International Journal of Advertising

Centering the societal impacts of AI, social media, and emerging technologies in advertising

With the rapid evolution of artificial intelligence, social media algorithms, robotic interfaces, and immersive technologies such as virtual and augmented reality (VR/AR), the boundaries between technology, persuasive influence, and human activities have blurred (Chu et al., 2024). In this evolving landscape, the individual and collective well-being of consumers, as well as society at large, is increasingly at risk and warrants critical scholarly attention. 

Consumer well-being is a multidimensional concept that encompasses individual and shared experiences, which can be manifested through cognitive (e.g., Tang et al., 2016), affective (e.g., Kemp et al., 2020), and psychophysical states (e.g., Zarantonello et al., 2024). Moreover, well-being is contextualized (Gilbert et al., 2021; Stafford and Pounders, 2021), with advertising holding the potential to enhance or diminish well-being at various micro, meso, and macro levels (Gurrieri, Zayer, and Coleman, 2022). At a broader level, societal well-being extends these concerns to collective equity, inclusion, and information integrity, highlighting how advertising technologies shape civic life, cultural representation, and public trust (e.g., BarGill et al., 2023; Zarouali et al., 2022). 

Emerging technologies such as generative AI have revolutionized how advertising messages are created, optimized, and delivered. Scholars have highlighted the emerging issues related to AI including societal and policy issues (Huh, Nelson, and Russell 2023; Rodgers 2021). While AI promises efficiency and personalization, research has also highlighted the risk of deception, diminishment of authenticity, and misrepresentation (Baek et al., 2024; Sands et al., 2024; Yang and Lee, 2026). At the same time, when human-AI collaboration is transparent and meaningful, it may enhance empowerment, creativity, and perceived control (Feng and Sun, 2025). 

Similarly, robotic and embodied agents, such as in-store robots or voice-enabled AI assistants, are emerging as persuasive venues. These technologies can increase comfort and engagement, such as through emotional support (Kim and Wang, 2025), but may also evoke discomfort or distrust if perceived as overly human-like or manipulative (Castelo et al., 2023; Khoa et al., 2024). Their deployment raises new ethical questions about safety, accessibility, labor replacement, and the equitable distribution of technological benefits (Santoni de Sio, 2024).

Aligned with the development of these technologies, examining the ethics and well-being impacts of social media platforms, drawing on advanced algorithmic targeting, is crucial due to the prevalence of deepfakes, misinformation, and extreme and misogynistic content. Although algorithms can enhance the relevance of information and increase efficiency and effectiveness, there could be negative results for consumer and societal well-being (Voorveld et al., 2024), along with other dark sides to social media (Hudders and Lou, 2022; Scheinbaum 2024; Breves et al., 2025). Building digital literacy among vulnerable populations and diminishing the harms of social media must be a priority for future scholarship.

Lastly, immersive and metaverse advertising further redefines persuasion through embodiment and sensory engagement and holds sociological and psychological implications as well as new challenges to consumer well-being and consciousness (Dwivedi et al. 2022). The heightened presence might enhance the learning and persuasion outcomes but could also result in consumer vulnerability if the ad boundaries or data protection are unclear (Song et al., 2021; Taylor, 2022; Yuan et al., 2021). Ethical design, consent, inclusion, and transparency are crucial to protecting autonomy in such sensor-rich environments. 

Collectively, these transformations signal both opportunity and responsibility. This special issue thus seeks contributions that advance conceptual, empirical, and policy insights to ensure that innovation in advertising remains human-centered, ethical, and responsible and enhances well-being. All methodological approaches are welcome.

In particular, we encourage works that discuss but are not limited to one of the following:

●      Ethical implications of AI, social media, AR, VR, metaverse, and other emerging technologies

●      Ethics and well-being related to AI companions, bots, and virtual influencers

●      Personalized content generation and psychological manipulation concerns

●      Algorithmic amplification of beneficial versus harmful advertising content

●      Anthropomorphic design ethics and emotional manipulation through physical presence of AI/Robot

●      Consent and autonomy in human-robot advertising/marketing communication interactions

●      Parasocial relationships with advertising/marketing communication robots and their exploitation

●      Consumer understanding and control of integrated AI advertising experiences

●      Data sharing and privacy concerns across social media platforms and emerging technologies

●      Building AI and digital media literacy

●      Children's well-being related to AI, social media and new technologies

●      Elderly consumers and potential exploitation through advertising systems embodied by emerging technologies

●      Consumer involvement in advanced advertising system design and governance

●      Professional ethics for AI-automated decision-making

●      Deepfakes and synthetic media ethics in commercial contexts

●      Synthetic data and digital twins in the advertising context

●      Automation and replacement of advertising industry employees by AI

●      Bias detection, measurement, and mitigation strategies across digital applications

●      Bias and representational harms of AI-generated advertising and other emerging technologies

●      Extremism, misogyny, and gender-based violence enabled by AI, social media, and emerging technologies

●      Immersive storytelling, embodiment, and consumer well-being in hybrid digital–physical advertising experiences.

●      Sustainability issues related to the use of AI in advertising industry

●      AI, energy consumption, and the carbon footprint of the advertising industry

References: 

Bar-Gill, O., Sunstein, C. R., & Talgam-Cohen, I. (2023). Algorithmic harm in consumer markets. Journal of Legal Analysis, 15(1), 1-47.

Baek, T. H., Kim, J., & Kim, J. H. (2024). Effect of disclosing AI-generated content on prosocial advertising evaluation. International Journal of Advertising, 1-22.

Breves, P. L., Liebers, N. T., Meijers, M. H., & van Berlo, Z. M. (2025). Beyond likes: the persuasive potential of romantic parasocial relationships with influencers. International Journal of Advertising, 44(4), 651-673.

Castelo, N., Boegershausen, J., Hildebrand, C., & Henkel, A. P. (2023). Understanding and improving consumer reactions to service bots. Journal of Consumer Research, 50(4), 848-863.

Chu, S. C., Yim, M. Y. C., & Mundel, J. (2024). Artificial intelligence, virtual and augmented reality, social media, online reviews, and influencers: a review of how service businesses use promotional devices and future research directions. International Journal of Advertising, 44(5), 798–828. 

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Feng, Y., & Sun, Y. (2025). Consumer empowerment through generative artificial intelligence: enhancing brand narrative with collaboration, creation, and communication. International Journal of Advertising, 1-33.

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Khoa, D. T., & Chan, K. W. (2024). Being alone or together: How frontline anthropomorphized robots affect solo (vs. joint) service consumption. Journal of Service Research, 27(4), 579-599.

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Stafford, R.M., & Pounders, K. (2021). The power of advertising in society: does advertising help or hinder consumer well-being?. International Journal of Advertising, 40(4), 487-490.

Rodgers, S. (2021). Themed Issue Introduction: Promises and Perils of Artificial Intelligence and Advertising. Journal of Advertising, 50(1), 1–10.

Sands, S., Demsar, V., Ferraro, C., Campbell, C., & Cohen, J. (2024). Inauthentic inclusion: Exploring how intention to use AI‐generated diverse models can backfire. Psychology & Marketing, 41(6), 1396-1413.

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Voorveld, H. A., Meppelink, C. S., & Boerman, S. C. (2024). Consumers’ persuasion knowledge of algorithms in social media advertising: identifying consumer groups based on awareness, appropriateness, and coping ability. International Journal of Advertising, 43(6), 960-986.

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Yuan, C., Wang, S., Yu, X., Kim, K. H., & Moon, H. (2021). The influence of flow experience in the augmented reality context on psychological ownership. International Journal of Advertising, 40(6), 922-944.

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