Identification of Determining Factors for Job Transitions that Trigger
Economic Structure Changes in North Sulawesi Province
HERMAN KARAMOY, HIZKIA HENDRICK DAVID TASIK, JULLIE JEANETTE SONDAKH,
STANLY WILNYSON ALEXANDER
Sam Ratulangi University,
Bahu, Malalayang district, Manado city, Sulawesi Utara,
INDONESIA
Abstract: - The challenges of North Sulawesi Province lie in the difficulty of developing sustainable industries
and economic structures in this region. With the evolution of technology and the growing government policies,
factors such as online motorcycle taxi services (ojek online), village funds (dana desa), business and production
incentives from the government, financial technology (fintech), and technological disruption have become
increasingly crucial in reshaping the economic activities in Indonesia including the creation of new jobs,
disruption in existing jobs, and job transitions. This research, however, aims to pinpoint the main drivers of job
transitions after significant changes in technology and government policies. To our knowledge, this is the first
study attempting to investigate what leads to a change in the profession or job of the actor of MSMEs
considering individuals’ demography characteristics, public insurance, and the advent of technology in
business. The findings of this study suggest that the fulfillment of electricity needs, personal income, and
business income are among the determinants of the job transitions of individuals in North Sulawesi Province.
Additionally, factors that can drive job transitions within the same industry or sector due to the presence of new
technologies include age, ownership of the National Health Insurance (BPJS/KIS), and residential and
workplace or school locations. Working or studying in urban areas increases the likelihood of changing
professions or jobs within the same sector or industry. On the other hand, the results suggest that the factors
above, along with marital status and higher education attainment, can also drive changes in professions or jobs
in different industries or sectors due to the presence of new technologies.
Key-Words: - Job transitions, profession changes, economic structures, technology disruption, survey, micro
and small-medium enterprises (MSMEs).
Received: September 7, 2023. Revised: April 9, 2024. Accepted: May 14, 2024. Published: May 31, 2024.
1 Introduction
North Sulawesi Province is a region endowed with
substantial economic potential. However, the
challenge lies in the difficulty of developing
sustainable industries and economic structures in
this area. According to BPS data, the Gross
Regional Domestic Product (PDRB) per capita of
North Sulawesi, based on current prices in 2021,
amounted to Rp54.04 million, which is still below
the national average of Rp62.26 million. Moreover,
the unemployment rate in North Sulawesi reached
7.06% in August 2021, higher than the national
average of 6.49%. According to the distribution map
of e-commerce businesses in Indonesia, North
Sulawesi falls into the fourth category out of five,
with a number of businesses ranging from 31,999 to
56,666 units. Meanwhile, provinces in Java are in
the top category with numbers exceeding 106,000
businesses.
With the development of technology and the
growing government policies in North Sulawesi
Province, factors like online motorcycle taxi
services (ojek online), village funds (dana desa),
business and production incentives, financial
technology (fintech), and technological disruption
are becoming increasingly vital in influencing the
economic structure in the province.
Therefore, this research aims to pinpoint the
main drivers of job transitions after significant
changes in technology and government policies
which potentially lead to changes in the economic
structure of North Sulawesi Province. The findings
of this research may serve as a basis to assist in
evaluating government policies. Additionally, the
results of this study are expected to contribute to our
comprehension of the job transitions and the main
drivers in North Sulawesi Province. To our
knowledge, this is the first study attempting to
investigate what leads to a change in the profession
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DOI: 10.37394/23207.2024.21.115
Herman Karamoy, Hizkia Hendrick David Tasik,
Jullie Jeanette Sondakh, Stanly Wilnyson Alexander
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or job of the actor of MSMEs considering
individuals’ demography characteristics, public
insurance, and the advent of technology in business.
2 Literature Review
The impact of technological advancements depends
on several elements, such as the type of industry and
the involvement of labor unions in that industry.
According to [1], in industries with labor unions,
such as healthcare, goods delivery, and culinary
retail, it was found that workers influenced the
adoption process of new technologies that could
benefit them. Conversely, in industries such as non-
food retail and warehouses that lack labor unions
and where large companies could dictate
competition requirements, the prospects for workers
to adopt new technologies were bleak.
On the other hand, conventional agriculture in
emerging markets like Indonesia was being
transformed by digital technology, as seen through
the lean start-up perspective, [2]. According to [3],
digital agriculture has the potential to bring
significant change to farming in Indonesia, and the
start-up ecosystem in Indonesia has witnessed rapid
growth in agriculture technology.
The use of new technology opens a gate to
produce new, cheaper goods, capital accumulation
and enhances international competitiveness, [4].
Based on an article by [5], while new technologies
required a change in old strategies, they also opened
new opportunities. In agriculture, this technology
offered increased productivity. In the service sector,
new technology was also crucial for economic
development as the roles of the primary and
secondary sectors were declining. Services such as
tourism have proven to be a more resilient source of
income against automation for countries that
successfully created desirable tourist destinations.
Service exports offered more growth benefits than
manufacturing-based export growth.
In the financial sector, the advent of new
technology provided convenience for people to
obtain loans easily and quickly. According to [6],
fintech has caused a significant alteration in the
banking sector. While fintech continued to grow,
credit tended to lag. People who once considered
interest rates as the standard for borrowing from
conventional banks now prefer to borrow from non-
banking institutions or fintech platforms that offer
easy access.
A paper by [7], showed that the power of
interest rates in changing loan rates was lower in the
fintech era compared to the pre-fintech era.
Furthermore, in many cases, the power of interest
rates statistically weakened in the fintech era. [8],
explained that compared to loans from banks, non-
bank or marketplace lenders had features such as
online application processes, quick responses,
higher approval rates, better credit analysis, and a
better customer experience. Studies by [6] and [7],
indicated changes in the amount of credit disbursed
and changes in the credit composition after the
Indonesian government launched the financial
technology program in 2016.
In the transportation sector, technological
advancements offered new hope for many
communities, particularly in terms of employment
and income. Ride-hailing drivers tended to arise
from lower-income environments with less regular
employment opportunities, [9].
Over the past decade, ride-hailing has become a
major force driving the development of smart
mobility worldwide. This development provided
passengers with flexible travel arrangements, short
travel times, no parking hassle, and increased
comfort, [10] and [11]. Additionally, this service
also had the potential to benefit cities by reducing
private car ownership and increasing the number of
public transportation passengers by solving first-
last-mile problems, [12] and [13]. On the other
hand, some studies found that ride-hailing could
increase vehicle traffic, raise energy consumption,
compete with public transportation, and worsen
transportation inequalities, [14] and [15].
Individuals with higher education and higher
incomes were more likely to use ride-hailing
services, [16 and [17]. Unfortunately, the supply
side of this business was disproportionately less
noticed, [18] and [19]. Transportation network
companies systematized drivers with no traditional
employment relationships. In contrast, they offer an
online platform to match supply and demand, [20].
Each ride-hailing driver affiliated with the platform
decided when and where to drive their car for work.
Therefore, platforms claimed that drivers were not
part of the company’s employment but individual
business partners who directly negotiated with
passengers. They were also referred to as gig
workers with non-fixed workplaces or employers,
[21]. Ride-hailing platform companies generate
profits by retaining a certain percentage of the
overall fare as their commission. This disruptive
impact was not only experienced by fellow business
players in the transportation sector, but the
emergence of new job opportunities in this sector
stimulated workers in other sectors to move to the
transportation sector.
On the other hand, to foster new businesses
within the community, the Indonesian government
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was actively releasing various productive programs
for the community, such as the Village Fund
program. This program encouraged people to
engage in various businesses supported by the
Village Fund. One of the priorities of this program
was the establishment, development, and capacity
building of Village-Owned Enterprises (BUM Desa)
or Joint Village-Owned Enterprises, which would
manage productive economic activities and the
development of tourism villages, [22].
Moreover, this program was observed to
suppress population migration from rural areas to
cities, [23], enabling rural communities to develop
their local economies. However, it was also
observed that the presence of online motorcycle
taxis still attracted attention from lower-income
communities in rural areas to seize job opportunities
with higher incomes in the cities, [9]. Government
incentive programs for businesses were also being
promoted at the national and regional levels. This
program helped the community start businesses
(start-ups) and stimulated the growth of existing
businesses, for example, with government subsidy
programs, [24]. The presence of factors such as
online motorcycle taxis, village funds, business
incentive programs, fintech, and technological
disruption had an impact on changing the economic
structure of a country and region.
The factors described above were grouped into
three categories: private factors, namely online
motorcycle taxis; government factors, namely
village funds and business incentives; and external
driving factors, namely the development of fintech
and technological disruption. This research will
analyze the strength of these three categories in
changing the economic structure of North Sulawesi,
which has not been previously studied.
3 Methodology
This study takes place from February 2023 until
December 2023 in North Sulawesi province,
sampling two hundred and forty respondents from
two regencies and four cities. These six areas are
selected because these areas are mostly exposed to
digital technology, Practically, online activities are
part of their daily lives. The areas include Manado
City, Tomohon City, Bitung City, Kotamobagu
City, North Minahasa Regency, and Minahasa
Regency. The selection of these areas is also based
on their proximity to the provincial capital of North
Sulawesi, Manado City. Proximity is crucial for this
study as it reflects the availability of
telecommunication facilities in each area. In other
words, the farther away a city from the provincial
capital, the more restricted telecommunication
facilities are. Specifically, Kotamobagu City,
despite its distance from the provincial capital, it
was chosen because of its city status, assuming that
economic activities in Kotamobagu City are like
other cities in the province.
In this study, the respondents are the actors (i.e.
the owners or individuals who have the rights to
lead the businesses) of small and medium
enterprises (SMEs). SMEs are chosen because they
are the keys to various social and economic issues in
society. The more SMEs, the more people are
employed, the less unemployment, the less crime,
and at the same time, the higher the average income
and the lower the poverty rate. Additionally, they
are more likely to get exposed to technological
advancements. The model used in this study is
based on the logistic regression model to evaluate
the influence of the factors that may lead to job
transitions. Job transitions may proxy the structural
changes in an economy. Let be the decision
whether an individual chooses either to stay in the
same profession or job or to switch to a
new one in a logistic regression model,
. Then, let be the probability of
“switching to a new profession or job”. Let be the
predictor variable, and be its corresponding
coefficient that determines the emphasis predictor
variable has on the outcome Y (or p). Also, let be
the intercept. Therefore, one can rewrite the
previous model to a linear form of logistic
regression model, where
is simply , and
.
4 Problem Solution
The following are the key variables used in the
model of this study (the codes Bs and Cs (e.g. B47,
C1 and so on) indicate specific questions or
statements used in the survey for this study).
1. B47. Does the presence of new technology
make you change the type or profession of
your job in the same industry/sector?
2. B48. Does the presence of new technology
make you change your job from one
sector/industry to another?
3. B49. Suppose with new technology there is
an opportunity in a new job type, but the
income is the same as the old job. Will you
switch to the new job?
4. B50. Suppose with new technology there is
an opportunity in a new job type, but the
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income is higher than the old job. Will you
switch to the new job?
5. B61. In which sector or industry was your
previous jobor business?
6. B62. In which sector or industry is your
current job or business?
7. B63. Is technology one of the reasons you
have EVER switched to a new
job/business/profession?
The predictor variables used in this study are:
1. C1. Current city of residence.
2. C2. Current residential location.
3. C3. Location where currently
working/attending school.
4. C4. Gender.
5. C5. Marital status.
6. C6. Employment status.
7. C7. Personal income (in Rupiah).
8. C8. Net income/Net profit of Business (in
Rupiah).
9. C9. Religion.
10. C10. Number of two-wheeled motor
vehicles owned (string).
11. C11. Number of four-wheeled motor
vehicles owned.
12. C12. Do you have a vehicle specifically for
business?
13. C13. Highest education ever taken.
14. C14. Do you have a public insurance
(BPJS) or Health Indonesian Card (KIS)?
15. C15. Age.
16. C16. How many Voltage Ampere (VA)
electric metres are in your residence?
17. C17. What type of residence do you have?
18. C18. Average monthly electricity
expenditure.
19. C19. Do you feel your monthly electricity
expenditure is expensive? (0 = very cheap, 9
= very expensive).
20. C20. With current electricity
usage/consumption, do you feel your
electricity needs are fulfilled? (0 = very
unfulfilled, 9 = very fulfilled).
Table 1 suggests the factors that can lead to a
change in profession or job:
1. Electricity needs fulfilment (C20): When
business owners feel that their electricity
needs are fulfilled, they are likely to change
their profession or job. This suggests that
electricity consumption plays a role in
someone's job change. In this case, one unit
increase in the individual’s perception that
its electricity needs are being met, the
which is the logit of probability
that the individual decides to switch to a new
profession or job increases by 0.140. In
other words, as =
increases by 0.140, will increase
by exp . This is a fifteen
percent increase in the odds of switching to a
new profession or job, assuming ceteris
paribus.
2. Personal Income (C7): When there is an
increase in personal income, business
owners are likely to change their profession
or job. This suggests that with an increase in
income, business owners have more options
for businesses or jobs that require larger
capital. Like in electricity needs case, one
rupiah increases in the personal income, the
which is the logit of probability
that the individual decides to switch to a new
profession or job increases by 0.118. Then,
the exp which means that
this is a thirteen percent increase in the odds
of switching to a new profession or job,
assuming ceteris paribus.
3. Business Income (C8): When there is an
increase in business income, business
owners are likely to change their profession
or job. This suggests that with an increase in
income, business owners have more options
for businesses/jobs that require larger
capital. Again, one rupiah increases in the
business income, the which is the
logit of probability that the individual
decides to switch to a new profession or job
increases by 0.235. Then, the exp
which means that this is a twenty-seven
percent increase in the odds of switching to a
new profession or job, assuming ceteris
paribus.
.
Table 1. Determinants of Profession or Job Change
VARIABLES (1) (2) (3)
C20
0.140*
(0.0791)
C7 0.118*
(0.0697)
C8 0.235***
(0.0726)
Constant -0.864 -0.209 -0.467**
(0.589) (0.247) (0.227)
Observations 240 240 240
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
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The dependent variable of the model in Table 1
is based on the difference between variables B61
and B62. If the responses to questions B61 and B62
are different, one can conclude that the respondents
have shifted their jobs or professions from one
sector or industry to another sector or industry.
Therefore, the value of the dependent variable is 1
when the responses are different meaning there is a
job transition, and 0 when the responses are similar
meaning there is no job transition.
Based on the results in Table 2, it can be
concluded that the following factors can drive a
change in profession or job within the same industry
or sector due to the presence of new technology.
1. Age (C15): As age increases, the likelihood
of changing professions or jobs within the
same sector or industry decreases. One-year
increases in the individual’s age, the
which is the logit of probability
that the individual decides to switch to a new
profession or job decreases by 0.127. Then,
the exp which means that
this is a fourteen percent decrease in the
odds of switching to a new profession or job,
assuming ceteris paribus.
2. Public Insurance (BPJS) or Health
Indonesian Card (KIS) (C14): Interestingly,
with the presence of BPJS/KIS, someone is
more likely to change professions or jobs
within the same sector/industry. For the
individual who owns a public insurance or
health Indonesian card, the which
is the logit of probability that the individual
decides to switch to a new profession or job
increases by 0.460. Then, the exp
which means that this is individuals
have fifty-eight percent higher in odds of
switching to a new profession or job
compared to those who do not own public
insurance or health Indonesian card,
assuming ceteris paribus.
3. Residential location (C2): By residing in
urban areas, someone is more likely to
change professions or jobs within the same
sector or industry. For the individual who
resides in urban areas, the which is
the logit of probability that the individual
decides to switch to a new profession or job
increases by 0.940. Then, the exp
which means that these individuals
have one hundred fifty-six percent higher
odds of switching to a new profession or job
compared to those who reside in rural areas,
assuming ceteris paribus.
4. Work or School location (C3): By working
or attending school in urban areas, someone
is more likely to change professions or jobs
within the same sector/industry. For the
individual who works or go to school in
urban areas, the which is the logit
of probability that the individual decides to
switch to a new profession or job increases
by 0.889. Then, the exp
which means that these individuals have one
hundred forty-three percent higher in odds of
switching to a new profession or job
compared to those who work or go to school
in rural areas, assuming ceteris paribus.
The dependent variable of the model in Table 2
is B47 (i.e. does the presence of new technology
make you change the type or profession of your job
in the same industry/sector?).
Table 2. Factors Driving Profession/Job Change
in the Same Industry/Sector Due to New
Technology
VARIABLES
(1)
(2)
(3)
(4)
(5)
(6)
C15
-0.127**
-0.127**
(0.0555)
(0.0555)
C14
0.460**
0.460**
(0.207)
(0.207)
C2
0.940***
(0.297)
C3
0.889***
(0.301)
Constant
-
1.033***
0.570
0.214
0.189
-1.033***
0.570
cut1
(0.280)
(0.487)
(0.255)
(0.262)
(0.280)
(0.487)
Constant
1.883***
3.485***
3.163***
3.128***
1.883***
3.485***
cut2
(0.320)
(0.549)
(0.342)
(0.346)
(0.320)
(0.549)
Obs
240
240
240
240
240
240
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
On the other hand, Table 3 suggests the
following factors that can drive a change in
profession or job in a different industry or sector
due to the presence of new technology (B47). The
directions and interpretation of the magnitudes of
age (C15), public insurance (C14), residential
location (C2), and work or school location (C3) in
Table 3 are the same as the variables in Table 2
On the other hand, individuals who are married
tend to refrain from changing their jobs in a
different industry or sector (C5). For the individual
who are married, the which is the logit of
probability that the individual decides to switch to a
new profession or job decreases by 0.378. Then, the
exp which means that these
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individuals have forty-six percent higher in odds of
switching to a new profession or job compared to
those who work or go to school in rural areas,
assuming ceteris paribus.
Individuals with a higher level of education tend
to refrain from changing their jobs to a different
industry or sector (C13). One- level increases in the
individual’s education attainment, the
which is the logit of probability that the
individual decides to switch to a new profession or
job decreases by 0.239. Then, the exp
which means that this is a twenty-seven percent
decrease in the odds of switching to a new profession
or job, assuming ceteris paribus.
Table 3. Factors Driving Profession/Job Change
in a Different Industry/Sector Due to New
Technology
VARIABLES
(1)
(2)
(3)
(4)
(5)
(6)
C2
0.555*
(0.295)
C3
0.500*
(0.300)
C5
-0.378*
(0.226)
C15
-0.0907*
(0.0548)
C13
-0.239*
(0.122)
C14
0.365*
(0.208)
Constant
0.0429
0.0085
4
-
1.006**
-
0.769***
-
1.155***
0.457
cut1
(0.256)
(0.263)
(0.402)
(0.275)
(0.425)
(0.490)
Constant
3.223***
3.182**
*
2.172**
*
2.406***
2.031***
3.636***
cut2
(0.366)
(0.369)
(0.451)
(0.353)
(0.466)
(0.567)
Obs
240
240
240
240
240
240
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
The dependent variable of model in Table 3 is
B48 (i.e. does the presence of new technology make
you change your job from one sector/industry to
another?).
Table 4 indicates that there are two different
groups of factors that emerge with the advent of
new technology that brings new jobs with income
like the old jobs.
1. Negative factors include marital status (C5)
and age (C15).
2. Positive factors include residential location
(C2), work or school location (C3), and
public insurance (C14).
Table 4. Factors Driving Job Change with the Same
Income When New Technology Introduces New
Jobs
VARIABLES
(1)
(2)
(3)
(4)
(5)
C2
1.374***
(0.366)
C3
1.593***
(0.392)
C5
-0.391*
(0.237)
C15
-0.140**
(0.0576)
C14
0.635***
(0.224)
Constant cut1
1.444***
1.642***
-0.294
-0.252
1.823***
(0.335)
(0.364)
(0.411)
(0.278)
(0.541)
Constant cut2
4.538***
4.751***
2.739***
2.800***
4.890***
(0.487)
(0.508)
(0.518)
(0.423)
(0.658)
Observations
240
240
240
240
240
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
The dependent variable of the model in Table 4
is B49 (i.e. Suppose with new technology there is an
opportunity in a new job type, but the income is the
same as the old job. Will you switch to the new
job?).
On the other hand, Table 5 shows almost similar
results as in Table 4 except the public insurance is
no longer affecting the dependent variable.
Therefore, the two groups of factors are as follows.
1. Negative factors include marital status (C5)
and age (C15).
2. Positive factors include residential location
(C2), work or school location (C3), and
public insurance (C14).
The dependent variable of model in Table 5 is
B50 (i.e. suppose with new technology there is an
opportunity in a new job type, but the income is
higher than the old job. Will you switch to the new
job?).
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Herman Karamoy, Hizkia Hendrick David Tasik,
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Table 5. Factors Driving Job Change with
Different Income When New Technology
Introduces New Jobs
VARIABLES
(1)
(2)
(3)
(4)
C2
0.497*
(0.286)
C3
0.481*
(0.291)
C5
-0.396*
(0.211)
C15
-0.0928*
(0.0528)
Constant cut1
-1.280***
-1.285***
-2.316***
-2.057***
(0.268)
(0.273)
(0.405)
(0.299)
Constant cut2
1.002***
0.994***
-0.0303
0.225
(0.261)
(0.266)
(0.370)
(0.261)
Observations
240
240
240
240
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Meanwhile, Table 6 indicates that those who
have previously decided to switch from one
profession to another are likely to change their jobs
when new technology introduces new jobs with
different incomes.
Table 6. Correlation Between the Decision to
Change Professions and the Willingness to
Change Jobs with Different Incomes When New
Technology Introduces New Jobs
VARIABLES
(1)
Difference of B61
and B62
(Changing
profession = 1,
otherwise 0)
0.445*
(0.247)
Constant cut1
-1.425***
(0.209)
Constant cut2
0.858***
(0.191)
Observations
240
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
The dependent variable of model in Table 6 is
B50 (i.e. suppose with new technology there is an
opportunity in a new job type, but the income is
higher than the old job. Will you switch to the new
job?).
5 Conclusion
This study has brought several inferences. The
fulfilment of electricity needs, personal income, and
business income are identified as determining
factors for someone's job transitions. Additionally,
various factors influencing the transitions of
professions or jobs within the same industry due to
the advent of new technology include age,
ownership of public insurance (BPJS) or Indonesian
Health Card (KIS), residential location, and
workplace or school location. Individuals working
or studying in urban areas are more likely to change
professions or jobs within the same sector/industry.
Additionally, marital status and higher
education level can drive the transitions of
professions or jobs in different industries or sectors
due to the introduction of new technology. Finally,
when categorised into two types of factors based on
their direction of impact on the likelihood of
changing professions or jobs, the factors can be
grouped as follows.
1. Negative Factors: These factors decrease the
likelihood of switching professions or jobs
and include marital status, educational level,
and age.
2. Positive Factors: These factors increase the
likelihood of switching professions or jobs
and encompass residential location, workplace
or school location, ownership of public
insurance (BPJS), or Indonesian Health Card
(KIS). These factors also affect the decision to
change professions and the willingness to
change jobs with different incomes when new
technology introduces new jobs.
The results of this study suggest that
policymakers should consider these factors for
adjustments in economic structures, whether
through providing incentives, technological
advancements, or creating new job opportunities in
order to maintain the stability of the economy.
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Herman Karamoy, Hizkia Hendrick David Tasik,
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DOI: 10.37394/23207.2024.21.115
Herman Karamoy, Hizkia Hendrick David Tasik,
Jullie Jeanette Sondakh, Stanly Wilnyson Alexander
E-ISSN: 2224-2899
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Volume 21, 2024
e00331.
https://doi.org/10.1016/j.jbvi.2021.e00331
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Herman Karamoy was responsible for the
development of problems, survey design and data
validation.
- Hizkia H. D. Tasik designed the survey and was
responsible for the data tabulation, methodologies,
and analyses.
- Jullie J. Sondakh developed the models.
- Stanly W. Alexander was responsible for the
development of problems, survey design, and
supervision.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This work was supported by the Non-Tax State
Revenue of Sam Ratulangi University under the
2023 University’s Applied Research Scheme.
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.115
Herman Karamoy, Hizkia Hendrick David Tasik,
Jullie Jeanette Sondakh, Stanly Wilnyson Alexander
E-ISSN: 2224-2899
1417
Volume 21, 2024
