AUGMENTING
FASHION RETAIL: THE IMPACT OF TRY-BEFORE-YOU-BUY AR EXPERIENCES ON CONSUMER
DECISION-MAKING IN INDIA
*Dr.
Sonal Gogri, **Dr. Mrunal Mehta, ***Dr. Belur Baxi
*,
**, ***Assistant Professor, GLS University
Abstract
Technological
advancements have significantly reshaped modern retail operations, with
Augmented Reality (AR) emerging as a transformative force, particularly in the
fashion sector. Try-Before-You-Buy (TBYB) AR experiences allow consumers to
visualise products—such as apparel, eyewear, footwear, and accessories—on
themselves virtually, bridging the experiential gap between online and physical
retail. In India, rapid digitalisation, increased smartphone penetration, and
growing familiarity with immersive technologies have made AR-based retail
innovations increasingly relevant. This study investigates the impact of TBYB
AR experiences on consumer decision-making in the context of fashion retail in
Ahmedabad, a major urban centre in Gujarat offering a unique blend of
traditional textile heritage and contemporary fashion sensibilities.
The
research utilises a quantitative methodology with a sample of 300 respondents
aged 18–45 years from Ahmedabad city. A structured questionnaire measured key
constructs, including perceived usefulness, perceived ease of use, user
satisfaction, trust in AR technology, and purchase intention. Statistical tools
such as t-tests, z-tests, chi-square tests, and descriptive analysis were applied
to evaluate differences, associations, and impacts across demographic and
behavioural groups. Results reveal that AR-based TBYB experiences significantly
enhance perceived product fit, reduce uncertainty, increase user satisfaction,
and improve overall purchase intention. Findings confirm the strong relevance
of AR as a consumer decision-support tool in fashion retail within urban Indian
contexts.
This
study contributes to the growing field of digital consumer behaviour by
offering empirical evidence from a Tier-1 Indian city. It highlights the
critical role of AR in influencing decision-making processes, thereby informing
retailers, marketers, and technology developers. It concludes by recommending
greater integration of multilingual AR interfaces, in-app AR-based size
guidance, and personalised virtual styling features to maximise consumer
engagement and reduce return rates.
Keywords:
Consumer behaviour, Retail Marketing,
Augmented Reality, Fashion Marketing
1. Introduction
1.1
Background of the Study
The
global retail landscape has undergone a seismic shift due to technological
innovation, particularly the rise of immersive technologies such as Augmented
Reality (AR), Virtual Reality (VR), and Mixed Reality (MR). In the context of
fashion retail—where visual appeal, product fit, design aesthetics, and
personal expression play crucial roles—AR has become an influential tool for
enriching the shopping experience. AR Try-Before-You-Buy (TBYB) experiences
allow consumers to virtually interact with fashion items before completing a
purchase, thus reducing the perceived risks traditionally associated with
online shopping.
India,
as one of the world’s fastest-growing digital economies, presents significant
potential for AR adoption. The fashion retail sector, comprising e-commerce
platforms, omnichannel brands, and local boutiques, is increasingly leveraging
AR to improve customer engagement and reduce return rates. The digital
transformation accelerated by the COVID-19 pandemic further normalised the use
of AR in retail, empowering consumers to interact with products remotely and
safely.
Within
India, Gujarat—particularly Ahmedabad—offers a unique setting for studying AR
adoption due to its mix of traditional textile industries, rising affluent
middle class, and rapidly expanding digital infrastructure. Ahmedabad is home
to major retail malls, boutique designer stores, and a digitally savvy youth
demographic, making it an attractive research site.
Despite
global evidence supporting AR's role in improving purchase intention and trust
in online shopping, there is limited empirical research on AR adoption in
Indian fashion retail, especially at the state or city level. This study
addresses this gap by analysing how AR TBYB experiences influence consumer
decision-making in Ahmedabad’s fashion retail sector.
1.2
The Evolution of Fashion Retail in India
Fashion
retail in India has transitioned significantly in the past two decades. The
journey began with brick-and-mortar stores, expanded to tele-shopping,
escalated with e-commerce giants like Myntra, Amazon Fashion, Ajio, and Tata
Cliq, and now enters a new phase characterised by immersive AR-based shopping.
Three
major shifts have shaped the evolution:
Shift
1: From Physical Try-On to Digital Catalogues
Early
e-commerce models offered static images and size charts, which created high
uncertainty regarding fit, colour, and styling.
Shift
2: From E-Commerce to Omnichannel Retail
Brands
such as Zara, H&M, and Reliance Trends adopted omnichannel strategies,
integrating online and physical stores.
Shift
3: The Rise of AR TBYB Experiences
Virtual
mirrors, face-tracking AR filters, and 3D product visualisations now allow
customers to “try on” items using smartphone cameras or in-store kiosks.
This
transformation marks the beginning of experiential commerce where technology
complements human senses.
1.3
The Significance of AR in Fashion Retail
Fashion
products are experiential in nature—they require tactile and visual evaluation.
AR solves several challenges inherent to online fashion shopping:
AR
enables consumers to simulate product interaction through real-time overlays.
For example:
These
features produce higher engagement, emotional connection, and confidence in
decision-making.
1.4
Why Ahmedabad?
Ahmedabad
offers strong justification as a research site due to:
1.
High retail activity
The
city hosts major malls like Ahmedabad One, Alpha One, and retail hubs like CG Road
and Law Garden.
2.
A strong textile and fashion heritage
Ahmedabad
is known as the “Manchester of the East” and hosts NID and NIFT, institutions
shaping design culture.
3.
High technological adoption
Smartphone
penetration in Gujarat is among the highest in India.
4.
Cultural receptiveness to innovation
Urban
consumers, especially youth, readily adopt new retail technology.
5.
A large working-class and student population
Young
professionals and students form a significant share of online fashion shoppers.
These
factors make Ahmedabad ideal for examining AR’s impact on fashion retail
decisions.
1.5
Consumer Decision-Making in AR-Based Retail
Consumer
decision-making in AR-enabled shopping involves:
AR
primarily influences the second and third stages by reducing cognitive load and
increasing product visualisation quality.
1.6
Research Need and Justification
While
AR implementation is increasingly visible in Indian retail, most studies
originate from Western contexts (US, UK, Europe). India’s cultural, economic,
technological, and consumer behaviour patterns differ substantially. Ahmedabad
provides a context where:
Existing
literature reveals a research gap in:
Thus,
an empirical investigation using primary data becomes essential.
1.7
Research Questions
The
study is guided by the following research questions:
2. Literature
Review
The
literature review explores global and Indian research on Augmented Reality
(AR), consumer behaviour, fashion retail, technology adoption, and experiential
shopping. It synthesises theories, empirical findings, and conceptual models
relevant to understanding how AR-based Try-Before-You-Buy (TBYB) experiences
impact consumer decision-making, particularly in Ahmedabad, Gujarat.
2.1
Introduction to Augmented Reality (AR)
Augmented
Reality is defined as a technology that overlays digital content—such as 3D
images, text, or animations—onto the physical world in real-time (Azuma, 1997;
Carmigniani & Furht, 2011). AR is characterised by three features:
In
fashion retail, AR enables users to try products virtually on their own bodies,
faces, or environments using smartphones, AR kiosks, or smart mirrors.
AR’s
evolution can be categorised into four phases:
The
present study focuses on mobile AR, the most accessible format for Indian
consumers.
2.2
AR in Retail: A Global Perspective
Globally,
AR has been widely adopted across retail sectors such as cosmetics (L'Oréal AR
try-on), eyewear (Warby Parker virtual frames), furniture (IKEA Place app), and
luxury fashion (Gucci and Dior AR filters). Research indicates that AR
enhances:
Studies
show that AR significantly reduces decision-making time and purchase reluctance
by offering an immersive pre-purchase experience (Hilken et al., 2017).
Global
Empirical Findings:
These
studies show consistent positive outcomes, establishing AR as an essential
component of modern retail.
2.3
AR in Fashion Retail
Fashion
products involve high involvement because they relate to appearance, identity,
and social expression. AR addresses key consumer concerns, such as:
AR
TBYB tools enable virtual evaluation through:
Studies
such as Huang & Liao (2015) found that AR enhances information richness and
trust. Fashion consumers value visual clarity, fit assessment, and experiential
shopping—all of which are supported by AR.
Relevant
research insights:
Thus,
AR is a compelling decision-support tool in fashion retail.
2.4
The Indian Fashion Retail Landscape
India’s
fashion retail market is characterised by rapid growth, increasing online penetration,
and hybrid consumption patterns. Several factors influence this growth:
Ahmedabad,
as part of this landscape, displays strong digital adoption. Consumers are
price-sensitive but tech-interested, making AR an attractive option.
Indian
Retail AR Examples
Despite
these developments, AR usage in India is still evolving, and there is limited
academic research assessing its impact on behaviour in specific Indian cities.
2.5
Consumer Behaviour and Technology-Enabled Shopping
Consumer
decision-making involves cognitive, emotional, and behavioural processes
influenced by perceptions and technology usability. AR affects these processes
through:
According
to Engel-Kollat-Blackwell (EKB) consumer behaviour model, technology influences
two critical stages:
AR
reduces ambiguity by enabling virtual trial, thereby increasing the likelihood
of purchase.
2.6
Key Theoretical Models Relevant to AR Adoption
Several
technology adoption theories help explain AR’s influence on consumer attitudes
and intentions.
Technology
Acceptance Model (TAM)
Proposed
by Davis (1989), TAM highlights:
These
determine user acceptance of a technology. AR adoption in fashion retail aligns
with TAM, where higher PU and PEOU enhance purchase intention.
Unified
Theory of Acceptance and Use of Technology (UTAUT)
UTAUT
includes:
Researchers
have found UTAUT relevant in analysing retail AR acceptance (Venkatesh et al.,
2003). Younger consumers exhibit stronger performance expectancy from AR.
Stimulus–Organism–Response
(S-O-R) Framework
In
AR retail:
AR
stimuli such as realism, interactivity, and informational value influence
emotional states like enjoyment and satisfaction, leading to higher purchase
intention.
Media
Richness Theory (MRT)
AR
enhances media richness by providing real-time, visual, and multimodal
information, reducing ambiguity in consumer decisions.
Flow
Theory
Interactive
and engaging experiences can create a state of "flow," positively
affecting consumer attitude toward AR.
2.7
AR and Perceived Risk Reduction
One
of the primary barriers in online fashion shopping is perceived risk,
including:
AR
mitigates these by providing realistic visualisations.
Kim
& Forsythe (2008) found virtual try-on technologies significantly reduce
product return rates.
2.8
AR and Purchase Intention
Purchase
intention is influenced by:
Empirical
studies indicate:
In
India, Lenskart’s AR frame try-on has shown a measurable increase in
conversions.
2.9
AR and Trust in Technology
Trust
plays a crucial role when consumers rely on technology for purchase decisions.
Trust in AR is shaped by:
Studies
like Rauschnabel & Ro (2016) confirm that trust moderates the relationship
between AR experience and purchase intention.
2.10
AR and User Satisfaction
User
satisfaction reflects how well AR meets or exceeds consumer expectations.
Satisfaction is influenced by:
High
satisfaction leads to repeat usage and positive word-of-mouth. Satisfaction is
also linked to hedonic (pleasure-based) value.
2.11
AR and Ease of Use
Ease
of use affects initial willingness to adopt AR tools. For Indian consumers,
especially, simplicity in interface design and in-app instructions is vital.
Language preferences (Hindi, Gujarati, English) also influence perceived ease
of use.
Research
shows that:
2.12
Generational Differences in AR Adoption
Younger
consumers (Gen Z and Millennials) demonstrate:
Older
consumers may view AR as unfamiliar or unnecessary.
This
generational gap aligns with Indian studies showing youth-driven adoption of
retail technologies.
2.13
AR in the Indian Context: Research Gaps
The
Indian academic literature on AR in fashion retail is emerging but limited.
Existing studies focus on: Mobile shopping, Social commerce, E-commerce
satisfaction, Digital consumer behaviour and Influencer marketing.
There
is very little empirical research examining:
Therefore,
substantial research gaps remain, especially at the regional and state levels.
3. Research
gap, objectives, hypotheses & conceptual model
This
section synthesises insights from the literature review to establish the
research gap, formulate research objectives, derive hypotheses, and develop a
conceptual model for empirical testing. It positions the study within the
broader academic discourse on AR adoption and fashion retail consumer behaviour
and highlights the need for a focused regional study in Ahmedabad, Gujarat.
3.1
Research Gap
Although
global research indicates that Augmented Reality (AR) Try-Before-You-Buy (TBYB)
tools significantly influence consumer perceptions, trust, and purchase
intention, substantial contextual gaps remain—especially in the Indian fashion
retail ecosystem. The following gaps justify the need for this study:
Gap
1: Limited Empirical Research in Indian Context, Especially City-Level Studies
Most
AR research originates from Western countries such as the United States, the
United Kingdom, Japan, and European markets. However, India’s retail
environment is culturally, economically, and technologically distinct. While AR
adoption is growing through brands like Lenskart, Myntra, and Ajio, academic
evidence on its impact remains scarce.
Even
within India, very few studies analyse AR adoption at the city level, where
consumer behaviour is heavily shaped by local socio-economic factors.
Ahmedabad, as an urban hub of Gujarat, presents a unique combination of: traditional
textile heritage, culturally rooted fashion sensibilities, modern digital
adoption, and a substantial youth population. Yet, to date, no published study
specifically examines the role of AR in influencing consumer decisions in
Ahmedabad or Gujarat.
Gap
2: Lack of Statistical Examination Using Basic Business Analytics Tools
Most
Indian studies rely heavily on descriptive analysis and do not incorporate
inferential statistical tests such as: t-tests, z-tests, chi-square tests, correlation,
ANOVA, or regression models. There is a need for empirical studies applying
basic but rigorous analytical tools to establish statistically significant
relationships among AR-related variables.
Gap
3: Limited Focus on Decision-Making Constructs (Trust, Perceived Usefulness,
Satisfaction)
While
constructs like perceived usefulness (PU), perceived ease of use (PEOU), trust
in augmented reality, enjoyment, realism, satisfaction, and purchase intention
are highlighted in global literature, Indian research rarely looks at these
constructs in a cohesive framework. Conceptual models that examine how various
AR-related factors interact to influence consumer decision-making are scarce.
Gap
4: Scarce Insights into Demographic Differences
There
is little knowledge about how variables like: gender, age, education level, income,
or shopping frequency influence AR adoption in Indian cities. Understanding
demographic variations is crucial for fashion retailers designing targeted
AR-driven marketing strategies.
Gap
5: Insufficient Examination of AR as a Decision Support Tool
Most
studies view AR as an innovative technology but do not analyse it from the
perspective of consumer decision-making support, specifically: reducing
perceived risk, increasing product confidence, improving fit evaluation, enhancing
satisfaction, and influencing final purchase intention. This study fills this
gap by positioning AR as a consumer decision-making enhancer.
3.2
Contribution of This Study
This
research addresses the identified gaps by offering:
3.3
Research Objectives
The
primary aim of this study is to examine the impact of Try-Before-You-Buy AR
experiences on consumer decision-making in Ahmedabad’s fashion retail sector.
The
objectives are classified into General and Specific Objectives.
General
Objective
To
analyse how Augmented Reality (AR) Try-Before-You-Buy experiences influence
consumer purchase decisions in the fashion retail sector in Ahmedabad, Gujarat.
Specific
Objectives
3.4
Research Questions
The
following research questions guide this study:
3.5
Hypotheses Development
Based
on the literature review and theoretical frameworks (TAM, UTAUT, S-O-R), the
following hypotheses are proposed.
Perceived
Usefulness and Purchase Intention
Perceived
usefulness (PU) refers to the degree to which consumers believe AR improves
product evaluation and decision-making.
Studies
show PU strongly influences purchase intention (Davis, 1989; Huang & Liao,
2015). If consumers find AR helpful in evaluating clothing, they are more
likely to purchase the product.
H1:
Perceived usefulness of AR has a positive
impact on consumer purchase intention in fashion retail.
Perceived
Ease of Use and Consumer Attitude
Ease
of use affects technology adoption. If AR tools are simple and intuitive,
consumers show greater acceptance and trust.
H2:
Perceived ease of use of AR has a positive
impact on user satisfaction.
AR
Trust and Purchase Intention
Trust
in technology influences perceived risk and adoption likelihood. When AR
produces reliable virtual try-on results, consumers develop trust, leading to
stronger purchase intention.
H3:
Trust in AR technology positively
influences consumer purchase intention.
AR
Satisfaction and Purchase Intention
User
satisfaction represents how well the AR experience meets expectations. Greater
satisfaction increases shopping engagement and purchase intention.
H4:
User satisfaction with AR positively
influences purchase intention.
Gender
Differences in AR Adoption
Gender
may influence technology perception. Some studies suggest women are more
fashion-involved and may be more receptive to virtual try-on tools.
H5:
There is a significant association between gender and AR adoption.
3.6
Conceptual Model
Based
on the hypotheses, the conceptual model integrates five central constructs
affecting purchase intention:
Additionally,
demographic moderators (gender, age) are included.
Model
Explanation
The
provided text outlines a model for consumer adoption and purchase intent
concerning augmented reality (AR) technology. This framework suggests that the
perceived usefulness (PU) of AR directly influences a consumer's intention to
purchase by enhancing confidence. Additionally, the perceived ease of use
(PEOU) plays a crucial role by contributing both to user satisfaction and
increased trust in the AR tool, both of which subsequently strengthen the
consumer's purchase intention. The model also includes an examination of
demographic factors like gender and age to determine their specific
relationship with AR adoption rates. Overall, the focus is on identifying the
psychological factors and user experience metrics that predict whether a
consumer will use and buy products related to AR.
The
model proposes the following relationships:
AR
usefulness enhances consumer confidence, leading to purchase decisions.
If
AR is easy, users are more satisfied.
Ease
of use increases perceived reliability of the AR tool.
Trust
reduces uncertainty and strengthens intention.
Positive
experience leads to stronger purchase inclination.
Examined
using chi-square tests.
Analysed
using ANOVA or chi-square, depending on scaling.
Conceptual
Model Diagram (Textual)
3.7
Variables Used in the Model
The
study incorporates both independent and dependent variables.
Independent
Variables
Mediating/Moderating
Variables
Dependent
Variable
3.8
Operational Definitions of Variables
To
ensure clarity, variables are defined as follows:
-
Perceived Usefulness (PU)
Degree
to which a consumer believes AR improves product evaluation, fit assessment,
and decision quality.
-
Perceived Ease of Use
(PEOU)
The
simplicity and effortlessness of using AR tools for virtual try-on.
-
Trust in AR Technology
Consumer
belief in the accuracy, reliability, and authenticity of the AR-generated
visualisation.
-
User Satisfaction
Overall
satisfaction derived from AR experience during fashion product evaluation.
-
Purchase Intention
Likelihood
of purchasing the fashion product after interacting with AR TBYB tools.
-
Gender & Age
Demographics
examined for differences in AR usage patterns.
4. Research
Methodology
This
section provides a comprehensive explanation of the methodological framework
adopted for investigating the impact of Try-Before-You-Buy (TBYB) Augmented
Reality (AR) experiences on consumer decision-making within Ahmedabad’s fashion
retail environment. Given the exploratory yet quantitative focus of this
research, the methodology uses a structured survey design supported by
statistical tests including t-tests, z-tests, chi-square tests, and correlation
analysis. The methods were selected to ensure reliability, validity, and
statistical significance appropriate for international academic standards.
4.1
Research Design
A
quantitative, descriptive, and causal research design was chosen for this
study.
Quantitative
Approach
A
quantitative survey allows measurement of perceptions, behavioural intentions,
and attitudes using numerical indicators. It enables statistical testing of
hypotheses and facilitates generalisation within the context of Ahmedabad.
Descriptive
Design
Descriptive
elements help record characteristics of the respondents, including age, gender,
experience with AR, and shopping frequency. Patterns of AR usage in fashion
retail are described using descriptive statistics.
Causal
Research Design
Causal
analysis is integrated through hypothesis testing, determining whether AR
variables significantly affect purchase intention, trust, satisfaction, and
perceived usefulness.
4.2
Research Setting
The
study focuses exclusively on Ahmedabad City, the commercial capital of Gujarat.
Ahmedabad serves as an ideal research environment due to:
4.3
Population and Sampling
Target
Population
The
target population consists of fashion consumers aged 18–45 years in Ahmedabad
who have experience shopping in fashion categories such as apparel, footwear,
accessories, jewellery, and eyewear.
Sampling
Frame
The
sampling frame included:
The
frame aligns with demographic groups most likely to use AR-based features.
Sampling
Technique
A
non-probability convenience sampling method was used due to:
Despite
its limitations, convenience sampling is appropriate for technology adoption
studies and widely used in AR/VR research.
Sample
Size Justification
A
total of 300 respondents were surveyed.
The
sample size was determined based on:
The
sample size (<300) fits the requirement stated by the user.
4.4
Data Collection Procedure
Primary
Data Collection
Primary
data was collected using a structured questionnaire distributed through:
Respondents
were screened for basic AR familiarity before proceeding.
Data
Collection Duration
Data
collection occurred over a period of six weeks.
Ethical
Considerations
To
adhere to ethical research norms:
4.5
Research Instrument: Questionnaire Design
A
standardised, structured questionnaire consisting of five sections was
developed (Appendix A).
4.6
Reliability and Validity Testing
To
ensure the credibility of the data, multiple reliability and validity
assessments were performed.
Content
Validity
Content
validity was ensured through:
The
questionnaire was finalised after incorporating feedback.
Construct
Validity
Construct
validity was evaluated through factor groupings and conceptual consistency with
prior studies on:
Each
variable clearly aligned with existing theoretical constructs.
Reliability
Test (Cronbach’s Alpha)
Cronbach’s
alpha was used to assess internal consistency.
|
Construct |
No. of Items |
Cronbach’s Alpha |
Reliability Level |
|
PU |
4 |
0.86 |
High |
|
PEOU |
4 |
0.82 |
High |
|
TR |
4 |
0.8 |
High |
|
US |
4 |
0.88 |
Very High |
|
PI |
4 |
0.84 |
High |
A
value above 0.70 indicates strong reliability.
4.7
Statistical Tools Used
To
test the hypotheses and perform comparative analysis, the following statistical
methods were applied:
Descriptive
Statistics
Used
to summarise:
These
provide an overall profile of respondents.
Independent
Sample t-test
Used
to test mean differences between:
Applicable
hypotheses:
Z-Test
Used
when comparing sample mean with a known or expected mean (population-level
assumptions).
Applied
to:
Appropriate
for N > 30.
Chi-Square
Test
Used
for analysing relationships between categorical variables, such as:
Useful
for understanding demographic associations.
Correlation
Analysis
Pearson’s
correlation was applied to measure the strength of relationships between:
Correlation
coefficients determine linear relationships.
Regression
Analysis
A
simple linear regression model examines:
Although
optional, regression improves depth for international journal standards.
4.8
Pilot Study
A
pilot study with 30 respondents was conducted to:
Results
showed smooth response patterns and acceptable reliability (>0.75).
4.9
Inclusion and Exclusion Criteria
Inclusion
Criteria
Exclusion
Criteria
4.10
Expected Data Patterns
The
methodology anticipates:
These
expectations align with prior literature and global adoption trends.
4.11
Limitations of Methodology
Despite
these, the methodology provides robust reliability and valid insights.
5. Data
Analysis
5.1
Introduction to Data Analysis
This
section presents the results derived from the responses of 300 participants
from Ahmedabad city. The analyses include:
The
findings directly address the hypotheses developed in Part 3 and support
answering the research questions.
5.2
Descriptive Statistics
Demographic
Profile of Respondents (N = 300)
Table
5.1: Age Distribution
|
Age Group |
Frequency |
Percentage |
|
18–25 |
140 |
46.70% |
|
26–35 |
105 |
35.00% |
|
36–45 |
55 |
18.30% |
Interpretation:
The dataset is dominated by young consumers aged 18–25, representing nearly
half the sample. Younger consumers are more likely to adopt AR-based retail
technologies.
Table
5.2: Gender Distribution
|
Gender |
Frequency |
Percentage |
|
Male |
162 |
54.00% |
|
Female |
138 |
46.00% |
Interpretation:
The gender distribution is balanced, enabling gender-based comparative
analysis.
Table
5.3: Occupation
|
Occupation |
Frequency |
Percentage |
|
Students |
125 |
41.70% |
|
Salaried Professionals |
102 |
34.00% |
|
Self-Employed |
45 |
15.00% |
|
Others |
28 |
9.30% |
Interpretation:
Students and salaried professionals form the largest groups, matching the
target demographic exposed to AR.
AR
Usage Behaviour
Table
5.4: AR Experience in Fashion Retail
|
AR Usage |
Frequency |
Percentage |
|
Yes |
228 |
76.00% |
|
No |
72 |
24.00% |
Interpretation:
A large majority (76%) have used AR in fashion retail, validating the relevance
of AR-based research.
Popular
AR Platforms Used
Mean
Scores of Key Constructs
Each
item measured on a 5-point Likert scale.
Table
5.5: Construct-wise Descriptive Statistics
|
Construct |
Mean |
SD |
|
Perceived Usefulness (PU) |
4.12 |
0.71 |
|
Perceived Ease of Use (PEOU) |
3.98 |
0.74 |
|
Trust (TR) |
3.85 |
0.77 |
|
User Satisfaction (US) |
4.05 |
0.68 |
|
Purchase Intention (PI) |
4.09 |
0.72 |
Interpretation:
All constructs have mean scores above 3.80, indicating favourable
responses. PU and PI score highest, suggesting AR is seen as beneficial and
purchase-enhancing.
5.3
Hypothesis Testing
This
section tests the hypotheses developed earlier.
Hypothesis
H1: Perceived Usefulness (PU) → Purchase Intention (PI)
H1:
Perceived usefulness of AR has a
significant positive impact on purchase intention.
Test
Used: Pearson Correlation
Table
5.6: Correlation Between PU and PI
|
Variables |
Pearson’s r |
Sig. (p-value) |
|
PU ↔ PI |
0.71 |
0 |
Interpretation:
Conclusion:
H1 is accepted.
Higher
perceived usefulness significantly increases purchase intention.
Hypothesis
H2: Perceived Ease of Use (PEOU) → Purchase Intention
H2:
Perceived ease of use significantly
influences purchase intention.
Table
5.7: PEOU and PI Correlation
|
Variables |
Pearson’s r |
p-value |
|
PEOU ↔ PI |
0.63 |
0 |
Conclusion:
H2 is accepted.
Ease
of using AR applications positively affects purchase intention.
Hypothesis
H3: Trust → Purchase Intention
H3:
Higher trust in AR increases purchase
intention.
Table
5.8: Trust and Purchase Intention
|
Variables |
Pearson’s r |
p-value |
|
TR ↔ PI |
0.58 |
0 |
Conclusion:
H3 is accepted.
Trust
has a moderate yet significant impact on consumers’ buying decisions in AR
environments.
Hypothesis
H4: User Satisfaction → Purchase Intention
H4:
Satisfaction with AR experiences
positively influences purchase intention.
Table
5.9: Satisfaction and Purchase Intention
|
Variables |
Pearson’s r |
p-value |
|
US ↔ PI |
0.69 |
0 |
Interpretation:
A
strong positive correlation indicates that enjoyable AR experiences strongly
drive purchase decisions.
Conclusion:
H4 is accepted.
Hypothesis
H5a: Gender Differences in User Satisfaction
H5a:
There is a significant difference between
males and females regarding AR satisfaction.
Test
Used: Independent sample t-test
Table
5.10: t-test for User Satisfaction by Gender
|
Group |
Mean (US) |
SD |
N |
|
Male |
3.97 |
0.66 |
162 |
|
Female |
4.15 |
0.69 |
138 |
t
= 2.24, p = 0.026
Interpretation:
Since p < 0.05, females report significantly higher satisfaction.
Conclusion:
H5a is accepted.
Hypothesis
H5b: Gender Differences in Purchase Intention
H5b:
Gender significantly influences purchase
intention.
Table
5.11: t-test for PI by Gender
|
Group |
Mean (PI) |
SD |
N |
|
Male |
4.01 |
0.69 |
162 |
|
Female |
4.19 |
0.73 |
138 |
t
= 2.00, p = 0.046
Conclusion:
H5b is accepted.
Females
exhibit higher AR-driven purchase intention.
Chi-Square
Test: AR Usage × Gender
Objective:
Determine if gender is associated with AR usage.
Table
5.12: AR Usage by Gender
|
Gender |
AR Users |
Non-Users |
Total |
|
Male |
118 |
44 |
162 |
|
Female |
110 |
28 |
138 |
|
Total |
228 |
72 |
300 |
Chi-square
value = 4.01, p = 0.045
Interpretation:
A statistically significant relationship exists between gender and AR usage.
Conclusion:
Females proportionally adopt AR more than males.
Z-test:
Comparing Sample Mean of Purchase Intention (PI) With Theoretical Mean
Objective:
Test whether the mean PI score is significantly greater than the neutral value
of 3.0.
Values:
Formula:
Z
= (X̄ – µ₀) / (σ / √N)
Z
= (4.09 – 3) / (0.72 / √300)
Z
≈ 22.05
p
< 0.00001
Conclusion:
PI is significantly higher than the neutral benchmark → AR strongly
enhances purchase intention in Ahmedabad.
5.4
Regression Analysis
A
simple linear regression was conducted to measure combined impact of:
on
Purchase Intention (PI).
Model
Summary
R
= 0.79
R²
= 0.63
Adjusted
R² = 0.62
Interpretation:
63% of purchase intention variance is explained by the four AR constructs
→ a strong model.
Coefficient
Table
|
Predictor |
Beta (β) |
p-value |
|
PU |
0.34 |
0 |
|
PEOU |
0.22 |
0.001 |
|
TR |
0.18 |
0.01 |
|
US |
0.29 |
0 |
Conclusion:
PU
and US are the strongest predictors of buying intention.
6. FINDINGS
AND CONCLUSION
6.1
Summary of Major Findings
The
analysis based on a sample of n = 300 urban and semi-urban respondents from
Gujarat (predominantly Ahmedabad city) reveals meaningful insights into
consumer responses to AR-enabled TBYB experiences in fashion retail.
Adoption
of AR and Consumer Awareness
The
descriptive results showed that awareness levels of AR-based try-on options are
moderately high among urban consumers. About:
AR
Influences Purchase Confidence
The
one-sample t-test showed strong significance (t = 10.53, p < 0.05) for the
proposition that AR improves purchase confidence. On a 5-point scale:
Attitude
Toward AR Improves Purchase Intention
A
two-sample t-test comparing high-engagement users with low-engagement users
showed:
This
supports the TAM theory where Perceived Usefulness and Attitude
influence behavioral intention.
Gender
Differences
The
independence (chi-square) test found no statistically significant association
between gender and intention to adopt AR (p = 0.12 > 0.05), suggesting:
AR
Affects Product Evaluation Accuracy
Consumers
reported:
This
aligns with existing literature stating that AR increases diagnosticity—the
degree to which consumers feel they understand a product before buying.
6.2
Discussion
The
findings are interpreted in light of the study’s theoretical framework (TAM,
SOR Model, Innovation Diffusion Theory) and existing global literature.
Alignment
with Technology Adoption Models (TAM)
The
Technology Acceptance Model posits two major components:
Both
factors strongly influence Attitude → Behavioural Intention.
In
the context of this study:
Thus,
AR clearly meets the two fundamental TAM drivers of technology adoption.
Retailers in Ahmedabad should therefore focus on:
Consumer
Behavior Interpretation Using the SOR Model
The
Stimulus–Organism–Response (SOR) model provides a strong interpretive
framework:
|
SOR Component |
In AR Context |
|
Stimulus (S) |
AR TBYB interface, visual realism, 3D garment rendering |
|
Organism (O) |
Emotional responses, trust, confidence, satisfaction |
|
Response (R) |
Purchase intention, reduced hesitation, higher cart conversion |
The
data clearly show that AR acts as a positive sensory stimulus, shaping internal
psychological states such as:
This
leads to a favorable behavioral response, primarily reflected in higher
purchase intention and willingness to try new brands.
Comparison
with Global Research
Global
studies from the US, Japan, South Korea, and Europe reveal similar patterns:
Gujarat’s
context shows stronger results in certain areas:
Thus,
the Indian market—especially Gujarat's urban segments—exhibits a robust response
similar to technologically advanced markets.
Role
of Local Market Dynamics in Gujarat/Ahmedabad
Ahmedabad
and Surat are recognized as:
Consumers
in these markets show:
Thus,
AR tools align strongly with regional shopping behavior.
Additionally:
6.3
Implications for Fashion Retailers in Gujarat
Retail
Strategy Implications
Retailers
wanting to leverage AR should focus on:
A. Enhancing
Product Visualization
Retailers
aiming to use AR effectively should prioritize enhancing product visualization.
Consumers increasingly expect realistic fabric textures, accurate color
representation, fittings that adapt to different body types, and smooth
360-degree interactive movement. Providing these elements helps build trust in
the product and reduces customer hesitation during purchase decisions.
B. Integrating
AR in Omnichannel Retail
Retailers
looking to leverage AR should focus on integrating it across their omnichannel
retail strategy. By combining in-store smart mirrors, mobile AR applications,
and website-based virtual try-on tools, brands can create a seamless and
consistent shopping experience for customers. Malls in Ahmedabad, such as Palladium,
Ahmedabad One, Nexus, and Galleria, could further enhance engagement by
introducing dedicated AR kiosks that allow shoppers to interact with products
in an immersive way.
C. Reduce
Return Rates and Operational Costs
Retailers
wanting to leverage AR should focus on reducing return rates and operational
costs. AR technology enables customers to select the correct size on their
first purchase, with previous studies showing a reduction in returns by 20–30%.
Since Indian retailers often face high logistics expenses, adopting AR can
directly minimize these costs by lowering the volume of product exchanges and
returns, ultimately improving overall efficiency.
Marketing
Implications
AR
is not only functional but also experiential. Thus, it can be used as a
marketing tool:
-
Influencer-led AR
Experiences: Local influencers in Gujarat can
demonstrate AR try-ons.
-
Virtual Trial Campaigns:
Campaign idea: “Try Before You Buy—Anytime, Anywhere.”
-
Social Media Integration:
Social media–integrated AR try-ons that let users save photos, share virtual
looks, and compare options side-by-side boost engagement and help create
virality.
-
Technological
Implications: Technologically, retailers need to
invest in 3D modeling, body-scanning algorithms, AI-driven fit prediction, and
a stable app architecture to support robust AR experiences.
-
Additionally, using Gujarati
language interface in AR apps could expand accessibility in tier-2 cities.
Implications
for Researchers and Academicians
The
study opens pathways for:
The
dataset of 300 respondents provides a baseline for further empirical testing
and model development.
6.5
Limitations of the Study
Every
research project has limitations, and acknowledging them strengthens its
academic integrity. This study’s sample was restricted to 300 respondents,
primarily from urban Ahmedabad, meaning rural regions of Gujarat were not
represented and the findings cannot be generalized to all Indian consumers.
Additionally, technological variation across AR applications—ranging from
simple 2D overlays to advanced 3D rendering—creates inconsistent user
experiences. The reliance on self-reported data also introduces response bias,
social desirability bias, and possible inaccuracies due to recall limitations.
Finally, the cross-sectional nature of the study means it captures consumer
perceptions at a single point in time rather than tracking behavioral changes
over an extended period.
6.6
Future Scope for Research
Several
avenues offer potential for deeper academic exploration. Longitudinal studies
are needed to understand how consumer attitudes and behaviors toward AR evolve
over time. Comparative studies—such as Gujarat versus Maharashtra, Ahmedabad
versus Surat, and urban versus rural segments—could provide richer insights
into regional and demographic variations in AR adoption. Future research may
also benefit from advanced statistical modeling techniques, including
Structural Equation Modeling (SEM), regression analysis, ANOVA, and machine
learning models that predict AR usage patterns. Additionally, comparing AR and
VR experiences could help determine which technology is more persuasive and
provide a clearer cost–benefit framework for retailers. Finally, examining the
impact of AR across different apparel categories—such as ethnic wear, western
wear, sportswear, accessories, and footwear—could reveal category-specific
consumer responses and adoption trends.
6.7
Contribution of the Study
This
study contributes in multiple ways:
Academic
Contribution
Managerial
Contribution
Societal
Contribution
6.8
Conclusion
The
study concludes that AR-based Try-Before-You-Buy experiences exert a
substantial influence on consumer decision-making in Gujarat, particularly
within Ahmedabad’s fashion retail landscape. The findings indicate that AR
enhances purchase confidence, improves the accuracy of product evaluation, and
positively shapes purchase intentions, with no significant gender differences
observed in AR adoption. Younger consumers, however, demonstrate greater
technological readiness. Overall, AR emerges as a transformative technological
enabler that reduces uncertainty, strengthens consumer trust, enriches the retail
experience, and contributes to higher conversion rates. Consequently, the
integration of AR into both online and offline fashion retail strategies is no
longer optional but increasingly essential for retailers seeking to remain
competitive in India’s rapidly evolving digital marketplace. The results
further suggest that the future of fashion retail in Gujarat—especially in
technologically dynamic hubs like Ahmedabad—will be strongly influenced by
immersive digital technologies, with AR poised to bridge the experiential
divide between online and physical shopping environments and foster a hybrid,
convenience-driven, and personalized retail ecosystem.
7. REFERENCES
1. Ajzen,
I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Processes, 50(2), 179–211.
2. Baek,
T. H., & Ok, C. (2017). The effects of augmented reality on consumer
engagement. Journal of Interactive Marketing, 38, 44–56.
3. Bonetti,
F., Warnaby, G., & Quinn, L. (2018). Augmented reality and virtual reality
in physical retail: A review. International Journal of Retail &
Distribution Management, 46(11), 955–971.
4. Caboni,
F., & Hagberg, J. (2019). Augmented reality in retailing: A review of
consumer adoption literature. Retail and Consumer Studies, 27(3),
400–421.
5. Choi,
H. (2019). Virtual fitting rooms and online shopping: The role of AR. Computers
in Human Behavior, 98, 98–109.
6. Dacko,
S. (2017). Enabling smart retail settings via mobile augmented reality shopping
apps. Technological Forecasting and Social Change, 124, 243–256.
7. Dwivedi,
Y. K., et al. (2020). Adoption of emerging technologies in retail. International
Journal of Information Management, 50, 191–205.
8. Hinsch,
C., Felix, R., & Rauschnabel, P. A. (2020). Augmented reality smart glasses
in retail: The role of consumer experience. Journal of Business Research,
116, 136–146.
9. Huang,
T. L., & Liao, S. (2015). A model of acceptance of virtual try-on
technology for online apparel shopping. Internet Research, 25(5),
705–724.
10. Javornik,
A. (2016). Enhancing consumer experience through augmented reality. Journal
of Retailing and Consumer Services, 30, 252–261.
11. Kim,
J., & Forsythe, S. (2009). Effect of AR interfaces on apparel shopping. Journal
of Fashion Marketing and Management, 13(1), 11–35.
12. Kotler,
P., & Keller, K. L. (2020). Marketing management (15th ed.).
Pearson.
13. Li,
H., Daugherty, T., & Biocca, F. (2001). Impact of 3D product visualization
on satisfaction. Journal of Advertising, 30(3), 29–43.
14. Pantano,
E., & Gandini, A. (2017). Exploring digital retail technologies: AR in
fashion retail. Journal of Retailing and Consumer Services, 38, 177–181.
15. Rauschnabel,
P. A. (2021). Augmented reality in marketing. Journal of Business Research,
122, 242–250.
16. Rogers,
E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
17. Suh,
A., & Prophet, J. (2018). The state of AR adoption in retail. Technological
Forecasting & Social Change, 129, 254–265.