Syntax Literate: Jurnal Ilmiah Indonesia p�ISSN: 2541-0849 e-ISSN: 2548-1398

Vol. 7, No. 10, Oktober 2022

 

THE EFFECT OF PERCEIVED USABILITY AND PERCEIVED CONVENIENCE ON USER SATISFACTION OF KHANZA HOSPITAL MANAGEMENT INFORMATION SYSTEM

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Dhiana Wijayanti, Bambang Setya Budi I

Master of Science in Accounting, Postgraduate Program, Jenderal Soedirman Purwokerto University, Indonesia

Jenderal Soedirman University, Indonesia

E-mail: [email protected], [email protected]

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Abstract

This study examines the effect of perceived usability and convenience on user satisfaction with management information systems in Khanza user hospitals. The population studied was hospital employees who used the khanza information system. The sampling technique in this study uses a non-probability sampling approach, namely by purposive sampling. This study used primary data sources. The data collection technique used is a questionnaire. The data analysis techniques used are descriptive analysis and multiple regression analysis. The theory used is the Technology Accepted Model. The results showed that the perception of usability and convenience significantly affected the satisfaction of information system users in hospital employees.�

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Keywords: information system, usability perception, convenience perception, user satisfaction, TAM

 

Introduction

Hospital MIS (Management Information System) is one of the essential parts of implementing hospital sustainability, especially in recording and reporting. The implementation of this Management Information System is in the Regulation of the Minister of Health of the Republic of Indonesia Number 82 of 2013 concerning the Hospital Management Information System in Article 3, paragraph 1. Namely, every hospital is required to organize a Hospital MIS (Kementrian Kesehatan Republik Indonesia, 2013).

There are many obstacles in implementing management information systems, such as human resources, lack of initiative to learn information technology, ignorance of the benefits of hospital MIS, ignorance of the effectiveness of hospital MIS information technology, and many other factors.

Various problems arise, such as using less than optimal information systems, low service of several hospital employees to patients in care, and medical activities to patients. So it requires an evaluation to determine the factors influencing the system's acceptance. Application for the assessment by collecting user response data, some appropriate variables, and according to analysis needs.

The Technology Accepted Model (TAM) acceptance method is one of the analytical methods to predict the response or acceptance of technology with statistical techniques so that it can calculate the value of the influence of use between variables. The model introduced by Davis is the most widely used in information systems research because it produces good validity or accuracy (Davis, 1989).

There are two main concepts in determining the acceptance of technology: perceived ease of use and perceived usefulness. Perceived ease of use is a variable to measure a person's confidence that using information technology will be easy and does not require hard effort. Perceived usefulness is a variable to measure a person's belief that using information technology will improve his performance and work (Davis, 1989).

Information system user satisfaction is one of the benchmarks for the success of accounting information system adoption. By showing pride in the information system, users feel the information system can meet expectations. In this study, the author will use TAM as a model used to understand the factors that affect satisfaction with using information technology, especially information systems (Setyowati &; Respati, 2017).

Based on the description above, the perception of usability and ease of use can affect the satisfaction of information system users. The perception of usability and ease of service in question is the assumption that the information system is easy to use and provides benefits that will satisfy users.

 

Literature Review and Hypothesis Formulation

Many researchers have previously researched the effectiveness or success of information system performance. However, there are still problems and inconsistencies in previous studies' results, so it becomes the basis for this study to reexamine and analyze the factors that affect the success of hospital information system performance.

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Hospital Management Information System

According to Regulation of the Indonesian Minister of Health, No.82 Article 1 Hospital Management Information System is an information communication technology system that processes and integrates the entire flow of hospital service processes in the form of coordination networks, reporting, and administrative procedures to obtain information precisely and accurately, and part of the Health Information System. Then according to Government Regulation No. 46 of 2014, health information systems is a set of arrangements that include data, information, indicators, procedures, technology, devices, and human resources that are interrelated and managed in an integrated manner to direct actions or decisions that are useful in supporting health development.

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User Satisfaction�

In general, user satisfaction is when users feel the usefulness of the performance of a system to expectations. Users feel satisfied if the usage of the application matches their expectations. Happy users tend to stay loyal longer and use it relatively more often. Information system user satisfaction is one of the benchmarks for the success of accounting information systems. According to the theory of DeLone and McLean (1992), the basis of the Information Systems Success model is on the process and causal relationship of six measuring dimensions: system quality; quality of information; Use; user satisfaction; individual impact; and organizational impact.

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Technology Acceptance Model (TAM)

Several models can be used to measure information system acceptance, such as the Theory of Reason Action (TRA), Technology Acceptance Model (TAM), End-User Computing Satisfaction (EUCS), and Task Technology Fit (TTF) Analysis. This research applies the Technology Acceptance Model (TAM). TAM is one of the most frequently used models in adoption research in information systems. Existing studies validate the correctness of TAM in testing various kinds of information technology used in multiple types of agencies and companies and are recognized by researchers worldwide (Setyawan, 2015).

TAM is one of the models built to explain and calculate user acceptance of information systems. Fred Davis was the one who first introduced TAM in 1986. Theory of Reasoned Action (TRA), a theory of reasoned action with a premise that a person's reactions and perceptions of something will determine that person's attitude and behavior, is the basic theory of TAM. TAM turns into a perception of practicality, and a perception of ease directly influences the behavioral intention to use (behavior intention to use) and ultimately shows the actual use of the system (actual system use). TAM is a model used to study several factors that can influence the acceptance of the use of technology. The purpose of TAM is to determine the determining factors of acceptance of an information-based technology. Researchers can discover why users may not get a system, so corrective action is needed to overcome it (Agung &; Tanamal, 2021).�

TAM is an adaptation of the theory developed by Fishbein, namely the Theory of Reasoned Action (TRA), which is a theory of action based on one assumption that a person's reaction and perception of something will determine the attitude and behavior of that person (Davis, 1989).

According to Davis (1989), the primary purpose of TAM is to establish a basis for tracing the influence of external factors on the beliefs, attitudes (personalization), and goals of computer users. According to Ajzen and Fisbein (1980), the TAM foundation of the Theory of Reasoned Action (TRA). Based on the TRA, the determination of users of accounting information systems from individual perceptions and attitudes to shape one's behavior in using accounting information systems.

TAM considers two main variables in adopting information systems: user perception of benefits and user perception of use. User perception of miracles has a meaning as the level of confidence someone uses a specific approach to improve their performance. While user perception of use has significance as a person's trust in a system that does not require effort (Davis, 1989).

Based on TAM, two factors predominantly influence technology integration: the perception of usability and ease of use of technology. The perception of usability through the system concerned will benefit its users and increase performance. While the perception of ease of use of technology is that users feel relief in operating the system and can understand it independently (Davis, 1989).

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Perception of Usefulness

Perception of usefulness is the degree to which a person believes using a system will improve performance (Tirtana &; Sari, 2014). The perception of usefulness determines the acceptance or rejection of a system. Usability perception is a belief about the decision-making process. Thus, if someone believes that information systems are helpful, users will continue using them. Conversely, if someone feels the information system is less valuable, the user will not use it. Indicators to measure perceived usefulness are working faster, job performance, increasing productivity, being practical, and making work more accessible and rewarding (Davis, 1989).

From a review of the perception of usefulness or usefulness and the results of previous research, the first hypothesis submission is:

H1: Perceived usability has a positive effect on user satisfaction at MIS Khanza Hospital.

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Perceived Ease of Use

The perception of ease of use is the degree to which a person believes technology is easy to understand. Ease is the extent to which a person believes using technology will be free from effort (Noviandini, 2012). The perception of ease of use is that users acknowledge that information technology will be free from action. From this definition, the construct of ease of use perception is a belief about the decision-making process.�� The indicators used to measure perceived ease of use adapted from Davis' research are easy to learn, controllable, easy to understand, flexible in use, and easy to use (Davis, 1989).

Few reviews still find the effect of perceived convenience on hospital MIS user satisfaction. Therefore, the submission of the second hypothesis is as follows:

H2: Perceived convenience positively affects user satisfaction at MIS Khanza Hospital.

 

Research Method

This study aims to determine the effect of usability and convenience on user satisfaction at MIS Khanza Hospital, which consists of two independent variables, Usability and Convenience, and User satisfaction as a dependent variable simultaneously or partially.

1.    Research Design

Research design is part of the initial step of conducting a study that contains the planned research stages. With the guidelines in the research design, researchers will not lose their way and can achieve their goals effectively. In this study, the approach used is a quantitative approach that uses TAM as a research framework.

2.    Population and Sample

The population is a generalized area of objects or subjects with specific qualities and characteristics determined by researchers to be studied and concluded (Sugiyono, 2011). The people in this study are hospitals that use MIS Khanza hospitals in Indonesia based on data obtained from the MIS Foundation of Khanza Indonesia hospitals, as many as 164 hospitals.

The sample is part of the number and characteristics possessed by the population (sugiyono, 2011). The sample is part of the number and characteristics maintained by that population. According to Roscoe, cited by Sekaran 2006, the exact sample size for the study is more than 30 and less than 500.�

In this study, the author narrowed the population. Namely, the total number of MIS users hospitals Khanza Hospital as much as 164 by calculating the sample size carried out using the Slovin technique according to Sugiyono (2011: 87). This study uses the Slovin formula because in determining the number of samples is representative. The goal of combining the research results and samples does not require a table of the number of samples but uses a simple formula. The Slovin formula for determining the sample is as follows:

n = ������� ���������������������������������������..(1)

Information:

n = Sample size/ number of respondents�

N= Population size

E = Percentage of leeway in sampling error accuracy that can still tolerate

e = 0.1�

In the Slovin formula, there are the following conditions:�

The value of e = 0.1 (10%) for a large population

The value of e = 0.2 (20%) for a small population

So the Solvin technique's sampling range is 10-20% of the study population. The total population of this study was 164. So the percentage of allowance is 10%. Correction of calculation results to achieve conformity. So to find out the number of research samples, with the following calculations:

n = �= 62.12; adjusted by researchers to 63

3.    Data collection techniques

This study uses data collection techniques by distributing questionnaires with questions to respondents to obtain responses to the questions asked. Researchers created questionnaires using the Google Forms application. Then the distribution of the questionnaire online to MIS users of Khanza Hospital in Indonesia. The scale used by all indicators of each variable using the Likert scale starts from 1 (strongly disagree), 2 (disagree), 3 (neutral), 4 (agree), and 5 (strongly agree). Researchers determined the questionnaire results by calculating the perceived usability and ease of using MIS Khanza Hospital.

4.    Variable measurement

� Data analysis of this research is a quantitative analysis, namely data analysis expressed in the form of numbers or quantitative data numbered (scoring) ranging from strongly disagreeing with a score of 1 to 5 strongly agree. Variable measurement using a 5-level Likert scale using alternative answers as follows:

1 = STS (strongly disagree)

2 = TS (Disagree)

3 = N (Neutral)

4 = S (Agree)

5 = SS (Strongly Agree)

Measurement of perceived variables of ease of use using indicators from Davis (1989), which include:

a. Easy to learn

b. Easy to Use

c. Clear and understandable

d. Flexible to use

e. Quickly skilled at using it

Measurement of benefit perception variables using indicators according to Davis (1989), which include:

a. work completed faster

b. Improve work performance

c. Increased work productivity

d. Increased work effectiveness

e. Making work easier

f. Useful�

5.    Data analysis techniques

�Quantitative Data Analysis

a.              Validity Test

�� The validity test is a tool to measure a level of ability. Measurement of questionnaire validity to respondents using product moment correlation coefficient assisted by SPSS (Statistical Package for Social Science) application with a significant level of ≤ 0.05 (Kusumah, 2018). If all instruments from the questionnaire tested are appropriate, then the instrument is said to be valid. The assessment criteria for the validity test is that if r counts > r table. Then the questionnaire item is correct. If r depends on < r table, then the questionnaire is invalid.

b.             Reliability Test

The reliability test is the level of confidence in the results of a measurement. To find out that the questionnaire is reliable, testing the reliability of the questionnaire will be carried out with the help of the SPSS computer program. The decision-making method in the reliability test uses a limit of 0.60, meaning that a variable is reliable if the value shows Cronbach's Alpha more significant than 0.6.

 

Classical Assumption Test

�The classical assumption test aims to determine the condition of the data used in the study. This research regression analysis model requires an assumption test of data which includes:

a.       Normality Test�

The data normality test aims to test whether, in the residual model, it has a normal distribution. To determine whether the collected data is normality test distributed can be done with a simple statistical test method often used to test the normality assumption, using the normality test from Kolmogorov Smirnov. The process of testing normal or abnormal distributed data by looking at the significance value of the variable. If the significance is more significant than 0.05 or 5%, it shows normal data distribution.

b.      Multicollinearity Test

���������� The multicollinearity test aims to test whether there is a correlation between independent variables in the regression model. The expected result in testing is that there is no correlation between independent variables. There are several ways to test whether or not multicollinearity is present in a regression model. In this test, researchers use mark analysis of the correlation between independent variables by looking at the Tolerance and Variance Inflation Factor (VIF) value. If the tolerance value is more significant than 0.10 or equal to the VIF value of less than 10, there is no multicollinearity in the regression model used in the study.

c.       Heteroscedasticity Test

�The heteroscedasticity test aims to test whether there is an inequality of variance from residuals from one observation to another in a regression model. Homokedasticity occurs when there is no difference in the results of observational conflict from one residue to another. At the same time, heteroscedasticity occurs when there are differences in the results of observational battles from one residue to another. A good regression model is homoscedasticity data, and heteroscedasticity does not happen. This study uses the glacier test as a basis for decision-making. Heteroscedasticity occurs if the significant independent variable is smaller than 0.05 or 5% and statistically affects the dependent variable. If an essential independent variable greater than 0.05 or 5% does not statistically affect the dependent variable, heteroscedasticity does not happen in the study.

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Multiple Regression Analysis

Multiple regression analysis is performed on models of more than one independent variable to determine the extent of its effect on the dependent variable. The research uses the SPSS application to facilitate the data processing process. Based on the SPSS application, researchers get the output of data processing results. Then researchers perform interpretation and analysis of the data. The production effects of data processing will be interpreted and analyzed. The multiple regression equation is as follows:

Y=α+β1.X1+β2.X2+e ������������������������������������(2)

Information:

Y: User satisfaction

α: constant

β1, β2: Regression coefficient�������������

X1: Benefits

X2: Ease

e: error

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Test the hypothesis

a.    Test t (Partial)

The t-test tests the effect of the independent variable, partial usability and convenience, on the dependent variable, user satisfaction. The trick is to look at each independent variable's calculated t-value and significance value with a significance level 0.05. If t counts < t table and the significant value > 0.05, then partially, the independent variable does not affect the dependent variable, namely user satisfaction. If the t-value is > t table, and the significant value is < 0.05. The independent variable partially depends on the dependent variable, user satisfaction.

b.            Simultaneous Significance Test (F Test)

The significance level is α = 5% (a significance of 5% or 0.05 is a standard provision widely used in research). In addition, to find out the significance value, if the significance value is < 0.05, Ha is accepted, and Ho is rejected. If the significance value > 0.05, Ha is rejected, and Ho is accepted.

c.            Coefficient of Determination Analysis

��� R2 (Adjust R Square) analysis or the coefficient of determination determines how much influence the independent variable has in explaining the dependent variable in research. The value of the coefficient of determination is between zero and one (0<R2<1). The independent variable's ability can explain the dependent variable's variation through an Adjusted R Square or a small R2 value. While the independent variable, which almost all provides the information needed in predicting the interpretation of the dependent variable, will show an Adjusted R Square or R2 value close to one.

 

Results and Discussion

A.  Validity Test

The validity test results are carried out by comparing the value of the r table and r count and looking at the significant value (sig). If the value of r count > r table = valid, if the value of r count < r table = invalid. r table = 0.2441, r count = 0.73, r count > r table, Sig = 0.00 < 0.05 then the questionnaire used is valid.�

B.  Reliability Test

Testing of reliability tests by looking at the consistency of Cronbach's Alpha coefficient based on all variable usage. Data is reliable with a Cronbach's Alpha value of > 0.60. Test results from reliability tests are shown in Tables 1 and 2:

 

Table 1

Case Processing Summary

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N

%

Cases

Valid

Excludeda

Total

63

100.0

0

0.0

63

100.0

a. Listwise deletion based on all variables in the procedure.

 

Table 2

Reliability Statistics

Cronbach's Alpha

N of Items

0.862

36

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Table 2 displays the reliability testing results with Cronbach Alpha testing. Namely, the variable has a Cronbach Alpha value of 0.862 > 0.6, so the instrument indicator is reliable.

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C.  Classical Assumption Test Results

Testing the classic assumptions in this study is testing normality, multicollinearity, autocorrelation, and heteroscedasticity with the following results:

1. Normality test

Test the normality of this study by looking at the points of data spread against diagonal lines on the graph. The data of this study spread out following a diagonal line. The results of the Normality Test based on Kolmogorov Smirnov's One-Sample Test can be seen in Table 3:

 

 

Table 3

One-Sample Kolmogorov-Smirnov Test

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Uses

Ease

Satisfaction

N

 

63

63

63

Normal

Parametersa,b

Most Extreme

Differences

Mean

Std. Deviation

Absolute

Positive

57.87302

4.619356

44.61905

4.255655

12.65079

2.222747

0.148

0.130

0.163

0.130

0.088

0.145

 

Negative

 

-0.148

-0.130

-0.163

Test Statistic

0.148

0.130

0.163

Asymp. Sig. (2-tailed)

 

0.002c

0.010c

0.000c

Monte Carlo ���� Sig.

Sig. (2-tailed) � 99% Confidence Interval

Lower

Bound

Upper

Bound

0.114d

0.217d

0.062d

0.106

0.207

0.056

0.122

0.228

0.068

 

From the results of data normality output using SPSS in Table 3, a significant value (Asymp. Sig 2-tailed) for variable X1 is 0.114, variable X2 is 0.217, and variable Y is 0.062. The cause of the data having a normal distribution value is because of the significant value (Asymp Sig 2-tailed) for each variable > 0.05. It means that variable data X1, X2, and Y can meet the assumption of normality, can use regression analysis techniques, and have normal distribution values.

2. Linearity Test

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Table 4

ANOVA

 

Sum of

Squares

df

Mean

Square

F

Sig.

Satisfaction Between�

�*�������������� Groups

Uses

(Combined)

174.041

16

10.878

3.783

0.000

Linearity

118.057

1

118.057

41.055

0.000

Deviation from

Linearity

55.98 4

15

3.732

1.298

0.242

Within Groups

132.276

46

2.876

�

�

Total

306.317

62

�

�

�

 

The results of the linearity test based on the data contained in Table 4 above show that the regression line of the usability variable (X1) with Satisfaction (Y) in deviation from linearity is 1.298 and a significant value of 0.242 > 0.05, thus between the satisfaction variable (Y) has a linear relationship with usability (X1).

 

Table 5

ANOVA

�

 

Sum of Squares

df

Mean

Square

F

Sig.

Satisfaction * Ease

Between ���� (Combined)

147.434

16

9.215

2.668

0.005

Groups ������� Linearity

75.737

1

75.737

21.927

0.000

Deviation from Linearity

71.697

15

4.780

1.384

0.196

Within Groups

158.883

46

3.454

 

 

Total

306.317

62

 

 

 

 

The results of the linearity test based on the data contained in Table 5 above show that the regression line of the satisfaction variable (Y) with ease (X2) in deviation from linearity is 1.384 > 0.05. The probability value of 0.196 > 0.05 thus between the variables of convenience (X2) has a linear relationship with satisfaction (Y).

3. Multicollinearity Test

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Table 6

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Collinearity Statistics

B

Std. Error

Beta

Tolerance

VIF

1 ��� (Constant)

-11.671

2.944

 

-3.965

0.000

 

 

Uses

0.262

0.043

0.545

6.067

0.000

0.963

1.039

Ease

0.205

0.047

0.392

4.364

0.000

0.963

1.039

a. Dependent Variable: Satisfaction

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Based on Table 6 of the results of the multicollinearity analysis, there is no significant multicollinearity between each independent variable in the regression model because the tolerance value is more critical than 0.10 and the VIF value is smaller than 10.00.

4. Autocorrelation test (Durbin Watson)

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Table 7

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

0.730a

0.533

0.518

1.543302

1.841

a.  Predictors: (Constant), Ease, Usability

b. Dependent Variable: Satisfaction

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Based on Table 7 above, Durbin Watson's value of 1.841 is between du (1.6581) and 4-du = 2.3419. The value of the du distribution in the Durbin-Watson table uses the formula for the independent variable (k) value, which is k = 2, and the sample value (N) = 63. Based on the provision of a significant value of 5%, there are no symptoms of correlation.

�5. Heteroscedasticity Test

Test heteroscedasticity in research by looking at the scatter plot graph. If the points spread above and below the zero on the Y-axis and do not form a specific pattern, heteroscedasticity does not occur. The dots in the scatter plot chart have no way and spread above and below zero. So that heteroscedasticity does not happen.

D.  Determinasi Coefficient of Determination Test

The coefficient analysis of the determination of this study uses the value of Adjusted R square (R2). If the Adjusted R square gets closer to 1, it can predict the bound variable (Y) and the more substantial independent variable (X1 and X2).

 

Table 8

Models Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-

Watson

1

0.730a

0.533

0.518

1.543302

1.841

a.  Predictors: (Constant), Ease, Usability

b. Dependent Variable: Satisfaction

�

Based on Table 8, the value of the coefficient of determination is at the Adjusted R Square value, which is 0.518. This right means that the ability of the independent variable to explain the dependent variable is 51.8%, and the remaining 48.2% is explained by other variables not discussed in this study.�

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E.   Regression Coefficient Test

The regression coefficient analysis in this study was measured by comparing significant values of 5% or 0.05. Then there is a considerable influence of the independent variable on the dependent variable.

 

Table 9

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t

Sig.

Collinearity Statistics

B

Std. Error

Beta

Toleranc

e

VIF

1 ��� (Constant)

Kegunaan

Kemudahan

-11.671

2.944

�

-3.965

0.000

�

�

0.262

0.043

0.545

6.067

0.000

0.963

1.039

0.205

0.047

0.392

4.364

0.000

0.963

1.039

a. Dependent Variable: Satisfaction

�

Based on data analysis using SPSS 23, the results of the regression equation are as follows:

Y = -11.671 + 0.262X1 + 0.205X2 + e

The regression equation above partially shows the relationship between the independent and dependent variables. Based on these equations, the conclusions are as follows:

1.      The value of Constanta is -11.671, meaning that if there is a change in the variables of usability and convenience (values of X1 and X2 are 0), then the satisfaction of users of MIS Khanza Hospital is -11,671. There is no error if there is a negative value of the constant if it meets the test of the normality assumption or other classical assumptions. In addition, as long as the slope value is not ZERO, there is no need to consider this negative constant.

2.      The value of the usability regression coefficient is 0.262, meaning that if the usability variable (X1) increases by 1%, assuming the convenience variable (X2) and constant (a) is 0 (zero), then the satisfaction of Khanza Hospital MIS users increases by 26.2% This shows that the usability variable contributes positively to user satisfaction, so that the greater the usefulness of Khanza hospital MIS, the greater the level of user satisfaction of Khanza hospital MIS.

3.      The value of the user convenience regression coefficient is 0.205, meaning that if the convenience variable (X2) increases by 1%, assuming the usability variable (X1) and constant (a) is 0 (zero), then the user satisfaction of Khanza hospital MIS increases by 20.5%. It shows that the convenience of the Khanza Hospital MIS provided contributes positively to the pleasure of Khanza Hospital MIS users, so the more significant the user convenience, the greater the satisfaction of Khanza Hospital MIS users.

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F.   Test Results t (Partial)

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Table 10

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1 ��� (Constant)

-11.671

2.944

�

-3.965

0.000

Kegunaan

0.262

0.043

0.545

6.067

0.000

Kemudahan

0.205

0.047

0.392

4.364

0.000

a. Dependent Variable: Satisfaction

�

Based on Table 10, observing the rows of columns t and sig can be explained as follows:

1.      Effect of usability variables on user satisfaction of MIS Khanza Hospital (H1)

The usability variable (X1) has a positive and significant effect on the satisfaction of MIS users of Khanza Hospital. This can be seen from the significant usability (X1) 0.000<0.05 and the value of t table = t (α/2: n-k-1=t (0.05/2:63-2-1)= (0.025:60)= 2.00030. The calculated t value exceeds the table t = (6.067>2.00030). Ho is rejected, and H1 is accepted so that the hypothesis about the positive influence of usability on user satisfaction at MIS Khanza Hospital was partially accepted.�

2.      The effect of convenience variables on user satisfaction at MIS Khanza Hospital (H2)

The user convenience variable (X2) has a positive and significant effect on the user satisfaction of MIS Khanza Hospital. It can be seen from the considerable ease of user (X2) 0.000<0.05 and the value of table t = (α/2:n-k-1=t(0.025:60)=2.00030. Meaning the calculated t value is greater than the table t = (4.364>2.00030), then Ho is rejected, and H2 is accepted so that the hypothesis about the effect of user convenience on satisfaction at MIS Khanza Hospital users was partially accepted.

G.  F Test Results (Simultaneous)

The use of Test F is to test the simultaneous effect of the independent variable on the dependent variable (Y). Testing of the F Test to compare the significance of F values of the count>F table. If the F value is calculated> F table, then it is appropriate to use the regression model by looking at the value of the F table = f(k:n-k), F=(2:63-2), and F= (2:61) = 3.148 with an error rate of 5%. The results of the F Test test can be seen in Table 11 below:

 

Table 11

F Test Results �(ANOVAa)

Model

Sum of Squares

df

Mean Square

F

Sig.

1 ����� Regression

Residual

Total

163.411

2

81.705

34.304

0.000b

142.907

60

2.382

306.317

62

 

a.  Dependent Variable: Satisfaction

b. Predictors: (Constant), Ease, Usability

 

Based on the test results in Table 11, the calculated F value is 34.304, and the table F value is 3.148. So that the value of F count>F table or 34,304>3,148. The significant level is 0.000<0.05, then Ho is rejected, and H1 is accepted. So usability variables (X1) and user convenience (X2) influence user satisfaction at MIS Khanza Hospital.

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Conclusion

After researchers carry out several stages of research, namely research preparation, research methodology, data collection, data analysis, and testing, results are obtained that can explain the relationship between the variables used, namely Perception of Usefulness and Perception of Ease. Both variables have a positive direction.

The results studied from usability perception variables significantly and positively influence user satisfaction. In this case, the T-test result shows a significance value of 0.000. It means that the usability perception dimension that discusses the benefits of the usefulness of the Khanza Hospital Management Information System can affect user satisfaction.

The results of all independent variables simultaneously affect user satisfaction. The F test result shows a significance value of 0.000. The independent variable has a coefficient of determination value of 0.518, so all dimensions of the free variable affect user satisfaction by 51.8%.

 

 

 

 

 

 

 

 

 

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Copyright holder:

Dhiana Wijayanti, Bambang Setya Budi I (2022)

 

First publication right:

Syntax Literate: Jurnal Ilmiah Indonesia

 

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