Transparent Algorithms, Trusted Brands: Ethical AI and Consumer Perceptions in Food Branding Literature review
DOI:
https://doi.org/10.65405/8v1rtz27الكلمات المفتاحية:
Artificial Intelligence (AI), Brand Authenticity, Consumer Trust, Algorithmic Transparency, Food Branding, Libyan Marketالملخص
As Artificial Intelligence (AI) reshapes branding practices globally, its implications for brand authenticity, transparency, and consumer trust particularly in culturally nuanced, trust-sensitive markets like Libya remain underexplored. This literature review addresses this gap by investigating how AI-driven branding influences consumer perceptions in the Libyan food industry, with a focused case on Aljaied for Food Industries. The central research question is: How can AI be ethically and effectively integrated into branding strategies without compromising brand authenticity and consumer trust in Libya’s culturally specific context?. The novelty of this study lies in its contextual grounding: it bridges global AI ethics frameworks with the socio-cultural realities of a North African emerging market, where interpersonal trust, community values, and Halal compliance heavily shape consumer behavior. Unlike most AI-branding research centered on Western or highly digitized economies, this review emphasizes the unique challenges and opportunities faced by local firms navigating digital transformation amid low AI literacy and high cultural sensitivity. Key contributions include: (1) a theoretical integration of Social Identity Theory and Signaling Theory to explain how AI-mediated interactions affect brand identification and trust; (2) a practical framework for ethical AI adoption that prioritizes explainability, data privacy, algorithmic fairness, and cultural alignment; and (3) contextual insight into the Libyan market, offering the first systematic analysis of AI’s role in a post-conflict, high-context society. This literature review also benefits Aljaied by providing actionable strategies to leverage AI for personalization and supply-chain transparency while preserving authenticity. By positioning AI as a trust-enhancing, not just efficiency-driven, tool, the study empowers Libyan brands to compete in a digital future without sacrificing the cultural and ethical foundations of their consumer relationships.
التنزيلات
المراجع
A Jetlawei et al., (2025).Temporal Intelligence and Algorithmic Equity: A Multi-Phase Framework for Predictive Student Success in Higher Education. The Journal of Comprehensive Sciences 9 (36), 1574-1595
A-abdullatef, M. M., Albaraesi, M. J. S., EL-sseid, M. A. M., Dalla, L. O. B., Ahmed, A. A., Agila, A., & Alsharif, A. (2026). Tri-Conditional Biomechanical Signature Extraction: A Hybrid Framework Integrating Multivariate Functional Clustering, Cross-Modal Regression, and Inter-Subject Classification for Discriminative Gait Pattern Analysis. Comprehensive Science Journal, 10(39), 1063-1087
Ahmed, S. M. M., Owais, M., Raza, M., Nadeem, Q., & Ahmed, B. (2025). The Impact of AI-Driven Personalization on Consumer Engagement and Brand Loyalty. Qlantic Journal of Social Sciences, 6(1), 311-323.
Al Chami, R. (2025). From talk to trust: Cultural dynamics of AI-driven social responsibility in negotiations for strategic communication. International Communication of Chinese Culture, 1-20.
Ali, L., Ali, F., & Alotaibi, S. (2025). Beyond the hype: Evaluating the impact of generative AI on brand authenticity, image, and consumer behavior in the restaurant industry. International Journal of Hospitality Management, 131, 104318.
Ali, S. M. S., Dakshinamurthy, T., Priyadarshi, P., & Mittal, M. (2025). Consumer Trust In Digital Brands: The Role Of Transparency And Ethical Marketing. Advances in Consumer Research, 2, 106-114.
Aljarah, A., Ibrahim, B., & López, M. (2025). In AI, we do not trust! The nexus between awareness of falsity in AI-generated CSR ads and online brand engagement. Internet Research, 35(3), 1406-1426.
Alsharif, A., Ibrahim, A. A., Waheshi, Y. A. A., Alsharif, M., Alhoudier, T. E., Esmail, E. M. A., & Dalla, L. O. F. B. (2025). An Analysis of Power Sector Investment by Technology in the Middle East and North Africa. African Journal of Academic Publishing in Science and Technology (AJAPST), 1(4), 25-39.
Arık, D. T., Karal, Ö., & Şahin, A. B. (2020). A Comparative Study of Artificial Neural Networks and Naïve Bayes Techniques for the Classification of Radar Targets. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9(4), 1779-1788.https://doi.org/10.17798/bitlisfen.676973
Arora, J., Bali, S., Aggarwal, N., Mamgain, G., & Sharma, T. (2025). AI-Driven Personalization Of Brand Voice: Enhancing Customer Engagement And Brand Identity. International Journal of Environmental Sciences, 935-943.
Ben Dalla, L, O, F. (2021).Literature review (LR) on the powerful of Research methodology processes life cycle. In 2021 The Powerful of Research Methodology Processes Life Cycle Conference (TPRMPLCC) (pp. 1-10). IEEE. https://doi.org/10.16543/TPRMPLCC 50717.2020.92876580
Bensalem, S., & Boudaoud, B. (2024). Ethical Algorithms: Balancing the Power of AI in Marketing with Consumer Trust-Case Studies of International Experiences. مجلة إدارة الأعمال والدراسات الاقتصادية, 10(1), 349-360.
Bensalem, S., Boudaoud, B., & Merrakchi, A. (2024). Symbiosis or Surveillance? Exploring the Relationship between AI, Big Data, and Consumer Privacy in Digital Marketing. افاق للعلوم, 9(4), 192-208.
Bentler, P. M. (1992). On the fit of models to covariances and methodology to the Bulletin. Psychological bulletin, 112(3), 400.
Bialkova, S. (2024). Data management. In The rise of AI user applications: Chatbots integration foundations and trends (pp. 167-186). Cham: Springer Nature Switzerland.
Blösser, M., & Weihrauch, A. (2024). A consumer perspective of AI certification–the current certification landscape, consumer approval and directions for future research. European Journal of Marketing, 58(2), 441-470.
Browne, M. W. and Cudeck R. (1993). Alternative ways of assessing model fit. In Bollen, K. A., & Long, J. S. (Eds.). (1993). Testing structural equation models (Vol. 154). Sage.
Brüns, A., & Meißner, M. (2024). Perceived genuineness in AI‑mediated brand communications. Journal of Interactive Marketing, 65, 1–14.
Brüns, J. D., & Meißner, M. (2024). Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity. Journal of Retailing and Consumer Services, 79, 103790.
Bulmer, S., Palakshappa, N., Dodds, S., & Harper, S. (2024). Sustainability, brand authenticity and Instagram messaging. Journal of Business Research, 175, 114547.
Byrne Barbara, M. (2016). Structural equation modeling with AMOS: basic concepts, applications, and programming. Routledge.
Charfou, A. R., & Naji, J. (2024). The Era of Ai-Generated Video Production: Exploring Consumers’ Attitudes.
Chen, Y., Wang, H., Hill, S. R., & Li, B. (2024). Consumer attitudes toward AI-generated ads: Appeal types, self-efficacy and AI’s social role. Journal of Business Research, 185, 114867.
Cicek, M., Gursoy, D., & Lu, L. (2025). Adverse impacts of revealing the presence of “Artificial Intelligence (AI)” technology in product and service descriptions on purchase intentions: the mediating role of emotional trust and the moderating role of perceived risk. Journal of Hospitality Marketing & Management, 34(1), 1-23.
Collier, J. (2020). Applied structural equation modeling using AMOS: Basic to advanced techniques. Routledge.
Crisci, A. (2024). Business-To-Business Brand Authenticity in the Age of Artificial Intelligence: Best Practices for Discussing Emotionally Charged Issues on Social Media From Top Technology Companies.
Crisci, L. (2024). Personalisation paradoxes in AI advertising. Marketing Science Review, 39(3), 55–73.
Dalla, L. O. B., Karal, Ö., & Degirmenciyi, A. (2025). Leveraging LSTM for Adaptive Intrusion Detection in IoT Networks: A Case Study on the RT-IoT2022 Dataset implemented On CPU Computer Device Machine. 5th International Conference on Engineering, Natural and Social Sciences, April 15-16, 2025: Konya, Turkey, 2025. Published by All Sciences Academy. https://www.icensos.com/
Dalla, L. O. B., Karal, Ö., Degirmenci, A., EL-Sseid, M. A. M., Essgaer, M., & Alsharif, A. (2025). Edge Intelligence for Real-Time Image Recognition: A Lightweight Neural Scheduler Via Using Execution-Time Signatures on Heterogeneous Edge Devices. Scientific Journal for Publishing in Health Research and Technology, 74-85.
Dalla, L. O. B., Medeni, T. D., & Medeni, İ. T. (2024). Evaluating the Impact of Artificial Intelligence-Driven Prompts on the Efficacy of Academic Writing in Scientific Research. Afro-Asian Journal of Scientific Research (AAJSR), 48-60.
Dalla, L. O. F. B. (2020). Convolutional Neural Network Baseline Model Building for Person Re-Identification. International Journal of Engineering and Modern Technology E-ISSN 2504-8848 P-ISSN 2695-2149 Vol. 6 No. 3 2020 www.iiardpub.org
Dalla, L. O. F. B. (2020). Dorsal Hand Vein (DHV) Verification in Terms of Deep Convolutional Neural Networks with the Linkage of Visualizing Intermediate Layer Activations Detection. International Journal of Engineering and Modern Technology E-ISSN 2504-8848 P-ISSN 2695-2149 Vol 6 No 2 2020 www.iiardpub.org
Dalla, L. O. F. B. (2020). IT security Cloud Computing. . In 2020 IT security Cloud Computing Applications Conference (ITSCC) (pp. 1-10). IEEE. https://doi.org/10.16377/ITSCC 50717.2020.9259880
Dalla, L. O. F. B. (2020). Lean Software Development Practices and Principles in Terms of Observations and Evolution Methods to increase work environment productivity. International Journal of Engineering and Modern Technology, 6(1), 23-45.
Dalla, L. O. F. B. (2020). Modeling by using Generic Modeling Environment (GME) Domain specific modeling language (DSL) for agile software development (ASD) types.
Dalla, L. O. F. B. (2020). The Influence of hospital management framework by the usage of Electronic healthcare record to avoid risk management (Department of Communicable Diseases at Misurata Teaching Hospital: Case study). EHRM, 20(4), 22–52. https://doi.org/20.51176/1954-9923-2020-4-22-52
DALLA, L., & AHMAD, T. M. A. (2024). THE DYNAMIC DELIVERY SERVICES BY USING ANT COLONY OPTIMIZATION ALGORITHM IN THE MODERN CITY BY USING PYTHON RAY SYSTEM.
Delios, A., Welch, C., Nielsen, B., Aguinis, H., & Brewster, C. (2023). Reconsidering, refashioning, and reconceptualizing research methodology in international business. Journal of World Business, 58(6), 101488.
Deryl, M. D., Verma, S., & Srivastava, V. (2023). How does AI drive branding? Towards an integrated theoretical framework for AI-driven branding. International Journal of Information Management Data Insights, 3(2), 100205.
Dubey, U. K. B., & Kothari, D. P. (2022). Research methodology: Techniques and trends. Chapman and Hall/CRC.
Dzwigol, H. (2022). Research methodology in management science: Triangulation. Virtual Economics, 5(1), 78-93.
Ejjami, R. (2024). The Holistic AI-Enhanced Marketing Framework Theory: Bridging Human Creativity and AI for Ethical Marketing. International Journal for Multidisciplinary Research, 6.
El-Annan, S. H., & Hassoun, R. (2025). Enhancing Consumer Trust Through Transparent Data Practices and Ethical Data Management in Business. In Innovation Management for a Resilient Digital Economy (pp. 105-148). IGI Global Scientific Publishing.
Elghaffi, F. S. A. (2026). Temporal Dynamics in Intraoperative Monitoring: A Novel LSTM-Based Framework for Multivariate Time Series Classification in Critical Care Events. Temporal Dynamics in Intraoperative Monitoring: A Novel LSTM-Based Framework for Multivariate Time Series Classification in Critical Care Events.Comprehensive Journal of Science. Volume (10), Issue (38), ( Dec, 2025) ISSN: 3014-6266
Farooq, M., Ramzan, M., & Yen, Y. Y. (2024). Exploring the Impact of Artificial Intelligence on Brand Management: A Multi-Dimensional Analysis. Transformative Impacts of AI in Management, 157.
Gao, Y., & Liang, J. (2025). The Impact of AI-Powered Try-On Technology on Online Consumers’ Impulsive Buying Intention: The Moderating Role of Brand Trust. Sustainability, 17(7), 2789.
Guerra-Tamez, C. R., Kraul Flores, K., Serna-Mendiburu, G. M., Chavelas Robles, D., & Ibarra Cortés, J. (2024). Decoding Gen Z: AI's influence on brand trust and purchasing behavior. Frontiers in Artificial Intelligence, 7, 1323512.
Gupta, A., & Gupta, N. (2022). Research methodology. SBPD publications.
Gursoy, D. (2025). Artificial intelligence (AI) technology, its applications and the use of AI powered devices in hospitality service experience creation and delivery. International Journal of Hospitality Management, 104212.
Hamzani, A. I., Widyastuti, T. V., Khasanah, N., & Rusli, M. H. M. (2023). Legal Research Method: Theoretical and Implementative Review. International Journal of Membrane Science and Technology, 10(2), 3610-3619.
Han, S. (2024). Consumer Perceptions of AI-Generated Content and Disclaimer in Terms of Authenticity, Deception, and Content Attribute.
Hasan, R., Shams, R., & Rahman, M. (2021). Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri. Journal of Business Research, 131, 591-597.
Helberger, N., Kas, B., Micklitz, H. W., Namysłowska, M., Naudts, L., Rott, P., ... & Veale, M. (2024). Digital Fairness for Consumers.
Hidayat, M. S., Muhammad, W., & Isdayanti, P. L. (2025). Digital Marketing Ethics in the Age of AI: A Comparative Analysis of Transparency and Consumer Trust in E-Commerce Platforms. Journal of Management and Informatics, 4(1), 723-740.
Holloway, S. (2024). Narratives of Transparency: How Supply Chain Communication Shapes Brand Image in Marketing.
Hooi, T. K. (2025). Examining Consumer Perceptions and Openness to AI in Business Advertising Campaigns: A Hybrid Methodology Study of Consumer Attitudes Toward AI in Marketing.
Huang, Q., & Maracic, J. (2024). Consumer perception of Deepfake Technology in Marketing: An abductive study on consumer attitude, trust and brand authenticity.
Huang, S., & Maracic, M. (2024). Explainable AI and consumer trust. Computers in Human Behavior Reports, 8, 100–114.
Hussain, W. (2025). The Impact of AI-Generated Brand Personalities on Consumer-Brand Relationship Quality: The Mediating Role of Emotional Resonance. In Impacts of AI-Generated Content on Brand Reputation (pp. 227-244). IGI Global Scientific Publishing.
Iqbal, M. N., & Jan, M. T. (2025). AI-Powered Business Strategies for Enhancing Consumer Behavior and Brand Loyalty in the Bancassurance Sector. In Elevating Brand Loyalty With Optimized Marketing Analytics and AI (pp. 101-132). IGI Global Scientific Publishing.
Islam, M. R., Khan, N. A., & Baikady, R. (Eds.). (2022). Principles of social research methodology. Springer Nature.
Jabagi, N., Croteau, A. M., Audebrand, L. K., & Marsan, J. (2024). Do algorithms play fair? Analysing the perceived fairness of HR-decisions made by algorithms and their impacts on gig-workers. The International Journal of Human Resource Management, 1-40.
Jetlawei, S. S., Dalla, L. O. B., Karal, Ö., Degirmenci, A., El-Sseid, M. A. M., Essgaer, M., & Alsharif, A. (2025). Temporal Intelligence and Algorithmic Equity: A Multi-Phase Framework for Predictive Student Success in Higher Education. Comprehensive Science Journal, 9(36), 1574-1595
Karal, Ö., & Dalla, L. O. F. B. Lung Nodule Characterization in CT Scans Using Hybrid 3D Attention U-Net Segmentation and Transfer Learning-Based Classification Approach.
Khan, A. W., & Mishra, A. (2024). AI credibility and consumer-AI experiences: a conceptual framework. Journal of Service Theory and Practice, 34(1), 66-97.
Kirk, C. P., & Givi, J. (2025). The AI-authorship effect: Understanding authenticity, moral disgust, and consumer responses to AI-generated marketing communications. Journal of Business Research, 186, 114984.
Kirkby, A., Baumgarth, C., & Henseler, J. (2023). To disclose or not disclose, is no longer the question–effect of AI-disclosed brand voice on brand authenticity and attitude. Journal of Product & Brand Management, 32(7), 1108-1122.
Knödler, L., & Rudeloff, C. (2024). Look Who’s Talking Now: The Effects of Pre-recorded and AI-generated Synthetic Brand Voices on Brand Anthropomorphism and Brand Equity. Journal of Creative Communications, 09732586241253651.
Krishnaa, S., & Pranava, P. (2024). AI Impact on Consumer Perception in Digital Marketing in Chennai City. Center for Development Economic, 11(19), 57-65.
Kuma, B. R. (2025). AI-Powered Marketing: Predictive Consumer Behavior and Personalized Campaigns.
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how?. International Journal of Information Management, 77, 102783.
Kunz, W. H., & Wirtz, J. (2024). Corporate digital responsibility (CDR) in the age of AI: implications for interactive marketing. Journal of Research in Interactive Marketing, 18(1), 31-37.
Lee, H., et al. (2024). Data disclosure and perceived fairness in AI services. Information Systems Journal, 34(1), 26–48.
Lee, H., Shin, M., Yang, J., & Chock, T. M. (2024). Virtual influencers vs. human influencers in the context of influencer marketing: The moderating role of machine heuristic on perceived authenticity of influencers. International Journal of Human–Computer Interaction, 1-18.
Lindholm, T. (2025). AI-powered ads: brand loyalty, trust, & ethical impact.
Longpre, S., Mahari, R., Obeng-Marnu, N., Brannon, W., South, T., Kabbara, J., & Pentland, S. (2024). Data Authenticity, Consent, and Provenance for AI Are All Broken: What Will It Take to Fix Them?.
Lopez, A., & Garza, R. (2023). Consumer bias against evaluations received by artificial intelligence: the mediation effect of lack of transparency anxiety. Journal of Research in Interactive Marketing, 17(6), 831-847.
Lou, C., Kiew, S. T. J., Chen, T., Lee, T. Y. M., Ong, J. E. C., & Phua, Z. (2023). Authentically fake? How consumers respond to the influence of virtual influencers. Journal of Advertising, 52(4), 540-557.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological methods, 1(2), 130.
Meggert, F. M. (2024). Art in the age of AI: consumer perceptions and purchase behavior towards human-created and AI-generated artworks (Doctoral dissertation).
Mishra, S. B., & Alok, S. (2022). Handbook of research methodology.
Muhajir, A. (2024). Building Trust in the Digital Age: Marketing Strategies to Enhance Brand Transparency and Authenticity. Management Studies and Business Journal (PRODUCTIVITY), 1(4), 706-717.
Nadeem, M. (2025). From Algorithms to Authenticity: Ensuring Ethical Customer Engagement in the Age of Artificial Intelligence. International Journal of Business and Management, 20(2).
Nallaperuma, K., Kaluarachchi, C. D., & Nguyen, L. (2024). Navigating the nexus of Authenticity, Value And Risk: A Situational Privacy Calculus Model in exploring the use of AI Influencers.
Nasution, M. K., Elveny, M., & Syah, R. (2023). Research methodology for computer science. In AIP Conference Proceedings (Vol. 2714, No. 1). AIP Publishing.
Nguyen, M. T., Thach, K. T. D., Nguyen, C. N. L., Nguyen, A. C., & Doan, H. K. (2025). The Influence of AI Chatbots on Green Satisfaction and Loyalty: Evidence from Sustainability-Driven Consumer Behavior. Journal of Global Marketing, 1-30.
Nizette, F., Hammedi, W., van Riel, A. C., & Steils, N. (2025). Why should I trust you? Influence of explanation design on consumer behavior in AI-based services. Journal of Service Management, 36(1), 50-74.
Onifade, A. Y., Ogeawuchi, J. C., & Abayomi, A. A. (2025). Scaling AI-Driven Sales Analytics for Predicting Consumer Behavior and Enhancing Data-Driven Business Decisions. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 2181-2201.
Osman, M. O. M., Dalla, L. O. F. B., Rashid, T. A., Albaraesi, M. J. S., El-Sseid, M. A. M., Ahmed, A. A., ... & Alsharif, A. (2026). A Comparative Analysis on Stacked Hybrid Intelligence: A Multi-Paradigm Machine Learning Framework for Robust Phishing URL Detection. Comprehensive Science Journal, 10(39), 1037-1062
Osman, M., Elghaffi, F., Ben Dalla, L., Karal, Ömer, & Rashid, T. (2026). A New Approach for Low-Latency, High-Accuracy Anomaly Detection at the Edge: Benchmarking Quantized Autoencoders, LSTMs, and Lightweight Transformers on RT-IoT2022 Time-Series Traffic. Wadi Alshatti University Journal of Pure and Applied Sciences, 4(1), 110-121. https://doi.org/10.63318/waujpasv4i1_12
Parveen, S. (2024). UNDERSTANDING THE DYNAMICS OF CHATBOT DESIGN, TRANSPARENCY, AND ETHICAL IMPLICATIONS IN CUSTOMER–BRAND RELATIONSHIP IN HOSPITALITY AND TOURISM INDUSTRY.
Pedersen, C. L., & Ritter, T. (2024). Digital authenticity: Towards a research agenda for the AI-driven fifth phase of digitalization in business-to-business marketing. Industrial Marketing Management, 123, 162-172.
Pedersen, C., & Ritter, T. (2024). Ambivalent consumer responses to AI. Journal of Business Research, 166, 114–126.
Putra, G. A. (2024). The Journey of Marketing Research in Driving Successful Marketing Strategies. Advances in Business & Industrial Marketing Research, 2(1), 26-35.
Raut, S., Chandel, A., & Mittal, S. (2025). Enhancing marketing and brand communication with AI-driven content creation. In AI, corporate social responsibility, and marketing in modern organizations (pp. 139-172). IGI Global Scientific Publishing.
Rawool, V., Foroudi, P., & Palazzo, M. (2024). AI-powered voice assistants: developing a framework for building consumer trust and fostering brand loyalty. Electronic Commerce Research, 1-33.
Rosário, A. T., & Dias, J. C. (2025). AI-Driven Consumer Insights in Business: A Systematic Review and Bibliometric Analysis of Opportunities and Challenges. International Journal of Marketing, Communication and New Media, (15).
Sahin, F., & Soylemez, C. (Eds.). (2024). Globalized Consumer Insights in the Digital Era. IGI Global.
Sajan, A., & Giri, P. (2025). Understanding artificial intelligence and its major role in branding. In Strategic Brand Management in the Age of AI and Disruption (pp. 127-154). IGI Global Scientific Publishing.
Saleh, R. A., & Zeebaree, S. R. (2025). Artificial Intelligence in E-commerce and Digital Marketing: A Systematic Review of Opportunities, Challenges, and Ethical Implications. Asian Journal of Research in Computer Science, 18, 395-410.
Saliya, C. A. (2023). Integrated-flexible research methodology: an alternative approach. In Social research methodology and publishing results: A guide to non-native English speakers (pp. 1-10). IGI Global.
Sarin, A. B. (2025). The Evolution of Consumer Behavior and the Role of Artificial Intelligence in Shaping It: AI in Marketing. In Decoding Consumer Behavior Using the Insight Equation and AI Marketing (pp. 21-40). IGI Global Scientific Publishing.
Schippers, T. D. (2025). The AI-Disclosure Dilemma: How AI labels shape advertisement Attitudes, Credibility, Transparency, and Purchase Intention in a digital environment (Bachelor's thesis, University of Twente).
Semwal, R., Tripathi, N., & Pandey, U. K. (2024). Leveraging Artificial Intelligence in Service Marketing to Enhance Electronic Word of Mouth (eWOM). In AI Innovations in Service and Tourism Marketing (pp. 249-269). IGI Global.
Siddiqui, U. A. (2024). Contemporary branding theories and frameworks. In Strategies for brand communications and management: Bridging Academia and Practice (pp. 1-39). Great Britain Press and Publications.
Singh, B., & Kaunert, C. (2024). Future of Digital Marketing: Hyper-Personalized Customer Dynamic Experience with AI-Based Predictive Models. In Revolutionizing the AI-Digital Landscape (pp. 189-203). Productivity Press.
Sipos, D. (2025). The Effects of AI-Powered Personalization on Consumer Trust, Satisfaction, and Purchase Intent. European Journal of Applied Science, Engineering and Technology, 3(2), 14-24.
Soyombo, D. A. (2024). Consumer insights and market trends in lactation supplements: Nigeria and the US perspective. International Journal of Management & Entrepreneurship Research, 6(4), 1103-1118.
Spais, G., & Jain, V. (2025). Consumer Behavior's Evolution, Emergence, and Future in the AI Age Through the Lens of MR, VR, XR, Metaverse, and Robotics. Journal of Consumer Behaviour, 24(3), 1275-1299.
Spence, A. M. (1973). Job market signaling . The Quarterly Journal of Economics , 87(3), 355–374.
Sreekumar, D. (2023). What is Research Methodology? Definition, Types, and Examples. Paperpal Blog-Academic Writing Guides.
Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict . In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47). Brooks/Cole.
Tanase, G. C. (2024). The Integration of Emotional Intelligence into AI Marketing: Connecting Brands with Consumers. Romanian Distribution Committee Magazine, 15(1), 29-37.
Tarnanidis, T., & Manaf, A. H. A. (2024). Research Notes on the Future of Marketing and Consumer Insights. In New Trends in Marketing and Consumer Science (pp. 324-336). IGI Global.
Tasnim, K., Abdullah, M. S., Karim, M. Z., & Hasan, R. (2025). AI-Driven Innovation, Privacy Issues, and Gaining Consumer Trust: The Future of Digital Marketing. Business & Social Sciences, 3(1), 1-7.
Teepapal, T. (2025). AI-driven personalization: Unraveling consumer perceptions in social media engagement. Computers in Human Behavior, 165, 108549.
Tenenhaus, M., Amato, S., & Vinzi, V. E. (2009). A global Goodness–of–Fit index for PLS structural equation modelling. 5.
Thapliyal, S., & Bindal, R. (2025). Leveraging AI for Personalized Customer Experiences. In Strategic Brand Management in the Age of AI and Disruption (pp. 303-324). IGI Global Scientific Publishing.
Ungunmerr-Baumann, M. R., Groom, R. A., Schuberg, E. L., Atkinson, J., Atkinson, C., Wallace, R., & Morris, G. (2022). Dadirri: An Indigenous place-based research methodology. AlterNative: An International Journal of Indigenous Peoples, 18(1), 94-103.
Vo, D. T., Nguyen, L. T. V., Dang-Pham, D., & Hoang, A. P. (2024b). When young customers co-create value of AI-powered branded app: the mediating role of perceived authenticity. Young Consumers.
Vo, D. T., Nguyen, L. V. T., Dang-Pham, D., & Hoang, A. P. (2025). What makes an app authentic? Determining antecedents of perceived authenticity in an AI-powered service app. Computers in Human Behavior, 163, 108495.
Vuyyuru, S. R. (2025). Understanding The Ethical Challenges Of AI In Retail And Addressing Data Privacy, Algorithmic Bias And Consumer Trust. International Journal of Engineering Development and Research, 13(2), 100-106.
Wang, X., & Qiu, X. (2024). The positive effect of artificial intelligence technology transparency on digital endorsers: Based on the theory of mind perception. Journal of Retailing and Consumer Services, 78, 103777.
Wang, Y., Sauka, K., & Situmeang, F. B. I. (2025). Anthropomorphism and transparency interplay on consumer behaviour in generative AI-driven marketing communication. Journal of Consumer Marketing, 42(4), 512-536.
Wu, J. J. X. (2024). Algorithmic Fairness in Consumer Credit Underwriting: Towards a Harm-Based Framework for AI Fair Lending. Berkeley Bus. LJ, 21, 65.
Yang, J., Teran, C., Battocchio, A. F., Bertellotti, E., & Wrzesinski, S. (2021). Building brand authenticity on social media: The impact of Instagram ad model genuineness and trustworthiness on perceived brand authenticity and consumer responses. Journal of Interactive Advertising, 21(1), 34-48.
Yang, Z., Hu, D., & Chen, X. (2025). The role of omnichannel integration and digital value in building brand trust: a customer psychological perception perspective. Internet Research, 35(3), 1029-1064.
Zhou, Y., Li, H., & Sun, H. (2022). Metalloproteomics for biomedical research: methodology and applications. Annual Review of Biochemistry, 91(1), 449-473.
Zhu, Z., & Liu, L. (2024). Investigating User Perceptions of Fairness in Social Media Marketing Platform.
Zou, P. X., & Xu, X. (2023). Research methodology and strategy: theory and practice. John Wiley & Sons.
Далла, Л. Б., Медени, Т. Д., Медени, И. Т., & Улубай, М. (2025). Повышение эффективности здравоохранения в больнице Алмасара: анализ распределенных данных и управление рисками для пациентов. Economy: strategy and practice, 19(4), 54-72.
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