Continuance intention to use MOOCs

Computers in Human Behavior 67 (2017) 221e232

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Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model Bing Wu*, Xiaohui Chen School of Economics and Management, Tongji University, Shanghai, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 June 2016 Received in revised form 21 September 2016 Accepted 26 October 2016 Available online 3 November 2016

The purpose of this study is to propose a unified model integrating the technology acceptance model (TAM), task fit technology (TTF) model, MOOCs features and social motivation to investigate continuance intention to use MOOCs. A sample of 252 participants in China that have already used MOOCs took part in this study. Structural equation modeling implemented via partial least squares (PLS) is conducted to test the research hypotheses. The results show that research framework for integrating the TAM for the adoption and TTF model for utility provides a more comprehensive understanding of the behaviors related to this context: (1) perceived usefulness and attitude are critical to the continuance intention to use MOOCs; (2) perceived usefulness is a significant mediator of the effects of perceived ease of use, tasktechnology fit, reputation, social recognition and social influence on continuance intention; (3) perceived ease of use, task-technology fit, reputation, social recognition and social influence are found to play important roles in predicting continuance intention; (4) individual-technology fit, task-technology fit, and openness affect the perceived ease of use; (5) unexpectedly, perceived ease of use and social influence have no significant effect on attitude, and individual-technology and openness do not affect perceived usefulness. © 2016 Elsevier Ltd. All rights reserved.

Keywords: MOOCs TAM TTF MOOCs features Social motivations Continuance intention

1. Introduction At the start of an education revolution, the number of Massive Open Online Courses (MOOCs) has increased in recent years. Demonstrating great differences from previous approaches to online education, MOOCs represent the latest stage in the evolution of open educational resources for students around the globe. MOOCs are considered to be a recent innovation in online learning with virtual technology-enhanced learning environments. Technology facilitates scalable peer-based learning and the dominant channel through which teachers and students can interact. Thus, social learning is a key aspect of MOOCs platforms. MOOCs are generally classified into two categories: xMOOCs and cMOOCs. xMOOCs are the most widespread and follow the so-called broadcast model, similarly to a traditional course with all content predefined by the instructor, which is the focus of this study. cMOOCs derive course materials and content from students during the course (Hew & Cheung, 2014). The advantages of MOOCs are large scale, openness and self-

* Corresponding author. E-mail address: [email protected] (B. Wu). http://dx.doi.org/10.1016/j.chb.2016.10.028 0747-5632/© 2016 Elsevier Ltd. All rights reserved.

organization. MOOCs are exploited to enhance teaching and learning. On the one hand, MOOCs offer teachers the opportunity to reach a large number of students worldwide (Alario-Hoyos et al., 2014). On the other hand, MOOCs enable students to access free and open education provided by the most reputable universities, which attract substantially larger audiences than traditional online education. Furthermore, MOOCs are communities of people that share common interests (Alario-Hoyos et al., 2014). A dozen MOOCs in Chinese have been developed and published on MOOCs platforms such as Coursera and edX. In China, elite institutions have launched MOOCs programs to promote pedagogical research on this platform. Despite public enthusiasm for MOOCs, it has been observed that MOOCs suffer from enormous dropout rates. On average, less than 10% of students attending MOOCs complete their course (Bartolome & Steffens, 2015). Considering the issue of MOOCs dropout and non-completion rates, a subject of great concern has been centered on issues of quality in learning and teaching (Diver & Martinez, 2015). However, completion rate may not be the best measure for evaluating learning in MOOCs (Jordan, 2014), because students enroll in MOOCs for a variety of reasons. For example, course completers tend to be more interested in the course content, whereas non-completers tend to be more interested in MOOCs as a

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type of learning experience (Chang, Hung, & Lin, 2015). Moreover, curiosity and job advancement are common motivating factors (Alraimi, Zo, & Ciganek, 2015). Exploring alternative dependent variables such as the percentage of content viewed, despite not participating in course assessments, is a different perspective that may offer value for MOOC providers. Consequently, the success of MOOCs is based on the continued usage. Considering the rapid development and adoption of MOOCs for distance learning, an investigation of factors that influence students' continued usage of MOOCs may reveal insights into its viability (Bhattacherjee & Premkumar, 2004) and sustainability (Barnes, 2011). However a limited amount of research has examined the factors that influence MOOCs continuance intention. In addition, MOOCs learning can be considered the behavior of users to obtain, use and spread MOOCs resources. This behavior includes two stages: the first stage is users' perception of MOOCs by attitude, adoption and habits; the second stage is the extent to which MOOCs meet the needs of users, which emphasizes the utility of MOOCs. The two stages are interrelated because the second stage of utility cannot be attained without the first stage of adopting MOOCs. Nevertheless, the best of our knowledge, the existing studies that capture students' intention to participate in MOOCs are too rough to combine both views. It is unreasonable to expect that a simple model can adapt to constantly changing information technology environments without modifications and thus explain behavior fully across a wide range of technologies and adoption situations. As a result, the current study aims to identify whether and to what extent the abovementioned factors influence MOOCs continuance intention relative to the two stages. Thus, we present a research framework for integrating the technology acceptance model (TAM) for adoption and the task fit technology model (TTF) for utility. The rest of this paper is organized as follows. The literature is reviewed in Section 2, which offers a short overview of MOOCs, TAM, and TTF. The research model is presented in Section 3, followed by a discussion of the research design and methodology in Section 4. The most relevant findings obtained from this study are presented and discussed in Section 5. Section 6 draws conclusions and summarizes the contributions of this study. Finally, Section 7 outlines research problems that will be investigated in our future work. 2. Literature review To gain a comprehensive understanding of our study problem, a literature review is conducted. The first section summarizes literature relevant to the development of MOOCs to justify using TAM and TTF in this specific domain. In the following sections, we review recent studies that have employed TAM and TTF to introduce how they operate. 2.1. MOOCs 2.1.1. Characteristics of MOOCs Recent research shows that MOOCs are becoming a widelydiscussed new topics in education. Open is the most important feature of a MOOC. There are at least five attributes of openness that are essential components of MOOCs: free access, adaptation, remixing, sharing and collaboration (Chiappe-Laverde, Hine, & Martinez-Silva, 2015). Furthermore, massiveness represents a second level of importance. Online indicates that all the learning experience is realized through the Internet. Thus, the course generates different experiences through free-access, self-learning video tutorials online. Because a MOOC not only has a clear pedagogical purpose but also has constituent components, most MOOCs consist of relatively

short video lectures, related content, and feedback, which are managed either through peer-review and group collaboration or by automation. Currently, Coursera, edX, and Udacity are the most popular MOOC platforms and are each associated with highly regarded institutions of higher education (Alraimi et al., 2015). 2.1.2. Activity research in MOOCs Because social learning is a key element of MOOCs, some researchers have proposed a unified generative model to improve the quality of learning via online discussion forums, by devising methods to sustain forum activities and to facilitate personalized learning (Brinton et al., 2014). In addition, the precise effectiveness strategy in MOOCs was proposed to define metrics for the effectiveness of students when interacting with educational resources and activities (Munoz-Merino, Ruiperez-Valiente, Alario-Hoyos, Perez-Sanagustin, & Kloos, 2015). At the individual learning level, some researchers have suggested that MOOCs appeal to students who are self-motivated and who perceive MOOCs to be useful (Zhou, 2016). To improve open teaching and learning through social, pedagogical or technological approaches, some researchers have discovered three activities and experiences that have received little attention in the MOOC literature: interactions in social networks outside of the MOOC platform, such as note taking and consuming content (Veletsianos, Collier, & Schneider, 2015); the strong correlation between procrastination and achievement; the preferred social tool (Alario-Hoyos, PerezSanagustin, Delgado-Kloos, Parada, & Munoz-Organero, 2014). Such studies provide insight into the views of actively engaged MOOC participants, but fail to account for the motivation for MOOCs. 2.1.3. Social motivation in MOOCs Because MOOCs provide an open and free online learning environment, participants tend to choose it to follow their goals and interests (Kizilcec & Schneider, 2015). It was speculated that learning styles could affect a learner's preference for MOOCs. Therefore, researchers investigated the influence of learning styles on learners' intentions to use MOOCs (Chang et al., 2015). Moreover, some researchers examined the psychological considerations inherent in learning and explored the psychological determinants of learner behaviors relevant to MOOCs (Terras & Ramsay, 2015). To further investigate the factors that affect students' perceptions and intentions in using MOOCs, one study explicitly highlighted the antecedents of the core constructs of TPB from a selfdetermination perspective (Zhou, 2016); and another study empirically extended the IS continuous model by incorporating perceived openness and perceived reputation to examine the effects of perceived openness, perceived reputation, and confirmation on users' motivation to MOOCs (Alraimi et al., 2015). Although these studies have successfully revealed various interesting findings, more research into these effects is required to understand the acceptance and the utility of MOOCs simultaneously. Thus, the TAM and TTF model are needed because they are two of the most frequently employed models for studying technology acceptance and utilization. 2.2. Extended technology acceptance model Some researchers have extended the TAM with a range of external factors to explain the likelihood of MOOCs acceptance or use. For example, a research model based on the information systems continuance expectation-confirmation model is proposed to research MOOCs continuance (Alraimi et al., 2015). To integrate the theory of planned behavior (TPB) and self-determination theory


(SDT) as a research framework, one study examined the factors that influence students' decisions to use MOOCs (Zhou, 2016). However, the TAM only concerns the short-term beliefs and attitude before or after the acceptance of MOOCS. A favorable outcome of using MOOCs is anticipated when a fit between the task and the technology is achieved, which is the focus of the task technology fit model. Therefore, task technology adaptation theory can compensate for the deficit of the TAM in this respect. Combined models of technology acceptance and the task technology fit provided a better explanation for the variance of IT utilization than either the TAM or TTF model alone (Chang, 2010). 2.3. Task technology fit model The task-technology fit (TTF) model is a widely used theoretical model for evaluating how information technology leads to performance, assessing usage impacts, and judging the match between the task and technology characteristics. Both task characteristics and technology characteristics can affect the task-technology fit, which in turn determines users' performance and utilization. Since its initial proposal, TTF has been actively researched and applied to a wide range of information systems (Aljukhadar, Senecal, & Nantel, 2014). Although research has investigated TTF in various contexts, little research has been conducted in MOOCs. To date, it is still unclear whether a good task-technology fit will impact a user's adoption of MOOCs and how well it will influence a user's adoption. Regarding MOOCs context, the TTF model does not address social factors, which may limit its predictive ability for social networking technology. The limitation can be overcome by extending it with social motivation drawing insights from social recognition and social influence. 3. Research model and hypotheses Using the theoretical background of the TAM, the TTF model, features of MOOCs, and social motivations, we propose a research model that identifies several attributes as predictors of MOOCs continuance intention. The relationships between these constructs are integrated in the conceptual model depicted in Fig. 1. The basic assumption is that MOOCs continuance intention is

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jointly determined by perceived usefulness and attitude, which are functions of perceived ease of use, TTF, MOOCs features, and social motivations. First, individual-technology fit and task-technology fit are incorporated into the model. Second, openness and reputation are featured in MOOCs. Finally, social motivation with social recognition and social influence are developed as constructs integrated in the research model. 3.1. The TAM To test the dependent variable of continuance intention of MOOCs, the TAM is applied to test the relationship between perceived ease of use, perceived usefulness, behavioral attitude, and continuance intentions. 3.1.1. Perceived ease of use In the context of MOOCs, the perceived ease of use can be defined as the extent to which a person believes that using MOOCs will be free of effort. An example of perceived ease of use is the ease of acquiring skills using the MOOCs. Previous studies showed that the perceived ease of use has a positive effect on users' attitudes and the perceived usefulness of using systems (Hong, Suh, & Kim, 2009). In online learning contexts, for example, perceived ease of use is vital for the perceived usefulness and attitudes towards using E-Learning 2.0 (Wu & Zhang, 2014). Similarly, perceived ease of use could affect the intention to accept MOOCs directly or indirectly through perceived usefulness. Thus, we propose the following research hypotheses: H1. Perceived ease of use has a positive effect on the perceived usefulness of MOOCs. H2. Perceived ease of use has a positive effect on attitudes towards using MOOCs.

3.1.2. Perceived usefulness Perceived usefulness reflects the users' subjective assessment of whether using a particular system would enhance job performance (Davis, Bagozzi, & Warshaw, 1989). The perceived usefulness of MOOCs can be described as the extent to which a person believes that MOOCs can be a driving force towards achieving learning goals.

Fig. 1. Proposed research model.

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Perceived usefulness is a construct that has been repeatedly revealed to influence attitude and is a direct determinant of continued IS usage intentions (Lee, Hsieh, & Chen, 2013). In addition, perceived usefulness mediates the effect of perceived ease of use on behavioral intention, a relationship that has been supported by many empirical studies. In the MOOCs literature, for example, it was reported that the intention to continue using MOOCs is significantly influenced by perceived usefulness (Alraimi et al., 2015). Thus, we propose the following research hypotheses: H3. Perceived usefulness has a positive effect on attitude towards using MOOCs. H4. Perceived usefulness has a positive effect on intention to continue using MOOCs.

3.1.3. Attitude and continuance intention The relationship between attitude and intention highlighted in the TAM suggests that attitude serves as an evaluative predisposition to behavior. The attitude towards using MOOCs has been regarded as the degree to which an individual perceives a positive or negative feeling related to MOOCs. Past research has found that attitude is the most powerful predictor of intention to use technology (Teo & Zhou, 2014). In the MOOCs context, using a Chinese sample, it was reported that the attitude towards MOOCs and perceived behavioral control (PBC) were significant determinants of intention to use them (Zhou, 2016). In another study, for example, a theoretical model was proposed based on the expectation confirmation model of information system to study factors that influence the continuance intention of MOOCs (Alraimi et al., 2015). Thus, we propose the following research hypothesis: H5. Attitude towards using MOOCs has a positive effect on intention to continue using them.

3.2. The TTF To understand the continuance use of MOOCs, we must consider not only individual interactions with the system but also taskoriented actions related to that system. The key to the user's evaluation of MOOCs lies in the individual-technology fit and the tasktechnology fit. 3.2.1. Individual-technology fit Students' effective use of MOOCs depends on factors associated with individual-technology fit, including whether teaching methods match learning styles, whether learning styles match the content of MOOCs, and whether content matches learning targets. Thus, individuals' interactions with an information system are often intertwined with their individual-technology adaptation behaviors (Yu & Yu, 2010). Those technology functions match task requirements and individual abilities. More experience, which means individual-technology fit, is associated with higher ease of use. In addition to the strong effect of tool experience on perceived ease of use, tool experience is also associated with perceived usefulness because more experienced users are better able to understand the usefulness of the tool. Thus, we propose the following research hypotheses: H6. Individual-technology fit has a positive effect on the perceived usefulness of MOOCs. H7. Individual-technology fit has a positive effect on the perceived ease use of MOOCs.

3.2.2. Task-technology fit The degree of task-technology fit (TTF), defined as a matter of how the capabilities of the IS match the tasks that the user must perform, is a major factor in explaining job performance levels (Goodhue, Klein, & March, 2000). Many previous empirical studies (Kim, Suh, Lee, & Choi, 2010) have suggested that the perception of whether a particular technology fits well with the present values of users, i.e., perceived ease of use and perceived usefulness, can be a basis for forming perceptions of actually utilizing the technology. Moreover, empirical results have demonstrated that perceived ease of use and perceived usefulness are affected by task-technology fit; that is, when fit between the task and technology is higher, users perceive the tool to be easier to use and useful for that task. Technology features are expected to influence online learning effectiveness. The prerequisite for the perceived usefulness of MOOCs is that individual users find a match between task and technology. When users actively choose to use MOOCs, the mechanism behind this choice is quite likely that task-technology fit influences their perceived ease use of MOOCs. Thus, we propose the following research hypotheses: H8. Task-technology fit has a positive effect on the perceived usefulness of MOOCs. H9. Task-technology fit has a positive effect on the perceived ease of use of MOOCs.

3.3. Features of MOOCs Because openness and reputation are prominent features of MOOCs, this study is interested in the causal effects of these features on the TAM's two main constructs, perceived ease of use and perceived usefulness. 3.3.1. Openness Over time, the open educational community, such as that of MOOCs, has focused on increased openness, such that educational materials emerge from the community are both visible and accessible with free access, greater choice and flexibility (Alraimi et al., 2015). As a result, a large proportion of students are interested in MOOCs because of their massive and open nature instead of receiving any type of certificate or gaining academic credits (Chiappe-Laverde et al., 2015). One of the most important elements underlying the idea of openness is adaptation, which has strong potential to change education practices (Jung, Sasaki, & Latchem, 2016). This aspect, which takes into account elements such as remixing, collaboration and open access, will inevitably impact MOOCs practices such as perceived ease of use, and perceived usefulness. Thus, we propose the following research hypotheses: H10. Openness has a positive effect on the perceived usefulness of MOOCs. H11. Openness has a positive effect on the perceived ease of use of MOOCs.

3.3.2. Reputation The reputation of an institution is attractive to students, because it represents the perceived excellence of the institution that guides the decisions of prospective students to attend the institution (Bourke, 2000). Therefore, the student's initial assessment of the reputation of the course or university may affect the decision to drop out. Thus, reputation might be a critical determinant of student's attitude towards a course or university in the early stages of a


program when the student has no experience upon which to base an assessment of the merits of the program or institution (DelgadoMarquez, Escudero-Torres, & Hurtado-Torres, 2013). Currently, the most popular MOOC platforms are each associated with highly-regarded institutions of higher education likely receive instant credibility from this association. As an inherent feature of MOOCs, reputation has a significant influence on an individual confirmation and satisfaction (Alraimi et al., 2015). As a result that reputation of the University and its programs are the most important factors in the students' decision of a place to further studies. Thus, we propose the following research hypothesis: H12. Reputation has a positive effect on the perceived usefulness of MOOCs. 3.4. Social motivations Because MOOCs represent an emerging issue, positive recognition and influence from others exert a strong influence on the sustainable development of MOOCs. This study identifies social motivation as social recognition and social influence. As a previous study suggests, individual user behavior may be influenced by other members. Thus, it is necessary to examine the effect of social motivation on MOOCs. 3.4.1. Social recognition In reality, recognition plays an essential role not only in realizing people's own abilities and skills but also in facilitating social interaction. Recognition enables social interaction, from which we may develop a profound understanding and awareness not only of self-confidence, self-respect, and self-esteem but also of relationships with others in society. As a result, establishment of recognition acts as a fundamental type of social interaction. Although existing research has already investigated various social recognition patterns and forms, little research thoroughly explain how social recognition can be implemented in MOOCs, as well as its holistic effect on perceived usefulness. Students' use of MOOCs may also be motivated by expected grade increases, reward structures, school support and other influences. Certification has been a major topic of recognition that has grown in recent years (BRAGG, 2014). From students' perspective, in MOOCs, the focus on certification recognition is associated with perceived usefulness of academic identity. Thus, we propose the following research hypothesis: H13. Social recognition has a positive effect on the perceived usefulness of MOOCs. 3.4.2. Social influence Information systems researchers have noted that individuals may adopt a particular technology not because of their own personal persuasions but because of the views of others (Ifinedo, 2016). The unified theory of acceptance and use of technology (UTAUT) proposes that social influence is a significant factor in determining user acceptance of an information technology (Venkatesh, Morris, Davis, & Davis, 2003). Social influence has also appeared in several models of user acceptance of information communication technology (Hsu & Lu, 2004), and empirically, it has received strong support as a driver of user behavior. The conceptual reasoning underlying this link lies in a person's motivation to comply with others' beliefs to strengthen relationships with group members (Hernandez, Montaner, Sese, & Urquizu, 2011). Our research considers social influence as the degree to which a user perceives that others explicitly approve and encourage their

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participation in MOOCs (Lee, 2010). When an individual observes that others use MOOCs and perceive the benefits of its employment, that individual will become more willing to use MOOCs, which can increase both present and future usage of MOOCs technologies. Likewise, we expect that social influence entails the student's perception of usefulness from others and plays an important role in driving attitudes towards the use of MOOCs. Thus, we propose the following research hypotheses: H14. Social influence has a positive effect on the perceived usefulness of MOOCs. H15. Social influence has a positive effect on the attitude towards using MOOCs. 4. Research method In this study, a survey is employed to test the hypotheses formulated in the previous sections; questionnaire development and data collection are discussed in the subsequent sections. 4.1. Questionnaire development We used a questionnaire survey with two sections to test our theoretical model. The first section includes demographic questions about the participants, whereas the second section features questions measuring the constructs in the research model. Considering the characteristics of MOOCs, the research model consisted of ten constructs, which were measured using multipleitem perceptual scales, as shown in Appendix A. Each questionnaire item corresponding to the constructs was measured using a seven-point Likert scale, anchored on “1 ¼ strongly disagree” and “7 ¼ strongly agree”. 4.2. Data collection The target participants of this study were those with experience with MOOCs. We sent online surveys by www.Sojump.com to those who were members of a MOOCs group in Tencent QQ (the biggest online social network in China). We collected data from January to March in 2016; overall, 252 valid surveys were returned. And all the respondents were Chinese from tier cities in China. Table 1 summarizes the demographics of the respondents. 5. Data analysis In analyzing the collected data, we followed a two-step procedure (Anderson & Gerbing, 1988). First, we examined the fitness and the construct validity of the proposed measurement model by assessing reliability, convergent validity, and discriminant validity. Then, we examined the structural model to investigate the strength and direction of the relationships among the theoretical constructs. 5.1. Construct validity 5.1.1. Evaluation of reliability and convergent validity Reliability was assessed using Cronbach's alpha. All multi-item constructs should meet the guidelines for a Cronbach's alpha of greater than 0.70. Convergent validity was assessed based on the criterion that the indicator's estimated coefficient was significant on its posited underlying construct factor. We evaluated the measurement scales using three criteria: all item factor loadings (k) should be significant and exceed than 0.7; composite reliabilities (CR) for each construct

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Table 1 Demographics characteristics of the respondents. Items

Type

Frequency (n ¼ 152)

Percent

Gender

Male Female Under 20 20e30 30e40 40 or above Less than Bachelor's Bachelor's Master's Doctoral Students Work Other First-tier cities in China Second-tier cities in China Third-tier cities in China Under 3 h 3e5 h 5e10 h 10 h or above Coursera edX Udacity Xuetangx MIT MOOCs iCourse in China Others

149 103 7 233 4 8 14 94 128 16 170 58 14 168 60 24 84 88 56 24 132 24 8 28 18 32 10

59.1 40.9 2.8 92.4 1.6 3.2 5.6 37.3 50.8 6.3 71.4 23.0 5.6 66.7 23.8 9.5 33.3 34.9 22.2 9.5 52.4 9.5 3.2 11.1 7.1 12.7 4.0

Age

Education

Occupation

Location

Time to participate in MOOCs per week

MOOCs Platform

should exceed 0.7; and the average variance extracted (AVE) for each construct should be greater than 0.50 (Fornell & Larcker, 1981). Table 2 demonstrates that item loading, the AVE, CR and Cronbach' s alpha values for all constructs in the measurement model exceeded the recommended threshold values. In sum, the adequacy

of the measurement model indicated that all items were reliable indicators of the hypothesized constructs. 5.1.2. Discriminant validity Discriminant validity was assessed based on the squared

Table 2 Construct reliability and convergent validity. Construct

Construct code

Items loading

AVE

C.R.

Cronbach's a

Perceived usefulness

PU1 PU2 PU3 PEOU1 PEOU2 PEOU3 ATU1 ATU2 ATU3 CITU1 CITU2 CITU3 ITF1 ITF2 ITF3 TTF1 TTF2 TTF3 TTF4 OP1 OP2 OP3 OP4 RP1 RP2 RP3 RP4 SR1 SR2 SR3 SI1 SI2 SI3

0.757 0.821 0.802 0.92 0.708 0.622 0.777 0.686 0.844 0.792 0.842 0.875 0.701 0.852 0.692 0.793 0.789 0.867 0.836 0.543 0.655 0.732 0.896 0.537 0.798 0.893 0.945 0.856 0.89 0.545 0.858 0.906 0.851

0.75

0.995

0.9922

0.79

0.942

0.9857

0.73

0.916

0.9005

0.79

0.947

0.9423

0.81

0.915

0.982

0.71

0.844

0.9786

0.77

0.932

0.9744

0.83

0.898

0.9925

0.83

0.822

0.9764

0.81

0.867

0.993

Perceived ease to use

Attitude towards using MOOCs

Continuance intention to use

Individual-technology fit

Task-technical fit

Openness

Reputation

Social recognition

Social influence


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correlations between variables and their extracted respective average variance. To test the discriminant validity, the average variance shared between a construct and its measures should be greater than the variance shared by the construct and any other constructs in the model (Fornell & Larcker, 1981). For the correlation analysis shown in Table 3, the extracted average variance value for the reflective variables is consistently greater than the off-diagonal squared correlations, suggesting satisfactory discriminant validity among variables.

Table 4 Overall model t indices for the research model.

5.1.3. Goodness of fit The structural model was tested to assess how well the model represented the data. We evaluated the following indices (Cangur & Ercan, 2015): the chi-square test statistic, the goodness-of-fit index (GFI), the normed fit index (NFI), the comparative fit index (CFI), Tucker Lewis Index (TLI), and the root mean square residual (RMSR). Table 4 presents the results and recommended values. The chi-square value is 0.128, and the remaining four indices are GFI ¼ 0.924; NFI ¼ 0.943; CFI ¼ 0.901; and RMSR ¼ 0.073. Therefore, we conclude that the goodness-of-fit indices met the recommended levels, suggesting that the research model provided a good fit for the data.

and attitude, resulting in an R2 of 0.957. In other words, the combined effects of perceived usefulness and attitude explain 95.7% of the variance in continuance intention. A summary of the hypotheses testing results of the standardized path coefficients and path significances is provided in Table 5. Most of the paths are significant in the expected direction. Overall, 11 out of 15 hypotheses are supported by the data.

5.2. Structural model for hypotheses testing The 15 hypotheses presented above were tested collectively using structural equation modeling (SEM) implemented via partial least squares (PLS). These techniques allow for the analysis of both a structural model, i.e., assessing relationships among theoretical constructs, and a measurement model, i.e., assessing the reliability and validity of measures. The test of the structural model includes the R2 values, which represent the amount of variance explained by the independent variables, and estimates of the path coefficients, which indicate the strengths of the relationships between the dependent and independent variables. Together, the R2 and the path coefficients indicate how well the data support the hypothesized model. Fig. 2 illustrates the R2 and the resulting path coefficients of the proposed research model. Perceived usefulness is found to be significantly determined by the four exogenous variables, i.e., tasktechnology fit, reputation, social recognition, and social influence, and through the direct effect of perceived ease of use, resulting in an R2 of 0.948. Thus, the above mentioned variables explain 94.8% of variance in the perceived usefulness. Likewise, perceived ease of use is found to be significantly determined by the three exogenous variables, i.e., individual-technology fit, task-technology fit and openness, resulting in an R2 of 0.468. Thus, the above mentioned exogenous variables explain 46.8% of variance in the perceived ease of use. Attitude is significantly determined by the perceived usefulness, resulting in an R2 of 0.89. The dependent variable continuance intention is significantly determined by perceived usefulness

Model t indices

Results value

Recommend value

Chi-square/degree of freedom Goodness-of-fit index (GFI) Normed fit index (NFI) Comparative fit index (CFI) Tucker Lewis Index (TLI) Root mean square residual (RMSEA)

0.128 0.924 0.943 0.901 0.945 0.073

3 0.9 0.9 0.9 0.9 0.9

5.2.1. Relationship in TAM Hypotheses 1e5 address the relationship in the TAM, which is related to perceived usefulness, perceived ease of use, behavioral attitude, and continuance intentions, and all except Hypothesis 2 are supported. The missing link between perceived ease of use and attitude (H2) is not expected, which stands in contrast to previously reported empirical results; perceived ease of use is a strong factor that affects attitude towards technology (Schepers, & Martin, 2007). A possible explanation for this result could be that MOOCs platforms are each accessible through a web browser and comprise similar capabilities and features, which may have made MOOCs easy to use; thus, the students' attitude towards MOOCs adoption depends completely on the perceived usefulness of MOOCs. 5.2.2. Relationship between TAM and TTF Hypotheses 6e9 explore the relationship between external variables of TTF and variables of the TAM, which posited that tasktechnology fit should influence the perceived usefulness and perceived ease of use; all hypothesis except for Hypothesis 6 are supported. In contrast to the prediction in H6, the hypothesis regarding the effect of individual-technology fit on perceived usefulness is not significant. However a significant indirect path exists between individual-technology fit and perceived usefulness, mediated by perceived ease of use. One possible explanation for this effect is that more experiences with MOOCs are prerequisites for individualtechnology fit such that more experienced users are better able to perceive the ease use of MOOCs, and MOOCs may be perceived to be useful only if they are also perceived to be easy to use (Dishaw, Strong, 1999). As such, a positive individual-technology fit may not have generated an increase in the perceived usefulness when students did not perceive the ease use of MOOCs.

Table 3 Inter-construct correlations and discriminant validity (i.e., bold numbers). Constructs

PU

PEOU

ATU

CITU

ITF

TTF

OP

RP

SR

SI

PU PEOU ATU CITU ITF TTF OP RP SR SI

0.75 0.622 0.607 0.055 0.195 0.369 0.474 0.413 0.235 0.015

0.79 0.260 0.52 0.432 0.211 0.550 0.380 0.337 0.369

0.73 0.173 0.246 0.105 0.183 0.405 0.387 0.294

0.79 0.379 0.312 0.444 0.672 0.434 0.122

0.76 0.438 0.118 0.579 0.661 0.054

0.74 0.856 0.373 0.215 0.492

0.77 0.336 0.085 0.128

0.83 0.663 0.681

0.81 0.431

0.80

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Fig. 2. Path analysis.

Table 5 Model path analysis. The hypothesis

Path coefficient

P value

H1: Perceived ease of use / Perceived usefulness H2: Perceived ease of use / Behavior attitude H3: Perceived usefulness / Behavior attitude H4: Perceived usefulness / Continuance intention H5: Behavior attitude / Continuance intention H6: Individual-technology fit / Perceived usefulness H7: Individual-technology fit / Perceived ease of use H8: Task-technolgoy fit / Perceived usefulness H9: Task-technolgoy fit / Perceived ease of use H10: Openness / Perceived usefulness H11: Openness / Perceived ease of use H12: Reputation / Perceived usefulness H13: Social recognition / Perceived usefulness H14: Social influence / Perceived usefulness H15: Social influence / Behavior attitude

0.320 0.021 0.507 0.470 0.509 0.614 0.161 0.163 0.334 0.546 0.199 0.686 0.217 0.126 0.075

p p p p p p p p p p p p p p p

5.2.3. Relationship between TAM and MOOCs features Hypotheses 10e12 address the relationship between external variables of the MOOCs features and variables of the TAM, which posited that MOOCs characteristics should influence the perceived usefulness and perceived ease of use. Significant relationships were verified between openness and perceived ease of use and between reputation and perceived usefulness, all hypotheses except for Hypothesis 10 are supported. In contrast to our predictions, Hypotheses 10, regarding the effects of openness on perceived usefulness, is not supported by the data. Although unexpected, one explanation for this unexpected finding is that because the users of MOOCs have different backgrounds, task differences might have influenced their responses. The perceived usefulness of MOOCs can vary significantly among individuals with different tasks, which might be reflected in the non-significant relationships between openness and perceived usefulness.

< > < < < > < < < > < < < < >

0.001*** 0.05 0.001*** 0.05* 0.01** 0.05 0.001*** 0.001*** 0.001*** 0.05 0.001*** 0.001*** 0.001*** 0.001*** 0.05

Support Yes No Yes Yes Yes No Yes Yes Yes No Yes Yes Yes Yes No

and behavior attitude, all hypotheses except for Hypothesis 15 were supported. Unlike in previous studies, Hypothesis 15, which concerns how social influence should positively affect behavior attitude, is not supported by the data. This finding suggests that external pressure or demand does not interfere with students' attitude towards MOOCs. However, there exists a significant indirect path between social influence and behavior attitude, mediated by perceived usefulness. There are two plausible reasons for this result. First, there may be a lack of strong and positive social interactions in MOOCs, which in turn could reduce the power of social influence in predicting attitude. Second, the positive effect of social influence on behavior attitude towards MOOCs depends on perceived usefulness of MOOCs. 6. Implications and discussions 6.1. Implications

5.2.4. Relationship between TAM and social motivation Hypotheses 13e15 address the relationship between external variables of the social motivation and variables of the TAM, positing that social motivation should influence the perceived usefulness,

In terms of theory building, this study attempts to integrate the TAM, the TTF model, social motivation and MOOCs features to examine the causal determinants of students' continuance


intentions in using MOOCs in China. Data were drawn from users across a wide range of courses regardless of their academic backgrounds or which platforms offer their MOOCs. The results of the empirical analysis provide strong support for 11 of our 15 hypotheses. With respect to each hypothesis, we offer the following insights into the TAM, the TTF model, social motivation and MOOCs features, respectively. 6.1.1. Implications for TAM To begin, the findings indicate that perceived ease of use is a strong predictor of perceived usefulness in that the more MOOCs are perceived to be easy to use, the more likely students will be to perceive the MOOCs as useful. This result is consistent with previous studies (Abdullah, Ward, 2016). Specifically, the indirect effect of perceived ease of use on attitude via the perceived usefulness of MOOCs was found to be apparent. The effect of perceived ease of use on attitude is more profound because students tend to focus on the utility of the system itself, rather than its ease of use in forming an attitude towards using of MOOCs. Therefore, perceived usefulness served as an important mediating variable between perceived ease of use and attitude towards using of MOOCs. That is, if MOOCs provide critically needed functionality, students tend to accept some difficulty of use. Perceived usefulness and attitude towards using were associated positively with continuance intention of MOOCs. This result indicates that perceived usefulness had a significantly positive effect on continuance intention of MOOCs, which is in agreement with the notion of technology acceptance as advocated by Davis et al. (1989). This study also demonstrates that the effect of attitude towards using on continuance intention of MOOCs is both significant and positive, which corresponds with the findings of Davis et al. (1989). In particular, attitude functioned as a crucial mediating variable between perceived usefulness on intention to use of MOOCs, because the indirect effect of perceived usefulness on intention to use via the attitude towards using of MOOCs was found to be apparent. Therefore, the above mentioned results agree with the findings of the TAM, illustrating that the TAM is applicable to the analysis of MOOCs. 6.1.2. Implications for TTF Our results indicate that combining the TAM and TTF constructs provides a better explanation for the variance in MOOCs utilization than either the TAM or TTF model can provide alone. Furthermore, the current study proposes a better hybrid technology utilization model to explain students' usage behavior regarding MOOCs. First, the study explores the implications of TTF for the TAM by explicitly highlighting the antecedents of the core constructs of the TAM from a TTF perspective. Second, with regard to TTF as an external factor, the direct effects of individual-technology fit and task-technology fit for MOOCs students were examined. Third, the model in this study shows modest support for what is so intuitively obvious regarding TTF. As predicted, matching the functionalities of MOOCs to specific tasks, i.e., task-technology fit, will enable students to perceive both the ease of use and the usefulness of MOOCs. This result is congruent with the conclusions of a prior study (Yu & Yu, 2010) indicating that TTF effects on perceived ease of use as well as perceived usefulness in the context of e-commerce. However individual-technology fit contributed to perceived usefulness mediating by the perceived ease of use in this study. This difference may be a result of the MOOCs context under study. When the degree of individual-technology fit becomes greater, students perceive MOOCs to be easier to use for that task, and thus more useful. In short, the greater the fit between the individual, the task and the technology employed, the better chances that MOOCs will

229

be perceived positively. In this context, our study informs provides further evidence of the applicability and suitability of the TAM and the TTF model for the investigation of continuance intention to use MOOCs. 6.1.3. Implications for MOOCs features This study considers the MOOCs features of openness and reputation as independent variables associated with the TAM in the perceived usefulness of MOOCs. The results emphasize the importance of reputation as a contributor in terms of explaining MOOCs perceived usefulness; openness is the predictor of MOOCs' perceived ease of use. In particular, the effect of MOOCs' features on perceived usefulness is stronger than that of MOOCs' features on the perceived ease of use of MOOCs. This result suggests that MOOCs' features are principally related to the perception of usefulness, rather than the perception of ease of use. This finding is unique in the context of MOOCs research. The research findings hold valuable implications for MOOCs practitioners to plan strategically and implement effective tools to improve students' performance. 6.1.4. Implications for social motivation The proposed model greatly expands the role of social motivation in MOOCs by incorporating social influence and social recognition. As social motivational factors, both social recognition and social influence exerted a significantly positive effect on the perceived usefulness of MOOCs. These findings support the results of previous studies, which have demonstrated that students show perceived usefulness of MOOCs when they know that others in their social network have the same values regarding the benefits of MOOCs and similar needs to establish or maintain satisfying relationships with others in their network. More specifically, social motivation was shown to have indirect effects on the attitude towards using MOOCs mediating perceived usefulness. This result is consistent with the previous conclusions (Venkatesh & Davis, 2000) suggesting that job relevance exerts a direct effect on perceived usefulness, after conceptualizing job relevance to be similar to TTF. Thus, management attention might be more fruitfully focused on the development of social motivation. 6.2. Discussions A limited amount of research has examined the factors that influence MOOCs adoption and even fewer, the continued use of MOOCs. The model proposed in this study not only contributes in several ways to the existing literature but also helps researchers and practitioners gain a better understanding of user behaviors in MOOCs. This research has value because it reveals multiple statistically significant relationships that explain why individuals choose MOOCs and why they continue to use MOOCs. 6.2.1. Theoretical discussions The application of the TAM and the TTF model to the MOOCs outlined in this study not only provides more accurate results than the TAM and the TTF model do individually but also enhances our understanding of the mechanisms of TTF as they relate to nurturing MOOCs. In this respect, the TAM extended with the TTF model, MOOCs features and social motivation should be considered a valuable tool for exploring behavior in MOOCs contexts. The current study contributes to the existing literature in three important ways. First, we extend prior work on MOOCs by highlighting the importance of achieving individual-technology fit and tasktechnology fit. Our results suggest that continuance intention to

230


use MOOCs is indirectly affected by perceived ease of use, individual-technology fit, task-technology fit, openness, reputation, social recognition and social influence. The proposed integrated model provides a better explanation and richer insights than the individual perspective of task-technology fit. We found that in addition to task-technology fit, individual-technology fit also has a significant effect on perceived usefulness of MOOCs. The findings of this study can be used as a reference for future research on MOOCs with the TTF model. With regard to perceived usefulness, the proposed research model also provides a mechanism for understanding the effect of TTF on the perceived usefulness of MOOCs. The findings suggest that students are more likely to expend effort to use MOOCs if they feel that doing so is beneficial in terms of tasktechnology fit. Second, we highlight importance of managing the perceived usefulness and perceived ease of use through openness and reputation. These components increase students' perceived usefulness and perceived ease of use, which may positively affect their continuance usage of MOOCs. Reputation, among all antecedent variables, has the strongest impact on perceived usefulness of MOOCs. Therefore, the relative importance of MOOCs features in the proposed research model is confirmed. Finally, our model proposes social motivation for increasing the perceived usefulness and attitude towards using MOOCs. Social motivation includes social recognition and social influence, which are reflected by social identity and group norms, respectively. Our results indicate that social recognition and social influence have significant effects on perceived usefulness. These results advance our understanding of user cognition, which has been mainly examined from a technology acceptance perspective. Based on the results outlined above, perceived usefulness is a critical factor affecting the attitude towards and continuance intention to use MOOCs. Therefore, to strengthen the continuance intention to use MOOCs, the factor of usefulness becomes key in enhancing MOOCs services. It is worth noting that this study's results enhance the understanding of factors influencing students' continuance intention to use MOOCs. 6.2.2. Practical discussions In addition to the theoretical contributions, the results of this study also provide some implications for practice. The current findings indicate that reputation is the dominant predictor of perceived usefulness in MOOCs. With regard to practice, based on these findings, a number of salient implications and important guidelines for MOOCs practitioners can be proposed. First, MOOCs practitioners must be aware that continuance intention depends not only on attitude towards MOOCs but also on perceived usefulness. Moreover, perceived usefulness of MOOCs is a significant mediator of the effects from perceived ease of use, tasktechnology fit, reputation, and social motivation on continuance intention. Because perceived usefulness is the most important determinant of continuance intention, the continuance intention of students can be increased by improving their beliefs in the effectiveness of MOOCs. These findings indicate that it is not enough to build MOOCs with a modern interface and friendly screens to influence users' continuance intention (Guo, Xiao, Van Toorn, Lai, & Seo, 2016). MOOCs practitioners should prioritize useful function over ease of use. Second, this study provides evidence that the task-technology fit of MOOCs determines perceived ease of use and perceived usefulness, and individual-technology fit determines perceived usefulness mediated by perceived ease of use. Thus, MOOCs should be organized to clarify the requirements and challenges of courses, including the levels of prior knowledge needed and the availability of resources necessary for students. MOOCs practitioners should be

particularly aware of the importance of individual-technology fit and task-technology fit, rather than the general usability of the tools to better match the individual-task-technology context. By offering opportunities related to students' specific tasks, MOOCs practitioners could ensure fits between MOOCs and students' current requirements. Third, openness and reputation are ways in which MOOC providers can both differentiate themselves from competitors and enhance an individual's perceived usefulness of MOOCs enrollment to thus attract students for continuance usage. Therefore, MOOCs practitioners can differentiate themselves by continuing to offer courses from renowned faculty or institutions of higher education. Moreover, the effects of openness on perceived usefulness are mediated by perceived ease of use because most MOOCs are offered free of charge and few switching costs exists. MOOCs practitioners should focus on factors associated with the perceived ease of use , 2013) by making full use of trajectory (Guardia, Maina, & Sangra available rich multimedia capabilities to better facilitate a dynamic loop, so that more user-generated experiences will be exchanged. Fourth, the study finds that social motivation influences perceived usefulness, which is a means to promote positive attitudes. MOOCs practitioners that leverage these insights are likely to both acquire and retain students within their course offerings. On one hand, they may distinguish their course offerings from others by ensuring that their courses are useful for students. On the other hand, they must attach importance to the effect of social influence, for which they can use peer influence to facilitate continuance usage. They can also enhance a user's sense of recognition by the organization and society to promote adoption behavior. Accordingly, the continuance intention of MOOCs can be improved in a friendly collaborative learning environment.

7. Limitations and future research Although a rigorous and comprehensive study was conducted, a few limitations associated with this research do exist. First, we conducted this research in China, where MOOCs are developing rapidly but are still in their initial stages. Thus, survey respondents participated of their own volition which may reflect a self-selection bias (Roca, Chiu, & Martínez, 2006). As the population of MOOC users increases, the ability to perform random probabilistic sampling will also improve. Second, we mainly conducted a cross-sectional study. However, user behavior is dynamic, and longitudinal research may provide more insight into the development of user behavior. Thus, it is also necessary to gather longitudinal evidence if we are to deepen our understanding of the interrelationships or causality among variables relevant to technology acceptance. As such, a longitudinal research design is a potential avenue for future research. Third, the cross-sectional design of the study makes it difficult to determine causal effects among the constructs. Although 95.7% of the variance in the intention to use MOOCs is explained, the remaining variance remains unexplained, possibly due to factors excluded from our research model. Future research must examine extra constructs, which are related to TTF, MOOCs features and social motivation constructs. A final limitation of this research on behavior in MOOCs is relatively new to researchers. The findings and implications presented in this study must be generalized for external validity because they were obtained from only a single study that examined MOOCs and targeted a specific user group in China. Further research is expected to help generalize our findings and discussions to include different cultures in which MOOCs are utilized.


Constructs

Items

Perceived usefulness (PU) PU1 PU2 PU3 Perceive ease of use (PEU) PEOU1 PEOU2 PEOU3 Attitude toward using ATU1 MOOCs ATU2 ATU3 Continuance intention to CIIU1 use (CITU) CITU2 CITU3 Individual technology fit (ITF)

ITF1 ITF2

ITF3 Task-technology fit (TTF) TTF1 TTF2 TTF3 TTF4 Openness (OP) OP1 OP2 OP3 OP4 Reputation (RP)

RP1 RP2 RP3

Social recognition (SR)

RP4 SR1 SR2

Social influence (SI)

SR3 SI1 SI2 SI3

Measures

References

I believe MOOCs improve my learning performance. Using MOOCs enhances my learning effectiveness. Using MOOCs easily translates the learning material into specific knowledge. Learning to use MOOCs is easy. It is easy to become proficient in using MOOCs. The interaction with MOOCs is clear and understandable. I believe that using MOOCs is a good idea. I believe that using MOOCs is advisable. I am satisfied in using MOOCs. I intend to continue to use MOOCs in the future. I will continue using MOOCs increasingly in the future. My intentions are to continue using MOOCs in the future, at least as active as today. I can independently and consciously complete courses in MOOCs. I actively participate in various types of discussion and evaluation in MOOCs. I try to win the awards for outstanding performance in MOOCs. MOOCs are fit for the requirements of my learning. Using MOOCs fits with my educational practice. It is easy to understand which tool to use in MOOCs. MOOCs are suitable for helping me complete online courses. I have the freedom to join any course without prerequisites. I have the freedom to access and use the course resources and materials for free of charge. I can reuse the course resources in my work. I feel free to combine the course materials with other to produce new one. Good reputation of MOOCs platform offers courses I am interested in. MOOCs' partners Universities have a good reputation. MOOCs tend to provide courses by professors from high reputation universities. MOOCs' Courses are offered by prestigious Universities. It is important for MOOCs to be adopted as on-the-job training by employers. It is important for MOOCs' quality to be appreciated and accepted by others. It is important for MOOCs' credits to be confirmed by universities. Other participants' beliefs about MOOCs encourage me to use them. Other participants' beliefs about MOOCs influence my degree of usage of them. Other participants' beliefs about MOOCs condition me to use them.

Wu and Zhang (2014); Kim et al. (2010)

Appendix A. Survey items

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Wu and Zhang (2014); Chang (2010)

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Kim et al. (2010); Yu and Yu (2010); Larsen, Sorebo, and Sorebo (2009); Lu and Yang (2014)

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