Corresponding Manager: Lucija Mihotic (lucijamihotic@gmail.com)
Track Manager(s): Lucija Mihotic, Ahmad Haidar
Description
AI adoption varies widely across business functions, with marketing and sales leading at 42% GenAI utilization (McKinsey, 2023), offering unprecedented capabilities for hyper-personalized engagement.
Recent research reveals AI’s dual nature in marketing. On the bright side, Gaczek et al. (2025) find that AI collaboration enhances ethical awareness—managers using AI alone (vs. human-AI teams) feel more responsible and make fewer unethical decisions. Conversely, Barari et al. (2024) demonstrate AI’s dark side: privacy concerns, perceived risks, customer alienation, and uniqueness neglect significantly harm customers’ cognitive (trust, perceived benefit), affective (attitude, satisfaction), and behavioural responses (loyalty, purchase intention, well-being). This paradox reveals AI’s ability to simultaneously enable moral accountability while eroding relational trust, demanding deeper inquiry into governing mechanisms.
This track bridges AI’s marketing value with responsibility, aligning with DTS’s mission. It invites interdisciplinary perspectives on how AI redefines marketing strategy, consumer experience, and brand authenticity.
We welcome, but are not limited to, contributions on:
Psychological and societal effects of AI in influencer and persuasive marketing
Generative AI, brand authenticity, and disinformation
Ethical governance, fairness, and inclusivity in AI-driven campaigns
Responsible intelligent influencer marketing
Keywords
1. Artificial Intelligence in Marketing , 2. Consumer Trust and Authenticity, 3. Algorithmic Personalization, 4. Ethical AI and Digital Well-Being, 5. Cross-Cultural Consumer Engagement
Key References
1. Allal-Chérif, O., Puertas, R., & Carracedo, P. (2024). Intelligent influencer marketing: how AI-powered virtual influencers outperform human influencers. Technological Forecasting and Social Change, 200, 123113.
2. Barari, M., Casper Ferm, L. E., Quach, S., Thaichon, P., & Ngo, L. (2024). The dark side of artificial intelligence in marketing: meta-analytics review. Marketing Intelligence & Planning, 42(7), 1234-1256.
3. Gaczek, P., Leszczyński, G., Wei, Y., & Sun, H. (2025). The Bright Side of AI in Marketing Decisions: Collaboration with Algorithms Prevents Managers from Violating Ethical Norms: P. Gaczek et al. Journal of Business Ethics, 1-24.
4. McKinsey & Company. (2023). The state of AI in 2023: Generative AI’s breakout year. Retrieved from https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year
5. Teng, D., Ye, C., & Martinez, V. (2025). Gen-AI’s effects on new value propositions in business model innovation: Evidence from information technology industry. Technovation, 143, 103191.
6. Vlačić, B., Corbo, L., e Silva, S. C., & Dabić, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187-203.