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cs.HC updates on arXiv.org

Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
Towards an Effective Organization-Wide Bulk Email System
Ruoyan Kong · 2023-08-18 · via cs.HC updates on arXiv.org

Bulk email is widely used in organizations to communicate messages to employees. It is an important tool in making employees aware of policies, events, leadership updates, etc. However, in large organizations, the problem of overwhelming communication is widespread. Ineffective organizational bulk emails waste employees' time and organizations' money, and cause a lack of awareness or compliance with organizations' missions and priorities. This thesis focuses on improving organizational bulk email systems by 1) conducting qualitative research to understand different stakeholders; 2) conducting field studies to evaluate personalization's effects on getting employees to read bulk messages; 3) designing tools to support communicators in evaluating bulk emails. We performed these studies at the University of Minnesota, interviewing 25 employees (both senders and recipients), and including 317 participants in total. We found that the university's current bulk email system is ineffective as only 22% of the information communicated was retained by employees. To encourage employees to read high-level information, we implemented a multi-stakeholder personalization framework that mixed important-to-organization messages with employee-preferred messages and improved the studied bulk email's recognition rate by 20%. On the sender side, we iteratively designed a prototype of a bulk email evaluation platform. In field evaluation, we found bulk emails' message-level performance helped communicators in designing bulk emails. We collected eye-tracking data and developed a neural network technique to estimate how much time each message is being read using recipients' interactions with browsers only, which improved the estimation accuracy to 73%. In summary, this work sheds light on how to design organizational bulk email systems that communicate effectively and respect different stakeholders' value.