Social media platforms increasingly employ proactive moderation techniques, such as detecting and curbing toxic and uncivil comments, to prevent the spread of harmful content. Despite these efforts, such approaches are often criticized for creating a climate of censorship and failing to address the underlying causes of uncivil behavior. Our work makes both theoretical and practical contributions by proposing and evaluating two types of emotion monitoring dashboards to enhance users' emotional awareness and mitigate hate speech. In a study involving 211 participants, we evaluate the effects of the two mechanisms on user commenting behavior and emotional experiences. The results reveal that these interventions effectively increase users' awareness of their emotional states and reduce hate speech. However, our findings also indicate potential unintended effects, including increased expression of negative emotions (Angry, Fear, and Sad) when discussing sensitive issues. These insights provide a basis for further research on integrating proactive emotion regulation tools into social media platforms to foster healthier digital interactions.

This study explores how input modality - voice versus text - affects self-disclosure in user feedback, leveraging a novel approach that uses transformer-based models to detect self-disclosure per token embedded in context. In an online experiment with 122 participants, results indicate that participants using voice input engaged significantly less in self-disclosure than those using text, a finding associated with reduced perceived anonymity in voice interactions. This effect persisted after accounting for response length, suggesting that the influence of voice input on self-disclosure is not merely due to brevity but also reflects unique psychological responses to voice-based communication. These findings contribute to a deeper theoretical understanding of input modality's role in shaping disclosure behavior in user feedback contexts. Practical implications offer design guidance for voice-based feedback systems to encourage more open and authentic feedback in sensitive settings.

The pervasiveness and increasing sophistication of artificial intelligence (AI)-based artifacts within private, organizational, and social realms are changing how humans interact with machines. Theorizing about the way that humans perceive AI-based artifacts is, for example, crucial to understanding why and to what extent humans deem these artifacts to be competent for decision-making but 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. We argue that 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 show 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 the artifact's actual agency, uncertainty, and user characteristics.

No AccessDigitale Transformation im Unternehmen gestaltenOct 2016Digitale Service-SystemeMahei Li, Christoph Peters, Jan Marco LeimeisterMahei LiSearch for more papers by this author, Christoph PetersSearch for more papers by this author, Jan Marco LeimeisterSearch for more papers by this authorhttps://doi.org/10.3139/9783446451148.003SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2016Pages: 29-38Print ISBN: 978-3-446-44678-6eISBN: 978-3-446-45114-8 Copyright & Permissions© 2016 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL 'I Will Follow You!' – How Recommendation Modality Impacts Processing Fluency and Purchase Intention Forty-Third International Conference on Information Systems, Copenhagen 2022 18 Pages Posted: 2 Dec 2022 See all articles by Melanie SchwedeMelanie Schwedeaffiliation not provided to SSRNNaim ZierauUniversity of St. GallenAndreas JansonUniversity of KasselMaik HammerschmidtMannheim Business School - Department of MarketingJ. M. LeimeisterUniversity of St. Gallen; University of Kassel - Information Systems Date Written: December 9, 2022 Abstract Although conversational agents (CA) are increasingly used for providing purchase recommendations, important design questions remain. Across two experiments we examine with a novel fluency mechanism how recommendation modality (speech vs. text) shapes recommendation evaluation (persuasiveness and risk), the intention to follow the recommendation, and how modality interacts with the style of recommendation explanation (verbal vs. numerical). Findings provide robust evidence that text-based CAs outperform speech-based CAs in terms of processing fluency and consumer responses. They show that numerical explanations increase processing fluency and purchase intention of both recommendation modalities. The results underline the importance of processing fluency for the decision to follow a recommendation and highlight that processing fluency can be actively shaped through design decisions in terms of implementing the right modality and aligning it with the optimal explanation style. For practice, we offer actionable implications on how to make effective sales agents out of CAs. Keywords: Conversational Agent, Recommendation Modality, Explanation of Recommendation, Processing Fluency, Purchase Intention Suggested Citation: Suggested Citation Schwede, Melanie and Zierau, Naim and Janson, Andreas and Hammerschmidt, Maik and Leimeister, Jan Marco, 'I Will Follow You!' – How Recommendation Modality Impacts Processing Fluency and Purchase Intention (December 9, 2022). Forty-Third International Conference on Information Systems, Copenhagen 2022, Available at SSRN: https://ssrn.com/abstract=4239822 Melanie Schwede affiliation not provided to SSRN Naim Zierau University of St. Gallen ( email ) Varnbuelstr. 14Saint Gallen, St. Gallen CH-9000Switzerland Andreas Janson University of Kassel ( email ) Fachbereich 05Nora-Platiel-Straße 134109 Kassel, Hessen 34127Germany Maik Hammerschmidt Mannheim Business School - Department of Marketing ( email ) Germany Jan Marco Leimeister (Contact Author) University of St. Gallen ( email ) Varnbuelstr. 14Saint Gallen, St. Gallen CH-9000Switzerland University of Kassel - Information Systems ( email ) Pfannkuchstraße 1Kassel, 34121Germany Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 2 Abstract Views 5 PlumX Metrics Related eJournals Consumer Behavioral Finance eJournal Follow Consumer Behavioral Finance eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 260 PAPERS 1,114 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo-5dc in 0.254 seconds

Conversational agents (CAs) provide opportunities for improving the interaction in evaluation surveys. To investigate if and how a user-centered conversational evaluation tool impacts users’ response quality and their experience, we build EVA - a novel conversational course evaluation tool for educational scenarios. In a field experiment with 128 students, we compared EVA against a static web survey. Our results confirm prior findings from literature about the positive effect of conversational evaluation tools in the domain of education. Second, we then investigate the differences between a voice-based and text-based conversational human-computer interaction of EVA in the same experimental set-up. Against our prior expectation, the students of the voice-based interaction answered with higher information quality but with lower quantity of information compared to the text-based modality. Our findings indicate that using a conversational CA (voice and text-based) results in a higher response quality and user experience compared to a static web survey interface.

Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

Conversational agents (CAs) provide opportunities for improving the interaction in evaluation surveys. To investigate if and how a user-centered conversational evaluation tool impacts users' response quality and their experience, we build EVA - a novel conversational course evaluation tool for educational scenarios. In a field experiment with 128 students, we compared EVA against a static web survey. Our results confirm prior findings from literature about the positive effect of conversational evaluation tools in the domain of education. Second, we then investigate the differences between a voice-based and text-based conversational human-computer interaction of EVA in the same experimental set-up. Against our prior expectation, the students of the voice-based interaction answered with higher information quality but with lower quantity of information compared to the text-based modality. Our findings indicate that using a conversational CA (voice and text-based) results in a higher response quality and user experience compared to a static web survey interface.

Innovation is one of the most important antecedents of a company's competitive advantage and long-term survival. Prior research has alluded to teamwork being a primary driver of a firm's innovation capacity. Still, many firms struggle with providing an environment that supports innovation teams in working efficiently together. Thereby, a team's failure can be attributed to several factors, such as inefficient working methods or a lack of internal communication that leads to so-called innovation blockages. There are a number of approaches that are targeted at supporting teams to overcome innovation blockages, but they mainly focus on the collaboration process and rarely consider the needs and potentials of individual team members. In this paper, we argue that Conversational Agents (CAs) can efficiently support teams in overcoming innovation blockages by enhancing collaborative work practices and, specifically, by facilitating the contribution of each individual team member. To that end, we design a CA as a team facilitator that provides nudges to reduce innovation blocking actions according to requirements we systematically derived from scientific literature and practice. Based on a rigorous evaluation, we demonstrate the potential of CAs to reduce the frequency of innovation blockages. The research implications for the development and deployment of CAs as team facilitators are explored.

Voice-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.

Conversational Agents (CAs) have become a new paradigm for human-computer interaction. Despite the potential benefits, there are ethical challenges to the widespread use of these agents that may inhibit their use for individual and social goals. However, besides a multitude of behavioral and design-oriented studies on CAs, a distinct ethical perspective falls rather short in the current literature. In this paper, we present the first steps of our design science research project on principles for a value-sensitive design of CAs. Based on theoretical insights from 87 papers and eleven user interviews, we propose preliminary requirements and design principles for a value-sensitive design of CAs. Moreover, we evaluate the preliminary principles with an expert-based evaluation. The evaluation confirms that an ethical approach for design CAs might be promising for certain scenarios.

Voice assistants’ increasingly nuanced and natural communication bears new opportunities for user experiences and task automation, while challenging existing patterns of human-computer interaction. A fragmented research field, as well as constant technological advancements, impede a common apprehension of prevalent design features of voice-based interfaces. As part of this study, 86 papers across domains are systematically identified and analysed to arrive at a common understanding of voice assistants. The review highlights perceptual differences to other human-computer interfaces and points out relevant auditory cues. Key findings regarding those cues’ impact on user perception and behaviour are discussed along with the three design strategies 1) personification, 2) individualization and 3) contextualization. Avenues for future research are lastly deducted. Our results provide relevant opportunities to researchers and designers alike to advance the design and deployment of voice assistants.

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.

Voice assistants are a novel class of information systems that fundamentally change human–computer interaction. Although these assistants are widespread, the utilization of these information systems is oftentimes only considered on a surface level by individuals. In addition, prior research has focused predominantly on initial use instead of looking deeper into post-adoption and habit formation. In consequence, this paper reviews how the notion of habit has been conceptualized in relation to biographical utilization of voice assistants and presents findings based on a qualitative study approach. From a perspective of post-adoption users, the study suggests that existing habits persist, and new habits hardly ever form in the context of voice assistant utilization. This paper outlines four key factors that help explain voice assistant utilization behavior and furthermore provides practical implications that help to ensure continued voice assistant use in the future.

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.

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, interactiondriven, 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.

Chatbots are predicted to play a key role in customer service based on recent advances in the area of Artificial Intelligence (AI). However, a lack of user trust impedes the widespread adaption of AI-based chatbots. Still, there is a lack of systematically derived design knowledge concerning user trust in those agents. In this short paper, we report on the first steps of our design science research project on which design principles are relevant for building trust in chatbots. Based on trust literature and user interviews, we propose preliminary requirements and design principles for trust-enhancing design features for chatbots in customer service. Furthermore, we present a first instantiation of those principles. These insights will support researchers and practitioners to better understand how user trust in chatbots can be systematically built to increase adoption and usage.

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.