This research has a purpose to examine the factors that influence the success of the accounting information system implementation in private universities (PU) in Bali. This is to measure the extent of success of the accounting software or application in terms of their usage in 8 PU in Bali. The data based on the source is primary data, collected using questionnaires which were directly distributed to the respondents. The sample were selected using the purposive sampling technique. The total research respondents are 55 people. The data analysis technique used is the Partial Least Square (PLS) with the help of the SmarPLS 3.0. program at a 5 percent level of significance. The research results state that system quality, information quality and the importance of the system have an influence on the usage and satisfaction of AIS users. In this research, the empirical evidence attained showed that system usage and system satisfaction have an influence on the net benefit gained by the private universities in Bali. Picture 1. Research Model An improvement in the system quality will result in the increase in system usage. The research results by (Tan et al., 2015)
An improvement in this research is the modification of (
Information system-related studies have been previously conducted by several researchers, such as (
The research model utilized in this research is as follows:
An improvement in the system quality will result in the increase in system usage. The research results by (
H1: System quality has an influence on AIS use in PU.
The better the system and system output quality provided, for example by improving the speed of access and system output usage, the less reluctant the users are to reuse it. Thus, the system usage intensity will increase, vice versa. DeLone and McLean stated that system quality is influenced by the satisfaction of users. If the system has characteristics that are in line with their expectations, the usage will increase the satisfaction of users. This research result is consistent with the research conducted by (
H2: System quality has an influence on the users satisfaction of AIS in PU.
In their study, DeLone and McLean revealed that information quality focuses on the alignment of the product or result of the information system (output) with the expected characteristics. Mason (
H3: Information quality has an influence on the AIS use in PU.
The influence of information usage (output) will influence the satisfaction of users. (
H4: Information quality has an influence on the users satisfaction of AIS in PU.
If the system user perceives that the information system quality is good, the perceived benefit will be high (
H5: System importance has an influence on the use of AIS in PU.
(
H6: System importance has an influence on the users satisfaction of AIS in PU.
Repeated usage would imply that the usage has a benefit for the user. The high degree of benefit attained would result in users becoming more satisfied. (
H7: The use has an influence on the users satisfaction of AIS in PU.
Shannon and Weaver (
H8: The users satisfaction has an influence on the use of AIS in PU.
(
H9: The system use has an influence on the net benefit of the AIS in PU.
(
H10 : User satisfaction has an influence on the net benefit of AIS in PU.
There are 60 private universities with an active status in Bali. The population in this research are eight private universities that participated in the AIS training which was held by KOPERTIS VIII of the private universities on the 21st of March 2017. They have attended and stated that they have an accounting information system which is integrated with the private universities’ academic system. Sampling in this research was conducted using the Purposive sampling technique. The sample selected are 10 employees as the respondents from each private university. The sampled employees comprise the financial bureau staffs, financial system operator, and the financial statement users from the eight private universities, with a total of 80 people. In distributing the questionnaires, there were 20 employees who had double positions, thus, the total number of respondents is 60 people. After the questionnaires were returned, there was 55 processable questionnaires, while the remaining 5 questionnaires which were not processable. The model is analyzed utilizing the SmartPLS 3.0 program at a 5 percent significant level. In the test using the Partial Least Square (PLS) variance, the inner and outer model test is conducted. The inner model is the relationship between the variables in the research model. The model developed identifies the research variables. The indicators of each variable can be seen in
Research Variables and Indicators
NO | CONSTRUCT | INDICATOR | CODE | REFERENCE | |
1. | SYSTEM QUALITY | System flexibility | X1.1 | (
|
|
System integration | X1.2 | ||||
Comfort in access | X1.3 | ||||
Language | X1.4 | ||||
2. | INFORMATION QUALITY | Completeness | X2.1 | (
|
|
Accuracy | X2.2 | ||||
Reliability | X2.3 | ||||
Output form | X2.4 | ||||
3. | SYSTEM IMPORTANCE | Sense of ownership | X3.1 | (
|
|
Interested to work on it | X3.2 | ||||
Develop abilities | X3.3 | ||||
Enhances confidence | X3.4 | ||||
Important to be used | X3.5 | ||||
4. | USE | Usage frequency | Y1.1 | (
|
|
System selection | Y1.2 | ||||
Time duration of usage | Y1.3 | ||||
5. | USER SATISFACTION | Satisfaction towards System and information quality | Y2.1 | (
|
|
Satisfaction towards the system facility and feature | Y2.2 | ||||
6. | NET BENEFIT | Productivity | Y3.1 | DeLone and McLean (
|
|
Effectivity | Y3.2 | ||||
Improvement in work development | Y3.3 |
Discussion of the Research Results
There were 60 questionnaires distributed to the sampled private universities. 5 questionnaires were not returned, thus, there are 55
questionnaires collected and computed. The descriptive statistics results in this research can be seen in
Descriptive Statistics Test Results
Variable | N | Min | Max | Mean | Range | Three box average method |
KS (X1) | 55 | 9 | 20 | 15.8 | 11 | 43.5 |
KI (X2) | 55 | 12 | 20 | 15.8 | 8 | 43.5 |
PS (X3) | 55 | 15 | 25 | 20.2 | 10 | 44.4 |
P (Y1) | 55 | 8 | 15 | 11.7 | 7 | 43.1 |
KP (Y2) | 55 | 4 | 10 | 7.7 | 6 | 42.1 |
NB (Y3) | 55 | 8 | 15 | 12.1 | 7 | 44.2 |
Based on
variable, using the three box method, is categorized as high.
The inner model or the structural model evaluation is conducted by assessing the relationship between variables, significance value, and R-square of the research model. The inner model test results is able to assess the relationship between constructs by comparing the significance value with the R-Square of the research model (Ghozali, 2015:42). The structural model is shown in
R-Square
Variable | R-Square |
Usage | 0.723 |
User Satisfaction | 0.762 |
Net Benefit | 0.641 |
The higher the R-Square value is, the greater the ability of the exogenous variable in explaining the endogenous variable, and the better the structural equation. Evaluation is subsequently conducted to calculate the Q-square predictive relevance, as follows:
Q
The Q2 value is above zero which means that the model designed has predictive relevance (
Output Bootstrap Results
Construct | Hypotheses | Original Sample (o) | Stand. Deviasi | T- statistics | P-value | Description | |
System Quality (KS) | H1 | KS -> P | 0.297 | 0.091 | 3.274 | 0.001 | Accepted |
H2 | KS -> KP | 0.192 | 0.097 | 1.993 | 0.047 | Accepted | |
Information Quality (KI) | H3 | KI -> P | 0.279 | 0.104 | 2.696 | 0.007 | Accepted |
H4 | KI -> KP | 0.257 | 0.113 | 2.268 | 0.024 | Accepted | |
System Importance (PS) | H5 | PS -> P | 0.404 | 0.101 | 4.001 | 0.000 | Accepted |
H6 | PS -> KP | 0.226 | 0.112 | 2.021 | 0.044 | Accepted | |
Usage (P) | H7 | KP -> P | 0.331 | 0.143 | 2.306 | 0.022 | Accepted |
H8 | P -> KP | 0.319 | 0.142 | 3.060 | 0.002 | Accepted | |
User Satisfaction (KP) | H9 | P -> NB | 0.474 | 0.155 | 2.021 | 0.044 | Accepted |
H10 | KP -> NB | 0.366 | 0.170 | 2.154 | 0.032 | Accepted |
It can be seen from
The test results provide empirical evidence that H1 is supported with a positive coefficient. This means that with a higher system quality, there will be an increase in the usage of the system. The implementation of AIS can increase the intention of users to use and recommend the AIS for the accounting process of PU in Bali. This research result is consistent with previous studies conducted by (
The test results provide empirical evidence which showed that H2 is supported with a positive coefficient. This means that system quality has an influence on the satisfaction of users, because the system has characteristics which meets user expectations and the usage increases the satisfaction of users. This research result is consistent with the research conducted by (DeLone & McLean, 1992:2003), (
Empirically, the system has given satisfaction to the AIS users and operators in performing their tasks. However, the features in AIS can be developed to minimize repeated posting process.
Descriptive Statistics of the System Quality Construct
Indicator | Percentage of Respondent’s Answer | Total * | Index ** | Category | ||||
1 | 2 | 3 | 4 | 5 | ||||
X1.1 | 0.00% | 1.86% | 20.93% | 44.65% | 32.56% | 215 | 43 | High |
X1.2 | 0.47% | 0.93% | 18.14% | 50.23% | 30.23% | 215 | 43 | High |
X1.3 | 0.00% | 0.92% | 19.35% | 49.77% | 29.95% | 217 | 43.4 | High |
X1.4 | 0.00% | 0.00% | 18.92% | 45.05% | 36.04% | 222 | 44.4 | High |
Total score | 173.8 | High | ||||||
Average | 43.45 |
Source: Computed data 2018
The quality construct is measured using
Information quality has an influence on the use and user
Satisfaction.
The test results provide empirical evidence which shows that H3 is supported. This means that information quality has a significant influence on AIS users. This empirical evidence has an implication in which with the high information quality, system users feel that the output or accounting report of the institution still must be developed, so that it can be easily understood and is in line with the institution’s needs. This empirical evidence is consistent with the research by (DeLone & McLean, 1992: 2003), (
The test results provide empirical evidence that H4 is supported by a positive coefficient. This means that the increase in information quality will influence the satisfaction of users. In other words, information quality has a positive relationship with the satisfaction of its users. The high quality of information produced by system users that are satisfied with the AIS application is able to provide accurate information for the institution’s accounting process. This research result is consistent with the research conducted by (
The Descriptive Statistics of the Information Quality Construct
Indicator | Percentage of Respondent’s Answer | Total * | Index ** | Category | ||||
1 | 2 | 3 | 4 | 5 | ||||
X2.1 | 0.00% | 0.00% | 19.35% | 55.30% | 25.35% | 217 | 43.4 | High |
X2.2 | 0.00% | 0.00% | 19.18% | 51.14% | 29.68% | 219 | 43.8 | High |
X2.3 | 0.00% | 0.92% | 17.97% | 53.46% | 27.65% | 217 | 43.4 | High |
X2.4 | 0.00% | 0.92% | 16.51% | 55.05% | 27.52% | 218 | 43.6 | High |
Total score | 174.2 | High | ||||||
Average | 43.55 |
Source: Computed data 2018
The quality construct is measured using
3. System importance has an influence on use and user satisfaction
The test results provide empirical evidence that H5 is supported with a positive coefficient. This means that an increase in system importance influences the usage. The implication of the empirical evidence is that if the system users perceive that the information system quality is good, the usefulness of the system will be high. The system usage has an influence on the dependency level of the system user. This research result is consistent with the research conducted by (
The test results provide empirical evidence which shows that H6 is supported with a positive coefficient. This means that with a higher system importance, there will be an influence on the user satisfaction. The empirical evidence has implications in which an increase in the system importance will have an influence on the level of user satisfaction. The benefit attained by operators and the users of the AIS will increase their satisfaction and the dependency of the user towards the system. This research result is consistent with the studies by (
Descriptive Statistics of the System Importance Construct
Indicator | Percentage of the Respondent’s Answers | Total * | Index ** | Category | ||||
1 | 2 | 3 | 4 | 5 | ||||
X3.1 | 0.00% | 0.93% | 23.61% | 40.74% | 34.72% | 216 | 43.2 | High |
X3.2 | 0.00% | 0.00% | 15.79% | 40.35% | 43.86% | 228 | 45.6 | High |
X3.3 | 0.00% | 0.93% | 20.83% | 48.15% | 30.09% | 216 | 43.2 | High |
X3.4 | 0.00% | 0.92% | 22.02% | 40.37% | 36.70% | 218 | 43.6 | High |
X3.5 | 0.00% | 0.00% | 18.18% | 27.71% | 54.11% | 231 | 46.2 | High |
Total score | 221.8 | High | ||||||
Average | 44.36 |
Source: Computed data, 2018
The system importance construct is measured with
4. Use has an influence on the users satisfaction
The test results provide empirical evidence that H7 is supported with a positive coefficient. This shows that usage has an influence on the satisfaction of users. The empirical evidence has implications in which an increase in usage entails an increase in the satisfaction of users. AIS can be useful for its users, and users will feel satisfied, as it is expected to be. This research result is in line with Delone and McLean’s theory of information system success which states that an increase in information system usage will increase the satisfaction of users. This research result is consistent with the study conducted by (
5. User satisfaction has an influence on use
The research results provide empirical evidence which shows that H8 is supported with a positive coefficient. This means that with a higher level of user satisfaction, there will be a higher level of usage. This empirical evidence has an implication in which an increase in user satisfaction will influence the usage, the more the AIS user feels that they gain decent benefit from the usage of the system in finishing their work, the less reluctant the user will be in reusing. Thus, the usage intensity of the system will increase. This result is in line with what was stated by (
The Descriptive Statistics of the Usage Construct
Indicator | Percentage of Respondent’s Answers | Total * | Index ** | Category | ||||
1 | 2 | 3 | 4 | 5 | ||||
Y1.1 | 0.00% | 0.00% | 28.17% | 41.31% | 30.52% | 213 | 42.6 | High |
Y1.2 | 0.00% | 3.69% | 17.97% | 36.87% | 41.47% | 217 | 43.4 | High |
Y1.3 | 0.00% | 1.85% | 25.00% | 31.48% | 41.67% | 216 | 43.2 | High |
Total score | 129.2 | High | ||||||
Average | 43.1 |
Source: Computed data, 2018
The construct is measured using three indicators, namely, usage frequency Y1.1, system selection Y1.
their response to item 2, system usage, and item
3, time duration of usage.
Viewed from the score value comparison of each indicator in
Descriptive Statistics of the User Satisfaction Construct
Indicator | Percentage of Respondent’s Answers | Total * | Index ** | Category | ||||
1 | 2 | 3 | 4 | 5 | ||||
Y2.1 | 0.00% | 4.74% | 22.75% | 32.23% | 40.28% | 211 | 42.2 | High |
Y2.2 | 0.00% | 4.76% | 21.43% | 38.10% | 35.71% | 210 | 42 | High |
Total score | 84.2 | High | ||||||
Average | 42.1 |
Source: Computed data 2018
The construct is measured using
6. Use has an influence on net benefit (SI)
The test results provide empirical evidence which shows that H9¬ is supported with a positive coefficient. This means that system usage has an influence on the net benefit. The existence of AIS becomes a positive stimulus and challenge for individuals in the organization to work better, which in turn will affect the organizational performance. This reaction can be in the form of new motivation to compete and increase performance. (
7. User satisfaction has an influence on the net benefit (SI)
The test results provide empirical evidence which showed that H10 is supported with a positive coefficient. This means that user satisfaction has an influence on the net benefit (SI). The empirical evidence shows that AIS user satisfaction is a dominant construct in explaining the net benefit of the AIS. This is consistent with (
The Descriptive Statistics of the Net Benefit Construct
Indicator | Percentage of the Respondent’s Answer | Total * | Index ** | Category | ||||
1 | 2 | 3 | 4 | 5 | ||||
Y3.1 | 0.00% | 0.00% | 24.77% | 38.53% | 36.70% | 218 | 43.6 | High |
Y3.2 | 0.00% | 0.89% | 18.67% | 33.78% | 46.67% | 225 | 45 | High |
Y3.3 | 0.00% | 0.00% | 25.91% | 30.91% | 43.18% | 220 | 44 | High |
Total score | 132.6 | High | ||||||
Average | 44.2 |
Source: Computed data 2018
The construct is measured with
Based on the empirical test results and discussions, it can be concluded that this research is able provide evidence that system quality, information quality and the importance of the system has an influence on the usage and satisfaction of AIS users, which afterward influences the net benefit of the PU. The better or greater the system quality, information quality, and the importance of the system, the higher the usage and satisfaction of the AIS users, which will provide better net benefit for the PU.
Based on this research result, some implications for the PU in Bali are: first, the systems should be developed in line with the needs of the PU to increase the efficiency of the system process through data integration so that there are less multiple posts of the same data. The information produced by the AIS application is expected to be easily comprehended, relevant, reliable, and comparable. Second, this research result revealed that most accounting information system users in private universities highly perceive that the system is important to be used in finishing their accounting tasks. Accounting information system is greatly needed and can be recommended to private universities. Third, the results attained from the user satisfaction construct shows that the system selection indicator and the satisfaction towards system facility and feature indicator have a very small difference in value. Therefore for future research, other information systems such as SIAK and online KRS can be used. Furthermore, other information system success models can be utilized such as TAM and UTAUT.
Delone, W., & McLean, E. (
Floropoulos, J., Spathis, C., Halvatzis, D., & Tsipouridou, M. (
Groho, Winarno, & Permanasari. (
Hussein, R., Karim, N. S. A., & Selamat, M. H. (
Istianingsih, & Wijanto. (
Laksana, Subroto, & Baridwan. (
Latifa. (
Livari, J. (
Muharor, L. afghan, Busaini, & Fitriah, N. (
Noviyanti. (
Purwanto, & Suharno. (
Radityo, & Zulaikha. (
Rai, A., Lang, S. S., & Welker, R. B. (
Seddon, & Kiew. (
Sudarmadi. (
Tan, Suyatno, & Aliyah. (
Urbach, N., & Müller, B. (
Wahyuni. (
Wang, & Liao. (
Wu, J. H., & Wang, Y. M. (
DeLone, W. H., & Mclean, E. R. (1992). Information Systems Success : The Quest for the Dependent Variable Author ( s ): William H . DeLone and Ephraim R . McLean Published by : INFORMS Stable URL : http://www.jstor.org/stable/23010781 REFERENCES Linked references are available on JSTOR for thi.
DeLone, & McLean. (1992). Information systems success: The quest for the dependent variable.
Darmawan. (2010).
Budiyanto. (2009). Dengan Pendekatan Model Delone dan MClean ( Studi Kasus Implementasi Billing System Di RSUD Kabupaten Sragen).
Azwar, Amriani, & Subekan. (2016). Evaluasi Atas Implementasi Aplikasi Sistem Akuntansi Instansi Basis Akrual ( SAIBA ) Dengan Pendekatan Delone & MClean Information System Succes Model ( Studi Kasus Mitra Kerja KPPN Gorontalo Dan Marisa ).
Al-Hiyari, Al-Mashregy, & Mat, N. (2013). Factors that Affect Accounting Information System Implementation and Accounting Information Quality: A Survey in University Utara Malaysia.
ADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY Al-adaileh. (2009). An Evaluation of Information Systems Success : A User Perspective - the Case of Jordan Telecom Group.