Online Streaming Services Uses During The COVID-19 Pandemic in Indonesia

Authors

  • Emerensia Tangkas Alma Wratsari Wratsari Universitas Atma Jaya Yogyakarta
  • MF. Sheellyana Junaedi Universitas Atma Jaya Yogyakarta
  • Mahestu Noviandra Krisjanti Universitas Atma Jaya Yogyakarta

DOI:

https://doi.org/10.24002/kinerja.v26i2.6068

Keywords:

online streaming services, netflix, TAM, UGT

Abstract

The COVID-19 pandemic accelerates the adoption of online streaming service around the world, including online streaming services, which is now starting to be used in Indonesia, like Netflix. This study assumes that consumers search for emotional and instrumental satisfaction when they start to watch any video or live streaming on their digital devices. Therefore, this study is based on two theories about technology adoption: the Technology Acceptance Model (TAM) and Uses Gratification Theory (UGT), to explore perceived ease of use and perceived usefulness from users' perceptions. By adding perceived risk perception related to the COVID-19 pandemic. This study aims to explore users' perceptions of online streaming services and describe their motivation to use the services. This study uses Smart PLS-SEM as analyze tool. There are 203 respondents used in this study. The research result shows that perceived ease of use positively and significantly affects perceived usefulness. Perceived usefulness, ritualized use, and instrumental use positively and significantly affect the intention to use Netflix. However, this study can't prove the effect of perceived ease of use and perceived risk perception on intention to use Netflix. Furthermore, perceived usefulness positively and significantly mediates the effect between perceived ease of use to intention to use Netflix.

References

Aji, H. M., Berakon, I., & Md Husin, M. (2020). COVID-19 and e-wallet usage intention: A multigroup analysis between Indonesia and Malaysia. Cogent Business and Management, 7(1). https://doi.org/10.1080/23311975.2020.1804181

Bhatti, A., Saad, S., & Gbadebo, S. M. (2018). Science Arena Publications International journal of Business Management Convenience Risk, Product Risk, and Perceived Risk Influence on Online Shopping: Moderating Effect of Attitude. 3(2), 1–11. www.sciarena.com

Camilleri, M. A. (2020). The online users' perceptions toward electronic government services. Journal of Information, Communication and Ethics in Society, 18(2), 221–235. https://doi.org/10.1108/JICES-09-2019-0102

Camilleri, M. A., & Camilleri, A. C. (2020). The students' readiness to engage with mobile learning apps. Interactive Technology and Smart Education, 17(1), 28–38. https://doi.org/10.1108/ITSE-06-2019-0027

Camilleri, M. A., & Falzon, L. (2021). Understanding motivations to use online streaming services: integrating the technology acceptance model (TAM) and the uses and gratifications theory (UGT). Spanish Journal of Marketing - ESIC, 25(2), 217–238. https://doi.org/10.1108/sjme-04-2020-0074

Cebeci, U., Ince, O., & Turkcan, H. (2019). UNDERSTANDING THE INTENTION TO USE NETFLIX: AN EXTENDED TECHNOLOGY ACCEPTANCE MODEL APPROACH. International Review of Management and Marketing, 9(6), 152–157. https://doi.org/10.32479/irmm.8771

Dryhurst, S., Schneider, C. R., Kerr, J., Freeman, A. L. J., Recchia, G., van der Bles, A. M., Spiegelhalter, D., & van der Linden, S. (2020). Risk perceptions of COVID-19 around the world. Journal of Risk Research, 23(7–8), 994–1006. https://doi.org/10.1080/13669877.2020.1758193

Fihartini, Y., Helmi, A., Hassan, M., & Oesman, Y. M. (2021). Perceived health risk, online retail ethics, and consumer behavior within online shopping during the covid-19 pandemic. Innovative Marketing, 17(3), 17–29. https://doi.org/10.21511/im.17(3).2021.02

Groshek, J., & Krongard, S. (2016). Netflix and engage? Implications for streaming television on political participation during the 2016 US Presidential Campaign. Social Sciences, 5(4). https://doi.org/10.3390/socsci5040065

Hair, J. F., Hult, G. Thomas. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (Second Edition). SAGE Publications.

Joo, J., & Sang, Y. (2013). Exploring Koreans' smartphone usage: An integrated model of the technology acceptance model and uses and gratifications theory. Computers in Human Behavior, 29(6), 2512–2518. https://doi.org/10.1016/j.chb.2013.06.002

Kaur, P., Dhir, A., Chen, S., Malibari, A., & Almotairi, M. (2020). Why do people purchase virtual goods? A uses and gratification (U&G) theory perspective. Telematics and Informatics, 53. https://doi.org/10.1016/j.tele.2020.101376

Kementerian Komunikasi dan Informatika. (n.d.). Retrieved November 10, 2021, from https://kominfo.go.id/index.php/content/detail/3834/siaran+pers+no.+17pikominfo22014+tentang+riset+kominfo+dan+unicef+mengenai+perilaku+anak+dan+remaja+dalam+menggunakan+internet+/0/siaran_pers

Lee, S. H., & Deale, C. (2021). Consumers' perceptions of risks associated with the use of Airbnb before and during the COVID-19 pandemic. International Hospitality Review, ahead-of-print(ahead-of-print). https://doi.org/10.1108/ihr-09-2020-0056

Leung, L. (2015). Using tablet in solitude for stress reduction: An examination of desire for aloneness, leisure boredom, tablet activities, and location of use. Computers in Human Behavior, 48, 382–391. https://doi.org/10.1016/j.chb.2015.01.068

Lim, J. S., Ri, S. Y., Egan, B. D., & Biocca, F. A. (2015). The cross-platform synergies of digital video advertising: Implications for cross-media campaigns in television, Internet and mobile TV. Computers in Human Behavior, 48, 463–472. https://doi.org/10.1016/j.chb.2015.02.001

Malik, A., Dhir, A., & Nieminen, M. (2016). Uses and Gratifications of digital photo sharing on Facebook. Telematics and Informatics, 33(1), 129–138. https://doi.org/10.1016/j.tele.2015.06.009

Michael Humbani, & Melanie Wiese. (2019). An integrated framework for the adoption and continuance intention to use mobile payment apps. International Journal of Bank Marketing, 37, 646–664.

Netflix Indonesia - Tonton Acara TV Online, Tonton Film Online. (n.d.). Retrieved November 10, 2021, from https://www.netflix.com/id/

Nikou, S. A., & Economides, A. A. (2017). Mobile-Based Assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance. Computers in Human Behavior, 68, 83–95. https://doi.org/10.1016/j.chb.2016.11.020

Pham, V. K., do Thi, T. H., & Ha Le, T. H. (2020). A study on the COVID-19 awareness affecting the consumer perceived benefits of online shopping in Vietnam. Cogent Business and Management, 7(1). https://doi.org/10.1080/23311975.2020.1846882

Qinghong Cui, Xiancun Hu, Xiao Liu, Lingmin Zhao, & Guangbin Wang. (2021). Understanding Architectural Designers' Continuous Use Intention Regarding BIM Technology: A China Case. MDPI.

Raf Buyle, Mathias Van Compernolle, Eveline Vlassenroot, Ziggy Vanlishout, Peter Mechant, & Erik Mannens. (2018). "Technology Readiness and Acceptance Model" as a Predictor for the Use Intention of Data Standards in Smart Cities. Media and Communication, 6(4), 127–139.

Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: An empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6–30. https://doi.org/10.1108/JEIM-04-2012-0011

Reuben M. Baron, & David A. Kenny. (1986). The Moderator-mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology.

Sarjono, H., & Julianita, W. (2019). Structural Equation Modeling (SEM) : Sebuah Pengantar, Aplikasi untuk Penelitian Bisnis. Salemba Empat.

Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers and Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009

Sheppard, M., & Vibert, C. (2019). Re-examining the relationship between ease of use and usefulness for the net generation. Education and Information Technologies, 24(5), 3205–3218. https://doi.org/10.1007/s10639-019-09916-0

Trivedi, S. K., & Yadav, M. (2018). Predicting online repurchase intentions with e-satisfaction as mediator: a study on Gen Y. VINE Journal of Information and Knowledge Management Systems, 48(3), 427–447. https://doi.org/10.1108/VJIKMS-10-2017-0066

Trivedi, S. K., & Yadav, M. (2020). Repurchase intentions in Y generation: mediation of trust and e-satisfaction. Marketing Intelligence and Planning, 38(4), 401–415. https://doi.org/10.1108/MIP-02-2019-0072

van der Linden, S., Nimmegeers, S., Geskens, K., & Weijters, B. (2020). Demographic and attitudinal antecedents of consumers' use and self-investment trajectories over time in an online TV content platform. Journal of Service Management, 31(3), 535–562. https://doi.org/10.1108/JOSM-10-2018-0346

Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A technology acceptance model (TAM) perspective. Information and Management, 51(2), 249–259. https://doi.org/10.1016/j.im.2013.12.003

Yang, H., & Lee, H. (2018). Exploring user acceptance of streaming media devices: an extended perspective of flow theory. Information Systems and E-Business Management, 16(1). https://doi.org/10.1007/s10257-017-0339-x

Zhong, Y., Oh, S., & Moon, H. C. (2021). What can drive consumers' dining-out behavior in China and Korea during the COVID-19 pandemic? Sustainability (Switzerland), 13(4), 1–17. https://doi.org/10.3390/su13041724

Published

2022-09-20

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Section

Articles