Consumer Behavior in Adopting Useetv Go Streaming & Video-On-Demand Services Using Unified Theory Of Acceptance And Use Of Technology 2 (UTAUT2)

Endang Rizkinawaty Sembiring, Sugih Harto Pujangkoro, Rulianda Purnomo Wibowo

Abstract


The increase in online activities is followed by increased spending to meet online needs, one of which is entertainment services. One of the most widely accessed entertainment facilities is digital TV and video-on-demand or VoD services. The service, which began to flourish in 2016, is an online video content provider system with a subscription payment mechanism. One of its appeals, the user has the freedom to decide what he wants to see. One way to entertain yourself which has become a new lifestyle pattern is to watch streaming shows or subscription videos online. A McKinsey & Company survey at the end of March said that as many as 45% of respondents spent more on home entertainment during the pandemic. On the other hand, 85% of respondents reduced their spending on outdoor entertainment. Subscription video streaming services (video-on-demand / VoD) are one of the entertainment options that can be done at home. UseeTV GO is an Over-the-Top (OTT) TV and Video mobile application service, using the publicly accessible Internet. The content is available via smartphones or tablets with broadband connections. Currently, UseeTV Go app users are around 300 thousand active users every month, far below Vidio.com and Netflix. With the low Monthly Active of UseeTV Go's Video-on-demand app users in the pre-growth area far below, Netflix, Vidio.com, and user activeness based on time, UseeTV Go is in the Average Performer area. Therefore, UseeTV Go consumer assessment of the variables contained in the modified UTAUT2 model (Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Condition, Hedonic Motivation, Price Value, Habit, and Content) was carried out on Behavioural Intention on UseeTV Go video-on-demand service in Indonesia.

Keywords


Shrimp Farming, Aquaculture, Machine Learning, Decision Tree, K-Nearest Neighbors, BPMN

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DOI: https://doi.org/10.21107/pamator.v16i2.19993

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