generative ai

Understanding disruption through the marketing lens: foundations, frameworks and future directions

Disruption, long treated in marketing scholarship as an episodic departure from equilibrium, has become a permanent feature of the competitive landscape. This study aims to lay the conceptual foundations for a marketing theory of persistent disruption, arguing that firms no longer experience disruption as isolated events with a clear before, during and after, but instead operate continuously within overlapping, interacting disruptive forces – technological, geopolitical, ecological and institutional. The authors first reconceptualize disruption itself, proposing that the conventional exogenous–endogenous binary be extended into a feedback system in which a firm’s response to one disruption becomes a source of disruption for others, with marketing positioned at the critical interface of this loop. Building on this, this study develops a triadic framework – sensing, seizing and shaping – to characterize marketing’s role under persistent disruption, showing that these capabilities are disrupted simultaneously rather than sequentially, and to introduce the Disruption–Marketing Response Matrix, a typology crossing disruption origin (endogenous/exogenous) with response domain (upstream/downstream) to identify underdeveloped theoretical territory. Drawing on the nine papers in this Special Issue, this study also develops an integrated five-stage marketing reinvention framework spanning disruption recognition, stakeholder engagement and agile operating models. The authors argue that emerging markets, with their institutional volatility and resource constraints, constitute an underutilized theoretical laboratory for understanding marketing under sustained disruption, and that new-age technologies (artificial intelligence [AI], generative AI, machine learning, extended reality, Internet of Things, robotics and blockchain) should be understood not as discretionary tools but as infrastructure that reconstitutes the marketing environment itself. Drawing on the nine contributions in this Special Issue, this study distills seven theoretical implications spanning the persistence of disruption, the feedback-based collapse of the exogenous–endogenous distinction, the simultaneous disruption of dynamic marketing capabilities, the diagnostic value of the Response Matrix, emerging markets as theory-generating contexts, technology as infrastructure and marketing’s expanding role in societal value creation. This conceptual study advances theory by proposing new constructs and frameworks for studying persistent disruption. It identifies opportunities for longitudinal research, measurement of market-shaping capability and greater integration of institutional theory and computational methods to advance marketing scholarship. This study presents a Disruption–Response Action Matrix that helps managers diagnose disruption types, assess sensing, seizing and shaping capabilities, prioritize capability investments and guide marketing reinvention under persistent disruption. The question facing academia and industry is not how to recover from disruption, but rather how to build the capabilities needed to function within it as a permanent condition, given disruption is shown to be becoming a constant phenomenon. Through this study (and this Special Issue), the authors offer that the aggregate effect of concurrent disruptions has created an environment that existing marketing theory does not fully cover. The authors see this as a call to develop new theoretical constructs that take the permanence of disruption as their starting point rather than their exception.

KSA Profiles for Standard Setting: A Generative AI Approach to Empirically Grounded Cut Score Evaluation

Generative AI is applied to AP U.S. History and AP World History essay data to demonstrate how this kind of evidence could help panels evaluate whether a proposed cut score captures the distinctions a policy intends and inform discussion of proposed cut scores or performance expectations considering evidence of the KSAs students demonstrate.

Reframing tour guiding in the age of generative AI: a framework for self-guided tourism experiences

A new framework for GAI-driven SGE is presented, highlighting three central aspects: personalization, real-time support and contextual relevance, and its difficulty in replicating the emotional connections, cultural understanding and narrative immersion that human guides provide.

Generative AI and digital transformation in organizational roadmaps: employee value perceptions and knowledge sharing

This study explains how employees translate the day-to-day value they derive from generative artificial intelligence into employees' perceived insider status and knowledge sharing behavior, and finds that emotional value is positively associated with perceived insider status, conditional value is negatively associated with perceived insider status, and functional and epistemic value show no direct associations.

Different routes, same storm: a three-dimensional paradox view of generative AI's governance

It is shown that the “dark side” of AI governance is not reducible to isolated ethical failures but emerges as an institutional outcome of competing governance logics, shifting attention from technical risks alone to the organizational architectures through which responsibility, control and legitimacy are continuously negotiated.

Developing trustworthy AI for climate services at scale

Two prototype AI systems designed to enhance access to climate data, support decision-making, and improve efficiency are illustrated here with key concerns identified around responsibility, quality, and trustworthiness of AI outputs.

From diffusion to negotiation: generative AI and academic integrity in higher education assessment

Two-Year Repeated Cross-Sectional Study of AI Integration and Learning in Global Game Jams

A repeated cross-sectional, survey-based study comparing data from two years of Global Game Jam participants at a Dalhousie University site, examining the development of AI integration through the framework of Self-Determination Theory (SDT).

A next-generation hybrid generative learning framework integrating dual GAN and Swin Transformer–CNN for robust early diagnosis of pomegranate diseases

A novel hybrid method is proposed that separates healthy from diseased pomegranates using generative AI and a dual-GAN system, thereby enhancing training and out-comes and provides a strong starting point for future AI-based crop health management.

Poetic Heritage for Culturally Grounded Emotional Support: An Interaction Design Framework and Its Multimodal Agentic Instantiation

The work shows how generative AI can mediate engagement with poetic heritage in culturally grounded emotional-support interactions and suggests that culturally grounded content and structured guidance should anchor system design, while multimodal presentation may strengthen resonance and engagement.

Expectations and Practices around AI Disclosure in CS Research

A categorization of research tasks by perceived necessity and a boilerplate template capturing expected details is suggested, suggesting a categorization of research tasks by perceived necessity and a boilerplate template capturing expected details.

Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applications

The Actionable Policy schema is presented, a YAML-based format for specifying what model responses can and cannot contain that enables exception-based policy governance, and a set of tools to help define the schema and enforce policy.