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Journal Of Optimization In Industrial Engineering (24233935) 16(2)pp. 257-273
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 behaviour. However, no such analysis has been conducted on using Agent-based modelling and simulation in marketing and consumer behaviour. 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 analyse d using bibliometrix R-Tool and VOS viewer. The results showed the 20 documents with the most citations were in the area of energy consumption (55%) and innovation diffusion behaviour (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 behaviour are multidisciplinary or interdisciplinary. 6 clusters were identified based on the analysis of the most frequent key-words: Cluster 1 (multi-agent systems and consumer behaviour), 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. © 2023 Qazvin Islamic Azad University. All rights reserved.
Deffuant, G. ,
Roozmand, O. ,
Huet, S. ,
Khamzina, K. ,
Nugier, A. ,
Guimond, S. IEEE Transactions on Computational Social Systems (2329924X) 10(3)pp. 922-933
In two studies about farming practices, the respondents who are particularly favorable to organic farming tend to have a higher intention to convert their farm to organic when they perceive other farmers as not very favorable to this practice. This intention can be considered as anticonformist, as it is in opposition to the general view of others. This article hypothesizes that this phenomenon can be explained by some biases on the perceptions of attitudes. It proposes an agent-based model which computes an intention based on the theory of reasoned action (TRA) and assumes some biases in the perception of others' attitudes according to the social judgment theory. It investigates the conditions on the model parameter values for which the simulations reproduce the features observed in the studies. The results show that perceptual biases are a possible explanation of anticonformist intentions. © 2014 IEEE.
International Journal of Organizational Analysis (19348835) 31(5)pp. 1364-1383
Purpose: This study aims to adopt a follower-centric approach in leadership and ethics research by investigating the impact of implicit followership theories (IFTs) on followers’ constructive resistance to leaders’ unethical requests. Specifically, it analyzes the mediating role of organizational citizenship behavior in the relationship between IFTs and constructive resistance. Indeed, this study aims to examine whether followers with more positive beliefs about the characteristics that a follower should have IFTs are more likely to resist unethical leadership and whether this relationship is mediated by organizational citizenship behavior as volunteering acts that exceed the formal job requirements. Design/methodology/approach: The proposed hypotheses were tested using survey data from 273 employees working in a steel manufacturer company in Iran. The variance-based structural equation modeling technique was used to analyze data. Findings: The results show that followership antiprototype negatively affects both follower’s constructive resistance and organizational citizenship behavior. Furthermore, organizational citizenship behavior mediates the relationship between IFTs and follower’s constructive resistance. Also, both followership prototype and organizational citizenship behavior have a positive effect on follower’s constructive resistance. Originality/value: Contrary to the dominant leader-centric approach in leadership and organizational ethics research, few studies have examined the role of followers and their characteristics. The results of this study provide important insights into the role of followers in resistance against the leader’s unethical request. © 2021, Emerald Publishing Limited.
International Journal of Management Science and Engineering Management (17509653) 18(2)pp. 77-87
One of the most important tools is an appropriate pricing mechanism to attract more customers and increase profits. The retailers’ main question is how to set the prices and inventory policies to maximize profit in a competitive heterogeneous market in presence of non-zero lead time and lost sales. A reinforcement learning algorithm is proposed to create appropriate decision-making mechanisms for pricing. A coordinated inventory policy in a competitive environment reduces logistic costs and leads to a higher profit. We use a reinforcement learning algorithm to investigate the performance of a retailer in a competitive environment. An agent-based modeling experimental environment combined with a simulation-optimization method in which a virtual market has been reproduced is used. The market is not homogeneous with respect to customer behavior. It is assumed that the retailer uses (R, Q) policy where the lead time is a fixed amount (L), and the shortage is permissible. The quality, distance, service level, and price are factors that influence customers’ choices. The simulation results for some randomly generated examples show that the algorithm in the competitive environment can make more profit than other available methods and the combined utilization of simulation-optimization methods has been able to find better solutions for the hybrid model of pricing and inventory management considering customer behavior. The results of simulation for three different categories of customers (more sensitive to price, equally sensitive to price, quality and service level, and more sensitive to quality (indicate that the average profit for the proposed algorithm is higher than that of other examined algorithms. © 2023 International Society of Management Science and Engineering Management.
Simulation Series (07359276) 46(1)pp. 77-83
In this paper we propose an agent-based model approach to determining the effects of consumer choice on aggregate demand (CCAD). Our overall goal is to better understand how the availability of information, heuristic decision making, and social norms affect: 1) total aggregate demand and 2) the aggregated demand for disposable vs. more durable goods. In the preliminary model presented here, consumer agents select among baskets of goods with different combinations of quality and disposability. Consumer choices are based on individual agent preferences and subject to a discretionary income constraint. Agents may be either maximizing, which means that they choose the best basket of goods that they can afford, or satisficing, which means that they choose the first affordable basket of goods that they can find with utility greater than their satisfaction threshold. When run at different price levels, the resulting models can be used to generate aggregated demand curves for each group of consumers. We also demonstrate that, satisficers buy more than maximizers overall. Further analysis shows that this is because maximizers focus their trading on more durable products to gain the highest utility, however satisficers purchase more disposable products because they shop for convenience rather than utility maximization.
Roozmand, O. ,
Ghasem-aghaee, N. ,
Hofstede, G.J. ,
Nematbakhsh, M.A. ,
Baraani, A. ,
Verwaart, T. Knowledge-Based Systems (09507051) 24(7)pp. 1075-1095
Simulating consumer decision making processes involves different disciplines such as: sociology, social psychology, marketing, and computer science. In this paper, we propose an agent-based conceptual and computational model of consumer decision-making based on culture, personality and human needs. It serves as a model for individual behavior in models that investigate system-level resulting behavior. Theoretical concepts operationalized in the model are the Power Distance dimension of Hofstede's model of national culture; Extroversion, Agreeableness and Openness of Costa and McCrae's five-factor model of personality, and social status and social responsibility needs. These factors are used to formulate the utility function, process and update the agent state, need recognition and action estimation modules of the consumer decision process. The model was validated against data on culture, personality, wealth and car purchasing from eleven European countries. It produces believable results for the differences of consumer purchasing across eleven European countries. © 2011 Elsevier B.V. All rights reserved.
Osinga, S.A. ,
Kramer, M.R. ,
Hofstede, G.J. ,
Roozmand, O. ,
Beulens, A.J. Lecture Notes in Economics and Mathematical Systems (00758442) 645pp. 177-188
This paper investigates the effect of a selected top-down measure (whatif scenario) on actual agent behaviour and total system behaviour by means of an agent-based simulation model, when agents' behaviour cannot fully be managed because the agents are autonomous. The Chinese pork sector serves as case. A multilevel perspective is adopted: the top-down information management measures for improving pork quality, the variation in individual farmer behaviour, and the interaction structures with supply chain partners, governmental representatives and peer farmers. To improve quality, farmers need information, which they can obtain from peers, suppliers and government. Satisfaction or dissatisfaction with their personal situation initiates change of behaviour. Aspects of personality and culture affect the agents' evaluations, decisions and actions. Results indicate that both incentive (demand) and the possibility to move (quality level within reach) on farmer level are requirements for an increase of total system quality. A more informative governmental representative enhances this effect. © Springer-Verlag Berlin Heidelberg 2010.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (03029743) 5097pp. 681-692
In this paper, we propose a flexible parallel ant colony algorithm for classification rule discovery in the large databases. We call this algorithm Parallel Ant-Miner2. This model relies on the extension of real behavior of ants and data mining concepts. The artificial ants are firstly generated and separated into several groups. Each group is assigned a class label which is the consequent parts of the rules it should discover. Ants try to discover rules in parallel and then communicate with each other to update the pheromones in different paths. The communication methods help ants not to gather irrelevant terms of the rule. The parallel executions of ants reduce the speed of convergence and consequently make it possible to extract more new high quality rules by exploring all search space. Our experimental results show that the proposed model is more accurate than the other versions of Ant-Miner. © 2008 Springer-Verlag Berlin Heidelberg.
Scientia Iranica (23453605) 14(6)pp. 631-640
In this paper, reinforcement learning is used in order to model the reputation of buying and selling agents. Two important factors, quality and price, are considered in the proposed model. Each selling agent learns to evaluate the reputation of buying agents, based on their profits for that seller and uses this reputation to dedicate a discount for reputable buying agents. Also, selling agents learn to maximize their expected profits by using reinforcement learning to adjust the quality and price of the products, in order to satisfy the buying agents' preferences. In contrast, buying agents evaluate the reputation of selling agents based on two different factors: Reputation based on quality and price. Therefore, buying agents avoid interacting with disreputable selling agents. In addition, the fact that buying agents can have different priorities on the quality and price of their goods is taken into account. The proposed model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/ buying agents that use the proposed algorithms in this paper obtain more satisfaction than the other selling/buying agents. © Sharif University of Technology, December 2007.
Journal of Theoretical and Applied Electronic Commerce Research (07181876) 2(1)pp. 1-17
In this paper, we propose a market model which Is based on reputation and reinforcement learning algorithms for buying and selling agents. Three important factors: quality, price and delivery-time are considered in the model. We take into account the fact that buying agents can have different priorities on quality, price and delivery-time of their goods and selling agents adjust their bids according to buying agents preferences. Also we have assumed that multiple selling agents may offer the same goods with different qualities, prices and delivery-times. In our model, selling agents learn to maximize their expected profits by using reinforcement learning to adjust product quality, price and delivery-time. Also each selling agent models the reputation of buying agents based on their profits for that seller and uses this reputation to consider discount for reputable buying agents. Buying agents learn to model the reputation of selling agents based on different features of goods: reputation on quality, reputation on price and reputation on delivery-time to avoid interaction with disreputable selling agents. The model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/buying agents that model the reputation of buying/selling agents obtain more satisfaction rather than selling/buying agents who only use the reinforcement learning. © 2007 Universidad de Talca.
