Bibliometrics has a long-standing interest in tracing epistemic change. The question of how epistemic change is reflected in publications is implicit to evaluative bibliometrics because the quality of a paper is measured through its impact, which can be understood as the extent to which a paper changes later research. Beyond this idea underlying bibliometric assessments, the question of how to identify and trace emerging fields or emerging technologies has attracted considerable attention, and the dynamics of topics has been traced with bibliometric methods. These approaches either identify emerging topics and technologies by searching for rapidly growing clusters of publication or trace changes that are known to exist and trace them using citation patterns, keywords or combinations thereof. The current interest in the identification of exceptional papers reflects a similar interest in epistemic change.

The application of bibliometric methods for tracing epistemic change also appears to be hindered by constraints that are not necessary but appear to be somewhat self-inflicted in that they are due to methodological traditions that are taken for granted in bibliometrics. Recent indicators like Innovativeness, Novelty, Originality or Disruptiveness describe single papers and attempt to codify the epistemic change they bring about. This design is based on three implicit assumptions. First, the construction of indicators of epistemic change from metadata of individual papers implies the assumption that the epistemic change is an event rather than a process. Second, the construction of these indicators assumes that the event that is measured by the indicator is fully reported in exactly one paper. Third, indicators sacrifice specificity for comparability, attempting to describe how much epistemic change is represented by a paper while being unable to report the kind of change that has occurred.

These three limitations appear to be the results of choices made by indicator designers rather than fundamental limitations of bibliometrics, and come on top of the only truly fundamental limitation: the dependence of bibliometrics on the signal about a publication's content that is contained by the publication's metadata. This limitation raises a fundamental question about the validity of bibliometric measures of epistemic change. Since we don't know how the content of a paper is reflected in bibliometric metadata, we cannot assess the content or strength of the signal metadata contain and thus cannot know if the signal is validly codified by an indicator.

The special collection presents papers that were developed in a series of workshops which addressed these four challenges, providing perspectives on conceptualisation and operationalisation of epistemic change, construction of ground truths, analysis of metadata content, validation of indicators, and application of artificial intelligence methods to publications.

Potential topics

• Conceptualisation and operationalisation of epistemic change for bibliometric tracing
• Construction of ground truths for development and testing of bibliometric methods
• Metadata content related to epistemic change in bibliographic records
• Validation of established bibliometric indicators against ground truths
• Application of large language models to full texts for identifying epistemic change

Timeline

• October 31, 2026: Submission deadline

Timeline

• October 31, 2026: Full paper submission deadline

Timeline

• December 31, 2026: Full paper submission deadline

Yichuan Wang, University of Sheffield

Minhao Zhang, University of Bristol

Francesco Schiavone, University of Naples Parthenope

Within mere hours of returning to the White House, President Donald Trump took a dramatic step in reshaping the future of artificial intelligence (AI) by repealing the guardrails put in place by former President Joe Biden. This abrupt policy reversal has sent shockwaves through the tech community, sparking both optimism about accelerated AI innovation and apprehension about a new "wild west" of limited oversight. While high-profile supporters such as venture capitalist welcome fewer restrictions, others caution that the Biden administration's initiatives on AI safety and ethical standards, many of which already yielded research findings and policy recommendations, risk being mitigated. As new US government remains tight-lipped about how it will regulate advanced AI, the international community is left to speculate: Will this herald an era of fast-paced development that bypasses cautionary measures, or will additional safeguards eventually surface to address concerns around privacy, discrimination, and global competition? This unfolding scenario sets the stage for a reexamination of AI governance, investment, and cross-border collaboration, an opportune moment for researchers to analyze the interplay between regulatory shifts and technological progress.

Scholars have noted that changes in political and policy interventions can profoundly affect how nations or organizations invest in and govern emerging technologies, particularly in AI. For example, some countries may double down on government-led AI initiatives to retain technological sovereignty, while others may opt for more laissez-faire approaches that prioritize market-driven innovation. As these dynamics unfold, they reshape competitive advantages, strategic alliances, and pathways of innovation. At the organizational level, a potential relaxation in AI governance, such as easing compliance requirements or loosening data protection measures, could bolster corporate and industrial innovations in the short run. Firms might benefit from lower costs associated with regulatory compliance and gain rapid market entry for AI-based products and services. However, insufficient oversight can exacerbate risks related to data privacy, algorithmic bias, and ethical concerns. Kronblad et al. (2024) propose the concept of "institutional blackboxing," describing how the technical complexities and operational procedures of AI decisions are obscured or hidden within institutional frameworks. This blackboxing prevents scrutiny and accountability, allowing injustices to persist unaddressed within the systems' operations.

This lack of accountability and transparency can further lead to fragmented standards across regions, as weakened governance makes it increasingly difficult for multinational companies to navigate varying legal requirements and compete effectively on a global scale. The risks associated with fragmented standards underscore the critical role of key enablers identified by Pramanik et al. (2024), which influence AI readiness across both developed and developing economies. These enablers, scientific research output, internet infrastructure, and public consumption expenses, highlight the universal challenges and opportunities faced by nations in harnessing AI. As these enablers play a pivotal role in shaping AI governance models, they also reflect each economy's ability to strategically leverage digital transformation. Therefore, in managing their AI ecosystems, industries and governments must carefully assess these factors to effectively navigate the complexities introduced by varied political and policy landscapes.

In light of these developments, there is a pressing need for scholarly investigations into how international and political uncertainties shape AI innovation, especially in terms of governance models, investment flows, cross-border collaborations, and competitive dynamics. We invite submissions that explore, but are not limited to, the following themes: (1) the influence of shifting geopolitical contexts on AI research and development priorities; (2) comparative studies of AI policy frameworks across different countries; (3) the implications of relaxed governance for ethical AI, data protection, and social welfare; and (4) strategies for multinational enterprises to navigate AI innovation in volatile regulatory environments. We particularly welcome interdisciplinary perspectives that draw on economics, political science, information systems, organizational studies, and operations management. By publishing in this special issue, authors will contribute to a deeper understanding of how AI innovation can be managed, sustained, and directed for societal benefit amidst evolving global uncertainties.

Potential topics

• How do shifting political contexts and leadership changes (e.g., the Trump administration's approach to AI) shape countries' AI R&D investments and strategic alliances?
• What are the short-term and long-term implications of relaxed AI governance on economic performance, data privacy, and algorithmic bias?
• Under what conditions can reduced AI regulations foster or hinder innovation ecosystems in sectors such as healthcare, finance, manufacturing, and transportation?
• How can policymakers and organizations balance the need for rapid AI innovation with the ethical and social risks arising from limited oversight or fragmented governance?
• How might relaxed AI governance in certain countries influence global competitive dynamics, international collaborations, and the uneven distribution of AI capabilities?
• What strategies can multinational enterprises adopt to navigate complex regulatory landscapes, protect intellectual property, and maintain data security while pursuing AI innovation?
• Which governance models or policy frameworks from different regions (e.g., EU vs. US vs. Asia) most effectively balance innovation, accountability, and social welfare in AI?
• How can scenario planning and forecasting methods be applied to model the impact of political volatility on AI investments, talent flows, and market structures?

Timeline

• November 15, 2026: Manuscript deadline

Varun Grover, University of Arkansas

Nik Rushdi Hassan, University of Minnesota Duluth

Tina Blegind Jensen, Copenhagen Business School

Paul Lowry, Virginia Tech

Kalle Lyytinen, Case Western Reserve University

Angsana Techatassanasoontorn, Auckland University of Technology

The pervasive and unprecedented digitization of organizational and human experience represents a major challenge to past and existing theorizing methods. Recent efforts emphasize that with the rise in complexity in this digitized world, we need to rethink our theories and create new theories to guide us. What is needed are new theories that connect new concepts to grasp the unprecedented and link them to existing concepts or other new concepts, that explain and answer why such phenomena are happening.

This special issue calls for native IS theories that explain the nexus between digital technology and social and organizational phenomena. Building theories is highly challenging, but essential to establish the field as a socially and intellectually influential discipline. Theory should not be restricted to causal mechanisms or viewed too inclusively, but rather take the form of scientific understanding and systematic explanation that contains logically interconnected sets of propositions, symbolic conjectures, and empirical generalizations, tied to the IS discourse.

To fulfill the criterion that theory be tied to IS discourse, the special issue seeks studies that focus on the uniqueness of phenomena, assumptions, boundary conditions, concepts, and logic to IS. Every theory proposed should be tied to an explicit why question about an IS phenomenon. Articles may apply one of two major approaches: either deeply engage with existing theory and demonstrate how these theories address or don't address unprecedented IS phenomena, or propose novel native IS theories that are different from established theories and explain why new theoretical positions are needed.

The special issue seeks articles that either deeply engage with existing theories or propose new native IS theories on unprecedented emergent IS phenomena. Researchers are encouraged to investigate emerging phenomena that are capturing the attention of society and in need of better explanations, such as digital technologies shaping public opinion, algorithmic amplification of social tendencies, threats to cybersecurity and privacy, deepfakes and synthetic newsrooms, ethical and legal implications from emerging technologies, disruption from artificial intelligence generative content, and societal impacts from misinformation and disinformation. There are also opportunities in areas such as energy informatics, green IS, and sustainability digital initiatives that can benefit from native IS theories.

Potential topics

• Digital strategy and technology-enabled business models
• IT-enabled fake news, misinformation, and disinformation
• Technology-infused hyperconnected society and social networks
• Disruptive technologies and their organizational impacts
• Surveillance capitalism and privacy concerns
• Big Data analytics and implications
• Social media effects and algorithmic amplification
• Generative artificial intelligence and AI-generated content
• Deepfakes and synthetic media
• Cybersecurity threats from technology-enabled organizations
• Ethical and legal implications of emerging technologies
• Energy informatics and green IS
• Sustainability digital initiatives
• Digital transformation and technological change
• Public opinion shaping through digital technologies

Timeline

• June 29, 2026: Deadline for Extended Abstracts
• December 1, 2027: Deadline for Paper submissions

Linda Tuncay Zayer, Loyola University Chicago

Jing Yang, Boston University

Shu-Chuan (Kelly) Chu, DePaul University

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. 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, affective, and psychophysical states. Moreover, well-being is contextualized, with advertising holding the potential to enhance or diminish well-being at various micro, meso, and macro levels. 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.

Emerging technologies such as generative AI have revolutionized how advertising messages are created, optimized, and delivered. While AI promises efficiency and personalization, research has also highlighted the risk of deception, diminishment of authenticity, and misrepresentation. At the same time, when human-AI collaboration is transparent and meaningful, it may enhance empowerment, creativity, and perceived control.

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, but may also evoke discomfort or distrust if perceived as overly human-like or manipulative. Their deployment raises new ethical questions about safety, accessibility, labor replacement, and the equitable distribution of technological benefits.

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. Building digital literacy among vulnerable populations and diminishing the harms of social media must be a priority for future scholarship.

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. 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. Ethical design, consent, inclusion, and transparency are crucial to protecting autonomy in such sensor-rich environments.

These transformations signal both opportunity and responsibility. This special issue 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.

Potential topics

• 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

Timeline

• August 31, 2026: Manuscript deadline