www.ijcrsee.com
417
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
Introduction
Education is one of the foremost concerns for countries worldwide. The sustainable development of
a nation relies not only on its economic, social, and cultural conditions but also on improving its education
system. Especially with the advent of the Fourth Industrial Revolution, the role of education is increasingly
emphasized in developing a high-quality workforce. There has been an increase in the number of students
enrolling in higher education institutions annually in foreign countries. However, the number of students
who want to leave university without obtaining a degree has also signicantly increased (Schnettler et al.,
2020). Approximately 15% of university students intend to drop out, which has become a severe issue
(Sheldon and Epstein, 2004). According to the STEM (Science, Technology, Engineering, Mathematics)
education approach, the estimated dropout rate of students is around 40-50%. The dropout status of
students not only negatively affects the students themselves and the university and society as a whole
(Schnettler et al., 2020).
In Vietnam, universities have also observed numerous cases of student dropouts. For example,
Industrial University of Ho Chi Minh City has issued warnings to 2,252 students who voluntarily dropped
out. University of Transport and Communication has warned 2,135 students regarding their academic
performance, with 257 students facing expulsion. The Ho Chi Minh City University of Technology and
Education has removed the names of over 450 students forced to discontinue their studies. The Ho Chi
Factors Inuencing Students’ Dropout Intentions in Ho Chi Minh City,
Vietnam
Mai Cam Binh1 , Tran Nha Ghi1* , Nguyen Ngoc Hien1 , Nguyen Thi Trang Nhung1 , Pham Hoang Bao Ngoc1
1Faculty of Business Administration, Industrial University of Ho Chi Minh City, Vietnam,
e-mail: maicambinh2002@gmail.com, trannhaghi@iuh.edu.vn, nguyenngochien@iuh.edu.vn,
trangnhunghk2002@gmail.com, nguyettieungoc2002@gmail.com
Abstract: The increasing number of students intending to drop out of universities in Vietnam has raised concerns. While
previous studies have addressed factors inuencing dropout intentions, several aspects still need to be explored, particularly
in developing countries like Vietnam. This research provides an overview of the factors inuencing students’ dropout intention
in Ho Chi Minh City. The study employs the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach with a
survey sample of 804 students from universities in Ho Chi Minh City. The research ndings reveal that factors such as Lack of
university commitment (LUC), degree and course commitment (DCC), ineffective time management (ITM), curriculum design
(CD), Ineffective adaptation to learning environment (IALE), low classroom participation (LCP) and personal circumstances
(PC) signicantly inuence students’ dropout intentions. Additionally, factors including skills and attitudes of instructors (SAI),
instructor support (IS), positive instructor feedback (PIF), university facilities (UF), cultural and social environment (CSE), and
access to support from academic advisors (ASA) do not show statistically signicant relationships with students’ dropout intention.
Furthermore, the study nds no signicant differences in dropout intention based on gender, area, and type of university, except
for ASA has a differential impact on students’ dropout intentions based on the type of university. The research results provide
valuable insights for researchers and educational experts to understand better the factors contributing to students’ dropout
intentions. Moreover, the ndings assist educational managers and instructors in developing appropriate support measures
and interventions to enhance student engagement throughout their academic journey. Finally, the study discusses limitations
and suggests future research directions.
Keywords: Dropout intentions, higher education, Ho Chi Minh City.
Original scientic paper
Received: September 05, 2023.
Revised: October 30, 2023.
Accepted: November 05, 2023.
UDC:
159.947.5.072-057.875(597
10.23947/2334-8496-2023-11-3-417-437
© 2023 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*Corresponding author: trannhaghi@iuh.edu.vn
www.ijcrsee.com
418
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
Minh City University of Industry and Trade has over 2,500 students with long-standing tuition fee debts,
putting them at risk of being banned from taking nal exams. The University of Sciences - Vietnam National
University Ho Chi Minh City has decided to expel 454 students and issue academic warnings to another
605 individuals. Statistical data demonstrates an increasing trend of student dropouts in Vietnamese
universities, highlighting the urgent need for measures to mitigate this issue.
Several studies have focused on the factors inuencing students’ intention to drop out before
completing their university education. Orion, Forosuelo, and Cavalida (2014) found that factors inuencing
students’ dropout intentions include school policies and practices, nancial resources, academic
performance, and teaching programs. Willcoxson (2010) concluded that the factors inuencing dropout
intentions differ among students in the rst, second, and third years of university. Farr-Wharton et al.
(2018) demonstrated the impact of lecturer-student exchange (student-LMX) on engagement, course
satisfaction, achievement, and intention to leave university among 363 students in an Australian university.
Schnettler et al. (2020) indicated that costs, age, and difculties in the learning process tend to make
students more likely to drop out. Lundquist, Spalding, and Landru (2002) concluded that females are more
prone to dropping out than males, and factors such as lack of faculty support, unresponsive faculty to
phone/email inquiries, and complicated faculty-student interactions increase students’ inclination to leave
the university. Bakker et al. (2021) found that supervisor and co-worker support are negatively related
to the intention to leave among nursing students. During the Covid-19 pandemic, several studies have
explored the factors inuencing students’ dropout intentions. Chi, Randall, and Hill (2021) showed that
the COVID-19 pandemic affects students’ mental health and dropout intentions, with those experiencing
anxiety or depression symptoms and burnout being more likely to consider dropping out compared to
those without mental health issues. Mtshweni (2021) investigated the factors inuencing the intention
to drop 955 students from a university in South Africa, including social adjustment, personal-emotional
adjustment, institutional attachment, and socioeconomic status. Baalmann et al. (2022) demonstrated
that parental educational aspirations, students living in partnerships, and close friends have an impact
on students’ dropout intentions among a sample of 7,169 students in a German university. Matteau et al.
(2023) revealed that excessive commitments and conicts between work, study, and personal life are
associated with higher levels of psychological stress and the intention to leave university.
The literature review shows that research on students’ dropout intentions has received signicant
attention from scholars worldwide. The factors inuencing students’ dropout intentions are diverse and
depend on each country’s timeframe and organizational cultural characteristics. Some factors inuencing
dropout intentions mentioned by Willcoxson (2010) are general, comprehensive, specic, and relevant to
the Vietnamese context. However, Willcoxson (2010) examined the differences in factors affecting dropout
intentions across semesters and among rst-, second-, and third-year students but needed to determine
the impact level of each factor on students’ dropout intentions. Moreover, the factors mentioned, such as
commitment to the institution, degree/course commitment, time management, teaching skills and attitudes
of instructors, accessibility and support from instructors, course design, feedback, ineffective adaptation
to the learning environment, class participation, infrastructure, socio-cultural environment, accessibility
and support from counseling, and personal circumstances that align with the context and culture of rst,
second, and third-year students in Vietnamese universities.
This research aims to provide an overview of the factors inuencing students’ dropout intentions
in universities within Ho Chi Minh City. While many studies have identied a range of factors that may
contribute to students’ dropout intentions, there still needs to be clear validation regarding the level of
impact of each factor. Therefore, the contribution of this study is to clarify the degree of inuence of
these factors on students’ dropout intentions in universities within Ho Chi Minh City, where extensive
validation studies still need to be completed. This research utilizes a non-probability and convenient
sampling method to collect data from the survey participants easily. The study’s geographical scope is
limited to the inner city and suburban areas of Ho Chi Minh City. The research has two main objectives:
1) identifying the factors inuencing students’ dropout intentions within the Ho Chi Minh City area, and 2)
proposing managerial implications to improve these factors to reduce students’ dropout intentions within
the Ho Chi Minh City area.
Denition of dropout intentions
According to, Pijl, Frostad, and Mjaavatn (2014), early dropout refers to needing to complete an
educational program or complete it with signicant delays. Additionally, Schwab (2018) suggests that
when individuals intend to leave school, they quickly focus on the desire to discontinue their education.
Therefore, dropout is considered the nal step in intending to leave school before early dropout occurs.
According to, Gury (2011), dropout occurs when students discontinue their studies without intending to
www.ijcrsee.com
419
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
continue in the initially registered eld of study or the institution they attend. Fitzpatrick and Yoels (1992),
dene dropout as students who leave an educational institution without completing their program within
the next four years, regardless of whether they return to school later and graduate. Furthermore, dropout
can refer to individuals participating in a school course who do not wish to complete the high school
program within ve years (Pijl, Frostad, and Mjaavatn (2014)).
Based on the denitions provided above, the dropout intentions can be dened as not completing
an educational program or completing it with signicant delays, not continuing in the initially registered
eld of study or institution, leaving school without graduating within a specic timeframe, or not wanting to
complete the academic program within a certain period.
Research hypothesis development
Students have various reasons for choosing to attend a university, including personal purposes
such as academic pursuit, proximity to their residence, reputation, quality of education, and the quality of
facilities at the institution. Based on their criteria, students can evaluate suitable universities and select a
school that meets their conditions. The choice of university has a specic impact on students’ subsequent
intention to drop out, especially in cases where students do not gain admission to their desired university
and must attend an alternative institution. Willcoxson (2010) also indicates that students are more
likely to leave university when they lack organizational commitment and receive insufcient guidance
regarding enrollment choices. This situation commonly occurs among rst-year students. From the
research ndings, Willcoxson (2010) determines that when students fail to gain admission to their desired
university, the likelihood of them forming an intention to drop out increases signicantly. Furthermore,
during their studies at the alternative university, students still hope to gain admission to their initial desired
university and attend the substitute university as a stepping stone to transfer to another university, thereby
increasing their intention to drop out (Willcoxson, 2010). Bean (1980) analyzed a model contributing to
student dropout and found a correlation between student commitment and the intention to drop out. This
study demonstrates that students need more commitment to the institution to increase their intention to
drop out and continue their educational journey.
H1: The lack of university commitment positively impacts student’s dropout intentions in Ho Chi
Minh City.
MacKie (2001) demonstrates that students who engage in courses over multiple years face similar
difculties as those who have dropped out before completing their studies. However, the remaining people
exhibit more substantial commitment and attachment to the institution. Students who stay in school are
more likely to overcome challenges than those who have dropped out (Nieudwoudt and Pedler, 2021).
Students with explicit purposes for pursuing a specic eld of study are more likely to intend to enroll in that
particular academic program at the university. Yorke and Longden (2008) indicate that strong academic
commitment is associated with stability and persistence in students’ studies, while weak commitment may
lead to an intention to drop out. Tinto (2012) also suggests that solid academic commitment positively
impacts students’ continued engagement in learning activities and reduces the likelihood of dropping out.
Therefore, universities with a clear commitment to degree programs and the career-related benets they
offer, aligning with students’ prospects, enhance students’ commitment and attachment to the university.
H2: Degree and course commitment negatively impact student’s dropout intentions in Ho Chi Minh
City
Swick (1987) argues that many students perceive the academic process as highly stressful. Time
management is a university counseling service (Macan and Shahani, 1990). Students also need help
to allocate their time effectively and balance it with work and personal life (Burke et al., 2017). Time is
a signicant factor inuencing students’ daily lives. When time management skills are weak, such as
inadequate time allocation or last-minute cramming for exams, it is discussed as a cause of stress and a
decline in academic performance (Longman and Atkinson, 1988). The issues mentioned above occurring
consistently over an extended period can discourage students, resulting in a gradual formation of the
intention to drop out (Nieudwoudt and Pedler, 2021). Students struggling to balance their personal time
and study time at the university and those struggling with effective time management are more likely to
have an increased intention to drop out (Willcoxson, 2010).
www.ijcrsee.com
420
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
H3: Ineffective time management positively impacts as students’ dropout intentions in Ho Chi Minh
City.
Willcoxson (2010) found that reducing students’ intention to drop out is related to building trust,
fostering learning expectations, improving teaching quality, providing support, and creating a vibrant
learning environment and social activities. Additionally, the emerging tendency of students to consider
dropping out is also linked to the teaching skills and attitudes of the faculty. Students perceive enthusiastic
support from instructors, a sense of closeness, provision of comprehensive learning materials, and timely
and positive feedback as factors that reduce their intention to drop out (Willcoxson, 2010). Social support,
learning experiences, and an engaging learning environment, along with institutional support, are factors
that inuence students’ decision to remain in school (Nieudwoudt and Pedler, 2021). Poor interaction or
communication with instructors and mentors can lead to students’ intention to drop out (Nieudwoudt and
Pedler, 2021).
H4: Skills and attitudes of instructors negatively impact student’s dropout intentions in Ho Chi Minh
City.
Glogowska, Young, and Lockyer (2007) demonstrate that students’ determination, career
commitment, social support, and student services provided by the university contribute to student retention.
Natoli, Jackling, and Siddique (2015) conclude that student support services offered by the university
are an essential factor in inuencing students’ intention to stay in school. The institution’s provision of
facilities, faculty, programs of study, student support services, and engagement in academic activities all
contribute to student retention (Kuh et al., 2007). Instructor members who are willing to address students’
concerns and understand their difculties in learning create enthusiasm for studying and reduce students’
thoughts of dropping out.
H5: Instructor support negatively impacts student’s dropout intentions in Ho Chi Minh City.
Willcoxson (2010) argues that carefully designed and logically structured courses with reliable
information yield high educational effectiveness. Instructors who incorporate real-life examples in their
lectures help students quickly understand and apply the subject to practical work situations (Willcoxson,
2010). Furthermore, university support for students to engage in experiential learning and work
opportunities in companies enhances their knowledge and skills, reducing their intention to drop out.
A exible curriculum can make students feel more comfortable dropping out. Rovai and Jordan (2004)
have demonstrated that program exibility can increase students’ commitment and intention to continue
their studies. Bransford (2000) emphasizes the importance of applying knowledge to real-life situations,
connecting knowledge with reality, and applying it in daily life to help students recognize the value of
learning and enhance their commitment to education.
H6: The curriculum design negatively impacts students’ intention to drop out in Ho Chi Minh City.
Case (2007) demonstrates that feedback is crucial in promoting student improvement by addressing
errors, lessons’ shortcomings, and areas needing improvement. Faculty support has a positive impact
on academic performance and student engagement. If instructors fail to meet students’ expectations or
requirements, harmful or ineffective feedback can lead to disappointment and strengthen the intention to
drop out (Hausmann, Schoeld and Woods, 2007).
H7: Positive instructor feedback negatively impacts students’ dropout intentions in Ho Chi Minh
City.
When participating in courses at school, students may encounter difculties in comprehending
knowledge, struggle to adapt and keep up with the teaching methods of instructors, nd it challenging
to understand specialized materials and feel overwhelmed by the workload. These factors can lead to
student frustration, a lack of self-belief in their ability to perform well in the courses, decreased motivation
to study, and an increased intention to drop out (Willcoxson, 2010). Eccles and Wigeld (2002) suggest that
students’ positive adaptation to the learning environment often leads to a more substantial commitment
to the learning process and a higher likelihood of sustaining their studies and completing the courses.
Effective adaptation can help students reduce stress and pressure in the learning process, which can
www.ijcrsee.com
421
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
contribute to the intention to drop out (Eisenberg et al., 2007). Kember, Biggs and Leung (2004) have
demonstrated the relationship between adaptation to the learning environment and students’ academic
performance, showing that well-adapted students tend to have higher grades, more stable academic
performance, and maintain their intention to study throughout the program.
H8: Ineffective adaptation to the learning environment positively impacts students’ dropout intentions
in Ho Chi Minh City.
According to empirical research surveys, many students who drop out initially show commitment
but fail to follow through (MacKie, 2001). Regular class truancy and non-participation positively correlate
to dropout (Willcoxson, 2010). Classroom engagement often provides valuable learning opportunities.
When students participate or participate minimally, they may take advantage of opportunities to
understand and acquire the necessary knowledge to achieve better results in assessments (Pascarella
and Terenzini, 1991). Kuh et al. (2008) have shown that classroom engagement is often correlated with
academic performance, with lower levels of engagement resulting in poorer academic outcomes and
higher intentions to drop out. Classroom engagement reects the commitment to the learning process.
Students who participate less in class may need more commitment and determination to complete the
course (Feldman, 1994).
H9: Low classroom participation positively impacts students’ dropout intentions in Ho Chi Minh City.
Good facilities create a conducive and comfortable learning environment that caters to the needs of
students and minimizes the likelihood of students intending to drop out. Good facilities inuence student
satisfaction, impact student condence (Omar et al., 2009), and shape future planning intentions (Clemes,
Gan and Kao, 2008). Reynolds (2007) analyzed the correlation between facilities and student recruitment
and retention. Classrooms that provide a high-quality learning environment, spacious and well-ventilated
libraries with diverse resources to support learning, and an information technology system that meets
students’ usage needs have a reverse correlation with students’ intention to drop out (Willcoxson, 2010).
H10: The university facilities negatively impact students’ dropout intentions in Ho Chi Minh City.
The cultural and social environment signicantly inuences students’ dropping out. Students may
feel helpless, isolated, and unwilling to continue their education when this environment is not friendly.
Conversely, when positive relationships characterize the environment, students will receive support and
encouragement to continue their studies. Research has shown that the cultural and social environment
impacts student satisfaction (Kahu, 2013). According to Willcoxson (2010), minimizing student dropout
requires providing facilities that meet social needs and are compatible with students’ religious/cultural
requirements.
H11: The cultural and social environment negatively impacts students’ intention to drop out in Ho
Chi Minh City.
Access to information and support from academic advisors increases student retention (Crosling,
Thomas and Heagney, 2009). Student retention depends not only on individual factors such as motivation
and academic achievement but also on external factors such as access to support and resources (Cabrera
et al., 2006). Access to information, guidance, and counseling from academic advisors and classmates, as
well as academic and social support services, can be crucial in the decision to continue or withdraw from
university (Tinto and Pusser, 2006). Students with access to high-quality support services are more likely
to be motivated and have higher retention rates (Tinto and Pusser, 2006). Students receiving good advice
from advisors regarding career choices or quickly receiving assistance when needed have a reverse
correlation with their intention to drop out (Willcoxson, 2010).
H12: Access to support from academic advisors negatively impact students’ dropout intentions in
Ho Chi Minh City.
Students who face nancial difculties often spend more time working than studying (Peltz et al.,
2021). Financial difculties negatively impact students’ commitment to their studies (Willcoxson, 2010).
Studies have found that students with high intentions to drop out often face nancial hardships and work
www.ijcrsee.com
422
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
an average of more than 16 hours per week (Leveson, McNeil and Joiner, 2013). Bean (1980) examined
the impact of personal circumstances on students’ decision to drop out. The results showed that the
impact of difcult personal circumstances can lead to an intention to drop out. Pascarella and Terenzini
(1991) demonstrated that the university environment and students’ circumstances inuence the decision
to continue their education. The results also indicated that personal circumstances can be essential to
the dropout decision process. Students’ concerns about mental health, physical health, homesickness, or
accumulating debt positively correlate with their intention to drop out (Willcoxson, 2010).
H13: Personal circumstances positively impact students’ dropout intentions in Ho Chi Minh City.
Figure 1. Proposed research model
Materials and Methods
Process research: A mixed-methods research approach combining qualitative and quantitative
research methods was used in this study.
Preliminary qualitative and quantitative research: A group interview method was employed with
15 students in the qualitative phase of the study. The research topic involved collecting opinions from rst-,
second-, and third-year students at public and private universities in suburban and urban areas of Ho Chi
Minh City. The group discussion aimed to identify factors inuencing the intention to drop out and rene
the measurement scales of the research concepts to align with the research context. The results of the
interviews were synthesized and adjusted to form a draft measurement scale to support the preliminary
quantitative research and the formal quantitative research. Subsequently, a survey was conducted with
80 students to evaluate the reliability using Cronbach’s Alpha coefcient and perform Exploratory Factor
Analysis (EFA) to examine the convergent and discriminant validity of the measurement scale.
Formal quantitative research: The study utilized the Bootstrapping technique with a sample size
of N = 5000 to test the hypotheses. This step was employed to evaluate the measurement model and the
structural model:
The measurement model was assessed by examining measurement scale reliability, composite
reliability, convergent validity, and discriminant validity. To ensure the reliability of the measurement scales,
Cronbach’s alpha coefcient and Composite Reliability (CR) should exceed 0.6 (Hair Jr et al., 2009). The
Average Variance Extracted (AVE) of each construct in the model should be greater than 0.5, based on
the criteria proposed by Shiau, Sarstedt and Hair (2019). The study followed the criteria of Fornell and
www.ijcrsee.com
423
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
Larcker (1981) to test the discriminant validity of the measurement scales, where the square root of the
AVE of each construct should be greater than the correlation coefcient between that construct and the
other constructs in the model.
The structural model was evaluated based on criteria such as the coefcient of determination (R2),
predictive relevance (Q2), and effect size (f2). The coefcient of determination (R2) values of the model
were interpreted as follows: weak (R2 = 0.02), moderate (R2 = 0.16), and robust (R2 = 0.26) explanations
of the model variance (Cohen, 2013). The Stone-Geisser Q² criterion was used for predictive relevance
assessment, following the evaluation standards proposed by Henseler, Ringle, and Sinkovics (2009):
weak prediction (Q2 < 0,02); moderate prediction (Q2 within [0,02; 0,35]), and strong prediction (Q2 >
0,35). Lastly, the effect size (f2) between corresponding components was examined, with weak effect (f2
= 0.02), moderate effect (f2 = 0.15), and substantial effect (f2 = 0.35) based on the criteria of Henseler,
Ringle and Sinkovics (2009).
Scale measurement: The research model consists of 13 research constructs. The dependent
variable is Dropout Intentions, which was adopted by Farr-Wharton et al. (2018). The independent
variables include Lack of university commitment, Degree and course commitment, Ineffective time
management, teaching skills and attitudes of instructors, Instructor support, Curriculum design, Positive
instructor feedback, Ineffective adaptation to the Learning Environment, Low Classroom Engagement,
University facilities, Cultural and social environment, Access to support from academic advisors, and
Personal Circumstances. These independent variables were inherited and adjusted from the study by
Willcoxson (2010). These independent variables were inherited and adjusted from the study by Willcoxson
(2010). There are a total of 74 observed variables, and they were measured using a 5-point Likert scale:
(1) Strongly Disagree, (2) Disagree, (3) Neutral, (4) Agree, and (5) Strongly Agree (see Table 1).
Table 1
Scale measurement
Formal sample
Survey Sample Criteria: First- year, second- year, and third- year university students studying at
expected public and private universities located within the inner city and suburban areas of Ho Chi Minh
City. This study did not survey fourth-year students as they rarely intend to drop out.
Sampling Method: The study employed a non-probability convenience sampling method. The
www.ijcrsee.com
424
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
survey questionnaire was distributed directly and online through Google Forms at various universities in
Ho Chi Minh City. The survey was conducted from February 13, 2023, to March 16, 2023.
Data Analysis Method: The study utilized Partial Least Squares Structural Equation Modeling (PLS-
SEM) to analyze the data. This method was chosen due to its advantage in handling small sample sizes
and data that do not follow normal distribution assumptions (Shiau, Sarstedt and Hai, 2019).
Formal Sample: The survey results yielded 804 valid responses. Therefore, the study used 804
observations as the formal sample for this research.
Results
Sample characteristics
Gender breakdown with the number of female students being 392 (48.8%) and the number of
male students being 412 (51.2%). Next is the breakdown of students by academic year, with 239 (29.7%)
rst-year students, 261 (32.5%) second-year students, 304 (37.8%) third-year students, and no fourth-
year students. Following that is the breakdown of students by major, with the corresponding number
of students in each major. The majors listed are Engineering with 163 students (20.3%), Economics
- International Trade with 133 students (16.5%), Business - Management with 158 students (19.7%),
Foreign Languages with 63 students (7.8%), Information Technology (IT) with 64 students (8.0%), Social
Sciences and Humanities with 25 students (3.1%), and other majors with 198 students (24.6%). Next is
the geographical breakdown, with the number of suburban students being 284 (35.3%) and the number
of downtown students being 520 (64.7%).
The university group includes various universities, with the corresponding number of students in
each university. The listed universities are Open University of Ho Chi Minh City with 70 students (8.7%),
Ho Chi Minh City University of Transport with 60 students (7.5%), Industrial University of Ho Chi Minh City
with 184 students (22.9%), Ho Chi Minh City University of Technical Education with 71 students (8.8%),
Nong Lam University with 80 students (10.0%), Van Lang University with 71 students (8.8%), UEF School
of Economics and Finance with 53 students (6.6%), HUTECH University with 62 students (7.7%), Nguyen
Tat Thanh University with 79 students (9.8%), and FPT University with 74 students (9.2%). Finally, the
group with intentions to drop out is divided into two categories: those with intentions to drop out, totaling
206 (25.6%), and those without intentions to drop out, totaling 598 (74.4%).
Table 2
Participants’ Characteristics
www.ijcrsee.com
425
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
Scale evaluation: In the PLS-SEM method, the outer loadings criterion is used to evaluate the
importance of predictor variables in the model. According to Henseler, Ringle and Sinkovics (2009), factor
loadings > 0.5 are considered. Factor loadings below 0.5 will be excluded from the measurement scale
in the model.
Table 3
Scale reliability
Table 3 presents the reliability testing results, including Cronbach’s Alpha, Composite Reliability
(CR), and Average Variance Extracted (AVE) for the measurement scales in the model. The statistical
table shows that the Cronbach’s alpha values of the measurement scales are all above 0.7, ensuring
reliability for use (Nunnally, 1978). Therefore, the variables will be retained and utilized in the subsequent
steps. Hair et al. (2019), state that a Composite Reliability (CR) value greater than 0.7 ensures reliability.
Based on the results in Table 3, all measurement scales have CR values above 0.6, except for the LCP,
IS, ALE and LUC scales. Lastly, the Average Variance Extracted (AVE) for all measurement scales is
more signicant than 0.5, ensuring reliability (Hair et al., 2019). Hence, most measurement scales in the
research model demonstrate satisfactory reliability.
Table 4
Scale statistical value
Items Mean SD Factor loadings
Student’s dropout inten-
tions (SDI)
SDI1: I often think about
dropping out of school. 2.065 1.208 0.75
SDI2: I am actively seeking
job opportunities and alter-
native learning options, so I
may leave the university.
2.044 1.123 0.815
SDI3: There is a possibility
that I will drop out of univer-
sity within the next year.
2.061 1.268 0.749
SDI4: I am looking for suit-
able timing to drop out of
school.
1.769 1.092 0.857
Lack of university commit-
ment (LUC)
www.ijcrsee.com
426
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
LUC1: I am studying at this
university as a steppingstone
to transfer to another univer-
sity.
1.924 1.138 0.922
LUC3: I am attending this
university because I did not
meet the requirements of
other preferences.
2.897 1.431 0.577
Degree and course com-
mitment (DCC)
DCC1: I have obvious rea-
sons for studying at this uni-
versity.
3.596 1.098 0.867
DCC2: I can enroll in the
course/program that I have
chosen.
3.799 0.969 0.856
DCC3: I know what profes-
sion I want to pursue in the
future.
3.506 1.138 0.807
Ineffective time manage-
ment (ITM)
ITM1: It is difcult to balance
personal time and study time
at the university.
3.073 1.119 0.798
ITM2: I struggle with manag-
ing study time effectively. 3.163 1.159 0.796
ITM3: It is challenging to bal-
ance family responsibilities
and university studies.
2.667 1.18 0.848
ITM4: It is challenging to
balance work and university
studies.
2.846 1.221 0.803
Skills and attitudes of in-
structors (SAI)
SAI1: The professors are en-
thusiastic and dedicated in
their teaching.
3.9 0.981 0.906
SAI2: The professors are
skilled at explaining things. 3.755 0.977 0.887
SAI3: The professors always
strive to make the classes in-
teresting.
3.795 0.986 0.865
SAI4: The faculty team clear-
ly communicates their ex-
pectations from the students
right from the beginning.
3.841 0.991 0.818
SAI5: The professors always
create a sense of closeness
with the students.
3.749 1.43 0.62
Instructor support (IS)
IS4: The faculty team is al-
ways available when I need
them.
3.68 1.699 0.768
IS5: My professors genuinely
make an effort to understand
the difculties students face
in their learning process.
3.641 0.974 0.9
Curriculum design (CD)
www.ijcrsee.com
427
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
CD1: My professors incorpo-
rate real-life examples into
their teaching curriculum.
3.851 0.92 0.856
CD2: What I am learning at
the university has been re-
searched and proven.
3.745 0.919 0.889
CD3: I am satised with the
job experiential opportunities
introduced by the university.
3.582 0.993 0.807
Positive instructor feed-
back (PIF)
PIF1: I received helpful feed-
back on the assessment
tasks.
3.519 0.923 0.964
PIF2: I received prompt feed-
back on the tasks. 3.437 0.954 0.891
Ineffective adaptation
to learning environment
(IALE)
IALE2: My study program is
too demanding. 3.085 0.969 0.793
IALE4: I nd it difcult to un-
derstand various study mate-
rials.
3.345 0.995 0.693
IALE5: I struggle to adapt to
the teaching methods at the
university.
2.988 1.064 0.841
Low classroom participation
(LCP)
LCP7: I frequently skip class-
es. 2.311 1.309 0.882
LCP8: I don't attend classes
because the study materials
are available on the website.
2.567 1.208 0.847
University facilities (UF)
UF1: The classrooms pro-
vide a high-quality learning
environment.
3.624 1.004 0.846
UF2: The library is spacious,
well-ventilated, and offers a
diverse range of study ma-
terials.
3.827 0.97 0.876
UF3: The information tech-
nology system meets my us-
age needs.
3.562 1.035 0.859
UF4: The classrooms are
very spacious. 3.586 1.012 0.817
Cultural and social envi-
ronment (CSE)
CSE1: The facilities of the
university meet my social
needs.
3.545 1.023 0.937
CSE2: The facilities of the
university are suitable for my
religious/cultural needs.
3.585 0.98 0.952
Access to support from
academic advisors (ASA)
www.ijcrsee.com
428
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
ASA1: I receive good advice
from the university regarding
career choices.
3.397 0.995 0.893
ASA2: I receive good advice
from a career counselor in
choosing a profession for
myself.
3.387 1.045 0.907
ASA3: I receive good advice
from the university regarding
career choices.
3.429 1.03 0.92
ASA4: I easily receive assis-
tance when needed from the
management team.
3.435 0.999 0.842
ASA5: The management
team is always ready to as-
sist when I need them.
3.427 1.019 0.754
Personal circumstances
(PC)
PC1: I worry about my men-
tal health. 3.075 1.21 0.82
PC2: I worry about my physi-
cal health. 3.039 1.202 0.821
PC3: I often feel homesick. 3.213 1.309 0.584
PC4: I worry about the accu-
mulating debt while studying
at university.
2.976 1.393 0.774
PC5: I have nancial issues. 3.06 1.351 0.742
(Source: own author)
Table 4 presents the descriptive statistics, standard deviations, and factor loadings of the variables
after variable elimination. The results show that all factor loadings are greater than 0.7, except for SAI5
and PC3, but they are retained to ensure content validity. The measurement scales used in the research
model exhibit convergence.
Table 5
The discriminant validity testing
(Source: own author)
Table 5 presents the discriminant validity test results for the model’s latent variables using the
criteria set by Fornell and Larcker (1981). The table shows that all square root of the average variance
extracted (AVE) values for each research variable are more signicant than the correlation coefcients
between that variable and the remaining variables in the model. Therefore, the measurement scales for
the research variables all demonstrate discriminant validity.
www.ijcrsee.com
429
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
Model evaluation: The estimation results of the model using the Bootstrapping method with a
sample size of 5,000 are depicted in Figure 2.
Figure 2. PLS-SEM estimation results
Table 6
Hypothesis test results
(Source: own author)
The quality of the proposed model is assessed through the R2 values and the Stone-Geisser index
(Q2). Table 6 shows that the R2 value for SDI is 0.30, more signicant than 0.26. According to Cohen
(2013) evaluation criteria, the model’s predictive power is considered strong. The Stone-Geisser value
www.ijcrsee.com
430
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
from Q²SDI is 0.178, falling within the range of (0.02-0.35). Following the evaluation criteria of Henseler
and Chin (2010), the model’s predictive ability is considered moderate. Additionally, the effect size (f2) of
the factors inuencing students’ intention to drop out is evaluated as weak. According to, Hair et al. (2019),
the inuence of factors with f2 values is all < 0.02.
The results of the hypothesis testing indicate that the lack of university commitment positively
impacts students’ intention to drop out (H1: B = 0.268; p = 0.000 < 0.01); thus, H1 is accepted. Next,
degree and course commitment negatively impact students’ intention to drop out (H2: B = -0.1, p = 0.006
< 0.01); thus, H2 is accepted. Similarly, ineffective time management positively impacts students’ intention
to drop out (H3: B = 0.172, p-value = 0.000 < 0.01). Thus, H3 is accepted.
However, the hypotheses H4 and H5 are not supported in this study (B = -0.064; p-value > 10%;
B = -0.014 > 10%). Additionally, the curriculum design negatively impacts students’ intention to drop out,
so H6 is accepted (B = -0.088, p-value = 0.079 < 10%). Moreover, the positive instructor feedback does
not impact students’ intention to drop out, so H7 is rejected. Furthermore, ineffective adaptation to the
learning environment and low classroom participation all have a positive impact on a student’s intention
to drop out; thus, H8 and H9 are supported (B = 0.116; p-value = 0.001 < 1%; B = 0.123; p-value = 0.001
< 0.001). Hypotheses H10, H11, and H12 are not supported in this study. Lastly, personal circumstances
positively impact a student’s intention to drop out; thus, H13 is accepted (B = 0.09; p-value = 0.008 < 1%).
Table 7
Differences in dropout intentions by gender, location, and type of university
www.ijcrsee.com
431
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
(Source: own author)
Table 7 presents the results of a multigroup analysis examining the differences in students’ intention
to drop out of school based on three variables: gender (Male, Female), location of activity (Urban,
Suburban), and type of university (Public, Private). The results of the statistical tests indicate that the
p-values are all greater than 0.05, suggesting that there is no signicant difference in students’ intention
to drop out of school based on gender, location of activity, or type of university, except for ASA - SDI, the
access to support from academic advisors (ASA) has a differential impact on students’ dropout intentions
based on the type of university (p = 0.023 < 0.05).
Discussion
The research ndings indicate a higher intention to drop out among students who need more
commitment to the institution, particularly in Ho Chi Minh City. These ndings are consistent with previous
studies such as Willcoxson (2010), Bean (1980). Willcoxson (2010) identied that students are likelier to
leave university when they lack organizational commitment. Bean (1980) found that students who lack
commitment to the institution tend to withdraw from the learning process.
The commitment to credentials and courses inversely impacts students’ intention to drop out.
Previous studies such as Yorke and Longden (2008), Tinto (2012) have shown that strong commitment
to academic qualications and courses is associated with stability and persistence in students’ learning,
reducing the likelihood of dropouts.
In addition, ineffective time management positively impacts students’ intention to drop out in
Ho Chi Minh City. The research ndings are consistent with previous studies such as Nieudwoudt and
Pedler (2021), Willcoxson (2010). Ineffective time management leads to student discouragement and the
formation of dropout intentions (Nieudwoudt and Pedler, 2021). Students who are unable to balance their
personal time and study time are more likely to develop intentions to drop out (Willcoxson, 2010).
The design of the course program has an inverse impact on students’ intention to drop out. The
research ndings align with previous studies as well. Rovai and Jordan (2004) demonstrated that exibility
in the curriculum can increase student commitment and reduce dropout intentions. Willcoxson (2010)
stated that carefully designed and logical courses can be highly effective in education and contribute to
reducing students’ intention to drop out.
Low classroom participation by students has a positive impact on their intention to drop out. Some
previous studies have also shown that students who frequently skip classes and do not participate in
classroom activities have a positive relationship with dropout intentions (Willcoxson, 2010). Kuh et al.
(2008) argued that low-engagement students have poorer academic outcomes and higher intentions
to drop out. Students with low classroom participation need more determination to complete the course
(Feldman, 1994). Lastly, personal circumstances positively impact students’ intention to drop out in Ho
Chi Minh City, which is consistent with previous research. Personal circumstances inuence students’
www.ijcrsee.com
432
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
decisions to drop out (Bean, 1980; Willcoxson, 2010).
The remaining factors, such as instructors’ teaching skills and attitude, instructor support, instructor
feedback, facilities, socio-cultural environment, and access to academic advisors, do not impact students’
intention to drop out. These research ndings contradict previous studies (Hausmann et al., 2007; Kuh
et al., 2007; Nieudwoudt and Pedler, 2021; Willcoxson, 2010). When interviewing a group of students in
various institutions, they expressed the belief that instructors’ teaching skills and attitude do not inuence
their intention to drop out. According to the interviewed student group, effective instruction requires
instructors to have practical experience, expertise, and in-depth knowledge. Students are concerned
with teaching methods and the enthusiasm and dedication of instructors. Instructor support helps
students effectively address difculties during the learning process. However, access to and support from
instructors is just one aspect, and if timely support from instructors is not received, students can seek
assistance from friends to resolve their issues. Whether students receive access to and support from
instructors does not impact their intention to drop out. The research results indicate that factors such
as timely feedback, facilities, socio-cultural environment, and access to academic advisors positively
but statistically insignicantly inuence students’ intention to drop out. The research ndings may not
be suitable for the actual situation in Vietnam and cannot be considered as factors affecting students’
intention to drop out.
Conclusion
Based on the practical context in Vietnam regarding students’ dropout rate and considering the
research by Willcoxson (2010), the study adjusted and identied factors inuencing students’ intention
to drop out in Ho Chi Minh City. The inuencing factors were explored by surveying 804 students from
public and private universities in suburban and urban areas. These factors include: 1) Lack of commitment
to the institution, 2) Degree/course commitment, 3) Time management, 4) Course design, 5) Students’
ineffective adaptation to the learning environment, 6) Limited classroom participation, and 7) Personal
circumstances. Additionally, the study found that the following factors did not impact students’ intention
to drop out in Ho Chi Minh City: Teaching skills and attitude of instructors, instructor support, instructor
feedback, facilities, socio-cultural environment, and access to academic advisors. Based on the ndings,
the research made two main contributions.
In terms of theoretical aspects, the research has identied factors that inuence students’ intention
to drop out in Vietnam, specically in Ho Chi Minh City, where previous studies were scarce. These factors
align with the practical situation for Ho Chi Minh City students. The study provides a comprehensive
understanding of the factors inuencing students’ intention to drop out. This helps researchers and
educational experts better understand the factors that may lead to student disengagement or loss of
interest in learning. Factors such as lack of commitment to the institution, degree/course commitment,
time management, course design, ineffective adaptation to the learning environment, and personal
circumstances have been identied to assess students’ intention to drop out. This can assist educational
managers and instructors develop appropriate support measures and interventions to maintain and
enhance students’ engagement and academic success.
In practical terms, the research ndings can be used to develop programs and educational policies
to reduce the student dropout rate. Universities and instructors can implement measures such as
enhancing student commitment, creating conducive learning environments, improving time management,
and offering better-designed courses to enhance student engagement and interest in learning. The
specic results from the study can also be used to propose individual support measures for students. This
may involve counseling and personal support to help students overcome personal and familial difculties
caused by their circumstances. The research also highlights that factor such as teaching skills and attitude
of instructors, instructor support, and academic advising are not decisive factors in students’ intention to
drop out in Ho Chi Minh City. This can help universities focus on other aspects of the learning experience
to create a positive learning environment and better support students.
The study is limited to the research scope within the urban and suburban areas of Ho Chi Minh
City. Therefore, expanding the research scope to include universities in other regions of Vietnam may
be necessary to gain a more comprehensive understanding of the issue. Additionally, the study needs to
address the nancial factors and cost of education. Surveying the impact of nancial factors and the cost
of education could be an essential part of understanding students’ intention to drop out.
www.ijcrsee.com
433
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
Acknowledgements
This study is the outcome of a university-level project (Grant number: 22/2QTKDSV01) and was
supported by funding from the Industrial University of Ho Chi Minh City.
Conict of interests
The authors declare no conict of interest.
Author Contributions
Conceptualization, M.C.B, N.T.T.N and P.H.B.N; methodology, T.N.G.; software, T.N.G.; formal
analysis, T.N.G. and N.N.H; writing—original draft preparation, T.N.G. and N.N.H.; writing—review and
editing, T.N.G. and N.N.H. All authors have read and agreed to the published version of the manuscript.
References
Baalmann, T., Brömmelhaus, A., Hülsemann, J., Feldhaus, M., & Speck, K. (2022). The Impact of Parents, Intimate
Relationships, and Friends on Students’ Dropout Intentions. Journal of College Student Retention: Research, Theory
& Practice, 15210251221133374. https://doi.org/10.1177/15210251221133374
Bakker, E. J. M., Roelofs, P. D. D. M., Kox, J. H. A. M., Miedema, H. S., Francke, A. L., van der Beek, A. J., & Boot, C. R. L.
(2021). Psychosocial work characteristics associated with distress and intention to leave nursing education among
students; A one-year follow-up study. Nurse Education Today, 101, 104853. https://doi.org/10.1016/j.nedt.2021.104853
Bandura, A. (1977). Self-efcacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.
https://doi.org/10.1037/0033-295X.84.2.191
Bean, J. P. (1980). Dropouts and turnover: The synthesis and test of a causal model of student attrition. Research in Higher
Education, 12(2), 155-187. https://doi.org/10.1007/BF00976194
Bransford, J. (2000). How People Learn: Brain, Mind, Experience, and School: Expanded Edition (2000). Retrieved from https://
nap.nationalacademies.org/catalog/9853/how-people-learn-brain-mind-experience-and-school-expanded-edition
Burke, P. J., Bennett, A., Bunn, M., Stevenson, J., & Clegg, S. (2017). It’s about time: Working towards more equitable
understandings of the impact of time for students in higher education. Retrieved from https://www.ncsehe.edu.au/
publications/its-about-time-working-towards-more-equitable-understandings-of-the-impact-of-time-for-students-in-
higher-education/
Cabrera, A. F., Deil-Amen, R., Prabhu, R., Terenzini, P. T., Lee, C., & Franklin, J. R. E. (2006). Increasing the College Preparedness
of At-Risk Students. Journal of Latinos and Education, 5(2), 79-97. https://doi.org/10.1207/s1532771xjle0502_2
Case, S. (2007). Reconguring and realigning the assessment feedback processes for an undergraduate criminology degree.
Assessment & Evaluation in Higher Education, 32(3), 285-299. https://doi.org/10.1080/02602930600896548
Chi, D. L., Randall, C. L., & Hill, C. M. (2021). Dental trainees’ mental health and intention to leave their programs during
the COVID-19 pandemic. The Journal of the American Dental Association, 152(7), 526-534. https://doi.org/10.1016/j.
adaj.2021.02.012
Clemes, M. D., Gan, C. E. C., & Kao, T.-H. (2008). University Student Satisfaction: An Empirical Analysis. Journal of Marketing
for Higher Education, 17(2), 292-325. https://doi.org/10.1080/08841240801912831
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. In. https://doi.org/10.1016/C2013-0-10517-X
Crosling, G., Thomas, L., & Heagney, M. (2009). Introduction: Student success and retention. Improving student retention
in higher education, 19-32. https://www.researchgate.net/publication/244990185_Crosling_G_Heagney_M_and_
Thomas_L_2009_Improving_Student_Retention_in_Higher_Education_Improving_teaching_and_learning_In_
Australian_Universities’_Review_Vol51_No_2
Eccles, J. S., & Wigeld, A. (2002). Motivational beliefs, values, and goals. Annual review of psychology, 53(1), 109-132.
https://doi.org/10.1146/annurev.psych.53.100901.135153
Eisenberg, D., Gollust, S. E., Golberstein, E., & Hefner, J. L. (2007). Prevalence and correlates of depression, anxiety, and
suicidality among university students. American journal of orthopsychiatry, 77(4), 534-542. https://doi.org/10.1037/0002-
9432.77.4.534
Farr-Wharton, B., Charles, M. B., Keast, R., Woolcott, G., & Chamberlain, D. (2018). Why lecturers still matter: the impact of
lecturer-student exchange on student engagement and intention to leave university prematurely. Higher Education, 75,
167-185. https://doi.org/10.1007/s10734-017-0190-5
Feldman, K. A. (1994). [What Matters in College? Four Critical Years Revisited., Alexander W. Astin]. The Journal of Higher
Education, 65(5), 615-622. https://doi.org/10.2307/2943781
Fitzpatrick, K. M., & Yoels, W. C. (1992). Policy, School Structure, and Sociodemographic Effects on Statewide High School
Dropout Rates. Sociology of Education, 65(1), 76-93. https://doi.org/10.2307/2112694
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement
Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Glogowska, M., Young, P., & Lockyer, L. (2007). Should I go or should I stay?: A study of factors inuencing students’ decisions
on early leaving. Active Learning in Higher Education, 8(1), 63-77. https://doi.org/10.1177/1469787407074115
Gury, N. (2011). Dropping out of higher education in France: a micro-economic approach using survival analysis. Education
Economics, 19(1), 51-64. https://doi.org/10.1080/09645290902796357
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European
www.ijcrsee.com
434
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
Hair Jr, J. F., Black, W. C., Babin, B. J., & Anderson, R. (2009). Multivariate Data Analysis. In. Retrieved from https://www.
drnishikantjha.com/papersCollection/Multivariate%20Data%20Analysis.pdf
Hausmann, L. R. M., Schoeld, J. W., & Woods, R. L. (2007). Sense of Belonging as a Predictor of Intentions to Persist Among
African American and White First-Year College Students. Research in Higher Education, 48(7), 803-839. https://doi.
org/10.1007/s11162-007-9052-9
Henseler, J., & Chin, W. W. (2010). A Comparison of Approaches for the Analysis of Interaction Effects Between Latent
Variables Using Partial Least Squares Path Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 17(1),
82-109. https://doi.org/10.1080/10705510903439003
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing.
In Advances in International Marketing, Vol. 20. R. R. Sinkovics & P. N. Ghauri (Eds.), New Challenges to International
Marketing (pp. 277-319). https://doi.org/10.1108/S1474-7979(2009)0000020014
Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758-773. https://doi.
org/10.1080/03075079.2011.598505
Kember, D., Biggs, J., & Leung, D. Y. P. (2004). Examining the multidimensionality of approaches to learning through
the development of a revised version of the Learning Process Questionnaire. 74(2), 261-279. https://doi.
org/10.1348/000709904773839879
Kuh, G. D., Cruce, T. M., Shoup, R., Kinzie, J., & Gonyea, R. M. (2008). Unmasking the Effects of Student Engagement on
First-Year College Grades and Persistence. The Journal of Higher Education, 79(5), 540-563. https://doi.org/10.1080
/00221546.2008.11772116
Kuh, G. D., Kinzie, J., Buckley, J. A., Bridges, B. K., & Hayek, J. C. (2007). Piecing together the student success puzzle:
research, propositions, and recommendations. In Vol. 32. https://eric.ed.gov/?id=EJ791634
Leveson, L., McNeil, N., & Joiner, T. (2013). Persist or withdraw: the importance of external factors in students’ departure
intentions. Higher Education Research & Development, 32(6), 932-945. https://doi.org/10.1080/07294360.2013.806
442
Longman, D. G., & Atkinson, R. H. J. P., MN: West Publishing Co. (1988). College Learning and Study Skills. In St. Paul, MN:
West Publishing Co. https://archive.org/details/collegelearnings0000long
Lundquist, C., Spalding, R. J., & Landrum, R. E. (2002). College student’s thoughts about leaving the university: The impact
of faculty attitudes and behaviors. Journal of College Student Retention: Research, Theory & Practice, 4(2), 123-133.
https://doi.org/10.2190/FLAL-7AM5-Q6K3-L40P
Macan, T. H., & Shahani, C. (1990). College Students’ Time Management: Correlations With Academic Performance and
Stress. Journal of Educational Psychology, 82(4), 760-768. https://psycnet.apa.org/doi/10.1037/0022-0663.82.4.760
MacKie, S. E. (2001). Jumping the Hurdles – Undergraduate Student Withdrawal Behaviour. Innovations in Education and
Teaching International, 38(3), 265-276. https://doi.org/10.1080/14703290110056371
Matteau, L., Toupin, I., Ouellet, N., Beaulieu, M., Truchon, M., & Gilbert-Ouimet, M. (2023). Nursing students’ academic
conditions, psychological distress, and intention to leave school: A cross-sectional study. Nurse Education Today, 129,
105877. https://doi.org/10.1016/j.nedt.2023.105877
Mtshweni, B. V. (2021). Adjustment and socioeconomic status: how do these factors inuence the intention to dropout of
university? South African Journal of Psychology, 52(2), 262-274. https://doi.org/10.1177/008124632110591
Natoli, R., Jackling, B., & Siddique, S. (2015). Insights into departure intention: A qualitative case study. Education Research
Perspectives, 42, 459-490. https://www.proquest.com/scholarly-journals/insights-into-departure-intention-qualitative/
docview/1765642305/se-2
Nieuwoudt, J. E., & Pedler, M. L. (2021). Student retention in higher education: Why students choose to remain at
university. Journal of College Student Retention: Research, Theory & Practice, 25(2), 326-349. https://doi.
org/10.1177/1521025120985228
Nunnally, J. C. (1978). Psychometric Theory: 2d Ed: McGraw-Hill.
Omar, N. A., Nazri, M. A., Abu, N. K., & Omar, Z. (2009). Parents’ perceived service quality, satisfaction and trust of a
childcare centre: Implication on loyalty. International Review of Business Research Papers, 5(5), 299-314. https://
www.researchgate.net/publication/228435945_Parents_Perceived_Service_Quality_Satisfaction_and_Trust_of_a_
Childcare_Centre_Implication_on_Loyalty
Orion., H. C., Forosuelo, E. J. D., & Cavalida, J. M. (2014). Factors affecting students’ decision to drop out of school. Education
Research International, 2(1), 1-16. https://doi.org/10.1155/2023/7704142
Pascarella, E. T., & Terenzini, P. T. (1991). How college affects students: Findings and insights from twenty years of research,
Academe 78(4), 44–47. https://doi.org/10.2307/40250363
Lewis, L. S. (1992). [Review of How College Affects Students: Findings and Insights from Twenty Years of Research, by E. T.
Pascarella & P. T. Terenzini]. Academe, 78(4), 44–47. https://doi.org/10.2307/40250363
Peltz, J. S., Bodenlos, J. S., Kingery, J. N., & Rogge, R. D. (2021). The role of nancial strain in college students’ work hours,
sleep, and mental health. Journal of American College Health, 69(6), 577-584. https://doi.org/10.1080/07448481.201
9.1705306
Pijl, S. J., Frostad, P., & Mjaavatn, P. E. (2014). Students with special educational needs in secondary education: are they
intending to learn or to leave? European Journal of Special Needs Education, 29(1), 16-28. https://doi.org/10.1080/08
856257.2013.830442
Reynolds, G. L. (2007). The impact of facilities on recruitment and retention of students. New Directions for Institutional
Research, 2007(135), 63-80. https://doi.org/10.1002/ir.223
Rovai, A. P., & Jordan, H. M. (2004). Blended Learning and Sense of Community: A comparative analysis with traditional and
fully online graduate courses. International Review of Research in Open and Distributed Learning, 5(2), 1-13. https://
doi.org/10.19173/irrodl.v5i2.192
Schnettler, T., Bobe, J., Scheunemann, A., Fries, S., & Grunschel, C. (2020). Is it still worth it? Applying expectancy-value
theory to investigate the intraindividual motivational process of forming intentions to drop out from university. Motivation
and Emotion, 44, 491-507. https://doi.org/10.1007/s11031-020-09822-w
www.ijcrsee.com
435
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
Schwab, S. (2018). Attitudes Towards Inclusive Schooling: A study on Students’, Teachers’ and Parents’ attitudes. International
Journal of Inclusive Education 27(2), 221-240. https://doi.org/10.1080/13603116.2020.1837267
Sheldon, S. B., & Epstein, J. L. (2004). Getting Students to School: Using Family and Community Involvement to Reduce
Chronic Absenteeism. The School Community Journal, 14(2), 39-56. https://doi.org/10.26530/OAPEN_389225
Shiau, W.-L., Sarstedt, M., & Hair, J. F. (2019). Internet research using partial least squares structural equation modeling (PLS-
SEM). Internet Research, 29(3), 398-406. https://doi.org/10.1108/IntR-10-2018-0447
Swick, K. J. (1987). Student Stress: A Classroom Management System. Analysis and Action Series. In. https://eric.
ed.gov/?id=ED307514
Tinto, V. (2012). Leaving college: Rethinking the causes and cures of student attrition. In. https://eric.ed.gov/?id=ED371658
Tinto, V., & Pusser, B. (2006). Moving from theory to action: Building a model of institutional action for student success.
National Postsecondary Education Cooperative, 1(51), 89-125. https://www.researchgate.net/publication/251378009_
Moving_From_Theory_to_Action_Building_a_Model_of_Institutional_Action_for_Student_Success
Willcoxson, L. (2010). Factors affecting intention to leave in the rst, second and third year of university studies: a semester-
by-semester investigation. Higher Education Research & Development, 29(6), 623-639. https://doi.org/10.1080/0729
4360.2010.501071
Yorke, M., & Longden, B. (2008). The rst-year experience of higher education in the UK. https://www.researchgate.net/
publication/225083945_The_First-Year_Experience_of_Higher_Education_in_the_UK_-_Final_Report
Appendix
Items
Student’s dropout intentions (SDI)
SDI1: I often think about dropping out of school.
SDI2: I am actively seeking job opportunities and alternative learning options, so I may
leave the university.
SDI3: There is a possibility that I will drop out of university within the next year.
SDI4: I am looking for suitable timing to drop out of school.
Lack of university commitment (LUC)
LUC1: I am studying at this university as a steppingstone to transfer to another univer-
sity.
LUC2: The reputation of my university is very important for job applications.
LUC3: I am attending this university because I did not meet the requirements of other
preferences.
LUC4: I am satised with the university I am currently studying at.
LUC5: I am satised with my personal experience at the university.
Degree and course commitment (DCC)
DCC1: I have obvious reasons for studying at this university.
DCC2: I can enroll in the course/program that I have chosen.
DCC3: I know what profession I want to pursue in the future.
Ineective time management (ITM)
ITM1: It is dicult to balance personal time and study time at the university.
ITM2: I struggle with managing study time eectively.
ITM3: It is challenging to balance family responsibilities and university studies.
ITM4: It is challenging to balance work and university studies.
Skills and attitudes of instructors (SAI)
SAI1: The professors are enthusiastic and dedicated in their teaching.
SAI2: The professors are skilled at explaining things.
SAI3: The professors always strive to make the classes interesting.
www.ijcrsee.com
436
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
SAI4: The faculty team clearly communicates their expectations from the students right
from the beginning.
SAI5: The professors always create a sense of closeness with the students.
SAI6: I have encountered diculties in understanding the accent of some instructors
while listening.
SAI7: I have had some unpleasant experiences with certain instructors.
Instructor support (IS)
IS1: I have received support from the instructors
IS2: Instructors are sensitive to the individual needs of students
IS3: Instructors often strive to meet my needs.
IS4: The faculty team is always available when I need them.
IS5: My professors genuinely make an eort to understand the diculties students face
in their learning process.
Curriculum design (CD)
CD1: My professors incorporate real-life examples into their teaching curriculum.
CD2: What I am learning at the university has been researched and proven.
CD3: I am satised with the job experiential opportunities introduced by the university.
Positive instructor feedback (PIF)
PIF1: I received helpful feedback on the assessment tasks.
PIF2: I received prompt feedback on the tasks.
Ineective adaptation to learning environment (IALE)
IALE1: I have the potential to succeed after completing your university education.
IALE2: My study program is too demanding.
IALE3: I believe that my essay writing skills are sucient for university-level study.
IALE4: I nd it dicult to understand various study materials.
IALE5: I struggle to adapt to the teaching methods at the university.
IALE6: I need good analytical skills in order to understand the content.
IALE7: I need a good memory in order to study eectively.
Low classroom participation (LCP)
LCP1: My classes are engaging and interesting.
LCP2: I enjoy the intellectual challenges that come with what I am studying.
LCP3: I appreciate the opportunity to interact with students from diverse cultural back-
grounds at the university.
LCP4: When working in groups, I enjoy collaborating with peers from dierent cultural
backgrounds.
LCP5: I actively participate in class discussions.
LCP6: I make it a habit to attend class and prepare the required materials in advance.
LCP7: I frequently skip classes.
LCP8: I don't attend classes because the study materials are available on the website.
LCP9: I often seek advice from my instructors.
LCP10: I am diligent in my studies at school.
University facilities (UF)
UF1: The classrooms provide a high-quality learning environment.
www.ijcrsee.com
437
Mai Cam Binh et al. (2023). Factors Inuencing Students’ Dropout Intention in Ho Chi Minh City, International Journal of
Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 417-437.
UF2: The library is spacious, well-ventilated, and oers a diverse range of study materi-
als.
UF3: The information technology system meets my usage needs.
UF4: The classrooms are very spacious.
UF5: The class schedule is convenient for me.
Cultural and social environment (CSE)
CSE1: The facilities of the university meet my social needs.
CSE2: The facilities of the university are suitable for my religious/cultural needs.
CSE3: I am sensitive to students from dierent cultural backgrounds.
CSE4: I appreciate the physical facilities and environment of the university campus.
CSE5: I feel a sense of belonging to the university community.
CSE6: I sometimes feel lonely in the university.
CSE7: I nd it easy to commute to the university.
Access to support from academic advisors (ASA)
ASA1: I receive good advice from the university regarding career choices.
ASA2: I receive good advice from a career counselor in choosing a profession for my-
self.
ASA3: I receive good advice from the university regarding career choices.
ASA4: I easily receive assistance when needed from the management team.
ASA5: The management team is always ready to assist when I need them.
ASA6: The sta at the university are often sensitive to the personal needs of students.
ASA7: Having an advisor at the university is very helpful.
Personal circumstances (PC)
PC1: I worry about my mental health.
PC2: I worry about my physical health.
PC3: I often feel homesick.
PC4: I worry about the accumulating debt while studying at university.
PC5: I have nancial issues.
