Examining the Factors Influencing Online Purchase Intention: A Digital Marketing Perspective

Abstract

This study explores the key determinants influencing online purchase intention in the digital marketplace. The findings indicate that various digital marketing strategies and consumer perceptions play a crucial role in shaping purchase decisions. Notably, while trust and engagement drive positive purchase behaviour, perceived risk also holds significance, suggesting that consumers acknowledge potential risks but proceed with purchases due to mitigating factors such as brand credibility, return policies, and digital security measures. The study provides valuable insights for businesses aiming to optimize their online presence, enhance customer trust, and improve digital marketing effectiveness. By leveraging data-driven strategies, businesses can refine consumer interactions and boost purchase confidence. The research contributes to the growing body of knowledge on online consumer behaviour, offering practical implications for marketers, policymakers, and e-commerce platforms in designing more effective engagement and conversion strategies.

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Copyright © 2025 The Author(s): This is an open-access article distributed under the terms of the Creative
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International Journal of Scientific Research in Science and Technology
Available online at : www.ijsrst.com
Print ISSN: 2395-6011 | Online ISSN: 2395-602X
doi : https://doi.org/10.32628/IJSRST251222656
1291
Examining the Factors Influencing Online Purchase Intention: A Digital
Marketing Perspective
Anita Kumari1, Pooja Thakur2
1Assistant Professor, Department of Commerce, Dr. Harisingh Gour Vishwavidyalaya, Sagar, Madhya Pradesh, India
2Research Scholar, Department of Commerce, Dr. Harisingh Gour Vishwavidyalaya, Sagar, Madhya Pradesh, India
A R T I C L E I N F O
A B S T R A C T
Article History:
Accepted : 20 April 2025
Published: 30 April 2025
This study explores the key determinants influencing online purchase
intention in the digital marketplace. The findings indicate that various
digital marketing strategies and consumer perceptions play a crucial role in
shaping purchase decisions. Notably, while trust and engagement drive
positive purchase behaviour, perceived risk also holds significance,
suggesting that consumers acknowledge potential risks but proceed with
purchases due to mitigating factors such as brand credibility, return
policies, and digital security measures. The study provides valuable insights
for businesses aiming to optimize their online presence, enhance customer
trust, and improve digital marketing effectiveness. By leveraging data-
driven strategies, businesses can refine consumer interactions and boost
purchase confidence. The research contributes to the growing body of
knowledge on online consumer behaviour, offering practical implications
for marketers, policymakers, and e-commerce platforms in designing more
effective engagement and conversion strategies.
Keywords: Consumer Behaviour; E-commerce; Digital Marketing;
Purchase Intention; Online Shopping Behaviour
Publication Issue :
Volume 12, Issue 2
March-April-2025
Page Number :
1291-1303
I. INTRODUCTION
The rapid expansion of e-commerce has
revolutionized consumer shopping behaviour, with
digital marketing playing a pivotal role in shaping
purchase decisions. Online purchase intention refers
to a consumer’s likelihood of buying products or
services through digital platforms, influenced by
various psychological, technological, and marketing-
related factors (Pavlou, 2003; Gefen et al., 2003). The
increasing penetration of the internet, social media,
and artificial intelligence has transformed how
businesses engage with consumers, making digital
marketing strategies more dynamic and data-driven
(Kotler et al., 2021).
Online shopping involves multiple decision-making
stages, including product evaluation, risk assessment,
and final purchase intent. Empirical research indicates
that online purchase intention is shaped by a
combination of rational and emotional factors (Kim et

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al., 2008; Gefen & Straub, 2004). Their decisions are
shaped by factors such as trust, perceived risk, ease of
use, brand reputation, and emotional engagement
(Kim et al., 2008; Lim et al., 2016). Digital marketing
strategies, including AI-driven content
personalization, influencer marketing, and targeted
advertisements, further influence consumer
preferences and behaviours (Kaplan & Haenlein,
2019). While rational decision-making models
emphasize product features, pricing, and
functionality, behavioural marketing research
highlights the role of emotions, social influence, and
cognitive biases in shaping online shopping behaviour
(Thaler, 1980; Madaan & Singh, 2019).
While extensive studies have examined individual
factors such as trust, perceived risk, and brand
reputation, limited research explores their combined
influence alongside AI-driven marketing, emotional
engagement, and dynamic pricing strategies on online
purchase intention. Existing literature often
investigates these factors in separately, overlooking
their cumulative effects in shaping consumer
decision-making within digital marketplaces.
Given these considerations, this study aims to provide
a comprehensive understanding of online purchase
intention by integrating perspectives from
technological acceptance, consumer psychology, and
digital marketing strategies. This research examines
how various factors collectively influence consumers'
decision-making processes in online shopping
environments. The factors influencing Purchase
Intention were initially identified through a
comprehensive literature review. These factors were
then refined using Exploratory Factor Analysis (EFA),
ensuring a data-driven approach to construct
validation. The identified constructs were further
tested using Multiple Regression Analysis to examine
their combined influence on Purchase Intention. This
approach allows for a more holistic understanding by
integrating theoretical insights with empirical
validation. The findings will offer data-driven insights
for businesses to optimize personalized marketing
strategies, risk mitigation approaches, and trust-
building mechanisms, ultimately enhancing consumer
confidence and engagement in e-commerce.
The remainder of this study is organised as follows:
Section 2. Conceptual framework and hypotheses
present the theoretical literature and hypotheses of
this study. Section 3. Methodology explains the
statement of the problem, research methods,
techniques, and sample selection. Section 4. Data
Analysis & Interpretation presents data insights,
descriptive statistics, and hypothesis testing. Section 5.
Results & Discussion includes the findings of the
regression model and their implications. Section 6.
Conclusion summarizes the study’s key findings,
highlights research limitations, and suggests directions
for future research.
II. CONCEPTUAL FRAMEWORK AND
HYPOTHESES
The online purchase intention of consumers has been
extensively studied in digital marketing and e-
commerce research. This pertains to the likelihood of
a consumer participating in an online transaction,
influenced by factors such as trust, perceived risk,
digital marketing strategies, and brand reputation
(Pavlou, 2003; Gefen et al., 2003). This section reviews
the relevant literature by categorizing key
determinants into technological, psychological, and
marketing-related factors that shape consumer
purchase decisions.
2.1 Online Purchase Intention and Consumer
Behaviour Theories
Several theories provide a foundation for
understanding consumer behaviour in online
shopping. The Technology Acceptance Model (TAM)
(Davis, 1989) suggests that perceived usefulness and
ease of use directly impact consumers' adoption of
digital platforms. The Theory of Planned Behaviour
(TPB) (Ajzen, 1991) extends this by incorporating
attitudes, subjective norms, and perceived behavioural

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control as determinants of online purchase intention.
More recently, the Unified Theory of Acceptance and
Use of Technology (UTAUT) (Venkatesh et al., 2003)
integrates various technological and psychological
factors to explain e-commerce adoption. In contrast,
behavioural economics emphasizes emotional,
cognitive, and social influences on consumer decision-
making. Prospect Theory (Kahneman & Tversky, 1979)
explains how individuals evaluate risk and potential
losses in online transactions, while behavioural
finance research (Thaler, 1980) highlights the role of
biases and heuristics in shaping purchasing behaviour.
2.2 Artificial Intelligence and Online Purchase
Intention
The rise of AI-driven content generation has
transformed consumer engagement with digital
platforms. Studies indicate that personalized product
recommendations, AI-generated advertisements, and
automated customer interactions enhance consumer
trust and perceived convenience, leading to higher
purchase intentions (Kaplan & Haenlein, 2019;
Dwivedi et al., 2021). AI-powered chatbots and virtual
assistants also reduce uncertainty in online shopping,
addressing common consumer concerns related to
product selection and post-purchase services (Luo et
al., 2019).
H01: Generative AI content positively influences
online purchase intention.
2.3 Perceived Risk and Trust in Online Shopping
Perceived risk remains one of the most significant
barriers to online purchases, encompassing financial,
privacy, and security risks (Kim et al., 2008).
Consumers evaluate the credibility of online platforms
based on secure payment methods, transparency in
product descriptions, and return policies (Lim et al.,
2016). Higher perceived risk discourages purchases,
whereas trust-building mechanisms, such as customer
reviews, transparent product descriptions, money
back guarantees, and third-party security
certifications, mitigate risk perception and improve
buying confidence (Gefen & Straub, 2004; McKnight
et al., 2002).
H02: Higher Perceived risk reduces online purchase
intention.
H03: Consumer trust positively influences online
purchase intention.
2.4 Brand Reputation and Online Consumer
Decisions
Brand reputation is a critical determinant of purchase
intention, as consumers often rely on brand credibility
and recognition when making online purchases
(Keller, 1993). Established brands are perceived as
more reliable, reducing the uncertainty associated
with digital transactions (Erdem & Swait, 2004).
Research indicates that positive online reviews,
influencer endorsements, and brand transparency
significantly enhance consumer trust and willingness
to buy (Chatterjee, 2001; Park et al., 2007).
H04: Brand Reputation positively influences online
purchase intention.
2.5 Mobile-Friendly Interfaces in Online Shopping
Behaviour
The usability of an websites and e-commerce platform
significantly impacts purchase intention. A well-
designed, user-friendly interface, seamless navigation,
and mobile responsiveness improve consumer
engagement and reduce friction in the buying process
(Venkatesh & Davis, 2000). Prior studies show that
platforms offering simplified checkout processes,
multiple payment options, and AI-driven assistance
increase conversion rates (Gefen & Straub, 2004).
H05: Ease of Use positively influences online purchase
intention.
2.6 Emotional Branding and Consumer Buying
Behaviour
Recent studies highlight the role of emotions in
shaping online purchase decisions. Emotional
engagement, driven by compelling digital storytelling,
brand interaction, and personalized experiences,
fosters a deeper connection between consumers and

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brands. Digital marketing campaigns leveraging
interactive content, social media engagement, and
influencer partnerships have been shown to enhance
consumer enthusiasm and brand loyalty (Barger et al.,
2016).
H06: Emotional engagement positively influences
online purchase intention.
2.7 Promotional Offers and Pricing in Online
Shopping
Eye-catching pricing and promotional offers play a
crucial role in influencing consumer purchase
behaviour. Time-limited discounts, personalized
pricing algorithms, and exclusive deals create a sense
of urgency, leading to impulse purchases (Grewal et
al., 2011). Prior research suggests that discount
framing, promotional timing, and personalized pricing
strategies significantly impact consumer decision-
making (Darke & Chung, 2005; Li et al., 2020).
H07: Dynamic pricing and offers positively influences
online purchase intention.
2.8 Digital Marketing and AI-Driven Consumer
Insights
The evolution of digital marketing strategies,
including influencer collaborations, celebrity
endorsement and AI-driven ad targeting, has reshaped
online consumer behaviour (Kannan & Li, 2017). AI-
powered algorithms analyze consumer preferences,
browsing history, and real-time engagement to deliver
hyper-personalized advertisements, significantly
increasing purchase intentions (Lemon & Verhoef,
2016). Recent studies also emphasize the role of
omnichannel marketing strategies, where brands
integrate multiple online and offline touchpoints to
create seamless shopping experiences (Verhoef et al.,
2015).
H08: Digital marketing strategies positively influences
online purchase intention.
III. METHODOLOGY
This study utilized both primary and secondary data
to validate and examine the factors influencing online
purchase intention among women consumers. Data
collection was conducted through a structured
questionnaire designed to capture insights into
women's online shopping behaviour in the digital
marketplace. The target population comprised 272
women actively engaging in online purchases. A non-
probability convenience sampling method was
employed to select respondents from Indore, Madhya
Pradesh, ensuring relevant insights from individuals
experienced in online shopping. Furthermore, EFA
facilitated the identification and revalidation of key
underlying constructs shaping women's online
purchase intentions. Building on these insights,
multiple regression analysis was applied to assess the
strength and significance of relationships between
these factors and purchase intention, providing a
comprehensive understanding of their predictive
influence.
IV. DATA ANALYSIS & INTERPRETATION
4.1 Factors influencing purchase intention
The study validated the previously identified factors
through EFA before incorporating them into the
research model. To ensure the internal consistency
and reliability of the measurement scale, Cronbach’s
alpha was computed. Its value above 0.7 is generally
considered acceptable, while values above 0.9 indicate
excellent reliability (Hair et al., 2019). A Cronbach’s
alpha value of 0.945, as shown in Table 1, indicates a
high level of reliability, suggesting that the 35 items
included in the study exhibit strong internal
consistency. This confirms that the survey instrument
is well-structured and capable of producing stable and
consistent results.
Table 1: Reliability Statistics
Cronbach's Alpha
No. of Items
.945
35
Source: Primary data
To assess the suitability of the data for factor analysis,
the Kaiser-Meyer-Olkin (KMO) Measure of Sampling
Adequacy and Bartlett’s Test of Sphericity were

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conducted. The results are presented in Table 2. The
KMO value of 0.922 indicates that the sample is
highly adequate for factor analysis, as a value above
0.6 is generally considered acceptable, while values
above 0.8 or 0.9 indicate excellent suitability (Kaiser,
1974). Additionally, Bartlett’s Test of Sphericity
yielded a significant chi-square value of 6109.487 (p <
0.001), confirming that the correlation matrix is not
an identity matrix and that there are significant
relationships among the variables. This indicates that
the dataset is appropriate for factor extraction and
further analysis.
Table 2: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.922
Bartlett's Test of Sphericity
Approx. Chi-Square
6109.487
df
595
Sig.
.000
Source: Primary data
Table 3, the Total Variance Explained table, shows
that the eight extracted factors account for 69.42% of
the total variance in women's online purchase
intention. After rotation, the first factor explains the
highest variance of 10.33%, followed by subsequent
factors contributing incrementally. This indicates a
robust factor structure, effectively capturing key
determinants influencing women's online shopping
behaviour.
Table 3: Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative %
Total
% of
Variance
Cumulative %
Total
% of
Variance
Cumulative %
1
12.650
36.143
36.143
12.650
36.143
36.143
3.615
10.328
10.328
2
2.891
8.259
44.402
2.891
8.259
44.402
3.531
10.090
20.418
3
2.157
6.164
50.566
2.157
6.164
50.566
3.161
9.032
29.451
4
1.685
4.814
55.380
1.685
4.814
55.380
3.043
8.694
38.145
5
1.408
4.022
59.401
1.408
4.022
59.401
2.924
8.356
46.500
6
1.311
3.745
63.147
1.311
3.745
63.147
2.780
7.943
54.443
7
1.180
3.371
66.518
1.180
3.371
66.518
2.623
7.495
61.938
8
1.017
2.905
69.422
1.017
2.905
69.422
2.620
7.484
69.422
9
.840
2.400
71.822
10
.735
2.101
73.923
11
.719
2.053
75.976
12
.645
1.842
77.819
13
.614
1.755
79.574
14
.586
1.674
81.248
15
.569
1.624
82.872
16
.515
1.470
84.343
17
.494
1.410
85.753
18
.456
1.302
87.055
19
.420
1.199
88.254

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20
.382
1.091
89.345
21
.373
1.065
90.411
22
.365
1.044
91.455
23
.351
1.002
92.457
24
.334
.953
93.410
25
.331
.945
94.354
26
.307
.878
95.232
27
.289
.827
96.059
28
.263
.751
96.810
29
.238
.679
97.489
30
.232
.663
98.151
31
.217
.619
98.770
32
.155
.442
99.213
33
.108
.308
99.521
34
.094
.268
99.789
35
.074
.211
100.000
Source: Primary data
The Scree Plot in Figure 1 illustrates the eigenvalues
of the principal components, aiding in determining
the optimal number of factors to retain. The sharp
decline in eigenvalues after the first few components,
followed by a gradual leveling off, marks the elbow
point, which indicates the number of significant
factors. In this plot, the elbow occurs around the 8th
component, supporting the extraction of eight factors
for further analysis. This confirms that the retained
factors capture the most meaningful variance in the
dataset while eliminating less significant components.
Figure 1: Scree Plot
Source: Primary data
Table 4 presents the Rotated Component Matrix,
which displays the factor loadings for each item after
applying Principal Component Analysis (PCA) with
Varimax rotation. The results indicate that the
extracted eight factors successfully group related
variables, confirming the presence of distinct
constructs influencing women's online purchase
intentions. The factor structure exhibits a well-
distributed pattern, ensuring that the selected
variables effectively capture key dimensions shaping
consumer behaviour. The high factor loadings suggest
strong correlations between items and their respective
factors, validating the factor extraction process.
Additionally, the rotation converged in seven
iterations, further affirming the stability and
reliability of the extracted factors for subsequent
analysis.
Factor 1 (Digital Marketing Strategies - DM) includes
DM1, DM2, DM5, DM4, DM6, DM3, and DM7,
indicating that these items are strongly associated
with digital marketing's influence on online purchase
behaviour.
Factor 2 (Consumer Trust - CT) comprises CT1, CT2,
CT4, and CT3, suggesting that trust plays a significant

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role in shaping consumer confidence in online
purchases.
Factor 3 (Perceived Risk - PR) consists of PR2, PR4,
PR3, and PR1, showing that concerns about security,
fraud, or uncertainty may affect purchase decisions.
Factor 4 (Ease of Use - EOU) includes EOU3, EOU4,
EOU1, EOU2, and EOU5, highlighting the importance
of user-friendly digital platforms in driving purchase
intentions.
Factor 5 (Dynamic Pricing & Offers - DP) consists of
DP3, DP4, DP2, DP5, and DP1, reflecting the
cognitive process consumers undergo before making
online purchase decisions.
Factor 6 (Generative AI - GAI) is represented by GAI1,
GAI3, GAI2, and GAI4, indicating that AI-generated
content influences consumer choices.
Factor 7 (Brand Reputation - BR) includes BR1, BR2,
and BR3, signifying that brand perception and
credibility are key determinants of purchase decisions.
Factor 8 (Emotional Engagement - EE) consists of EE2,
EE1, and EE3, suggesting that emotional engagement
through storytelling contribute to online purchase
behaviour.
Table 4: Rotated Component Matrix
Component
1
2
3
4
5
6
7
8
DM1
.733
DM2
.725
DM5
.708
DM4
.615
DM6
.585
DM3
.572
DM7
.561
CT1
.891
CT2
.883
CT4
.791
CT3
.762
PR2
.672
PR4
.664
PR3
.662
PR1
.618
EOU3
.717
EOU4
.677
EOU1
.669
EOU2
.599
EOU5
.528
DP3
.733
DP4
.712
DP2
.629
DP5
.574
DP1
.524
GAI1
.793
GAI3
.760
GAI2
.610
GAI4
.569
BR1
.808
BR2
.796
BR3
.789
EE2
.848
EE1
.824
EE3
.782
Source: Primary data
4.2 Influence of factors on online purchase intention
This section presents the multiple regression analysis
to examine the impact of the identified factors on
women's online purchase intentions. The extracted
factors from EFA serve as independent variables,
while purchase intention (PI) is the dependent
variable. The regression analysis aims to determine
the strength and significance of these relationships,
providing deeper insights into how different digital
marketing elements influence consumers purchase
intentions.
Table 5 presents key statistical measures of the
regression analysis. The R value (0.733) indicates a
strong positive correlation between the predictor
variables (EE, BR, GAI, DP, EOU, PR, CT, DM) and
the dependent variable (PI). The R Square value
suggests that 53.7% of the variance in women's online
purchase intention (PI) is explained by the
independent variables, demonstrating a good model
fit. The Adjusted R Square (0.523) accounts for the

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number of predictors in the model, providing a more
accurate estimate of explanatory power. The Standard
Error of the Estimate (0.66862) reflects the average
deviation of observed values from the predicted
values, indicating the model’s predictive accuracy.
Lastly, the Durbin-Watson statistic (2.032) suggests
that there is no significant autocorrelation in the
residuals, confirming the reliability of the regression
model.
Table 5: Model Summary
Model
R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Durbin-
Watson
1
.733a
.537
.523
.66862
2.032
a. Predictors: (Constant), EE, BR, GAI, DP, EOU, PR,
CT, DM
b. Dependent Variable: PI
The ANOVA table evaluates the overall significance
of the regression model. The F-statistic (38.163) and
its corresponding p-value (.000) indicate that the
model is statistically significant at the 0.05 level. This
confirms that the independent variables (EE, BR, GAI,
DP, EOU, PR, CT, DM) collectively have a significant
impact on the dependent variable (PI). The regression
sum of squares (136.490) is notably higher than the
residual sum of squares (117.576), suggesting that the
model explains a substantial proportion of the total
variance in women's online purchase intention. This
validates the suitability of the regression model for
analyzing the relationships between the predictor
variables and the outcome variable.
Table 6: ANOVA
Model
Sum of
Squares
df
Mean
Square
F
Sig.
1
Regression
136.490
8
17.061
38.163
.000b
Residual
117.576
263
.447
Total
254.066
271
a. Dependent Variable: PI
b. Predictors: (Constant), EE, BR, GAI, DP, EOU, PR,
CT, DM
Table 7 presents the regression coefficients,
highlighting the impact of various factors on women's
online purchase intentions. The results indicate that
all independent variables significantly influence
purchase intention, as evidenced by their p-values
being below 0.05. Among these, Brand Reputation
(BR) (β = 0.518, p = .000) emerges as the most
influential factor, suggesting that a strong brand image
plays a crucial role in shaping consumer trust and
purchase decisions. Consumer Trust (CT) (β = 0.262, p
= .000) and Digital Marketing Strategies (DM) (β =
0.238, p = .000) also exhibit a substantial impact,
reinforcing the importance of credibility and targeted
marketing efforts. Additionally, Decision Process (DP)
(β = 0.237, p = .000) and Perceived Risk (PR) (β =
0.182, p = .000) contribute to understanding purchase
behaviour, indicating that a structured decision-
making approach and perceived risks affect consumer
choices. Factors such as Generative AI Content (GAI)
(β = 0.141, p = .001), Ease of Use (EOU) (β = 0.103, p
= .015), and Engagement Experience (EE) (β = 0.153, p
= .000) also play a significant role, demonstrating that
AI-driven content, user-friendly interfaces, and
interactive engagement enhance consumer confidence.
The multicollinearity statistics, with Tolerance values
of 1.000 and VIF values of 1.000, confirm the
independence of predictor variables, ensuring a stable
and reliable model. These results indicate no
multicollinearity concerns. Based on the regression
coefficients, the estimated regression equation for
predicting women's online purchase intention is as
follows: PI = β0 + β1 (DM) + β2 (CT) + β3 (PR) + β4
(EOU) + β5 (DP) + β6 (GAI) + β7 (BR) + β8 (EE) + ϵ
Using unstandardized coefficients:
PI = 4.735 + 0.230 (DM) + 0.253 (CT) + 0.177 (PR) +
0.100 (EOU) + 0.230 (DP) + 0.137 (GAI) + 0.502 (BR) +
0.148 (EE) + ϵ
where:
PI (Dependent variable) & DM, CT, PR, EOU, DP,
GAI, BR, EE (Predictors)
4.735 = Constant (intercept)
ε = Error term

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Table 8: Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity Statistics
B
Std. Error
Beta
Tolerance
VIF
1
(Constant)
4.735
.041
116.802
.000
DM
.230
.041
.238
5.663
.000
1.000
1.000
CT
.253
.041
.262
6.237
.000
1.000
1.000
PR
.177
.041
.182
4.346
.000
1.000
1.000
EOU
.100
.041
.103
2.453
.015
1.000
1.000
DP
.230
.041
.237
5.660
.000
1.000
1.000
GAI
.137
.041
.141
3.371
.001
1.000
1.000
BR
.502
.041
.518
12.357
.000
1.000
1.000
EE
.148
.041
.153
3.652
.000
1.000
1.000
Source: Primary data
The residuals statistics confirms that the residuals are
approximately normally distributed, with a mean of
zero and standard deviation close to one. The
predicted values range from 2.7015 to 6.3994, while
the standardized residuals fall within ±3, indicating no
significant outliers. This suggest that the regression
model meets key assumptions of normality and
homoscedasticity, ensuring the reliability of the
findings.
V. RESULTS & DISCUSSION
This study examined the influence of various
traditional and modern factors on online purchase
intention using multiple regression analysis. The study
confirms that Generative AI content, Consumer Trust,
Brand Reputation, Ease of Use, Emotional
Engagement, Dynamic Pricing, and Digital Marketing
Strategies significantly influence online purchase
intention.
The positive impact of Generative AI content suggests
that AI-driven recommendations, chatbots, and
automated content enhance consumer decision-
making by providing personalized shopping
experiences, real-time assistance, and tailored product
suggestions. These AI-driven tools reduce search
efforts, address consumer queries instantly, and create
engaging interactions, leading to increased trust and
purchase confidence. The ability of AI to analyze
consumer preferences and deliver relevant content
improves user satisfaction, ultimately driving higher
online purchase intentions. This finding emphasizes
the growing role of AI in digital marketing and the
need for businesses to integrate intelligent automation
to enhance consumer engagement.
Consumer trust and brand reputation emerged as
strong predictors of purchase intention, indicating
that consumers are more likely to make online
purchases when they perceive a brand as credible and
trustworthy. Empirical studies confirm that higher
consumer trust leads to a greater willingness to engage
in online transactions (Chatterjee, 2001; Park et al.,
2007). Strategies such as trust seals, influencer
endorsements, and interactive customer service
contribute to building long-term consumer trust in e-
commerce platforms (Luo et al., 2019). Businesses
must prioritize transparency, quality assurance, and
customer engagement to strengthen consumer
confidence and drive purchase decisions.
Ease of use was also found to be a crucial factor,
highlighting that a user-friendly shopping experience
improves purchase likelihood. A seamless, user-
friendly interface, easy navigation, and a smooth
transaction process enhance consumer confidence and

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1300
willingness to purchase online. When online
platforms minimize complexity and offer a hassle-free
shopping experience, consumers are more likely to
complete their purchases. This underscores the
importance of optimizing website and app usability to
improve engagement and repeat sales.
Furthermore, emotional engagement plays a key role
in shaping purchase intention, emphasizing the need
for brands to create personalized and emotionally
appealing content. Consumers are more likely to make
purchases when they feel a personal connection with
the brand through emotionally appealing content,
storytelling, and interactive experiences. Dynamic
pricing and promotional offers significantly drive
online purchases, proving that consumers respond
positively to personalized discounts and competitive
pricing strategies.
Digital marketing strategies such as influencer
marketing, targeted advertisements, and social media
engagement also have a strong impact on purchase
decisions. Interestingly, the study found that
perceived risk also positively influences online
purchase intention. Despite recognizing potential
risks, consumers still proceed with their purchases,
suggesting that factors like brand trust, reputation,
security measures, and flexible return policies help
mitigate risk concerns. This highlights the importance
of reassuring customers through clear policies and
secure transaction processes.
Table 9: Summary of Hypotheses
Hypotheses
Sig.
Results
H01 Generative AI content positively influences online purchase intention.
0.001
Supported
H02 Higher Perceived risk reduces online purchase intention.
0.000
Not Supported
H03 Consumer trust positively influences online purchase intention.
0.000
Supported
H04 Brand Reputation positively influences online purchase intention.
0.000
Supported
H05 Ease of Use positively influences online purchase intention.
0.015
Supported
H06 Emotional engagement positively influences online purchase intention.
0.000
Supported
H07 Dynamic pricing and offers positively influences online purchase
intention.
0.000
Supported
H08 Digital marketing strategies positively influences online purchase
intention.
0.000
Supported
VI. IMPLICATIONS
➢ This study contributes to digital marketing
literature by integrating AI-generated content
and emotional engagement as key drivers of
purchase intention.
➢ The positive impact of perceived risk challenges
conventional risk-aversion theories, suggesting a
possible shift in consumer behaviour due to
evolving digital trust mechanisms.
➢ Brands should leverage AI-generated content to
personalize recommendations, enhance
engagement, and boost trust among online
shoppers.
➢ E-commerce platforms and business should focus
on brand reputation management through
customer reviews, influencer endorsements, and
robust return policies.
➢ Marketers should refine digital strategies by
integrating emotional engagement techniques,
interactive content, and targeted promotions to
drive higher conversions.
➢ Risk perception should be managed strategically
by offering flexible refund policies and
highlighting security measures.

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VII. CONCLUSION
This research contributes to the growing field of
consumer behaviour and digital marketing, offering
empirical evidence on the factors that drive online
purchase intention. The findings reveal that
Generative AI content, Consumer Trust, Brand
Reputation, Ease of Use, Emotional Engagement,
Dynamic Pricing, and Digital Marketing Strategies
positively impact online purchase intention,
supporting the majority of the hypotheses. While
Perceived Risk has traditionally been seen as a barrier
to online purchasing (Pavlou, 2003; Gefen & Straub,
2004), recent studies suggest that its impact may vary
based on industry, consumer experience, and brand
trust (Lim et al., 2016; Luo et al., 2019). Our findings
indicate that perceived risk is significant to purchase
intention, suggesting that consumers acknowledge
potential risks but may still proceed with a purchase
due to mitigating factors such as brand reputation,
return policies, and digital security measures. These
insights provide valuable implications for marketers
and e-commerce businesses, emphasizing the need for
trust-building strategies, AI-driven personalization,
and enhanced security measures to optimize consumer
engagement and conversion rates.
VIII. LIMITATIONS AND FUTURE SCOPE
This study, based on regression analysis, provides
insights into the factors influencing online purchase
intention. However, it is limited to a specific
population and geographic scope, which may affect
the generalizability of the findings. Future research
can expand the sample size and include diverse
demographics to enhance applicability. Additionally,
while this study examines key factors such as
Generative AI content, Consumer Trust, Brand
Reputation, Perceived Risk, and Digital Marketing
Strategies, future studies can explore additional
psychological and social influences. Employing
Structural Equation Modeling (SEM) can provide a
more comprehensive analysis by assessing direct,
indirect, and mediating effects. Longitudinal studies
could also help capture evolving consumer behaviours
in the rapidly changing digital marketplace.
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