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INDUSTRY PERSPECTIVES ON DIGITAL COMPETENCES AMONG MBKM
INTERNS IN INDONESIA
Linasari Santioso
Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
linasari.santio[email protected]
ABSTRACT
Indonesia's digital economy is expanding at a breakneck pace, highlighting the urgent need for digital
competencies that align with industry demands. Addressing this critical issue, the Ministry of Education,
Culture, Research, and Technology of Indonesia has initiated the Merdeka Belajar Kampus Merdeka
(MBKM) program, one of the programs, emphasizing practical internships to enhance undergraduates'
digital skills. This research aims to examine the effectiveness of the MBKM initiative in narrowing the
disparity between graduates' digital competencies and the evolving needs of various industries, shedding
light on the program's role in fostering digital readiness among undergraduatesThis research scrutinizes
the impact of the MBKM initiative on bridging the gap between graduates' digital competencies and the
expectations of diverse industries. It specifically assesses the efficacy of the program in cultivating
essential digital skills through IT-related tasks in various sectors. Employing a Likert-scale questionnaire
rooted in the DigComp 2.0 framework, the research surveyed host companies across Jakarta, evaluating
the digital proficiency of MBKM interns and their readiness for employment. Analysed through the
Smart PLS, the research employed PLS-SEM to unravel the influence of digital competencies on
employability. Findings indicate that one digital competence, Digital Content Creation, is significantly
associated with employability prospects. Conversely, Information and Data Literacy, Communication
and Collaboration, Safety, and Problem Solving did not exhibit a substantial direct effect on
employability. This research underlines the critical nature of enhancing specific competencies within the
MBKM framework to better prepare Indonesia's workforce for the demands of a digital future. The
insights gleaned offer strategic implications for refining educational programs and underscore the
importance of continual alignment with industry needs, thereby strengthening the nation's digital talent
pool.
Keywords : Digital Competences; Internship; MBKM
INTRODUCTION
Indonesia’s Digital economy is projected to reach USD 146 billion by 2025 representing
approximately 40% of the Southeast Asia digital economy (BI, 2022). Digital transformation
plays an important role that contributes to significant economic growth in Indonesia, one of the
examples is digitalisation in the finance industry. With society joining the digital era, financial
inclusion has increased in which 65.4% of the adult population has a formal bank account with
83.6% of aforementioned population having used financial products and services (BI, 2022). This
accomplishment optimistically will bring Indonesia to achieve the target of 90% financial
inclusion by 2024. It is predicted that with the immense growth taking place, the digital economy
will reach approximately USD 315.5 billion by 2030 (Sekertariat Kabiner, 2021).
Looking at the aforementioned high trajectory of digital economy, an immense digital
transformation in every sector has to take place and digital talent demand will definitely increase
to serve the digital ecosystem (Gong & Ribiere, 2021). According to the Minister of Communication
and Information Technology, Indonesia, at current rate, needs at least 600,000 digital talents every
year to navigate the driver of the digital ecosystem (Kementerian Komunikasi dan Informatika,
2022). It is projected that there will be a shortage of 47 million digital talents in 2030
(Kementerian Komunikasi dan Informatika, 2022) and at national level, it is recorded that at least
50% of the talents have only basic and intermediate digital skills while only 1% that have
advanced digital skills (Andarningtyas & Ad, 2022).
During the pandemic COVID-19 situation, digital transformation was not delayed, but it
even has taken place aggressively. Lots of activities or daily routines that were done offline have
shifted online, one of the examples is the education system. During the pre-pandemic, education
Industry Perspectives On Digital Competences Among MBKM Interns in Indonesia
42 Return: Study of Management Economic And Business, Vol 3 (1), January 2024
system was rarely run online whereby students always came to their schools or other education
institutions. Due to the rule of physical distancing or social distancing, students had to study at
home and familiarise themselves with technologies such as Google Meet, Zoom and any other
online medium that helped the education be delivered without having to attend the class
physically. This rule-changing condition has also happened in workplaces imposing a new
working rule which was Working from Home (WFH) or the other term used was Working from
Anywhere (WFA) by which employees did not have to attend the work physically and their
performance indicator suddenly changed to result-oriented rather than process-oriented as their
superiors could not monitor them closely (UNESCO, 2018).
Innovation and technology have brought a bunch of benefits to today’s world, however,
there is one thing to remember that digital transformation is a changing process and it’s not only
applied to the technology, but also to the talents. From the employment perspective, despite digital
transformation promotes various new job creations, a new set of digital skills are required to fill
those jobs. The talents have to be transformed into digital talents that are equipped with digital
skills, therefore, upskilling and reskilling contribute a crucial role in digital transformation. As
digital competence is a lifelong learning, digital talents have to keep enhancing their skills in
order to adapt to this fast-changing digital ecosystem. Future is unpredictable, hence the ability
to adapt to an even more digital future depends on developing the next generation of skills so as
to close the gap between talent supply and demand (Frankiewicz & Chamorro-Premuzic, 2020).
In the midst of the enormous demand of digital talent, there is a gap between digital talent
availability and job opportunities, simply because current available talents or graduates are
considered lacking of digital competence (Vuorikari et al., 2016). While the demand for digital
talent is reaching 600,000 every year, Indonesia’s unemployment rate in 2022 exists at 5.83% or
equals to 8.4 million people currently unemployed with the detailed information provided in Table
1 according to BPS (2022). The highest rank of unemployment is dominated by senior high school
(SMA) and vocational high school (SMK) graduates. According to Ali et al. (2020), there is a
substantial mismatch between vocational education provider and the needs of the industries due
to the limitations in terms of quality human resources, facilities, infrastructure, curricula and work
culture that meets industries’ standards while vocational education itself should be promoting
ready-to-work graduates
Table 1 Unemployment Status
Education
2021
2022
February
August
February
Not School
20.461
23.905
24.852
Not graduated
342.734
431.329
437.819
Elementary School
1.219.494
1.393.492
1.230.914
Junior High School
1.515.089
1.604.448
1.460.221
Senior High School
2.305.093
2.472.859
2.251.558
Vocational High School
2.089.137
2.111.338
1.876.661
Academy/ Diploma
254.457
216.021
235.359
University
999.543
848.657
884.769
Total
8.746.008
9.102.052
8.402.153
The gap of digital talent in Indonesia has been a huge challenge that needs to be solved in
order to promote digital transformation that contributes to the growth of the digital economy. The
collaboration between the stakeholders including government, educators and industries will be
pivotal to the attainment of a digital ecosystem. Thus, digital talent is one crucial component to
support Indonesia in achieving top 10 in world’s major economies by 2030 (Sekretariat Kabinet,
2018).
Indonesian digital startups have been growing significantly and it is stated that there are
more than 2,000 startups running in 2022. These startups are reinforcing the digital transformation
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and the formation of the digital economy by supporting the incumbents to become digital. At this
rate, digital talent will be hugely demanded not only by the startups, but also by the incumbents
and unfortunately, digital talent transformation is not an instant process.
There is a mismatch between graduates’ competences and the industry requirements. Lots
of industries have difficulty in finding the right digital talent and this is because what industry
needs is digital competences, which consist not only the digital hard skills, but also digital soft
skills, to adapt with the current situation. Based on the finding obtained by Santoso & Putra (2017),
adequate knowledge is not sufficient if the graduates are not equipped with necessary
competencies demanded by industries which will affect the chance of employability of the
graduates (Oberländer et al., 2020). In addition, the fast development in information and
communication technology (ICT) makes it difficult for education institutions to assess the gap
between current skills taught and new skills required by the jobs so they fail to instantly adapt to
the fast changes of the skills requirements (Mohammad Akhriza et al., 2017).
The homework that is necessary to be accomplished is upgrading existing talents,
transforming inadequate talents including the unemployed based on the Table 1, and cultivating
the future talents in order to match current and future industry needs or requirements. In
conclusion, bridging the digital competences gap between potential talents or graduates and
industries is the initial step to increase the employability and reduce the mismatch occurred.
According to Collins (2021), there are several ways to bridge the digital competences gap
which are apprenticeship, paid internship, integrating career service into postsecondary
institutions, and building strong postsecondary-industry relationship. In Australia and New
Zealand, a few models of internships have been promoted and one of the successful programs is
cadetship program (Ismail, 2018; Kempegowda & Chaczko, 2018). Cadetship is a mix of industry
placement combined with formal study in tertiary education or universities that ensure the
students or the cadets gain the right knowledge of technical/hard skills and soft skills through real
industry experience (SnelL & Snell-Siddle, 2017). Cadetship programs have shown a positive-
proven result and it has been implemented by organizations and industries across Australia and
New Zealand to address skills shortage and bridge the skills gap. In Scotland, especially in
computing, apprenticeship is more popular because of the integration with work experience and
it is a way to ensure that graduates are equipped with the necessary skills for sustainable
employment (Taylor-Smith et al., 2019).
In Indonesia, as the initial strategy to address the digital competences gap, the Ministry of
Communication and Information Technology (MCIT) has introduced four module of digital
literacy which are digital culture, digital safety, digital ethics, and digital skills (Sasongko, 2021).
There are three main programs supporting this digital literacy that are categorized into three
levels: advanced, intermediate and basic digital skills. In advance level, Digital Leadership
Academy (DLA) is initiated to increase the competences of digital decision maker both in public
and private sectors, starting with 300 participants conducting online while in intermediate level,
there is Digital Talent Scholarship (DTS) that is dedicated to prepare and train 100,000 talents
and potential talents including the skills of artificial intelligence, machine learning, cloud
computing, cyber security, digital entrepreneurship and digital communication. In the basic level,
there is National Digital Literacy Movement (GNLD) Siberkreasi program in collaboration with
34 provincial governments including 514 city governments together with Project Implementation
Unit in MICT to address bigger segment that represents 12.4 million Indonesian (Torres-Coronas
& Vidal-Blasco, 2011).
Furthermore, The Ministry of Education, Culture, Research and Technology (MoECRT)
has also imposed a new policy, named Merdeka Belajar Kampus Merdeka (MBKM), which is a
crucial collaboration between universities, industries and government, in relation with cultivating
the next wave of digital talents, to achieve and align the goals to minimise the skills gap in
Indonesia (Ingsih et al., 2022). MBKM programs are dedicated to give opportunities for
undergraduate students exploring their passion and ability outside their current program in
university. MBKM offers a variety of activities that can be undertaken by students within or even
outside their campus. Students undertake five semesters of their major in the normal
Industry Perspectives On Digital Competences Among MBKM Interns in Indonesia
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circumstances and as part of the MBKM program, they have the right to spend one semester in
another major or faculty at the same university and two semesters outside their campus
(Rahmawanti & Nurzaelani, 2021). These 3 semesters’ activities are designed to assist students to
define their real capabilities as well as to equip them with additional knowledge that might not be
experienced in their current major and will be useful during their transition into the working
world.
There are eight activities offered in MBKM policy which are Student Exchange, Internship,
Educational Assistance, Research, Humanity Project, Entrepreneurship, Independent Study, and
Community Service Program (Kemeterian Pendidikan, 2020). The most popular activity offered
in MBKM that is taken by the students is internship which can be converted into SKS system
(Kemeterian Pendidikan, 2020) and the potential host companies have to prepare proper
internship material or curriculum that can accommodate the SKS conversion system in order to
be accepted and eligible to take the students becoming their interns. This process helps to
standardise the industry internship program so the interns can gain valuable experiences more
than just administrative working. It is expected that the internship, especially in tech-based
companies, can nurture the final year undergraduate students to prepare themselves including
their competences when they enter into the digital ecosystem after they graduate from universities
so as to increase the chance of employability.
There exists a global framework on digital competences that have been applied and become
an integral tool worldwide which is the DigComp 2.0 Framework as detailed in Table 2. The
DigComp 2.0 is an EU framework offering a detailed description of all the competences needed
to be proficient in today’s digital society (Ferrari & Punie, 2013; Law et al., 2018; Martin & Grudziecki,
2006). It encompasses areas like information and data literacy, communication and collaboration,
digital content creation, safety, and problem-solving. By comprehensively understanding and
incorporating this framework, the gap between industry requirements and digital competences of
graduates could be bridged.
Table 2 Conceptual Framework DigComp 2.0
Competence Areas (Dimension 1)
1. Information and data literacy
2. Communication and
collaboration
3. Digital content creation
4. Safety
5. Problem solving
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The MBKM internship aims to provide students with real-world experience and
understanding of company needs, hence equipping them with necessary hard and soft skills.
Industries, in return, can evaluate and train these future talents, ensuring they meet industry
standards upon graduation. The incorporation of the DigComp 2.0 Framework into such
initiatives would further bolster the digital competences of graduates.
In conclusion, addressing the digital talent gap is crucial for the continuous growth of
Indonesia's digital economy. By leveraging comprehensive tools like the DigComp 2.0
Framework and initiatives like the MBKM program, Indonesia could be better poised to nurture
digital talent and reduce the mismatch between potential talents or graduates and industry
requirements. As a beginning, this research will therefore focus on assessing the digital
competences of the MBKM interns from the industry's perspective.
RESEARCH METHOD
This research aims to assess the digital competences of the MBKM interns with industries
serving as the primary stakeholders as they are the ultimate beneficiaries of this digital talent pool.
The recent implementation of the MBKM internship program in early 2020 and the paucity of
existing data from the industry perspective make this research context unique. The initial step of
this research entails identifying the digital competencies that industries expect. This is followed
by an evaluation of how the MBKM internship program aligns with these expectations in terms
of enhancing the digital competencies of potential graduates.
In light of the lack of comprehensive existing data and the specialized focus of this research,
the research will predominantly rely on a primary data collection approach. As such, a Likert-
scale questionnaire has been developed to assess the industry's evaluation of the proficiency level
in each digital competency exhibited by the MBKM interns. The results obtained from this
questionnaire will be analysed using Smart PLS. Moreover, this research will use SEM method
which allows for a robust examination of the relationships between observed indicators and latent
variables concerning industry-rated digital competencies and the skills exhibited by MBKM
interns.
Given the unknown population size of industry representatives who have interacted with
the MBKM internship program, a purposive sampling method will be employed (Muijs, 2010).
This method facilitates the selection of individuals who have had direct interactions with MBKM
interns, thereby ensuring that the responses gathered are informed and relevant to the research
objectives.
Aligned with the research questions and hypotheses, the trajectory of this research is
exploratory in nature. This approach is geared towards methodically answering the research
questions and testing the hypotheses by delving into the relationship between industry
expectations, the MBKM internship program, and the digital readiness of potential graduates.
Through the exploration and analysis of collected data, the research strives to provide a
comprehensive understanding of how the MBKM internship program prepares the digital
competencies of potential graduates, and whether this aligns with the expectations of the industry
stakeholders. This endeavour is crucial as it informs on the efficacy of the MBKM internship
program in preparing graduates for the digital demands of the modern workforce.
The research will begin with gathering responses through a Likert-scale questionnaire
which. The aim is to understand what digital skills industries expect from potential employees
and how well MBKM interns demonstrate these skills according to industry representatives.
serves as a pivotal tool for this research, designed to assess industry representatives' perceptions
of the proficiency levels of MBKM interns across various digital competencies and their
employability rates within the industry. This questionnaire, based on the DigComp 2.0
framework, ensures comprehensive coverage of digital competence areas and aligns with the
research objectives. Prior to administering the questionnaire, a validation process is undertaken
Industry Perspectives On Digital Competences Among MBKM Interns in Indonesia
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to confirm the eligibility and experience of industry representatives in evaluating digital skills.
Once validated, data collection proceeds, targeting industry representatives overseeing MBKM
interns, particularly those from IT-related host companies, through purposive sampling (Palinkas
et al., 2015). This approach ensures a minimum of 60 respondents, in line with SEM guidelines,
for a robust analysis of the relationships between interns' digital competencies and their
employability rates.
The responses collected will be analysed using Smart PLS. Initially, basic statistics will be
used to see if there are any clear trends in the data and following this, a deeper analysis to test the
hypotheses will be conducted using SEM method. SEM will help understand the complex
relationships between the proficiency levels of MBKM interns and the employability chance for
the interns (Pagano, 2012).
Before diving into SEM, validation and reliability tests will be conducted to ensure the data
is accurate and consistent. This step is crucial to ensure the trustworthiness of the data before
proceeding to test the hypotheses and drawing conclusions regarding the digital competences of
the MBKM interns.
Table 3 Research Flow Summary
Research Processes
Summary
Data Collection
• A survey will be conducted utilizing a questionnaire informed by
the DigComp 2.0 Framework to assess digital competencies.
• The focus is on host companies across Jakarta with MBKM
interns engaged in IT-related tasks.
• Data of minimum 60 samples will be collected, to provide a
representation of industry perspective.
Data Validation
• Preliminary validation will use 30 respondents to test the
instrument’s validity and reliability.
• R values should be above 0.361 in order to indicate a valid
instrument.
• Cronbach’s Alpha will be utilized and value above the accepted
threshold of 0.7 will indicate high reliability.
Data Analysis
• Smart PLS software will be employed to analyse the data,
utilizing descriptive and inferential statistics.
• PLS-SEM will be applied to investigate the relationships among
digital competencies, perceived proficiency levels of MBKM
interns, and their employability.
• The analysis will reveal the level of industry’s perception on
proficiency levels of key digital competencies.
Hypotheses Testing
• Five hypotheses will be tested using PLS-SEM.
• The result is expected to indicate a significant relationship for
each digital competencies with employability, through the
threshold value of p-value < 0.05 and t-statistics >1.96 which will
imply statistical significance, as well as the value of path
coefficient which will show the relationship positivity.
Conclusion
• The research will conclude which digital competence is more
influential in determining the employability of MBKM interns
from the industry perspective.
• Whether the MBKM internship program positively impacts
digital competency development
• The research's implications will also be provided to suggest an
urgent need to align educational initiatives with industry
expectations to optimize the digital talent pool in Indonesia's
evolving digital economy.
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RESULT AND DISCUSSION
PLS-SEM Analysis
In this research, the influence between variables will be analysed using the PLS-SEM
analysis technique. The stages in PLS-SEM analysis consist of testing the outer model and testing
the inner model (Hair et al., 2017). This research model includes six latent variables: Information
and Data Literacy, Communication and Collaboration, Digital Content Creation, Safety, Problem
Solving, and Employability. All these variables are first-order latent constructs measured by
several indicators. The Information and Data Literacy construct is a first-order construct with 3
measurement indicators, the Communication and Collaboration construct is a first-order construct
with 6 indicators, the Digital Content Creation construct is a first-order construct with 4
indicators, the Safety construct is a first-order construct with 4 indicators, the Problem Solving
construct is a first-order construct with 4 indicators, and the Employability construct is a first-
order construct with 10 measurement indicators. The PLS-SEM model specification to be
estimated in this research is pictured in Figure 1 SEM-PLS Model Specification.
Figure 1 PLS-SEM Model Specification
1. Testing the Outer Model
The measurement model testing phase includes the assessment of Convergent Validity,
Discriminant Validity, and Composite Reliability. The results of the PLS analysis can be used to
test research hypotheses if all indicators in the PLS model meet the criteria for convergent
validity, discriminant validity, and composite reliability. To generate the results of the outer
model test, the PLS model must be estimated using an algorithm technique. The figure 2 is the
results of the PLS-SEM model estimation after being estimated using the algorithm technique.
Industry Perspectives On Digital Competences Among MBKM Interns in Indonesia
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Figure 2 Initial Phase SEM Model Estimation Results
a. Convergent Validity
Based on the SEM model estimation results shown in Figure 2, all remaining variables in
the model are valid in their measurement, allowing the testing to proceed to the Average Variance
Extracted (AVE) stage. The loading factor values and AVE of the model can be more clearly seen
in Table 4 Convergent Validity Test Results.
Table 4 Convergent Validity Test Results
Variable
Indicator
Loading
factor
Cut Value
AVE
Convergent
Validity
Communication and
Collaboration
CC1
0.844
0.7
0.743
Valid
CC2
0.945
0.7
Valid
CC3
0.879
0.7
Valid
CC4
0.925
0.7
Valid
CC5
0.724
0.7
Valid
CC6
0.837
0.7
Valid
Digital Content
Creation
CCre1
0.850
0.7
0.813
Valid
CCre2
0.914
0.7
Valid
CCre3
0.941
0.7
Valid
CCre4
0.900
0.7
Valid
Employability
EMP1
0.784
0.7
0.666
Valid
EMP10
0.748
0.7
Valid
EMP2
0.923
0.7
Valid
EMP3
0.774
0.7
Valid
EMP4
0.785
0.7
Valid
EMP5
0.733
0.7
Valid
EMP6
0.843
0.7
Valid
EMP7
0.911
0.7
Valid
EMP8
0.748
0.7
Valid
EMP9
0.883
0.7
Valid
Information and Data
Literacy
ID1
0.872
0.7
0.834
Valid
ID2
0.911
0.7
Valid
ID3
0.955
0.7
Valid
Problem Solving
PS1
0.791
0.7
0.749
Valid
PS2
0.892
0.7
Valid
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PS3
0.894
0.7
Valid
PS4
0.881
0.7
Valid
Safety
SA1
0.881
0.7
0.776
Valid
SA2
0.840
0.7
Valid
SA3
0.905
0.7
Valid
SA4
0.896
0.7
Valid
The SEM model estimation results in Table 4 show that all constructs are valid and have
an Average Variance Extracted (AVE) > 0.5, meaning that they meet the required convergent
validity based on loading factor and AVE values.
b. Discriminant Validity
Discriminant validity is essential for ensuring that each latent variable in a model distinctly
captures its intended construct, separate from others in the model. An established criterion for
assessing discriminant validity is the Fornell-Larcker criterion, which posits that a model exhibits
strong discriminant validity when the square root of the AVE (Average Variance Extracted) for
each construct is greater than the construct's correlations with other constructs. Table 53 presents
the results of the discriminant validity assessment.
Table 5 Discriminant Validity according to the Fornell-Larcker Criterion
CC
CCre
EMP
ID
PS
SA
CC
0.862
CCre
0.564
0.902
EMP
0.588
0.915
0.816
ID
0.004
0.353
0.353
0.913
PS
0.465
0.731
0.715
0.188
0.865
SA
0.562
0.875
0.839
0.303
0.795
0.881
The results from Table 5 largely support the discriminant validity of the constructs within
the PLS model, as the square roots of the AVEs for most constructs are higher than their inter-
construct correlations, as per the Fornell-Larcker criterion. There are some instances where the
correlations slightly exceed the square roots of the AVEs.
However, these instances are minimal and do not significantly detract from the overall
discriminant validity of the model. This is further corroborated by additional robustness checks
through cross-loadings, where each indicator's loading on its own construct is compared with its
cross-loadings on other constructs. According to this criterion, an indicator demonstrates adequate
discriminant validity if its loading on its own construct is higher than on any other construct.
These results underscore the distinctiveness of the constructs and support the validity of the
model, reinforcing the reliability of the findings and the soundness of the PLS model's application
in this research.
Table 6 Discriminant Validity according to Cross Loading Values
CC
CCre
EMP
ID
PS
SA
CC1
0.844
0.580
0.575
0.128
0.468
0.593
CC2
0.945
0.553
0.555
-0.013
0.453
0.559
CC3
0.879
0.461
0.488
-0.017
0.478
0.513
CC4
0.925
0.532
0.535
0.048
0.420
0.491
CC5
0.724
0.309
0.365
-0.179
0.245
0.314
CC6
0.837
0.425
0.485
-0.018
0.295
0.382
CCre1
0.514
0.850
0.745
0.324
0.577
0.756
CCre2
0.412
0.914
0.835
0.411
0.662
0.792
CCre3
0.572
0.941
0.889
0.315
0.701
0.817
CCre4
0.538
0.900
0.824
0.225
0.690
0.790
Industry Perspectives On Digital Competences Among MBKM Interns in Indonesia
50 Return: Study of Management Economic And Business, Vol 3 (1), January 2024
EMP1
0.509
0.635
0.784
0.287
0.423
0.612
EMP10
0.416
0.572
0.748
0.163
0.391
0.521
EMP2
0.577
0.889
0.923
0.341
0.657
0.798
EMP3
0.459
0.671
0.774
0.185
0.580
0.554
EMP4
0.508
0.655
0.785
0.234
0.667
0.643
EMP5
0.300
0.678
0.733
0.201
0.668
0.665
EMP6
0.414
0.887
0.843
0.397
0.634
0.800
EMP7
0.588
0.866
0.911
0.367
0.659
0.792
EMP8
0.425
0.703
0.748
0.284
0.504
0.668
EMP9
0.571
0.806
0.883
0.341
0.600
0.714
ID1
-0.030
0.244
0.249
0.872
0.036
0.160
ID2
-0.105
0.287
0.271
0.911
0.169
0.267
ID3
0.096
0.399
0.407
0.955
0.260
0.360
PS1
0.349
0.671
0.588
0.255
0.791
0.762
PS2
0.514
0.631
0.683
0.149
0.892
0.692
PS3
0.385
0.650
0.604
0.154
0.894
0.677
PS4
0.345
0.580
0.590
0.096
0.881
0.623
SA1
0.485
0.826
0.752
0.275
0.769
0.881
SA2
0.566
0.794
0.747
0.289
0.560
0.840
SA3
0.456
0.721
0.746
0.291
0.700
0.905
SA4
0.472
0.738
0.709
0.210
0.775
0.896
Based on the results in Table 6, all indicators have the highest loading on their respective
constructs, fulfilling the discriminant validity requirements. Besides the Fornell-Larcker and
cross-loading tests, discriminant validity can also be assessed using the Heterotrait-Monotrait
Ratio (HTMT) between constructs. HTMT, an alternative method recommended for assessing
discriminant validity, uses a multitrait-multimethod matrix as its basis. The HTMT value should
be less than 0.9 to ensure discriminant validity between two reflective constructs (Henseler et al.,
2015). In this testing, constructs in the PLS model meet discriminant validity if the HTMT value
between constructs does not exceed 0.9.
Table 7 HTMT Between Latent Constructs
CC
CCre
EMP
ID
PS
S
CC
CCre
0.599
EMP
0.620
0.968
ID
0.126
0.372
0.357
PS
0.499
0.807
0.774
0.201
S
0.603
0.958
0.900
0.317
0.890
Based on the results in Table 7, while most construct pairs in PLS model are well within
this threshold, a few constructs pairs exhibit HTMT values marginally above it. Despite these
minimal exceedances, the overall pattern of HTMT values supports the assertion that the
constructs in the PLS model retain distinct conceptual definitions. The instances where the HTMT
values slightly surpass the threshold can be attributed to the inherent complexity and
interrelatedness of the constructs under study, which does not substantially compromise the
model's discriminant validity. The outer model of the PLS, therefore, still demonstrates a robust
discriminant validity.
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a. Construct Reliability
Construct reliability is assessed by the Cronbach's Alpha and Composite Reliability values
of each construct. The recommended values for composite reliability and Cronbach's Alpha are
more than 0.7.
Table 8 Composite Reliability
Construct
Cronbach's Alpha
Composite Reliability
Reliability
CC
0.929
0.945
Reliable
CCre
0.923
0.946
Reliable
EMP
0.943
0.952
Reliable
ID
0.902
0.938
Reliable
PS
0.887
0.922
Reliable
Sa
0.903
0.933
Reliable
Based on the analysis in Table 8, the composite reliability and Cronbach's Alpha values of
all constructs exceed 0.7, indicating that all constructs meet the required reliability. Considering
the overall results of the validity and reliability tests in the outer model testing phase, it can be
concluded that all indicators validly measure their constructs and all constructs are reliable,
allowing the testing to proceed to the next phase, the inner model testing.
2. Testing the Inner Model
a. Testing the Goodness of Fit of the Model
The goodness of fit of a PLS model can be determined by the R Square, Q Square, and
SRMR (Standardized Root Mean Square Residual) values. The R Square value indicates the
model's strength in predicting dependent variables, Q Square indicates the level of predictive
relevance of the model, and SRMR indicates the goodness of fit of the model, categorizing it as
either perfect fit, fit, or bad fit.
• Assessment of R Square Model
According to Chin (2013), an R Square value > 0.67 indicates a strong PLS model in
predicting endogenous variables, an R Square between 0.33 – 0.67 indicates a moderately strong
model, and an R Square between 0.19 – 0.33 indicates a weak model in predicting endogenous
variables. The analysis results in Table 9 show that the R Square for employability is 0.854,
categorizing it as strong.
Table 9 R Square Values
Variable
R Square
Criteria
Employability
0.854
Strong
• Assessment of Q Square Model
Q Square indicates the predictive relevance of the model. A Q Square value between 0.02
– 0.15 indicates low predictive relevance, a Q Square between 0.15 – 0.35 indicates moderate
predictive relevance, and a Q Square > 0.35 indicates high predictive relevance (Chin, 2013). The
analysis results in Table10 show that the Q Square for employability falls into the category of
high predictive relevance.
Table 10 Q Square Values
Variable
Q Square
Criteria
Employability
0.824
High
• Assessment of SRMR Model
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In addition to R Square and Q Square values, the goodness of fit of the model is also
assessed by the SRMR of the estimated model. A model is considered a perfect fit if the SRMR
is < 0.08 and fit if the SRMR is between 0.08-0.10. The analysis results in the table 11 show that
the SRMR of the estimated model is 0.086, categorizing it as a fit.
Table 11 SRMR
Component
SRMR
Criteria
Saturated Model
0.086
Fit
Estimated Model
0.086
b. Multicollinearity
Multicollinearity assessment within the PLS-SEM framework is conducted through the
Variance Inflation Factor (VIF) of the inner model constructs. A VIF value below 5.00 typically
suggests an absence of multicollinearity, ensuring that the regression model's estimates remain
unbiased and reliable. According to the value presented in Table 12, the VIF values for the
majority of constructs in the inner model are comfortably below the threshold, signifying a
multicollinearity-free model. However, one construct marginally exceeds this limit with a VIF
value of 5.639, which suggests a need for a more nuanced interpretation (Fisher, 1922).
This slightly elevated VIF value warrants further inspection but does not necessarily imply
a significant multicollinearity issue within the model. Given the conservative nature of the VIF
threshold, values that marginally exceed 5.00 may not dramatically affect the validity of the
regression estimates. Consequently, while the presence of a VIF value above 5.00 merits
acknowledgement and consideration, it does not invalidate the model. The integrity of the PLS-
SEM model is upheld by the overall pattern of low VIF values across the constructs, and the
analysis proceeds with a mindful recognition of the indicated multicollinearity check.
Table 12 VIF Inner Model
CC
CCre
EMP
ID
PS
SA
CC
1.617
CCre
4.765
EMP
ID
1.239
PS
2.802
SA
5.639
c. Testing Direct Effects
In PLS analysis, after the model is proven to be fit, testing the influence between variables
can be conducted. This includes testing direct effects, indirect effects, and total effects. The
following Figure 3 shows the results of the PLS-SEM model estimation using the bootstrapping
method.
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Figure 3 Bootstrapping Model Estimation Results
Based on the PLS model estimation results using the bootstrapping technique with 500
samples, the following Table 13 are the results of the testing of the influence between variables.
Table 13 Results of Testing Direct Effects
Path
Path Coefficient
T-Statistics
P-Values
CC -> EMP
0.109
1.874
0.062
CCre -> EMP
0.702
5.391
0.000
ID -> EMP
0.064
1.200
0.231
PS -> EMP
0.065
0.703
0.482
SA -> EMP
0.092
0.825
0.410
Direct effects, often referred to as direct impact, are the influences of exogenous variables
directly on endogenous variables without going through other (intervening) variables. In PLS-
SEM analysis, the significance and direction of direct effects are determined by the p-value, t-
statistics, and path coefficients for each path connecting endogenous and exogenous variables. If
the p-value obtained in the relationship between variables is < 0.05 and t-statistics > 1.96 (two-
tailed t-value, α 5%) and t-statistics > 1.65 in one-tailed tests, it is concluded that the exogenous
variable significantly influences the endogenous variable in the direction indicated by the sign of
its path coefficient. Conversely, if the p-value is > 0.05 and t-statistics < 1.96 (two-tailed t-value,
α 5%) in two-tailed tests and t-statistics < 1.65 in one-tailed tests, it is concluded that the
exogenous variable does not significantly influence the endogenous variable (Hair et al., 2019).
Based on these test results, the following conclusions are drawn:
• Communication and Collaboration → Employability
The relationship between communication and collaboration and employability, while
positive, is not statistically significant with a p-value of 0.062 > 0.05. The path coefficient is
0.109, and the t-statistics are 1.874 < 1.96, just below the conventional significance threshold.
This suggests that the influence of communication and collaboration on employability, although
present, is not significant.
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• Digital Content Creation → Employability
The analysis reveals a strong positive relationship between digital content creation and
employability. The path coefficient of 0.702 signifies a substantial effect, and with t-statistics of
5.391 > 1.96, this relationship is statistically significant beyond the 0.05 p-value threshold. The
findings suggest that proficiency in digital content creation is a key driver of employability,
reflecting the growing demand for these skills in the job market.
• Information and Data Literacy → Employability
The relationship between information and data literacy and employability is observed with
a path coefficient of 0.064. However, the associated t-statistics of 1.200 indicate that this effect is
not statistically significant, as the p-value of 0.231 > 0.05. This suggests that, within the scope of
this analysis, information and data literacy do not have a direct measurable impact on
employability.
• Problem Solving → Employability
Problem-solving is not statistically significant to employability, as reflected by a p-value
of 0.482 > 0.05 and t-statistics of 0.703 < 1.96. This indicates a weak influence of problem-solving
skills on employability.
• Safety → Employability
Safety's influence on employability is not statistically significant with a p-value of 0.410 >
0.05 and t-statistics of 0.825 < 1.96. This suggests that the level of safety does not have a strong
predictive power or does not have a significant influence on employability.
d. Coefficient of Determination
In a structural model, the exogenous variables in the research model simultaneously
influence the endogenous variable. The extent of the contribution of all exogenous variables to
the endogenous variable can be seen from the coefficient of determination. The coefficient of
determination is indicated by the Adjusted R Square value, which ranges between 0-1, or can also
be interpreted as a percentage (0-100%). A higher coefficient of determination indicates a greater
proportion of the variance in the endogenous variable explained by its exogenous variables, while
a lower coefficient of determination suggests a relatively low influence of the exogenous variables
on the endogenous variable. This is because there are still many factors outside of these exogenous
variables that can influence the endogenous variable.
Table 14 Coefficient of Determination
R Square
R Square Adjusted
Employability
0.854
0.842
The analysis results in Table 14 show that the adjusted R square for employability is 0.842,
meaning that 84.2% of the variance in employability is influenced by Information and Data
Literacy, Communication and Collaboration, Digital Content Creation, Safety, and Problem
Solving, while the remaining 15.8% of employability is influenced by other factors outside of
Information and Data Literacy, Communication and Collaboration, Digital Content Creation,
Safety, and Problem Solving.
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Hypothesis Testing
The hypothesis testing in this research is based on the results of the PLS-SEM analysis.
Below is a summary of the hypothesis testing results in this research as disclosed in Table 153.
Table 15 Hypothesis Testing Results
No.
Hypothesis
Regression Coefficients
Conclusion
H1
Information and Data
Literacy has significant
relationship with
Employability
Path Coefficient = 0.064;
T-Statistics = 1.200;
P-Value = 0.231
Not Significant
H2
Communication and
Collaboration has significant
relationship with
Employability
Path Coefficient = 0.109;
T-Statistics = 1.874;
P-Value = 0.062
Not Significant
H3
Digital Content Creation has
significant relationship with
Employability
Path Coefficient = 0.702;
T-Statistics = 5.391;
P-Value = 0.000
Significant
H4
Safety has significant
relationship with
Employability
Path Coefficient = 0.092;
T-Statistics = 0.825;
P-Value = 0.410
Not Significant
H5
Problem Solving has
significant relationship with
Employability
Path Coefficient = 0.065;
T-Statistics = 0.703;
P-Value = 0.482
Not Significant
The explanations of the hypothesis testing results are as follows:
• Hypothesis 1 (H1): states that information and data literacy have a positive, but not
significant effect on employability. The analysis results show a p-value of 0.231, t-statistics
of 1.200, and a positive path coefficient of 0.064. Since the p-value is > 0.05 and t < 1.96, it
can be concluded that information and data literacy positively, but not significantly affect
employability, therefore, this result indicates a rejection of H1.
• Hypothesis 2 (H2): states that communication and collaboration have a positive, but not
significant effect on employability. The analysis results show a p-value of 0.062, t-statistics
of 1.874, and a positive path coefficient of 0.109. Since the p-value is > 0.05 and t < 1.96, it
can be concluded that communication and collaboration positively, but not significantly
affect employability, therefore, this result indicates a rejection of H2.
• Hypothesis 3 (H3): states that digital content creation has a positive and significant effect
on employability. The analysis results show a p-value of 0.000 and t-statistics of 5.391. Since
the p-value is < 0.05 and t > 1.96, it can be concluded that digital content creation positively
and significantly affects employability, therefore, this result indicates a clear confirmation
of H3. Furthermore, digital content creation has the highest path coefficient among other
digital competence components which indicates this digital competence influences
employability more than the other digital competencies.
• Hypothesis 4 (H4): states that safety has a positive, but not significant effect on
employability. The analysis results show a p-value of 0.410, t-statistics of 0.825, and a
positive path coefficient of 0.092. Since the p-value is > 0.05 and t < 1.96, it can be concluded
that safety positively, but not significantly affects employability, therefore, this result
indicates a rejection of H4.
• Hypothesis 5 (H5): states that problem solving has a positive, but not significant effect on
employability. The analysis results show a p-value of 0.482, t-statistics of 0.703, and a
positive path coefficient of 0.065. Since the p-value is > 0.05 and t < 1.96, it can be concluded
that problem solving positively, but not significantly affects employability, therefore, this
result indicates a rejection of H5.
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Discussion on each Digital Competencies
a. Re-evaluating the Role of Information and Data Literacy in Employability
The research indicates that information and data literacy, while positively associated with
employability, does not have a statistically significant impact. This finding prompts a re-
examination of how these competencies are perceived and valued in the industry. While data
literacy remains a critical skill for navigating a data-driven landscape, its direct correlation
to employability may not be significant. This nuanced understanding calls for a broader
approach to developing digital competencies that reflect the multifaceted nature of
employability.
b. Communication and Collaboration’s Subtler Influence on Employability
The research suggests that communication and collaboration, essential skills in the
workplace, contribute positively but not significantly to employability. This nuanced result
challenges the assumption of their direct impact on job prospects. While these competencies
are undoubtedly valued, the findings imply that additional factors may play a more pivotal
role in employability, necessitating a more comprehensive skill set for MBKM interns.
c. Digital Content Creation as a Key Driver of Employability
In a significant finding, digital content creation emerges as a vital competence with a
substantial and significant positive effect on employability. This marks the positioning
creative digital skills as a critical component for the graduates seeking to stand out in the job
market. The research underscores the necessity of integrating digital content creation into
curricular activities to enhance job readiness and align with industry trends.
d. Safety’s Unexpected Minor Role in Employability
In this research, safety does not significantly influence employability, though it maintains a
positive relationship. This suggests that while important, safety competencies alone may not
be sufficient indicators of job readiness. This insight calls for a re-evaluation of safety within
the digital competence framework, potentially integrating it with broader skill sets to meet
the complex demands of the digital workforce.
e. Re-thinking the Impact of Problem-Solving Skills on Employability
This research highlights that problem-solving, while positively aligned with employability,
does not significantly determine job prospects. This unexpected outcome indicates that while
problem-solving is critical, it may operate synergistically with other skills rather than
independently influencing employability. It suggests that a holistic approach, combining
problem-solving with other digital and non-digital competencies, might better prepare
MBKM interns for the multifarious challenges of the modern workplace.
Managerial Implications
The important findings of this research have been summarized, followed by the managerial
responses as well as the persons who are responsible to take such implications as shown in
Table14.
Table 16 Managerial Implications of the Research
No.
Research Finding
Managerial Response
Executors
1
Digital Content Creation
as a Key Competency
Amplify focus on Digital Content
Creation within MBKM training,
recognizing its substantial impact on
employability.
Educational
Institutions and
MBKM Program
Coordinators
2
Moderate Proficiency in
Information and Data
Literacy, Communication
and Collaboration, Safety
and Problem Solving
Intensify training in Information and
Data Literacy, Communication and
Collaboration, Safety and Problem
Solving for MBKM interns, given
their moderate proficiency and
positive relationship on
employability.
MBKM Program
Developers and
Educational
Institutions
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3
Alignment with Industry
Standards
Strengthen the synergy between
educational institutions and
industries to customize MBKM
programs to industry-specific digital
skill requirements.
Educational
Institutions, Industry
Partners, and MBKM
Program Developers
4
Policy Development
Advocate for policies that facilitate
the integration of digital
competencies in MBKM programs,
particularly those most impactful on
employability.
Government Bodies,
Educational Policy
Makers, and Industry
Leaders
Limitation of the Research
Despite the insights gained from this research, it's important to acknowledge certain limitations
that may have influenced the outcomes, including sample specificity and methodological
constraints.
a. Sample Specificity: The research focused on IT-related tasks, potentially overlooking the
importance of competencies in non-IT sectors.
b. Small Sample Size: The limited sample size might not fully represent the diverse digital
competency landscape across industries.
c. Industry Specificity: The research centered primarily on IT-related industries, possibly
neglecting the needs of other sectors
d. Temporal Scope: The research's timeframe may not capture the evolving nature of digital
competencies and industry requirements.
e. Methodological Limitations: Reliance on self-report questionnaires could introduce biases.
f. Limited Generalizability: The findings may not be broadly applicable outside the Indonesian
context.
Recommedation for Future Research
Building upon the findings and limitations, several recommendations emerge for future research
and practical applications, aiming to address gaps in knowledge and enhance the effectiveness
of educational programs in aligning with industry needs.
a. Diversify Research Focus: Investigate the importance of different competencies across
various industry contexts.
b. Curriculum Enhancement: Integrate key findings into educational programs to better prepare
students for employability.
c. Industry-Education Collaboration: Foster closer collaboration between industries and
educational institutions to ensure relevance and alignment of training programs.
d. Policy Alignment: Formulate policies that align educational programs with industry needs.
e. Promote Lifelong Learning: Encourage continuous skill development to keep pace with
technological advancements.
CONCLUSION
In conclusion, this research provides valuable insights into the alignment between the
digital competencies of MBKM interns and industry requirements in Indonesia. Through rigorous
analysis, it has identified Digital Content Creation as a crucial competency directly impacting
employability, emphasizing the significance of creative and technical skills in the digital realm.
However, there exists a moderate perception gap in proficiency levels among interns, particularly
in competencies like Information and Data Literacy and Safety, suggesting a need for
recalibration in educational programs to better meet industry demands.
The MBKM internship program demonstrates a positive influence on competency
development, bridging theoretical knowledge with practical application. Yet, the diversity in
competency significance underscores the necessity for a nuanced program design. Prioritizing
digital content creation skills within MBKM could ensure interns possess both theoretical
knowledge and practical capabilities highly sought after in today's job market.
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58 Return: Study of Management Economic And Business, Vol 3 (1), January 2024
Moving forward, it is imperative to acknowledge the limitations of this research, such as
the focus on IT-related tasks and the small sample size. These constraints inform the interpretation
of findings and guide future research endeavours. Addressing these limitations involves
broadening research focus across industries, integrating diverse methodological approaches, and
considering longitudinal studies to track competency evolution over time.
The main implications of these findings are twofold: firstly, educational programs,
particularly the MBKM initiative, should prioritize the development of digital content creation
skills to enhance interns' job readiness; secondly, stakeholders, including educational institutions,
industry partners, government bodies, and policymakers, should collaborate to tailor educational
programs to industry needs, ensuring graduates are equipped with the necessary digital
competencies for the evolving job market
Recommendations stemming from this research highlight the need for future studies to
delve into the varying importance of competencies across different industry contexts.
Additionally, integrating insights from this study into curriculum development, fostering closer
collaboration between industries and educational institutions, and formulating policies to align
educational programs with industry needs are crucial steps forward.
In essence, this research lays a foundational blueprint for enhancing the MBKM internship
program's efficacy in Indonesia. By aligning program objectives with identified digital
competencies, stakeholders can ensure interns are equipped not only for current digital demands
but also for future technological advancements. This alignment is pivotal for sustaining
Indonesia's growth and competitiveness in the global digital landscape, fostering a workforce
adaptable to the rapid changes defining this era of digital transformation.
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