Pricing and timing strategies for new product using agent-based simulation of behavioural consumers

JASSS

Volumn 17, Issue 2, 2014, Pages

  Lee, Keeheon ^a   Lee, Hoyeop ^a   Kim, Chang Ouk ^a  

^a Yonsei University   (South Korea)

Author keywords

Korean Mobile Phone Market; Pricing and Time Strategies; Product Diffusion; Sensitivity Analysis

Indexed keywords

EID: 84897371212     PISSN: 14607425     EISSN: None     Source Type: Journal    
DOI: 10.18564/jasss.2326     Document Type: Article

References (50)

1. ABDELLAOUI, M., Bleichrodt, H., & Paraschiv, C. (2007). Loss aversion under prospect theory: a parameter-free measurement. Management Science, 53, 1659-1674.
2. BARBERIS, N., Huang, M., & Santos, T. (2001). Prospect theory and asset prices. Quarterly Journal of Economics, 116(1), 1-53.
3. BASS, F.M. (1969). A new product growth model for consumer durables. Management Science, 15(5), 215-227.
4. BEGGS, A., & Graddy, K. (2009). Anchoring effects: evidence from art auctions. American Economic Review, 99(3), 1027-1039.
5. BENTZ, Y., Merunka, D. (2000). Neural Networks and the Multinomial Logit for Brand Choice Modelling: a Hybrid Approach. Journal of Forecasting, 19, 177-200.
6. BLEICHRODT, H., Pinto, J.L., Wakker, P.P. (2001). Making Descriptive Use of Prospect Theory to Improve the Prescriptive Use of Expected Utility. Management Science, 47(11), 1498-1514.
7. BROMILEY, P. (2009) A Prospect theory model of resource allocation. Decision Analysis, 6(3), 124-138.
8. CAMERER, C., & Thaler, R. H. (1995). Anomalies: ultimatums, dictators and manners. The Journal of Economic Perspectives, 9(2), 209-219.
9. CAMERER, C.F. (2008). Neuroeconomics: opening the gray box. Neruon, 60(3), 416-419.
10. CHAPMAN, G.B., & Johnson, E.J. (2002). Incorporating the irrelevant: anchors in judgements of belief and value. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and Biases: The psychology of Intuitive Judgement (pp. 120-138). Cambridge: Cambridge University Press.
11. Chou, C-C. (2003). The canonical representation of multiplication operation on triangular fuzzy numbers. Computers and Mathematics with Applications, 45, 1601-1610.
12. DAVIS, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003.
13. DAVIS, F. D. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology.
14. DELRE, S.A., Jager W., & Janssen, M.A. (2007a). Diffusion dynamics in small-world networks with heterogeneous consumers. Computational and Mathematical Organization Theory, 13, 185-202.
15. DELRE, S. A., Jager, W., Bijmolt, T. H. A., & Janssen, M. A. (2007b). Targeting and timing promotional activities: an agent-based model for the takeoff of new products. Journal of Business Research, 60(8), 826-835.
16. DIAO J., Zhu, K., & Gao, Y. (2011). Agent-based simulation of durables dynamic pricing. Systems Engineering Procedia, 2, 205-212.
17. FEINSCHREIBER, R. (1969). Accelerated depreciation: a proposed new method. Journal of Accounting Research, 7(1), 17-21.
18. FISHBEIN, M. & AJZEN, I. (1975). Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research, Addison-Wesley, Reading, MA.
19. GENESOVE, D., & Mayer, C. (2001). Loss aversion and seller behaviour: evidence from the housing market. Quarterly Journal of Economics, 116(4), 1233-1260.
20. GERSHOFF, A.D., Kivetz, R., & Keinan, A. (2012). Consumer response to versioning: how brands' production methods affect perceptions of unfairness. Journal of Consumer Research, 39(2), 382-398.
21. GEYER, R., & Blass, V.D. (2010). The economics of cell phone reuse and recycling. International Advanced Manufacturing Technology, 47, 515-525.
22. GODES, D., & Mayzlin, D. (2004). Using Online Conversations to Study Word-of-Mouth Communication. Marketing Science, 23(4), 545-560.
23. GUARDAGNI, P., & J. Little. 1983. A logit model of brand choice calibrated on scanner data. Marketing Science, 2, 203-238.
24. GUNTHER, M., Stummer, C., Wakolbinger, LM, & Wildpaner, M. (2011). An agent-based simulation approach for the new product diffusion of a novel biomass fuel. Journal of the Operational Research Society, 62, 12-20.
25. HARDIE, B.G.S., Johnson, E.J., & Fader, P.S.(1993). Modeling loss aversion and reference dependence effects on brand choice. Marketing Science, 12(4), 378-394.
26. HARRISON, G.W., & Rutström, E.E. (2009). Expected utility theory and prospect theory: one wedding and a decent funeral. Experimental Economics, 12, 133-158.
27. HE, X.D., & Zhou, X.Y. (2011). Portfolio choice under cumulative prospect theory: an analytical treatment, Management Science, 57(2), 315-331.
28. HONG, T-P., & Lee, C-Y. (1996). Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets and Systems, 84, 33-47.
29. JAGER, W. (2007). The four P's in social simulation, a perspective on how marketing could benefit from the use of social simulation, Journal of Business Research, 60, 868-875.
30. JANSSEN, M.A., & Jager, W. (2003). Simulating market dynamics interactions between consumer psychology and social networks. Artificial Life, 9, 343-356.
31. KAHNEMAN, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk, Econometrica, 47(2), 263-292.
32. KEMPE, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. In Proc. of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining.
33. KIM, S., Lee, K., Cho, J. K., & Kim, C. O. (2011). Agent-based diffusion model for an automobile market with fuzzy TOPSIS-based product adoption process. Expert Systems with Applications, 38(6), 7270-7276.
34. LEE, K., Kim, S., Kim, C. O., & Park, TH. (2013). An agent-based competitive product diffusion model for the estimation and sensitivity analysis of social network structure and purchase time distribution. Journal of Artificial Societies and Social Simulation, 16(1), 3 http://jasss.soc.surrey.ac.uk/16/1/3.html
35. LI, X., Ortiz, P.J., Browne, J., Franklin, D., Oliver, J.Y., Geyer, R., Zhou, Y., & Chong, F.T. (2010). Smartphone Evolution and Reuse: Establishing a more Sustainable Model. 2010 39th International Conference on Parallel Processing Workshops.
36. MAHAJAN, V., Muller, E., & Bass, F.M. (1990). New product diffusion models in marketing: a review and directions for research. Journal of Marketing, 54, 1-26.
37. MARDIA, K.V., Kent, J.T., & Bibby, J.M. (1979). Multivariate Analysis. Academic Press.
38. MCFADDEN, D. (1974), Conditional logit analysis of qualitative choice. In P. Zarembka (Ed.), Frontiers in Econometrics, New York: Academic Press.
39. MCFADDEN, D. (1980). Economeric Models for Probabilistic Choice Among Products. Journal of Business, 53(3), S13-S29.
40. ORRELL, D., & McSharry, P. (2009). System economics: overcoming the pitfalls of forecasting models via a multidisciplinary approach. International Journal of Forecasting, 25, 734-743.
41. PARK, Y.R., Kim, M.S., & Lee, K.H. (2011). Empirical analysis on consumer behaviour in using smart device (In Korean). Technical Report, 11-3, Korea Information Society Development Institute. Archived at: .
42. TOM, S. M., Fox, C.R., Trepel, C., & Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515-518.
43. TVERSKY, A., & Kahneman, D. (1974). Judgement under uncertainty: heuristics and biases, Science, 185, 1124-1131.
44. TVERSKY, A., & Kahneman, D. (1991). Loss aversion and riskless choice: a reference dependent model. Quarterly Journal of Economics, 106 (November), 1039-1061.
45. Villas-Boas, J.M., & Winer, R.S. (1999). Endogeneity in Brand Choice Models. Management Science, 45(10), 1324-1338.
46. WATTS, D. J., & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393(6684), 440-442.
47. WILENSKY, U. (1999). NetLogo. Center for connected learning and computer-based modeling. Northwestern University.
48. XU, H., Zhou, J., & Xu, W. (2011). A decision-making rule for modelling travellers' route choice behaviour based on cumulative prospect theory. Transportation Research Part C: Emerging Technologies, 19(2), 218-228.
49. ZENOBIA, B., Weber, C. & Daim, T. (2009). Artificial markets: a review and assessment of a new venue for innovation research. Technovation, 29, 338-350.
50. th eds.). Dordrecht; Boston: Kluwer Academic Publishers.