PurposeThe purpose of this study is to examine factors affecting mobile phone users' attitude toward watching TV content on a mobile device. Under the uses and gratifications theory, the current study examined attitude toward watching TV content on a mobile phone, with antecedents of affinities for both watching TV content and a mobile phone use.Design/methodology/approachThe current study adopted a construct of relative advantage as a mediator between affinities and attitude, and four moderators that affect the relationship between relative advantage and attitude. The study then analyzed 430 survey responses from public mobile phone users with a generalized linear model.FindingsResearch findings reveal that both affinities are significantly associated with the relative advantage of watching TV content on a mobile phone. Relative advantage plays a salient role in explaining attitudes toward watching TV content on a mobile phone. The relationship between attitude and relative advantage was significantly affected by relaxation, fashion status and accessibility.Originality/valueThis study contributes to media literature, especially where new, applied technology is considered. Particularly, the current research theoretically explains rationale behind a mobile phone user's positive attitude toward watching TV content on a mobile phone, and potential implication of the current and increasing trend of broadcasting individual content through social media outlets.

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Since the popularity of blockchain-based cryptocurrency investments has increased among the public, people have directly purchased cryptocurrency through the cryptocurrency market or joined initial coin offering (ICO) projects. This research explores what informational cues are captured before, during, and after ICO projects that can be considered as signals and a fulfillment of information asymmetry. We adopted two theoretical underpinnings to achieve our research goal-agency and signaling theory. Using information from Twitter, we selected the best-performing ICO project based on the highest return on investment (ROI). Then, we extracted 5,085 tweets related to the selected ICO project. Tweets are categorized by pre-ICO, during and post-ICO, by topic, and dispersion. Analyzing the tweets, we found multiple categories of informational cues for each ICO project. Implications and limitations are discussed.

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