International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 18, No. 5, 8 Oct. 2026
Cover page and Table of Contents: PDF (size: 974KB)
Application of Artificial Intelligence for Engagement Personalization in B2C Business Models: A Literature Review
PDF (974KB), PP.90-108
Views: 0 Downloads: 0
Author(s)
Laleska Fabiola Estrella Copaja
1
Jose Antonio Rojas Garcia
2
Juan Carlos Quiroz-Flores
3,*
Index Terms
Artificial Intelligence, Recommendation Systems, Customer Engagement, Customer Experience, Digital Marketing, Consumer Behavior, B2C (Business to Consumer), E-commerce.
Abstract
In an increasingly competitive world, retail companies are implementing marketing strategies aimed at gaining customer preference by building lasting emotional relationships. This facilitates the conversion of so-called followers into loyal customers and brand ambassadors, thereby increasing return on investment (ROI), facilitating direct feedback to improve the product mix, and reducing customer acquisition costs over time, generating competitive advantages. The purpose of this systematic review is to analyze recent scientific production (2020-2025) on the application of Artificial Intelligence (AI) for the personalization of engagement in B2C business models, in order to analyze the predominant techniques, evaluate their impact on the consumer and determine the critical factors that condition long-term customer loyalty; the methodology used consisted of a systematic review in the main scientific databases in publications of high-impact journals. The main findings suggest that personalization tends to be dominated by machine learning models focused on algorithmic efficiency; however, a critical asymmetry was observed: the literature shows a hyper-focus on quantitative studies and functional efficiency, while studies on trust and ethics are the least relevant. Additionally, knowledge is biased by Asian dominance (53%) and the "novelty effect," limiting the applicability of strategies in markets with high demands for transparency, such as the American market. Furthermore, an emerging trend toward an Ethical-Relational Model has been observed in the literature. It is concluded that moving beyond the functional efficiency-centric approach and integrating Ethical AI as a strategic consideration may be key to fostering sustainable engagement, as evidenced by the reviewed literature.
Cite This Paper
Laleska Fabiola Estrella Copaja, José Antonio Rojas García, Juan Carlos Quiroz-Flores, "Application of Artificial Intelligence for Engagement Personalization in B2C Business Models: A Literature Review", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.5, pp. 90-108, 2026. DOI:10.5815/ijieeb.2026.05.06
Reference
[1]Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how?. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2024.102783
[2]Dritsas, E., & Trigka, M. (2025). Machine Learning in E-Commerce: Trends, Applications, and Future Challenges. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3572865
[3]Vlačić, B., Corbo, L., Silva, S. C. e., & Dabić, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2021.01.055
[4]Yrjölä, M., Hautamäki, P., & Ranta, R. (2025). Value propositions of artificial intelligence in retailing. The International Review of Retail, Distribution and Consumer Research, 35(4), 355-382.
[5]Gao, Y., Wang, J., Li, Z., & Peng, Z. (2023). The Social Media Big Data Analysis of Customer Experience in AI-Enabled Tourism Services. Journal of Global Information Management. https://www.igi-global.com/article/325217
[6]Bawack, R. E., Wamba, S. F., & Carillo, K. D. A. (2021). Exploring the role of personality, trust, and privacy in customer experience performance during voice shopping: Evidence from SEM and fuzzy set qualitative comparative analysis. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2021.102309
[7]Roy, S. K., Tehrani, A. N., Pandit, A., Apostolidis, C., & Ray, S. (2025). AI-capable relationship marketing: Shaping the future of customer relationships. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2025.115309
[8]Qin, M., Zhu, W., Zhao, S., & Zhao, Y. (2022). Is Artificial Intelligence Better than Manpower? The Effects of Different Types of Online Customer Services on Customer Purchase Intentions. Sustainability. https://doi.org/10.3390/su14073974
[9]Gomes, S., Lopes, J. M., & Nogueira, E. (2025). Anthropomorphism in artificial intelligence: a game-changer for brand marketing. Journal of Innovation and Entrepreneurship. https://doi.org/10.1186/s43093-025-00423-y
[10]Tulcanaza-Prieto, A. B., Cortez-Ordoñez, A., & Lee, C. W. (2023). Influence of Customer Perception Factors on AI-Enabled Customer Experience in the Ecuadorian Banking Environment. Sustainability. https://doi.org/10.3390/su151612441
[11]Haque, A., Akther, N., Khan, I., Agarwal, K., & Uddin, N. (2024, October). Artificial intelligence in retail marketing: Research agenda based on bibliometric reflection and content analysis (2000–2023). In Informatics (Vol. 11, No. 4, p. 74). MDPI.
[12]Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.
[13]Yang, X., Li, H., Ni, L., & Li, T. (2021). Application of artificial intelligence in precision marketing. Journal of Organizational and End User Computing (JOEUC), 33(4), 209-219.
[14]Lara-Cabrera, R., González-Prieto, Á., & Ortega, F. (2020). Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems. Applied Sciences. https://doi.org/10.3390/app10144926
[15]Bharti, P., & Park, B. (2023). The Ethics of AI in Online Marketing: Examining the Impacts on Consumer privacyand Decision-making. International Journal of Internet, Broadcasting and Communication, 15(2), 227-239.
[16]Saura, J. R., Škare, V., & Dosen, D. O. (2024). Is AI-based digital marketing ethical? Assessing a new data privacy paradox. Journal of Innovation & Knowledge, 9(4), 100597.
[17]Ghosh, M. (2025). Artificial intelligence (AI) and ethical concerns: a review and research agenda. Cogent Business & Management, 12(1), 2551809.
[18]Liu-Thompkins, Y., Okazaki, S., & Li, H. (2022). Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience. Journal of the Academy of Marketing Science, 50(6), 1198-1218.
[19]Xu, Y., Shieh, C. H., van Esch, P., & Ling, I. L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian marketing journal, 28(4), 189-199.
[20]Nicolescu, L., & Tudorache, M. T. (2022). Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review. Electronics. https://doi.org/10.3390/electronics11101579
[21]Vebrianti, R., Aras, M., Putri, M. S. S., & Swandewi, I. A. (2025). AI Chatbots in E-Commerce: Enhancing Customer Engagement, Satisfaction and Loyalty. Paper Asia. https://doi.org/10.59953/paperasia.v41i2b.445
[22]Mei, L., Tang, N., Zeng, Z., & Shi, W. (2025). Artificial Intelligence Technology in Live Streaming E-commerce: Analysis of Driving Factors of Consumer Purchase Decisions. International Journal of Computers Communications & Control. https://doi.org/10.15837/ijccc.2025.1.6871
[23]Wasilewski, A., Chawla, Y., & Pralat, E. (2025). Enhanced E-Commerce Personalization Through AI-Powered Content Generation Tools. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3550956
[24]Raza, A., Salahuddin, Ali, G., Soomro, M. H., & Batool, S. (2025). Analyzing the Impact of Artificial Intelligence on Shaping Consumer Demand in E-Commerce. International Journal of Information Engineering and Electronic Business. https://doi.org/10.5815/ijieeb.2025.05.04
[25]Hardcastle, K., Vorster, L., & Brown, D. M. (2025). Understanding Customer Responses to AI-Driven Personalized Journeys: Impacts on the Customer Experience. Journal of Advertising. https://doi.org/10.1080/00913367.2025.2460985
[26]Filahi, Y., Gul, O. M., Elghirani, A., Arican, E., Parlak, I. B., Kadry, S., & Karpouzis, K. (2025). Enhanced E-commerce decision-making through sentiment analysis using machine learning-based approaches and IoT. PLOS ONE. https://doi.org/10.1371/journal.pone.0326744
[27]Islam, M. A., Fakir, S. I., Masud, S. B., Hossen, M. D., Islam, M. T., & Siddiky, M. R. (2024). Artificial intelligence in digital marketing automation: Enhancing personalization, predictive analytics, and ethical integration. Edelweiss Applied Science and Technology, 8(6), 6498-6516
[28]Rakee, F., Hamzeh, A., & Mozafari, N. (2025). TEMUL: Tensor-Based Deep Learning Approach for Multi-Criteria Recommender System. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3626692
[29]Kravets, O., Korolova, U., Nosachenko, O., Vasiltsova, O., & Koberniuk, S. (2025). AI-Powered Digital Marketing: Enhancing Customer Behaviour Predictions. European Journal of Sustainable Development. https://doi.org/10.14207/ejsd.2025.v14n2p84
[30]Gao, J., Opute, A. P., Jawad, C., & Zhan, M. (2025). The influence of artificial intelligence chatbot problem solving on customers’ continued usage intention in e-commerce platforms: an expectation-confirmation model approach. Journal of Business Research, 200, 115661.
[31]Febrian, A., Purnomowati, I., Mahardiyanto, A., Lina, L. F., & Novita, D. (2025). The Role of Artificial Intelligence in Forming Favorable Customer Attitudes on E-Commerce Websites. Journal of IA and Technology. https://ojs.istp-press.com/jait/article/view/696
[32]Prentice, C., Weaven, S., & Wong, I. A. (2020). Linking AI quality performance and customer engagement: The moderating effect of AI preference. International Journal of Hospitality Management, 90, 102629.
[33]Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & intelligent systems, 7(1), 439-457.
[34]He, H., Zhou, G., & Zhao, S. (2022). Exploring e-commerce product experience based on fusion sentiment analysis method. Ieee Access, 10, 110248-110260.
[35]Kilimci, Z. H. (2022). Prediction of user loyalty in mobile applications using deep contextualized word representations. Journal of Information and Telecommunication, 6(1), 43-60.
[36]Zhou, X., & Zhou, Y. (2022). Research on the Innovative Marketing Model of E‐Commerce Platform Driven by Big Data in the Era of Network Economy. Mobile Information Systems, 2022(1), 1551264.
[37]Volkmar, G., Fischer, P. M., & Reinecke, S. (2022). Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management. Journal of Business Research, 149(2), 599-614.
[38]Javaid, K., Siddiqa, A., Naqvi, S. A. Z., Ditta, A., Ahsan, M., Khan, M. A., ... & Khan, M. A. (2022). Explainable artificial intelligence solution for online retail. Computers, Materials & Continua, 71(3), 4425-4442.
[39]Morsi, S. (2023). Artificial Intelligence in Electronic Commerce: Investigating the Customers' Acceptance of the Use of Chatbots. J. Syst. Manag. Sci , 13 (3), 156-176, doi: 10.33168/JSMS.2023.0311
[40]Chi, N. T. K., & Hoang Vu, N. (2023). Investigating the customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction. CAAI Transactions on Intelligence Technology, 8(1), 260-273.
[41]Li, W. (2023). Intelligent recommendation system based on the infusion algorithms with deep learning, attention network and clustering. International Journal of Computational Intelligence Systems, 16(1), 83.
[42]Li, L., Huang, H., Li, Q., & Man, J. (2023). Personalized movie recommendations based on deep representation learning. PeerJ Computer Science, 9, e1448.
[43]Bodduluri, K. C., Palma, F., Kurti, A., Jusufi, I., & Löwenadler, H. (2024). Exploring the landscape of hybrid recommendation systems in e-commerce: a systematic literature review. IEEE Access, 12, 28273-28296.
[44]Guo, C., & Zhang, X. (2024). Intelligent prediction of Cross-Border E-Commerce customer satisfaction using deep learning embeddings. IEEE Access.
[45]Mustafa, G., Jhamat, N. A., Arshad, Z., Yousaf, N., Abdal, M. N., Maray, M., ... & Ahmad, T. (2024). OntoCommerce: Incorporating ontology and sequential pattern mining for personalized e-commerce recommendations. IEEE Access, 12, 42329-42342.
[46]Hong, S., Li, X., Yang, S., & Kim, J. (2024). Based Recommender System Using Outer Product on CNN. IEEE Access, 12, 65650-65659.
[47]Li, J., Chen, J., Shi, Y., & Cui, Y. (2025). Exploring the impact of differences between AI streamers and human streamers on consumer purchase intention in live e-commerce: A grounded theory approach. PLoS One, 20(10), e0334530.
[48]Chen, Y., & Prentice, C. (2025). Integrating artificial intelligence and customer experience. Australasian Marketing Journal, 33(2), 141-153.
[49]Ledro, C., Nosella, A., Vinelli, A., Dalla Pozza, I., & Souverain, T. (2025). Artificial intelligence in customer relationship management: A systematic framework for a successful integration. Journal of Business Research, 199, 115531.
[50]Kaponis, A., Maragoudakis, M., & Sofianos, K. C. (2025). Enhancing User Experiences in Digital Marketing Through Machine Learning: Cases, Trends, and Challenges. Computers, 14 (6), 211.
[51]Li, C., Hao, R., Li, N., & Zhang, C. (2025). Measuring customer experience in ai contexts: A scale development. Journal of Theoretical and Applied Electronic Commerce Research, 20(1), 31.
[52]Ribeiro, A., Rivero, A. J. L., & Abrantes, J. L. (2025). The impact of artificial intelligence on consumer behavior towards brands: a systematic review. Electronic Commerce Research, 1-42.
[53]Beyari, H., & Hashem, T. (2025). The Role of Artificial Intelligence in Personalizing Social Media Marketing Strategies for Enhanced Customer Experience. Behavioral Sciences, 15(5), 700.
[54]Chau, H. K. L., Ngo, T. T. A., Bui, C. T., & Tran, N. P. N. (2025). Human-AI Interaction in E-Commerce: The Impact of AI-Powered Customer Service on User Experience and Decision-Making. Computers in Human Behavior Reports, 100725.
[55]Karthik, R. V., Pandiyaraju, V., & Ganapathy, S. (2025). A context and sequence-based recommendation framework using GRU networks. Artificial Intelligence Review, 58(6), 170.
[56]Kim, S., Boo, S., Jeon, G., Shin, D., & Lee, S. (2025). Balancing Diversity in Session-Based Recommendation Between Relevance and Unexpectedness. IEEE Access.
[57]Seol, J., & Choi, J. (2025). Context-aware Negative Sampling for Sequential Recommendation. IEEE Access.
[58]Chaudhari, A., Hitham Seddig, A. A., Raut, R., & Sarlan, A. (2025). FedRecSys: A Federated Learning-Based Recommender System for Concept Drift Adaptation. IEEE Access, 13, 66478-66491.
[59]Oprea, S. V., & Bâra, A. (2025). Graph-Based User-Centric Recommender System Using Neo4j, Cypher, and Jaccard Similarity in the Field of e-Commerce. IEEE Access, 13, 135089-135097.
[60]Chornous, G., Dimitrov, R., Fareniuk, Y., Penkova, M., & Nosko, R. (2025). Integration of feeling AI tools to support marketing solutions in e-commerce. Access Journal, 6(2), 415-436.