Using micro-lectures in small private online courses: what do we learn from students’ behavioural intentions?

Abstract

The purpose of this study is to apply the Technology Acceptance Model to identify the determinants affecting students’ behavioural intentions when it comes to using micro-lectures. The conceptual framework included three antecedents of behavioural intentions – perceived usefulness of micro-lectures; perceived ease of use of micro-lectures; and user satisfaction. Moreover, the four first-order constructs of content richness, user satisfaction, vividness and self-efficacy were incorporated into the framework based on the theoretical construction. Employing a structural equation modelling approach, the hypothesised model was validated empirically using data collected from China. This study examines two factors (perceived usefulness of micro-lectures and user satisfaction in terms of micro-lectures) related to learners’ behavioural intentions to continue using micro-lectures. Additionally, micro-lecture compatibility, micro-lecture self-efficacy and the vividness of micro-lectures emerged as critical predictors of the perceived usefulness of micro-lectures. The findings provide practical implications for educators and micro-lecture learning system developers.

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Using micro-lectures in small private online
courses: what do we learn from students’
behavioural intentions?
Kai Wang , Chang Zhu & Jo Tondeur
To cite this article: Kai Wang , Chang Zhu & Jo Tondeur (2020): Using micro-lectures in small
private online courses: what do we learn from students’ behavioural intentions?, Technology,
Pedagogy and Education, DOI: 10.1080/1475939X.2020.1832565
To link to this article: https://doi.org/10.1080/1475939X.2020.1832565
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Using micro-lectures in small private online courses: what do we
learn from students’ behavioural intentions?
Kai Wang
a
, Chang Zhu
b
and Jo Tondeur
b
a
Center for Teacher Education Research, Beijing Normal University, Beijing, China;
b
Department of Educational
Sciences, Vrije Universiteit Brussel, Brussels, Belgium
ABSTRACT
The purpose of this study is to apply the Technology Acceptance Model to
identify the determinants aecting students’ behavioural intentions when
it comes to using micro-lectures. The conceptual framework included
three antecedents of behavioural intentions – perceived usefulness of
micro-lectures; perceived ease of use of micro-lectures; and user satisfac-
tion. Moreover, the four rst-order constructs of content richness, user
satisfaction, vividness and self-ecacy were incorporated into the frame-
work based on the theoretical construction. Employing a structural equa-
tion modelling approach, the hypothesised model was validated
empirically using data collected from China. This study examines two
factors (perceived usefulness of micro-lectures and user satisfaction in
terms of micro-lectures) related to learners’ behavioural intentions to
continue using micro-lectures. Additionally, micro-lecture compatibility,
micro-lecture self-ecacy and the vividness of micro-lectures emerged as
critical predictors of the perceived usefulness of micro-lectures. The nd-
ings provide practical implications for educators and micro-lecture learn-
ing system developers.
ARTICLE HISTORY
Received 6 August 2018
Accepted 26 July 2020
KEYWORDS
Micro-lectures; small private
online courses; behavioural
intentions; technology
acceptance model
Introduction
During the second decade of the twenty-rst century, massive open online courses (MOOCs) and
small private online courses (SPOCs) (Fox, 2013) have had a major inuence on higher education.
Students can spend considerable time outside of class engaging with self-paced instructional videos
and quizzes, with immediate feedback available 24 hours a day that students can use as frequently as
desired. Interactive technology-based tools allow users to control videos by playing, pausing and
forwarding (Delen et al., 2014). At one time, the instructors in the classroom focused on facilitating
discussion for a deeper understanding of the overall themes of the course. With the development of
digital technology, delivery speed on the part of the network used is gradually growing. David
Penrose, who is the manager of online services at San Juan College in the USA, and also the proposer
of ‘micro-lectures’, came up with the idea that the traditional class-video can be replayed by pieces of
a lecture lasing one minute dealing with specic topics (Shieh, 2009). When the micro-lecture was
introduced to China, its length was often about 5–8 minutes instead of the original 60 seconds.
Besides the length, other aspects of the micro-lecture have also been adapted to t the Chinese
context (Song, 2016). If the content is too long, it will decrease the eectiveness and eciency of
learning (Wilson & Korn, 2007). Accordingly, instructors are doing most of the lectures themselves in
order to avoid disconnecting from the course material. In general, micro-lectures maximise the
CONTACT Chang Zhu chang.zhu@vub.be Department of Educational Sciences, Vrije Universiteit Brussel, Pleinlaan 2B -
1050 Brussels, Belgium
TECHNOLOGY, PEDAGOGY AND EDUCATION
https://doi.org/10.1080/1475939X.2020.1832565
© 2020 Technology, Pedagogy and Education Association

leverage of the scarce resource – instructor time – and play an important role in course design with
regard to the support of SPOCs.
As a new form of text representation, attention about how to eciently use the micro-lecture has
continued to grow (Cheng et al., 2017; Mitra et al., 2010). Students can access high-quality materials
and go back to watch them repeatedly if they nd that they have insucient knowledge, in order to
complete the exercises at their own pace. This might enhance learner engagement and so improve
learning eectiveness. Studies on adoption and usage of micro-lectures start from the point where
technology is introduced as an innovation and try to analyse its acceptance and behavioural
intentions during this study. There have been certain studies on student adoption of technology
for learning (Giannakos & Vlamos, 2013; S. Y. Park et al., 2012). These studies have suggested that
students’ acceptance of technology for learning should be predicted and analysed through dierent
factors. These factors include perceived self-ecacy (Lai et al., 2012) for learning, user satisfaction
with a specic learning context (D. Lee & Lehto, 2013), perceived usefulness or awareness of the
educational potentials of technological resources (Davis, 1989; Liaw & Huang, 2013) and technical
support (Sánchez & Huero, 2010). However, none of the theories and models in the usage and
adoption literature could predict behavioural intentions in micro-lecture-based learning environ-
ments. Moreover, the variables that these theories were based on also varied. What is missing in
current literature is a conceptualisation of the complex relationships among these variables and how
they work together to aect behavioural intentions on use of micro-lectures for learning. In order to
gain knowledge of what factors will aect students’ perceptions of micro-lectures in SPOCs, this
study tries to answer two main research questions:
RQ1: Will the three factors of perceived usefulness of micro-lectures, perceived ease of use of micro-
lectures and user satisfaction with micro-lectures inuence students’ behavioural intentions to use
micro-lectures?
RQ2: Will other factors such as the content richness of the micro-lectures, the vividness of the micro-
lectures, and micro-lecture compatibility and micro-lecture self-ecacy inuence students’ percep-
tions of the usefulness of micro-lectures?
Theoretical background
Micro-lectures and SPOCs
The SPOC refers to on-campus lectures using the MOOC technology platform and teaching
methods. SPOC is also known as ‘private lesson’, that is, small private online courses. It is a new
concept proposed by Harvard University after the MOOC (Kaplan & Haenlein, 2016), which limits
numbers of participants. MOOCs and SPOCs are two innovative ways of distance learning con-
ducted online, which dier primarily in the number of participants to which they cater (Kaplan &
Haenlein, 2016). MOOCs are a specic type of provision of online courses without participation
restrictions. SPOCs are online courses that are oered only to a limited number of participants and
therefore require some form of formal enrolment. MOOC technology uses collaborative methods
based on building knowledge with students worldwide to enhance their learning. Universities
could also use this knowledge to reach out to a greater audience through the MOOC. Universities
could also introduce more participative methods on campus through similar environments such as
SPOCs. Universities can implement SPOCs because they represent an innovation in learning
methods, their network presence and students’ enrolment (Freitas & Paredes, 2018). SPOCs allow
teachers to use a ‘blended learning’ approach and ‘ipped learning’ approach that combines
classroom teaching with online learning (Chen & Yang, 2015). Compared with MOOC, micro-
2K. WANG ET AL.

lectures used by SPOC are more attractive than the designated reading materials to prepare
students for their participation, especially for students who are not motivated to learn. Micro-
lectures, such as videos, are the most diversied and distinct virtual learning media. They can assist
traditional classrooms just as projectors and PowerPoints do. The nal purpose of micro-lectures is
to actively encourage student learning based on the learners’ needs and involving new technol-
ogies. Many researchers have explored several issues related to the ease of use, acceptance and
usefulness of dierent e-learning media such as Moodle (Sánchez & Huero, 2010) and other
systems (Ngai et al., 2007). At present, micro-lectures are always used in college teaching for
supporting the ‘ipped learning’ approach, which can meet students’ requirements for individua-
lised learning (Blair et al., 2016; Davies et al., 2013). Empirical analysis of behavioural intention to
use micro-lectures in SPOCs is lacking.
Hypotheses development
The Technology Acceptance Model (TAM), acclaimed for explaining individual behavioural inten-
tions, serves a useful frame of reference when it comes to helping to conceptualise factors that
might inuence users’ acceptance of an information system (Davis, 1989). It has also proved to be
useful in elaborating university students’ adoption or use of new technology for learning (McGill &
Klobas, 2009). The TAM assumes that perceived usefulness (PU) and perceived ease of use (PEOU)
are the key antecedents of behavioural intention (BI) towards using the information technology.
Behavioural intention is ‘the degree in which a user formulates conscious plans to develop or not
a particular future behaviour’ (Warshaw & Davis, 1985, p. 214). The rst key belief, PU, is the degree
to which the user supposes that a technology would improve his/her job performance (Davis,
1989). PU has consistently been shown to be the strongest predictor of continuance behaviour in
variance contexts (Hartshorne & Ajjan, 2009; Lee et al., 2013). PEOU, the second key belief, refers to
the degree to which the particular technology is free of eort (Davis, 1989). Previous studies show
that PEOU has a positive eect on users’ behavioural intention to use systems (Sharma & Chandel,
2013; Teo, 2010). Thus, we include PU and PEOU as important predictors in our theoretical
framework.
Hypothesis 1: Students’ perceived usefulness positively inuences their behavioural intentions with
regard to using micro-lectures.
Hypothesis 2: Students’ perceived ease of use positively inuences their behavioural intentions to
use micro-lectures.
However, at the same time, a variety of previous studies have reformed the original version of the
TAM by reinterpreting the underlying structures of some constructs in terms of the particularities of
the educational context (Chen, 2011; Lai et al., 2012). TAM2 incorporates several other theoretical
factors, including social inuence process and cognitive instrumental processes (Venkatesh & Davis,
2000). The TAM was further extended to incorporate another variable – user satisfaction (D. Lee &
Lehto, 2013). Ives et al. (1983) indicated that user satisfaction is the user’s opinion of the system and
that the capacity of the system should enhance the user’s power to make decisions. When used in
this study, satisfaction is usually conceptualised as the important construct aecting the success of
a learning system (Shee & Wang, 2008; Wu et al., 2010). A higher level of satisfaction towards
a system indicates a higher degree of willingness to use it (Liaw & Huang, 2013). M. C. Lee (2010)
identied that students’ continuance intention to learn is greatly inuenced by their satisfaction.
Therefore, we present the following hypothesis:
Hypothesis 3: User satisfaction positively aects behavioural intentions with regard to micro-
lectures.
TECHNOLOGY, PEDAGOGY AND EDUCATION 3

Further, researchers have studied contextual factors that aect the perceived usefulness of
a particular new technology. Content richness is operationally dened here as a large amount of learning
resources that learners can access to enrich their learning activity (D. Lee & Lehto, 2013). Generally,
measuring content richness or similar concepts (i.e. information quality, Tung & Chang, 2008) involves
three dimensions: relevance, timeliness and suciency (Jung et al., 2009). In a variety of previous studies,
aspects of content richness such as content quality and information quality (Park et al., 2012) oered by
a system have proven to be a factor that inuences perceived usefulness. Y. C. Lee (2006) noted that
content qualities, which are similar to content richness, were signicant antecedents of perceived
usefulness when using an e-learning system. According to the study by Huang et al. (2017), the
relationship among students’ learning, content richness and vividness was strong. Richness has focused
on the category of the media used to deliver information in traditional studies dealing with media
richness. The user can access this useful information in such a way as to enrich their learning activity.
Steuer (1992, p. 80) described vividness as ‘the ability of a technology to produce a sensorial rich
mediated environment’. Vividness is signicant because it will aect the expression of virtual objects
and the natural combination of virtual objects and real environment. Vividness of the course content
is also especially signicant (Desai et al., 2008) for both educational modes, especially SPOCs,
because, as previously 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
particular importance. Learner groups in micro-lectures with vivid content in a SPOC system can
communicate through a variety of dierent cues, such as visual (for example, teacher’s gestures in
videos, images) and auditory (teacher’s voice, background music, etc.) cues; they accordingly under-
stand each other better. We posit
Hypothesis 4: Content richness positively aects perceived usefulness.
Hypothesis 5: Vividness positively aects perceived usefulness.
Agarwal and Prasad (1998) modied the TAM by adding the factor of compatibility. In the eld of
information systems (IS), compatibility is considered as one of the basic prerequisites for users to
adopt new technologies or applications (Cheng, 2015). Compatibility is dened as the extent to
which technology ts with potential existing values and experiences (Rogers, 2005). Compatibility is
the intensity with which online learning technology is perceived to be consistent with student
beliefs, values and lifestyles (Ozturk et al., 2016). Students consider online learning systems are more
useful when they believe in their capability of taking online courses successfully (J. W. Lee &
Mendlinger, 2011). In this study, we treated educational compatibility in our theoretical framework
as an important predictor with regard to perceived usefulness.
Hypothesis 6: Compatibility positively aects perceived usefulness.
Agarwal and Karahanna (2000) proposed to modify TAM by adding factors like playfulness, self-
ecacy and personal innovativeness. There is substantial evidence that self-ecacy inuences
competence when it comes to learning and persistence in the face of challenges (Zhu et al., 2011).
Terzis and Economides (2011) considered computer self-ecacy has a causal link with perceived
usefulness. Marakas et al. (2007) suggested that users with high technology self-ecacy were more
likely to perceive higher perceptions of perceived usefulness. This means that students who have
higher self-ecacy are more likely to perceive learning systems as more useful (J. W. Lee &
Mendlinger, 2011). Consequently, the following hypothesis is developed:
Hypothesis 7: Micro-lecture self-ecacy positively aects perceived usefulness.
4K. WANG ET AL.

Thus, in this study, we incorporated some constructs into the existent technology acceptance
model according to the ndings from student technology adoption. The conceptual framework of
this study is presented in Figure 1. The relationships among variables were discussed, and conse-
quently, we developed the hypotheses derived from this discussion.
Methodology
Context of this research
Since 2015, Tianjin University in China has gradually uploaded micro-lectures recorded by its own
teachers to the university’s e-learning platform. Only students in this school with a password can
watch the micro-lectures to ensure privacy. The psychological course was a four-credit course and
was oered during the winter semester. The course dealt with the work responsibilities and role
positioning of psychological committee members that are relevant to the eld of mental health
education work. Before the class, students need to watch four to ve micro-lectures per class for
a total length of almost 30 minutes.
As shown in Figure 2(a), the module listed the structure of the selected course including units,
courseware and activities, which were dened by the teachers. More specically, every course
comprised several units; every unit comprised several pieces of micro-lectures. Figure 2(b) is an
example of a micro-lecture with slides. Then, students were asked to nish the assignments on the
platform after watching the micro-lectures. During the face-to-face class, the students were discuss-
ing in groups for about 45 minutes, and the instructor gave individual guidance to students who
asked questions. After the discussion, students presented major themes of the course and their
shared insights by group presentation. Afterwards, the instructor highlighted and gave a focused
explanation of the complex or controversial issues covered by the course content. Face-to-face
lessons lasted two hours each. Upon completion of the course, students were required to write
a reection document. The reection reported on the role of psychological committee members in
identifying students with psychological problems in real life and guiding students to seek profes-
sional teachers’ help.
Participants
The survey was carried out at the beginning of the second semester, in the school year 2016–17. Not
all Tianjin University students attend SPOCs. A signicant criterion for selecting the participants was
that they had attended some courses involving micro-lectures in the past. In sum, 285 individual
Vividness of
micro-
lectures
Perceived ease of use of
micro-lectures
Micro-lecture compatibility
User satisfaction
in terms of
micro-lectures
Behavioural
intentions
Perceived usefulness of
micro-lectures
Content
richness of
micro-lectures
Micro-lecture self-efficacy
H2
H3
H5 H6
H1
H4
H7
Figure 1. The proposed conceptual framework.
TECHNOLOGY, PEDAGOGY AND EDUCATION 5

surveys were returned. However, incomplete responses and inappropriate responses that featured
uniform answers to the entire set of questions were excluded. A total of 202 fully answered
responses were used for the analysis (Table 1), including 125 (61.9%) males and 77 (38.1%) females.
Approximately 82.2% of the participants had one to two courses involving the use of micro-lectures.
Each course consists of several micro-lectures. A majority of the participants (95.5%) were working
towards a bachelor’s degree and ranged in age from 18 to 23.
Discussion
forum
Assignment
Resources
Lecture slide Micro-lecture
Figure 2. (a) The SPOCs system: learning resources in a selected course. (b) The SPOCs system: an example of a micro-lecture with
slides.
Table 1. Sample profile of the survey.
Item Demographic Frequency Percentage (%)
Gender Male 125 61.9
Female 77 38.1
Age 18–23 194 96.0
Over 23 8 4.0
Micro-lecture experience 1–2 166 82.2
3–4 28 13.9
5–6 5 2.5
6 and more 3 1.5
Subject Art 11 5.4
Science 191 94.6
Level of education Bachelor 193 95.5
Other 9 4.5
Total N = 202 100.0
Micro-lecture experience means the number of courses watched delivered as micro-lectures.
6K. WANG ET AL.

Instruments
The questionnaire consisted of two parts: (1) questions concerning the demographics of the
participants; and (2) measures of antecedents aecting the behavioural intentions to use micro-
lectures in SPOCs. The second part of the survey instrument was adapted from previous studies (D.
Lee & Lehto, 2013; Liaw, 2008; Taylor & Todd, 1995). The 27 adjusted items were carefully modied to
t the context of this study (see Appendix). Measurements for PEOU and PU of the micro-lectures
were based on Davis (1989), Lai et al. (2012) and Venkatesh et al. (2002), and with semantic
dierential scale of four items in the PU latent variable and semantic dierential scale of three
items in the PEOU latent variable. Measurement for the user satisfaction of the micro-lectures was
based on Liaw (2008), with semantic dierential scale of four items. Measurement for the BI to use
micro-lectures was adapted from Lee et al. (2011), with semantic dierential scale of four items.
Measurement for the content richness of the micro-lectures was based on D. Lee and Lehto (2013),
with semantic dierential scale of four items. Measurement for the user satisfaction of the micro-
lectures was based on Liaw (2008), with semantic dierential scale of four items. Measures of
vividness of micro-lectures and micro-lecture compatibility consist of ve items. The two items for
the vividness construct were adapted from Jiang and Benbasat (2007). The three items for the
compatibility construct were adapted from Taylor and Todd (1995). The three items for the micro-
lecture self-ecacy were based on Zhao et al. (2010). All items were assessed using a 5-point Likert
scale with anchors ranging from 1 (= not at all true) to 5 (= very true).
Data analysis
Amos 20, a software package designed to perform a structural equation model (SEM) approach to
path analysis, was used to test the hypothesised relationships of the full structural model. Sample
size requirements for structural equation models are at least 200 cases (Kline, 2011). Given that the
sample of the present study consists of 202 people, it can be said that the present study has
a reasonable sample size. Analyses started with correlation analyses to investigate the strength of
the relationships between PU, PEOU, US and BI, and then relationships among PU and other
predictor variables.
Results
Descriptive statistics of the measurement instruments
The descriptive statistics of mean and standard deviation were calculated for the constructs and the
items measured (see Table 2). The means of all the constructs were rated above 3.0 on the one-to-
ve scale, ranging from 3.20 (Compatibility: SD = 1.07) to 3.42 (Micro-lecture self-ecacy: SD = 1.10).
Among the constructs, the compatibility of micro-lectures is the lowest (Mean = 3.20, SD = 1.07),
while the highest is micro-lecture self-ecacy (Mean = 3.42, SD = 1.10). The means of items ranged
from 3.10 (BI2, SD = 1.17) to 3.45 (US2, SD = 1.13). Skewness and kurtosis were examined to conrm
the normalised distribution of the collected data.
Analysis of the measurement model
The Cronbach’s alpha scores, shown in Table 3, indicate that each construct exhibited strong internal
reliability. Convergent validity can be established based on the criterion that two similar indicators
correspond with one another. Convergent validity was evaluated based on Fornell and Larcker's
(1981) criteria: all indicator factor loadings (k) should be signicant and exceed 0.5, and average
variance extracted (AVE) by each construct should exceed 0.5. All standard factor loading (k) values in
conrmatory factor analysis of the measurement model exceed 0.5 (from 0.70 to 0.85), and the AVE,
ranging from 0.501 to 0.712, was greater than the variance because of measurement error. In
TECHNOLOGY, PEDAGOGY AND EDUCATION 7

addition, composite reliability values of 0.60 to 0.70 are acceptable in exploratory research (Nunnally
& Bernstein, 1994). All composite reliability, in this research, was greater than 0.6, ranging from 0.667
to 0.883. Consequently, all construct met the requirements for reliability and convergent validity. As
Table 2. Descriptive statistics of the measurement instruments.
Construct Item Mean SD Skewness Kurtosis
Behavioural intention (Mean = 3.23, SD = 1.06) BI1 3.29 1.115 –.315 –.299
BI2 3.10 1.174 –.204 –.580
BI3 3.26 1.148 –.290 –.440
BI4 3.25 1.192 –.294 –.633
Perceived usefulness (Mean = 3.32, SD = 1.06) PU1 3.21 1.195 –.268 –.614
PU2 3.31 1.149 –.415 –.479
PU3 3.42 1.118 –.489 –.268
PU4 3.33 1.121 –.470 –.347
Perceived ease of use (Mean = 3.36, SD = 1.09) PEOU1 3.48 1.185 –.466 –.551
PEOU2 3.30 1.155 –.268 –.591
PEOU3 3.32 1.281 –.253 –.928
Compatibility (Mean = 3.20, SD = 1.07) COM1 3.27 1.142 –.328 –.472
COM2 3.17 1.144 –.164 –.591
COM3 3.17 1.108 –.193 –.462
Self-efficacy (Mean = 3.42, SD = 1.10) MLSE1 3.42 1.195 –.391 –.636
MLSE2 3.44 1.196 –.331 –.714
MLSE3 3.42 1.144 –.466 –.503
User satisfaction (Mean = 3.38, SD = 1.03) US1 3.41 1.126 –.419 –.314
US2 3.45 1.128 –.482 –.272
US3 3.38 1.137 –.380 –.355
US4 3.34 1.135 –.424 –.401
Content richness (Mean = 3.32, SD = 0.98) CR1 3.31 1.100 –.327 –.320
CR2 3.32 1.119 –.307 –.343
CR3 3.32 1.120 –.383 –.229
CR4 3.32 1.102 –.328 –.391
Vividness (Mean = 3.32, SD = 1.09) VID1 3.30 1.127 –.316 –.367
VID2 3.33 1.173 –.410 –.448
Table 3. Construct validity and convergent validity.
Construct
Questionnaire
items
Standardised factor
loading
Composite
reliability
Average variance
extracted
Cronbach
alpha
Behavioural intention BI1 0.77 0.883 0.654 0.938
BI2 0.76
BI3 0.85
BI4 0.85
Perceived usefulness PU1 0.75 0.844 0.577 0.942
PU2 0.74
PU3 0.70
PU4 0.84
Perceived ease of use PEOU1 0.70 0.775 0.535 0.883
PEOU2 0.79
PEOU3 0.70
Compatibility COM1 0.81 0.881 0.712 0.941
COM2 0.87
COM3 0.85
Micro-lecture self-efficacy MCSE1 0.75 0.807 0.583 0.923
MCSE2 0.75
MCSE3 0.79
User satisfaction US1 0.78 0.882 0.653 0.944
US2 0.85
US3 0.80
US4 0.80
Content richness CR1 0.74 0.842 0.572 0.906
CR2 0.78
CR3 0.80
CR4 0.70
Vividness VID1 0.81 0.667 0.501 0.894
VID2 0.79
8K. WANG ET AL.

shown in Table 4, discriminant validity was conrmed based on the square root of the AVE statistics;
the values of the square roots of the AVE values are greater than the other values of the inter-
construct correlation.
Analysis of the structural model
Table 5 reports the result of the chi-square to degree-of-freedom ratio of 1.780, goodness-of-t index
(GFI) of 0.854, adjusted goodness-of-t index (AGFI) of 0.801, comparative t index (CFI) of 0.963,
normed t index (NFI) of 0.921 and root mean square error of approximation (RMSEA) of 0.062. Doll
et al. (1994) suggested that the value of GFI and AGFI is acceptable if above 0.8. Therefore, the overall
statistics reveal a moderately acceptable t model.
Hypotheses examination
The results of the SEM analysis are illustrated in Figure 3. Two variables of perceived usefulness of
micro-lectures and user satisfaction in term of micro-lectures together accounted for 65.4% of the
variance in inuencing students’ behavioural intentions to use micro-lectures. In this study, three
hypothesised associations were strongly signicant at p < 0.001, two hypothesised associations were
signicant at p < 0.01, and hypothesis 2 and hypothesis 4 were not signicant. Consistent with H1
and H3, perceived usefulness of micro-lectures was positively related to behavioural intention to use
micro-lectures (β = 0.233, p < 0.001) and user satisfaction in terms of micro-lectures (β = 0.563,
p < 0.001) was positively related to behavioural intention to use micro-lectures. Based on the result, it
was found that the perceived usefulness and user satisfaction aect the behavioural intention to use
micro-lectures. Similar to a previous study conducted by Liaw (2008), user satisfaction was found to
be the factor that inuences students’ behavioural intention to use micro-lectures the most highly.
Nevertheless, the impact of perceived ease of use of micro-lectures on behavioural intention
(Hypothesis 2) was not supported (β = 0.402, p > 0.05). Therefore, the user’s behavioural intention
to use micro-lectures is not directly aected by perceived ease of use. Regarding H5–H7, vividness
was positively related to perceived usefulness (β = 0.233, p < 0.001), compatibility was positively
related to perceived usefulness (β = 0.233, p < 0.001) and micro-lecture self-ecacy was positively
related to perceived usefulness (β = 0.233, p < 0.001). These three predictors explained 72.1% of the
total variance in perceived usefulness. This emphasises the importance of management considering
Table 4. Construct correlations and discriminant validity.
BI PU PEOU COM MLSE US CR VIV
BI 0.88
PU 0.45 0.86
PEOU 0.41 0.43 0.84
COM 0.44 0.51 0.46 0.90
MLSE 0.33 0.31 0.53 0.44 0.84
US 0.24 0.52 0.55 0.61 0.44 0.95
CR 0.73 0.46 0.45 0.41 0.24 0.43 0.88
VIV 0.55 0.44 0.71 0.51 0.61 0.43 0.47 0.84
BI = behavioural intentions, PU = perceived usefulness, PEOU = perceived ease of use, COM = compatibility, MLSE = micro-
lectures self-efficacy, US = user satisfaction, CR = content richness, VIV = vividness.
Table 5. Model-fit indices for the model.
Model X
2
/df NFI CFI GFI AGFI RMSEA
1.780 0.921 0.963 0.854 0.801 0.062
Recommended values ≦3.0 ≧0.9 ≧0.9 ≧0.8 ≧0.8 ≦0.8
NFI = normed fit index, CFI = comparative fit index, GFI = goodness-of-fit index, AGFI = adjusted goodness-of-fit index,
RMSEA = root mean square error of approximation.
TECHNOLOGY, PEDAGOGY AND EDUCATION 9

the form of presentation and user’s estimation of his or her ability when it comes to using micro-
lectures. However, regarding H4, content richness does not predict perceived usefulness.
Discussion and conclusions
The role of perceived usefulness, perceived ease of use and user satisfaction in inuencing
behavioural intentions
Except for perceived ease of use, all path coecients were found to be statistically signicant.
Consistent with our predictions, the students’ perceptions of usefulness were found to be a direct
predictor of their behavioural intentions. Perceived usefulness has found to be an extrinsic motivator
when it comes to determining students’ behavioural intentions in many educational environments
(Lai et al., 2012; Nazarenko, 2015). The present study contributes to the knowledge community by
investigating the impact of perceived usefulness of micro-lectures on the behavioural intention to
use micro-lectures. In the SPOC environment, students appreciated accessible resources that meet
their needs. This is probably closely related to the fact that students are likely to use resources to
achieve their specic learning goals. This could be primarily true amongst university students since
they can benet from wide and easy access to the resources used in the learning process. D. Lee and
Lehto (2013) did similar research to investigate users’ acceptance of YouTube for procedural learning
by introducing TAM. They indicated that perceived usefulness can be an extrinsic motivator when it
comes to determining learners’ behavioural intentions when they use YouTube in order to achieve
their specic learning goals.
Perceived
Ease of Use
Behavioural
Intention
Perceived
Usefulness
Micro-lectures
Self-efficacy
User
Satisfaction
Vividness
Compatibility
Content
Richness
-0.306 0.315**
*
0.559**
0.367***
0.563***
0.233**
0.402
R2=0.654
R2=0.72
Figure 3. Results of hypotheses tests. Note: **p < 0.01; ***p < 0.001.
10 K. WANG ET AL.

In the hypothesised model, the present study conrms the claim that behavioural intention to use
micro-lectures was signicantly predicted by user satisfaction in terms of micro-lectures. This partly
varies from previous studies (see, e.g. Davis, 1989) which indicate that the constructs of usefulness
and ease of use have a direct inuence on intentions concerning technology. It seems that user
satisfaction is typically treated as the evaluation of the emotion based on the outcomes associated
with the system and cannot shape the initial users’ acceptance. However, Dalcher and Genus (2003)
reported that end-user satisfaction was depicted as a likely eect on acceptance and, subsequently,
increased usage. Actually, user satisfaction with a single system can be asserted as the most
signicant determinant of long-term foundation intention to use the system (Bhattacherjee, 2001).
In the present study, even though students could use micro-lectures with satisfaction at rst for
some reasons (e.g. novelty eects), since the use of systems with the target behaviour is mandatory
(Baroudi et al., 1986), they will not continue to use micro-lectures when they are dissatised with
them (Joo et al., 2017). When students have an alternative system choice, user satisfaction is
a reliable measure of the behavioural intention to use micro-lectures, because user satisfaction is
viewed as a perceptual or subjective measure of system success which will inuence intrinsic
motivation, commitment to learning and learning performance (Shee & Wang, 2008; Wu et al.,
2010). The ndings suggest that educators should be aware of the distinctiveness of user satisfaction
in continuing to use micro-lectures. Users’ behavioural intentions can be better predicted by the
integration of user satisfaction and TAM, the predictive power of user satisfaction can be improved
and the utility of TAM can be augmented.
However, contrary to conventional predictions from TAM, perceived ease of use was found to be
unrelated to the behavioural intention to use micro-lectures. Past research has discussed the weak
role of perceived ease of use in predicting user acceptance (Wang et al., 2009). For example, in
a study of user acceptance of YouTube for procedural learning (D. Lee & Lehto, 2013), the authors
noted that participants did not give much priority to retaining their perception of ease of use when
making acceptance decisions. This means that in most cases, students tend to have the inclination to
use micro-lectures directly, rather than thinking about their perceived ease of use. In the present
study, a sensible explanation for the insignicant nding may be that using micro-lectures does not
create a practical barrier to acceptance given that the students involved had experienced some
micro-lectures. They have control over the skills in taking micro-lectures and will not be frustrated
and discouraged by using them. Consequently, most students may not need to think and ponder
once they want to look for something. For example, when they browse a website to watch some
micro-lectures, they will usually nd it immediately due to a specic link to it. Similarly, over time,
Internet technology is increasingly becoming user-friendly and accessible. Experienced students can
watch online lectures they need to watch with just a click of mouse.
Factors aecting students’ perception of usefulness of micro-lectures
The TAM and its evolutions show the importance of four additional variables including content
richness, vividness, compatibility and micro-lecture self-ecacy as moderating variables of perceived
usefulness. The result of this research support previous research, suggesting that perceived useful-
ness could be inuenced by vividness, compatibility and micro-lecture self-ecacy (Hernandez,
2011; Lai et al., 2012). In particular, increased levels of vividness were found to increase perceived
usefulness. However, this is dierent from a previous study where content richness emerged as
a signicant predictor (De Wulf et al., 2006). In this study, content richness failed to demonstrate
a signicant eect on perceived usefulness. This nding is partly in line with the work of Huang et al.
(2017), which indicates that content richness is less likely to help students learn if the course is
complex. The more complex a course is perceived by students, the less powerful the eect of course
content richness is on their intention to revisit. With respect to the nature of dierent courses (i.e.
diculty level of the course content), the ndings suggest further that course diculty negatively
aects the relationship between the richness of course content and students’ perceived usefulness.
TECHNOLOGY, PEDAGOGY AND EDUCATION 11

In this regard, institutions and educators should simplify the content of micro-lectures only by clearly
teaching a concept, a formula, an example or an experiment.
The study examined the antecedent variables that predict experienced students’ behavioural
intentions when it comes to using micro-lectures in small private online courses based on the
Technology Acceptance Model. The results showed user satisfaction is one of the most signicant
determinants of students’ behavioural intentions to use micro-lectures for learning, and the study
failed to substantiate the eect of content richness on perceived usefulness. These ndings may
have practical implications for organisational planning when it comes to designing and implement-
ing SPOCs involving micro-lectures. One of the limitations of this study is that there is a low level of
participation in the survey. Besides, it would be interesting to see if any dierences are seen between
male students and female students. Another limitation is the missing consideration of other vari-
ables, such as social norms. Future research may need to add other measurements to explain better
technology acceptance and draw more reliable conclusions. Furthermore, detailed discussion of
TAM, course contents, learners’ tasks and instructional strategies can be done in future research.
Note
The data for this study can be accessed by email request to Kai Wang personally (wangkai.edu@outlook.com). The study
was carried out under the approval of the university to which the participants belonged. The participants were asked to
respond to the survey items anonymously.
Disclosure statement
There is no conict of interest regarding this study.
Notes on contributors
Kai Wang is a post-doctoral researcher in the Center for Teacher Education Research of Beijing Normal University. In
more recent work, he is especially interested in exploring the interplay between micro-lectures and ipped learning.
Chang Zhu is a professor in the Department of Educational Sciences of Vrije Universiteit Brussel in Belgium. Her main
topics and areas of research include blended learning and innovations in higher education.
Jo Tondeur is an assistant professor at the Vrije Universiteit Brussel. His research interests are in the eld of instructional
design and educational innovation.
ORCID
Kai Wang http://orcid.org/0000-0002-3795-4719
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Appendix.
Construct
Item
no. Measurement items Reference
Perceived
Usefulness
PU1 I feel that micro-lectures are useful for finishing school assignments. Lai et al., 2012
PU2 I find micro-lectures are useful to me in this course.
PU3 I feel that micro-lectures are useful for enhancing understanding.
PU4 Using micro-lectures helps me accomplish my learning effectively.
Perceived ease of
use
PEOU1 I find micro-lectures to be easy to use. Venkatesh et al.,
2002PEOU2 It is easy to become skilful at using micro-lectures.
PEOU3 I think it is easy to get micro-lectures to do what I want it to do.
Compatibility COM1 Using micro-lectures fit well with the way I study. Taylor & Todd,
1995COM2 Using micro-lectures are compatible with the way I study.
COM3 Using micro-lectures fit into my study-style.
Micro-lectures self-
efficacy
MLSE1 I feel confident navigating micro-lectures by following hyperlinks. Zhao et al., 2010
MLSE2 I feel confident going backward and forward to previously visited micro-
lectures pages without being lost.
MLSE3 I feel confident looking for information by querying the micro-lectures
database.
User Satisfaction US1 I was satisfied with my overall experience of the use of micro-lectures. Liaw, 2008
US2 My overall experience of the use of micro-lectures was pleasing.
US3 Using micro-lectures was a wise choice.
US4 I am satisfied with using micro-lectures as a learning-assisted tool.
Content Richness CR1 The micro-lectures contain very useful information. D. Lee & Lehto,
2013CR2 The content of micro-lectures fits my need.
CR3 Micro-lectures provide up-to-date content that I need.
CR4 I find a satisfactory amount of learning content that I need on micro-lectures.
Vividness VIV1 I feel that the micro-lectures are lively. Jiang &
Benbasat,
2007
VIV2 I can acquire the content of micro-lectures from different sensory channels.
Behavioural
Intentions
BI1 Given the opportunity, I would use micro-lectures to assist my learning. Lee et al., 2011
BI2 I will strongly recommend others to use it.
BI3 I intend to use micro-lectures in the future.
BI4 I intend to use micro-lectures as an autonomous learning tool.
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