Purpose: This study aims to examine the relationship between emotional attachment (EA) and intelligent personal assistant (IPA) continuance intention. While existing theories emphasize purely rational and goal-oriented factors in terms of information technology (IT) continuance intention, this research examines how users' EA toward technology impacts their continuance intention in the absence of cognitive and habitual factors.Design/methodology/approach: This study contextualizes attachment theory from the social psychology/consumer psychology literature to an IT application and formulates and tests a new model that is proposed in the context of IPA continuance. Five research hypotheses developed from contextualization and application of the theory were posited in a structural model and empirically validated using survey results from IPA users.Findings: The results show that users' EA to IPA use significantly influences their IPA continuance intention, along with emotional trust and interaction quality with the IPA.Originality/value: This study contextualizes attachment theory developed in the social psychology/consumer psychology literature to formulate and test a new model in the context of IPA continuance. This work contributes to the theoretical understanding by investigating IPA continuance intention in the absence of cognitive or habitual factors and fills a critical research gap in IT post-adoption literature. IPA is just one example of technologies to which individuals can form attachments and this research provides an important foundation for future research by positing and testing the value of EA in IT post-adoption behavior. This research also contributes to practical knowledge by inferring that IPA manufacturers, managers and vendors could extend their revenue streams by integrating product features that capture emotion.

Purpose: The application of mobile augmented reality (MAR) for enhancing user experiences and consumer patronizing intention has been the focus of recent MAR literature. Few studies examine the differences between apps. This study fills the research gap by examining how consumers assess their experiences with different MAR applications and how their decision-making process is performed, particularly in the setting of smartphones.Design/methodology/approach: A web-based online survey was administered to collect data on consumers' perceptions of two different MAR apps: utilitarian and hedonic apps. Reliability and validity of the measurement scales, non-response bias and comment method bias were assessed. With the support of measurement model, partial least square (PLS) was employed to test the research hypotheses.Findings: This study reveals that the technological attributes of augmented reality (AR) apps have significant effects on consumer perceptions of their utilitarian and hedonic benefits, including interactivity, visual quality, service quality, technicality and aesthetics. Moreover, this study shows that consumers of hedonic apps place more importance on their enjoyment with the MAR app; consumers of utilitarian apps focus more on the accrued functional values. The findings provide practical insights for retailers in AR marketing and application development in the MAR environment.Originality/value: This study provides a comprehensive viewpoint for analyzing ongoing use and purchase intentions simultaneously in a unified theoretical framework. In addition, it compares different types of MAR apps: hedonic and utilitarian. Furthermore, it is one of the first few studies attempting to provide a comprehensive understanding of the predictive role of MAR technologies by incorporating privacy concerns into the research model based on user and gratification framework.

Generative artificial intelligence (GenAI), synthetic media, social bots, and agentic AI are turning information disorder from a content-verification problem into a governance problem for multimedia information ecosystems. Research on fake news, deepfakes, GenAI misuse, and cyber-enabled influence has developed in largely separate streams, and none of these streams offers an integrated governance model for influence activity coordinated through AI-supported workflows. This study develops such a model through a structured evidence synthesis of 35 peer-reviewed sources published between 2019 and 2026, identified through four structured discovery queries and citation chaining and appraised with evidence-type-specific criteria. The synthesis identifies five mechanisms through which AI reshapes information disorder: influence orchestration, ambiguity production, selective persuasion, hybrid human–machine dissemination, and a trust paradox in countermeasures. On this basis, the study defines agentic influence operations as workflow-level risks and organizes them in a twelve-dimension framework that links AI capabilities, content modality, platform infrastructure, data access, evidentiary trust, organizational harm, and governance control points. It also sets out construct boundary conditions, five propositions with explicit evidence status, a managerial control set, and a phased implementation roadmap. Only the proposition on synthetic media and uncertainty about authentic evidence has direct empirical support; the others are offered for testing through simulation, experimental, design-science, and expert-validation studies.