Consumer Decision-making Style of Gen Z: A Generational Cohort Analysis

Abstract

The media and consumer research groups have been keeping the Millennials in spotlight for many years now; perhaps it is time to turn some of the attention on Gen Z, which began its foray into mainstream consumption. This exploratory study examines the shopping orientation of Gen Z online shoppers using the generational cohort theory (GCT) as a framework and provides insights to e-retailers to understand how this generation approaches the online shopping. The penetration of Internet and accelerated growth of online shopping have enthused the e-retailers to offer a wide range of goods at greater efficiency than the traditional players. By cluster analysis (K-means) of nine online shopping orientation factors (two were eliminated prior due to low factor loading scores), four segments were identified: (a) ‘Economic-quality seekers’, (b) ‘Convenience shoppers’, (c) ‘Deal hunting-convenience seekers’ and (d) ‘Brand and quality conscious shoppers’, and the study profiled each segment based on the demographic data through chi-square analysis. Finally, implications for online retailers and marketing practitioners are enumerated towards the end of the article.

Full text

Original Article
Consumer Decision-making Style
of Gen Z: A Generational
Cohort Analysis
Packiaraj Thangavel1
Pramod Pathak1
Bibhas Chandra1
Abstract
The media and consumer research groups have been keeping the Millennials in spotlight for many years
now; perhaps it is time to turn some of the attention on Gen Z, which began its foray into mainstream
consumption. This exploratory study examines the shopping orientation of Gen Z online shoppers
using the generational cohort theory (GCT) as a framework and provides insights to e-retailers to
understand how this generation approaches the online shopping. The penetration of Internet and
accelerated growth of online shopping have enthused the e-retailers to offer a wide range of goods
at greater efficiency than the traditional players. By cluster analysis (K-means) of nine online shopping
orientation factors (two were eliminated prior due to low factor loading scores), four segments were
identified: (a) ‘Economic-quality seekers’, (b) ‘Convenience shoppers’, (c) ‘Deal hunting-convenience
seekers’ and (d) ‘Brand and quality conscious shoppers’, and the study profiled each segment based on
the demographic data through chi-square analysis. Finally, implications for online retailers and marketing
practitioners are enumerated towards the end of the article.
Keywords
Generation Z, consumer behaviour, shopping style inventory, generational cohort, e-commerce,
customer profiling, Asia
Introduction
The digital revolution has influenced the life of all the generations, especially Gen Y and Z. The
emergence of Internet, Smart devices and Social media brought in new approaches to life and changed
the way people reach out to each other and the shopping-related decisions they make (Iorgulescu, 2016;
Singh, Chaudhuri, & Verma, 2017; Turner, 2015). This study aims to segment Gen Z (the generation
Global Business Review
1–19
© 2019 IMI
Reprints and permissions:
in.sagepub.com/journals-permissions-india
DOI: 10.1177/0972150919880128
journals.sagepub.com/home/gbr
1 Department of Management Studies, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India.
Corresponding author:
Packiaraj Thangavel, Doctoral student, Department of Management Studies, Indian Institute of Technology (ISM), Dhanbad, Jharkhand
826004, India.
E-mail: packiaraj7@gmail.com

2 Global Business Review
after the Millennials) online shoppers into unique shopping orientation groups and establish a profile for
each of them. Shopping behaviour of today’s young generation tends to be quite different from that of
preceding generations owing to the constant political, cultural and socio-economic changes that occur in
the society as a continuum (Delafrooz, Paim, & Khatibi, 2010). As the use and popularity of e-commerce
continues to increase, the question of why the young consumers prefer to buy on the Internet needs to be
understood. Moreover, the recent researches by Puiu (2016) and Desai and Lele (2017) established that
Gen Z exhibits different consumer values, preferences and ideas from those of earlier generations;
therefore identifying the consumption characteristics of Gen Z is essential. Further, understanding who
shops online and what drives them to do so have been important and challenging questions for both
marketing practitioners and academics (Rehman, 2017). Shopping orientations or shopping style
inventory have been found to be reliable predictors of consumers’ buying behaviour in physical retail
formats such as mall shopping and catalogue shopping (Gehrt & Shim, 1998). Additionally, many studies
have been carried out in the past to predict the shopping behaviour of Gen X and Millennials (otherwise
referred to as Gen Y) using the shopping style inventory (Shopping orientations) in the e-commerce
platform too (e.g., Girard, Korgaonkar, & Silverblatt, 2003; Loureiro & Breazeale, 2016; Vijayasarathy
& Jones, 2000). Against this backdrop, this study employs a tool—shopping style inventory—to segment
and profile Gen Z E-shoppers. Further, the study of Kantar Millward Brown (Multinational market
research firm) also asserted that Gen Z have adopted different media consumption and shopping values
compared to that of Millennials (Southgate, 2017). Despite this compelling opportunity, there is paucity
of research focusing on Generation Z. Therefore, it is expected that the exploration of shopping orientation
of emerging consumer group, that is, Gen Z in the e-commerce platform could bring out new implications
to the practitioners and contribute to the existing literature. This article is organized as follows: After the
introduction, the article critically reviews the literature on generational cohort theory (GCT), Gen Z,
existing researches on shopping style inventory and the status of online shopping in Asia and India. The
second section deals with rationale of the study and its propositions. We then describe our methodology
and research findings. Finally, conclusion and managerial implications are drawn.
Review of Literature
Generational Cohort Theory
This study brings together two areas of literature, that is, GCT and shopping orientation to explore the
online shopping behaviour of Gen Z. Generation is denoted as an ‘identifiable sect of people who share
common birth years, experience similar life events and grow up in comparably alike environment with
equal resources, opportunities and challenges’ (Krbová & Pavelek, 2015; Kupperschmidt, 2000;
Seemiller & Grace, 2017). As each generation matures through such identical events and exposures, they
tend to develop uniform belief system, values and personality traits that differ from that of preceding and
succeeding generations. This generational difference is also believed to be impacting the communication
style, knowledge, skills and all other aspects of life including socializing and purchasing (Howe &
Strauss, 1992; Srinivasan, 2012). Often, generational studies are criticized as an upper middle-class
phenomenon and fail to reflect the true essence of the society. However, the study of Anderson and Jiang
(2018) found that 90 per cent of the youngsters (Gen Z) from median and below median income families
have Smartphones and their preference to shop online is in increasing trend (Anderson & Jiang, 2018).
Looking at these statistics, we can assert that access to the technology is no more restricted only to upper
class individuals and the differences in the society are closing down.

Thangavel et al. 3
Generation Z
Gen Z is the generation after the Millennials, often referred as iGeneration, Post-Millennials, Gen Wii or
NextGen (Raphelson, 2014; Turner, 2015). There is a considerable dispute and debate about the definition
of this generation. Demographers place its beginning anywhere from early 1990s to until 2000 (Addor,
2011; Iorgulescu, 2016; Seemiller & Grace, 2017; Tulgan, 2013). Gen Z are the descendants of Baby
boomers and a fraction of them have Millennials as their parents too. Almost a third of this generation
was born with Internet-technology and ‘being virtually connected’ was the way it had always been for
them (Kebritchi & Sharifi, 2016; Turner, 2015). The revolution of web and the unprecedented access to
information have equipped them to understand the global imperatives (Rehman, 2017). This generation
is uniquely diverse than any other preceding generations in the USA (Shatto & Erwin, 2016). The
Accenture (2017) survey states that social media has greater influence on Gen Z than it does on Gen Y,
and three factors based on which Gen Z consumers generally make purchase decisions: lowest price/best
deal, display of the merchandise and reviews of users. They were also found to be placing greater
emphasis on turning to friends and family members for their suggestions before deciding what to buy
(Accenture, 2017). The study conducted by Ernst and Young (2015) compared the online shopping
behaviour of Millennials (Gen Y) and Gen Z. The results portrayed that Gen Z is less brand loyal than
Gen Y, whereas Gen Y cares more about prices than Gen Z does. It is slightly contradictory to the
findings of Accenture study (2017). Further, Seemiller and Grace (2017) stated in their study that while
Generation Z shares some characteristics with Millennials, it is quite a different generational cohort with
its own specifics and characteristics. This young generation is ‘the next big disrupter for apparel and
retail industry’ according to a Women’s wear daily and they are also widely perceived and reported as
possessing great purchasing power due to the widespread opportunities opened up by digitalization and
globalization of economies, and it is estimated that they would constitute about 40 per cent of all
consumers globally and 22 per cent of the workforce world over by the year 2020 (Lanvin & Evans,
2016). Though they seem to be such a potential segment, research on Generation Z remains thin and
there is lot to be explored about them (Chillakuri & Mahanandia, 2018; Haddouche & Salomone, 2018).
Shopping Orientation or Purchase Decision-Making Style
A consumer’s predisposition to the function of shopping can be referred as shopping orientation and it is
otherwise known as ‘purchase decision-making style’. It is defined by Sprotles and Kendall (1986) as ‘a
mental orientation characterizing a consumer’s approach to making choices’, and they asserted that
consumers embrace a ‘shopping personality’ that is relatively permanent and predictable in the same way
Psychologists look at the personality. Researchers have found that shopping orientation impacts all
aspects of consumers’ decision-making behaviour right from recognition of need to post-consumption
evaluation (Hiu, Siu, Wang, & Chang, 2001). The basic principle on which shopping orientation works
is that consumers with different orientations have different consumption attributes with unique market
behaviours including distinctive requirement for information (Girard et al., 2003), different psychographic
characteristics (with regard to Product and allied services) and demographics (Brosdahl & Carpenter,
2011) with different emphases on store attributes (Lumpkin, 1985).
Among the earlier shopping orientation studies, Stone (1954) was first to state that consumers
purchase from a retailer for other than just economic reasons and identified four types of shoppers:
‘economic shoppers’, ‘personalizing shoppers’, ‘apathetic shoppers’ and ‘ethical shoppers’. Following
Stone’s footsteps, Lumpkin (1985) segmented the elderly consumers into three groups such as ‘active

4 Global Business Review
apparel shoppers’, ‘economic shoppers’ and ‘apathetic shoppers’. Sprotles and Kendall (1986) established
eight consumer decision-making styles through literature survey and validated those eight factors
through empirical factor analysis. Shim and Kotsiopulos (1993) examined shopping orientation among
female apparel shoppers and unearthed three distinct shopping orientations: ‘highly involved apparel
shoppers’, ‘apathetic apparel shoppers’ and ‘convenience-oriented catalogue shoppers’, and nine
shopping orientations were found in the study of in-home shoppers of Singapore consumers by
Shamdasani and Yeow (1995). The study of shopping orientations of French shoppers (Gehrt & Shim,
1998) resulted in five segments, namely ‘catalogue shoppers’, ‘store segment’, ‘apathetic segment’,
‘relationship segment’ and ‘non-aesthetic segment’. The empirical exploration of Chinese consumers’
decision-making style by Hiu et al. (2001) resulted in five orientations: ‘Perfectionist’, ‘novelty-fashion
conscious’, ‘recreational’, ‘price-cconscious’ and ‘confused by over-choice’. The study of Indian
consumers by Sinha (2003) unearthed two shopping styles such as ‘the fun shoppers’ and ‘the work
shoppers’. Moreover, Bakewell and Mitchell (2003) studied the consumer decision-making style of Gen
Y female shoppers, identifying five distinct consumer groups, namely ‘recreational quality seekers’,
‘recreational discount seekers’, ‘trend-setting loyals’, ‘shopping and fashion uninterested’ and ‘confused
time/money conserving’. The examination of Japanese online shoppers by Gehrt, Onzo, Fujita, and
Rajan (2007) resulted in four segments such as ‘shopping enjoyment segment’, ‘brand browsers’, ‘price
browsers’ and ‘shopping dislikes’, and Brosdahl and Carpenter (2011) studied the shopping orientations
amid US male shoppers, identifying eight shopping orientations: ‘shopping enjoyment/recreational/
market mavenism’, ‘price-conscious/frugality’, ‘shopping antipathy’, ‘brand loyalty’, ‘confused by
over-choice’, ‘store loyalty’, ‘shopping confidence’, ‘brand consciousness’. Consecutively, Loureiro and
Breazeale (2016) studied Generation Y’s clothing shopping in the e-commerce platform and suggested
that ‘in-home shopping tendency’, ‘convenience consciousness’ and ‘impulse purchase’ are the most
important factors that drive consumers’ Internet shopping orientation. Krbová and Pavelek (2015)
examined the shopping orientation of Gen Y consumers based on the importance of e-commerce shopping
attributes and clustered them into three distinct groups: ‘quality and reliability’, ‘free delivery’ and ‘extra
services’. While adequate researches exist on shopping orientations of other generational cohorts in the
context of online and offline retailing environment, attention needs to be paid to the emerging cohort,
that is, Gen Z.
Online Shopping
E-commerce continues to demonstrate accelerated growth across the globe. As of February 2019, Internet
penetration was reported to be 57 per cent of global population with the growth rate of 13 per cent
between 2014 and 2018 (Lamb, 2019). Internet has proliferated in Asia, with approximately little over
two and half billion users and it is expected to grow multifold in the coming years (Agarwal, 2018). One
of the Asia’s biggest nations in terms of population—India; its total consumption is forecasted to be
tripled from US$1.3 trillion in 2016 to US$3.6 trillion in 2027, of which a significant chunk to take place
over the Internet due to the strong IT infrastructure the country has been building over the years (India
Brand Equity Foundation [IBEF], 2018). The surge in women entering workforce, emergence of double
income households coupled with strong appetite for convenience were some of the drivers behind
changing consumer trends (Kartik, Willis, & Jones, 2016). These market characteristics present
opportunities for online retailers. Besides, the greater efficiency and the convenience that e-commerce
brings to the purchasers help in reducing the cost of search that leads to overall low consolidated price
for a range of goods compared to their offline counterparts (Kumar & Sadarangani, 2018; Nagar &

Thangavel et al. 5
Gandotra, 2016). This could be the reason why online shoppers often found to be exhibiting the price
consciousness (Vijayasarathy & Jones, 2000). The most important online shopping attributes for Gen Z
are found to be reviews of customers who bought and used the item and the ability to compare the
competing products (Van den Bergh & Pallini, 2018). Male shoppers stated that the width of assortments
in the e-commerce platform was a more important attribute than the female consumers (Krbová &
Pavelek, 2015). Experts and the market research firms indicate that Gen Z would be a viable consumer
sect for online retailing in Asian and other developing economies due to the penetration of cheap Chinese
Smartphones and the ever-increasing appetite for sophistication. Consequently, the shopping orientations
of new generation must reflect the socio-economic and cultural changes in the society. Despite this
compelling opportunity, there is a paucity of research on this front and that prompted the researchers to
explore this generational cohort.
Objectives
While substantial research exists with respect to consumer decision-making styles of other generational
cohorts (Gen X and Millennials), attention needs to be paid to the purchasing style of Gen Z as they are
the emerging consumer segment. Therefore, the first objective of the study is to determine the shopping
orientation styles that exist among Gen Z consumers using factor-analytic examination. The second
objective is to segment them into different consumer groups through cluster analysis technique. The final
objective is to profile each of those segments based on demographic data, employing chi-square analysis.
Rationale of Study
From the above section, it is clear that today’s young consumers (Gen Z) carry different consumer values
from those of other generational cohorts (Gen X and Millennials) owing to the constant socio-economic
and technological changes that the human society underwent. As a generational cohort, Gen Z is expected
to account for about 40 per cent of all consumers worldwide by 2020 and recognized as the next consumer
power house (Heitzman, 2018)). Although a number of blogs and columns have been written on
shopping behaviour of Gen Z, no empirical studies have been found. Moreover, they are important not
only because of their buying power but also because they directly influence the buying decision of their
parents (Batra & Ali, 2015). Therefore, the study addresses the following research propositions,
The Era of E-Commerce: Does the Concept of
Brand Loyalty Exist Among Gen Z?
‘Brand loyalty’ is the tendency of customers to choose one brand consistently over its competitors for
products and services. There are evidences which suggest that brand loyalty is losing its significance
among Gen Z consumers. The Internet made access to information (reviews, product comparisons, etc.)
so easy that almost no young consumer buys a product just because of the brand name. The Deloitte
study, which was conducted of late, claims that Millennials are prone to make a purchase based on value
for money and are least loyal to the brands (Deloitte, 2018). The younger generation has more choices
than any generation had ever before (Taylor, 2018). With low-involvement products, most people are

6 Global Business Review
brand loyal as long as it is convenient to them. The idea of working for a single company for decades,
for instance, seems archaic now. People are loyal to quality now, not to names. So it is rational that Gen
Z would choose less expensive and convenient products over the well-established expensive brands.
Taylor (2018) in his study of generations and impact of advertising suggested that ‘consumers today
have become more critical and cynical about the advertisements and they have difficulty trusting a
brand’s intentions, and if they do not get what they expect, one will certainly hear about it on social
media’. This led to our first proposition that ‘Many Generation Z shoppers tend to consistently switch
between brands that is convenient to them’.
Choice Overload: Is Gen Z Drowning in Choices?
Everybody gets so much information all day long that they lose their common sense.
Gertrude Stein, ‘Reection on the Atomic Bomb’ (1946)
The term ‘information overload’ had first appeared in the work of Bertram M. Gross (1964) ‘The
Managing of Organizations: The Administrative Struggle’. Gen Z lives in a world of continuous updates
and processes lot more information than any other generation did at their age. According to Gao and
Simonson (2016), the current phenomenon in the online retail environment is that ‘an abundance of
choice, perhaps too much’. For many years, Fast-moving consumer goods (FMCG) businesses were
convinced that consumers prefer to have many choices. But the research studies lately suggest that too
many choices often lead to ‘analysis paralysis and indecision’. The academic research conducted by
Iyengar and Lepper (2000) has indicated that abundance of choices is bad to consumers as it results in a
kind of ‘mind freeze’ at the point of purchase. The young consumers (Gen Z) face the dilemma that
whatever they select, the other options seem to be better (Tugend, 2010). This leads to our second
proposition that Gen Z consumers tend to be ‘confused by over-choice’ and many of them will limit their
product search using the filters in the e-commerce platforms.
Rivalry Among E-Retailers: Does it Make Gen Z to
Feel That Deals/Discounts as Their Birthright?
In a survey conducted in 2016 among the Gen Z European consumers, it was found that the popularity of
the virtual retailing is attributed to the flexibility, enhanced convenience, wider outreach, low cost and
huge collection of products and services. The revolutionary growth and success of the virtual mode of
retailing are attributed to the fact that it eliminates the need to have a well-maintained physical store in a
posh location, which costs a fortune for the seller, and the tragedy is that this cost is passed on to the
customers to pay. The e-commerce has eliminated these overheads and inadequacies of offline retailing to
a greater extent possible across the value chain (Lieber & Syverson, 2012). The study conducted by Kartik
et al. (2016) found that youths from developing nations are thriftier when it comes to shopping, as they
wait for the right deal to pop up and do extensive search and compare before making up their mind to
ensure that every penny is spent worthily. Many business columnists and practitioners too asserted that
Gen Z want material gain—some free products or a good discount and it is increasingly encouraged by
predatory pricing or deep discounting by competing rivals to win them over. This led to the third
proposition that ‘many of the Gen Z consumers will show value consciousness when they shop’.

Thangavel et al. 7
Methodology
Data Source
A three-page questionnaire was used as the survey instrument. It was pilot tested among the fellow
scholars, professors and professional friends from other disciplines. Validity of the research instrument
can be appraised by a panel of able professionals whose expertise can judge whether the scale measures
what it intends to measure (Zikmund, Babin, Carr, & Griffin, 2013). Based on the input received, few
items in the questionnaire, which were considered unnecessary, were removed and wordings changed to
enhance the understandability. Data were collected among the online shoppers aged between 16 and 23
(Gen Z), including high school and college students and the individuals those who just began corporate
career. Convenient sampling was used to obtain the sample and care was taken to have equal representation
from high school, college and those who just entered the workforce. Moreover, to elicit the honest and true
responses from the chosen sample, each respondent was gifted with a ballpoint pen (Cello—Butterflow)
worth ₹20 when they began to fill up the questionnaire. Offering gifts (Coupons, Cash incentives and
Samples) is widely in practice among the researchers to motivate survey respondents (Chen, Lei, Li,
Huang, & Mu, 2015; Guo, Kopec, Cibere, Li, & Goldsmith, 2016; Jackson, Stoel, & Brantley, 2011; Kolb,
2008; National Research Council, 2013; Park & Kim, 2003; Singer, 2002; Toepoel, 2015). Besides
motivation, respondents are bound to be honest and legitimate in their responses as they owe something
in return for the kind gesture extended by the survey administrators (Falk & Fischbacher, 2006).
Considering the online shopping context and the literature review, the following 11 shopping orientations
were employed in the survey: ‘quality consciousness’, ‘brand loyalty’, ‘influence of reference group’, ‘online
shopping confidence’, ‘convenience consciousness’, ‘impulsiveness’, ‘brand consciousness’, ‘market
mavenism’, ‘price/value-conscious/economic shoppers’, ‘online store loyalty’, and ‘confused by over-
choice’. Items representing the shopping orientations were not ordered sequentially so that the statements
from one category do not get grouped together in the questionnaire. Respondents were specifically requested
to reflect on online shopping context, while responding to the questions. The questionnaire also included
demographics factors such as gender, age, current status, monthly family income, years of Internet usage,
average amount of money spent in E-shopping (in the last 12 months) and the frequency of shopping.
Respondent Profile
A total of 244 workable questionnaires with complete information were received. Table 1 provides the
demographic profile of the sample. The respondent pool comprised 138 (56.5%) males and 106 (43.5%)
of females; 78 (32%) were high school students, 104 (43%) were college goers and 62 (25%) were in the
beginning of their career. A majority of the sample (79%) were in the age group between 16 and 19. A
sizeable number (67%) of the chosen sample revealed that they shopped online for about 19 times on an
average in the last 12 months and spent about ₹18,000.
Analysis
Exploratory Factor Analysis of Shopping Orientations
The survey questionnaire consisted of 37 psychographic statements and 7 demographic measures.
Psychographics are the components employed in the analysis of shopping orientation. The demographic

8 Global Business Review
data were gathered to aid in establishing the profile of the personnel associated with each segment.
Although all the items employed in this study to measure the orientation were adopted from various past
studies, none of them have used all those orientation constructs in the same single study. Moreover, not
all those orientations were employed to study the Gen Z consumers in the e-commerce environment.
Therefore, considering the exploratory nature of the work, EFA (Exploratory Factor Analysis) was to be
carried out. Thus, to ensure the factorability of the collected data, the inter-item correlations were
examined to ascertain that the items under a construct are significantly correlated (>0.30) as the lower
correlation (<0.30) suggest items that are unreliable and have weaker relation with the rest (Churchill Jr,
1979). The correlation matrix revealed numerous higher-to-moderate correlations, which is an indication
that the matrix is factorable. Subsequently, the data were also subjected to ‘Kaiser-Meyer-Olkin’ (KMO)
measure for sampling adequacy (Kaiser, 1974) and ‘Bartlett’s test of sphericity’. The study obtained the
value of 0.745 for KMO measure and χ2 = 3646.706 with the significance level (p) of < 0.001 for
‘Bartlett’s test of sphericity’ (Table 2). The meritorious KMO value coupled with high significance level
of ‘Bartlett’s test of sphericity’ suggests that the given dataset is fit for factor analysis.
Table 1. Demographic Profile of Respondents and Their Internet Usage and Shopping Frequency
Variable Frequency Valid Frequency Percentage
Gender
Male
Female
Others
149
111
0
138
106
0
56.5
43.5
00.0
Age
16–17
18–19
20–21
22–23
89
116
27
28
85
108
24
27
34.8
44.2
09.8
11.0
Current status
At High school
At College
At Work
83
111
66
78
104
62
32.0
43.0
25.0
Family’s monthly income (₹)
Below 30,000
31,000–60,000
61,000–90,000
Above 90,000
26
128
77
29
24
121
72
27
09.8
49.5
29.5
11.0
Years of Internet usage
Less than 3 years
4–6 years
Above 6 years
62
83
115
59
78
107
24.1
31.9
43.8
Frequency of online shopping in
the past 12 months
1–4 times
5–8 times
9–12 times
13–16 times
17–20 times
21 and more
04
05
06
59
174
12
04
05
06
55
163
11
01.6
02.0
02.4
22.5
66.8
04.5
(Table 1 Continued)

Thangavel et al. 9
Variable Frequency Valid Frequency Percentage
Money spent on online shopping in
the past 12 months
Less than ₹4000
₹4,001–8,000
₹8,001–12,000
₹12,001–16,000
₹16,001–20,000
Above ₹20,000
04
05
05
53
158
35
04
05
05
48
149
33
01.6
02.0
02.0
19.6
61.0
13.5
Source: The authors.
Table 2. KMO and Bartlett’s Test for Shopping Orientation Attributes
‘Kaiser–Meyer–Olkin’ measure of sampling adequacy 0.745
Approx. chi-square 3646.706
‘Bartlett’s test of sphericity’ df 435
Sig. 0.000
Source: The authors (Factor Analysis Data Reduction, SPSS 20.0).
‘Principal factor analysis with promax’ method of factor extraction was performed on 37 shopping
orientation variables that were expected to represent the 11 dimensions. This technique was
employed as it includes only the shared variance in the solution and for the items to be retained
factor loading of 0.50 or more were required (Hair Jr, Wolfinbarger, Money, Samouel, & Page,
2015). Further, the promax rotation was chosen as it is better able to identify the simple structure
than varimax rotation in a context where there is moderate-to-high inter-component correlation
(Finch, 2006). The analysis produced nine dimensions with factor loading of 0.50 or more and
explaining 63.4 per cent of cumulative variance. The nine factors were: ‘brand loyal shoppers’,
‘brand consciousness’, ‘quality consciousness’, ‘confused by over-choice’, ‘Price consciousness’,
‘convenience consciousness’, ‘online store loyal’, ‘online shopping confidence’ and ‘socially
desirable shopping tendency’. A total of seven items that did not demonstrate high internal validity
were eliminated (One item in ‘online shopping confidence’, and three items each in ‘Impulse
purchase’ and ‘Market mavenism’ that led to their elimination from further processing). Table 3
illustrates the loading of the 30 items and Cronbach’s alpha for the nine retained components.
Segmentation Using Cluster Analysis
K-means, a non-hierarchical clustering algorithm has been used to categorize the shopping segments, as
it is suitable for large dataset and overcomes the limitations of other algorithms by selecting initial
cluster centres with well-separated values unlike other cluster methods that rely on random initial
assignments (Hair, Black, Babin, & Anderson, 2015; Norusis, 2008). The supremacy of K-means
algorithm lies in the fact that it iteratively re-assigns observations to clusters until the optimized clustering
solution is achieved. This optimization procedure allows for reassignment of observations, with the goal
to create most distinct clusters (Hair, Black, Babin, & Anderson, 2015). Thus, the validity and stability
of the cluster solution is enhanced.
(Table 1 Continued)

10 Global Business Review
Table 3. Exploratory Factor Analysis of Shopping Orientations of Gen Z
Factor Labels
(Cronbach’s alpha) Items
Factor
Loading
Variance
Explained
(%)
Grand
Mean
Brand loyal shoppers
(α = 0.84)
I don’t like to buy the same brand every time*
I buy my favourite brands over and over
Once I find a brand I like, I stick to it
I try to stick to certain brands
0.846
0.769
0.765
0.643
15.9 3.4
Brand consciousness
(α = 0.89)
The higher the price of the product, better the quality is
Even though it may be costly, I prefer to buy popular
brands
I purchase only branded items
0.910
0.872
0.828
10.1 3.2
Quality consciousness
(α = 0.82)
I do not mind to pay a higher price if I can get a quality
product
I carefully considers the quality of products I buy
I make special efforts to choose the best quality
products
My expectations from the products I buy are very high
0.880
0.734
0.686
0.666
7.8 3.9
Confused by over-
choice
(α = 0.87)
All the information I get on different products confuses me
The more I learn about different brands, the harder it
seems to choose one
Availability of many brands often make me confused
when I shop
0.852
0.843
0.825
6.8 3.5
Price consciousness
(α = 0.84)
I pay attention to the advertisements announces discounts
I usually use discounts to save money
I carefully watch how much I spend
0.930
0.770
0.755
6.3 3.3
Convenience
consciousness
(α = 0.84)
I prefer to shop online because it helps me to save time
I like to get things delivered home than go and buy
from shop
Offline shopping takes lot of effort in terms of travel,
parking and carrying back the items
0.839
0.818
0.784
5.2 3.2
Online store loyalty
(α = 0.82)
I tend to buy mostly from a particular online store
Only my favourite online store provides me the best
products
I like to try new online shopping sites*
0.879
0.826
0.692
4.3 3.5
Online shopping
confidence
(α = 0.77)
I think I am a good online shopper
I have the ability to choose the right products online
I feel comfortable with the level of security online
stores provide
0.878
0.762
0.659
3.7 3.6
Influence of reference
group/socially
desirable/Information
seeking
(α = 0.76)
I seek the opinion of others before buying something
What others may think of my purchases often
influences my shopping decision
I like to buy things that people I admire use
I would discuss with others before deciding on the
purchase
0.846
0.665
0.600
0.548
3.3 4.0
Source: The authors.
Note: *Reversed items were re-coded before the principal component analysis.

Thangavel et al. 11
Cluster solutions of three to six groups were generated. The obtained solutions were assessed based
on the significance level of univariate F-ratio (see Table 4) for each of the nine shopping orientations for
each cluster solutions (three to six). The F-ratio was not significant for three-cluster solution, so it was
not considered as a probable solution. The 6-cluster solution had significant F-ratio but has picked up
small numbers 10 (cluster 1) and 11 (cluster 4) in ‘number of cases in each cluster’, so it got disqualified
to be an effective cluster solution. Thus, the focus was shifted to four- and five-cluster solutions. Finally,
the four-cluster solution was chosen as the optimal solution, as it had better pairwise differences compared
to five-cluster solution. The examination of F-ratios associated with the extent to which each of the
shopping orientations that differed across the four shopping segments reveals that quality consciousness
has the largest value, that is, 68.1 (Table 4). This was followed by Price/value consciousness (F-ratio =
50.1), convenience seeking (F-ratio = 46.3), being socially desirable (F-ratio = 43.4), online store loyalty
(F-ratio = 34.7), brand consciousness (F-ratio = 30.7), online shopping confidence (F-ratio = 23), brand
loyalty (F-ratio = 19.9), confused by over-choice (F-ratio = 9.9). All of them were significant at ≤0.001
level, which is an indication that shopping orientations robustly differentiate between the identified
shopping segments. The final cluster centres for each of the nine dimensions for respective segments are
given in Table 4.
Table 4. Cluster Analysis with Cluster Centres on Shopping Orientation Criteria
Segments ANOVA
Shopping Orientation
(Listed based on factor loading
scores—descending order)
Economic-
quality Seekers
Convenience
Shoppers
Deal
Hunting-
convenience
Seekers
Brand- and
Quality-
conscious F p-Value
I. Brand loyal shoppers
II. Brand-conscious shoppers
III. Quality-conscious shoppers
IV. Confused by over-choice/
availability of too many choices
V. Price/value-conscious/
economic shoppers
VI. Convenience (Time and
energy conserving) Shoppers
VII. Online store loyal shoppers
VIII. Online shopping confidence
IX. Influence of reference
group/socially desirable/
information seeking
−0.45961
−0.68304
0.10841
−0.10604
0.15597
−0.84368
−0.07808
−0.24269
−0.28437
−0.43442
−0.45522
−1.50555
−0.68904
−0.11659
0.10808
−1.17031
−0.82256
−1.11557
0.54716
0.24091
0.18035
0.21794
0.71281
0.67651
0.40710
0.08139
0.41882
0.03718
0.53744
0.44237
0.19537
−0.78623
−0.03216
0.21079
0.50771
0.34323
19.863
30.663
68.992
9.843
50.941
46.296
34.658
22.941
43.352
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
Source: The authors.

12 Global Business Review
Table 5. Chi-square Analysis of Demographics, Internet Usage and Shopping Behaviour
Frequency
‘Economic-
Quality
Seekers’
‘Convenience
Shoppers’
‘Deal Hunting-
Convenience
Seekers’
‘Brand and
Quality Conscious
Shoppers’
Row
Totals
Gender (not significant)
Male
% within Cluster
Female
% within Cluster
32
52.5
29
47.5
22
62.9
13
37.1
45
60.8
29
39.2
39
52.7
35
47.3
138
56.6
106
43.4
Age (not significant)
16–17
18–19
20–21
22–23
18
29.5
32
52.5
2
3.3
9
14.8
16
45.7
13
37.1
3
8.6
3
8.6
27
36.5
31
41.9
8
10.8
8
10.8
24
32.4
32
43.2
11
14.9
7
9.5
85
34.8
108
44.3
24
9.8
27
11.1
Current status (not
significant)
At high school
At college
At work
14
23.0
29
47.5
18
29.5
14
40.0
13
37.1
8
22.9
26
35.1
30
40.5
18
24.3
24
32.4
32
43.2
18
24.3
78
32.0
104
42.6
62
25.4
Monthly family income
(=0.1 significant)
<30,000
31,001–60,000
61,001–90,000
>90,001
10
16.4
28
45.9
20
32.8
3
4.9
2
5.7
21
60.0
9
25.7
3
8.6
8
10.8
33
44.6
25
33.8
8
10.8
4
5.4
39
52.7
18
24.3
13
17.6
24
9.8
121
49.6
72
29.5
27
11.1
Years of Internet usage
(≤0.05 significant)
<3 years
4–6 years
>6 years
19
31.1
21
34.4
21
34.4
12
34.3
13
37.1
10
28.6
10
13.5
23
31.1
41
55.4
18
24.3
21
28.4
35
47.3
59
24.2
78
32.0
107
43.9
(Table 5 Continued)

Thangavel et al. 13
Frequency
‘Economic-
Quality
Seekers’
‘Convenience
Shoppers’
‘Deal Hunting-
Convenience
Seekers’
‘Brand and
Quality Conscious
Shoppers’
Row
Totals
Online shopping frequency
in the past 12 months (not
significant)
1–4 times
5–8 times
9–12 times
13–16 times
17–20 times
≥21 times
1
1.6
3
4.9
1
1.6
14
23.0
39
63.9
3
4.9
0
0.0
1
2.9
0
0.0
6
17.1
27
77.1
1
2.9
1
1.4
0
0.0
2
2.7
18
24.3
50
67.6
3
4.1
2
2.7
1
1.4
3
4.1
17
23.0
47
63.5
4
5.4
4
1.6
5
2.0
6
2.5
55
22.5
163
66.8
11
4.5
Average amount of money
spent shopping online in
the past 12 months (not
significant)
<4,000
>4,001 to <8,000
>8,001 to <12,000
>12,001 to <16,000
>16,001 to <20,001
>20,001
1
1.6
3
4.9
0
0.0
12
19.7
38
62.3
7
11.5
0
0.0
1
2.9
0
0.0
5
14.3
24
68.6
5
14.3
1
1.4
0
0.0
2
2.7
17
23.0
42
56.8
12
16.2
2
2.7
1
1.4
3
4.1
14
18.9
45
60.8
9
12.2
4
1.6
5
2.0
5
2.0
48
19.7
149
61.1
33
13.5
Discussion and Managerial Implications
Profiling the Shopping Segments
This study segments the Gen Z online shoppers based on their predisposition towards shopping and
develops a profile for each segment. Gen Z consumers enjoy shopping significantly; this due to the fact
that they have been brought up by both working parents and were socialized at the very young age to
assist with the family shopping shores (Deloitte, 2018; Kebritchi & Sharifi, 2016; Turner, 2015). In
addition to their age, the informational era they live in drives them to be more savvy consumers than any
other preceding generational cohorts. From the nine psychographic-based shopping orientation
constructs, Gen Z online buyers have been classified into four segments, 1. ‘Economic-quality seekers’
(Table 5 Continued)

14 Global Business Review
(25%), 2. ‘Convenience shoppers’ (14.3%), 3. ‘Deal hunting-convenience seekers’ (30.3%) and 4. ‘Brand
and quality-conscious shoppers’ (30.3%). The profile of each segment is described as follows:
Economic-quality seekers: The first segment has been named as the ‘economic quality seekers’ (25%
of subjects). This cluster indicates that about one-fourth of Gen Z shoppers seek out for quality products
at a reasonable price, and they are most likely to ‘compare and contrast’ available products in the
e-commerce platforms before they make the final purchase. The online sellers who target this segment
must try and offer quality products at competitive prices. Since this segment is large enough, the sellers
could take advantage of economies of scale. Sellers are also cautioned not to displease this segment with
quality as they are hardcore switchers and least guilty about returning the product. This segment is also
noteworthy as it had the highest negative score for the convenience orientation (–0.84368) and brand
consciousness (–0.68304). These negative scores suggest that this segment has the audience who are
least likely to be influenced by brand names and convenience. It leads to further implication to e-retailers
that they can make this segment to collect their products at the common pick-up point rather than
delivering at their doorsteps. So, the cost saved can be handed over to these shoppers in terms of reduced
price to win their loyalty. About 47 per cent of this segment is in the college and a greater portion of them
(63.9%) shopped online about 17–20 times in the past 12 months. They were also found to be above
average spenders in e-commerce platform and about 45.9 per cent of them belong to the working-class
families, and this segment is found to be equivalent with respect to gender across the sample (see Table
5). The segment reaffirms our first research proposition that ‘many Generation Z buyers tend to
consistently switch between brands that suits them’. This segment also reinforces our third proposition
that ‘many of the Gen Z consumers will exhibit value consciousness when they shop online’.
Source: The authors.
Convenience shoppers: The second segment has been designated as the ‘convenience shoppers’ (14.3
% of the subjects), as it had the positive score only for the convenience orientation (0.10808) in this
cluster. It indicates that the personnel associated with this segment value convenience more than anything,
and this segment’s highest negative score for the ‘Loyalty’ (–1.17031), further reinforced their
convenience orientation. Van den Bergh and Pallini (2018) suggested that Gen Z are least loyal to
retailers and tend to choose them more wisely than any other preceding generations, and long-term
benefits like loyalty cards or coupons to redeem in the subsequent purchases seem to hold little value for
Gen Z, while the short-term benefits such as discounts, freebies and free delivery were rated by this
cohort as very attractive. Thus, this segment seems to be attractive to the online retailers. The door-step
delivery and paid-fast delivery seem to be effective means to satisfy this segment’s convenience
orientation needs. Moreover, easy return policy and flexible delivery schemes of e-retailers are also
appealing to this segment. Hence, e-retailers need to focus more on those convenience and flexibility
attributes of serving this segment. Chi-square analysis (Table 5) reveals that this segment possesses
maximum number of shoppers (77.7%) with above average online shopping frequency compared to
other segments. They are relatively younger compared to other segments, with higher percentage of them
in the age group of 16–17 years (45.7%) and it has the highest percentage of males (62.9%). Additionally,
it is also to be noted that this segment has the maximum number of shoppers with middle-class status
compared to other segments which explains why the individuals associated with this segment are
convenience oriented.
Deal hunting-convenience seekers: The third segment termed as the ‘deal hunting convenience
seekers’ (30.3% of the subjects) had the highest scores for the price consciousness (0.71281) and

Thangavel et al. 15
convenience orientation (0.67651). These scores suggest that the personnel associated with this segment
value the discounts and the convenience attributes. They quite often might look out for ‘lightening deals’
and ‘discounted sales’ to reap the maximum value for their money. The e-retailers can e-mail the ‘daily
deals’ and discounts to this consumer segment directly, which the e-commerce industry in Asia at present
does to the extent of irritating the customers with inundating e-mails, and this segment also detest going
out for shopping facing the traffic and waiting in the queue to get their items billed. The online retailers
who target this segment must focus on the convenience attributes which are elaborated above. This
segment’s lowest score for the ‘Confusion by over-choice’ (0.21794) lead to the inference that they are
tech-savvy. Moreover, this segment also has the highest number of shoppers with longer period of
Internet usage (6 Years or more) compared to other segments. With respect to age and current status, this
segment was found to be equivalent to the overall sample but differs with gender. This cluster carries the
highest representation of males (60.8%). This is in line with the study of Leggatt-Cook’s (2007) who
stated that men indulge in more shopping than women do, and about a half of this segment belongs to the
middle-class families. This cluster’s lowest score for the factor ‘confusion by over-choice’ gives no
support for our second proposition that ‘Gen Z shoppers likely to be confused by over-choice’. This
probably would be true as this generation was early introduced to the ‘era of information’ and ‘social
media’ that would have made them to get accustomed with constant updates and navigate through large
information with ease.
Brand- and quality-conscious shoppers: The last segment has been designated as the ‘Brand- and
quality-conscious shoppers’ (30.3% of the subjects), as it had the highest scores for Brand consciousness
(0.53744) and quality consciousness (0.44237). The scores suggest that the individuals in this segment
are brand-conscious, as they might believe that only the well-known brands offer them the best quality.
This segment’s negative score for ‘Price consciousness’ (–0.78623), further reinforcing their orientation
towards brand and quality. This leads to the inference that the online retailers who target this segment
may try and build a reputation for their private label brands through advertising, celebrity endorsement
and associating their brand with social causes. Further to building reputation for their brand, they should
not compromise on the quality of the product, as it might backfire if they prefer one over the other. The
segment’s online shopping frequency and money spent in e-shopping for the past 12 months was found
to be above average. With respect to gender, this segment is more or less equivalent to the overall
sample. In addition, about 48 per cent of the shoppers in this cluster have been using the Internet for more
than 6 years now.
Conclusion
The study contributes to the theory of shopping motivation and generational cohort approach in consumer
segmentation. The empirical findings of the study suggest that value consciousness and convenience
driven are the dominant shopping orientations that drive the Gen Z consumers. This could be the prime
reason why they overtly favour e-retailers. At the same time, they are most likely to ‘compare and
contrast’ available products in the e-commerce platforms before they make the final purchase. So the
e-retailers should continue to look for ways to enhance these features to lead the disruption they caused
in the retailing industry. This also leads to the assertion that Gen Z are less brand loyal compared to the
previous generations and the same is validated by this study. Therefore, instead of spending millions of
dollars in advertising and promotion to reinstate the brand consciousness and loyalty among the
customers, the marketers should adopt the approach that ‘the fortune at the bottom of the pyramid’
(Prahalad, 2009), whereby serving the large number of aspiring poor and middle-income people the

16 Global Business Review
company can realize growth and profitability through volume (What Sam Walton’s Walmart had done to
the country people of USA in the 1970s and 1980s (Walton & Huey, 1993) to be repeated in Asia by the
e-commerce players). Finally, the authors conclude that the shopping orientations of Gen Z differs
substantially from that of preceding generations, and the marketing strategies that target the Gen Z online
shoppers must be customized.
Limitations and Scope for Future Research
Although the present study contributes significantly to the literature on generational studies and
e-retailing, it too can be seen with some limitations. This study had survey participants who live in one
Asian country, as it has been widely reported that people from different countries will go through
different events during their growing up years and those events will have strong bearing on their attitude,
belief and value system, hence this study which was primarily conducted among the shoppers who are
from an Asian country cannot be generalized for consumers across the globe. Future research work is
suggested into shopping orientation between Asian Gen Z consumers and those of other continents. We
used only three to four items to measure each shopping orientation construct, so the questionnaire would
be at reasonable length and the respondents’ irk is not evoked. Ideally, we should have employed more
than five items to measure each of those 11 shopping orientations. Given the humungous socio-economic
and demographic differences that prevail in this country, the sample drawn for this study is not true
representative of the population. Similar research can be done on a larger population. The respondents
were specifically requested to reflect on online shopping context, while responding to the questions, is
another limitation of the study. Future studies could evaluate the shopping orientations, specific to
product categories (e.g. clothing, footwear, cosmetic and consumable (pantry) product categories) in the
e-commerce environment. Even though previous studies have added to the researchers’ understanding,
no other published work has studied the shopping orientation of Gen Z in the Asian context.
Acknowledgement
The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve
the quality of the article. Usual disclaimers apply.
Declaration of Conflicting Interests
The author(s) declared no potential conicts of interest with respect to the research, authorship and/or publication of
this article.
Funding
The author(s) received no nancial support for the research, authorship and/or publication of this article.
ORCID iD
Packiaraj Thangavel https://orcid.org/0000-0001-8608-0969
References
Accenture. (2017). Gen Z and Millennials leaving older shoppers and many retailers in their digital dust. Accenture
LLP. Retrieved from https://www.accenture.com/t20170503T114448Z__w__/us-en/_acnmedia/PDF-44/
Accenture-Retail-Customer-Research-Executive-Summary-2017.pdf

Thangavel et al. 17
Addor, M. L. (2011). Generation Z: What is the Future of Stakeholder Engagement. Institute for Emerging Issues – NC
State University, 1–7. Retrieved from https://iei.ncsu.edu/wp-content/uploads/2013/01/GenZStakeholders2.pdf
Agarwal, S. (2018). Internet users in India expected to reach 500 million by June: IAMAI. The Economic Times.
Retrieved from https://economictimes.indiatimes.com/tech/internet/internet-users-in-india-expected-to-reach-
500-million-by-june-iamai/articleshow/63000198.cms?from=mdr
Anderson, M., & Jiang, J. (2018). Teens’ social media habits and experiences. Pew Research Center. Retrieved from
http://www.pewinternet.org/2018/11/28/teens-social-media-habits-and-experiences/
Bakewell, C., & Mitchell, V. W. (2003). Generation Y female consumer decision-making styles. International
Journal of Retail & Distribution Management, 31(2), 95–106.
Batra, D. K., & Ali, A. (2015). Parent’s opinion of children’s influence in purchase decisions: A comparative analysis
between rural and urban Delhi. Global Business Review, 16(6), 1100–1111.
Brosdahl, D. J., & Carpenter, J. M. (2011). Shopping orientations of US males: A generational cohort comparison.
Journal of Retailing and Consumer Services, 18(6), 548–554.
Chen, K., Lei, H., Li, G., Huang, W., & Mu, L. (2015). Cash incentives improve participation rate in a face-to-face
survey: An intervention study. Journal of Clinical Epidemiology, 68(2), 228–233.
Chillakuri, B., & Mahanandia, R. (2018). Generation Z entering the workforce: The need for sustainable strategies
in maximizing their talent. Human Resource Management International Digest.
Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of
Marketing Research, 16(1), 64–73.
Delafrooz, N., Paim, L. H., & Khatibi, A. (2010). Students’ online shopping behavior: An empirical study. Journal
of American Science, 6(1), 137–147.
Deloitte. (2018). Millennials disappointed in business, unprepared for Industry 4.0. The Deloitte Millennial Survey.
Retrieved from https://www2.deloitte.com/content/dam/Deloitte/global/Documents/About-Deloitte/gx-2018-
millennial-survey-report.pdf
Desai, S. P., & Lele, V. (2017). Correlating internet, social networks and workplace—a case of generation Z students.
Journal of Commerce and Management Thought, 8(4), 802.
Ernst & Young. (2015). What if the next big disruptor isn’t a what but a who? Ernst & Young LLP. Retrieved from
https://www.ey.com/Publication/vwLUAssets/EY-rise-of-gen-znew-challenge-for-retailers/%24FILE/EY-rise-
of-gen-znew-challenge-for-retailers.pdf
Falk, A., & Fischbacher, U. (2006). A theory of reciprocity. Games and Economic Behavior, 54(2), 293–315.
Finch, H. (2006). Comparison of the performance of varimax and promax rotations: Factor structure recovery for
dichotomous items. Journal of Educational Measurement, 43(1), 39–52.
Gao, L., & Simonson, I. (2016). The positive effect of assortment size on purchase likelihood: The moderating
influence of decision order. Journal of Consumer Psychology, 26(4), 542–549.
Gehrt, K. C., Onzo, N., Fujita, K., & Rajan, M. N. (2007). The emergence of internet shopping in Japan: Identification
of shopping orientation-defined segments. Journal of Marketing Theory and Practice, 15(2), 167–177.
Gehrt, K. C., & Shim, S. (1998). A shopping orientation segmentation of French consumers: Implications for catalog
marketing. Journal of Interactive Marketing, 12(4), 34–46.
Girard, T., Korgaonkar, P., & Silverblatt, R. (2003). Relationship of type of product, shopping orientations, and
demographics with preference for shopping on the internet. Journal of Business and Psychology, 18(1),
101–120.
Gross, B. M. (1964). The managing of organizations: The administrative struggle, 2. New York, NY : Free Press of
Glencoe.
Guo, Y., Kopec, J. A., Cibere, J., Li, L. C., & Goldsmith, C. H. (2016). Population survey features and response
rates: A randomized experiment. American Journal of Public Health, 106(8), 1422–1426.
Haddouche, H., & Salomone, C. (2018). Generation Z and the tourist experience: Tourist stories and use of social
networks. Journal of Tourism Futures, 4(1), 69–79.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2015). Multivariate data analysis: Pearson new
international edition. London, UK: Pearson Higher Ed.

18 Global Business Review
Hair, J. F., Jr, Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J. (2015). Essentials of business research
methods. Abingdon-on-Thames, UK: Routledge.
Heitzman, A. (2018). The 101 on Generation Z and how marketing will adapt. Inc. Retrieved from https://www.inc.
com/adam-heitzman/the-101-on-generation-z-how-marketing-will-adapt.html
Hiu, A. S., Siu, N. Y., Wang, C. C., & Chang, L. M. (2001). An investigation of decision-making styles of consumers
in China. Journal of Consumer Affairs, 35(2), 326–345.
Howe, N., & Strauss, W. (1992). Generations: The history of America’s future, 1584 to 2069. New York, NY: Harper
Collins.
India Brand Equity Foundation (IBEF). (2018, September 14). E-commerce Industry in India. Retrieved from
https://www.ibef.org/industry/ecommerce.aspx
Iorgulescu, M. C. (2016). Generation Z and its perception of work. Cross-Cultural Management Journal, 18(1), 9.
Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing?
Journal of Personality and Social Psychology, 79(6), 995.
Jackson, V., Stoel, L., & Brantley, A. (2011). Mall attributes and shopping value: Differences by gender and
generational cohort. Journal of Retailing and Consumer Services, 18(1), 1–9.
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36.
Kartik, D., Willis, R., & Jones, C. (2016). Consumer identity and marketing implications: Indian urban youth.
International Journal of Consumer Studies, 40(4), 435–443.
Kebritchi, M., & Sharifi, Y. (2016). Multigenerational perspectives on the gen Z effect. Journal of Psychological
Issues in Organizational Culture, 6(4), 83–87.
Kolb, B. (2008). Marketing research: A practical approach. Los Angeles, CA: SAGE Publications.
Krbová, P., & Pavelek, T. (2015). Generation Y: Online shopping behaviour of the secondary school and university
students. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 63(2), 567–575.
Kumar, S., & Sadarangani, P. (2018). An empirical study on shopping motivation among generation Y Indian.
Global Business Review. doi:10.1177/0972150918807085.
Kupperschmidt, B. R. (2000). Multigeneration employees: Strategies for effective management. The Health Care
Manager, 19(1), 65–76.
Lamb, K. (2019). Philippines tops world internet usage index with an average 10 hours a day. The Guardian.
Retrieved from https://www.theguardian.com/technology/2019/feb/01/world-internet-usage-index-philippines-
10-hours-a-day
Lanvin, B., & Evans, P. (2016). The global talent competitiveness index. INSEAD Business School, Adecco Group
and Human Capital Leadership Institute. Fontainebleau, France.
Leggatt-Cook, C. (2007). Health, wealth and happiness? Employers, employability and the knowledge economy.
Auckland: Labour Market Dynamics Research Programme, Massey University.
Lieber, E., & Syverson, C. (2012). Online versus offline competition: The Oxford handbook of the digital economy
(p. 189). Oxford, UK: Oxford University Press.
Loureiro, S. M. C., & Breazeale, M. (2016). Pressing the buy button: Generation Y’s online clothing shopping
orientation and its impact on purchase. Clothing and Textiles Research Journal, 34(3), 163–178.
Lumpkin, J. R. (1985). Shopping orientation segmentation of the elderly consumer. Journal of the Academy of
Marketing Science, 13(1–2), 271–289.
Nagar, K., & Gandotra, P. (2016). Exploring choice overload, internet shopping anxiety, variety seeking and online
shopping adoption relationship: Evidence from online fashion stores. Global Business Review, 17(4), 851–869.
National Research Council. (2013). Nonresponse in social science surveys: A research agenda. Washington, DC:
National Academies Press.
Norusis, M. (2008). SPSS 16.0 advanced statistical procedures companion. Upper Saddle River, NJ: Prentice Hall
Press.
Park, C. H., & Kim, Y. G. (2003). Identifying key factors affecting consumer purchase behavior in an online
shopping context. International Journal of Retail & Distribution Management, 31(1), 16–29.
Prahalad, C. K. (2009). The fortune at the bottom of the pyramid, revised and updated 5th anniversary edition:
Eradicating poverty through profits. New Jersey, NJ: FT Press.

Thangavel et al. 19
Puiu, S. (2016). Generation Z–A new type of consumers. Revista Tinerilor Economişti, 27, 67–78.
Raphelson, S. (2014). From GIs To Gen Z (Or Is It iGen?): How Generations Get Nicknames. NPR: Special Series, New
Boom. Retrieved from https://www.npr.org/2014/10/06/349316543/don-t-label-me-origins-of-generational-names
-and-why-we-use-them
Rehman, V. (2017). Looking through the glass of Indian culture: Consumer behaviour in modern and postmodern
era. Global Business Review, 18(3_suppl), S19–S37.
Seemiller, C., & Grace, M. (2017). Generation Z: Educating and engaging the next generation of students. About
Campus, 22(3), 21–26.
Shamdasani, P. N., & Yeow, O. G. (1995). An exploratory study of in-home shoppers in a concentrated retail market:
The case of Singapore. Journal of Retailing and Consumer Services, 2(1), 15–23.
Shatto, B., & Erwin, K. (2016). Moving on from Millennials: Preparing for Generation Z. The Journal of Continuing
Education in Nursing, 47(6), 253–254.
Shim, S., & Kotsiopulos, A. (1993). A typology of apparel shopping orientation segments among female consumers.
Clothing and Textiles Research Journal, 12(1), 73–85.
Singer, E. (2002). The use of incentives to reduce nonresponse in household surveys. Survey Nonresponse, 51,
163–177.
Singh, V., Chaudhuri, R., & Verma, S. (2017). E-personality of the young Indian online shopper: A scale validation.
Global Business Review, 18(3 suppl), S157–S171.
Sinha, P. K. (2003). Shopping orientation in the evolving Indian market. Vikalpa, 28(2), 13–22.
Southgate, D. (2017). The emergence of generation Z and its impact in advertising: Long-term implications for
media planning and creative development. Journal of Advertising Research, 57(2), 227–235.
Sprotles, G. B., & Kendall, E. L. (1986). A methodology for profiling consumers’ decision-making styles. Journal
of Consumer Affairs, 20(2), 267–279.
Srinivasan, V. (2012). Multi generations in the workforce: Building collaboration. IIMB Management Review, 24(1),
48–66.
Stone, G. P. (1954). City shoppers and urban identification: Observations on the social psychology of city life.
American Journal of Sociology, 60(1), 36–45.
Taylor, C. R. (2018). Generational research and advertising to Millennials. International Journal of Advertising,
37(2), 165–167.
Toepoel, V. (2015). Doing surveys online. London, UK: SAGE Publications.
Tugend, A. (2010, February 26). Too many choices: A problem that can paralyze. The New York Times. Retrieved
from https://www.nytimes.com/2010/02/27/your-money/27shortcuts.html
Tulgan, B. (2013). Meet Generation z: The second generation within the giant ‘Millennial’ cohort (pp. 1–12).
Connecticut, US: Rainmaker Thinking Inc.
Turner, A. (2015). Generation Z: Technology and social interest. The Journal of Individual Psychology, 71(2),
103–113.
Van den Bergh, J., & Pallini, K. (2018). Marketing to generation Z. Research World, 2018(70), 18–23.
Vijayasarathy, L. R., & Jones, J. M. (2000). Intentions to shop using internet catalogues: Exploring the effects of
product types, shopping orientations, and attitudes towards computers. Electronic Markets, 10(1), 29–38.
Walton, S., & Huey, J. (1993). Sam Walton, made in America: My story. New York, NY: Bantam.
Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business research methods. Cengage Learning.