A verification of motivations for over-the-top binge and short viewing of audio-visual content

Yoo, Jongsu; Lee, Jieon; Lee, Daeho

doi:10.1080/13614568.2020.1865464

Cited by 16 publications

(12 citation statements)

References 57 publications

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“…The observed phenomena of binge-watching or returning films highlight this automaticity. This viewpoint is consistent with Yoo et al's [78] investigation of changing entertainment preferences, emphasizing the favorable impact of habit creation via streaming platforms to total consumption behavior. As a result, this shortened study retains the depth and rigor observed in the preceding discussion on hedonic motivation, stressing the critical role of habituation in determining the dynamic landscape of interactive entertainment adoption.…”

Section: Discussionsupporting

confidence: 89%

Machine Learning Decision System on the Empirical Analysis of the Actual Usage of Interactive Entertainment: A Perspective of Sustainable Innovative Technology

Guste,

Ong

2024

Computers

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This study focused on the impact of Netflix’s interactive entertainment on Filipino consumers, seamlessly combining vantage points from consumer behavior and employing data analytics. This underlines the revolutionary aspect of interactive entertainment in the quickly expanding digital media ecosystem, particularly as Netflix pioneers fresh content distribution techniques. The main objective of this study was to find the factors impacting the real usage of Netflix’s interactive entertainment among Filipino viewers, filling a critical gap in the existing literature. The major goal of using advanced data analytics techniques in this study was to understand the subtle dynamics affecting customer behavior in this setting. Specifically, the random forest classifier with hard and soft classifiers was assessed. The random forest compared to LightGBM was also employed, alongside the different algorithms of the artificial neural network. Purposive sampling was used to obtain responses from 258 people who had experienced Netflix’s interactive entertainment, resulting in a comprehensive dataset. The findings emphasized the importance of hedonic motivation, underlining the requirement for highly engaging and rewarding interactive material. Customer service and device compatibility, for example, have a significant impact on user uptake. Furthermore, behavioral intention and habit emerged as key drivers, revealing interactive entertainment’s long-term influence on user engagement. Practically, the research recommends strategic platform suggestions that emphasize continuous innovation, user-friendly interfaces, and user-centric methods. This study was able to fill in the gap in the literature on interactive entertainment, which contributes to a better understanding of consumer consumption and lays the groundwork for future research in the dynamic field of digital media. Moreover, this study offers essential insights into the intricate interaction of consumer preferences, technology breakthroughs, and societal influences in the ever-expanding environment of digital entertainment. Lastly, the comparative approach to the use of machine learning algorithms provides insights for future works to adopt and employ among human factors and consumer behavior-related studies.

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Section: Discussionsupporting

confidence: 89%

Machine Learning Decision System on the Empirical Analysis of the Actual Usage of Interactive Entertainment: A Perspective of Sustainable Innovative Technology

Guste,

Ong

2024

Computers

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“…Also, Khan (2017) concludes that the strongest predictor for liking and disliking YouTube videos was the relaxing entertainment motive; commenting and uploading were strongly predicted by the social interaction motive, and the information-giving motive anticipated sharing. The results of the study by Yoo et al (2020) demonstrate that media audiences show different motivations towards the tendency of binge-watching and short-watching. As Camilleri and Falzon (2021) concluded, their study's research participants sought emotional gratification from the streaming technologies.…”

Section: Literature Reviewmentioning

confidence: 90%

“…Depending on the socio-spatial context, consumers may, for example, prefer to watch a drama series individually on a mobile phone while commuting on public transport but would cast the same series to a bigger screen while being together with friends or family. Yoo et al (2020) analyse the addiction effects of both binge-watching and short-watching. They considered several motivation variables and concluded that social interaction significantly positively affects attitudes towards short-watching.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We also emphasise that currently, several studies are being carried out regarding the profile and consumer behaviour of TV series such as Netflix and HBO,…, that is of programs whose each episode has a longer duration (e.g., Martínez-Sánchez et al, 2021; Nagaraj et al, 2021), and other studies to analyse the profile and behaviour of the consumer for videos of short/medium duration, such as YouTube, TikTok, Reels (e.g., Blagojević, n.d.; Chen, 2016; Iqbal, 2023). As referred by Yoo et al (2020), like binge-watching, short-watching is also a representative way of consuming media. In congruence with the continuing growth of subscription video-on-demand (SVoD) services such as Netflix and Hulu, studies on binge-watching appear to receive more attention than research conducted on short-watching.…”

Section: Introductionmentioning

confidence: 99%

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Streaming Consumers: Series Versus Videos, What Distinguishes Them?

Clímaco,

Larguinho

2023

Media Watch

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In recent years, countless studies have sought to explain and characterise video streaming consumption. Its facet of excessive consumption, the so-called binge-watching, has been privileged in an attempt to define and quantify, if possible, the phenomenon. In this work, we intend to analyse streaming video consumers, aiming to identify the factors that differentiate two specific types of consumers: TV series streamers and medium/short-duration video streamers. The data was obtained using a structured questionnaire, shared online between 16 June and 16 August 2022, to individuals residing in Portugal, aged between 18 and 64, with 496 valid responses. Our sample consists of 67.3% women, and 83.5% of the participants are of Portuguese nationality. About 75% of the participants assumed themselves as consumers of TV series, and about 25% were medium/short-duration video consumers. This study used several statistical techniques, including descriptive statistics, chi-square independence test and logistic regression. The factors that differentiate these two groups of consumers are gender, age group, environment where they live, type of platforms used, the device they usually watch, with whom they typically watch and the system recommendations.

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“…For example, Azza Ahmed (2017, p. 193) considers it watching "more than one episode of a TV show in a short period of time". According to Jongsu Yoo et al (2020), it means "watching more than two episodes of the same TV show in one session more than twice a week", and Caroline L. Jarzyna (2021, p. 417) sees BW as "the ability to watch an entire season of a TV series in one sitting over many hours". Researchers often highlight the intense nature of watching Perks, 2019a;Forte et al, 2021), pointing to the number of episodes watched or the total time spent in front of a screen.…”

Section: Introductionmentioning

confidence: 99%

Binge-watching as an interdisciplinary research field

Kisilowska-Szurmińska,

Jupowicz-Ginalska,

Szurmiński

2023

MKKS

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The authors of this text aim to quantitatively describe the state of the academic literature on binge-watching, formally characterize the research on this phenomenon (methods, techniques, population) and present its motives and consequences. The paper applies an in-depth literature review analysis of binge-watching (126 publications for the period 2014–2021) and discusses the development of research trends within the interdisciplinary field of binge-watching.

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A verification of motivations for over-the-top binge and short viewing of audio-visual content

Cited by 16 publications

References 57 publications

Machine Learning Decision System on the Empirical Analysis of the Actual Usage of Interactive Entertainment: A Perspective of Sustainable Innovative Technology

Machine Learning Decision System on the Empirical Analysis of the Actual Usage of Interactive Entertainment: A Perspective of Sustainable Innovative Technology

Streaming Consumers: Series Versus Videos, What Distinguishes Them?

Binge-watching as an interdisciplinary research field

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