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Assessing Students' Satisfaction with AI Tools in Higher Education
Siti Haslini Zakaria
*
., Nik Nur Amiza Nik Ismail., Mardhiyah Ismail., Nurul Hafiza Ismail., Fadila
Normahia Abd Manaf
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Kelantan, Malaysia
DOI:
https://dx.doi.org/10.47772/IJRISS.2025.910000022
Received: 26 September 2025; Accepted: 01 October 2025; Published: 01 November 2025
ABSTRACT
Nowadays, AI-powered applications are increasingly integrated into academic fields, and numerous studies
have discussed the acceptance of this technology among higher education students. With AI worldwide
establishment, empirical research remains necessary to evaluate user satisfaction, effectiveness, and long-term
sustainability. Since cultural, social, and economic factors influence how AI is implemented in education, the
level of acceptance of AI tools among students also may vary from one country to another. This study aims to
explore university students' satisfaction with AI tools in the context of higher education in Malaysia,
specifically in Kelantan. This study examines how satisfied students are with AI technologies used for their
learning, with an emphasis on emotional well-being, content quality, and perceived utility of the tools. Using a
cross-sectional approach, 105 undergraduate students from various faculties at Universiti Teknologi MARA
(UiTM) Kelantan were selected to participate in the study. Students were given a self-administered
questionnaire using Google Forms to obtain the data. Simple random sampling was used in the study, and the
data analysis was conducted using Multiple Linear Regression (MLR). The findings showed that the only
significant variables influencing students' satisfaction with AI tools in their education are emotional well-being
and perceived utility, while the quality of the content is not statistically significant. The findings show that how
students feel when using AI tools together with their perception of the tool’s benefit is crucial despite the
content itself. The results indicate that, to drive user satisfaction and long-term usage of this technology,
developers may prioritize usability, perceived benefits, and emotional engagement rather than solely enhancing
algorithmic reliability or refining instructional content.
Keywords: artificial intelligence tools, student satisfaction, emotional well-being, perceived utility, higher
education.
INTRODUCTION
Beginning in the 1950s, the idea of computers producing content marked the start of a remarkable development
in Artificial Intelligence (AI) [1]. In some of the early attempts, computers were used to imitate human
creativity through producing music and visual art that was radically unrealistic and distinct from humans [2].
For generated content to reach a high level of realism, years and major advances in AI were required.
Nowadays, AI tools for education are currently among the most widely used tools by numerous parties. The
way researchers, teachers, and students interact with educational content has been profoundly altered by the
quick adoption of AI technologies in academic contexts. These technologies, which range from personalized
learning platforms to AI-powered writing assistance and intelligent tutoring systems, provide previously
unattainable chances to improve academic performance. However, beyond their practical advantages, there is
growing demand for studying how these tools affect users' emotional well-being, the quality of academic
material, and their perceived usefulness in accomplishing learning objectives.
The impact of AI tools on students' emotions, feelings, confidence, and general affective state throughout
interaction with AI is all incorporated into the concept of emotional well-being. Tools that offer clear, helpful,
and less cognitively demanding content can reduce negative feelings and increase satisfaction. According to
Almufarreh, students who used these technologies and saw an improvement in their emotional well-being were
likely more satisfied [3]. Alsaiari et al. found that the feedback that was enhanced with motivational or
emotional content was seen far more favorably implying the significance of the factor as an indicator with the
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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tool’s satisfaction [4].
Numerous recent research has demonstrated that one of the main factors influencing user satisfaction with AI
technologies in education is content quality. It is believed that AI's capability to modify the content besides
pacing the needs of use is an essential factor that boosts both engagement and productivity. Content quality
refers to aspects such as relevance, accuracy, clarity, freshness, depth, and appropriateness of the content
generated by or mediated through AI tools. A mismatch between content quality and learners’ expectations,
like poorly aligned material, can affect user satisfaction. In study at King Faisal University, information quality
emerged as a strong predictor of satisfaction with ChatGPT for research tasks, confirming the importance of
content being accurate, relevant, and useful [5]. For many students, if the AI tool is seen as directly helpful in
preparing assignments, clarifying difficult concepts, or giving feedback, the satisfaction with the tools tends to
increase. Research into ChatGPT usage revealed that perceived usefulness strongly influenced how beneficial
students feel the tool is, which in turn boosts their overall satisfaction [6]. Another study of 328 college
students has demonstrated that variables like perceived ease of use, compatibility, efficiency, and perceived
usefulness affect user’s satisfaction and willingness to keep using ChatGPT [7].
Furthermore, research from an established Chinese university showed that although students appreciated AI
technologies for growing support and efficiency, they also raised concerns about the reliability of the content
information and highlighted the importance of emotional support and personalization [8]. Collectively, these
results imply that overall satisfaction with AI technologies for academic use incorporates more than just
functionality or accessibility but also the emotional and qualitative experience. Almufarreh [3] has used a two-
stage method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network
(ANN) to measure the satisfaction with AI tools among Saudi Arabian universities. The result demonstrated
that emotional well-being is the most critical factor in user satisfaction. However, the findings might restrict
their wide applicability to a certain demographic and cultural setting.
Although the acceptance of AI technology has been the subject of many studies, the findings might vary in
each country due to digital literacy, infrastructure, educational traditions, and trust in technology.
Understanding satisfaction with the tools ensures that technological innovation genuinely delivers significant
educational results. To determine whether AI technologies are empowering students, continuous study should
be done to help track students' views over time to ensure that these tools remain relevant. In addition, studies
on AI tool satisfaction must be grounded in real student feedback to provide evidence-based guidance for
governments and institutions. Here, in the context of the Malaysia region, satisfaction with AI tools in
education has been analysed among 105 students from UiTM Kelantan Branch. Regression analysis is then
used to analyze the data. The purpose of this study is to investigate the overall satisfaction of academic users
with AI tools besides giving additional insight into how these technologies serve as valuable academic utilities,
influence users' emotional well-being, and meet expectations for content quality. Besides, this study can
contribute to the expanding body of knowledge regarding the acceptability of AI tools in Malaysia, as adoption
factors can vary across regions. This alone can provide additional empirical data on the adoption of technology
in education by comparing the findings. The results of this study will provide a thorough and detailed
understanding of how much university students rely on AI tools for their academic work. By examining usage
trends, the uses of AI tools, and the perceived advantages and difficulties of using them, this study can show
how embedded AI technologies have become in students' educational experiences. Additionally, the findings
may assist Malaysian policymakers, businesses, professional development programs, and the government in
developing tools and strategies for incorporating AI into the educational system that meet the student needs
and cultural expectations.
LITERATURE REVIEW
Satisfaction with AI Tools
The rise of AI technology drove widespread interest in the rapid global recognition of these tools. Numerous
previous research has examined how satisfied and accepted users of AI tools are [9]. Past studies analyzed the
technology acceptance model theory, which enables the prediction of users' intentions and behaviors. User
satisfaction is an essential performance indicator in the educational technology field, particularly when
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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measuring the effectiveness and acceptance of platforms, apps, and tools. Satisfaction is typically measured
using both quantitative and qualitative methods across students, educators, administrators, and many more. The
satisfaction in AI tools includes usefulness, trust, transparency, engagement, accuracy, and emotional response
while using the tools. According to [10], user experience and trust were influential on user satisfaction and
played partial mediating roles between predictors and user satisfaction. The studies included the determinants
of ChatGPT adoption among university students and its impact on learning satisfaction.
There are many factors influencing satisfaction [5]. User satisfaction with AI tools is influenced by a
multifaceted interplay of factors that collectively shape the user experience. Accuracy is important because
accuracy and reliable precise information enhance user trust as well as reinforce the perceived utility of the
tools. A study by Xing and Jiang [11], for example, found that accuracy is a crucial factor in determining user
satisfaction in AI chat systems, emphasizing its indirect impact through perceived utility. Likewise, usefulness
has a direct impact on the system's perceived value. This is due to users are more likely to employ AI
technologies when they find them useful for completing tasks. In addition, lowering cognitive effort and
complexity increases user satisfaction, which is in line with Davis's (1989) Technology Acceptance Model,
(TAM) which believes that user acceptance is significantly influenced by perceived ease of use.
According to recent research by Cetinkaya and Krämer [12], user satisfaction is strongly correlated with
ethical AI concepts like transparency and fairness. The findings by Glassberg et al. [13] show that transparency
and well-designed visuals of the tools are important mediators that affect user confidence in AI-powered
digital adoption. According to [14], user satisfaction and trust in AI tools are augmented by the tool's
performance expectancy. However, concerns about privacy and security affected user comfort and ethical trust.
Phua et al. [15] highlighted the need of addressing security and privacy concerns to enhance student
acceptances toward AI tools, which are crucial for their continuous use. Long-term engagement is believed to
increase motivation and retention, thus encouraging consistent use of the tools. According to [7], engagement
has a major impact on students' continuous use of ChatGPT in higher education. Furthermore, many
researchers have adopted technologies due to their ability to support self-regulated learning have changed and
motivated AI as a research assistant [16]. Together, these factors work together to determine how satisfied
people are with AI technologies in general.
Emotional Wellbeing
Emotional well-being has a substantial factor in people's satisfaction with AI tools. People's perceptions of the
threats and benefits of AI, their willingness to interact with AI, and their ability to give honest and unbiased
responses regarding the tools are all greatly influenced by this factor. It plays a major role in determining how
users interact with AI in education, especially in terms of their learning experience, tool usage, and
engagement. Feelings of competence additionally affect the relationship between AI and well-being by
influencing how people respond to inquiries regarding AI [17]. To conclude, learning systems use emotional
well-being as a feedback loop regarding something.
The emotional well-being of students is a top priority of administrators, policymakers, and scholars [18]. It is
positively influenced when users find the tools can boost confidence, motivation, and support. While
emotionally supportive AI designs can enhance engagement, learning results, and overall user satisfaction,
poor user experiences lead to frustration and reduced tool usage. AI tools may elicit user dissatisfaction and
disengagement when their use leads to psychological discomfort, including anxiety, stress, or confusion [19].
This is due to users being satisfied with the tools whenever they provide interactions that feel safe,
nonjudgmental, and empowering, besides helping users feel less anxious about their tasks [20]. Other forms of
psychological discomfort include when users perceive the interaction as judgmental, emotionally
disconnecting, or undermining their personal autonomy and control over the task or process at hand [21, 22].
Negative experiences of AI tools were associated with the perception of threat extended to AI technologies,
regarded as a threat to several aspects of human life, including jobs, resources, identity, uniqueness, and value
[23].
Students experiencing emotional distress struggle to stay focused and engaged with AI-supported learning
environments, which negatively affects their overall experience and satisfaction [24]. However, a positive
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emotional state can influence academic engagement, leading to higher satisfaction due to its ability to enhance
a user's perception of the AI's usefulness, making the user more likely to find the tool valuable and satisfactory.
When users feel emotionally secure and supported, they are more likely to be motivated and engaged with AI
tools, leading to better interaction and higher satisfaction [25]. As stated by Choudhry et al. [26], students who
are emotionally good are better able to concentrate and cooperate with AI tools, which enhances learning
outcomes and overall satisfaction.
On the other hand, negative emotions like depression or anxiety can lead to disengagement. AI tools may be
used as an escape by people who are struggling emotionally, which might result in dependence and a vicious
cycle that eventually undermines both well-being and tools satisfaction [27]. Relationships between social
anxiety, learning adaptability, AI tools usage, and behavioral problems among primary school students have
been studied [2]. High levels of AI-related stress or anxiety might result in negative views of the technology
and an inability to give useful or adequate responses. In addition, negative feelings may result from uncertainty
and anxiety about future employment opportunities brought on by AI advancements [28].
Nonetheless, users' responses indicate that perceived competence with AI influences people's attitude and well-
being. As AI technology continues to develop, addressing public concerns and controlling its application is
necessary for the benefit of society. Therefore, it can also be argued regarding generative AI that satisfaction
and adaptability of generative AI will be closely associated with the development of emotional well-being
[29]. Surveys about AI may receive more thorough and useful answers from those with positive emotional
states and have higher job satisfaction who are engaged with AI technologies more. Earlier research has shown
that people who are emotionally stable are more likely to be satisfied with their lives [30]. A positive
emotional state is frequently associated with a belief in the efficiency of AI, which improves job satisfaction
and productivity, thus, influencing how people respond to answers regarding the advantages of AI [31].
Content Quality
Other key factors that contribute to the student’s satisfaction in using AI tools are the quality of content
produced. The content quality of AI tools depends on their authenticity, accuracy, legitimacy, and relevancy
[32]. Among the many AI tools available on the current market, educational chatbots produce high-quality,
personalized, and real-time feedback [33]. This tool is well-received since it is easy to use, the content
produced is up to date, and it can support students regardless of their background, abilities, and needs.
To enhance users’ satisfaction, especially in education, AI tools should focus on prioritizing the quality of
users’ experience when handling the tools, accuracy of the information produced, and have an interactive
engagement platform [34]. Of all AI tools available, the most common and popular AI tools and platforms
used by students at all levels are ChatGPT and Grammarly [35, 36]. These two tools were mainly used as
grammar-checking tools and research information retrieval. Students value these AI platforms as they can
simplify complex content, enhance personalized learning, improving writing quality, and optimize time [37,
38, 39]. Another key determinant of student satisfaction with the content quality produced by AI tools depends
on its clarity and comprehensibility. Its ability to simplify the explanation of the concept used using clear
language and visuals plays an important role for students’ satisfaction. Students reported greater satisfaction
and perceived the AI tool as more useful, accessible, and effective than traditional resources [3]. Learners in
STEM fields often report higher satisfaction, as AI tools can explain complex concepts or provide problem-
solving support [40].
Quality of information produced by AI tools was vast from real-world applications, case studies to hands-on
practice. Relevant material produced by AI increases engagement and satisfaction since it offers more
engaging and personalized content to its target audiences [41]. It also plays an important role in fostering
student engagement as it has personalized tutoring quality, good technical assistance, relevant content
generation [42] and hence, enhances the efficiency of learning.
There are mixed findings from other research on the relationship between the content quality of AI tool and
student satisfaction. Content quality was reported as equally significant as perceived utility in predicting the
outcome of satisfaction [3]. High satisfaction reported by [43], where students are very satisfied with the
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effectiveness of AI tools, emphasizing that AI tools can address academic queries and generate quality content.
Similar result obtained by [44] where the AI tool gave positive impact in students’ learning and students are
highly satisfied with the engaging and interactive academic content produced. However, contrasting evidence
shows the level of satisfaction with adopting AI educational applications is decreasing despite moderate
comfort with the technology [45]. This study concludes that there are other important factors other than
content quality that influence students' acceptance of AI tools.
Perceived Utility (usefulness quality, ease of use, and effectiveness)
Perceived utility refers to a user's belief that using a tool will help them achieve their goals (helping in
learning, improving performance, saving time). In educational settings, this is crucial because students are
more likely to be satisfied with tools they find genuinely helpful for understanding complex topics, completing
assignments faster or more effectively, getting personalized and instant feedback, and enhancing their learning
experience. If students believe an AI tool added value to their academic experience, satisfaction levels are
likely to increase.
Three components highly stimulate the perceived utility of AI tools, which are their usefulness, ease of use,
and effectiveness. In education, if AI tools can give support in the teaching and learning process and promote
effective learning outcomes, the tools will be highly accepted [46]. Based on the report by Louly [47], the use
of AI was proven to be practical to enhance students’ academic performance. From the experience using the
AI tools, the platforms used are tailored to the students’ needs, provide a personalized learning experience, and
can adjust content delivery in real time to achieve learning objectives.
Much research on AI tools proved that perceived utility will significantly impact students’ satisfaction. The
intelligent feedback mechanism and personalized experience obtained from AI tools greatly improve students’
engagement and achievement in the learning process and hence increase their satisfaction [48]. AI tools can
enhance users’ satisfaction in education via their personalization and instant feedback mechanism. 74% of
students reported that they received tailored content [49], 72.5% of students rated AI personalization
experience highly, and 80% found the AI tools very helpful from the instant feedback received [50]. Perceived
usefulness and ease of use positively influenced students’ satisfaction when handling ChatGPT, as reported by
[7]. Since the medium used is easy to handle, it will eventually increase the learning satisfaction. Based on the
findings obtained, when students perceive AI tools as useful and effective, their satisfaction with this
educational aid will increase substantially.
METHODOLOGY
Study Design and Sampling Technique
A cross-sectional study has been applied for this study with data collection over three (3) weeks. This study
was conducted at the Universiti Teknologi MARA (UiTM), Kelantan Branch. In this study, convenience
sampling was used to select respondents. The reason for choosing this method is due to low cost and ease of
use. Therefore, a total of 105 respondents participated in this study. This study was conducted to assess
students’ satisfaction with AI Tools Education in UiTM Kelantan Branch.
Data Collection Method and Research Instrument
An online survey was created through Google Forms to get the information from respondents. This method
was used due to its many benefits and applicability for this study. One of the benefits included a lower budget
requirement since this survey was sent via student’s email. A set of questionnaires was adapted from [3]. There
are two (2) main sections in this questionnaire. The first section represents Section A, which contains four (4)
items on the demographic profile of respondents, such as gender, age, semester, and faculty. While the second
section represents Section B consists of 20 items on a 5-point scale ranging from strongly disagree to strongly
agree. Strongly disagree receives a score of 1 and strongly agrees to receive a score of 5. This section assesses
the degree of agreement among respondents on four (4) variables, which are Satisfaction on AI Tools,
Emotional Wellbeing, Content Quality, and Perceived Utility.
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Table 1 Number of Measurement Items
Variables
No. of items
Per Variable
Satisfaction with AI Tools
5
Q1 – Q5
Emotional Wellbeing
5
Q6 – Q10
Content Quality
5
Q11 – Q15
Perceived Utility
5
Q16 – Q20
Figure 1 reveals the research framework of this study. The dependent variable was satisfaction with AI Tools,
while the three (3) independent variables were emotional wellbeing, content quality, and perceived utility.
Figure 1 Research Framework
RESULTS AND FINDINGS
Reliability testing
A test of reliability was conducted to check whether the respondents answered all the items with consistency or
not. According to Taber (2018) stronger dependability is indicated by values near 1.0, which is a Cronbach’s
Alpha value of 0.7 or greater is typically considered adequate. The measure of internal consistency items for
each variable in this study must exceed a minimum value of 0.7. The reliability test was carried out to confirm
the internal consistency of items for satisfaction on AI tools, emotional well-being, content quality and
perceived utility. Table 2 reveals that all the Cronbach’s Alpha values greater than 0.7 indicate that the
instruments are sufficiently reliable and consistent to be used.
Table 2 Reliability Test for All Variables
Variables
No. of items
Satisfaction with AI Tools
5
Emotional Wellbeing
5
Content Quality
5
Perceived Utility
5
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Normality Test
Assessing the normality of the data is a must when using parametric statistics such as Multiple Linear
Regression (MLR). This study used skewness and kurtosis to check the normality of data. The data is normally
distributed if the values of skewness and kurtosis are between -2 and 2 [51]. The result in Table 3 indicates that
all the variables have skewness values between -0.270 and 0.220, while for the kurtosis values between -0.555
and -0.324, which are within the acceptable range. This result implies that the data in this study are normally
distributed for all variables.
Table 3 Normality Results
Variables
Skewness
Kurtosis
Satisfaction with AI Tools
-0.270
-0.326
Emotional Wellbeing
-0.244
-0.365
Content Quality
0.220
-0.555
Perceived Utility
0.036
-0.324
Descriptive Statistics
Table 4 depicts the characteristics of the entire sample in terms of gender, age, semester, dan faculty among
selected respondents. Respondents for this study consist of 23.8% males (25 respondents) and 76.2% females
(80 respondents). The mean (standard deviation) respondent’s age is 20.35 years (1.160 years). Most of the
respondents come from semester 5 groups, which is 52.4% (55 respondents). Relating to the faculty, a slight
majority of the respondents are from KPPIM (50.5%, n = 53 respondents) and only 3.8% (4 respondents) from
FSPPP.
Table 4 Characteristics of Respondents
Item
Frequency (n = 105)
Percentage (%)
Gender
Male
Female
25
80
23.8
76.2
Age
(mean ± std. deviation)
20.35±1.160
Semester
1
2
3
4
5
6
9
2
21
4
55
12
8.6
1.9
20.0
3.8
52.4
11.4
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7
2
1.9
Faculty
ACIS
KPPIM
KPSK
FP
FSPPP
FPM
7
53
9
10
4
22
6.7
50.5
8.6
9.5
3.8
21.0
Regression Analysis
According to the correlation results between emotional well-being, content quality, perceived utility and
satisfaction on AI tools in Table 5, all the independent variables have a significant relationship with
satisfaction on AI tools. The relationship between emotional well-being and satisfaction with AI tools is 0.796
(p < 0.01), which indicates a positive relationship between these two variables. While the relationship between
content quality and satisfaction with AI tools is 0.667 (p < 0.01), it indicates that there is a positive relationship
between these variables. Similarly, the relationship between perceived utility and satisfaction on AI tools
among students is also a positive relationship, which is 0.704 (p < 0.01). Therefore, university students with
positive emotional wellbeing, content quality, and perceived utility tend to have a positive relationship of
satisfaction with AI tools.
Table 5 Correlation of Regression Analysis
Variables
Satisfaction with
AI Tools
Emotional
Wellbeing
Content
Quality
Perceived Utility
Satisfaction with AI Tools
1
0.796**
0.667**
0.704**
Emotional Wellbeing
1
0.685**
0692**
Content Quality
1
0.738**
Perceived Utility
1
Based on Table 6, there is a positive relationship (r = 0.827) between According to the correlation results
between satisfaction with AI tools and the independent variables, which are emotional well-being, content
quality, and perceived utility. The R-squared value of 0.683 indicates that 68.3% of the total variation in
satisfaction with AI tools is explained by all the independent variables. While the other 31.7% are explained by
the other factors that are not included in this study. The model is statistically significant as the F-test is 72.612
with a p-value (0.000) less than 0.05. This finding reveals that satisfaction with AI tools can be predicted by at
least using one of the independent variables in this study.
Table 6 Model Summary of Regression Analysis
Model
Sum of Squares
df
Mean Square
F
Sig.
Regression
23.225
3
7.742
72.612
0.000
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Residual
10.769
101
0.107
Total
33.994
104
R
0.827
R-Square
0.683
From the results in Table 7, there are only two (2) independent variables that are statistically significant, which
are emotional well-being (t = 6.675, p = 0.000 < α = 0.05) and perceived utility (t = 2.647, p = 0.009 < α =
0.05). While content quality is not significantly assessed to satisfaction with AI tools among students.
Therefore, the final model shows that only emotional well-being and perceived utility are significantly
assessed to satisfaction with AI tools in education among students. The equation of the final model regression
is written as:
Satisfaction on AI Tools= 0.570 + 0.524(Emotional Wellbeing) + 0.240(Perceived Utility) + ε
Table 7 Coefficient of Regression
Model
Unstandardized B
t
Sig.
Constant
0.570
2.276
0.025
Emotional Wellbeing
0.524
6.675
0.000
Content Quality
0.113
1.266
0.208
Perceived Utility
0.240
2.647
0.009
DISCUSSIONS AND CONCLUSIONS
This study is to assess the satisfaction with AI tools in education among university students. The data
collection from 105 students at UiTM Kelantan Branch was conducted through cross cross-sectional study and
it was analysed using regression analysis. The findings revealed that only emotional well-being and perceived
utility are statistically significant assess to satisfaction on AI tools in education among university students.
This finding is similar to the previous study that emotional well-being is found significantly influenced by AI
systems that can boost confidence, motivation, and sense of support [18]. There are lot of previous studies that
proved that perceived utility significantly impacts students’ satisfaction on AI tools. The intelligent feedback
mechanism and personalized experience obtained from AI tools greatly improve students’ engagement and
achievement in the learning process and hence increase their satisfaction [48].
In conclusion, this study offers a comprehensive exploration assessing satisfaction with AI tools in education
among university students. The study emphasizes the key roles of emotional well-being and perceived utility in
assessing satisfaction with AI tools in education among students. These findings are significant for theoretical
advancement, practical interventions, and policy formulation.
Limitations And Future Research Directions
Despite the contributions of the study, there are several limitations. The sample size was relatively small and
limited only to UiTM Kelantan branch, which could restrict the applicability of the findings to a broader
context. Future research can extend to a larger sample size from different universities throughout Malaysia.
Other than that, this study only assesses three (3) independent variables; future research should include other
variables that are not involved in this study.
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