Continuance Intention to Use Bilibili for Online Learning: An Integrated Structural Equation Model

Continuance Intention to Use Bilibili for Online Learning: An Integrated Structural Equation Model

Xindi Liu, Zhonggen Yu
Copyright: © 2023 |Pages: 24
DOI: 10.4018/IJAET.322387
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Abstract

Bilibili, a popular video-sharing and danmaku-themed platform, has played a crucial role in online learning. However, limited research has been conducted to examine the factors that influence its users' continuance intention. Thus, this study proposes an integrated model of technology acceptance model, expectation-confirmation model, and task-technology fit based on the data collected from 265 Chinese participants. The model also includes content richness and a newly introduced variable, danmaku interface, to fill the research gap. The results show that 1) content richness has no positive influence on satisfaction and perceived usefulness, 2) danmaku interface significantly and positively influences satisfaction at the significant 0.05 level but has no positive effect on perceived usefulness, 3) task-technology fit positively and significantly predicts perceived ease of use and perceived usefulness at the significant 0.05 level, and 4) the other hypotheses within TAM and ECM are well accepted. The features and educational functions of Bilibili could be further explored in the future.
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Introduction

As proposed by Curtain (2002), online learning can be defined as the use of the internet to facilitate teacher-student interactions, which encompasses both asynchronous and synchronous activities, such as using web-based course materials, email, and conferencing tools. The terms “web-based education” and “e-learning” are often used interchangeably with online learning. By implementing online learning, the teaching-learning process can become more flexible, student-centered, and innovative (Dhawan, 2020). Technology and education have led to the development of several websites and mobile applications, including YouTube (Dubovi & Tabak, 2020; Liu & Luo, 2022), WhatsApp (Mpungose, 2020), Google Classroom (Sobaih et al., 2022), Superstar Learning System (Yu, 2022), and Tencent Meeting (Quadir & Zhou, 2021), which have been widely applied to online learning. Moreover, online learning platforms can enhance students’ learning outcomes (Yu et al., 2022), improve their learning proficiency, and reduce cognitive loads (Yu et al., 2019).

Figure 1.

Video categories on Bilibili

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Founded in 2004, Bilibili (www.bilibili.com) is an influential and popular video-sharing platform in China. In the third quarter of 2022, Bilibili reported 90.3 million average daily active users (Bilibili, 2022). It allows users to create their original content and upload it and it illustrates a clear classification of the content, thus improving user guidance (see Figure 1). Videos of all kinds, such as animations, TV shows, online courses, etc., are available for users. It is also worth noting that Bilibili divides instructional videos into three categories: class, knowledge, and open courses. As shown in Figure 2, users can either subscribe to paid courses or follow free courses uploaded by others. Another prominent feature of Bilibili is the use of danmaku (see Figure 3), which is a type of video comment that appears as streams superimposed on video screens, moving horizontally from right to left (R. Wang, 2022). This allows users to receive instant feedback from their co-viewers while watching the video.

Figure 2.

Examples of paid and free courses

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Previous studies (Lin et al., 2018; Shen & Liang, 2022; Wu et al., 2022) have confirmed the educational implications of Bilibili, whereas there is scarce research on the contributing factors to its use and acceptance in an educational setting. Meanwhile, Liu et al., (2021) proposed an extended Unified Theory of Acceptance and Use of Technology model to study users’ experience of using Bilibili. Hu et al. (2016) investigated users’ participation by applying self-construal and community interactivity in a structural equation model. However, few studies have proposed a model to investigate the adoption of Bilibili in online learning. Therefore, this study aims to identify the predictors of the continuance intention to use Bilibili for online learning and formulate two research questions: 1) Can danmaku interface and content richness contribute to learning on Bilibili? 2) Can this study establish an integrated model of technology acceptance model (TAM), expectation-confirmation model (ECM), and task-technology fit (TTF)?

Figure 3.

An instructional video with danmaku comments

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