Lailatul Saadah, Achmad Fajar Hendarman | http://devotion.greenvest.co.id
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THE RELATIONSHIP BETWEEN NEW LEARNING
ORGANIZATION ON HUMAN CAPITAL READINESS OF
SMEs IN INDUSTRY 4.0
Lailatul Saadah, Achmad Fajar Hendarman
1
Master of Science in Management Program, School of Business and Management, Institut Teknologi Bandung
(SBM-ITB), Indonesia,
2
School of Business and Management, Institut Teknologi Bandung (SBM-ITB),
Indonesia
lailatul_saadah@sbm-itb.ac.id
1
, achmad.fajar@sbm-itb.ac.id
2
KEYWORDS
Human Capital
Readiness, New
Learning Organizations,
Industry 4.0
ABSTRACT
Learning organization is one of the strategic plans that support the
development of an organization. Based on organizational literacy
known that learning organizations are aimed at developing human
resources as the main driver in creating and developing an
organization. Learning organization in the Era of Industry 4.0 forces
a rapid change in organizational models and has no exception.
Implementing diversity within organizations and change-resistant is
a global challenge that critically required us to increase the readiness
for change. The purpose of this study is to examine the relationship
between new learning organizations and human capital readiness to
continue to develop as a phenomenon at the individual level in
SMEs in facing the era of industrial revolution 4.0. The population
of this research is all SMEs in Indonesia. By using the probability
sampling technique, namely simple random sampling, the minimum
number of samples is 45 SMEs. The statistical method using
multiple linear regression. The results showed that there is a
significant relationship between new learning organizations (holistic
people experience and agile, digitally enable infrastructure) on
human capital readiness in Industry 4.0. The significance value ≤
0.05. This means that the learning organizations especially the better
holistic people experience and agile, digitally enable infrastructure,
the higher the human capital readiness in a SMEs in the Era of
Industry 4.0.
INTRODUCTION
Organizations have an important influence on the environment. The organization is a
system in which some sub-systems interact with the environment. In learning organizations,
not all organizations can learn quickly to survive. Therefore, the organization must always
responsive and adaptive to complex environmental development, and always responsive in
the face of world competition that continues to grow. Organizational ability is needed in
making changes in knowledge with the learning process. That's why commitment is needed
high in building and developing strategic resources. Learning organization (LO) is a concept
in a dynamic organizational environment and LO is a strategy in the success of the
organization.
Volume 3, Number 10, August 2022
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Organizational learning is learning together, where humans learn continuously expand
their capacity to create results that do them want, a place to find new patterns and think
broadly, a place to gathering these shared aspirations is liberated and a place where people
continually learn how to learn together (Senge, PM, 2004). Therefore, companies need to
implement learning organizations in the industrial era with the current increase in the Internet
of Things.
Enhancement of the Internet of Things (IoT), Big Data, AI, and Virtual Reality is a
big challenge for SMEs in Indonesia. SMEs in Indonesia is increasing from the previous year.
The development of cooperatives and MSMEs in Indonesia in the future is direct at
strengthening economic resilience to support the rise of the economy. Based on the
Regulation of the Minister of Cooperatives and Small and Medium Enterprises of the
Republic of Indonesia No. 5 of 2020 concerning the Strategic Plan of the Ministry of
Cooperatives and Small and Medium Enterprises for 2020-2024, it is stated that the growth of
MSMEs in Indonesia continues to increase, (figure 1).
Source: Minister of Cooperatives and Small and Medium Enterprises of the Republic of
Indonesia, 2020
Figure 1. SMEs in Indonesia
In line with the increase in SMEs in Indonesia, it is necessary to implement learning
organizations to have human resource readiness. Learning organization is one of the strategic
plans that support the development of an organization. Based on organizational literacy
known that learning organizations are aimed at developing human resources as the main
driver in creating and developing organizations (Runhaar, P, 2017). The organizational
model in the previous study focused on the processes (considering humans as machines
oriented towards efficient performance). Nowadays, the organizational model is supposed to
change on people (considering people as capital are valuable for organizational development.
Traditional organizations have to transform into digital-based organizations. Therefore, the
company should pay attention to the importance of implementing learning organizations. The
idea of the learning organization is a must to stay alive in an organization (Pedler, M. and
Burgoyne, J, 2017).
Currently, companies are required to continue to compete, therefore the
implementation of a learning organization is important for the company. Learning
organization in the Era of Industry 4,0 forces a rapid change in organizational models and has
no exception (Stephen M. Mutula, 2009). The new approach in a learning organization is the
59,260,000
61,650,000
62,922,617
64,194,057
65,471,134
56,000,000
58,000,000
60,000,000
62,000,000
64,000,000
66,000,000
2015 2016 2017 2018 2019
SMEs in Indonesia 2015-2019
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new learning organization. Build the new learning organization will be achieving strategic
advantage through a commitment to learning.
Implementing diversity within organizations and change-resistant is a global
challenge that critically required us to increase the readiness for change (Adserias, Ryan P.;
Charleston, LaVar J.; Jackson, Jerlando F. L., 2017). This research aim contributes to the
sustainable competitive advantage for SMEs in Indonesia. The purpose of this study is to
examine the relationship between new learning organizations and human capital readiness to
continue to develop as a phenomenon at the individual level in SMEs in facing the era of
industrial revolution 4.0. From this research, it will be known how new learning
organizations affect the readiness of human capital to face the development (Industry 4.0),
especially in SMEs.
METHOD RESEARCH
The research paradigm in this research is positivism using the quantitative method.
This research was deductive by using a cross-sectional study design approach, meaning that
the sampling method is done at the same time simultaneously (Saunders M, Lewis P, and
Thornhill A, 2017). The research design in this thesis used a survey with a questionnaire.
Survey research can be generalized because the sample tends to be representative of a large
population (Saunders M, Lewis P, and Thornhill A, 2017). The total population is 8.000.000
SME’s in Indonesia. The sampling technique used in this study is probability sampling.
Probability sampling is a sampling technique that provides equal opportunities for each
element of the population to be selected as a member of the sample (Sugiyono, 2017). This
study using the Slovin formula as follows (Silalahi, 2017):
Where:
n = number of samples
N = total population
e = precision value 0,15 or 15%
Then:
This research using statistical techniques namely multiple linear regression. Prior to
the multiple linear regression analysis, the data were tested for classical assumptions which
included normality test, multicollinearity test, and heteroscedasticity test (Ghozali, 2016).
The multiple linear regression to examine the relationship of new learning organizations on
the human capital readiness of SMEs in Industry 4.0. Regression analysis carried out to
predict/knowing the effect of one or more independent variables on the dependent variable
(Ghozali, 2016). The regression equation in this study is:
Y = α + β
1
X
1
+ β
2
X
2
+ β
3
X
3
+ β
4
X
4
+ β
5
X
5
+ β
6
X
6
+ e
Where:
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Y : Human capital readiness
X
1
: Clarity of purpose
X
2
: Holistic people experience
X
3
: Thriving ecosystem
X
4
: Agile, digitally enabled infrastructure
X
5
: Continual engagement
X
6
: Intelligent decision-making
α : Score intercept (Constant), Value of Y When X = 0 (Constant).
β : The regression direction coefficient, the direction number or the regression coefficient
that shows the increase or decrease in the dependent variable based on the independent
variable. If β (+) then there is an increasing trend and if β (-) then there is a decreasing
trend.
e : error
The hypothesis testing used t-test, F test and R-square (R
2
) (Ghozali, 2016). data
processing by using software R.
RESULT AND DISCUSSION
The data of this study obtained from questionnaires that were distributed by google
form to SMEs. The sample in this study was calculated based on the probability sampling
technique with the Slovin formula so that a minimum sample in this study is 45 SMEs. After
examining the completeness of the questionnaire data, then the data was processed for
research.
A normality test is used to determine whether the data is normally distributed or not.
The normality test in this study used p-plot normal test. The following shows the results of
the R cloud program output normality test using the normal p-plot test.
Figure 2. Results of Normality Test
Based on the normal residual p-plot, it can be seen, that the data normally distributed.
This can be seen from the data that spreads close to the diagonal or follows the direction of
the diagonal line. It can be concluded that the regression model meets the assumption of
normality.
The multicollinearity test aims to test whether the regression model found a
correlation between the independent variables. A good regression model should not correlate
with the independent variables. If the independent variables are correlated with each other,
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then these variables are not orthogonal. Orthogonal variables are independent variables
whose correlation value between independent variables is equal to zero.
To detect the presence or absence of multicollinearity in the regression model, it can be seen
from the tolerance value or variance inflation factor (VIF). These two measures indicate
which of each independent variable is explained by the other independent variables.
Tolerance measures the variability of the selected independent variables that cannot be
explained by other independent variables. A low tolerance value is the same as a high VIF
value (because VIF = 1/tolerance) and indicates high collinearity. The cut-off value used is a
tolerance value of 0.10 or equal to the VIF value above 10.
Table 1. Results of Multikolinerity Test
Multicolinearity test
vif(lm_model)
Clarity of Purpose
Holistic People Experience
Thriving Ecosystem
Agile, Digitally Enabled Infrastructure
Continual Engagement
Intelligent Decission Making
2.919977
3.140107
2.484750
2.254666
3.820542
2.575517
Source: Output R, (2022)
The results above indicate that the value of each independent variable has a VIF value
< 10. So it can be concluded that there is no multicollinearity between independent variables
in the regression model.
A heteroscedasticity test was used to determine whether the data had the same
variance (homogeneous). The heteroscedasticity test of the data carried out using the
Breusch-Pagan test. Following are the results of the heteroscedasticity test using the R
version 2.0 software:
Table 2. Results of Heteroscedasticity Test
studentized Breusch-Pagan test
data: lm_model
BP = 8.6288, df = 6, p-value = 0.1956
Source: Output R, (2022)
By using R software, the results of the regression coefficients are as follows:
Table 3. Results of Multiple Linear Regression
Call:
lm(formula = hcr ~ cp + hpe + te + adei + ce + idm, data = data2)
Coefficients:
(Intercept) cp hpe te adei ce idm
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6.7611 0.0561 0.3672 -0.2374 0.3055 0.3143 -0.1048
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.7611 3.6894 1.833 0.0747 .
cp 0.0561 0.1603 0.350 0.7283
hpe 0.3672 0.1389 2.642 0.0119 *
te -0.2374 0.1422 -1.670 0.1031
adei 0.3055 0.1338 2.283 0.0281 *
ce 0.3144 0.1650 1.905 0.0644 .
idm -0.1048 0.1340 -0.783 0.4387
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.392 on 38 degrees of freedom
Multiple R-squared: 0.6348,
Adjusted R-squared: 0.5772
F-statistic: 11.01 on 6 and 38 DF,
p-value: 4.362e-07
Source: Output R, (2022)
Based on the results above, there are only 2 variables that are declared significant,
namely the holistic people variable and agile, digitally enable infrastructure. Due to the p-
value (sig) < 0.05 then H0 is rejected, meaning that the holistic people and agile, digitally
enabling infrastructure to have a significant effect on the human capital readiness. This is in
line with the results state that the learning organization in digital era 4.0. New learning
organization determined whether an organization can prepare human capital readiness for
their organisation (Daly J, Overton L, 2017). Organizational learning has a positive and
significant effect on innovation capability, both directly and through mediating hard skills
and soft skills (Goestjahjanti FS, Asbari M, Purwanto A, et al. , 2020).
Based on the results above, it is known that the F count is 11,01 and R-square value is
0.6348 or 63.48% which shows the meaning that the new learning organization variable
simultaneously has an effect of 63.48% on human capital readiness. While the remaining
36.52% is influenced by other factors that are ignored by the author.
CONCLUSION
The purpose of this study is to examine the relationship between new learning
organizations and human capital readiness to continue to develop as a phenomenon at the
individual level in SMEs in the era 4.0. From this research, known that new learning
organizations (holistic people and agile, digitally enabling infrastructure) affect the human
capital readiness in Industry 4.0, especially in SMEs.
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