[pt] Assistentes de Voz (AVs) trazem diversos benefícios para os usuários e estão se tornando progressivamente populares, mas algumas barreiras para adoção de AVs ainda persistem, como atitudes dos usuários, preocupações com privacidade e percepções negativas desses sistemas. Uma abordagem para mitigar os obstáculos e melhorar as interações pode ser entender os modelos mentais dos usuários de AVs, uma vez que estudos indicam que o entendimento dos usuários não é alinhado com as reais capacidades desses sistemas. Assim, considerando a importância de um modelo mental correto para as interações, explorar fatores geradores de percepções inadequadas e soluções para lidar com tal questão pode ser essencial. O objetivo desta pesquisa foi identificar fatores influentes para as percepções inadequadas de usuários e oferecer recomendações de design para alinhar os modelos mentais de usuários com as reais capacidades desses sistemas. Para alcançar esse objetivo, nós conduzimos uma revisão sistemática de literatura, entrevistas exploratórias com experts e um estudo Delphi de três rodadas com base em questionários. Os resultados indicam que os aspectos de design como a humanização dos AVs e a transparência em respostas do sistema são influentes para os modelos mentais. Apesar desses fatores terem sido indicados como causas para incorreções em modelos mentais, remover a humanização dos AVs e apresentar informações excessivas pode não ser uma solução imediada. Indica-se que designers devem avaliar o contexto de uso e os domínios de tarefa em que os AVs serão usados para guiar as soluções de design. Além disso, os designers devem entender os perfis e backgrounds dos usuários para ajustar as interações uma vez que as características dos usuários são influentes para sua percepção do produto. Finalmente, o time de desenvolvimento deve ter um entendimento correto e homogêneo do AVs, e deve possuir o conhecimento necessário para aplicar soluções corretamente. Esse último requisito é desafiador porque os AVs são produtos relativamente novos e podem demandar que os profissionais dominem novas habilidades e ferramentas. / [en] Voice Assistants (VAs) bring various benefits for users and are increasingly popular, but some barriers for VA adoption and usage still prevail, such as users attitudes, privacy concerns, and negative perceptions towards these systems. An approach to mitigating such obstacles and leveraging voice interactions may be understanding users mental models of VAs, since studies indicate that users understandings of VAs are unaligned with these systems actual capabilities. Thus, considering the importance of a correct mental model for interactions, exploring influential factors causing misperceptions and solutions to deal with this issue may be paramount. The objective of this research was to identify leading causes of users misperceptions and offer design recommendations for aligning users mental models of VAs with these systems real capacities. In order to achieve this goal, we conducted a systematic literature review (SLR), exploratory interviews with experts, and a questionnaire-based three-round Delphi study. The results indicate that design aspects such as VAs high humanness and the lack of outputs transparency are influential for mental models. Despite the indication that these drivers lead to users misperceptions, removing VAs humanness and excessively displaying information about VAs might not be an immediate solution. In turn, developers should assess the context and task domains in which the VA will be used to guide design decisions. Moreover, developers should understand the users profiles and backgrounds to adjust interactions, as users characteristics are influential for how they perceive the product. Finally, developing teams should have a correct and homogeneous understanding of VAs and possess the necessary knowledge to employ solutions properly. This latter requirement is challenging since VAs novelty might demand professionals to master new skills and tools.

Digitalization crosses all areas of life (Hess et al. 2014). Recent progress in artificial intelligence (AI) opens new potentials for further developments and improvements, with virtual coaching being a prime example. Virtual coaches (VCs) aim to optimize the user’s life by transforming cognition, affection, and behavior towards a stated goal. Since they emerged from the health and sports domain, a typical example are VCs in the form of digital avatars, which instruct physical exercises, shape health-related knowledge and provide motivational support to achieve the user’s goals (e.g., weight loss) (Ding et al. 2010; Tropea et al. 2019). Nonetheless, the application areas of VCs are versatile and exploring the potential areas (e.g., healthcare, work, finance, leisure, and environment) constitutes an essential topic of future research and development. According to Gartner’s hype cycle for human capital management technology, VCs are still in their infancy but are considered innovation triggers for the following years (Gartner, Inc. 2021). Specifically, VCs can be a replacement or complement for traditional human-to-human coaching scenarios and promise broad access to personalized coaching services independent of place and time (Graßmann and Schermuly 2021). As a result, VCs may contribute to solving challenges posed by an aging society and skilled labor shortage (European Commission 2016; Edwards and Cheok 2018). Last but not least, the recent COVID-19 pandemic additionally showcased the need for VCs as an alternative to traditional face-to-face interventions. Against this background and driven by the potential and promises of VCs, research has recently engaged in developing and understanding VC applications (Tropea et al. 2019; Lete et al. 2020; Graßmann and Schermuly 2021). To introduce the concept in information systems (IS) research and provide a basis for researchers and practitioners alike, this catchword aims at providing a holistic view on VCs. The structure of this paper is as follows. Section 2 elaborates a definition, delimits VCs from related system classes, and proposes a research framework. Section 3 aggregates existing research into the framework and concludes with an outlook on future IS research perspectives.

Turn-taking is a fundamental process that governs social interaction. When humans interact, they naturally take initiative and relinquish control to each other using verbal and nonverbal behavior in a coordinated manner. In contrast, existing approaches for controlling a robot's social behavior do not explicitly model turn-taking, resulting in interaction breakdowns that confuse or frustrate the human and detract from the dyad's cooperative goals. They also lack generality, relying on scripted behavior control that must be designed for each new domain. This thesis seeks to enable robots to cooperate fluently with humans by automatically controlling the timing of multimodal turn-taking. Based on our empirical studies of interaction phenomena, we develop a computational turn-taking model that accounts for multimodal information flow and resource usage in interaction. This model is implemented within a novel behavior generation architecture called CADENCE, the Control Architecture for the Dynamics of Embodied Natural Coordination and Engagement, that controls a robot's speech, gesture, gaze, and manipulation. CADENCE controls turn-taking using a timed Petri net (TPN) representation that integrates resource exchange, interruptible modality execution, and modeling of the human user. We demonstrate progressive developments of CADENCE through multiple domains of autonomous interaction encompassing situated dialogue and collaborative manipulation. We also iteratively evaluate improvements in the system using quantitative metrics of task success, fluency, and balance of control.