Factors Determining the Service Performance of Government Employees: Lesson Learned from Indonesia

Factors Determining the Service Performance of Government Employees: Lesson Learned from Indonesia

Helwin Ginting¹, Tjahya Supriatna², Musa Hubeis³, Dewi Sulistyani⁴, Carunia Mulya Firdausy^5*

¹Ph.D Student University of Satyagama, Jakarta, Indonesia

²Professor of Governmental Science Institute of Home Affairs Governance, Indonesia

³Professor of Management, University of IPB

⁴Senior Lecturer, University of Satyagama

⁵Professor of Economics, National Research and Innovation Agency and University of Tarumanagara

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.805186

Received: 16 May 2024; Revised: 25 May 2024; Accepted: 29 May 2024; Published: 25 June 2024

ABSTRACT

Limited studies examining the influence of budgeting and employee resources available in organizations on the service performance of government employees in Indonesia To fill this gap, this study aims to examine whether or not policy implementation, employee resources, budgeting, and infrastructure affect the service performance of government employees by taking one of the government office located in North Jakarta, the Capital City of Indonesia as a case study. A random sample of 131 government employees, community leaders, and the community members who received the government services was selected. Random sampling was used since it is generally less biased and more generalizable. The questionnaire instrument was distributed directly to the sample respondents at the government office in North Jakarta, the Capital City of Indonesia. The method to analyze the data was by using a multiple linear regression model as suggested in the literature. The results showed that the four factors of policy implementation, employee resources, budgeting, and infrastructure were found to positively and significantly affect the service performance of government employees in Indonesia. These findings may be considered lessons learned for other developing countries in improving the service performance of their government employees. It is now or never.

Keywords: policy implementation, employee resources, budgeting, infrastructure, service performance

JEL codes: J18, 015, Z18.

INTRODUCTION

Studies examining the key factors behind government policy failure in many developing countries have increased in recent years (Hudson et al. 2019; Nimrah et al. 2021; Nirwana et al. 2022; Saadouli & Al-Khanbashi, 2020). Of the factors behind the policy failure, the low quality of service performance of the government employees was found to be the key factor (Ahmad et al. 2019;  Anyakoha, 2019; Bhatti et al. 2020; Buil et al, 2019; Eliyana et al, 2019; Khaeriah et al. 2015; Rozanna et al. 2019; Saadouli & Al-Khanbashi, 2020). As pointed out by Anyakoha (2019), for example, employee performance is a cornerstone of any organization‘s formula for success.

However, policy suggestions to improve the service performance of government employees vary from one study to other studies. Saadouli & Al-Khanbashi (2020) from their study in government offices in the Sultanate of Oman suggest that technology is a more significant factor than leadership and organizational structure changes in improving the service performance of government employees. Their finding was supported by Khin & Ho (2019), Lember et al. (2019), Martin- Rojas et al. (2019), and Oreg & Berson, (2019). For Khin & Ho (2019) technology has played a more profound role in organizations with increased digital capability and innovation, while top management supports technological acquisition and integration (Martin- Rojas et al. 2019).

Further, Buil et al (2019) and Meng & Berger (2019) suggest the importance of organizational changes to improve the service performance of government employees. To do this, there is a need to address organizational structure and/or culture (Ahmad et al. 2019; Eliyana et al. 2019; Kucharska & Kowalczyk, 2019; Rozanna et al. 2019; Tarba et al. 2019). Organizational structure is necessary for situations involving higher degrees of engagement and trust (Meng & Berger, 2019) as well as organizations that have a distinct reputation and a long-term orientation (Kucharska & Kowalczyk, 2019). Finally, Afsar et al. (2019) suggest leadership is needed to improve the quality of service performance of government employees. This factor plays an important role in situations with increased job crafting behaviors and decreased task conflict (Kammerhoff et al. 2019).

The above suggestions imply that on one hand employee performance is a cornerstone in any organization‘s formula for success (Anyakoha, 2019), while on the other hand improving the service performance of government employees is a complex issue involving many independent factors (Audenaert et al. 2019). Also, it depends greatly on the organization‘s micro and macro environments (Bhatti et al. 2020), whether the organization is public or private (George et al. 2019) as well as the different degrees of accountability and job security between the two types of organizations (Cooper, 2020).

In Indonesia, past studies indicated that factors determining the quality service performance of government employees included as follows. First, it was influenced by the limited ability to implement policies. Second, it is due to the lack of ability of government employees to master and use digital communication and information technology to serve the public. Third, it is because of the lack of facilities and infrastructure for service administration. Fourth, government employees have low awareness to function their role in serving the public. Fifth, lack of coordination between the provincial government and the institutions under it, such as districts and sub-districts. Finally, employees who work do not have the work spirit to achieve the targets set by the leadership (Dermawan Dwi Putra et al. 2019; Selamat & Heryanto, 2019).

However, from a systematic literature review, it was found that there are limited studies examining the influence of budgeting and employee resources available in organizations on the service performance of government employees in Indonesia. For that reason, the novelty of this present study apart from reconfirming the generic results of technology/infrastructure and policy implementation factors in affecting the service performance of government employees as mentioned above also aims at examining the influence of budgeting and employee resource variables on the service performance of government employees in Indonesia. The urgency to accommodate budgeting and employee resources variables in the model is partly because the government faced budget constraints to support human resources development. Also, it is because there is a great tendency of national job seekers to work in government. So, by undertaking this study, further knowledge of factors influencing the service performance of government employees will be contributed on one hand, and policy lessons to improve the service performance of government employees on the other hand.

SOURCE OF DATA AND METHOD

The source of data to determine factors affecting the service performance of government employees was collected from the government office in the Penjaringan Sub-District Office – North Jakarta. A random sample of 131 respondents out of 330 total population in the sub- district of the Capital City of Jakarta was selected. These respondents consisted of government employees, community leaders, and the community members that received services given by this government office. Random sampling was employed since it is generally less biased and more generalizable (Gujarati & Porter, 2019). The questionnaire was distributed to 131 respondents in the location. Detailed questions asked in the questionnaire can be seen in Appendix 1.

The questionnaires distributed to the 131 respondents consisted of two parts. The first part requested demographical data, while the second part assessed the respondent‘s perception of the service performance of government employees relative to the four investigated factors: policy implementation, employee resources, budgeting, and infrastructure. To assess the perceived employee performance, ten items of three dimensions were adopted from Samsuddin et al. (2023). For policy implementations, there were ten items of two dimensions adopted from Nimrah et al (2021). For employee resources, there were 10 items from three dimensions adopted from Risky Nor Chalisa & Dian Prawitasari (2024). Furthermore, there were ten indicators of four dimensions of budgeting variables taken from  Nurina Risfaqul Laili &  Citra Mulya Sari (2022). For the infrastructure variable, there were 10 items of two dimensions adopted from. The reason for adopting dimensions and indicators of each variable developed by Indonesian scholars is simply because this study focused on Indonesia as a case study. It also aims to be used for the discussion of the results of the study. Note that to measure the responses of all indicators of each variable, we used a 5-point Likert scale.

A complete response from 131 respondents was received and prepared for analysis. The first analysis was by testing the validity and reliability. These tests aimed to ensure the quality of the data before being processed further. After completing the validity and reliability tests, we further tested classical assumptions of the multiple regression. The reason for applying multiple regression analysis followed previous studies conducted by Nirwana et al. (2022), Saadouli & Al-Khanbashi (2020), and Selamat & Heryanto (2019). The classical assumption test used consisted of a normality test, multicollinearity test, and heteroscedasticity test. The normality test is carried out by looking at the spread of data on diagonal sources on the Normal P-P Plot of regression standardized residual (graphic method) chart or by using the One-Sample Kolmogorov Smirnov test (Gujarati & Porter, 2019).

A Multicollinearity test was conducted to test the presence or absence of correlation between independent variables in the regression model. The multicollinearity test was done by calculating the Variance Inflation Factor (VIF) and the tolerance value. If the VIF value is 10 or the tolerance value is 0.1, then multicollinearity is free. The heteroscedasticity test was carried out by analyzing the Scatterplot graph. If there is no clear pattern, namely the points spread above and below the number 0 (zero) on the Y axis, or in other words, the distribution of the points does not form a certain pattern, this means that there is no heteroscedasticity (see, Gujarati & Porter, 2019 for detailed statistical tests for OLS assumptions).

Having completed the above tests, the next step was to run the multiple linear regression analyses with the help of the SPSS software program version 25. The model of multiple linear regression analysis selected followed used as suggested by Nirwana et al. (2022) and can be written mathematically as follows.

Y = a + b₁X₁ + b₂X₂ +b₃ X₃ + b₄X₄ + e

Where:

Y = Service performance of the government employee;

a = constant

b₁, b₂, b₃, and b₄ = coefficients of X₁, X₂, X₃, and X₄

X1 = Policy implementation

X2 = Employee Resources

X3 = Budgeting

X4 = Infrastructure

e = error terms.

RESULTS AND DISCUSSION

a. Results of Validity, Reliability, and Classical Assumption Tests

As mentioned in the method section above before running the multiple regression analysis we tested the validity and reliability of the quality of the data and the classical assumption tests. The results of each test are as follows.

Under the validity test, the results showed that the r-count value of each statement item of the four variables underestimation was greater than the r-table value (0.1703). For the policy implementation variable, for example, it was found that the r-count value of each statement item or indicator in this variable was greater than the r-table (see Table 1). This suggests that 10 statement items under the policy implementation variable were valid.

Table 1.  The results of validity test of policy implementation (X1)

Source: calculated from the questionnaire by using SPSS version 25.

The same results were also found in all items for the rest of the variables under the study, namely, employee resources (X2), budgeting (X3), infrastructure (X4), and service performance (Y). The r-count values of all items of these four variables are greater than the r-table (0.1703). Thus, it can be concluded that all statement item indicators of these four variables were valid.

Concerning the reliability test, the results are shown in Table 2. This reliability test aimed to determine the extent to which the measurement results remain consistent if it is carried out twice or more on the same symptoms using the same measuring instrument. A variable is said to be reliable if it has a Cronbach Alpha value > 0.60. As can be seen in Table 2 the four variables examined are also reliable.

Table 2.  The results of the reliability test

Source: Results of data processing with SPSS Version 25.

Further, the results of classical assumption tests consisting of the normality test, multicollinearity test, and heteroscedasticity test are as follows. First, by using the One-sample Kolmogorov Smirnov test (Gujarati and Porter, 2019) was found that the Sig Kolmogorov Smirnov value was 0.200 which was greater than 0.05. This indicates that the model met the requirements of the normality test. The results of the multicollinearity test also showed that all independent variables have a tolerance value of 0.1 and a VIF value of 10. Thus, multicollinearity does not occur in all independent variables in this study. Finally, the result of the heteroscedasticity test confirmed that the multiple regression model has met the homoscedasticity assumption. Thus, the multiple regression model developed in this study does not violate the classical OLS assumptions. This suggests that the model was justifiable for inference or prediction.

b. Determinant Factors Affecting the service performance of government employees

After completing the above tests to make sure the model under study did not violate any statistical requirements, we further ran the regression analysis using SPSS program software v.25. The results of the estimated multiple regression model were as follows.

Ŷ = -0.184 + 0.446 X₁ + 0.213 X₂ + 0.138 X₃ + 0.244 X₄

The estimated regression model above showed that the policy implementation (X₁) and the infrastructure (X₄) have greater coefficient values than the employee resources (X₂) and the budgeting variable (X₃). The coefficient of the policy implementation (X₁), for instance, was found to be 0.446 which means that for one unit increase/decrease of the policy implementation, it will increase/decrease the service performance of government employees by 0.446. While for the infrastructure variable (X₄), the beta coefficients were 0.244 meaning that one unit change in infrastructure will change the service performance of government employees by 0.244.

The employee resources (X₂) and the budgeting variables (X₃), however, contribute only 0.213 and 0.138 respectively. This means that an increase/decrease of one unit of employee resources will increase/decrease the service performance of government employees by 0.213. While an increase/decrease of one unit government budget will increase/decrease the service performance of government employees by 0.138. Note that, the use of the unstandardized coefficients (Beta coefficients) to interpret the regression result is simply because the unit of independent variables used was not standardized. In other words, the unit used for each independent variable differs from one another. Besides, the use of an unstandardized coefficient is easy to interpret as we do not need to relate it with the deviation standard of variables (Gujarati & Porter, 2019).

The study also found that those four independent variables partially have positive and significant influences in determining the service performance of government employees. Each variable is significant since the estimated t-value of the independent variable is greater than the t- statistical tables at a 5% level. Similarly, in terms of the probability sig values, each independent variable has probability sig values less than 0.05 (Table 3). These indicate that the null hypothesis which stated that each independent variable does not affect the dependent variable was rejected. This finding suggests that each independent variable of policy implementation, employee resource, budgeting, and infrastructure partially has a positive and significant influence on the service performance of government employees in Indonesia.

Table 3. Results of Multiple Regression Analysis

Source: estimated from SPSS version. 25.

The test result of the jointly influence of the four independent variables on the service performance of government employees is exhibited in Table 4. As can be seen from this Table 4, four independent variables of policy implementation, employee resources, budgeting, and infrastructure jointly have significant effects on the service performance of government employees. This can be seen from both the F-sig value and F calculated value in that the probability of the F-sig value was less than 5%, while the estimated F-value was 702.4 which is greater than the F-statistical table. This finding suggests that these four independent variables simultaneously or jointly are significant in determining the service performance of government employees in Indonesia.

Table 4 ANOVA Table

Source: estimated by using SPSS Version 25.

Further, the significant effects of those four independent variables on the service performance of government employees were also confirmed by the R square adjusted value. The adjusted value of the R square was found to be 0.956 (Table 5). This means that 96 percent of the variation on the service performance of government employees was explained by four variables in this study. The remaining 4 percent can be attributed to other (unknown) variables that are not accommodated in the estimated model.

Table 5. Coefficient determination

Source: estimated by using SPSS Version 25.

The findings above were supported by many empirical studies. The significance of the policy implementation on service performance, for instance, was supported by studies conducted by Samsuddin et al. (2023), Nirwana et al. (2022), and Nimrah et al (2021). Nirwana et al. (2022) from their study in Enrekang district (South Sulawesi province) found that the successful policy implementation and quality of employees’ resources were very significant in influencing the service performance of the organization. They pointed out that indicators under the policy implementation that need to be given attention to improve the service performance of government employees are well-organized administration, employing service standards, and employees’ responsibility to the public. These indicators were also viewed as important by respondents in this study.

Similarly, the significant effect of employee resources on the service performance of government employees in this study supports previous studies by Samsuddin et al. (2023),  Nurina Risfaqul Laili &  Citra Mulya Sari (2022) and Selamat & Heryanto (2019). Selamat and Heryanto (2029), in particular, from their study on the effect of policy Implementation and human resource development on the quality of public services in Koto Baru, West Sumatera found that both of these variables have significant effects on the service performance of government employees in Koto Baru, West Sumatera. They confirmed that the ability of employees to work can have a significant effect on the service performance of government employees. So the government in Koto Baru needs to pay more attention to employees’ resources. This can be done, for example, by giving employees additional assignments and training to government employees.

Like policy implementation and employee resources, budgeting was also found to be partially significant in influencing the service performance of government employees. Studies that supported this finding include studies conducted by Tsai & Mezher (2020). These studies confirmed that the higher the budget, the quality of the service performance of government employees will also be higher and vice versa. Similarly, the infrastructure factor was partially significant in affecting the service performance of the government employees in the survey location. This finding was supported by previous studies (Zaidan, Al-Saidi, & Hammad, 2019). This indicates that office facilities and other relevant infrastructures play important components in improving the quality of service performance of government employees. An adequate infrastructure reduces the workload and makes performing tasks more efficient (Zaidan, Al-Saidi, & Hammad, 2019).

The significance of the joint effect of the four variables on the service performance of government employees found in this study was also supported by other past studies (Nirwana et al 2022; Tsai & Mezher, 2020; Diamantidis & Chatzoglou, 2019; Selamat & Heryanto, 2019). Therefore, the government in Indonesia must improve all four factors, namely, policy implementation, employee resources, budgeting, and infrastructure to improve the service performance of government employees. However, the implementation of these findings cannot be fully justified for any developing country without any research base. The reason is simply that factors contributing to employee performance depend greatly on the organization‘s micro and macro environments (Bhatti et al. 2020). As stated by Audenaert et al. (2019) improving employee performance is a complex issue involving many interdependent factors.

CONCLUSIONS

This study based on primary data collected by random sampling method confirms that determinant factors of the service performance of government employees in Indonesia are policy implementation, employee resources, budgeting, and infrastructure. The policy implications of these findings at least are as follows. First, there is a need to widely disseminate any public policies that are going to be implemented to the public so that organizational objectives and goals can be achieved optimally. In implementing public policies, several things can be done, namely by maintaining order in the administration and ensuring fair services to every citizen, and employees need to carry out services with high responsibility and accountability.

Second, improvement and empowerment of employee human resources are also very much needed to improve the service performance of government employees in Indonesia. This, for instance, can be done by giving training and coaching to the employees so that they will have better skills and productivity as well as a high attitude of responsibility. In addition, it is also necessary to apply punitive sanctions to employees who are not disciplined both in the level of absenteeism and at work.

Third, budgeting is also important to be managed more effectively, because it will be able to improve service programs even better. The budgeting process can be carried out by determining the Government accounting standards that will be used, the process of socialization to regional officials and apparatus as well as supervision and control in its implementation. Finally, adequate infrastructure to improve service performance is also very much needed. The infrastructure here is not only work equipment but it can also be in the form of activities or programs that support improving service performance.

These research findings provide some evidence that the service performance of government employees in Indonesia has not significantly changed despite the tremendous efforts by the central and local governments to implement more efficient and accountable public employment systems. Much remains to be done by the government of Indonesia to improve the service performance of government employees. The above four factors may also be considered as lessons learned for other countries in reforming their government bureaucracies for better shape. However, research-based evidence is a must to support this suggestion. It is now or never.

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APPENDIX 1.

QUESTIONNAIRE

Policy implementation (X₁)

Note: FA = Fully agree;

A = Agree;

LA = Less agree; DA = Disagree

TDA = Totally disagree

Employee resources (X₂)

Note: FA = Fully agree;

A = Agree;

LA = Less agree; DA = Disagree

TDA = Totally disagree

Budgeting (X₃)

Note: FA = Fully agree;

A = Agree;

LA = Less agree; DA = Disagree

TDA = Totally disagree

Infrastructure (X₄)

Note: FA = Fully agree;

A = Agree;

LA = Less agree; DA = Disagree

TDA = Totally disagree

Service performance (Y)

Note: FA = Fully agree;

A = Agree;

LA = Less agree; DA = Disagree

TDA = Totally disagree