What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors

Accepted Manuscript What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors Bo Li, Xinghua Wang, Seng Chee Tan PII:

S0747-5632(18)30192-4

DOI:

10.1016/j.chb.2018.04.028

Reference:

CHB 5483

To appear in:

Computers in Human Behavior

Received Date: 4 January 2018 Revised Date:

5 April 2018

Accepted Date: 12 April 2018

Please cite this article as: Li B., Wang X. & Tan S.C., What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors, Computers in Human Behavior (2018), doi: 10.1016/j.chb.2018.04.028. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors

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Institute of Higher Education, Linyi University, PR China

National Institute of Education, Nanyang Technological University, Singapore

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Authors Note

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Bo Li1, Xinghua Wang2*, Seng Chee Tan2

Bo Li, Institute of Higher Education, Linyi University, PR China, 276005, Email: [email protected]; Xinghua Wang, Blk3-02-02, National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore, 637316, Email: [email protected];

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Seng Chee Tan, Blk7-03-118A, National Institute of Education, Nanyang Technological University, 1

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Nanyang Walk, Singapore, 637616, Email: [email protected].

* Correspondence regarding this article should be addressed to Xinghua Wang, Email:

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[email protected]

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What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors Abstract

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This study investigated how network externalities affect users’ persistence in completing massive online open courses (MOOCs) through the mediation of human factors. 346 students from a public university were recruited into the study. The data were collected using a survey

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and analyzed by partial least square structural equation modelling (PLS-SEM). The findings indicate that users’ persistence in completing MOOCs was a function of network benefit, user

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preference, and motivation to achieve. Network benefit, which was strongly predicted by network size (direct network externalities) and perceived complementarity (indirectly network externalities), also indirectly influenced users’ persistence in completing MOOCs through user preference and motivation to achieve. Furthermore, this study found that the duration of MOOC

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usage made a significant difference in the effect of network externalities on users’ persistence in completing MOOCs. For instance, user preference had a stronger influence on users’ persistence in completing MOOCs for one-year users than above-one-year users, while motivation to

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achieve in MOOCs had a stronger effect on users’ persistence in completing MOOCs for aboveone-year users than one-year users. This study could benefit MOOC providers and researchers

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seeking to improve the retention and completion rates of MOOCs. Keywords: network externalities; human factors; MOOCs; PLS-SEM; completion

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1. Introduction Network externalities are concerned with the factors that yield network effects, including network size and complementary goods or services (Economides, 1996), and have been

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considered of high importance in generating and diffusing technological innovation (Kathuria, 1999; K.Y. Lin & Lu, 2011). One example is Microsoft Windows operating systems. As more people use the systems, Microsoft collects more feedback to fix the system bugs, thereby refining

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its systems. Consequently, existing and new users have a better user experience with its systems. Furthermore, with the increased user base, more third-party developers develop application tools

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and software related to its systems, giving it an edge over its competitors such as Linux or Macintosh. The wide range of third-party tools and software, in turn, not only improves the work efficiency for existing users, but also serves as a great attraction for new users. The phenomenon of network externalities also applies to MOOCs and the improvement of their low completion

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rates.

In the context of MOOCs, network effects are manifested when the benefits that people attain from completing certain MOOCs depend on the number of other people joining the same

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MOOCs and the availability of complementary products or services (e.g., official recognition by conventional universities and employers in the market) that generate additional value for people

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attending these MOOCs (C. P. Lin & Bhattacherjee, 2008). Specifically, the size of learner network may have strong influence on learners’ use and

completion of MOOCs (Zhou, 2016). Courses with a wide audience are more likely to attract others to join them. For instance, when MIT launched its first MOOC, 6.002x (introductory course in circuits and electronics) in March 2012, 120,000 users registered for it, but the number continued to rise to 155,000 in 14 weeks (Breslow et al., 2013). When more people join the same

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courses, a huge number of feedback and comments will be generated, which, in turn, will drive MOOC providers (i.e., MOOC content and platform creators) to refine their courses and platforms for the next round of course release. The improved courses and platforms will further

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attract more people to join these courses. Such positive cycle of network effects will eventually benefit learners and strengthen their willingness to persist in completing the whole courses.

Further, the availability of complementary services that MOOC providers have is equally

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important in affecting learners’ registration and completion of MOOCs (C. P. Lin, Tsai, Wang, & Chiu, 2011). Although currently many MOOCs are provided by conventional onsite

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universities, they are mostly not accredited by these universities (Sandeen, 2013). If MOOCs could be accredited by these conventional universities, particularly the elite ones, they are likely to attract more learners to join and attain high completion rates since they offer learners more choices outside of their conventional curricula and carry equivalent credit, often at lower costs.

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Additionally, if MOOC certificates could be officially recognized by employers in the job market, this could appeal to more people since the certificates indicating the successful completion of MOOCs will increase people’s likelihood of securing their desired jobs.

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Moreover, when a MOOC platform is integrated with a wide range of third-party tools, such as social media (e.g., Facebook, Tweet, and Gmail), and learning support tools (e.g.,

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artificial intelligence-based learning analytics or adaptive learning tools), the likelihood of learners persisting in and completing MOOCs would be enhanced (Sharrock, 2015). Social media can facilitate the interaction among peers attending the same course, thus strengthening peer support. Learning support tools enable learners to constantly monitor and regulate their learning progress, thereby improving their learning effectiveness.

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In view of the analyses above, network externalities have the potential to improve MOOCs’ low completion rates, which have been one of the most serious issues of MOOCs (e.g., Sandeen, 2013; Schuwer et al., 2015). Nevertheless, despite the increasing importance of

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network externalities in the development and application of online technologies (e.g., C. P. Lin et al., 2011; Zhao & Lu, 2012) and the ever-growing significance of social networks of users and MOOC providers (e.g., Fidalgo-Blanco, Sein-Echaluce, & García-Peñalvo, 2016; Kellogg, Booth,

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& Oliver, 2014; Taneja & Goel, 2014; Veletsianos, 2017), a paucity of attention has been given to them in existing research on MOOCs. With the rapid advancement of MOOCs and the

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increasing number of users joining them (Brahimi & Sarirete, 2015; Sandeen, 2013), network externalities are likely to have important implications for the improvement of MOOC quality, development of complementary services and products for MOOCs, collaborations between MOOC providers and other stakeholders in society, and particularly, the continued use of

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MOOCs and their completion rates. As such, this study set out to bridge this gap by exploring how network externalities influence learners’ persistence in completing MOOCs. In addition, existing research on internet technologies has indicated a significant role

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played by human factors in their design and applications (e.g., Chang, Hung, & Lin, 2015; Hong & Zhu, 2006; Lehto & Landry, 2012). Human factors refer to people’s cognitive, affective, and

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social properties that influence their interactions with technologies (Bandura, 2001; Lehto & Landry, 2012). Integrating human needs and reactions into the design of technologies can increase the chance of their success (Clegg, 2000). In this regard, this study hypothesized that human factors could mediate the effect of network externalities on MOOC users’ persistence in completing MOOCs. User experience, user preference, and motivation related to the use of technologies have been considered important human factors in research focusing on human-

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computer interactions (e.g., Cockburn, Quinn, & Gutwin, 2017; Jung, Hong, & Kim, 2002; Kuniavsky, 2003; Lehto & Landry, 2012). Therefore, in this study, we sought to answer the following research question: How do network externalities affect users’ persistence in

preference, and motivation to achieve in MOOCs? 2. Theoretical framework

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2.1. MOOCs and the low completion rate

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completing MOOCs through the mediation of human factors such as user experience, user

MOOCs have been considered an educational revolution that can widen access to quality

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education and enhance social inclusion (Evans, Baker, & Dee, 2016; Wu & Chen, 2017). With their fast advancement and potential influence in education, they have been enlisted in the modernization agenda for many universities around the world (Sharrock, 2015). Nevertheless, MOOCs have been plagued by the serious issue of low completion rates, which have also

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become one of their common features (Macleod, Haywood, Woodgate, & Alkhatnai, 2015). Although there are researchers who question the relevance of the completion rate as a measure of the success of MOOCs (e.g., Jona & Naidu, 2014), it is considered an important measure

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indicating learners’ behavioral and cognitive engagement (Hew, 2016), as persistence in completing MOOCs often leads to greater learning (Evans et al., 2016).

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Existing research efforts to address this issue mainly focus on improving the design,

delivery, and assessment of content knowledge (e.g., Bali, 2014; Hew, 2016; Nawrot & Doucet, 2014; Salmon, Pechenkina, Chase, & Ross, 2016). Although these research efforts are essential, they explored this issue largely from the content provider’s perspective, with limited considerations of external factors, such as networks of MOOC users, collaborations among MOOC providers, conventional educational institutions, and third-party technology/service

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providers. In view of the increasing importance of social networks of users and MOOC providers in the design and application of online learning technologies, it is therefore necessary and

particularly, the improvement of MOOCs’ completion rates. 2.2. Network externalities

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meaningful to consider the effect of network externalities on people’s use of MOOCs and,

With the rapid advancement of information technologies in recent years, network

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externalities have been successfully applied to a wide range of human-computer interactions, for instance, the intention to use interactive information technologies (C. P. Lin & Bhattacherjee,

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2008), the use of social network sites (K.Y. Lin & Lu, 2011; Hong, Cao, & Wang, 2017), microblogging service satisfaction and continuance intention (Zhao & Lu, 2012), and human-computer relationships in e-service (C. P. Lin et al., 2011). All these studies demonstrated the relevance and importance of network externalities in shaping and reshaping people’s attitudes and

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behaviors toward new information technologies.

Existing research distinguishes between two forms of network externalities: direct and indirect (Katz & Shapiro, 1985). Direct network externalities are associated with the number of

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users in a given network (Katz & Shapiro, 1985; Zhang, Li, Wu, & Li, 2017). With increasing numbers of users utilizing network products, existing users are likely to have access to greater

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network benefits, which consist of the utilitarian benefit concerning the practical value generated by network products, and the hedonic benefit related to the pleasurable experience associated with using network products (C. P. Lin & Bhattacherjee, 2008; Lowry, Gaskin, Twyman, Hammer, & Roberts, 2012; Venkatesh, Morris, Gordon, & Davis, 2003). For instance, the increase in the number of learners joining a MOOC platform will strengthen the interaction among learners and produce a huge amount of data related to their learning experiences,

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academic challenges, and bugs in courses and platforms. These data will help MOOC providers refine their courses and platforms. Consequently, existing MOOC users will benefit from such refinements while new users will be attracted to these MOOCs.

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Indirect externalities concern the additional benefits users can get as a result of the

network growth, including the development of complementary products and services, which result indirectly from the increased number of users (Katz & Shapiro, 1985; C. P. Lin &

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Bhattacherjee, 2008; Zhang et al., 2017). For example, MOOC users’ increased social needs would lead more third-party tools such as social media (e.g., Facebook, Tweet, and Gmail) to be

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integrated into MOOC platforms. This facilitates social interactions among the users, which, in turn, encourage their continued use of MOOCs (Brahimi & Sarirete, 2015). Moreover, products or services complementary to MOOCs are likely to further enhance the completion rates. For instance, the course-review and credit-recommendation service that American Council on

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Education provides to MOOCs will motivate users to complete the courses so as to attain academic credits associated with them (Sandeen, 2013). Once more conventional universities accredit MOOCs that are provided by themselves and, particularly, by other universities, they

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will increase existing and new MOOC users’ choices of course selection, which will further increase their chances of finding desired courses and subsequently strengthen their willingness to

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persist in finishing these courses.

In line with the analyses above, direct externalities basically stem from the demand side

of networks, while indirect network externalities are due to the supply side (Katz & Shapiro, 1985; Strader, Ramaswami, & Houle, 2007). Existing studies (e.g., C. P. Lin & Bhattacherjee, 2008; K.Y. Lin & Lu, 2011; Zhao & Lu, 2012) have indicated that both direct and indirect network externalities are essential factors underlying people’s use of technologies mediated by

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network benefit. Thus, the measurement of either single externality is insufficient. Guided by these studies, we measured network externalities from three dimensions in this study: network size (direct network externalities), perceived complementarity (indirect network externalities),

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and network benefit. 2.3. Hypotheses development

Drawing on existing studies on network externalities, human factors related to the use of

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technologies, and MOOCs by taking into consideration the current research context, we

developed a theoretical framework for this study (see Fig.1). The development of specific

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hypotheses is presented in the following paragraphs.

Fig.1. Proposed research model for this study.

In practice, MOOC users often base their course-selection decisions on the number of

people attending the same courses (C. P. Lin & Bhattacherjee, 2008; Zhou, 2016). People are more likely to join a course which has a large number of participants. Also, people tend to select the course which many of their friends or colleagues in their social circle had also registered for (Xiong, Payne, & Kinsella, 2016). As more people join a MOOC, the MOOC provider will get

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more information to improve its course quality and platform usability. The refined course and platform will serve existing and new users better and subsequently strengthen their determination to complete the whole course. Hence, we develop the following hypothesis:

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H1. Network size will positively affect the network benefit of MOOCs.

Complementary tools and services produce auxiliary network benefit for MOOC learners (Katz & Shapiro, 1985; K.Y. Lin & Lu, 2011). As the network of a MOOC platform grows, a

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wide range of third-party developers will be attracted to use its application programming

interface to develop tools or services for MOOC users (C. P. Lin & Bhattacherjee, 2008; Zhang

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et al., 2017). For instance, MOOC providers collaborate with third-party testing corporations to tackle the cheating and plagiarism in online tests, such as the collaboration between Udacity and Pearson UAE (https://home.pearsonvue.com/ ), a company specializing in computer-based test development. In doing so, MOOC providers strengthen their academic integrity and the

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trustworthiness and value of their certificates. This will benefit existing and potential MOOC users, and eventually increase their persistence in completing MOOCs. Further, with MOOCs being integrated with adaptive learning technologies, MOOC users will be able to get

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personalized support easily and meet their learning needs effectively. In this scenario, people are more likely to be attracted to MOOCs and want to achieve academic success. Therefore, the

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following hypotheses are proposed:

H2. Perceived complementarity will positively affect the network benefit of MOOCs.

H3. Network benefit will positively affect users’ persistence in completing MOOCs. Motivation for achievement refers to the desire for success and attainment of excellence

with respect to achievement and standards in studies or work (Harackiewicz, Barron, Carter, Lehto, & Elliot, 1997; McClelland, 2015). It is part of individuals’ goal structures and

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perceptions of what is important (Middleton & Spanias, 1999). Thus, we propose that users with a high motivation to achieve in MOOCs tend to be persisted in their learning endeavors. As such, the following hypothesis is developed:

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H4. MOOC users’ motivation to achieve will positively affect their persistence in completing MOOCs.

Network benefit constitutes an important motivation for people using network-related

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technologies (C. P. Lin et al., 2011; Lowry et al., 2012; Venkatesh et al., 2003). MOOCs with substantial network benefit are likely to motivate people to attain high achievements in their

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studies or work (K.Y. Lin & Lu, 2011). For instance, MOOCs that carry equal credit with onsite courses in conventional universities may incentivize people to invest a lot of effort and time so as to attain the credit. In doing so, MOOC users, even if they are at work places, can easily continue their formal education. Thus, we develop the following hypothesis:

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H5. Network benefit will positively affect users’ motivation to achieve in MOOCs. Users’ experience with MOOCs refer to their feelings and perceptions about the use of MOOCs and constitutes an important aspect of human-computer interaction (Kuniavsky, 2003).

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MOOCs that support active interactions among users worldwide and have sufficient tools and services facilitating users’ learning are likely to give users a more favorable experience (Chang

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et al., 2015). Further, as users’ preference for certain technology is linked to the attributes of the technology (Jung et al., 2002), MOOCs with substantial network benefit are thus helpful in developing users’ preference towards them and their associated learning mode. As such, we propose the following hypotheses: H6. Network benefit will positively affect users’ experience with MOOCs. H7. Network benefit will positively affect users’ preference towards MOOCs.

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Compared with traditional classroom learning, MOOCs and the associated learning mode are relatively new to users (Wu & Chen, 2017). It may be difficult to ask for a high investment of effort and time from users to stay in MOOCs and achieve high performance. Users often gauge

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their commitment into MOOCs based on their experience with MOOCs (K. M. Lin, 2011).

Optimal user experience will decrease users’ psychological resistance towards MOOCs (Cheon, Lee, Crooks, & Song, 2012). Hence, we propose the following hypotheses:

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H8. Users’ experience with MOOCs will positively affect their motivation to achieve in MOOCs.

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H9. Users’ experience with MOOCs will positively affect their persistence in completing MOOCs.

User preference is an important construct that predicts user’s intention and behavior towards certain technologies (Cockburn et al., 2017; Jung et al., 2002). MOOC users who like

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the way in which learning and teaching are organized in MOOCs are naturally motivated to commit themselves to these courses and persist in completing them regardless of the challenges they face (Chang et al., 2015). Hence, we propose that:

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H10. User preference will positively affect users’ motivation to achieve in MOOCs. H11. User preference will positively affect users’ persistence in completing MOOCs.

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3. Methodology

3.1. Participants

Purposive sampling method was utilized with the aim of reaching the targeted samples

quickly and producing deep insights into how MOOC users interacted with network externalities of MOOCs. Participants came from a public university in mainland China (the focal university hereafter), which has launched two MOOC platforms, i.e., ZhiHuiShu

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(http://www.zhihuishu.com/) and ChaoXing (http://mooc.chaoxing.com/), starting in early 2014. Some of the courses carry equivalent credit as onsite courses. Students in the focal university are encouraged to choose one or more courses from other platforms, for instance, Coursera and edX.

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The MOOC platforms come with their mobile versions, together with the desktop versions, which support students in learning anywhere and anytime. Students in the focal university

mainly come from the middle-income families. 500 participants in total were approached with

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their informed consent through an online survey application during the pilot and main studies. In the end, 346 valid responses were attained, with a response rate of 69.2%. The demographic

Table 1

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information of the recruited participants is presented in Table 1.

Demographic information of the participants (N=346).

Gender

Female Male

Total (N)

197

346

149 183

1-2 Years (including 2 years)

140

1-2 Years (including 2 years)

17

3-4 Years (including 4 years)

6

346

17-22 years old

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Age

0-1 Year (including 1 year)

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Duration of MOOC usage

N

3.2. Instrument development and validation

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The original survey instrument for this study, which contained 7 constructs with 36 items,

was adapted from existing studies with the aim of enhancing its validity. The wording was modified to tailor to the context of MOOCs. The items measuring the three constructs of network externalities (i.e., network size, perceived complementarity, and network benefit) were adapted from C. P. Lin and Bhattacherjee (2008), with the reported composite reliability ranging from 0.80 to 0.91. User preference items were developed based on the studies by Chang et al. (2015) and Cheon et al. (2012), who reported composite reliability above 0.80. Items measuring user

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experience were modified from the scale of negative critical incidents during e-learning in K. M. Lin’s (2011) study; the reported composite reliability was above 0.83. For the constructs of motivation to achieve in MOOCs and persistence in completing MOOCs, we consulted the

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studies by Evans et al. (2016) and Wu and Chen (2017), who reported composite reliability

between 0.84 and 0.95. In the survey instrument used in this study, all items were evaluated on a 5-point Likert scale with 1 representing Strongly Disagree and 5 Strongly Agree with the items.

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Besides the 36 items, there were also three questions asking for students’ demographic information, including gender, age, and years of usage of MOOCs. As the survey was in English,

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a back-translation procedure was conducted to ensure no differences existed between the English and Chinese versions.

During the pilot study, 100 students were invited, from which we obtained 75 valid responses. Reliability check and factor analysis were performed on the pilot data. Three items

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that were below 0.7 in Cronbach alpha and did not fit factor analysis were removed (see the items denoted with * in Appendix A). Following this, the refined survey was administered to 400 students in the focal university, from whom we received 271 valid responses. Considering that

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the original survey and the refined one only differed in the removed items and that the increased sample size could enhance the research findings, we then combined the 346 valid responses from

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the pilot and main studies for the final analysis. To further ensure the quality of the survey instrument, another round of reliability and

validity checks was performed on the data (see the Results section for specific procedures of factor analysis). The final version of the survey instrument was presented in Appendix A, which contained 7 constructs with 33 valid items in total. Moreover, as self-report data were used in this study, there might be a possibility of common method bias. To solve this issue, Harman’s

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single-factor test (Podsakoff, MacKenzie, & Podsakoff, 2012) was conducted. When all items were loaded onto one common factor, the total variance for the single factor was 14.86%, which was substantially less than 50%. Therefore, the validity of the study was not threatened by

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common method bias. 3.3. Data analysis

PLS-SEM was utilized to analyze the research model that explored how network

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externalities and human factors collectively affected MOOC users’ persistence in completing MOOCs. The use of PLS-SEM was based on two reasons: (a) this study was exploratory in

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nature; (b) PLS-SEM is primarily prediction-oriented and exploratory with the objective of maximizing the variance explained for the dependent variables (Willaby, Costa, Burns, MacCann, & Roberts, 2015), thereby fitting the objective of the current study. The PLS-SEM package (Sanchez, 2013) in the R programming language was employed to analyze the data and build the

4. Results

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model.

We followed a two-step analytical procedure in analyzing the PLS-SEM model in this

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study, with the measurement model being checked first and then the structural model (Hair, Anderson, Tatham, & William, 2010). Then the dataset was split based on MOOC users’ years

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of usage with the aim of exploring the effect of time on the functioning of network externalities and human factors. To balance the number of participants in the contrasting groups, users who indicated 0-1 Year MOOC usage were labeled as One-Year Users (N=183), whereas users who indicated longer usage durations (1-2 Years, 2-3 Years, and 3-4 Years) were combined and labeled as Above-One-Year Users (N=163; see Table 1). The two sub-datasets were compared to examine whether there was any significant difference in terms of the path coefficients.

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4.1. Measurement model The measurement model was assessed on four aspects: item reliability, internal consistency, convergent validity, and discriminant validity.

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4.1.1. Item reliability

In PLS-SEM, item reliability is assessed by evaluating the loadings of the items with their respective latent variable (or construct). The standardized loadings of the indicators should

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be greater than 0.7 (Hulland, 1999). As shown in Table 2, all item loadings met the requirement.

Table 2

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Thus, the item reliability in the research model of this study was supported.

Cronbach’s alpha, composite reliability, average variance extracted (AVE), and factor loadings of the constructs and items in the research model. Cronbach’s

Constructs/Items

alpha

Network size

0.86

AVE

reliability 0.90

Factor loadings

M (SD)

0.70

0.78

3.85 (1.06)

0.88

4.19 (0.94)

0.80

3.88 (1.07)

0.89

4.09 (1.00)

0.74

3.49 (1.22)

0.80

3.53 (1.16)

0.74

3.73 (1.12)

0.72

3.48 (1.62)

0.81

3.50 (1.08)

0.75

3.35 (1.11)

0.81

3.99 (1.08)

0.85

3.62 (1.14)

0.89

3.78 (1.07)

NB4

0.87

4.03 (1.03)

NB5

0.85

3.90 (1.10)

UE1

0.89

2.98 (1.25)

UE2

0.88

2.65 (1.20)

NS2 NS3 NS4 Perceived complementarity PC1 PC3

PC6

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PC4 PC5

Network benefit NB1 NB2 NB3

0.85

0.89

0.57

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PC2

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NS1

Composite

User experience

0.91

0.87

0.93

0.92

0.73

0.79

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UE3

0.90

2.69 (1.14)

UP1

0.86

3.66 (1.02)

UP2

0.88

3.70 (1.03)

UP3

0.87

3.73 (1.04)

UP4

0.79

3.51 (1.16)

0.88

3.67 (1.07)

0.77

3.78 (1.03)

0.78

3.51 (1.12)

0.80

3.57 (1.03)

0.91

0.93

0.73

UP5 Motivation to achieve

0.91

0.93

0.64

MO1 MO2

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MO3

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User preference

MO4 MO5 MO7 Persistence in completing

0.85

MOOCs PERS1 PERS2

4.1.2. Convergent validity

0.91

3.68 (1.01)

0.83

3.81(1.04)

0.82

3.70 (1.04)

0.73

3.83 (1.04)

0.87

3.80 (1.01)

0.86

3.66 (1.04)

0.91

3.71 (1.06)

0.78

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PERS3

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MO6

0.87

Convergent validity examines to what extent the items of a scale that are theoretically related are related in reality (Hair, Ringle, & Sarstedt, 2011). The convergent validity was

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evaluated based on two criteria: (1) the composite reliability for each latent variable should be

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higher than 0.70, and (2) the average variance extracted (AVE) for each latent variable should be above 0.50 (Fornell & Larcker, 1981). Internal consistency. Internal consistency for a given block of indicators was evaluated

using composite reliability. For a model to be considered internally consistent, composite reliability should be greater than 0.7 (Nunnally, 1978). The research model met this requirement (see Table 2).

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Average variance extracted (AVE). AVE measures the amount of variance that a latent variable captures from its indicators in relation to the amount of variance owing to measurement error. In this study, AVE was evaluated based on the minimum criteria of AVE = 0.50, which

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indicates that at least 50% of the indicator’s variance is explained (Hair et al., 2011). AVEs for the research model met the requirement (see Table 2).

Overall, the convergent validity of the research model was confirmed.

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4.1.3. Discriminant validity

Discriminant validity for the current research model was examined based on two criteria:

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(a) the square root of the AVE for each latent variable should exceed the correlation between that and all other latent variables (Chin, 1998), and (b) the items should load more highly on the latent variables which they are intended to measure than on other latent variables (Chin, Marcolin, & Newsted, 2003). According to these criteria, the discriminant validity for the

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research model was largely satisfied (see Table 3 and Table 4), except for the seventh item of motivation (MO7; see Table 4). But deleting this item did not significantly change the explanatory power of the research model. Therefore, we decided to keep it as it could enrich our

Table 3

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understanding of the measurement of MOOC users’ motivation.

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Discriminant validity of the research model. Constructs

NS

NS

0.84

PC

PC

NB

UE

UP

MO

0.58

0.76

0.75

0.63

0.85

-0.16

0.31

-0.20

0.89

UP

0.52

0.62

0.57

-0.31

0.86

MO

0.58

0.72

0.66

-0.35

0.75

0.80

PERS

0.54

0.61

0.65

-0.31

0.74

0.79

NB UE

PERS

0.88

Note. The bold values in the diagonal row are the square roots of the average variance extracted for the constructs in the research model.

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Table 4 Cross-loadings of variables in the research model. NS

PC

NB

UE

UP

NS1

0.78

0.51

0.52

-0.16

0.42

NS2

0.88

0.52

0.71

-0.13

0.47

NS3

0.80

0.44

0.52

-0.10

0.39

NS4

0.89

0.49

0.71

-0.15

0.45 0.46

MO

PERS

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Network size 0.45

0.37

0.51

0.51

0.43

0.34

0.54

0.54

0.53

0.47

0.47

0.58

0.48

0.49

0.55

0.48

0.46

0.51

0.41

Perceived complementarity 0.39

0.74

0.49

-0.25

PC2

0.47

0.80

0.48

-0.26

PC3

0.51

0.74

0.47

-0.21

PC4

0.42

0.72

0.47

-0.21

PC5

0.45

0.81

0.49

-0.24

0.47

0.53

0.44

PC6

0.41

0.74

0.47

-0.25

0.48

0.57

0.49

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Network benefit

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PC1

0.66

0.45

0.81

-0.13

0.40

0.47

0.46

0.56

0.52

0.85

-0.18

0.43

0.55

0.55

NB3

0.60

0.56

0.89

-0.18

0.48

0.60

0.57

NB4

0.70

0.61

0.87

-0.20

0.54

0.59

0.58

NB5

0.66

0.53

0.85

-0.16

0.57

0.59

0.58

UE1

-0.09

-0.28

-0.15

0.89

-0.29

-0.33

-0.31

UE2

-0.16

-0.27

-0.18

0.88

-0.25

-0.29

-0.21

UE3

-0.18

-0.29

-0.20

0.90

-0.29

-0.30

-0.30

User experience

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User preference

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NB1 NB2

0.44

0.52

0.48

-0.31

0.86

0.67

0.61

0.45

0.54

0.50

-0.23

0.88

0.65

0.62

UP3

0.45

0.54

0.52

-0.26

0.87

0.65

0.64

0.37

0.47

0.42

-0.32

0.79

0.57

0.62

0.50

0.57

0.53

-0.23

0.88

0.67

0.68

0.52

0.55

0.51

-0.25

0.59

0.77

0.66

0.37

0.61

0.51

-0.31

0.64

0.78

0.71

MO3

0.40

0.60

0.51

-0.32

0.60

0.80

0.66

MO4

0.48

0.61

0.60

-0.30

0.63

0.87

0.71

MO5

0.49

0.56

0.53

-0.25

0.58

0.83

0.62

MO6

0.49

0.56

0.56

-0.24

0.63

0.82

0.71

MO7

0.50

0.51

0.49

-0.26

0.55

0.73

0.56

UP4 UP5

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UP1 UP2

Motivation to achieve MO1 MO2

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Persistence in completing MOOCs PER1

0.50

0.52

0.54

-0.20

0.69

0.72

0.87

PER2

0.43

0.57

0.55

-0.30

0.63

0.74

0.86 0.91

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PER3 0.48 0.51 0.61 -0.31 0.64 0.72 Note. The bold values are the loadings of each item on its latent variable in the research model.

4.2. Structural model

The structural model was examined by checking the significance levels of the path

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coefficients in the research model and the explanatory power (i.e., R2) of the structural model.

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The validation outcomes of the structural model are presented in Fig. 2.

Fig.2. Structural model for the whole participants. Note. ** p<0 .01; *** p< 0.001; ns= nonsignificant.

As PLS-SEM does not rest on any distributional assumptions, parametric approaches for

examining significance levels are not applicable. Thus, bootstrapping analyses were carried out to evaluate the statistical significance of the path coefficients for the structural model (Chin, 2010; Sanchez, 2013). The default resamples in the PLS-SEM package in R programming

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language are 100 (Sanchez, 2013). The validation outcomes are presented in Table 5. Except for H6, H8, and H9, the remaining hypotheses were supported. Table 5 Path coefficients #

Hypotheses

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Validation outcomes for the research model. t

Results

NS -> NB

0.578 ***

14.10

Support

H2

PC -> NB

0.294 ***

7.16

Support

H3

NB -> PERS

0.144 **

3.78

Support

H4

MO -> PERS

0.539 ***

11.20

Support

H5

NB -> MO

0.339 ***

8.76

Support

H6

NB -> UE

-0.202 **

-3.83

Not support

H7

NB -> UP

0.572 ***

12.90

Support

H8

UE -> MO

-0.114 **

-3.40

Not support

H9

UE -> PERS

-0.015

-0.49

Not support

H10

UP -> MO

0.523 ***

31.10

Support

H11

UP -> PERS

0.249 ***

5.71

Support

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H1

Note. ** p<.01; *** p<.001; NS=Network size; PC=Perceived complementarity; NB=Network benefit;

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PERS=Persistence; MO=Motivation; UE=User experience; UP=User preference; # Three decimals places were kept here so as to preserve the statistical precision; The bold values indicate the hypotheses that were supported.

Since the main objective of PLS-SEM is to maximize the variance explained in all

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endogenous constructs, R2 values for the endogenous variables are considered the essential criterion for structural model testing (Henseler, Ringle, & Sinkovics, 2009). Considering that

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there are no generally agreed-upon optimal values for R2, in this study we refer to the research of Cohen (1988) on R2. Cohen (1988) suggested that R2 values of 0.02, 0.13, and 0.26 indicate small, medium, and large effect sizes, respectively. In the research model of this study, the R2 values for network benefit, user preference, motivation to achieve, and persistence in completing MOOCs were 0.618, 0.328, 0.657, and 0.728, respectively (see Fig.2). Except for the construct of user experience (R2=0.041), the R2 values for the remaining endogenous variables were high. Thus, the predictive power of the research model was generally substantial.

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Although there are not any overall goodness-of-fit criteria for the PLS-SEM analysis (Henseler et al., 2009), a global criterion of goodness-of-fit (0
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average communality and average R2, and is embedded in the PLS-SEM package in R

programming language. The value of GoF is defined as small (0.10), medium (0.25), and large (0.36). The GoF value of our research model was 0.57, which was significant.

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4.3. Comparison between one-year users and above-one-year users

The reliability and validity of the research model of this study were confirmed in the

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analyses above. To find out whether years of usage would impose any possible effect on how network externalities and human factors functioned, we made a comparison between one-year users and above-one-year users. Generally, multi-group analyses are realized by comparing the differences at the structural level, specifically, the path coefficients (Sanchez, 2013). The main

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reason lies in the objective of path modeling with latent variables, which is to estimate the linear relationships among constructs. This study utilized the bootstrap t-test approach, which is more robust, to conduct the group comparison. This procedure involves separating the data into groups

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followed by running bootstrap samples with replacements for each group. Subsamples are compared via a t-test in respect of the standard error estimates of path coefficients.

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As shown in Table 6, Fig. 3, and Fig. 4, there was a significant difference between one-

year users and above-one-year users in terms of (a) the path coefficient of network size on network benefit, indicating that above-one-year users cared more about the network benefit brought by network size than one-year users; (b) the path coefficient of perceived complementarity on network benefit, implying that one-year users cared more about the network benefit brought by perceived complementarity than above-one-year user; (c) the path coefficient

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of user preference on persistence in completing MOOCs, suggesting that user preference had a greater influence on one-year users’ persistence in completing MOOCs than above-one-year users; and (d) the path coefficient of motivation to achieve on persistence in completing MOOCs,

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indicating that motivation to achieve in MOOCs had a stronger influence on above-one-year users’ persistence in completing MOOCs than one-year users.

Global

Group: Above-One-year

Group: One-year

diff.abs

NS -> NB

0.578

0.698

0.508

0.190

2.323

344

0.010

Yes

PC -> NB

0.294

0.178

NB -> PERS

0.144

0.067

MO -> PERS

0.539

0.717

NB -> MO

0.339

0.225

NB -> UE

-0.202

-0.212

NB -> UP

0.572

0.571

UE -> MO

-0.114

-0.151

UE -> PERS

-0.015

UP -> MO

0.523

UP -> PERS

0.249

t

df

p

Sig.05

0.366

0.188

2.105

344

0.018

Yes

0.196

0.129

1.492

344

0.068

No

0.450

0.267

2.114

344

0.018

Yes

0.403

0.178

1.507

344

0.066

No

-0.193

0.019

0.204

344

0.419

No

0.575

0.004

0.141

344

0.444

No

-0.094

0.057

0.814

344

0.208

No

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Path

#

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Comparison between One-year-users and Above-one-year-users.

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Table 6

-0.041

-0.003

0.038

0.566

344

0.286

No

0.615

0.470

0.145

1.156

344

0.124

No

0.105

0.311

0.206

1.753

344

0.040

Yes

Note. NS=Network size; PC=Perceived complementarity; NB=Network benefit; PERS=Persistence;

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MO=Motivation; UE=User experience; UP=User preference; diff.abs= absolute difference; # Three decimals places were kept here so as to preserve the statistical precision; The bold rows indicate the paths where one-year users

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significantly differed from above-one-year users.

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Fig.3. Structural model for one-year users.

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WHAT MAKES MOOC USERS PERSIST

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Note. * p< 0.05; ** p<0 .01; *** p< 0.001; ns= nonsignificant.

Fig.4. Structural model for above-one-year-users. Note. * p< 0.05; ** p<0 .01; *** p< 0.001; ns= nonsignificant.

5. Discussion and conclusion

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This study investigated how network externalities influenced users’ persistence in completing MOOCs mediated by human factors. There were 346 students from a public university in mainland China recruited into this study. A research model was built by taking into

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consideration network externalities, human factors, and persistence in completing MOOCs. Data were collected using a survey instrument. Findings from PLS-SEM analyses (see Fig. 2 and Table 5) showed that users’ persistence in finishing MOOCs was a function of network benefit,

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user preference, and motivation to achieve, among which motivation to achieve constituted the main contributor. Together, the three factors explained 72.8% variance in users’ persistence in

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completing MOOCs, which was substantial. Besides the direct effect of 0.144, network benefit also had a strong indirect effect of 0.502 on users’ persistence in finishing MOOCs through user preference and motivation to achieve (see Appendix B). This indicated that network benefit tended to exert more influence on users’ persistence in completing MOOCs in an indirect way

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through user preference and motivation to achieve.

Network size and perceived complementarity of MOOCs significantly predicted users’ perceived network benefit of MOOCs. Furthermore, network benefit significantly affected user

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preference and motivation to achieve, among which the former also had a strong effect on the later. Nevertheless, user experience did not contribute to the research model in this study.

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Consistent with previous research regarding the effect of network externalities on

people’s perceptions and use of technologies (e.g., C. P. Lin & Bhattacherjee, 2008; C. P. Lin et al., 2011; Zhao & Lu, 2012), this study confirmed the direct influence of network externalities on users’ persistence in completing MOOCs. More importantly, this study highlighted that network externalities were more likely to take effect indirectly through human factors. This further underpinned the important role of human factors in future refinement of MOOCs (Chang et al.,

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2015; Lehto & Landry, 2012). However, in contrast to the studies of Cheon et al. (2012) and K. M. Lin (2011), user experience made little contribution to the research model in this study. This was largely due to the low user experience that participants rated the MOOCs they joined.

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The findings that both network size and perceived complementarity contributed to

network benefit with network size being the greater contributor are largely in line with the theory of network externalities (e.g., Hong et al., 2017; Seol, Lee, Yu, & Zo, 2016). Network size, as

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the factor measuring direct network externalities, constitutes the basis of network effect

(Gallaugher & Wang, 1999; Strader et al., 2007). Meanwhile, perceived complementarity, which

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measures indirect network externalities, results from the effect of network size indirectly (Katz & Shapiro, 1985). Without a large user base, third-party tool or course developers are not likely to develop specific tools or courses for MOOC platforms, thereby decreasing the network benefit users can enjoy.

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In addition, this study found that length of MOOC usage made a significant difference in the effect of network externalities on users’ persistence in completing MOOCs (see Table 6, Fig. 3, and Fig. 4). First, network size had a stronger effect on network benefit for above-one-year

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users than one-year users, while perceived complementarity having a stronger effect on network benefit for one-year users than above-one-year users.

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These findings imply that as people use MOOCs for a longer period of time, they tend to

care more about the network size of MOOCs, as opposed to the perceived complementarity of MOOCs. This may be because larger network sizes are more likely to bring about more interactions among MOOC users in the world (Zhang et al., 2017). For instance, users can easily get responses to their inquiries from other users worldwide and even assess one another’s work when their MOOCs have a large user base. The substantial usage data generated by the user base

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can help MOOC developers refine their courses more precisely through the use of artificial intelligence analytical tools (Kay, Reimann, Diebold, & Kummerfeld, 2013). For above-one-year MOOC users, the increased interactions and refined courses would be critical to sustain their

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determination to complete the course.

On the other hand, for MOOC starters, they were likely to be more concerned about the perceived complementarity of MOOCs, instead of the network size of MOOCs. Although

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network size influences people’s selection choice of courses (K. Y. Lin & Lu, 2011), people’s early decision to stay in certain MOOCs may be partly driven by the availability of their

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complementary services and tools. For example, the MOOCs that offer official certificates carrying equivalent credit with onsite courses of traditional universities or are co-developed with employers in the job market may be more likely to retain users in the first place (Jobe, 2014). Second, user preference had a stronger influence on users’ persistence in completing

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MOOCs for one-year users than above-one-year users. Motivation to achieve in MOOCs had a stronger effect on users’ persistence in finishing MOOCs for above-one-year users than one-year users. This may be due to that for MOOC starters, their preference for MOOCs tends to

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constitute an important factor that keeps them staying with MOOCs in the first place, especially when they are facing a variety of choices of learning (Chang et al., 2015; Jung et al., 2002). But

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for learners who have used MOOCs for a longer time, they may have sensed the benefits and future potential associated with MOOCs, which are likely to further motivate them for better performance in MOOCs (McClelland, 2015). These findings suggested that for starters of MOOCs, cultivating their preference towards MOOCs tend to be essential for enhancing their persistence in accomplishing MOOCs. While for long-time users of MOOCs, preserving their

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motivation to achieve may be crucial for strengthening their persistence in accomplishing MOOCs. To sum up, as indicated by Table 5, Fig. 2, and Appendix B, network externalities had a

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significant direct effect on users’ persistence in accomplishing MOOCs and, particularly, a strong indirect effect on it through user preference and motivation to achieve. Therefore,

persistence in learning throughout their use of MOOCs.

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regardless of the usage duration, network benefit always posed a significant effect on users’

6. Contributions, limitations, and implications for future research

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This study could contribute to existing research on MOOCs and network externalities in the following two ways:

First, the findings of this study underscored the significant influence of network externalities on users’ persistence in completing MOOCs via two factors: network size (direct

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network externality) and perceived complementarity (indirect network externality). Network benefit, which was strongly predicted by network size and perceived complementarity, not only directly affected users’ persistence in finishing MOOCs, but also indirectly exercised its

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influence through user preference and motivation to achieve. Second, as compared to previous studies that looked into network externalities, the

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unique contribution of the present study is the exploration of the effect of length of usage on network externalities. This study hypothesized that the effects of network externalities and human factors on learners’ persistence in completing MOOCs vary as a function of the length of MOOC usage. It was found that as learners use MOOCs for a longer time, the same factors tend to vary their effects on learners’ perceived network benefit and persistence in completing MOOCs. As such, MOOC providers are advised to differentiate their marketing strategies based

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on learners’ usage history. For instance, for MOOC starters, MOOC providers can enhance the perceived complementarity by (a) stressing the collaboration with conventional universities and employers in the job market and (b) emphasizing the availability of third-party tools or services

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supportive of learning. For MOOC users of more than one year, sustaining their motivation to achieve in MOOCs via network externalities is more likely to enhance their persistence in completing MOOCs.

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Nevertheless, there are several limitations in this study. First, participants in this study came from a university setting. Thus, it is unclear regarding the extent to which the research

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findings from this study can be generalized to the work setting. Second, the socio-economic and cultural factors deserve appropriate attention, as people from different socio-economic and cultural backgrounds tend to have different expectations of and experiences with MOOCs (Bagozzi, 2007; Nistor, Göğüş, & Lerche, 2013). Participants in this study are mostly from

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middle-income families in China. As such, attempts to generalize the research findings to other samples should be cautioned. Third, the cross-sectional nature of this study may generate spurious cause-effect inferences. Fourth, as with any self-report instrument, the findings of this

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study are subject to report errors associated with individuals’ perceptions (Zhou, 2016). Therefore, further studies are necessary to triangulate self-report data with observation data and

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in-depth interviews with MOOC users, providers, and other stakeholders to offer stronger arguments for the jointed effect of network externalities and human factors on MOOC completion.

In view of the significance of network externalities (i.e., network size, network

complementarity, and network benefit) to users’ persistence in completing MOOCs, several implications for MOOC providers and researchers can be drawn from this study.

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6.1. For MOOC providers First, besides improving the quality of MOOCs from the aspects of content and design, MOOC providers need to communicate the information regarding network size and

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complementary tools and services to their users in a timely and routine manner. This may help cultivate users’ learning preference and strengthen their motivation to achieve in MOOCs,

subsequently enhancing their persistence in completing the MOOCs that they have registered for.

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Second, even though it is difficult for MOOC providers to control network size, they can influence users’ perceived complementarity by strengthening collaborations with third-party

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developers and organizations. For instance, MOOC platforms can work closely with Coursetalk (https://www.coursetalk.com/ ; a MOOC review and search platform) so as to inform users’ course selection decisions. If users are able to select their desired courses, the completion rate of courses will be improved. Further, instead of simply giving completion certificates, MOOC

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providers are advised to have deeper collaborations with traditional universities to award MOOC users certificates that carry equal credit as onsite courses. As to collaborating with employers in the job market, we can refer to the Google-Udacity partnership

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(https://www.udacity.com/google ). It offers MOOC users three months of access to courses and projects co-developed by Google and Udacity staff, leading to a Google validated certificate of

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programming proficiency, which may impress potential employers in the job market more than an academic degree attained from a conventional university. 6.2. For MOOC researchers First, as one’s learning should not be separated from social contexts and is dependent on

interactions with others (Schunk, 2012), the same principle also applies to the learning that happens in MOOCs. As such, MOOC researchers may consider applying network externalities to

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a wide range of topics related to MOOCs. For instance, network externalities may be combined with self-determination theory to explain MOOC users’ decisions of course selection. As network externalities are mainly related to external factors, while self-determination theory

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explains people’s innate psychological needs and internal motivation behind their thoughts and behaviors, the combined use of both may offer a comprehensive insight into the reasons leading to people’s perceptions and behaviors toward MOOCs.

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Second, due to the low user experience as indicated by participants regarding the MOOCs they joined (see Table 2), the research model in this study did not maximize its explanatory

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power. In this regard, MOOC researchers may further explore the current research model by comparing participants from two MOOC platforms with similar user bases but different user experience. Such comparison could deepen our understanding with respect to how human factors (e.g., use experience or user preference) mediate the effect of network externalities on users’

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persistence in completing MOOCs. It could also shed light on how MOOC platforms with good and poor user experience, respectively, differ from one another in terms of the effect of network

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externalities on users’ perceptions and behaviors toward MOOCs.

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Highlights Network externalities greatly affect learners’ persistence in completing MOOCs.

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Network externalities take effect directly and indirectly through human factors.

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Network externalities’ effect on MOOC completion vary with the usage duration.

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