Corresponding Author: Tirza
Victorina Rosette Mantik
E-mail: [email protected]
INTRODUCTION
Micro, Small, and Medium Enterprises (MSMEs) are
productive businesses owned by individuals or business entities that fulfill
the criteria of MSMEs as stipulated in Law Number 7 of 2021 on the Ease, Protection,
and Empowerment of Micro, Small, and Medium Enterprises. MSMEs contribute to
the Indonesian economy by absorbing up to 97 percent of the workforce and
attracting up to 60.4 percent of total investment. In March 2021, a Bank
Indonesia survey showed that 87.5% of MSMEs were affected by the COVID-19
pandemic. This prompted the government to recognize the need to digitize MSMEs
by utilizing e-commerce platforms to market their products (Coordinating
Ministry for Economic Affairs, 2021). In 2019, as many as 4.10 billion people
used the Internet, which accounted for 54 percent of the world's population.
This number increased to 4.90 billion in 2021, representing 63 percent of the
population, in line with the growth of information and communication technology
(ICT). The Development Index for 2021 is 5.76, indicating a 3% increase from
the previous year. These figures demonstrate the importance of information and
communication technology in maintaining the continuity of worlds of business,
work, education, services, entertainment, and socialization (Central Bureau of
Statistics Indonesia, 2022).
MSMEs are tasked with selling goods and services to
customers (Malesev & Cherry,
2021). MSMEs have an essential role in economic growth,
especially in Indonesia. If the income of MSMEs increases, it will positively
impact Indonesia's GDP (Erlanitasari et al.,
2020). MSMEs face external constraints and challenges to
competitiveness due to business globalization, such as modern markets,
information technology advances, and consumer changes (Qosasi et al., 2019). According to (Qosasi et al., 2019), small businesses operating in traditional markets
face various constraints ranging from weak business resources, management,
bargaining power, legality, competitiveness challenges, and technology.
Contrary to traditional systems, information technology offers significant
opportunities for MSMEs (Chouki et al., 2020), which can compete with other business sectors,
improve customer interaction, management effectiveness, rapid delivery, and
globalization (Matarazzo et al.,
2020); (Kurniawati et al.,
2021); (Thathsarani &
Jianguo, 2022).
Some MSMEs in Indonesia still rely on traditional
systems and may require attention from industry researchers, as MSMEs have
unique characteristics that differ from large-scale businesses (Sugandini et al.,
2018). Despite the potential benefits, there are still
areas for improvement in the adoption of information technology by MSMEs, as
they still need to fully utilize the technology (Malesev & Cherry,
2021). Conversely, existing MSME technologies may not be
helpful, causing some MSMEs to distrust information technology opportunities (Malesev & Cherry,
2021).
On the other hand, adopting technology can provide new
opportunities for business, market development, customer insight, and a better
experience (Zamani, 2022). Technology adoption is an action taken by using
technology to complete tasks that can positively impact performance and is used
according to the needs of the user's task (Daradkeh, 2019). The relationship between technology adoption and
individual performance has given rise to theoretical models (DeLone & McLean,
2016), two of which are task technology fit (TTF) and the
technology acceptance model (TAM), which have been used to study technology
adoption in different user contexts (Daradkeh, 2019). TTF is used to measure users' task fit with
technology (Erskine et al., 2019); (Sinha et al., 2019) and service characteristics (Huang et al., 2017), while TAM is used to look at differences in
attitudes and engagement of technology users and indicates that a person has
the intention to use technology (Daradkeh, 2019). TAM is also considered adequate to predict user
acceptance and technology adoption in various fields (Cranmer et al., 2016); (Granić, 2023). Previous research found a relationship between TTF
and TAM, with TTF values positively increasing perceived ease of use (PEOU) (Alqatan et al., 2017); (Daradkeh, 2019). However, some MSMEs have tried to adopt technology
but struggle to keep up with frequent changes, and they do not prioritize human
resources to integrate technology, resulting in wasted time (Malesev & Cherry,
2021). On the other hand, previous research states that
perceived ease of use (PEOU) is influenced by self-efficacy and the belief that
a system provides convenience in its capabilities (Ozturk, 2016); (Zhang et al., 2017). So, higher levels of self-efficacy will lead to a
higher willingness to adopt and use technology (Venkatesh et al.,
2003).
Therefore, further research is needed to find evidence
of other contributions to the existing literature and look at the integration
between the tasks performed by MSMEs by utilizing technology and the influence
of one's beliefs on the acceptance of technology use. Thus, the researcher aims
to produce recommendations to answer how self-efficacy and technology adoption
can help stakeholders and MSME actors utilize technology in their business
activities.
Task Technology Fit
Task Technology Fit is a model where
technology helps individuals carry out their tasks. When technology can support
existing tasks, it can help users carry out their tasks smoothly and
efficiently. TTF refers to matching technological capabilities with job
requirements, namely technological capabilities to support tasks, and can be
defined as the extent to which technology enables individuals to perform a
portfolio of tasks (Bere, 2018); (Daradkeh, 2019); (Fu et al., 2020).
Based on the TTF perspective, task characteristics (TC) are actions so that
users can use information technology, and technology characteristics (TNC) are
tools used by system users in carrying out their tasks (Diar et al., 2018); (Hsieh & Lin, 2020); (V�r�s et al., 2021).
Tasks are defined broadly as the actions
performed by individuals in converting inputs into outputs; tasks can be
thought of as a part of a person's work through a sequence of actions to
achieve a goal (�The Effect of Individual and Task Characteristics on
Decision Aid Reliance,� 2017); (V�r�s et al., 2021).
There have been two types of task characteristics: equivocality and
interdependence. When task equivocality and task interdependence are high,
there is greater enthusiasm for technology, and this combination can be
utilized to find relevant technological requirements (Lee & Lee, 2018); (Fu et al., 2020).
Tasks are supported by technological facilities that will assist in
decision-making (Daradkeh, 2019),
which influences the compatibility between tasks and technology. When the task
and technology do not match, the user may feel uninterested in using the
technology for the task. In addition, the influence exerted by the task is
based on the complexity of the task (Muchenje & Sepp�nen, 2023);
when it is more complex, it will affect the tasks and technology fit (Rai & Selnes, 2019).
Based on the literature, we propose the following hypothesis:
H1 : Task characteristic has a positive effect on TTF.
Technology is a tool used to perform
specific tasks. Technology can provide convenience to a system, accuracy,
effective customer relations, flexibility, and reliability (Wu & Chen, 2017); (Omotayo & Haliru, 2020).
Technology has become ubiquitous through online shopping and web-based search
engines, supporting consumer and company interactions and creating marketing
innovations (Hoffman et al., 2022).
Based on research by (Blom et al., 2021),
technology can improve customer service by identifying data collected on
purchase history so that promotions can be aligned and effectively tailored to
customer needs. The use of technology makes business activities in MSMEs more
efficient, which affects the product innovation that will be carried out
(Ardolino et al., 2017)(Marco et al., 2018). It
will impact the match between technology and tasks as well as the functionality
of the technology, whether it suits the user or not (Muchenje & Sepp�nen, 2023).
Therefore, the following hypotheses were proposed:
H2 : Technology characteristic has a positive effect on
TTF.
Integrated TTF and TAM
Previous research indicates that the
integrated model of TTF and TAM provides more explanatory power than the model
alone and thus should lead to a better understanding of choices about the use
of IT (Daradkeh, 2019).
The higher the TTF, the higher the level of information system use, which shows
the perceived benefits of the technology itself (Ratna et al., 2018).
The use of technology is considered to be better if it combines the TTF and TAM
models (Vanduhe et al., 2020).
However, the TTF model does consider not only beliefs about perceived ease of
use (PEOU), but also the extent to which a system can meet the capabilities and
requirements of business actors' analytical tasks (Daradkeh, 2019). (Vanduhe et al., 2020)
said that TTF is used to evaluate user performance. The effectiveness of
technology acceptance is based on the user and how the user maximizes the task (Wu & Chen, 2017).
The utility match between technology and task requires acceptance from the user
(Vanduhe et al., 2020), so
it is possible to match the technology and task when the user accepts and finds
it easy to use the technology (Wu & Chen, 2017).
Based on the literature, we propose the following hypothesis:
H3 : TTF has a positive effect on PEOU.
Technology Acceptance Model (TAM)
TAM is a theory that has been widely
embraced in theoretical studies and is frequently used to forecast technology
acceptance (Vanduhe et al., 2020).
Before new technologies are widely adopted, TAM aims to forecast how people
will use them and investigate how information systems are created. The Theory
of Reasoned Action (TRA) was used to establish TAM, which was then used to
investigate how external factors can affect beliefs, attitudes, and intentions
about the usage of new technologies. The willingness of the user to employ
technology to assist intended tasks is referred to as TAM.� Perceptions of usefulness and ease of use, in
accordance with TAM, moderate the effect of other external factors on the
user's intentions (N.-H. Chen, 2019).
TAM has evolved into a crucial framework for comprehending how people embrace
or reject technology throughout time (Granić & Marangunić, 2019).
According to (Kamal et al., 2020),
TAM has been used to anticipate how new technologies will be received and has
been shown to explain acceptance behavior in a variety of information system
domains. TAM identifies two beliefs as the primary determinants of technology
acceptance behavior, namely perceived ease of use (PEOU) and perceived
usefulness (PU), and explains and estimates the acceptance of system
information based on these beliefs (Anggraeny & Baihaqi, 2021).
In the context of this research, the
author examines Davis' TAM theory of Perceived Ease of Use (PEOU), Perceived
Usefulness (PU), Attitude Toward Use (ATU), and Intention to Use (ITU) by
developing Self-Efficacy (SE) as an external variable.
Self-Efficacy, Perceived Ease of Use, Perceived
Usefulness, Attitude Toward Use, Intention to Use
Self-efficacy is defined as the belief
that a person has the ability to perform certain behaviors (Anggraeny & Baihaqi, 2021). In
the context of technology use, self-efficacy is an assessment of a person's
ability to use technology (Musyaffi et al., 2021). In
the context of entrepreneurship, self-efficacy indicates the ability possessed
by the entrepreneur to complete tasks and achieve goals within a specified time
frame (Jegadeeswari et al., 2020).
Self-efficacy influences people's behavior choices, willingness to put out
effort, and length of time spent overcoming challenges; in addition, it is the
best indicator of perceived ease of use (Ozturk, 2016); (Murillo et al., 2021).
Based on the literature, we propose the following hypothesis:
H4 : Self-efficacy has a positive effect on PEOU.
Perceived ease of use is a crucial
concept that influences how a technology is adopted (L. Chen & Aklikokou, 2020).
PEOU is a perception in which a person believes that the ease of use of a
system is considered an essential factor for adopting technology in the long
term and does not need to make various efforts to use it. Besides, the greater
the perceived use of technology, the greater the possibility of using a
platform (Prastiawan et al., 2021).
While perceived usefulness (PU) refers to "the degree to which a person
believes that using a particular system would be free from effort," PEOU
stands for "the degree to which a person believes that using a particular
system would be free from effort�. PEOU can increase PU and improve
understanding of technology adoption (Barhoumi, 2016).
Likewise, when developing companies, entrepreneurs use new technologies that
are easy to implement (Ojo et al., 2019).
Previous studies have shown that PEOU has a positive effect on PU (Huang et al., 2017).
Based on the literature, we propose the following hypothesis:
H5 : PEOU has a positive effect on PU.
PU is a perception felt by consumers
where they believe that technology will increase efficiency at work (Lanlan et al., 2019). PU
is defined as the probability that consumers use certain applications to
improve performance, but some consumers are said to expect the use of systems
or applications that do not require effort to use them (Daradkeh, 2019).
Two variables PEOU and PU have an impact on users' intentions to accept and use
modern technology. These two elements influence how users feel about new
technologies (Wu & Chen, 2017).
The characteristics of technological acceptance are PEOU and PU (Huang et al., 2017).
The concept of perceived ease of use has been applied in a variety of contexts,
including email, e-commerce, mobile commerce, and the intention to use internet
services (Moslehpour et al., 2018).
PEOU and PU are assumed to be the main factors influencing a person�s attitude
and behavior toward intending to use technology. According to recent literature
on technology adoption, PU and PEOU are crucial elements for enhancing the
predictive validity of the TAM (Pipitwanichakarn & Wongtada, 2019).
Previous research has demonstrated that PEOU has a positive effect on ATU (Huang et al., 2017).
Users typically adopt good attitudes toward technology and believe it to be
useful when they find it simple to use (Huang et al., 2017).
PEOU reflects user convenience, which impacts technology acceptance and user
behavior (Lanlan et al., 2019).
People's attitudes towards use of technology are also influenced by PEOU and
PU, while PU itself is also influenced by PEOU (Dhingra & Mudgal, 2019).
Based on the literature, we propose the following hypothesis:
H6: PU has a positive effect on attitude toward use.����
H7: PEOU has a positive effect on attitude toward use.
Attitudes are the user's perspectives
and emotions relating to a psychological issue (Siyam, 2019).
Researchers define attitude as an evaluation of a person's liking or disliking
of an object or behavior. This concept has been studied in several research
contexts (Nedra et al., 2019).
Attitude toward use of technology is defined as the extent to which a person is
interested in or not interested in using internet-based technology for
learning. Successful system adoption is greatly influenced by a person's
attitude toward using new technology and systems; if users are unwilling to
accept the new technology and systems, they will not fully benefit from them.
Accordingly, the more accepting users are of the new system, the more likely
they are to change their practices and begin using the system (Yoon et al., 2020).
Similar to how attitude toward using a system can be a sign of system
effectiveness, success is not always reliant on the system's objective quality (Yoon et al., 2020).
The word intention refers to the
subjective possibility of people taking specific actions. Intention to use is
considered a behavior that emphasizes the intention to use and positively
affects the actual user; therefore, intention to use has a positive relationship
with the user (Izkair & Lakulu, 2021).
Successful system adoption is greatly influenced by a person's attitude toward
utilizing new technology and systems (Yoon et al., 2020). A
person's disposition (attitude) toward an intent behavior that displays either
avoidance of or acceptance of a particular technology is represented by the
nature of technology usage factors, which are the stimuli that cause an
emotional response or reaction (Bervell et al., 2020).
Intention to use was significantly influenced by attitude toward use. Attitude
toward use of technology has a significant impact on the user's intention (Dhingra & Mudgal, 2019)
Based on the literature, we propose the following hypothesis:
H8: Attitude toward use has a positive effect on
intention to use.
Based on previous research and the
theoretical concepts explained above, the conceptual research framework will be
formed according to Figure 1.
Figure 1. The
Conceptual Research Framework
METHOD
This research is
descriptive with a quantitative approach. In this study, quantitative data will
be collected with various questions in the form of a questionnaire. The
sampling method used in this study is nonprobability sampling using convenience
sampling. The unit of analysis in this study is MSME actors who have yet to use
IT in their business activities. This study's target population was Micro,
Small, and Medium Enterprises (MSMEs) in North Sulawesi, specifically about
385,000 MSME actors in the microbusiness sector (Ministry of Cooperatives and
MSMEs North Sulawesi). Sample size using the calculation method from (Black & Babin, 2019) contains 41 item indicators, so the number of samples we
used was 269 respondents with the following specific criteria :
1. Respondents met the retail trader business segmentation
criteria for Micro, Small, and Medium Enterprises (MSMEs) under Law No. 20 of
2008 concerning MSMEs.
2. Respondents are MSMEs in Manado City who have been
running businesses without using technology in their business activities.
This study will
test the data collected using the following sequence: reliability testing,
validity testing, hypothesis testing, and R square using partial least squares
structural equation modeling (PLS-SEM), which is widely applied to represent
structural division models (Black & Babin, 2019), allowing researchers to simultaneously model and
display complex relationships between several dependent and independent
variables (Hair Jr et al., 2021) using the SmartPLS software.
The construct
measures were adopted from previous studies and measured using a six-point
Likert scale. The first section of the questionnaire was used to collect basic
information about the respondents, such as their gender, age, and education.
The second part was developed based on the three variable of task technology
fit model (TTF) that are task characteristic (TC1-TC6) were measured in terms
of the item suggested by (Tam & Oliveira, 2016)
and (Daradkeh, 2019), technology characteristic (TNC1-TNC7) were measured in
terms of the item suggested by (Tam & Oliveira, 2016), and (Daradkeh, 2019), task technology fit (TTF1-TTF6) were measured in terms
of the item suggested by (Daradkeh, 2019), and five variable of technology acceptance model (TAM)
that are self efficacy (SE1-SE4) were measured in terms of the item suggested
by (Rahmawati, 2019), perceived ease of use (PEOU1-PEOU5) were measured in
terms of the item suggested by (Rafique et al., 2020), Majid and Mawaddah (2020), perceived usefulness
(PU1-PU5) were measured in terms of the item suggested by (Daradkeh, 2019), (Sukendro et al., 2020), and (Majid & Mawaddah,
2022), attitude toward use (ATU1-ATU4) were measured in terms
of the item suggested by (Sukendro et al., 2020) and (Majid & Mawaddah,
2022), intention to use (ITU1-ITU4) were measured in terms of
the item suggested by (Sukendro et al., 2020)
and (Rafique et al., 2020).
RESULTS AND DISCUSSION
Respondent Characteristic
During the data collectiodataod, 269 respondents
completed the survey form, which c,ould be categorized as an acceptable data
set after conducting an outlier checking procedure. Table 1 presents an
overview of the respondents who were involved.
Table 1. Respondent Characteristics
|
|
Frequency
|
Percentage (%)
|
|
Male
|
123
|
45.7
|
|
Gender
|
Female
|
146
|
54.3
|
|
Total
|
269
|
100.0
|
|
<20
|
7
|
2.6
|
|
20-30
|
78
|
29.0
|
|
Age
|
31-40
|
93
|
34.6
|
|
>40
|
91
|
33.8
|
|
Total
|
269
|
100.0
|
|
Elementary
School
|
9
|
3.3
|
|
Junior High
School
|
20
|
7.4
|
|
Senior High
School
|
124
|
46.1
|
|
Education
|
Associate's
Degree
|
27
|
10.0
|
|
Bachelor's
Degree
|
83
|
30.9
|
|
Master's
Degree
|
6
|
2.2
|
|
Total
|
269
|
100.0
|
|
Culinary
|
112
|
41.6
|
|
Fashion
|
41
|
15.2
|
|
Educational
|
4
|
1.5
|
|
Retail
|
68
|
25.3
|
|
Type of
|
Creative
Economy
|
6
|
2.2
|
|
Business
|
Agriculture
|
4
|
1.5
|
|
Livestock
|
9
|
3.3
|
|
Fishery
|
6
|
2.2
|
|
Others
|
19
|
7.1
|
|
Total
|
269
|
100.0
|
|
<2
|
43
|
16.0
|
|
3-5
|
119
|
44.2
|
|
Age of
Business
|
6-8
|
48
|
17.8
|
|
>8
|
59
|
21.9
|
|
Total
|
269
|
100.0
|
|
<IDR
3.000.000
|
42
|
15.6
|
|
IDR 3.000.000
- IDR 6.000.000
|
118
|
43.9
|
|
Average
|
IDR 6,000,001
- IDR 9,000,000
|
67
|
24.9
|
|
Earnings
|
IDR 9.000.001
- IDR 12.000.000
|
22
|
8.2
|
|
>IDR
12.000.000
|
20
|
7.4
|
|
Total
|
269
|
100.0
|
Measurement
Model Evaluation
The measurement model's evaluation focused on assessing the reliability
and validity of each construct where factor loading and AVE are more
significant than 0.5 and composite reliability. Cronbach's, alpha is more
significant than 0.7 (Hair
Jr et al., 2021). As seen in Table 2, factor
loadings of TC 1, TC 2, and ATU 2 are lower than 0.5. Besidessthisthe, the Task
Characteristics variable values of AVE and Croncbach's alpha are lower than the
minimum threshold, so they are unacceptable.
Table 2.
The Summary of The Measurement Model Evaluation
|
Construct
|
Test Item
|
Validity
|
Test
|
Composite reliability
|
Reliability Test
|
|
Factor Loading
|
AVE
|
Cronbach's alpha
|
|
Task
Characteristics
|
TC 1
|
0.42
|
0.38
|
0.77
|
0.63
|
|
(TC)
|
TC 2
|
0.21
|
|
|
|
|
TC 3
|
0.69
|
|
|
|
|
TC 4
|
0.74
|
|
|
|
|
TC 5
|
0.76
|
|
|
|
|
TC 6
|
0.68
|
|
|
|
|
Technology
Characteristics
|
INC 1
|
0.86
|
0.61
|
0.91
|
0.89
|
|
(TNC)
|
TNC 2
|
0.83
|
|
|
|
|
TNC 3
|
0.71
|
|
|
|
|
TNC 4
|
0.71
|
|
|
|
|
TNC 5
|
0.76
|
|
|
|
|
TNC 6
|
0.80
|
|
|
|
|
TNC 7
|
0.77
|
|
|
|
|
Task
Technology Fit
|
TTF 1
|
0.50
|
0.51
|
0.86
|
0.80
|
|
(TTF)
|
TTF 2
|
0.77
|
|
|
|
|
TTF 3
|
0.68
|
|
|
|
|
TTF 4
|
0.75
|
|
|
|
|
TTF 5
|
0.76
|
|
|
|
|
TTF 6
|
0.78
|
|
|
|
|
self-efficacy
|
SE 1
|
0.93
|
0.73
|
0.91
|
0.87
|
|
(SE)
|
SE 2
|
0.67
|
|
|
|
|
SE 3
|
0.92
|
|
|
|
|
SE 4
|
0.88
|
|
|
|
|
Perceived
Ease of Use
|
PEOU 1
|
0.86
|
0.67
|
0.91
|
0.87
|
|
(PEOU)
|
PEOU 2
|
0.84
|
|
|
|
|
PEOU 3
|
0.74
|
|
|
|
|
PEOU 4
|
0.79
|
|
|
|
|
PEOU 5
|
0.84
|
|
|
|
|
Perceived
Usefulness
|
PU 1
|
0.91
|
0.78
|
0.95
|
0.93
|
|
(PU)
|
PU 2
|
0.89
|
|
|
|
|
PU 3
|
0.86
|
|
|
|
|
PU 4
|
0.88
|
|
|
|
|
PU 5
|
0.87
|
|
|
|
|
Attitude
Towards Use
|
ATU 1
|
0.88
|
0.62
|
0.86
|
0.77
|
|
(ATU)
|
ATU 2
|
0.34
|
|
|
|
|
ATU 3
|
0.87
|
|
|
|
|
ATU4
|
0.91
|
|
|
|
|
Intention to
Use
|
ITU 1
|
0.92
|
0.82
|
0.95
|
0.92
|
|
(ITU)
|
ITU 2
|
0.86
|
|
|
|
|
ITU 3
|
0.92
|
|
|
|
|
ITU 4
|
0.91
|
|
|
|
Based on Table 2, items that have a value below the minimum threshold are
removed and recalculated. The final results of the PLS-SEM calculation for the
validity and reliability tests are in Table 3, where several removed items also
affected the value of HTMT, so the final results for the validity and
reliability tests are in Table 3. The task characteristic variable has the
lowest AVE value and reliability test, where the AVE value of TC shows that an
average of 58% of the variance captured by the indicator is still acceptable.
Overall, the values of the validity test and reliability test are acceptable.
Table 3.
The Final Summary of the
Measurement Model Evaluation
|
Construct
|
Test Item
|
Validity
|
Test
|
Composite Reliability
|
Reliability Test
|
|
Factor Loading
|
AVE
|
Cronbach's alpha
|
|
Task
Characteristics
|
TC3
|
0.80
|
0.58
|
0.85
|
0.76
|
|
(TC)
|
TC4
|
0.85
|
|
|
|
|
TC5
|
0.72
|
|
|
|
|
TC6
|
0.67
|
|
|
|
|
Technology
Characteristics
|
TNC 2
|
0.85
|
0.72
|
0.89
|
0.81
|
|
(TNC)
|
TNC 6
|
0.87
|
|
|
|
|
TNC 7
|
0.82
|
|
|
|
|
Task
Technology Fit
|
TTF 2
|
0.81
|
0.68
|
0.86
|
0.76
|
|
(TTF)
|
TTF 5
|
0.82
|
|
|
|
|
TTF 6
|
0.84
|
|
|
|
|
Self Efficacy
|
SE 1
|
0.93
|
0.73
|
0.91
|
0.87
|
|
(SE)
|
SE 2
|
0.67
|
|
|
|
|
SE 3
|
0.92
|
|
|
|
|
SE 4
|
0.88
|
|
|
|
|
Perceived
Ease of Use
|
PEOU 2
|
0.87
|
0.67
|
0.90
|
0.83
|
|
(PEOU)
|
PEOU 3
|
0.77
|
|
|
|
|
PEOU 4
|
0.76
|
|
|
|
|
PEOU 5
|
0.87
|
|
|
|
|
Perceived
Usefulness
|
PU 2
|
0.90
|
0.78
|
0.93
|
0.91
|
|
(PU)
|
PU 3
|
0.86
|
|
|
|
|
PU 4
|
0.89
|
|
|
|
|
PU 5
|
0.88
|
|
|
|
|
Attitude
Towards Use
|
ATU 1
|
0.89
|
0.80
|
0.92
|
0.88
|
|
(ATU)
|
ATU 3
|
0.88
|
|
|
|
|
ATU4
|
0.92
|
|
|
|
|
Intention to
Use
|
ITU 1
|
0.92
|
0.82
|
0.95
|
0.93
|
|
(ITU)
|
ITU 2
|
0.86
|
|
|
|
|
ITU 3
|
0.92
|
|
|
|
|
ITU 4
|
0.91
|
|
|
|
Furthermore, the discriminant validity of the construct is referred to as
the heterotrait-monotrait ratio (HTMT). The rule of thumb threshold for HTMT is
below 0.90 (Hair
Jr et al., 2021). According to Table 4, it can
be inferred that HTMT values were highly accepted.
Table 4.
The discriminant validity test (Heterotrait-Monotrait
Ratio (HTMT)) summary
|
Construct
|
TC
|
TNC
|
TTF
|
SE
|
PEOU
|
PU
|
ATU
|
ITU
|
|
TC
|
|
|
|
|
|
|
|
|
|
TNC
|
0.470
|
|
|
|
|
|
|
|
|
TTF
|
0.466
|
0.824
|
|
|
|
|
|
|
|
SE
|
0.430
|
0.646
|
0.702
|
|
|
|
|
|
|
PEOU
|
0.501
|
0.884
|
0.851
|
0.879
|
|
|
|
|
|
PU
|
0.426
|
0.840
|
0.739
|
0.585
|
0.868
|
|
|
|
|
ATU
|
0.407
|
0.877
|
0.894
|
0.649
|
0.850
|
0.822
|
|
|
|
ITU
|
0.385
|
0.852
|
0.823
|
0.667
|
0.865
|
0.897
|
0.898
|
|
As observed in Table 4, the constructs of ITU-ATU and ITU-PU exhibit
relatively high values, which are still considered acceptable.
Structural Model Evaluation
The coefficient provides the squared relationship between the actual and
expected values of the variables; thus, it incorporates the notion of variance
degree inside the endogenous constructs by default. Every exogenous construct
supports this. Furthermore, it is identifiable. According to (Chin,
1998), the R square value is considered
high when it is over 0.67, 0.33 to 0.67 is moderate, and 0.19 to 0.33 is weak.
According to Table 4, the R Square values of Task Technology Fit, Perceived
Ease of Use, Perceived Usefulness, Attitude Toward Use, and Intention to Use
were categorized as moderate. So, it is concluded that the effect of the Task
Characteristic and Technology Characteristics variables on Task Technology Fit
is 45.9%. In addition, the effect of Task Technology Fit and Self Efficacy on
Perceived Ease of Use is 67.2%, and the effect of Perceived Ease of Use on
Perceived Usefulness is 56.70%. Furthermore, Perceived Ease of Use and
Perceived Usefulness strongly affect Attitudes toward Use by 61%. Lastly, the
attitude toward use on intention to use increased by 65.6%.
Table 5.
R square of the endogenous latent variables
|
Construct
|
R
Square
|
R
Square Adjusted
|
Result
|
|
TTF
|
1.459
|
0.455
|
Moderate
|
|
PEOU
|
0.672
|
0.670
|
Moderate
|
|
PU
|
0.567
|
0.566
|
Moderate
|
|
ATU
|
0.610
|
0.607
|
Moderate
|
|
ITU
|
0.655
|
0.655
|
Moderate
|
The structural model evaluation aims to analyze the provided hypotheses.
The path coefficient analysis was used to do this. The model was designed to
process a data set using a bootstrap resampling technique to determine the
path's importance. A total of 5,000 resamples were used in this study. In this
investigation, a two-tailed t-test was used. Table 6 displays the hypothesis
testing findings of the integrated model used in this investigation.
Table 6.
Result of Structural Model Research Hypotheses
|
Hypothesis
|
Relationship
|
Path Coefficient (B)
|
t-value
|
p-value
|
Significance
|
|
H1
|
TC -> TTF
|
0.140
|
2.624
|
0.0009
|
Yes
|
|
H2
|
TNC -> TTF
|
0.613
|
12.353
|
0.0000
|
Yes
|
|
H3
|
TTF -> PEOU
|
0.352
|
6.263
|
0.0000
|
Yes
|
|
H4
|
SE -> PEOU
|
0.562
|
10.807
|
0.0000
|
Yes
|
|
H5
|
PEOU -> PU
|
0.753
|
23.010
|
0.0000
|
Yes
|
|
H6
|
PU -> ATU
|
0.429
|
5.352
|
0.0000
|
Yes
|
|
H7
|
PEOU -> ATU
|
0.405
|
4.979
|
0.0000
|
Yes
|
|
H8
|
ATU -> ITU
|
0.810
|
25,054
|
0.0000
|
Yes
|
Note (s): t-value > 1.96� (Significance level: 5% (p<0.05) (Hair Jr et al., 2021).
The authors focused on hypothesis testing of the structural model
utilized in the current research. The significance level of 5% is used in the
evaluation of research hypotheses (p<0.05) and t-value > 1.96 of the
relationship among the variables (Hair
Jr et al., 2021). The table above shows that
the relationship between variables has a significant positive effect where the
t-value is > 1.96 and the p-value is 0.05. Therefore, H1 to H8 are proven
and accepted. TabTables shows that Technology CharacteristiCharacteristicsaracteristiCharacteristicsws
more influence Task Technology Fit that the specific characteristics of the
technology used in completing the task have a more significant influence on the
perceived fit. In other words, the technology's features, functions, or
capabilities play a more significant role in determining how good TTF is.
On the other hand, Self-efficacy provides a more significant influence
than Task Technology Fit, which shows that individual beliefs in their
abilities have a more significant impact on perceptions of how easily the
technology can be used compared to how well the technology can fit the task.
Figure 2. The
output of path coefficient, t-value, and R square structural model evaluation
Based on the results of the validity and reliability tests, it can be
concluded that all items are acceptable. The HTMT value shows that all
indicators have validity in their latent variables. Each independent variable
has a moderate influence on the dependent variable. From the existing hypothesis,
TC and TNC positively influence TTF, so the better the value of task
characteristics and technology characteristics, the more it will affect MSMEs
to utilize technology in business activities. Furthermore, PEOU is influenced
by SE, which shows that the beliefs of MSME actors in the use of technology
positively influence their perceptions of the ease of using technology. In
addition, PEOU and PU positively influence ATU, which shows that the more MSME
actors feel the ease and usefulness of technology, which provides efficiency in
business activities, the more it will influence the acceptance of technology
use. Furthermore, ATU has a positive influence on ITU, so when MSME actors
accept the use of technology, it will impact their tendency to use technology
in business activities.
CONCLUSION
Utilizing technology is an important
competitive advantage in today's business environment. Businesses are adopting
technology for use on their premises. This study examines the acceptance and
application of technology by MSMEs that still carry out their business
activities traditionally. It also develops and tests the integration of TTF and
TAM models in technology adoption. Analysis of the survey data in this study
yielded significant findings that support the eight research hypotheses. The
results showed that the TTF model with the variables TC, TNC, and TTF had a
positive effect on the TAM model with the PEOU, PU, ATU, and ITU variables, but
when compared to TTF, SE had a greater influence on PEOU. So that the
integration of TTF and TAM by combining SE as an external variable can provide
a better explanation of the use of technology than using only the TTF or TAM
models. This study has several important implications for research and practice
as it offers an amalgamation of insights from SE and two competing technology
adoption models, TTF and TAM. Thus, this is an initial effort to predict
technology adoption in MSMEs business activities.
The managerial implications of the
results of this study are that they can be used as a reference for MSMEs to
adopt technology and be more active in their technological capabilities in
their business activities. The results of this study make a positive
contribution to existing problems where there is a gap in technology
utilization and some MSMEs have tried to use technology but currently need to
use technology. This study offers several practical recommendations for
stakeholders with an interest in empowering MSMEs, including the government,
entrepreneurs, and MSMEs. Based on respondent data, the highest percentage at
the education level of MSME actors is senior high school. These data are
relevant to the results of testing the hypothesis that SE has a greater
influence on PEOU. So to increase the self-efficacy of MSMEs actors, the
government can provide programmes such as outreach, training, enrichment, and
so on related to how to use technology so as to improve self-efficacy in the
use of technology for MSMEs actors. Apart from providing opportunities for the
government and entrepreneurs, it can also be an opportunity to collaborate with
banking and non-banking companies to provide a digital ecosystem for MSMEs. In
addition, every need for technology utilisation by MSMEs is influenced by the
type of business being run. Technology can be offered according to existing
needs because convenience and usability also affect the intention of MSMEs to
use it. Entrepreneurs can see most of the business fields that have not used
technology, so they can offer tools to run MSMEs businesses.
Managerial and theoretical implications
must be considered because this study has limitations. In this research, the
measurement of self-efficacy in the use of technology for MSMEs actors is only
carried out in general; there is no grouping of MSMEs who are fluent and not
fluent in using technology, and educational background may show different
research results, so that specific factors cannot be identified that influence
the decisions and behaviour of SMEs towards the use of technology. In addition,
this study also has limited target types of businesses for MSMEs because the
types of existing businesses can influence their decisions to use technology,
as seen from the results of hypothesis testing, which show that technology
characteristics have a positive effect on TTF. Therefore, it is hoped that in
future research, categorization needs to be done to find out specifically the
factors that significantly influence the decisions and behaviour of MSMEs
towards the use of technology.
REFERENCES
Alqatan, S., Noor, N. M. M., Man, M., & Mohemad, R.
(2017). A theoretical discussion of factors affecting the acceptance of
m-commerce among SMTEs by integrating TTF with TAM. International Journal of
Business Information Systems, 26(1), 66�111.
Anggraeny, R. D., & Baihaqi, I. (2021). Analysis of
E-Marketplace Use in East Java�s MSMEs Using the Technology Acceptance Model
Approach. KnE Social Sciences, 707�727.
Barhoumi, C. (2016). User acceptance of the e-information
service as information resource: A new extension of the technology acceptance
model. New Library World, 117(9/10), 626�643.
Bere, A. (2018). Applying an extended task-technology fit for
establishing determinants of mobile learning: an instant messaging initiative. Journal
of Information Systems Education, 29(4), 239�252.
Bervell, B., Nyagorme, P., & Arkorful, V. (2020).
LMS-enabled blended learning use intentions among distance education tutors:
Examining the mediation role of attitude based on technology-related
stimulus-response theoretical framework. Contemporary Educational Technology,
12(2), ep273.
Black, W., & Babin, B. J. (2019). Multivariate data
analysis: Its approach, evolution, and impact. In The great facilitator:
Reflections on the contributions of Joseph F. Hair, Jr. to marketing and
business research (pp. 121�130). Springer.
Blom, A., Lange, F., & Hess, R. L. (2021). Omnichannel
promotions and their effect on customer satisfaction. European Journal of
Marketing, 55(13), 177�201.
Chen, L., & Aklikokou, A. K. (2020). Determinants of
E-government adoption: testing the mediating effects of perceived usefulness
and perceived ease of use. International Journal of Public Administration,
43(10), 850�865.
Chen, N.-H. (2019). Extending a TAM�TTF model with
perceptions toward telematics adoption. Asia Pacific Journal of Marketing
and Logistics, 31(1), 37�54.
Chin, W. W. (1998). The partial least squares approach to
structural equation modeling. Modern Methods for Business Research, 295(2),
295�336.
Chouki, M., Talea, M., Okar, C., & Chroqui, R. (2020).
Barriers to information technology adoption within small and medium
enterprises: A systematic literature review. International Journal of
Innovation and Technology Management, 17(01), 2050007.
Cranmer, E., Jung, T., tom Dieck, M. C., & Miller, A.
(2016). Understanding the acceptance of augmented reality at an organisational
level: the case of Geevor Tin Mine Museum. Information and Communication
Technologies in Tourism 2016: Proceedings of the International Conference in
Bilbao, Spain, February 2-5, 2016, 637�650.
Daradkeh, M. (2019). Visual analytics adoption in business
enterprises: An integrated model of technology acceptance and task-technology
fit. International Journal of Information Systems in the Service Sector
(IJISSS), 11(1), 68�89.
DeLone, W. H., & McLean, E. R. (2016). Information
systems success measurement. Foundations and Trends� in Information Systems,
2(1), 1�116.
Dhingra, M., & Mudgal, R. K. (2019).
Applications of perceived
usefulness and perceived ease of use: A review. 2019 8th International
Conference System Modeling and Advancement in Research Trends (SMART),
293�298.
Diar, A. L., Sandhyaduhita, P. I., &
Budi, N. F. A. (2018). The
determinant factors of individual performance from task technology fit and IS
success model perspectives: A case of public procurement plan information
system (SIRUP). 2018 International Conference on Advanced Computer Science
and Information Systems (ICACSIS), 69�74.
Erlanitasari, Y., Rahmanto, A., &
Wijaya, M. (2020). Digital economic
literacy micro, small and medium enterprises (SMES) go online. Informasi, 49(2), 145�156.
Erskine, M. A., Gregg, D. G., Karimi,
J., & Scott, J. E. (2019). Individual
decision-performance using spatial decision support systems: a geospatial
reasoning ability and perceived task-technology fit perspective. Information
Systems Frontiers, 21, 1369�1384.
Fu, J., Shang, R.-A., Jeyaraj, A., Sun, Y., & Hu, F.
(2020). Interaction between task characteristics and technology affordances:
task-technology fit and enterprise social media usage. Journal of Enterprise
Information Management, 33(1), 1�22.
Granić, A. (2023). Technology acceptance and adoption in
education. In Handbook of open, distance and digital education (pp.
183�197). Springer.
Granić, A., & Marangunić, N. (2019). Technology
acceptance model in educational context: A systematic literature review. British
Journal of Educational Technology, 50(5), 2572�2593.
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M.,
Danks, N. P., Ray, S., Hair, J. F., Hult, G. T. M., Ringle, C. M., &
Sarstedt, M. (2021). An introduction to structural equation modeling. Partial
Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook,
1�29.
Hoffman, D. L., Moreau, C. P., Stremersch, S., & Wedel,
M. (2022). The rise of new technologies in marketing: A framework and outlook.
In Journal of Marketing (Vol. 86, Issue 1, pp. 1�6). SAGE Publications
Sage CA: Los Angeles, CA.
Hsieh, P.-J., & Lin, W.-S. (2020). Understanding the
performance impact of the epidemic prevention cloud: an integrative model of
the task-technology fit and status quo bias. Behaviour & Information
Technology, 39(8), 899�916.
Huang, L.-C., Shiau, W.-L., & Lin, Y.-H. (2017). What
factors satisfy e-book store customers? Development of a model to evaluate
e-book user behavior and satisfaction. Internet Research, 27(3),
563�585.
Izkair, A. S., & Lakulu, M. M. (2021). Experience
moderator effect on the variables that influence intention to use mobile
learning. Bulletin of Electrical Engineering and Informatics, 10(5),
2875�2883.
Jegadeeswari, S., Sudarvel, J., & Velmurugan, R. (2020).
Factors influencing sustainability of micro small medium enterprise
entrepreneurs. International Journal of Scientific & Technology Research,
9(2), 5501�5503.
Kamal, S. A., Shafiq, M., & Kakria, P. (2020).
Investigating acceptance of telemedicine services through an extended
technology acceptance model (TAM). Technology in Society, 60,
101212.
Kurniawati, E., Idris, I., Handayati, P., & Osman, S.
(2021). Digital transformation of MSMEs in Indonesia during the pandemic. Entrepreneurship
and Sustainability Issues, 9(2), 316.
Lanlan, Z., Ahmi, A., & Popoola, O. M. J. (2019).
Perceived ease of use, perceived usefulness and the usage of computerized
accounting systems: A performance of micro and small enterprises (mses) in
china. International Journal of Recent Technology and Engineering, 8(2),
324�331.
Lee, S. Y., & Lee, S. W. (2018). The effect of Facebook
use on office workers� job performance and the moderating effects of task
equivocality and interdependence. Behaviour & Information Technology,
37(8), 828�841.
Majid, R., & Mawaddah, H. (2022). FinTech and MSMEs: The
role of product knowledge. Asian Journal of Islamic Management (AJIM), 4(1),
15�24.
Malesev, S., & Cherry, M. (2021). Digital and social
media marketing-growing market share for construction SMEs. Construction
Economics and Building, 21(1), 65�82.
Marco, A., Rapaccini, M., Nicola, S., Paolo, G., Giovanni,
C., & Carlo, R. (2018). The role of digital technologies for the service
transformation of industrial companies. International Journal of Production
Research, 1�17.
Matarazzo, M., Penco, L., & Profumo, G. (2020). How is
digital transformation changing business models and internationalisation in
Made in Italy SMEs? Sinergie Italian Journal of Management, 38(3),
89�107.
Moslehpour, M., Pham, V. K., Wong, W.-K., & Bilgi�li,
İ. (2018). E-purchase intention of Taiwanese consumers: Sustainable
mediation of perceived usefulness and perceived ease of use. Sustainability,
10(1), 234.
Muchenje, G., & Sepp�nen, M. (2023). Unpacking
task-technology fit to explore the business value of big data analytics. International
Journal of Information Management, 69, 102619.
Murillo, G. G., Novoa-Hern�ndez, P., & Rodriguez, R. S.
(2021). Technology Acceptance Model and Moodle: A systematic mapping study. Information
Development, 37(4), 617�632.
Musyaffi, A. M., Mulyani, S., Suraida, I., & Sukmadilaga,
C. (2021). Lack of readiness of digital banking channel acceptance: Study on
TAM 3 and technology readiness. Academy of Strategic Management Journal,
20, 1�18.
Nedra, B.-A., Hadhri, W., & Mezrani, M. (2019).
Determinants of customers� intentions to use hedonic networks: The case of
Instagram. Journal of Retailing and Consumer Services, 46, 21�32.
Ojo, A. O., Raman, M., & Downe, A. G. (2019). Toward
green computing practices: A Malaysian study of green belief and attitude among
Information Technology professionals. Journal of Cleaner Production, 224,
246�255.
Omotayo, F. O., & Haliru, A. (2020). Perception of
task-technology fit of digital library among undergraduates in selected
universities in Nigeria. The Journal of Academic Librarianship, 46(1),
102097.
Ozturk, A. B. (2016). Customer acceptance of cashless payment
systems in the hospitality industry. International Journal of Contemporary
Hospitality Management, 28(4), 801�817.
Pipitwanichakarn, T., & Wongtada, N. (2019). Mobile
commerce adoption among the bottom of the pyramid: A case of street vendors in
Thailand. Journal of Science and Technology Policy Management, 10(1),
193�213.
Prastiawan, D. I., Aisjah, S., & Rofiaty, R. (2021). The
effect of perceived usefulness, perceived ease of use, and social influence on
the use of mobile banking through the mediation of attitude toward use. APMBA
(Asia Pacific Management and Business Application), 9(3), 243�260.
Qosasi, A., Permana, E., Muftiadi, A., Purnomo, M., &
Maulina, E. (2019). Building SMEs� competitive advantage and the organizational
agility of apparel retailers in Indonesia: The role of ICT as an initial
trigger. Gadjah Mada International Journal of Business, 21(1),
69�90.
Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., &
Bashir, A. K. (2020). Investigating the acceptance of mobile library
applications with an extended technology acceptance model (TAM). Computers
& Education, 145, 103732.
Rahmawati, R. N. (2019). Self-efficacy and use of e-learning:
A theoretical review technology acceptance model (TAM). American Journal of
Humanities and Social Sciences Research, 3(5), 41�55.
Rai, R. S., & Selnes, F. (2019). Conceptualizing
task-technology fit and the effect on adoption�A case study of a digital
textbook service. Information & Management, 56(8), 103161.
Ratna, S., Astuti, E. S., Utami, H. N., Rahardjo, K., &
Arifin, Z. (2018). Characteristics of tasks and technology as a driver of
task-technology fit and the use of the hotel reservation information system. VINE
Journal of Information and Knowledge Management Systems, 48(4),
579�595.
Sinha, A., Kumar, P., Rana, N. P., Islam, R., & Dwivedi,
Y. K. (2019). Impact of internet of things (IoT) in disaster management: a
task-technology fit perspective. Annals of Operations Research, 283,
759�794.
Siyam, N. (2019). Factors impacting special education
teachers� acceptance and actual use of technology. Education and Information
Technologies, 24(3), 2035�2057.
Sugandini, D., Purwoko, P., Pambudi, A., Resmi, S., Reniati,
R., Muafi, M., & Adhyka Kusumawati, R. (2018). The role of uncertainty,
perceived ease of use, and perceived usefulness towards the technology
adoption. International Journal of Civil Engineering and Technology (IJCIET),
9(4), 660�669.
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B.,
Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended
Technology Acceptance Model to understand students� use of e-learning during
Covid-19: Indonesian sport science education context. Heliyon, 6(11).
Tam, C., & Oliveira, T. (2016). Understanding the impact
of m-banking on individual performance: DeLone & McLean and TTF
perspective. Computers in Human Behavior, 61, 233�244.
Thathsarani, U. S., & Jianguo, W. (2022). Do Digital
Finance and the Technology Acceptance Model Strengthen Financial Inclusion and
SME Performance? Information, 13(8), 390.
The effect of individual and task characteristics on decision
aid reliance. (2017). Behaviour & Information Technology, 36(2),
165�177.
Vanduhe, V. Z., Nat, M., & Hasan, H. F. (2020).
Continuance intentions to use gamification for training in higher education:
Integrating the technology acceptance model (TAM), social motivation, and task
technology fit (TTF). IEEE Access, 8, 21473�21484.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F.
D. (2003). User acceptance of information technology: Toward a unified view. MIS
Quarterly, 425�478.
V�r�s, Z., Kehl, D., & Rouet, J.-F.
(2021). Task characteristics
as source of difficulty and moderators of the effect of time-on-task in digital
problem-solving. Journal of Educational Computing Research, 58(8),
1494�1514.
Wu, B., & Chen, X. (2017). Continuance intention to use
MOOCs: Integrating the technology acceptance model (TAM) and task technology
fit (TTF) model. Computers in Human Behavior, 67, 221�232.
Yoon, J., Vonortas, N. S., & Han, S. (2020).
Do-It-Yourself laboratories and attitude toward use: The effects of
self-efficacy and the perception of security and privacy. Technological
Forecasting and Social Change, 159, 120192.
Zamani, S. Z. (2022). Small and Medium Enterprises (SMEs)
facing an evolving technological era: a systematic literature review on the
adoption of technologies in SMEs. European Journal of Innovation Management,
25(6), 735�757.
Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X., & Lin,
J. (2017). User acceptance of mobile health services from users� perspectives:
The role of self-efficacy and response-efficacy in technology acceptance. Informatics
for Health and Social Care, 42(2), 194�206.
