CHAPTER 4. RESULTS AND DISCUSSION
4.1 Introduction
In Chapter 3, the author presented the research process, the measurement scales for
research concepts, the research design, and the methodology. This chapter also
elaborates on the descriptive statistics of the research data, the validation of the research
model, checking of the structural model, and the discussion of the research results. The
research model validation section focuses on assessing the reliability of the
measurement scales, evaluating convergent validity, and examining discriminant
validity. Meanwhile, the structural model validation section addresses key aspects such
as multicollinearity assessment, hypothesis testing, evaluation of the adjusted R²
coefficient, assessment of the F² effect size and testing for bias caused by the research
method.
4.2 Descriptive statistics
The data was collected by the author through Google Form questionnaire, resulting in
147 responses. Of these, 144 valid responses were retained, the author excluded 3
responses due to issues such as incomplete answers or inconsistencies in responses to
reverse-coded questions. Therefore, 144 responses were used to analyze the research
findings.
Through qualitative data on the respondents, the author uses Microsoft Excel software
to analyze data including demographic factors to help leaders have a more general view
of the characteristics of these factors for the study. Factors include: (1) Gender, (2) Age,
(3) Major.
Among the 144 survey participants, who were students for the most of part, the
respondents were predominantly females (99), comprising 68.8%. Meanwhile, the
participation of men was at 31.3% (45). More than half of the total survey sample
consisted of female respondents. This is evidenced by the fact that in universities and
colleges especially in the field of economics in general, female students are usually
more numerous than male students.
Table 4.1. Descriptive Statistics on the Number of Students by Gender
Frequency
Rate (%)
Female
99
68,8%
Male
45
31,3%
Total
144
100%
The study focused on people aged 18 to 20 (75 students), accounting for 52,1%, and
those aged 21 to 23 (61 students), accounting for 42,4%. Objectively, since the main
research subjects are students, the age range will be mainly from 18 to 23. This is also
the age group that has contact with AI and has the purpose of using AI, so the survey
results show that students from 18 to 20 and 21 to 23 have useful perceptions in using
AI.
Table 4.2. Descriptive Statistics on the Number of Students by
Frequency
Rate (%)
From 18 to 20
75
52,1%
From 21 to 23
61
42,4%
From 24 and above
8
5,6%
Total
144
100%
In terms of research fields, students from the Economics and Management majors
accounted for the majority (69.4%). This may be due to the larger enrollments
typically seen in economics and management majors at universities. We also collected
samples from five different majors. This indicates that there is diversity in the use of
artificial intelligence across current majors.
Table 4.3. Descriptive Statistics on the Number of Students by their Majors
Major
Frequency
Rate (%)
Natural Sciences
5
3,5%
Economics - Administration
100
69,4%
Social Sciences and Humanities
10
6,9%
Health Sciences
5
3,5%
Engineering - Technology
24
16,7%
Total
144
100%
4.3 Result research model
This section evaluates the research model by systematically analyzing the reliability, validity,
and overall quality of the measurement and structural models. This method is based on
established criteria and frameworks, such as Hair et al. (2016), to ensure the reliability and
relevance of the results.
The analysis begins with assessing the reliability of the scales used, including metrics such as
Cronbach's Alpha and composite reliability. This is followed by assessing convergent
validity, using average variance extracted (AVE) and factor loadings. Furthermore,
discriminant validity is examined through methods such as Heterotrait-Monotrait (HTMT)
ratio and Fornell-Larcker criteria.
The goal of this comprehensive assessment is to verify the robustness of the measurement
model and confirm the hypothesized relationships between the constructs in the structural
model. These results not only contribute to validating the research model but also provide
valuable insights into the relationships between the variables under study.
Figure 4.1 Adjusted measurement model
Source: Author built with Smart PLS software
4.3.1 Reliability of the scale
The results show that all scales have reliability coefficients > 0.7 and alpha coefficients
ranging from 0.705 to 0.919.
According to Henseler and Sarstedt (2013), a composite reliability of 0.7 or greater is
considered good for a model with the purpose of confirming the relationship between
variables, and 0.80 or greater is considered good for confirmatory research (Daskalakis
& Mantas 2008). Also according to Hair Jr. et al. (2017), a reliability of 0.6 to 0.7 is
acceptable for exploratory research, 0.7 to 0.9 is appropriate, and if higher than 0.95 is
inappropriate.
Cronbach’s
alpha
Composite
reliability
Composite
reliability
Average
variance
(rho_a)
Attitude
(rho_c)
extracted (AVE)
0.705
0.733
0.870
0.770
positive
0.919
0.971
0.941
0.801
Perceived ease of use
0.861
0.864
0.906
0.707
Perceived usefulness
0.876
0.876
0.915
0.729
Subjective norm
0.764
0.849
0.855
0.665
Lecture
response
Figure 4. : Construct reliability and validity
4.3.2 Convergence value assessment
Factor loadings of observed variables and average variance extracted are used to assess
convergent validity for outcome-type scales.
For assessing the convergent validity of latent variables based on the outer loadings and
AVE indices, an outer loading of a variable is considered ideal if it is > 0.7, while a
range between 0.4 and 0.7 should be considered before deletion (Reinartz, Haenlein,
and Henseler 2009). According to Hair et al. (2016), to evaluate the convergent validity
of the scale, it is necessary to consider the average variance extracted (AVE). He also
stated that the scale achieves convergent validity when AVE is greater than 0.5.
According to the results from the table, the outer loading coefficients of the observed
variables all meet the conditions from 0.693 to 1.000. Therefore, all factors have good
reliability and convergent validity.
Attitude
ATT3
0.846
ATT4
0.908
Age
Gender
LPR1
Age
Gender
Lecture
positive
response
1.000
1.000
0.936
Major
Perceived Perceived Subjective
ease of use usefulness
norm
LPR2
0.889
LPR3
0.858
LPR4
0.896
Major
1.000
PEOU1
0.863
PEOU2
0.817
PEOU3
0.826
PEOU4
0.855
PU1
0.886
PU2
0.851
PU3
0.838
PU4
0.839
SN1
0.858
SN2
0.883
SN3
0.693
Figure 4. : Convergent Value Assessment result
4.3.3 Evaluation of discriminant value
To evaluate the discriminant value when using PLS-SEM, the HTMT criterion
(Heterotrai t- monotrait ratio) is the first criterion to consider, then evaluate the other
criteria.
Criteria for evaluating discriminant value include
- HTMT criterion
- Fornell-Larcker criterion
According to Garson (2016), the discriminant validity between two related variables is
demonstrated when the HTMT index is less than 1. In addition, Henseler et al. (2015)
suggested that the HTMT value should be less than 0.9. The results after running the
data show that the HTMT index values of the factors are all less than 0.9. Thus, the
discriminant value of the model is satisfied.
Attitude
Age
Gender
Lecture
positive
response
Major
Perceived
ease of
use
Perceived Subjective
usefulness
norm
ATT
Age
0.023
Gender
0.096
0.002
LPR
0.507
0.057
0.187
Major
0.091
0.153
0.227
0.048
PEOU
0.689
0.087
0.076
0.314
0.131
PU
0.830
0.081
0.078
0.193
0.141
0.708
SN
0.827
0.051
0.155
0.794
0.171
0.543
0.551
Figure 4.: Discriminant Validity Assessment Based on HTMT Criterion
According to the results of the Fornell and Larcker conditional region, all square roots
of AVE have coefficients higher than 0.5 (ranging from -0.155 to 1.000 ) meeting the
requirements.
In each factor, the square roots of AVE have values higher than the correlation
coefficients of other factors in the same column. Therefore, all factors have discriminant
validity.
Attitude
Age
Gender
Lecture
positive
response
Major
ATT
0.887
Age
-0.005
1.000
Gender
0.080
0.002
1.000
LPR
0.415
-0.060
0.172
0.895
Major
-0.078
0.153
-0.227
-0.052
1.000
PEOU
0.551
0.081
0.073
0.299
-0.123
Perceived
ease of
use
0.841
Perceived Subjective
usefulness
norm
PU
0.661
0.075
0.060
0.185
-0.132
0.617
0.854
SN
0.622
-0.045
0.136
0.590
-0.155
0.447
0.495
Figure 4. : Fornell - Larcker criterion
4.4 Checking the structural model
4.4.1. Multicollinear evaluation
Multicollinearity poses a threat to the accurate identification and efficient estimation of
structural correlations that are commonly sought using regression techniques (DE
Farrar, RR Glauber, 1967). VIF <2 indicates no multicollinearity, VIF between 2 and 5
indicates moderately correlated variables, and VIF greater than 5 indicates highly
correlated variables, per Hair et al. (2009). The likelihood of multicollinearity increases
with VIF, necessitating additional study. There is substantial multicollinearity that
requires correction when VIF exceeds 10. The results of the Multicollinearity test of the
structural model show that the range is from 1,000 to 3,871, of which 10 variables are
greater than 2, namely PEOU3 PU4, PEOU1, PEOU4, LPR3, PU2, PU1, LPR4, LPR1,
LPR2 with the numbers 2.059; 2.099; 2.185; 2.249; 2.310; 2.447; 2.886; 3.743; 3.863;
3.871, respectively, so the authors accept
the above results.
ATT3
1,421
ATT4
1,421
Age
1,000
Gender
1,000
LPR1
3,863
LPR2
3,871
LPR3
2,310
LPR4
3,743
Major
1,000
PEOU1
2,185
PEOU2
1,859
PEOU3
2,059
PEOU4
2,249
0.816
PU1
2,886
PU2
2,447
PU3
1,992
PU4
2,099
SN1
1,762
SN2
1,522
SN3
1,488
Table 4.5: Multicollinear evaluation
Source: Calculation on SmartPLS software
4.4.2. Testing of research hypothesis
In this study, the bootstrapping technique in SmartPLS is used to examine the
constructs' direct relationships. To examine the statistical significance of the previously
described effects, a bootstrapping test using 5000 oversamples from the original 144
sample is utilized. Figure 4.x and Table 4.x highlight the path coefficients and show
how the structural model variables affect the structural model test results. Attitudes
positively influence perceived usefulness. The bootstrapping test yielded the following
results:
H1: Attitudes have a positive impact on Perceived usefulness
With the initial sample size of 0.438 and after running the bootstrapping test is 0.431
and with T statistics= 5.137> 1.96, P values = 0.000< 0.05 showing statistical
significance. Therefore, the author accept hypothesis H1 (Table 4.x).
H2: Age has a positive impact on perceived usefulness
With the initial sample size of 0.053 and after running the bootstrapping test is 0.052
and with T statistics= 0.906< 1.96, P values = 0.365> 0.05 showing statistical
significance. Therefore, the authors reject hypothesis H2 (Table 4.x).
H3: Gender has a positive impact on perceived usefulness
With the initial sample size of 0.000 and after running the bootstrapping test is -0.023
and with T statistics= 0.002< 1.96, P values = 0.998> 0.05 showing statistical
significance. Therefore, the authors reject hypothesis H3 (Table 4.x).
H4: Lecturer positive response has a positive impact on perceived usefulness
With the initial sample size of -0.209 and after running the bootstrapping test is -0.186
and with T statistics= 2.548> 1.96, P values = 0.011< 0.05 showing statistical
significance. Therefore, the author accept hypothesis H5 (Table 4.x).
H5: Major has a positive impact on perceived usefulness
Age
Gender
Experience
Attitude
0,438
Perceived
Ease of
0,053
0
-0,045
0,344
Perceived
Usefulness
-0,209
Lecturer
positive
0,187
Subjective
norm
Figure 4.x: Structure model
With the initial sample size of -0.045 and after running the bootstrapping test is -0.050
and with T statistics= 0.684< 1.96, P values = 0.494> 0.05 showing statistical
significance. Therefore, the authors reject hypothesis H5 (Table 4.x).
H6: Perceived ease of use has a positive impact on perceived usefulness
With the initial sample size of 0.344 and after running the bootstrapping test is 0.336
and with T statistics= 4.368> 1.96, P values = 0.000< 0.05 showing statistical
significance. Therefore, the author accept hypothesis H6 (Table 4.x).
H7: Subjective norms have a positive impact on perceived usefulness
With the initial sample size of 0.187 and after running the bootstrapping test is 0. 181
and with T statistics= 1.874< 1.96, P values = 0.061> 0.05 showing statistical
significance. Therefore, the authors reject hypothesis H7 (Table 4.x).
Stand T
ard statis
Origina
devia tics
l
tion (|O/S Path
Hypothesis
Relations sample Sample
(STD TDE Coeffici
Testing
hip
(O)
mean (M) EV) V|)
ent
P values Result
No.
Hypothesis
1
Attitudes
have
a
positive impact on ATT
perceived usefulness PU
0,438
0,431
0,085 5,137 0,438
0,000
Accept
2
Age has a positive
impact on perceived
usefulness
Age -> PU 0,053
0,052
0,058 0,906 0,053
0,365
Reject
3
Gender has a positive
impact on perceived Gender ->
usefulness
PU
0,000
-0,023
0,168 0,002 0
0,998
Reject
4
Lecturer
positive
response has a positive
impact on perceived LPR
usefulness
PU
-0,209
-0,186
0,082 2,548 -0,209
0,011
Accept
5
Major has a positive
impact on perceived Major
usefulness
PU
-0,045
-0,050
0,066 0,684 -0,045
0,494
Reject
6
Perceived ease of use
has a positive impact
on
perceived PEOU ->
usefulness
PU
0,344
0,336
0,079 4,368 0,344
0,000
Accept
7
Subjective norms have
a positive impact on
perceived usefulness SN -> PU 0,187
0,181
0,100 1,874 0,187
0,061
Reject
->
->
->
Table 4.x: Results of testing hypotheses
Source: Calculation on SmartPLS software
4.4.3 Evaluation of adjusted coefficient of determination R2
The R2 and adjusted R2 are important indicators, ranging from 0 to 1. If R2 approaches
1, the independent variables explain more of the variance in the dependent variables.
Conversely, if R2 approaches zero, the independent variables explain less of the
variance in the dependent variables. There is no exact standard for which the R2 value
makes the model acceptable.
Note that a regression model with a high R2 does not always indicate a high research
value, nor does a model with a low R2 indicate a low research value. The fit of the
regression model did not have a causal relationship with the study value. In repeated
research, we often choose an intermediate level of 0.5 to differentiate between strong
and weak significance, and we expect that an R² value from 0.5 to 1 indicates a good
model, while an R2 value less than 0.5 indicates a suboptimal model.
PU
R2
Adjusted R2
0.565
0.552
Table 4.x. Evaluation of R2 and Adjusted R2 Levels
The table shows that the coefficient of determination (R²) for Perceived Usefulness
reaches 56.5%, and the adjusted R² is 0.552 (> 50%). Thus, it can be concluded that the
independent variables in the research model explain 55.2% of the variance in the
dependent variable, or in other words, 55.2% of the variation in the dependent variable
is explained by the independent variables, with the remaining 44.8% being explained
by other variables outside the model and random errors.
4.4.4 Evaluation of the impact factor f ²
Exogenous variables
Endogenous variables
f2 coefficient
ATT
PU
0.228
Age
PU
0.006
Gender
PU
0.000
LPR
PU
0.064
Major
PU
0.004
PEOU
PU
0.181
SN
PU
0.037
Table 4.x. Results of f ² Coefficient Analysis
Cohen (1988) proposed a table for interpreting f² coefficients to assess the importance
of independent variables as follows:
● f ² < 0.02: Effect size is extremely small or negligible.
● 0.02 ≤ f ² < 0.15: Small effect size.
● 0.15 ≤ f ² < 0.35: Medium effect size.
● f ² ≥ 0.35: Large effect size.
The analysis presented in the table indicates that the variables Age, Gender, and Major
exhibit f² coefficients of 0.006, 0.000, and 0.004, respectively. These values suggest
that none of these variables exert a significant influence on the endogenous variable, as
all fall below the threshold of 0.02. In contrast, the variables LPR and SN demonstrate
small effects on the Perceived Usefulness (PU) of the system, with f² values ranging
from 0.02 to 0.15 and associated coefficients of 0.064 and 0.037, respectively.
Moreover, the remaining variables, ATT and PEOU, show medium effects on PU, as
indicated by their f² coefficients of 0.228 and 0.181, both of which are situated within
the range of 0.15 to 0.35.
This highlights the more pronounced influence that ATT and PEOU have on PU
compared to the other variables examined. Therefore, these results indicate that the
major predictors in forecasting are ATT and PEOU for predicting PU. Overall, the
predictive capability of the structural model used in this study was adequate.
4.5. Discussing research results
The research findings provide critical insights into the hypotheses tested in this study.
Out of the total seven hypotheses, four were accepted, and three were rejected. These
results not only corroborate some established findings in prior research but also
contribute new perspectives to the academic discourse.
Attitudes positively impact Perceived Usefulness. This result supports existing theories,
such as the Technology Acceptance Model (TAM), which highlight the pivotal role of
attitudes in shaping perceptions of usefulness. A favorable attitude towards the system
enhances its perceived utility, emphasizing the importance of fostering positive user
experiences.
Lecturer Positive Responses positively affect Perceived Usefulness. The study confirms
that supportive feedback from lecturers significantly impacts students’ perceived
usefulness. This aligns with pedagogical research, which underscores the importance of
a positive learning environment and guidance in improving the perceived value of
academic tools and processes.
Perceived Ease of Use positively influences Perceived Usefulness. Consistent with prior
studies (e.g., Davis, 1989), ease of use was shown to be a significant determinant of
perceived usefulness. This finding highlights the necessity of designing user-friendly
and accessible systems to encourage acceptance and engagement.
Subjective Norm does not significantly impact Perceived Usefulness. While subjective
norm have often been linked to behavioral outcomes in previous research (e.g., Diekhoff
et al., 1996), this study did not observe a significant relationship. A possible explanation
is that peer influence might be less impactful in individual decision-making contexts or
due to the specific nature of the research sample.
The hypothesis that a student’s major significantly influences Perceived Usefulness was
rejected. This finding implies that the evaluation of perceived usefulness transcends
academic disciplines, as students from diverse fields may prioritize similar factors such
as functionality, ease of use, and relevance to personal or professional goals. This
universality in perception may be attributed to the cross-disciplinary nature of
technological tools, which are often designed to cater to a broad user base regardless of
their academic background. Furthermore, the increasing integration of technology
across various fields of study could minimize the impact of disciplinary differences on
perceived usefulness. Future studies could investigate whether the role of major
becomes more prominent in contexts involving highly specialized tools or domainspecific applications.
Age does not significantly impact Perceived Usefulness. The hypothesis that age
positively affects perceived usefulness was rejected. This outcome might be explained
by the homogeneous age distribution of the sample, which predominantly consisted of
young adults, limiting the variability necessary to observe such effects.
Gender does not significantly impact Perceived Usefulness. Contrary to some earlier
findings suggesting gender-based differences in technology adoption, this study did not
find significant differences. This result could reflect evolving gender norms and a
growing universal familiarity with technological tools among both genders.
The adjusted R² value of 55.2% indicates that the independent variables explain a
substantial portion of the variance in perceived usefulness. Furthermore, the f² analysis
highlights that attitude and perceived ease of use have the most substantial influence,
with medium effect sizes, while variables like age, gender, and major exhibited
negligible effects.
These findings emphasize the importance of designing systems that prioritize user
experience and ease of interaction while fostering positive perceptions. Future studies
should explore broader demographic samples and include additional variables, such as
cultural or environmental factors, to expand on these findings and further validate the
rejected hypotheses.
CHAPTER 4 SUMMARY