This article examines the intersection of the rapidly growing creator economy and artificial intelligence (AI). Addressing the lack of a comprehensive conceptualization of AI’s role in the creator economy, the authors conceptualize the impact of cocreating with AI on customer engagement. The conceptual framework distinguishes between the domain of AI cocreation (content vs. process) and degree of AI involvement (augmentation vs. substitution), while incorporating two critical boundary conditions (privacy concerns and transparent disclosure of AI use) that inform the relationship between AI deployment and customer engagement. Nine research propositions integrating resource-based theory, self-determination theory, and signaling theory alongside service-dominant logic examine how these human-AI configurations shape audience engagement. The multi-theoretic grounding represents a significant advance over prior conceptual work in this domain. This paper offers a novel multi-actor cocreation framework, provides strategies for marketers, creators, platforms, and policymakers, and outlines directions for future research.
We develop a stochastic generalization of the Bass diffusion model that incorporates uncertainty directly into the adoption dynamics. The resulting stochastic diffusion captures innovation and imitation effects and yields a quasi-analytical solution via a martingale-based transformation. The framework delivers managerially relevant state-dependent probabilities, including the likelihood of attaining a target penetration conditional on current penetration, and characterizes how these probabilities vary with innovation, imitation, and diffusion variance coefficients. The stochastic formulation also implies a structured error process for discrete-time estimation: errors are heteroscedastic and may be serially correlated. Using standard consumer durable series, we find that inference is largely unchanged after applying heteroscedasticity and autocorrelation corrections, indicating that nonlinear least squares estimates from the deterministic Bass specification remain reliable in common applications. Finally, examining pre-Internet and Internet-era products, we find that diffusion curves continue to fit well and that imitation effects are stronger in the digital era, consistent with accelerated interpersonal influence through “word of mouse.” Collectively, our results show that introducing uncertainty improves managerial interpretation through state-dependent probabilities yet preserves the practical reliability of deterministic Bass estimation.
Against the background of the intention-behaviour-gap prevalent in empirical labelling research, this study investigates the potential of a multi-level colour-coded carbon label at the point of sale (POS) to impact purchase decisions of consumers in a field study. For four categories of fruits and vegetables (bananas, apples, cucumbers and tomatoes), we computed and displayed climate scores for a duration of 24 days in three German retail stores. Using hierarchical linear modelling, we compared purchases during the trial period with the month before the introduction of the label and found that purchases of less carbon-intensive products increased during the days the carbon label was displayed, particularly driven by the tomato category. The POS labelling trial was followed by a consumer survey on their attitudes and label perception. We conclude that while carbon labels on food products have a significant influence on purchasing decisions, their effectiveness is limited and contingent on demand patterns and promotional activities. This research gives guidance for food retailers and decision-makers on how to design and apply labels on products to drive climate-friendly purchase decisions and serves as a stepping stone for further research on effective between-category colour-coded carbon labels at the POS.
As AI technologies rise to prominence in consumers’ lives, marketing scholars and practitioners face an urgent need to understand how different consumer segments perceive, trust, and adopt these tools. However, current literature often treats consumer AI sentiment as monolithic, and practical guidance for addressing diverse consumer concerns remains sparse. To address this gap, we introduce the RISE framework—organized around Relational meaning differences, In-context segmentation, Skepticism-usage paradox, and Equitable AI integration—as a diagnostic lens for understanding AI adoption heterogeneity. We offer RISE not as a grand theory but as pre-theoretic diagnostic scaffolding: each dimension classifies the type of heterogeneity problem researchers or managers face, determining which mechanisms to investigate and which strategic levers to pull. Drawing on illustrative evidence from a U.S. census-representative survey (n = 2,144), we document patterns that motivate each framework dimension and articulate twelve formal propositions (P1–P12) that provide specific, testable entry points for future research. We develop a structured research agenda comprising twelve priority research questions with specific methodological recommendations and boundary conditions. We also include a managerial diagnostic appendix with 25 strategic questions that practitioners can use to assess their customer base on each RISE dimension. By mapping these heterogeneous patterns, our research provides a nuanced perspective on AI acceptance and offers implications for both marketing theory and practice, including guidance on building consumer trust and tailoring AI solutions to diverse needs across consumer segments.
Privacy-enhancing technologies are reshaping online advertising, making it increasingly difficult for advertisers to track customer journeys and link marketing actions to economic outcomes. As a result, interest in probabilistic measurement techniques such as media and marketing mix modeling (m/MMM) has surged, particularly among digital-first advertisers. Many of these advertisers are small and midsize firms that rely heavily on digital channels yet lack the resources to commission bespoke proprietary models. Against this backdrop, grassroots analytics communities and large online advertising platforms such as Meta Platforms and Google have started developing open-source packages for m/MMM. The packages share core principles but differ on important aspects such as their degree of automation, ease of use, and modeling paradigms. This article provides a narrative review and practice-oriented synthesis of these developments and discusses the challenges and opportunities that come with “packaged up” m/MMM. It concludes with an outline of topics that marketing scholars can engage with to vet and extend open-source m/MMM, and to align its continued development with its users’ needs.
