JVK
JURNAL VOKASI KESEHATAN
http://ejournal.poltekkes-pontianak.ac.id/index.php/JVK
APPLICATION OF UTAUT 3 MODEL TO PREDICT EMR ACCEPTANCE BY
NURSES
Annissa Ambaravista Nasution1 , Ermi Girsang2, Sri Lestari Ramadhani Nasution3
1,2,3

Master of Public Health Study Program, Faculty of Medicine, Dentistry and Health Sciences,
Universitas Prima Indonesia, Indonesia.
Article Info
Article History:
Received : 21-04-2025
Approved : 21-07-2025
Published : 31-07-2025
Keywords:
Electronic Medical
Record, UTAUT3,
behavioral intention, use
behavior, nurses, health
technology.

Abstract
The implementation of an Electronic Medical Record (EMR) system plays
an important role in improving the efficiency and quality of nursing services
in hospitals. However, the level of user acceptance, especially nurses, is often
a major challenge in its implementation. This study aims to analyze the
factors that influence EMR acceptance by nurses at Graha Bunda Hospital
using the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3)
approach. The method used is quantitative with an explanatory survey
design. The study population included all active nurses at Graha Bunda
Hospital, totaling 67 people, who were used as respondents through the total
sampling technique. Data were collected through questionnaires and
analyzed using the Partial Least Square (PLS) method with the help of
SmartPLS software. The results of the analysis show that the variables of
performance expectancy (β = 0.287; p < 0.05), effort expectancy (β = 0.224;
p < 0.05), social influence (β = 0.231; p < 0.05), facilitating conditions (β =
0.265; p < 0.05), hedonic motivation, leadership and management, regulation
and policy, technology readiness, and security & privacy have a significant
influence on behavioral intention and use behavior. Moderating variables
such as age, work experience, and level of volunteerism strengthen some of
the relationships between variables. This study suggests that perceived
usefulness, ease of use, organizational support, and technology readiness are
key factors in EMR adoption. Hospitals need to focus on strengthening
training, improving digital infrastructure, and creating a technology-based
work culture to encourage optimal sustainability of EMR use.

Correspondence address:
Master of Public Health Study Program, Faculty of Medicine, Medicine
Dentistry and Health Sciences, Universitas Prima Indonesia, Indonesia
Email: ermigirsang@unprimdn.ac.id

Introduction
Application Electronic Medical Record (EMR)
is an essential aspect in the modernization of health
services, especially in improving the efficiency of
recording and accessing patient medical data by
medical personnel, including nurses. EMRs allow
quick access to important information such as
disease history, allergies, medications, and test
results, thus supporting informed clinical decision-

ISSN 2442-5478

making and lowering the risk of errors due to
incomplete information (Kim & Kang, 2023).
EMRs help accelerate coordination between
professionals in medical services, support real-time
information exchange, and encourage safer, highquality care (Burridge et al., 2018).
In addition to the clinical benefits, EMRs also
simplify administrative burdens and speed up
services, thereby improving the operational

Jurnal Vokasi Kesehatan Volume 11 Nomor 2, hlm. 33-41

efficiency of hospitals (Devi Delvita & Dety
Mulyanti, 2023). The reduction in documentation
errors and the reduction of patient waiting times
show the positive impact of EMR implementation
in healthcare (Mahal Alrehaili & Zayyan Alsharqi,
2021). The application of EMR has even been
proven globally to increase productivity, reduce
workload, and save costs, while strengthening
collaboration between healthcare providers (Liasari
et al., 2024; Onigbogi et al., 2018). Nurses also
value EMR as a system that facilitates their work,
especially in improving communication and
coordination of teams between professions
(Massora et al., 2024).
EMR plays an important role not only in
individual care, but also in supporting approaches
patient-centered care, especially in service units
such as rehabilitation (Squirrel Siregar et al., 2022;
Fauziah et al., 2024). However, in developing
countries, EMR implementation challenges are still
significant, including technological limitations,
resistance to change, and the need to adapt to the
local context (Ariffin et al., 2018). Studies in
Nigeria and Indonesia show that readiness and
willingness to use EMRs are directly correlated
with improved service quality and cost savings
(Amalia et al., 2021).
Nurses, as a profession that is face-to-face
with patients for 24 hours, have a critical role in the
successful implementation of EMR. Today's
generation of nurses is generally familiar with
digital technology, so they are better prepared to use
computer-based applications and software (Batara
et al., 2018). They also understand the strategic
benefits of EMRs in accelerating access to
information and improving the quality of clinical
decision-making (Darmin et al., 2022; Rubiyanti,
2023). EMRs are also thought to support
collaboration between professionals and aid in
evidence-based care, with features such as clinical
reminders and data analysis tools (Susanti et al.,
2023; Wahyuni et al., 2021).
Nevertheless, the acceptance of EMRs by
nurses is also affected by various barriers. The high
learning curve, the already large workload, and
resistance from senior colleagues are challenges.
Inadequate infrastructure and data security and
privacy issues also often hinder the optimal
implementation of these systems (O'Donnell et al.,
2018). Therefore, it is important for healthcare
institutions to provide ongoing training, technical
support, and investment in information systems and
data protection to ensure the successful
implementation of EMR (Rahal et al., 2021).
In responding to these challenges and
opportunities, the Model Unified Theory of
Acceptance and Use of Technology 3 (UTAUT3)
can be used to predict the level of acceptance of
technology by nurses. UTAUT3 is a development
of previous models, such as TAM and TPB, which
provide a comprehensive approach to analyzing

individual and social factors influencing
technology adoption (Almarzouqi et al., 2022;
Simatupang et al., 2022). Factors such as intrinsic
motivation, experience with technology, and the
social influence of the work environment are
important elements in this framework (Resnawaty
et al., 2024).
The advantage of UTAUT3 also lies in its
ability to accommodate contextual variables such
as habits, hedonistic motivations, and perceptions
of value or cost. Habits in the use of technology can
accelerate nurses' adaptation to EMR, while the
aspect of pleasure and comfort (hedonistic) also
increases interest in adopting technology (Hwang et
al., 2019). The perception of cost is also no less
important, especially for healthcare institutions that
operate on a limited budget (Zurynski et al., 2021).
In UTAUT3, the expectation of performance
(Performance expectancy) and ease of use (Effort
expectancy) becomes the main predictor of
behavioral intentions (Behavioral Intention) to use
technology. Social factors, such as the support of
colleagues or superiors, as well as the condition of
support facilities (Facilitating Condition) also
determines the actual behavior of the user (Oo et al.,
2021). The advantage of this model is its flexibility
and ability to be empirically validated in a variety
of healthcare contexts (Stuart & Scott, 2021).
Graha Bunda General Hospital is a relevant
research because the hospital represents the
challenge of implementing information technology
in regional health facilities, which generally face
limited infrastructure and human resources. The
implementation of the Electronic Medical Record
(EMR) system in this environment does not
necessarily run optimally without a comprehensive
understanding of the acceptance of end users, in this
case nurses, who are at the forefront of health
services. Understanding the extent to which nurses
receive and use EMRs in hospitals provides
valuable input in the development of strategies to
increase technology acceptance that are appropriate
to local conditions.
This study enriches the local literature related
to the implementation of EMR, because so far
similar studies have been more conducted in large
hospitals, metropolitan cities, or in developed
countries. The novelty of this study lies in the
application of the Unified Theory of Acceptance
and Use of Technology 3 (UTAUT3) model in the
regional hospital environment, which presents a
holistic approach by considering individual,
organizational, and technological variables
simultaneously. In addition, the use of Structural
Equation Modeling – Partial Least Squares (SEMPLS) provides a more robust analysis of the
relationship between latent variables.
However, studies that specifically examine the
factors that influence EMR acceptance by nurses in
regional hospitals, particularly with the UTAUT3
approach, are still very limited. Therefore, this

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Jurnal Vokasi Kesehatan Volume 11 Nomor 2, hlm. 33-41

research is expected to fill the gap and become a
reference for the development of information
technology policies in the regional health sector.
This study aims to analyze the factors that affect the
intention and behavior of using Electronic Medical
Record (EMR) by nurses at Graha Bunda Hospital
using the UTAUT3 approach.

Model evaluation in PLS involves two components,
namely theMeasurement Model (Outer Model) - To
test the validity and reliability of the measuring tool
andthe Structural Model (Inner Model) - To predict
the relationship between latent variables.
Evaluation of Measurement Model (Outer
Model) of Convergent Validity
Convergent validity testing uses outer loading
or loading factor values. If the outer loading value
of an indicator is greater than 0.7, then it is widely
accepted as an indicator that has high convergent
validity. The total outer loading of each indicator
for the research variable is listed below: The results
of the outer loading measurement in the reflective
indicator show that most of the research indicators
have met the requirements as variable measurement
indicators because they have an outer loading
value exceeding 0.7 (outer loading > 0.7).
Therefore, all indicators are considered valid and
suitable for use in the follow-up analysis of this
study.

Method
This study uses a quantitative approach with
an expplanatory survey design to analyze the extent
of nurses' acceptance of the use of the Electronic
Medical Record (EMR) system at Graha Bunda
General Hospital. The main objective of this study
is to identify and explain the relationship between
various factors that influence EMR use behavior,
both from the individual, organization, and
technology sides. The research location was chosen
purposively, taking into consideration the
availability of infrastructure, the readiness of
human resources, and the support of the hospital
management. The implementation of the research
lasted for three months, starting from June to
August 2024.
The study population includes all active
nurses at Graha Bunda Hospital, totaling 67 people.
Because the number did not reach 100 people, the
total sampling technique was used, so that all
members of the population were made respondents.
The inclusion criteria used include nurses who are
actively working, used to using technology, and
have at least two years of work experience.
Meanwhile, nurses who are on leave or refuse to be
respondents are grouped in the exclusion criteria.
The data collection instrument is in the form
of a questionnaire compiled based on indicators
from the Unified Theory of Acceptance and Use of
Technology 3 (UTAUT 3) model. Independent
variables include social influence, effort expectancy,
performance expectancy, facilitating condition,
technology readiness, habit, price value, hedonic
motivation, regulation and policy, leadership and
management, as well as security and privacy factors.
The bound variables included behavioral intention
and use behavior, while the moderation variables
included age, gender, experience, and voluntariness
of use.
The data collected consisted of primary data
(through questionnaires), secondary data (hospital
profiles), and tertiary data from scientific sources.
The analysis technique used is Partial Least Square
(PLS) with SmartPLS software version 4, which
includes the evaluation of the outer model through
validity and reliability tests, as well as the inner
model to test the predictive strength between latent
variables.

Discriminatory Validity
Discriminant validity is measured using two
approaches:
1. Heterotrait-Monotrait Ratio (HTMT) All
variables show HTMT values below 0.9,
which indicates that all constructs of the
variables have good discriminating ability.
2. Average Variance Extracted (AVE) All study
variables met the standard of AVE values
exceeding 0.5 (AVE > 0.5), as shown in the
following table:
Table 1. Average Variant Extracted (AVE)
Variable
AVE
Behavioral Intention
Effort Expectancy
Experience
Facilitating Condition
Habit

0,849
0,853
0,937
0,941
0,877

Hedonic Motivation
Leadership and Management
Perceived Value
Performance Expectancy
Regulation and Policy
Security & Privacy
Social Influence
Technology Readiness

0,895
0,799
0,826
0,872
0,895
0,936
0,898
0,884

Use Behavior
Voluntariness of Use

0,864
0,903

Composite Reliability
All variables had a Composite Reliability
value (rho_a and rho_c) above 0.7, indicating that
all the study variables had high reliability.

Result
Analytical Approach
This study uses the SmartPLS 3.0 application
to conduct Partial Least Squares (PLS) analysis.

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Table 2. Composite Reliability
Composite
Reliability
(rho_a)
Behavioral Intention
0,911
Effort Expectancy
0,921
Experience
0,934
Facilitating
Condition
0,969
Habit
0,944
Hedonic Motivation
0,945
Leadership and
Management
0,887
Perceived Value
0,922
Performance
Expectancy
0,928
Regulation and
Policy
0,947
Security & Privacy
0,973
Social Influence
0,946
Technology
Readiness
0,949
Use Behavior
0,922
Voluntariness of Use
0,916

Variable

Composite
Reliability
(rho_c)
0,944
0,946
0,968

RSquare

R-Square
Adjusted

Behavioral
0,846
0,784
Intention
Use Behavior
0,697
0,608
The R-Square value for Behavioral Intention
of 0.846 indicates that 84.6% of the variation in the
variable can be explained by independent variables
in the model. While the R-Square value for Use
Behavior of 0.697 indicates that 69.7% of
variations in Use Behavior can be explained by
independent variables.

0,979
0,955
0,962
0,923
0,934

F-Square Test
The results of the f-square analysis showed
that several variables had varying degrees of
influence on the intention and behavior of using the
EMR system by nurses. The interaction between
age and performance expectancy on behavioral
intention showed an f-square value of 0.198, which
was classified as having a strong influence. Similar
things can also be seen in the interaction between
age and social influence on behavioral intention,
with an f-square value of 0.166. The variable effort
expectancy on behavioral intention also showed a
strong influence with an f-square value of 0.162.
Meanwhile, the security & privacy variable on
behavioral intent had an f-square value of 0.141,
indicating a moderate influence. The relationship
between behavioral intention and use behavior
obtained an f-square value of 0.130, also included
in the category of moderate influence. These
findings indicate that the perception of convenience,
age factors, as well as security and privacy aspects
are important elements that need to be considered
in improving the intentions and behaviors of nurses
in using EMR systems.

0,953
0,962
0,978
0,964
0,958
0,950
0,949

Cronbach's Alpha
All of the study variables showed Cronbach's
Alpha values above 0.8, which indicates good
reliability for all variables.
Table 3. Cronbach's Alpha
Cronbach's
alpha
Behavioral Intention
0,911
Effort Expectancy
0,914
Experience
0,933
Facilitating Condition
0,968
Habit
0,930
Hedonic Motivation
0,941
Leadership and Management
0,875
Perceived Value
0,896
Performance Expectancy
0,926
Regulation and Policy
0,941
Security & Privacy
0,966
Social Influence
0,943
Technology Readiness
0,935
Use Behavior
0,921
Voluntariness of Use
0,894

Hypothesis Test
Of the 26 hypotheses tested, 15 hypotheses
were accepted and 11 hypotheses were rejected.
The following is the accepted hypothesis, namely
H₁: Performance Expectancy has a positive and
significant effect on Behavioral Intention (T=3.130,
P=0.002). H₂: Effort Expectancy has a positive and
significant effect on Behavioral Intention (T=2.359,
P=0.018), H₃: Social Influence has a positive and
significant effect on Behavioral Intention (T=2.278,
P=0.023), H₄: Facilitating Condition has a positive
and significant effect on Use Behavior (T=3.070,
P=0.002), H₅: Hedonic Motivation has a positive
and significant effect on Behavioral Intention
(T=4.565, P=0.000), H₈: Habit has a positive and
significant effect on Use Behavior (T=2.601,
P=0.010), H₉: Leadership and Management has a
positive and significant effect on Behavioral
Intention (T=5.580, P=0.000), H₁₀: Regulation and
Policy has a positive and significant effect on
Behavioral Intention (T=4.660, P=0.000), H₁₂:
Technology Readiness has a positive and

Evaluation of Structural Model (Inner Model)
Path Coefficient Test
The results showed that all the variables in the
model had a path coefficient with a positive value,
which signifies that the higher the value of the path
coefficient from the independent variable to the
dependent variable, the stronger the relationship
between the two.
Coefficient of Determination Test (R²)
Table 4. Coefficient of Determination

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significant effect on Use Behavior (T=2.882,
P=0.004), H₁₃: Security & Privacy has a positive
and significant effect on Behavioral Intention
(T=2.190, P=0.029), H₁₅: Age moderates the effect
of Performance Expectancy on Behavioral
Intention (T=2.143, P=0.032), H₁₇: Age moderates
the effect of Social Influence on Behavioral
Intention (T=2.370, P=0.018), H₂₁: Experience
moderates the effect of Effort Expectancy on
Behavioral Intention (T=3.117, P=0.002), H₂₅:
Voluntariness of Use moderates the effect of
Technology Readiness on Use Behavior (T=2.584,
P=0.010).

as Effort Expectancy and Social Influence. For
example, Chao's study found that both PE and
Effort Expectancy had a significant effect on
Behavioral Intention, which suggests that ease of
use complements the perceived benefits of the
technology (Chao, 2019). In addition, Kadir and
Ismail emphasized that Performance Expectancy
positively affect Behavioral Intention among
millennials who adopt online delivery services,
emphasizing the importance of perceived benefits
in the adoption of technology (Kadir & Ismail,
2022).
Further,
the
relationship
between
Performance Expectancy and Behavioral Intention
can be moderated by variables such as Perceived
Risk and Trust. For example, Chayomchai's
research shows that personal trust and innovation
directly affect Performance Expectancy, which in
turn influenced the intention to use online
technology during the COVID-19 pandemic
(Chayomchai, 2020). This indicates that although
Performance Expectancy is a strong predictor of BI,
its effectiveness can be strengthened or weakened
by individuals' trust in technology and their
willingness to innovate.
Important Performance Expectancy also
extends to the context of the organization. Verhees
discusses how performance expectations influence
small companies' decisions regarding radical
product innovation, which shows the implications
Performance Expectancy in business strategic
decision-making (Verhees et al., 2020). In line with
that, Mubarok noted that Performance Expectancy
significantly affect students' intention to adopt
QRIS (Quick Response Code Indonesian Standard)
in their financial transactions, demonstrating their
relevance in the context of education and
economics (Mubarok et al., 2023).
Thus, it can be concluded that Performance
Expectancy is one of the main factors that drive
user intent to adopt technology, although its
influence can vary depending on the type of
technology used and the characteristics of the user.

Discussion
The Effect of Performance Expectancy on
Behavioral Intention
The results of the hypothesis test showed that
the T value of statistics was 3.130 and the P value
was 0.002. Table T is a type of distribution used to
test hypotheses. This distribution table relies on ttest statistics, and in this study, the table's T-value
is 1.668. The statistical T value > the T table
(3.130 > 1.668), and the P value of 0.018 < the
standard alpha of 5% (0.002< 0.05) showed a
significant influence of Performance Expectancy
on Behavioral Intention. A positive path coefficient
indicates a positive influence of Performance
Expectancy on Behavioral Intention. Thus, it can be
concluded that there is a positive and significant
influence of Performance Expectancy on
Behavioral Intention. In other words, the better
Performance Expectancy is able to increase the
Behavioral Intention or the first Hypothesis (H1) is
accepted.
Performance Expectancy refers to the extent
to which individuals believe that the use of a
particular technology will improve their work
performance or overall effectiveness. Various
studies have shown a positive correlation between
Performance Expectancy and Behavioral Intention,
implying that when users see technology as
something beneficial, they are more likely to intend
to use it.
According to Unified Theory of Acceptance
and Use of Technology (UTAUT), Performance
Expectancy reflects the extent to which individuals
believe that using a technology will improve their
performance in certain tasks (D. Liu et al., 2019).
These beliefs have a significant influence on an
individual's intention to adopt a new technology, as
they are more likely to use systems that they
perceive will provide beneficial results. For
example, Liu et al.'s research shows that
Performance Expectancy positively correlated with
intention to use physical activity apps, highlighting
the role of Performance Expectancy in motivating
users to interact with health technology.
Further research shows that Performance
Expectancy not only directly affect Behavioral
Intention, but also interacts with other factors such

The Effect of Effort Expectancy on Behavioral
Intention
The results of the hypothesis test showed that
the T value of statistics was 2.359 and the P value
was 0.018. Table T is a type of distribution used to
test hypotheses. This distribution table relies on ttest statistics, and in this study, the table's T-value
is 1.668. The statistical T value of the T > table
(2.359 > 1.668), and the P value of 0.018 < the
standard alpha of 5% (0.018< 0.05) showed a
significant influence of Effort Expectancy on
Behavioral Intention. A positive pathway
coefficient indicates a positive influence of Effort
Expectancy on Behavioral Intention. Thus, it can be
concluded that there is a positive and significant
influence of Effort Expectancy on Behavioral
Intention. In other words, the better Effort

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Expectancy is able to increase the Behavioral
Intention or the second Hypothesis (H2) is accepted.
Effort Expectancy (EE) is an important
construct in the Unified Theory of Acceptance and
Use of Technology (UTAUT), which states that the
perception of ease of use of technology
significantly influences the user's behavioral
intention to adopt and utilize the technology.
Studies have shown a positive correlation between
Effort Expectancy and Behavioral Intention in a
variety of contexts, indicating that when users find
a technology easy to use, they are more likely to
intend to take advantage of it.
For example, research by Lee and Song shows
that Effort Expectancy, along with other factors
such as trust and risk perception, explains most of
the variance in behavioral intentions to use new
technology services, specifically CeDA services.
This confirms the importance of user perception of
ease of use in the acceptance of technology (Lee &
Song, 2013). In addition, Haengnam et al.
emphasized that Effort Expectancy is a significant
factor influencing behavioral intent in mobile
learning services, suggesting that users' perceptions
of ease of use directly affect their willingness to
interact with technology (Sung et al., 2015).
Another study by Wichean and Sungsanit
focusing on chicken farmers in Thailand also found
that Effort Expectancy was the most influential
factor in the adoption of poultry farm management
systems, reinforcing the idea that the perception of
ease of use is crucial in technology adoption
decisions (Wichean & Sungsanit, 2022). These
findings are consistent with a study by Liu, who
notes that Effort Expectancy, along with
Performance Expectancy and Social Influence,
significantly influences behavioral intent in the
context of online shopping for fresh agricultural
products (L.-W. Liu et al., 2016).
However, some studies report mixed results
regarding the relationship between Effort
Expectancy and Behavioral Intention. For example,
research by Nejadrezaei et al. found no significant
association between Effort Expectancy and
intention to adopt pressure irrigation technology in
olive growers, suggesting that in certain contexts,
other factors may influence more than the
perception of ease of use (Nejadrezaei et al., 2018).
Research by Handayani and Sudiana also showed
that although Performance Expectancy and Social
Influence had a significant impact on Behavioral
Intention, Effort Expectancy did not show a
significant influence in their study of academic
information systems (Handayani & Sudiana, 2015).
Although there are mixed findings, consensus
in the literature confirms that Effort Expectancy
plays an important role in shaping behavioral
intentions toward technology adoption. The study
by Horas et al. further reinforces this, in which they
confirm that both Performance and Effort
Expectancy positively affect usage intent,

confirming that improving user perceptions of ease
of use can facilitate wider acceptance of new
technologies (Horas et al., 2023).
As such, Effort Expectancy plays a significant
role as a significant predictor in the early adoption
of the technology, although its influence may
diminish over time, depending on the context of the
technology and the demographics of the user.
Social Influence (SI) has a significant
influence on Behavioral Intention (BI) with values
of T = 2.278 and P = 0.023. SI is a social influence
of other individuals that can influence decisions in
adopting technology. Cultural effects also
contribute to reinforcing this influence. Facilitating
Condition (FC) showed a significant influence on
Use Behavior (UB) with values T = 3.070 and P =
0.002. FC includes technical support and the
availability of resources needed in the use of
technology. This support is important in facilitating
the adoption of technology. Although some of the
results show different findings, the majority of
studies support the importance of FC in influencing
technology use behavior.
Hedonic Motivation (HM) showed a very
significant influence on BI with values of T = 4.565
and P = 0.000. HM reflects the level of pleasure or
enjoyment felt in using technology. In various
contexts such as e-commerce, education, and
tourism, HM plays an important role in
encouraging the intention of using technology.
Perceived Value (PV) had no significant effect
on BI (T = 0.605; P = 0.545). However, in certain
contexts, PV can have an indirect influence on
behavioral intent, for example through user
satisfaction. On the other hand, there are also
studies that do not find a significant direct
relationship between PV and BI.
Habit had no significant effect on BI (T =
0.478; P = 0.633). Some studies have shown that
the effect of habit on behavioral intent tends to be
inconsistent and depends on specific contexts. In
fact, in some cases, the relationship can deteriorate
over time.
Habit had a significant influence on UB (T =
2.601; P = 0.010). Habitual use drives continuous
user engagement. The relationship between habit
and intention and behavior is reciprocal, and is
influenced by social factors and intrinsic
motivations.
Leadership and Management showed a very
significant influence on BI (T = 5,580; P = 0.000).
Leadership styles such as transformational and
servant leadership are able to reduce exit intentions
and strengthen positive behavior intentions.
Organizational commitment and managerial
support also play an important role in this.
Regulations and policies have a significant
influence on BI (T = 4,660; P = 0.000). Policies that
are designed effectively are able to increase
behavioral intentions through the perception of

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justice, relevance, and the formation of social
norms.
Technology Readiness (TR) had no significant
effect on BI (T = 0.087; P = 0.931). This suggests
that although individuals have a high readiness for
technology, there are other external factors that can
hinder the intention to use the technology.
Nevertheless, TR had a significant effect on UB (T
= 2.882; P = 0.004). Optimism and innovation that
individuals possess can encourage active
technology use behavior. Technology readiness is
an important factor in supporting the application of
technology, especially in the health and education
sectors.
Security & Privacy has a significant influence
on BI (T = 2,190; P = 0.029). Users tend to have
higher intentions in using technology if they feel
safe and their privacy is protected. However, there
is a contradictory phenomenon where users
continue to use technology despite being aware of
privacy risks, and this can be mediated by the user's
level of trust in the system. No significant effect
was found between Security and Privacy on UB (T
= 0.313; P = 0.754). This shows that there is a gap
between the perception of risk to security and
privacy and the actual behavior of using technology.
Age moderated the relationship between
Performance Expectancy (PE) and BI (T = 2,143; P
= 0.032). Older individuals tend to need additional
support in using technology, and this reinforces
their intention to participate in the use of
technology.
No significant age-related moderation effect
was found on the relationship between Effort
Expectancy (EE) and BI (T = 0.143; P = 0.886).
These findings suggest that different age groups
have varying priorities and considerations for the
ease of use of technology. Social Influence on BI
was significantly moderated by age (T = 2,370; P =
0.018). Age is a factor that strengthens the
relationship between social influence and the
conversion of intentions into real behaviors in the
use of technology.

The success of EMR implementation is
greatly influenced by the perception of benefits,
ease of use, managerial support, and readiness of
available technology. Therefore, strategies to
increase the adoption of information technology in
hospitals, especially EMR, should be focused on:
(1) increasing the perception of the benefits of
technology (performance expectancy), (2)
simplifying interfaces and workflows (effort
expectancy), (3) building positive social influence
through the role of leadership or peer influence, and
(4) providing adequate supporting facilities such as
periodic training and reliable digital infrastructure.
For further research, it is recommended that a
mixed methods approach be carried out to explore
more deeply non-technical factors, such as
organizational cultural values or resistance to
change. In addition, testing the UTAUT3 model in
hospitals of different types or locations is also
important to expand the generalization of the
results of this study.
Acknowledgments
The researcher expressed his greatest
appreciation to the Head of Graha Bunda General
Hospital for all forms of support that have been
provided in the implementation of this research.
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