Human-Computer Interaction in the Digital Economy
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Item The Impact of ChatGPT on People’s Engagement with Online Advice Communities(2025-01-07) Gao, Yuting; Hahn, JungpilOne of the most influential technological innovations in recent years is ChatGPT, an LLMs- enabled chatbot that can quickly respond to a wide variety of prompts with human-like text in various contexts, ranging from marketing and customer service to education and healthcare. Much research has explored how ChatGPT affected users’ engagement with online Q&A communities such as Stack Overflow. However, it is unclear how ChatGPT may influence people’s engagement with online advice communities (e.g., online communities for users to get advice on relationships, career development, health...). Therefore, we conducted an analysis of secondary data, and we plan to conduct another analysis of secondary data and a lab experiment to explore the impact of ChatGPT on people’s engagement with online advice communities and why. This study contributes to the literature on the impact of AI on online advice communities and social interactions and enhances our understanding of the social implications of AI.Item Trust is Earned (Unless Your Website is Flawed): How Presentation Flaws and Delays Affect Swift Trust Between Individuals(2025-01-07) Wells, Taylor; Bullock, TaylorAs interactions and work moves online, the experience users have with interfaces becomes increasingly more important. In this study, we examined how two types of website malfunction, presentation flaws and delays, affect swift trust impressions of other users. We draw upon signaling theory to theoretically explain how presentation flaws and delays affect users’ swift trusting beliefs and intentions to delegate to other users. We conducted an online experiment (n=514) with a 2x2 factorial design to test our model. The presence of presentation flaws significantly biased swift trust evaluations in the negative direction as predicted. Surprisingly, we found no impact of delays on swift trust impressions, but we did find that delays increase the effect of presentation flaws on swift trust in some conditions. Our results provide guidance for practitioners seeking to keep users engaged with their interface and avoiding inadvertent distrust of other users of the platform.Item Identifying and Predicting Consumer Informational Friction: A Digital Behavioral Biometric Approach(2025-01-07) Weisgarber, Paul; Valacich, Joseph; Jenkins, Jeff; Wilson, David; Kim, DavidInformational consumer friction—resulting from insufficient or overly complex product information—can negatively affect consumer purchase decisions. This paper proposes that monitoring an individual’s digital behavior through mouse dynamics offers a novel method to identify and predict conditions of higher or lower friction. We examine this proposition in an exploratory study that assesses the relationship between informational consumer friction and mouse dynamics. By manipulating the difficulty of evaluating product features, we found that three mouse dynamic metrics—sub-movements, x-flips, and area under the curve—are significantly related to friction conditions. We also developed machine learning models to predict whether individuals were evaluating a product under higher or lower friction conditions and achieved a classification accuracy of over 67%. The findings suggest that digital behavior, particularly mousing dynamics, provides valuable insights that can allow researchers and practitioners to identify informational friction and ultimately enhance consumer experiences.Item Introduction to the Minitrack on Human-Computer Interaction in the Digital Economy(2025-01-07) Schneider, Christoph; Valacich, Joseph; Jenkins, Jeffrey; Nah, Fiona Fui-HoonItem CochlearMotion: Head Gesture Recognition Leveraging Ear Canal Deformation Sensing(2025-01-07) Lee, Youngone; Tan, Sheng; Wang, ZiHands-free interfaces have become increasingly popular due to the growing demands for convenient interaction with mobile and wearable devices. Among all of hands-free interfaces, head gesture interaction has shown great potential in providing alternatives in various real-world scenarios, such as interfaces for people with disabilities and Virtual/Augmented Reality applications. However, existing head gesture recognition systems require either Line-Of-Sight or specialized/customized hardware. Additionally, some approaches could raise potential privacy concerns. In this work, we propose CochlearMotion, a novel in-ear wearable system that achieves head gesture recognition by utilizing off-the-shelf earbuds with a built-in microphone. Specifically, we leverage sonar-like techniques to sense the unique deformation of the ear canal, which closely correlated with each head motion for cross-user head gesture recognition. Our extensive experimental evaluation shows that our system can achieve over 95% recognition accuracy for six typical head gestures and works well in various real-world environments and scenarios.