Conversational agents (CAs) are natural language user interfaces that emulate human-to-human communication. Because of this emulation, research on CAs is inseparably linked to questions about anthropomorphism—the attribution of human qualities, including consciousness, intentions, and emotions, to nonhuman agents. Past research has demonstrated that anthropomorphism affects human perception and behavior in human-computer interactions by, for example, increasing trust and connectedness or stimulating social response behaviors. Based on the psychological theory of anthropomorphism and related research on computer interface design, we develop a theoretical framework for designing anthropomorphic CAs. We identify three groups of factors that stimulate anthropomorphism: technology design-related factors, task-related factors, and individual factors. Our findings from an online experiment support the derived framework but also reveal novel yet counterintuitive insights. In particular, we demonstrate that not all combinations of anthropomorphic technology design cues increase perceived anthropomorphism. For example, we find that using only nonverbal cues harms anthropomorphism; however, this effect becomes positive when nonverbal cues are complemented with verbal or human identity cues. We also find that CAs’ disposition to complete computerlike versus humanlike tasks and individuals’ disposition to anthropomorphize greatly affect perceived anthropomorphism. This work advances our understanding of anthropomorphism and contextualizes the theory of anthropomorphism within the IS discipline. We advise on the directions that research and practice should take to find the sweet spot for anthropomorphic CA design.

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Information overload influences users' satisfaction and performance when completing a complex task. In e-commerce interactions, this has the effect that customers' decision making becomes confused, less accurate and less effective. For websites, numerous countermeasures to mitigate information overload have been presented, whereas not many attempts have been made to reduce cognitive load when conversational agents are used instead. Conversational agents are expected to increase the perceived overload due to the voice interface characteristics. In this pilot study, the cognitive load of subjects was measured during an online shopping task which required different custom shopping skills for Amazon Alexa. It was tested if the countermeasure filtered repetition can reduce subjects' perceived overload when using the voice assistant and which load differences can be found in comparison to a shopping website. To measure the mental load, the skin conductance level was recorded. Keywords Information overload • Conversational agents • Skin conductance level 1 IntroductionThe great convenience, large product range, and high amount of product-related information offered by online retailers ensures that an increasing number of customers use online channels for shopping. According to [1], the share of online shoppers in Germany has increased significantly over the last years. However, the vast amount of information and cognitive constraints of human information processing often cause