THE INFLUENCE OF JOB LEVEL AND WORK
STRESS ON LEARNING MOTIVATION OF MASTER'S STUDENTS AT HARAPAN BANGSA BUSINESS
SCHOOL CLASS OF 2023-2024
Damianus Tulus Rahmat1, Mentiana Sibarani2
Sekolah Tinggi Ilmu Ekonomi Harapan Bangsa, Bandung,
Indonesia
[email protected] , [email protected]
Keywords: job level, job stress, learning motivation,
master of management
Corresponding Author: Damianus Tulus Rahmat
E-mail: [email protected]
INTRODUCTION
Along with technological advancements,
especially in education, many colleges have expanded their reach by offering
online-based classroom study programs. This phenomenon opens the door for
workers who want to improve their academic qualifications without leaving their
jobs
According to data from the Ministry of
Education and Culture
Work stress is a primary factor affecting
student learning motivation
Previous research has consistently
highlighted the influence of job roles and stress on motivation within
educational and professional contexts. Sobaih et al.
Unlike previous studies that mainly focus
on either job motivation or the effects of work stress in isolated professional
or educational contexts, this research uniquely examines the combined influence
of job level and work stress on the learning motivation of Master’s students
actively balancing professional and academic responsibilities. While earlier
studies have provided insights into how job stress can affect performance or
how motivation impacts productivity, few have investigated these dynamics in a setting
where students simultaneously manage career and academic aspirations
This study examines the influence of
position level and work stress on the learning motivation of Master of
Management students at Harapan Bangsa Business School. The initial results of a
questionnaire on 35 students showed that 50% of respondents experienced a
decrease in learning motivation when they felt stressed at work. This
emphasizes the importance of paying attention to students' mental well-being,
as stress can interfere with focus and enthusiasm for learning, ultimately
impacting academic performance. By focusing on individuals concurrently managing professional and
academic roles, this research reveals how job hierarchy and work stress
interact uniquely within this context, impacting learning motivation differently
from traditional student populations. The novelty of this research lies in its
approach of examining both external job-level factors and internal stress
factors together, providing a dual perspective that offers a more nuanced
understanding of what drives or detracts from learning motivation in a
real-world, professional-academic setting. The findings contribute to a more
affluent theoretical foundation for developing educational and institutional
support strategies tailored to working students.
This study aims to analyze the influence
of position level and work stress on the learning motivation of S2 Harapan
Bangsa Business School (HBBS) students of the 2023–2024 batch. Based on the
background that has been explained, the main question to be answered is whether
the level of position and work stress, both individually and together, affect
students' motivation to learn. The results of this study are expected to be
useful for academics as a reference for future research and for the HBBS
community in designing policies or programs that can increase student learning
motivation.
METHOD
This study
employs a quantitative research approach to explore the influence of job level
and work stress on the learning motivation of Master of Management students at
Harapan Bangsa Business School, Class of 2023-2024. The research used a
structured questionnaire as the primary data collection tool, carefully
designed to ensure clarity and relevance to each variable (job level, work
stress, and learning motivation). The questionnaire's development process
included validity and reliability testing to establish the instrument’s
credibility. Content validity was confirmed through expert review, and
construct validity was tested using factor analysis. Reliability was assessed
using Cronbach's alpha, with all variables exceeding the threshold of 0.7,
indicating high internal consistency.
The study’s
population consisted of all active students enrolled in the Master of
Management program. Participants were selected through purposive random
sampling, ensuring all respondents were actively enrolled students with
concurrent job responsibilities. Responses were rated on a Likert scale to
capture levels of agreement on items relating to job level, work stress, and
learning motivation. For data analysis, we utilized SPSS version 20, applying
multiple regression analysis to examine the relationships between job level,
work stress, and learning motivation. Specifically, stepwise regression was
used to explore the most predictive variables for learning motivation. This
method allows for a sequential inclusion of predictor variables based on their
statistical significance, thus refining the model to focus on the variables
with the most significant explanatory power.
The measurement
instrument in this study consists of a questionnaire containing questions
related to the operational definition of each variable. This questionnaire
includes questions about job title levels, which includes categories such as
Entry, Intermediate, Supervisory, Managerial, and Executive Levels; work stress,
which includes perceived job demands, perceived job control, work-related
challenges, and work environment pressures; as well as learning motivation
which includes intrinsic aspects such as interest, curiosity, and enjoyment in
the learning process, as well as extrinsic aspects such as awards, academic
grades, and academic-related social pressures. Each question was structured
using a Likert scale to measure respondents' agreement with the statements
given.
The normality test was carried out
through regression calculation using the SPSS version 20 program using 2 graph
approaches: histogram graph analysis and P-Plot average graph analysis. The
latter compared two observations with distributions close to the normal
distribution. The following is an explanation of the graphs.
Figure 1.
Histogram
Chart
The histogram above results from a
normality test conducted using SPSS version 20 on 120 respondents. This graph
depicts the distribution of the normalized standard residuals, which is the
difference between the actual value of the dependent variable and the value
predicted by the regression model. Based on the results of the normality test
shown by the histogram chart above, it can be concluded that the residual data
from the regression model is usually distributed. Fulfilling this normality
assumption is one of the essential requirements in regression analysis because
this will affect the validity and reliability of the analysis results.
P-P plot graphs (probability plots)
evaluate whether the data is usually distributed. In this graph, the data
points representing the cumulative distribution of observations are plotted
against the expected normal cumulative distribution. The data points will
follow a straight diagonal line if the data is normally distributed.
Figure 2.
P-P plot graph (probability plot)
The plot P-P graph from the SPSS results
shows that most data points follow the diagonal line quite well. This indicates
that the residual data from the regression model tends to be normally
distributed. Although some data points deviate slightly from the diagonal line,
overall, the data distribution pattern conforms with the normal distribution.
Based on the results of the normality test shown by the P-P graph of the plot
above, it can be concluded that the residual data from this regression model
meets the normality assumption. Fulfilling this normality assumption is one of
the crucial requirements in regression analysis because this will affect the
validity and reliability of the analysis results.
The VIF value in the table shows the variance
inflation rate caused by the correlation between independent variables. The
higher the VIF score, the greater the level of multicollinearity. The commonly
used threshold value is 10. If the VIF value of a variable is more than 10,
then the variable is considered to have a severe multicollinearity problem. In
addition to the VIF value, tolerance is an essential indicator for testing
multicollinearity. The tolerance value is the opposite of the VIF value
(1/VIF). A low tolerance value (close to 0) indicates the presence of high
multicollinearity, while a high tolerance value (close to 1) indicates the
absence of multicollinearity
Figure 2.
All VIF values in the table above are
below 10. This suggests that your regression model has no serious
multicollinearity issues. In other words, the independent variables in this
model do not have a very high correlation with each other, so they will not
interfere with the estimation of the regression coefficient. The tolerance
value of all independent variables was not below 0.01. This is an excellent
indication that there are no serious multicollinearity problems in this
regression model. Each independent variable uniquely contributes to explaining
the variation of dependent variables, and this regression model is quite
stable. The VIF and tolerance values provide consistent information and the
absence of significant multicollinearity problems in this regression model.
The scatterplot below illustrates the
relationship between the normalized residual (Y-axis) and the normalized
predicted value (X-axis) in this regression model. This graph is used to detect
the presence of heteroscedasticity, a condition in which the variance of the
residual is not constant for all observations. ( Y-axis = SRESID plots, X-axis
= ZPRED plots)
Figure 3.
Heteroscedasticity analysis used a
standardized residual scatterplot against the standardized prediction value.
The results of the analysis showed that there was no clear pattern or funnel
shape in the scatterplot. The data points are scattered randomly around the
zero horizontal line. This indicates that there is no strong evidence to reject
the hypothesis that the residual variance is constant. Thus, the assumption of
homoscedasticity in this regression model can be considered fulfilled. These
results strengthen the validity of the built regression model.
Multiple Linear Regression Analysis
Multiple Linear Regression Coefficient Analysis
Figure 4.
Results of Multiple Linear Regression Coefficient
Analysis
This study uses a multiple linear
regression model to test the influence of job level and work stress on learning
motivation. The regression model can be expressed as Y = 1.487 + 0.535X1 +
0.078X2 + ε. In this model, Y represents learning motivation, X1
represents job level, X2 represents work stress, and ε is the error term. The
regression coefficient for the position level (β1) was 0.535, which showed that
every increase in one unit at the position level would increase learning
motivation by 0.535 units, assuming other variables were controlled. Meanwhile,
the regression coefficient for work stress (β2) was 0.078, which indicates that
every increase in work stress level will increase learning motivation by 0.078
units. This regression analysis aims to test the statistical significance of
these coefficients and confirm whether job level and work stress significantly
influence learning motivation. Based on the results of multiple linear
regression analysis, it can be seen that both the position level (X1) and work
stress (X2) have a significant influence on the learning motivation (Y1) of the
Harapan Bangsa Business School Master of Management students.
This study found that the level of
position and work stress significantly influenced the learning motivation of
master management students, with the influence of the position level being more
dominant. The positive regression coefficient at the job title level shows that
individuals in higher organizational hierarchical positions tend to have a
stronger drive to continue learning and developing themselves. Meanwhile, work
stress also had a positive effect, albeit to a lesser extent, suggesting that
for some individuals, stress can spark a passion for learning as a way to cope
with work pressure. However, the effects of this stress vary, depending on the
coping mechanism of each individual, where some experience a decrease in
motivation when stressed excessively. Overall, these findings emphasize that
occupational factors such as job level play an important role in increasing
learning motivation. At the same time, work stress has a more complex and
varied influence, which is in line with previous studies on the influence of
social status on motivation.
The determination coefficient, often notated with
R-squared, is a statistic that shows the proportion of variance of dependent
variables that independent variables can explain in a regression model. The
value of the determination coefficient ranges from 0 to 1. The higher the
R-squared value, the better the model explains the variation in data.
Figure 5.
Regression Analysis Results
Based on the regression
analysis results, the model used can explain around 35.3% of the variability of
learning motivation. This shows that Job Level and Work Stress significantly
contribute to predicting Learning Motivation. However, there is still a
considerable proportion of variability in learning motivation that this model
cannot explain. This indicates that other factors outside the model, such as
job satisfaction or individual characteristics, also play an important role in
influencing Learning Motivation. Therefore, further research is needed to
identify and measure the influence of these additional variables.
A hypothesis test was carried out to test
the significance of the regression model and the influence of independent
variables on dependent variables. The F test will be used to test the null
hypothesis that dependent variables have no effect on dependent variables. Suppose
the significance value of the F test is less than the predetermined
significance level (0.05). In that case, the null hypothesis is rejected, and
it can be concluded that overall, the independent variable has a significant
influence on the dependent variable. Furthermore, a t-test will be conducted to
partially test each independent variable's influence. The t-test will show
whether each independent variable makes a significant contribution to
explaining the variation of the dependent variable.
The F test is carried out to test whether
the regression model that has been built is statistically significant. The
results of the F test will show whether the developed regression model can
explain the variation in the dependent variable significantly.
Figure 6.
Test results F
The results of the F test showed that the
significance value of 0.000 was smaller than the significance level of 0.05.
This means that we can reject the null hypothesis (H0) and conclude that,
overall, the regression model constructed is significant. In other words, the
independent variables of Job Level and work Stress (X1 and X2) together significantly
contribute to explaining the variation of the dependent variable of Learning
Motivation (Y1). These findings indicate that the regression model that has
been developed can be used to predict the value of dependent variables based on
the values of independent variables. The results of the F test provide strong
support for the research hypothesis that job level and work stress together
have a significant effect on learning motivation. However, to determine the
influence of each variable individually, it is necessary to conduct a t-test on
the regression coefficient.
A t-test was
conducted to test the influence of the significance of each independent
variable (job level and work stress) on the dependent variable (learning
motivation). This t-test aims to find out whether the influence of each
independent variable on the dependent variable is statistically significant.
The level of significance used in this study is 5%. That is, we would reject
the null hypothesis (H0) if the p-value was less than 0.05. With the number of
respondents as many as 120 and there are 2 dependent variables, the degree of
freedom (df) used in the calculation of the t-test is 120 - 3 = 117. This
degree of freedom is obtained by subtracting the total number of observations from
the number of estimated parameters (i.e., the coefficients for both independent
variables and constants). The t-value of the table for degrees of freedom (df) is
117 at a significance level of 5% (0.05) and is about 1.980 for a two-sided
test.
Figure 7.
T-test results
Based on
the coefficient table displayed, we can interpret the t-test results for each
independent variable (X1 and X2) to the dependent variable (Y1). It can be
concluded as follows:
Learning Motivation Variable (X1):
· The calculated t-value for variable X1 is 7.559.
· The significance value (Sig.) for variable X1 is 0.000.
Since the t-value of the calculation (7.559) is
much greater than the t-value of the table (1.980) and the significance value
is much smaller than 0.05, then we reject the null hypothesis. This means there
is strong evidence to state that the X1 variable has a significant positive
influence on the Y1 variable. In other words, an increase of one unit in
variable X1 will be accompanied by a significant increase in variable Y1.
Work Stress Variable (X2):
· The calculated t-value for variable X2 is 0.883.
· The significance value (Sig.) for variable X2 is 0.379.
Since the t-value of the calculation
(0.883) is smaller than the t-value of the table (1.980) and the significance
value is greater than 0.05, then we fail to reject the null hypothesis. This
means that there is no strong enough evidence to state that variable X2 has a
significant influence on variable Y1. In other words, changes in variable X2 do
not have a significant effect on changes in variable Y1. Based on the results
of regression analysis and t-test, it can be concluded that the Position Level
variable (X1) has a significant positive influence on the Learning Motivation
variable (Y1). This indicates that an increase of one unit in the Position
Level variable (X1) will be accompanied by a significant increase in the
Learning Motivation variable (Y1). On the other hand, the Work Stress variable
(X2) did not show a significant influence on the Learning Motivation variable
(Y1). Thus, it can be concluded that the variation in the Learning Motivation
variable (Y1) is more influenced by changes in the Position Level variable (X1)
compared to the Work Stress variable (X2).
Multiple Regression
Result Analysis
The regression analysis results showed that the
position level had a significant and positive influence on the learning
motivation of master's students (β = 0.535, p < 0.05). Each increase of one
unit on the position-level scale was associated with an average increase of
0.535 units on the learning motivation scale after controlling for the effects
of work stress. These findings are consistent with motivation theory, which
states that individuals with higher positions tend to have higher intrinsic
motivation to achieve their goals. In contrast, work stress did not show a
significant relationship with learning motivation (β = 0.079, p > 0.05).
This indicates that other factors outside of work stress, such as intrinsic
factors such as interests and career goals, may play a greater role in
explaining the variation in learning motivation among master's students.
However, keep in mind that this study has some limitations, such as limited
sample size and generalization of results can only be done in similar populations.
Questionnaire
Result Analysis
Analysis of the questionnaire conducted
on the Harapan Bangsa Business School Master of Management students shows that
the position level has a significant contribution to increasing learning
motivation. As many as 80% of respondents reported that the higher their job
level, the more motivated they were to learn. In contrast, work stress did not
show a significant correlation with learning motivation, although 60% of
respondents stated they experienced stress in some aspect of their lives. These
findings indicate that external factors such as organizational structure and
institutional support, often related to job titles, have a stronger influence
on increasing learning motivation compared to internal factors such as stress.
This is in line with previous research that showed that individuals with higher
positions tend to have higher intrinsic motivation and better access to
resources to support their learning.
The results of in-depth interviews revealed that
master's students with manager positions and above generally have higher
motivation to learn compared to those at the staff level. Managers stated that
the responsibility they carry to lead the team and complete projects encourages
them to continue to develop their competencies. In contrast, master's students
at the staff level more often cited uncertainty regarding their career future
as a source of stress, although this did not significantly affect their overall
learning motivation.
CONCLUSION
This study reveals that
job level significantly enhances the learning motivation of Master of
Management students, while job stress does not show a direct influence. Higher
job positions appear to boost motivation, likely due to increased
responsibilities and the drive for professional growth. This suggests that
intrinsic motivators related to job level play a stronger role in sustaining
student motivation compared to stress factors, which may impact well-being in
other ways but are not central to learning motivation.
Based on these findings,
educators and policymakers could implement strategies to support motivation by
enhancing autonomy and competence in academic settings. Educational
institutions might offer project-based learning and leadership roles to foster
ownership and mastery, while policymakers could support flexible,
stress-responsive programming like online courses and personalized deadlines.
Additionally, mentorship programs could pair students with higher-level
professionals, offering career guidance and motivational support. Implementing
competency-based workshops that align with career objectives may further help
students balance academic and professional growth, encouraging both groups to
pursue learning with greater commitment.
REFERENCE
Abduh, M., Rivai, F., & Kadir, R. (2018). Pengaruh Role
Stress, Self Efficacy, Adaptability, Terhadap Kinerja Perawat Di Rumah Sakit
Pupuk Kaltim Siaga Ramania Samarinda. Jurnal Kesehatan Masyarakat Maritim,
1(2).
Akgunduz, Y. (2015). The influence of self-esteem and role
stress on job performance in hotel businesses. International Journal of
Contemporary Hospitality Management, 27(6), 1082–1099.
Chu, C. C., & Chou, C. Y. (2024). Hybrid work stressors
and psychological withdrawal behavior: A moderated mediation model of
emotional exhaustion and proactive personality. Journal of Vocational
Behavior, 155. https://doi.org/10.1016/j.jvb.2024.104053
Daoud, J. I. (2017). Multicollinearity and regression
analysis. Journal of Physics: Conference Series, 949(1), 012009.
Deci, E. L., & Ryan, R. M. (2008). Self-determination
theory: A macrotheory of human motivation, development, and health. Canadian
Psychology/Psychologie Canadienne, 49(3), 182.
fit surya Inul, I., & Lewangka, O. (2022). THE EFFECT
OF JOB STRESS, JOB SATISFICATION ORGANIZATIONALCOMMITMENT AND SELFESTEEM ON
EMPLOYEE PERFORMANCE. Hasanuddin Journal of Applied Business and
Entrepreneurship, 5(2), 60–78.
Getaneh Mekonen, E., Shetie Workneh, B., Seid Ali, M.,
Fentie Abegaz, B., Wassie Alamirew, M., & Aemro Terefe, A. (2022).
Prevalence of work-related stress and its associated factors among bank
workers in Gondar city, Northwest Ethiopia: A multi-center cross-sectional
study. International Journal of Africa Nursing Sciences, 16.
https://doi.org/10.1016/j.ijans.2021.100386
Goulmy, J. P., Toualbi, L., Boyer, V., Kanoute, P.,
Retraint, D., & Rouhaud, E. (2024). A model for the influence of work
hardening and microstructure on the evolution of residual stresses under
thermal loading – Application to Inconel 718. European Journal of
Mechanics, A/Solids, 107.
https://doi.org/10.1016/j.euromechsol.2024.105361
Hakim, A. L., & Sudarmiatin, S. (2018). The effect of
work stress on turnover intention with work satisfaction and commitment as
intervening variable (study at pt infomedia solusi humanika in Malang). European
Journal of Business and Management, 10(12), 85–95.
Hu, Y., Zhang, S., Zhai, J., Wang, D., Gan, X., Wang, F.,
Wang, D., & Yi, H. (2024). Relationship between workplace violence, job
satisfaction, and burnout among healthcare workers in mobile cabin hospitals
in China: Effects of perceived stress and work environment. Preventive
Medicine Reports, 40. https://doi.org/10.1016/j.pmedr.2024.102667
Khosa, A., Burch, S., Ozdil, E., & Ren, C. (2024).
Casual academics’ motivation and well-being: evidence from Australia. Accounting
Education, 1–35.
Le, C. M., Huynh, S. Van, Luu, K., Ho, Q. N. N., Nguyen, V.
T. Le, & Tran-Chi, V. L. (2024). Exploring the Interplay between Job
Stress, Work Performance, and Attitudes toward Professional Psychological Help
among Employees. International Journal of Mental Health Promotion, 26(7),
531–545. https://doi.org/10.32604/IJMHP.2024.050961
Lu, C., Zhang, Z., Li, D., Huang, C., Liu, C., Peng, J.,
Xue, Y., Zhang, J., & Xu, J. (2024). The mediating effect of lifestyle
behaviors on the relationship between self-perceived work stress and
suboptimal health status: a cross-sectional study involving Chinese urban
workers. Public Health, 236, 396–403.
https://doi.org/10.1016/j.puhe.2024.07.024
Phanniphong, K., Na-Nan, K., Asavanatthakorn, N., &
Chuen-u-ra, K. (2024). Job stress matters: Understanding how job stress
moderates the relationship between person-environment fit and innovative work
behavior in the Thai food industry. Acta Psychologica, 250.
https://doi.org/10.1016/j.actpsy.2024.104513
Qiang, W., & Li, F. (2025). Work stressors and
aggressive driving: The mediating roles of stress appraisals. Safety
Science, 181. https://doi.org/10.1016/j.ssci.2024.106656
Rachor, G., Krebs, B., Yamamoto, S., Dick, B., Brown, C.,
Senthilselvan, A., Straube, S., Els, C., Jackson, T., Voaklander, D.,
Bremault-Phillips, S., Berry, T., Stastny, J., & Gross, D. (2022). Worker
Characteristics and Factors Associated with Return-to-Work Among Workers with
or without Co-Occurring Posttraumatic Stress Injuries and Physical Injuries. Safety
and Health at Work, 13, S291.
https://doi.org/10.1016/j.shaw.2021.12.1661
Setyawan, H., Denny, H. M., Nurjazuli, N., & Dewi, E.
K. (2022). Personal and Environmental Risk Factors of Work-Related Stress: A
Cross-Sectional Study among Female Workers of a Textile Industry in Indonesia.
The Open Public Health Journal, 15(1).
https://doi.org/10.2174/18749445-V15-E2210190
Sobaih, A. E. E., & Hasanein, A. M. (2020). Herzberg’s
theory of motivation and job satisfaction: Does it work for hotel industry in
developing countries? Journal of Human Resources in Hospitality &
Tourism, 19(3), 319–343.
Soelton, M., Amalia, D., Noermijati, N., & Wahyudiono,
B. (2020). Self-esteem: the levels of religiosity in job insecurity and stress
in government company. 4th International Conference on Management,
Economics and Business (ICMEB 2019), 302–310.
Solikhah, M. (2023). ANALYSIS OF THE INFLUENCE OF WORK
STRESS ON EMPLOYEE PERFORMANCE WITH JOB SATISFACTION AS AN INTERVENING
VARIABLE. Journal Research of Social Science, Economics & Management,
3(2).
Sönmez, S., & Betül Kolaşınlı, I. (2021). The effect of
preschool teachers’ stress states on classroom climate. Education 3-13,
49(2), 190–202.
Turnip, W. J. (2020). Effect of Motivation and Work
Environment on Employee Performance of PT. XYZ. Enrichment: Journal of
Management, 10(2, May), 39–43.
van Dinter, R., Reneman, M. F., & Post, M. W. M.
(2024). Relation between work hours, work control, work stress, and quality of
life in persons with spinal cord injury. Disability and Health Journal.
https://doi.org/10.1016/j.dhjo.2024.101682
Zare, R., Kazemi, R., Choobineh, A., Cousins, R., Smith,
A., & Mokarami, H. (2024). Development of a Work Systems Stress
Questionnaire to predict Job Burnout: A mixed methods study based on a
macroergonomics approach. Heliyon, e40226.
https://doi.org/10.1016/J.HELIYON.2024.E40226
Zimbico, O., & Manuel, A. (2021). The Methodological
Consistency of Master’s of Education Dissertations at Eduardo Mondlane
University (2013-2018). International Journal of African Higher Education,
8(3), 1–23.
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