International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 01, January 2019, pp. 1710-1721, Article ID: IJMET_10_01_170
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=01
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
Scopus Indexed
A CRITICAL EVALUATION OF GEN-Y BUYING
BEHAVIOR TOWARDS ONLINE FASHION
RETAILERS
Dr. Kiran.G
Associate Professor
Welingkar Institute of management Development & Research
Dr. D.N. Murthy
Dean-Marketing & Research
Welingkar Institute of management Development & Research
Dr. Nila Chotai
Professor
Jain University
Dr. Pradeepa
Marketing Manager
Malnad Hi Tech Diagnostics
ABSTRACT
In India the pace of online users has been faster with users being more comfortable
to buy online for apparels, footwear and fashion accessories when compared to other
countries (Forrester research)1 and the consumers are becoming more mature by passing
time.On the other hand business to consumer e-commerce has also shown a major growth
in the GDP from the year 2009 from 0.13% to 0.21% in 2017 as percentage of GDP in
India (The Statistics Portal) and expected to touch 0.24% by end of 2018.A number of
business today have gone online from the traditional stores not just to sell online but also
for various marketing strategy. For consumers who want to shop online the platform well
known for e-commerce is business to consumers (B2C) which includes online shopping
for all their needs. This study was conducted to analyze the shopping patterns of
millennials when to comes to the clothing brands online the study was conducted in
Bangalore city covering the age group of 23-35 year youngsters and the study indicated
a significant relationship between the buying behavior and the factors influencing the
young buyers to buy the clothing brands from the online portals. The study was
exploratory in nature covering important variables identified.
Keywords: Buying, online, shopping, millennial, behavior, customer, fashion
http://www.iaeme.com/IJMET/index.asp
1710
editor@iaeme.com
Dr. Kiran.G, Dr. D.N. Murthy, Dr. Nila Chotai and Dr. Pradeepa
Cite this Article: Dr. Kiran.G, Dr. D.N. Murthy, Dr. Nila Chotai and Dr. Pradeepa, A
Critical Evaluation of Gen-Y Buying Behavior Towards Online Fashion Retailers,
International Journal of Mechanical Engineering and Technology, 10(01), 2019, pp.1710–
1721
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&Type=01
1. INTRODUCTION
Online fashion retailers with the help of internet have changed the way consumer shop and buy
products online. A large number of e-tailer’s enter the online business with an objective of not
just selling the product but also to convey, communicate and to take valuable feedback from the
consumers. Online retailers are rapidly growing over the period of time in various aspect of doing
business online.
Internet world stats (2017) shows that 55.1% of the world population using internet for
shopping online, making payments, browsing information and many such activates are from Asia.
The stats also show that Indian internet users have rapidly increase over a period of time from
7.5% in 2010 to 34.8% of population in 2016 which provides a huge platform for the e-tailers to
set up their business online because stats shows that number of users are going to increase in the
coming years.
Source: Original from the author.
2. MILLENNIAL CONSUMER BEHAVIOR
Millennial’s are the group of consumers who are very different and unique from the other
category of consumers. The below table is the comparison of how they are different
Characteristic
Role in the family
Purchase behavior
Consumer type
Generation X
Information getter
Money oriented
Sincere
Media habits
Television
Technology
Price-quality
Diversity
Shopping behavior
Use technology
Loyal till they find a better new
deal
Price oriented
Accept it
Self-reliant
Appealing products
Snacks, footwear, music
Loyalty
Millennial’s
Strong influencers
Realistic and Savvy
Smart
Television, social media and
various internet source
Assume technology
As per their convince
Value and quality oriented
Celebrate it
Self-inventive
Fashion, accessories, health,
entertainment
Source: Accenture, Insight Outlook
3. REVIEW OF LITERATURE
Ambujakshi (2017) conducted a study to have a better understanding of how youth of age group
15-24 a young target segment which is growing ever faster play a major role in buying decision
making process, get influenced by advertising, this study was based on the level of influence
advertising has on the youth buying behavior of electronic gadgets and found that Majority of the
youth agree that advertising does influence them in their buying behavior were on the other hand
they feel advertising should be more creative and effective.
http://www.iaeme.com/IJMET/index.asp
1711
editor@iaeme.com
A Critical Evaluation of Gen-Y Buying Behavior Towards Online Fashion Retailers
AnushaSreeram, AnkitKesharwani, Sneha Desai (2017) explored to see if technology,
visual appeal, product prototype and self-expression have a major influence on purchase behavior
of online grocery buying intention through attitude and pleasure,this paper aims to test an model
of online grocery buying intention by extending technology model by adding time pressure,
efforts, value, design etc. to understand if consumer would purchase grocery online they found
that Product assortment had a significant impact and it supports the notion of one stop solution
as a major reason of buying grocery online. A structure equation model was used to test the
technology impact with factors such as time, efforts and value.
CherukuriJayasankara Prasad, AnkisettiRamachandraAryasri (2013)with an objective
to enable retailer to segment their market and marketing strategy which would be helpful to meet
their retail needs found that shoppers' age, gender, occupation, education, monthly household
income, family size and distance travelled to store have significant association with retail format
choice decisions. A total of 1040 food grocery stores were selected were it was found that all the
factors have a significant association with retail format choice decision.
Dipanjan Kumar Dey, AnkurSrivastava (2017) conducted a study to understand the
shopping perspective of young consumers who buy from different value dimension such as fun,
social interaction, novelty and outside appreciation, for which results said that the impulsive
buying intentions of young consumers are positively associated with all the dimension of hedonic
shopping value with time and money having a positive relationship. A structured questionnaire
was used for the age group of 15-23.
Dr. Renuka Sharma, Dr. Kiran Mehta, Shashank Sharma (2014) did a research based on
structured questionnaire on millennial's based out in Punjab to understand the online buying
behavior of consumers in India and to provide useful information to marketing professional to
develop a better and effective strategy and results stated that the young Indian consumer are being
addicted to online shopping but research still states the e-tail stores are not fully developed and a
lot needs to be changed to make it more effective
Johan Hagberg, MalinSundstrom, NiklasEgels-Zandén (2016) analyzed the phenomenon
of the digitalization of retailing by developing a conceptual framework that can be used to further
delineate current transformations of the retailer-consumer interface and result stated that
digitalization transforms in a number of ways such as factor of exchange, communication, price
and participation of consumers and retailers.This paper addresses a significant and on-going
transformation in retailing and develops a framework that can both guide future research and aid
retail practitioners in analyzing retailing’s current transformation due to digitalization
Ms. Dipti Jain, Ms. Sonia Goswami, Ms. ShipraBhutani (2014) The main purpose of their
study was to understandthe impact of perceived risk, perceived enjoyment, perceived usefulness
and perceived ease of use factors on online shopping behavior of consumers, 160 online shoppers
of different age, income and occupation with a prior experience in online shopping were
considered on 5 point Likert scale basis and found that Only one factor perceived risk
significantly affected online shopping behavior of young consumer.
4. STATEMENT OF RESEARCH PROBLEM
The purpose of this project is to identify the factors which would influence the millennial
consumers towards online fashion retailers. This study aims to answer the following question:
• Does risk play a role in the purchase intention of millennial’s towards online fashion
risk
• Peer group have an influence on decision making of millennial’s?
• Does trust play a very important factor when millennial’s shop online?
• What influence the attitude of millennial’s towards online fashion shopping?
http://www.iaeme.com/IJMET/index.asp
1712
editor@iaeme.com
Dr. Kiran.G, Dr. D.N. Murthy, Dr. Nila Chotai and Dr. Pradeepa
5. RESEARCH QUESTION
What factors influence the purchase intention of millennial’s towards online fashion retailers in
India?
5.1. SCOPE OF RESEARCH
This research would be carried out in Bengaluru urban were majority millennial are tech savvy
and are the ones who do online fashion shopping on a regular basis. This study would further
help the marketers and online fashion retailers to understand the purchase intention of
millennial’s, what attracts these millennial’s and what else can be done to attract those who still
don’t make a purchase from online
6. RESEARCH METHODOLOGY
The first step of a research study is to develop a blue print or a research design. Which give an
overview of how the research should be carried on? It’s either exploratory, experimental or/and
descriptive in nature which would help the researcher answer the question which they would like
to find the answer for with respect to the subject or topic been selected to research upon. The next
step after deciding the kind of research to be carried the researcher would go ahead to design a
set of question to be asked depending upon the objective and scope of research, after which
researcher goes ahead and collects the data and then the analysis of data is been done using
different statistical tools. Only through interpretation a research would be able to explain the
relationship between variables and arrive with findings before coming up with conclusion
In this study the technique of research adopted is exploratory research. It’s a study which is
been done for a problem which has not been defined clearly. It has the following objectives:
• Specific formulation of problem
• Setting priorities for further research
• A clear research design about collection of data and information
• It help is defining the relationship between factors related to it
• Gives a depth knowledge to the researcher
6.1. RESEARCH DESIGN
A research design is a basic plan which guides the process of data collection and analysis of the
research begin carried out. It’s a framework that clearly specifies the type of information to be
collected, the source of information and process of data collection.
According to (Kinnear & Taylor, 1996; Chruchill&Lacobucci 2005) define research design
“it is the blue print that is followed to complete the study” and “it ensure that study is relevant to
the problem and will use economical procedure”.
In the present study exploratory research design has been adopted which means that it is a
research that has been carried out for a problem which has not been studied more clearly which
will help to have a better understanding of the problem. This study does not aim to provide a final
answer to research question but with an aim of exploring the research topic.
6.2. RESEARCH OBJECTIVE
1. To determine the of role trust in influencing purchase intention of millennial’s
shopping online from fashion retailers
2. To study the impact of perceived risk on purchase intention of millennial’s shoppers
3. To assess the importance of perceived ease of use, perceived usefulness and peer
group opinion on purchase intention of millennial shoppers
http://www.iaeme.com/IJMET/index.asp
1713
editor@iaeme.com
A Critical Evaluation of Gen-Y Buying Behavior Towards Online Fashion Retailers
This study is limited to online fashion retailers of India and only in the city of Bengaluru
6.3. SCOPE OF RESEARCH
This research would be carried out in Bengaluru urban were majority millennial are tech savvy
and are the ones who do online fashion shopping on a regular basis. This study would further
help the marketers and online fashion retailers to understand the purchase intention of
millennial’s, what attracts these millennial’s and what else can be done to attract those who still
don’t make a purchase from online. Further the study is based out only on the working
professional millennial’s from the age group of 23-34 who have a disposable income which would
provide the online marketers to understand the purchase intention of these millennial’s and what
they need to improvise on to build upon the trust towards them.
6.4. METHODS OF DATA COLLECTION
Data: it is a set of values of qualitative or quantitative variables. It is the process of collection,
measurement and analysis and then visualized.
The types of data are:
1. Primary Data: The process of collecting raw data or unused data directly from
sources like survey, interview, observation, questionnaire and from an
experimentation which are not been replicated before.
Advantages of primary data:
• It is accurate as it is collected directly from a predetermined population
• It’s an unbiased data
• It a basic and specific source of information
Disadvantages of primary data:
• Need a large size of sample
• It’s quite costly and time consuming
• It’s very subjective in nature.
2. Secondary data: Secondary data is a data which is readily available and processed
into useful information available from the sources like government, journals,
magazine, reports and statistical,
Advantages of secondary data:
• It is less expensive
• It is time saving
• It helps in problem solving
• It provides a base for comparison of data
Disadvantages of secondary data:
• Accuracy is not known
• Data sometime may be out dated
• Information may not be same as required
In the present study journals from published source were reviewed for better understating of
the subject and the conceptual model. Apart from journal the researcher also collected statistical
data from various trusted online sites for more information about millennial's in India and around
few developed nations in the world.
http://www.iaeme.com/IJMET/index.asp
1714
editor@iaeme.com
Dr. Kiran.G, Dr. D.N. Murthy, Dr. Nila Chotai and Dr. Pradeepa
6.5. SAMPLING METHODS
Its tool applied to define the population which would be selected for the purpose of the survey.
A process of obtaining sample from the given population with a predefined plan, it is also known
as sample design
6.6. Target Population
It is a process of collection of object or elements which is related to research study. In the study
the aim was to examine and to identify those factors which influence the millennial's consumers
towards online fashion shopping in Bengaluru context. The study focused on target population
that is working professional of age group of 24-34.
6.6.1. Sampling Frame and Sampling Location
The place or an area from which sample is drawn. The questionnaire was distributed to the
working professional respondent from various location of Bengaluru.
6.6.2. Sampling Elements
The research was conducted in Bangalore through questionnaire. The target population are
working professional of age group 23-34 were selected because they have the required
knowledge, information, access and disposable income, and respondents with prior experience in
online shopping.
6.6.3. Sampling Technique
The two commonly used methods of sampling are probability and non-probability sampling
techniques used in the research study. In this study non-probability technique has been adopted
as it is widely used, less expensive and with smaller population size.
In this research Judgmental sampling technique was bee used because it’s fast and lower cost
6.6.4. Sampling Size
Specifying appropriate sample extent is a tricky and sophisticate task. The rules of thumb for
determining samples, more than 30 and less than 500 are appropriate for most research (Roscoe,
1975). In this study the sample size: Field study of 350 respondents
7. DATA ANALYSIS AND TOOL FOR RESEARCH
The analysis of collected data was done through SPSS (Statistical Package for the Social
Sciences) statistical software. The methods of statistical tools of analysis used are KMO and
Bartlett's Test, Cronbach’s Alpha, Mean, Standard deviation, variance, chi-square test, Anova,
coefficients and correlation.This study would use confirmatory factor analysis; confirmatory
factor analysis is a statistical tool which is used commonly to test the hypothesis that would
develop a relationship between the variable of the study. This is further done on the basis of
theory and then to test the hypothesis statistically.
7.1. Kaiser-Meyer-Olkin (KMO) Test for Sampling Adequacy
Kaiser-Meyer-Olkin (KMO) a test which is used to measures the suitability of data for factor
analysis which measures the sampling adequacy of individual variable in the model and also for
the entire model used in the research. It also measures the proportion of variance in the given set
of variable which may be common variance. That is to say if the proportion is lower than the data
becomes more suitable for factor analysis.
KMO returns values between 0 and 1 indicate:
• The sampling is adequate when the KMO value lies between 0.8 t0 1
http://www.iaeme.com/IJMET/index.asp
1715
editor@iaeme.com
A Critical Evaluation of Gen-Y Buying Behavior Towards Online Fashion Retailers
The sampling is not adequate when the KMO value is less than 0.6 and for which a
remedial action needs to be taken
• When KMO value is near to zero that there are large partial correlation
For reference, Kaiser put the following values on the results:
• When the KMO value is 0.00 to 0.49 then it is unacceptable.
• When the KMO value is 0.50 to 0.59 then it is miserable.
• When the KMO value is 0.60 to 0.69 then it is mediocre.
• When the KMO value is 0.70 to 0.79 then it is middling.
• When the KMO value is 0.80 to 0.89 the it is meritorious and
• When the KMO value is 0.90 to 1.00 then the sampling is marvelous
The result of the pilot study and KMO value were:
•
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.850
Approx. Chi-Square
2073.194
Bartlett's Test of Sphericity
Df
595
Sig.
.000
The value of .850 and the result satisfy condition of KMO values between 0.8 and 1 which
indicate the sampling is adequate.
Individual KMO Value of the Variables of the Study
Table 1: Perceived Ease of Use
Construct
Question
I find most online shopping sites easy to use.
Perceived Ease of Use
KMO Value
I find it easy learning to use most online shopping sites.
I find it easy to use most online shopping sites to find what I want
I find it easier to compare products when shopping at online retailers.
.809
I feel that most online shopping sites are flexible to interact with.
The above table shows the construct of Perceived Ease of Use using five items. The above
have been adopted and modified from Weng Marc Lim(April 2012)
Table 2: Perceived Usefulness
Construct
KMO
Value
Question
I am able to accomplish my shopping goals more quickly when I shop online.
I am able to improve my shopping performance when I shop online (e.g. save time or
money).
Perceived
Usefulness
I find the website of online retailers useful in supporting my purchase decisions.
Shopping from online retailers improves my purchase decisions.
.876
Shopping from online retailers makes it easier for me to satisfy my needs.
The above table shows the construct of Perceived Usefulness using five items. The above
have been adopted and modified from Weng Marc Lim (April 2012)
http://www.iaeme.com/IJMET/index.asp
1716
editor@iaeme.com
Dr. Kiran.G, Dr. D.N. Murthy, Dr. Nila Chotai and Dr. Pradeepa
Table 3: Attitude towards Online Fashion Shopping
Construct
Attitude
Question
I am comfortable to shop from online shopping sites.
I like to purchase what I need from online shopping sites.
I like to seek for product information from online shopping sites.
I feel happy when I do my shopping online
I hold a positive evaluation of shopping online.
KMO Value
.895
The above table shows the measurement items of Attitude towards Online Fashion Shopping
and is been adopted and modified from Weng Marc Lim (April 2012)
Table 4: Online Purchase Intention
KMO
Value
Construct Question
Intention
It is likely that I will continue to purchase the product from online retailer
I intend to continue purchase products from the internet in the future
I would likely visit an online shopping site to shop for my needs
I plan to do more of my shopping via online shopping sites.
When I need to buy a particular product, I would search for an online
retailer which has the product.
.804
The above table shows a five item construct of purchase intention of millennial's towards
online fashion shopping. It has been adopted and modified from Weng Marc Lim(April 2012)
Table 5: Risk towards online fashion shopping
Construct
Question
KMO
Value
Risk
It is hard to judge the quality of the merchandiser over the internet
I feel that there will be difficulty in settling disputes when I shop online (e.g.
while exchanging products).
I might not get what I ordered through online shopping.
I might receive malfunctioning merchandiser.
I do not shop online because of non-availability of reliable &well-equipped
shipper
.703
The above table shows the five item risk construct which may have an impact on attitude of
millennial's towards online fashion shopping it has been adopted and modified from Hashim
Shahzad (Spring 2015)
Table 6: Peer Group Opinion
Construct
Question
KMO
Value
Peer
Group
My friends influence me to do shopping online.
The members of my family (relatives, parents, children, and spouse)
believe that I should shop online.
My colleagues influence me when I go for shopping online.
I ask my friend about the product before shopping online
I often observe what my friends are buying
.851
http://www.iaeme.com/IJMET/index.asp
1717
editor@iaeme.com
A Critical Evaluation of Gen-Y Buying Behavior Towards Online Fashion Retailers
The above table shows a five items construct of peer group opinion and its impact on purchase
intention of millennial's towards online fashion shopping. It has been adopted and modified from
Yu et al. (2005)
Table 7: Trust
Construct
Trust
Question
KMO Value
I believe that the website would act in my best interest
I believe that the website is trustful in its dealing with me
I believe that the website would keep its commitment
.849
I feel my personal details would not be share by the retailer to third party
I feel that my card details would not be misused
The above table shows the measurement item of trust which may have a direct influence on
purchase intention of millennial's towards online fashion shopping. It has been adopted from
McKnight, Choudhury&Kacmar (2002)
Cronbach's Alpha
It is the processes of estimating the reliability based on inter- correlation of the variables.The
value of Cronbach’s alpha reliability coefficient will increase while the inter-correlations among
test items increase, it usually ranges between 0 and 1 (Bajpai, 2011).
Alpha Coefficient Range
Less than 0.6
0.6 to < 0.7
0.7 to < 0.8
0.8 to < 0.9
0.9 and above
Strength of Association
Poor
Moderate
Good
Very Good
Excellent
Table 8 PARTIAL LEAST SQUARE PATH MODELLING
COMPOSITE RELIABILITY INDEX
Latent variable
Dimensi
ons
Perceived ease
of use
5
Perceived
usefulness
5
Risk
5
Cronbach's
alpha
0.878
0.916
0.867
http://www.iaeme.com/IJMET/index.asp
D.G. rho
(PCA)
Condition
number
0.912
4.415
0.938
4.323
0.908
5.256
1718
Critical
value
1.240
1.037
1.529
Eigenval
ues
4.198
0.850
0.617
0.321
0.215
3.898
0.519
0.321
0.236
0.209
5.136
0.987
0.941
0.392
0.186
editor@iaeme.com
Dr. Kiran.G, Dr. D.N. Murthy, Dr. Nila Chotai and Dr. Pradeepa
Attitude
5
Peer group
opinion
5
Trust
5
Purchase
intention
5
0.937
0.896
0.903
0.931
0.952
5.767
0.924
3.908
0.930
5.193
0.948
5.917
1.293
1.377
1.278
1.166
5.175
0.572
0.328
0.236
0.156
4.884
0.724
0.552
0.406
0.320
4.667
0.751
0.475
0.321
0.173
4.577
0.639
0.278
0.203
0.131
In this application, latent variable are reflective. The blocks have to be one-dimensional.
Dillon-Goldstein’s rho is higher than 0.7
Table 9: Goodness of Fit
GOODNESS OF FIT
GoF GoF (Bootstrap)
Absolute
0.783
0.781
Relative
0.969
0.963
Outer model 0.992
0.990
Inner model 0.976
0.972
GOF was 0.783 very close to GOF bootstrap. This value was hard to interpret; it could be
useful when comparing the global quality of two different models.
PARTIAL LEAST SQUARE PATH MODELLING
Figure 1: PLS Path Model
http://www.iaeme.com/IJMET/index.asp
1719
editor@iaeme.com
A Critical Evaluation of Gen-Y Buying Behavior Towards Online Fashion Retailers
All hypothesis were supported in the PLS Path Modeling approach. Thus, R square 0.869
which can be considered as a good result, 86.9%variance in online consumer purchase intention
towards customer attitude.
Table 1: Result of Path Coefficient and Hypothesis Testing
Result of path coefficient and hypothesis testing
Latent variable
Perceived ease of use
Perceived usefulness
Risk
Attitude
Peer group opinion
Trust
Value
0.291
0.643
0.017
0.732
0.063
0.184
Standard error
0.043
0.042
0.025
0.039
0.024
0.039
t
6.718
15.489
0.678
18.996
2.600
4.708
Decision
Supported
Supported
Supported
Supported
Supported
Supported
The above table presented the results for the path coefficients and hypothesis study in PLS
path Modeling approach. The path coefficient value for perceived ease of use has a value of
0.291 and standard error of 0.043. The empirical t-value is 6.718 and it is greater than the
significance t value of 2.57 for a probability error of 1%. It shows that Perceived ease of use has
a strong positive influence on consumers’ purchase intention.
With regards to attitude, the path coefficient value shows 0.732. The empirical t-value is
18.996, which is greater than significance level for probability error of 4%. Thus it has a weak
influence on online consumer purchase intention. With regards to perceive risk, the path
coefficient value shows 0.017. The empirical t-value is 0.678, which is lesser than the theoretical
t-value of 1.65 for probability error of 3%. It indicates that H6 has a strong influence on
consumers’ purchase intention at significance level of 5. Contrarily, the Perceived usefulness has
a positive path coefficient value of 0.643. The t-value is 15.489 and it is higher than the theoretical
t-value of 1.65 for probability error of 5%. Thus, it has a positive relationship with online
consumer purchase intention. The path coefficient for trust shows 0.184. The empirical t-value is
4.708, which is more than the theoretical t-value of 1.96 for probability error of 4%. Thus, it
shows that trust has a significant stimulus on online consumers’ purchase intention at significant
level of 5%. Thus, the hypothesis is supported. Finally, the outcome revealed that all variables
are supported towards online consumer purchase intention.
8. SUMMARY OF FINDINGS & CONCLUSION
This study was done with an objective to know the factors which influence the millennial
consumer to make a purchase decision from online fashion retailers in Bengaluru context. The
findings of the study concluded that all the hypotheses employed in the study were supported
which may be keeping in mind the fact that today’s millennial's are tech savvy, have an
experience in online shopping and also most importantly they have access to all the information.
The study also adds on to literature on retailing, consumer behavior, and retailing and also to the
theory employed in this study. The study also gives a valuable insights into millennial's purchase
intention. Lastly to conclude this study also ended with few limitation and recommendation
which can be used as a source of information for the upcoming research to study on.
http://www.iaeme.com/IJMET/index.asp
1720
editor@iaeme.com
Dr. Kiran.G, Dr. D.N. Murthy, Dr. Nila Chotai and Dr. Pradeepa
REFERENCES:
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
Alan, A. E., Eyuboğlu, E., & Maltepe, I. (2012). 11th international marketing trends congress
19-21th January. Venice-Italy.
Barton, C., Fromm, J., & Egan, C. (2012). The Millennial consumer: Debunking stereotypes.
The Boston Consulting Group, 16.
Fabrigar, L. R., MacDonald, T. K., & Wegener, D. T. (2005). The structure of attitudes. The
Handbook of Attitudes, 79-124.
Lachance, M. J., Beaudoin, P., & Robitaille, J. (2003). Adolescents’ brand sensitivity in
apparel: Influence of three socialization agents. International Journal of Consumer Studies,
27(1), 47-57.
Martin, C. A., & Bush, A. J. (2000). Do role models influence teenagers’ purchase intentions
and behavior? Journal of Consumer Marketing, 17(5), 441-453.
Nusair, K. K., et al. (2011). Building a model of commitment for generation Y: An empirical
study on e-travel retailers. Tourism Management, 32(4), 833-843.
Parment, A. (2013). Generation Y vs. baby boomers: Shopping behavior, buyer involvement
and implications for retailing. Journal of Retailing and Consumer Services, 20(2), 189- 199.
Ryder, N. B. (1965). The cohort as a concept in the study of social change. American
Sociological Review, 843-861.
Sullivan, P., & Heitmeyer, J. (2008). Looking at gen Y shopping preferences and intentions:
Exploring the role of experience and apparel involvement. International Journal of Consumer
Studies, 32(3), 285-295.
Yarrow, K., & O’Donnell, J. (2009). Gen buy: How tweens, teens and twenty-somethings are
revolutionizing retail. John Wiley and Sons.
Young, K. E., & Kim, Y. K. (2004). Predicting online purchase intentions for clothing
products. European journal of Marketing, 38(7), 883-897.
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end
model and synthesis of evidence. The Journal of Marketing, 2-22.
Ramesh R Kulkarni, a Study on the Factors Restricting Online Buying Behavior of SemiUrban College Students of North Karnataka. International Journal of Management, 7(7),
2016, pp. 114–121.
Aparna Goyal, Sanjeev Bansal and Sanjay Srivastava, Organic Food Market and Its Consumer
Buying Behavior of Ecologically Sustainable Green Products, International Journal of
Mechanical Engineering and Technology 8(12), 2017, pp. 1030–1041.
http://www.iaeme.com/IJMET/index.asp
1721
editor@iaeme.com