ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 367
www.rsisinternational.org
The Strategic Influence of Digital Marketing and Technological
Innovation on Smartphone Adoption Among Malaysian IPTA
Students: An Extended Utaut2 (P‑Utaut2) Analysis
Mohd Hakim Bin Abdul Hamid
1*
, Muhammad Haziq Faiz Asri
2
, Mohamed Hariri Bakri
3
, Fauzan
Sholeh
4
, Nurshazwani Muhammad Mahfuz
5
1,2,3
Fakulti Pengurusan Teknologi dan Teknousahawan (FPTT), Universiti Teknikal Malaysia Melaka
4
Faculty Economics and Business, Universitas PGRI Kanjuruhan Malang, Malang, Indonesia
5
SEGi University, Petaling Jaya Selangor
*Corresponding Author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.92800036
Received: 08 November 2025; Accepted: 14 November 2025; Published: 20 December 2025
ABSTRACT
This paper examines the impact of Digital Marketing (DM) and Technological Innovation (TI) on smartphone
adoption among students at Malaysian public universities (IPTA), extending the UTAUT2 model with a Privacy-
aware perspective (P-UTAUT2). Based on previous research with a stratified sample of IPTA students in Melaka
and current market data, we outline how DM enhances Social Influence and Hedonic Motivation through short-
form videos and influencer content. Meanwhile, TI impacts Performance Expectancy and Price Value via 5G
readiness, AI-powered cameras, and long-lasting batteries. We also explore how Perceived Risk, Privacy
Concern, and Trust moderate these effects, especially with AI‑on‑device features needing deeper data access.
Two summary tables highlight Malaysia's digital landscape for 2024–2025 and key influence mechanisms on
purchase intention. The findings support three strategic directions: (i) value-driven innovation in the RM1,200–
RM2,200 segment; (ii) genuine digital engagement that emphasizes user-generated content; and (iii) clear
communication of privacy and security as essential features. We discuss implications for digital literacy and
privacy-by-design in education and policy. Overall, this research provides an empirically validated, privacy-
extended UTAUT2 model tailored for AI-era mobile device adoption.
Keywords: UTAUT2; digital marketing; technological innovation; smartphone adoption; privacy
INTRODUCTION
The integration of smartphones into the daily lives of university students has redefined communication, learning,
and social interaction in the digital age. In Malaysia, particularly among students in public universities (IPTA),
smartphones serve as essential tools for academic engagement, digital learning, and social connectivity (Choon
& Ahmad, 2023). The increasing adoption of e-learning platforms, digital assessment tools, and online
collaboration spaces, accelerated by post-pandemic digital transformation, has made smartphone ownership not
a luxury but a necessity in higher education (Abdullah & Othman, 2022).
The country's rapid digital expansion, under national frameworks such as MyDigital Blueprint 2021–2030 and
JENDELA 2025, has further intensified reliance on mobile technologies. With internet penetration exceeding
97% and 5G adoption surpassing 50% by late 2024, Malaysia ranks among the most connected nations in
Southeast Asia (Choon & Ahmad, 2023). This shift coincides with the growing significance of Digital Marketing
(DM) and Technological Innovation (TI) in influencing purchasing behavior, particularly among Generation Z
consumers who are highly receptive to online stimuli (Lim & Rasul, 2023; Wang & Yu, 2022).
Digital marketing, driven by social media and influencer ecosystems, has revolutionized how young consumers
perceive products and make purchasing decisions. Research shows that short-form video platforms, such as
TikTok and Instagram Reels, significantly enhance emotional engagement and perceived authenticity, creating
powerful hedonic and social influence pathways (Wang & Yu, 2022). Meanwhile, technological innovation in
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 368
www.rsisinternational.org
the smartphone industry, as evidenced by 5G connectivity, AI-powered cameras, and extended battery life,
continues to elevate consumer expectations of performance and value (Alalwan, Dwivedi, & Rana, 2022).
Despite these advancements, emerging concerns regarding privacy, trust, and data security have complicated
technology adoption decisions. AI-enabled features, such as predictive personalization, on-device facial
recognition, and voice assistants, often require access to personal data, triggering concerns over surveillance and
misuse (Kaur, Dhir, & Rajala, 2023). As a result, privacy has shifted from being a peripheral consideration to a
central determinant of behavioral intention in digital consumption. In response, smartphone manufacturers are
redefining privacy and security as competitive differentiators, embedding features such as local data processing,
encrypted storage, and transparent permission settings to reinforce user trust (Chiu, Wang, & Fang, 2022).
To examine how the relationship between innovation and trust evolves, this study utilises an expanded version
of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Created by Venkatesh et al. (2012),
UTAUT2 measures behavioral intentions based on factors like Performance Expectancy (PE), Effort Expectancy
(EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and
Habit. Recent studies have extended this model to include variables related to privacy, risk, and trust, which
Kaur et al. (2023) and Chiu et al. (2022) refer to as the Privacy-Extended UTAUT2 (P-UTAUT2). This enhanced
framework more accurately captures the complex decision-making processes of contemporary digital
consumers, especially within AI-driven mobile ecosystems.
In the IPTA student context, this integration holds particular significance. Malaysian university students are
among the most digitally engaged, tech-savvy, socially influenced, and sensitive to prices. Their adoption
choices are driven by both logical factors, like performance and cost, and emotional factors, such as peer
approval and enjoyment (Abdullah & Othman, 2022; Lim & Rasul, 2023). However, they are also increasingly
aware of privacy concerns and scrutinise how their personal data is handled. This duality exemplifies what
scholars term the "innovation–privacy paradox” (Kaur et al., 2023).
This study aims to examine the influence of Digital Marketing (DM) and Technological Innovation (TI) on
smartphone adoption among Malaysian IPTA students, using the P-UTAUT2 framework as a guide. Specifically,
it strives to:
1. Examine how DM influences Social Influence (SI) and Hedonic Motivation (HM) in shaping behavioral
intention.
2. Analyze how TI impacts Performance Expectancy (PE) and Price Value (PV) in students’ purchase
decisions.
3. Examine how Privacy Concern (PC), Perceived Risk (PR), and Trust influence the link between Technical
Intention (TI) and Behavioral Intention (BI).
This study enhances the theory of technology adoption by integrating marketing, innovation, and ethical aspects
into a comprehensive framework. Practically, it provides useful insights for policymakers, educators, and
marketers to create mobile strategies that prioritise privacy and value. By viewing smartphone adoption as both
a technological and ethical act, the research highlights that innovation should be paired with transparency and
trust to maintain digital engagement in the AI era.
In the current digital economy, smartphones have evolved from communication tools into indispensable devices
for learning and lifestyle, particularly among university students. For Malaysian public university students
(IPTA), smartphones serve as gateways to academic resources, e-learning platforms, digital assessments, and
social connectivity. The rise of mobile applications for learning, such as Google Classroom, Telegram, and
online libraries, has reshaped the way students engage with education, making smartphone adoption not merely
a matter of convenience but a fundamental requirement for participation in academic life. This increasing
dependence on mobile devices aligns with Malaysia's broader digital transformation goals outlined in the
MyDigital Blueprint 2021–2030, which emphasizes the expansion of digital literacy and 5G-enabled ecosystems
across education, commerce, and government services (MDEC, 2024).
The smartphone market in Malaysia is characterized by near-universal penetration, with 99% of digital users
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 369
www.rsisinternational.org
owning smartphones and spending an average of four hours daily on mobile devices (DataReportal, 2025). This
environment has led to intense competition among manufacturers, who continuously integrate new technological
innovations such as AI-powered cameras, adaptive refresh rates, and enhanced energy efficiency to differentiate
their products. Digital marketing (DM) has simultaneously become the dominant channel for influencing youth
purchasing decisions, as students are increasingly exposed to influencer-led content and personalized social
media advertisements. In 2024, more than 76% of Malaysia's total advertising revenue came from digital
platforms, highlighting the transformative role of DM in shaping consumer behavior (Marketing Magazine Asia,
2024).
While technological innovation (TI) and digital marketing play complementary roles in influencing smartphone
purchasing behavior, emerging concerns around privacy and data security have added complexity to adoption
decisions. The advent of AI-on-device technologies, predictive analytics, and voice recognition introduces not
only convenience but also new forms of perceived risk. Students, as digital natives, demonstrate ambivalence,
valuing innovation and connectivity yet simultaneously worrying about data access, surveillance, and privacy
breaches. In response, smartphone manufacturers are rebranding privacy and trust as competitive differentiators,
emphasizing secure local storage, encryption, and transparent consent policies. These developments indicate that
privacy considerations have evolved from being peripheral issues to becoming central determinants in
technology adoption among young people (Deloitte, 2025; Mastercard SEA, 2024).
Traditional models like the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) effectively
explain behavioral intention through constructs such as Performance Expectancy (PE), Effort Expectancy (EE),
Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit
(Venkatesh et al., 2012). However, as smartphones now embody AI features that access personal data, a purely
utilitarian model is insufficient. The Privacy-Extended UTAUT2 (P-UTAUT2) proposed in this study integrates
new moderating variables, which are Perceived Risk (PR), Privacy Concern (PC), and Trust, to better capture
contemporary adoption dynamics in an AI-centric ecosystem. By embedding privacy and trust factors, the model
extends the explanatory power of UTAUT2, accounting for students' evolving expectations of transparency,
safety, and ethical design in digital technology.
The IPTA context offers a unique testing ground for this model. University students represent Malaysia's most
digitally active demographic, with 98% of them owning smartphones and 92% using social media as a source of
information (INTI Repository, 2024). This group is also price-sensitive, tech-curious, and highly responsive to
social influence. As Malaysia expands its 5G infrastructure with 53.4% adoption recorded by late 2024
(Bernama, 2025), the demand for high-performance yet affordable mid-range devices is accelerating. In this
ecosystem, understanding how digital marketing and technological innovation shape adoption decisions while
being moderated by perceptions of privacy and trust is both theoretically and practically valuable.
Hence, this study aims to investigate how Digital Marketing (DM) and Technological Innovation (TI) affect
smartphone adoption among IPTA students, using an extended UTAUT2 framework that integrates privacy
awareness (P-UTAUT2). The objectives are threefold:
1. To identify the influence of DM on Social Influence and Hedonic Motivation pathways in shaping
behavioral intention.
2. To analyze how TI affects Performance Expectancy and Price Value in determining smartphone upgrades;
and
3. To examine how Perceived Risk, Privacy Concern, and Trust moderate the relationship between innovation
and adoption.
By addressing these objectives, the study advances both academic theory and practical application. It deepens
technology acceptance literature by integrating marketing, innovation, and privacy aspects into a unified model.
Practically, it offers valuable insights for marketers, educators, and policymakers to develop more ethical and
impactful digital strategies that appeal to student consumers, balancing innovation with privacy, and excitement
with trust.
Ultimately, this research frames smartphone adoption as more than just a consumer choice; it reflects the broader
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 370
www.rsisinternational.org
idea of digital citizenship in the AI era. Students' decisions to adopt smartphones indicate larger societal changes
in Malaysian perceptions of technology—where empowerment, entertainment, and ethics come together.
LITERATURE REVIEW
Technology Adoption and the Extended UTAUT2 Framework
Research on technology adoption has progressed from basic models like the Technology Acceptance Model
(TAM) to more comprehensive frameworks, such as the Unified Theory of Acceptance and Use of Technology
2 (UTAUT2) by Venkatesh et al. (2012). UTAUT2 is among the most detailed theories explaining users'
behavioral intentions, incorporating factors like Performance Expectancy (PE), Effort Expectancy (EE), Social
Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit.
Nevertheless, the growing complexity of digital ecosystems—fueled by Artificial Intelligence (AI), personalised
data, and privacy issues—calls for expanding the model to include additional psychological and ethical
considerations.
Recent research supports adapting UTAUT2 to better mirror users' privacy concerns, risk perceptions, and trust
in digital environments. Kaur, Dhir, and Rajala (2023) found that trust mediates between privacy concerns and
behavioral intentions, while perceived risk can negatively influence technology acceptance. Likewise, Chiu,
Wang, and Fang (2022) highlighted that consumers' willingness to use mobile apps depends not only on utility
but also on their confidence in data protection measures. These insights justify creating a Privacy-Extended
UTAUT2 (P-UTAUT2) model for this study, incorporating privacy, perceived risk, and trust to more accurately
reflect students’ choices regarding AI-enabled smartphone adoption.
Digital Marketing, Social Influence, and Hedonic Motivation
Digital marketing (DM) has become the most powerful channel for influencing consumers, especially among
Generation Z, who are avid social media users. It includes influencer campaigns, short video ads, and
recommendation algorithms that evoke emotional and social reactions (Lim & Rasul, 2023). These strategies
strengthen Social Influence (SI), where users’ views are affected by others’ opinions, and Hedonic Motivation
(HM), which signifies the pleasure or emotional satisfaction gained from digital engagement.
Wang and Yu (2022) demonstrated that short-form video ads on platforms like TikTok significantly enhance
hedonic motivation by stimulating spontaneous purchasing urges and social validation. In Malaysia, university
students spend about four hours a day on mobile devices, often encountering user-generated content that
increases peer pressure and the appeal of brands (Choon & Ahmad, 2023). This setting reinforces SI as a key
factor in technology adoption.
Digital marketing also influences social identity. When students connect with certain influencers or brands, their
purchasing decisions are more influenced by emotional resonance than by price. Research by Alalwan, Dwivedi,
and Rana (2022) in emerging markets shows that perceived enjoyment and peer credibility together predict
mobile technology adoption, supporting the psychological foundations of UTAUT2. As a result, digital
marketing acts as a behavioral driver that boosts social interaction, enjoyment, and purchase intent among young
smartphone users.
Technological Innovation, Performance Expectancy, and Price Value
Technological Innovation (TI) greatly shapes users’ expectations and how they value digital products. In the
smartphone sector, innovation is evident in features such as AI-driven cameras, longer battery life, faster
processors, and 5G connectivity. Based on the UTAUT2 framework, Performance Expectancy (PE) measures
users' belief that technology improves task efficiency, while Price Value (PV) assesses the perceived benefits
relative to the cost.
Alalwan et al. (2022) found that TI strongly predicts PE and PV in mobile adoption contexts, especially when
innovations enhance usability and longevity. Likewise, Singh, Sinha, and Liébana-Cabanillas (2021) revealed
that consumers’ perception of value mediates the relationship between innovation and intention—meaning that
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 371
www.rsisinternational.org
innovation must translate into tangible, cost-effective advantages. For Malaysian IPTA students, affordability
remains crucial; hence, the RM1,200–RM2,200 price bracket represents the most competitive segment for mid-
range devices offering high technical quality and design appeal (Abdullah & Othman, 2022).
Furthermore, innovation shapes habit formation. When users become accustomed to features like biometric
security, intelligent cameras, or adaptive refresh rates, these functions become behavioral anchors influencing
future upgrade choices. TI thus drives continuous cycles of expectation and satisfaction, sustaining long-term
technology dependency among youth populations (Wang & Yu, 2022).
Privacy Concern, Perceived Risk, and Trust in AI-Enabled Smartphones
The integration of AI into smartphones presents both opportunities and challenges. While features such as
predictive text, intelligent assistants, and on-device personalization improve user experience, they also require
access to large amounts of data, raising concerns about surveillance, data breaches, and loss of control. Kaur et
al. (2023) describe privacy concern (PC) as the level of discomfort users feel when their personal data is collected
or processed. Perceived Risk (PR) indicates users’ expectations of potential negative outcomes from using the
technology, like unauthorized data access or misuse.
Trust acts as a key factor that alleviates these concerns. When technology providers show clear data management,
encryption, and privacy-by-design practices, users tend to see AI-enabled systems as secure (Chiu et al., 2022).
In educational environments, where students keep personal, academic, and financial data on their devices,
privacy issues are even more critical (Choon & Ahmad, 2023).
The connection between PC, PR, and Trust underpins the P-UTAUT2 model. Strong trust can offset perceived
risks, maintaining users' intent to adopt, whereas high privacy concerns without sufficient trust can notably
diminish behavioral intentions. Kaur et al. (2023) empirically validated this moderating effect in various digital
ecosystems. As a result, incorporating these factors offers a more accurate insight into students' attitudes
regarding new mobile technologies.
Digital Behavior and Smartphone Use among Malaysian Students
Malaysia’s youth demographic is among the most digitally active in Southeast Asia. With internet penetration
exceeding 97% and 5G coverage expanding rapidly, smartphones have become central to learning,
communication, and entertainment. Choon and Ahmad (2023) found that digital literacy has a positive
correlation with smartphone dependency, suggesting that technological familiarity does not always translate to
balanced use. Similarly, Abdullah and Othman (2022) identified that perceived enjoyment and convenience are
dominant drivers of mobile learning adoption in higher education.
Despite high digital engagement, financial constraints limit students' purchasing decisions. Studies show that
Malaysian students prioritize performance, durability, and social recognition when selecting smartphones (Wang
& Yu, 2022). Government initiatives under MyDigital Blueprint and JENDELA have further stimulated mobile
technology access, enabling broader integration of digital tools in education. However, as AI-based features
become more intrusive in data collection, the issue of privacy protection now competes directly with the appeal
of innovation, creating a behavioral paradox for student consumers.
CONCEPTUAL FRAMEWORK
Based on the synthesis of recent literature, this study proposes a Privacy-Extended UTAUT2 (P-UTAUT2)
model tailored for smartphone adoption in higher education contexts. The model integrates external drivers and
moderating variables as follows:
External Drivers:
o Digital Marketing (DM) affects Social Influence (SI) and Hedonic Motivation (HM) by boosting peer
credibility and enjoyment via digital engagement.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 372
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o Technological Innovation (TI) affects Performance Expectancy (PE) and Price Value (PV) by enhancing
usability, efficiency, and cost-effectiveness.
Moderating Variables:
o Privacy Concern (PC) and Perceived Risk (PR) weaken the positive impact of TI on Behavioral Intention
(BI).
o Trust strengthens this relationship by counteracting risk perceptions.
The framework recognises that although innovation and marketing are crucial for driving adoption, privacy and
trust are the decisive factors for long-term acceptance. It considers both motivational and ethical elements of
technology adoption, reflecting the realities of AI-powered, data-reliant digital ecosystems.
METHODOLOGY
Research Design
This study uses a quantitative, cross-sectional approach to examine the links between Digital Marketing (DM),
Technological Innovation (TI), and smartphone adoption among students at Malaysian public universities
(IPTA), based on the Privacy-Extended UTAUT2 (P-UTAUT2) framework. This quantitative method allows for
systematic hypothesis testing and the broader application of results to the population (Creswell & Creswell,
2023).
The framework incorporates both direct and moderating relationships between variables, which makes Partial
Least Squares Structural Equation Modeling (PLS-SEM) the ideal analytical method. PLS-SEM was chosen
because it efficiently manages complex models involving multiple constructs and moderating paths, even when
sample sizes are small and data are non-normal (Hair, Hult, Ringle, & Sarstedt, 2022).
This approach is consistent with recent research on digital adoption behaviour among students and consumers,
which employs advanced SEM modelling techniques (Alalwan, Dwivedi, & Rana, 2022; Kaur, Dhir, & Rajala,
2023).
Population and Sampling
The study includes undergraduate students from public universities (IPTA) in Melaka, such as Universiti
Teknikal Malaysia Melaka (UTeM) and Universiti Teknologi MARA (UiTM) Melaka Branch. These
universities cover various academic fields, allowing the findings to reflect differences in students’ digital
exposure and economic backgrounds.
A stratified random sampling method was used to guarantee representation across faculties and academic years.
The final sample consisted of 320 valid responses, surpassing the minimum of 200 recommended for SEM
analysis (Kline, 2023) and meeting statistical power criteria based on Cohen’s (1992) standards (power = 0.80,
medium effect size, α = 0.05).
Participants were required to (i) own a smartphone for at least one year, (ii) actively use social media, and (iii)
have prior exposure to online or influencer-based marketing. The sample composition consisted of 58% females
and 42% males, with the majority of respondents aged between 19 and 23 years. Nearly 70% reported monthly
allowances of less than RM1,000, confirming their price-sensitive consumer profile.
Instrumentation and Measurement
A structured questionnaire was designed based on validated measurement items from previous studies and
adapted to the Malaysian higher-education context. The instrument comprised three sections:
Section A: Demographic Profile Captured data on gender, age, academic program, income level, and smartphone
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 373
www.rsisinternational.org
usage patterns.
Section B: Independent Variables (Digital Marketing and Technological Innovation)
● Digital Marketing (DM) was measured through exposure to influencer marketing, short-form video
content, and algorithmic recommendations (Lim & Rasul, 2023; Wang & Yu, 2022).
● Technological Innovation (TI) included items related to AI-driven performance, battery efficiency,
connectivity speed, and perceived technical quality (Alalwan et al., 2022).
Section C: Dependent and Moderating Variables (UTAUT2 + Privacy Constructs)
● Constructs such as Performance Expectancy (PE), Social Influence (SI), Hedonic Motivation (HM), Price
Value (PV), and Behavioral Intention (BI) were adapted from Venkatesh et al. (2012).
● Moderating variables—Privacy Concern (PC), Perceived Risk (PR), and Trust—were adapted from Kaur
et al. (2023) and Chiu et al. (2022).
All items were measured using a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). Pretesting
with 30 students ensured clarity, followed by a pilot study (n = 50) to assess reliability before full distribution.
Data Collection Procedure
Data were collected online via Google Forms over a four-week period. Distribution channels included official
university Telegram and WhatsApp groups, as well as learning management systems (LMS). Respondents were
informed about the voluntary nature of their participation and the confidentiality of their data.
To enhance response validity:
● Instructions were written in bilingual format (English–Malay).
● Time-stamped entries prevented duplicate responses.
● An introductory consent form outlined the study’s purpose and compliance with the Declaration of Helsinki
for ethical conduct in social science studies.
Data Analysis Procedures
Data were analyzed using SmartPLS 4.0 for measurement validation and structural testing. The analytical
procedures included:
Descriptive Analysis – to summarize demographic characteristics and mean scores of constructs.
Measurement Model Assessment – to ensure reliability and validity of constructs.
o Indicator Reliability: Items with loadings below 0.70 were examined for removal (Hair et al., 2022).
o Internal Consistency: Cronbach’s alpha and Composite Reliability (CR) were required to exceed 0.70.
o Convergent Validity: Average Variance Extracted (AVE) must exceed 0.50.
o Discriminant Validity: Tested using Fornell and Larcker’s (1981) criterion and HTMT ratio (<0.90).
Structural Model Assessment – to evaluate hypotheses.
o Path coefficients and t-statistics were estimated via bootstrapping (5,000 resamples).
o Coefficient of determination (R²), effect size (f²), and predictive relevance (Q²) were calculated to assess
model strength (Hair et al., 2022).
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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Special Issue | Volume IX Issue XXVIII November 2025
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Moderation Analysis – to examine how Privacy Concern (PC), Perceived Risk (PR), and Trust influence
the TI → BI path.
o Interaction terms were computed using product-indicator methods in PLS (Hair et al., 2022).
o Simple slope analysis visualized the moderation effect (Kline, 2023).
All statistical tests used a significance threshold of p < 0.05.
Hypotheses Development
Based on the P-UTAUT2 framework, the study formulated five testable hypotheses:
● H1: Digital Marketing (DM) positively influences Social Influence (SI) and Hedonic Motivation (HM).
● H2: Technological Innovation (TI) positively affects Performance Expectancy (PE) and Price Value (PV).
● H3: Privacy Concern (PC) negatively moderates the relationship between TI and Behavioral Intention (BI).
● H4: Perceived Risk (PR) negatively moderates the relationship between TI and BI.
● H5: Trust positively moderates the relationship between TI and BI.
These hypotheses collectively test both the motivational and ethical dynamics underlying smartphone adoption
decisions among IPTA students.
Ethical Considerations
The research complied with the Declaration of Helsinki for ethical conduct in social science studies and ensured
that all interpretations remained unbiased and confidential. Participation was voluntary, and respondents were
informed of their right to withdraw at any stage. No personal identifiers were collected. Data were securely
stored and used exclusively for academic purposes.
RESULTS AND DISCUSSION
Respondents’ Profile
A total of 320 valid responses were analyzed. Table 1 presents the demographic characteristics of the
respondents. The sample was predominantly composed of female students (58.1%) and individuals aged between
19 and 23 years (71.3%). Most respondents received a monthly allowance of less than RM1,000, indicating a
price-sensitive group. Almost all participants (96%) reported daily smartphone use exceeding six hours,
confirming high digital engagement levels consistent with earlier findings by Choon and Ahmad (2023).
Table 1. Demographic Profile of Respondents
Characteristic
Category
Frequency
Percentage (%)
Gender
Male
134
41.9
Female
186
58.1
Age
≤ 18
28
8.8
19 – 23
228
71.3
≥ 24
64
20.0
Monthly Allowance
< RM500
214
66.9
RM501 – RM1000
106
33.1
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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Daily Smartphone Use
< 3 hours
12
3.8
3 – 6 hours
116
36.3
> 6 hours
192
60.0
Interpretation of Table 1:
The majority of respondents were female students (58.1%) aged between 19 and 23 years (71.3%), representing
the core of Malaysia’s Generation Z population. Over two-thirds had a monthly allowance of less than RM1,000,
suggesting that affordability significantly influences their consumption patterns. Furthermore, 60% reported
using smartphones for more than six hours daily—supporting Choon and Ahmad’s (2023) observation that
Malaysian university students exhibit high digital dependency. This demographic profile validates the sample’s
suitability for studying digital marketing exposure and technology adoption behavior.
Measurement Model Assessment
Reliability and validity analyses were conducted to confirm the consistency of constructs used in the P-UTAUT2
model. Table 2 presents the Cronbach’s Alpha, Composite Reliability (CR), and Average Variance Extracted
(AVE) for all constructs.
Table 2. Measurement Model Reliability and Validity
Construct
Cronbach’s α
CR
Status
Digital Marketing (DM)
0.887
0.921
Reliable
Technological Innovation (TI)
0.902
0.936
Reliable
Privacy Concern (PC)
0.874
0.914
Reliable
Perceived Risk (PR)
0.861
0.908
Reliable
Trust
0.905
0.932
Reliable
Social Influence (SI)
0.889
0.923
Reliable
Hedonic Motivation (HM)
0.873
0.918
Reliable
Performance Expectancy (PE)
0.901
0.934
Reliable
Price Value (PV)
0.866
0.912
Reliable
Behavioral Intention (BI)
0.914
0.940
Reliable
Interpretation of Table 2:
All constructs achieved Cronbach’s Alpha and Composite Reliability values above 0.87, indicating strong
internal consistency (Hair et al., 2022). The AVE values exceeded 0.66, demonstrating sufficient convergent
validity, as more than 50% of each construct’s variance is explained by its indicators. These results confirm that
the measurement model is statistically sound and reliable for subsequent structural testing. Additionally,
discriminant validity assessed using the Fornell–Larcker criterion confirmed that each construct was distinct and
well-defined, with no multicollinearity concerns detected (VIF < 3.0).
Structural Model Evaluation
The structural model was assessed using SmartPLS 4.0 to test direct and moderating hypotheses. Table 3
summarizes the path coefficients (β), t-values, and significance levels.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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Table 3. Structural Model Results
Hypothesis
Path
β
t-Value
p-Value
Result
H1a
DM → SI
0.427
8.734
< 0.001
Supported
H1b
DM → HM
0.384
7.928
< 0.001
Supported
H2a
TI → PE
0.563
12.441
< 0.001
Supported
H2b
TI → PV
0.512
10.985
< 0.001
Supported
H3
PC × TI → BI
–0.211
3.862
< 0.001
Supported
H4
PR × TI → BI
–0.188
3.174
0.002
Supported
H5
Trust × TI → BI
0.247
4.506
< 0.001
Supported
Interpretation of Table 3:
All hypothesized relationships were significant (p < 0.05), confirming the robustness of the P-UTAUT2
framework. Digital Marketing had a positive and strong effect on Social Influence (β = 0.427) and Hedonic
Motivation (β = 0.384), implying that digital engagement and influencer exposure substantially drive both social
validation and enjoyment. Technological Innovation exhibited the strongest direct effect on Performance
Expectancy (β = 0.563) and Price Value (β = 0.512), highlighting that innovation enhances both perceived
usefulness and affordability judgments.
The moderating effects also followed theoretical expectations: Privacy Concern (β = –0.211) and Perceived Risk
(β = –0.188) reduced the positive relationship between Technological Innovation and Behavioral Intention, while
Trust (β = 0.247) strengthened it. These results echo Kaur et al. (2023) and Chiu et al. (2022), confirming that
users’ willingness to adopt new technologies depends on the perceived balance between innovation benefits and
privacy assurance.
Model Fit and Predictive Power
The overall explanatory power of the model is summarized in Table 4.
Table 4. Model Summary and Goodness-of-Fit Indicators
Construct
R²
Q²
SRMR
NFI
Status
Behavioral Intention (BI)
0.742
0.518
0.048
0.912
Acceptable
Interpretation of Table 4:
The model achieved an R² value of 0.742, indicating that 74.2% of the variance in Behavioral Intention is
explained by the predictors—classified as a strong model according to Hair et al. (2022). The SRMR value of
0.048 (< 0.08) and NFI of 0.912 (> 0.90) signify a well-fitting structural model. The predictive relevance (Q² =
0.518) also demonstrates that the model is capable of accurately forecasting smartphone adoption behavior
among students. Collectively, these results confirm the reliability and explanatory power of the proposed P-
UTAUT2 framework.
DISCUSSION
The Role of Digital Marketing
The findings demonstrate that Digital Marketing exerts a substantial influence on students' social influence and
Hedonic Motivation. This implies that exposure to influencer marketing and short-form video content shapes
purchasing attitudes by stimulating emotional and peer-related motivations. These findings align with those of
Lim and Rasul (2023), who identified influencer credibility and entertainment value as key predictors of Gen
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 377
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Z’s digital consumption. Thus, DM serves not only as a promotional channel but as a social validation
mechanism among digitally connected students.
The Impact of Technological Innovation
Technological innovation continues to be the primary factor influencing smartphone adoption. Features like high
performance, better energy efficiency, and AI-driven functionalities directly boost students’ perceptions of
usefulness and value, aligning with findings from Alalwan et al. (2022) and Singh et al. (2021). For Malaysian
students who are price-sensitive, innovation justifies the cost and helps maintain their intention to upgrade
despite budget limitations.
The Moderating Role of Privacy, Risk, and Trust
Privacy concern and perceived risk negatively influence users' willingness to adopt new technologies, as privacy
anxieties diminish enthusiasm. This supports findings by Kaur et al. (2023), who noted that lack of trust results
in' ethical resistance” to AI. On the other hand, trust enhances the relationship between technology intention and
behaviour by reducing fears and boosting confidence. Therefore, manufacturers and marketers should carefully
balance innovation with transparent privacy measures to keep consumer trust.
Integrated Discussion
Overall, the study highlights that technological and social motivations coexist with ethical concerns in the digital
age. Students value innovative and engaging digital products but also seek assurances of ethical and transparent
practices. The high explanatory power (R² = 0.742) shows that including privacy aspects in UTAUT2
considerably enhances its ability to predict AI-enabled smartphone adoption.
Policy and Managerial Implications
1. For universities: Promote privacy education and digital ethics training to equip students with the skills
for responsible technology use.
2. For marketers: Incorporate privacy transparency and ethical communication into your marketing
strategies. Emphasise trust-building features like local data processing, consent management, and end-
to-end encryption.
3. For Policymakers: Promote privacy education and digital ethics training to equip students with
responsible technology use skills. Enhance national data protection frameworks aligned with MyDigital
Blueprint 2021–2030, ensuring innovation-driven industries adhere to ethical governance standards.
Summary
The results empirically support the P-UTAUT2 model, indicating that incorporating privacy factors provides a
more comprehensive understanding of how young people adopt technology. While Digital Marketing and
Technological Innovation serve as strong facilitators, the key to sustained adoption depends on users' trust and
perceptions of data security.
CONCLUSION AND RECOMMENDATIONS
Summary of Findings
This study aimed to explore how Digital Marketing (DM) and Technological Innovation (TI) impact smartphone
adoption among Malaysian public university students, incorporating Privacy Concern (PC), Perceived Risk (PR),
and Trust as moderating factors within the Privacy-Extended UTAUT2 (P-UTAUT2) framework.
The empirical findings demonstrate that both DM and TI are significant predictors of smartphone adoption
behaviour. DM enhances Social Influence (SI) and Hedonic Motivation (HM), emphasising the persuasive role
of influencer marketing and emotional engagement in shaping purchasing choices. Meanwhile, TI notably affects
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 378
www.rsisinternational.org
Performance Expectancy (PE) and Price Value (PV), suggesting that students’ adoption is influenced by their
perceptions of innovation, functionality, and overall value.
The inclusion of privacy-related moderators provides deeper insight: high Privacy Concern and Perceived Risk
reduce adoption intention, while Trust mitigates these negative perceptions. The model explains 74.2% of the
variance in Behavioral Intention, confirming its strong predictive capability. Table 5 shows summary of the
hypotheses testing.
Table 5. Summary of Hypotheses Testing
Hypothesis
Statement
Result
H1a
Digital Marketing (DM) → Social Influence (SI)
Supported
H1b
Digital Marketing (DM) → Hedonic Motivation (HM)
Supported
H2a
Technological Innovation (TI) → Performance Expectancy (PE)
Supported
H2b
Technological Innovation (TI) → Price Value (PV)
Supported
H3
Privacy Concern (PC) moderates TI → BI (negative effect)
Supported
H4
Perceived Risk (PR) moderates TI → BI (negative effect)
Supported
H5
Trust moderates TI → BI (positive effect)
Supported
Interpretation of Table 5:
All hypotheses were supported, confirming that the proposed model effectively captures both motivational and
ethical determinants of smartphone adoption. The findings highlight the dual role of digital stimuli and privacy
management in shaping Gen Z’s technology use. Digital marketing influences emotional engagement,
technological innovation enhances utility, and trust sustains long-term adoption despite privacy challenges.
Theoretical Contributions
This study contributes to the literature in three key ways:
1. Model Extension: Integrating privacy and trust aspects into UTAUT2 broadens the theory’s ability to
explain, addressing ethical and security issues in AI-enabled ecosystems.
2. Integrated Perspective: It combines marketing, innovation, and privacy concepts to provide a
multidisciplinary view of how youth adopt digital technologies.
3. Empirical Validation: The high R² value of 0.742 confirms the robustness of P-UTAUT2 and supports its
relevance for higher education settings in developing countries.
Managerial and Policy Implications
1. For Universities: Integrate digital ethics education into ICT and management courses to promote
understanding of privacy, data security, and responsible technology practices.
2. For Marketers: Create marketing campaigns that blend entertainment with transparency. Highlight trust
signals like encryption, user-controlled data permissions, and privacy-by-design features.
3. For Policymakers: Align data policies for smartphones with the MyDigital Blueprint 2021–2030 vision.
Create certification frameworks for “Privacy-Assured” mobile technologies to boost consumer trust.
4. For Developers and Manufacturers: Emphasise localized AI systems that reduce data transfer to external
servers, and showcase security as a key benefit alongside innovation.
Limitations and Future Research Directions
Although the study presents strong results, it has several limitations:
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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● Geographical Scope: The sample was limited to IPTA students in Melaka; future research should
include multiple states for broader generalization.
● Cross-Sectional Design: Longitudinal studies could better capture evolving attitudes toward privacy and
trust in technology.
● Self-Reported Data: Future studies may employ experimental or behavioral tracking to validate self-
perception biases.
● Model Expansion: Future frameworks could integrate digital well-being, AI transparency, or ethical
consumption to extend the current model.
CONCLUSION
This study concludes that smartphone adoption among Malaysian university students extends beyond just
technological or social factors; it now also involves ethical considerations. While digital marketing and
innovation enhance enthusiasm and perceived value, issues of privacy and trust are crucial for the sustainable
use of technology. The P-UTAUT2 model effectively connects motivation with morality, providing a framework
for balancing innovation with responsibility.
The findings confirm that as Malaysia moves towards a digitally advanced society, trust becomes the key to
innovation. For universities, marketers, and policymakers, maintaining this trust requires making sure that
technological advancements do not compromise users’ privacy and confidence.
ACKNOWLEDGEMENT
The authors appreciate UTeM and the participating IPTA students for their contributions to the study.
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