INTRODUCTION
In today's rapidly changing and highly competitive business,
organizations are searching for ways to improve performance and maintain a
sustainable advantage in today's rapidly evolving and highly competitive
environment. As a result, knowledge management has emerged as a critical
success factor for organizations. Knowledge management encompasses a wide range
of activities aimed at capturing, organizing, storing, and sharing knowledge
within organizations (Abualoush, Masa�deh, Bataineh, &
Alrowwad, 2018). By
using knowledge management (KM) effectively, organizations can optimize their
resources, improve efficiency, drive innovation, and gain competitiveness (Ghani Al-Saffar & Obeidat, 2020).
Knowledge management (KM) is the process of connecting, storing, organising, managing, and using knowledge to improve
organizational performance (Girard, Georgia, & College, 2022). In today's highly competitive environment, organizations are
increasingly relying on KM practices to gain a competitive advantage over their
rivals (Anwar & Abdullah, 2021). The Central Bureau of Statistics (BPS) of Cilacap
Regency is one organization that has recognized the importance of KM in
improving its performance� (Wijaya & Hong, 2018). This journal will analyze the effect of knowledge management on
organizational performance at Central Statistics Agency of Cilacap
Regency. This research will explore how the implementation of effective KM
practices can lead to improved decision-making, increased innovation, and
increased productivity in organizations. The purpose of this research is to
investigate the effect of knowledge management implementation on the
organizational performance of BPS Cilacap Regency.
The Central Bureau of Statistics (BPS) of Cilacap
Regency plays an important role in collecting, analyzing, and disseminating
statistical data for policy formulation, economic development, and community
welfare in the region� (Ardiansyah, Brilian Putra, Wicaksono,
Inayah, & Perpajakan, 2023). As a government institution responsible for producing reliable and
accurate data, BPS faces the challenge of managing large amounts of information
while ensuring its accessibility, accuracy, and relevance to support evidence-based
decision-making.
In recent years, BPS has faced several challenges in fulfilling its
mission due to limited resources and staff expertise. To overcome these
challenges, BPS Cilacap Regency needs to adopt
innovative approaches such as Knowledge Management (KM). KM involves
identifying critical areas of knowledge within the organization and developing
strategies to capture this knowledge so that it can be shared effectively
across departments or divisions� (Ferreira, Mueller, & Papa, 2020). This approach aims to improve organizational learning processes by
enabling people from different functional areas to share experiences more easily.
This study aims to investigate the impact of knowledge management
implementation on the organizational performance of BPS Cilacap
Regency, with specific objectives outlined as follows. Firstly, the study
endeavors to analyze existing knowledge management practices within BPS Cilacap Regency, assessing processes, strategies, and tools
employed for knowledge asset management. This includes an evaluation of the
effectiveness of knowledge creation, acquisition, dissemination, and
utilization within the organization. Secondly, the research aims to evaluate
BPS organizational and performance indicators, encompassing data accuracy,
timeliness, and efficiency. Key performance indicators (KPIs) relevant to BPS,
such as data accuracy, timeliness of data collection and reporting, and
efficiency in data processing, will be measured and assessed. Thirdly, the
study seeks to examine the relationship between knowledge management practices
and organizational performance indicators, analyzing how effective knowledge
management contributes to improved data accuracy, timeliness, efficiency, and
overall BPS performance. Additionally, the research aims to identify potential
barriers and challenges faced by BPS in implementing effective knowledge
management strategies, with the goal of proposing recommendations and
strategies to overcome these obstacles and enhance knowledge management
effectiveness. Finally, the study aims to provide practical recommendations and
strategies for improving knowledge management and enhancing organizational
performance within BPS, offering insights applicable to similar organizations
in optimizing knowledge management processes to achieve strategic goals.
METHOD
The research method used for this study is descriptive-quantitative (Nassaji, 2019). Based on survey questionnaires distributed among employees working in
different departments/units within BPS Cilacap
Regency who have experience implementing or benefitting from KM practices. Sampler
size was determined using the Krejcie and Morgan formula,
which estimates the sample size based on population size and margin of
error (Godden, 2004). The quantitative methodology employed in this study aims to provide
statistical evidence and objective insights into the relationship between
knowledge management practices and organizational performance indicators. It
involves the selection of a representative sample of BPS employees, data
collection through structured surveys, and rigorous statistical analysis using
appropriate techniques such as correlation analysis and regression analysis.
This research also uses validity and reliability tests (Gowda et al., 2019). Validity is an index that shows that a measuring instrument truly
measures what it intends to measure (Surucu & Maslakci, 2020). The higher the validity, the more accurate it will be. Meanwhile, the
reliability test in this study is used to determine whether a questionnaire
used to collect research data can be considered reliable. The analysis of data
used in this study is multiple linear regression analysis. A multiple linear
analysis test is one independent variable with many dependent variables (Ottaviani & Marco, 2021).
Data Analysis is using IBM SPSS Statistics
Version 25.0.
RESULTS AND DISCUSSION
This study involved 30 employees and was given 15 question items
related to the research variables. In conducting research, the validity test
uses the Pearson product-moment correlation method with a confidence level of
0.05 if the value of the correlation score is greater r on the table, then the
statement tested is valid. The r table used is 0.361 because the number of
respondents is 30 employees, in this study. The results of the validity tests
are shown in Table 1.
Table 1
Validity Test Results
|
Variabel
|
Correlation score
|
r
|
Desc
|
|
Knowledge Management (X1)
|
0.742
|
0.361
|
Valid
|
|
0.635
|
0.361
|
Valid
|
|
0.729
|
0.361
|
Valid
|
|
0.452
|
0.361
|
Valid
|
|
0.670
|
0.361
|
Valid
|
|
Skills (X2)
|
0.643
|
0.361
|
Valid
|
|
0.768
|
0.361
|
Valid
|
|
0.670
|
0.361
|
Valid
|
|
0.671
|
0.361
|
Valid
|
|
0.712
|
0.361
|
Valid
|
|
Organisational Performance (Y1)
|
0.719
|
0.361
|
Valid
|
|
0.617
|
0.361
|
Valid
|
|
0.711
|
0.361
|
Valid
|
|
0.460
|
0.361
|
Valid
|
|
0.637
|
0.361
|
Valid
|
From
Table 1, it can be seen that all the items have a coefficient value greater
than Table r. In summary, it can be said that the data collection tools used in
this research is valid, so that the research can proceed to the next stage,
namely reliability testing.
The
reliability test for this study is tested using Cronbach's alpha technique, in
which a statement is considered reliable if the calculated Cronbach's alpha
value is greater than 0.60. The results of the reliability test are shown in
Table 2.
Table 2
Reliability Test
Results
|
Variabel
|
Cronbach Alpha
|
|
Knowledge
Manajemen (X1)
|
0,647
|
|
Skills
(X2)
|
0,729
|
|
Organisational
Performance (Y1)
|
0,712
|
Table 2
shows that all question items produce alpha coefficients greater than 0.60. So
that it can be summarized that all items of the data collection questionnaire
used in this research are reliable, so the questionnaire is declared logical
and feasible as a statement item to measure each indicator and regression
analysis.
The next
step is the regression analysis testing. The goal is to be able to calculate
the independent variables on organizational performance. If the Sig value is
less than 0.05 or if the t-count is greater than the t-table (2.052), then
there is an influence of variable X on variable Y. The results of the
calculation with the IBM SPSS program version 25. The results are presented in
Table 3.
Table 3
Results The Regression
Analysis Testing using IBM SPSS Version 25.
|
Coefficientsa
|
|
Model
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
|
B
|
Std. Error
|
Beta
|
|
1
|
(Constant)
|
5,982
|
3,263
|
|
3,833
|
0,008
|
|
X1
|
,709
|
,134
|
,730
|
5,287
|
0,000
|
|
X2
|
,016
|
,142
|
,015
|
2,111
|
0,003
|
|
a. Dependent Variable: Y1
|
Based on
Table 3, we will do testing to find out if the variables X1 and X2 partially
affect variable Y. After that, regression testing is continued to find out the
relationship between the independent variable and the dependent variable,
whether the value is positive or negative. To test the research hypothesis,
first, we must know about the basis for decision-making in the Partial t-test.
In this regard, two references can be used as a basis for decision-making.
Firstly, by looking at the significance value of Sig. And secondly, by
comparing the value of the t count with the t table (2.052). If the value of
significance (Sig) has a probability smaller than 0.05, then there is an
influence of the independent variable (X) on a dependent variable (Y), or the
hypothesis is accepted. The partial test results can be seen in Table 4.
Table 4
Results of Partial
Variable Testing
|
Coefficientsa
|
|
Model
|
Unstandardized
Coefficients
|
Standardized
Coefficients
|
t
|
Sig.
|
|
B
|
Std. Error
|
Beta
|
|
1
|
(Constant)
|
5,982
|
3,263
|
|
1,833
|
0,008
|
|
X1
|
,709
|
,134
|
,730
|
5,287
|
0,000
|
|
X2
|
,016
|
,142
|
,015
|
2,111
|
0,003
|
|
a. Dependent Variable: Y1
|
Based on
Table 4 above, it can be seen that the Sig. value for the effect of variable X1
on variable Y is 0.00 smaller than 0.05, and the t-count value of 5.287 is
greater than the t-table (2.052). Based on this, it can be summarized that
knowledge management has a significant effect on organizational performance.
Then, in the second test, the regression test also shows the effect of skills (X2) on organizational performance (Y1). The
significant value of the results of the skills (X2) table is 0.003 with a value
of significance level of 0.05 in the coefficients table, and the t-test gives a
figure of 0.003 with a t-table (2.052). So, it can be summarized that skills
have an effect on employee performance.
So the
next step is the f-test, which is to determine the effect simultaneously of the
dependent variable Y. These tests are carried out to see the effect of all
independent variables on the dependent variable. The standard used is 0.05. If
the F significance value is less than 0.05, it is understood that the
independent variable has an effect on the dependent variable simultaneously or
vice versa. The F test results can be seen in Table 5.
Table 5
F Test Results
|
ANOVAa
|
|
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
1
|
Regression
|
102,184
|
2
|
51,092
|
14,997
|
.000b
|
|
Residual
|
91,982
|
27
|
3,407
|
|
|
|
Total
|
194,167
|
29
|
|
|
|
|
a. Dependent Variable: Y1
|
|
b. Predictors: (Constant), X2, X1
|
|
|
From
Table 5 given above, a significance value is 0.000. Because the significance
value of 0.000 is less than 0.05, then under the decision-making basis in the F
test, It can be concluded that the hypothesis is
accepted. In addition, both the X1 and X2 variables have a simultaneous effect
on the Y variable.
CONCLUSION
These result
obviously show the positive impact of knowledge management variables on
organizational performance. Comprehensive hypothesis testing results, including
simultaneous, partial, validity, and reliability testing, unequivocally prove
that better knowledge and skills management significantly contribute to
improved organizational performance.
REFERENCES
Abualoush,
Shadi, Masa�deh, Ra�ed, Bataineh, Khaled, & Alrowwad, Ala�aldin. (2018).
The role of knowledge management process and intellectual capital as
intermediary variables between knowledge management infrastructure and
organization performance. Interdisciplinary Journal of Information, Knowledge,
and Management, 13, 279�309. https://doi.org/10.28945/4088
Anwar, Govand, & Abdullah,
Nabaz Nawzad. (2021). The impact of Human resource management practice on
Organizational performance. International Journal of Engineering, Business and
Management, 5(1), 35�47. https://doi.org/10.22161/ijebm.5.1.4
Ardiansyah, Adit, Brilian
Putra, Bally, Wicaksono, Galih, Inayah, Resvi, & Perpajakan, Program Studi.
(2023). Analysis of the Role of Hotel Tax Effectiveness and Contribution To
Cilacap Regional Income. Yudishtira Journal : Indonesian Journal of
Finance and Strategy Inside, 3(1), 10�20.
Ferreira, Jo�o, Mueller, Jens,
& Papa, Armando. (2020). Strategic knowledge management: theory, practice
and future challenges. Journal of Knowledge Management, 24(2), 121�126.
https://doi.org/10.1108/JKM-07-2018-0461
Ghani Al-Saffar, Nasser Abdul,
& Obeidat, Abdallah Mishael. (2020). The effect of total quality management
practices on employee performance: The moderating role of knowledge sharing.
Management Science Letters, 10(1), 77�90.
https://doi.org/10.5267/j.msl.2019.8.014
Girard, John, Georgia, Middle,
& College, State. (2022). Defining knowledge management : Toward an
applied compendium Defining knowledge management : Toward an applied
compendium. Online Journal of Applied Knowledge Management, 3(June 2015), 1�20.
Godden. (2004). Sample Size
Determination Using Krejcie and Morgan Table - KENPRO. (February), 81�83.
https://doi.org/10.13140/RG.2.2.11445.19687
Gowda, Guru S., Komal, Sai,
Sanjay, Tarasingh N., Mishra, Saumya, Kumar, Channaveerachari N., & Math,
Suresh B. (2019). Internal, External, and Ecological Validity in Research
Design, Conduct, and Evaluation. Indian Journal of Psychological Medicine,
41(2), 138�143. https://doi.org/10.4103/IJPSYM.IJPSYM
Nassaji, Hossein. (2019).
Qualitative and descriptive research: Data type versus data analysis. LANGUAGE
TEACHING RESEARCH, 19, 129�132.
https://doi.org/https://journals.sagepub.com/doi/10.1177/1362168815572747
Ottaviani, Filippo Maria,
& Marco, Alberto De. (2021). Multiple Linear Regression Model for Improved
Project Cost Forecasting. Procedia Computer Science, 196(2021), 808�815.
https://doi.org/10.1016/j.procs.2021.12.079
Surucu, Lutfi, & Maslakci,
Ahmet. (2020). Business & Management Studies : Business & Management
Studies: An International Journal, 8(3), 2694�2726.
Wijaya, Arwan Putra, &
Hong, Jung Hong. (2018). Quantitative assessment of social vulnerability for
landslide disaster risk reduction using gis approach (case study: Cilacap
regency, province of central Java, Indonesia). International Archives of the
Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS
Archives, 42(4), 77�85. https://doi.org/10.5194/isprs-archives-XLII-4-703-2018
Effect of knowledge management on organizational performance of
Central Statistics Agency (BPS) of Cilacap Regency