* Corresponding Author: [email protected]
A Study of Efficacy of Information Systems and Performance
Accountability in Governmental Agency
HAMZAH RITCHI*
Faculty of Economics and Business, Universitas Padjadjaran, Indonesia ANDI IRAWAN
Finance and Development Supervisory Agency, Indonesia ZALDY ADRIANTO
Faculty of Economics and Business, Universitas Padjadjaran, Indonesia WINA APRILIANISA
Faculty of Economics and Business, Universitas Padjadjaran, Indonesia
Abstract: This study aims at determining the efficacy of adopting the information system (IS) on performance accountability in the Indonesian government based on DeLone McLean's success model. Based on performance accountability reports being lodged by 77 ministries and institutions, 41 units responded. One hundred sixty-four respondents completed the questionnaire using purposive sampling, filling up two proportionate categories of independent and dependent variables. Path analysis was employed for the data analysis. The results confirm that quality elements as the exogenous factor, individually and together, significantly affect governmental performance accountability, satisfying the respected hypotheses. IS quality also shows a significant effect on information quality.
Keywords: Information System Quality, Information Quality, System Service Quality, Performance Accountability
Abstrak: Penelitian ini bertujuan untuk mengetahui efektivitas penerapan sistem informasi (SI) terhadap akuntabilitas kinerja di pemerintahan Indonesia berdasarkan model sukses DeLone McLean. Berdasarkan laporan akuntabilitas kinerja yang disampaikan oleh 77 kementerian dan lembaga, 41 unit telah merespon. 164 responden mengisi kuesioner dengan menggunakan purposive sampling, mengisi dua kategori proporsional dari variabel independen dan dependen. Analisis jalur digunakan untuk analisis data. Hasil tersebut mengkonfirmasi bahwa elemen kualitas sebagai faktor eksogen, secara individu dan bersama-sama secara signifikan mempengaruhi akuntabilitas kinerja pemerintah, memenuhi hipotesis yang dianut. Kualitas SI juga menunjukkan pengaruh yang signifikan terhadap kualitas informasi.
Kata Kunci: Kualitas Sistem Informasi, Kualitas Informasi, Kualitas Sistem Layanan, Akuntabilitas Kinerja
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1. Introduction
From the perspective of good government governance, accountability is not limited to preparing government organizations' financial statements and operational budgets.
Accountability is required for benefit evaluation of what activities have been held, especially those directly affecting the government organization and indirectly impacting the improvement of the welfare of society in general (Khairunsyah and Efni, 2018). It is the public's expectation of the governmental bodies toward the ideal condition to maximize benefits and create people's prosperity (Afriyanti et al., 2015).
Klitgaard (1998) and Faiki (2020) stated the equation "C = M + D - A", (C = Corruption, M = Monopoly, D = Discretion, A = Accountability), explainingcorruption as the function of an official's absolute power (monopoly), big discretion (discretion), and the diminishing level of accountability. Transparency International (2015) found that the Indonesian corruption case position was number 88 with a corruption perception index (CPI) of 3,6 (scale of 1-10), indicating a higher corruption acuteness in Indonesia. The country was still below the average of most countries in Southeast Asia. This finding illustrates a strong linkage between corruption and performance accountability, given the low CPI.
According to WEF (2015, 2016), Indonesia dropped dramatically from rank 37 to rank 41. The drop reveals the need to improve performance accountability in Indonesia (Asmawanti et al., 2020). Khairunsyah and Efni (2018) stated that the performance accountability of government agencies is among the strategic policy issues in Indonesia, as improving the performance accountability of government agencies lends an impact to performing good governance. However, a recent report regrettably showed that there were only 32% of the 77 Ministries/Institutions being observed obtained the category
"A" and "BB", whereas the remaining 68% comprised predicates "B" and "CC". In the last five years, the acquisition of LAKIP for the Ministry/Institution score for "A" rank was only 2.60% to 6.49% (KemenPAN-RB, 2015). This finding shows that the actual accountability performance of government agencies was still below the expected ideal
367 condition, calling for further examination of factors determining the solvent performance accountability.
Another indicator from the Global Competitive Index is concerned with the availability of technology. Indonesia has not been ready regarding these technological aspects, as shown by WEF (2015), its rank number 91 (down six ranks) from 85, with a score of 3.5. Davis et al. (1989) state that organizational performance would not develop without fusing suitable computer systems (technology/information systems) with the running bureaucracy business process. Al-Debei and Al-Lozi (2013) and Liu et al. (2009), whose discussion focused on information systems/portals, suggest that in developing countries, portals are the most promising services, even though the performance is relatively small. Furthermore, the proper strategic balance between the capability of technology and the government goals is imperative for the successful performance of e-government, as the case in northern India (Dahiya and Mathew, 2016) and the quality of Government Information System in Indonesia (Berlilana Et al., 2017).
Previous research has sought aspects to explain ex-ante attributes that determine performance accountability. However, there is a lack of analysis within the information systems dynamics that contribute to performance accountability. A recent study proposed compliance with legislation, the competence of human resources, and clarity of budget as factors affecting performance accountability reports of government agencies (Eprianto, 2023). Additionally, built around institutional theory, Han (2020) stated that deficits in information provision, assessment, and consequence can relatively affect the success or failure of federal agency performance accountability. In light of the information provision and assessment, Yuesti et al. (2022) found the positive effect of conforming to government accounting standards, adopting government information systems, and complying with regulations regarding the quality of financial statements and performance of local governments. Given the relative void in the efficacy of information system quality towards the quality of performance accountability, more studies to unveil the factors should be warranted.
The efficacy of an information system (e.g., quality, affordance, acceptance) has been of enduring interest to the information system community over several decades.
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DeLone and McLean's (1992, 2003) model is arguably the most extensive of the commonly adopted frameworks that explain an information system's efficacy. Many adaptations and extensions were carried out using constructs similar or associated with the model for independent purposes (e.g., task-technology fit or self-efficacy) or reaffirming in different contexts (Jeyaraj, 2020). The success model stated that the representation of the success model of an information system could be seen from the classification of six main variables: System Quality, Information Quality, Use, User Satisfaction, Individual Impact, and Organizational Impact. DeLone and McLean (2003) refined the instruments by extending more constructs with Service Quality, Intention to Use, and Net Benefit on the equation (hereafter called DMIS).
From the discussion above, the authors strive to examine how the efficacy of the information systems elements - the system's quality, information quality, and service quality would impact organizational performance accountability as the embodiment of organization benefit/net benefit. Despite the importance of attaining a successful information system and service to support government performance accountability, there is little published research on performance accountability at Ministries and Agencies in Indonesia. IS successfulness measurement model proposed by DeLone and Mclean (1992, 2003, 2016) would be acceptable given its long-standing substantiation.
The following sections will describe the theoretical foundation and the formulation of hypotheses. Next, the method by which the research will be conducted is discussed, continued with the discussion of the result. The section conclusion will summarize the overall study.
2. Theoretical Framework and Hypothesis Development 2.1. Performance Accountability
Accountability is the obligation to answer and explain the performance and actions of a person/group of an organization to those who have the right or authority to request information or accountability (Muchlis et al., 2019). According to Turner and Hulme (1997), accountability is a complex concept that is more difficult to apply than eradicate corruption. Accountability is accountability for the actions of public officials.
369 Implementing its current governance involves effective accounting and auditing, decentralization within the micro accountability level, consumers, and non- governmental organizations who play significant roles. Hughes (2003) argues that accountability is necessary when there is a hierarchical relationship or relationship between the principal and agent to ensure that the authority acts on purpose. Thus, every government policy must be widely approved by the community. Accountability is the public accountability of every activity undertaken. Nasution and Atika (2019) define accountability as the obligation of the agent to provide accountability, presenting, reporting, and disclosing all activities and obligated activities to the principal, who has the right and authority to hold such accountability. The form of the dimension of public accountability by the government is presented by Ellwood (1993) with dimensions of the measurement of performance accountability variables, namely probity and legality, process, program, and policy.
Performance accountability management in Indonesia has been orchestrated by several central government bodies whose responsibility is complementary to one another authorized institutions. The Audit Board of the Republic of Indonesia, or BPK- RI, assesses the accountability opinion of financial statements of each Government agency. The Ministry of Administrative and Bureaucratic Reform or KemenPAN-RB evaluates the performance of government agencies in the form of The Performance Accountability Report of Government Agencies or LAKIP. The Corruption Eradication Commission, or KPK, provides an Anti-Corruption Initiative Assessment or PIAK.
Lastly, the Ministry of Internal Affairs, or MoIA, conducts performance assessments for local governments. Each of these institutions is supported by strong regulations so that each government agency in both the central government and local government regularly presents the performance information about their achievements in the form/format of reports required according to the rules during one year of the current budget (Afriyanti et al., 2015).
Building on agency conceptualization, the complex relationship between various government agencies, specifically between superiors and subordinates, can be explained (Lupia, 2001; Mulgan, 2000; Shi and Svensson, 2002). Arifin (2021) argues
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that the legislature and the executive (government), as community agents, must maintain accountability for their performance in society.
Information systems in the business world are imperative for the success and growth of the company. It also plays a vital role in administering the company managerial (Latchu and Singh, 2022). The existence of a company can lead to agency costs in which the company's ownership is separated from its management. Government performance accountability is closely related to the government as an executive, functioning as an agent who must account for the budget and its performance to the community/people in this matter, represented by the legislative body as the principal.
2.2. D&M Information System Success Model
DMIS Success Model gets many responses, and it is one of the most cited studies on measuring the success of information systems because the model is considered quite simple but valid enough because a good model is complete but simple (Marianana, 2006). Variable DMIS Success Model by DeLone and McLean (1992) consists of system quality, information quality, user satisfaction, use, individual impact, and organizational impact; these dimensions affect each other. Tate et al. (2011) explain that the work of DeLone and McLean (2003) evaluates the success of the information system model as initially introduced by DeLone and McLean (1992). It then comes with adding categories, breakdown categories, and simplified categories. The addition is Service quality, while the use here is specified as the purpose of using Usage Intentions and System use, simplifying individual and organizational impacts into Net Benefits, eventually known as the DMIS Success Model.
2.3. Information System Quality
Detailed information systems are outlined by Romney et al. (2012), which explain that the system is a set of two or more interconnected components that interact to achieve a goal. While Information is data that has been compiled and processed to provide meaning and improve the decision-making process. Data is a collection of facts collected, recorded, stored, and processed by information systems. Businesses need to collect some types of data, such as ongoing activities, activity-affected resources, and
371 people participating in activities. There are three stages for the model of information system components: Raw data and/or stored data, which serves as the input, Process:
process data, and Information as the result/output. Thus, the information system is a solution to overcome the limitations of human memory to store and manage the amount of Information that can be absorbed/accepted and thought by humans. While the quality of information systems, according to DeLone and McLean (1992), is a desirable characteristic of the information system, which is a system that can produce Information. Thus, the quality of Information is the output generated by the information system.
2.4. Information Quality
Romney et al. (2012) state that Information is the data managed and processed to be submitted to users who need it; it is useful for decision/policy makers for achieving organizational goals. Whereas from the point of view of the profit-oriented organization in question, the Information that has value is the advantage of processing it if it has reduced the cost incurred to process it. The quality of Information is the assessment of the citizens of the Information on the website from the accuracy, validity, and punctuality (Teo et al., 2008). Petter et al. (2008) state that information quality is a characteristic of expected system output, including management reports and web pages.
2.5. System Service Quality
Pitt et al. (1995) suggest the quality-of-service system is an instrument developed from the scope of marketing with the five dimensions of System Service Quality:
tangibles, reliability, responsiveness, assurance, and empathy. Kettinger and Lee (1995) complement it with indicators: tangibles include physical facilities, equipment, and personal appearance; reliability includes the ability of services performed reliably and accurately; responsiveness is the ability of the reaction include the willingness to help customers and provide fast service; assurance includes knowledge of employee politeness and the ability to convince and inspire; empathy includes caring and giving individual attention to the service provider. The research results become the reference for DeLone and McLean (2003), simplifying indicators of service quality instruments consisting only of assurance, empathy, and responsiveness. Thus, DeLone and McLean
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(2016) state that the quality-of-service system is the quality of support received by system users from information systems and IT support units with five indicators adopted from system service quality (Pitt et al., 1995).
2.6. Hypothesis Development
Gorla et al. (2010) believe that the integrated system provides complete and accurate information, so the information output is useful for daily work and relevant for decision-making. Therefore, high information content is relevant for decision-making, so the quality of the information-feeding system will produce useful Information to support decision-making. The quality of information systems positively and significantly influences the performance accountability mediated by the quality of Information. Suryanto et al. (2016) state the hypothesis to prove a positive relationship between information systems' quality and information quality of 1.17. The quality of Information is related to the quality of the executive information system output (EIS) with the relevance, punctuality, and accuracy dimensions of Information generated by the EIS (Marianana, 2006). Based on the consideration above, H1 is proposed:
H1: The quality of information systems positively affects the quality of Information The literature shows differences of opinion on the construct definition of net benefit, one of the variables of the success model of Information Systems DMIS (Al- Debei et al., 2013). According to Petter et al. (2008), the net benefit is a capable and successful system to make good contributions to individuals, groups, organizations, and state industries. The two models of the I/S measurement success according to DMIS is linked to the quality of the system with organizational impact (DeLone and McLean, 1992) and net benefit (DeLone and McLean, 2003), information system quality affecting organizational performance is expressed by more than ten research which concludes that there is a significant relationship and/or correlation between system usage and Net Benefit/impact on the organization (DeLone and McLean, 2003).
Implementing the ERP System positively affects organizational performance, and intensive implementation occasionally makes big/small and good impacts (Gavrea et al., 2011). However, some research has found no relationship between information
373 system quality and organizational impact (Liu et al., 2020; Iivari, 2002; Wu and Wang, 2006). For that reason, the hypothesis is:
H2: The quality of information systems has a positive effect on performance accountability
Organizational impact is defined as the effect of Information on organizational performance. Some other researchers also focus more on the quality of information systems output and quality information, which is considered a capable system of producing reports (DeLone and McLean, 1992). Gorla et al. (2010) examine the dimensions of information quality and its impact on the organization. The authors view that the more quality information presented in the report can improve the accountability of organizational performance. This leads to hypothesis three. We formulate the hypothesis as follows:
H3: Information Quality has a positive effect on performance accountability
According to Liu et al. (2009), the Internet portal owned by Yahoo, which was phenomenal enough with its inbox message (e-mail), search engine, and equipped with information/world news, has now faded its performance along with the emergence of Google with its simple and powerful search engine, since Google does not include any display that is usually slowing the display. Hence, users are more interested in using it, and can grow and even dominate the business world. Until now, Google has penetrated the operating system for mobile phones/gadgets, which beats Windows, Apple, and Java. Ticketing information system applied by PT. Kereta Api Indonesia (Persero) can improve the performance of excellent service quality (Septianita et al., 2014). The interaction between government and society in implementing governmental and developmental tasks requires an optimal, effective, and efficient public service system (Rehman et al., 2011). The proposed hypothesis for this analysis is:
H4: Quality of system service has a positive effect on performance accountability The influence of the three variables (system quality, information quality, and service quality) on the accountability of organizational performance, if checked against DMIS view, can be observed both directly or indirectly through the notions of
“organizational impact” (DeLone and McLean, 1992) and “net benefit” (DeLone and
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McLean, 2003). One includes researching IS Success DMIS models by Gorla et al.
(2010), that the overall dimensions of system quality, information quality, and service quality have significant positive leverage on organizational impact. The authors thus propose this hypothesis:
H5: Information System Quality, Information Quality, and Quality of System Service show a positive effect on performance accountability
Figure 1.
The conceptual framework of this study
3. Research Method
The unit of analysis in this study is The Ministry/Institution, with 77 populations.
The data was based on a performance accountability report by The Ministry of PAN- RB Ministries/Institutions. The researchers selected a purposive sampling method for the sample, resulting in 41 Ministries/Institutions as the unit of analysis. By assigning several Ministries/Institutions, it represents the predicate of LAKIP, the predicate "A"
of four Ministries/Institutions, 11 Ministries/Institutions for the predicate "BB", 13 Ministries/Institutions for predicate "B," and 13 Ministries/Institution for predicate "C".
Each Ministry/Institution was represented by four respondents, comprising two independent variables (X) and two dependent variables (Y). This resulted in 164 respondents spanning across all types of performance accountability predicates.
The independent variable consists of information system quality (X1), information quality (X2), and service quality of the system (X3). Performance Accountability serves
Information Quality (X2) Performance Accountability (Y)
System Service Quality (X3) Information System Quality
(X1) Ƹ
H1
H2
H3
H4
H5
375 as the dependent variable (Y). Information system quality variables were measured using DMIS's (1992,2003) information system success models covering five dimensions, including easy to use (Al-Debei et al., 2013; Davis et al., 1989; Petter et al., 2008; Urbach et al. 2012, 2010), flexibility (Gorla et al., 2010; Urbach et al., 2012), the response time (DeLone and McLean,2003; Petter et al., 2008; Urbach et al., 2012), reliability (DeLone and McLean, 2003; Petter et al., 2008; Urbach et al., 2012) and Security (Al-Debei et al., 2013).
Information quality variables were measured using DMIS's success information system model (1992,2003) including four dimensions of accuracy (DeLone and McLean, 1992; Petter et al., 2008; Urbach et al., 2012), time series (Petter et al., 2008;
Urbach et al., 2012), thorough (DeLone and McLean, 2003; Gorla et al., 2010; Petter et al., 2008; Urbach et al., 2012, 2010) and Format (Gorla Et al., 2010; Urbach et al., 2012).
The System Service Quality variable was measured using DMIS's success information system model (1992, 2003, 2016) and based on research by Kettinger and Lee (1995), Pitt et al. (1995), Dyke et al. (1997), Xie and Goh (1998), Liu et al. (2009), and DeLone and McLean (2003, 2016) include five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The Independent variable in this research is the performance accountability variable. This variable was developed by Ellwood (1993) and was measured by four dimensions, namely law and honesty, processes, programs, and policies. Path analysis was used as the data analysis technique following Schumacker and Lomax's Mediated Path Model (1996: 41-42) with the help of SPSS 22.0 software.
4. Results and Discussion 4.1. Data Collection Results
Based on the results of questionnaires distributed to structural and internal audit auditors, there are respondents as dependent variable (performance accountability) and managers of financial data/performance as user/admin information system planning and evaluation, user information, and user service system of two respondents as the
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independent variable of each Ministry/Institution, the authors show the characteristic data as follows:
Table 1
Table of Demographic Characteristics of Respondents
Classification Respondent Criteria Frequency (%)
Age X 21-35 43 52%
36-45 25 30%
>46 14 17%
Y <20 1 1%
21-35 37 45%
36-46 33 40%
>46 13 13%
Gender X Woman 36 45%
Man 46 56%
Y Woman 32 44%
Man 40 56%
Job Period X < 5 years 12 15%
6-10 years 40 49%
11-20 years 15 18%
>21 years 15 18%
Y <5 years 12 15%
6-10 years 38 46%
11-20 years 21 26%
>21 years 11 13%
Title X D3 7 9%
S1 50 61%
S2 23 28%
S3 2 2%
Y D3 7 9%
S1 40 49%
S2 31 38%
S3 4 5%
X: Respondent represents Independent variable Y: Respondent represents the Dependent variable
377 4.2. Descriptive Statistics Analysis Results
To see respondents' responses to each item submitted in the questionnaire, the writer conducted a descriptive analysis to interpret the result. The weight of the questions shows the total number of question items multiplied by the total respondents to get Information about the proportion of questions of each variable. The response for each item's question is called an actual score, multiplying the chosen score by the total number of respondents. For each variable, the maximum score that can be obtained is called the Ideal Score, a cumulative score from the total item's question multiplied by the optimal score "5" and the Total respondent. The average score is calculated by dividing the actual score with the weight. Finally, the percentage of the actual and ideal scores shows that all criteria are fulfilled, and all variables have been sufficiently fulfilled.
Table 2
Descriptive Analysis
No Variable Weight Actual Score
Ideal
Score Average % Criteria
1 Information System Quality
1.148 4.155 5.740 3,60 71,95 Highly Sufficient
2 Information Quality
656 2.358 3.280 3,59 71,89 Highly
Sufficient
3 Service Quality System
1.148 4.085 5.740 3,55 71,04 Highly Sufficient
4 Performance Accountability
1.148 4.212 5.740 3,67 73,48 Highly Sufficient
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4.3. Statistical Hypotheses Test Results 4.3.1. Construct validity
Validity Test with the correlation coefficient value, its tested item statements must be greater than the critical value of 0.3 (>0.3); the test results of the questionnaire validity for the variables studied are presented in Appendix A. For construct reliability in this research model, the value of Composite reliability and Cronbach's Alpha had values above 0.6, presented in Appendix B. So; it can be concluded that the research model has been reliable or has met the reliability test.
4.3.2. Correlation Coefficient
This correlation coefficient indicates a positive and negative relationship (strong and weak) in determining the value of the correlation coefficient and the significance given by the independent variable to the dependent variable. Results of data processing with SPSS 22.0 are presented in the following table:
Table 3
Partial test (t-test)
(X1) (X2) (X3) (Y)
X1 Pearson Correlation 1 0,877** 0,934** 0,904**
Sig. (1-tailed) 0,000 0,000 0,000
N 82 82 82 82
X2 Pearson Correlation 0,877** 1 0,890** 0,901**
Sig. (1-tailed) 0,000 0,000 0,000
N 82 82 82 82
X3 Pearson Correlation 0,934** 0,890** 1 0,909**
Sig. (1-tailed) 0,000 0,000 0,000
N 82 82 82 82
Y Pearson Correlation 0,904** 0,901** 0,909** 1
Sig. (1-tailed) 0,000 0,000 0,000
N 82 82 82 82
379 We conducted a partial test to determine whether each independent variable of System Efficacy affects the dependent variable of Performance Accountability. Based on the table above, it is known that the significance value of the variable X1 with X2 is 0,000 <0.05, which means there is a significant correlation or with the coefficient of 0.877 included in the category of a very strong relationship because it resides in the correlation interval of 0.800-1000. Variable X1 with Y significance value 0,000 < 0.05 means a significant correlation of 0.904 is included in the category of a very strong relationship. Variable X2 with a Y significance value of 0,000 <0.05, indicating a significant correlation or with a coefficient of 0.901, belongs to the category of a very strong relationship. Variable X3 with a Y significance value of 0,000 <0.05 means there is a significant correlation or with the coefficient of 0.909 included in the category of a very strong relationship.
The simultaneous test shows that the value of F count is greater than Ftable
(185,668> 2,722) with a significance level of <0,05 which is 0.000. In conclusion, the independent variables simultaneously significantly affect the bound variable.
4.3.3. Path Analysis Coefficient
The coefficient of path analysis is the number that indicates the summary of data processing results using SPSS 22.0, presented in the following table:
Table 4
Path Analysis Coefficients
Standardized Coefficients (Beta) X1
X2
X3
0,288 0,376 0,306
The table above provides Information about the path coefficient for the quality of the information system (ρyX1) is 0,288, the quality of Information (ρyX2) is 0,376, and the System Service Quality (ρyX3) is 0,306.
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4.3.4. Coefficient of Determination (𝑅2)
The coefficient of determination is a number that indicates the magnitude of the influence contribution given by the independent variable to the dependent variable.
Summary of data processing results using SPSS 22.0, presented in the following table:
Table 5
Coefficient of Determination
Var Coefisien Direct (%)
In Direct
(%)
Total
% 𝜌- value X1 X2 X3
X1 0,288 8,29 - 9,51 8,22 17,73 26,02 0,015 X2 0,376 14,17 9,51 - 10,23 19,74 33,11 0,000 X3 0,306 9,33 8,22 10,23 - 18,45 27,78 0,014
Total Influence (R2) 87,72
The table above describes that the quality of information systems contributes an influence of 26.02%, the quality of Information contributes an influence of 32.11%, and the quality of service contributes an influence of 27.78% so that the total effect given by all three is equal to 87.72%.
4.4. Evaluation of Significance Relationships
The hypothesis needs to be tested to test the significance or significant influence that occurs. The statistical method used to test this partial hypothesis is Test-T. The Ttable value used as the critical value in this partial hypothesis test is 1.991, the recommended critical number for the test.
From the table 6, it can be seen that the Tcount value obtained by the quality of the information system (X1), the quality of Information (X2), and the quality of the information system (X3) is greater than the value of Ttable of 1.991. The independent variable is simultaneously Tcount> Ttable (185,668> 2,772). Based on Table 6, it is shown that Information System Quality influences Information Quality; hence H1 is accepted.
The same table also shows that the Quality of the Information System, The Quality of Information, and The Quality of the Service System partially positively influence
381 performance accountability. Therefore, it supported theH2, H3, and H4. Lastly, the H5 is also supported because the Information Quality, Information System Quality, and Service System Quality simultaneously have positively influenced Performance Accountability.
Table 6 t-Test
Latent Variable
Path Coefficient
tcount ttable Conclusion
X1 -> X2 0,872 16,325 1,991 H1Accepted Significant X1 -> Y 0,288 1,482 1,991 H2Accepted Significant X2 -> Y 0,376 4,156 1,991 H3 Accepted Significant X3 -> Y 0,306 2,507 1,991 H4 Accepted Significant
Latent Variable
R Square
fcount ftable Conclusion
X1, X2, X3->
Y
0,877 185,668 2,722 H5 Accepted Significant
The quality of the information system influences the quality of Information positively. Thus, it can be concluded that the higher the quality of information systems in the Ministry/Institution, the higher the potential to improve the quality of Information. This is in line with the results of previous research that the Ministry/Institution information system is more qualified; it will have great potential to improve the quality of Information to improve the accountability of Ministry/Institution performance indirectly. This is stated by Gorla et al. (2010), Marianana (2006), and Suryanto et al. (2016). This finding can be explained by exploring government shifts that emphasize governance and management of the information system process. The enforcement of Government Agency Performance Accountability System (SAKIP) implementation across local and central government units is increasingly linked to the assessment of e-government maturity (Sistem Pemerintahan Berbasis Elektronik or SPBE). SAKIP entails a high degree of information fidelity to allow for proper
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measurement of unit performance. Therefore, the sound and optimal management of the information system is necessary for reliable Information in the form of a Report of Performance Accountability of Government Agencies.
Similarly, the finding shows that information system quality affects performance accountability. Thus, it can be concluded that the higher the quality of information systems in a Ministry/Institution, the higher the potential for Ministry/Institution performance accountability. This is in line with the results of previous research that the Ministry/Institution information system was more qualified; it will have great potential in improving its performance to improve Ministry/Institution performance accountability. This is stated by Al-Debei et al. (2013), DeLone and McLean (1992, 2003), Gavrea et al. (2011), Gorla et al. (2010), Iivari (2002), Jones and Straub (2006), Petter et al. (2008), and Wu and Wang (2006). Ministries/Institutions must use planning information systems and monitoring and evaluation of the management of performance/financial data, regardless of the type of application, depending on internal policies. With quality information technology support, Ministries/Institutions can manage financial or performance data from planning, implementation/realization, and monitoring implementation to the evaluation of its implementation properly and easily.
It would be helpful for a unit to improve performance from the financial context and the output of such a unit.
The quality of Information has a significant effect on performance accountability.
Thus, it can be concluded that the higher the quality of Information on Ministry/Institution, the higher the potential for Ministry/Institution performance accountability. This is in line with the results of previous research that the Ministry/Institution has more qualified Information; it will have great potential in improving its performance to improve Ministry/Institution performance accountability.
This is stated by DeLone and McLean (1992), Gorla et al. (2010), and Teo et al. (2008).
At the Finance and Development Supervisory Agency (BPKP), information input and transmitted by Yearly Work Plan and Yearly Work Plan Evaluation management information systems allowed the agency to win the A title for two consecutive years in 2013 and 2014. This indicates the capability of the systems to elicit useful and reliable
383 performance accountability information. That means the information systems in question showcase the accuracy, timeline, and completeness in an understandable format.
The existence of system service quality has a positively significant effect on performance accountability. Therefore, it can be concluded that the higher quality of the service system at the Ministry/Institution will increasingly have the potential to increase accountability in the Ministry/Institution. This is in line with the results of the previous research that high-quality system services had a potentially improved performance, so it was able to improve performance accountability (Liu et al., 2009; Rehman et al., 2011;
Septianita et al., 2014). From field observation, it was shown that the quality of system services is bundled with the system itself. The quality of information systems constitutes a holistic picture of the system service. Following the enactment of e-business practice on a government agency's business processes, performance accountability demands not only mere automation and proper Information. Technical support, infrastructure, and service support provided by the system are imperative. Service improvements are necessary to increase LAKIP quality. These improvements may include maintaining steadfast technical support and infrastructure readily accessible to users.
Lastly, the field results showed that Quality of Information Systems, Quality of Information, and Service Quality simultaneously significantly affect performance accountability. The correlation coefficient marked positive, indicating the relationship between them is unidirectional, meaning the better the quality of the information system, the quality of Information, and the quality of service had an impact on the potency of the better performance accountability of Ministry/Institution in Indonesia.
This is in line with the results of previous research that the quality of information systems, information quality, and high System Service Quality potentially improved the performance so it could improve Ministry/Institution performance accountability. This was stated by DeLone and McLean (1992, 2003, 2016) and Gorla et al. (2010).
384
5. Conclusion, Implication, and Limitations 5.1. Conclusion
This research provides an understanding of the quality of information systems, information quality, and service system quality to the performance quality in the Ministries or Government institutions in Indonesia. A clear finding of this studycan be summarized: (i) The quality of information systems has a positive and significant impact on information quality. (ii) The information systems quality significantly affects the performance accountability. (iii) The quality of Information positively influences the performance accountability. And (iv) The quality of information systems, information quality, and system service quality has a positive and heavy influence on performance accountability simultaneously.
This finding clearly shows that to improve performance accountability, the Ministries and local Governments should invest in information systems and technology that can integrate all Information from various applications or sources and powered by contemporary features, such as prediction tools and big data analytics. The successful implementation of the integrated information system and support service system, such as SAKIP (Government Agency Performance Accountability System), will become the foundation for the Indonesian Ministries and Local Government to maintain performance following good governance and accountability.
5.2. Implication and Limitation
This study provides evidence of the importance of information system quality, information quality, and service quality to ministries and institution performance accountability in Indonesia. Therefore, the government's attempts to improve its performance by evaluating the performance and financial accountability mandated to the government institutions, such as the assessment of LAKIP, should be accompanied by the development of good planning and quality evaluation of the information system.
One of the aspects is by giving attention to the quality of Information generated.
Regarding the quality of the service system, Ministries/Institutions are expected to
385 implement linearly between information system and information quality with the improvement of the service system to its users.
This research is inseparable from the limitations. Therefore, researchers provide suggestions for the next research: First, Even though this study implements a well- known IS Success Model (Delone and McLean, 2016), there was a limitation associated with the subjectivity in the collected data, therefore future research to use new methods such as the mixed methodology in terms of data collection techniques that are by interview or observation techniques to understand the dynamics of performance accountability in Ministries/ Institutions and Local government agencies and exploring more in-depth analysis. Second, incorporate more constructs of DMIS, such as system usage, system user perceptions, organizational impact, and individual performance.
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Appendix
Table A
Instrument Validity Test Results
Variable Dimension Item Coefficient Value
Min Result
Information System Quality
(X1)
Easy of use X1-1 0.784 0,3 Valid
X1-2 0.893 0,3 Valid
X1-3 0.780 0,3 Valid
X1-4 0.892 0,3 Valid
X1-5 0.844 0,3 Valid
Flexibility X1-6 0.756 0,3 Valid
X1-7 0.728 0,3 Valid
Response time X1-8 0.949 0,3 Valid
X1-9 0.829 0,3 Valid
Reliability X1-10 0.833 0,3 Valid
X1-11 0.850 0,3 Valid
X1-12 0.868 0,3 Valid
Security X1-13 0.886 0,3 Valid
X1-14 0.698 0,3 Valid
Information Quality
(X2)
Accuracy X2-1 0.943 0,3 Valid
X2-2 0.922 0,3 Valid
Timeline X2-3 0.933 0,3 Valid
X2-4 0.892 0,3 Valid
Completeness X2-5 0.957 0,3 Valid
X2-6 0.895 0,3 Valid
Format X2-7 0.937 0,3 Valid
X2-8 0.836 0,3 Valid
391 Variable Dimension Item Coefficient
Value
Min Result
System Service Quality
(X3)
Tangibles X3-1 0.858 0,3 Valid
X3-2 0.734 0,3 Valid
Reliability X3-3 0.844 0,3 Valid
X3-4 0.940 0,3 Valid
X3-5 0.827 0,3 Valid
Responsiveness X3-6 0.865 0,3 Valid
X3-7 0.875 0,3 Valid
X3-8 0.897 0,3 Valid
Assurance X3-9 0.927 0,3 Valid
X3-10 0.875 0,3 Valid
X3-11 0.938 0,3 Valid
Empathy X3-12 0.755 0,3 Valid
X3-13 0.901 0,3 Valid
X3-14 0.893 0,3 Valid
Performance Accountability
(Y)
Accounting for probity and legality
Y-1 0.874 0,3 Valid
Y-2 0.762 0,3 Valid
Y-3 0.907 0,3 Valid
Y-4 0.640 0,3 Valid
Process accountability
Y-5 0.874 0,3 Valid
Y-6 0.834 0,3 Valid
Y-7 0.923 0,3 Valid
Y-8 0.877 0,3 Valid
Y-9 0.868 0,3 Valid
Program accountability
Y-10 0.883 0,3 Valid
Y-11 0.873 0,3 Valid
Y-12 0.877 0,3 Valid
Policy Accountability
Y-13 0.846 0,3 Valid
Y-14 0.890 0,3 Valid
392 Table B
Reliability of Constructs
Variable Reliability Coefficient Min. Result
Information System Quality 0,964 0,6 Reliable
Information Quality 0,970 0,6 Reliable
System Service Quality 0,974 0,6 Reliable
Performance Accountability 0,968 0,6 Reliable