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Examining Healthcare Profesional’s Acceptance of Electronic 

Medical Records System using Extended UTAUT2 
 

Milenia Ayukharisma1, Dian Budi Santoso2 

1Vocational School, Gadjah Mada University 

2Department of Health Services and Information, Vocational School, Gadjah Mada University 

1mileniaayukharisma@gmail.com, 2dianbudisantoso@ugm.ac.id 

 

Abstract  

This study aims to analyze user acceptance of the Electronic Medical Record system using the extended 

Unified Theory of Acceptance and Use Technology 2 model at PKU Muhammadiyah Bantul hospital. 

The UTAUT 2 model was chosen because it is the latest technology acceptance model which is a 

unification, synthesis, or summary of the eight pre-existing technology acceptance models. The subjects 

in this study were PKU Muhammadiyah Bantul hospital employees who used an electronic medical 

record system specifically for outpatient care. The object of this study is user acceptance of using the 

RME system in health services. Data collection techniques in this study are using questionnaires and 

observation. This research is a type of quantitative analytic research with data analysis using descriptive 

analysis. Data processing in this study used Smart-PLS software version 4.0 with SEM-PLS data 

analysis. The results showed that the aspects of the extended UTAUT2 model that had a positive and 

significant effect on user acceptance were performance expectancy (t=1.816), while the aspects of effort 

expectancy (t=0.419), social influence (t=0.635), facilitating conditions (t=0.139), hedonic motivation 

(t=0.909), price value (t=.304) habit (t=1.458), trust (t=0.032) and perceived risk (1.365) have no effect 

on user acceptance of the EMR system. Gender and age moderation variables were found to have no 

effect on the relationship between variables. 

 

Keywords: EMR, Hospital, User, UTAUT2 

 

I. Introductions 
Technology is developing rapidly in the current era of globalization. Advances in technology have 

penetrated into various fields including the health sector. Hospitals are required to build on quality of 

health services by utilizing currently developing technology. One of the implementations of 

technological advances in the health sector is the application of EMR in health services [1]. EMR is an 

electronic record that includes information such as a person's health that is created, collected, managed, 

used, and purchased by a doctor or health worker who is entitled to a health care institution. [1]. Quality 

electronic medical records will produce optimal patient health services and produce complete 

information to support organizational or hospital decision making [2]. Quality health services are 

supported by an optimal information system design [3]. In addition, user acceptance of the use of a 

system needs to be measured to produce a system that meets user needs [4]. User perceptions can help 

provide the right recommendations to maximize the development of electronic medical record systems 

[5]. One of the theory that can be used to measure the level of user acceptance of a system is using 

UTAUT2 theory [6]. 

UTAUT 2 was chosen to be used because this theory is the latest theory regarding user acceptance 

which adds new variables to support research in looking at acceptance of the use of new technology [7]. 

UTAUT2 was developed based on the UTAUT model which has four main constructs including 1) 

performance expectancy, 2) effort expectancy, 3) social influence, and 4) facilitating conditions which 

are then added again the three main constructs to support more accurate research include 1) hedonistic 

motivation, 2) price value, and 3) habit. Through UTAUT2 it can be understood that users' reactions 

and perceptions of a technology can influence their attitude in accepting and using technology [23]. A 

technology can increase productivity optimally if users can accept the use of a technology and according 

to user needs [8]. In this study also adds new variables to UTAUT 2 to optimize research results that 

P-ISSN : 2715-2448 | E-ISSN : 2715-7199 
Vol.5 No.1 Januari 2024 
Buana Information Technology and Computer Sciences (BIT and CS) 

 

mailto:Dian%20Budi%20Santoso@ugm.ac.id


Vol. 5, No.1 Januari 2024 | 20  

 

are appropriate in the field. The added variables are trust and perceived risk. There are moderator 

variables in this study, namely gender and age. 

PKU Muhammadiyah Bantul Hospital is a private hospital that has implemented electronic medical 

records since 2018. The EMR system used was designed independently by the hospital. Currently EMR 

is implemented in outpatient services and continues to be developed in inpatient services. Electronic 

medical record system at PKU Muhammadiyah Bantul Hospital still has various kinds of obstacles, 

including system errors, duplication of data entry, incomplete data output, patient data missing from the 

system without a known cause, and other obstacles that impede health services provided to patients. 

This needs to be analyzed to produce an optimal electronic medical record system and according to user 

needs. A good electronic medical record system will make patient treatment more optimal due to the 

continuity of medical history data owned by the patient.  

 

II. Methods 
In conducting this research, to measure user acceptance of using new technology, the extended 

UTAUT2 research concept was used because it has variables that match the research being carried out. 

The extended UTAUT2 conceptual framework is as follows. 

 

 

Figure 1.  Research concept framework 

Based on the Figure 1, it can be seen that there are 9 independent variables and one dependent variable, 

as well as two moderator variables in this study, namely gender and age that using in this research. 

A type of quantitative analytical research is used in this study. This study used cross-sectional 

research. This research was conducted from May to July 2023. The population in this study were all 

PKU Muhammadiyah Bantul hospital staff who used the outpatient EMR system in health services. 

Total population in this study was 152 officers who work as doctors, nurses, medical recorders, 

laboratories, radiographers, pharmacists, and health insurance center officers.  

Sample is the object under study and is considered to represent the entire population [8]. Sampling 

is the process of taking several elements from the population under study to be sampled, and 

understanding the various characteristics or characteristics of the subjects being sampled, which later 

can be generalized from the population elements [3]. Proportional stratified random sampling was used 

in this study. This technique is used for populations that have heterogeneous and proportionally 

stratified members or elements [21]. Utilizing the Slovin formula to determine the number of samples 

as shown below. 

 

𝑛 =  
𝑁

1 +  𝑁 (𝑒)2
 

 



Vol. 5, No.1 Januari 2024 | 21  

 

n = size of sample 

N = size of population 

e = percent allowance for sampling error that is acceptable for inaccuracy. 

Calculation of the percent allowance in this study uses a percent of 10% and the sample calculation 

results are obtained as follows. 

𝑛 =  
152

1 +  152 (0,1)2
 

𝑛 =  
152

1 +  152 (0,01)
 

𝑛 =  
152

1 +  1,52
 

𝑛 =  
152

2,52
 

𝑛 =  60 

Based on the above calculation, a minimum of 60 samples must be taken. 

The research instrument is a tool that is observed [21]. The research instruments used in this study 

were questionnaires and observation sheets. Questionnaires are data collection techniques in the form 

of statements or questions given to respondents to answer [21]. The questionnaire used a Likert scale 

of 1 to 5. The Likert scale on the questionnaire is used to analyze the items in this variable research.   

Questionnaire was distributed to employees of PKU Muhammadiyah Bantul who utilized an 

electronic medical record system for data collection. In this study, the processes of data analysis were 

SEM-PLS analysis and univariate analysis. To determine the tendency of respondents' responses to the 

statement items on the research questionnaire, univariate was used to describe the characteristics of the 

respondents and display the distribution of each variable. PLS is a variance based structural equation 

analysis that can test structural models and measurement models simultaneously. 

 

III. Results And Discussions 
1. The results of the description of the characteristics of the respondents 

Characteristics respondents were divided based on gender, age, education background, and the 

profession of the respondents. Respondents in this study amounted to 60 respondents with the details 

of the respondents as shown in the following table. 

 

Table 1. Gender of Respondents 

Gender Totals 

Female 42 

Male 18 

 

The ages of the respondents in this study were grouped into five age groups, as follows. 

 

Table 2. Ages Range of Respondents 

Ages Range Totals  

17-25 years old 12 

26-35 years old 18 

36-45 years old 10 

46-55 years old 14 

>56 years old 6 

 

The results of the study show that the last educational background of the respondents is as follows. 

 

Table 3. Educational Background of Respondents 

Graduate Totals  

Senior High School 1 

Diploma III 31 



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Bachelor 10 

Masters 18 

 

Respondents involved in this study shown in the following table. 

 

Table 4. Profession of Respondent 

Profession Totals  

Doctors 18 

Nurses 9 

Medical recorders 12 

Radiographers 5 

Pharmacists 4 

Laboratories 7 

Health insurance officers 5 

 

2. SEM-PLS Analysis Results 

The outer model test and the inner model test are the two methods that used in partial least square 

testing.  

a. Outer model (measurement model) 

1) Convergent Validity Test 

 

Table 5. Result of Convergent Validity  
Variabel Item Loading 

Factor 

AVE 

Performance Expectancy PE1 0,804 0,536 

 PE2 0,680  

 PE3 0,570  

 PE4 0,841  

Effort Expectancy EE1 0,749 0,630 

 EE2 0,914  

 EE3 0,882  

 EE4 0,590  

Social Influence SI1 0,914 0,846 

 SI2 0,957  

 SI3 0,887  

Facilitating Condition FC1 0,739 0,601 

 FC2 0,716  

 FC3 0,763  

 FC4 0,873  

Hedonic Motivation HM1 0,882 0,726 

 HM2 0,902  

 HM3 0,766  

Price Value PV1 0,726 0,627 

 PV2 0,714  

 PV3 0,919  

Habit H1 0,806 0,604 

 H2 0,717  

 H3 0,836  

 H4 0,745  

Trust T1 0,823 0,623 

 T2 0,768  

 T3 0,754  

 T4 0,810  

Perceived Risk PR1 0,884 0,722 

 PR2 0,844  

 PR3 0,821  

Acceptance and Use of 

Technology 

AUS1 0,949 0,894 

 AUS2 0,958  

 AUS3 0,930  

Gender ZG 1,000 1,000 



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Variabel Item Loading 

Factor 

AVE 

Age ZU 1,000 1,000 

 

Table 5 shows the loading factor value of each item on the variable has a value of 

> 0,5 so that the instrument is declared valid according to convergent validity testing 

as well as the AVE value of each construct in the entire model has a value of > 0,5 

so it can be concluded that all constructs are valid and fulfill validity converge well. 

 
2) Discriminant Validity Test 

 

 

Figure 2. Discriminant Validity Test Result 

 

The results in this study in Figure 2 show that all constructs have a construct correlation 

with measurement items that is higher than the other constructs. It means that the 

requirements for a good discriminant validity test have been fulfilled. 

 

 

 

 

 

 

               AUS EE FC H HM PE PR PV SI T ZG ZU 

AUS1 0.949 0.628 0.572 0.513 0.556 0.579 -0.351 0.309 0.523 0.603 0.029 -0.125 

AUS2 0.958 0.581 0.599 0.568 0.651 0.541 -0.336 0.322 0.576 0.630 0.073 -0.102 

AUS3 0.930 0.511 0.485 0.613 0.639 0.461 -0.274 0.416 0.496 0.582 -0.034 -0.072 

EE1 0.362 0.749 0.370 0.445 0.211 0.279 -0.267 0.017 0.321 0.443 -0.041 -0.079 

EE2 0.477 0.914 0.377 0.305 0.392 0.424 -0.262 0.136 0.281 0.467 -0.008 -0.073 

EE3 0.635 0.882 0.538 0.405 0.381 0.352 -0.262 0.143 0.241 0.534 0.083 -0.251 

EE4 0.376 0.590 0.444 0.032 0.440 0.319 -0.191 0.376 0.368 0.324 0.067 -0.118 

FC1 0.330 0.365 0.739 0.359 0.249 0.282 -0.110 0.227 0.209 0.455 0.260 -0.088 

FC2 0.360 0.442 0.716 0.365 0.296 0.273 -0.201 0.219 0.322 0.482 0.118 -0.113 

FC3 0.521 0.478 0.763 0.151 0.664 0.393 -0.205 0.494 0.294 0.392 0.115 -0.118 

FC4 0.540 0.422 0.873 0.343 0.568 0.426 -0.192 0.454 0.367 0.594 0.157 -0.099 

H1 0.626 0.458 0.449 0.806 0.282 0.222 -0.196 0.074 0.341 0.568 -0.072 -0.190 

H2 0.382 0.247 0.034 0.717 0.270 0.077 0.076 0.220 0.415 0.456 -0.006 0.029 

H3 0.404 0.243 0.307 0.836 0.221 0.141 -0.077 0.115 0.419 0.541 -0.000 -0.017 

H4 0.342 0.149 0.278 0.745 0.103 0.092 -0.034 0.244 0.339 0.533 -0.020 -0.038 

HM1 0.546 0.420 0.551 0.156 0.882 0.507 -0.208 0.505 0.405 0.402 0.166 0.023 

HM2 0.666 0.400 0.512 0.444 0.902 0.594 -0.271 0.472 0.535 0.564 0.077 0.088 

HM3 0.407 0.326 0.524 0.075 0.766 0.373 -0.106 0.592 0.413 0.329 0.159 0.224 

PE1 0.431 0.397 0.367 0.234 0.308 0.804 -0.023 0.094 0.279 0.324 0.028 0.129 

PE2 0.386 0.045 0.276 0.240 0.451 0.680 -0.008 0.380 0.383 0.325 -0.061 0.234 

PE3 0.326 0.342 0.189 -0.106 0.413 0.570 -0.073 0.311 0.248 0.110 0.023 0.276 

PE4 0.475 0.461 0.461 0.138 0.558 0.841 -0.103 0.310 0.303 0.276 -0.006 0.134 

PR1 -0.322 -0.270 -0.126 -0.038 -0.272 -0.152 0.884 -0.254 -0.226 -0.381 -0.052 0.258 

PR2 -0.215 -0.262 -0.229 -0.132 -0.056 -0.011 0.844 -0.088 -0.060 -0.329 0.004 0.423 

PR3 -0.305 -0.255 -0.253 -0.101 -0.241 0.001 0.821 -0.008 -0.050 -0.287 0.007 0.528 

PV1 0.158 0.263 0.443 0.242 0.278 0.140 -0.116 0.726 0.257 0.473 0.224 0.057 

PV2 0.166 0.092 0.245 0.222 0.255 0.290 -0.146 0.714 0.531 0.391 0.217 0.160 

PV3 0.418 0.165 0.443 0.101 0.674 0.370 -0.111 0.919 0.366 0.383 0.103 0.054 

SI1 0.505 0.383 0.414 0.429 0.555 0.418 -0.151 0.466 0.914 0.519 0.105 0.103 

SI2 0.532 0.378 0.323 0.455 0.460 0.343 -0.175 0.398 0.957 0.456 0.100 -0.001 

SI3 0.516 0.242 0.345 0.438 0.463 0.379 -0.062 0.398 0.887 0.467 0.141 -0.016 

T1 0.624 0.486 0.546 0.675 0.404 0.335 -0.370 0.279 0.506 0.823 0.107 -0.234 

T2 0.323 0.314 0.383 0.646 0.268 0.123 -0.282 0.329 0.377 0.768 0.148 -0.150 

T3 0.522 0.505 0.509 0.332 0.621 0.442 -0.233 0.568 0.371 0.754 0.085 -0.074 

T4 0.460 0.429 0.469 0.508 0.296 0.159 -0.339 0.358 0.361 0.810 0.099 -0.239 

ZG 0.025 0.040 0.199 -0.040 0.148 -0.006 -0.019 0.186 0.125 0.134 1.000 -0.023 

ZU -0.105 -0.179 -0.135 -0.093 0.117 0.250 0.468 0.094 0.030 -0.225 -0.023 1.000 



Vol. 5, No.1 Januari 2024 | 24  

 

3) Reliability Test 

Table 6. Reliability Test 

Variabel Cronbach’s 

Alpha 

Composite 

Reliability 

(rho_a) 

Composite 

Reliability 

(rho_c) 

Result 

Acceptance and Use of 

Technology 

0,941 0,942 0,962 Reliable 

Effort Expectancy 0,795 0,857 0,869 Reliable 

Facilitating Condition 0,783 0,810 0,857 Reliable 

Habit 0,789 0,832 0,859 Reliable 

Hedonic Motivation 0,814 0,860 0,888 Reliable 

Performance Expectancy 0,701 0,728 0,819 Reliable 

Perceived Risk 0,810 0,830 0,886 Reliable 

Price Value 0,744 1,056 0,833 Reliable 

Social Influence 0,908 0,909 0,943 Reliable 

Trust  0,802 0,824 0,869 Reliable 

ZG 1,000 1,000 1,000 Reliable 

ZU 1,000 1,000 1,000 Reliable 

 

Based on Table 6, all variables have met the reliability requirements, that is all 

variables have a value of > 0,7 so that the measurement model used in this study 

can be declared reliable. 

 
b. Inner model (structural model) 

1) Coefficient of Determination 

                            Table 7. Coefficient Determination Result 

Variable  (R2) Chategory 

AUS 0,756 Strong 

 

The results based on Table 7 show that (R2) value on the dependent variable AUS of 0,712, 

meaning that this explains that all variables have an influence of 75,6% on Acceptance and 

Use of Technology. 

 

2) Hypothesis test 

Table 8. Hypothesis Test Result 

 T 

Statistic 

P 

Values 

Result 

H1 PE→ AUS 1.816 0.035 Accepted 

H2 EE → AUS 0.419 0.338 Rejected 

H3 SI → AUS 0.635 0.263 Rejected 

H4 FC → AUS 0.139 0.445 Rejected 

H4a FC*ZG→AUS 0.290 0.386 Rejected 

H4b FC*ZU→AUS 0.691 0.245 Rejected 

H5 HM → AUS 0.909 0.182 Rejected 

H5a HM*ZG→AUS 0.292 0.385 Rejected 

H5b HM*ZU→AUS 0.389 0.349 Rejected 

H6 PV → AUS 0.304 0.381 Rejected 

H6a PV*ZG→AUS 0.713 0.238 Rejected 

H6b PV*ZU→AUS 0.231 0.409 Rejected 



Vol. 5, No.1 Januari 2024 | 25  

 

 T 

Statistic 

P 

Values 

Result 

H7 H → AUS 1.458 0.073 Rejected 

H7a H*ZG→AUS 0.153 0.439 Rejected 

H7b H*ZU→AUS 0.407 0.342 Rejected 

H8 T → AUS 0.032 0.487 Rejected 

H9 PR → AUS 1.365 0.086 Rejected 

 

H1: Performance expectancy have a positive and significant effect on user acceptance 

of the Electronic Medical Record system 

The results of data processing obtained a t value of 1.816 (> 1.64) with a p value of 0.035 

(< 0.05). Based on the test results, hypothesis 1 is accepted. Performance expectations have 

a positive and significant effect on user acceptance of the RME system. The results of this 

study are in line with [14], and [15]. which state that performance expectations affect user 

acceptance. The results of this study were also reinforced by researchers [12]. that this 

research shows that performance expectancy affects the acceptance and use of systems or 

technology. 

H2: Effort expectancy have a positive and significant effect on user acceptance of the 

Electronic Medical Record system 

The results of data processing obtained a t value of 0.419 (<1.64) and a p value of 0.338 

(>0.05). Hypothesis 2 is rejected, effort expectations have no effect on user acceptance of 

the EMR system. The results of this study are in line with research [11] which states that 

effort expectations have no effect on user acceptance of the system. The results of this 

study are also reinforced by research conducted [25] which states that effort expectations 

do not have a significant effect on user acceptance of systems or technology. 

H3: Social influence has a positive and significant effect on user acceptance of the 

Electronic Medical Record system 

The results of data processing obtained a t value of 0.635 (<1.64) and a p value of 0.263 

(>0.05). The t value and the p value show that hypothesis 3 is rejected so that social 

influence does not affect user acceptance of the RME system. This research is in contrast 

to research [19] in which social influence has a significant influence on the behavioral 

intention to use the system. However, the results of this study are in line with previous 

research [5] which states that social influence has no influence on someone in using a 

system. 

H4: Facilitating conditions have a positive and significant effect on user acceptance 

of the Electronic Medical Record system 

The results of data processing obtained a t value of 0.139 (<1.64) and a p value of 0.445 

(>0.05). Based on the test findings, hypothesis 4 is rejected so that facilitating conditions 

do not affect user acceptance of the RME system. The results of this study are in line with 

research [2] which also found that facilitating conditions did not have a significant effect 

on behavioral interest in accepting the use of the system. 

H4a: Gender moderates the effect of facilitating conditions on user acceptance of the 

Electronic Medical Record system 

The results of data processing obtained t = 0.290 (<1.64) and p = 0.386 (> 0.05). The 

hypothesis is rejected so that gender has no effect in moderating conditions that facilitate 

user acceptance of the RME system. 

H4b: Age moderates the effect of facilitating conditions on user acceptance of the 

Electronic Medical Record system 

The results of data processing obtained t = 0.691 (<1.64) and p = 0.245 (> 0.05). The 

hypothesis is rejected so that age has no effect in moderating conditions that facilitate user 

acceptance of the RME system. 

H5: Hedonistic motivation has a positive and significant effect on user acceptance of 

the Electronic Medical Record system 

The results of data processing obtained a t value of 0.909 (<1.64) with a p value of 0.182 

(>0.05). Hypothesis 5 is rejected. Hedonistic motivation has no effect on user acceptance 



Vol. 5, No.1 Januari 2024 | 26  

 

of the RME system. This research is in line with research conducted (Ismarmiaty and 

Etmy, 2018) which states that hedonistic motivation has no effect on system user 

acceptance. The results of the study which stated that hedonism motivation had no 

influence on user acceptance were also strengthened by research conducted by [3] who had 

the same research results. 

H5a : Gender moderates the effect of hedonistic motivation on user acceptance of the 

Electronic Medical Record system 

The results of data processing obtained t = 0.292 (<1.64) and p = 0.385 (> 0.05). The 

hypothesis is rejected so that gender has no effect in moderating hedonistic motivation on 

user acceptance of the RME system. 

H5b: Age moderates the influence of hedonistic motivation on user acceptance of the 

Electronic Medical Record system 

The results of data processing obtained t = 0.389 (<1.64) and p = 0.349 (> 0.05). The 

hypothesis is rejected so that age has no effect in moderating hedonistic motivation on user 

acceptance of the RME system. 

H6: Price value has a positive and significant effect on user acceptance of Electronic 

Medical Records 

The results of data processing obtained a t value of 0.304 (<1.64) and a p value of 0.381 

(> 0.05). Based on the test findings, hypothesis 6 is rejected. The price value has no effect 

on user acceptance of the RME system. Research of (Ismarmiaty and Etmy, 2018) has the 

same results, price value has no effect on system user acceptance. 

H6a: Gender moderates the effect of price value on acceptance of Electronic Medical 

Record users 

The results of data processing obtained t = 0.713 (<1.64) and p = 0.238 (> 0.05). The 

hypothesis is rejected so that gender has no effect in moderating the value of prices on user 

acceptance of the RME system. 

H6b: Age moderates the effect of price value on user acceptance of the Electronic 

Medical Record system 

The results of data processing obtained t = 0.231 (<1.64) and p = 0.409 (> 0.05). The 

hypothesis is rejected so that age has no effect in moderating the price value on user 

acceptance of the RME system. 

H7: Habits have a positive and significant effect on user acceptance of the Electronic 

Medical Record system 

The results of data processing obtained a t value of 1.458 (<1.64) and a p value of 0.073 

(>0.05). Hypothesis 7 is rejected, habit has no effect on user acceptance of the RME 

system. This results are in line with research [4] which suggests that habits have no effect 

on user acceptance of the system. The results of the study that habit has no effect on system 

user acceptance is also reinforced by research results [6] habit has no effect on system user 

acceptance. 

H7a: Gender moderates the influence of habits on user acceptance of the Electronic 

Medical Record system 

The results of data processing obtained t = 0.153 (<1.64) and p = 0.439 (> 0.05). The 

hypothesis is rejected so that gender has no effect in moderating habits on user acceptance 

of the RME system. 

H7b: Age moderates the influence of habits on acceptance of users of the Electronic 

Medical Record system 

The results of data processing obtained t = 0.407 (<1.64) and p = 0.342 (> 0.05). The 

hypothesis is rejected so that age has no effect in moderating habits on user acceptance of 

the RME system. 

H8: Trust has a positive and significant effect on user acceptance of the Electronic 

Medical Record system 

The results of data processing obtained a t value of 0.032 (<1.64) a p value of 0.487 (> 

0.05). Hypothesis 8 is rejected, trust has no effect on user acceptance of the RME system. 

The results of this study are in line with research [11] which also obtained the result that 

trust does not have a significant effect on user acceptance of the system. 



Vol. 5, No.1 Januari 2024 | 27  

 

H9: Perceived risk has a positive and significant effect on user acceptance of the 

Electronic Medical Record system 

The results of data processing obtained a t value of 1.365 (<1.64) with a p value of 0.086 

(<0.05). Hypothesis 9 is rejected, meaning that risk perception has no effect on user 

acceptance of the RME system. This research is supported by research (Anggraeni et al, 

2023) which explains that perceptions of risk have no effect on user acceptance of systems 

or technology. The results of this study are reinforced by research conducted [22] and [8] 

which state that risk perception has no effect on system user acceptance. 

Test analysis on the influence of moderating variables 

Gender and age moderation has no effect in this study. This was also found in other studies 

where there were several factors that could cause the moderating variable not to affect the 

independent variable on the dependent variable. In research conducted by [10] it was also 

found that gender had no effect in moderating the independent variable on the dependent 

variable UTAUT2. [13] stated that gender and age have no effect due to the evolution of 

individuals into modern society. This makes there is no difference or no difference between 

gender and age level in using technology from the user's perspective. In terms of gender 

[7] states that gender roles can no longer be used as a benchmark in predicting the use of 

new technology. In terms of age, the reason why age does not moderate the relationship 

between variables is because there is a balanced age grouping of respondents where a 

balanced age distribution will better describe the gender effect. 

 

IV. Conclusions 

In this study, based on the tests that have been carried out, interesting results are obtained that the 

factors that can influence a person to accept and use a system or technology are aspects of business 

expectations. Based on this research, it can be concluded that in making a new innovation in a system 

or technology, developers must pay attention to aspects of user performance efficiency in order to 

increase work productivity.  

 

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