Abstract The application and use cases for conversational agents (CAs) are versatile. Smart speakers such as Alexa and Google Home are used in smart home environments, digital agents are integrated into car systems and chatbots are increasingly used in customer service processes. However, human–computer interaction researchers identify and investigate a wide-ranging variety of aspects impeding the usage of CAs by end-users. In general, impediments differ depending on use case contexts, user group characteristics and the CA’s technological infrastructure. Hence, it is difficult and often ambiguous for designers and developers to generate an appropriate awareness about aspects impeding CA usage. We address this problem, by conducting a systematic review of 65 publications surveying impeding aspects of the usage of CAs.

There has been increasing interest in the application of service robots in retail service domains in recent years. In most cases, deployed robot systems focus on serving customer needs autonomously. Specific and individual customer needs often cannot be addressed by these systems, promoting frustration and dissatisfaction in customers. In this study, we investigate the potential of human-robot-collaboration in service domains and how humans may be kept in the loop while customers interact with autonomous service robots. Therefore, we developed a graphical user interface allowing users to control a service robot remotely. We tested the interface with 19 participants to understand their perceptions on the usability of the interface and their user experiences while serving customers in two different use cases. Results illustrate that participants easily learned interacting with the robot and successfully completed the service use cases. They reported diverse user experiences, ranging from feeling odd to having great experiences while remotely operating the robot. We discuss implications of our results for the design of human-robot-interaction in service domains and emphasize a shift of focus from full robot automatization to human-robot collaboration.

Aim of this study is to investigate the effects of user experience (UX) on shopping mall customers’ intention to use a social robot. Therefore, we used a Wizard of Oz approach that enabled data collection in situ. Quantitative data was obtained from a questionnaire completed by shopping mall customers who interacted with a social robot. Data was used in a regression analysis, where user experience factors served as predictors for robot use in retail. The regression model explains up to 23.2% of the variance in customers’ intention to use a social robot. In addition, we collected qualitative data on human-robot-interactions and used the data to complement the interpretation of statistical results. Our findings suggest that only hedonic qualities significantly contribute to the prediction of customers’ intention, that shopping mall customers are reluctant to grant pragmatic qualities to social robots, and that UX evaluation in HRI requires additional predictors.

There has been increasing interest in the application of humanoid robots in service domains like retail or care homes in recent years. Here, most use cases focus on serving customer needs autonomously. Frequently, human intervention becomes necessary to support the robot in exceptional situations. However, direct intervention of service operators is often not possible and requires specialized personnel. In a co-design process with 13 service operators from a pharmacy, we designed a remote working environment for human-robot collaboration that enables first-time experiences and collaboration with robots. Five participants took part in an assessment study and reported on their experiences about the utility, usability and user experience. Results show that participants were able to control and train the robot through the remote control environment. We discuss implications of our results for future work in service domains and emphasize a shift of focus from full robot automatization to human-robot collaboration forms.