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PurposeThis study presents one of the earliest empirical investigations on how brand chatbots' anthropomorphic design and social presence communication strategies may improve consumer evaluation outcomes via the mediators of parasocial interaction and perceived dialogue.Design/methodology/approachThis study employs a 2 (high vs. low social presence communication) by 2 (anthropomorphic vs. non-anthropomorphic bot profile) between-subject experimental design to evaluate how chatbots' high social presence communication and anthropomorphic profile design may enhance perceptions of parasocial interactions and dialogue with the chatbot, which in turn drive user engagement, interaction satisfaction and attitude toward the represented brand.FindingsThe influences of chatbots' high social presence communication on consumer engagement outcomes are mediated by perceived parasocial interaction and dialogue. Additionally, chatbots' anthropomorphic profile design can boost the positive effects of social presence communication via the psychological mediators.Originality/valueThis study advances the interactive marketing literature by focusing on an emerging interactive technology, chatbots. Additionally, distinct from prior chatbot studies that focused on the utilitarian use of chatbots for online customer support, this study not only examines which factors of chatbot communication and profile design may drive chatbot effectiveness but also examines the mechanism underlying the messaging and design effects on consumer engagement. The findings highlight the mediating role of interpersonal factors of parasocial interaction and perceived dialogue.

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys, and facilitates the identification of grand challenges so as to set the stage for new breakthroughs.

As a descriptor, artificial intelligence (AI) is polysemous and problematic. Like the muddled "big data" before it, this term du jour tends to be invoked broadly and haphazardly, by boosters as well as critics in some cases, making it difficult to discern exactly what AI is supposed to represent in the world, let alone how it is intended to work as a means of performing human tasks-from recognizing images, blocking spam email, and serving up algorithmic newsfeed recommendations to the more complicated challenges of autonomously flying drones and driving cars. Because science fiction and Hollywood so often depict AI in the form of sentient machines, many people associate AI with "thinking" robots or computers that can mimic human reasoning and behavior with uncanny accuracy-though, as Meredith Broussard points out in this special forum and in her book Artificial Intelligence: How Computers Misunderstand the World (Broussard, 2018), nothing could be further from the truth.Rather, AI more narrowly refers to a branch of computer science focused on simulating human intelligence, one that recently has been especially engaged in the subfield of machine learning: the training of a machine to learn from data, recognize patterns, and make subsequent judgments, with little to no human intervention. More narrowly still, and turning to the particular relevance for this journal, "communicative AI" may refer to AI technologies-such as conversational agents, social robots, and automated-writing software-that are designed to function as communicators, rather than merely mediators of human communication, often in ways that confound traditional conceptions of communication theory and practice . Amid this confusion surrounding the definition of AI and its emergent role in everyday life, this special forum attempts to carve out an elaboration on AI in the context of journalism-a key domain through which to illustrate many of the opportunities and challenges that AI presents for the broader realm of communication, media, and society.The implications of AI for journalism must be foregrounded in the larger context of the digitization of media and public life-a transition to apps, algorithms, social media, and the like in ways that have transformed journalism as institution: undercutting business models, upending work routines, and unleashing a flood of information alternatives to news, among other things. In that sense, AI technologies, regardless of how transformative they yet prove to be in the short, medium, or long term, may be understood as part of a broader story of journalism's reconfiguration in relation to computation.

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