JRSSEM 2023, Vol. 02, No. 09, 2116 – 2133
E-ISSN: 2807 - 6311, P-ISSN: 2807 - 6494
DOI : 10.59141/jrssem.v2i09.441 https://jrssem.publikasiindonesia.id/index.php/jrssem/index
BARCODE SYSTEM MANAGEMENT MODEL ON OIL PALM
COMPANY PERFORMANCE: FIT VIABILITY APPROACH
Zulham
1
Zulkarnain Lubis
2
Muhammad Zarlis
3
Ardian Arifin
4
Muhammad Reza Aulia
5
1
Program of Agriculture Science, University Medan Area, Medan, Indonesia
2
Agricultre Economy, University of Medan Area, North Sumatra, Indonesia
3
Information Systems Management Department, Bina Nusantara University, Jakarta, Indonesia
4
Faculty of Technology and Informatics, University Technology Malaysia, Malaysia
5
Department of Agribusiness, Faculty of Agriculture, University of Teuku Umar, Aceh, Indonesia
*
e-mail: zulham@gmail.com, zulkarnain[email protected], muhammadzarlis@gmail.com,
ardianarifin@gmail.com, muhammadrezaaulia@gmail.com
*Correspondence: zulham@gmail.com
Submitted
: April 02
th
2023
Revised
: April 15
th
2023
Accepted
: April 25
th
2023
Abstract: The use of innovative technology, such as the barcode system, has the potential to
enhance the performance of palm oil companies. To better understand the impact of the Fit-
Viability-Use barcode system on business performance, a study was conducted. This research
aimed to examine the relationship between fit, viability, and use of the barcode system with
business performance in the context of an oil palm company. The study utilized a sample of 237
employees, selected through stratified proportional random sampling, from PT LNK between April
2022 and June 2022. The data was analyzed using SEM-PLS analysis with SMART PLS 3 software.
This paper presents an overview of the Fit-Viability approach to the Barcode System. The results
showed that business performance was positively influenced by viability and use, while fit had no
significant effect. The findings can be useful in further developing the barcode system at PT LNK,
and it is recommended that other palm oil companies adopt this technology and integrate it into
cloud-based data for real-time production data.
Keywords: Fit; Viability; Use; Business Performance; Oil Palm Company; Barcode System; SEM-PLS.
Zulham
1
Zulkarnain Lubis
2
Muhammad Zarlis
3
Ardian Arifin
4
Muhammad Reza Aulia
5
| 2117
INTRODUCTION
Indonesia has earned the coveted title
of being the foremost creator and supplier
of Crude Palm Oil (Cherie, Rini, and Makky
2019; Makky and Berd 2018). With a steady
increase from 1998 until 2017, Indonesian
CPO maintains its comparative market
edge (Yanita, Napitupulu, and Rahmah
2019). This rise in production is in tandem
with the rapid expansion of oil palm
plantations across Indonesia, symbolizing a
revolution in the cultivation of oil palms.
Spread across 22 out of the 33 provinces,
Sumatra and Kalimantan Islands serve as
the two primary centers of oil palm
plantation development in Indonesia
(Purba and Sipayung 2017). Notably, land
area emerges as a key factor having a
substantial impact on palm oil production
in (Yanita and Suandi 2021).
In Indonesia, the use of technology in
improving the performance of local
companies is still very low. This is
supported by Jelita et al. (2020) who
concluded that the use of renewable
technology is still dominated by foreign
private companies. The novelty of
technology will be able to improve the
performance of palm oil companies.
Therefore, a study is needed to find out
how much influence the Fit-Viability-Use
barcode system has on the performance of
palm oil companies.
To accurately forecast palm oil
production, companies in the industry are
turning to Information and
Communication Technology (ICT) as a
solution for enhanced efficiency. ICT can be
leveraged through various applications,
such as an expert system for diagnosing oil
palm plant disease. Irawan et al. (2018)
recommend coconut seeds and ideal palm
oil mill locations in (Annisa et al. 2020), and
an information system for oil palm
production in (Bakti 2020).
One of the most effective ICT tools
available to palm oil companies is the
barcode system. This technology is used to
track employee data, with the aim of
increasing work efficiency and productivity.
The HR department plays a critical role in
ensuring efficiency, particularly in relation
to work activities and time management
(Samsuni 2017). Therefore, an optimal
work system design is necessary to achieve
a successful production process through
the use of barcode technology. PT. Langkat
Nusantara Kepong (LNK), an Operational
Cooperation company engaged in agro-
industry business specializing in palm oil
production, is an example of a company
that has implemented the barcode system.
In Indonesia, the utilization of
technology for improving the performance
of local companies remains low. As noted
by Jelita et al. (2020), renewable
technology is still predominantly used by
foreign private companies. The
implementation of innovative technologies
could significantly improve the
performance of palm oil companies.
Hence, further research is required to
examine the impact of the Fit-Viability-Use
barcode system on the performance of
palm oil companies.
2118 | Barcode System Management Model on Oil Palm Company Performance: Fit Viability
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MATERIALS AND METHODS
Framework
The theoretical framework presented
is a refined version of the Task Technology
Fit model (Liang et al. 2007), with a focus on
the individual-level analysis of technology
adoption. The Fit-Viability Theory serves as
a tool for evaluating the efficacy of an
Information and Communication
Technology (ICT) object within an
organization, based on two dimensions: Fit
and Viability. The Fit dimension gauges the
consistency of a new ICT object in aligning
with the core values, structure, and culture
of an organization. The Viability dimension
evaluates the potential added value of the
ICT object in relation to human resource
requirements, capital requirements, and
other relevant factors. In essence, this
theory explains how the suitability of a
technology for a specific task and the
continuity of its use can significantly impact
performance.
Figure 1. Fit-Viability Model (Liang, 2007).
The previously discussed framework
indicates that Task and Technology serve as
indicators of the Fit construct. Essentially,
the Fit construct is evaluated by measuring
its dimensions, which are Task and
Technology. Similarly, economic factors, IT
infrastructure, and organization are
indicators of the Viability construct for an
implemented IT object. Thus, the Viability
construct is assessed by measuring its
dimensions, which are economy, IT
infrastructure, and organization/
management.
In addition to the previous discussion,
it is worth noting that the Fit and Viability
constructs have a significant impact on
performance. This highlights the
importance of using the Fit-Viability Theory
to evaluate the effectiveness of the barcode
system in palm oil companies. Specifically,
the theory can be applied in the following
ways:
1. Task construct is a significant construct
that is closely related to employee
attendance and the calculation of fruit
harvest. By evaluating the extent to
which the barcode system fits into
these tasks, companies can determine
whether the technology is effective in
improving task-related performance.
2. Technology construct describes the
characteristics of the technology used
in the barcode system. This construct
can be evaluated to assess whether the
technology is suitable for the needs of
the company and whether it is effective
in enhancing productivity.
3. Economic construct considers how the
budget fits and transaction costs
required to implement the barcode
system. By evaluating the economic
feasibility of the technology,
companies can determine whether the
benefits of using the system outweigh
the costs.
4. IT Infrastructure construct describes
software and hardware availability and
data management in the barcode
system. This construct can be used to
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1
Zulkarnain Lubis
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3
Ardian Arifin
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Muhammad Reza Aulia
5
| 2119
evaluate the effectiveness of the
system in managing data, and whether
the infrastructure supports the
successful implementation of the
technology.
5. Organizational construct discusses
how the production process takes
place, the existence of re-
engineering, and the competency
of employees with ICT. By assessing
how the barcode system fits into the
production process and how it
impacts the competency of
employees, companies can
determine the overall effectiveness
of the technology in improving
organizational performance.
6. Performance construct describes
satisfaction, positive influence, and
consistency of the barcode system
in helping work. This construct can
be evaluated to determine the level
of satisfaction among employees
and the extent to which the
technology has a positive impact on
performance.
The intensity of use is an additional
construct that can impact the performance
construct, which has been established
through several studies. According to a
research conducted by Gozi and Felicia
(2019), the usage of ICT can have an impact
on the quality of employee performance.
Since employee performance is a critical
component of a company's overall
performance, it can be inferred that ICT
utilization can influence enhancing the
company's performance. The proposed
research framework is outlined below.
Figure 2. Framework
Figure 2 illustrates that the Fit
Construct is made up of multiple
dimensions, namely Task and Technology,
while the Viability Construct also comprises
of several dimensions, namely Economic, IT
Infrastructure, and Organization.
Furthermore, the Constructs of Use, Fit, and
Viability can all have an impact on the
Performance Construct. The study intends
to measure various responses and
expla(Sugiyono 2010)natory variables
concurrently, and for this purpose, Partial
Least Square (PLS) will be utilized as an
analytical method. PLS is a suitable
alternative for solving intricate multilevel
models that do not require a large sample
size. Additionally, PLS has numerous
benefits, including optimal implications for
prediction accuracy, and it is a potent
analytical method as it does not assume a
FIRM
PERFORMANCE
FIT
TASK
TECHNOLOGY
VIABILITY
ECONOMIC
IT
INFRASTRUCTURE
ORGANIZATION USE
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data measurement scale, and it can be
utilized to confirm the theory.
Sampling Method
The data used in this study included
both primary and secondary sources.
Primary data was collected through
employee interviews, while secondary data
was obtained from various sources such as
production data and records of production
increases from each estate.
The focus of this study was on the
estates of PT Langkat Nurasantara Kepong,
including Padang Brahrang, Bekiun,
Tanjung Keliling, Marike, Bukit Lawang,
Johor Lama, and Tanjung Beringin. From
these estates, a sample of four were
selected for the research: Basilam, Bekium,
Padang Brahrang, and Johor Lama. The
total number of employees in these four
estates was 1,870 individuals. The stratified
proportional random sampling technique
was used to ensure that each sampling unit
in the population was represented in the
sample (Lubis 2021). This technique was
chosen due to the relative homogeneity of
the variables under study, which were the
employees of oil palm plantations at PT.
Langkat Nusantara Kepong.
Determination of the samples number
from a certain population developed by
Issac and Michael for error rates of 1%, 5%,
and 10% can be calculated using the
following formula (Sugiyono 2010) :
󰇛
󰇜

Notice :
s = Sample
N = Population
X
2
= The value of chi squared
with degrees of freedom = 1
P = Probability of accepting that
an event is said to be true,
assuming the value = 0,5
(50%)
Q = Probability of accepting that
an event is said to be false,
assuming the value = 0,5
(50%)
d = The value of
precision/percentage of difference in
answers from the questionnaire for
each question item, assuming the
value = 0,05 (5%)

󰇛󰇜󰇛 󰇜 󰇛󰇜
󰇛󰇜
Table 1 illustrates the random sampling
technique used in each estate to ensure the
representativeness of the population. It
displays the distribution of the population
and samples in each oil palm plantation,
which served as the research location.
Table 1. Population and Sample
No
Estates
Population
Samples
1
Basilam
1048
132
2
Bekiun
250
32
3
Gohor Lama
373
47
4
Padang Brahrang
199
26
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Zulkarnain Lubis
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Ardian Arifin
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Amount
1.870
237
Source: PT. Langkat Nusantara Kepong, 2021.
Partial Least Square Analysis
To test complex hypotheses about the
direct or indirect relationships between
variables, the Partial Least Square (PLS)
analysis method can be employed by
combining regression and path analysis.
This method can depict the whole
relationship between the dependent and
independent variables in a single analysis.
The SMART PLS 3 software is one of the
programs that can be utilized for PLS
analysis. It can depict all the relationships
constructed in the theory-based model,
enabling the analysis of the impact of Fit
Viability and barcode system usage on
Company Performance, as well as other
variables that influence Fit Viability.
Partial Least Square (PLS) analysis has
several advantages over other statistical
methods, as pointed out by Hair et al.
(2017). One of the key advantages is that
PLS can operate complex models, where
there are a large number of dependent and
independent variables. This means that PLS
is an effective method for analyzing
complex data sets that might be difficult to
analyze using other methods. Another
advantage of PLS is that it can handle
multicollinearity problems between
independent variables. This is important
because multicollinearity can cause
problems in traditional regression analysis.
Additionally, PLS is able to process data
with missing or abnormal data, while still
producing solid and reliable results.
Another advantage of PLS is that it can
be used with both reflective and formative
constructs. Reflective constructs are used to
measure a single variable, while formative
constructs are used to measure a
composite variable that is made up of
several underlying variables. PLS can also
be used on small samples, and the data
does not have to be normally distributed,
which makes it a flexible analytical tool.
Lastly, PLS can handle data with different
scale types, including nominal, ordinal, and
continuous, making it suitable for a wide
range of data types.SEM and PLS have
some differences. PLS is predictive whereas
SEM generally tests theory. PLS has a
measurement model and a structural
model. The measurement model is the
relationship between the observation
variable and the latent variable. While the
structural model explains the relationship
between latent variables. Therefore, the
measurement model must be valid and
reliable, while the structural model is
assessed by evaluating the explanatory
power and the significance level of the path
coefficient.
In order to evaluate the accuracy of the
model, the first step is to examine the outer
reflective indicator model. There are three
criteria used to evaluate the outer reflective
indicator model: Convergent validity,
Compositer reliability, and Discriminant
validity. Once these criteria have been met,
the second step can be taken to evaluate
the inner model. This is done by analyzing
R-square, Q-square, Goodness of fit (GoF)
and F-square. R-square assesses the degree
to which the dependent latent variable has
a significant impact. Q-square measures
the accuracy of the values generated by the
2122 | Barcode System Management Model on Oil Palm Company Performance: Fit Viability
Approach
model. Goodness of Fit (GoF) assesses the
validity of the structural model.
Variables
In this study, the variables being
investigated are concepts that can be
measured. These variables include both
latent variables and manifest variables that
act as indicators of the latent variables. In
order to measure the latent variables,
indicator variables are used. Manifest
variables, also known as indicator variables,
are variables that describe or quantify the
underlying latent variable being studied.
Table 2. Latent and Indicator Variable
Latent
Variable
Keterangan
Task
The level of discipline of workers on the
barcode system
Planning, implementation and evaluation
Carried out continuously with the same system
and there is always maintenance
Workers understand the tasks assigned
Technology
Barcodes on sales
Barcodes on fruit collection
Barcodes in shipping tracking
Economic
Cost compatibility with budget
More efficient transaction fees
IT
Infrastructure
App availability
Device availability
Management of stored data every day
Organization
Structuring the position structure within the
company
The role of the organization in the production
process
The ability of workers to carry out work
Culture formed in the working environment of
the barcode system
Fit
The suitability of the work system desired by
workers
Dependence of workers with a work system
with a barcode system
Viability
Possibility of continuous system improvement
Continuity between planning, implementation
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to evaluation
Use
Intensity of using barcode system and
irreplaceable
Use of barcodes in planning, implementation
and evaluation
Performance
Number of fruits produced
Increased production during the
implementation of the barcode system
Barcode error rate
Source : Liang
et al.
2007
Measurement Model Evaluation
To ensure that the manifest variable
(indicator) accurately measures the latent
variable (construct), an assessment is
necessary. The assessment involves
determining the validity of the manifest
variable based on the loading factor value.
If the loading factor value is greater than
0.7, the manifest variable is considered
valid. Conversely, if the loading factor value
is less than 0.7, the manifest variable must
be excluded because it is deemed
inadequate in measuring the latent
variable.
Figure 3. First Model
Table 3. L
oading Factor
Value
Variabel Manifest
λ
Descriptio
n
Worker Discipline (TS1)
0.888
Valid
Work System (TS2)
0.881
Valid
Business Process Sustainability (TS3)
0.884
Valid
2124 | Barcode System Management Model on Oil Palm Company Performance: Fit Viability
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Task Understanding (TS4)
0.900
Valid
Selling System (TC1)
0.158
Not Valid
Collecting system (TC2)
0.945
Valid
Delivery Tracing System (TC3)
0.945
Valid
Total Cost(EC1)
0.944
Valid
Transaction Cost (EC2)
0.920
Valid
Software (IT1)
0.797
Valid
Hardware (IT2)
0.851
Valid
Management Data (IT3)
0.857
Valid
Organization System (OG1)
0.886
Valid
Production Process (OG2)
0.865
Valid
Worker Competencies (OG3)
0.700
Valid
Work Culture (OG4)
0.893
Valid
Suitability (FT1)
0.861
Valid
Dependency (FT2)
0.857
Valid
Improvment (VB1)
0.974
Valid
System Continuity (VB2)
0.975
Valid
Intensity (US1)
0.933
Valid
Planning, implementation and evaluation
(US2)
0.856
Valid
Production Volume (PF1)
0.955
Valid
Increase of Production (PF2)
-
0.888
Not Valid
Error System (PF3)
0.735
Valid
The initial model created in Figure 4
was evaluated based on the loading factor
value of each variable, and the results are
presented in Table 4. The evaluation
showed that two variables, Sales System
(TC1) and Production Increase (PF2), had a
loading factor value less than 0.7, indicating
invalidity. This implies that the barcode
system implemented by PT LNK has not
reached the sales stage, and the Production
Increase variable is not an accurate
measure of performance due to the slight
increase in production in the estates with
the highest average production compared
to those with low production average but
significant increase after the barcode
system implementation. Therefore, a
second-stage test was conducted without
including the invalid variables to obtain a
final valid model. In this model, all indicator
variables had a loading factor value greater
than 0.7, indicating their validity. The final
model can be seen in Figure 4, and all
values leading to the yellow box (loading
factor value) have a value above 0.7,
indicating that all variables are valid and
can be used in the model.
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DOI : 10.59141/jrssem.v2i09.441 https://jrssem.publikasiindonesia.id/index.php/jrssem/index
Figure 4. Final Model
The model's reliability is crucial in
ensuring trustworthy results. A model can
be considered reliable if it has an AVE value
and composite reliability greater than 0.5
and 0.7, respectively. The AVE value
indicates the amount of variance in the
construct that is captured by its indicators,
while the composite reliability represents
the internal consistency of the construct. In
Table 5, all variables have an AVE value
greater than 0.5 and a composite reliability
greater than 0.7, indicating that the model
is reliable and can be trusted to measure
the constructs.
Table 5. Average Extracted (AVE), Compose Reliability, Cronbach Alpha, and Cross Loading
Value
Latent Variable
AVE
Compose
Reliablity
Cronbach
Alpha
Cross
Loading
Task
0.789
0.937
0.911
0.888
Technology
0.894
0.944
0.882
0.946
Economic
0.869
0.930
0.850
0.932
IT Infrastructure
0.698
0.874
0.787
0.836
Organization
0.705
0.905
0.760
0.840
Fit
0.738
0.849
0.645
0.859
Viability
0.949
0.974
0.947
0.974
Use
0.803
0.890
0.760
0.896
Performance
0.781
0.877
0.722
0.884
The following step in evaluating the
measurement model involves checking its
unidimensionality using the Cronbach
alpha indicator. A value of 0.6 or higher
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indicates that the indicator is good. As
presented in Table 5, all variables in the
model have a Cronbach alpha value above
0.6, indicating good unidimensionality of
the model.
The next stage is to assess discriminant
validity, which ensures that different
constructs are not highly correlated. The
Fornell and Larcker method can be used to
test discriminant validity, which examines
the cross-loading value that must be at
least 0.50, meaning that at least 50% of the
variation of the indicator can be explained.
In Table 5, all variables have a cross-loading
value greater than 0.500, indicating no
problem of discriminant validity in the
evaluated models. Thus, after meeting all
requirements, the model can be declared as
the final model.
Structural Model Evaluation
Table 6. R-square Value
Latent Endogen
Variable
R-
Square
Keteranga
n
Viability
0.711
Sedang
Fit
0.610
Sedang
Performance
0.892
Kuat
To evaluate the structural model, one
can examine the R-square on the
endogenous variables and the estimated
value of the path parameter coefficients. A
strong model has an R-square of 0.75, a
medium model is 0.50, while a weak one
has an R-square of 0.25. The values of R-
square in Table 6 indicate that the factors
used to measure the Fit, Viability, and
Performance variables can explain 71.1
percent, 61 percent, and 89.2 percent of the
values, respectively, while the remaining
percentage is explained by other variables
not included in the model.
The next step is to assess the Q-square
value (predictive relevance) to determine
how well the observed values produced by
the model and the parameter estimates
match. The Q-square value ranges from 0
to 1, where a higher value indicates a better
fit.
Q-
square
=
1 - (1 - 0.711) (1 -
0.610) (1 - 0.892)
=
1 - (0.289) (0.490) (0.108)
= 1 - 0.015
= 0.985
The result of Q-square is 0.985,
meaning the model can explain 98.5
percent of the phenomena that occur, and
the rest is explained by other variables that
are not in the model.
Goodness of Fit
Goodness of Fit (GoF) is used to
measure structural model is valid or not.
The GoF value is obtained manually by
using the formula for the root mean of the
AVE multiplied by the average R-square.
Calculation of goodness of fit using the
formula:
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The result of the Goodness of Fit on the
model is 0.770, meaning the suitability of
the model is high.
Hypothesis Testing
Hypothesis testing can use the t-table
value, for alpha 5% is 1.96. The hypothesis
is accepted if t-statistics > t-table and the
hypothesis is rejected if t-statistics < t-
table. The results of direct effect hypothesis
testing on the model can be seen in Table 9
which the hypothesis of the influence of
Technology on Fit and Fit on Performance
is declared rejected and the rest of the
other hypotheses are accepted. The result
of indirect effect hypothesis testing can be
seen in Table 10 which the hypothesis of the
Fit on Performance and Technology on
Performance is declared rejected and the
rest of the other hypotheses are accepted.
Table 7. Direct Effect
Original
Sample
T-
statistik
Descripti
on
0.647
8.999*
Accepted
0.154
1.943
Rejected
0.314
7.621*
Accepted
0.250
2.823*
Accepted
0.403
4.324*
Accepted
-0.071
1.893
Rejected
0.567
11.726*
Accepted
0.490
8.885*
Accepted
Table 8. Indirect Effect
Original
Sample
T-
statistik
Descripti
on
-0.046
1.803
Rejected
0.011
1.393
Rejected
0.178
7.158*
Accepted
0.142
2.844*
Accepted
0.229
3.673*
Accepted
RESULTS AND DISCUSSION
PT. Langkat Nusantara Kepong (LNK)
is a joint venture between PT. Perkebunan
Nusantara II (PTPN II Persero) and Kuala
Lumpur Kepong (KLK) Plantation Holdings
2128 | Barcode System Management Model on Oil Palm Company Performance: Fit Viability
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Malaysia in the agro-industry business.
LNK has implemented a barcode system
across all levels of employees, from
assistants to senior managers and field
staff, for attendance and fruit counting
data. This system allows employees to
input information such as harvest date,
time, number of fruit, harvester's name,
and location, which is then processed for
wage distribution. Attendance data is also
collected through the barcode system,
including employee names, dates and
times, types of work, and fields. In
addition, the system manually inputs data
for overtime, off days, premiums, and
adjustments. The barcode system aims to
improve employee work efficiency and
increase palm oil production. Despite its
benefits, many other palm oil companies
have yet to adopt this system. The
barcode system can increase employee
productivity, find out production and
productivity data, and evaluate any
problems that arise. Research has also
shown that the barcode system
significantly improves employee
performance and reduces paper costs.
By implementing the barcode system,
PT. LNK can ensure that their employees
are responsible for the designated harvest
areas and that they cannot move without
recording their actions in the system. The
barcode system also allows the company
to identify unharvested areas and track
crop rotations and fruit quality. The
harvest foreman can easily access data on
workers who are not meeting standards,
leading to an increased focus on crop
checks and improvements in harvesting
techniques. Additionally, the barcode
system provides comprehensive data on
production and productivity, making it
easier for the company to identify any
issues and develop solutions. With the
barcode system, employees can also
calculate their bonus and monthly income
based on their performance, which can
boost their motivation and productivity.
Overall, the use of the barcode system has
proven to be a valuable investment for PT.
LNK can potentially benefit other palm oil
companies in the industry.
Fit
The Fit variable was explained by the
Suitability (FT1) and Dependency (FT2)
variables. Part of the Task indicators is
Worker Discipline (TS1), Work System (TS2),
Business Process Sustainability (TS3), and
Task Understanding (TS4). The results
showed that the Fit variable was influenced
by Task.
After analyzing the data, the
researchers found that the Fit variable was
influenced by the Task. This suggests that
the nature of the task being performed, and
the conditions under which it is being
performed, are important factors in
determining whether an individual or
system is well-suited for the task at hand.
Different from the Technology
variable which does not affect Fit. Part of
the Technology indicators is the
Collecting System (TC2), and Delivery
Tracing System (TC3). some employees
say it doesn't match the barcode system.
Viability
The indicator variables, Improvement
(VB1) and System Continuity (VB2) provide
a comprehensive explanation of the
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Viability variable. The study finds that the
Viability variable is positively impacted by
the Economy, IT Infrastructure, and
Organization, indicating that an increase in
these variables will enhance the viability of
the Barcode System in palm oil companies.
Notably, the Organizational variable has
the greatest influence with an original
sample value of 0.403. This aligns with prior
research on the role of the Barcode System
as a Management Information System,
facilitating decision-making and problem-
solving for leaders. As Paoki (2012)
suggests, an information system can
identify issues by generating alternative
designs, selecting actions, and assessing
feasibility.
Use
According to the findings of the study,
implementing a barcode system has a
positive impact on the performance of
palm oil companies. It offers various
advantages, such as reducing errors in
calculations, facilitating fruit quality
determination, minimizing paper usage,
and speeding up information delivery.
Other research by Istiqomah et al (2019)
demonstrates that applying barcodes in
warehouse management can reduce errors
in receiving and storing goods, speed up
the receipt and retrieval of goods, help
determine storage locations, assess the
quality of goods, and reduce paper usage.
Furthermore, the use of barcodes can
enhance the efficiency of information and
data reporting, making processes faster
and more streamlined. Consequently,
warehouses that have implemented
barcode systems are more efficient and
productive compared to those that rely on
manual handling. This paper is also in line
with Suriwan's Wanitwattanakosol,
Attakomal, and Suriwan (2015) research,
which demonstrates that implementing
barcodes in warehouses has lots of
benefits, including the ability to minimize
errors in the receipt of goods and speeds
up the receipt of goods, can automatically
determine the location of storage, minimize
errors in storing goods at the storage area,
minimize location and goods picked errors
by the picker, minimize location and goods
picked errors by the picker, minimize
location and goods picked errors by the
picker, minimize location and goods picked
errors by the picker, minimize location.
Fresh fruit bunches in oil palm must be
harvested at optimal ripe conditions
(Misron et al. 2017). The Barcode system
rejects unripe fruit. The use of a barcode
system supports companies to produce
high-quality CPO. The use of a barcode
system helps to ensure that only ripe fruit is
harvested, which can improve the overall
quality of the CPO produced. This is
because unripe fruit can negatively affect
the oil yield and quality, resulting in lower
overall production and revenue. By using a
barcode system to reject unripe fruit,
companies can improve the overall
efficiency and effectiveness of their
operations, and ultimately deliver a higher
quality product to their customers (Boulos
et al. 2015).
The Effect of Fit, Viability, and Use of
Barcode System on Performance
The original sample values of 0.567 and
0.490 show that Viability and use have a
significant impact on business
performance. The hypothesis suggests that
2130 | Barcode System Management Model on Oil Palm Company Performance: Fit Viability
Approach
any increase in Viability and use will lead to
improved performance. However, Fit does
not affect business performance as per the
hypothesis, with an original sample value of
-0.071. In practice, some employees dislike
the barcode system, which prevents them
from manipulating harvest data for
personal gain, particularly in high-
production estates. Although the
implementation of the system has
increased efficiency and production, there
are some unhappy parties due to its impact
on employee income, which the company
may not be fully aware of. It's worth noting
that the barcode system has only been in
place for the past 5 years at PT LNK, and
while it has received positive feedback from
many, there are still some concerns that
need to be addressed.
The results from the original sample
values indicate that the performance of a
business is greatly affected by Viability and
use, with values of 0.567 and 0.490
respectively. The hypothesis further states
that any increase in Viability and use will
lead to an improvement in business
performance. On the other hand, Fit was
found to not affect business performance,
with a sample value of -0.071, which goes
against the hypothesis. It has been
observed that some employees are not in
favor of the barcode system because it
prevents them from manipulating harvest
data for personal benefit, especially in
high-production estates. While the
implementation of the barcode system has
brought about increased efficiency and
production, there are still some employees
who are unhappy with its impact on their
income. It is possible that the company may
not have a complete understanding of the
issue. It is important to note that the
barcode system has only been in use at PT
LNK for the past five years and, while it has
received positive feedback from many,
there are still some issues that need to be
addressed.
The Effect of Task, Technology, Economic, IT
Infrastructure and Organization on
Performance
IT Infrastructure, Economic factors, and
Organization indirectly impact business
performance, while Task and Technology do
not directly affect performance.
Surprisingly, the implementation of a
barcode system, specifically for attendance
tracking, did not seem to impact employee
discipline, contradicting some established
theories. This finding aligns with Cay's et al.
(2021) research, which similarly concluded
that the use of fingerprint attendance
tracking did not affect employee discipline.
While technology such as the barcode
system can bring many benefits, it is
essential to recognize its potential
limitations and unintended consequences
to make informed decisions about its use in
the workplace.
Policy Impact
The implementation of a barcode
system in an oil palm company can have
significant policy implications, particularly
concerning environmental sustainability
and labor practices. On the one hand, the
use of a barcode system can help
companies to produce higher-quality crude
palm oil (CPO) by ensuring that only ripe
fruit is harvested. This can help to increase
efficiency and reduce waste, which can in
turn have positive environmental
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implications by reducing the overall
environmental footprint of the company
(Zulham et al. 2022).
However, there are also potential risks
and challenges associated with the
implementation of a barcode system in the
oil palm industry. One concern is that the
increased efficiency and productivity
associated with the use of such technology
could lead to greater pressure to clear more
land for oil palm cultivation. This could have
significant negative environmental
consequences, including deforestation,
habitat loss, and increased greenhouse gas
emissions.
In addition, there are also concerns
about labor practices and worker rights in
the context of the implementation of a
barcode system. For example, some
workers may be negatively impacted by the
use of this technology, particularly if it
results in reduced pay or loss of bonuses.
Furthermore, there is a risk that the
implementation of such technology could
exacerbate existing labor abuses, including
forced labor and exploitation, particularly
in areas where labor laws and regulations
are weak or not effectively enforced
(Zulham et al. 2023).
In light of these concerns, it is
important for oil palm companies to
carefully consider the potential impact of
implementing a barcode system and to
take steps to ensure that the technology is
used in a socially and environmentally
responsible manner. This may include
investing in training and education for
workers, working with local communities
and stakeholders to minimize the
environmental impact of oil palm
cultivation, and ensuring that workers are
fairly compensated and their rights are
protected.
CONCLUSIONS
The barcode system is a powerful tool
that provides numerous benefits to
businesses, particularly in the palm oil
industry. Firstly, the use of the barcode
system can result in significant cost savings
for companies, as it minimizes the need for
manual labor and reduces the occurrence
of errors in counting and data entry.
Moreover, the barcode system also ensures
the accuracy of fruit counting, which is
crucial in the palm oil industry, as it affects
the quality and quantity of oil produced.
Secondly, the barcode system can
improve the work culture within the
organization by creating a more efficient
and effective work environment. With the
implementation of the barcode system,
employees can perform their tasks more
efficiently and rely on the system to provide
accurate data and information. This can
result in increased job satisfaction and
morale among employees, leading to a
positive work culture.
Finally, the barcode system can make it
easier for leaders to evaluate and
determine the right policies for the
company. By providing real-time data and
information, the barcode system can help
leaders make informed decisions and
quickly respond to changes in the market.
This can ultimately lead to improved
business performance and profitability.
In conclusion, the barcode system is a
valuable tool that can provide significant
benefits to companies in the palm oil
industry, including cost savings, improved
2132 | Barcode System Management Model on Oil Palm Company Performance: Fit Viability
Approach
work culture, and ease of evaluation for
leaders. Company performance is strongly
influenced by Usage and Viability, meaning
an increase in these two variables will
improve Company Performance. In contrast
to compatibility, which does not affect
performance, this is presumably because
the barcode system has only been used in
the last 5 years, and needs further studies
in the next 5 years. In summary, this
research shows that the barcode system
can be used in all palm oil companies for
performance improvement.
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