Department of Communication and Culture

Journal of Retailing and Consumer Services 94 p. 104981-104981 Doi: https://doi.org/10.1016/j.jretconser.2026.104981

Journal of Destination Marketing & Management 42 Doi: https://www.sciencedirect.com/science/article/pii/S2212571X26000697

Digital nomads (DNs) blend work and travel, creating opportunities and management challenges for destinations. Drawing on 571 participants in China and the United States, an expanded version of the theory of planned behavior (TPB) is used, examining life satisfaction, policy expectation, and travel experience as context relevant antecedents. PLS-SEM shows that policy expectation is the strongest correlate of intention, while perceived behavioral control (PBC) has the larger association with current DN status. Permutation multi-group analysis identifies four significant country differences: travel experience-PBC and PBC-current status associations are stronger in the Chinese sample, while PBC-intention and intention-current status associations are stronger in the US sample. The findings contextualize TPB for digital nomadism and show how motivational, experiential, and policy-related associations vary across country contexts. They help destinations tailor positioning, onboarding, mobility services, and community integration while managing local impacts.

European Journal of Social Psychology Doi: https://doi.org/10.1002/ejsp.70106

Creativity and flourishing are core aims of higher education (HE) institutions and government initiatives globally. Large‐scale projects have linked creativity and flourishing with cultural diversity, yet their associations with majority‐group acculturation (i.e., how members of dominant culture groups adopt minority‐cultural elements and/or maintain their national culture) remain unexplored. We tested relationships between majority‐group acculturation and seven components of creativity and flourishing in UK (Study 1, N = 323) and Chinese (Study 2, N = 174) majority‐group university students and a three‐wave, English majority‐group longitudinal sample (Study 3, N at Time 1 = 219). Results found integration (i.e., adopting other cultures while maintaining one's own) predicted higher cognitive flexibility (Study 1), meaning‐in‐life (Studies 1 and 2) and quality of interpersonal relationships (Studies 1 and 2), while cultural adoption predicted higher cognitive flexibility, curiosity‐exploration and flourishing over time (Study 3). Findings align majority‐group acculturation with key priorities of HE and government programmes.

Academy of Management Proceedings Doi: https://doi.org/10.5465/amproc.2026.21871abstract

Large Language Models (LLMs) fundamentally transform human–machine communication, and in turn, professional communication in organizations. Unlike earlier systems that processed language without participating in interaction, LLMs generate context-sensitive discourse, modulate tone and stance, and filter information through selective summarization. This article introduces Algorithmic Pragmatics, a theoretical framework explaining how LLMs reshape communicative action despite lacking intentions or grounded understanding. The theory is developed within the context of international business communication by drawing on language-sensitive international business and management research. The Algorithmic Pragmatics framework identifies four mechanisms through which LLMs influence meaning-making: algorithmic generation of linguistic acts, algorithmic modulation of interpersonal tone, algorithmic filtration of salient information, and cybernetic interaction, whereby recursive human–model feedback loops normalize algorithmically patterned forms of communication. These mechanisms collectively reorganize the conditions under which linguistic meaning is produced, interpreted, and distributed in business communication. They also carry far-reaching societal and ethical implications, including shifts in epistemic authority, the emergence of algorithmically enforced linguistic norms that contribute to linguistic homogenization, widening accountability gaps in hybrid human–machine authorship, and new forms of communicative inequality shaped by differential access to AI literacy and the privileging of dominant language varieties. The article concludes with three implications for professional communication, specifically in relation to hybrid communication systems; cognitive offloading and sensemaking; and communicative convergence.

AI & Society: Knowledge, Culture and Communication Doi: https://doi.org/10.1007/s00146-026-03189-1

Generative artificial intelligence is transforming how creativity is organised, attributed, and governed across cultural and creative industries. This study examines how generative systems redistribute creative functions within human-led workflows by comparing AI-generated outputs with live human performances across visual (VNAI) and performative (PEAI) domains. Focusing on systems such as Stable Diffusion and OpenAI’s Sora, the analysis adopts a mixed-methods approach combining semantic alignment, latent feature analysis, reverse image similarity, and motion tracking to investigate how originality, authorship, and embodied expressiveness are structured in practice. The findings show that while generative models achieve high semantic fidelity and stylistic coherence, their outputs remain constrained by training data patterns and probabilistic recombination. In contrast, live human performance exhibits greater variability, responsiveness, and affective nuance. Across both modalities, meaningful differentiation emerges through iterative prompting, selection, and contextual framing, indicating that creative value is shaped through distributed workflows in which human judgement, evaluation, and embodied interpretation play a constitutive role. These results demonstrate that generative AI systems function as augmentative components rather than autonomous creative agents. By empirically analysing how creative agency is organised across human and algorithmic processes, the study provides an analytical framework for examining authorship, labour, and value formation in AI-mediated cultural production.