INTRODUCTION
Education is the main
foundation for efforts to develop human quality, through education humans get
the opportunity to know more new things, adapt, and create what is needed to
ensure their survival. In addition, education spillovers in the form of
tertiary education of bordering regions are incorporated (Benos, & Karagiannis, 2016).
Higher education
as a part of formal education offers opportunities for people to gain various
kinds of knowledge and experience, therefore higher education is also often
known as a place to prepare oneself before facing the wider social world. one
of the institutions which are supporting higher education is called a
university as a place for various kinds of people to meet, who then form their
community structure, namely the academic community with one of the constituent
elements known as students.
In
education perspective, innovation is defined as a new or considerably enhanced
product, process, organizational approach, or organization developed by or
having a major impact on the activities of a Higher Education institution or
other Higher Education stakeholders (Menzil, et al, 2022).
To maximize the potential of students, especially in universities, there are
various choices of internal organizations that specialize in specific fields,
some of which are spread through the dimensions of culture, arts, sports, and
student-level government organizations such as the Student Association or the
Student Executive Board where student involvement in it adjusts to the
tendencies towards their interests and talents.
Student
organizations are seen as the other side of the learning process at university
and are also known as the place of field implementation of the theories
obtained through formal classes can be actualized. Furthermore Smith
and Chenoweth (2015)
remarked how the leadership roles in co-curricular activities such as student
organizations indicate a significant impact on student development such as
specific leadership skills and interpersonal abilities so that involvement in
student organizations supports student success during and after college. Involvement
in student organizations encourages affective and cognitive changes in
students, and the benefits can often appear beyond classroom learning, participation
in organizational activities contributes to one's intellectual, social, and
emotional changes from time to time, and things such as critical thinking, new
knowledge, synthesis, and decision making as well as personality development
such as attitudes, values, aspirations, and personality dispositions become an
inseparable part of organizational activities (Montelongo,
2002).
In some cases, the organization also sometimes negatively affects student
learning while in other cases it helps positively. Therefore, student
organizations differentially affect academic performance, depending on the type
of organization and the race and gender of the students (Baker,
2008).
Besides
from the positive effects previously, University is facing a challenge on how
to encourage active participation from its students who come from the Z
generation to join student activities, the initial stage that can be done is to
identify student motivation in participating in student activities (Kumendong
& Panjaitan, 2021).
However, the current condition of student organizations shows a lack of
performance in carrying out its role as a place for self-development, this
happens due to the lack of member participation in various internal
organizational activities that lead organizational performance to become
unproductively. Previous research has shown that the organizational climate is
not optimal that supports student organization activities and the low
motivation of student organizations affects student participation in
organization activities, the results of research in the field show that the
campus organizational climate affects student organization motivation, these
two things are interrelated each other, where the climate optimal campus
organization will foster student organizational motivation well and vice versa,
besides that the results of further observations also show that the campus
organizational climate affects student participation in student organizations (Yuanita,
2017).
Especially for the organizational climate students are more likely to join if
they have a positive perception of the organization and see a benefit to
themselves in joining (Trolian,
2019).
The
development of the role of student organizations through student participation
is considered not to have received serious attention, it is also can found in
the Hasanuddin University Political Science Student Association (Himapol
Unhas). Direct experience, observation, and preliminary discussions conducted
on several implementers of organizational and non-implementer activities
indicated that the not-so-optimal campus organizational climate and low student
organizational motivation were related to the level of student participation in
organizational activities within the Himapol Hasanuddin University. therefore,
the approximate value of the three variables is less than 60%. However, there
is a fundamental question that needs to be criticized, that is whether student
participation in campus organizations is indeed at a low level and how it
relates to the state of the organization and the perception of the students
themselves, this diagnosis is important because although the relationship
between the variables is positive and significant, the effectiveness of the
outputs constructed from the results of the analysis may not be appropriate to
minimize problems. Since participation is a concept, it will be possible if
there are various subjective meanings from people who are around the
organization, therefore, stating the participation of members from an
organization at a low level, it is clear that it should explicitly affect the
sustainability of the organization's work program, but in fact, this does not
happen at Himapol Unhas because all programs completely run according to the
initial design that has been set. This situation becomes more interesting with
the swift discourse related to the current state of student organizations that
came from various circles, including the general public, university
administrators, alumni, and even the implementer of the organization itself
which ultimately blame the low level of participation.
Regrettably,
this study of campus organizational problems especially related to student
participation is far from sufficient and difficult to access, unlike other
organizational areas such as industry or government which are full of attention
from practitioners. Moreover, it is important to note that studying and
reporting on the concept of student participation differs from one nation to
another simply because of the presence of different cultures and education
policies (Shahabul,
Muthanna, & Sultana, 2022).
Worth pondering that student organizations provide many benefits to human
development, especially when they have entered the workforce (Simmons,
Creamer, & Yu, 2017).
Most of the studies on student organizations end with conclusions about the
influence and significance of certain independent variables and lack in
association these correlations with descriptive data in deep assumptions. This
research is present as a fundamental effort to find the pattern of the problems
described previously. The consideration for choosing Organizational Climate and
Motivation as an exogenous variable and Participation as an endogenous variable
is to provide a balanced description of the two sides of the research object.
First, how members of the organization assess their organization through
measuring the quality of the organizational climate, and secondly, how members
of the organization asses themselves as an entity that runs the organization
and how they confirm their contribution to the organization. To obtain desired
results, the research constructs using quantitative methods with survey
techniques. The
collected data then be analyzed through partial least squares structural
equation modeling (PLS-SEM) to estimate a complex causal relationship model
between latent variables using the Smart-pls software. This study aims to
examine how PLS-SEM is applied in student organization research so that the
results of this study are expected to show the pattern of the relationship
between descriptive data and evidence of the significant effect of the
relationship between variables that can be used in the formulation of internal
policies of student organizations in dealing with circulating discourse.
METHOD
The method used in this
research is through approach with survey techniques that are the quantitative
approach uses a deductive perspective, involves many subjects, uses measurement
instruments, and data in the form of scores, and is analyzed statistically (Periantalo, 2019). The survey research examines
large and small populations by selecting and examining selected samples from
the population, to find the incidence, distribution, and relative interrelation
of sociological and psychological variables (Kerlinger, 2014).
The
research is located at the Association of Political Science Students, Faculty
of Social and Political Sciences Hasanuddin University, Makassar, Indonesia.
The subject of this research is the population of all members of the Hasanuddin
University Political Science Student Association with a total population of 160
people, which is then further managed using a sampling technique that is simple
random sampling without replacement (SRS-WOR) which is practically done by
taking one by one from the existing units until the required number of samples
is obtained the supporting software used in conducting random sampling is the R
program (Asra & Prasetyo, 2016). However, before determining
the selected sample it is necessary to determine
the sample size to be used, in this case, the sample size calculation using the
Isaac and Michael formula with an error rate of 5% and a 95% confidence interval
(Isaac & Michael, 1995).
Based on the formula and population, the number of samples used in this study
is 113 members of Himapol Unhas.
There
are three variables in this study, namely organizational climate (X1),
motivation (X2), and participation (Y). The model of the relationship between
variables in structural equation modeling with two exogenous variables and one
endogenous variable. The schema of the relationship between the variables can
be seen in Figure 1.
Figure
1
Research
Framework
Organizational climate
according to Schneider,
Ehrhart, and Macey (2013)
emerges through the process of social information concerning the meaning
attached by employees to the policies, practices, and procedures they
experience, and the behavior they observe can be appreciated, supported, and
expected. Organizational climate is an alternative construct used to conceptualize
the way people experience and describe their working conditions, this includes
not only business but also schools and government. Furthermore, according to Milton
(1981)
organizational climate is defined as the quality of the internal environment
that is relatively enduring, becomes an experience for every member of the
organization, influences their behavior, and can be discussed in a set of
characteristics or attributes (traits) and becomes a differentiator between one
organization with other organizations. The definition according to Litwin
and Stringer (1968)
organizational climate is the effect of subjective perceptions of the formal
system, the manager's informal style, and other environmental factors that
influence the attitudes, beliefs, values, and motivation of people who work in
a certain company.
Motivation refers to the
forces within the individual that explain the level, direction, and persistence
of effort expended at work. Meanwhile, according to Hall and
Goetz (2013)
motivation refers to the processes that underlie the initiation, control,
maintenance, and evaluation of goal-oriented behavior, so motivation refers to
the psychological mechanisms that occur throughout the process of pursuing
one's goals.
Participation focuses
specifically on participation in decision making which then reaches two basic
conclusions (Cotton,
Vollrath, Froggatt, Lengnick-Hall, & Jennings, 1988).
First, it emphasizes that participation can take several diverse forms (short
or long term, formal or informal, direct or indirect). Second, confirms that
the effect of participation on satisfaction and performance varies according to
the form of participation. Meanwhile, according to Glew,
O’Leary-Kelly, Griffin, and Van Fleet (1995)
participation is defined as a conscious and deliberate effort by individuals at
higher levels in the organization to provide an extra visible role or role
expansion opportunities for individuals or groups at lower levels in the organization
to have a greater voice. in one or more areas of organizational performance. In
addition, participation can also be in the form of the participation of a
person or community group in the development process, both in the form of
statements and in the form of activities by providing input such as thoughts,
energy, time, expertise, and capital (Sumaryadi, 2005).
If the organizational climate
is seen as a person's perception of his environment and motivation refers to
the forces of internal individual that explain the level, direction, and
persistence of effort expended in the workplace, both organizational climate
and motivation have shaped human behavior. This organizational behavior can be
manifested in the form of participation which is a conscious and intentional
act either directly or indirectly, this decision-making occurs after
accumulation which refers to the overall psychological mechanism including the
perception of the environment.
There
are three instruments developed in this study, namely the instrument on
organizational climate (10 dimensions), motivation (3 dimensions), and
participation (4 dimensions) a total of 17 item dimensions. All instruments
were developed with a Likert scale using the lowest alternative answer 1 and
the highest 5. The data collection instrument used in this study was a
questionnaire compiled through an online application or commonly called a
self-administered questionnaire with the final result of 84 people responding
from a total of 113 predetermined samples. In other words, the response rate in
this study is 75%.
Table 1
Operational Research
Variables
|
NO
|
Variable
|
Dimension
|
|
1.
|
Organizational Climate (X1). Developed from
The Organizational Climate Questionnaire (OCQ) (Furnham and Goodstein, 1997)
were then adapted to research needs.
|
1.
Role clarity.
|
|
2.
Self-respect.
|
|
3.
Communications.
|
|
4.
Reward systems.
|
|
5.
Surveillance systems.
|
|
6.
Support systems.
|
|
7.
Conflict management.
|
|
8.
commitment.
|
|
9.
Practice and learning.
|
|
10.
Directional alignment.
|
|
2.
|
Organizational Motivation (X2). Developed
from An empirical test of a new theory of human
needs (Alderfer, 1969) and then adapted to research needs.
|
1.
Existence needs.
|
|
2.
Relatedness needs.
|
|
3.
Growth needs.
|
|
3.
|
Participation (Y). Developed from
Understanding the Role of Participation in Public Service Delivery (Nayak and
Samanta, 2014) were then adapted to research needs.
|
1.
Attend activities.
|
|
2.
Propose a discussion.
|
|
3.
File a complaint.
|
|
4.
Contribution.
|
After the methodological completeness is met, including
data from research subjects which are the points of assessment for each member
of Himapol Unhas on the three variables. Furthermore, testing the feasibility
of the data through the Validity Test is used to measure the accuracy of an
instrument in measuring a variable, and the Reliability Test is used to test
the stability, consistency, and accuracy of the data pattern. Several types of
testing must meet the standard, especially if using the partial least squares
method. The following are the results of the data test:
1. Outer loading
Outer
loading is a table that contains a loading factor to show the magnitude of the
correlation between indicators and latent variables, in the assessment of the
outer loading value of 0.70 or higher the priority. but if it is exploratory
research, 0.4 or higher is acceptable (Hulland, 1999).
Table 2
Outer Loading Value
|
Variables
|
Outer
Loading
|
|
|
Organizational
Climate
|
0.75
|
0.84
|
0.76
|
0.72
|
0.80
|
0.83
|
0.71
|
0.68
|
0.68
|
0.69
|
0.80
|
0.76
|
0.79
|
|
Organizational
Motivation
|
0.71
|
0.73
|
0.76
|
0.79
|
0.77
|
|
|
|
|
|
|
|
|
|
Member
Participation
|
0.69
|
0.63
|
0.81
|
0.76
|
0.73
|
0.76
|
|
|
|
|
|
|
|
Source: Primary Data
(Processed)
Based on the data from table 2, the outer
loading value can be seen that all items or indicators of the outer loading
value are > 0.5. Referring to the Outer Loading value limit, the researcher
sets > 0.5 as the acceptance standard with consideration of the validity and
reliability of the constructs meeting the requirements and the model is still
newly developed, so that based on the outer loading validity of all items or
indicators then it can be stated as valid.
2. Average Variance Extracted
The
convergent validity of a construct with reflective indicators was evaluated by
Average Variance Extracted (AVE). The AVE value should be 0.5 or more. An AVE
value of 0.5 or more means that the construct can explain 50% or more of the
variance of each item (Bagozzi & Yi, 1988).
Table 3
Average Variance Extracted Value
|
Average Variance
Extracted Value (AVE)
|
|
X1
|
0.570
|
|
X2
|
0.543
|
|
Y
|
0.576
|
Source: Primary Data
Based
on the Average Variance Extracted (AVE) value, it can be concluded that all
constructs have been achieved, namely the convergent validity requirements of
the AVE value of the three variables > 0.50.
3. Fornell-Larcker Criterion
A
construct is said to be valid by comparing the root value of the AVE
(Fornell-Larcker Criterion) with the correlation value between latent
variables. The AVE root value must be greater than the correlation between a
latent variable. To assess discriminant validity is done by looking at the
Fornell Larcker Criterion value, which is a method that compares the square
root value of the Average Variance Extracted (AVE) of each construct with the
correlation between other constructs in the model (Henseler, Ringle, &
Sarstedt, 2015). If the AVE square root value
of each construct is greater than the correlation value between constructs and
other constructs in the model, then the model is said to have a good
discriminant validity value (Fornell & Larcker,
1981).
Table 4
Fornell-Larcker Criterion
Value
|
|
X1
|
X2
|
Y
|
|
X1
|
0.759
|
|
|
|
X2
|
0.577
|
0.755
|
|
|
Y
|
0.526
|
0.586
|
0.737
|
Source: Primary Data
Based
on table 4, all roots of the AVE (Fornell-Larcker Criterion) of each construct
are greater than their correlation with other variables. As an explanation of
the X1 variable: the AVE value is 0.570, and the AVE root is 0.759, which value
is greater than the correlation with other constructs, that is X2 0.577 and Y
of 0.526. This also applies to other latent variables, where the root value of
AVE > Correlation with other constructs. Because all latent variables have a
root value of AVE > Correlation with other constructs, the discriminant
validity conditions in this model have been met.
4. Cross Loading
Cross-loading is another method to determine discriminant validity, if
the cross-loading value of each item to other constructs is greater than the
cross-loading value belonging to the variable being compared is greater than
the research variable could say to be valid (Ab Hamid, Sami, &
Sidek, 2017).
Table 5
Cross Loading Value
|
|
X1
|
X2
|
Y
|
|
lK1
|
0.754
|
0.364
|
0.374
|
|
lK10
|
0.843
|
0.346
|
0.301
|
|
lK11
|
0.764
|
0.481
|
0.384
|
|
lK12
|
0.720
|
0.503
|
0.587
|
|
lK13
|
0.804
|
0.421
|
0.391
|
|
lK2
|
0.833
|
0.468
|
0.432
|
|
lK3
|
0.714
|
0.419
|
0.356
|
|
lK4
|
0.684
|
0.446
|
0.310
|
|
lK5
|
0.680
|
0.320
|
0.240
|
|
lK6
|
0.696
|
0.279
|
0.284
|
|
lK7
|
0.805
|
0.438
|
0.458
|
|
lK8
|
0.759
|
0.479
|
0.466
|
|
lK9
|
0.786
|
0.564
|
0.413
|
|
M1
|
0.429
|
0.717
|
0.336
|
|
M2
|
0.406
|
0.730
|
0.300
|
|
M3
|
0.519
|
0.762
|
0.468
|
|
M4
|
0.457
|
0.792
|
0.571
|
|
M5
|
0.347
|
0.771
|
0.471
|
|
PA1
|
0.185
|
0.321
|
0.691
|
|
PA2
|
0.321
|
0.464
|
0.632
|
|
PA3
|
0.474
|
0.405
|
0.818
|
|
PA4
|
0.536
|
0.461
|
0.762
|
|
PA5
|
0.363
|
0.463
|
0.735
|
|
PA6
|
0.346
|
0.440
|
0.768
|
Source: Primary Data
Based
on the table of cross-loading values for each items,
it can be concluded that for example, the IK1 item is part of the X1 variable
(0.754) with a higher cross-loading value than the other constructs, namely X2
(0.364) and Y (0.374). The same thing applies to items from each other
construct which shows that all cross-loading values belonging to the compared
variables are larger and can be said to be valid.
5. Internal Consistency
Reliability
Internal
Consistency Reliability is a confidence measure used to evaluate the extent to
which different test items relate to the same construct and produce similar
results. Internal Consistency Reliability can also be understood as to how
capable the indicator can measure its latent construct. The tools used to
assess this are composite reliability and Cronbach's alpha. The composite
reliability value must be 0.7 or higher. however, for exploratory research, 0.6
or higher is acceptable. (Bagozzi & Yi, 1988), while for Cronbach's alpha,
the expected value is above 0.7 (Taber, 2018).
Table 6
Internal Consistency
Reliability Value
|
|
Cronbach’s Alpha
|
Composite
Reliability
|
|
X2
|
0.813
|
0.869
|
|
Y
|
0.831
|
0.879
|
|
X1
|
0.938
|
0.946
|
Source: Primary Data
Based on table 6, it can be concluded
that all constructs have Cronbach's Alpha value > 0.7 so it can be said that
all of these constructs are reliable. The same thing also applies to composite
reliability values, all range values reach > 0.7.
Refers to the previous data,
it can be concluded that the model built, the instrument used, and the
collected data can be processed to the next stage by descriptive analysis and
measure the effect on relationship and significance. When testing the
descriptive hypothesis, based on the data held in the form of interval data,
the one-sample t-test hypothesis test or One tail test is used, the technique
is used when H0 reads "Lower or equal to (<) and Ha reads Bigger (>)."
Furthermore, to test the inferential analysis, the Structural Equation Model
technique is used which is a method of multivariate data analysis to analyze
complex relationships between constructs and indicators (Hair et al, 2021)
which are later determined based on an inner model in the application of
Partial least squares, one of the relevant reasons for using this technique is
because Partial Least Square is best used when analyzing a small population
that limits the sample size ( Joe F Hair Jr, Sarstedt, Hopkins, &
Kuppelwieser, 2014).
RESULTS AND DISCUSSION
A. Hypothesis
Testing
1.
Descriptive Hypothesis
This testing is carried out
to obtain a generalization of research results based on one sample, in this
study the test was carried out as a one-sided test, namely on the left side.
The test value used is determined by the formula: “Highest score for each item”
x “number of items” x “number of respondents” / "number of respondents.”
Table 8
Testing
Results
|
Variables
|
Hypothesis
Testing
|
T-Score
|
T-Table
|
P-Value
|
Status
|
|
Organizational
Climate
|
H0 : < 60%
|
5.551
|
1.797
|
.00001
< 0.05
|
Reject
H0
|
|
Organizational
Motivation
|
H0 : < 60%
|
12.236
|
1.797
|
.00001
< 0.05
|
Reject
H0
|
|
Member
Participation
|
H0 : < 60%
|
2.571
|
1.797
|
.005962
< 0.05
|
Reject
H0
|
Source:
Primary Data (Processed)
The rule of thumb used in
this test is to reject H0 if the t-score is greater than the t-table. In
addition, reject H0 if the p-value is less than the confidence level (0.05).
Based on the data listed in table 8, the three variables have met the
conditions set so that H0 rejected.
2. Path Coefficient
Measurement of path
coefficients is carried out to see the significance and strength of the
relationship between the constructs and also to test the hypothesis. The value
of path coefficients ranges from -1 to +1, if the value of path coefficients is
closer to +1 then the relationship between the two constructs is getting
stronger, on the other hand, a relationship that is close to -1 indicates that
the relationship is negative (Sarstedt
& Christian, 2017).
Table
9
Path
Coefficient Value
|
|
X1
|
X2
|
Y
|
|
X1
|
|
0.577
|
0.281
|
|
X2
|
|
|
0.424
|
Source:
Primary Data
Based on value table 9, the
direct effect of X1 on Y is 0.281, which means that if X1 increases by one
unit, Y can increase by 28.1%. Furthermore, the direct effect of X2 on Y is
0.424, which means that if X2 increases by one unit, Y can increase by 42.4%.
As for the direct effect of X1 on X2, the value is 0.577, which means that if
X1 increases by one unit, then X2 can increase by 57.7%. Due to the path
coefficient value of the three variables leading to +1 the connection between
the three variables is positive.
3. Bootstrapping
a.
Direct Effect
Bootstrapping is a process to
assess the level of significance or probability of direct effects, indirect
effects, and total effects which is also a test of statistical hypotheses, in
this case, the interpretation of p-value < 0.05 or 95% confidence interval
based on the percentile method or in the case of skewed bootstrap distribution
can be accepted as significant (Hair,
Risher, Sarstedt, & Ringle, 2019).
Table
10
Direct
Effects Value
|
|
T
Statistics (l O/ STDEV l)
|
P
Values
|
|
X1
-> X2
|
4.709
|
0.000
|
|
X1
-> Y
|
2.965
|
0.003
|
|
X2
-> Y
|
4.639
|
0.000
|
Source:
Primary Data
Direct effects of X1 on Y can
be explained based on calculations using bootstrap or resampling, where the
test results of the estimated coefficient of X1 on X2 with the t value of 2.635
and p-value of 0.000 < 0.05 so reject H0 or which means the direct effect of
X1 on X2 is significant. statistically significant.
Furthermore, for the direct effects
of X1 on Y, the test results for the estimated coefficient of X1 on Y with the
t-count value of 2.965 and a p-value of 0.003 < 0.05, reject H0 or which
means that the direct effect of X1 on X2 is statistically significant. The same
thing can also be found in the interpretation of the direct effects of X2 on Y,
where the test results for the estimated coefficient of X2 on Y with the t
value of 4.639 and a p-value of 0.000 < 0.05 so reject H0 or which means
that the direct effect of X2 on Y is statistically significant.
b. Specific Indirect Effects
Apart from the direct effects
X1 to X2, the structural equation modeling method also has a path relationship
or specific indirect effects mediated by an intermediate construct with the
following pattern: X1 → X2 → Y, which can be interpreted as mediation that
occurs when the mediator variable (X2) intervenes between two other related
constructs. More precisely, changes in exogenous constructs lead to changes in
mediator variables which turn the result of endogenous constructs into the
partial least squares pathway model. Thus, the mediator variable regulates the
nature of the mechanism or process that underlies the relationship between the
two constructs (Hair
Jr, Hult, Ringle, & Sarstedt, 2021).
Table 11
Specific
Indirect Effects Value
|
|
T
Statistics (l O/ STDEV l)
|
P
Values
|
|
X1
-> X2 -> Y
|
3.812
|
0.000
|
Source:
Primary Data
Specific
indirect effects X1 on X2 and then on Y can be explained based on calculations
using bootstrap or resampling, where the results of the estimation coefficient
test X1 → X2 → Y with the t value of 3.812 and p-value of 0.000 < 0.05 so
accept H1 or which means the direct effect of X1 on X2 and so on Y was
statistically significant.
B.
Effect on Relationship
1.
F-Square
The interpretation of the F
Square value is an assessment of the magnitude of the influence between
variables with Effect Size or f-square. F Square value of 0.02 is small, 0.15
is medium, and 0.35 is large. Values less than 0.02 can be ignored or
considered to have no effect (Sarstedt
& Christian, 2017).
Table
12
F-Square
Value
|
|
X1
|
X2
|
Y
|
|
X1
|
|
0.498
|
0.088
|
|
X2
|
|
|
0.199
|
Source:
Primary Data
Based on table 12, the large
effect size with F Square criteria > 0.35 is found in the effect of X1 on
X2. Furthermore, the medium effect with F Square between 0.15 to 0.35 is the
effect of X2 on Y. Meanwhile, the effect of X1 on Y is small because the F
Square value is in the range of 0.02 to 0.15.
2.
R Square
k coefficient
of determination (r square) is a way to assess how much endogenous constructs
can be explained by exogenous constructs. The value of the coefficient of
determination is expected to be between 0 and 1. R Square values of 0.75, 0.50,
and 0.25 indicate that the model is strong, moderate, and weak (Sarstedt
& Christian, 2017).
Meanwhile, Adjusted R Square is the corrected R Square value based on the
standard error value. The value of Adjusted R Square provides a stronger
picture than R Square in assessing the ability of an exogenous construct to
explain endogenous constructs.
Table
13
R-Square
Value
|
|
R
Square
|
R
Square Adjusted
|
|
X2
|
0.333
|
0.324
|
|
Y
|
0.396
|
0.381
|
Source:
Primary Data
The value of the effect of r
square X1 on X2 is 0.333 with an adjusted r-square value of 0.324. so it can be explained that the exogenous construct X1
affects X2 by 0.324 or 32.4%, This means that the adjusted r square is less
than 0.50 or 50%, so the effect of the exogenous construct X1 on X2 is weak. As
for the value of the effect of r square together, namely X1 and X2 on Y, it is
0.396 with an adjusted r-square value of 0.381, so it can be explained that the
two exogenous constructs (X1 and X2) simultaneously affect Y by 0.381 or 38.1%,
This means that the adjusted r square is less than 50%, so the influence of the
two exogenous constructs X1 and X2 on Y is weak.
C. Predictive
Relevance (Blindfolding)
Blindfolding is an analysis
used to assess the level of relevance of predictions from a construct model. Q
Square is the benchmark value used when interpreting the level of predictive
relevance. If the value is greater than zero, it means that the value is higher
than 0, 0.25, and 0.50 which means the level of prediction accuracy is small,
medium, and large, so that through the partial least squares path model it can
be concluded that the constructed model is relevant and the variables that used
to predict endogenous variables are correct (Hair
et al., 2019).
Table
14
Predictive
Relevance Value
|
|
Q2
(+1-SSE/SS0)
|
|
X1
|
|
|
X2
|
0.168
|
|
Y
|
0.176
|
Source:
Primary Data
The relevance of the
prediction of X1 to X2 based on the value of Q Square is 0.168 > 0.05 so H0
is accepted. It can be concluded that the exogenous variable X1 has been
relevant to be used as a predictor of the X2 construct as an endogenous
variable. As for the relevance of predictions X1 and X2 to Y, based on the value
of Q Square is 0.176 > 0.05 so H0 is accepted. It can be concluded that the
exogenous variables X1 and X2 have been relevant to be used as predictors of
the Y construct which is an endogenous variable.
D.
Discussion
The
relationship between the points in the subsection will be discussed here. This
is an overview of how each item that has been determined represents the views
of the respondents by the overall mean of the object study. Determination of
the standard interpretation of each item is using the categorization of five
optional choices from the Likert scale. The distribution results of the total
respondents' answers are in the following table:
Table 15
Descriptive Value
|
Variables
|
Range of Mean
Score
|
|
|
4.21 – 5.00
Highest
|
3.41 – 4.20
High
|
2.61 – 3.40
Medium
|
1.81 – 2.60
Low
|
1.00 – 1.80
Lowest
|
|
Organizational
Climate
|
|
3.468
|
|
|
|
|
Organizational
Motivation
|
|
3.905
|
|
|
|
|
Member
Participation
|
|
|
3.216
|
|
|
Source: Primary Data
(Processed)
As
can be seen from Table 15, the value of each variable is not at the low or
lowest level, especially for the variable participation is at a medium level
with a range of values quite far from the lower limit. Recall the condition
of the initial study, the problems in student organizations centered on the
discourse of the lack of participation levels that is worth questioning again.
Based on the mean values of the two exogenous variables, it can consider. Firstly,
Organizational climate is an alternative construct used to conceptualize
people's experiences and describe their working conditions that influence the
motivation of people who work in an organization. Therefore, based on the
concept that the higher the member's perception of the quality of the
organizational climate the better it will motivate. In this case, the results
are positive in path coefficient X1 → X2 (0.577 or 57%) and the direct effect X1 → X2 (p-value 0.000 < 0.05)
is statistically significant. Secondly, motivation refers to the processes that
underlie the initiation, control, maintenance, and evaluation of goal-oriented
behavior, and also refers to the forces of internal individuals that explain
the level of effort expended in the workplace that manifested in the form of
participation. Previous studies have shown a relationship between motivation
and participants in organizations. In this study, the results also show the
same thing, it is positive in path coefficient X2 → Y (0.424 or 42%), and the
direct effect of X2 →
Y (p-value 0.000 < 0.05) is statistically significant.
Regarding
the main problem, even if the relationship between the variables is positive
and significant, the value of the endogenous variable, namely participation,
shows the opposite of the hypothesis because it is not at a low level. The
evidence is in table 15, Descriptive Value (3.126 or Medium), and table 8,
Descriptive Hypothesis (.005962 < 0.05) that is Reject H0. This is implicating
in how students identify their participation in organizational activities as
they are. The fact that both the Organizational itself (Organizational
Climate), students involved in the organization (Motivation), and how students
are involved in the organization (Participation) are in the medium-high value
range. There is no sufficient descriptive reason to justify organizational
performance becoming unproductive due to the low level of student
participation. Two things should be noticed from this discussion. First, the
regression analysis carried out to minimize problems in student organizations
should start by paying attention to the descriptive measures of variables used,
especially in testing the hypothesis, even if it has been through literature
research or qualitative interviews. It is necessary to consider the concept of
student participation differs from one to another simply because of the
presence of different cultures and education policies. Second, in the case of
Himapol Unhas, there is a possibility that the discourse on the problem of the
lack of member participation in organizational activities is inappropriate as
the cause of the lead organizational performance becoming unproductive. To
simplify, the core of this problem, there is a pattern that is not right
because of the inconsistency between external conditions (discourse about the
lack of student participation) and internal conditions (values from study
results). This strange happened because this discourse developed from students
who were involved in running the organization, on the other hand, students
identify themselves as highly motivated, who do not have significant problems
with the organizational climate and are active in carrying out organizational
activities.
Regardless
of the results of the studies, there is always a possibility that studies will
be inaccurate. The reasons that need to be considered are. First, although it
has gone through various anticipatory actions, including escort in every data
collection process, the instrument used in this research is an online-based
questionnaire, so the findings from online surveys may inaccurate
(Chittaranjan, 2020). Second, the model built in the study is not broad enough
to understand the problems in student organizations. Therefore, it is necessary
to conduct a multivariate analysis with a variety of variables that make it
possible to answer complaints against student organizations.
Critical
questions have been answered. Therefore, continuing problem-solving efforts
that focus on the issue of participation in the Himapol Unhas cases becomes
less impactful but does not mean it has no effect. Since another purpose of
this research is to show how to study student organizations using the PLS-SEM
method, several discussion points can be used as material for organizational
evaluation, as follows:
1.
Organizational Climate
Although
it has a positive and significant relationship to the level of participation,
the effect of X1 on Y is small (F Square 0.088), and Adjusted R-Square X1 and
X2 on Y (0.381 or 38%) is also weak. It can be maximized by improving the
quality of the organizational climate, see figure 2.
Figure 2. Organizatioanal
Climate
Source: Primary Data
(Processed)
Increasing
student participation can be started by improving the quality of the two lowest
dimensions, namely Conflict Management which is related to the organization's
ability to manage internal conflicts, and the Reward System related to
recognition or appreciation for members who carry out their roles.
2.
Participation Motivation
These
two variables are explained in combination because motivation has a relatively
strong effect on participation, which has a medium effect with F Square
(0.199), and there is also a pattern that can provide insight, notice in Figure
3:
Figure
3. Motivation on Participation
Source: Primary Data
(Processed)
Considering
table 15, the means of the total variable of organizational motivation is 3905
which is at a high level, and the variable participation is 3216 or at a medium
level. Based on figure 3, Although motivation has the highest value, especially
in the "Relatedness needs" dimension which is the desire of each
member in the organization to occupy a certain position in a structure,
conversely, it is not in line with one of the lowest points of the dimensions
in variable participation that is named "proposes a discussion,"
which is related to self-involvement in submitting a discussion in
organizational forums as well as being active inviting other members to have a
relaxing discussion, from this it can be said that if an individual has high
motivation, it does not mean that it will then transform into action, so there
is a possibility that there are other conceptions that hinder the actualization
of the motivation of members who have not been examined as an example, the
problem of self-confidence.
CONCLUSION
As explained in the introduction, the research is here to
answer various doubts about the decline in student participation in
organizational activities at the university. On the other hand, the research
results found show different meanings than some of the descriptive hypotheses
at the beginning discussion. Based on initial observations and literature
references, researchers assume that the level of participation in Himapol Unhas
is < 60% of the criteria set, and therefore some constructs in the form of
organizational climate and motivation are considered to affect the level of
participation are also at the same level.
In contrast to the previous assumption, the results of the
analysis of the research data show that both the exogenous organizational
climate (X1) and motivation (X2) and endogenous (Y) variables are at a value
level > 60% in the sense that H0 is rejected. This implies that the majority
of respondents who are members of Himapol Unhas accumulatively have an
assessment level of organizational climate, motivation, and participation at
the medium level or above. This is different from the claim that the problem of
a student organization has entirely centered on the lack of student
participation, especially this applies in the case of Himapol Unhas, where
there is a possibility that placing the problem of participation in an
inappropriate manner as a cause of organizational performance to be
unproductive.
However, in another assumption, placing participation as
an object that needs to be improved will still have an effect on the
organization considering the lack of research that specifically examines the
problems of student participation in organizations, especially based on the
results of this study, it can be concluded that the direct relationship between
X1 → X2, X1 → Y, X2 →
Y, and the indirect specific relationship X1 →
X2 → Y shows the path coefficient
and p-value values that have met the test criteria so that all variables
reflect there is a positive and significant relationship, certainly with
varying levels of value. The implications of the finding of a positive and
significant relationship through the two exogenous variables are then expected
to provide benefits for policymaking efforts to maximize the level of student
participation in organizations. However, it should be underlined that referring
the coefficient of the R-value of the two exogenous variables, has a value
below 0.50 which means the relationship between them is weak, so it is
necessary to find other constructs that may occupy the remaining value of the R
coefficient.
In the end, research that has been completed can only
explain what has been planned, therefore to provide clarity on other factors
that may affect the level of student participation in the university's internal
organization needs to be studied further, several variables such as leadership,
education system, cultural organization, self-confidence, financial problems,
and institutional factors allow to be investigated to complement the results of
this study.
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ANALYSIS OF STUDENT PARTICIPATION IN UNIVERSITY ORGANIZATION
USING PARTIAL LEAST SQUARE-STRUCTURAL EQUATION MODELLING