Analysis of Work Effectiveness and Work Based on Tupoksion Service Quality at UPT BAPENDA South Sulawesi Province

Asnani Mappa¹, Mattalatta², Hasmin Tamsah³, Umi Farida⁴, Ansar⁵, Gunawan⁶, Kurniawati Sandra⁷

STIE AMKOP, Makassar, South Sulawesi, Indonesia^{1,2,3,4,5,6,7}

Email: [email protected]¹, [email protected]², [email protected]³, [email protected]⁴, [email protected]⁵, [email protected]⁷

Keywords

Abstract

Work Effectiveness, Function Based Work, Service Quality.

This study at UPT Bapenda in South Sulawesi Province aims to evaluate the impact of work effectiveness and task-based work on service quality, utilizing a quantitative research approach with a sample of 89 employees. Analysis conducted through multiple linear regression using SPSS Version 22 reveals that work effectiveness (X1) does not significantly affect service quality (Y), with a significance value of 0.630, greater than the threshold of 0.005. Consequently, the hypothesis that X1 influences Y is rejected. Conversely, task-based work (X2) shows a significant positive effect on service quality, with a significance value of 0.000, below the 0.005 threshold, confirming the hypothesis that X2 impacts Y. Furthermore, the combined effect of X1 and X2 on service quality is significant, as indicated by an F-test result of 624.562, surpassing the F table value of 2.02619, with a significance level of 0.000. Thus, both factors together significantly influence service quality. In summary, while work effectiveness alone does not impact service quality, task-based work significantly enhances it, and their combined effect is also positive and significant.

for possible open access publication

under the terms and conditions of the Creative Commons Attribution (CC BY SA) license (https://creativecommons.org/licenses/by-sa/4.0/).

1. Introduction

The Province of South Sulawesi's Regional Revenue Agency, as an extension of the Governor's arm in carrying out government duties in implementing decentralization and deconcentration authority in the field of Regional Revenue, in accordance with the policies stipulated based on applicable laws and regulations (Ardiansyah, Marthen, & Amalia, 2015). The South Sulawesi Province's Regional Revenue Agency is the subject of this study, in accordance with the Regulation of the Governor of South Sulawesi Number 99 of 2016, The Head of the Agency, who reports to the Governor via the Regional Secretary, oversees the Regional Revenue Agency of South Sulawesi Province (Makawi, Pranata, Abudan, & Pawirosumarto, 2020). The 2018-2023 Strategic Plan (Renstra), South Sulawesi Province Regional Revenue Agency was formulated with stages, procedures and approaches regulated in statutory regulations in order to ensure consistent planning and selection of priority programs/activities in keeping with Bapenda's responsibilities and roles.

The Sulawesi Provincial Regional Revenue Agency as an element of regional apparatus which carries out government and development tasks in the field of regional revenue management (Darmi, 2018), is regulated in the South Sulawesi Provincial Regional Regulation based on Governor's Regulation Number 10 of 2016 concerning the Formation and Composition of Regional Apparatus. Regional Revenue Agency of the Province of South Sulawesi is committed to continuously changing the regional financial management paradigm (Morasa, Suwetja, & Mintalangi, 2021). Increasing regional revenues from better regional taxes and levies is a demand that government administrators must pay attention to (Martinez-Vazquez, 2015). This is important because taxes and levies are a component of regional income to improve the regional economy as well as a measure of the success of implementing autonomy (Ardiansyah et al., 2015). South Sulawesi Provincial Regional Revenue Agency, in order to carry out certain supporting technical tasks, based on South Sulawesi Governor Regulation Number 52 of 2018, at the South Sulawesi Provincial Regional Revenue Agency.

The purpose of the study is to determine and examine how knowledge and analysis of job effectiveness and work based on primary tasks and functions affects the quality of services provided by UPT Bapenda, South Sulawesi Province. It is hoped that this research can help organizations become more competitive and sustainable in facing ongoing changes (Palayukan & Tandi, 2023).

2. Materials and Methods

This research is quantitative research because it emphasizes more on the aspect of objectively measuring social phenomena (Tandi, 2024). This research aims to explain the causal relationship of a number of variables studied and is included in the type of explanatory research (explanatory research). Research based on the philosophy of positivism is used to research certain populations/samples, sampling techniques are generally carried out randomly, data collection uses research instruments, data analysis is quantitative/statistical with the aim of testing hypotheses that have been established (Sugiyono & Kuantitatif, 2009). Research that explains the causal relationship between the variables Work Effectiveness and Main Duties and Functions (Tupoksi) on the Service Quality of the UPT Regional Revenue Office of the Province of South Sulawesi.

All Civil Servants (PNS) employed by the UPT of the South Sulawesi Province's Regional Revenue Agency included the study's population. The total number of civil servants within the UPT of the Province of South Sulawesi's Regional Revenue Agency, is based on data from the Personnel Management Information System (SIMPEG) at the UPT of the Regional Revenue Agency of South Sulawesi Province, there are 490 employees with the status of Civil Servants (PNS)(Source: South Sulawesi Provincial BAPENDA Personnel, December, 2022).

The sample in this study was determined using the Slovin formula, This is a formula for figuring out the bare minimum sample size when the population's behavior is still uncertain (Aloysius Rangga Aditya Nalendra, et al. 2021: 27-28). The error rate value determines the research sample size using the Slovin formula. as follows:

Information :

n = Sample size/number of respondents

N = Population size

It is = Percentage of allowance for sampling accuracy that can still be tolerated; e=0.1, in the Slovin formula there is a provision that the value of e = 0.1 (10%) for a large population The value of e = 0.2 (20%) for a small population is the sample range that can be taken from the technique Solvin is between 10-20 % of the study population.

The total population in this study was 490 people, where researchers used an allowance percentage of 10% and To ensure appropriateness, the computation results could be rounded off.

Both primary and secondary data were used in this study; primary data came from South Sulawesi Province's Regional Revenue Agency's UPT (Rum & KUSUMAWARDANI, 2020). Primary data are those that were gathered straight from research participants using questionnaires, while secondary data is obtained from supporting data such as journals, books and other references. The data collection method in this research is a survey method where the instrument is a questionnaire, where the questionnaire is distributed and distributed to respondents (Minnaar & Heystek, 2013).

3. Results and Discussions

Results of Analysis of Multiple Linear Regression Equations

Regression analysis is used to measure how big the influence is between the independent variable and the dependent variable (Wagschal, 2016). If there is only one independent variable and one dependent variable, then the regression is called simple linear regression (Gogtay, Deshpande, & Thatte, 2017). To ascertain the direction and degree of the independent variable's influence on the dependent variable, multiple linear regression analysis was performed (Wicherts et al., 2016).

Multiple linear regression analysis is used in this study to ascertain and examineWork Effectiveness and Tasks-Based Work on Service Quality at the Regional Revenue Agency UPT of South Sulawesi Province. The variables in this research consist of 2 (three) independent variables, namely: Work Effectiveness variable (X1) and work based on main tasks (X2), while the dependent (bound) variable in this study consists of 1 (one) variable, namely: the Service Quality variable (Y).

The researcher then used SPSS 22 to perform a multiple linear regression test, the test was carried out partially or simultaneously between the independent/free variable (X) and the dependent/bound variable (Y), which can be described as follows:

Table 1.Multiple Linear Regression Test Results

Coefficients^a
Model		Unstandardized Coefficients		Standardized Coefficients	t	Say.
Model		B	Std. Error	Beta	t	Say.
1	(Constant)	1.456	.800		1.819	.072
	Work Effectiveness(X₁)	-.018	.038	-.015	-.483	.630
	Work based on main tasks (X₂)	1.066	.033	.970	31.910	.000
a. Dependent Variable: Service Quality (Z)

In accordance with the analysis using SPSS 22, the results of the regression equation are obtained as follows:

Y = -1.456+ -0.18X1 + 1.066X2 + and

The regression equation above shows the relationship between the Independent variable and the dependent variable partially (Kafle, 2019). From this equation it can be concluded that the Constanta Value is-1.456, meaning that there is no change in the Work Effectiveness variable, the Task Based Work variable (X value1 and X2 is 0) then Service Quality (Z) is equal to-1.456, this shows that:

1. The coefficient value of the Work Effectiveness variable (X1) is -0.18, meaning that if the Work Effectiveness variable (X1) increased by 1%, assuming the variable Work Based on Main Functions (X2), and the constant (a) is 0, then Service Quality (Z) increases by -0.18. This shows that the Work Effectiveness variable (X1) has a significant effect on Service Quality (Z), so that work effectiveness is good and comfortable at the Regional Revenue Agency UPT of South Sulawesi Province, then Service Quality (Z) will be better.

2. The regression coefficient value of the Function Based Work variable (X2) 1.066, meaning the variable Working Based on Main Tasks (X2) increased by 1%, assuming the Work Effectiveness variable (X1), and the constant (a) is 0 (zero), then the Service Quality (Z) of the UPT Regional Revenue Agency of South Sulawesi Province, then the Service Quality (Z), will increase by 1,066. This shows that work is based on main tasks (X2) that is complete will make a positive contribution to employee performance, so that the better the work based on main tasks and functions, The more the UPT of the South Sulawesi Province's Regional Revenue Agency's quality of service improves.

t Test Results (Partial)

The partial test (t test) is used to determine how much an independent variable partially affects the variation in the dependent variable (Bariroh, 2018). The significance value in the Coefficients table is used to make decisions. Tests of regression results are conducted using a 95% confidence level or a significance level of5% (α = 0.05). The calculated t value is used to test the partial effect of X1 and X2 to the dependent variable (Y). Does variable X have a significant influence on Y or not with an error rate of 5%. The criteria for the t statistical test (Ghozali, 2006)

a. H0 is accepted and Ha is rejected if the t test's significance value is greater than 0.05. This indicates that the independent factors have no effect on the dependent variable.

b. Ha is accepted and H0 is rejected if the t test's significance value is less than 0.05. This indicates that the independent and dependent variables are influenced by one another.

This test is carried out by looking at the significance of each independent variable with a significance level of ˂ 0.05, as in the following table:

Table 2.

T test (Partial)

Coefficients^a
Model		Unstandardized Coefficients		Standardized Coefficients	t	Say.
Model		B	Std. Error	Beta	t	Say.
1	(Constant)	_-1.456	.800		1.819	.072
	Work Effectiveness(X₁)	-.018	.038	-.015	-.483	.630
	Work based on main tasks (X₂)	1.066	.033	.970	31.910	.000
a. Dependent Variable: Service Quality (Z)

According to the Partial Test table above, by observing the row, column t and sig, the following explanation is obtained:

Influence of Work Effectiveness (X1) has a noteworthy and favorable impact on Service Quality (Y)

Hypothesis 1 (X1 against Y), X1 sig value. 0.630 this value is greater than 0.005, ( 0.630 ˃ 0.005), meaning H₀ accepted and H1 rejected. This means that there is no influence between the independent variables on the dependent variable. X1 has no significant effect on Y.

The Influence of Task Based Work (X2) has a noteworthy and favorable impact on service quality(Y)

Hypothesis 2 (X2 against Y), X2 sig value. 0.000, this value is smaller than 0.005, (0.000 ˂ 0.005), meaning H₀ rejected and H2 accepted. This means that there is an influence between the independent variable and the dependent variable. X2 significant effect on Y.

F Test Results (Simultaneous)

The F test (Simultaneous) is carried out by comparing the significance between the calculated f values ˃ f table, then the model formulated is correct (Hothorn, Bretz, & Westfall, 2008). If the regression model is valid, as indicated by the computed f value ʃ in the f table, meaning that the influence is joint or simultaneous, by looking at the f table value = f (a/2;n-k) f= (0.005/2;98-2-1 ), f table = 0.025;95) = 1.98525(source: see f distribution table, probability = 0.05), with an error rate of 5%. The f test carried out can be seen in the following table:

Table 3.

FANOVA Test Table Resultsb

ANOVA^b
Model	Sum of Squares	df	Mean Square	F	Say.
Regression	711.861	2	355.930	624.562	.000a
Residual	54.139	95	.570
Total	766.000	97
a. Predictors: (Constant), Working Based on Functions (X₂), Work Effectiveness(X₁)
b. Dependent Variable: Service Quality (Z)

In accordance with the test results in the table above, it can be seen that the calculated F value is 624,562 with the F table value being 1.98525(source: see f distribution table, probability = 0.05), so that the calculated F value ˃ F table or 624.562 ˃ 2.02619, and the significant level is 0.000 ˂ 0.05, then H0 rejected and H1 accepted, it can be concluded that variable1 and X2 simultaneously (together) have a significant effect on variable Y.

This research shows that the Work Effectiveness Variable (X1), and Functional Based Work Variables (X2) concurrently (collectively) have a noteworthy and favorable impact on service quality(Y) at the Regional Revenue Agency UPT of South Sulawesi Province.

Coefficient of Determination Test Results (R2)

Coefficient of Determination (R2) to measure how far the model's ability to explain variations in the dependent variable (Nakagawa, Johnson, & Schielzeth, 2017). The coefficient values are Zero and one. R value2 low means that the ability of the independent variables to explain variations in the dependent variable is very limited (Adenugba, Ige, & Kesinro, 2016). A value close to one means that the independent variables provide almost all the information needed to predict variations in the dependent variable (Daud et al., 2022). According to the results of the Determination Coefficient Test (R2) carried out can be seen in the following table:

Table 4.

Coefficient of Determination Test Results (R2)

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.964^a	.929	.928	.755
a. Predictors: (Constant), Working Based on Functions (X₂), Work Effectiveness(X₁)

4. Conclusion

In accordance with the table above, it can be seen that the coefficient of determination value is found in the value Adjusted R Sguare of 0.928. This indicates that 92.8% of the dependent variable can be explained by the independent variable (0.928x100%), with other variables accounting for the remaining 7.2%, which are not explained and not discussed in this research.

5. References

Adenugba, Adesoji Adetunji, Ige, Abayomi Akinyemi, & Kesinro, Olalekan Rasheed. (2016). Financial leverage and firms’ value: A study of selected firms in Nigeria. European Journal of Research and Reflection in Management Sciences, 4(1).

Ardiansyah, Fitrian, Marthen, Andri Akbar, & Amalia, Nur. (2015). Forest and land-use governance in a decentralized Indonesia: A legal and policy review.

Bariroh, Siti. (2018). The Influence of Parents’ Involvement on Children with Special Needs’ Motivation and Learning Achievement. International Education Studies, 11(4), 96–114.

Darmi, Titi. (2018). Locally-Generated Revenue as A Capacity Parameters of New Regional Autonomy Management. Jurnal Kebijakan Dan Administrasi Publik, 22(1), 1–13.

Daud, Ibrahim, Nurjannahe, Dewi, Mohyi, Achmad, Ambarwati, Titiek, Cahyono, Yoyok, Haryoko, Andrean Eko, Handoko, Agus Leo, Putra, Riyan Sisiawan, Wijoyo, Hadion, & Ariyanto, Aris. (2022). The effect of digital marketing, digital finance and digital payment on finance performance of indonesian smes. International Journal of Data and Network Science, 6, 37–44.

Ghozali, Imam. (2006). Aplikasi analisis multivariate dengan program SPSS. Badan Penerbit Universitas Diponegoro.

Gogtay, N. J., Deshpande, S. P., & Thatte, U. M. (2017). Principles of regression analysis. J. Assoc. Physicians India, 65(48), 48–52.

Hothorn, Torsten, Bretz, Frank, & Westfall, Peter. (2008). Simultaneous inference in general parametric models. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 50(3), 346–363.

Kafle, Sarad Chandra. (2019). Correlation and regression analysis using SPSS. Management, Technology & Social Sciences, 126.

Makawi, Panhar, Pranata, Muhammad Rizky, Abudan, Muhammad, & Pawirosumarto, Suharno. (2020). GENERAL REVIEW OF THE APPOINTMENT OF ACTIVE POLRI MEMBERS TO ANSWER AS REGIONAL HEAD (Case study: Appointment of Pol Commissioner General Mochamad Iriawan as Acting Governor of West Java).

Martinez-Vazquez, Jorge. (2015). Tax assignments at the regional and local levels. In Handbook of multilevel finance (pp. 358–388). Edward Elgar Publishing.

Minnaar, Lorinda, & Heystek, Jan. (2013). Online surveys as data collection instruments in education research: A feasible option? South African Journal of Higher Education, 27(1), 162–183.

Morasa, Jenny, Suwetja, I., & Mintalangi, Syermi. (2021). Village Fund Management in Public Sector Accounting System: Case from East Indonesian Region. Proceedings of the 1st International Conference on Law, Social Science, Economics, and Education, ICLSSEE 2021, March 6th 2021, Jakarta, Indonesia.

Nakagawa, Shinichi, Johnson, Paul C. D., & Schielzeth, Holger. (2017). The coefficient of determination R 2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface, 14(134), 20170213.

Palayukan, Renita Sau, & Tandi, Asrin. (2023). Analisis kinerja pegawai pada Perusahaan Umum Daerah Air Minum Tirta Bantimurung Kabupaten Maros. Humantech: Jurnal Ilmiah Multidisiplin Indonesia, 3(1), 104–113.

Rum, Muh, & KUSUMAWARDANI, Anisa. (2020). Industrial growth and environmental resource toward the tax potential: a case study in South Sulawesi Province. The Journal of Asian Finance, Economics and Business, 7(10), 201–210.

Sugiyono, M. P. P., & Kuantitatif, P. (2009). Kualitatif, dan R&D, Bandung: Alfabeta. Cet. Vii.

Tandi, Asrin. (2024). Analysis of Employee Work Motivation In The Scope of Tanralili Sub-district Gov-ernment, Maros Regency. Eduvest-Journal of Universal Studies, 4(1), 183–201.

Wagschal, Uwe. (2016). Regression analysis. In Handbook of Research Methods and Applications in Political Science (pp. 359–382). Edward Elgar Publishing.

Wicherts, Jelte M., Veldkamp, Coosje L. S., Augusteijn, Hilde E. M., Bakker, Marjan, Van Aert, Robbie C. M., & Van Assen, Marcel A. L. M. (2016). Degrees of freedom in planning, running, analyzing, and reporting psychological studies: A checklist to avoid p-hacking. Frontiers in Psychology, 7, 1832.