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ORIGINAL RESEARCH
published: 28 April 2022
doi: 10.3389/frai.2022.768831
Frontiers in Artificial Intelligence | www.frontiersin.org 1April 2022 | Volume 5 | Article 768831
Edited by:
Ralf Klamma,
RWTH Aachen University, Germany
Reviewed by:
Jyotir Moy Chatterjee,
Lord Buddha Education Foundation
(LBEF), Nepal
Chien-Sing Lee,
Sunway University, Malaysia
*Correspondence:
Gesselle B. Batucan
gesselle.batucan@ctu.edu.ph
Specialty section:
This article was submitted to
AI for Human Learning and Behavior
Change,
a section of the journal
Frontiers in Artificial Intelligence
Received: 08 October 2021
Accepted: 31 March 2022
Published: 28 April 2022
Citation:
Batucan GB, Gonzales GG,
Balbuena MG, Pasaol KRB, Seno DN
and Gonzales RR (2022) An Extended
UTAUT Model to Explain Factors
Affecting Online Learning System
Amidst COVID-19 Pandemic: The
Case of a Developing Economy.
Front. Artif. Intell. 5:768831.
doi: 10.3389/frai.2022.768831
An Extended UTAUT Model to Explain
Factors Affecting Online Learning
System Amidst COVID-19 Pandemic:
The Case of a Developing Economy
Gesselle B. Batucan 1
*, Gamaliel G. Gonzales 1,2 , Merly G. Balbuena 1,
Kyla Rose B. Pasaol 1, Darlyn N. Seno 1and Roselyn R. Gonzales 1,2
1College of Education at Danao Campus, Cebu Technological University, Cebu City, Philippines, 2Educational Research and
Resource Center, Cebu Technological University, Cebu City, Philippines
From a developing country perspective, this study explains the factors affecting online
learning amidst the COVID-19 pandemic. The paper empirically tests the proposed
extended unified theory of acceptance and use of technology (e-UTAUT) model in the
students’ intention and use behavior toward the online learning system. Understanding
the acceptance of online learning technology is crucial, especially among developing
countries caught off-guard by the abrupt transition of face-to-face classes to pure online
learning. The enjoyment, interactivity, flexibility, and quality of online learning systems
were added as antecedent variables to the UTAUT model. Eight hundred eighty valid
responses from selected college students in the Visayas regions, Philippines, were
collected. Structural equation modeling (SEM) was employed to verify the research
hypotheses. The results supported the proposed model with acceptable fit measures and
substantial explanatory power. The extended constructs provide different views on online
learning based on the significant cluster of antecedents to explain technology acceptance
through behavioral intentions and actual system usage. The paper implies that despite
the challenges of connectivity in developing countries, the variations still conform with
emerging literature about the topic. Insights for higher education institutions and policy
directions are recommended.
Keywords: COVID-19 pandemic, e-UTAUT, online learning, developing economies, higher education
INTRODUCTION
The outbreak of COVID-19 changed the landscape of the educational systems and has made the
learning institutions shift from the traditional face-to-face to online teaching-learning modality.
The changes have set unprecedented transition challenges that are more pronounced among
developing economies, highlighting the internet infrastructure as one of the most daring barriers
(Costan et al., 2021; Szopi´
nski and Bachnik, 2022). For a developing country like the Philippines,
the COVID-19 pandemic has accelerated the adoption of online tools that support remote-based
teaching, helping institutions leapfrog into adopting tools that support twenty-first-century
learning. Researchers even believe that the pandemic disrupted an education system long lost its
Batucan et al. Extended UTAUT in Online Learning
relevance and provided an opportunity to introduce digital
learning (Pokhrel and Chhetri, 2021). Online learning helps
academic managers to go along with the changes by using
online platforms that enable teachers and students to continue
the teaching-learning process while supporting the students in
developing abilities, skills, and attitudes (Vlachopoulos, 2020).
Online learning platforms’ can cover a course with about 50% less
time than face-to-face learning (Li and Lalani, 2020). The online
environment supports self-directed learning where students can
revisit concepts and set personal goals. For this modality to be
effective, there is a need for a comprehensive understanding
among learners, instructors, and organizations about its benefits
(Adedoyin and Soykan, 2020). As the world responds to the
challenges of the pandemic, the learning institutions are also
reassessing the acceptance of online learning systems as the global
educational environment (UNESCO, 2020).
Emerging literature revealed various aspects of the acceptance
of online learning systems. Kim et al. (2021) reviewed the
acceptance of online learning using the social psychology theories
investigating the mediating impact of user innovativeness amidst
the disruption of classes due to COVID-19. The pandemic
has created opportunities to reassess the effects of behavioral
constructs on the intentions to use and the actual usage of
online learning systems. In a survey of 1,009 students from four
countries (USA, Peru, Mexico, and Turkey) about the use and
acceptance of emergency online learning, cognitive engagement
and self-efficacy vary with students’ attitudes toward online
learning. Understanding the factors affecting online learning
amidst the COVID-19 pandemic is essential for the success of
technology use. Hypothetically, the acceptance of online learning
technology may affect other related latent constructs, especially
when the technology has been introduced abruptly due to the
COVID-19 pandemic. Recently, university students expressed
discontent about the emerging online education amidst the
pandemic due to lack of preparations. For instance, 99 percent of
the students in Korea, during a survey of 203 Korean universities
on a student council network, expressed discontent with online
lectures. The main reason for the reported dissatisfaction was
the poor quality of online classes, the inability to utilize the
school facilities, and difficulty finding a job (Yonhap News, 2020).
The structures of this behavioral dismay are best described in
a structural equation modeling (SEM) research specifically on
the acceptance of technology. However, when online learning
became a necessity nowadays, the reality is that online learning
is arduous from many developing countries geographically due
to the lack of internet and computers services or the inability to
afford the high cost of internet access (Qiao et al., 2021). It should
be noted how online learning can be improved in the respective
of technological evolution if it is the only way for education when
facing new influencers, such as COVID-19.
Hassanzadeh et al. (2012) argued that higher education
changes with the advent of information technology. The
behavioral aspects of the use of technology are important
factors. Teo (2011) viewed technology acceptance as a person’s
willingness to embrace technology to facilitate tasks based on
the support it intends to provide. Recently, the acceptance of
the online learning system has been examined by researchers
in various educational institutions around the world, using
multiple models based on distinct criteria. For example, Pham
and Dau (2022) revealed that the perceptions of the students
in higher education in Vietnam on the uses of online learning
system is not an assurance to gain in their performance and
that the effort expectancy on online learning readiness has
been criticized. These relationships of these factors have been
examined using technology acceptance theories such as the
technology acceptance model (TAM) and the unified theory
of acceptance and use of technology (UTAUT). Other factors
such as planning, structural and organizational aspects, the
components of a system and the interfaces between them,
and various related issues, such as human resources, decision-
making, and training, were used to extend the TAM and UTAUT
(Anderson, 2008).
The current paper explains students’ perspectives on the
factors that affect online learning amidst the Covid-19 pandemic
by extending the UTAUT model with relevant factors on
mandatory e-learning environment (Deˇ
cman, 2015). The
researchers aimed to empirically test the factors that affect online
learning by adding system enjoyment,system interactivity,system
flexibility, and system quality as antecedent variables to the
UTAUT model. The theoretical underpinning of this work was
based on the work of Venkatesh et al. (2003), and the extended
constructs were derived from Nelson et al. (2005),Kulkarni et al.
(2006),Barki et al. (2007),Saraf et al. (2007), and Zhang et al.
(2008). The extension is based on the idea that the acceptance of
online learning is affected by the characteristics of the learning
management systems and internet connectivity.
The remainder of this paper is arranged as follows; section
research model and hypothesis development provides the
hypotheses development based on the proposed model, while
section method explains the methodology. Section results
and discussion presents the results and discussions of SEM
comprising the following steps: (1) the model specification and
extraction of factors through confirmatory factor analysis (CFA),
(2) the determination of how well the measured indicators
represent the specified constructs, and (3) the evaluation of causal
model through path analysis. The implications of the findings
are offered in section implication, and the paper ends with
concluding remarks.
RESEARCH MODEL AND HYPOTHESIS
DEVELOPMENT
Venkatesh et al. (2003) developed the UTAUT model (see
Figure 1), associating the elements of eight models as follows:
(i) the theory of reasoned action (TRA) (Ajzen and Fishbein,
1975), (ii) the technology acceptance model (TAM) (Davis, 1989),
(iii) the motivational model (MM) (Davis et al., 1992), (iv)
the theory of planned behavior (TPB) (Ajzen, 1991), (v) the
combined TAM and TPB (C-TAM-TPB) (Taylor and Todd,
1995), (vi) the model of PC utilization (MPCU) (Triandis,
1977; Thompson et al., 1991), (vii) the social cognitive theory
(SCT) (Compeau and Higgins, 1995), and (viii) innovation
diffusion theory (Rogers, 1995). In this context, the UTAUT
Frontiers in Artificial Intelligence | www.frontiersin.org 2April 2022 | Volume 5 | Article 768831
Batucan et al. Extended UTAUT in Online Learning
model is a theoretical model unifying major theories about
information technology acceptance. Many scholars have used
various theories/models to examine and predict technology
adaptation. The UTAUT model has been used effectively in
various studies on technology acceptance and is inferred as a
convenient instrument for executives to measure the success of
Information technology (Šumak and Šorgo, 2016; Kalavani et al.,
2018). Among these theories/models, the authors argue that the
new model successfully integrates antecedent variables to better
explain the variances in IT behavioral intention and use behavior
in the current situation.
UTAUT is considered a useful and comprehensive model
by many researchers as it looks at all of the available
theories about technology adoption. Its explanatory power in
technology is the highest compared with other technology
acceptance theories (Venkatesh et al., 2011). It has also
been used to study technological innovations supporting
higher education (Halili and Sulaiman, 2018). Specifically,
the theory was also used in broad-spectrum educational
environments such as virtual learning technologies on a cloud
basis, virtual learning environments, desktop web conferencing,
and interactive whiteboards (Suki and Suki, 2017). Similarly,
education research applied the UTAUT model to highlight
the determinants of students’ acceptance and use of various
technologies in many countries (Khechine et al., 2014). Extended
models in UTAUT are also applied to several phenomena, such
as the user acceptance of technology in the consumer context
(Venkatesh et al., 2012).
The extended unified theory of acceptance and use of
technology (e-UTAUT) study develops an integrated model by
modifying the UTAUT by adding use behavior as an independent
variable. Several researchers modified the classical form of
the UTAUT model by redesigning the classical model and
adding independent variables and determinants or resigning the
specific classical determinants and moderators. The exemplary
modifications regard new variables or determinants such as
system flexibility (Hsia and Tseng, 2008), system enjoyment
(Moon and Kim, 2001), system interactivity (Alrawashdeh,
2012), and system quality (DeLone and McLean, 1992) many
other inclusions of independent variables and moderators.
Several UTAUT extensions (variables) and empirical studies were
discovered during the literature review. For instance, Nassuora
(2013) applied the model by modifying it to add a relationship to
understand intent to use online learning. Therefore, this model
is a helpful tool for examining students’ acceptance of the online
learning system during the COVID-19 pandemic.
System Enjoyment
System enjoyment is defined as “the degree of pleasure and
enjoyment that users believe they experience while interacting
with a given IT system” (Moon and Kim, 2001). Several studies
have shown the impact of enjoyment on behavioral intention
based on specific cases of technology adoption, such as a single
platform on mobile payments (Sudono et al., 2020); and online
transportation services (Septiani et al., 2017). It was found that
enjoyment significantly affects user behavioral intention to use
an online learning system. Accordingly, Chao (2019) maintained
that system enjoyment regarding online learning significantly
affects performance expectancy and effort expectancy. Thus, the
following are the proposed hypothesis:
H1. System enjoyment directly impacts behavioral intention.
H2. System enjoyment directly impacts effort expectancy.
H3. System enjoyment directly impacts
performance expectancy.
System Interactivity
System interactivity refers to the ability to customize the site’s
look, feel, and content and interact with the user (Palmer,
2002). Through learners themselves and learners’ interaction
with the organization itself, the interactions between instructors
and learners are the critical elements of the learning process
(Abbad et al., 2009). The development of technologies used in the
online learning context increases individuals’ ability to interact
from anywhere (Alrawashdeh and Al-Mahadeen, 2013). Abbad
et al. (2009) suggested that system interactivity indirectly impacts
users’ intention to use online learning systems through perceived
usefulness and ease of use. Consequently, Venkatesh et al.
(2003) agree that performance expectancy is similar to perceived
usefulness, and effort expectancy is similar to perceived ease of
use. In a study by Compeau and Higgins (1995), the interaction
of the high learning system effectively and efficiently increases the
perceived usability of a computerized learning system.
Venkatesh (2000) found that the performance expectancy
also increased as the learning system’s experience increased.
According to expectancy theory, individual expectations lead
to a decision to perform a specific activity. Thus, the student’s
decision to interact with the learning system depends on
their perception of the usefulness of the learning system.
Sun et al. (2008) agree that positive interaction of the
learning system improves the usefulness of a particular learning
system in learners’ perception. Thus, the following are the
proposed hypothesis:
H4. System interactivity directly impacts effort expectancy.
H5. System interactivity directly impacts
performance expectancy.
System Flexibility
System flexibility refers to the degree to which a learner believes
that they can access the learning system anywhere at any
time (Hsia and Tseng, 2008). Arbaugh (2000) suggested that
online learning gives students a high degree of flexibility when
taking courses online. In other words, learners prefer online
learning because of the flexibility of time and place that comes
with it. Moreover, flexibility allows students to conveniently
manage their learnings, school work, and personal activities
(Arbaugh, 2000). The mobile learning environment shows that
the perceived flexibility advantages, related to the time and place
flexibility, may be closely related to learners’ intent to continue
learning on mobile devices (Sripalawat et al., 2011). For instance,
Evans (2008) suggested that students emphasize flexibility in their
behavioral intention to adopt mobile learning. Therefore, the
following is the proposed hypothesis:
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Batucan et al. Extended UTAUT in Online Learning
H6. System flexibility directly impacts behavioral intention.
System Quality
DeLone and McLean (1992) defined system quality as the
characteristics that reflect the system’s technical level regarding
information generation. Tajuddin et al. (2013) showed that system
quality and users’ satisfaction have a positive relationship. Roca
et al. (2006) argued that system quality improves user satisfaction
with technology by encouraging users to use it. Thus, system
quality is a prominent factor associated with users’ satisfaction.
Chuan-Chuan Lin and Lu (2000) used the internet to emphasize
usefulness on the intention to use online learning. They stated
that many people resist using it due to the slow response time
despite the internet’s popularity. With the website’s poor design
or merely heavy traffic on the internet, the lack of accessibility
of the system induces the availability of related information
systems (computers, modems, online services, software, etc.).
Therefore, the quality of the information system is considered
necessary to influence the user’s beliefs of a Web site (Chuan-
Chuan Lin and Lu, 2000). Thus, this study strives to test the
following hypothesis:
H7. System quality directly impacts behavioral intention.
Social Influence
Venkatesh et al. (2003) defined social influence as the degree
to which individuals perceive that someone accepts that they
should use the new system. Social influence refers to the students,
teachers, friends, classmates, and family members using the
online learning system in the educational context. Sripalawat
et al. (2011) found that social influence is an influential factor
in explaining the use of technology. For instance, women are
more sensitive to the opinions of others and are therefore
more aware of social influence when they intend to use new
technologies (Venkatesh, 2000). Other literature indicates that
social influence significantly impacts behavioral intention to use
online learning (Abu-Al-Aish and Love, 2013). For instance, for
young students, the intention to use mobile learning is influenced
by the opinion of parents and teachers about the importance of
mobile technologies in education. Thus, this study strives to test
the following hypothesis:
H8. Social influence directly impacts behavioral intention.
Effort Expectancy
The effort expectancy construct is the perceived ease of use
of the system (Venkatesh et al., 2003). In the online learning
context, this variable refers to the students’ easiness of using
online learning. The relationship between effort expectancy and
behavioral intention was significant and positive. Another study
by Alrawashdeh (2012) reported that the relationship between
effort expectancy and the behavioral intention was significant in
Jordan’s online learning. For instance, the more effort it takes to
use technology, the less useful it is perceived to be (Venkatesh,
2000; Venkatesh and Davis, 2000). Accordingly, we propose the
following hypothesis:
H9. Effort expectancy directly impacts behavioral intention.
Performance Expectancy
Performance expectancy is the degree to which student believes
that using the system will help them achieve job performance.
In the online learning context, this variable refers to the
students’ study performance. Thus, Venkatesh et al. (2003)
showed that performance expectancy is the most vital determinant
of a user’s behavioral intention to adopt a technology. Davis
(1989) pointed out that performance expectancy showed a
stronger and more consistent relationship with BI than other
variables described in the literature, including various attitude,
satisfaction, and perception measures. An additional study by van
Dijk et al. (2008) showed that performance expectancy and related
constructs are the strongest predictors of BI. Another study
by Abu-Al-Aish and Love (2013),Chang (2013) suggest that
performance expectancy positively influences behavioral intention
to use online learning. For instance, the more individuals expect
technology to improve their productivity, the more likely they
will use it (Venkatesh, 2000). Accordingly, we propose the
following hypothesis:
H10. Performance expectancy directly impacts
behavioral intention.
Facilitating Conditions
Facilitating conditions refers to how an individual perceives that
technical and organizational infrastructure is required to use
the intended system that is available. Venkatesh et al. (2003),
in a study about Users Acceptance of Information Technology,
revealed that facilitating conditions directly affect use behavior.
Raza et al. (2021) found that facilitating conditions positively
affects students’ behavioral intention. Also, a study by Boontarig
et al. (2012) suggested that facilitating conditions positively
influences the behavioral intention and use behavior of using
smartphones for health services. Based on this discussion, the
following hypotheses emerged:
H11. Facilitating conditions directly impacts
behavioral intention.
H12. Facilitating conditions directly impacts use behavior.
Behavioral Intention
Behavioral intention measures the strength of one’s own intention
to perform a certain behavior and the willingness of the
respondent to use the system (Fishbein and Ajzen, 1977).
Warshaw and Davis (1985),Davis (1989) defined behavioral
intention as a degree to which students formulate a mindful plan
to perform specific future behavior and considered as one of the
primary dependent variables of the UTAUT model. Behavioral
intention and use behavior are strongly associated, and behavioral
intention predicts actual use behavior (Bhattacherjee and Hikmet,
2008). Also, a study by Venkatesh and Davis (2000) tested that
behavioral intention assesses the actual use behavior of users.
Thus, this study strives to test the following hypothesis
H13: Behavioral intention directly impacts use behavior.
Gender as a Moderating Variable
Venkatesh et al. (2003) proved that age and gender affect
the behavioral intention of using technology. The performance
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Batucan et al. Extended UTAUT in Online Learning
TABLE 1 | Demographic information of the participants (N=880).
Category Total N =880
n%
Gender
Male 249 28.3
Female 631 71.7
Age
≤20 521 59.2
21 259 29.4
21+100 11.4
Regions in the Philippines
6 180 20.4
7 502 57.1
8 198 22.5
expectancy was moderated by gender toward behavioral intention
to use an online learning system. For example, Nysveen
and Pedersen (2014) proposed that the effect of performance
expectancy is stronger for men than for women. Gender
differences moderate the effects of social influence and the self-
management of mobile learning. Age and gender are moderating
variables for the relationship between effort expectancy and
behavioral intention (Zhang et al., 2008).
H14a: The impact of performance expectancy on behavioral
intention is moderated by gender.
H14b: The impact of social influence on behavioral intention is
moderated by gender.
H14c: The impact of effort expectancy on behavioral intention
is moderated by gender.
Use Behavior
Davis (1989) suggested that the use behavior construct is often
operational by self-reporting participants’ degree of current
system usage. However, like behavioral intention,use behavior
was not explicitly defined in the UTAUT model’s development,
although it measures via system logs (Oh and Yoon, 2014).
Thus, Venkatesh et al. (2003) used system logs to provide
a logical alternative and may be a preferred method for
measuring use behavior in research on information systems.
Consequently, Venkatesh et al. (2003) suggested that behavioral
intention significantly influences use behavior without assuming
a moderation effect between intention and use.
METHOD
Participants
A total of 1,238 College students from the Visayas regions,
Philippines participated in the study. In the data quality
audit, 358 responses were discarded due to duplication, failure
to qualify year level, failure to qualify the scope of the
area, and failure to hold sincerity test. Hence, 880 responses
were considered valid for further analysis. Table 1 reflects the
demographic information of the final participants.
Instruments
A questionnaire was created and divided into two parts: (1) the
demographic information, (2) the constructs associated with the
study.
System Enjoyment
The following items measured the system enjoyment (Simon
et al., 1996; Venkatesh, 2000; Venkatesh et al., 2003). “The
online learning systems make school works more attractive.” “I
find using the online learning system to be enjoyable.” “The
information provided by the online learning system meets my
exact needs in learning.” “I find contentment with the accuracy
of the online learning system.” “The online learning system
provides sufficient information.” The items were measured along
a 5-point Likert scale, which ranges from “strongly agree” (1) to
“strongly disagree” (5). Cronbach’s alpha for the scale was 0.871.
System Interactivity
The following items measured the system interactivity (Barki
et al., 2007): “I use the online learning system (or application)
to exchange with other people.” “I use the online learning system
(or application) to coordinate” “I use the online learning system
(or application) to solve various” “For accomplishing my tasks,
an online learning system is essential.” “I use the online learning
system (or application) to plan or follow up on my tasks.” The
items were measured along a 5-point Likert scale, which ranges
from “strongly agree” (1) to “strongly disagree” (5). Cronbach’s
alpha for the scale was 0.828.
System Flexibility
The following items measured the system flexibility (Nelson
et al., 2005; Saraf et al., 2007): “The online learning system
is versatile to meet the needs as they arise.” “The online
learning system can flexibly adapt to the new demands and
circumstances” “The online learning systems can be adapted to
address various learning needs.” “The online learning system is
highly adaptable.” “The online learning system is designed to
accommodate changes” The items were measured along a 5-point
Likert scale, which ranges from “strongly agree” (1) to “strongly
disagree” (5). Cronbach’s alpha for the scale was 0.862.
System Quality
The following items measured the system quality (Barki et al.,
2001; Kulkarni et al., 2006): “The online learning system allows
me to add useful knowledge.” “The online learning system is user-
friendly or easy to use.” “The online learning system is accessible
from anywhere by anyone.” “The range of functions offered
by the online learning system is adequate.” “The information
provided by the online learning system is precise.” The items
were measured along a 5-point Likert scale, which ranges from
“strongly agree” (1) to “strongly disagree” (5). Cronbach’s alpha
for the scale was 0.791.
Social Influence
The following items measured the social influence (Venkatesh
et al., 2003; Flynn and Ames, 2006): “People who influenced my
behavior think that I should use the online learning system.” “I
have to use the online learning system because that’s how the
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Batucan et al. Extended UTAUT in Online Learning
people who are close to me think.” “People in my organization
who use the online learning system have more prestige than
those who do not.” “I can direct and guide meetings in my favor
in the online learning system.” “I can build effective learning
relationships with others in the online learning system.” The
items were measured along a 5-point Likert scale, which ranges
from “strongly agree” (1) to “strongly disagree” (5). Cronbach’s
alpha for the scale was 0.726.
Effort Expectancy
Derived from Venkatesh et al. (2003),Brown et al. (2010), the
effort expectancy of the students is measured by the following
items: “My interaction with the online learning system is clear
and understandable.” “Using the online learning system helps me
to become skillful quickly.” “Learning to use the online learning
system is easy for me.” “Using an online learning system will
not require a lot of mental effort.” “I believe the online learning
system (tool) will be easy to use.” The items were measured along
a 5-point Likert scale, which ranges from “strongly agree” (1) to
“strongly disagree” (5). Cronbach’s alpha for the scale was 0.708.
Performance Expectancy
The following items measured the performance expectancy
(Venkatesh et al., 2003, 2012): “The online learning system allows
me to achieve the task faster.” “The online learning system
increases my work performance.” “I find the online learning
system useful for communication.” “I find the online learning
system useful in daily life.” “If I use the online learning system,
it will increase my chances of getting higher grades.” The items
were measured along a 5-point Likert scale, which ranges from
“strongly agree” (1) to “strongly disagree” (5). Cronbach’s alpha
for the scale was 0.778.
Facilitating Conditions
Derived from Venkatesh et al. (2003, 2008), we measure students’
facilitating conditions using the following items: “The guidance
from someone helps me in the selection of the online learning
system.” “Specialized instructions concerning the online learning
system were available to me.” “Using the online learning system
fits well with the way I like to deal with my school works.” “I
have the resources needed to use the online learning system.” “I
am aware of how to use the online learning system. The items
were measured along a 5-point Likert scale, which ranges from
“strongly agree” (1) to “strongly disagree” (5). Cronbach’s alpha
for the scale was 0.737.
Behavioral Intention
The following items measured the behavioral intention (Hong
et al., 2002; Malhotra and Galletta, 2005): “I intend to continue
using the online learning system in the future.” “I intend to
use the online learning system to communicate with others as
part of my studies/classes.” “I intend to use the online learning
system in doing performance-based activities.” “I intend to use
the online learning system for coordinating and collaborating
with my classmates.” “I intend to use the online learning system
in my daily school activities.” The items were measured along a
5-point Likert scale, which ranges from “strongly agree” (1) to
“strongly disagree” (5). Cronbach’s alpha for the scale was 0.852.
Use Behavior
The following items measured the use behavior (Davis et al., 1989;
Ajzen, 1991; Venkatesh et al., 2003): “If I had the opportunity
to use an online learning system, I would prefer to use it.” “If I
can proceed with my schooling using an online learning system,
I will.” “I am satisfied with my decision to use the online learning
system.” “I use an online learning system to manage my school
tasks.” “I will use the online learning system in the future.” The
items were measured along a 5-point Likert scale, which ranges
from “strongly agree” (1) to “strongly disagree” (5). Cronbach’s
alpha for the scale was 0.848.
Data Analysis
This study used SPSS software to analyze the items in terms of
reliability and validity, while the AMOS 27 software was used
to evaluate the measurement model and the path analysis. SEM
is a powerful statistical method that simultaneously examines a
series of separate multiple regression equations (Pedhazur, 1997).
This study evaluated and tested the structural relationship of the
UTAUT constructs, as shown in Figure 2.
The reliability of the survey instrument was examined by
calculating the Cronbach’s alpha of each construct to indicate the
internal consistency. Then, convergent validity in this study was
examined based on the standard that the estimated coefficient
of the indicator was significant. CFA was conducted to assess
the measurement model. The three criteria suggested by Fornell
and Larcker (1981), that is, standardized loadings, composite
reliabilities (CR), and average variance extracted (AVE), were
used in this study. These criteria can verify the validity and
reliability of the constructs.
The testing of the hypotheses were conducted by path analysis
using the SEM approach. We evaluated the structural model
of the hypothesized relationships to determine the model’s fit.
In as much as the overall goodness-of-fit using chi-square is
sensitive to large sample size, we alternatively use the minimum
discrepancy of chi-square value (CMIN/DF) to evaluate the
adequacy of the hypothesized model (Hair, 2009). Other fit
indices (i.e., TLI, SRMR, CFI, and RMSEA) were also measured
for the sensitivity of the chi-square test to sample size.
RESULTS AND DISCUSSION
This study used a two-step approach to SEM analysis. After
conducting CFA to validate the measurement model, the
structural model was used to test the hypotheses (see Figure 3).
Preliminary Analysis
The preliminary analysis is to find the internal reliability indices
of each construct using Cronbach’s alpha of the original survey
items. These indices ranging from 0.708 to 0.871 were reflected
in the instrument section. All indices provide a reliable measure
of internal consistency (Awang, 2012). Table 2 shows the visual
inspection of multicollinearity and discriminant validity using
the correlation matrix.
The correlation coefficients are all significant at 0.01 (∗∗)
alpha levels. The intercorrelations between the constructs ranged
from 0.445 to 0.709. The results revealed good discriminant
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Batucan et al. Extended UTAUT in Online Learning
FIGURE 1 | The UTAUT model.
FIGURE 2 | The proposed model.
validity since the correlation indices of the study variables are all
<0.90 (Lischetzke, 2014). The strongest positive correlation was
found between system quality and system flexibility (0.709). All
other coefficients had moderate correlation ranging from 0.445
to 0.681.
Measurement Model Results
A total of 880 respondents were loaded to CFA to evaluate the
construct validity and measurement reliability. Four measures
have been applied to assess the overall metric model fit,
namely: the root mean square error approximation (RMSEA), the
standardized root mean square residual (SRMR), comparative fit
index (CFI), and the Tucker–Lewis index (TLI). Guided by Hu
and Bentler (1999), we implement the following cutoff scores to
achieve a good model; SRMR must be ≤0.080, RMSEA must be
≤0.060, TLI must be ≥0.900, and CFI must be ≥0.900. Table 3
reflects the standardized loadings, CR, AVE, and Cronbach’s
alpha of the final model.
Convergent validity demonstrates in two ways, the factor
loadings must be significant and higher than 0.5 (Bagozzi and Yi,
1988), and then, the AVE for each of the factors is >0.5 (Fornell
and Larcker, 1981). Facilitating conditions has an AVE that is less
than the threshold level of 0.5. However, Fornell and Larcker
(1981) argued that an AVE of <0.5 is adequate if it bears a CR of
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Batucan et al. Extended UTAUT in Online Learning
FIGURE 3 | The final study.
TABLE 2 | Zero-order correlations of the study variables.
Study variable 1 2 3 4 5 6 7 8 9 10
1. BI 1
2. EE 0.445** 1
3. FC 0.575** 0.614** 1
4. PE 0.506** 0.637** 0.582** 1
5. SE 0.596** 0.576** 0.561** 0.629** 1
6. SF 0.555** 0.476** 0.544** 0.531** 0.715** 1
7. SInf 0.542** 0.566** 0.586** 0.576** 0.569** 0.512** 1
8. SInt 0.559** 0.394** 0.543** 0.462** 0.471** 0.546** 0.456** 1
9. SQ 0.579** 0.532** 0.576** 0.544** 0.706** 0.709** 0.545** 0.532** 1
10. UB 0.676** 0.498** 0.575** 0.575** 0.681** 0.602** 0.551** 0.554** 0.584** 1
Mean (x) 2.77 3.02 2.57 2.76 3.17 2.72 2.89 2.45 2.8 3.01
Standard deviation(s) 0.768 0.623 0.585 0.65 0.763 0.685 0.575 0.598 0.67 0.761
BI, behavioral intention; EE, effort expectancy; FC, facilitating conditions; PE, performance expectancy; SE, system enjoyment; SF, system flexibility; SInf, social influence; SInt, system
interactivity; SQ, system quality; UB, use behavior. ** p<0.01.
higher than 0.6. The reliability of the scale is confirmed because
the CR indices of each of the constructs obtained are higher than
0.6 (Bagozzi and Yi, 1988), with levels ranging from 0.608 to
0.869. The overall measurement model showed very satisfactory
fit measures of the RMSEA (0.047), SRMR (0.0384), TLI (0.931),
and CFI (0.942).
Relationships Between the Latent
Variables
We used the correlational analysis through the Pearson
correlation coefficient to support the path analysis of the SEM.
The study followed the r-value guidelines (Schober et al., 2018):
negligible correlation (0.00–0.09), weak correlation (0.10–0.39),
moderate correlation (0.40–0.69), strong correlation (0.70–0.89),
and very strong correlation (0.90–1.00).
Table 4 revealed the correlation matrix among the constructs
included in the CFA. All correlations were positive and
significant at 0.01 alpha level, ranging from 0.30 to 0.729.
More specifically, the correlation between system quality and
system flexibility was 0.729, p<0.001, and between system
flexibility and system enjoyment, 0.715, p<0.001, was found
to be strong. All other coefficients were found to be moderate,
ranging from 0.36 to 0.651. The dependent variable (use
behavior) was found to be significantly correlated with all
nine of the other variables. The correlation of all constructs
was higher than the zero-order correlation in the preliminary
analysis.
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Batucan et al. Extended UTAUT in Online Learning
TABLE 3 | CFA results of the final measurement model.
Items Standardized loadings CR AVE α
System enjoyment (SE) SE1 0.701 0.869 0.571 0.871
SE2 0.72
SE3 0.816
SE4 0.798
SE5 0.736
System flexibility (SF) SF1 0.737 0.863 0.557 0.862
SF2 0.732
SF3 0.773
SF4 0.731
SF5 0.758
Behavioral intention (BI) BI1 0.659 0.856 0.543 0.852
BI2 0.758
BI3 0.752
BI4 0.731
BI5 0.779
System interactivity (SInt) SInt2 0.689 0.808 0.513 0.807
SInt3 0.663
SInt4 0.764
SInt5 0.744
System quality (SQ) SQ1 0.738 0.755 0.507 0.756
SQ4 0.712
SQ5 0.686
Use behavior (UB) UB1 0.725 0.768 0.526 0.787
UB2 0.688
UB5 0.76
Performance expectancy (PE) PE1 0.72 0.729 0.574 0.774
PE2 0.794
Effort expectancy (EE) EE1 0.675 0.678 0.514 0.674
EE2 0.756
Social influence (SInf) SInf1 0.825 0.78 0.64 0.808
SInf2 0.774
Facilitating condition (FC) FC2 0.578
FC3 0.739 0.608 0.44 0.727
Structural Model
The final model fit measures are acceptable (CMIN =1372.401,
df =459, chi/df =2.99, CFI =0.94, TLI =0.931),
whereas RMSEA =0.048 suggests an excellent fit between the
hypothesized model and the observed data (Hair, 2009). The
significance of each hypothesized structural path is tested using
standardized path coefficients and the p-values.
Table 5 showed four paths are significant at p<0.001, two at
p<0.01, one at p<0.05 and five paths are not significant. It is
noteworthy to mention that H11 (facilitating conditions directly
impacts behavioral intentions) was removed in the final model
due to problems on multicollinearity during CFA. The result
revealed that system enjoyment significantly and inversely affects
the behavioral intention (β=-0.492, p<0.01), which is a
contradiction to the findings of existing literature Alqahtani et al.
(2018). This finding adds to the body of literature, specifically in
the case of developing economies. In addition, system enjoyment
directly impacts effort expectancy (β=0.781, p<0.001), and
performance expectancy (β=0.634, p<0.001). Chao (2019)
demonstrated that perceived enjoyment significantly influenced
performance expectancy and effort expectancy of using mobile
learning. Therefore, system enjoyment is a key external variable
in the UTAUT model.
Moreover, system interactivity directly impacts effort
expectancy (β=0.114, p<0.05). When students intend to
use online learning systems to interact with their peers, they
also believe that online learning will improve their learning
performance. In addition, the result also indicates that the system
quality directly impacts behavioral intention (β=0.631, p<
0.01). Thus, the seventh hypothesis (H7) is confirmed. Therefore,
the result shows that system quality is considered necessary in
affecting the behavioral intention of students in online learning.
Furthermore, the twelfth hypothesis (H12) revealed that
facilitating conditions is positively significant to behavioral
intention (β=0.764, p<0.001). This finding has also been
confirmed by Sangeeta and Tandon (2021), who indicates that
infrastructural support is well-established in schools to facilitate
online teaching, and it can enable behavioral intention. Moreover,
the hypothesis of behavioral intention directly impacts use
behavior. Therefore, H13 is accepted. It means that students who
have a higher behavioral intention level to use online learning
systems will positively influence use behavior (β=0.79, p<
0.001). Like Raza et al. (2021), the study concluded a significantly
positive link between behavioral intention and use behavior.
Analysis of Moderating Effects
The moderating effect of gender on the structural model was
analyzed using multigroup analyses. The moderating variable was
divided into two groups and analyzed using the critical ratios
approach (Byrne, 2010). The comparison of the gender-variable
moderator group was split into male (N=249) and female (N=
631) respondents.
As shown in Table 6,social influence,performance expectancy,
and effort expectancy were not moderated by gender toward
behavioral intention. Both males and females do not significantly
affect students’ intention to use the online learning system.
Although not hypothesized, the result showed that gender
significantly moderated system interactivity to effort expectancy.
System enjoyment is positively significant to performance
expectancy and effort expectancy for both males and females. Also,
use behavior is moderated by gender toward behavioral intention.
From our sample, both men and women are college students
who do not have the same quality education and access to
technology. Therefore, gender did not demonstrate a moderating
effect on performance expectancy,effort expectancy, and social
influence on the behavioral intention of students’ widespread use
of technology.
IMPLICATION
The results of this study showed several implications. First,
the e-UTAUT model makes it relevant to the present situation
caused by the Covid-19 and its application in higher education
to explain factors affecting online learning, most especially from
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Batucan et al. Extended UTAUT in Online Learning
TABLE 4 | Correlation results among the constructs in CFA.
Study variable 1 2 3 4 5 6 7 8 9 10
1. BI 1
2. EE 0.473** 1
3. FC 0.483** 0.458** 1
4. PE 0.390** 0.601** 0.384** 1
5. SE 0.596** 0.598** 0.466** 0.552** 1
6. SF 0.555** 0.494** 0.457** 0.441** 0.715** 1
7. SInf 0.428** 0.437** 0.400** 0.360** 0.444** 0.397** 1
8. SInt 0.544** 0.368** 0.457** 0.360** 0.465** 0.538** 0.359** 1
9. SQ 0.578** 0.531** 0.490** 0.448** 0.718** 0.729** 0.409** 0.556** 1
10. UB 0.651** 0.459** 0.409** 0.445** 0.648** 0.573** 0.448** 0.475** 0.565** 1
BI, behavioral intention; EE, effort expectancy; FC, facilitating conditions; PE, performance expectancy; SE, system enjoyment; SF, system flexibility; SInf, social influence; SInt, system
interactivity; SQ, system quality; UB, use behavior. ** p<0.01.
TABLE 5 | SEM results.
Hypothesis Path βSE CR Label
H1 System enjoyment →Behavioral intention −0.492** 0.194 −2.59 Yes
H2 System enjoyment →Effort expectancy 0.781*** 0.05 13.354 Yes
H3 System enjoyment →Performance expectancy 0.634*** 0.051 11.439 Yes
H4 System interactivity →Effort expectancy 0.069 0.046 1.453 No
H5 System interactivity →Performance expectancy 0.114* 0.051 2.368 Yes
H6 System flexibility →Behavioral intention −0.047 0.166 −0.295 No
H7 System quality →Behavioral intention 0.631** 0.311 2.606 Yes
H8 Social influence →Behavioral intention −0.047 0.062 0.854 No
H9 Effort expectancy →Behavioral intention 0.045 0.157 0.345 No
H10 Performance expectancy →Behavioral intention −0.116 0.104 −1.234 No
H12 Facilitating conditions →Behavioral intention 0.764*** 0.207 3.934 Yes
H13 Use behavior →Behavioral intention 0.79*** 0.046 21.062 Yes
*** p<0.001, ** p<0.01, and *p<0.05.
TABLE 6 | Effects of moderating variables.
Gender Male Female
estimate estimate z-score
System enjoyment →Effort expectancy 0.632*** 0.682*** 0.444
System enjoyment →Performance
expectancy
0.478*** 0.632*** 1.421
System interactivity →Effort expectancy 0.322** −0.015 −2.81***
Social influence →Behavioral intention −0.001 0.062 0.38
Effort expectancy →Behavioral intention −0.162 0.161 0.894
Performance expectancy →Behavioral
intention
−0.11 −0.131 −0.068
Facilitating conditions →Behavioral
intention
0.676 0.904*** 0.489
Use behavior →Behavioral intention 1.088*** 0.923*** −1.412
*** p<0.001 and ** p<0.01.
the experiences of a developing economy. This inference is based
on the significance of system enjoyment to intentions to use,
the expected effort, and the expected performance of the online
learning system. These findings supported Audet et al. (2021),
which states that students’ adjustment to online learning amidst
the COVID-19 pandemic is engaging. Hence, the advantage of
using an online learning system in pandemics where institutions
are closed are supported with reasonable factor loadings implying
flexibility of the students to respond to the situational crisis.
The results suggest that higher education institutions build a
stable online portal where teachers can teach and guide students
without any difficulties.
Secondly, the perceived interactivity and quality of the online
learning system significantly explains the students’ belief to
perform better and, consequently, add to their willingness to
use the system. This supports the findings that higher education
students are still abreast of digitizing their activities despite
being challenged by technological infrastructure in developing
economies and actively aspire to develop their technological
knowledge (Gonzales and Gonzales, 2021). The clear advantage
of system interactivity and quality is that it allows a consolidated
variety of information combined. It permits us to store all
information in one place, and students can locate them anytime,
Frontiers in Artificial Intelligence | www.frontiersin.org 10 April 2022 | Volume 5 | Article 768831
Batucan et al. Extended UTAUT in Online Learning
using compatible devices. It reduces administrative hassles
related to maintaining learning materials in multiple areas.
Lastly, the behavioral aspects that facilitate the desire to use
the online learning system significantly explain the students’
intentions. Thus, this is a reason to believe that this are facilitated
with the availability of specialized instructions, awareness, and
enough guidelines concerning online learning systems (Yates
et al., 2021). The use of an online learning system through
successful implementation is recommended to help students
examine the benefits of technology. Thus, the utilization of the
system is proof that it can make other educational learning
activities done online.
CONCLUSION
Amidst the COVID-19 pandemic, educational institutions use
online learning to meet the needs of students. The complexity
of the learning environment in online learning constrains the
need to investigate critical latent factors in understanding the
usage behavior. The paper extended the UTAUT with enjoyment,
interactivity, flexibility, and quality. It is believed that these
factors differ among developing economies.
The results revealed that the model had high internal
consistency and reliability, indicating that the proposed model
possesses substantial explanatory power. This study shows that
intention is a key factor that significantly influences students’
use behavior toward online learning. Students’ system enjoyment
played an important factor in affecting performance expectancy
and effort expectancy. The significance of the negative effect
of system enjoyment to behavioral intention suggests that there
is a need for further investigation on the contrariety of
the results in developing economies. The significant effect of
system quality in behavioral intention indicates that despite
the challenges of connectivity in developing countries, the
variations still conform with emerging literature about the topic.
Finally, the positive effect of facilitating conditions on behavioral
intention could be attributed to the technical and organizational
infrastructure. For example, specialized instructions on online
learning and the resources needed were available. Determining
what motivates online learning can enrich learning quality and
facilitate pedagogical and instructional uses of online learning.
Therefore, this study will have significance for decision-makers
in higher education institutions.
In the future, it is recommended that the model should be
extended to encompass additional constructs, such as system
satisfaction and confirmation, along with various moderating
variables (i.e., age, experiences, and voluntariness of use).
The study also recommends exploring further the variables
or indicators of online learning acceptance on usage behavior
concentrated on the digital education revolution. The model
will then be integrated into an application that will support
the growth of technology in education. This can provide a step
forward to digital education and technology-rich learning.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included
in the article/supplementary material, further inquiries can be
directed to the corresponding authors.
ETHICS STATEMENT
The studies involving human participants were reviewed
and approved by Local Research Ethics Committee,
Cebu Technological University-Danao Campus. The
patients/participants provided their written informed consent to
participate in this study.
AUTHOR CONTRIBUTIONS
GB: project leader, revising the research paper, and analyzing
data. GG: research adviser. MB: writes the methods and
implications. KP: writes the review of related literature. DS:
technical writer and writes the introduction. RG: revising
research paper. All authors contributed to the article and
approved the submitted version.
FUNDING
This work was funded under the Student Trust Fund (STF)
the Cebu Technological University Danao Campus, Danao City
Cebu, Philippines.
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