Proceedings of 2013 4th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2013) pp 595-604 | Cite as
The Self-Organized Criticality of Customer System
Abstract
The purpose of this paper is to explore the evolution law of crowd behavior of customers under the effect of Word-of-Mouth (WOM) by theoretical analysis on the nature of customer system and the effect of WOM. The integration of customers of a business unit is defined as customer system with the individual customer as sub-unit and the nature of the system is discussed. Then, the two dimensional cellular automation (CA) model is built and five experiment schemes are designed to simulate the aggregate crowd behavior of the customer group. The results generated from the research were quantitatively analyzed based on the concept of Self-Organized Criticality (SOC).
Keywords
Crowd behavior of customers Customer system Cellular automation (CA) Self-Organized Criticality (SOC) Word-of-Mouth (WOM)Notes
Acknowledgment
This research is supported by the research fund of humanities and social sciences of ministry education of P.R. China (Project No.11YJA630074).
References
- 1.Roberts JH (2000) Developing new rules for new markets. J Acad Mark Sci 28(1):31–44CrossRefGoogle Scholar
- 2.Zeithaml VA (2000) Service quality, profitability, and the economic worth of customers: what we know and what we need to learn. J Acad Mark Sci 28(1):67–85CrossRefGoogle Scholar
- 3.Keaveney M, Parthasarathy M (2001) Customer switching behavior in online services: an exploratory study of the role of selected attitudinal, behavioral, and demographic factors. J Acad Mark Sci 29(4):374–390CrossRefGoogle Scholar
- 4.Triest S, Bun M, Raaij E, Vernooij M (2009) The impact of customer-specific marketing expenses on customer retention and customer profitability. Mark Lett 20(2):125–138CrossRefGoogle Scholar
- 5.Liao Z, Shi X (2009) Consumer perceptions of internet-based e-retailing: an empirical research in Hong Kong. J Serv Mark 23(1):24–30CrossRefGoogle Scholar
- 6.Athanassopoulos A, Gounaris S, Stathakopoulos V (2001) Behavioral responses to customer satisfaction: an empirical study. Eur J Mark 35(5/6):687–707CrossRefGoogle Scholar
- 7.Carbonara N (2009) Managing the complexity of the supply chain. In: Proceedings of the 16th industrial engineering and engineering management, IEEM’09, Hongkong, pp 553–557Google Scholar
- 8.Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of Word-of-Mouth. Mark Lett 12(3):211–223CrossRefGoogle Scholar
- 9.Mason RB (2008) Word of mouth as a promotional tool for turbulent markets. J Mark Commun 14(3):207–231CrossRefGoogle Scholar
- 10.Wollin D, Perry C (2004) Marketing management in a complex adaptive system: an initial framework. Eur J Mark 38(5):556–638Google Scholar
- 11.Zhang G (2009) Customer value of social network service website: key components and impacts on customer loyalty. In: Proceedings of the 17th industrial engineering and engineering management, IEEM’09, Macao, pp 714–717Google Scholar
- 12.Cheng S, Lam T, Hsu C (2006) Negative word-of-mouth communication intention: an application of the theory of planned behavior. J Hosp Tour Res 30(1):95–116CrossRefGoogle Scholar
- 13.Bak P, Tang C, Wiesenfeld K (1987) Self-organized criticality: an explanation of the 1/f noise. Phys Rev Lett 59(4):381–384CrossRefGoogle Scholar
- 14.Matutinovic I (2006) Self-organization and design in capitalist economies. J Econ Issues 40(3):575–601Google Scholar
- 15.Luo XG, Chi MY, Ma SQ (2011) The analysis of subgroup in customer word-of-mouth communication network. In: Proceedings of the 5th international conference on management science and engineering management, ICSEM’11, Liverpool, pp 48–52Google Scholar
- 16.Luo XG (2011) The study of customer system robustness based on the negative Word-of-Mouth. In: Proceedings of the 18th international conference on management science and engineering management, IEEE’11, Changchun, pp 453–458Google Scholar