Journal of the Korea Society for Simulation (한국시뮬레이션학회논문지)

The Korea Society for Simulation (한국시뮬레이션학회)
권호 표지
DOI QR코드

DOI QR Code

Consumer-Agent Based Sensitivity Analysis of Product Diffusion Dynamics for Domestic Automobile Market

국내 자동차 시장에서 소비자 에이전트 모형 기반의 제품 확산 다이나믹스 민감도 분석

  • 김신태 (삼성SDS SCL컨설팅팀) ;
  • 김창욱 (연세대학교 정보산업공학과)
  • Received : 2011.01.24
  • Accepted : 2011.05.08
  • Published : 2011.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper focuses on the sensitivity analysis for the calibration of an agent-based simulation that analyzes the brand-level diffusion dynamics of competing products in the domestic premium mid-sized car market. In this paper, we employ a consumer-agent model that imitates the purchasing characteristics and behaviors of the consumers. The group of consumer agents that are socially interconnected represents a virtual market. By spreading the product information from previous adopters to potential consumer agents in the virtual market, the word-of-mouth phenomenon emerges like in the real market. The phenomenon influences the product choice of potential consumer agents that causes the variation of the product diffusion dynamics. In this simulation model, it is important to calibrate the virtual market parameters(e.g., ratio of innovators, social network structure, purchase time decision method) so that the virtual market can simulate the real market. However, it is difficult to measure these parameters directly from the real market. In this paper, we analyze the diffusion dynamics of simulations under various conditions in comparison with real sales data to calibrate the parameters.

본 연구에서는 에이전트 모형 기반 시뮬레이션 기법을 이용하여 국내 중형 고급승용차 제품시장에서 경쟁 제품들의 확산 다이나믹스를 예측하기 위한 환경조건을 도출하고자 민감도 분석을 실시하였다. 본 연구에서는 소비자의 구매 특성과 행동을 모방한 소비자 에이전트 모형을 이용하며 사회적 네트워크로 연결된 소비자 에이전트들의 집단은 하나의 가상시장을 이룬다. 제품을 구매한 소비자 에이전트가 이웃 에이전트들에게 제품정보를 전달함으로써 실제 시장처럼 구전현상이 나타나고 이는 잠재적 소비자 에이전트들의 제품선택에 영향을 주게 되어 확산 다이나믹스 패턴이 변화하게 된다. 가상시장의 확산 다이나믹스가 실제 시장의 확산다이나믹스를 반영하기 위해서는 초기채택자 비율, 사회적 네트워크의 구조, 소비자 에이전트의 구매시점 결정방법 등의 가상시장 환경설정이 중요하다. 그러나 이러한 환경조건들은 실제시장에서 측정하기가 어렵기 때문에 본 연구에서는 다양한 환경조건하에서의 확산다이나믹스패턴을 실제 데이터와 비교 분석하여 적합한 환경조건을 찾고자 한다.

Keywords

References

  1. 곽상만, 김기찬, 안수웅, 장원혁, 홍정석, "시스템다이내믹스 기법을 활용한 차급별 월간 자동차 수요예측모델 개발," 한국 시스템다이내믹스 연구, 3(1), pp. 79-104, 2002년 5월.
  2. 한국자동차공업협회, 2010 한국의 자동차산업(통권 22호), 한국자동차공업협회, pp. 12-21, 2010년 5월.
  3. F. Alkemade and C. Castaldi, "Strategies for the diffusion of innovations on social networks," Computational Economics, vol. 25, no. 1-2, pp. 3-23, Feb. 2005. https://doi.org/10.1007/s10614-005-6245-1
  4. F.M. Bass, "A New Product Growth for Model Consumer Durables," Management Science, vol. 15, no. 5, pp. 215-227, Jan. 1969. https://doi.org/10.1287/mnsc.15.5.215
  5. C.-C. Chou, "The canonical representation of multiplication operation on triangular fuzzy numbers," Computers & Mathematics with Applications, vol. 45, no. 10-11, pp. 1601-1610, May-Jun. 2003. https://doi.org/10.1016/S0898-1221(03)00139-1
  6. S.A. Delre, W. Jager, T.H.A. Bijmolt and M.A. Janssen, "Targeting and timing promotional activities: an agentbased model for the takeoff of new products," Journal of Business Research, vol. 60, no. 8, pp. 826-835, Aug. 2007. https://doi.org/10.1016/j.jbusres.2007.02.002
  7. R. Garcia, "Uses of agent-based modeling in innovation/ new product development research," Journal of Product Innovation Management, vol. 22, no. 5, pp. 380-398, Sept. 2005. https://doi.org/10.1111/j.1540-5885.2005.00136.x
  8. W.J. Hopp and M.L. Spearman, Factory Physics, 2nd Ed., McGraw-Hill, Singafore, pp. 428, 2000.
  9. C.L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications, Berlin, Springer, 1981.
  10. M.A. Janssen and W. Jager, "Simulating market dynamics: interactions between consumer psychology and social networks," Artificial Life, vol. 9, no. 4, pp. 343-356, Fall. 2003. https://doi.org/10.1162/106454603322694807
  11. S. Kim, K. Lee, J.K. Cho, and C.O. Kim, "Agent-based diffusion model for an automobile market with fuzzy TOPSIS-based product adoption process," Expert Systems with Application, vol. 38, no. 6, pp. 7270-7276, Jun. 2011. https://doi.org/10.1016/j.eswa.2010.12.024
  12. T.V. Krishnan, F.M. Bass, and V. Kumar, "Impact of a late entrant on the diffusion of a new product/service," Journal of Marketing Research, vol. 37, no. 2, pp. 269-278, May. 2000. https://doi.org/10.1509/jmkr.37.2.269.18730
  13. B. Libai, E. Muller, and R. Peres, "The role of seeding in multi-market entry," International Journal of Research in Marketing, vol. 22, no. 4, pp. 375-393, Dec. 2005. https://doi.org/10.1016/j.ijresmar.2005.09.004
  14. V. Mahajan, S. Sharma, and R.D. Buzzell, "Assessing the impact of competitive entry on market expansion and incumbent sales," Journal of Marketing, vol. 57, no. 3, pp. 39-52, Jul. 1993. https://doi.org/10.2307/1251853
  15. V. Mahajan, E. Muller, and Y. Wind, New-product diffusion models, Kluwer academic publishers, pp. 3-24, 2000.
  16. J.H. Roberts and G.L. Urban, "Modelling multiattribute utility, risk, and belief dynamics for new consumer durable brand choice," Management Science, vol. 34, no. 2, pp. 167-185, Feb. 1988. https://doi.org/10.1287/mnsc.34.2.167
  17. P.R. Steffens, "An aggregate sales model for consumer durables incorporating a time-varying mean replacement age," Journal of Forecasting, vol. 20, no. 1, pp. 63-77, Jan. 2001. https://doi.org/10.1002/1099-131X(200101)20:1<63::AID-FOR758>3.0.CO;2-D
  18. D.J. Watts and S.H. Strogatz, "Collective behavior of 'small-world' networks," Nature, vol. 393, no. 6684, pp. 440-442, 1998. https://doi.org/10.1038/30918
  19. U. Wilensky, NetLogo, http://ccl.northwestern.edu/netlogo/, Center for connected learning and computer-based modeling, Northwestern University, Evanston, IL, 1999.
  20. B. Zenobia, C. Weber, and T. Daim, "Artificial markets: a review and assessment of a new venue for innovation research," Technovation, vol. 29, no. 5, pp. 338-350, May. 2009. https://doi.org/10.1016/j.technovation.2008.09.002