The Role of Course Difficulty

(1)

Antecedents of student MOOC revisit intention: Moderation effect of course difficulty ⁶

Liqiang Huang

^a

, Jie Zhang

^b

, Yuan Liu

^a,∗

a School of Management, Zhejiang University, Hangzhou, China

b School of Information and Management Engineering, Zhejiang University of Finance and Economics, Hangzhou, China

A R TI C L E I N F O A B S T R A C T

Article history:

Received 29 March 2016 Received in revised form 10 November 2016 Accepted 19 December 2016 Available online 7 January 2017

Keywords:

Content vividness Teacher subject knowledge MOOC interactivity Course difficulty Intention to revisit

In response to the research gap in the current literature regarding the low student retention of Massive Open Online Courses (MOOCs), this study uses task-technology fit theory to understand how MOOCs’

technological factors in three dimensions (i.e., course vividness, teacher subject knowledge, and interactivity) influence students’ revisiting of MOOCs. Going deeper, this study also takes course difficulty into consideration and investigates the interactive effects of course difficulty on the main factors identified above. The empirical results show that the vividness of course content, teacher subject knowledge, and MOOC interactivity can positively affect students’ intention to revisit MOOCs. However, the relationships between the three dimensional factors and student intention to revisit are affected in different ways by course difficulty. Specifically, the findings show that course difficulty negatively moderates the relationship between course content vividness and students’ intention to revisit, and positively moderates the relationship between teacher subject knowledge and students’ intention to revisit. In addition, course difficulty typically does not have a significant influence on the relationship between technology interactivity and students’ intention to revisit. Theoretical and practical implications are discussed.

1. Introduction

The MOOC, an innovative educational model that has emerged in the last few years, has attracted broad attention from both researchers and practitioners. As with most new technologies, MOOCs have advantages and disadvantages. On the one hand, unlike traditional face-to-face education, this new model has few temporal and spatial limitations: Learners are able to access knowledge through a MOOC anytime and anywhere, so long as they have internet access (Kushik, Yevtushenko, & Evtushenko, 2016;

Saadatdoost, Sim, Jafarkarimi, & Mei Hee, 2015). MOOCs also offer learners greater choice, through multiple offerings of the same course or subject, or the opportunity to access reputable educational resources produced by distinguished professors from MIT or Harvard, which is not possible in traditional learning environments (Liyanagunawardena, 2015; Xing, Chen, Stein, & Marcinkowski, 2016). Meanwhile, MOOCs are also considered to be cost-effective

6 This paper was fully supported by grants from the National Nature Science Foundation of China (Project No: 71401152; 71401154).

∗ Corresponding author.

E-mail addresses: [email protected] (L. Huang), [email protected] (J. Zhang), [email protected] (Y. Liu).

because more people worldwide can share the cost, compared with traditional courses for a review, see Zhang, Zhao, and Zhou (2004).

On the other hand, both teachers and learners face challenges when using MOOCs. Preparing a MOOC course requires many times energy and time than is needed for the same course in the traditional learning environment, which significantly lowers teachers’

motivation to participate in MOOC teaching (Kolowich, 2013; Roth, 2013). Meanwhile, unlike in traditional learning environments, it is almost impossible for students to get direct responses to their questions during the learning process. Teacher-student interaction is also inefficient because, in most situations, one teacher instructs a large number of students scattered across the world (Daniel, 2012;

Martin, 2012).

As referred, although the advantages and disadvantages co-exist in both educational modes (i.e., MOOCs and traditional learning modes) (see Zhang et al., 2004; Zhang, 2016), as a new educational mode under the rapid development of innovative technology, MOOC has been widely argued having bright future in contribut- ing educational progress. For instance, in one seminal study, Zhang et al. (2004) argued that “in the midst of this transition, corpora- tions, government organizations, and educational institutions must understand the e-learning phenomenon and make strategic decisions on how to adopt e-learning techniques in their unique environments”.

Therefore, it is essential and potentially valuable that both corpo- http://dx.doi.org/10.1016/j.ijinfomgt.2016.12.002

Contents lists available at ScienceDirect

International Journal of Information Management

jou rn al h om epa ge : w w w. e l se v ier . co m/l ocate/ij in fo mgt

(2)

rations and educational institutions encourage learners to adopt a new type of learning mode. However, low rate participant retention is still a key problem widely existing (Baggaley, 2013; Zutshi, O’Hare, & Rodafinos, 2013). That is, even if learners attend a MOOC and gain relevant knowledge, many of them drop out before the end of the course. Thus, both practitioners and researchers call for research into the underlying reasons for this drop off, and for corresponding solutions (e.g., (Selwyn, Bulfin, & Pangrazio, 2015). There are few studies in this direction. To fill this gap, this study examines the related antecedents that may influence learners’ intention to revisit MOOCs.

Understanding learners’ (students, in our context) revisit behavior is imperative for both theoretical and practical reasons. In theoretical terms, most studies examining MOOCs focus on initial participation (e.g., (Barak, Watted, & Haick, 2016; Littlejohn, Hood, Milligan, & Mustain, 2016; Zhang, 2016), while little is understood of students’ revisit behavior. Although these studies offer a great deal of rich knowledge, the exploration of students’ MOOC revisit behavior remains necessary. In addition, considering the nature of human behavior, the antecedents of initial visits and revisits should logically be different (Chiu & Wang, 2008; De Guinea & Markus, 2009). When a student initially attends a MOOC, teachers’ and/or peers’ encouragements are dominant factors; students’ own initial experiences are more influential in regard to their revisit behavior.

The exploration of this issue can extend researchers’ understanding of motivation in different stages of learning. In practical terms, this study can, to some extent, provide a series of guidelines for both platforms and teachers on how to encourage learners to revisit MOOCs, thus improving their retention rate.

Drawing on the advertising persuasion framework and task- technology fit theory, we propose that students’ intention to revisit MOOCs is not only influenced by MOOCs’ technological factors, manifested by MOOC course vividness, teacher subject knowledge, and interactivity, but also by the underlying interaction effects aroused by the nature of the course itself (course difficulty). The findings of this study make several theoretical contributions to the literature. For instance, as noted, MOOCs’ low retention rates are widely acknowledged as a serious practical issue, but few empirical academic studies have explored the underlying reasons for this.

This study represents an initial attempt to uncover key dimensional antecedents, thus supplementing the extant knowledge. In addition, unlike a considerable number of studies that take task- technology fit as a cohesive concept (e.g., Dishaw & Strong, 1999;

Lee & Lehto, 2013), this study examines the technology itself from three dimensional factors (i.e., content vividness, teacher subject knowledge, and interactivity), and shows that these dimensions and their influence on students’ intention to revisit MOOCs are affected in different ways by course difficulty.

The rest of the paper is organized as follows. In Section 2, we introduce the underlying theory; based on that theory, we also propose a series of hypotheses for further testing. In Section 3, in order to test the related hypotheses, we introduce the research method used, describe how the study was conducted, and present the results. The findings are discussed in Section 4, where we compare the findings with those in the literature, and detail their implications.

2. Literature review and hypotheses 2.1. MOOCs

MOOCs, as a specific e-learning style, have been widely used in corporations, government organizations, and educational institutions for training and learning, for a review, see Chang (2016).

Although scholars have argued that it is essential to understand

how to adopt e-learning techniques in unique environments (Zhang et al., 2004), the extant literature mainly focuses on the initial adoption of e-learning, leaving room for a large exploration of the continuous adoption issue. Relating to student education, research has revealed that students’ intention to use a new technology like a MOOC can be widely influenced by factors including learners’ own personality characteristics e.g., (Zhang 2016) and their motivations e.g., (Littlejohn et al., 2016), teacher support and encouragement e.g., (McGill & Klobas, 2009), peer influence/norms e.g., (Lin, Zimmer, & Lee, 2013; Zhao, Lu, Wang, & Huang, 2011), and self-efficacy (Kuo, Tseng, Lin, & Tang, 2013). The findings of these works typically provide rich knowledge. However, as the revisit issue has rarely been the focus of these studies, the differences between initial intention and revisit intention have largely not been discussed (Alraimi, Zo, & Ciganek, 2015). In fact, initial and revisit behavior share distinct characteristics (Lee, 2010; Lin, 2011). For instance, learners’ initial adoption of MOOCs can be affected by peers’ suggestions and teacher support or encouragement, but their revisit behavior is more likely to be determined by their own experiences and evaluations of MOOCs (Lee and Choi, 2013). If they have good experiences and assess MOOCs positively, they are more likely to revisit MOOCs.

In broader areas, some interesting findings have been uncov- ered from previous studies in relation to continuous e-learning. For instance, Chiu et al. (2007) identified that students’ continuance intention to learn is greatly influenced by their satisfaction, which is further determined by various types of value, such as attainment value, utility value, and intrinsic value. Lee (2010) also demonstrated that students’ continuance intention is greatly impacted by their satisfaction in initial usage, but, different from Chiu et al.

(2007) work, suggested that satisfaction is greatly determined by whether or not students’ initial expectations are confirmed. Fur- ther, extending the previous knowledge, Lin (2011) took students’

own causal attribution into consideration and further demonstrated that casual attribution is a key factor that determines students’ satisfaction in initial learning, thus further impacting continuance behavior. In addition, Terzis et al. (2013) focus on exploring the relationships among the system’s ease of use, usefulness, confirmed content, and continuance behavior. To summarize, it is realized the great value these studies hold in providing new knowledge through which we can understand continuance learning. However, the aforementioned studies examine continuance learning behavior from a relatively abstract level through studying students’ overall satisfaction and their confirmation perception, while little is known about the detailed antecedents that determine learners’ continuance intention. This study moves in this direction, drawing on the nature characteristics of MOOC technology and the complex nature of the course itself in exploring the underlying mechanisms and relationships.

Different from traditional education model that students and teacher can interact with each other directly, MOOC, as a new model, relies on the technology to transfer information from teacher to students (Lee & Lehto, 2013). That is, under the MOOC educational model, students learn from the MOOC technology itself.

Therefore, whether a MOOC course can play its significant role on influencing students is much more like the persuasion process of advertising, which leads us to consider what the main factors are having to be included in studying the MOOC on students. Accordingly, before we specify the related factors, we draw on the advertising persuasion framework (Sundar & Kim, 2005), which suggest that whether a piece of advertising could be per- suasive is determined by the quality of the advertising includes;

how the content is transferred (i.e., ad shape); and the perceived interactivity by consumers. Considering the nature characteristics of MOOCs, it is not difficult to theorize that whether a MOOC can elicit students to revisit is typically impacted by three decisive fac-

(3)

tors, i.e., whether the teacher of the MOOC is knowledgeable (Shih

& Chuang, 2013); whether the content is vivid (Al-Samarraie, Teo, &

Abbas, 2013; Blasco-Arcas, Buil, Hernández-Ortega, & Sese, 2013);

and whether the presentation mean is interactive (Lee and Lehto, 2013). In line with previous studies, e.g., Al-Samarraie et al. (2013) and Shih & Chuang (2013), we conceptualize course content vividness as the degree to which a course’s presentation is rich and attracts students’ attention; teachers’ subject knowledge is defined as the degree to which a teacher is perceived by students to have mastered a subject.

In order to clearly define the MOOC interactivity, we revert back to the previous literature. Our comprehensive review of the extant literature shows that there are two seminal types of interactivity having been widely used and discussed, namely functional view of interactivity and a contingency view of interactivity (Sundar, Kalyanaraman, & Brown, 2003). A medium’s functional interactivity refers to whether a medium includes some functions or features that enable people to perceive that the medium is interactive, but not relate to the actual behavior. For instance, a website that inlays features such as a video download button or chat rooms are perceived as having higher interactivity than the ones without such provisions (Massey & Levy, 1999). Similarly, in the context of advertising, a piece of advertising that is constituted by rich features could also be perceived as having higher levels of interactivity (Coyle & Thorson, 2001). Different from a functional view of interactivity, a contingency view of interactivity is typically related to the actual behavior, rather than the perception itself (see Sundar &

Kim, (2005)). That is, from a contingency view of interactivity, specific messages should be transferred in the process of interaction, to either a user-to-user interaction or a user-to-computer interaction.

Relating to our study, we consider the interactivity with the former type, i.e., functional interactivity, and conceptualize the interactivity as the degree to which the MOOC includes rich presentations of interactions (i.e., a MOOC is considered to have higher levels of interactivity when the MOOC includes frequent discussions between teacher and students, and relatively lower levels of interactivity without any interactions between teacher and students; namely, the teacher of the MOOC simply presents most of the course content, leaving less room for interaction with students). In addition to presenting an understanding of the manifestations of MOOC technology, we also consider the complexity of a course in order to understand whether technology manifestations are similarly important to any course (Gialamas, Nikolopoulou, &

Koutromanos, 2013). In this study, the course difficulty is defined as the degree of complexity that is perceived by students through considering the course nature. For instance, some students may consider mathematics as being especially difficult, while history is relatively easier, but other students may perceive otherwise. We examine students’ evaluations in relation to course difficulty in order to uncover whether the MOOC learning mode is suitable for some courses but not for others.

In order to examine how students’ personal experiences of MOOC technology and the nature of MOOCs affect revisit intention, we draw on the MOOC technology literature and task-technology fit theory.

2.2. Impacts of MOOC technology

In line with traditional educational modes, whether a course is perceived as good and will elicit students’ further intention to revisit the course is always greatly determined by the teacher’s knowledge (Blasco-Arcas et al., 2013; McCutchen et al., 2002). In our specific research context, there is no evidence that can reject this proposition and we also conjecture that the teacher’s knowledge is especially important in whether a MOOC course can be evaluated with higher quality, thus leading to students’ further

revisit intention. Therefore, the relationship between a teacher’s subject knowledge and students’ intention to revisit should be positively significant. With the same logic, other than the importance of a teacher’s knowledge on a typical course, good preparation (e.g., vividness of course content) of the course content is also especially significant (Desai, Hart, & Richards, 2008) for both educational modes, especially under the MOOC situation because, as mentioned, students cannot directly and instantly interact with teachers as they can in traditional face-to-face educational modes, which makes the provision of vivid content of prominent importance (Lee & Lehto, 2013). We posit:

H1. The vividness of course content is positively associated with students’ intention to revisit.

H2. Teachers’ subject knowledge is positively associated with students’ intention to revisit.

Generally, in a traditional educational model, whether a course is perceived as high quality is mainly determined by the teacher and content themselves. However, in MOOC educational model, even courses by the same teacher with the same content can be differently organized or presented. For example, the course could be delivered in a classroom with a large quantity of students, or it could be presented in a way that includes the teacher and slides only, without any student appearing in the video. These two different types of organization would typically cause different levels of attendance perception of students in their learning (Bringula, 2013; Bulu, 2012). As mentioned, in our context, the interactivity is defined as the degree of interactions between a teacher and learners in MOOC video. The interactivity is especially significant for students watching the MOOC video because MOOCs cannot provide any direct interaction between teachers in the video and students who are watching, and so it is impossible for students to have prompt responses to their questions from the teacher, which may in fact be delayed. While frequent teacher-student interactivity in a MOOC course, to some extent, not only provides rich social perceptions (Fortin & Dholakia, 2005), it also enables students to learn more from the interactions between the teacher and learners in the MOOC, thus increasing the possibility of the students’ own potential questions being addressed. Therefore, we hypothesize:

H3. Interactivity is positively associated with students’ intention to revisit.

2.3. Task-technology fit

The notion of fit has been widely discussed in various disciplines, generating a number of helpful theories. For instance, focusing on individual decision-making, Aljukhadar et al. (2014) pioneering work proposed and demonstrated that, in order to fulfill a specific task, an appropriate presentation of the problem is essential; the effectiveness of the solution is contingent upon how the problem is presented. This proposition, which is the basis of cognitive fit theory, has been widely tested in a variety of studies (Chen, Vogel,

& Wang, 2015; Shaft & Vessey, 2006). In addition, on the basis of a series of empirical findings, scholars have suggested that providing the most resources possible is not the best way to address a problem, but a match between task requirements and resource provision generates a better outcome (Mantel and Kellaris, 2003).

This stream of knowledge is called resource matching theory (Tan, Teo, & Benbasat, 2010). Other related theories, such as regulatory fit theory (Higgins, 1998, 2005), can also be referred to in order to better understand how fit plays a significant role in judgment, decision-making, and task fulfillment. The central thesis of fit theories suggests that the quality of decision-making or task fulfillment is always decided by two main factors, and task-technology fit theory is no exception (Goodhue & Thompson, 1995; Lin, 2006).

(4)

Fig. 1. Research Model.

Based on the main effects of the three dimensional factors of MOOC technology and in line with task-technology fit theory, we conjecture that the effects of MOOC technology on students’

intention to revisit are affected by the very nature of the courses (i.e., course difficulty). Specifically, although it is hypothesized that teachers with better subject knowledge, vivid course content, and greater interactivity could inspire learners to revisit MOOCs, this does not mean that all courses need all three dimensional factors to be optimal. Take course difficulty as an example: It is obvious that the more complex a course, the higher expectations and requirements students have of teachers’ knowledge and content vividness (Lin & Huang, 2008), because more knowledgeable teachers with more vivid content can be more beneficial to students when they face difficult courses. On the other hand, it is common that students generate more questions and concerns in understanding the course content when taking a perceived difficult course, which makes them be more eager to learn something from peers’ questions, such as the interactions between learners and teacher in the MOOC video. This leads us to consider that more frequent teacher-student interactivity in a complex course can be especially helpful for students. Based on this, we conjecture that the higher the complexity of a course, the stronger the effects of these dimensional factors (i.e., teacher subject knowledge, content vividness and interactivity) on students’ revisit intention. We hypothesize (Fig. 1):

H4. Course difficulty positively moderates the effect of content vividness on students’ intention to revisit.

H5. Course difficulty positively moderates the effect of teachers’

subject knowledge on students’ intention to revisit.

H6. Course difficulty positively moderates the effect of interactivity on students’ intention to revisit.

3. Research methodology

A survey method was adopted to test the hypotheses in order to consider the generalizability issues (Dooley, 2001).

3.1. Operationalization of constructs

and Lin et al. (2014): 1) “What is the likelihood that you would learn with MOOC again?” 2) “How likely are you to learn with a MOOC again?” and 3) “I think I will learn with a MOOC again”.

Items measuring course content vividness were adapted from Jiang

& Benbasat (2007) and Lee & Lehto (2013): 1) “Procedure instructional content on MOOCs is animated”; 2) “Procedure instructional content on MOOCs is lively”; 3) “MOOCs contain procedure instructional content that is exciting to the senses”; and 4) “I can acquire procedure instructional content on MOOCs from different sensory channels”. The construct of teachers’ subject knowledge was mea- sured by items derived from Shih & Chuang (2013): 1) “The teacher knows the content that he/she teaches very well”; 2) “The teacher knows the development and history of the theories and principles of the subject”; 3) “The teacher makes good decisions regarding the depth, scope, and extension of concepts taught”; and 4) “The teacher does a good job of planning the sequence of concepts taught in class”. Items measuring perceived interactivity were adapted from Al-Samarraie et al. (2013) and Blasco-Arcas et al. (2013): The interactivity of teacher and students in MOOCs 1) “Enables me to understand the content better”; 2) “Enables me to learn more from the course”; 3) “Enables me to use summaries and compare them with others”; and 4) “Enables me to address my concerns”. Items relating to course difficulty were derived from Sun et al. (2012) and Gialamas et al. (2013): 1) “I find that completing MOOCs is: 1 = not difficult at all, 7 = extremely difficult”; 2) “Completing MOOCs is a challenge to me”; and 3) “I find MOOCs very complex”. All questions were designed to be answered on a seven-point scale, from

“strongly disagree” to “strongly agree”.

3.2. Data collection

The data were collected from two large public universities in China. University students were chosen as the main sample focus of this study because they constitute the majority of MOOC learners. Although we acknowledge this may affect the generalizability of our findings, university student samples are still ideal for this study. The data collection was undertaken in the students’ classes, and the samples from the two difference universities face different courses.¹The detail procedure is as follows. At the beginning of the All the items used to measure the constructs were adapted from

previous studies in order to ensure their validity (Stone 1978). Items measuring students’ revisit intention were adapted from Lee (2010)

1 Students from the two universities watched two different MOOCs, for the follow- ing reasons: First, the in-class courses offered by these two universities are different,

(5)

class, the importance of MOOC education in both students’ in-class learning and self-motivated after-class learning was explained to the students. After that, they were also told that MOOCs, as a supplementary tool for education, will also be used in this class.

Meanwhile, the students were further informed that some of the content introduced in the MOOC they were about to watch would appear in their final exam, in order to motivate their involvement.

They were then invited to watch a specific MOOC that was closely related to the in-class course they were then attending (i.e., students in university A watched a different MOOC video from the students in university B, but the MOOC video is from the same specific course the student took). When the MOOC video had finished, the students were asked to complete a questionnaire. They were not declared regarding the objective of this research. Ultimately, 246 questionnaires were collected. The average age of the respondents was 20.03 (SD = 1.146) and 26.83% were male.

3.3. Data analysis

All of the cases were used in the final data analysis without any exclusion. We adopt the most widely used technique suggested by Podsakoff, MacKenzie, Lee, and Podsakoff (2014) i.e., Harman’s single-factor test, to check for common method bias. We per- form exploratory factor analysis with unrotated factor solution. The results show that (a) we did not find a single factor that emerged from the factor analysis; and (b) three factors with eigenvalues larger than 1 were extracted, which indicates that common method bias is likewise of no major concern in this paper. The reliability of the constructs was assessed by Cronbach’s alpha. Values larger than 0.70 indicate good reliability. The Cronbach’s alpha values of the constructs were as follows: course content vividness (0.915), teacher subject knowledge (0.912), interactivity (0.884), course difficulty (0.890), and intention to revisit MOOC learning (0.865). All of the constructs thus had adequate reliability. In testing validity, two dimensions must be considered: convergent validity and discriminant validity. Convergent validity is used to assess whether items within the same construct are highly correlated with each other. Discriminant validity is used to assess whether items load more on their intended construct than on others (Lai & Chen, 2011).

Construct validity was tested using factor analysis with principal component analysis and varimax rotation. A loading between 0.45 and 0.54 is generally considered fair; loading between 0.55–0.62 is good; loading between 0.63–0.70 is very good; and loading is considered excellent if it is higher than 0.71 (Comrey & Lee, 2013).

The factor loading analysis indicated that all the constructs in the model had both good convergent and discriminant validity (see Table 1). Meanwhile, we also performed the correlations among all the variables (including the control variables) to make a robustness examination of discriminant validity (Fornell & Larcker, 1981). As shown in Table 2, we found that the square root of AVE is greater than the correlations for all constructs, indicating sound discriminant validity.

3.4. Hypotheses testing

PLS software was used to the test the research model (the results are shown in Fig. 2). In order to obtain rigorous results, control variables, such as students’ age, gender, and their expe-

which required us to design two different MOOC courses; second, the main objective of this study is to examine the dimensional factors that influence students’ revisit intention, which includes content vividness, teachers’ subject knowledge, and interactivity. We used two different MOOCs because, theoretically, this gives results that are more generalizable, as different courses typically have different levels of these dimensions and students’ evaluations of their complexity therefore differ.

rience with MOOCs, were also taken into consideration. None of the control variables had a significant influence on the final results.

As regard to the main model, the results show that content vividness (β = 0.271, p < 0.05), teacher subject knowledge (β = 0.323, p < 0.001), and interactivity (β = 0.254, p < 0.001) significantly and positively influenced students’ intention to revisit MOOCs. This implies that better MOOC design, including course content vividness, teacher knowledge, and good interactivity, can drive students’

intention to learn from MOOCs. Thus, hypotheses 1, 2, and 3 are fully supported. With respect to the moderation effects, interesting findings are revealed: The results show that course difficulty negatively affected the relationship between content vividness and students’

intention to revisit (β = −0.147, p < 0.05), positively affected the relationship between teacher subject knowledge and students’

intention to revisit (β = 0.137, p < 0.05), and had no significant influence on the relationship between technology interactivity and students’ intention to revisit (β = −0.029, p > 0.05). Fig. 3 presents the characteristics regarding the way in which the moderation effect happens. Therefore, hypothesis 5 is supported, but hypotheses 4 and 6 are not supported.

4. Discussions 4.1. Findings

This study explores the antecedents of student MOOC revisit intention. The empirical findings show that the three dimensional factors of MOOCs − the course content itself, teacher subject knowledge, and MOOC interactivity − positively influence students’

intention to revisit MOOCs. These findings are consistent with previous studies on traditional educational environments, which show that teachers, course content, and MOOC interactivity are the main determinants in students’ motivation to learn (e.g., (Shih &

Chuang, 2013; Zhao et al., 2011). With respect to the nature of different courses (i.e., course difficulty), the findings further suggest that course difficulty negatively affects the relationship between the richness of course content and students’ intention to revisit MOOCs, but positively affects the relationship between teacher subject knowledge and students’ intention to revisit MOOCs. In other words, the more complex a course is perceived by students, the less powerful the effect of course content richness on their intention to revisit, while the effect of teacher subject knowledge on their intention to revisit increases. This may because content vividness is less likely to help students learn if the course is complex, but teachers’ knowledge and the way they present the subject matter can help students better understand the central thesis of even complex course content. These findings reveal the underlying dynamic relationship between course content itself and teacher subject knowledge. Although both course content and teacher subject knowledge are important in any situation, teachers’ knowledge is more important to students’ learning when the course is complex.

The findings also show that interactivity does not make a significant difference in either relatively easy or complex courses.

4.2. Implications

The findings of this study have several implications for both theory and practice.

First, this is an initial attempt to explore the antecedents of students’ intention to revisit MOOCs. Drawing on advertising persuasion framework, the findings show that the three dimensional factors of MOOC technology (i.e., course content vividness, teacher subject knowledge, and interactivity) significantly influence students’ intention to revisit MOOCs. Since this study has not employed a comprehensive theoretical framework, including

(6)

Table 1

Factor Loading Analysis.

Content Vividness Teacher Subject Knowledge Interactivity Course Difficulty Intention to Revisit

(CV) (TSM) (INT) (CC) (ItoR)

CV 1 0.702 0.365 0.270 0.171 0.202

CV 2 0.843 0.190 0.250 0.146 0.175

CV 3 0.727 0.275 0.211 0.123 0.315

CV 4 0.775 0.281 0.212 0.196 0.227

TSM 1 0.193 0.738 0.323 0.096 0.286

TSM 2 0.355 0.761 0.258 0.179 0.206

TSM 3 0.310 0.723 0.283 0.080 0.279

TSM 4 0.354 0.686 0.354 0.136 0.210

INT 1 0.311 0.206 0.696 0.121 0.113

INT 2 0.238 0.219 0.789 0.074 0.299

INT 3 0.180 0.273 0.797 0.166 0.243

INT4 0.158 0.294 0.740 0.130 0.181

CC 1 0.070 0.028 0.115 0.885 0.118

CC 2 0.175 0.140 0.156 0.879 0.044

CC 3 0.161 0.130 0.070 0.870 0.098

ItoR 1 0.299 0.324 0.221 0.139 0.710

ItoR 2 0.354 0.236 0.277 0.153 0.714

ItoR 3 0.180 0.234 0.271 0.076 0.809

Table 2 Correlations.

Intention to Revisit Interactivity MOOC experience Subject Knowledge Course Difficulty Vividness Age Gender Intention to Revisit 0.887

Interactivity 0.647 0.862

MOOC experience 0.136 0.133 1.000

Subject Knowledge 0.699 0.708 0.220 0.890

Course Difficulty 0.331 0.341 0.019 0.346 0.906

Vividness 0.676 0.628 0.096 0.730 0.395 0.893

age −0.017 −0.005 −0.140 −0.024 0.184 0.017 1.000

gender 0.126 0.186 0.385 0.222 0.139 0.124 −0.053 1.000

Fig. 2. Results.

all the major antecedents of students’ revisit intention behavior, the findings serve to provide an initial insight; further research is required in this direction. In addition, compared with previous studies regarding an understanding of continuance behavior from relatively abstract levels (e.g., ease of use; usefulness and satisfaction) (Chiu, Sun, Sun, & Ju, 2007; Lee, 2010), this study is successful in uncovering the underlying effects of MOOC technology itself on continuance learning behavior.

Second, the findings of this study also contribute to the applica- tion of task-technology fit theory. A considerable number of studies have applied this theory in the examination of problems in different contexts. However, most have approached technology from a particular perspective (i.e., scholars consider a technology as a uni- fied concept in its measurement) (Dishaw & Strong, 1999; Lee &

Lehto, 2013), while typically neglecting the fact that a technology can be perceived from various aspects, which may transmit differ-

(7)

Fig. 3. Moderation effects of course content vividness and teacher subject knowledge.

ent meanings to people. Relating to this specific research, MOOC technology is typically perceived from different dimensions and these dimensional factors play different roles in influencing students’ intention to revisit in contexts of differing course difficulty.

The empirical investigation provides additional knowledge to the current literature.

Third, it is well established that both course content vividness and teacher subject knowledge are especially important to students’ learning (Lee & Lehto, 2013; Shih & Chuang, 2013); however, it is scantly researched regarding whether these two factors are always constantly important. The findings of this study uncover the different situational dynamics, i.e., with the increasing complexity of a course, teacher knowledge is of prominent significance, compared with course content vividness, which offers new knowledge about the effect of course content and suggests that the effect of teacher knowledge on students’ learning intention with MOOC is contingent on the course difficulty.

Fourth, this study has successfully identified a moderator variable that influences the effects of antecedents on students’

intention to revisit MOOCs. As noted, the way in which the characteristics of MOOC technology influence students’ revisit intention is typically affected by the complexity of the course itself. The findings not only extend the literature with new theoretical knowledge, but also provide insightful practical implications. Based on these findings, MOOC platform managers and university teachers should pay great attention to providing “fit” courses to students for their learning needs. Although course content, teacher subject knowledge, and interactivity are important to students, if a course is complex, it is more important for practitioners to have excellent subject knowledge. The two other dimensions (i.e., course content vividness and interactivity) are relatively less important.

4.3. Limitations and research directions

As with all studies, this work has its limitations, which may be mitigated by future research.

First, the exploration of antecedents to the intention to revisit MOOCs is valuable. However, this study used only the task- technology fit framework to understand how course content vividness, teacher subject knowledge, and interactivity influence students’ intention to revisit under the consideration of course difficulty, while leaving room for the exploration of other factors that potentially influence students’ intention to revisit behavior.

Future studies could explore a wide range of variables to provide a comprehensive framework for understanding this phenomenon.

Meanwhile, this study focuses on students’ learning only, and it is little understood in industrial area, for a review, see Chang (2016).

It is significant for future research in exploring the antecedents that influence learners’ continuance behavior in industry or government organizations.

Second, a survey method was used in this study to test the proposed hypotheses. Although this is recognized as a useful way to explore such problems, survey data are typically obtained in an uncontrolled environment. We therefore call for future research to use other methods, such as a controlled lab experiment, or students’

self-reported interviews, to obtain more findings from different channels.

Third, in our study, interactivity is conceptualized as the interactions between students and teacher in MOOC video, but does not take student–student interactions into consideration. As a matter of fact, the student–student interaction is very important in this context. Future studies could consider extending the conceptualization of interactivity and investigating it in a much more comprehensive way.

5. Conclusion

The MOOC, as an innovative educational model, poses a series of challenges to the traditional education model. MOOCs’ success faces many challenges, such as how to attract learners to attend and how to address low learner retention. The development of such an innovative educational model certainly offers researchers great opportunities for future exploration. This study, based on the knowledge gap in the current literature regarding MOOC revisit behavior, has examined the antecedents of students’ intention to revisit MOOCs under the consideration of course difficulty. The findings provide both theoretical and practical implications. How- ever, there is still a very long way to go in this research direction and we call for further research to expand our knowledge.

References

Al-Samarraie, H., Teo, T., & Abbas, M. (2013). Can structured representation enhance students’ thinking skills for better understanding of E-learning content? Computers & Education, 69, 463–473.

Aljukhadar, M., Senecal, S., & Nantel, J. (2014). Is more always better? Investigating the task-technology fit theory in an online user context. Information &

Management, 51(4), 391–397.

Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: the role of openness and reputation. Computers & Education, 80(1), 28–38.

Baggaley, J. (2013). MOOC rampant. Distance Education, 34(3), 368–378.

Barak, M., Watted, A., & Haick, H. (2016). Motivation to learn in massive open online courses: examining aspects of language and social engagement.

Computers & Education, 94, 49–60.

Blasco-Arcas, L., Buil, I., Hernández-Ortega, B., & Sese, F. J. (2013). Using clickers in class. The role of interactivity, active collaborative learning and engagement in learning performance. Computers & Education, 62, 102–110.

Bringula, R. P. (2013). Influence of faculty- and web portal design-related factors on web portal usability: a hierarchical regression analysis. Computers &

Education, 68(10), 187–198.

Bulu, S. T. (2012). Place presence, social presence, co-presence, and satisfaction in virtual worlds. Computers & Education, 58(1), 154–161.

Chang, V. (2016). Review and discussion: e-learning for academia and industry.

International Journal of Information Management, 36(3), 476–485 [6//].

(8)

Chen, Z.-J., Vogel, D., & Wang, Z.-H. (2015). How to satisfy citizens? Using mobile government to reengineer fair government processes. Decision Support Systems, 82, 47–57.

Chiu, C.-M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: the role of subjective task value. Information & Management, 45(3), 194–201.

Chiu, C.-M., Sun, S.-Y., Sun, P.-C., & Ju, T. L. (2007). An empirical analysis of the antecedents of web-based learning continuance. Computers & Education, 49(4), 1224–1245.

Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Psychology Press.

Coyle, J. R., & Thorson, E. (2001). The effects of progressive levels of interactivity and vividness in web marketing sites. Journal of Advertising, 30(3), 65–77.

Daniel, J. (2012). Making sense of MOOCs: musings in a maze of myth, paradox and possibility. Journal of Interactive Media in Education, 3(18).

De Guinea, A. O., & Markus, M. L. (2009). Why break the habit of a lifetime?

Rethinking the roles of intention, habit, and emotion in continuing information technology use. MIS Quarterly, 33(3), 433–444.

Desai, M. S., Hart, J., & Richards, T. C. (2008). E-learning: paradigm shift in education. Education, 129(3), p327.

Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs, Information &. Management, 36(1), 9–21.

Dooley, K. (2001). Year social research methods. NJ, Citeseer: Upper Saddle River [4th ed.].

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Fortin, D. R., & Dholakia, R. R. (2005). Interactivity and vividness effects on social presence and involvement with a web-based advertisement. Journal of Business Research, 58, 387–396.

Gialamas, V., Nikolopoulou, K., & Koutromanos, G. (2013). Student teachers’

perceptions about the impact of internet usage on their learning and jobs.

Computers & Education, 62(0), 1–7 [3//].

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.

Higgins, E. T. (1998). Promotion and prevention: regulatory focus as a motivational principle. Advances in Experimental Social Psychology, 30, 1–46.

Higgins, E. T. (2005). Value from regulatory fit. Current Directions in Psychological Science, 19(4), 209–213.

Jiang, Z., & Benbasat, I. (2007). Research note- Investigating the influence of the functional mechanisms of online product presentations. Information Systems Research, 18(4), 454–470.

Kolowich, S. (2013). The professors who make the MOOCs. The Chronicle of Higher Education, 59(28), 20–23.

Kuo, F.-Y., Tseng, F.-C., Lin, C. I. C., & Tang, W.-H. (2013). Critical success factors for motivating and sustaining women’s ICT learning. Computers & Education, 67(0), 208–218 [9//].

Kushik, N., Yevtushenko, N., & Evtushenko, T. (2016). Novel machine learning technique for predicting teaching strategy effectiveness. International Journal of Information Management [forthcoming].

Lai, H.-M., & Chen, C.-P. (2011). Factors influencing secondary school teachers’

adoption of teaching blogs. Computers & Education, 56(4), 948–960.

Lee, Y., & Choi, J. (2013). A structural equation model of predictors of online learning retention. Internet and Higher Education, 16, 36–42.

Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: an extension of the Technology Acceptance Model. Computers &

Education, 61, 193–208 [2//].

Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: an extension of the expectation–confirmation model. Computers &

Education, 54(2), 506–516.

Lin, T.-C., & Huang, C.-C. (2008). Understanding knowledge management system usage antecedents: an integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410–417.

Lin, S., Zimmer, J. C., & Lee, V. (2013). Podcasting acceptance on campus: the differing perspectives of teachers and students. Computers & Education, 68, 416–428 [10//].

Lin, H., Fan, W., & Chau, P. Y. (2014). Determinants of users’ continuance of social networking sites: a self-regulation perspective. Information & Management, 51(5), 595–603.

Lin, A. (2006). The acceptance and use of a business-to-business information system. International Journal of Information Management, 26(5), 386–400.

Lin, K.-M. (2011). e-Learning continuance intention: moderating effects of user e-learning experience. Computers & Education, 56(2), 515–526 [2//].

Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs:

motivations and self-regulated learning in MOOCs. Internet and Higher Education, 29, 40–48.

Liyanagunawardena, T. R. (2015). Massive open online courses. Humanities, 4(1), 35–41.

Mantel, S. P., & Kellaris, J. J. (2003). Cognitive determinants of consumers’ time perceptions: the impact of resources required and available. Journal of Consumer Research, 29(4), 531–538.

Martin, F. G. (2012). Will massive open online courses change how we teach?

Communications of the ACM, 55(8), 26–28.

Massey, B. L., & Levy, M. R. (1999). Interactivity, online journalism, and English-language Web newspapers in Asia. Journalism & Mass Communication Quarterly, 76(1), 138–151.

McCutchen, D., Abbott, R. D., Green, L. B., Beretvas, S. N., Cox, S., Potter, N. S., et al.

(2002). Beginning literacy links among teacher knowledge, teacher practice, and student learning. Journal of Learning Disabilities, 35(1), 69–86.

McGill, T. J., & Klobas, J. E. (2009). A task–technology fit view of learning management system impact. Computers & Education, 52(2), 496–508 [2//].

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

Roth, M. S. (2013). My modern experience teaching a MOOC. Chronicle of Higher Education, 59(34), 18–21.

Saadatdoost, R., Sim, A. T. H., Jafarkarimi, H., & Mei Hee, J. (2015). Exploring MOOC from education and Information Systems perspectives: a short literature review. Educational Review, 67(4), 505–518.

Selwyn, N., Bulfin, S., & Pangrazio, L. (2015). Massive open online change?

exploring the discursive construction of the ‘MOOC’ in newspapers. Higher Education Quarterly, 69(2), 175–192.

Shaft, T. M., & Vessey, I. (2006). The role of cognitive fit in the relationship between software comprehension and modification. MIS Quarterly, 30(1), 29–55.

Shih, C.-L., & Chuang, H.-H. (2013). The development and validation of an instrument for assessing college students’ perceptions of faculty knowledge in technology-supported class environments. Computers & Education, 63, 109–

118.

Stone, E. F. (1978). Research methods in organizational behavior. Goodyear Publishing Company.

Sun, Y., Fang, Y., & Lim, K. H. (2012). Understanding sustained participation in transactional virtual communities. Decision Support Systems, 53(1), 12–22.

Sundar, S. S., & Kim, J. (2005). Interactivity and persuasion: influencing attitudes with information and involvement. Journal of Interactive Advertising, 5(2), 5–18.

Sundar, S. S., Kalyanaraman, S., & Brown, J. (2003). Explicating Web Site interactivity impression formation effects in political campaign sites.

Communication Research, 30(1), 30–59.

Tan, C.-H., Teo, H.-H., & Benbasat, I. (2010). Assessing screening and evaluation decision support systems: a resource-matching approach. Information Systems Research, 21(2), 305–326.

Terzis, V., Moridis, C. N., & Economides, A. A. (2013). Continuance acceptance of computer based assessment through the integration of user’s expectations and perceptions. Computers & Education, 62, 50–61 [3//].

Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: reaching the low hanging fruit through stacking generalization. Computers in Human Behavior, 58, 119–129.

Zhang, D., Zhao, J. L., Zhou, L., & Nunamaker, J. F., Jr. (2004). Can e-learning replace classroom learning? Communications of the ACM, 47(5), 75–79.

Zhang, J. (2016). Can MOOCs Be interesting to students? an experimental investigation from regulatory focus perspective. Computers & Education, 95, 340–351.

Zhao, L., Lu, Y., Wang, B., & Huang, W. (2011). What makes them happy and curious online? An empirical study on high school students’ Internet use from a self-determination theory perspective. Computers & Education, 56(2), 346–356.

Zutshi, S., O’Hare, S., & Rodafinos, A. (2013). Experiences in MOOCs: the perspective of students. American Journal of Distance Education, 27, 218–227.

Liqiang Huang is an associate professor of information systems at Zhejiang Univer- sity. His research interests include electronic commerce, platform ecosystem as well as students learning. His work has appeared in IS journals like Journal of Management Information Systems, Information & Management, International Journal of Information Management, International Journal of Electronic Commerce, and others.

Jie Zhang is currently an assistant professor at Zhejiang University of Finance and Economics. She has received her two PhD degrees from City University of Hong Kong and University of Science and Technology of China. Her research interests include student learning, electronic commerce, consumer behavior as well as supply chain management. She has published research works at Omega-International Journal of Management Sciences, Journal of Strategic Marketing, among others.

Yuan Liu is professor of information systems at Zhejiang University. He focuses on studying how the information technology changes individual behavior and organization regulations. He has published papers in Government Information Quarterly, Information & Management, among others.