Carlos Diaz Ruiz, Hanken School of Economics

Sofia Ulver, Lund University

James Pamment, Lund University

The online information landscape is a site for contested ideas, but it is also a marketplace; under the platform economy, every engagement has monetary value. This setup encourages content creators to circulate the kinds of material most likely to capture attention, even if it is incendiary. Central to this business model are the algorithmic recommendation systems, which are designed to monetize attention while simultaneously amplifying disinformation.

The term "Disinformation Economy" was introduced in a report published by the McCain Institute and The Carter Center. It highlights how the sources circulating disinformation extract financial benefits from digital marketing infrastructures, particularly through the architecture of advertising technologies (AdTech). Researchers estimate that 1.68% of digital advertising budgets are redirected to fake news websites, amounting to approximately US$2.4 billion in the US alone and nearly US$6 billion worldwide. This figure suggests that brands are inadvertently funding fake news and disinformation with their budgets.

The special issue aims to extend existing research on how platform architectures monetize and, at times, underwrite the spread of online disinformation, incentivizing the production of fake news websites, and the circulation of incendiary content through the creator economy. Although we know that polarization thrives in the online "conflict markets" designed to reward click maximization, we do not know well whether influencers profiting from radicalizing content is a likely outcome of the system or a regrettable side effect.

Policymakers increasingly recognize that malicious actors exploit digital advertising infrastructures to undermine democratic institutions and divide society, quickly becoming "hybrid threats," which is when state or non-state actors seek to exploit the vulnerabilities of democracies to their own advantage while remaining below the threshold of formal warfare. For instance, the infrastructures of the advertising market allow "dark money" actors to run influence campaigns unchecked. A recent report by the Swedish Psychological Defense Agency illustrates the emergence of a hybrid organization that applies digital marketing tools to conduct state-sponsored influence operations. And yet, we do not know to what extent influence operations co-opt malpractice in digital marketing.

This special issue invites contributions that seek to conceptualize and theorize disinformation research at the intersection of business, marketing, and society. However, we believe that it is insufficient merely to appreciate the problem; instead, we call for research that dares to find solutions that can inform public policy, change managerial practice, and strengthen society. We welcome contributions that can enhance social resilience against the spread of disinformation online.

We approach disinformation broadly, incorporating not just falsehoods but also dual-use technologies (like AI-generated synthetic media like deepfakes), which can disrupt social realities. Critical researchers on marketing and consumer research have shown that the accelerating speed of automation, big data, and algorithmic intensification give rise to new "manufactured consumers", becoming an ever more fragmented, yet controlable, societal formation. Thus, we would need to know more about how disinformation, scams, and fraud have become integral to the ebbs and flows of a digitally mediated consumer culture.

Multidisciplinary research contributions from across business disciplines, media studies, the social sciences, and public policy are welcome. We are particularly interested in research that addresses marketing technologies and practices in connection to disinformation. Other relevant research areas include understanding and countering deceptive online practices such as fake news, enhancing resilience through media literacy, fact-checking, platform governance, platform economies, and critical disinformation studies.

Potential topics

• Whether and how programmatic advertising funds fake news?
• How do the recommender algorithms amplify incendiary content?
• How can AI-generated content be used to spread disinformation?
• Do the monetization schemes of the creator economy reward polarization?
• How do influence operations exploit marketing infrastructures?
• What initiatives and interventions can strengthen societal resilience to disinformation?
• What forms of platform governance can protect liberal deliberative democracies?
• What methodologies, including Open-Source Intelligence (OSINT), can be used to investigate disinformation on AdTech and social media?

Timeline

• September 15, 2026: Manuscript deadline

Kirsten Cowan, University of Edinburgh

Yang Feng, University of Florida

Seth Ketron, University of St. Thomas

Aidin Namin, Loyola Marymount University

The rapid evolution of generative artificial intelligence (AI) is revolutionizing the way consumers search, retrieve, and interact with product information, offerings, and brands. The traditional search landscape, once dominated by Google's algorithmic filtering and search engine optimization (SEO) strategies, is being transformed by AI-generated summaries and zero-click searches. This paradigm shift is disrupting conventional SEO approaches and revenue models, reshaping how consumers encounter and process information and make choices while forcing the industry to adapt and reinvent.

As AI continues to influence search results and decision-making, several key trends are emerging. First, the nature of search is changing, with AI increasingly mediating our interactions and altering the way we engage with search results. Is search still a pull activity, where consumers initiate queries and receive advertising responses aligned with their intent, or are we witnessing a shift towards AI-led search experiences that unfold without explicit human initiation? Second, we are delegating search-related tasks to AI, potentially leading to a future where AI autonomously performs search functions, bypassing traditional some stages of the consumer journey, including information processing. Third, our current interactions with AI are largely functional, focusing on utilitarian offerings and cognitive evaluations. However, as AI evolves, we may see a shift towards more experiential, hedonic, and social evaluations, where AI assists us in discovering new goods and services that cater to our emotional, social, and moral needs.

The implications for advertising are profound. As AI-generated content and zero-click searches become more prevalent, advertisers must adapt their strategies to reach consumers in a landscape where traditional search results are no longer the primary touchpoint. This may involve shifting focus from keyword-based advertising to more contextual, conversational, and experiential approaches, such as sponsored content, influencer partnerships, and immersive brand experiences. Further, the rise of AI-driven search raises important questions about the role of advertising in shaping consumer decision-making, the potential for bias in AI-generated recommendations, and the need for greater transparency and accountability in advertising practices.

Building upon recent contributions that focus on GenAI search and advertising, this special section aims to inspire the future of advertising applications within the consumer search phase. We welcome submissions that make substantive theoretical and practical contributions in the broader domain of advertising research, irrespective of methods. This includes either conceptual or empirical work. Empirical work (experiments, surveys, modeling, qualitative, etc.) should be appropriately executed, given best practices in the discipline.

Potential topics

• How advertisers can effectively leverage AI-generated content and zero-click searches to reach their target audiences
• The impact of AI-generated content on search results and consumer decision-making
• The evolution of SEO strategies in response to AI-driven search algorithms
• The role of AI in shaping consumer journeys and experiential marketing
• How AI has shifted and/or will shift psychological mechanisms in search, including but not limited to empowerment, attention, memory, processing styles, and cognitive bias
• How social, cultural, and other contextual differences might lead different consumer segments or markets to react to and utilize AI differently in search
• The role of AI in relation to marketer-controlled vs. nonmarketer-controlled sources of information (e.g., ads or website content vs. customer reviews or social media) and consumers' relative reliance on each
• The effects of AI on speed, locus of control, and effort in consumer search
• The potential for AI-driven advertising to enhance or undermine consumer trust and brand loyalty
• The impact of AI on ad pricing and revenue models, including the potential for more nuanced and contextual pricing strategies
• The role of AI in facilitating more personalized and relevant advertising search experiences, and the potential trade-offs with consumer privacy
• The need for new metrics and benchmarks to measure the effectiveness of AI-driven advertising campaigns, and the potential for AI to provide more granular and actionable insights into consumer behavior and preferences
• The potential for AI to create more intimate and human-like interactions with consumers
• The ethical implications of AI-driven search and advertising practices
• The future of search interfaces, including voice-activated, image-based, and multimodal search
• The interplay between AI-generated search experiences and consumer autonomy
• The implications of AI-driven search ecosystems for market competition and brand visibility
• The role of AI transparency and explainability in consumer trust and regulatory compliance
• The sustainability and environmental impact of AI-driven search and advertising ecosystems
• Cross-platform integration and continuity in AI-mediated consumer journeys
• How the reduced visibility of paid versus organic influence in conversational AI affects consumer understanding, disclosure, and trust in recommendations
• How the growing tendency to treat AI systems as social or companion-like partners might reshape the value of impressions, expectations of authenticity, and marketplace pricing for AI-mediated advertising interactions

Timeline

• June 1, 2026: Submission window opens
• September 7, 2026: Manuscript deadline