Abstract
This paper presents an Agent Based Modeling Framework that seeks to provide a generic model that can be used to simulate any internet based business. The model captures the unique characteristics that define how online users interact, share information, and take product adoption decisions. This model can be used to simulate business performance, make business forecasts, and test business strategies. To demonstrate the applicability of the model, we choose two social networks with opposing scales: Facebook and a startup educational social network—weduc.pt.
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Aliprantis D, Tesfatsion L, Zhao H (2010) An agent-based test bed for the integrated study of retail and wholesale power system operations. Agent Technol Energy Syst ATES 2010:6
Anderson C (2009) Free: the future of a radical price. Hyperion Books, New York
Bass FM (1969) A new product growth for model consumer durables. Manage Sci 15(5):215–227
Brown J, Broderick AJ, Lee N (2007) Word of mouth communication within online communities: conceptualizing the online social network. J Interact Mark 21(3):2–20
Bruyn AD, Lilien GL (2008) A multi-stage model of word-of-mouth influence through viral marketing. Int J Res Mark 25(3):151–163
Delre SA, Jager W et al (2007) Targeting and timing promotional activities: an agent-based model for the takeoff of new products. J Bus Res 60(8):826–835
Diao J, Zhu K, Gao Y (2011) Agent-based simulation of durables dynamic pricing. Syst Eng Procedia 2:205–212
Docters R, Tilstone L et al (2011) Pricing in the digital world. J Bus Strategy 32(4):4–11
Feng J, Papatla P (2011) Advertising: stimulant or suppressant of online word of mouth. J Interact Mark 25(2):75–84
Gallagher S, West J (2009) Reconceptualizing and expanding the positive feedback network effects model: a case study. J Eng Technol Manage 26(3):131–147
Gatignon H (1985) A propositional inventory for new diffusion research. J Consum Res, pp 849–867
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):211–223
Goldenberg J, Libai B et al (2007) The NPV of bad news. Int J Res Mark 24(3):186–200
Goldenberg J, Libai B, Muller E (2010) The chilling effects of network externalities. Int J Res Mark 27(1):4–15
Goodnight GT, Green S (2010) Rhetoric, risk, and markets: the dot-com bubble. Q J Speech 96(2):115–140
Grewal D, Janakiraman R et al (2010) Strategic online and offline retail pricing: a review and research agenda. J Interact Mark 24(2):138–154
Herr PH, Kardes FR, Kim J (1991) Effects of word-of-mouth and product-attribute information on persuasion: an accessibility-diagnosticity perspective. J Consum Res 17:454–462
Kalyanam K, McIntyre S, Masonis JT (2007) Adaptive experimentation in interactive marketing: the case of viral marketing at Plaxo. J Interact Mark 21(3):72–85
Manyika J, Roxburgh C (2011) The great transformer: the impact of the internet on economic growth and prosperity. McKinsey Glob Inst, pp 1–10
McGrath RG (2010) Business models: a discovery driven approach. Long Range Plan 43(2):247–261
Montgomery A (2001) Applying quantitative marketing techniques to the internet. Interfaces (Providence) 31:90–108
North MJ, Macal CM (2007) Managing business complexity: discovering strategic solutions with agent-based modeling and simulation. Oxford University Press, Oxford
Schramm ME, Trainor KJ et al (2010) An agent-based diffusion model with consumer and brand agents. Decis Support Syst 50(1):234–242
Stonedahl F, Rand W, Wilensky U (2008) Multi-agent learning with a distributed genetic algorithm. In: AAMAS: ALAMAS + ALAg Workshop, Citeseer
Stonedahl F, Rand W, Wilensky U (2010) Evolving viral marketing strategies. In: Proceedings of the 12th annual conference on genetic and evolutionary computation, pp 1195–1202
Swatman PMC, Krueger C, Beek KVD (2006) The changing digital content landscape: an evaluation of e-business model development in European online news and music. Internet Res 16(1):53–80
Teece DJ (2010) Business models, business strategy and innovation. Long Range Plan 43(2):172–194
Walter TP, Back A (2010) Crowdsourcing as a business model: an exploration of emergent textbooks harnessing the wisdom of crowdsourcing as business model innovation. Business, pp 555–568
Acknowledgments
The authors also would like to thank Fundação da Ciência e Tecnologia for supporting the research center UNIDEMI through the grant PEst-OE/EME/UI0667/2011. Also, the authors are grateful to ISOFIN and VORTALWAY projects (QREN) for funding Aneesh Zutshi’s research work.
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Zutshi, A., Grilo, A., Jardim-Gonçalves, R. (2014). DYNAMOD—An Agent Based Modeling Framework: Applications to Online Social Networks. In: Xu, J., Cruz-Machado, V., Lev, B., Nickel, S. (eds) Proceedings of the Eighth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55182-6_31
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DOI: https://doi.org/10.1007/978-3-642-55182-6_31
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