-7552(98)00110-X
  3. Brooks AW, Gino F, and Schweitzer ME (2015). Smart People Ask for (My) Advice: Seeking Advice Boosts Perceptions of Competence, Management Science, 61(6), 1421-1435. https://doi.org/10.1287/mnsc.2014.2054
  4. Cross R and Parker A (2004). The Hidden Power of Social Networks: Understanding How Work Really Gets Done in Organizations, Harvard Business School Press, Boston.
  5. Fruchterman TMJ and Reingold EM (1991). Graph Drawing by Force-directed Placement, Software - Practice and Experience, 21(11), 1129-1164. https://doi.org/10.1002/spe.4380211102
  6. Granovetter M (1974). Getting a Job: A Study of Contacts and Careers, The University of Chicago Press, Chicago.
  7. Guk H (2012). A Study on the Personal Tendency and Work Characteristics that Influence Advice Behavior: Focusing on the advice provider's point of view, Korea University, Seoul.
  8. Hunter DR, Goodreau SM, and Handcock MS (2005). Goodness of Fit of Social Network Models, CSSS Working Paper, 47.
  9. Hunter DR, Handcock MS, Butts CT, Goodreau SM, and Morris M (2008). ergm: A package to fit, simulate and diagnose exponential-family models for networks, Journal of Statistical Software, 24(3), 1-29.
  10. Kang M and Park J (2018). A study on gender differences in organizational network structures, Gender and Culture, 11, 89-123. https://doi.org/10.20992/gc.2018.12.11.2.89
  11. Kang YK, Bae SY, and Hong SH (2021). Analysis of middle school students' friends network in class using ERGM: homophily and relationship in gender, grade, academic achievement and family economic status, Forum For Youth Culture, 67, 5-27. https://doi.org/10.17854/ffyc.2021.07.67.5
  12. Kleinberg JM (1999). Authoritative sources in a hyperlinked environment, Journal of the ACM, 46(5), 604-632. https://doi.org/10.1145/324133.324140
  13. Kolaczyk ED and Csardi G (2014). Statistical Analysis of Network Data with R, Springer, New York.
  14. Lee JW, Kang HJ, Oh SH, Cho GW, Jang PS, Seo HJ, Son SA, Na DY, and Song CH (2021). A Study on Developing Panel Data ofthe Korean Innovation Survey, Science and Technology Policy Institute.
  15. McPherson M, Smith-Lovin L, and Cook JM (2001). Birds of a feather: Homophily in social networks, Annual Review of Sociology, 27(1), 415-444. https://doi.org/10.1146/annurev.soc.27.1.415
  16. Park HH (2019). Using ERGM in exploring network effects: A case study of policy networks, Modern Society and Public Administration, 29, 35-61.
  17. Rapoport A and Horvath WJ (1961). A study of a large sociogram, Behavioral Science Journal, 6(4), 279-291. https://doi.org/10.1002/bs.3830060402
  18. Roy V (2020). Convergence diagnostics for Markov chain Monte Carlo, Annual Review of Statistics and Its Application, 7(1), 387-412. https://doi.org/10.1146/annurev-statistics-031219-041300
  19. Schwarz G (1978). Estimating the dimension of a model, Annals of Statistics Journal, 6(2), 461-464.
  20. Seo IS (2013). A mechanism of collaborative network structure : Focusing on settlement support program, Korea Research Institute for Local Administration, 27, 75-102.
  21. Snijders T (2002). Markov chain Monte Carlo estimation of exponential random graph models, Journal of Social Structure, 3.
  22. Song MS (2017). Communication Makes Good Results in the Enterprise, HR Monthly Review, 4, 141-144.
  23. Stallings MM (2010). Reaching up: The influence of gender, status, and relationship type on men's and women's network preferences, Publicly Accessible Penn Dissertations, 144.
  24. Vega Yon GG, Slaughter A, and Haye KDL (2021). Exponential random graph models for little networks,Social Networks, 64, 225-238. https://doi.org/10.1016/j.socnet.2020.07.005
  25. Woehler ML (2017). Gender and Networking: Building and Benigitting from High Status Ties in the Workplace, Theses and Dissertations-Management, 8.
  26. Yoon YH and Kim HK (2011). Mining and manufacturing panel analysis: Focusing on analysis of the mining and manufacturing industry survey from 2000 to 2009, Statistical Research Institute.