The Level of Impact of Brand Image on Purchase Intention in the Cosmetic Industry:
A Study of Female University Students at UIC
BRM (1006)
Group 6
赵桐瑶 Bella
2030006256
杨佳韵 Angela
2030006211
蔡家欣 Anthea
2030006005
Eunice
2030006299
刘姿
高蕊芯 Isabella
2030006052
崔校溶 Subrina
2030006284
陈旖阳 Sylvia
2030006026
陈姿桦 Olivia
2030006031
2023.05.16
1
Table of Contents
Abstract ................................................................................................................................................................................. 3
1. Introduction ..................................................................................................................................................................... 4
1.1 Background ..........................................................................................................................................................4
1.2 Significance and Objectives ............................................................................................................................ 6
2. Literature Review ........................................................................................................................................................7
2.1 Brand Image ...................................................................................................................................................... 7
2.2 Purchase Intention ...........................................................................................................................................8
2.3 Relationships Between Variables ............................................................................................................. 10
2.4 Research Gaps ................................................................................................................................................ 11
3. Hypothesis Development .......................................................................................................................................... 12
3.1 Independent Variables (IV) & Dependent Variables (DV) ....................................................................12
3.2 Hypotheses ........................................................................................................................................................ 13
3.3 Control Variables (CV) ...................................................................................................................................14
4. Methodology .................................................................................................................................................................17
4.1 Target Population ............................................................................................................................................. 17
4.2 Survey and Procedures ................................................................................................................................... 17
5. Result ..............................................................................................................................................................................25
5.1 Correlation Analysis ........................................................................................................................................25
5.2 Multiple Linear Regression Analysis ..........................................................................................................29
6. Limitations .................................................................................................................................................................... 37
7. Discussion .....................................................................................................................................................................39
8. Recommendation .......................................................................................................................................................41
9. Conclusion ................................................................................................................................................................... 44
Reference............................................................................................................................................................................46
2
Abstract
With the development of China's economy and the improvement of personal living
standards, female college students are playing an increasingly important role in the
cosmetics consumer market, becoming the main buyers of cosmetics. Therefore,
understanding the factors that influence female college students' purchase intention has
become a key factor for cosmetics companies in formulating brand strategies. However,
current research on purchase intention is limited to the exploration of cosmetics brand
image, without categorizing the product functionality of cosmetics or segmenting the brand
image. Therefore, this study aims to investigate the influence of functional benefits,
experiential benefits, and symbolic benefits under the brand image on the purchase
intention of make-up, personal care, and skincare products. By conducting multiple linear
regression analysis on the purchase intention data of 102 UIC female college students, the
results indicate that functional benefits are positively correlated with purchase intention and
have the greatest impact. Experiential benefits also show a positive correlation with
purchase intention, but with a slightly smaller effect. Interestingly, the impact of symbolic
benefits is the smallest, and in some cases, it even exhibits a negative correlation. Overall,
these findings will help businesses better understand the purchase decisions of female
college students, enabling them to develop more precise marketing and brand strategies,
and enhance product competitiveness.
3
1. Introduction
1.1 Background
According to data from iiMedia Research (2022), the market size of the cosmetics industry
in China reached 455.3 billion yuan in 2021 and has essentially recovered to pre-pandemic
levels. Driven by factors such as the continuous increase in disposable income,
strengthened aesthetic awareness, and emphasis on high-quality appearance, domestic
cosmetics consumption is expected to continue to rise, with the market size estimated to
exceed 500 billion yuan by 2023 (Sun, 2022). Additionally, Statista’s (2022) data shows
that women are the primary consumers of the cosmetics market in China, accounting for
over 74 percent of the market share; Particularly, among college students, the consumption
growth rate of cosmetics is much higher than that of women in other age groups. Therefore,
cosmetics entities need to attract the attention of young female consumers by formulating
more precise market positioning and brand strategies, in order to stand out from the fierce
market competition.
In order to help cosmetic entities stand out in the competitive market, previous studies have
been conducted on factors that influence consumers' intention to purchase. According to a
case study from the perspective of Thai women, brand, product packaging, product quality,
and price have a significant positive impact on purchase intention (Nakpathom, 2022).
Furthermore, the results of research conducted by Cindy, L., & Innocentius, B. (2019) have
also reported that brand image significantly influences purchase intentions.
Although there has been research conducted on brand image, it has not gone into a deep and
categorized analysis. Devita (2019) did provide some categorization of brand image,
dividing it into product attributes, consumer benefits, and brand personality, with consumer
benefits further divided into functional, experiential, and symbolic benefits. However, there
is still a lack of granularity in brand image analysis, which is why this study aims to address
this gap by investigating the impact of experiential, functional, and symbolic benefits of
brand image on the purchase intention of female university students at UIC.
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To conduct this study, female college student consumers from Beijing Normal University –
Hong Kong Baptist University United International College (UIC) were selected as our
research. UIC’s students, as the new generation of consumers, possess relatively high
purchasing power and willingness to consume. Therefore, using UIC female college
students as research subjects is a suitable and feasible choice.
This study addresses the research problem by employing the following approach: Firstly, a
research model was proposed to examine the impact of functional, experiential, and
symbolic benefits on customers' purchase intention for various types of cosmetics,
including makeup products, personal care products, and skincare products.
Secondly, a questionnaire survey was conducted to collect information on purchase
preferences and intentions from 102 female UIC university students, aiming to validate the
proposed model. This model will be tested on a specific number of UIC female university
student samples.
Subsequently, the collected data from UIC female university students underwent regression
analysis to determine the extent to which functional, experiential, and symbolic benefits
influence purchase intention. Through regression analysis, the importance of these factors
in influencing the purchase decisions of UIC female university students is revealed, along
with quantitative results and conclusions.
The research findings indicate that for the purchase intention of cosmetics, the three
benefits (functional, experiential, and symbolic) are positively correlated. Among them,
functional benefits have the greatest influence on purchase intention, while symbolic
benefits have the least influence.
For personal care products, the three benefits are unrelated to purchase intention. Regarding
skincare products, experiential and functional benefits positively impact purchase intention,
with functional benefits having a stronger influence. However, symbolic benefits have a
negative impact on purchase intention, producing the opposite effect. For makeup products,
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symbolic, experiential, and functional benefits all positively influence purchase intention,
with their impact gradually increasing.
1.2 Significance and Objectives
This study is important because cosmetic companies are facing a management dilemma.
While they are aware that a good brand image can attract more consumers to purchase their
products, they lack in-depth understanding and cognition on how to enhance their brand
image in different ways for different cosmetic types, such as makeup, personal care, and
skincare.
In order to enhance the competitiveness of cosmetic companies, this study aims to provide
valuable insights for the development of effective brand image strategies in the cosmetics
industry. To achieve this objective, this study will utilize several methods.
Firstly, an extensive literature review will be conducted to explore the composition
dimensions of cosmetic brand image. Secondly, an influence model will be constructed to
analyze the impact of different dimensions of cosmetic brand image on the purchase
intention of female college students in the field of cosmetics. Finally, empirical research
will be conducted to test the degree of influence of three components of brand image on
purchase intention. In addition to these methods, this study also aims to provide relevant
business management suggestions to help cosmetics enterprises formulate more suitable
brand images with accurate positioning. By gaining a better understanding of college
students’ preferences and developing more targeted marketing strategies and market
positioning, cosmetics companies can stimulate purchase intentions and remain competitive
in the marketplace.
In conclusion, the results of this study will contribute to the development of effective brand
image strategies for cosmetic companies to solve the management dilemma and provide
valuable insights into the cosmetics industry.
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2. Literature Review
2.1 Brand Image
According to Kotler & Keller (2021), a brand image is some beliefs, ideas, and impressions
held by a person about an object. Brand image is consumer's perspective on the experience
and information generated from a brand (Yohana et al., 2020). Brand image is not only a
mental image but also conveys emotional value. It’s the perception consumer generates
about the product.
Consumers’ purchase decisions can be made based on brand image in their minds (Agustini
et al., 2019). When buyers are buying a product, they are also buying its brand image.
Customers can be attracted by a strong brand image (Suria, Kusumawati, & Pangestuti,
2016). Aaker and Keller hold the view that a brand with a positive image can enhance
consumer loyalty, build trust in the product, and increase the consumer's inclination to
purchase the brand.
There are three components in brand image: Product attributes, consumer benefits and
brand personality (Agustini & Devita, 2019).
(1) Product attributes refer to the benefits that the product offers. It can be shown in the
quality, features and design of the product.
(2) Consumer benefits are the value consumer gets from product. There are three
components to consumer benefits: functional benefits, experiential benefits and
symbolic benefits.

Functional benefits are benefits of meeting consumers' needs for practical
functionality. These are the basic needs of physiological needs, safety needs, and
problem-solving. For example, the functions of whitening, and hydrate of cosmetics
fulfil consumers’ physical needs.
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
Experiential benefits refer to what consumers feel when using the product. These
feelings are more related to perceptual and cognitive novelty. Such as fulfilling the
needs of variation-seeking and cognitive stimulant.

Symbolic benefits are benefits that product functions meet consumer intrinsic needs,
such as social acceptance, reputation and human relationships.
(3) Brand personality relates to a set of humanistic characteristics associated with a brand
name (Susanto & Wijanarko, 2004). The main types of brand personality include
excitement, sincerity, ruggedness, competence and sophistication. Consumers are more
likely to choose a brand if its personality fits their own.
2.2 Purchase Intention
Purchase intention is explained as part of cognitive behavior about how one intends to buy
a product or brand (Elseidi & El-Baz, 2016). According to Assael (2004), purchase
intention is defined as a behavioral response to an object and also signifies the customer's
interest and desire to make a purchase. Mowen (2006) said that purchase intention is
shaped through a learning process and cognitive deliberation. It is the stage of the
decision-making process before the transaction has made (Keller, 2012). In short, it can be
defined as the willingness of consumers to buy a particular product or service, and refers to
the plan or decision to purchase a product or service within a specific time frame.
According to Shih et al. (2018), purchase intention can be assessed by the willingness to
study product information, product consideration, recommending a product, brand
influence on buying motives, preferring a product despite other options, and interest in
promotions.
Several factors can influence purchase intention. Imbayani & Gama (2018) explain that
purchase intention is influenced by external factors and personal characteristics that play a
role in the decision-making process. According to Albari &Khoirunnisa (2023), consumer
purchase intention is influenced by product knowledge, brand image, e-WOM and so on.
Among the influencing factors, brand image is an important factor that can influence
8
consumers' purchase intention, as it can shape consumers’ perception of the brand and
ultimately affect their purchasing behavior.
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2.3 Relationships Between Variables
According to Dea Khoirunnisa and Albari (2023), the brand image variable has a positive
and significant effect on consumer purchase intention, where the consumer purchase
intention becomes higher when the brand image becomes higher. Therefore, it can be
reasonably considered that the final intention of people buying products can be affected by
the image of brands. Moreover, brand images affect e-WOM, which means that they can
influence consumer preferences and the intention of buying goods, thus leading to e-WOM
influence on purchase intention.
Another research written by Lie and Bernarto (2019) shows that perceived quality and
brand image have a positive and significant impact on purchase intentions because it
reflects the consumers’ personal judgments and evaluations regarding the act of buying
products publicly. Therefore, a consistent brand image and positioning can increase the
future success of brands. Moreover, the research done by the publication (Sulhaini, 2019),
shows that a better brand image of cosmetic products originating from South Korea will
increase purchase intention on cosmetic products originating from South Korea. Products
with good brand images are not difficult to build consumer views of the product. If the
product has a high brand image, it will increase purchase intention. In summary, we can
easily observe that brand image is a vital factor to influence purchase intentions.
The other research written by Elvina Marsha Devita and M.Y. Dwi Hayu Agustini (2019)
divides the factors that affect brand image into three parts, which are product attributes
(characteristics of the product that reflect benefits it provides), consumer benefits (personal
value a consumer can get from product attributes), and brand personality (a set of
humanistic characteristics associated to a brand), and it measures the brand image through a
survey of three benefits of consumer benefits. The three consumer benefits are functional
benefits, which relate to the function of a product in fulfilling consumers’ needs;
experiential benefits which relate to consumers’ feeling aroused when consuming the
product; and symbolic benefits which relate to the need for social acceptance. Since brand
image can shape consumers' perceptions of a brand and ultimately influence their purchase
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behavior, consumer benefits from the consumer perspective (three subdivisions: functional
benefits, experiential benefits, and symbolic benefits) will be the central factor in our study
of brand image.
2.4 Research Gaps
From Lie and Bernarto (2019) and Sulhaini (2019), we can see that Etude House and some
cosmetic products originating from South Korea existed a limited scope of brands which
only focus on some brands in a specific region that researchers also mention in the
relationship of brand image and purchase intention. Nevertheless, we will not just focus on
only one cosmetic brand for purchase intention by brand image, what we are more
concerned about is different brands in different images for respondents’ purchase intention.
In the other word, we will not setting an specific brand at the beginning, the participants
can construct an brand that they more rely on to fulfill familiar the influence of brand image.
The reason is people may have stereotypes about some familiar or unfamiliar brands.
Moreover, the scope of the research (Lie & Bernarto, 2019) is too wide and lacks
granularity in cosmetic products, so we define the research scope of purchase intention to
make-up, skin care products and personal care products to be more comprehensive to
extend the ranges to collect more consumers’ various views in various cosmetic products
through consumers’ spending behaviors that people will not only use one aspects of
cosmetic products in the daily life. To be more specific, consumers expose themselves in
diversity categories of cosmetic products to find out the suitable products.
Last but not the least, the respondents will be UIC female undergraduate students which
differ from people from Indonesian university students who are graduates and
undergraduates and no gender distinguished in the research (Sulhaini, 2019). As female
spend more money in cosmetics than male and youngsters are also one the potentially vast
groups in consuming cosmetics.
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3. Hypothesis Development
3.1 Independent Variables (IV) & Dependent Variables (DV)
According to the previous literature review, we learned that many studies found the
influence of brand image on buying decisions and showed a positive relationship between
brand image and purchase intention (Romadon et al.,2014); (Silvia et al.,2014); (Suria et
al.,2016); (Wangean & Mandey, 2014). There are also some limitations to the existing
studies. Therefore, we have selected several key points from these gaps (the specific group
and region of the research object, the segmentation of brand images, the scope of the brand,
and the classification of cosmetics) and established our research model to propose the
following hypotheses.
Based on the previous studies (Hao et al., 2010), the brand image can be jointly measured
by the functional benefits, experiential benefits, and symbolic benefits, which can be
quantified by questioning and scoring. Therefore, we set the brand image as an independent
variable (IV) and set the hypothesis relating to the three benefits of brand image for better
data collection and analysis. Our dependent variable (DV) is the purchase intention of the
cosmetics industry. We assume the three benefits of the brand image have a positive impact
12
on the cosmetics purchase intention (DV) separately, and we will further collect data and
conduct subgroup analysis under 3 major categories of cosmetics: make-up, personal care,
and skin care products. Considering other factors that may influence the purchase intention,
we also assign demographic, peer effect, and purchase frequency as the control variables
(CV).
3.2 Hypotheses
Assume the functional brand image has a positive impact on purchase intentions.
We find a positive relationship between functional brand image and purchase intention in
different industries.
Firstly, in the food industry, (Lim, Sung-Yun, 2022). This paper deduces the dietary pursuit
benefit (a kind of functional benefit, satisfying people's demand for health and body shape)
into four factors: healthy nutrition pursuit, taste pursuit, trend experience pursuit, and
symbol pursuit. Among them, healthy nutrition pursuits and trends experience pursuit meet
people's demand for staying healthy and keeping up with the trend. They have a positive
effect on purchase intentions.
Secondly, in the clothing industry, Hong et al. (2009) indicated that “Pragmatic benefit
positively affected the purchase intention of eco-friendly apparel and health-functional
apparel.” which also counts as a sort of functional benefit.
Thirdly, in the textile industry, Choi et al. (2016) show that the aesthetic benefit influenced
product identification positively. The aesthetic benefit, functional benefit, and product
identification were all positively related to purchase intention.
Based on the inductive inference, we can know that the functional benefit of brand image
has a positive impact on customers’ purchase intentions.
H1: the functional benefit of brand image has a positive impact on purchase intention.
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According to previous studies, it was verified that the three benefits of brand images have a
positive impact on consumers' brand attitudes separately, then consumers' brand attitudes
can positively affect the purchase intention (Wu & Wang, 2014). With the same logic, we
attempt to remove brand attitudes and explore the direct impact of brand image on purchase
intentions. Therefore, we assume that the experiential benefit of brand image has a positive
impact on customers’ purchase intentions.
H2: The experiential benefit of brand image has a positive impact on purchase intention.
Some researchers showed that social value has a positive influence on undergraduate
students’ purchase intention toward Korean beauty products (Lim et al., 2020). However,
“social value” refers to the symbolic or ostentatious consumer value of a product, which
means that individuals purchase a specific brand or product to gain a unique or prestigious
status and image (Candan et al., 2013). At the same time, according to our literature review,
the symbolic benefit of brand image is product functions that meet consumer intrinsic needs,
such as consumer social acceptance and reputation (Hao et al., 2010). Therefore, both of
them have very similar definitions and goals, then we assume that the symbolic benefit of
the brand image also has a positive impact on cosmetic purchase intentions.
H3: The symbolic benefit of brand image has a positive impact on purchase intention.
3.3 Control Variables (CV)
In the process of studying brand image and purchase intention, there are some other
variables that may affect the relationship between independent variables and Dependent
variables, so we introduce four control variables to avoid the deviation of key variables.
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(1) Peer Effect
According to the study conducted by Wang, You, et al. (2021), the examination of platform
recommendations and recommendations from friends aimed to determine whether they
have different effects on users' purchase intentions. The findings revealed a significant
positive correlation between recommendations from friends and purchase intentions.
Similarly, Samaraweera (2016) conducted a study that specifically focused on Sri Lankan
consumers. The research findings emphasized the significant influence of peer
recommendations on consumers, resulting in a notable reduction in their purchase intention
for crisis brands. This highlights the moderating effect of peer recommendations, indicating
that consumers are more likely to be swayed by the opinions and suggestions of their peers
when considering their purchase decisions.
Moreover, Wang, Sun, et al. (2021) conducted a comprehensive study that examined the
impact of familiarity and intimacy on purchase intentions. The results of their research
further supported the notion that familiarity and intimacy positively influence consumers'
purchase intentions.
Taking these findings into consideration, this study recognizes peer effect as a crucial
factor.
(2) Consumer Buying Power
Demographics variables such as Consumer Buying Power are also considerable factors
influencing the purchase intention of Cosmetics products.
According to Eze (2016), the income decides Consumer Buying Power of an individual and
thus, the more the personal income, the more the expenditure on other items and vice-versa.
There is even another study Lohrey (n.d.) which suggests that Consumer buying power is
disposable, after-tax income, a sum of money you are able to spend, save or invest.
15
From these points of view, it can be determined that consumer buying power may be
closely associated with personal income, accounting for providing a material basis for
consumption level and the ability to spend.
(3) Major
Students in each department have different personality tendencies. For example,
management students may be more outgoing, data science students may be more rigorous.
There is a correlation between consumer personality and consumer purchase intention (Liu
et al., 2016).
Besides, students in different faculties will pursue different careers. Different professions
have different perceptions of brand image on purchase intention. In the study conducted by
Nair and R (2006), professionals and executives are not very strict about brands, while
respondents who belong to all other career groups including teachers, businessmen,
government employees, are more willing to buy specific brands which they are loyal to.
(4) Category
The brand image of different types of cosmetics has different degrees of influence on
purchase intention. We divided cosmetics into three categories, skincare, makeup and
personal care. Each category of cosmetics showed a relationship between their brand image
and purchase intention in past research.
Brand image of skincare products has a positive and significant effect on purchase
intentions (Johan & Then, 2021). According to Boateng (2021), eWOM signals enhance
brand image, and then influence consumers’ makeup purchase intentions. Brand image of a
personal care product can influence consumer attitudes towards the product to stimulate the
purchase intention (Huthasuhut et al., 2022). Each of their results shows different P. Thus,
the category of cosmetics is controlled for.
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4. Methodology
4.1 Target Population
Target population in this study is female college students from year 1 to year 4 who are
primarily from UIC in China. As Zhuhai is in the Greater Bay Area, which has relatively
superior economic conditions, UIC students are selected as the target population for the
sufficient potential of cosmetic consumption and greater insights into various brands of
cosmetics.
4.2 Survey and Procedures
4.2.1 Data Collection Method
(1) Sampling Method
Quota sampling method is used to select a predetermined number or proportion of units, in
a non-random manner. It is simple to make a convenience sample more representative. As
our survey is conducted on campus, we can have access to a very limited number of
respondents (such as our friends), in line with non-probabilistic sampling techniques.
Besides, quota controls such as majors (Four departments in UIC) could be applied.
Through the selection and induction of the department information of the collected
questionnaire information, we obtained the sample sizes of Personal Care Products, Skin
Care Products and Make-up Products were 16, 40, and 46 respectively.
However, the results may be subject to biases (Jager et al., 2017). It may be incorrect and
not universal to expand the results beyond that specific sample.
(2) Questionnaires Method & 7-point Likert Scale
Quantitative research is done to carry out the study. Regarding data collection, the primary
data has been collected with an online questionnaire, which is a vital part indicating UIC
17
students’ perception of cosmetics Brand Image and Purchase Intention. The advantages of
using the questionnaire method are less time-consuming and inexpensive. At least 100
samples are collected, which return with a 100% response rate, to attain a large enough
sample size and analyze the relationship between variables.
The survey questionnaire starts by informing the respondents of the anonymity and
confidentiality of our survey to eliminate their concerns about information leakage. Then
the respondents are asked to choose products (Personal Care products, Make-up products or
Skin Care products) more likely to purchase, about which they are required to answer the
questions. Then according to our IV (Functional benefits, Experiential benefits, and
Symbolic benefits), we will set 3 questions related to each IV individually. The answers
given by each respondent will be measured using a Likert Scale (Strongly disagree1Strongly agree 7).
Due to the objective existence of the halo effect, that is, the evaluation of one trait
influences the evaluation of other traits. The halo effect is often associated with a lack of
participant attention, therefore, an Instructed Response Item (IRI), can also be applied as
well to reduce Rater Errors. During the period of data collection, the questionnaire reveals
one trait per page, and respondents may rate one trait at a time. In order to attract the
attention of the respondents, the reversed scales are adopted when setting the questionnaire
to obtain some effective response data.
Despite collecting a large amount of data through a fast and inexpensive method,
participants may provide dishonest answers or avoid some questions (Jones et al., 2013).
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(3) Sample
This study employs a correlational research design based on an electronic survey answered
by 138 questionnaires. Through the IRI test, we excluded 35 invalid questionnaires. and
then 102 valid questionnaires are obtained through sample screening (we exclude one
questionnaire because of the gender of our respondent). The effective recovery rate of our
questionnaire is 74.6%.
(4) Measurement for Dependent, Independent and Control Variables
The measurement items of each research variable in the questionnaire are obtained by
referring to the research (Hao et al., 2010), and adjusted according to the actual situation of
UIC students. The accuracy and validity of the questionnaire contents are tested repeatedly.
At the same time, we look for the assistance of relevant marketing personnel and ask them
to modify opinions on the settings of the questions in the questionnaire.
This questionnaire is divided into four parts:
The first part contained questions regarding the demographic profile of the respondents to
ensure that quota controls were in place.
The second part includes measuring the influence of brand image (symbolic, experiential,
functional) on consumers’ intentions to buy cosmetics.
The third part measures the dependent variable, namely the purchase intention of cosmetics,
which contains questions about customers’ attitudes and behaviors.
The fourth part measures whether purchase intentions are influenced by other factors.
Dependent and Independent Variables
The indicators of respondents’ brand image perception and purchase intention in each
dimension were measured on average.
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Hao (2010) quantifies Purchase Intention into three questions: "I am willing to buy", "I will
buy" and "I will recommend others to buy these products", according to which the averaged
result will be concluded.
Our independent variables will be measured from three dimensions:
Symbolic brand image is quantified through three dimensions: social status enhancement,
interpersonal relationship enhancement and individual uniqueness.
Experiential brand image measures whether it conforms to fashion requirements, diversity,
and the pursuit of life pleasure.
Functional brand image is quantified by asking respondents whether it achieves the desired
effect, quality, and benefits.
Based on data status, the respondents’ general impression and opinions can be further
realized.
Control Variables
We controlled other factors that might influence consumers' purchase intentions. First of all,
when filling in the questionnaire, we ask the respondents to choose the categories that are
more likely to take Brand Image into consideration when buying cosmetics. Secondly, for
different majors, taking UIC as a sample, we measured the major by dividing it into four
departments of FBM, SCC, FHSS and FST. Third, for consumer buying power, the buying
power of respondents is divided into four levels (monthly): Consumer buying power level 1
(0≤ CBP< 300), level 2 (300≤ CBP < 500), level 3(500≤ CBP < 1000) and level
4(CBP≥1000). Finally, for peer effect, its influence degree (low, moderate, and high) is
distinguished.
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Table 1. Construct Operationalizations
Measurement of Brand Image
Construct
Item
Description
I think using this brand of makeup represents my social
Symbolic
Symbolic
brand
Benefit
status.
I think using this brand of cosmetics can enhance my
image
interpersonal relationship.
I think using this brand of cosmetics can make me unique.
I think using this brand of cosmetics can satisfy my
demand for pursuing fashion.
Experienti
al brand
image
Experiential
Benefit
I think using this brand of cosmetics can satisfy my
demand for pursuing diversified life
I think using this brand of cosmetics can satisfy my
pursuit of fun in life
I think I can achieve the desired effect with this makeup.
Functional
Functional
I think using this brand of makeup will achieve the
brand
Benefit
desired quality.
image
I think it is beneficial to use this brand of cosmetics.
Measurement of Purchase Intention
I am willing to purchase the product.
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Purchase
Purchase
intention
intention
I will buy this product.
I will recommend others to buy this product.
Measurement of Control Variables
Make up
Dummy variable indicates if people prefer buying Make
up Products when considering brand imagine
Cosmetic
Category
Personal
Dummy variable indicates if people prefer buying
Care
Personal Care Products
when considering brand imagine
Skin care
Dummy variable indicates if people prefer buying Skin
care Products
when considering brand imagine
FBM
Dummy variable indicates if people come from the FBM
division
Majors
SCC
Dummy variable indicates if people come from the SCC
division
FHSS
Dummy variable indicates if people come from the FHSS
division
FST
Dummy variable indicates if people come from the FST
division
Level 1
Dummy variable indicates if people spend 0 to 300
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yuan(exclude 300) on cosmetics each month
Level 2
Dummy variable indicates if people spend 300 to 500
yuan(exclude 500) on cosmetics each month
Consumer
Buying
Level 3
Dummy variable indicates if people spend 500 to 1000
yuan(exclude 1000) on cosmetics each month
Power
Level 4
Dummy variable indicates if people spend more than
1000 on cosmetics each month
Peer
Low
Dummy variable indicates if people are rarely influenced
Effect
by others
Moderate
Dummy variable indicates if people are moderately
influenced by others
High
Dummy variable indicates if people are easily influenced
by others
4.2.2 Data Analysis Method
(1) Correlation Analysis
Pearson correlation analysis will be conducted firstly on variables (Purchase intention,
Symbolic brand image, Experiential brand image and Functional brand image). P-value and
Pearson correlation coefficient will be used to determine whether the variables are
statistically significant, as well as their specific correlation and correlation strength. If
P>0.05, whether the Variance Inflation Factor (VIF) is greater than 10 should be taken into
consideration; if it is less than 10, it is still statistically significant.
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(2) Multiple Linear Regression Analysis
The Multiple Linear Regression model can be used to examine the hypotheses by
calculating the relevant statistical data. The Durbin-Watson (DW) test is adopted to
measure whether our Independent Variables have auto-correlation problems. If the value of
DW is closer to 2, it means that the autocorrelation of our variables is less obvious, and the
model design is satisfactory. Variance Inflation Factor (VIF) is used to measure whether
there is a multidisciplinary among independent variables (Symbolic brand image,
Experiential brand image and Functional brand image). If VIF is less than 5, it indicates
that the variables are independent enough and the model designing is satisfied. Finally, the
degree of fitting of the model is measured by R2. The closer the value of R2 is to 1, the
more satisfactory the explanatory ability of Independent Variables (Symbolic brand image,
Experiential brand image and Functional brand image) is to the Dependent Variable. This
indicates the model design is mature.
Finally, the unstandardized coefficientβof the Multiple Linear Regression model will be
discussed in detail to study the influence of Independent Variables and Control Variables
on Dependent Variables.
24
5. Result
5.1 Correlation Analysis
5.1.1 Correlation Analysis for All Categories of cosmetic products
Table 2 displays the results from our correlation analyses for testing H1-3 under all
categories of cosmetic products. The results show that three benefits have significant
two-tailed benefits that are positive, and all of them are statistically significant at p<0.001
level. Their Pearson correlation is all positive and increases in the order of symbolic benefit,
experiential benefit, and functional benefit. For the control variables, the category of
make-up products and skin care products is significant at p<0.001 level. The peer effect at
moderate and high levels is also significant at p<0.001 level. Level (100-300) of consumer
buying power is significant at the p<0.01 level. For other control variables, the variance
influence factor (VIF) for them is all lower than 10, which means significant in the field of
statistics.
25
Thus, for the purchasing intention under all categories of cosmetics and all dimensions of
brand image, H1, H2, and H3 are supported. All control variables have an effect on
purchase intention of all categories of cosmetic products.
5.1.2 Correlation Analysis for Skin Care Products
Table 3 displays the results from our correlation analyses for testing H1-3 under the
categories of skin care products. The results show that the significant two-tailed of three
benefits are all positive. Symbolic benefit is statistically significant at p<0.05 level, while
experiential benefit and functional benefit are statistically significant at p<0.001. For the
three independent variables, their Pearson correlation is all positive and increases in the
order of symbolic benefit, experiential benefit, and functional benefit.For the control
variables, the moderate level of peer effect, and level 3 (500-1000) of consumer buying
power are significant at p<0.05 level. For other control variables, the VIF value for them is
all lower than 10, which means they are significant statistically.
Thus, for the purchasing intention under the category of skin care products and all
dimensions of brand image, H1, H2, and H3 are supported. All control variables have
26
an effect on purchasing intention of all categories of cosmetic products.
5.1.3 Correlation Analysis for Make-up Products
Table 4 displays the results from our correlation analyses for testing H1-3 under the
category of make-up products. Three benefits' significant two-tailed are all positive and
statistically significant at p<0.001 level. Their Pearson correlation is all positive and
increases in the order of symbolic benefit, functional benefit, and experimental benefit. For
the control variables, the moderate level of peer effect and level 3（500-1000）of consumer
buying power are significant at p<0.05 level. For other control variables, the VIF value for
them is all lower than 10, which means they are significant statistically.
Thus, for the purchase intention under the category of make-up products and all
dimensions of brand image, H1, H2, and H3 are supported. All control variables have
effect on purchasing intention of all categories of cosmetic products.
27
5.1.4 Correlation Analysis for Personal Care Products
Table 5 displays the results from our correlation analyses for testing H1-3 under the
category of personal care products. The results show that the three benefits' significant
two-tailed are all positive. However, only the functional benefit is statistically significant at
p<0.001 level, while the other two benefits’ Pearson correlation are exceeding 0.05, and
both their VIF are 11, which exceeds 10. So the experiential benefit and symbolic benefit
haven’t a significant correlation with the purchasing intention, and they can’t conduct the
linear equation.
Therefore, only H1 is supported, but H2 and H3 are not supported for affecting
purchase intention under the category of personal care products.
28
5.2 Multiple Linear Regression Analysis
5.2.1 Model 1: All Categories of Cosmetic Products
After confirming the correlation of each hypothesis (H1-3), the study proceeded to conduct
the regression equation.
29
From Table 6 above,we get the unstandardized linear regression model as follows:
Y = 0.973 + 0.038 Symbolic Benefit + 0.232 Experiential Benefit + 0.612 Functional
Benefits
- 0.027 Personal Care Products - 0.215 Make-up Products
- 0.278 FST - 0.301 SCC -0.064 FHSS
- 0.083 Level2 + 0.233 Level3 + 0.293 Level 4
- 0.115 Moderate -0.243 Low
Table 6 displays the results from our regression analyses for testing H1-3 under all
categories of cosmetic products. The result of the Durbin-Watson test (2.128) is very close
to 2, which can determine that all observed values of the regression model are independent
of each other without autocorrelation, so the effectiveness of the regression model can be
inferred. In this regression model, the VIF of the three benefits and all control variables is
less than 5, confirming that there is no multicollinearity problem between the variables. The
coefficient of R-Squared indicates that the interpretability of the regression model for
changes in the purchasing intention of cosmetics is 69.4%.
The beta values of symbolic benefit, empirical benefit, and functional benefit are all
positive, so the impact of the three benefits on purchasing intention is positively
correlated. Functional benefit has the greatest impact, while symbolic benefit has the
smallest impact.
For the control variable of cosmetic categories, based on past research, we believe that skin
care products are generally more expensive than the other two types of cosmetics, so we
speculate that consumers may value the brand image of skin care products more. We set the
individuals who purchase skin care products as the reference group. After excluding the
influence of majors, consumer buying power, and peer effect, the beta coefficients of the
sample who purchase personal care products and those who purchase make-up are both
negative compared to those who purchase skincare products. This means that when
30
considering cosmetic purchases, the purchasing intention of individuals who purchase
personal care products is 0.027 less than that of those who purchase skin care products, and
the purchasing intention of people who purchase make-up is 0.215 less than that of people
who purchase skin care products.
For the control variable of majors, since the proportion of FBM in the total sample is the
largest, we set FBM as the reference group. Compared to the FBM department, the FST
department, SCC department, and FHSS department all have negative beta coefficients.
This indicates that in this regression model, after excluding the influence of cosmetics
categories, consumer buying power, and peer effect, compared to students majoring in FBM,
the influence of students from the FST department, SCC department, and FHSS on
cosmetics purchasing intention decreased by 0.278, 0.301, and 0.064 respectively.
For the control variable of consumer buying power, we set Level 1 (0-300) as the reference
group since the largest proportion in the total sample. Compared to Leve 1, only the beta
coefficients of the Level 2 group are negative, while the beta coefficients of the Level 3
group and Level 4 group are positive. This indicates that in this regression model, after
excluding the influence of cosmetics categories, major, and peer effect, then compared to
Level 1, the influence of the Level 2 group on cosmetics purchasing intention decreased by
0.083, while the influence of Level 3 and Level 4 on purchasing intention increased by
0.233 and 0.293 respectively.
For the control variable of peer effect, we set the high peer effect (6-7) as the reference
group since the largest proportion in the total sample. Compared to individuals who are
easily affected by the peer effect (reference group), the beta coefficients of the individuals
with low peer effect and individuals with moderate peer effect are both negative. This
means that in this regression model, after excluding the influence of cosmetics categories,
major, and consumer buying power, then compared to the high peer effect people, their
influence on cosmetics purchasing intention is reduced by 0.015 and 0.243 respectively.
31
5.2.2 Model 2: Skin Care Products
From Table 7 above,we get the unstandardized linear regression model as follows:
Y = 0.889 - 0.125 Symbolic Benefit + 0.295 Experiential Benefit + 0.685 Functional
Benefits
- 0.096 FST - 0.402 SCC - 0.415 FHSS
- 0.052 Level 2 + 0.214 Level 3 + 0.158 Level 4
- 0.514 Moderate - 0.615 Low
32
Table 7 displays the results from our regression analyses for testing H1-3 under the
category of skin care products.
The result of the Durbin-Watson test (1.906) is very close to 2, which can determine that all
observed values of the regression model are independent of each other without
autocorrelation, so the effectiveness of the regression model can be inferred. In this
regression model, the VIF of the three benefits and all control variables is less than 5,
confirming that there is no multicollinearity problem between the variables. The coefficient
of R-Squared indicates that this regression model has a more ideal fit compared to
regression model 1, with an interpretable degree of 71.0% for changes in the purchasing
intention of cosmetics.
The beta coefficient of symbolic benefit is -0.125, while the beta coefficient of
experiential benefit and functional benefit are 0.295 and 0.685 respectively. This
indicates that both experiential benefit and functional benefit have a positive
correlation with cosmetic purchasing intention, and functional benefit has a greater
impact. However, the symbolic benefit has a negative correlation with purchasing
intention and has opposite effects on it.
For the control variable of majors, we set FBM as the reference group. Compared to the
FBM department, the FST department, the SCC department, and the FHSS department all
have negative beta coefficients. This indicates that in this regression model, after excluding
the influence of cosmetics categories, consumer buying power, and peer effect, compared to
students majoring in FBM, the influence of students from the FST department, SCC
department, and FHSS on cosmetics purchasing intention decreased by 0.096, 0.402, and
0.415 respectively.
For the control variable of consumer buying power, we set Level 1 (0-300) as the reference
group. Only the Beta coefficients of the Level 2 group were negative, while the Beta
coefficients of the Level 3 group and the Level 4 group were positive. This indicates that in
this regression model, after excluding the influence of cosmetics categories, major, and peer
33
effect, then compared to Level 1, the influence of Level 2 on cosmetics purchasing
intention decreased by 0.052 compared to the reference group, while Level 3 and Level 4
increased by 0.214 and 0.158 compared to the reference group, respectively.
For the control variable of peer effect, we set the high peer effect (6-7) as the reference
group. Compared to those who are easily affected by the peer effect (reference group), the
beta coefficients of the low peer effect group and the moderate peer effect group are all
negative. This indicates that in this regression model, after excluding the influence of
cosmetics categories, major, and consumer buying power, then compared to the high peer
effect group, the influence of the moderate peer effect group and low peer effect group on
cosmetics purchasing intention decreased by 0.514 and 0.615 respectively.
5.2.3 Model 3: Make-up Products
34
From Table 8 above,we get the unstandardized linear regression model as follows:
Y = 0.376 + 0.130 Symbolic Benefit + 0.377 Experiential Benefit + 0.455 Functional
Benefits
- 0.068 FST - 0.559 SCC -0.689 FHSS +
0.174 Level 2 + 0.185 Level 3 + 0.083 Level 4
- 0.120 Moderate - 0.123 Low
Table 8 displays the results from our regression analyses for testing H1-3 under the
category of make-up products.
The result of the Durbin-Watson test (2.129) is very close to 2, which can determine that all
observed values of the regression model are independent of each other without
autocorrelation, so the effectiveness of the regression model can be inferred. In this
regression model, the VIF of the three benefits and all control variables is less than 5,
confirming that there is no multicollinearity problem between the variables. The coefficient
of R-Squared indicates that the explanatory degree of the variable to the change in
purchasing intention of cosmetics is 78.3%, and this regression model has the most ideal fit
among all models.
For the symbolic benefit, empirical benefit, and functional benefit in this regression model,
their beta scores are all positive and are 0.130, 0.377, and 0.455, respectively. This
indicates that their influence on purchasing intention is positive, and functional
benefit has the greatest influence, while symbolic benefit has the smallest influence.
For the control variable of majors, we set FBM as the reference group. Compared to the
FBM department, the FST department, the SCC department, and the FHSS department all
have negative beta coefficients. This indicates that in this regression model, after excluding
the influence of cosmetics categories, consumer buying power, and peer effect, compared to
35
students majoring in FBM, the influence of students from the FST department, SCC
department, and FHSS on cosmetics purchasing intention decreased by 0.068, 0.559 and
0.689 respectively.
For the control variable of consumer buying power, we set Level 1 (0-300) as the reference
group. The Level 2 group, Level 3 group, and Level 4 group have positive beta coefficients.
This indicates that in this regression model, after excluding the influence of cosmetics
categories, major, and peer effect, then compared to Level 1, Level 2, Level 3, and Level 4
have a greater positive impact on purchasing intention compared to the reference group,
with increases of 0.174, 0.185, and 0.083 respectively.
For the study of the Peer effect, compared to the reference group "High", the Low group,
and the Moderate group, the beta coefficients were all positive. This indicates that in this
regression model, after excluding the influence of cosmetics categories, major, and
consumer buying power, then compared to the high peer effect group, the influence of the
moderate peer effect group and low peer effect group on cosmetics purchasing intention
increased by 0.120 and 0.123 respectively.
36
6. Limitations
Source of our data: Our data sources are limited to UIC female college students, and
ignore the views of other age groups, as well as men, which may lead to one-sided views.
Each age group's preferences, and attention to the three groups of cosmetics of personal
care, makeup, and skin care may be different. Our results may not be applicable to other
areas or groups. This may limit the generalizability of our study. Further study can do
research on different university’s students; on both male and female; or on other age
group’s cosmetics users.
Sample capacity: To achieve more accurate conclusions, a larger sample would be useful.
Our current sample is 102. In future studies, they can find more samples to improve
accuracy.
Data bias 1: Among the collected data, there are fewer users of personal care selection,
which may cause a large bias in the results of personal care. This problem is indeed
encountered in our actual calculations. According to our hyphotheses and other literature
research, brand image should have an impact on consumers' willingness to buy no matter
what category of cosmetics, but since the sample size of personal care is only 16, we failed
to discover correlations or conduct multiple linear regression on this category.
Data bias 2: Most of our data comes from students in the DBM department, that is, the
department of business management. Students in different departments have different
majors and may have personality differences, which brings about differences in preference.
As we collected most of the data from the DBM questionnaire that may lead to bias. It is
recommended that future researchers change their methods and collect more balanced data.
IV measurement: In terms of the factors influencing purchase decisions, we only chose
consumer benefits to determine brand image, while not choosing consumer benefits and
brand personality which are also components of brand image. However, the other two
components may influence consumers' purchase intentions, but they are difficult for us to
control. Perhaps in the future, it can be controlled to CV.
IV division: We only focus on 3 categories of cosmetics. Personal care, makeup, skincare.
we did not discuss hair care, hands and feet, etc., which are also a category of cosmetics
37
according to the State Drug Administration classification. In addition, we do not break it
down detailly under each category. Future research can do more on detailed categories of
cosmetics.
Theoretical: If we want to argue based on data, our data should be based on experiment.
However, all of our data is from the survey, so we are arguing causality. It’s advised that
future research can research the effect of brand image on purchase intention using
experiments, which can show causality based on data.
Bias raised by the nature of survey: We use surveys to collect the preferences and
feelings of respondents, however, these are dynamic. And our results and the findings of
the study are based on those preferences and feelings only. Subjective factors such as
personal biases or the way questions are phrased can further affect the results.
The limitation of Likert scale: Since purchase intentions are only roughly measured by an
aggregate score, we cannot further characterize structural differences in attitudes. Further
study can be conducted using other method.
38
7. Discussion
Our results provide several insights into the effect of brand image on purchase intentions
for cosmetic categories. The findings are mostly consistent with the results of previous
empirical studies. Overall dimensions of brand image of manifold kinds of cosmetics have
positive effects on the purchase intention (h1, h2, h3 are supported). Therefore, from the
perspective of the industry, companies are required to endeavor to preserve customer trust
and corporate reputation to enhance the brand image of their specific products.
If categorized by dimensions, functional benefits of cosmetics are the most significant
factor affecting consumer purchase intention compared with other benefits (h1 has the
largest influence value).
However, across categories, the brand image of personal care products has no significant
correlation with purchase intention. The primary reason is that there may be other factors
playing a greater role in the purchase of personal care products, thus overlooking, or even
negating the importance of brand image of personal care products on the purchase intention
(h1, h2, h3 are not supported). According to the research of organic personal care products
(Kim & Chung, 2011), consideration of consumers’ past behaviors can provide better
predictions of behavioral intentions. Therefore, this research can reasonably infer that past
purchasing behaviors may be a potential contributing factor that explains the extraordinary
data point.
The results of our control variables induce additional cognition into the effect of brand
image on purchase intention. At the category level, due to its substantial target consumers,
skin care products are taken as the reference group to eliminate other variables’ impact.
People have a weaker purchase intention for make-up compared with skincare products,
with moderate personal care products.
Majors are closely related to personal characteristics. Owing to the limitations of the field
of investigation, FBM students account for a massive proportion of our survey objects.
39
Among them, SCC has the least influence on purchase intentions, followed by FST and
FHSS, which highlights the necessity to target sales according to consumer characteristics.
Purchase intention varies at different levels of consumer buying power. As for the outliers,
the results reveal that the group whose expenditure reaches level 2 in cosmetics has lower
purchase intention than that of level 1 (level 2 more than level 1). Furthermore, as the
questionnaire fails to reach a variety of consumer groups vertically, the size and diversity of
samples are relatively restricted. More in-depth research may contribute to a more
rationalized explanation.
When it comes to peer effect, it is also conducive to purchase intention, implying that
appropriate marketing tools may lead to appreciable economic benefits.
40
8. Recommendation
In light of the above findings, several recommendations can be made to help businesses
operating in this sector improve their brand image and appeal to the UIC female college
student demographic.
First of all, from the perspective of the overall model, the brand image has a positive impact
on the purchase intention of UIC female college students, and the shaping of the brand
image will affect the performance of the entire brand. Therefore, we recommend that
enterprises can cooperate with authoritative and well-known brands for promotion in the
marketing stage, so as to attract more potential customers.
Secondly, businesses should carefully consider their branding strategies and tailor them
based on the product category they are selling. For skincare products, companies should
position themselves as trusted and reliable sources of effective solutions that meet their
customers' needs. In contrast, companies selling personal care products should focus more
on their brand story, values, and personality to resonate with customers on a symbolic level.
As far as model three is concerned, in the skincare category, we can obtain through the
regression coefficient and correlation coefficient that functional brand image has a greater
impact on consumers' purchase decisions, so enterprises should pay more attention to the
promotion of brand functional characteristics. Functional bias refers to the use of the
physical feeling brought by the product can meet the consumer's pursuit of actual function,
for example, on the cosmetics shelf can be used function diagram and specific use operation
diagram to describe the function clearly, so that consumers get the clearest and most
intuitive functional cognition. Simply speaking, businesses should prioritize functionality in
their product development process. They should invest in research and development to
create products that are effective, reliable, and practical.
While for the makeup category, symbolic experiential brand image is the most relevant. We
recommend corporates to grasp the cutting-edge information of the industry to fill in the
41
market gap, innovate product forms and promote forms when designing products, and
provide customers with novel consumer experience. It can be innovated and upgraded for
product appearance, storage methods, personalized customization, and incidental services.
For example: the introduction of virtual image in makeup promotion, brand image
combined with Internet celebrity IP to increase brand story, VR one-click makeup try-on,
etc.
Furthermore, based on the analysis of control variables, we have the following
recommendations.
We can know through the influence of control variables that UIC female college students
are more likely to make purchase decisions under the utility of their peers for skin care
products, so corporates can introduce sales measures recommended by peers, such as
classmates recommending discount coupons or using social networks to forward brand
information for promotion.
In terms of purchasing power, customers have a low willingness to buy skincare products in
the 300-500 purchasing power range, and cosmetics in the 0-300 range. Both skin care and
color cosmetics have high purchasing power in the range of 500-1000. We suggest that the
company develop a pricing strategy based on this situation and calculate the purchasing
power distribution accordingly to get a different pricing range with different focus. We
recommend companies to adopt the bundled pricing method to bundle skin care and
cosmetics products to blur consumers' psychological expectation pricing. The anchoring
effect can also be used to set price anchors for consumers, such as setting a low price for an
item with high sales volume and whose price is relatively familiar to customers to attract
them to buy it thus increasing the overall brand sales volume.
In the major analysis, students in FBM are more willing to buy both makeup and skin care.
We suggest that companies can target customer groups and develop different marketing
strategies for different professions and occupations, for example, they can set specific
makeup and skin care sections for business people as well as design specific products for
42
them.
Finally, businesses should consider leveraging digital marketing channels such as
influencers, social media, and online forums to connect with their target market and build
brand awareness. This approach can help businesses generate buzz and build an engaged
audience that is more likely to consider their products when making purchasing decisions.
In conclusion, the findings of this study suggest that businesses must carefully consider the
type of brand image they want to create and position themselves accordingly to appeal to
UIC female college students. By prioritizing functionality, tailoring branding strategies, and
leveraging digital marketing tactics, companies can build a strong brand image, connect
with their customers, and increase sales in this highly competitive market.
43
9. Conclusion
To summarize, this study examines the extent of correlation to which brand image
influences the intention to purchase cosmetics among female college students at UIC. The
research model is based on previous literature on brand image and purchase intention of
cosmetics. 102 responses were collected from female college students in UIC on how they
were perceived by brand image (The three consumer benefits) when purchasing cosmetics
through questionnaires in three dimensions (make-up products, skincare products, and
personal care products).
The results show that brand image does have a positive effect on UIC female students'
purchase intention for cosmetics, as the data analysis shows that brand image is highly
correlated with purchase intention, so almost every hypothesis is supported.
This research also looks at the different results that emerge when segmenting the brand
image from the three consumer benefits:
 For skincare products: Symbolic benefits will have a significant correlation with
purchase intention of UIC female college students for skincare products. Functional
benefits and experimental benefits are highly correlated, with functional benefits
having the highest correlation.
 For make-up products: Symbolic benefits, functional benefits and experimental
benefits are all highly correlated with the purchase intention of make-up products
among UIC female college students, with the correlation of experimental benefits being
the highest.
 Personal care products: Because the sample supporting this data was very small,
personal care products did not appear in the data analysis.
Therefore, in future research, scholars can use more subjects and objects for the brand
image of different categories of cosmetic products to conduct more research on the impact
44
of the three consumer benefits on potential consumers' purchase intentions, such as
collecting more information on personal care products. In addition, the study only did a
survey to collect data, so that the possibilities of the research model become more tested.
The subsequent research can be based on more methods, such as experiments and taking
more samples to go deeper. Besides, different areas of cosmetics (e.g. color cosmetics,
skincare, and personal care products) should improve their brand image based on the
different needs of customers for the products and thus increase their purchase intention.
45
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