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Given the growing interest in combinations of fashion and digital innovations, it is critical for both researchers and retailers to understand how consumers respond to new technologies, especially artificial intelligence (AI). The purpose of the study was to examine consumers’ attitudes and purchase intention toward an AI device. By adapting the technology acceptance model, a conceptual model was constructed and tested related to consumers’ attitudes and purchase intention toward an AI device—Echo Look. A total of 313 subjects (61% female) between 18 and 65 years old in the top 10 metropolitan areas in the United States participated in the study. The results indicated that perceived usefulness, perceived ease of use, and performance risk were significant in consumers’ attitude toward AI. Positive attitudes toward technology positively influenced the purchase intention. Based on these results, theoretical and practical implications are discussed.

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The need for highly scalable and accurate detection and filtering of misbehaving users and obscene content in online video chat services has grown as the popularity of these services has exploded in popularity. This is a challenging problem because processing large amounts of video is compute intensive, decisions about whether a user is misbehaving or not must be made online and quickly, and moreover these video chats are characterized by low quality video, poorly lit scenes, diversity of users and their behaviors, diversity of the content, and typically short sessions. This paper presents EMeralD, a highly scalable system for accurately detecting and filtering misbehaving users in online video chat applications. EMeralD substantially improves upon the state-of-the-art filtering mechanisms by achieving much lower computational cost and higher accuracy. We demonstrate EMeralD's improvement via experimental evaluations on real-world data sets obtained from Chatroulette.com.

Online video chat services, such as Chatroulette, Omegle, and vChatter are becoming increasingly popular and have attracted millions of users. One critical problem encountered in such applications is the presence of misbehaving users ("flashers") and obscene content. Automatically filtering out obscene content from these systems in an efficient manner poses a difficult challenge. This paper presents a novel Fine-Grained Cascaded (FGC) classification solution that significantly speeds up the compute-intensive process of classifying misbehaving users by dividing image feature extraction into multiple stages and filtering out easily classified images in earlier stages, thus saving unnecessary computation costs of feature extraction in later stages. Our work is further enhanced by integrating new webcam-related contextual information (illumination and color) into the classification process, and a 2-stage soft margin SVM algorithm for combining multiple features. Evaluation results using real-world data set obtained from Chatroulette show that the proposed FGC based classification solution significantly outperforms state-of-the-art techniques.