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Jurnal Teknologi Informasi Komunikasi (e -Journal)
Vol. 11 No. 1 (2024) 148 - 154

ISSN Media Elektronik: 2443-3640

Exploration of Acceptance Factors of Online Learning Platforms:
A Theory of Planned Behavior Perspective
Adi Dharma Putra1, Addin Aditya2*, Arif Tirtana3
1,2Program Studi Sistem Informasi, Sekolah Tinggi Informatika & Komputer Indonesia,
3 Program Studi Informatika, Sekolah Tinggi Informatika & Komputer Indonesia
1161131008@mhs.stiki.ac.id, 2addin@stiki.ac.id*, 3arif.tirtana@stiki.ac.id

Abstract
E-learning is acknowledged as a form of education enabled by the use of information and communication technologies. Its
effective implementation is contingent upon various influencing factors. This study seeks to determine the key factors that drive
the acceptance of online learning platforms, utilizing the Theory of Planned Behavior (TPB) as a theoretical framework. The
research specifically examines three independent variables: attitudes, subjective norms, and perceived behavioral control. As
explanatory research with a quantitative orientation, data were collected from active students who met predetermined criteria
and were provided access to an online survey. The research sample was selected through purposive sampling. Multiple linear
regression analysis was employed, facilitated by computational tools. The findings from hypothesis testing reveal that two
hypotheses—subjective norms affecting intention and intention affecting behavior—have a positive and significant impact. This
indicates that individuals' perceptions of their social surroundings strongly influence their intention to engage with online
learning platforms. However, the other three hypotheses demonstrate a positive yet statistically insignificant effect, suggesting
that behavioral control and attitudes do not play a critical role in the actual use of these platforms. Overall, the four variables
explain only 43.7% of the variance in behavioral outcomes, implying that the remaining 56.3% is likely influenced by factors
outside the purview of this study.
Keywords: E-Learning; Theory of Planned Behavior; Multiple Linear Regression

Abstrak
E-learning diakui sebagai salah satu bentuk pendidikan yang diselenggarakan melalui penerapan teknologi informasi dan
komunikasi. Keberhasilan penerapan e-learning dipengaruhi oleh berbagai faktor. Penelitian ini bertujuan untuk
mengidentifikasi faktor-faktor yang menentukan penerimaan platform pembelajaran daring berdasarkan perspektif Theory of
Planned Behavior (TPB). Penelitian ini memfokuskan pada tiga variabel independen, yaitu sikap, norma subjektif, dan kontrol
perilaku yang dirasakan. Dengan pendekatan eksplanatori dan fokus pada data kuantitatif, data penelitian diperoleh dari
mahasiswa aktif yang memenuhi kriteria tertentu, yang kemudian diberi akses untuk mengisi kuesioner daring. Sampel
penelitian dipilih melalui metode purposive sampling. Penelitian ini menggunakan analisis regresi linier berganda yang
dilakukan secara komputasi. Hasil pengujian hipotesis menunjukkan bahwa dua hipotesis, yaitu pengaruh norma subjektif
terhadap niat dan pengaruh niat terhadap perilaku, memiliki dampak positif dan signifikan. Ini menunjukkan bahwa persepsi
sosial seseorang dapat berperan penting dalam membentuk niat untuk menggunakan platform pembelajaran daring. Namun,
tiga hipotesis lainnya menunjukkan pengaruh positif yang tidak signifikan, yang mengindikasikan bahwa kontrol sikap dan
perilaku tidak secara signifikan memengaruhi penggunaan platform tersebut. Secara keseluruhan, keempat variabel tersebut
hanya menjelaskan 43,7% dari perilaku pengguna, sementara 56,3% sisanya kemungkinan dipengaruhi oleh faktor lain yang
tidak tercakup dalam penelitian ini.
Kata kunci: E-Learning; Theory of Planned Behavior; Multiple Linear Regression

1. Introduction
Information technology has become an essential part of
daily life, facilitating various activities, including Elearning. E-learning, a form of information technology

designed for remote education, plays a crucial role in
streamlining the process for students to acquire skills
through online platforms [1], [2], [3]. E-learning
technology is essential when it provides user-friendly
features and ensures seamless remote access. As a mode

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of education, E-learning encompasses a broad spectrum
of activities that harness information and
communication technology (ICT) to enrich and
optimize the teaching and learning process. The
adoption of E-learning, however, largely hinges on the
user's viewpoint. Typically, technology is embraced
when it becomes indispensable, when there are
adequate support services, financial resources, and
backing from policy makers.
E-learning, short for Electronic Learning, represents a
contemporary approach to education, utilizing
electronic media, particularly the Internet, as its
foundation. The E-learning platform developed by
STIKI Malang is known as e-belajar application.
Currently, E-learning is not intended to replace
traditional classroom settings, where students and
lecturers engage in face-to-face learning. Instead, it
serves as a supplementary tool that enhances the
learning process and facilitates interaction between
lecturers and students, offering flexibility through
online access. The effectiveness of such an information
system is significantly influenced by behavioral factors,
which play a crucial role in determining its success or
failure [4], [5], [6], [7]. A system or technology
frequently fails due to a lack of user acceptance. To
address this, it is crucial to either modify the rejection
behavior or ensure that the system is designed in a way
that encourages acceptance. Consequently, identifying
the underlying factors that lead to either rejection or
acceptance of the system is essential.
The Theory of Planned Behavior (TPB) provides a
framework for assessing the degree of user acceptance
of technology. It extends the foundations established by
the Theory of Reasoned Action (TRA), which posits
that an individual’s response and perception towards
something are key determinants of their attitude and
behavior [8], [9]. The Theory of Reasoned Action
(TRA) posits that an individual’s intention to perform a
specific behavior is shaped by two primary factors: their
attitude towards the behavior and the subjective norms
influencing it. Ajzen later expanded upon TRA by
introducing the concept of perceived behavioral control,
which was absent from the original model. This
addition, integrated into the Theory of Planned
Behavior (TPB), addresses the influence of personal
limitations and resource constraints on an individual's
capability to carry out the intended behavior [10]. As
can be seen in figure 1, The Theory of Planned Behavior
(TPB) posits that an individual's actions are guided by
their intention to perform the behavior. This intention is
influenced by three key elements: the individual's
attitude towards the behavior, the pressure from
subjective norms, and their perceived control over
executing the behavior. [11], [12].
Attitude refers to an individual's feelings about the
outcomes of engaging in certain behaviors. In contrast,
attitude toward behavior specifically pertains to a

person's perception of performing a particular action
[13], [14]. Subjective norms pertain to an individual's
perception of societal expectations, which shapes their
inclination to engage in particular behaviors [15].
Subjective norms are formed by deeply held beliefs
about what others deem acceptable, coupled with the
motivation to conform to these expectations, known as
normative beliefs. As outlined by Ajzen, perceived
behavioral control represents an individual’s
confidence in their capacity to perform a given behavior
[16]. Perceptions of behavioral control, often referred to
as control beliefs, are influenced by an individual's
understanding of the factors that could either enable or
hinder the execution of a behavior, along with their
evaluation of the relative strength of these factors.

Figure 1. Framework of TPB

This research is grounded in a review of several
previous studies, the foremost being the Model of
Acceptance and Utilization of Technology: E-Learning
in Higher Education. The study seeks to predict and
elucidate the factors that require enhancement in elearning adoption. The findings underscore the
significance of addressing users' attitudes toward the
technology and their perceptions of its ease of use when
deploying e-learning systems [17]. The subsequent
study, titled Assessing Student Adoption of E-Learning:
An Integration of TAM and TPB Framework, aims to
pinpoint the critical factors influencing students'
acceptance of e-learning platforms. By introducing an
extended model that links the C-TAM-TPB framework
with three external variables, this research has
substantially enriched the understanding of learners'
intentions in using e-learning systems. Moreover, the
findings offer key insights for more effectively
designing and optimizing e-learning systems to enhance
student engagement within educational environments.
[18].
The next study, titled Acceptance Study of NUADU as
an Online Education Platform, utilizes the Technology
Acceptance Model (TAM) to assess the acceptance of
the NUADU online learning platform among users in
private schools in Balikpapan. The findings from
hypothesis testing reveal a strong level of acceptance
for NUADU as a distance learning tool. Additionally,
seven recommendations were developed from the

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descriptive analysis to improve the quality of NUADU
as an online learning platform [19]. The Technology
Acceptance Model (TAM) is another framework
employed to assess students' adoption of e-learning. In
the study Exploring Students' Acceptance of ELearning Through the Development of a
Comprehensive Technology Acceptance Model,
researchers applied the TAM model to pinpoint the
factors that shape students' acceptance of e-learning.
The findings indicate that three critical factors—system
quality, computer self-efficacy, and enjoyment of
computer use—substantially affect the perceived ease
of using e-learning platforms. Additionally, the
perceived usefulness and ease of use are positively
impacted by information quality, accessibility, and the
perceived enjoyment of these systems [20]. The
selection of e-learning platforms plays a crucial role in
determining user satisfaction. As highlighted in the
study Students' Happiness and Continued Intention
Toward E-Learning: A Theory-Based Study, the
successful adoption of an online learning system that is
both intuitive and effective is key to improving students'
engagement and overall satisfaction with e-learning
experiences. [21]. This study employs the Theory of
Planned Behavior (TPB) framework to identify the
factors influencing the acceptability of online learning
platforms, as informed by a comprehensive literature
review. Conducted using a quantitative methodology,
the findings of this research offer valuable insights that
could serve as a foundation for future studies, thereby
enhancing the role of online learning platforms in
supporting the teaching and learning process.
2. Research Method
This study employs a quantitative method with a focus
on explanatory research to identify the factors
influencing the acceptance of the e-learning platform.
According to Figure 2, The research process begins
with data collection through an online questionnaire
distributed to a sample of active students at STIKI
Malang, selected using purposive sampling techniques.
The sample consists of students who meet specific
criteria, such as active participation in courses and
involvement with the e-learning platform. The
minimum number of respondents is determined using
the Taro Yamane formula, with a margin of error set at
10%. Once the data is collected, the study applies
multiple linear regression analysis to evaluate the
relationships between variables such as attitudes,
subjective norms, behavioral control, intention, and
actual behavior. All statistical tests are conducted using
computational tools to ensure the accuracy and validity
of the results.

Literature
Review and
Problem
Statement

Data
Acquisition

Developing
Conceptual
Framework

Variable
Definition,
Validity and
Reliability
Test

Hypotheses
Testing

Result
Intepretation

Figure 2. Research Flow

2.1. Data Acquisition
All of the study's participants were STIKI Malang
students who were enrolled full-time. The number of
investigated samples was calculated using the Taro
Yamane formula. Consequently, using the Taro
Yamane formula as shown in Equation 1.
n=

1447
1447.0,12 +1

=

1447
15,47

= 93,5

(1)

The minimum sample size for this study was
determined to be 100 respondents, based on calculated
parameters. A 10% margin of error was applied. A
purposive sampling method, a non-random selection
approach, was employed in conjunction with nonprobability sampling techniques to ensure a
representative sample, guided by the researcher's
criteria. The criteria included active students at STIKI
Malang and visitors to the e-learning platform.
Questionnaires were distributed to qualifying students
by STIKI Malang, serving as the main data collection
instrument for this research.
2.2. Conceptual Frameworks
The research framework is a concept in interconnected
research. Where the depiction of one variable with
another depiction can be connected in detail and also
systematically. In addition, the research framework
needs to be assembled and carried out so that research
can be more easily understood. Referring to the TPB
model, Figure 3 is a model framework that will be used
in this study.

Figure 3. Conceptual Frameworks

The objective of the Operational Definition of Variables
is to identify the factors that impact STIKI Malang
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users' acceptance and utilization of the e-learning
platform. This study uses specific measurement
indicators for each construct within the Theory of
Planned Behavior (TPB). Attitude reflects how
individuals perceive the decision-making process.
Perceived behavioral control represents the belief that
one can make decisions based solely on personal
preferences, while subjective norms refer to the
influence of others' opinions on decision-making.
Behavior is defined as an action driven by personal
interests, whereas interest denotes a person's need or
desire for a particular outcome. The definitions of
operation for each variable derived from the TPB model
are explained in Table 1.

Var.

Indicator

Control belief
power

Table 1. Variable Definition
Var.
Attitude
X1

Indicator
Belief strength

Outcome
evaluation

Subjective
Norm
X2

Normative
beliefs)

Motivation to
comply

Behavior
Control
X3

Control belief
strength

Items
I think using elearning is a
good idea
(X1.1)
It would be
very beneficial
for me to use
e-learning.
(X1.2)
I don't think it
is necessary to
use e-learning
for lectures
(X1.3)
I think using ebelajar is not a
good idea and
is difficult to
learn (X1.4)
Most of the
people I know
suggest I use
e-learning
regularly
(X2.1)
Most people
who are
important to
me suggest
that I don't
need to use elearning
frequently
(X2.2)
I am confident
that I can
fulfill the
expectations of
those closest to
me to use elearning more
often (X2.3)
I think it is too
difficult if I
have to use elearning
regularly
(X2.4)
I can use elearning easily
(X3.1)

Source
[11]

Intention
Z

Interest

Situation

[13]
Behavior
Y

Actual usage

Items
I use elearning based
on my own
desire (X3.2)
If I am sick,
tired or
something
unexpected
happens and
puts a strain on
my time, it will
make me more
irregular in
using elearning (X3.3)
I do not have
sufficient skills
and knowledge
in and
resources
needed to use
e-learning
(X3.4)
I want to use elearning during
my studies
(Z1.1)
I want to get
learning
materials
through elearning (Z1.2)
I want to use elearning more
often (Z1.3)
I want to get
learning
materials
through elearning (Z1.4)
I often use elearning (Y1.1)
I use and try in
my
assignments
(Y1.2)
I am more
comfortable
using elearning (Y1.3)
Using elearning is the
best way for
me to learn
(Y1.4)

Source

[1]

[1],
[11],
[22]

3. Result and Discussions
3.1. Validity and Reliability Test

[11]

The validity test indicates the difference in the level of
consistency within the data that the researcher gathered
and the data that happens on the item. [23], [24]. Using
the SPSS software and the Pearson variable correlation
algorithm, the validity test was conducted. The purpose
of the validity test is to evaluate the questionnaire's
degree of validity for data collection. In the validity test,
questions are considered invalid if, at a significance
level of 5%, the R-count is less than the R-table.

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Conversely, if R-count > R-table at a threshold for
significance of 5%, the questionnaire questions are
considered legitimate
It is known that the significance level is 0.05 and the
number of samples is 100, so from the calculation of the
R value, R-table is obtained at 0.195. Table 2 is the
validity test value that has been calculated from 100
respondents who meet the sampling criteria. Table 3
shows the validity test results of the research
questionnaire.
Table 2. Validity Test Result
Variable
Attitude
X1

Indicator
(X1.1)
(X1.2)
(X1.3)

R-Count
0,396
0,474
-0,174

(X1.4)

-0,170

(X2.1)
(X2.2)

0,508
0,138

(X2.3)
(X2.4)

0,423
-0,122

Behavior
Control
X3

(X3.1)
(X3.2)
(X3.3)
(X3.4)

0,336
0,489
0,302
-0,052

Intention Z

(Z1.1)
(Z1.2)
(Z1.3)
(Z1.4)
(Y1.1)
(Y1.2)
(Y1.3)
(Y1.4)

0,651
0,663
0,720
0,740
0,671
0,550
0,716
0,626

Subjective
Norm
X2

Behavior Y

Result
Valid
Valid
Not
Valid
Not
Valid
Valid
Not
Valid
Valid
Not
Valid
Valid
Valid
Valid
Tidak
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid
Valid

The aim of the reliability test is to evaluate the
consistency of the study's questionnaire, ensuring that it
yields stable and dependable results upon repeated
administration. A questionnaire is deemed unreliable if
its Cronbach's alpha value falls below 0.60, while a
value exceeding 0.60 indicates that the instrument is
reliable. The reliability test was conducted on a sample
of 100 respondents who met the selection criteria and
completed the questionnaire. Based on the results
presented in Table 4, the research questionnaire can be
regarded as reliable.
Table 3. Reliability Test Result
Reliability Statistics
Cronbach's Alpha

N of Items

0.858

15

3.2. Assumption Classic Test
The purpose of the Classical Assumption Test is to
guarantee that the regression equation is consistent,
does not exhibit crazy fluctuations, and has an accurate
estimate. When a model in research satisfies
conventional presumptions or circumvents issues with
multicollinearity, heteroscedasticity, normalcy, and

linearity tests, it is said to as BLUE (Best Linear
Unbiased Estimator) outstanding.
Table 4. Assumption Classic Test Result
Var.
X1
X2
X3
Z
Y

Normality
Test
Normal
Normal
Normal
Normal
Normal

Multicollinearity
Test (Y/N)
N
N
N
N
N

Heteroskedasticity
Test
N
N
N
N
N

The research experiments come next. Every hypothesis
will be examined and explicated in light of the
outcomes of many linear regression analysis
examinations. Five different hypotheses in all need to
be evaluated.
The Impact of Attitude Variables on Intention (H1): The
inferential analysis reveals that the attitude variable
(X1) does not significantly influence the intention to
use (Z), as indicated by a significance value of 0.317,
which exceeds the alpha threshold of 0.05.
Furthermore, the t-statistic of 1.005 is below the critical
t-table value of 1.984, suggesting that an individual's
attitude toward a system does not necessarily affect
their acceptance of it. In this study, the measurement of
intention is based on indicators related to user
confidence, such as ideas for system usage (X1.1) and
perceived benefits of the system (X1.2). Additional
indicators include assessments of the outcomes of
system usage (X1.3), system utilization during lectures
(X1.4), and challenges encountered while using the
system. The descriptive analysis findings show that
participants generally agree, implying a belief that the
e-learning platform offers advantages in the context of
lectures.
The Impact of Perception on Intention (H2): The
findings from the inferential analysis reveal that the
Perception variable (X2) has a significant positive
effect on the intention to use (Z), with a significance
value of 0.018, which is below the alpha level of 0.05.
Additionally, the t-statistic of 2.395 exceeds the critical
t-value of 1.984, indicating that an individual's
perceptions significantly influence others' willingness
to engage with an e-learning platform. The study
assesses intention by evaluating confidence indicators
influenced by others, such as (X2.1) frequent
recommendations to use the website and (X2.2)
suggestions from important individuals to limit elearning usage. Other indicators include motivation to
meet others' expectations (X2.3) and the challenge of
consistent website use (X2.4). The descriptive analysis
suggests that respondents generally agree, indicating
that environmental factors play a role in their
engagement with the e-learning platform.
The Impact of Behavioral Control on Intention (H3):
The inferential analysis results show that the behavioral
control variable (X3) does not significantly affect user

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intention (Z), as indicated by a p-value of 0.091, which
exceeds the significance level of α = 0.05, and a tstatistic of 1.705, which falls short of the critical t-value
of 1.984. This suggests that despite the presence of
factors that facilitate performance, behavioral control
does not influence the intention of e-learning users. The
study evaluates intention by examining beliefs related
to factors that simplify e-learning use (X3), such as
convincing oneself of its ease of use (X3.1) and trusting
in its voluntary use (X3.2). Indicators of personal
confidence in utilizing e-learning include feeling
assured in using it under any circumstances and
recognizing limitations in knowledge and skills needed
to use the required resources. The descriptive analysis
results suggest that respondents tend to strongly agree,
indicating they have both the necessary tools and
interest in using the e-learning platform.
The impact of Behavioral Control on Behavior (H4):
The results of the inferential analysis reveal that the
behavior control variable (X3) does not have a
significant effect on behavior (Y), as evidenced by a
significance value of 0.108, which exceeds the α
threshold of 0.05, and a t-statistic of 1.705, which is
lower than the critical t-value of 1.984. This suggests
that, despite the presence of factors that support
performance, behavioral control does not influence the
behavior of e-learning users. The behavioral control
coefficient is calculated at 0.108, representing 10.8% of
the total effect. In this study, intention is measured
through beliefs associated with factors that ease the use
of e-learning, including self-assurance in using elearning with ease (X3.1) and trust in its voluntary use
(X3.2). Additional indicators of personal belief in elearning use include confidence in all situations (X3.3)
and the perceived lack of necessary skills and
knowledge for using e-learning resources (X3.4).
Descriptive analysis results suggest that participants
tend to strongly agree, indicating that they possess both
the necessary resources and motivation to engage with
the e-learning platform.
The Influence of Intention on Behavior (H5): The
inferential analysis results demonstrate that the
intention variable (Z) has a significant positive
influence on behavior (Y), as indicated by a
significance value of 0.000, which is well below the α
threshold of 0.05, and a t-statistic of 8.183, surpassing
the critical t-value of 1.985. This suggests that intention
or desire significantly impacts the frequency with which
behavioral variables are employed in accessing the elearning platform. The study assesses the intention
construct through the Interest in Using E-Learning (Z)
indicator, which includes statements such as (Z1.1) "I
desire to utilize e-learning in college" and (Z1.2) "I aim
to access learning materials through e-learning."
Additional indicators of e-learning usage intention
include (Z1.3) a desire to increase e-learning usage and
(Z1.4) a preference for accessing learning materials

online. The descriptive analysis results indicate that
most respondents tend to strongly agree, reflecting a
high level of interest in engaging with e-learning.
4. Conclusions
This study seeks to identify the factors that affect the
acceptance of online learning platforms through the lens
of the Theory of Planned Behavior (TPB). The analysis
and hypothesis testing indicate that attitude does not
significantly influence the intention to use, suggesting
that an individual’s willingness to adopt the system is
not determined by their attitude. However, perception
plays a pivotal role in shaping the willingness to engage
with the e-learning platform. Behavioral control neither
affects the intention to use nor actual behavior,
implying it does not influence users' actions on the
platform. In contrast, interest strongly impacts
behavior, highlighting the crucial role of intention in the
effective use of e-learning websites.
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