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The television (TV) content ecosystem has shifted from traditional broadcasting systems to dedicated content producers and over-the-top (OTT) services. However, less empirical effort has been paid to the actual behaviors of the mobile users who watch TV content when explaining the impact of OTT service and mobile network profiles in watching TV content. This study aims to investigate the impact of gratifications and attitude formed by mobile TV users on actual mobile TV watching behaviors, as well as the moderating impacts of paid OTT service subscriptions and mobile network profiles, based on gratification theory, cognition–affect–behavioral (CAB) framework, sunk cost effect and walled-garden effect.

This study employs the generalized linear model (GLM) with generalized estimating equations (GEE) to test hypothesized relationships. A total of 338 mobile phone users who have been watching TV content using a mobile phone participated in the survey. The moderating variables, 4 types of paid streaming platform subscriptions, were classified based on the walled gardens formed by mobile telecom services.

The study’s results revealed that obtained gratifications and opportunity constructs substantially influenced a mobile phone user’s attitude and behaviors. Additionally, mobile network profiles and the degree of access to paid platform services played significant moderating roles in the relationship between users’ attitudes and behavior.

This research enriches the existing OTT service literature and is one of the pioneering studies investigating the walled-garden effect’s role in mobile phone users’ actual watching behaviors, offering valuable practical implications for the OTT platform providers.

This study aims to conduct text mining and semantic network analysis of muscle-supportive and posture-corrective wearable robots for the elderly to understand key terms related to the topic and to identify considerations for developing these types of clothing.

The authors searched and identified the key terms wearable robot, muscle-supportive, posture correction and elderly using the text-mining software Textom to extract terms as well as the network analysis software UCINET 6 to process and visualize the relationships among the terms. The authors compared and analyzed the term frequency (TF), the TF-inverse document frequency and the degree centrality of the terms, and the authors visualized and summarized the terms using NetDraw.

The key terms and their relationships in 3–4 groups were identified: wearable robot, muscle-supportive, posture correction and elderly. The authors identified the aspects of designing muscle-supportive and posture-corrective wearable robots for the elderly.

This study contributes to the field of muscle-supportive clothing and wearable robotics by deriving insights into what people are discussing and interested in, and by offering recommendations when developing these types of clothing for the elderly.