Abstract
IntroductionNew approaches to the study of the binge-watching phenomenon require new technology, leading to the development of a non-intrusive and low-cost analytical research software that facilitates a holistic understanding of binge-watching in an uncontrolled environment remotely (e.g., the home). BWDAT was developed to allow the collection of three types of data: users’ physiological data gathered from a smartwatch, users’ interactions from video-on-demand interfaces, and self-reported data. This tool offers the possibility to generate automatic data analysis reports, facilitating researchers’ data analysis tasks. MethodsTwo trial studies and a long-term study were used to evaluate the design and the technical implementation of the BWDAT tool. The metrics used were the BWDAT smartwatch’s App data coverage of the viewing sessions, and the data’s reliability of the viewer’s interactions with the Netflix interface, collected by the BWDAT Chrome Extension. ResultsHigh percentages of data coverage and content coverage were verified in the sessions collecting the smartwatch’s data. The reporting system developed proved to be useful in the collection and synchronization of physiological and users’ interaction data with Netflix interface, both generated in uncontrolled environments. Furthermore, the BWDAT tool facilitated the analysis of a large amount of nuanced data. ConclusionThe results obtained confirm the reliability, accuracy, and usability of BWDAT. This tool has the potential to help researchers shed new light on the field of media and audience studies, and in particular on binge-watching.
Highlights
New approaches to the study of the binge-watching phenomenon require new technology, leading to the development of a non-intrusive and low-cost analytical research software that facilitates a holistic under standing of binge-watching in an uncontrolled environment remotely
(b) The development of a flexible smartwatch app that accurately collects physiological data during VOD consumption at home. We opted for these two kinds of data because the heart rate reflects emotional activity (Man dryk & Inkpen, 2004), and wrist inertial data allows for a better interpretation of the heart rate due to the impact that movement has on the cardiovascular system
Most participants reported to have had a stressful week and not clearly remembering the instructions when performing the viewing session. In those sessions when the browser extension did not register the closing Netflix tab action, we considered the end of the session as the last action performed by the participant
Summary
New approaches to the study of the binge-watching phenomenon require new technology, leading to the development of a non-intrusive and low-cost analytical research software that facilitates a holistic under standing of binge-watching in an uncontrolled environment remotely (e.g., the home). BWDAT was developed to allow the collection of three types of data: users’ physiological data gathered from a smartwatch, users’ in teractions from video-on-demand interfaces, and self-reported data. This tool offers the possibility to generate automatic data analysis reports, facilitating researchers’ data analysis tasks. Conclusion: The results obtained confirm the reliability, accuracy, and usability of BWDAT This tool has the potential to help researchers shed new light on the field of media and audience studies, and in particular on binge-watching. Data shows that the time spent consuming streaming TV and video in the weekend of March 13–14, 2020 grew by more than 40% in countries like Austria and Spain.
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