Korean Academic Society of Business Administration
[ Article ]
korean management review - Vol. 49, No. 1, pp.129-153
ISSN: 1226-1874 (Print)
Print publication date 29 Feb 2020
Received 05 Nov 2019 Accepted 02 Dec 2019
DOI: https://doi.org/10.17287/kmr.2020.49.1.129

ERGM을 활용한 자동차산업 공급 네트워크 분석

박철순* ; 강아롬**
*(제1저자) 숙명여자대학교 경영학부 부교수 cspark@sookmyung.ac.kr
**(교신저자) 숙명여자대학교 경영학과 박사과정 clara121@sm.ac.kr
Exploring Endogeneous Processes in Automobile Supply Network: An Exponential Random Graph Model Analysis
Chulsoon Park* ; Ahrom Kang**
*Associate Professor, Sookmyung Women’s University, First Author
**Ph.D candidate, Sookmyung Women’s University, Corresponding Author

초록

본 논문은 자동차산업의 공급 네트워크를 구성하는 내재적 프로세스를 분석했다. Exponential Random Graph Model (ERGM) 모형을 2017년 자동차산업 편람 데이터에 적용하여 공급 네트워크의 근본적인 프로세스를 탐색하였다. 그 결과 자동차산업의 공급 네트워크는 기본적으로 사슬 구조였다. 물자를 공급받은 납품처가 가공 후 다른 업체에게 공급하는 사슬 형태가 유의하게 많았다. 기본 형태의 공급사슬은 중개업체를 공유하거나 층위를 건너뛰지 않았다. 또한, 고객사의 고객사에게 직접 납품하는 구조는 찾기 어려웠다. 즉, 이행성은 나타나지 않았다. 하지만, 공유하는 중개업체가 많을수록 둘 간의 직접적인 거래 관계가 나타날 가능성은 커지는 것으로 확인됐다. 납품처의 경우 다양한 업체로부터 납품받을 경우 추가적인 납품 가능성이 생기는 빈익빈 부익부 현상을 확인하였다. 공급업체의 경우 자원의 한계로 인해 다양한 납품처에 납품할수록 추가적인 납품 가능성은 작아짐을 확인했다.

Abstract

This paper analyzes the endogeneous processes that make up the supply network of the automotive industry. A supply network is basically a network in which one company provides goods and information to another. The actual network we observe is a structurally emergent form in which several individual endogenous processes interact. The supply network is a complex adaptation system created by interaction processes. Nevertheless, previous studies have overlooked the interaction of these attributes or the endogenous processes. This is largely due to the limitations of existing research methodologies. Therefore, this study will examine the fundamental aspect of supply network as a complex adaptation system by using a new network analysis method that recognizes interactions in supply network.

An Exponential Random Graph Model (ERGM) model was applied to the 2017 Automotive Handbook to explore the underlying processes of the supply network. An ERGM is an novel approach that incorporates endogenous structural effects of network and allows the interactions among various covariates of nodes or links. As a result, the supply network in the automotive industry was basically a chain structure. There were many types of chains that were supplied by suppliers to other companies after its own processing. The basic supply chain did not share intermediaries or skip tiers. In addition, it was difficult to find a structure that directly delivered to customers. In other words, there was no transitivity which can be easily observed in human networks. However, the more intermediaries they share, the more likely they are to have a direct trade relationship. In the case of the purchasers, we confirmed the phenomenon of the rich get richer, which is the possibility of additional transaction when receiving from various companies. As for the suppliers, it is revealed that the possibility of additional delivery is lower as they are delivered to various suppliers due to resource limitations.

This study revealed for the first time the endogenous process that constitutes the supply network of the Korean automobile industry. The ERGM model, which recognizes the dependencies between explanatory variables, was used to analyze the fundamental processes of the automotive supply network. Comprehensive inclusion of various explanatory variables to identify the supply relationship in the automotive industry could lead to new conclusions, including the results of previous studies.

Keywords:

Automobile Industry, Supply Network, Exponential Random Graph Models

키워드:

자동차산업, 공급 네트워크, ERGM

Acknowledgments

이 논문은 2017년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2017S1A5A2A03069011)This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A5A2A03069011)

References

  • 오중산(2009), “생산자원과 생산역량 및 성과 간의 인과관계: 자원기반이론에 근거를 둔 실증연구,” 한국생산관리학회지, 20(4), pp.91-121.
  • Akaike, H.(1998), “Information Theory and an Extension of the Maximum Likelihood Principle.” Selected Papers of Hirotugu Akaike. New York, NY, Springer, pp.199-213. [https://doi.org/10.1007/978-1-4612-1694-0_15]
  • Bellamy, M. A. and R. C. Basole(2013), “Network Analysis of Supply Chain Systems: A Systematic Review and Future Research,” Systems Engineering, 16(2), pp.235-249. [https://doi.org/10.1002/sys.21238]
  • Bellamy, M. A., S. Ghosh and M. Hora(2014), “The Influence of Supply Network Structure on Firm Innovation,” Journal of Operations Management, 32(6), pp.357-373. [https://doi.org/10.1016/j.jom.2014.06.004]
  • Borgatti, S. P., M. G. Everett and J. C. Johnson (2013), Analyzing Social Networks, SAGE Publications Limited.
  • Borgatti, S. P. and X. U. N. Li(2009), “On Social Network Analysis in a Supply Chain Context,” Journal of Supply Chain Management, 45 (2), pp.5-22. [https://doi.org/10.1111/j.1745-493X.2009.03166.x]
  • Carnovale, S. and S. Yeniyurt(2015), “The Role of Ego Network Structure in Facilitating Ego Network Innovations,” Journal of Supply Chain Management, 51(2), pp.22-46. [https://doi.org/10.1111/jscm.12075]
  • Chen, H. and T.-J. Chen(2003), “Governance Structures in Strategic Alliances: Transaction Cost Versus Resource-Based Perspective,” Journal of World Business, 38(1), pp.1-14. [https://doi.org/10.1016/S1090-9516(02)00105-0]
  • Choi, T. Y., K. J. Dooley and M. Rungtusanatham (2001), “Supply Networks and Complex Adaptive Systems: Control Versus Emergence,” Journal of Operations Management, 19(3), pp.351-366. [https://doi.org/10.1016/S0272-6963(00)00068-1]
  • Choi, T. Y. and Z. Wu(2009), “Triads in Supply Networks: Theorizing Buyer–Supplier–Supplier Relationships,” Journal of Supply Chain Management, 45(1), pp.8-25. [https://doi.org/10.1111/j.1745-493X.2009.03151.x]
  • Choi, T. Y., Z. Wu, L. Ellram and B. R. Koka (2002), “Supplier-Supplier Relationships and Their Implications for Buyer-Supplier Relationships,” IEEE Transactions on Engineering Management, 49(2), pp.119-130. [https://doi.org/10.1109/TEM.2002.1010880]
  • Cranmer, S. J., P. Leifeld, S. D. McClurg and M. Rolfe(2017), “Navigating the Range of Statistical Tools for Inferential Network Analysis,” American Journal of Political Science, 61 (1), pp.237-251. [https://doi.org/10.1111/ajps.12263]
  • Das, T. K. and B.-S. Teng(1998), “Resource and Risk Management in the Strategic Alliance Making Process,” Journal of Management, 24(1), pp.21-42. [https://doi.org/10.1177/014920639802400103]
  • Desmarais, B. A. and S. J. Cranmer(2012a), “Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks,” Policy Studies Journal, 40(3), pp.402-434. [https://doi.org/10.1111/j.1541-0072.2012.00459.x]
  • Desmarais, B. A. and S. J. Cranmer(2012b), “Statistical Inference for Valued-Edge Networks: The Generalized Exponential Random Graph Model,” PLOS ONE, 7(1), pp.e30136. [https://doi.org/10.1371/journal.pone.0030136]
  • Dyer, J. H. and H. Singh(1998), “The Relational View: Cooperative Strategy and Sources of Interorganizational Competitive Advantage,” The Academy of Management Review, 23 (4), pp.660-679. [https://doi.org/10.5465/amr.1998.1255632]
  • Edward, H. and W. Mark(2013), “A Complex Network Approach to Supply Chain Network Theory,” International Journal of Operations and Production Management, 33(4), pp.442-469. [https://doi.org/10.1108/01443571311307343]
  • Ghosh, A., R. Ranganathan and L. Rosenkopf(2016), “The Impact of Context and Model Choice on the Determinants of Strategic Alliance Formation: Evidence from a Staged Replication Study,” Strategic Management Journal, 37(11), pp.2204-2221. [https://doi.org/10.1002/smj.2570]
  • Goodreau, S. M., J. A. Kitts and M. Morris(2009), “Birds of a Feather, or Friend of a Friend? Using Exponential Random Graph Models to Investigate Adolescent Social Networks,” Demography, 46(1), pp.103-125. [https://doi.org/10.1353/dem.0.0045]
  • Holland, J. H.(1996), Hidden Order: How Adaptation Builds Complexity, Reading, MA, Addison-Wesley.
  • Hunter, D. R.(2007), “Curved Exponential Family Models for Social Networks,” Social Networks, 29(2), pp.216-230. [https://doi.org/10.1016/j.socnet.2006.08.005]
  • Hunter, D. R., S. M. Goodreau and M. S. Handcock (2008a), “Goodness of Fit of Social Network Models,” Journal of the American Statistical Association, 103(481), pp.248-258. [https://doi.org/10.1198/016214507000000446]
  • Hunter, D. R., M. S. Handcock, C. T. Butts, S. M. Goodreau and M. Morris(2008b), “ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks,” Journal of Statistical Software, 24(3), pp. 29. [https://doi.org/10.18637/jss.v024.i03]
  • Kim, D.-Y.(2014), “Understanding Supplier Structural Embeddedness: A Social Network Perspective,” Journal of Operations Management, 32(5), pp.219-231. [https://doi.org/10.1016/j.jom.2014.03.005]
  • Kim, J. Y., M. Howard, E. Cox Pahnke and W. Boeker(2016), “Understanding Network Formation in Strategy Research: Exponential Random Graph Models,” Strategic Management Journal, 37(1), pp.22-44. [https://doi.org/10.1002/smj.2454]
  • Kim, Y., T. Y. Choi, T. Yan and K. Dooley(2011), “Structural Investigation of Supply Networks: A Social Network Analysis Approach,” Journal of Operations Management, 29(3), pp.194-211. [https://doi.org/10.1016/j.jom.2010.11.001]
  • Kito, T., A. Brintrup, S. New and F. Reed-Tsochas (2014), “The Structure of the Toyota Supply Network: An Empirical Analysis,” Available at SSRN 2412512, [https://doi.org/10.2139/ssrn.2412512]
  • Lee, Y., I. W. Lee and R. C. Feiock(2012), “Interorganizational Collaboration Networks in Economic Development Policy: An Exponential Random Graph Model Analysis,” Policy Studies Journal, 40(3), pp.547-573. [https://doi.org/10.1111/j.1541-0072.2012.00464.x]
  • Leifeld, P., S. J. Cranmer and B. A. Desmarais (2018), “Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals,” Journal of Statistical Software, 83(6), pp.1-36 [https://doi.org/10.18637/jss.v083.i06]
  • Lomi, A. and F. Fonti(2012), “Networks in Markets and the Propensity of Companies to Collaborate: An Empirical Test of Three Mechanisms,” Economics Letters, 114(2), pp.216-220. [https://doi.org/10.1016/j.econlet.2011.10.004]
  • Lomi, A. and P. Pattison(2006), “Manufacturing Relations: An Empirical Study of the Organization of Production across Multiple Networks,” Organization Science, 17(3), pp.313-332. [https://doi.org/10.1287/orsc.1060.0190]
  • Louch, H.(2000), “Personal Network Integration: Transitivity and Homophily in Strong-Tie Relations,” Social Networks, 22(1), pp.45-64. [https://doi.org/10.1016/S0378-8733(00)00015-0]
  • Lusher, D., J. Koskinen and G. Robins(2013), Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications, Cambridge University Press. [https://doi.org/10.1017/CBO9780511894701]
  • Lusher, D. and G. Robins(2013), “Formation of Social Network Structure.” in D. Lusher, J. Koskinen & G. Robins (Eds.) Exponential Random Graph Models for Social Networks. New York, NY, Cambridge University Press, pp.16-28. [https://doi.org/10.1017/CBO9780511894701.004]
  • MacDuffie, J. P. and S. Helper(2007), “Collaboration in Supply Chains: With and without Trust.” in C. Heckscher & P. S. Adler (Eds.) The Firm as a Collaborative Community. Oxford University Press, pp.417-466.
  • Mello John, E. and P. Stank Theodore(2005), “Linking Firm Culture and Orientation to Supply Chain Success,” International Journal of Physical Distribution & Logistics Management, 35(8), pp.542-554. [https://doi.org/10.1108/09600030510623320]
  • Morris, D., T. Donnelly and T. Donnelly(2004), “Supplier Parks in the Automotive Industry,” Supply Chain Management: An International Journal, 9(2), pp.129-133. [https://doi.org/10.1108/13598540410527024]
  • Nanda, A.(1993), Resources, Capabilities, and Competencies, Division of Research, Harvard Business School.
  • Pathak, S. D., Z. Wu and D. Johnston(2014), “Toward a Structural View of Co-Opetition in Supply Networks,” Journal of Operations Management, 32(5), pp.254-267. [https://doi.org/10.1016/j.jom.2014.04.001]
  • Pfeffer, J. and G. R. Salancik(1978), The External Control of Organizations: A Resource Dependence Perspective, Stanford University Press.
  • Podolny, J. M.(1993), “A Status-Based Model of Market Competition,” American Journal of Sociology, 98(4), pp.829-872. [https://doi.org/10.1086/230091]
  • Reyes Levalle, R. and S. Y. Nof(2015), “Resilience by Teaming in Supply Network Formation and Re-Configuration,” International Journal of Production Economics, 160, pp.80-93. [https://doi.org/10.1016/j.ijpe.2014.09.036]
  • Robins, G., P. Pattison, Y. Kalish and D. Lusher (2007), “An Introduction to Exponential Random Graph (p*) Models for Social Networks,” Social Networks, 29(2), pp.173-191. [https://doi.org/10.1016/j.socnet.2006.08.002]
  • Shalizi, C. R. and A. C. Thomas(2011), “Homophily and Contagion Are Generically Confounded in Observational Social Network Studies,” Sociological Methods & Research, 40(2), pp.211-239. [https://doi.org/10.1177/0049124111404820]
  • Simmel, G.(1950), “The Dyad and the Triad,” The Sociology of Georg Simmel, pp.59-68.
  • Snijders, T. A. B.(2017), “Stochastic Actor-Oriented Models for Network Dynamics,” Annual Review of Statistics and Its Application, 4(1), pp.343-363. [https://doi.org/10.1146/annurev-statistics-060116-054035]
  • Snijders, T. A. B., G. G. van de Bunt and C. E. G. Steglich(2010), “Introduction to Stochastic Actor-Based Models for Network Dynamics,” Social Networks, 32(1), pp.44-60. [https://doi.org/10.1016/j.socnet.2009.02.004]
  • Uzzi, B.(1997), “Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness,” Administrative Science Quarterly, 42(1), pp.35-67. [https://doi.org/10.2307/2393808]
  • Wang, P., G. Robins, P. Pattison and E. Lazega (2013), “Exponential Random Graph Models for Multilevel Networks,” Social Networks, 35(1), pp.96-115. [https://doi.org/10.1016/j.socnet.2013.01.004]
  • Wasserman, S. and K. Faust(1994), Social Network Analysis: Methods and Applications, Cambridge University Press. [https://doi.org/10.1017/CBO9780511815478]
  • Wasserman, S. and P. Pattison(1996), “Logit Models and Logistic Regressions for Social Networks: I. An Introduction to Markov Graphs And p,” Psychometrika, 61(3), pp.401-425. [https://doi.org/10.1007/BF02294547]
  • Wernerfelt, B.(1984), “A Resource-Based View of the Firm,” Strategic Management Journal, 5(2), pp.171-180. [https://doi.org/10.1002/smj.4250050207]
  • Wincent, J.(2005), “Does Size Matter?,” Journal of Small Business and Enterprise Development, 12(3), pp.437-453. [https://doi.org/10.1108/14626000510612330]
  • Wu, Z. and T. Y. Choi(2005), “Supplier–Supplier Relationships in the Buyer–Supplier Triad: Building Theories from Eight Case Studies,” Journal of Operations Management, 24(1), pp.27-52. [https://doi.org/10.1016/j.jom.2005.02.001]
  • Wu, Z., T. Y. Choi and M. J. Rungtusanatham (2010), “Supplier–Supplier Relationships in Buyer–Supplier–Supplier Triads: Implications for Supplier Performance,” Journal of Operations Management, 28(2), pp.115-123. [https://doi.org/10.1016/j.jom.2009.09.002]

• 저자 박철순은 한국과학기술원(KAIST) 산업공학과에서 학사 및 석사학위를, 한국과학기술원(KAIST) 경영공학과에서 박사학위를 취득하였다. 현재 숙명여자대학교 경영학부 부교수로 재직 중이며, 주요 관심 분야는 네트워크 시뮬레이션, 공급 네트워크 분석, 네트워크에서의 확산, 개방형 혁신 등이다.

• 저자 강아롬은 현재 숙명여자대학교 일반대학원 경영학부 박사과정에 재학 중이다. 동 대학에서 경영학 및 중어중문 학사, 경영학 석사 학위를 취득하였다. 주요 연구분야는 구매업체와 공급업체 간 공급사슬관리, 자동차산업에서 국내외 완성차 업체의 공급 네트워크 실증 분석 등이다.