INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
Online ISSN 1841-9844, ISSN-L 1841-9836, Volume: 16, Issue: 6, Month: December, Year: 2021
Article Number: 4398, https://doi.org/10.15837/ijccc.2021.6.4398

CCC Publications 

Exploring the Antecedents of Online Learning Satisfaction: Role of
Flow and Comparison Between use Contexts

Quan Xiao, Xia Li

Quan Xiao
School of Information Management
Jiangxi University of Finance and Economics, Nanchang
No.165 West Yu-Ping Street, 330032, Nanchang, Jiangxi, China
xiaoquan@foxmail.com

Xia Li*
School of Information Management
Jiangxi University of Finance and Economics, Nanchang
No.165 West Yu-Ping Street, 330032, Nanchang, Jiangxi, China
*Corresponding author: 2202020581@stu.jxufe.edu.cn

Abstract

Learners’ satisfaction plays a critical role in the success of online learning platform. Many
factors that affect online learning satisfaction have been addressed by previous studies. How-
ever, the mechanisms by which these factors are associated with online learning satisfaction are
not sufficiently clear. Moreover, the difference in the antecedents of online learning satisfaction
between two use contexts- Mobile context and PC context, was rarely examined. Based on the
Stimulus-Organism-Response (S-O-R) framework, we investigate the key factors (self-efficacy, so-
cial interaction, platform quality, teacher’s expertise) affecting flow and highlights its role in online
learning satisfaction, which is empirically tested through an online survey of 333 online learners.
Results show that self-efficacy, teacher’s expertise, platform quality, and social interaction positively
affect online learning satisfaction through the mediation of flow. Use contexts not only moderate
the relationship between flow and online learning satisfaction, but also between social interaction,
platform quality, teacher’s expertise, and flow. These new findings expand educators with ways to
increase flow, add to knowledge about the relationship between flow and online learning satisfaction
and provide references for online learning platforms to enhance learners’ online learning satisfaction
under multiple-version affordances.

Keywords: online learning satisfaction, flow, use contexts, S-O-R framework.

1 Introduction
The outbreak of the COVID-19 pandemic in 2020 spread rapidly around the world, causing huge

ripples in education in different countries [32]. Isolation measures have impacted the learning and
education activities of colleges and universities, leading traditional offline learning to be transferred



https://doi.org/10.15837/ijccc.2021.6.4398 2

to online [51], thus the demand for online learning has exploded. Considering the spread of the
epidemic, most American universities temporarily shifted most of their teaching activities online [73].
For example, Harvard University, Stanford University, Massachusetts Institute of Technology, and
many other universities have all announced to set up online courses for students on campus. Besides,
Romanian scholars have developed an online platform to actively facilitate and support the publication
of high-quality scientific literature produced in their country [61]. The Chinese government has put
forward the policy of "suspending classes, ongoing learning", and learning informatization are rapidly
advancing under the leadership of the government. Online learning has become an inevitable trend in
the development of global education [8].

With this trend, an increasing diversity of technology platforms have been adopted to support
online learning [64]. For example, learning management systems (LMS) are acknowledged as one
of the most essential online learning platforms, which facilitates online learning without the time
and space constraints [46]. The LMS can support learners in having classes, writing assignments,
taking exams online, so the quality of online learning is influenced by LMS. Further, the LMS was
recognized as an irreplaceable emergency learning tool for the transition from traditional learning
to online learning during the COVID-19 pandemic. Although LMS is continuing to develop and is
expected to continue, the rapid growth of the technology platforms does not mean that online learning
will bring learners a positive learning experience. Even many learners have suffered reduction in their
satisfactions to online learning after the first experiences. This necessitates a deeper study of the
antecedents of online learning satisfaction.

Research on online learning satisfaction have become a hotspot attracting much attention. Hiltz
first proposed the concept of online learning, which refers to placing the homepage of a course and
related materials on the PC to form shared virtual learning space to achieve a face-to-face (FTF)
learning [28]. Malaysian scholars Ramayah and Lee used structural equation models to study sys-
tem characteristics and online learning satisfaction, aiming to reveal the relationship between online
learning system quality, information quality, service quality and learning satisfaction [57].

Though scholars have done a great deal of theoretical exploration and empirical analysis on online
learning, there are still problems that need to be studied. First, unlike traditional offline learning,
online learning context allows for greater flexibility in learning and more intelligent forms in teaching.
Therefore, it cannot be determined whether the factors that influence the learning satisfaction to
traditional offline learning can also apply to online learning contexts. Secondly, fewer scholars have
introduced mediating effect into the study of online learning satisfaction models, especially there are
scarce researches in the literature that use flow as a mediating variable in online learning. Third,
previous research only focused on only one type of use context, such as PCs [5], mobile phones [76].
Nevertheless, the rise of the information age has given creation to a diverse media context [38]. It is
worth extending the findings of previous studies to compare different media contexts in the field of
online learning.

Motivated by these gaps, this research is developed based the following three questions:
Question 1: What are the antecedents of online learning satisfaction?
Question 2: What is the mechanism of flow in the establishment of online learning satisfaction?
Question 3: Does the use of context play a moderating effect in the influence mechanism of online

learning satisfaction?
Subsequently, this study facilitates the development of literature on the antecedents, mediator,

moderator, and outcomes of online learning satisfaction in the framework of the S-O-R model. Gov-
ernment, schools, platform, educators, and learners can use this as a basis to strengthen the quality
of online learning and increase learner satisfaction in order to promote the development of the online
learning community.

The remainder of the paper is structured as follows. In Section 2 the related literatures are
reviewed. In Section 3, we construct the research model of this paper and put forward the research
hypothesis based on the S-O-R framework. In Section 4, we discuss the collection of the data and
sample characteristics. Section 5 presents the empirical results. Section 6 discusses the research
results, and highlights the research significance and future research directions. Finally, conclusions
with limitations and future direction are drawn in Section 7.



https://doi.org/10.15837/ijccc.2021.6.4398 3

2 Literature review

2.1 Online learning satisfaction

Satisfaction is the goal pursued by products/services [77], and is a measure of user’s emotions. After
the user’s evaluation of use, they develop an overall emotional attitude towards the product/service
[14, 66]. Kotler believed that customer satisfaction refers to the customer’s feelings of happiness or
disappointment after using the product, which is formed by comparing the user’s perceived utility
after use with the expected expectations before use [39]. Martin applied the theory of satisfaction to
the field of learning, suggesting that a learner is satisfied when the learning process feels greater than
what was expected before formal learning, otherwise he would be unsatisfied [49]. Many scholars have
conducted empirical studies on online learning satisfaction currently. Hsiu-Feng has determined the
relationship among big five traits, online learning motivation and online learning satisfaction through
a questionnaire survey of 153 college English learners [62]. Cong Wang found that in online learning
situations, need satisfaction and need dissatisfaction have a significant impact on students’ motivation
and learning outcomes [70]. Online learning satisfaction is a widely recognized indicator to measure
quality of teaching and learning [75]. A summary of the literature relevant to all the factors vital to
the activities of online learning, and affecting learners’ satisfaction with online learning is presented in
Table 1. We define online learning satisfaction as experience of online learners interacting via learning
products or learning services in all processes, which accumulates over time to form an overall evaluation
of the product or service. Hence, we employ the online learning satisfaction as a response(R) in the
current theoretical model.

2.2 Online learner factor: self-efficacy

Self-efficacy refers to the predictions and inferences made by individuals about whether they can
complete a certain behavior. Self-efficacy is important because the more self- efficacious learners will
make plans, seek appropriate help, and motivate themselves to immerse themselves in a learning
state and potentially enhance their learning satisfaction. In Bandura’s research, self-efficacy exerts its
influence through four major processes of cognitive, motivational, affective, and selection [3]. Compeau
defined online learning self-efficacy as the extent to which people feel confident in their ability to
successfully use online learning technology to complete the learning task [27]. Liaw demonstrated that
perceived satisfaction, perceived usefulness, and interactive learning environments were all found to
predict perceived self-regulation in online learning [43]. High-value learning content will encourage
learners to devote more learning energy to achieve better learning achievement goals, and they will
be more likely to be satisfied with online learning. This study defines self-efficacy as the degree to
which online learners are confident to work effectively in different characters and use self-efficacy as a
stimulating(S) factor.

2.3 Educator factor: teacher’s expertise

The role of teacher in online learning is different from traditional face-to-face learning. Pei-Chen
Sun conducted that the existence and guidance of teachers, the ability of teachers to teach online and
the flexibility of courses is an important predictor of online learning satisfaction [65]. Therefore, in
addition to having knowledge of teaching, teachers are encouraged to pay attention to the topics that
learners are talking about in online learning communities and can use platform learning tools to serve
teaching and learning goals. Siritongthaworn conducted a survey on online learning in Thai universities
and found that the key barrier was found to be student preference for instructor-led learning [63]. It
is assumed that brilliant teachers’ expertise focusses more on student learning and pedagogical issues
and, thus, can be more flexible in adopting new approaches to promote learner flow. Specifically, the
teacher’s knowledge reserve, teaching level and teaching style will all affect online learning satisfaction.
Therefore, this study defines teacher’s expertise as online learners’ perception of teachers’ mastery of
educational knowledge and professional knowledge, which is a stimulus(S) factor.



https://doi.org/10.15837/ijccc.2021.6.4398 4

Table 1: Related references about online learning satisfaction
Reference Country Theory/Model Methodology Findings

Lin
(2005) [45] America

TRA,TAM
and TPB

187 students in
a Midwest state

university to learn
knowledge and skills

in a physical and virtual
place and analysis

using SEM

Self-efficacy and technology
facilitating conditions had the

strongest impact on
students’ satisfaction

Suarez
(2008)[18] European None

Test the acquired
experience in the
development and
use of multimedia

contents for e-learning
applications

The result of the work shows the
students satisfaction in the

development and use of multimedia
contents fore-learning applications

Pei-Chen Sun
(2008)[65] Taiwan

Six dimensions were
chosen based on a
comprehensive

literature
review.

295 data were collected and
analyzed using multiple

regression analysis

Earner computer anxiety,
instructor attitude toward
e-Learning, e-Learning

course flexibility, e-Learning
course quality, perceived

usefulness, perceived ease of
use, and diversity in

assessments are the import
factors affecting learners’
perceived satisfaction

Jen-Her Wu
(2010)[72] Taiwan

Social Cognitive
Theory

212 data were collected and
analyzed using PLS

Learning climate and
performance expectations

significantly affect
learning satisfaction

Ke F
(2013)[37] America

The design theory of
student-centered

learning environment

680 respondents from 13
countries and

analyzed using SEM

Learner relevance, active
learning, authentic learning,

learner autonomy, and
computer technology competence
predicted students’ perceived

satisfaction

Xusen Cheng
(2016)[12] China Yield Shift Theory

113 participants were
tested by experiment

Satisfaction is higher
in online collaborative

learning

HueiChuaWei
(2020)[72] Taiwan None

356 students were gathered
and analyzed using SEM.

Self-efficacy for online learning
readiness had a mediated effect

on online learning perceptions and
online discussion score effect on online
learning perceptions and satisfaction

Haozhe Jiang
(2020)[34] China TSM

928 students were gathered,
analyzed using SEM
and the Rasch model

Self-efficacy and the perceived
ease of use and usefulness
of the platforms is directly
and indirectly impacted by

Chinese university
students’ satisfaction with
online learning platforms

Nam-Hyun Um
(2021)[68] South Korea None

236 students were gathered
and analysis using SEM

Students’ satisfaction with online
learning was positively related

to interactions, teaching presence,
self-management of learning,
and academic self-efficacy



https://doi.org/10.15837/ijccc.2021.6.4398 5

2.4 Platform factors: platform quality and social interaction

Online learning is different from traditional classroom learning, which can carry out multiple com-
bined learning modes in space and time dimensions. Sevgi Ozkan analyzed actual cases and showed
that good platform quality could allow school learners to enjoy better learning experiences and en-
hancing online learning satisfaction [55]. Shun Li and Quan Xiao pointed out that optimizing the
design features of mobile applications would get a payback of higher user satisfaction [42]. Adriana-
Meda UDROIU helped Romanian public institutions to improve platform quality by integrating their
own resources and specific activities [67]. Fredrickson conducted a study on the factors influencing
online learning satisfaction at New York University. The results show that perceived value of instruc-
tion is the most important reflector of online learning satisfaction. This value is reflected in online
discussions, the interaction between learners and teachers, and the interaction between learners [22].
Thus, the learning platform should pay attention to the construction of social interaction, as learners
can not only interact with teachers through the platform, but also exchange learning opportunities
with friends in different fields, high-quality and high-frequency social interaction has a driving effect
on online learning satisfaction. Based on existing literature, it is believed that platform quality refers
to the ease of use of the platform perceived by online learners and the degree to which it supports
teaching. Social interaction is defined as an online learning process in which individual users or groups
of users interact with each other by acquiring information and sharing experiences. Also, platform
quality and social interaction are social stimuli(S) factors.

2.5 Mediating role of flow

Flow, also known as "fluid experience", is a concept first proposed by American psychologist
Mihaly Csikszentmihalyi in 1975. It refers to people’s interest in a challenging activity and a task
driven by internal motivation [15]. Csikszentmihalyi constructed a model of the interaction between
psychological factors and situational factors, which promoted the formation of flow theory. This theory
has been applied in various research fields. Australian scholar Jackson conducted research on flow for
outstanding athletes in the field of sports. He believed that flow was the feelings obtained during sports
[33]. Hoffman and Novak applied the concept of flow experience to network navigation behavior for
the first time, and defined immersion in network navigation as: a series of non-stop and seamless
responses caused by human-computer interaction, truly enjoyable, accompanied by selflessness, and
the state of motivation [53]. Based on these studies, this study defines flow as a positive emotion that
individuals have a strong sense of participation in the process of activities, which can promote the
individual to devote themselves to learning activities. At the same time, our study focuses on the
mediating role of flow. The theoretical basis for the mediation role is provided by the S-O-R. model.
This model theorizes that self-efficacy, teacher’s expertise, platform quality and social interaction (S)
create a flow state (O) which further triggers the online learning satisfaction (R).

2.6 Moderating role of use contexts

With the progress of society and economic development, in addition to the electronic information
that needs to be organized and managed in personal computers at personal fixed offices and learning
locations, personal mobile terminal devices such as Pad and smartphones have also become a new
base for the collection, storage and use of personal information [4]. Among them, smartphones are
undoubtedly becoming an important tool for personal online learning. David Mutambara discovered
through the technology acceptance model that mobile learning can be used to alleviate the challenges
faced by STEM education in rural areas [52]. Lee regards mobile learning as the evolution of online
learning, in which mobile devices and wireless connections replace elements such as fixed computers
and wired networks [41]. This paper takes use contexts as a moderating variable, and divides use
contexts into two groups: Mobile context and PC context.



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2.7 The S-O-R framework

The S-O-R framework involves three components: stimulus, organism, and response [50]. The
S-O-R model explains that various environmental aspects can act as a stimulus (S) that influences an
individual’s internal state (O), which subsequently derives the individual’s behavioral response (R).
The model explicates how stimuli in the outer environment can fortify the inner states of individuals
[20]. Many researchers have applied the S-O-R model to the field of online learning [23]. Xuesong
Zhai focused on how privacy concern developed knowledge hiding perceptions of the learners, thereby
affecting their online collaboration based on the S-O-R paradigm [79]. Buxbaum examined students’
learning flow experiences and other personality traits using the S-O-R model [7]. As the global COVID-
19 pandemic spreads, many students begun to transform from offline classroom to online classroom,
the sudden change in the learning environment forced students to experiment with multimedia tools
for learning [80]. The flow changes may induce students to have different learning styles and online
learning satisfaction. Thus, it is necessary to utilize the S-O-R model to further explore the antecedents
of online learning satisfaction. More specifically, self-efficacy, teacher’s expertise, platform quality and
social interaction are important stimuli (S) that help create flow in students’ online learning (O) to
enhance the online learning satisfaction (R).

3 Research model and hypotheses
Drawing on the related studies, we proposed a conceptual model as shown in Fig.1, which presents

the relationships among self-efficacy, teacher’s expertise, platform quality, social interaction, flow,
online learning satisfaction and use contexts.

Self-efficacy

Teacher’s expertise

Platform quality 

Social interaction

Flow  Online learning satisfaction

Use context 

H2

H1(a)

H1(b)

H1(c)

H1(d)

H3(a)

H3H3(b)
H3(c)

H3(d)

Figure 1: Conceptual model

In terms of the antecedent variables of flow, prior studies have shown that self-efficacy significantly
impacts learners on learning satisfaction within an online environment [11], and literature also suggests
that expert teachers are more flexible with new methods and focus more on student learning [29].
Teachers with expertise are more likely to succeed in high-satisfaction classrooms [35]. As online
learning is dependent on the technology platform, the smoothness and clarity of which has an impact
on the flow and learning satisfaction [13]. On the other hand, social interaction provides learners with
a unique collaborative and social experience, which may raise learning satisfaction. Riva suggests
that the more interactive users feel, the more immersed they will be in the virtual environment [58].
Considering all above mentioned, we have the following hypotheses:

H1 (a): Self-efficacy positively affects flow.
H1 (b): Teacher’s expertise positively affects flow.
H1 (c): Platform quality positively affects flow.
H1 (d): Social interaction positively affects flow.
Flow is often described as the best state of excitement that occurs when people are fully involved in

an activity [40]. Because flow brings happiness to the learning activity itself, it can internally encourage
online individuals to feel more attractive to the virtual environment, or to form an emotional attitude
towards the virtual environment [31]. Greater attractiveness and positive emotional state will increase
learner satisfaction. Thus, we suggest the following hypothesis:



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H2: Flow positively affects online learning satisfaction.
Previous research has indicated that LMS quality attributes have different impacts on students’

satisfaction in the Mobile context from the PC context [13]. More specifically, learner experience of
online learning can vary from Mobile context to PC context. Hence, this article divides use contexts
into two groups- Mobile context and PC context. We test each path in the model in different contexts,
focusing on whether the effects under different contexts are different. Hence, the following hypotheses
are developed:

H3: Use context moderates the relationship between flow and online learning satisfaction.
H3 (a): Use context moderates the relationship between self-efficacy and online learning satisfac-

tion.
H3 (b): Use context moderates the relationship between teacher’s expertise and online learning

satisfaction.
H3 (c): Use context moderates the relationship between platform quality and online learning

satisfaction.
H3 (d): Use context moderates the relationship between social interaction and online learning

satisfaction.

4 Research methodology

4.1 Questionnaire design and measurement

All measurement items from existing instruments were adapted to the context of online learning
to ensure content validity. A total of 23 items associated with the constructs were included in the
initial questionnaire. The initial scales in English were translated into Chinese by using the traditional
back-translation method. Each item was measured with seven-point Likert scale (1= point strongly
disagree, 7= points strongly agree) [44]. All items of scales are shown in Table 2.

4.2 Data collection

This study takes learners who have participated in online learning as the survey objects. The online
survey was conducted through the professional survey platform “wjx.com”, via its sample service. The
questionnaire clearly states that only users who have taken part in the online learning can participate.
In addition, we ensured that the responses were confidential.

A total of 428 online questionnaires were collected through online questionnaire survey. All re-
turned questionnaires were carefully examined. After removing 25 careless and invalid questionnaires,
and 70 questionnaires that are unclear about the context of use, a total of 333 valid questionnaires
are obtained for the empirical analysis. The ages of the respondents are mainly between 16 and 40
years old. In terms of gender, there are 182 females (54.7%) and 151 males (45.3%). Most respondents
have more than one year of online learning experience, and 175 people use Mobile devices to learn,
accounting for 52.6% while 158 people learn on PC, accounting for 47.4%.

5 Results

5.1 Measurement model

In data analysis, we used partial least squares (Smart PLS3.0), a variance-based latent variable
structural equation modeling (SEM) technique [24, 74]. In order to ensure the consistency of the latent
variables in the model construction, the reliability and validity of survey data are tested first. Prior
to evaluating the research model, we conducted several analyses to ensure that the latent constructs
exhibited validity and reliability. Reliability results are given in Table 3.

In terms of construct validity test, this study conducted two tests: convergent validity and dis-
criminative validity. If the factor load of the indicator variable is greater than 0.5, AVE>0.5, and
reliability>0.7, it means that it has convergence validity [2]. As shown in Table 3, CR values are
all above 0.7, and all values of Cronbach’s α coefficient are also above 0.7, indicating an adequate



https://doi.org/10.15837/ijccc.2021.6.4398 8

Table 2: Structure and source of questionnaire
Construct Measurement items Reference

SE

1. I feel confident of using the online learning platform.

2. I feel confident of searching information on the online learning

platform.

3. I feel confident of reading others’ messages on the online learning

platform.

4. I feel confident of providing information or respond to someone

else on the online learning platform.

Kao, Wu, and Tsai (2011);

Chen et al. (2001)[9, 36] .

TE

1. The teacher is knowledgeable enough about content.

2. The teacher follows up student problems and tries to find out

solution via the online learning platform.

3. The teacher is proficient with all content used in the course.

4. The teacher is good at communication with students within the

online learning platform.

Ozkan& Koseler (2009);

Yoon (2006)[55, 78].

PQ

1. The graphical user interface of the online learning platform is

suitable for online learning.

2. I have not faced any system errors on the online learning platform.

3. In the online learning platform I can easily navigate where I want.

4. I can find required information easily on the online learning

platform.

5. Help option is available on the system.

Ozkan& Koseler (2009);

Hassanzadeh et al. (2012)[25, 55].

SI

1. The interaction with the classmates increase my interest in the

online course.

2. The interaction with the classmates helps me answer questions

raised in course activities.

3. The interaction with the classmates helps me construct

explanations/ solutions.

Diep et al. (2017);

Arbaugh et al. (2008)[1, 17].

FL

1. I have (at some items) experienced ‘flow’ on the online

learning platform.

2. In general, how frequently would you say you have experienced

flow when you use the online learning platform?

3. Most of time when I use the online learning platform, I feel that

I am in flow.

Rodriguez-Ardura&

Meseguer-Artola (2016)[59].

OLS

1. I developed knowledge and competencies within the online

learning platform.

2. The courses in online learning platform were a good fit for the way

I like to learn.

3. The online learning platform met my expectations for what

I had hoped to learn.

4. The knowledge and competencies taught through the online

learning platform are personally meaningful and important to me

Lin (2005);

Wu et al. (2010)[45, 75].

Note1:SE= Self-efficacy;TE= Teacher’s expertise;PQ= Platform quality;

SI= Social interaction;FL=Flow;OLS= Online learning satisfaction



https://doi.org/10.15837/ijccc.2021.6.4398 9

Table 3: Assessment of the measurement model
Construct No. of item Mean (SD) Cronbach ‘s α AVE CR

SE 4 4.195(1.221) 0.916 0.798 0.941

TE 4 4.289(1.329) 0.933 0.833 0.952

PQ 5 4.225(0.923) 0.788 0.540 0.854

SI 3 4.150 (1.241) 0.871 0.794 0.921

FL 3 4.520(1.245) 0.882 0.809 0.927

OLS 4 4.436(1.255) 0.938 0.842 0.955

Note1:SE= Self-efficacy;TE= Teacher’s expertise;PQ= Platform quality;

SI= Social interaction;FL=Flow;OLS= Online learning satisfaction

Table 4: Discriminant validity (intercorrelations) of variable constructs
variables SE TE PQ SI FL OLS

SE 0.894

TE 0.280 0.913

PQ 0.170 0.249 0.735

SI 0.281 0.275 0.479 0.891

FL 0.298 0.480 0.366 0.345 0.900

OLS 0.254 0.289 0.258 0.186 0.537 0.918

Note1:SE= Self-efficacy;TE= Teacher’s expertise;PQ= Platform quality;

SI= Social interaction;FL=Flow;OLS= Online learning satisfaction

consistency reliability. Moreover, the average variance (AVE) ranged from 0.540 to 0.842, which is
higher than the suggested threshold value of 0.5. This shows sufficient convergent validity.

We evaluated the square root of the AVE and structural dependence to test the discriminant
validity. Fornell et al. suggested that it can be tested by testing the magnitude of AVE value of the
latent variable and comparing the square root of the AVE of the latent variable with the correlation
coefficient of other latent variables [21]. That is, when AVE value of the latent variable is greater
than 0.5 and the square root of AVE value is greater than the correlation coefficient with other latent
variables, it shows that it has good discrimination validity. It can be seen from Table 4 that all latent
variables meet the conditions, so the discriminative validity of the measurement model also passes the
test.

We verified the effectiveness of convergence by extracting the factors and cross-loads of all index
items to their respective potential structures [18]. As shown in Table 5, all items show a high load for
their relevant factors, but a low crossover load for other factors, which confirm the convergent validity
of these indicators as representing distinct latent constructs.

5.2 Structural model

The results of the path coefficients and the corresponding significance levels for testing the struc-
tural model are shown in Fig.2.

As is indicated, self-efficacy (beta=0.135, p<0.05), teacher’s expertise (beta=0. 398, p<0.001);
platform quality (beta=0. 246, p<0.001) and social interaction (beta=0.090, p<0.05) are strong
predictors of flow, accounting for 32.5 percent of variance in flow (R2 = 0.325). As we hypothesized
that self-efficacy (H1a) and teacher’s expertise (H1b), platform quality (H1c) and social interaction
(H1d) would positively affect flow, hypotheses H1 (a), H1 (b), H1 (c) and H1 (d) are supported.
Meanwhile, flow is found positively affecting online learning satisfaction (beta = 0.583, p < 0.001) and
explains 28.8 percent of variance in online learning satisfaction (R2 = 0.288). Thus, H2 is supported.



https://doi.org/10.15837/ijccc.2021.6.4398 10

Table 5: Factor loadings (bolded) and cross loadings
SE TE PQ SI FL OLS

FL1 0.283 0.402 0.360 0.350 0.896 0.484
FL2 0.257 0.418 0.284 0.277 0.902 0.459
FL3 0.264 0.474 0.340 0.302 0.902 0.503
OLS1 0.173 0.209 0.215 0.109 0.406 0.910
OLS2 0.261 0.224 0.226 0.138 0.461 0.917
OLS3 0.239 0.289 0.249 0.207 0.474 0.93
OLS4 0.248 0.318 0.249 0.209 0.591 0.914
PQ1 0.091 0.155 0.735 0.196 0.248 0.262
PQ2 -0.030 0.093 0.696 0.158 0.216 0.148
PQ3 0.140 0.233 0.728 0.194 0.314 0.202
PQ4 0.255 0.234 0.767 0.617 0.263 0.166
PQ5 0.136 0.176 0.745 0.566 0.283 0.167
SE1 0.883 0.236 0.155 0.199 0.255 0.256
SE2 0.905 0.274 0.154 0.252 0.267 0.201
SE3 0.918 0.295 0.208 0.343 0.314 0.239
SE4 0.868 0.173 0.064 0.180 0.211 0.209
SI1 0.224 0.173 0.439 0.857 0.256 0.131
SI2 0.250 0.243 0.392 0.914 0.323 0.199
SI3 0.272 0.304 0.453 0.902 0.335 0.162
TE1 0.327 0.903 0.211 0.244 0.450 0.249
TE2 0.223 0.918 0.216 0.248 0.463 0.283
TE3 0.206 0.922 0.247 0.247 0.414 0.230
TE4 0.264 0.909 0.239 0.265 0.422 0.293
Note1:SE= Self-efficacy;TE= Teacher’s expertise;PQ= Platform quality;

SI= Social interaction;FL=Flow;OLS= Online learning satisfaction

5.3 Mediating effect tests

In management research, exploring research questions related to mediation has implications for
understanding the drivers of success or failure of certain processes or factors [26]. We use the boot-
strapping method proposed by Preacher to conduct the mediation analysis [56]. The results showed
(Table 6) that the 95% confidence interval for each mediating effect did not contain 0, indicating that
the indirect effects were all significantly different from 0. Hence, the mediation effects of flow were
significant.

5.4 Multi-group analysis

The structural model was further compared between the two use contexts (Mobile context and PC
context) using multi-group permutation tests. For hypothesis testing, it is to test whether there are
differences between path coefficients. The key lies in the construction of test statistics, the core of
which is the mathematical expectation and variance of the variables to obtain the t-value.

 Self-efficacy

SE1

SE1

SE1

SE1

TE1

TE2

TE3

TE4

PQ1

PQ2

PQ3

PQ4

PQ5

SI1

SI2

SI3

Teacher’s 

expertise

 Platform 

quality

 Social 

interaction

0.918

0.918

0.728

0.857

0.914

Flow

R
2
=0.325

 Online learning 

satisfaction

R
2 
=0.288

FL1 FL2 FL3

OLS1 OLS2 OLS3 OLS4

0.896 0.902 0.902

0.910

0.917 0.930 0.914

0.583***

Figure 2: PLS analysis of research model



https://doi.org/10.15837/ijccc.2021.6.4398 11

Table 6: Mediating test results
Hypotheses Parameters Estimated T-values 95% CI Decision

H2a SE -> FL ->OLS 0.070* 2.281 [0.014,0.134] Supported

H2b TE -> FL -> OLS 0.195*** 6.145 [0.133,0.257] Supported

H2c PQ-> FL-> OLS 0.107*** 3.563 [0.014,0.134] Supported

H2d SI -> FL-> OLS 0.061* 1.919 [0.002,0.123] Supported

Note1: Path Coefficients: *p-value< 0.05; ** p-value< 0.01; *** p-value< 0.001

Note2: SE= Self-efficacy;TE= Teacher’s expertise;PQ= Platform quality;

SI= Social interaction;FL=Flow;OLS= Online learning satisfaction

Table 7: Multi-group analysis result

Path
Coefficient (Standard error)

t-values Difference
PC(N=158) Mobile(N=175)

SE -> FL (Mobile<PC) 0.134(0.069) 0.126(0.091) 0.90 N

TE -> FL(Mobile<PC) 0.386(0.069) 0.347(0.080) 4.74*** Y

PQ-> FL (Mobile >PC) 0.161(0.082) 0.223(0.069) -7.49*** Y

SI -> FL(Mobile<PC) 0.165(0.089) 0.087(0.077) 8.57*** Y

FL-> OLS (Mobile <PC) 0.573(0.052) 0.514(0.056) 9.93*** Y

Note1: *p-value< 0.05; ** p-value< 0.01; *** p-value< 0.001; Y=Yes, N=No.

Note2: SE= Self-efficacy;TE= Teacher’s expertise;PQ= Platform quality;

SI= Social interaction;FL=Flow;OLS= Online learning satisfaction.

Use the following formula to calculate the t-value.

Spooled =
√

N1 − 1
N1 + N2 − 2

× SE21 +
N2 − 1

N1 + N2 − 2
× SE22 (1)

t =
PC − Mobile

Spooled ×
√

1
N1

+ 1
N2

(2)

Spooled is pooled estimator for the variance; t is t-statistic with (N1 + N2 -2) degree of freedom;
Ni is sample size of dataset for group i; SEi is standard error of path in structural model of group i;
PC and Mobile is path coefficient in structural model of group i.

It can be seen from Table 7 that there is a significant moderating effect of the use contexts on
the four path relationships (Flow-Online Learning Satisfaction, Platform Quality-Flow, Social Interac-
tion-Flow, Teacher’s expertise -Flow) in the model. More specifically, the degree of influence of flow
on online learning satisfaction is significantly different under two groups. In the PC context, a more
fixed learning medium can eliminate more interference, and the learner’s ability to concen-trate may
be higher. Therefore, flow has a significant impact on online learning satisfaction. In contrast, in the
case of Mobile context, small size of the screen does not facilitate learning and the learner’s attention
is easily distracted, making the impact of flow on online learning satisfaction less than on the PC
context. At the same time, in the influence of teacher’s expertise and social in-teraction on flow, use
contexts have a significant moderation effect. The path coefficient under the PC context is greater.
In the past two years, the application of Mobile in learning has developed fast. Mahmoud designed
an e-learning content model system based on mobile technology architec-ture [48]. In the context of
Mobile, platform quality has a much greater impact on flow than in the PC context. In conclusion, H3,
H3 (b), H3 (c), H3 (d) are all confirmed. However, there is no signif-icant difference in the influence
of self-efficacy on the flow in the two contexts, thereby rejecting H3(a).



https://doi.org/10.15837/ijccc.2021.6.4398 12

6 Discussion and implication

6.1 Discussion

Based on the S-O-R paradigm, self-efficacy, teacher’s expertise, platform quality and social in-
teraction are studied as environmental stimulus that develops flow as an organism, and further in-
fluence online learning satisfaction as a response. The moderating effect of use contexts is also inves-
tigated. Specifically, self-efficacy is an important determinate in flow, which has a significant indirect
effect upon online learning satisfaction, and this is consistent with prior studies such as Wang and
Newlin [71]. The results show that learners with a high sense of self-efficacy are more confident in
completing online learning activities and have significantly better flow and higher online learning sat-
isfaction. Similarly, teacher’s expertise affects the effectiveness and satisfaction of students’ learning
activities, which is consistent with earlier studies [19, 30]. In addition, social interaction has a signifi-
cant effect on online learning satisfaction. Therefore, online learning satis-faction will increase if the
teacher’s timely assistance encourages learners to continue learning and the platform is responsive to
students’ needs and problems. Platform quality positively contributes to online learning satisfaction,
and flow mediates the relationship between them. The study indi-cates good quality online learning
courses help learners to effectively motivate continuous online learning, enhance flow and increase sat-
isfaction. In summary, flow is an important mediator linking the four antecedents and online learning
satis-faction.

The study demonstrates that use context moderates the positive relationship between flow and
online learning satisfaction, and teacher’s expertise and online learning satisfaction through flow, social
interaction, and online learning satisfaction through flow. The learner is the center and sub-ject of
online learning. Due to the large PC screen and richer features learners tend to experience better
flow in the PC context. Additionally, significant differences in the effect of teacher’s exper-tise on
online learning satisfaction exist between the two groups. Teacher’s expertise has a greater impact
on online learning satisfaction in the PC context than in the Mobile context. A possible ex-planation
is that most learners choose online courses to "increase knowledge" and "learn skills", where teacher’s
expertise prepares learners well for teaching [6]. With richer and more powerful learning resources on
PC, the teacher’s expertise on PC not only meets the basic need for content mastery, but also increases
online learning satisfaction. The social interaction has a greater impact on satisfaction in the Mobile
context than in the PC context. Probably because it is easier to achieve content interaction, teacher
interaction, learner interaction and human-computer interac-tion in the PC context. A step further,
online learning needs to enhance and improve the interactive learning experience of learners in terms
of interface design, platform quality and other instrumen-tal support dimensions in PC to attract
more learners to actively participate in online courses [60]. However, we have also found a moderating
role of use context between self-efficacy and flow not supported. Probably because self-efficacy is more
subjective and directly affects flow independent of the external context.

6.2 Implication

This research has enriched relevant research in the field of online learning, and has wealth value
and significance in both theory and practice. In terms of theory, this paper enriches and validates
the S-O-R model. The findings promote the development of literature on exploring the antecedents,
mediating effect, moderating effect, toward online learning satisfaction based on the framework of the
S-O-R model. This study contributes to literature by investigating the antecedents of online learning
satisfaction from the three levels of learners, educators, and platforms. The four factors (self-efficacy,
teacher’s expertise, platform quality and social interaction) play a vital role in the development of
flow of learners and as responses affect online learning satisfaction. Besides the direct effect, our
study highlights the mediation effect of flow. The findings suggest that gaining a compelling flow is
critical for learners to enhancing online learning satisfaction. The flow and evaluating its essential
impacts in online learning context are of significance. This could encourage future researchers to
develop the concept of flow in different contexts in educational backgrounds and non-educational
environments. Finally, our study presents new theoretical knowledge and pro-vides a model that
can systematically compare the effects of antecedents on online learning satis-faction between Mobile



https://doi.org/10.15837/ijccc.2021.6.4398 13

context and PC context. This argument points to a theoretical gap in the research so far. The changes
of the path coefficients of the model and the significance verification under the two groups are tested
respectively,which imply that the influences of teacher’s expertise and social interaction on flow among
PC context are greater than Mobile context, whereas the ef-fect of platform quality on flow among
Mobile context is lower than PC context. On the other hand, the influence of flow on online learning
satisfaction among PC context is greater than Mobile context.

In terms of practice, for platforms that provide online education services, it is essential to under-
stand the factors that affect users’ online learning satisfaction. Studies have shown that students
are willing to use VR technology for long hours of online learning [69]. Applying new technologies
to the field of online learning fundamentally enhance the quality of online learning platform devel-
opment. Meanwhile, learner’s reviews and comments posted during social interaction are important
information [10]. The platforms extract implicit preferences and emotions about product or service
characteristics through social interaction [47], thereby attracting users to retain and improving user
stickiness. For educators, the continuous renewal of teaching methods brings new opportunities and
challenges, but the professional level and teaching quality of teachers have always been the most
important factors affecting learner satisfaction. Furthermore, educators may incorporate soft skills
into education to equip learners with skills, habits, attitudes, knowledge, and concepts for personal
use [54]. Lastly, educators and the platform are appropriately linked to jointly do the community
function of online learning, increase teacher-student and student-student interaction, and strengthen
the feedback mechanism. For users, understanding the influencing factors that drive their online
learning satisfaction can more clearly recognize their requirements, to choose online learning services
that more closely match their own needs.

7 Conclusion
For improving the quality of online education services, improving the teaching quality evalua4tion

system, and enhancing flow experience of learners, it is necessary to explore the antecedents of online
learning satisfaction to help learners achieve better experiences. To investigate this issue, our research
provides substantial theoretical contributions to the literature and inspiring managerial implications
for practitioners. Firstly, we empirically examines the effects of self-efficacy, teacher’s expertise, plat-
form quality and social interaction motivations on online learning satisfaction based on the of the
S-O-R framework to explore the antecedents of online learning satisfaction. Secondly, the paper shows
that the impacts of self-efficacy, teacher experience, platform quality, and social interaction on flow are
significant. Likewise, the impact of flow to online learning satisfaction is significant, which suggests
flow has important role in building learners’ online learning satisfaction. Furthermore, this study
contributes to existing literature by assessing the differences between two types of use contexts (i.e.,
Mobile and PC). Findings suggest that the influences of teacher’s expertise and social interaction on
flow among PC context are greater than Mobile con-text. Similarly, the influence of flow on online
learning satisfaction among PC context is greater than Mobile context. However, the effect of platform
quality on flow is lower for PC context than Mobile context. These findings are useful as we deepen
the understanding of what antecedents and how they may affect online learning satisfaction.

Though the present research has certain uniqueness in terms of perspective and method, and offers
theoretical and practical implications, there also has certain limitations, which provides a possible
direction for the further study. First, considering the generalization of the research conclu-sions, the
sample size can be expanded to improve the randomness and universality in coming fu-ture research.
Second, because this study is conducted in China, we cannot directly generalize our findings to other
countries, although online learning has been implemented by schools all around the world during
the COVID-19 pandemic. Finally, the current study examines the role of flow and use contexts on
online learning satisfaction, other mediators and moderators are not examined. The future research
is expected to consider additional moderating (such as age and education level) and other mediating
factors (such as ease of use and learner’s perception of achievement).



https://doi.org/10.15837/ijccc.2021.6.4398 14

8 Acknowledgements
This work was partially supported by the grant of National Natural Science Foundation of China

[No. 71861014], China Postdoctoral Science Foundation [No. 2019M652272], Science and Technol-
ogy Project of Jiangxi Education Department [No. GJJ60458], Priority Postdoctoral Research Pro-
jects of Jiangxi Province [No. 2018KY10], Social Science Project of Jiangxi Province [No. 17BJ31],
Educational Teaching Reform Research Project of Jiangxi Province, and Educational Teaching Re-
form Research Project of Jiangxi University of Finance and Economics [JG2021036].

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https://doi.org/10.15837/ijccc.2021.6.4398 19

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Cite this paper as:
Xiao, Q.; Li X. (2021). Exploring the Antecedents of Online Learning Satisfaction: Role of Flow and
Comparison Between use Contexts, International Journal of Computers Communications & Control,
16(6), 4398, 2021.

https://doi.org/10.15837/ijccc.2021.6.4398


	Introduction
	Literature review
	Online learning satisfaction
	Online learner factor: self-efficacy
	Educator factor: teacher’s expertise
	Platform factors: platform quality and social interaction
	Mediating role of flow
	Moderating role of use contexts 
	The S-O-R framework

	Research model and hypotheses
	Research methodology
	Questionnaire design and measurement
	Data collection

	Results
	Measurement model
	Structural model
	Mediating effect tests
	Multi-group analysis 

	Discussion and implication
	Discussion
	Implication

	Conclusion 
	Acknowledgements