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Generation Z - The Global Market’s New Consumers- And Their Consumption Habits: Generation Z Consumption Scale



Along with globalization, the structure of markets has changed. In today's markets, it is necessary to analyse the consumers’ profile in order to appeal to consumers or compete with other companies and survive against them. Today's changing consumer structure reveals the differentiation of consumption habits as well. The Generation Z, which is included in the young age profile of the consumer segment, represents the year 1995 and beyond. This generation is also known as the mobile generation. They are interested in more technology than their predecessors (Generation X and Y), and they are actualizing their social lives more and more through smart devices such as mobile phone, tablets. This situation has also changed the perception of time and space in consumption habits. The shopping mall culture that emerged with globalization is now taking its place to Internet shopping. Ads made via social media and shopping made by these ads are among the preferences of Generation Z. In this study, we focus on changing the general consumption habits and the role of the Generation Z’s profile in these habits. For this purpose, questionnaires developed for our study were applied to 200 people who are members of the relevant Generation Z. And the data obtained from the field are evaluated by reliability and factor analysis. Findings are interpreted as the Generation Z Consumption Scale.
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European Journal of
Multidisciplinary Studies
May-Aug 2017
Vol.5, Nr.1
Generation Z - The Global Market’s New Consumers- And Their Consumption Habits:
Generation Z Consumption Scale
Asst. Prof. PhD. Mustafa Özkan
Giresun University, Faculty of Economics and Administrative Sciences, Department of Business Administration
Research Assistant Betül Solmaz
Istanbul University, Faculty of Economics, Department of Labour Economics
Along with globalization, the structure of markets has changed. In today's markets, it is necessary to analyse
the consumers’ profile in order to appeal to consumers or compete with other companies and survive against
them. Today's changing consumer structure reveals the differentiation of consumption habits as well. The
Generation Z, which is included in the young age profile of the consumer segment, represents the year 1995
and beyond. This generation is also known as the mobile generation. They are interested in more technology
than their predecessors (Generation X and Y), and they are actualizing their social lives more and more through
smart devices such as mobile phone, tablets. This situation has also changed the perception of time and space
in consumption habits. The shopping mall culture that emerged with globalization is now taking its place to
Internet shopping. Ads made via social media and shopping made by these ads are among the preferences of
Generation Z. In this study, we focus on changing the general consumption habits and the role of the Generation
Z’s profile in these habits. For this purpose, questionnaires developed for our study were applied to 200 people
who are members of the relevant Generation Z. And the data obtained from the field are evaluated by reliability
and factor analysis. Findings are interpreted as the Generation Z Consumption Scale.
Keywords: Consumption Habits, Generation Z, Factor Analysis, Reliability Analysis, Generation Z Consumer Scale
1. Introduction
The Generation Z (or Gen Z), which is included in the young age profile of the consumer segment, represents the year
1996 and beyond. The Generation Z, which will be the most dynamic actors of the trade sector in five to ten years, can be
defined as a mass that is not much affected by classical sales and marketing activities. For this reason, it is very important
to know the definition and characteristics of these individuals for every segment, because the future will consist of this
generation. The aim of this study is to understand the compatibility of the Generation Z with the characteristics of Generation
Z in literature; and at the same time, to determine the factors that affect the shopping preferences of this generation over
the Internet. It is inevitable that these individuals who are growing in an environment where the Internet is used extensively
and realize their social experiences on the Internet, will play an active role in shaping the social and economic structure of
the country where they live. This generation, constantly seeking rapid change and innovation, also stimulates the
environment of commercial competition or it will stimulate.
When considering that there is an increasingly young population especially in Turkey, it is a necessity to consider while
determining the market policies for enterprises and entrepreneurs. In this context, our work will contribute to the literature
about the Generation Z which has a restricted examination rate in this area. In this study, we will focus on changing the
general consumption habits and the role of the Generation Z’s profile in these habits. For this purpose, questionnaires
ISSN 2414-8385 (Online)
ISSN 2414-8377 (Print
European Journal of
Multidisciplinary Studies
May-Aug 2017
Vol.5, Nr.1
developed for our study were applied to 200 people who are members of the relevant Generation Z. And the data obtained
from the field are evaluated by reliability and factor analysis. Findings are interpreted as the Generation Z Consumption
Scale. This scale, which is the result of the analysis, consists of 17 questions and 5 factors in total.
2. Generation Z: A Conceptual Framework
The word "generation" is of Greek origin and emerged from the "genos", and means "getting out of the best possible
presence." The word refers not only to biological/conceptual birth, but also to continuous change over time in terms of
origin. In other words, it describes the development of something new in the societies (Clarke, 2012: 41).
Kupperschmidt (2000: 66) describes the generation concept as "a group that shares the birth-years and birth-places and
critical-social events at the same time."
While the classification is made for this concept, it is considered that there are groups of people who are born in the same
time within the same age groups and share the same history and culture (Weingarten, 2009: 27). The start and end dates
are not precise, but the generation periods generally cover 15-20 years (Stapley, 2010: 847).
Along with making different evaluations in the literature, the common view for the classifications of generations has been
gathered on four generations: they can be classified as follows (De Cooman and Drics, 2012: 44);
The Silent Generation (1925-1944)
Baby Boom (1945-1964)
Generation X (1965-1979)
Generation Y (1980-1994)
Generation Z (1995 -...)(
There is no consensus on the beginning of the Generation Z in the literature. Some researchers accept different beginning
years for this generation. These are; 1990 and after, 1995 and after, 2000 and after. In our study, 1995 and after was
accepted as the starting year.
Generation Z is also referred to by different names; there are "Generation V", "Generation C", "Generation Cox", "Internet
Generation", "Homeland Generation", or "Google Generation"( Apart from these, Strauss and Howe
(1999: 335) describe it as "The New Silent Generation."
The characteristic features of the Generation Z are different from the other generations. The globalization of the world and
the rapid spread of the Internet in the world are very influential in their characteristic structure. For this generation that is
growing with computers and technological breakthroughs, technology and Internet are indispensable. They are constantly
living together with the Internet, social media and their applications such as mp3 players, text messages, mobile phones,
PDAs, YouTube, IPADs, media technologies. This generation can also be called the “mobile generation” (Kapil and Roy,
2014: 10-11.)
In another definition, it is thought that the Generation Z, known as “selfie generation”, is less narcissistic than the preceding
generation Y. They prefer to spend less because they witness global wars and economic recessions more often than others.
According to previous generations, they are more optimistic about their health. They are aware that the world needs to be
"better able to live". They prefer quick communication (
We can say that they are more conscious than previous generations because they spend their time on the In ternet and
they shape their life according to this atmosphere. Their friendships are mostly on social media. Because their world is a
digital environment, their characteristic features are shaped like a virtual environment.
Although these individuals are still too young to get involved in the business world by age, consumption habits and general
consumption trends belong to their generations. They still live as economically dependent on their parents. Therefore, they
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are more selective in spending money and more selective in choosing products they will buy. They are sensitive to the
concept of brand, but they are not more loyal than the previous generations. Because they have many alternatives in
products and service, they expect constant innovation from the products they use. Visuality and design are important to
them, as they prefer products. General expectations are the preferred product for convenience in their lives. Generation Z
consumers make a detailed research on the product before purchasing a product.(Wood, 2013: 3) They absolutely test that
product and make purchases by choosing the seller who offers the best price ( This study was
carried out in order to reveal the general trends of Generation Z in shopping habits.
3. Factor Analysis
Factor Analysis was developed by the psychologist Charles Spearman in the early 1900s with a study of measuring human
intelligence. It is a technique that seeks a causal relationship. For this, it is based on the principle of measuring the
correlation between the set of observed variables with the help of linear combinations of unrecognized sub-factors (Timm,
2002: 496). In other words, it can be said that factor analysis is a statistical technique indicating, which, and to what degree,
variables relate to an implicit and unmarked factor (Kim and Mauller, 1978: 56).
It is used to assign the number of distinct constructs assessed by a set of evaluations (Fabrifar and Wegener, 2012: 3).
The widespread use of Factor Analysis comes after the 1970’s when the use of computer technology accelerated in the
world. (Öngen, 2010: 1).
Factor analysis can generally be grouped under two main categories. These are called explanatory factor (EFA) analysis
and confirmatory factor analysis (CFA). EFA gives the information to the researcher about the direction of a possible
relationship, since there is no anticipation between variables. On the other hand, CFA is used to test the accuracy of a
predetermined relationship (Altunışık, 2010: 262-264). EFA has been used to understand the possible underlying
factors structure of a set of evaluated variables without imposing any biased structure on the outcomes (Suhr, 2005: 2).
Explanatory Factor Analysis has some assumptions. These can be explained as follows:
The data are at least equally spaced.
Random sampling technique is used.
There is a linear relationship between the observed variables.
Variables have normal distribution.
Observed variable pairs have bivariate normal distribution.
The variables observed in the last place are multivariate normal distribution.
The CFA has also some assumptions like as EFA. These can be listed as follows:
Multivariable normality,
An adequate sample size (n> 200),
The correct a priori model specification,
The data are based on random sample (
Factor analysis has four basic stages. These are, firstly the calculation of the correlation matrix for all variables, then
determining the factor numbers, and then the rotation of the factors (or factor conversion process), finally, calculation of
factor scores and describing its names. In addition, three methods are used to evaluate the suitability of the data set: the
creation of the correlation matrix, the Kaiser-Meyer-Olkin (KMO) and the Bartlett tests. (Akgül and Çevik, 2005: 419-428).
The general factor model (GFA) can explained that for p observed variables and q factors or implicit variables:
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In the above form, are residuals. , , ,… refer to common factors or latent variables. , , and
are named the factor loadings. is known that constant term and it has any role fitting and interpreting the analysis
model (Bartholomew et al, 2002:180-181).
4. Material
The purpose of this study is to understand how members of Generation Z determine the product preference and consumer
priorities in the purchasing process. We use the " A Survey of Generation Z Consumption Scale" questions developed by
us to achieve this goal.
At the outset, a questionnaire consisting of 37 questions excluding demographic questions was sent to 200 randomly
selected respondents. Subsequently, the obtained data were tested by factor analysis and reliability analysis. Thus, we
developed a scale with 5 factors and a total of 17 questions.
According to the obtained data, the demographic evaluations of the individuals who are member of Generation Z are as
shown in Table 1.
able 1: Demographic Information
Monthly Personal Income
0-400 TL
401-600 TL
601-800 TL
801-1000 TL
1001-1200 TL
More than 1200 TL
When Table 1 is examined, the following information is obtained. 55% (110 people) are female and 45% (90 people) are
male respondents of the survey. In addition, 2% (4 people) of participants are in the age of 18, 15% (39 people) were in
the age of 19, 27,5% (55 people) are in the age of 20, 27% (54 people) are in the age of 21, 19,5% (39 people) are in the
age of 22 and 9% (18 people) are over 23 years old. The monthly budgets for the individual monthly expenditures of the
participants are classified as follows: 39,6% (77 people) of participants have a budget between 0-400 TL, 27,1% (52 people)
have a budget between 401-600 TL, 13,5% (26 people) have a budget between 601-800 TL, 9,4% (18 people) have a
budget between 801-1000 TL, 6,3% (12 people) have a budget between 1001-1200 TL, 4,2% (8 people) Person) have a
budget more than 1200 TL.
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5. Application
Since the study will be evaluated by factor analysis, it is firstly assessed whether the study structure is appropriate for factor
analysis. For this purpose, the KMO test value was calculated and the result was 0,694. However, when the MSA values
that show the value of conformity to the factor analysis of each item were examined, they were eliminated from the
questionnaire because the items A-2, A-29, A- 31 and A- 32 in questionnaire were less than 0.50. It is customary to remove
the factor weight from the analysis so that the questionnaire can become stronger. However, there is no consensus on
which items with the necessary value to be taken from the questionnaire. In general, items with a score below 0.50 are
excluded from the analysis. However, there are also researchers who say that this ratio is 0.70. In our study, we preferred
the other variables below 0.55. As a result of the reconstituted factor analysis after removal of the relevant items from the
analysis, the KMO test value increased to 0.724. According to the "KMO" test values, the variables used for the study are
"good" for factor analysis (Durmuş et al., 2013: 80-87). As a general evaluation, if the test value found in the "KMO" test is
below 0,50, it is assumed that the variables are not suitable for factor analysis. In addition; it is known about the “KMO
tests that 0,50 weak, 0,60 moderate, 0,70 good, 0,80 very good, 0,90 perfection (Sharma, 1996: 116).
Table 2: Total Variance Values Explained
Initial Eigenvalues
Sum of Transformed Squared Weights
Variance %
Cumulative %
Variance %
Table 2 provides information on the number of dimensions of the questionnaire used. This structure, consisting of 5 sub-
dimensions, has the capacity to explain 56,229% of the total variance. When the factors are assessed one by one; the first
factor has 13,076% of the total variance, the second factor 11.922%, the third factor 11,672%, the fourth factor 11,120%
and the fifth factor 8,439%.
Table 3: Factor Loads Table
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Table 3 gives information on the factor loadings of the variables and the weights of the sub-factors. According to the above
information; the factors and factors affecting the determination of the purchasing priorities of the individuals are regulated
in Table 4.
Table 4: Factor Analysis Outputs
Old Factor Names
New Factor Names
Numbers of Questions in Factor
Substances in Factor
Factor 1
Internet Affect
A16, A15, A10, A8,
Factor 2
Product Description
A13, A12, A17, A11
Factor 3
External Factors
A24, A28, A26,
Factor 4
A33, A34, A35
Factor 5
Product Identity
A9, A36, A1
At the beginning, there were a total of 37 questions for our work. As a result of the factor analysis, the number of questions
decreased to 17. You can see in Table 5 what are the questions of scale developed in our work.
Table 5: Factor Analysis Outputs
Factor Names
Internet Affect
If I find the prices of the products at the internet websites cheaper than stores, I prefer purchasing on
the internet.
If a product in the store is cheaper on the internet website, I would prefer to buy it from the website.
I do official branding of products from official websites.
I rely on internet purchasing for brand products, which I know.
Product Description
I will pay high fees if a product longevity.
I will buy a high price for the comfort that the product gives us.
I prefer personalized products to serial products.
I think that the reliability of Internet stores can be determined from different blog sites and comments
made about them.
External Factors
While choosing to buy a product, I read comments about that product on the social media.
I am impressed by the comparative evaluations on the product, made on internet websites.
I would prefer not to pay more for brand-value products for the same benefit.
Opportunities (points / money points) offered by Internet websites in stores will lead me to purchasing
on that website.
When I buy a product, I prefer to offer applications in the form of "where can I find the cheapest", and I
prefer to shop by visiting these websites.
If Internet websites have advantages for me (cinema, theater, bus, air plane tickets), that will affect my
purchasing on those websites.
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Product Identity
When shopping, I buy environmentally sensitive products (nature friendly, recyclable, respectful of
animal rights, etc.).
I think it is a more practical way to enjoy a product on the internet websites and buy it from the store.
In the preferred product when shopping, I evaluated according to the quality of the product brand.
Reliability analysis was performed for Generation Z Consumption Scale” so that the output obtained could be used as a
scale. As a result of the reliability analysis, the developed questionnaire could be regarded as valid. Cronbach's Alpha value
was calculated as 0,793.
6. Conclusion
It is important to note that the preferences of life for the Generation Z are much different from the previous generations.
The purchasing culture of this generation, which has spent a great majority of its days on the Internet, also sharply affects
their priorities and expectations too. A questionnaire was developed to understand the Z people by us, and applied to the
200 people who are member of Generation Z. As a result, we identified 5 factors that determine the purchasing preferences
of them. These factors have been named as Internet affect, product description, external factors, deals and product identity.
The reliability analysis result was calculated as 0,793. This result shows that the questionnaire data is strongly reliable.
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Digital ecosystems have gained tremendous importance in the B2C sector in recent years. They have the ability to satisfy the entire spectra of needs, leading to an increase in customer loyalty, which in return represents a crucial role in securing a company’s existence in the online business environment. However, Generation Z, which represents the purchasing power of the future, tends to exhibit decreasing loyalty behavior. Nevertheless, no scientific studies were found, that comprehensively examine the relationship between "digital ecosystems”, "customer loyalty” and "Generation Z" in a single investigation. The goal of this master's thesis is therefore to identify the factors, which lead to customer loyalty in a digital B2C ecosystem among Generation Z representatives. Using a scientific qualitative investigation approach via focus group interviews and a real-life case study using the practical example of Amazon, new influencing factors were identified, based on the characteristics of digital ecosystems and Generation Z representatives and previously researched factors were examined regarding their relevance for Generation Z and digital ecosystems in relation to e-loyalty. The results show, that the most decisive factors to build customer loyalty amongst Generation Z representatives within digital B2C ecosystems are price, service quality, trust, perceived value, user-friendliness, variety of offerings, speed and convenience.
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Aktualna rzeczywistość bywa postrzegana z jednej strony jako nieprzewidywalna, różnorodna, z drugiej jako dynamiczna i ambiwalentna. Procesy globalizacji, przemiany świata społeczno-kulturowego, postęp naukowo-techniczny przyczyniają się do przeobrażeń stylu życia, systemu wartości i codziennego funkcjonowania współczesnych młodych ludzi. Dodatkowo, jak pokazał ostatni czas, oprócz wyzwań technologicznych i społecznych młodzi muszą się również zmagać z doświadczeniami i następstwami kryzysu COVID-19. Badania prowadzone przez Shuxian Jin i współpracowników1 wykazały, że pandemia COVID-19 miała największy negatywny wpływ na osoby w wieku 18–24 lata. W tej grupie dostrzegalny był wzrost samotności, niepewność pracy oraz trudnościekonomiczne. Sytuacja przygotowania zawodowego oraz aktywność pokolenia Z na rynku pracy będzie miała znaczenie dla funkcjonowania wielu społeczeństw. Sposób radzenia sobie młodych osób na rynku pracy, pokonywanie trudności spowodowanych pandemią COVID-19 będą niewątpliwie zależały od ich wykształcenia, rozwijania posiadanych umiejętności i doświadczeń zawodowych. Celem tego artykułu jest ukazanie cech pokolenia Z oraz trudności napotykanych przez osoby z tej generacji na rynku pracy. Ważne jest również przedstawienie form wsparcia umożliwiających przygotowanie do rozpoczęcia aktywności zawodowej, jak i jej kontynuację w sytuacji trudnych warunków pandemicznych.
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La Generación centennial es de los recientes grupos sociales, que surgen en este nuevo milenio, como esa población que es de gran interés, para comprender aspectos esenciales, como los hábitos de consumo y compra, ya que, en la actualidad de mercado, los estudios de consumo se vuelven cada vez más profundos y fuertes de comprender. El presente propósito del estudio es, identificar las principales características cuantitativas y cualitativas de los centennial, sobre uso y consumo de los bienes y servicios. Para el estudio de la investigación en cuestión, se utiliza el enfoque mixto; ya que incluye un corte transversal, realizada durante el período 2019, con edades comprendidas entre los 14 a los 25 años, del sector de la GAM (Gran Área Metropolitana). Los resultados del estudio demuestran, la importancia que este grupo generacional enfatiza sobre el uso y compra de bienes y servicios, además de que la información es clave, para los procesos de toma de decisiones sobre consumo de tangibles e intangibles, es fundamental que el mercadeo y la publicidad, para este tipo de consumidor, esté al alcance en tiempo real, a través de dispositivos tecnológicos.
Purpose As Generation Z (Gen Z) represents one of the most important segments in the travel and tourism sector, the present work aims to analyse Gen Z's behaviour in a post-pandemic scenario. In particular, the present work deepens the factors that may influence future accommodation choices. Design/methodology/approach This study uses data from a web-based questionnaire targeting Italian tourists belonging to Generation Z, reaching 221 complete responses. After the statistical analysis of the sample, an exploratory factor analysis (EFA) was then carried out. Findings Data provided evidence about the main factors affecting Gen Z tourist choices. In particular, these young consumers are positively influenced by the information (especially from institutional and verifiable sources), more flexible purchase terms (e.g. full refund in case of cancellation due to contagion of the coronavirus disease 2019 (COVID-19) and digital payments) and structure features (e.g. cleanliness, sanitisation, digitalisation and sustainability). Originality/value This work attempts to contribute to the academic literature by looking at the tourism experience from Gen Z's perspective. From this angle, this work underlines the influence some factors exert on the final accommodation choice, especially in the presence of exogenous events. Moreover, as this study focusses on a post-pandemic scenario, the study seeks to provide valuable indications, both at the theoretical and managerial level, for the tourism sector to boost the sector's recovery and prepare the sector to face similar events.
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In the society of the 21st century, obsolescence as a phenomenon is all around us, it affects everyone, even if there are those who are less aware of it and do not deal with it. The aim of our study is to identify consumers' attitudes towards planned obsolescence, as well as to learn about their clothes buying habits. We conducted primary research through an online questionnaire, which was distributed using the snowball method. A total of 176 respondents participated in the research. The data analysis was carried out with the help of Microsoft Excel program. Based on our research results, the majority of respondents buy new fashion products every month or every six months. The reason for the exchange is usually that the product is no longer wearable or the customer simply wants something new. More than half of the people participating in the research fell victim to functional obsolescence as a result, but psychological obsolescence also appears. When buying new fashion products, the most important aspects are comfort and durability. On average, 34.42% of the respondents' wardrobe consists of products that are worn less often than every six months.
Introduction.- Vector and Matrix Algebra.- The Multivariate Normal Distribution, Multivariate Normality, and Covariance Structure.- One- and Two-Sample Tests.- Multivariate Analysis of Variance.- Discriminant Analysis.- Canonical Correlation.- Principal Component Analysis.- Factor Analysis.- Structural Equations.
Chapter 1: Introductory Factor Analysis Concepts Chapter 2: Requirements for and Decisions in Choosing Exploratory Common Factor Analysis Chapter 3: Requirements and Decisions for Implementing Exploratory Common Factor Analysis Chapter 4: Factor Analysis Assumptions Chapter 5: Implementing and Interpreting Exploratory Factor Analysis Chapter 6: Summary, Conclusions, and Recommendations
With many countries facing current or impending nursing shortages, considering the impact of the multigenerational workforce, their needs and behaviours may impact on efforts to recruit and retain nurses. As the nursing workforce changes, with ageing nurses, an increased demand for nursing care and an ageing population, greater emphasis is placed on an understanding of the workforce's needs and the differences highlighted by various generational groups. Offered is a literature review and definitions of the four different generational groups evident in the current nursing workforce. These are offered as an insight into the different needs and attitudes that multigenerational groups bring to the nursing workforce. KEY ISSUE(S): Making the most of the multigenerational workforce and offering recommendations for meeting their unique challenges. Understanding the different generational groups may allow nursing leaders and managers to consider what drives, motivates or hinders nurses from different generations. Recruiting and retaining a workforce increasingly made up of nurses from many generational groups is a challenge that can be tackled with a deeper understanding of each of the commonly identified generational groups.
Based on a longtime course for master's level students at the London School of Economics and Politics, where the authors are based, this text concentrates on the multivariate methods so useful to social science problems involving correlational rather than causal relationships. Chapters with application examples and further readings cover data preliminaries, cluster analysis, multidimensional scaling, correspondence analysis, principal components analysis, factor analysis, and latent variable methods. While mathematical demands are minimal, these methods require use of a computer software package; an auxiliary website supplies data sets and code for use with SPSS.