A Review of Three Decades Using Agent-Based Modelling and Simulation in Marketing and Consumer Behavior
Subject Areas :
Zahra Sadeqi-Arani
1
,
Omid Roozmand
2
1 -
2 -
Keywords: Marketing, Consumer behavior, Agent-Based Modelling and Simulation, Science Mapping,
Abstract :
Agent-based modelling and simulation (ABMS) is one of the topics which has been extensively studied by researchers in the field of marketing and consumer behavior. However, no such analysis has been conducted on using Agent-based modelling and simulation in marketing and consumer behavior. An extensive bibliometric analysis, as well as a thorough visualization and science mapping, was carried out in this field from 1995 to 2022, in response to capturing recent ABMS development in this field. A total of 1210 documents from the WOS and Scopus databases were analyze d using bibliometrix R-Tool and VOSviewer. The results showed the 20 documents with the most citations were in the area of energy consumption (55%) and innovation diffusion behavior (20%). The USA has the most publications in this field, with the production of 188 documents. The “EXPERT SYSTEMS WITH APPLICATIONS” is a productive journal publishing in this field. Generally, the major journals that publish research on the use of ABM in marketing and consumer behavior are multidisciplinary or interdisciplinary. 6 clusters were identified based on the analysis of the most frequent keywords: Cluster 1 (multi-agent systems and consumer behavior), Cluster 2 (agent-based simulation and SCM), Cluster 3 (ABM and energy consumption), Cluster 4 (AMB and innovation diffusion), Cluster 5 (complex system and Simulation) and Cluster 6 (ABM and TAM). Prediction is one of the goals that has attracted the most attention of ABMS researchers among many goals such as optimization, description, self-organization, and adaptability, and there are many recent works in this field. These results show that many topics that were of interest in the past, such as the ontology of ABMS, are no longer of much interest to researchers, and the attention of researchers has been directed toward issues such as the diffusion of innovation, energy consumption, and pricing in recent years. This topic can determine the appropriate approach for other researchers to research in this field.
Al-Alawi, B. M., & Bradley, T. H. (2013). Review of hybrid, plug-in hybrid, and electric vehicle market modelling studies. Renewable and Sustainable Energy Reviews, 21, 190-203.
Al Mamun, M. A., Azad, M. A. K., & Boyle, M. (2022). Review of flipped learning in engineering education: Scientific mapping and research horizon. Education and information technologies, 27(1), 1261-1286.
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020). Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renewable and Sustainable Energy Reviews, 130, 109899.
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics, 11(4), 959-975.
Axtell, R. L., & Farmer, J. D. (2022). Agent-based modelling in economics and finance: past, present, and future. Journal of Economic Literature.
Berger, T. (2001). Agent‐based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural economics, 25(2‐3), 245-260.
Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social science information, 22(2), 191-235.
Castro, J., Drews, S., Exadaktylos, F., Foramitti, J., Klein, F., Konc, T., Savin, I., & van den Bergh, J. (2020). A review of agent‐based modelling of climate‐energy policy. Wiley Interdisciplinary Reviews: Climate Change, 11(4), e647.
Chang, H. H. (2010). Task-technology fit and user acceptance of online auction. International Journal of Human-Computer Studies, 68(1-2), 69-89.
Concari, A., Kok, G., & Martens, P. (2022). Recycling behavior: Mapping knowledge domain through bibliometrics and text mining. Journal of environmental management, 303, 114160.
Deng, Z., Wang, B., Xu, Y., Xu, T., Liu, C., & Zhu, Z. (2019). Multi-scale convolutional neural network with time-cognition for multi-step short-term load forecasting. IEEE Access, 7, 88058-88071.
Derviş, H. (2019). Bibliometric analysis using Bibliometrix an R Package. Journal of Scientometric Research, 8(3), 156-160.
Ding, Z., Hu, T., Li, M., Xu, X., & Zou, P. X. (2019). Agent-based model for simulating building energy management in student residences. Energy and Buildings, 198, 11-27.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
Echchakoui, S. (2020). Why and how to merge Scopus and Web of Science during bibliometric analysis: the case of sales force literature from 1912 to 2019. Journal of Marketing Analytics, 8(3), 165-184.
Elsawah, S., Guillaume, J. H., Filatova, T., Rook, J., & Jakeman, A. J. (2015). A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: from cognitive maps to agent-based models. Journal of environmental management, 151, 500-516.
Eppstein, M. J., Grover, D. K., Marshall, J. S., & Rizzo, D. M. (2011). An agent-based model to study market penetration of plug-in hybrid electric vehicles. Energy policy, 39(6), 3789-3802.
Farmer, J. D., Patelli, P., & Zovko, I. I. (2005). The predictive power of zero intelligence in financial markets. Proceedings of the National Academy of Sciences, 102(6), 2254-2259.
Feng, L., Li, B., Podobnik, B., Preis, T., & Stanley, H. E. (2012). Linking agent-based models and stochastic models of financial markets. Proceedings of the National Academy of Sciences, 109(22), 8388-8393.
Foruzan, E., Soh, L.-K., & Asgarpoor, S. (2018). Reinforcement learning approach for optimal distributed energy management in a microgrid. IEEE Transactions on Power Systems, 33(5), 5749-5758.
Gholami, H., Abu, F., Sharif, S., Abdul-Nour, G., & Badar, M. A. (2023). A Review of Global Research Trends on Sustainable Manufacturing. Sustainable Manufacturing in Industry 4.0: Pathways and Practices, 1-17.
Gilbert, N. (2019). Agent-based models. Sage Publications.
Gómez-Cruz, N. A., Saa, I. L., & Hurtado, F. F. O. (2017). Agent-based simulation in management and organizational studies: a survey. European Journal of Management and Business Economics, 26(3), 313-328.
He, Q. (1999). Knowledge discovery through co-word analysis.
Jiang, Y., Ritchie, B. W., & Benckendorff, P. (2019). Bibliometric visualisation: An application in tourism crisis and disaster management research. Current Issues in Tourism, 22(16), 1925-1957.
Kanta, L., & Zechman, E. (2014). Complex adaptive systems framework to assess supply-side and demand-side management for urban water resources. Journal of Water Resources Planning and Management, 140(1), 75-85.
Kieckhäfer, K., Wachter, K., & Spengler, T. S. (2017). Analyzing manufacturers' impact on green products' market diffusion–the case of electric vehicles. Journal of Cleaner Production, 162, S11-S25.
Kiesling, E., Günther, M., Stummer, C., & Wakolbinger, L. M. (2012). Agent-based simulation of innovation diffusion: a review. Central European Journal of Operations Research, 20(2), 183-230.
Krupa, J. S., Rizzo, D. M., Eppstein, M. J., Lanute, D. B., Gaalema, D. E., Lakkaraju, K., & Warrender, C. E. (2014). Analysis of a consumer survey on plug-in hybrid electric vehicles. Transportation Research Part A: Policy and Practice, 64, 14-31.
Lamba, H. K., Kumar, N. S., & Dhir, S. (2023). Circular economy and sustainable development: a review and research agenda. International Journal of Productivity and Performance Management, ahead-of-print(ahead-of-print). https://doi.org/10.1108/IJPPM-06-2022-0314
Marcal, J., Bishop, T., Hofman, J., & Shen, J. (2021). From pollutant removal to resource recovery: A bibliometric analysis of municipal wastewater research in Europe. Chemosphere, 284, 131267.
Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional de la Información, 29(1), e290103. https://doi.org/https://doi.org/10.3145/epi.2020.ene.03
Nagaiah, M., Thanuskodi, S., & Alagu, A. (2021). Application Of Lotka's Law To The Research Productivity In The Field Of Open Educational Resources During 2011-2020. Library Philosophy and Practice, 1-13.
Negahban, A., & Yilmaz, L. (2014). Agent-based simulation applications in marketing research: an integrated review. Journal of Simulation, 8, 129-142.
Niu, L., Zhao, X., Wu, F., Tang, Z., Lv, H., Wang, J., Fang, M., & Giesy, J. P. (2021). Hotpots and trends of covalent organic frameworks (COFs) in the environmental and energy field: Bibliometric analysis. Science of the Total Environment, 783, 146838.
North, M. J., Macal, C. M., Aubin, J. S., Thimmapuram, P., Bragen, M., Hahn, J., Karr, J., Brigham, N., Lacy, M. E., & Hampton, D. (2010). Multiscale agent‐based consumer market modelling. Complexity, 15(5), 37-47.
Nunna, H. K., & Doolla, S. (2012). Multiagent-based distributed-energy-resource management for intelligent microgrids. IEEE Transactions on Industrial Electronics, 60(4), 1678-1687.
Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: A critical review and research directions. International journal of research in marketing, 27(2), 91-106.
Rahman, T., Taghikhah, F., Paul, S. K., Shukla, N., & Agarwal, R. (2021). An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic. Computers & Industrial Engineering, 158, 107401.
Rai, V., & Henry, A. D. (2016). Agent-based modelling of consumer energy choices. Nature Climate Change, 6(6), 556-562.
Rand, W., & Rust, R. T. (2011). Agent-based modelling in marketing: Guidelines for rigor. International journal of research in marketing, 28(3), 181-193.
Rand, W., & Stummer, C. (2021). Agent‐based modelling of new product market diffusion: an overview of strengths and criticisms. Annals of Operations Research, 305(1-2), 425-447.
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., & Koffel, J. B. (2021). PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews. Systematic reviews, 10(1), 1-19.
Ringler, P., Keles, D., & Fichtner, W. (2016). Agent-based modelling and simulation of smart electricity grids and markets–a literature review. Renewable and Sustainable Energy Reviews, 57, 205-215.
Rojas-Lamorena, Á. J., Del Barrio-García, S., & Alcántara-Pilar, J. M. (2022). A review of three decades of academic research on brand equity: A bibliometric approach using co-word analysis and bibliographic coupling. Journal of Business Research, 139, 1067-1083.
Roozmand, O., Ghasem-Aghaee, N., Hofstede, G. J., Nematbakhsh, M. A., Baraani, A., & Verwaart, T. (2011). Agent-based modelling of consumer decision making process based on power distance and personality. Knowledge-Based Systems, 24(7), 1075-1095.
Safdarian, A., Fotuhi-Firuzabad, M., & Lehtonen, M. (2014). A distributed algorithm for managing residential demand response in smart grids. IEEE Transactions on Industrial Informatics, 10(4), 2385-2393.
Schwarz, N., & Ernst, A. (2009). Agent-based modelling of the diffusion of environmental innovations—An empirical approach. Technological Forecasting and Social Change, 76(4), 497-511.
Segovia, J. E. T., Di Sciorio, F., Mattera, R., & Spano, M. (2022). A Bibliometric Analysis on Agent-Based Models in Finance: Identification of Community Clusters and Future Research Trends. Complexity, 2022.
Sensfuß, F., Ragwitz, M., & Genoese, M. (2008). The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany. Energy policy, 36(8), 3086-3094.
Shafiei, E., Thorkelsson, H., Ásgeirsson, E. I., Davidsdottir, B., Raberto, M., & Stefansson, H. (2012). An agent-based modelling approach to predict the evolution of market share of electric vehicles: A case study from Iceland. Technological Forecasting and Social Change, 79(9), 1638-1653.
Sim, K. M. (2011). Agent-based cloud computing. IEEE transactions on services computing, 5(4), 564-577.
Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
Yousefi, S., Moghaddam, M. P., & Majd, V. J. (2011). Optimal real time pricing in an agent-based retail market using a comprehensive demand response model. Energy, 36(9), 5716-5727.
Zehra, A., & Urooj, A. (2022). A Bibliometric Analysis of the Developments and Research Frontiers of Agent-Based Modelling in Economics. Economies, 10(7), 171.
Zenobia, B., Weber, C., & Daim, T. (2009). Artificial markets: A review and assessment of a new venue for innovation research. Technovation, 29(5), 338-350.
Zhang, H., & Vorobeychik, Y. (2019). Empirically grounded agent-based models of innovation diffusion: a critical review. Artificial Intelligence Review, 52, 707-741.
Zhao, J., Mazhari, E., Celik, N., & Son, Y.-J. (2011). Hybrid agent-based simulation for policy evaluation of solar power generation systems. Simulation Modelling Practice and Theory, 19(10), 2189-2205.
Zhu, X., & Zhang, Y. (2020). Co-word analysis method based on meta-path of subject knowledge network. Scientometrics, 123(2), 753-766.
