Presenting an Artificial Intelligence Intelligent Agent Model for Monitoring Consumer Behavioral Responses of Handbags and Shoes on the Blockchain Platform

Document Type : Original Article

Authors

1 PhD. Student, Department of Business Administration, Emirates Branch, Islamic Azad University, Dubai, United Arab Emirates

2 Associate Professor, Department of Business Management, Firozabad Branch, Islamic Azad University, Firozabad, Iran

3 Associate Professor, Department of Business Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

Purpose: The Artificial Intelligence Intelligent Agent for Consumer Behavioral Responses is a system that, based on acquired knowledge and experiences, recognizes the dominant behavioral style
of the consumer, identifies behavioral anomalies, examines behavioral models and dimensions of consumer behavioral responses, and can intelligently decide which behavioral model to use to have the maximum impact on consumer behaviors and habits to create value and increase the customer life cycle. In this regard, the present study aimed to present an Artificial Intelligence Intelligent Agent Model for Monitoring Consumer Behavioral Responses to Handbags and Shoes on the Blockchain Platform.
Method: The present study employs a qualitative approach, utilizing two systematic and data-driven review methods in combination. The statistical population for the data-driven method consisted of experts in business management, information management, and computer science. The sample size was determined to be 16 individuals, based on theoretical saturation, using purposive sampling. The data collection tool used in the systematic review method was library studies, while the data-based method employed semi-structured interviews with experts. The validation of this research was conducted based on the qualitative criteria established by Lincoln and Guba.
Findings: First, the systematic review method was employed to identify the antecedents and central phenomena of the pattern. A total of 361 articles were identified based on the research topic. After screening, 26 articles in Persian and English were selected for inclusion based on their relevance to the topic and content. Given that the existing literature and research background were not sufficiently comprehensive to complete the paradigmatic model, a data-driven approach was employed in the continuation of the research. This method aimed to identify intervening factors, contextual factors, strategies, and consequences of Grounded Theory method by utilizing expert opinions.
Conclusion: The present research led to the presentation of a new paradigm model with the title of an intelligent artificial intelligence operating system for monitoring the behavioral responses of bag and shoe consumers on the blockchain platform.

Keywords

References

Ab Azizi, A. & Farif Jemili, M. (2022). Conceptual Design of a Socially Intelligent Agent with Triadic Empathy and Theory of Mind for Mental Health Support. Journal of human center technology, 1(1): 28-39. https://doi.org/10.11113/humentech.v1n1.12
Abu Hadba, S.Y. & Nafea, I. (2021). An Intelligent Agent Model and a Simulation for a Given Task in a Specific Environment. American Journal of Artificial Intelligence, 5(1): 1-16.       
https://doi.org/10.11648/j.ajai.20210501.11
Ali Ahmadi, A. & Hour Ali, M. (2004). Customer relationship management relying on web-based tools and intelligent agents. Tomorrow's Management, 7(8): 101-108. [in persian]
Alkinani, M.H., Ali Almazroib, A., Aadhikaric, M. & Menond, V.G. (2022). Desing and analysis of logistic agent-based swarm-neural network for intelligent transportation system. Alexandria Engineering Journal, 61(10): 8325-8334.
Binos, T. & Bruno, V. (2022). Intelligent agent based framework to augment warehouse management systems for dynamic demand environments. Australasian Conference on Information Systems: 198-216. https://doi.org/10.3127/ajis.v25i0.2845
Fafadar, Sh. & Abdulzadeh Barforosh, A. (2011). Patterns of learning stability analysis in intelligent software agents. Electrical Engineering and Computer Engineering of Iran, 9(3): 119-132.
[in persian]
Fathima, K.M., Santhiyakumari, N. & Suganthi, M. (2022). Augmentation of Intelligent Agent for Multiple Access Protocols in Wireless Sensor Networks. Second International Conference on Artificial Intelligence and Smart Energy, 3(1): 61-78.        
https://doi.org/10.1109/ICAIS53314.2022.9742944
Francis, V., Francelin, S., Daniel, J. & Velliangiri, S. (2022). Intelligent agent and optimization-based deep residual network to secure communication in UAV network. International journal of intelligent systems, 5(2): 68-82. https://doi.org/10.1002/int.22800
Guba, E.G. & Lincoln, Y.S. (2005). Paradigmatic Controversies, Contradictions, and Emerging Confluences. In: N.K. Denzin & Y.S. Lincoln (Eds.), The Sage handbook of qualitative research: 191-215.
Hamdi Ben, A., Byrns, A. & Frasson, C. (2021). Optimizing Alzheimer’s Disease Therapy Using
a Neural Intelligent Agent-Based Platform. International Journal of Intelligence Science, 6(3): 321-343. https://doi.org/ 10.4236/ijis.2021.112006
Hassanzadeh, M. & Mohammadkhani, A. (2008). An overview of intelligent agents and their role in library services. Peek Noor, 6(2): 42-52. [in persian]
Hassanzadeh, M. (2021). The fifth data revolution, the irreplaceable role of intelligent agents and the necessity of a national data organization. Information Management Sciences and Techniques, 7(3): 7-16. [in persian]
Jungwon, K. & Yongjun, S. (2022). Artificial Intelligence Is Safer for My Privacy: Interplay Between Types of Personal Information and Agents on Perceived Privacy Risk and Concerns. Cyberpsychology, Behavior, and Social Networking, Vol. 25: 587-611.             
https://doi.org/10.1089/cyber.2021.0076
Kaswan, K.S., Dhatterwal, J.S. & Balyan, A. (2022). Intelligent Agents based Integration of Machine Learning and Case Base Reasoning System. International Conference on Advance Computing and Innovative Technologies in Engineering: 168-183.  
https://doi.org/ 10.1109/ICACITE53722.2022.9823890
Kavossi, K. & Moshiri, B. (2004). Improving the intelligent methods of combining information. Journal of Technical Faculty of Tehran University, no. 83: 85-98. [in persian]
Khan Mohammadi, M. (2021). Examining the obstacles to the use of intelligent agents in independent auditing. Accounting and Management Perspectives, 4(38): 100-114. [in persian]
Kridalukmana, R., Eridani, D., Septiana, R. & Rochim, A.F. & Setyobudhi, CHT. (2022). Artificial Situation Awareness for an Intelligent Agent. International Joint Conference on Computer Science and Software: 47-63. https://doi.org/10.1109/JCSSE54890.2022.9836282
Mansourian, Y. (2004). Intelligent search agents and information retrieval in the web environment. Informatics, no. 4: 164-163. [in persian]
Moradi, M., Aghaei, A. & Hosseini, M. (2012). Application of intelligent multi-agent system in decision making with knowledge management approach. Information Technology Management, 5(4): 244-219. [in persian]
Mousawi, S., Koufaris, M. & Benbunan-Fich, R. (2022).The role of user perceptions of intelligence, anthropomorphism, and self-extension on continuance of use of personal intelligent agents. European Journal of Information Systems, Vol.108: 22-41.
https://doi.org/10.1080/0960085X.2021.2018365
Mukherjee, S. & Chittipaka, V. (2022). Analysing the Adoption of Intelligent Agent Technology in Food Supply Chain Management. IGI Global’s Online Bookstore Extended: 258-279.       
https://doi.org/10.1177/23197145211059243
Nami, M., Kamali Dehghan, M., Abbasi, M. & Farsi, A. (2008). The role of intelligent agents in improving e-government activities. Electrical Engineering, 2(4): 42-37. [in persian]
Nandy, S., Adhikari, M., Chakraborty, S., Alkhayyat, A. & Kumar, N. (2022). Intelligent Agent-based Internet of Medical Things framework for detecting brain response from Electroencephalography signal using Bag-of-Neural Network. Future Generation Computer Systems, Vol. 130: 241-252. https://doi.org/10.1016/j.future.2021.12.019
Rin Shim, H. & Gwan Kim, B. (2020).The Effect of Customer Value on User Satisfaction With Dialogue Characteristics of Apple's Intelligent Agent Siri. Journal of Organizational and End User Computing (JOEUC), Vol. 92: 28-46. https://doi.org/10.4018/JOEUC.2020010104
Sargazi Moghadam, H. & Shahesvari, M. (2016). An overview of the role of intelligent software agents in supply chain management. Supply Chain Management, 18(53): 14-26. [in persian]
Sidlauskiene, J. (2021). What Drives Consumers’ Decisions to Use Intelligent Agent Technologies? Journal of Internet Commerce, Vol.113: 8-22. https://doi.org/10.1080/15332861.2021.1961192
Yang,Y., Yue, L., Xingyang, L., Jin, A. & Yifan, L. (2022). Anthropomorphism and customers’ willingness to use artificial intelligence service agents. Journal of Hospitality Marketing & Management, Vol. 31: 12-35. https://doi.org/10.1080/19368623.2021.192603
 
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Sciences and Techniques of Information Management
Volume 10, Issue 2
November 2024
Pages 155-184

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