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Naim Zierau

Wissenschaftlicher Mitarbeiter
Müller-Friedbergstrasse 8
9000 St. Gallen
+41 71 224 3237


Gendered voice based on pitch is a prevalent design element in many contemporary Voice Assistants (VAs) but has shown to strengthen harmful stereotypes. Interestingly, there is a dearth of research that systematically analyses user perceptions of different voice genders in VAs. This study investigates gender-stereotyping across two different tasks by analyzing the influence of pitch (low, high) and gender (women, men) on stereotypical trait ascription and trust formation in an exploratory online experiment with 234 participants. Additionally, we deploy a gender-ambiguous voice to compare against gendered voices. Our findings indicate that implicit stereotyping occurs for VAs. Moreover, we can show that there are no significant differences in trust formed towards a gender-ambiguous voice versus gendered voices, which highlights their potential for commercial usage.

Based on recent advances in Artificial Intelligence (AI), chatbots are now increasingly offered as an alternative source of customer service. For their uptake user trust in critical. However, little is known about how these interfaces fundamentally influence trust perceptions. In particular, it’s unclear what exactly causes perceptual differences - the change towards a conversational interface or the usage of anthropomorphic design elements. In this study, an online experiment with 160 participants was conducted to examine the differential effects of conversational interaction and anthropomorphism on trust in the interface or the provider within the context of online loan applications. The results show that both treatment conditions affect trust in the interface and the provider by increasing perceptions of social presence. Meanwhile, trust in the interface significantly effects the intention to share information, while trust in the provider has no effect on behavioral intention.

Smart Personal Assistants (SPA) fundamentally influence the way individuals perform tasks, use services and interact with organizations. They thus bear an immense economic and societal potential. However, a lack of trust - rooted in perceptions of uncertainty and risk - when interacting with intelligent computer agents can inhibit their adoption. In this paper, we conduct a systematic literature review to investigate the state of knowledge on trust in SPAs. Based on a concept-centric analysis of 50 papers, we derive three distinct research perspectives that constitute this nascent field: user interface-driven, interaction-driven, and explanation-driven trust in SPAs. Building on the results of our analysis, we develop a research agenda to spark and guide future research surrounding trust in SPAs. Ultimately, this paper intends to contribute to the body of knowledge of trust in artificial intelligence-based systems, specifically SPAs. It does so by proposing a novel framework mapping out their relationship.

The knowledge base related to user interaction with conversational agents (CAs) has grown dramatically but remains segregated. In this paper, we conduct a systematic literature review to investigate user interaction with CAs. We examined 107 papers published in outlets related to IS and HCI research. Then, we coded for design elements and user interaction outcomes, and isolated 7 significant determinants of these outcomes, as well as 42 themes with inconsistent evidence, providing grounds for future research. Building upon the insights from the analysis, we propose a research agenda to guide future research surrounding user interaction with CAs. Ultimately, we aim to contribute to the body of knowledge of IS and HCI in general and user interaction with CA in particular by indicating how developed a research field is regarding the number and content of the respective contributions. Furthermore, practitioners benefit from a structured overview related to CA design effects.

Conversational agents (CAs) represent a paradigm shift in regards to how humans use information systems. Although CAs have recently attracted considerable research interest, there is still limited shared knowledge about the distinctive characteristics of CAs from a user experience-based perspective. To address this gap, we conducted a systematic literature review to identify CA characteristics from existing research. Building on classifications from service experience theory, we develop a taxonomy that classifies CA characteristics into three major categories (i.e. functional, mechanic, humanic clues). Subsequently, we evaluate the usefulness of the taxonomy by interviewing six domain experts. Based on this categorization and the reviewed literature, we derive three propositions that link these categories to specific user experience dimensions. Our results support researchers and practitioners by providing deeper insights into service design with CAs and support them in systematizing and synthesizing research on the effects of specific CA characteristics from a user experience-based perspective.