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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
The Effects of Cultural dimensions and Demographic Charac...
Ali Tarhini · 2016-07-06 · via cs.HC updates on arXiv.org

This study aims to develop and test an amalgamated conceptual framework based on Technology Acceptance Model (TAM) and other models from social psychology, such as theory of reasoned action and TAM2 that captures the salient factors influencing the user adoption and acceptance of web-based learning systems. This framework has been applied to the study of higher educational institutions in the context of developing as well as developed countries (e.g. Lebanon and UK). Additionally, the framework investigates the moderating effect of Hofstedes four cultural dimensions at the individual level and a set of individual differences (age, gender, experience and educational level) on the key determinants that affect the behavioral intention to use e-learning. A total of 1197 questionnaires were received from students who were using web-based learning systems at higher educational institutions in Lebanon and the UK with opposite scores on cultural dimensions. Confirmatory Factor Analysis (CFA) was used to perform reliability and validity checks, and Structural Equation Modeling (SEM) in conjunction with multi-group analysis method was used to test the hypothesized conceptual model. Our findings suggest that individual, social, cultural and organizational factors are important to consider in explaining students behavioral intention and usage of e-learning environments. The findings of this research contribute to the literature by validating and supporting the applicability of our extended TAM in the Lebanese and British contexts and provide several prominent implications to both theory and practice on the individual, organizational and societal levels.