Abstract :
This research paper examines the transformative impact of behavioral economics on consumer decision making in an era where artificial intelligence (AI), data science, business analytics, and the Internet of Things (IoT) are increasingly influential. These technologies, particularly IoT-enabled “servgoods,” allow for a seamless blend of products and services, fostering intelligent, adaptive, and personalized consumer interactions that fundamentally shift traditional consumer behaviour models. AI-powered systems equip businesses with capability to anticipate consumer preferences and adjust offerings instantaneously, creating a dynamic interplay of digital, economic, and psychological factors that re-shape engagement strategies. Leveraging Big Data and sophisticated analytics, companies achieve greater marketing precision and data-driven insights, leading to highly personalized consumer engagement that aligns with individual preferences. However, while AI and IoT facilitate more efficient and engaging consumer experiences, they introduce complex ethical and practical concerns, particularly around data privacy, algorithmic fairness, and maintenance of consumer trust. The integration of behavioral economics with these technologies presents unique challenges for decision-makers, emphasizing the urgent need for ethical frameworks to guide the use of predictive analytics and algorithm-driven personalization. The convergence of AI with behavioral economics is therefore critical for both understanding consumer behavior and optimizing it in a manner that respects ethical boundaries. This paper underscores the significance of these technological advancements and their alignment with behavioral economics principles, recognizing their potential to profoundly influence, and responsibly steer, consumer decision-making in the digital age.

Keywords :
Artificial Intelligence, Big Data, Behavioral Economics, Business Analytics, Consumer Decision-Making, Data Privacy, Digital Marketing, Internet of Things, Predictive Analytics, Servgoods.

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References :

• Abrardi, L., Cambini, C., & Rondi, L. (2022). Artificial intelligence, firms and consumer behavior: A survey. Journal of Economic Surveys, 36(4), 969-991.
• Chaudhary, K., & Alam, M. (2022). Big data analytics: applications in business and marketing. Auerbach Publications.
• Chkoniya, V. (2023). Challenges in decoding consumer behavior with data science.
• De Mauro, A., Sestino, A., & Bacconi, A. (2022). Machine learning and artificial intelligence use in marketing: a general taxonomy. Italian Journal of Marketing, 2022(4), 439-457.
• Gerlick, J. A., & Liozu, S. M. (2020). Ethical and legal considerations of artificial intelligence and algorithmic decision-making in personalized pricing. Journal of Revenue and Pricing Management, 19, 85-98.
• Giannakopoulos, N. T., Terzi, M. C., Sakas, D. P., Kanellos, N., Toudas, K. S., & Migkos, S. P. (2024). Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling. Information, 15(2), 67.
• Han, M. (2023). Consumer Behavior Analysis and Personalized Marketing Strategies for the Internet of Things. Revista Ibérica de Sistemas e Tecnologias de Informação, (E63), 367-376.
• Hicham, N., Nassera, H., & Karim, S. (2023). Strategic framework for leveraging artificial intelligence in future marketing decision-making. Journal of Intelligent Management Decision, 2(3), 139-150.
• Kliestik, T., Kovalova, E., & Lăzăroiu, G. (2022). Cognitive decision-making algorithms in data-driven retail intelligence: consumer sentiments, choices, and shopping behaviors. Journal of Self-Governance and Management Economics, 10(1), 30-42.
• Musiolik, T. H., Rodriguez, R. V., & Kannan, H. (Eds.). (2024). AI impacts in digital consumer behavior. IGI Global.
• Pei, J. (2020). A survey on data pricing: from economics to data science. IEEE Transactions on knowledge and Data Engineering, 34(10), 4586-4608.
• Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.
• Raji, M. A., Olodo, H. B., Oke, T. T., Addy, W. A., Ofodile, O. C., & Oyewole, A. T. (2024). E-commerce and consumer behavior: A review of AI-powered personalization and market trends. GSC Advanced Research and Reviews, 18(3), 066-077.
• Rane, N. (2023). Enhancing customer loyalty through Artificial Intelligence (AI), Internet of Things (IoT), and Big Data technologies: improving customer satisfaction, engagement, relationship, and experience. Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience (October 13, 2023).
• Reddy, S. R. B. (2021). Predictive Analytics in Customer Relationship Management: Utilizing Big Data and AI to Drive Personalized Marketing Strategies. Australian Journal of Machine Learning Research & Applications, 1(1), 1-12.
• Sarker, I. H. (2021). Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective. SN Computer Science, 2(5), 377.
• Smith, A. (2019). Consumer behaviour and analytics. Routledge.
• Tien, J. M. (2017). Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4, 149-178.
• Zaman, K. (2022). Transformation of marketing decisions through artificial intelligence and digital marketing. Journal of Marketing Strategies, 4(2), 353-364.

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