Analysis of Interactions of Key Stakeholders on B2C e-Markets - Agent Based Modelling and Simulation Approach

• Authors: Aleksandar Marković , Sava Čavoški , Andrej Novović
• Languages of publication: EN

Abstracts

EN

Background/purpose: This paper discusses the application of ABMS - agent-based modelling and simulation in the analysis of customer behaviour on B2C e-commerce websites as well as in the analysis of various business decisions upon the effects of on-line sales. The continuous development and dynamics in the field of e-commerce requires application of advanced decision-making tools. These tools must be able to process, in a short time period, a large amount of data generated by the e-commerce systems and enable the use of acquired data for making quality business decisions. Methodology: The methodology of the agent-based simulation used in this paper may significantly enhance the speed and quality of decision making in electronic trade. The models developed for the needs of this research aim to improve the use of practical tools for the evaluation of the B2C online sales systems in that they allow for an investigation into the outcomes of varied strategies in the e-commerce site management as regards customer behaviour, website visits, scope of sales, income earned, etc. Results: An agent-based simulation model developed for the needs of this research is able to track the interactions of key subjects in online sales: site visitors - prospective consumers, sellers with different business strategies, and suppliers. Conclusion: Simulation model presented in this paper can be used as a tool to ensure a better insight into the problem of consumer behavior on the Internet. Companies engaged in the B2C e-commerce can use simulation results to better understand their consumers, improve market segmentation and business profitability and test their business policies.

Keywords

EN

ABMS; B2C; e-commerce; website; customer behavior

• Publisher: Sciendo
• Journal: Organizacija
• Year: 2016
• Volume: 49
• Issue: 2
• Pages: 138-149

Dates

published

2016-05-01

received

2015-03-19

revised

2015-04-07

accepted

2016-04-29

online

2016-06-10

Contributors

author

Aleksandar Marković

• University of Belgrade, Faculty of Organizational Sciences, Jove Ilića 154, 11000 Belgrade, Serbia

author

Sava Čavoški

• MDS informatički inženjering, Bulevar Milutina Milankovića 7d, 11000 Belgrade, Serbia

author

Andrej Novović

• University of Belgrade, Faculty of Organizational Sciences, Jove Ilića 154, 11000 Belgrade, Serbia

References

• Aggarwal, R., Gopal, R., Gupta, A., & Singh, H. (2012). Putting Money Where the Mouths Are: The Relation Between Venture Financing and Electronic Word-of- Mouth, Journal Information Systems Research, 23, 976-992.
• Bagozzi, R., Gurhan-Canli, Z., & Priester, J. (2002). The Social Psychology of Consumer Behaviour, Buckingham: Open University Press.
• Bailey, P. (1998). Electronic Commerce: Prices and Consumer Issues for Three Products: Books, OECD Digital Economy Papers, Retrieved January 5, 2016 from http://www.oecd.org/sti/35497325.pdf
• Currie, C. S., & Rowley, I. T. (2010). Consumer behaviour and sales forecast accuracy: What’s going on and how should revenue managers respond?, Journal of Revenue & Pricing Management, 9(4), 374-376, http://dx.doi.org/10.1057/rpm.2010.22
• Dellarocas, C. (2003). The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms, Management Science, 49, 1407-1424, http://dx.doi.org/10.1287/mnsc.49.10.1407.17308
• Dyner, I., & Franco, C. J. (2004). Consumers’ bounded rationality: The case of competitive energy markets, Systems Research and Behavioral Science, 21(4), 373-389, http://dx.doi.org/10.1002/sres.644
• Engel, J., Blackwell R., & Miniard P. (1994). Consumer Behavior, The Dryden Press (8th ed.).
• Furaiji F., Łatuszyńska, M., & Wawrzyniak, A. (2012). An empirical study of the factors influencing consumer behaviour in the electric appliances market, Contemporary Economics, 6(3), 76-86.
• Godes, D., & Mayzlin, D. (2004). Using Online Conversations to Study Word of Mouth Communication, Marketing Science, 23(4), 545-560, http://dx.doi.org/10.1287/mksc.1040.0071
• Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., & DeAngelis D. L. (2008). A standard protocol for describing individual-based and agent-based models, Ecological modelling, 198(1-2), 115-126, http://dx.doi.org/10.1016/j.ecolmodel.2006.04.023
• Harrison-Walker. L. J. (2001).The measurement of wordof- mouth communication and investigation of service quality and customer commitment as potential antecedents, Journal of Service Research, 4(1), 60-75, http://dx.doi.org/10.1177/109467050141006
• Hummel, A., Kern, H., Kuhne, S., & Dohler, A. (2012). An Agent-Based Simulation of Viral Marketing Effects in Social Networks, 26th European Simulation and Modelling Conference.
• Hyung, A. (2010). Evaluating customer aid functions of online stores with agent-based models of customer behavior and evolution strategy, Information Sciences, 180(9), 1555-1570, http://dx.doi.org/10.1016/j.ins.2009.12.029
• Jager, W. (2008). Simulating consumer behaviour: a perspective, paper prepared for the Netherlands Environmental Assessment Agency project “Environmental policy and modelling in evolutionary economics”. Retrieved January 5, 2016 from http://www.pbl.nl/sites/default/files/cms/publicaties/eem_paper_wj_revised.pdf
• Kim, B., Blattberg, R. & Rossi, P. (1995). Modelling the distribution of price sensitivity and implications for optimal retail pricing, Journal of Business & Economic Statistics, 13(3), 291-303, http://dx.doi.org/10.2307/1392189
• Kim, D., Ferrin, D. & Raghav, R. H. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents, Decision Support Systems, 44(2), 544-564, http://dx.doi.org/10.1016/j.dss.2007.07.001
• Klever, A. (2009). Behavioural Targeting: An Online Analysis for Efficient Media Planning?, Hamburg: Diplomica Verlag.
• Liu, X., Tang, Z., Yu, J., & Lu, N. (2013). An Agent Based Model for Simulation of Price War in B2C Online Retailers, Advances in Information Sciences and Service Sciences, 5(5), 1193-1202, http://dx.doi.org/10.4156/AISS
• Michael, S., & Sinha, I. (2000). The Impact of Price and Extra Product Promotions on Store reference, International Journal of Retail & Distribution Management, 28(2), 83-9253, http://dx.doi.org/10.1108/09590550010315269
• Moe, W. (2003). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream, Journal of Consumer Psychology, 13, 29-39, http://dx.doi.org/10.1207/S15327663JCP13-1&2_03
• Moe W. Fader S. (2002), Fast-Track: Article Using Advance Purchase Orders to Forecast New Product Sales, Marketing Science, 21, 347-364, http://dx.doi.org/10.1287/mksc.21.3.347.138
• North, M., Macal, C., Aubin, J., Thimmapuram, P., Bragen, M., Hahn, J., Karr, J., Brigham, N., Lacy, M., & Hampton D. (2010). Multiscale agent-based consumer market modelling, Complexity, 15(5), 37-47, http://dx.doi.org/10.1002/cplx.20304
• Okada, I., & Yamamoto, H. (2009). Effect of online wordof- mouth communication on buying behavior in agentbased simulation, Proc. of the 6th Conference of the European Social Simulation Association, http:// www.isslab.org/study_work/essa2009proc.pdf
• Poh, H., Yao, J., & Jasic, T. (1994). Neural Networks for the Analysis and Forecasting of Advertising and Promotion Impact, International Journal of Intelligent Systems in Accounting, Finance and Management, 7, 253-268, http://dx.doi.org/10.1002/(SICI)1099-1174(199812)7:4%3C253::AID-ISAF150%3E3.0.CO;2-X
• Railsback, S., & Grimm, V. (2012). Agent-Based and Individual- Based Modeling, Princeton University Press, Princeton and Oxford.
• Roozmand, O., Ghasem-Aghaee, N., Hofstede, G. J., Nematbakhsh, M., Baraani, A., & Verwaart, T. (2011). Agent-based modeling of consumer decision making process based on power distance and personality. Knowledge-Based Systems, 24 (7), 1075-1095, http://dx.doi.org/10.1016/j.knosys.2011.05.001
• Russell, S., & Norvig, P. (2003). Artificial Intelligence: A Modern Approach, Prentice Hall.
• Said, L., Bouron, T. & Drogoul, A. (2002). Agent-based interaction analysis of consumer behaviour. In: Proceedings of the first international joint conference on Autonomous agents and multi-agent systems, IFAAMAS, p. 184-190.
• Schiffman, L., & Kanuk, L. (2009). Consumer Behavior, New Jersey: Prentice Hall (10th ed).
• Schramm, M., Trainor, J., Shanker, M., & Hu, Y. (2010). An agent-based diffusion model with consumer and brand agents, Decision Support Systems, 50(1), 234-242, http://dx.doi.org/10.1016/j.dss.2010.08.004
• Solomon, M., Bamossy, G., & Askegaard, S. (2009). Consumer Behaviour: A European Perspective, Prentice Hall
• Wang, Z., Wang, W., & Dong, L. (2010). Research on Influencing Factors of Perceived Risk in Online Shopping by Consumers, E-Business and E-Government (ICEE), 2010 International Conference on, Guangzhou, 2010, pp. 342-345, http://dx.doi.org/10.1109/ICEE.2010.94
• Zhang, T., & Zhang, D. (2007). Agent-based simulation of consumer purchase decision-making and the decoy effect, Journal of Business Research, 60(8), 912-922, http://dx.doi.org/10.1016/j.jbusres.2007.02.006
• Zhu, D. S., Lee, Z., O’Neal, G., & Chen, Y. (2009). The Effect of Trust and Perceived Risk on Consumers’ Online Purchase Intention, International Conference on Computational Science and Engineering, ISBN: 978-1-4244-5334-4, pp. 771-776.
• Zutshi, A., Grilo, A., Jardim-Gonçalves, R. (2014). A Dynamic Agent-Based Modeling Framework for Digital Business Models: Applications to Facebook and a Popular Portuguese Online Classifieds Website, Digital Enterprise Design & Management, Volume 261 of the series Advances in Intelligent Systems and Computing, pp. 105-117, http://dx.doi.org/10.1007/978-3-319-04313-5_10

Identifiers

DOI

10.1515/orga-2016-0010