40 Jurnal Teknik Informatika dan Sistem Informasi ISSN 2407-4322 Vol. 10, No. 2, Juni 2023, Hal. 40-49 E-ISSN 2503-2933
Analysis Acceptance Of Academic System Using TAM And DeLone & McLean (D&M ISSM)
Hanifa Huzaima1*, Muhammad Luthfi Hamzah2, Megawati3, Febi Nur Salisah4, Zarnelly5 State Islamic University of Sultan Syarif Kasim Riau, HR. Soebrantas Street, No.155 Tuah
Madani Tampan, Pekanbaru 589026
1,2,3,4
Department of Information System, Faculty of Sains and Technology.
e-mail: 1*[email protected], 2[email protected],
3[email protected], 4[email protected], 5[email protected]
Abstract
SIAK is an academic information system that has been implemented since 2018. Users of the SIAK system are active students, while in its implementation SIAK experiences several obstacles such as some menus available there are SIAK services in its application are considered ineffective, such as the forgot password menu that cannot be used, but the system is not user-friendly. The obstacles experienced will affect the acceptance of SIAK users. The main objective of the study is to ascertain how SIAK consumers feel about the service. The modified TAM model includes, perceived usability, ease and acceptance, information quality variables, system quality, and service quality of Delone and Mclean ISSM models. Using the Slovin formula and sampling techniques using Simple Random Sampling, this research obtained one hundred students as research samples. SmartPLS V 3.0 for PLS-SEM data processing. Of the 8 hypotheses given, 7 were approved, and 1 was the rejected variable is Service Quality which does not show a positive relationship with the Perceived Ease of Use.
Keywords— SIAK, TAM, Delone and Mclean ISSM, Simple Random Sampling, SmartPLS.
1. INTRODUCTION
Analysis of the reception of information systems explains how users may perceive a system as well as how users act and feel about it [1][2]. An information system is successful if it can take in data, process that data to forward corporate goals, and produce useful outputs. To improve user acceptance and pinpoint the system's flaws, an acceptability analysis of the information system is essential [3]. A system is a collection of components that are arranged, communicate with one another, and cooperate to accomplish a particular objective [4]. An information system can be defined as a system that supports the operations, transactional and management activities of an organization and that generates information as a result. Information systems are essential to an organization because they enable decision-making, operating models, and processes [5][6]. User acceptability has a significant impact on how successfully technology is implemented, so the factor that determines user acceptance also determines the success of implementation. According to Davis, the implementation of a system can be accepted or rejected based on two factors: the user perceived benefits and ease of use[7].
The Academic Information System is a system created to manage all student administrative tasks and academic data, providing online access to users of academic administration tasks on campus [8]. The many benefits of information systems in an organization, sometimes due to poor user acceptance, can cause the system to be unable to be implemented. Furthermore, a thorough analysis is needed to identify the variables influencing the organization's acceptance of the implementation of information technologies [9]. The University of Muhammadiyah West Sumatra (UMSB) has used an information system to assist
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the organization's operational activities. UMSB uses the Academic Information System (SIAK) to carry out academic processes in the campus environment The services contained in SIAK UMSB are filling out KRS, study result cards (KHS), student grades, downloading lecture materials, collecting assignments, seeing details of tuition payments, submitting final project proposals, and graduation registration.
Positive effects on the adoption of SIAK, such as improved student administration services through greater centralization and automation, can be observed as a result of the use of this academic information system. However, in the implementation of SIAK, it is also known that there are complaints or obstacles experienced by users while using SIAK. The SIAK system has problems, according to observations and interactions with UMSB system operators and active students, such as some menu features contained in SIAK are still ineffective when used, for example the forgot password feature is available but cannot be used, errors in the system make users feel uncomfortable, system display that is considered less user friendly, and other errors.
Studies on the AIS UIN Jakarta Mobile application, such as that conducted by Nur Aeni Hidayah 2020, have utilized TAM and Delone and Mclean ISSM methodologies to identify system receiving factors. Based on the study, 7 out of 8 hypotheses were accepted, as the relationship between the services provided had no effect on the user's youthfulness was rejected (the relationship hypothesis did not exist because the T-Test value for the variable was less than 1.96. One of the seven hypotheses approved by both PU and AI has a big effect size, whereas the other six have lesser effect sizes. In addition, Herlina and Ninik Sri Lestari's research[10], used the TAM and Delone and Mclean methods on the academic information system of the Indonesian Institute of Cultural Arts Bandung, with the findings indicating that the variables of ease of using the Information system and the variables of information system quality have a positive and significant influence on the satisfaction of users of the Academic information system. In 2020, Yakubu did research in which he concluded that Information Quality and System Quality had the greatest influence on student behavior intentions out of a total of nine hypotheses provided.
Previous research conducted by Al-Fraihat 2018 [11] E-Learning analysis using the TAM and Delone and Mclean models in the analysis results there were 26 hypotheses submitted 19 hypotheses accepted and 7 hypotheses rejected. According to the four research components, the quality of the educational system, the support systems, the students, and the perceived advantages of e-learning all had a role in the 34.1% adoption rate. Then in 2022, Emil Robert Kaburuan [12] conducted a study using the TAM and Delone and Mclean models to determine the approval factor of the DAPURQ application; The findings include 9 hypotheses, of which 2 are rejected, that the information provided has no positive effect on the user's usability and ease.
From the presentation of previous research above, basically the research has similarities in both the research model and the results obtained. However, this study has differences from previous studies, where in terms of data processing previous research using SPSS with data analysis using AMOS as passed by researchers Herlina and Ninik Sri Lestari, 2020[10], research conducted by Yakub at the stage of data analysis of the study and in this study using data analysis with SEM and data processing using AMOS. However, in other studies conducted by their data processing techniques using SmartPLS and data analysis with SEM-PLS, the difference with this research is in the sampling technique, in the research [11] the research used Purposive Sampling, while for this study it used Simple Random Sampling. The research aims to provide an overview of the organization to help them improve their academic system's (SIAK) acceptance by combining variable from the TAM and Delone and Mclean ISSM models.
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2. RESEARCH METODHOLOGY
The research methodology that will be carried out in this study in order to solve the problem with several stages can be seen in Figure 1.
Figure 1. Research Methodology
2.1 Problem Identification
This study's first stage was identifying the issue. The issues were then modified as a result of observations and interviews with users and operators of the SIAK system, which were based on variables obtained from models that emphasized user acceptance, namely the TAM model and Delone and Mclean ISSM
2.2 Literature Studi
For the purpose of this study, a selection of the prior scholarly work associated with the investigation currently being carried out was investigated. In the body of work known as the literature, several references to articles, books, and studies done in the past are cited in order to explain the object of study that will be utilized in order to determine the admission factor of the academic system through the utilization of the TAM and Delone & Mclean variables. These references come from a variety of sources, including journals, books, and studies [11][13].
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2.3. Population and Sample
Respondent in this analysis, which employed the Simple Random Sampling technique, consisted of students at the University of Muhammadiyah West Sumatra Campus III Aur Kuning, where there were 2,232 active SIAK users among the students. In order to calculate the appropriate number of sample sizes, the author uses the Slovin formula and sets the error tolerance threshold at 10%. The conclusion drawn from the calculation is that there are one hundred samples in total.
2.4 Quistonare Preparation
The preparation of questionnaires in this study is by adapting from the model indicators Technology Acceptance Model, from Davis.F research [14], and using the Delone and Mclean ISSM model from Delone and Mclean research [15]. The questionnaire asks questions regarding the SIAK system with the goal of gauging user acceptability.
2.5 Data Anlysis
With the help of the application SmartPLS V 3.0, we analyzed the Outer Model and the Inner Model based on the findings of the data collection stage, which involved the distribution of questionnaires and the running of hypothesis tests. This stage was very similar to the stage where we analyzed the data. The final step is to derive conclusions from the findings of the testing of the hypothesis that has already been carried out
3. RESULT AND DISCUSSION
For this research, we used a SEM strategy built on the PLS method for analyzing the data. There are two kinds of models used in SEM-PLS analysis, structural models, and measurement models. The PLS algorithm is used to provide a descriptive account of the route analysis that approximates the relationship specified by the system's structural equations in the path diagram [16].
Figure 2. Path Diagram
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3.1 Measurement Model Evaluation (Outer Model)3.1.1 Validity Test
However, there is a loading factor value that is less than 0.7, but the indicator is still used, as shown in Table 1. Even if this value is lower than 0.7, the unit value of the score indication can be acceptable if it is greater than 0.7.
Table 1. Outer Loading Value
Variable Indicator Outer Loading Description System Quality
SQ1 0,844 Validity Value
Fulfilled
SQ2 0,795
SQ3 0,758
SQ4 0,748
Service Quality SEQ1 0,747
SEQ2 0,785
SEQ3 0,771
Information Quality IQ1 0,818
IQ2 0,665
IQ3 0,756
IQ4 0,613
IQ5 0,704
Perceived Usefulness PU1 0,724
PU2 0,783
PU3 0,660
PU4 0,782
PU5 0,757
Perceived Ease of Use PEOU1 0,828
PEOU2 0,695
PEOU3 0,845
PEOU4 0,743
Acceptance of IT AIT1 0,823
AIT2 0,792
AIT3 0,833
3.1.2 Average Variance Extracted (AVE)
When the variable's value exceeds 0.5 [17], it may be claimed that the AVE value is valid and that it satisfies the standards, as shown in Table 2.
Table 2. AVE Score Variable AVE
SQ 0,605
SEQ 0,590
IQ 0,511
PU 0,551
PEOU 0,609
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AIT 0,666
3.2 Internal Consistency Reliability
When it comes to the Cronbach Alpha and Composite Reliability tests, a score that is greater than 0.7 is excellent, while a value of 0.6 is still considered to be adequate. The results of the calculations for the Composite Reliability Analysis are shown in Table 3.
Table 3. Consistencu Reability Value Variabel CA CR Description
SQ 0,782 0,860 Reliable
SEQ 0,652 0,812 Reliable
IQ 0,758 0,838 Reliable
PU 0,796 0,860 Reliable
PEOU 0,786 0,861 Reliable
AIT 0,750 0,857 Reliable
3.3 Structural Model Evaluation (Inner Model)
3.3.1 Path Coefficient (β)
The path coefficients value >0.1 then the path influences the model.
Table 4. Path Diagram Path Coefficients (β)
SQ - PU 0,324
SQ - PEOU 0,338
SEQ - PU 0,325
SEQ - PEOU 0,101
IQ - PU 0,340
IQ - PEOU 0,462
PU- AIT 0,463
PEOU - AIT 0,379
This study has eight research paths, the Path Coefficient Score which is obtained by all variables above 0.1, then all variables then the path affects the model and the ability to predict the dependent structure is stronger
3.3.2 R-Square
The research model is thought to be better when the R-Square score is higher. The value of 0.67 in the r-square can be classified as moderate, the value of 0.33 as medium, and the value of 0.19 as weak.
Table 5.R-Square Score Variable R-Square
AIT 0,604
PEOU 0,669
PU 0,648
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The findings indicate that the measure of IT Acceptance has a substantial correlation with both the Perceived Usefulness and the Perceived Ease of Use variables (R-Square = 0.604, which is a correlation coefficient of 60.4%). The medium or middle range is where this value should be placed. The R-Square value of "perceived ease of use" (PEOU) is 0.669, or 66.9%, placing it in the "strong" category, showing that it can be influenced by "system quality,"
"quality of service," and "quality of information." The Perceived Usefulness measure is moderately significant (R-Square = 0.648, or 64.8%).
3.4 T-Test
The T-Statistical test is presented in Table 6
Table 6. T-Test Value
Variable Original Sample T-Statistic P-Values Description
SQ - PU 0,324 3,323 0,001 Accepted
SQ - PEOU 0,338 2,985 0,003 Accepted
SEQ - PU 0,235 2,401 0,017 Accepted
SEQ - PEOU 0,101 0,918 0,359 Rejected
IQ - PU 0,340 3,555 0,000 Accepted
IQ - PEOU 0,462 3,993 0,000 Accepted
PU - AIT 0,463 4,226 0,000 Accepted
PEOU - AIT 0,397 3,416 0,001 Accepted
Hypothesis 1: The inner model findings and T-Test scores of the eight submitted hypothesis models showed that seven of the hypotheses were accepted and one was rejected.
Using a T-statistic of 3.323, Hypothesis 1 demonstrates the relationship between the System Quality Variable and the Perceived Usefulness Variable. Hypothesis 1 is supported since the T- Statistical value exceeds the T-Table value (1.96). This is consistent with prior research [17][12], the data supports the first hypothesis variable's significant association with and positive impact.
Hypothesis 2: It is determined that there is a correlation between System Quality and Perceived Usefulness using a T-Statistic 2.985, supporting acceptance of the second hypothesis.
This research is inversely proportional to research [17], where the variable has no positive effect, this is consistent with previous research [11], which found that high-quality systems have a beneficial effect on users on how easy they use them. When the interface is intuitive and the learning process is easy. This connection mentioned that the quality of the system can make it easier for users to utilize the system, and that a good quality system produces an environment in which it is easier for students to use the system.
Hypothesis 3: The T-statistic of 2.401 indicates that the third hypothesis, that Service Quality affects Perceived Usefulness, is supported. This accords with earlier studies that found a favorable correlation between service quality and perceived usefulness. Results indicate service quality, such as the availability of user guides presented in an easily navigable format, and explanations are concise and straightforward. The impact of this variable has the effect of making the services offered more aware of the user, which in turn brings advantages to their utilization of the system.
Hypothesis 4: the T-statistical value of the variable (H4) was 0.918, a number lower than the cutoff of 1.96 indicating that an increase in service quality did not lead to an increase in
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reports on how simple the interface is being used. Similar findings found in previous studies [13][14], this study assume that Variable 4 does not contribute positively to the Hypothesis.
Studies have shown that people's impressions of how easy services are to use are favorably influenced by the quality of the services they use, therefore this seems to go against those findings. According to the results of the research, the quality of service had no effect on users' levels of satisfaction with the system, even though certain menus on the system were excellent at creating that impression.
Hypothesis 5: The test results support hypothesis five (H5) where the variable has a positive effect with a T-Statistical value of 3.555. An example where the user is confident that all relevant data has been provided by the system and that the data displayed is accurate and trustworthy. This is consistent with previous studies showing that this variable increase has a beneficial effect on Perceived Usability.
Hypothesis 6: With a T-statistical value of 3.993, the findings of the experiment that tested the sixth hypothesis demonstrated that the hypothesis variable in question had a substantial and favorable influence; hence, the sixth hypothesis was validated. The results of this research are consistent with the findings of earlier studies, which found that a good influence on system users can be attributed to the students' perception that the information provided is accurate and not difficult to acquire.
Hypothesis 7: the results derived from hypothesis 7 showed that, the hypothesis variable had a significant and positive effect with a T-statistical value of 4.226, so H7 was accepted. This is in accordance with previous studies[18], where participants reported that the presence of the system made their work easier and faster. This result not only makes the usability of the system a key factor towards the acceptance of the system, but it also makes the perceived usability of the system offer benefits for the user, so that it can affect how well the system is accepted.
Hypothesis 8: Testing hypothesis eight yielded a T-Statistic of 3,416, which indicates that the variable has a positive effect, so hypothesis 7 is accepted, this confirms the findings of previous research [18], in which the hypothesis suggests that this system is easy to understand and is very much liked by its end user. In addition to this, it makes it simple for users to operate the system, which is helpful regardless of whether the system is accepted. This is also backed by the TAM theory, which suggests that the ease of user aspects in utilizing the system should determine whether the system is acceptable
4. CONCLUSION
According to the findings of the research that has been conducted, it is possible to state that out of the eight hypotheses that were proposed, seven of them are accepted and have a significant effect, and one of them is rejected because the T-Test value is lower than 1.96, specifically the SEQ-PEOU variable, which has a value of 0.359. With a T-Statistical score of 4,226, the PU variable exerts a preponderant amount of control on the admissions decisions made by the SIAK academic system. The R-Square value of the AIT variable or endogenous variable is 0.604, the PEOU variable with a score of 0.669, and the PU variable with a score of 0.648, based on this value the exogenous variable SQ SEQ IQ accurately explains. The research gives colleges, universities, and other institutions of higher learning with a model and set of instruments that are valid, reliable, and comprehensive in nature, allowing for the evaluation of user acceptability of their academic system.
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5. SUGGESTION
The suggestion of this study is the need to analyze user acceptance of academic information systems, using the analytical methods that have been presented. While the author of this study hopes that future researchers will develop the same research using other methods to measure the level of user acceptance.
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