Anuschka Schmitt
Last Name
Schmitt
First name
Anuschka
Email
anuschka.schmitt@unisg.ch
Phone
+41 71 224 3225
17 results
Now showing 1 - 10 of 17
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Item type:Publication, The Role of AI-Based Artifacts’ Voice Capabilities for Agency Attribution(2023-07-04); ; ; The pervasiveness and increasing sophistication of artificial intelligence (AI)-based artifacts within private, organizational, and social realms change how humans interact with machines. Theorizing about the way humans perceive AI-based artifacts is crucial to understanding why and to what extent humans deem these as competent for, i.e., decision-making, yet has traditionally taken a modality-agnostic view. In this paper, we theorize about a particular case of interaction, namely that of voice-based interaction with AI-based artifacts. The capabilities and perceived naturalness of such artifacts, fueled by continuous advances in natural language processing, induce users to deem an artifact as able to act autonomously in a goal-oriented manner. We argue that there is a positive direct relationship between the voice capabilities of an artifact and users’ agency attribution, ultimately obscuring the artifact’s true nature and competencies. This relationship is further moderated by an artifact’s actual agency, uncertainty, and user characteristics.Type:journal articleJournal:Journal of the Association for Information Systems (JAIS)Volume:24Issue:4Scopus© Citations 25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Charting the Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and New Frontiers(Springer Nature, 2023-04-20); Scopus© Citations 110 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Conversational Agents for Information Retrieval in the Education Domain: A User-Centered Design Investigation(2022-11-11); ; Text-based conversational agents (CAs) are widely deployed across a number of daily tasks, including information retrieval. However, most existing agents follow a default design that disregards user needs and preferences, ultimately leading to a lack of usage and an unsatisfying user experience. To better understand how CAs can be designed in order to lead to effective system use, we deduced relevant design requirements from both literature and 13 user interviews. We built and tested a question-answering, text-based CA for an information retrieval task in an education scenario. Results from our experimental test with 41 students indicate that following a user-centered design has a significant positive effect on enjoyment and trust in a CA as opposed to deploying a default CA. If not designed with the user in mind, CAs are not necessarily more beneficial than traditional question-answering systems. Beyond practical implications for effective CA design, this paper points towards key challenges and potential research avenues when deploying social cues for CAs.Type:journal articleJournal:Proceedings of the ACM on Human-Computer Interaction (PACMHCI)Volume:6Issue:CSCW2DOI:10.1145/3555587Scopus© Citations 20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Voice bots on the frontline: Voice-based interfaces enhance flow-like consumer experiences & boost service outcomesVoice-based interfaces provide new opportunities for firms to interact with consumers along the customer journey. The current work demonstrates across four studies that voice-based (as opposed to text-based) interfaces promote more flow-like user experiences, resulting in more positively-valenced service experiences, and ultimately more favorable behavioral firm outcomes (i.e., contract renewal, conversion rates, and consumer sentiment). Moreover, we also provide evidence for two important boundary conditions that reduce such flow-like user experiences in voice-based interfaces (i.e., semantic disfluency and the amount of conversational turns). The findings of this research highlight how fundamental theories of human communication can be harnessed to create more experiential service experiences with positive downstream consequences for consumers and firms. These findings have important practical implications for firms that aim at leveraging the potential of voice-based interfaces to improve consumers' service experiences and the theory-driven ''conversational design'' of voice-based interfaces.Type:journal articleJournal:Journal of the Academy of Marketing Science (JAMS)Volume:51Issue:4Scopus© Citations 94 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving AI-Assisted Decision-Making: Insights into Example-Based Explanations and Cognitive Load in Sales Forecasting(2025); ; ; AI-assisted decision-making often underperforms due to users' difficulties in effectively interacting with AI-based systems. This study investigates how example-based explanations—specifically factual and counterfactual explanations—impact users' decision-making performance and their tendency to overrule algorithmic advice in a sales forecasting task. We also examine the mediating role of cognitive load. By analyzing 1330 forecasts made in an online lab experiment, we find that factual explanations significantly enhance forecasting performance by enabling users to more effectively overrule algorithmic advice. While counterfactual explanations also result in performance gains, the increase is smaller and operates primarily through reduced deviation from AI advice due to cognitive overload. Our findings suggest that factual explanations align well with human cognitive processes, facilitating better decision outcomes, while counterfactuals may overwhelm users cognitively. This study contributes to a deeper understanding of explainable AI design in decision-making contexts, emphasizing the importance of aligning explanations with users' cognitive capacities.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Ensuring Human Agency: A Design Pathway to Human-AI Interaction(2024)The augmentation of human work through Artificial Intelligence (AI) promises to be a panacea to the role of technology in organizations. While frameworks on augmentation theorize how to best divide work between humans and AI, the empirical literature on human-AI interaction offers unexpected and inconclusive findings. Interaction challenges—including overreliance and selected engagement with the algorithmic output—call into question how theorized augmentation benefits can be realized. Rooted in cognitive learning theory, we develop a conceptual model on the design of algorithmic output. We argue that human-AI interaction can lead to multiple beneficial outcomes when algorithmic output is designed in a reciprocal manner. By providing humans with reflection-provoking feedback, reciprocal algorithmic output does not prescribe any actions, and thereby necessitates humans to expend cognitive effort. We identify three crucial augmentation outcomes that reciprocal algorithmic output enables: task performance, human agency, and human learning.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Promise of Evaluative Algorithmic Advice: A Field Experiment on Writing Improvement(2024-08-09); ; ; Krzysztof GajosThe design and impact of algorithmic advice has become more important than ever with generative artificial intelligence’s (AI) diffusion in organizational and private realms. Unfortunately, challenges associated with the computational nature of AI-based systems and human sensemaking can hinder augmentation of human work. Prevalent forms of algorithmic advice commonly provide a user with ‘one best’ solution that can induce the user to fixate on the advice and neglect own critical reasoning. To overcome such interaction challenges, we explore the potential of evaluative algorithmic advice that provides users with more open-ended and engaging feedback. As part of two controlled experiments, we find that humans prefer evaluative over contrastive advice for writing feedback. We then conducted a field experiment in the context of an educational business pitch writing task with two conditions: (i) contrastive algorithmic advice improving users’ writing without any further feedback; and (ii) evaluative algorithmic advice providing feedback in form of open-ended questions and pro and contra arguments. Users’ writing could be improved regardless of the type of algorithmic advice, yet we show that users exposed to evaluative advice engaged significantly more with the task and the advice. Our study explores how algorithmic advice may serve as a critical stimulator rather than attenuating human agency in AI augmentation.Type:conference paperJournal:Academy of Management (AOM) Meeting - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring the Synergies in Human-AI Hybrids: A Longitudinal Analysis in Sales Forecasting(2023-08-10); Despite the promised potential of artificial intelligence (AI), insights into real-life human-AI hybrids and their dynamics remain obscure. Based on digital trace data of over 1.4 million forecasting decisions over a 69-month period, we study the implications of an AI sales forecasting system's introduction in a bakery enterprise on decision-makers' overriding of the AI system and resulting hybrid performance. Decisionmakers quickly started to rely on AI forecasts, leading to lower forecast errors. Overall, human intervention deteriorated forecasting performance as overriding resulted in greater forecast error. The results confirm the notion that AI systems outperform humans in forecasting tasks. However, the results also indicate previously neglected, domain-specific implications: As the AI system aimed to reduce forecast error and thus overproduction, forecasting numbers decreased over time, and thereby also sales. We conclude that minimal forecast errors do not inevitably yield optimal business outcomes when detrimental human factors in decision-making are ignored.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Disentangling Trust in Voice Assistants - A Configurational View on Conversational AI Ecosystems(2023); ; Bevilacqua, TatjanaVoice assistants’ (VAs) increasingly nuanced and natural communication via artificial intelligence (AI) opens up new opportunities for the experience of users, providing task assistance and automation possibilities, and also offer an easy interface to digital services and ecosystems. However, VAs and according ecosystems face various problems, such as low adoption and satisfaction rates as well as other negative reactions from users. Companies, therefore, need to consider what contributes to user satisfaction of VAs and related conversational AI ecosystems. Key for conversational AI ecosystems is the consideration of trust due to their agentic and pervasive nature. Nonetheless, due to the complexity of conversational AI ecosystems and different trust sources involved, we argue that we need a more detailed understanding about trust. Thus, we propose a configurational view on conversational AI ecosystems that allows us to disentangle the complex and interrelated factors that contribute to trust in VAs. We examine with a configurational approach and a survey study, how different trust sources contribute to the outcomes of conversational AI ecosystems, i.e., in our case user satisfaction. The results of our study show four distinct patterns of trust source configurations. Vice versa, we show how trust sources contribute to the absence of the outcome, i.e., user satisfaction. The derived implications provide a configurative theoretical understanding for the role of trust sources for user satisfaction that provides practitioners useful guidance for more trustworthy conversational AI ecosystems.Type:conference paperJournal:Annual Meeting of the Academy of Management (AOM) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Social Audio: Conceptualizing Voice-Based Online Social Networks and their Privacy Implications(2023);