FACTORS INFLUENCING THE BRANDED SPORTSWEAR PURCHASE
INTENTION AMONG YOUNG CONSUMERS IN BANGKOK
*
Thiha Soe Htike, **Asst Prof Dr. Siriwan Kitcharoen, ***Dr. Bhumiphat
Gilitwala
* School
of Business and Advanced Technology Management, **Assistant Vice President for Educational
Innovation and Graduate Studies, ***Program Director MBA, Assumption
University
This study examines how brand
attitude, brand image, perception, and subjective norm influence purchase
intention toward branded sportswear among young consumers in Bangkok, Thailand.
The purpose is to identify the most influential psychological and brand-related
drivers of consumer purchase intention in a growing sportswear
market. The study used a quantitative approach, gathering responses from 413
participants aged 18–35 through a structured survey. Data analysis was
performed with Partial Least Squares Structural Equation Modeling (PLS-SEM) to
test reliability, validity, and the proposed relationships. Results showed that
all four variables had a significant influence on purchase intention. Among
these, brand attitude demonstrated the strongest effect, followed by brand
image, subjective norm, and perception. The model accounted for 74.1% of the
variation in purchase intention, showing strong predictive capability. These
results highlight that young consumers’ decisions are not based solely on
product quality or features but are shaped by positive emotions toward the
brand and social influence from peers and family. The study concludes that
sportswear companies should enhance brand attitude, strengthen brand image, and
leverage social influence to build stronger connections with young consumers.
The findings provide both theoretical contributions and practical guidance for
the sportswear industry in Thailand.
Keywords: Purchase Intention, Brand Attitude,
Brand Image, Perception, Subjective Norm
The sportswear market in Thailand has grown rapidly as young consumers increasingly
adopt active lifestyles and fashionable sportswear. Activities such as gym
workouts, yoga, and running have become popular among urban youth, leading to
higher demand for products that combine comfort, performance, and style
(Euromonitor International, 2024). In 2024, the Thai sportswear market expanded
by 22 percent, reaching USD 67 million, showing strong recovery and potential
for future growth (IndexBox, 2024). Young consumers are the main drivers of
this market, motivated not only by functionality but also by brand image and
social influence. According to the Ministry of Tourism and Sports, Thai people
spend an average of THB 7,054 annually on sports-related products, reflecting
their strong purchasing power (Nation Thailand, 2024).
Sportswear has evolved beyond functional use and is now closely tied to
personal identity and social expression. For many young people in Bangkok,
branded sportswear represents lifestyle choices, social image, and
self-expression. Their purchase decisions are influenced by friends, family,
and online communities, highlighting the importance of both psychological and
social factors.
Despite the growing market, many sportswear companies still struggle to
understand what truly drives young consumers’ purchase intention. Local brands
often compete mainly on price and overlook the need to build stronger consumer
connections through positive brand associations and social engagement. This
limitation has created a gap in understanding the deeper drivers of purchase
decisions among young people in Bangkok.
Hence, this research investigates the
key factors that drive young consumers in Bangkok to intend to purchase branded
sportswear. By addressing this research gap, the study provides insights that
can help companies design effective marketing strategies, enhance consumer
engagement, and remain competitive in a dynamic market.
1.1 Research Objectives
1. To investigate how brand attitude,
brand image, perception, and subjective norm affect young consumers’ intention
to buy branded sportswear in Bangkok, Thailand.
2. To determine which factor plays
the most significant role in shaping purchase intention.
3. To apply the research findings to
provide recommendations for sportswear brands targeting young consumers in the
Bangkok market.
Purchase intention describes the
likelihood or intention of a consumer to make a purchase. Studies show that it
is influenced by individual feelings and social support. Osman et al. (2023)
found that students were more likely to buy branded sportswear when they had
positive attitudes and encouragement from peers or family. Nam et al. (2017)
noted that intention strengthens when products fit consumers’ lifestyles and
beliefs. Tan et al. (2023) added that Generation Z values product design and
personal fit, showing that perception also plays a role. In short, purchase
intention is shaped by product features, emotions, and social influence.
Brand attitude is the consumer’s
general assessment of a brand, usually shaped by emotions and past interactions
with it. If consumers hold a positive view of a brand, they are more inclined
to intend to buy its products. As demonstrated by Osman et al. (2023),
university students who perceived branded sportswear positively, based on
visual appeal, brand message, or emotional relevance, exhibited a higher
willingness to consider purchasing.
Brand image
reflects how consumers perceive and emotionally respond to a brand. Widyastuti
and Said (2020) found it strongly affects intention to buy sports shoes. Nam et
al. (2017) confirmed that lifestyle alignment improves intention, while Gaurav,
Ray, and Sahu (2020) highlighted product quality and recognition as key
drivers.
Perception
reflects how consumers judge product quality, design, and usefulness. Ming et
al. (2022) showed that Generation Z emphasizes design and fit. Baek et al.
(2017) found that global brand recognition increases purchase intention. Tran
et al. (2022) noted that sustainability improves perception and intention.
Gaurav et al. (2020) added that durability and quality shape favorable
perceptions.
Subjective norm
is social pressure from peers, family, or groups. Osman et al. (2023) found
that encouragement from friends and family strengthened purchase intention,
while Nam et al. (2017) also confirmed the role of social approval.
According to
Osman et al. (2023), students who held positive attitudes toward a brand
displayed greater intention to purchase. Nam et al. (2017) confirmed that
lifestyle alignment reinforces positive attitudes and intention.
Osman et al.
(2023) observed that students who viewed a brand’s image positively showed a
stronger likelihood of making purchases. Widyastuti and Said (2020) confirmed
that strong image motivates purchase, and Hafez and Hasan (2019) highlighted
CSR as a factor that strengthens image and intention.
Nam et al.
(2017) showed that favorable perceptions of usefulness and fit increase
intention. Ming et al. (2022) found that Generation Z values design and product
match, while Tran et al. (2022) highlighted sustainability.
Osman et al. (2023) found that
encouragement from peers and family members greatly enhanced consumers’
intention to purchase. Nam et al. (2017) also confirmed that social approval is
a key driver in consumer intention.
This study was guided by a framework
linking four independent variables: brand attitude, brand image, perception,
and subjective norm to purchase intention. The framework was based on the
Theory of Planned Behavior (Ajzen, 2002), the Customer-Based Brand Equity model
(Keller, 2001), and the Stimulus-Organism-Response framework (Keller, 2001).
Previous studies also support these relationships. Osman et al. (2023)
confirmed that brand attitude and subjective norm influence purchase intention.
Nam et al. (2017) and Widyastuti and Said (2020) emphasized the role of brand
image, while Ming et al. (2022), Tran et al. (2022), and Putra et al. (2022)
showed that perception including product design and sustainability predicts
purchase intention. These results offer solid support for selecting the four
independent variables used in the framework of this study.
Figure 1: Conceptual Framework
Ho1: There is no significant relationship
between brand attitude and purchase intention.
Ha1: There is significant relationship between Brand Attitude and
purchase intention.
Ho2: There is no significant relationship
between brand image and purchase intention.
Ha2: There is significant relationship between brand image and purchase
intention.
Ho3: There is no significant between perception
and purchase intention.
Ha3: There is significant relationship between perception and purchase
intention.
Ho4: There is
no significant between subjective norm and purchase intention.
Ha4: There
is significant between subjective norm and purchase intention.
This research applied a quantitative
design and employed non-probability sampling, combining purposive and
convenience methods. The target population was young consumers aged 18–35 in
Bangkok interested in branded sportswear.
The questionnaire was divided into
three sections. The initial section included a screening item to confirm that
participants were prospective buyers of branded sportswear. The second section
collected demographic details including gender, age, education level, and
monthly income. The final section covered measurement items related to brand
attitude, brand image, perception, subjective norm, and purchase intention,
which were adapted from established studies to maintain content validity.
A pilot survey of 30 participants
confirmed reliability, with Cronbach’s alpha values exceeding the 0.70
threshold. The final survey yielded 413 valid responses through Google Forms.
Data collection was conducted both online and offline; in addition, I visited
Assumption University’s Bangna campus to distribute the Google Form directly to
students. Data were analyzed using SmartPLS 4. Reliability and validity were
assessed through the measurement model, while the structural model was
evaluated using path coefficients, R², and bootstrapping with 5,000 samples.
A pilot study
was carried out with 30 participants before the main survey to evaluate the
reliability of the questionnaire. Reliability was measured using Cronbach’s
alpha, where scores higher than 0.70 indicate satisfactory consistency (Hair et
al., 2017). The analysis revealed that all constructs met this requirement,
with alpha values ranging from 0.820 to 0.949. These results confirm the
internal consistency of the measurement items, showing that the questionnaire
was suitable for full-scale data collection.
Table 1 - Pre-testing Result by SMART
PLS Software (N =30)
|
Cronbach’s Alpha |
Number of Item |
Strength of Association |
|
|
Independent Variables |
|||
|
Brand Attitude |
0.936 |
5 |
Excellent |
|
Brand Image |
0.820 |
5 |
Good |
|
Perception |
0.925 |
5 |
Excellent |
|
Subjective Norm |
0.869 |
5 |
Good |
|
Dependent Variable |
|||
|
Purchase Intention |
0.949 |
5 |
Excellent |
As shown in Table 2, A total of 413 valid responses were collected from
young consumers in Bangkok.
For gender, the sample included 231
females (55.9%), 144 males (34.9%), and 38 respondents (9.2%) who preferred not
to state. This suggests that women form a key market segment for branded
sportswear.
Regarding occupation, most
respondents were employees (168; 40.7%), followed by students (152; 36.8%) and
self-employed individuals (92; 22.3%), with only 1 respondent (0.2%) working as
a freelancer. This indicates a balanced mix of working professionals and
students.
For education level, the largest
group had completed a bachelor’s degree (128; 31.0%), followed by high school
graduates (120; 29.1%), master’s degree holders (73; 17.7%), and diploma
holders (65; 15.7%). A smaller proportion held a PhD (25; 6.1%) or other
qualifications (2; 0.4%). This demonstrates a diverse educational background.
In
terms of monthly income, 161 respondents (39.0%) earned less than 15,000 THB,
125 respondents (30.3%) earned between 15,000 and 30,000 THB, 93 respondents
(22.5%) earned between 30,000 and 50,000 THB, and 34 respondents (8.2%)
reported incomes of more than 50,000 THB.
This
distribution shows that a large proportion of the sample had modest to
mid-level income, which is an important factor when considering affordability
in sportswear purchases.
Table 2 - The summary of Frequency
and Percentage of Demographic Factors
|
Demographic Factors |
Frequency |
Percentage |
|
Gender Male Female Prefer not to say |
231 144 38 |
55.9 34.9 9.2 |
|
Total |
413 |
100 |
|
Occupation Employee Freelance Self-employed Student |
168 1 92 152 |
40.7 0.2 22.3 36.8 |
|
Total |
413 |
100 |
|
Education Bachelor's Degree Diploma High School Master's Degree PhD Other |
128 65 120 73 25 2 |
31 15.7 29.1 17.7 6.1 0.4 |
|
Total |
413 |
100 |
|
Monthly Income 15,000-30,000 THB 30,001-50,000 THB More than 50,000 THB Less than 15,000 THB |
125 93 34 161 |
30.3 22.5 8.2 39 |
|
Total |
413 |
100 |
Source: Created by Author
The measurement model was evaluated
through factor loadings, Cronbach’s alpha, composite reliability (CR), and the
average variance extracted (AVE). A loading value of 0.70 or higher was
considered acceptable (Hair et al., 2017), indicating strong relationships
between constructs and their indicators. Cronbach’s alpha values above 0.70
suggest satisfactory internal consistency (George & Mallery, 2003), while
CR values above 0.70 and AVE above 0.50 confirm convergent validity (Fornell
& Larcker, 1981). The results are summarized in Table 3.
All constructs achieved Cronbach’s
alpha values greater than 0.86, composite reliability values exceeding 0.90,
and AVE values above 0.65, satisfying the suggested cut-off levels. These
findings confirm that the measurement items for brand attitude, brand image,
perception, subjective norm, and purchase intention demonstrate strong
reliability and convergent validity, and are suitable for further structural
model analysis.
Table 3: Construct reliability and
validity
|
|
Factor Loading |
Cronbach’s
Reliability Coefficient |
Composite
reliability (ρa) |
Composite
reliability (ρc) |
Mean
variance extracted (AVE) |
|
Brand
Attitude |
|||||
|
BA 1 |
0.787 |
|
|
|
|
|
BA 2 |
0.829 |
|
|
|
|
|
BA 3 |
0.842 |
0.866 |
0.867 |
0.903 |
0.651 |
|
BA 4 |
0.794 |
|
|
|
|
|
BA 5 |
0.780 |
|
|
|
|
|
Brand
Image |
|||||
|
BI 1 |
0.728 |
|
|
|
|
|
BI 2 |
0.822 |
|
|
|
|
|
BI 3 |
0.873 |
0.867 |
0.871 |
0.904 |
0.655 |
|
BI 4 |
0.814 |
|
|
|
|
|
BI 5 |
0.803 |
|
|
|
|
|
Perception |
|||||
|
P 1 |
0.811 |
|
|
|
|
|
P 2 |
0.846 |
|
|
|
|
|
P 3 |
0.864 |
0.890 |
0.893 |
0.919 |
0.695 |
|
P 4 |
0.823 |
|
|
|
|
|
P 5 |
0.823 |
|
|
|
|
|
Purchase
Intention |
|||||
|
PI 1 |
0.794 |
|
|
|
|
|
PI 2 |
0.834 |
|
|
|
|
|
PI 3 |
0.850 |
0.886 |
0.886 |
0.916 |
0.686 |
|
PI 4 |
0.837 |
|
|
|
|
|
PI 5 |
0.827 |
|
|
|
|
|
Subjective
Norm |
|||||
|
SN 1 |
0.686 |
|
|
|
|
|
SN 2 |
0.858 |
|
|
|
|
|
SN 3 |
0.832 |
0.872 |
0.883 |
0.908 |
0.664 |
|
SN 4 |
0.824 |
|
|
|
|
|
SN 5 |
0.861 |
|
|
|
|
Source: Created by Author
Figure 2: PLS-SEM Results of the
Sportswear Purchase Intention Model
Source: Created by Author
Discriminant validity was tested
following the Fornell and Larcker (1981) method, and the results are shown in
Table 4. The evaluation revealed that for every construct, the square root of
AVE exceeded its correlations with other constructs. This outcome suggests that
the constructs are distinct from one another and account for unique variance.
Moreover, the measurement items were verified to accurately capture their
intended variables. Overall, the findings confirm that the model satisfies both
convergent and discriminant validity requirements, making it suitable for
subsequent structural analysis.
Table 4: Discriminant validity
|
Brand Attitude |
Brand Image |
Perception |
Purchase Intention |
Subjective Norm |
|
|
Brand Attitude |
|
|
|
|
|
|
Brand Image |
0.885 |
|
|
|
|
|
Perception |
0.897 |
0.874 |
|
|
|
|
Purchase Intention |
0.923 |
0.891 |
0.845 |
|
|
|
Subjective Norm |
0.866 |
0.841 |
0.745 |
0.844 |
|
Source: Created by Author
The model’s overall fit was assessed
using several indices, including SRMR, d_ULS, d_G, Chi-square, and NFI, as
shown in Table 5. n this research, the SRMR was 0.053, indicating satisfactory
fit. In this study, the SRMR value was 0.053, supporting satisfactory fit. The
values of d_ULS (0.921) and d_G (0.433) also confirmed model reliability
(Henseler et al., 2014). Although the Chi-square value was relatively high
(1025.615), such results are common in studies with large sample sizes and do
not necessarily suggest poor fit (Kline, 2015). Furthermore, the NFI value of
0.863 exceeded the minimum recommended level of 0.80 (Bentler & Bonett,
1980), confirming that the model is adequate.
Overall, these indices verify that
the model fits the data well and is appropriate for further structural testing.
The results are displayed in Table 5.
Table 5: Model Fit
|
|
Saturated
model |
Estimated model |
|
SRMR |
0.053 |
0.053 |
|
d_ULS |
0.921 |
0.921 |
|
d_G |
0.433 |
0.433 |
|
Chi-square |
1025.615 |
1025.615 |
|
NFI |
0.863 |
0.863 |
Source: Created by Author
The explanatory strength of the model
was measured using both R-square and Adjusted R-square, as shown in Table 6.
The R-square result of 0.741 suggests
that 74.1% of the variation in purchase intention is accounted for by the
independent variables in the model, indicating strong predictive ability.
The adjusted R-square value of 0.739
is almost identical, confirming model stability and showing that the results
are not affected by overfitting. These findings suggest that the predictors
consistently explain purchase intention and provide a reliable model for
interpretation.
Table 6: Adjusted R Square
|
R-square |
R-square adjusted |
|
|
Purchase Intention |
0.741 |
0.739 |
Source: Created by Author
Table 7 shows the path coefficients
and statistical results obtained from PLS-SEM. All four hypotheses were
supported, as the path coefficients were significant (p < 0.05).
H1: Brand Attitude also showed a significant relationship with Purchase
Intention, with a standardized coefficient of 0.342 (t = 5.609, p < 0.001).
This finding aligns with Osman et al. (2023), who reported that students with
favorable attitudes toward sportswear were more likely to intend to purchase.
H2: Brand Image also showed a
significant relationship with Purchase Intention, with a standardized
coefficient of 0.252 (t = 3.612, p < 0.001). This result is consistent with
Nam et al. (2017) and Widyastuti & Said (2020), highlighting that a strong
and positive image motivates consumers’ purchasing behavior.
H3: Perception also showed a
significant relationship with Purchase Intention, with a standardized
coefficient of 0.161 (t = 2.505, p = 0.012). This outcome supports the work of
Ming et al. (2022) and Tran et al. (2022), which emphasized the role of product
design, comfort, and sustainability in shaping consumer intentions.
H4: Subjective Norm also showed a
significant relationship with Purchase Intention, with a standardized
coefficient of 0.196 (t = 3.727, p < 0.001). This finding is in line with
Osman et al. (2023) who found that social influence from peers and family plays
a significant role in consumers’ purchasing decisions.
Table 7: Hypothesis Testing Results (PLS-SEM Path
Coefficients)
|
Original Sample (O) |
Sample mean (M) |
Standard deviation (STDEV) |
T statistics (O/STDEV) |
Pvalues |
Support |
|
|
BA-> PI |
0.342 |
0.340 |
0.061 |
5.609 |
0.000 |
No |
|
BI-> PI |
0.252 |
0.253 |
0.070 |
3.612 |
0.000 |
No |
|
P-> PI |
0.161 |
0.162 |
0.064 |
2.505 |
0.012 |
No |
|
SN->PI |
0.196 |
0.196 |
0.052 |
3.727 |
0.000 |
No |
Source: Constructed by author
Note: BA=
Brand Attitude, BI= Brand Image, P= Perception, SN= Subjective Norm,
PI= Purchase Intention
This research investigated how brand
attitude, brand image, perception, and subjective norm affect the purchase
intention of young consumers in Bangkok toward branded sportswear. The results
revealed that all four factors significantly influenced purchase intention,
accounting for 74.1% of its variance. Among these predictors, brand attitude
had the greatest impact, followed by brand image, subjective norm, and
perception.
The findings emphasize that purchase
intention is shaped not only by product features but also by psychological
evaluations and social influences. A positive brand attitude strengthens
willingness to buy, consistent with Osman et al. (2023), who found that
students with favorable attitudes toward branded sportswear showed higher
purchase intention. Brand image was also a critical factor, supporting previous
research by Nam et al. (2017) and Widyastuti and Said (2020), which highlighted
that a strong image increases consumer trust and motivation to purchase.
Subjective norm further demonstrated significant impact, aligning with Osman et
al. (2023), who showed that peer and family influence enhances consumer
confidence in buying sportswear. Although perception had the weakest effect, it
remained significant, in line with Ming et al. (2022) and Tran et al. (2022),
who noted that product design, comfort, and sustainability play an important
role in shaping consumer evaluations.
Overall, this study reinforces the
theoretical understanding that purchase intention in the sportswear market of
Bangkok is driven by both internal attitudes and external social pressures. By
linking the results to prior studies, it provides empirical evidence that
psychological and social variables jointly determine purchase intention in this
context.
The findings provide several
directions for sportswear companies to increase purchase intention. First,
consumers value products that match their style, look attractive, and remain
durable with consistent quality. Firms should therefore invest in innovation, design,
and quality control to ensure their products meet these expectations.
Second, as consumers hold favorable
attitudes toward branded sportswear, companies should reinforce brand attitude
through emotional branding, loyalty programs, and campaigns that highlight
authenticity and benefits. Strengthening this emotional connection will deepen
customer trust and commitment.
Third, peer and family influence
strongly affect purchase decisions. Brands should leverage social influence by
working with influencers, encouraging peer-to-peer sharing, and building online
communities that make products more socially accepted.
Finally, brand image remains a
critical driver. Companies must maintain a consistent identity, promote
recognizable logos and features, and differentiate themselves through clear
communication. Sustaining this image, together with innovative and reliable
products, will help brands secure consumer confidence and long-term loyalty in
the competitive Bangkok sportswear market.
Although this study provides valuable
insights, some limitations should be acknowledged. First, the data were
collected only from young consumers in Bangkok, which reduces the ability to
generalize the findings to other age groups or regions. Different outcomes
might appear if future research includes broader demographics or covers
different locations. Second, the study examined only four independent
variables: brand attitude, brand image, perception, and subjective norm. Other
relevant factors such as brand loyalty, digital marketing, or price perception
were not included, which restricts a full understanding of all potential
drivers of purchase intention. Third, this study was based on a cross-sectional
approach, meaning that the relationships were measured at only one point in
time, and therefore could not reflect changes in consumer behavior across
longer periods.
From a practical perspective, these
limitations imply that companies should be cautious when generalizing the
results to all consumers. However, the findings still provide useful
implications for marketing managers. By improving brand attitude, image, and
perception, and by leveraging social influence, firms can enhance purchase
intention. Managers should also recognize that these factors may vary across
consumer groups and industries, and adapt their strategies accordingly.
Future research can build on this
study in several directions. The sample should be broadened to include
consumers from different age groups and regions, as this would strengthen the
generalizability of findings and highlight variations across demographic
groups. Researchers could also extend the scope of variables beyond those
tested here by considering brand loyalty, digital marketing exposure, corporate
social responsibility, and price perception, which may offer additional
insights into purchase intention.
Longitudinal research is recommended
to capture how consumer behavior develops over time, especially in response to
lifestyle changes, fashion trends, and sustainability practices. This approach
would provide a deeper understanding of long-term purchase intention compared
to a single time study. Scholars may also consider applying other theoretical
perspectives beyond TPB, CBBE, and S-O-R. Models such as the Technology
Acceptance Model and UTAUT could be particularly relevant in digital or
technology-driven retail contexts.
Finally, qualitative or mixed-method
approaches, such as interviews and focus groups, could complement quantitative
findings by revealing consumer motivations, emotions, and cultural influences
in greater depth. Pursuing these directions would give future research a more
holistic view of the drivers of purchase intention in the sportswear industry.
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