INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XI November 2025
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Preliminary Study to Understand the Gen Z Student’s Behaviour
towards Adoption AI Chatbots with the Moderating Role of Gender
Using Modified UTAUT2
Noor Hafiza Mohammed, Nur Syaahidah Mohamad, Suzila Mat Salleh, Siti Fatimah Mardiah
Hamzah, Yaumee Hayati Mohamed Yusof, Sholehah Abdullah, Nor Hamiza Mohd Ghani
Faculty of Business and Management, Universiti Teknologi MARA, Cawangan Terengganu
DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100139
Received: 20 November 2025; Accepted: 27 November 2025; Published: 03 December 2025
ABSTRACT
The prompt progression of Artificial Intelligence (AI) spread to all sectors including the education sectors. The
development of AI chatbots as advanced instruments can enhance the teaching and learning in higher learning
institutions. Regardless of powerful chatbots, the effective implementation use of AI chatbots among students in
higher learning institutions depends on a composite relationship among technological, behavioural, and
demographic circumstances. Furthermore, this study focuses on the Generation Z (Gen Z) students that are
known as digital citizens born between the middle 1990s and early 2010s. The main objective of this preliminary
study is to understand the Gen Z students’ behaviour towards adoption of AI chatbots by using Unified Theory
of Acceptance and Use of Technology (UTAUT2). There are two variables added to this study. The population
for this study is the students from public and private higher learning institutions in Terengganu. Hence, this
study is using the convenience sampling technique to get the respondents. However, the sample size for this
study is 118 students based on the G-Power. The instruments for this study were conducted online and as a result,
205 respondents have completed and returned the questionnaires. The data collected is analysed using SPSS
28.00 and PLS 4.1. There are nine behavioural intentions factors and 12 hypotheses that were constructed for
this study. Nevertheless, only four were supported and the rest eight were rejected. As a result, the gender as a
moderating effect between behavioural intention and adoption use of AI chatbots was rejected. This study is
suggested to apply in other higher learning institutions to see the comparison between them. Furthermore, it is
recommended for future research to use new variables as mediating effects or new variables as moderating
effects.
Keywords: Adoption AI chatbots, UTAUT2, Students’ behaviour, Gen Z
INTRODUCTION
Nowadays, AI chatbots are tremendously implemented in higher learning institutions to enhance the process of
teaching and learning. However, the behavioural factors that influence the adoption of AI chatbots among Gen
Z students in higher learning institutions remain lack of investigations [31][32]. Gen Z can be categorised as
extraordinary digital literacy, favouring prompt interaction, and they anticipated in technology motivated
clarification [1]. A modified UTAUT2 model can replicate the adoption of AI chatbots usage in higher learning
institutions by adding two factors such as trust and technology anxiety and demonstrated to have influence on
adoption of AI technology in the past studies [2][3]. Nevertheless, past studies implemented the UTAUT2 model
to explore the adoption of technology, gender as a moderating variable that influences the behavioural factors
towards adoption of technology is rarely being considered in the study. Previous study has revealed that the
awareness of technology adoption diverges between male and female students especially on behavioural factors
such as social influence [4]. Therefore, this preliminary study is conducted to understand the Gen Z student’s
behaviour towards the adoption of AI chatbots by using the modified UTAUT2 model as well as to analyse the
moderating role of gender in this study.
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LITERATURE REVIEW
Generation Z and Technology Adaption
The first generation born utterly in the digital era is the generation Z that was born between 1997 and 2012 [6].
Gen Z are considered by their extraordinary technological knowledge, multitasking capability, and favour in
flexible-first interaction. Conversely, past research indicates that Gen Z demonstrates quicker concentration
time and superior probabilities for user experience and validity
UTAUT2 Model
This study is using the modification of original UTAUT2 [7] and extends this model by adding two more
constructs related to adoption of AI chatbots among students in higher learning institutions. Based on the
recommendation from the past studies, it is suggested to understand the alternative behavioural components of
adoption of AI chatbots that is crucial for development of chatbot for improving chatbot execution in higher
learning institutions. Therefore, the constructs used in this study are performance expectancy (PE), effort
expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), price value (PV),
and habit (HT).
PE can be defined as the level of believing using AI chatbots will improve the student performance in completing
their task and learning process in higher learning institutions. Past studies discovered PE has a positive impact
towards the intention to adopt AI in the learning process [8][9]. Besides, PE was found as the most influential
construct of a student's intention to adopt AI [9]. EE construct is known as the perceived ease of using AI
chatbots, where the user adopts chatbots more readily when they believe the system is straightforward and
involves minimal effort [10][11]. SI construct is the degree to which individuals comprehend that significant
others think that they must use AI chatbots, affecting intention and usage [10][12][15]. FC displays user’s
awareness of available resources, technical support, and infrastructure that allow efficient chatbot use [10][13].
HM portrays the pleasure, excitement, or enjoyable experience consequent from relating with AI chatbots, which
can improve adoption intentions [10][11]. Past study indicates that HM plays a strong role in behavioural
intentions to adapt technology [15]. PV measures the interchange between perceived benefits and costs when the
user feels that AI chatbots' benefits compensate for financial or effort-related costs, the adoption of AI chatbots
multiplies [10][14]. Habit indicates the level to which chatbots use becomes usual or routine, drastically
forecasting ongoing usage [10][13].
H1: The higher the behavioural intention, the higher the student’s adoption of AI chatbots in higher learning
institutions.
H2: Performance expectancy positively influences the behaviour intention to adopt AI Chatbots in higher
learning institutions.
H3: Effort expectancy positively influences the behaviour intention to adopt AI Chatbots in higher learning
institutions.
H4: Social influence positively influences the behaviour intention to adopt AI Chatbots in higher learning
institutions.
FC displays user’s awareness of available resources, technical support, and infrastructure that allow efficient
chatbot use [10][13].
H5: Facilitating conditions positively influences the behaviour intention to adopt AI Chatbots in higher learning
institutions.
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H6: Hedonic motivation positively influences the behaviour intention to adopt AI Chatbots in higher learning
institutions.
H7: Price value positively influences the behaviour intention to adopt AI Chatbots in higher learning institutions.
H8: Habit positively influences the behaviour intention to adopt AI Chatbots in higher learning institutions.
H9: Habit positively influences the behaviour intention to adopt AI Chatbots moderated by gender in higher
learning institutions.
Modified UTAUT2 Model for This Study
Therefore, the modified UTAUT2 in this study preserves PE, EE, SI, FC, HM, PV and HT, while theoretically
adding two more constructs that includes Trust and Technology Anxiety to better explain student adoption
behaviour in AI motivated circumstances.
Trust displays user’s idea that AI chatbots are trustworthy, secure, able for delivering precise assistance, making
it an essential element of adoption [16][17]. Otherwise, researchers have recommended adding new constructs
such as trust to capture emotional and ethical components [18]. Distinguished risk can be moderated by trusting
the new technologies and readily participating with new technologies such as financial services or smart homes
[34][35]. 32 studies found that the integrating trust in UTAUT2 develops expectation of user acceptance of new
technology and trust can be a mediator between UTAUT2 behavioural factors and behavioural intention [34].
TA is the worry or nervousness users feel when relating with AI directed tools, that lead to decreasing adoption
of AI chatbots [19][20]. Past studies showed that TA can be identified as moderating variable or external
variables that can reduce the behavioural intention of using new technologies. [36][37]. Technology anxiety was
commonly used to highlight the gaps in non-adoption studies especially in education and healthcare sectors
[36][37][38]. As a result, past studies recognized that the academicians with high technology anxiety were less
likely to adopt the new technologies [36].
H10: Performance expectancy positively influences the behaviour intention to adopt AI Chatbots in higher
learning institutions.
H11: Performance expectancy positively influences the behaviour intention to adopt AI Chatbots in higher
learning institutions.
Gender as a Moderating Variable
Information systems literature has extensively examined the gender differences in technology adoption. Past
studies indicated that men reveal deeper insights of performance expectancy, while community and emotional
factors are able to influence women. [21][22]. For this study, gender may moderate how users assess the
behavioural intentions used in this study. Male users are more reactive to PE and EE [12]. In contrast, female
users are persuaded by SI and HM [12].
H12: The behavioural intention is positively related to adoption of AI chatbots moderated by gender in higher
learning institutions.
UTAUT2 in AI Chatbots Adoption in Higher Learning Institution
PE, EE, and HM are found to be substantial forecasters of AI Chatbots adoption in higher learning institutions
[11]. Besides, SI and Habit intensely influence intention to use AI chatbots among students [23]. On the other
hand, numerous studies found out that addition of trust in UTAUT2 for AI chatbots in higher learning institutions
has revealed enhanced clarifying power [24]. Furthermore, gender moderated the relationship between UTAUT2
constructs and behavioural intentions to use AI chatbots in higher learning institutions [25]. Therefore, the
conceptual framework for this study is illustrated in Figure 1.
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Figure 1: Conceptual Framework
RESEARCH METHODOLOGY
The study has been conducted to full time students from public and private higher learning institutions in
Terengganu. Therefore, this study is limited only to the students in Terengganu and to test their adoption of AI
Chatbots during their study. Hence, this study is using the convenience sampling technique to get the
respondents. According to G-Power, the sample size for this study based on the construct is 118 respondents
[26]. The instruments for this study were conducted online and this study was able to get 205 completed and
returned questionnaires more than the required sample size. The survey instruments were adapted by modified
UTAUT2 that included nine behavioural intention factors towards adoption use of AI chatbots [21]. The survey
instruments are using both five-point Likert scale and seven-point Likert scale. The data collected from the
respondents were analysed using SPSS 28.00 and PLS 4.1.
RESULTS AND FINDINGS
Profile of Respondents
Table 1.0: Demographic Background
Variable
Frequency
Percent
(%)
Gender
Male
46
22.40
Female
159
77.60
Total
205
100.0
Age (Generation Z)
18 – 20
118
57.60
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21 – 23
82
40.00
24 - 26
5
2.40
Total
205
100.00
Higher Learning Institution
Public
194
94.6
Private
11
5.40
Total
205
100.0
AI Chatbots
ChatGPT only
75
36.60
ChatGPT, Gemini
38
18.50
ChatGPT, MS Copilot
22
10.70
ChatGPT, Gemini, Perplexity
20
9.80
ChatGPT, Gemini, MS Copilot
18
8.80
ChatGPT, Gemini, Perplexity, MS Copilot
10
4.90
ChatGPT, Perplexity
9
4.40
ChatGPT, Perplexity, MS Copilot
6
2.90
Others
7
3.50
Total
205
100.00
According to Table 1, 159 respondents (77.6%) were female, and 46 respondents were male (22.4%). The
majority of the respondents’ age between 18 – 20 with 118 students (57.6%) followed by 82 students’ age
between 21 – 23 (40%) and only five students’ age between 24 – 26 (2.4%). As a result, 198 students were using
ChatGPT as one of tools of AI chatbots in this study and only 75 students (36.6%) only use ChatGPT as a tool
of AI chatbot.
Table 2: Crosstabulation
Behaviour Intention and Gender
I regularly use AI Chatbots in my study. * Gender Crosstabulation Count
Gender
Total
Female
Male
Very strongly disagree
2
0
2
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I I regularly use AI Chatbots in
my study.
Strongly disagree
9
1
10
Disagree
19
2
21
Not sure
36
11
47
Agree
50
19
69
Strongly agree
31
5
36
Very strongly agree
12
8
20
Total
159
46
205
Table 2.1: Crosstabulation 1
50 female students agree that they use AI chatbots regularly in their study followed by 31 female students who
strongly agree and only 12 female students with very strongly agree to use AI chatbots regularly in their study.
Besides, 19 male students agree to use AI chatbots regularly followed by 5 male students who strongly agree
and 8 students with very strongly agree to use AI chatbots regularly in their study.
AI Chatbots usage is a pleasant experience. * Gender Crosstabulation Count
Gender
Total
Female
Male
AI Chatbots usage is a
pleasant experience.
Very strongly disagree
1
1
2
Strongly disagree
4
0
4
Disagree
11
2
13
Not sure
48
15
63
Agree
42
13
55
Strongly agree
38
7
45
Very strongly agree
15
8
23
Total
159
46
205
Table 2.2: Crosstabulation 2
Majority of the students including male and female students acknowledge that AI chatbots are a pleasant
experience. 95 female students agree that AI chatbots are a pleasant experience and only 28 male students, of
which more than half male students agree the same thing.
I currently use AI Chatbot as a supporting tool in my study. * Gender Crosstabulation Count
Gender
Total
Female
Male
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I currently use AI Chatbots as a
supporting tool in my study.
Very strongly disagree
0
1
1
Strongly disagree
5
1
6
Disagree
11
0
11
Not sure
32
12
44
Agree
49
6
55
Strongly agree
45
12
57
Very strongly agree
17
14
31
Total
159
46
205
Table 2.3: Crosstabulation 3
14 male students very strongly agree to use AI chatbots as a supporting tool in their study followed by 12 male
students who strongly agree and 6 male students with agree that AI chatbots as a supporting tool in study. In
contrast, the large number of female students agree that AI chatbots as a supporting tool in study. 49 female
students agree, followed by 45 female students strongly agree and 17 female students very strongly agree that
AI chatbots as a supporting tool in study.
Construct
Item
Loading
CR
AVE
Effort Expectancy
EE1
0.897
0.935
0.783
EE2
0.866
EE3
0.912
EE4
0.865
Facilitating Conditions
FC1
0.898
0.921
0.745
FC2
0.898
FC3
0.889
FC4
0.759
Hedonic Motivation
HM1
0.957
0.968
0.909
HM2
0.956
HM3
0.947
Habit
HT1
0.895
0.946
0.813
HT2
0.931
HT3
0.893
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HT4
0.887
Performance Expectancy
PE1
0.851
0.927
0.762
PE2
0.895
PE3
0.883
PE4
0.861
Price Value
PV1
0.818
0.911
0.775
PV2
0.927
PV3
0.891
Social Influence
SI1
0.925
0.951
0.867
SI2
0.931
SI3
0.936
Trust
T1
0.875
0.932
0.732
T2
0.844
T3
0.831
T4
0.893
T5
0.833
Technology Anxiety
TA1
0.753
0.898
0.691
TA2
0.685
TA3
0.932
TA4
0.927
Use AI Chatbots
USE1
0.881
0.908
0.768
USE2
0.879
USE3
0.868
Behaviour Intention
BI1
0.837
0.924
0.802
BI2
0.914
BI3
0.933
Table 3: Convergent Validity
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Table 3 presents the dataset, named Student Adoption to AI Chatbots (n=205), used to assess the reflective
measurement model in Figure 1. The exogeneous variables data where performance expectancy consists of four
indicators, effort expectancy consists of four indicators, facilitating conditions with four indicators, habit with
four indicators, hedonic motivation with three indicators, price value with three indicators, social influence with
three indicators, trust with five indicators, and technology anxiety consist of four indicators. In contrast, the
endogenous variables data were behavioural intention with three indicators and use AI chatbots with three
indicators.
In addition, Table 3 presents the reliability and validity of the study. The composite reliability (CR) values >0.70
indicated that these constructs have adequate level of internal consistency. Thus, the average variance extracted
(EVA) values has met the satisfactory level of AVE with >0.50. The results showed that items in each construct
explain more than 50% of the construct variance [27]. Item loading higher than 0.5 for indicator reliability is a
necessity [28]. However, there are no item loadings that had value <0.50 and deleted in this study.
BI
EE
FC
HT
HM
PE
PV
SI
TA
T
EE
0.545
FC
0.592
0.854
HT
0.652
0.464
0.540
HM
0.532
0.732
0.748
0.449
PE
0.709
0.821
0.845
0.550
0.777
PV
0.458
0.532
0.655
0.461
0.538
0.596
SI
0.649
0.486
0.609
0.704
0.433
0.603
0.555
TA
0.186
0.079
0.092
0.366
0.083
0.167
0.267
0.435
T
0.689
0.624
0.704
0.704
0.598
0.707
0.605
0.678
0.319
USE
0.779
0.678
0.740
0.581
0.653
0.772
0.459
0.544
0.107
0.677
Table 4: Discriminant Validity (HTMT)
Table 4 shows the discriminant validity of all entry variables have been established by using the
heterotraitmonotrait (HTMT) ratio of correlation criterion [29]. The discriminant validity was determined in the
measurement model when the correlative values correspond to the respective constructs that do not exceed the
HTM 0.90 criterions threshold. All HTMT data in this study do not exceed the 0.90 criterion threshold.
BETA
SE
T VALUES
P VALUES
VIF
F2
DECISION
BI -> Adoption USE AI
Chatbots
0.533
0.102
5.227
0.000
6.583
0.104
SUPPORTED
Gender x BI -> Adoption
USE AI Chatbots
-0.160
0.138
1.164
0.122
6.674
0.007
NOT
SUPPORTED
FC -> Adoption USE AI
Chatbots
0.364
0.068
5.368
0.000
1.571
0.204
SUPPORTED
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FC -> BI
0.146
0.197
0.743
0.229
18.343
0.003
NOT
SUPPORTED
HT -> Adoption USE AI
Chatbots
-0.101
0.106
0.948
0.172
6.085
0.004
NOT
SUPPORTED
HT -> BI
0.105
0.236
0.444
0.329
20.016
0.001
NOT
SUPPORTED
Gender x HT -> Adoption
USE AI Chatbots
0.257
0.149
1.726
0.042
6.697
0.019
SUPPORTED
EE -> BI
-0.189
0.174
1.088
0.139
17.271
0.005
NOT
SUPPORTED
HM -> BI
-0.195
0.214
0.913
0.181
17.918
0.005
NOT
SUPPORTED
PE -> BI
0.605
0.191
3.170
0.001
14.587
0.059
SUPPORTED
PV -> BI
0.146
0.163
0.900
0.184
9.215
0.005
NOT
SUPPORTED
SI -> BI
0.185
0.146
1.273
0.102
9.548
0.008
NOT
SUPPORTED
TA -> BI
-0.184
0.146
1.262
0.104
8.530
0.009
NOT
SUPPORTED
Trust -> BI
0.112
0.229
0.489
0.313
18.303
0.002
NOT
SUPPORTED
Table 5: Path Coefficient and Hypotheses Testing and Effect Size
The bootstrapping procedure has been applied to test the hypotheses for this study and generate results for each
path relationship in Table 5. Bootstrap sub-samples with 1,000-sample cases have been computed to allow the
procedure to estimate the model of each sub-sample [33]. There were 12 hypotheses tested and only four
hypotheses were supported. The path relationship between the behavioural intention and adoption to use AI
chatbots was positively related, ß=0.00, p<0.001 at the 95% confidence level. Facilitating conditions positively
related to adoption use of AI chatbots with ß=0.00, p<0.001 at the 95% confidence level. Thus, performance
expectancy is positively related to behavioural intention, ß=0.00, p<0.001 at the 95% confidence level. Gender
on the other hand mediated the relationship between habit and adoption use of AI chatbots with ß=0.42, p<0.001
at the 95% confidence level. Lastly, the performance expectancy was positively related to behavioural intention,
ß=0.01, p<0.001 at the 95% confidence level. Gender was not moderated the relationship between behavioural
intention and adoption use of AI chatbots, ß=0.122, p<0.001 at the 95% confidence level.
Table 5 presents the effect size (f2) of all the exogenous constructs on the endogenous construct. The f2 effect
size values have exhibited the importance of each exogenous construct to the endogenous construct. The value
of 0.02 has a small effect size, 0.15 has a medium effect size, and 0.35 has a medium-to-large effect size [4]. The
effect size of behavioural intention on adoption to use AI chatbots is (f2=0.104) is medium effect size.
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CONCLUSION
Since the scope of this study is limited to only higher learning institutions from public and private in Terengganu
only, this study only generalises the result for this state only. To conclude, performance expectancy was proven
as the most influential behavioural intentions factor on adoption to use AI chatbots in this study [8][9]. Gender
moderating effects in this study fail to support the hypotheses and only habit has a positive effect on adoption to
use AI chatbots moderated by gender. Even though many behavioural intentions factors were rejected in this
study, the behavioural intention to adopt use of AI chatbots are positively related and supported the hypothesis.
Since this is only the preliminary study, it is recommended to apply the same instruments with other higher
learning institutions and to add new constructs as mediated variables or to use other moderated variables.
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