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Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
10.5281/zenodo.14274261 EISSN: 2735-9891
415
Thriving in Uncertainty: The Relationships between Future Job
Predictions, Learning Agility, Responsive Attitude, and Adaptability
Encep Saefullah
https://orcid.org/0000-0002-6255-934X
*Bambang Dwi Suseno
https://orcid.org/0000-0001-8196-6146
Nani Rohaeni
https://orcid.org/0000-0002-4685-7160
Universitas Bina Bangsa, Indonesia
Jalan Raya Serang Km 03 No.1b, Pakupatan Kota Serang,
*2Corresponding author, email: bambangdwisuseno1@gmail.com
Abstract
Background: The 2023 World Economic Forum report indicates that the influence of
Artificial Intelligence (AI) and automation on the labour sector was more significant than
initially anticipated. While a 2018 study predicted substantial employment losses offset by
job creation, recent evidence suggests a different outcome.
Objectives: This study aims to identify the principal determinants affecting the
adaptability of college graduates in Indonesia. The study equally measures the intensity of
the interactions among these variables to comprehend their collective impact on graduate
adaptability.
Methodology: The researchers used descriptive survey research in this study, examining
284 Indonesian ICIL policy students who were selected using the purposive sampling
technique. A structured questionnaire was used to collect data for the study. This study
employed quantitative analysis, using Structural Equation Modeling (SEM) with SmartPLS
4.0.
Results: It was found that job trend forecasting significantly affects responsiveness, with a
correlation coefficient of 0.69, while responsiveness strongly influences learning agility,
with a coefficient of 0.43. However, there exists no significant direct correlation between
job trend forecasts and adaptability (t-value = 0.56).
Conclusion: Adaptability is a multidimensional concept that incorporates job forecasting
trend analysis, responsive practices, and learning accelerators. Institutions ought to
improve their human resources tactics to better equip graduates for the ever-evolving job
market.
Unique Contribution: This study has provided empirical evidence that could guide
policies and programmes for preparing graduates for the labour market.
Keywords: Adaptation Abilities, Job Prediction, Learning Agility, Responsive Attitudes,
Digital Technology Revolution.
Introduction
The policy of The Independent Campus Independent Learning (ICIL) strategy,
launched by the Ministry of Education, Culture, Research, and Technology of the
Republic of Indonesia in 2020, was anticipated to face considerable obstacles due to
Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
10.5281/zenodo.14274261 EISSN: 2735-9891
416
unpredictable external factors. The National Labor Force Survey (Sakernas, 2024)
performed by the Central Statistics Agency (BPS, 2024) reported that in February 2024,
Indonesia had 7.195 million unemployed persons. The unemployment rate for diploma
and college graduates was 5.49%, equating to roughly 0.395 million individuals (BPS,
2024). This high unemployment rate is a pressing issue that demands urgent attention
and proactive measures. Addressing this challenge requires a combination of strategic
policies in the higher education sector to better align curricula with job market
demands, as well as initiatives in the business sector to stimulate job creation and
provide opportunities for graduates.
The 2023 World Economic Forum report indicates that the influence of Artificial
Intelligence (AI) and automation on the labour sector was more significant than initially
anticipated. While a 2018 study predicted substantial employment losses offset by job
creation, recent evidence suggests a different outcome. From 2023 to 2027, it is
projected that 69 million new jobs will emerge due to developments in AI; however,
this will be counterbalanced by eliminating 83 million jobs, resulting in a net reduction
of 14 million jobs globally. Positions associated with AI, digitalisation, and
sustainability, including AI specialists and renewable energy engineers, are anticipated
to expand, but roles in clerical and administrative fields are most susceptible to
reduction (World Economic Forum, 2023). This transition highlights the inevitability of
job changes and the urgency for reskilling and adaptation to changing sectors since
around 44% of workers' abilities will be disrupted by 2027. The necessity for analytical
thinking, technological proficiency, and adaptability will escalate as organisations
progressively embrace frontier technologies (World Economic Forum, 2023).
However, the amount of work on predicting changes in graduates' employment patterns
due to adaptability indicates that it is limited at best and often contradictory. Studies
suggest that adaptability, an ability to adjust cognitively, behaviorally and emotionally
in new, different or uncertain circumstances, is critical for predicting academic
professional success (Holliman et al. 2021). However, the study has not looked at
whether adaptability influences psychological well-being.
Heterogeneity and generalisability need further investigation (Zenebe et al., 2021).
Lastly, predictors of psychological well-being are adaptability and social support (Ryan
& Deci, 2017) which should be explored as affective variables to investigate their
differentiated effects respectively (Buzzai et al., 2020). Based on the gaps in previous
literature, this study aimed to explore various government policies, such as the MBKM
initiative. That is, challenge graduates in workspaces.
The purpose of this study was to (1) identify the key variables that influence
adaptability among Indonesian college graduates, (2) assess relationship strengths
between them and combine effects on graduate life-long adaptation, and (3) generate
theoretical recommendations for policy and practical implications based on the results
mentioned above will further improve ICIL.
Literature review and hypothesis development
Predicting Future Job Trends (PFJ) and Fostering Responsive Learning
In terms of curricula at colleges and universities, these are largely focused on academic
aspects of the course, especially about specific courses. Career exploration in the early
stages promotes flexibility, which is essential for achievement later (Tokar & Kaut,
Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
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2018). Despite its benefits in nurturing critical-minded professionals of tomorrow, these
strategies are seldom adopted by colleges and universities (Ashour, 2020).
Learning agility (LA) by (Murphy, 2021; De Meuse et al., 2010) helps deal with
uncertainties and create new values. Point out that the ability to adapt to careers is a
human lifelong learning process that can be achieved automatically by young people
themselves (Öztemel & Akyol, 2021; Suseno et al., 2021). An individual’s emphasis on
protean career orientation. This leads to the following hypothesis:
H1: The sharper the ability to predict future job, the higher the responsiveness.
1.1
Predictions of Future Job Trends and Learning Agility
Students and parents are paying a premium for the ability to forecast employment
trends, establish LA in accompanying university studies or participate. According to
Strielkowski et al., (2020), emotional regulation has an important effect on career
adaptability and job search success. Demonstrated that reducing anxieties like stress,
social anxiety, or frustration improved self-restraint and confidence in oneself, which
can become an advantage. George and Park (2022) mentioned some of the focused
training intersessions, like mock interviews, along with stress control.
The study of Jian et al. (2022) saw LA as an intervening factor in the relationship
between ASE and long-term learning outcomes. LA challenges its users to understand
and apply new information quickly (De Meuse, 2010; Suseno & Barowi, 2023).
Finally, EI and LA play a significant role in predicting students' career success. A
hypothesis was formulated.
H2: The better students can predict future career trends, the greater the chances of career
success.
The Relationship between Responsive Attitude (RA) and Learning Agility
Higher education is grappling with theoretical knowledge, and the media needs to bring
that in balance with helping students learn practical skills, so they are prepared for
something more uncertain and less stable than ever already (Wrigley et al., 2021;
Munawir & Suseno, 2024) . When it comes to emphasising theory, graduates may exit
school unprepared for the labour market needs (Gunzenhauser, 2021), and
overemphasising practical skills can fail to promote adaptational capacities and problem-
solving capabilities in the dynamics of daily life. Learning Agility (LA) helps students
gain the ability to learn quickly, use knowledge in new situations, develop adaptability,
synthesise information, and understand diversity (Alhadabi & Karpinski, 2020;
Wulandari et al, 2024). A responsive attitude (RA) to environmental change increases
learning capabilities and adaptation speed, supporting an education system that balances
high theoretical loads with hands-on practical skills to foster adaptive, globally
responsible actors. Based on insights from previous literature, the following hypothesis is
proposed:
H3: The stronger the responsive attitude, the higher the learning agility.
The Correlation Between Responsive Attitude and Adaptability of University Graduates
(AOUG)
Educators are confronted with the challenge of balancing the acute knowledge and skills
that need to be taught while at the same time preparing students for the changing face of
Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
10.5281/zenodo.14274261 EISSN: 2735-9891
418
media work and its systemic issues (Wrigley et al.,. More often than not, too much
theoretical knowledge will leave graduates poorly prepared for the realities of life,
whereas an overemphasis on practical skills may not make it possible to address
systemic problems in a better way (Motlova & Honsova, 2021; Maskudi et al., 2024).
Career exploration through self-understanding and environmental awareness is
significant for developing sound career plans and adjusting to job changes (Smith et al.,
2020). Parental support plays an essential role in shaping graduates’ ability to adjust
themselves to workplace changes (Kim & Park, 2022; Suseno et al.,.2924). That is why
it is posited that a strong responsiveness attitude enhances the ability of graduates to
adapt, which is essential for navigating a volatile and precarious labour market. Based
on a thorough review of the literature, we can propose the following hypothesis:
H4: The stronger the responsive attitude, the higher the adaptability of university
graduates.
Learning Agility and Adaptability of University Graduates
Learning Agility (LA) is an individual's proficiency and ambition to acquire experience-
based knowledge and use it effectively when confronted with novel situations. According
to Lombardo and Eichinger (2000), LA stems from the ability and willingness to learn
through experiences and the capacity to adapt to different situations. LA is when
someone is very good at learning from the world around them and then applying that
learning to new situations. Lombardo and Eichinger (2000) and Suseno (2019) tell us that
LA brings together experience-based knowledge with the ability to learn from these
experiences. It also allows for flexibility in various circumstances. This means these
people can solve new problems by transferring knowledge, combining different things,
and respecting different opinions. Studies show that you have Academic Self-Efficacy
(ASE) affecting your academic motivation and engagement by means of Learning Agility
(LA). Therefore, this hypothesis has been proposed:
H5: The higher the learning agility, the higher the adaptability of university graduates.
Future Job Prediction Ability and High Adaptability of University Graduates
PFJ is important for students since it directs career planning and brings forth a
matching of skills as well as interests. Accurate career forecasts enhance adaptability
and life satisfaction for students and their parents (Haratsis et al., 2015; Mustofa et al.,
2023). Career support provided by parents and students’ skills in anticipating future
jobs mediate the link between life satisfaction and readiness for work. Tokar and Kaut
(2018) term self- and environmental explorations as two dimensions of career
exploration. The former aids personal understanding, while the latter looks into jobs
and organisations to unearth facts.
Typically, during adolescence and young adulthood, career exploration is vital in career
development (Smith et al., 2020; Suseno & Mukhlis, 2023). Predicting labour market
trends enables students to adapt more easily to fast-paced changes in the work
environment. The following hypothesis was proposed following an extensive literature
review:
H6: The sharper the ability to predict future work, the higher the adaptability of university
graduates.
Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
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Methodology
Design of the study
For this investigation, the researchers utilized a quantitative methodology based on a survey,
following Creswell and Plano Clark (2011). The operational definition of variables is
delineated as follows:
• Forecasting Future Employment Trends (FFET) Projected essential trends derived
from integrated data, varied viewpoints, professional growth dynamics, personal
profile comprehension, and digital literacy (Haratsis et al., 2015; Strielkowski et al.,
2020; Park et al., 2022).
• Responsive Attitude (RA): The readiness and capacity to react suitably to
circumstances and problems, represented by teamwork, critical thinking, pragmatism,
responsiveness, and problem-solving skills (OECD, 2018; Wrigley et al., 2021;
Schmid et al., 2022; Banwo, 2023).
• Learning Agility (LA): The ability to learn and adapt swiftly, demonstrated by
academic motivation, environmental context, diversity comprehension, and value
adoption (Lombardo).
• The adaptability of University Graduates (AOUG) is their capacity to adjust to diverse
circumstances, which is demonstrated by their inventiveness, personal flexibility, task
involvement, emotional intelligence, and accomplishment orientation (Ma et al.
(2019), Chui et al. (2020), Öztemel and Akyol (2021), Parola and Marcionetti (2021),
Badiozaman, 2023).
Population of the study
The research population comprised students involved in the Indonesian ICIL policy.
Furthermore, of the 57,822 candidates, 16,250 students (Kemendikbud et al. 2024),
geographically distributed across several islands such as Sumatra, Java, Sulawesi, Bali, and
West Nusa Tenggara, representing Western, Central, and Eastern Indonesia, were chosen.
Sample size
The rationale for the sample size followed Hair et al. (2010), who advocated for a minimum
of five times the amount of indicators. Nonetheless, with 19 indications, the minimum
advisable sample size was 95 (5 x 19), guaranteeing consistent parameter estimates Kline,
(2015).
Sample techniques
A purposive sample strategy was employed to identify participants who precisely matched the
research population. A total of 293 students participated; however, after a rigorous screening
process, only 284 replies were deemed suitable for the research. The replies surpassed the
minimal sample size suggested by Hair et al. (2010) and, according to Kline (2015), ensured
the stability of parameter estimates.
Instrument for data collection
• The trial test, a comprehensive process conducted from February to June 2024, was followed
by the meticulous development of the survey questionnaire. This questionnaire was crafted by
transforming indicators from each evaluated variable and was deemed valid and reliable after
preliminary testing on a limited sample. The purposive sample technique exclusively targeted
students who engaged in the ICIL program to complete the questionnaire using the Google
Form link. The survey consisted of 19 multiple-choice questions designed with precision and
care.
• Prediction of Future Employment (PFE) utilizing multiple-choice inquiries 1) Synthesizing
information, 2) Varied perspectives, 3) Dynamics of professional development, 4)
Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
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Understanding individual profiles, and 5) Incentive for digital literacy training (Haratsis et al.,
2015; Strielkowski, 2020; Park, 2022).
• Multiple-choice inquiries regarding a Responsive Attitude 1) Collaboration, 2) Critical
thinking, 3) Realism, 4) Responsiveness, and 5) Problem-solving skills (OECD, 2018;
Wrigley et al., 2021; Schmid et al., 2022; Banwo, 2023).
• Learning Agility (LA) consists of multiple-choice questions that assess 1) academic
motivation, 2) environmental factors, 3) understanding of the variety, and 4) absorption of
new values (Lombardo & Eichinger, 2000; De Meuse et al., 2010; Jian, 2022).
• Evaluating the adaptability of university graduates (AOUG) with multiple-choice questions
1) Innovation, 2) Individual adaptation capabilities, 3) Task involvement, 4) Emotional
intelligence (EI), and 5) Achievement orientation (Ma et al. (2019), Chui et al. (2020),
Öztemel and Akyol (2021), Parola and Marcionetti (2021), Badiozaman, 2023).
Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) were chosen
as the predominant data analysis techniques for their practicality and applicability. These
methodologies allowed scholars to assess conceptual frameworks and measure constructs,
making the research findings directly applicable to real-world scenarios.
Reliability of the instrument
Composite Reliability. Data exhibiting composite reliability greater than 0.7 demonstrates
strong dependability. Cronbach's Alpha. Cronbach's Alpha enhances the robustness of
reliability testing.
Validity of the instrument
• Convergent Validity The convergent validity value is the factor loading associated
with the latent variable and its manifest counterpart. The anticipated value surpasses
0.7, Although researchers frequently employ a threshold of 0.6 as the minimal
criterion for factor loading values.
• Discriminant Validity. This value represents the cross-loading factor, which is
essential for assessing the construct's discriminant validity; specifically, the targeted
construct's loading value must exceed the other constructs' loading values.
• Average Variance Extracted (AVE). The anticipated AVE value surpasses 0.5.
Method of data analysis
Primary data collected from respondents were analyzed using SmartPLS 4.0 software,
which is well-suited for processing Structural Equation Modeling (SEM) data.
Result
Descriptive statistics
The study encompassed a diverse group of 284 respondents, with a slight majority being
female (172 or 60.56%) and the rest male (112 or 39.44%). The participants represented a
variety of academic programs, with the management program being the most prevalent (162
or 57.04%), followed by accounting (57 or 20.07%), business administration (24 or 8.45%),
and public administration and Islamic communication broadcasting and information
systems, each with a small but significant representation of approximately eight individuals,
accounting for roughly 2.82%.
The youngest students were in semester 6, including 141 individuals or 49.65%, while the
oldest was in semester 8, totaling 143 individuals or 50.35%. These students offered varied
Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
10.5281/zenodo.14274261 EISSN: 2735-9891
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perspectives throughout the educational stages. During the sixth semester, intermediate
students articulated concepts on pivotal topics and culminating projects. Students in their
eighth semester engaged with their education, drawing upon insights from previous
seminars on final papers and theses and the maturity gained from their program learning.
ICIL comprises student exchanges (45.77%), village development (18.66%), internships
(11.27%), and entrepreneurship (24.30%). This distribution indicates a preference for
mentorship-enhanced education to foster communities and develop industry-relevant
entrepreneurial skills.
Model Testing
This test assesses the validity of each association between the manifest and the construct or
other variables. Researchers evaluate the convergent validity of the measuring model with a
manifest reflection by analyzing the association between item or component scores and
latent variable or construct scores computed by SmartPLS software. As depicted in Figure
1, there is a correlation between the two data sets.
Figure 1. Output from Testing a Structural Equation Model by SmartPLS 4.0 (2024)
The results in Figure 1 indicate that the values generated by each variable are above the threshold of
0.7, signifying that the model is suitable for future testing.
Subsequently, table 1 presents the loading factor values for each manifest inside the variable.
Table 1. Outer Loading
Construct
Learning
Agility
Responsive
Attitude
Prediction
of Future
Job Trends
Adaptability
of University
Graduates
1
0,931
2
0,919
3
0,937
Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
10.5281/zenodo.14274261 EISSN: 2735-9891
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4
0,922
5
0,928
6
0,936
7
0,945
8
0,921
9
0,947
10
0,933
11
0,947
12
0,891
13
0,921
14
0,939
15
0,926
16
0,902
17
0,944
18
0,805
19
0,940
20
0,909
21
0,958
22
0,914
23
0,935
24
0,929
25
0,930
26
0,939
27
0,939
28
0,908
29
0,934
30
0,934
Sources: Outputl SmartPLS (2024)
The findings presented in Table 1 above indicate that each variable has satisfied the necessary
criteria, with values over 0.7, which can be regarded as having passed the validity test between
variables, permitting further testing.
R-Square Test
The R2 value signifies the degree of determination of the exogenous variable in relation to its
endogenous counterpart. A higher R2 number signifies a superior degree of determination. A model is
classified as strong with an R-square value of 0.75, moderate with an R-square value of 0.50, and
weak with an R-square value of 0.25, which is visualised in Table 2.
Table 2. R-Square
R Square
R Square Adjusted
Adaptability of
University Graduates
0,963
0,962
Learning Agility
0,945
0,944
Responsive Attitiude
0,926
0,925
Sumber : Output SmartPLS (2024)
The test results presented in Table 2 indicate that the R-Square values for the adaptability of
university graduates variable model is 0.963, for the learning agility variable is 0.945, and for the
responsive attitude variable is 0.926, demonstrating a robust model for all three variables.
Bootstrapping Outcomes
Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
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Bootstrapping testing was used to ascertain the correlation between factors regarding data fit and data
mismatch, as illustrated in Figure 2 below.
Figure 2. Bootstrapping
Sources: Output SmarPLS (2024)
The results depicted in Figure 2 indicate that the relationship between variables, understood as
hypothesis tests, is presented in Table 3. The p-value determines the acceptance of this association; a
value less than 0.05 indicates acceptance, but a value over the threshold established in smartPLS
signifies rejection (not significant).
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Tabel 3. Path Coefficient
Original
Sample
(O)
Sample
Mean
(M)
Standard
Deviation
(STDEV)
T Statistics
(|O/STDEV|)
P-
Values
Information
Learning Agility -
> Adaptability of
University
Graduates
0,202
0,212
0,088
2,286
0,011
Significant
Prediction of
Future Job Trends
-> Adaptability of
University
Graduates
0,299
0,310
0,106
2,818
0,003
Significant
Prediction of
Future Job Trends
-> Learning
Agility
0,309
0,299
0,094
3,283
0,001
Significant
Prediction of
Future Job Trends
-> Responsive
Attitude
0,962
0,963
0,011
88,125
0,000
Significant
Responsive
Attitude ->
Adaptability of
University
Graduates
0,492
0,471
0,112
4,376
0,000
Significant
Responsive
Attitude ->
Learning Agility
0,671
0,681
0,091
7,338
0,000
Significant
Sources : Output SmarPLS (2024)
Following the bootstrapping method on the variable measurements, researchers can derive the
following conclusions from the hypothesis testing results:
1. H1: The projection of prediction of future job trends influences responsive attitudes.
The path coefficient results indicate a strong positive correlation of 0.962 between predicting future
job trends and responsive attitude. Additionally, the T statistic value of 88.125, with a P-value of
0.000 ≤ 0.05) supports the acceptance of hypothesis H1 regarding this relationship.
2. H2: Predicting future job trends influences learning agility.
The path coefficient results of 0.309 indicates a positive correlation between predicting future job
trends and learning agility. Additionally, the T statistic value of 3.283, with a P-value of 0.001,
suggests a significant influence of future job trend predictions on learning agility, leading to the
acceptance of H2.
3. H3: A responsive attitude influences learning agility.
The results of the path coefficient obtained between responsive attitude and learning agility are 0.671,
which means it has a positive value. In contrast, the T statistic value of 7.338 with a P-value of 0.000
< 0.05 can be interpreted that there is a significant influence between responsive attitude and learning
agility, then H3 is accepted.
4. H4: A responsive attitude influences the adaptability of university graduates.
The path coefficient between responsive attitude and adaptability of university graduates is 0.495,
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indicating a positive relationship. The T statistic value is 4.376, and the P-value is 0.000, less than
0.05. This indicates a significant influence of a responsive attitude on the adaptability of university
graduates, so H4 is accepted.
5. H5: Learning agility influences the adaptability of university graduates.
The path coefficient of 0.202 indicates that learning agility positively influences the adaptability of
university graduates. The T-statistic value of 2.286 and a P-value of 0.001, which is less than 0.05,
demonstrate a significant relationship between learning agility and adaptability. So, H5 is accepted.
6. H6: Predicting future job trends influences the adaptability of university graduates.
The path coefficient between the prediction of future job trends and the adaptability of university
graduates is 0.299, indicating a positive relationship. Additionally, the T statistic value of 2.818 and a
P-value of 0.003, which is less than 0.05, demonstrate a significant influence of predicting future job
trends on the adaptability of university graduates. So, H6 is accepted.
Discussion
The first hypothesis states a positive correlation exists between the prediction of future job
trends and responsive attitude. (Murphy (2021) bolsters this claim with data indicating that
workers become more responsive when accurately predicting job trends. It was also
observed that this dynamic was influenced by contextual factors such as the type of industry
and economic uncertainty. De Meuse et al. (2010) conducted longitudinal studies
demonstrating predictive analytics' role in helping employees respond better and adjust to
changes.
According to the second hypothesis, greater learning agility is associated with more
accurate job trend prediction skills; learning agility is the capacity for people to draw
lessons from the past and apply those lessons to new situations. Predicting future job trends
is unique in today's globalised and technologically advanced world. Furthermore, past
research has demonstrated evidence of a very strong positive. In this context, capacity
forecasting is emphasised. Those individuals who can precisely predict job trends will adapt
better to market changes, predict the required skills, continue educating themselves
throughout their lives, keep up with modifications in education sectors, and gain knowledge
that will be of use in future careers (Smith et al., 2020; Munawir & Suseno, 2024).
The third hypothesis proposes a positive correlation between high learning agility and solid
relational analytics. The statistical evidence indicating a positive link between a responsive
attitude and learning agility validates this proposition. According to Gunzenhauser and
Nückles (2020), learning agility can be described as an ability to quickly and smartly adapt
to changes or situations. Even though the current study found a strong direct correlation
between a responsive attitude and learning agility, raising a responsive attitude was essential
for raising learning agility.
The Fourth Hypothesis argues that a robust, responsive attitude increases adaptability among
universities, which is important for workplace success. This hypothesis was confirmed,
meaning that a responsive attitude could increase the adaptability of up to 43 per cent in
graduates. These results are consistent with theories such as De Meuse et al. (2010) that
acknowledge the significance of both personal and organisational factors related to agility at
work.
Hypothesis five suggests that graduates in a rapidly transforming labour market require
adaptability, including problem-solving and acquiring knowledge (Lombardo & Eichinger,
2000; Suseno & Dwiatmadja, 2016). A significant link between learning ability and
adaptability was found. Broad-based programs should be provided to foster learning ability.
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The sixth hypothesis proposing a connection between adaptability and the capability of
foretelling job trends was supported. A substantial correlation exists between forecasts of
future employment trends and the adaptability of college graduates, indicating that a high
capacity for adaptation to technological changes, work patterns, automation, and uncertainty
enables students to remain pertinent to emerging job trends. According to Haratsis et al.
(2015) and Maskudi et al. (2024), graduate adaptability is influenced more by cognitive
flexibility, resilience, and social skills than by predicting future job markets.
Conclusion
Predicting job trends and responsiveness are linked. Economic uncertainty and industry type
alter these dynamics—predictive analytics aid employee change. Job trend prediction
improves with learning agility. Today's technologically advanced culture rarely predicts job
trends worldwide. Previous studies have demonstrated significant benefits. The focus is on
capacity predictions. Job trend predictors can better react to market changes, predict required
abilities, continue to educate themselves, stay up with education sector developments, and
learn for future careers.
Relational analytics improves learning agility. Statistics suggest a link between
responsiveness and learning agility. Despite a strong correlation between responsiveness and
learning agility, increasing responsiveness is essential to promote learning agility. That good
responsiveness promotes collegiate adaptability, which is crucial for employment success.
College graduates' adaptability improves with the response, proving the notion. Young people
in a fast-changing job environment need problem-solving and knowledge-acquisition skills.
Learning capacity strongly influences adaptation. Learning should improve with broad
programs. We support the sixth hypothesis connecting adaptation to job trend forecasts. The
projections say college graduates' adaptability to technology, work patterns, automation, and
unpredictability keeps them relevant to job trends. Cognitive flexibility, resilience, and social
skills better predict graduates' adaptation than job market projections.
The current study faced several limitations, as the researchers restricted the sample to
undergraduates involved in the ICIL program. Consequently, the findings needed to be more
consistent with the broader graduate community. This research employed a cross-sectional
methodology, evaluating variables at a singular moment, neglecting to account for
fluctuations or developments in graduates' adaptability over time.
Moreover, mediating factors such as technology, organizational culture, and training were
mentioned solely in a conceptual context and not subjected to empirical analysis, leading to a
deficient comprehension of their particular mediating roles.
Future studies need to expand the sample to include graduates from diverse programs and
institutions, improving the findings' generalizability. Longitudinal evaluations are also
required to evaluate the progression of graduates' adaptation over time. Furthermore, further
research should investigate the impact of mediating factors such as technology, organizational
culture, and training in greater depth to improve the comprehension of the relationship among
PFJ, RA, LA, and adaptability. Future evaluations should examine effective educational and
training methodologies to enhance responsiveness and learning agility in graduates.
References
Alhadabi, A. & Karpinski, A. C. (2020).Grit, self-efficacy, achievement orientation goals, and academic
performance in University students, International Journal of Adolescence and Youth, 25(1), pp. 519–535.
doi: 10.1080/02673843.2019.1679202.
Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
10.5281/zenodo.14274261 EISSN: 2735-9891
427
Ashour, S. (2020). How technology has shaped university students perceptions and expectations around higher
education: an exploratory study of the United Arab Emirates. Studies in Higher Education, 45(12), .
2513–2525. doi: 10.1080/03075079.2019.1617683.
Badiozaman, I. F. A.(2023). Exploring online readiness in the context of the COVID 19 pandemic’, Teaching in
Higher Education, 28(8), pp. 1974–1992. doi: 10.1080/13562517.2021.1943654.
Banwo, B. O.(2023). A Community within a Community: Collectivism, Social Cohesion and Building a Healthy
Black Childhood’, Anthropology & Education Quarterly, 54(2), pp. 122–143. doi: 10.1111/AEQ.12448.
BPS .(2024). Tingkat Pengangguran Terbuka (TPT) sebesar 4,82 persen dan Rata-rata upah buruh sebesar
3,04 juta rupiah per bulan Agustus,( The Open Unemployment Rate is 4.82 percent and the average
wage of workers is 3.04 million in August 2024), Badan Pusat Statistik Republik Indonesia. Available at:
https://www.bps.go.id/id/pressrelease/2024/05/06/2372/tingkat-pengangguran-terbuka--tpt--sebesar-4-
82-persen-dan-rata-rata-upah-buruh-sebesar-3-04-juta-rupiah-per-bulan.html (Accessed: 14 September
2024).
Buzzai, C. Sorrenti, L. Orecchio, S. Marino, D. Filippello, P.(2020). The relationship between contextual and
dispositional variables, well-being and hopelessness in school context, Frontiers in Psychology, 11, p.
533815. doi: 10.3389/FPSYG.2020.533815/BIBTEX.
Chui, H. Li, H. and Ngo, H.(2020). Linking protean career orientation with career optimism: career adaptability
and career decision self-efficacy as mediators, Journal of Career Development, 49(1), pp. 161–173. doi:
10.1177/0894845320912526.
Gunzenhauser, C. and Nückles, M. (2021). Training executive functions to improve academic achievement:
tackling avenues to far transfer, Frontiers in Psychology, 12, p. 624008. doi:
10.3389/FPSYG.2021.624008/BIBTEX.
Hair, JF. Black, WC. Babin, BB. Anderson, RE.(2010). Multivariate data analysis (7th edition), Pearson
Education, Inc.New York.
Haratsis, JM. Hood, M. Creed, PA.(2015). Career goals in young adults: personal resources, goal appraisals,
attitudes, and goal management strategies’, Journal of Career Development, 42(5), pp. 431–445. doi:
10.1177/0894845315572019.
Holliman, A. J., Collie, R. J. and Martin, A. J. (2020) ‘Adaptability and Academic Development’, The
Encyclopedia of Child and Adolescent Development, pp. 1–11. doi: 10.1002/9781119171492.wecad420.
Jian, Z.(2022).Sustainable engagement and academic achievement under impact of academic self-efficacy
through mediation of learning agility—evidence from music education students, Frontiers in Psychology,
13, p. 899706. doi: 10.3389/FPSYG.2022.899706/BIBTEX.
Kim, D. and Park, J. (2022). The way to improve organizational citizenship behavior for the employees who
lack emotional intelligence, Current Psychology, 41(9), pp. 6078–6092. doi: 10.1007/S12144-020-
01104-5/METRICS.
Kline, R B. (2015). Principles and Practice of Structural Equation Modeling. 4th edition, The Guilford Press.
4th edition. New York. doi: 10.15353/cgjsc-rcessc.v1i1.25.
Lombardo, M M. and Eichinger, R W. (2000). High potentials as high learners’, Human Resource Management,
39(4), pp. 321–329. doi: 10.1002/1099-050X(200024)39:4<321::AID-HRM4>3.0.CO;2-1.
Ma, Y. You, J. and Tang, Y. (2019). Examining predictors and outcomes of decent work perception with
chinese nursing college students, International Journal of Environmental Research and Public Health,
Vol. 17, Page 254, 17(1), p. 254. doi: 10.3390/IJERPH17010254.
Mustofa, MA. Suseno, BD. Basrowi.(2023). Technological innovation and the environmentally friendly
building material supply chain: Implications for sustainable environment, Uncertain Supply Chain
Management 11, 1405–1416, doi: https://doi.org/10.5267/j.uscm.2023.8.006
Munawir, A. Suseno, BD.(2024). Employee performance: exploring the nexus of nonstandard services,
psychological contracts, and knowledge sharing, Human Behavior And Emerging Technologies, Volume
2024, issue 1, 16 pages, DOI. https://doi.org/10.1155/2024/6746963
Motlova, V. & Honsova, P. (2021). The effects of a 13-week career development programme on career-adapting
thoughts and behaviours. International Journal for Educational and Vocational Guidance, 21(3), 571–
588. https://doi.org/10.1007/S10775-020-09454-Z/METRICS
Maskudi, Suseno BD. Munawir A. Firjatullah S. (2024). Employee innovation performance: Exploring non-
standard service relationships, psychological contracts, and knowledge sharing in green manufacturing
industry development. Journal of Infrastructure, Policy and Development. 8(7): 5111.
De Meuse, K P. Dai, G. and Hallenbeck, GS. (2010). Learning agility: A construct whose time has come’,
Consulting Psychology Journal, 62(2), pp. 119–130. doi: 10.1037/A0019988.
Motlova, V. and Honsova, P.(2021). The effects of a 13-week career development programme on career-
adapting thoughts and behaviours, International Journal for Educational and Vocational Guidance,
21(3), pp. 571–588. doi: 10.1007/S10775-020-09454-Z/METRICS.
Murphy, S M. (2021). Learning Agility and Its Applicability To Higher Education. USA: Columbia University.
Ianna Journal of Interdisciplinary Studies, Volume 7 Issue 1, January 2025 DOI: https://doi.org/
10.5281/zenodo.14274261 EISSN: 2735-9891
428
OECD .(2018). Education at a Glance 2018, doi: 10.1787/EAG-2018-EN.
Öztemel, K. and Akyol, EY. (2021). From adaptive readiness to adaptation results: implementation of student
career construction inventory and testing the career construction model of adaptation, Journal of Career
Assessment, 29 (1), pp. 54–75. doi:
10.1177/1069072720930664/ASSET/IMAGES/LARGE/10.1177_1069072720930664-FIG3.JPEG.
Park, SY. Cha, SB. Joo, MH. Na, H. (2022). A multivariate discriminant analysis of university students career
decisions based on career adaptability, social support, academic major relevance, and university life
satisfaction, International Journal for Educational and Vocational Guidance, 22(1), pp. 191–206. doi:
10.1007/S10775-021-09480-5.
Parola, A. and Marcionetti, J. (2021). Career decision-making difficulties and life satisfaction: The role of
career-related parental behaviors and career adaptability, Journal of Career Development, 49(4), pp.
831–845. https://doi.org/10.1177/0894845321995571
Ryan, R M. and Deci, E L. (2017). Self-determination theory: Basic psychological needs in motivation,
development, and wellness, Revue québécoise de psychologie 38(3):231, DOI: 10.7202/1041847ar
Suseno, B D. (2019). The strength of justified knowledge sharing on good manufacturing practices: Empirical
evidence on food beverage joint venture company of Japan – Indonesia. Quality - Access to Success,
Vol. 20 (170), 130-135.
Suseno, BD. & Mukhlis, A. (2023). The role of collectivism's innovation capacity as a predictor of recovery in
industrial estate performance, SCMS Journal of Indian Management, Vol. 20 No. 2 (April-Juni), pp.19-
33.
Smith, R. W. Baranik, LE. & Duffy, R D.(2020). Psychological ownership within psychology of working
theory: A three-wave study of gender and sexual minority employees. Journal of Vocational Behavior,
118, 103374. https://doi.org/10.1016/J.JVB.2019.103374
Suseno, BD. Rochmaedah, D. Firjatullah, F. Munawir, A. Idrus, I. (2024). The influence of exceptional service
and product quality on online purchase, Advances in Business-Related Scientific Research Journal
(ABSRJ), Vo. 15 No. 1 pp.1- 19
Schmid, J. Morgenshtern, M. and Turton, Y. (2022). Contextualized Social Work Education: A Critical
Understanding of the Local’, Journal of Social Work Education, 58(4), pp. 719 –732. doi:
10.1080/10437797.2021.1969300..
Suseno, BD; Basrowi. (2023). Role of the Magnitude of Digital Adaptability in Sustainability of Food and
Beverage Small Enterprises Competitiveness, HighTech and Innovation Journal, Vol. 4, No. 2, pp. 270-
282. http://dx.doi.org/10.28991/HIJ-2023-04-02-02.
Strielkowski, W., Kiseleva, L. S. and Sinyova, A. Y. (2020) ‘Trends in international educational migration: A
case of Finland’, Integration of Education, 24(1), pp. 32–49. doi: 10.15507/1991-
9468.098.024.202001.032-049.
Suseno, B. D. & Dwiatmadja, C. (2016) ‘Technology transfer motive of managers in Eastern Asia: Empirical
results from manufacture industry in Banten province, Indonesia’, Problems and Perspectives in
Management, 14(2). doi: 10.21511/ppm.14(2).2016.04 .
Suseno, BD, Yusuf, FA, Kurnia, D. (2021). Development of patronage ambidexterity and the
performance of joint venture shopping centers in indonesia, Journal Calitatea, 22 (181), 30-34.
Tokar, DM. & Kaut, K P. (2018). Predictors of decent work among workers with Chiari malformation: An
empirical test of the psychology of working theory’, Journal of Vocational Behavior, 106, pp. 126–137.
doi: 10.1016/J.JVB.2018.01.002
Wulandari, SS. Suryapermana, N. Fauzi, A. and Suseno, BD. (2024). Development of an empirical model and
using community sport organizations as the basis for intervening variables in Islamic sports, Journal of
Islamic Marketing, 15(2) pp. 1519-1533. https://doi.org/10.1108/JIMA-04-2023-0109,.
World Economic Forum (2023) Future of Jobs Report. doi: 10.1142/11458.
Wrigley, C. Wolifson, P. and Matthews, J.(2021). Supervising cohorts of higher degree research students:
design catalysts for industry and innovation, Higher Education, 81(6), pp. 1177–1196. doi:
10.1007/S10734-020-00605-3/TABLES/2.
Zenebe, Y. Kunno. K. Mekonnen, M. Bewuket. A. Birkie, M. Necho. Seid, M. 2 , Tsegaw, M. Akele, B.
(2021). Prevalence and associated factors of internet addiction among undergraduate university students
in Ethiopia: a community university-based cross-sectional study’, BMC Psychology, 9(1), pp. 1–10. doi:
10.1186/S40359-020-00508-Z/FIGURES/2.