Université de Strasbourg

Master Grand Ecole

2012/2013

Master Thesis

Marketing Green Products – An Empirical Comparison of

the Consumers’ Willingness to Pay for Green Products in

Germany and France

Author:

Simon Burkel

Supervisor:

Prof. Dr. Andreas Munzel

29 August 2013

Université de Strasbourg

École de Management Strasbourg

Master Grand École

2012/2013

Master Thesis

Marketing Green Products – An Empirical Comparison of

the Consumers’ Willingness to Pay for Green Products in

Germany and France

Author:

Simon Burkel

Supervisor:

Prof. Dr. Andreas Munzel

29 August 2013

I

Acknowledgement

This thesis was written between March and August 2013 at the École de Manage-

ment in Strasbourg.

I would like to thank Prof. Dr. Andreas Munzel for his invaluable support and guid-

ance for this thesis. Furthermore I want to express my gratitude to all participants in

the pretest. Their time and feedback was invaluable for the obtained results. In addi-

tion I would like to thank all survey participants for sharing their knowledge and expe-

rience, on the basis of which relevant findings could be generated in this thesis.

I want to express my highest gratitude to my parents, who allowed me to follow my

interests and supported me throughout my whole studies. Finally, I would like to sin-

cerely thank Sarah, for her patience, emotional support and understanding during the

time I was writing this thesis.

Simon Burkel

Brussels, 24.08.2013

II

LIST OF CONTENTS

LIST OF CONTENTS ................................................................................................. II

LIST OF FIGURES .................................................................................................... IV

LIST OF TABLES ...................................................................................................... IV

LIST OF APPENDIXES .............................................................................................. V

LIST OF ABBREVIATIONS ....................................................................................... VI

1. INTRODUCTION AND DISCUSSION OF THE RESEARCH PROBLEM ............ 1

1.1. Introduction.................................................................................................. 1

1.2. Problem Discussion and Research Gap .................................................... 3

1.3. Thesis Outline .............................................................................................. 6

2. LITERATURE REVIEW ........................................................................................ 7

2.1. Definition Green marketing ........................................................................ 7

2.2. Green Marketing Mix ................................................................................... 9

2.3. Defining Green Products .......................................................................... 11

2.4. Attitude towards the Environment and Green Products ........................ 13

2.5. Intention to Purchase Green Products .................................................... 15

2.6. Green Prices and Willingness to Pay ...................................................... 17

2.7. Green Pricing Strategies ........................................................................... 20

2.8. Model and Hypothesis Definition ............................................................. 22

3. METHODOLOGY ............................................................................................... 26

3.1. Research Approach ................................................................................... 26

3.2. Methods to Measure the Consumers’ Willingness to Pay ..................... 27

3.3. Critics of the Methods to Measure WTP .................................................. 30

3.4. Survey Design............................................................................................ 31

3.5. Data Collection .......................................................................................... 35

4. RESULTS .......................................................................................................... 36

4.1. Demographic Data ..................................................................................... 36

4.2. Frequency Analysis and Descriptive Statistics ...................................... 38

4.2.1. Attitude towards the Environment .................................................... 38

4.2.2. Attitude towards Green Products ...................................................... 40

4.2.3. Intention to Purchase Green Products.............................................. 42

4.2.4. Willingness to Pay for Green Products ............................................. 44

4.2.5. Social Desirability ............................................................................... 47

III

4.3. Independent and ANOVA-test .................................................................. 48

4.4. Correlation Analysis .................................................................................. 52

4.4.1. Correlation Matrix ............................................................................... 52

4.4.2. Bivariate Analysis and Pearson’s Coefficient .................................. 53

4.5. Multiple Regression Analysis ................................................................... 56

5. FINDINGS & IMPLICATIONS ............................................................................ 59

5.1. Findings ..................................................................................................... 59

5.2. Test of Hypotheses and Discussion ........................................................ 60

5.3. Research Questions and Implications ..................................................... 65

6. CONCLUSION ................................................................................................... 68

6.1. Conclusion ................................................................................................. 68

6.2. Limitations and Further Research ........................................................... 69

REFERENCES ......................................................................................................... 71

APPENDIXES .......................................................................................................... 81

IV

LIST OF FIGURES

Figure 1: Willingness to Pay (WTP) .......................................................................... 18

Figure 2: Ideal-Typical Price Positions...................................................................... 21

Figure 3: Model and Hypotheses .............................................................................. 22

Figure 4: WTP Price Premium .................................................................................. 44

Figure 5: Reason for no WTP ................................................................................... 46

Figure 6: Revised Model ........................................................................................... 64

LIST OF TABLES

Table 1: Numerical Analysis WTP Total and Percentage ......................................... 45

Table 2: Correlation Matrix ....................................................................................... 53

Table 3: Bivariate Regression Analysis .................................................................... 54

Table 4: Pearson’s Coefficient .................................................................................. 55

Table 5: Chi2 test Demographics WTPYesNo and PastBB ...................................... 56

Table 6: Estimated Regression Model to Explain AEmean....................................... 57

V

LIST OF APPENDIXES

Appendix 1: Questionnaire English Version

Appendix 2: Questionnaire German Version

Appendix 3: Questionnaire French Version

Appendix 4: Nationality Cross Tabulations

Appendix 5: Frequencies and Percentage Analysis

Appendix 6: Numerical Analysis Attitude Measures

Appendix 7: Correlation Matrix Attitude towards the Environment

Appendix 8: Factor Analysis Attitude Measures

Appendix 9: Cronbach’s alpha

Appendix 10: Correlation Attitude Items with SDmean

Appendix 11: ANOVA test

Appendix 12: ANOVA WTPtotal by Nationality

VI

LIST OF ABBREVIATIONS

AE Attitude environment (items)

AG Attitude green product (items)

AGP Attitude towards green products

AIC Akaike Information Criterion

AMA American Marketing Association

ANOVA Analysis of Variance

ATE Attitude towards the environment

BIC Bayesian Information Criterion

B-to-B Business to Business

CO2 Carbon Dioxide

c.p. Ceteribus Paribus

CVM Contingent Valuation Method

e.g. Exempli Gratia (for example)

etc. Et cetera (and so forth)

fig. Figure

GfK Gesellschaft für Konsumforschung

GWP Gloabal Warming Potential

HC Hydrocarbon

HFC Hydrofluorocarbon

HVAC&R Heating, Ventilation, Air-Conditioning & Refrigeration

i.e. id est (“in other words”)

MNC Multinational Company

NH3 Ammonia

OECD Organization for Economic Cooperation and Development

p. Page

PhD Doctor of Philosophy

PI Purchase intention (items)

VII

RCmdr R Commandeur (statistic software)

RP Reference Price

SD Social Desirability

Sig. Significance (niveau/level)

TEWI Total Equivalent Warming Impact

UK United Kingdom

US United States

WTP Willingness to Pay

WOM Word of Mouth

% Per Cent

& And

VIII

Bibliothèque:

Résumé:

This thesis compares German and French consumers’ willingness to pay (WTP) for

green products sold in supermarkets using natural refrigerants. The results of a sur-

vey conducted among a total of 1.045 German and French individuals demonstrates

that the general WTP for green products is directly influenced by the attitude towards

the environment (ATE) as well as the demographic variables ‘gender’, ‘education’

and ‘income’. The results indicate that French and German consumers are willing to

pay an average price premium of 16,4 % for products sold in an environmentally

friendly supermarket. However, even if French consumers are characterized by a

more positive ATE and higher general WTP compared to German respondents, the

level of the price premium they are willing to pay is significantly lower.

#Mots clés: Green Marketing, Willingness to Pay, Attitude towards the Environment,

Consumer Behavior #

1

1. INTRODUCTION AND DISCUSSION OF THE RESEARCH PROBLEM

1.1. Introduction

“Marketers in the past have based their strategies on the assumption of infinite re-

sources and zero environmental impact“ (Kotler, 2011, p. 132). But in today’s indus-

trialized world, both businesses and consumers are increasingly aware of environ-

mental sustainability issues and the future of our planet (Ghosh, 2010, p. 82). As a

result of the rapid economic growth in the past decades and consequently the in-

crease of consumption worldwide, the environmental deterioration caused by over-

consumption and utilization of natural resources has increased. The consequences

are numerous and range from global warming, acid rains, the destruction of the

ozone layer, air pollution, pollution of sea and rivers, contaminated food supplies

health problems due to pollution and toxins, destruction of land and forests, endan-

gered wildlife, noise and light pollution to desertification (Datta, 2011, pp. 124 - 125;

Peattie, 1999, p. 57;Vaccaro & Cohn, 2009, p. 596).

As the environmental consequences are well known, going green has gained im-

portance and large companies such as Coca Cola, General Electric, Toyota, IBM,

General Motors, Volkswagen, BP, Shell, Esso/Exxon, Total, Renault and many oth-

ers have started to focus on sustainability (Bush, 2008, p. 1; Hartmann & Apaolaza-

ibáñez, 2009, p. 715). According to research by Vandermerwe & Oliff (1990, p. 12) in

the 90’s, 92% of multinational companies (MNC) in Europe started to change their

products and to launch green campaigns in order to respond to the growing environ-

mental concerns of the consumers. An orientation towards green marketing is not

exclusively found at companies serving consumer markets. Increasingly also busi-

ness to business (B-to-B) marketers started to capitalize on making their services

and products environmentally friendly and energy efficient (Vaccaro, L., 2009, p.

315). However, this new orientation towards green marketing has also lead to credi-

bility issues for companies and raised consumer’s skepticism (Callahan, 2006, p. 1).

The growing consumers’ skepticism is one of the reasons why since the “promising

90’s” many green products have left the market and companies are cautious about

launching green communication campaigns for fear of being accused of ‘green wash-

ing’ (Peattie & Crane, 2005, pp. 357-358; Lane, 2013, p. 20; Ginsberg & Bloom,

2004, p. 79).

2

Since the last decade, which was marked by drawbacks for green marketers, green

marketing has now entered a new phase in which advertising green products, ser-

vices and business practices has become successful and profitable (Lane, 2013,

p.20). The recent upswing of green marketing is driven by a constantly improving

consumer attitude towards environmental issues and sustainability. In addition com-

panies superior profitability expectations of green products, growing concerns about

their social responsibility, cost and profit issues as well as governmental and com-

petitive pressure have supported their growing prominence (Ghosh, 2010, pp. 84-86).

Ottmann (2011, p. 2) states that today green has become mainstream. This devel-

opment has lead to an increasing demand for green products and raw materials from

both consumers and industrial buyers (Kumar, 2011, p. 59) and consequently to re-

newed interest in, importance of and use of green marketing (Ghosh, 2010, p. 86).

According to Kumar (2011, p. 60) “Green marketing is a global concern and it is go-

ing to have a better future ahead”.

A positive trend towards environmentally friendly products is also measurable for re-

frigeration technologies used in supermarkets (Guide: shecco publications, 2012, p.

54). A 100% green and environmentally friendly alternative to greenhouse gases

used in conventional refrigeration technologies are natural refrigerants. The most

commonly used natural refrigerants today are “ammonia (NH3), carbon dioxide

(CO2) and hydrocarbons (HCs), such as propane, iso-butane, and propylene also

known as propane, as well as to a minor extent water and air(Guide: shecco publica-

tions, 2012, p. 10). These natural refrigerants can be seen as eco-friendly products

as they lessen the impact on the environment due to reduced or no greenhouse gas

emissions. Furthermore they feature components and recycling techniques which are

less harmful for the environment than those of conventional products (Carbajal &

Kanter, 2099, p. 16).

The decision to equip new supermarkets with refrigeration technology using natural

refrigerants or to retrofit existing refrigeration technology is to a large extend influ-

enced by factors such as initial costs, maintenance, safety, legislation, corporate re-

sponsibility and strategy, training, availability and amortization of the investment

(Guide: shecco publications, 2012, p. 52 ff.). Still, even if natural refrigerants become

serious alternatives to conventional cooling technologies in commercial refrigeration,

it is a question of high interest for retailers, whether the consumer is actually willing to

pay a price premium for products sold in a supermarket using this green technology.

3

And if so, which factors influence the consumers’ willingness to pay (WTP) and which

implications can be drawn for the marketing and pricing of these products that would

allow to increase sales and profits.

1.2. Problem Discussion and Research Gap

As a majority of the environmental issues mankind faces today are the result of mod-

ern development and economic growth, a rethinking to sustainable production and

consumption is required (Peattie, 1999, p. 58). In order to realize this necessary

change, a paradigm shift on an individual, societal and economic level is needed

(Hartmann & Apaolaza-Ibáñez, 2006, p. 676). To achieve these objectives govern-

ments have to develop and implement environmental policies and corporations have

to invest in greening as part of their business strategy (Peattie, 1999, p. 58; Polonsky

& Rosenberger, 1999, p. 23). In addition consumers need to be not only interested in

green products but also purchase them (D’Souza, Taghian, Lamb, & Peretiatko,

2007, p. 375). Researchers on the topic seem to agree that the consumers’ interest

in green products is constantly growing. A McKinsey survey (Bonini & Oppenheim,

2008, p. 1) conduced in 2007 showed that 87% of consumers in Brazil, Canada, Chi-

na, France, Germany, India, the United Kingdom (UK), and the United States (US)

take into account the environmental and social impact of the products they buy1.

However, at the same time attitude and action differ. Only 33% of the consumers that

participated in the 2007 McKinsey survey stated that they are actually ready to buy

green products or have already done so (Bonini & Oppenheim, 2008, p. 1). Further

research proves that there is a wide gap between what consumers state about their

environmental awareness and their actual purchase decisions (Michaud & Llerena,

2011, p. 411). This gap between attitude and action leads to the first research ques-

tion of this thesis:

Research Question 1: “Are consumers with an environmental friendly atti-

tude ready to buy green products?”

Even if consumers are ready to buy green products it has to be questioned if they are

also willing to pay for these products. A survey about the Europeans’ attitude towards

the issue of sustainable consumption and production conducted in 2009 with more

than 26.500 participants showed that even if a majority of all respondents (60%) stat-

1 Another survey conducted by Hukahodo Inc. (Hakuhodo, 2007, p. 1) in 2007 for the Japanese mar-

ket stated that 92,9% of the consumers are concerned about global warming

4

ed that environmental impact is more important than e.g. a product’s brand name

when making a purchase decision, only a minority of 19% rated environmental impact

as more important than a product’s price (Eurobarometer, 2009, p. 7). According to

an OECD survey (2002a, p. 12), 27 % of consumers in OECD countries can be la-

beled as green consumers due to their high WTP as well as strong environmental

concern and activism. But the same organization stated in the same year that a ma-

jority of consumers in OECD countries show high interest in green products but only

a medium to low WTP for these products (OECD, 2002b, p. 97). According to a Eu-

ropean Commission survey (2013b) 90% of European consumers state to buy green

products at least sometimes. But even if growing consumer preferences for green

products have been reported in research, only few consumers are willing to purchase

green products if they have to make compromises in performance, quality, or price

(Wang et al., 2008, p. 2). Another conducted research for the UK market shows that

78% of consumers state that price is a more important buying criteria than its envi-

ronmental attributes (Costa, 2010, p. 26). In general, research proves that there is a

wide gap between what consumers state about their environmental awareness and

their actual purchase decisions (Nidumolu, Prahalad, & Rangaswami, 2009, p. 57;

Michaud & Llerena, 2011, p. 411).

At the same time research indicates that consumers in industrialized economies are

not only aware of environmental issues and green products but are also willing to pay

higher prices for these products in order to maintain a cleaner and greener environ-

ment (Cherian & Jacob, 2012, p. 117; Mishra & Sharma, 2012, p. 41; Sammer &

Wüstenhagen, 2006, p. 194).

These contradictory findings regarding the consumers’ WTP for green products make

it difficult to make general and specific statements about the consumers’ WTP for

products sold in supermarkets using natural refrigerants. However, a key objective of

green marketing is to develop products that meet consumers’ needs for performance,

convenience, are affordable and have a minimal impact on the environment (Ottman,

1993, p. 125). Thus companies have to know the price a consumer is willing to pay

for a green product while achieving its environmental satisfaction. Past experiences

and research have shown that the consumers’ WTP varies between different markets

and industries. According to Michaud & Llerena (2011, p. 411) it is still an open ques-

tion “whether consumers are effectively willing to pay more for green products or not”.

Therefore the second research question is:

5

Research Question 2: “Are consumers with a positive attitude towards pur-

chasing green products willing to pay a price premium

for products from supermarkets using natural refrig-

erants?”

It seems that consumers often want to enjoy the benefits of a cleaner environment

but do not want to pay for these benefits directly (Prakash, 2002, p. 290). This gap

between attitude and action might be a result of the fact that for consumers a product

has to offer an additional benefit instead of just being ‘green’. A consumer is only

ready to pay a premium for a green product if the general performance of the product

meets or exceeds their expectations and offers some additional benefits (compared

to conventional alternatives) (Bonini & Oppenheim, 2008, p. 4) which allow to achieve

an advantage respectively additional value (Rakhsha & Majidazar, 2011, p. 757).

Former research on the consumers’ WTP for eco-labeled washing machines indi-

cates that consumers are willing to pay a price premium of about 30%, even above

the expected savings due to reduced energy or resource consumption. But the re-

search also shows that the WTP is not only linked to environmental attributes but al-

so to other product attributes such as the brand name (Sammer & Wüstenhagen,

2006, p. 194 - 196).

For marketers of green products it is consequently of high interest to know the factors

that influence a consumers green purchasing decision and WTP a price premium.

Influencing factors might be demographic factors as well as situational factors such

as the expected personal benefit of a green product (Drozdenko, Jensen, & Coelho,

2011, p. 106 - 109). Therefore the third research question is:

Research Question 3: “Which demographic factors influence consumer with

a positive environmental attitude to actually pay a

price premium for products from supermarkets using

natural refrigerants ?”

By studying the existing literature on the consumers purchase behavior and WTP for

green products, the findings differ between different product groups and cultures, re-

spectively nationalities. Studies show that environmental attitudes (Eurobarometer,

2008, p. 11) as well as the importance of price when making a purchase decision

(Eurobarometer, 2009, p. 51) differ among European nationalities. Furthermore sev-

eral international studies on the consumers environmental attitude and WTP indicate

6

significant differences in the consumers behavior among different nationalities and

cultures (Drozdenko et al., 2011, p. 112; Shen, 2012, p. 93; Rahbar & Wahid, 2010,

p. 332 ff.). Therefore the fourth research question aims to examine if differences in

the French and German consumers WTP and factors that influence the purchase

behavior of green products can be found.

Research Question 4: “Are there differences in the amount and the factors

that influence the willingness to pay a price premium

for products from supermarkets using natural refrig-

erants between French and German consumers?”

1.3. Thesis Outline

To answer the research questions formulated in chapter 1.2, this thesis will apply a

research process composed of six parts. The first chapter provides the reader with a

general introduction to the topic of green marketing and gives background infor-

mation and insights into the historical evolution of this comparably new marketing

discipline. Furthermore this chapter will discuss existing findings that lead to the for-

mulation of the research questions for the specific case examined in this thesis: natu-

ral refrigerants. The second chapter will provide the reader with a review of literature

relevant for this study. The presented literature is composed of secondary data such

as books, articles from scientific journals, magazines, press and publications from

organizations and research institutions that supported the formulation of the model

and hypotheses to be tested. In the third chapter the methodology of this study is out-

lined by presenting the selected research approach, measures and design of the

questionnaire to test the formulated hypotheses. The fourth chapter will analyze the

results of the conducted survey. In the fifth chapter, the findings and the compliance

of the hypotheses testing with existing literature will be discussed. Furthermore the

findings will be outlined with regard to the formulated research questions and implica-

tions will be highlighted. The last chapter will summarize the thesis findings and point

out limitations of the conducted study and propose fields for further research.

7

2. LITERATURE REVIEW

2.1. Definition Green marketing

Researchers agree that it is not easy to define the term of green marketing as several

meanings intersect and seem to contradict each other (Mishra & Sharma, 2012, p.

35; Khan & Khan, 2012, p. 3). Instead of green marketing the terms ecological, envi-

ronmental and sustainable marketing are also used (Mishra & Sharma, 2012, p. 35;

Peattie, 2001, p. 129). Furthermore the term of green marketing has different mean-

ings in international business, sometimes associated with organic or clear water, hu-

man rights or politics, nature or even low cost and low quality (Peattie, 1995, p. 25).

Furthermore, green marketing is a relatively young discipline, with changing interest

of research within the last decades. In order to understand and structure the devel-

opment of green marketing from its early stage, the evolution of the term green mar-

keting in literature is presented as follows:

In 1976 the American Marketing Association held its first seminar on ecological mar-

keting. In the same year the first book about environmental marketing was published

by Henion & Kinnear (1976) containing the first definition of green marketing, reading

as follows: green marketing is "the implementation of marketing programs directed at

the environmentally conscious market segment" (cited in Rakhsha & Majidazar,

2011, p. 755). According to Peattie (2001, p. 130) the 70’s marked the first stage of

green marketing. Ecological marketing used to be the predominant term in those

days. Ecological marketing focused on specific environmental problems such as pol-

lution and resource depletion, the identification of particular products, companies or

industries that caused, or that were in a position to help solve these particular envi-

ronmental problems. In these early days of the discipline the increasing ratification of

environmental and legal regulations were of main relevance for green marketing as it

was still a niche and only few consumers and companies were ready to change their

behavior in this regard (Peattie, 2001, p.130).

After its initial appearance in the 70’s, the term of green marketing re-emerged in the

late 1980’s and beginning of 90’s (Szocs, 2011, p. 254; Peattie & Crane, 2005, p.

358). This comeback was driven by incidents and discoveries such as the Bhopal

tragedy in 1984, the discovery of the Antarctic hole in the ozone layer in 1985, Cher-

nobyl in 1986 and the Exxon-Valdez oil spill in 1989. These incidents brought the po-

8

tential vulnerability of the environment, and therein human life, into the public focus

and made the environment a mainstream issue. Within that time end of the 1980’s

and beginning of 90’s the term green marketing emerged (Peattie & Crane, 2005, p.

358). According to Ghosh (2010, p. 83) green marketing had been defined by many

researchers such as Stanton and Futrell (1987), Mintu and Lozada (1983) and

Polonsky (1994). In a broad sense Stanton and Futrell, Mintu and Lozada as well as

Polonsky have in common, that they postulate that it is the marketing activities which

facilitate exchanges to satisfy consumer needs and wants by minimizing the impact

of these activities on the physical environment. In addition, the relationship between

business activities and the environment was rethought and consequently the term

sustainability came up in green thinking and marketing. As a result the focus of green

marketing shifted towards clean technologies that involved designing innovative new

products, which were to take care of pollution and waste issues (Shil, 2012, p. 76).

During that time companies realized that they can generate competitive advantages

by providing environmental excellence. Porter and van der Linde showed in a 1995

Harvard Business Review article, that “superior green solutions leads to innovation

and the creation of more efficient and effective technologies and ways to use re-

sources” (cited in Peattie, 2001, p. 133).

However with the end of the 90’s it became clear that it was not longer sufficient to

just pretend to be green. A growing number of consumers became better educated

and knowledgeable about green products which forced many companies to take

green products off the market and rethink their green communication because of the

fear to being accused of “green washing” the way they promoted their green products

(Peattie & Crane, 2005, pp. 357-358; Lane, 2013, p. 20; Ginsberg & Bloom, 2004, p.

79). Nowadays, in times of educated consumers, green marketing is entering a new

phase in which just advertising green products, services and business practices be-

comes successful and profitable (Lane, 2013, p.20). But still today there is no unique

definition of green marketing approach due to its youngness (Rakhsha & Majidazar,

2011, p. 756).

However the latest definitions indicate that the term green marketing underlines eco-

logically acceptable behavior in the producer-customer domain (Khan & Khan, 2012,

p. 3).

9

Due to the growing consumer interest in environmental issues, the classical market-

ing definition is not appropriate. Classical marketing is based on “discovering, devel-

oping, and sale of products and services whose price, quality, and characteristics will

best fulfill customer needs without taking into consideration a wider social or envi-

ronmental sensitivity” (Khan & Khan, 2012, p. 4). According to the American Market-

ing Association, green marketing has to be defined from a retailing, social marketing

and environment perspective. It can be summarized as the “marketing of products

that are presumed to be environmental friendly and the development of products de-

signed to minimize negative effects on the physical environment or to improve its

quality” as well as “the efforts by organizations to produce, promote, package and

reclaim products in a manner that is sensitive or responsive to ecological concerns”

(AMA, 2013a). Mishra & Sharma (2012, p. 35) point out that nowadays green market-

ing refers to a holistic marketing concept, which includes the production, marketing,

consumption and disposal of products and services in an environmental friendly

manner. Khan & Khan (2012, pp. 3-4) add that modern green marketing comprises

green products, green packaging, green prices and green communication. In addi-

tion, besides the classical marketing approach of developing a product that satisfies

customer needs and desires with regard to quality, price, and convenience to a high-

er extend than competitors products by having a minimum influence on the environ-

ment, green marketing also has to project a “quality image of the product as well as

the manufacturer with respect to environmental sensitivity”.

2.2. Green Marketing Mix

This thesis examines the marketing of green products in general and the WTP for

green products in detail. The consumers’ WTP for a product is influenced by various

factors, among them the product or service itself, the place where it is sold as well as

its promotion. The price has a huge influence on the WTP for a good. The WTP also

determines the price at which a product can be sold and is consequently relevant for

this thesis. Therefore and as the marketing mix concept with its 4 P’s (price, product,

place, promotion) is a core concept in classical marketing theory, its applicability for

green marketing will be presented as follows.

The marketing mix concept helps to classify marketing activities in order to be easily

memorized, systematically diagrammed and to position products in the market (D.

Kumar, Kumar, Rahman, Yadav, & Goyal, 2011, p. 62.1). The 4 P concept was es-

10

tablished 1960 by Jerome McCarthy and is still the predominant scheme to classify

marketing activities (Waterschoot & Bulte, 1992, p. 84) and is widely accepted in

marketing education (Meffert, Burmann & Kirchgeorg, 2008, p. 21). The 4 P’s are

considered to be one of the main idea in marketing (Waterschoot & Bulte, 1992, p.

83).

Nevertheless, the marketing mix concept has been criticized by several researchers

in the past as it does not take into account the human factor, lacks strategic dimen-

sions, personalization and interactivity between the four P’s (Constantinides, 2006, p.

430). Therefore research has reevaluated the usefulness of the marketing mix for

several marketing sub-disciplines. Research shows that organizations are redefining

their marketing mix to respond to the changing scenario and to position their products

in the context of sustainability and environmental friendliness (D. Kumar, Kumar,

Rahman, Yadav, & Goyal, 2011, p. 62.1).

Khan & Khan (2012, p. 5) propose that if green marketing is considered to be a form

of marketing supporting ecological issues, “each element of the marketing mix must

have a green outlook, before developing and introducing the product in the market”.

Chitra (2007, p. 173) is going one step further asking for a marketing mix that consid-

ers the environmental threats business is facing and consumers are asking for.

Therefore Chitra suggests a marketing mix which “preserves the green resources on

the one hand and delivers value added products and services to the needy on the

other hand”. To achieve that goal she proposes a green marketing mix consisting of

the traditional 4 P’s product, price, place, promotion added by 3 further P’s, namely

process, physical distribution and people with utmost concern for eco friendliness

(Chitra, 2007, p. 174).

The two P’s product and price (out of the 7 P’s of the green marketing mix proposed

by Chitra (2007)) will be further elaborated in chapters 2.3. and 2.6. as they are fun-

damental for the research purpose of this thesis. However, the remaining five P’s are

presented with regard to their role in the green marketing mix as follows:

- Green promotion comprises all forms of communication, advertising, sales,

publicity, personal sales and public relations. The focus lies on the introduction

of green products in the market, the creation of customer demand for such

products, the provision of additional information and the promotion of the com-

pany as ‘green’. In addition advertising has to be educational and create

11

awareness regarding the ecological problems the company’s products can

help to solve (Khan & Khan, 2012, p. 6). According to Chitra (2007, p. 174)

green promotion also refers to other, more internally orientated sales promo-

tion measures which also take into account eco friendly approaches with re-

gard to the utilization of material, manpower and other resources.

- Process is added to the green marketing mix as the processing of green prod-

ucts requires a special treatment in order to eliminate or reduce the impact or

creation of pollution in the manufacturing and service creation.

- Place and physical distribution refer to activities which involve the storage,

warehousing and logistics such as transportation that should not have a nega-

tive impact on the environment (Chitra, 2007, p. 174). According to Khan &

Khan (2012, p. 6) distribution is the lever with the maximum effect on the envi-

ronment and can consequently play a major role on the way of greening.

- People is an additional “P” in the proposed green marketing mix and refers to

internal and external customers associated with green marketing. Whereas in-

ternal customers are defined as employees involved in the production and de-

livery of the product or service, external customers represent the target mar-

ket. “The internal and external customers are expected to have a greater de-

gree of concern over eco friendliness in each and every aspect of production

and consumption so that the objective of green marketing will be meaningfully

fulfilled” (Chitra, 2007, p. 174).

2.3. Defining Green Products

As this thesis aims to study the marketing of green products in general and the WTP

for green products in detail, a clear definition of the term green product is required.

Researchers agree that the definition of ‘green product’ is unclear and particularly

complex. One reason is that the academic research on the green product concept

was characterized by limited interest in the past (Durif, Boivin, & Julien, 2010, p. 31).

But nowadays, according to Villano (2011, p. 52) “green is the new black”. But the

term green as well as green products have different meanings to different consum-

ers.

According to (Mishra & Sharma, 2012, p. 36) green products are characterized by a

green manufacturing process and a low environmental impact and can be defined as

12

follows: “green products are originally grown, are recyclable, reusable, and biode-

gradable, have natural ingredients, contain recycled contents, non-toxic chemical, do

not harm or pollute the environment, are not tested on animals and have eco-friendly

packaging”.

The European Commission (2013a) defines green products' as “those that use re-

sources more efficiently and cause less environmental damage along their life cycle,

from the extraction of raw materials, to their production, distribution, use, up to the

end of life (including reuse, recycling and recovery) compared to other similar prod-

ucts of the same category. 'green products' exist in any product category regardless

of being eco-labeled or marketed as green; it is their environmental performance that

defines them as 'green'”.

A further definition proposed by Wang, Chen, Hu, & Bidanda (2008, p. 1) additionally

points out the importance of health when referring to the term green product. They

propose that green products or services “have a lesser or reduced effect on human

health and the environment when compared with competing products or services that

serve the same purpose. This comparison may consider raw materials acquisition,

production, manufacturing, packaging, distribution, reuse, operation, maintenance, or

disposal of the product or service."

The three definitions have in common that they point out the lower negative impact

on the environment green products have by taking into account the whole product life

cycle. Accordingly and in line with the definitions presented, this thesis will refer to a

definition by Durif, Boivin, & Julien (2010, p. 31), who distilled the following definition

out of 35 existing green product definitions:

"A green product is a product whose design and/or attributes (and/or production

and/or strategy) uses recycling (renewable/toxic-free/biodegradable) resources and

which improves environmental impact or reduces environmental toxic damage

throughout its entire life cycle".

Applying above definition of green products to natural refrigerants, the main object of

research of this thesis, shows that natural refrigerants perfectly fit in the category of

green products. According to Greenpeace (Carbajal & Kanter, 2009, p. 16) natural

refrigerants are naturally occurring, non-synthetic substances that can be used as

cooling agents in refrigerators and air conditioners. The five main natural refrigerants

13

in use are hydrocarbons, ammonia, carbon dioxide, water and air which don’t harm

the ozone layer nor the climate.

2.4. Attitude towards the Environment and Green Products

The concept of „attitude towards” a specific object, an advertisement, a product or the

environment is well-known and often discussed in marketing, or more precisely con-

sumer behavior. Following the definition of the AMA (2013b), attitude is “a person's

overall evaluation of a concept; an affective response involving general feelings of

liking or favorability. A cognitive process involving positive or negative valences, feel-

ings, or emotions. An attitude toward an object always involves a stirred-up state a

positive or negative feeling or motivational component. It is an interrelated system of

cognition, feelings, and action tendencies.” Consequently the attitude determines

what a consumer likes or dislikes.

The attitude of a consumer is of high interest as it influences the behavior of an indi-

vidual.2 According to Chyong, H., Phang, G, Hasan, H. and Buncha, M. (2006) (cited

in Chen & Chai, 2010, p. 30) “attitudes are the most consistent explanatory factor in

predicting consumers’ willingness to pay for green products”. Therefore, and as con-

cern for environmental issues is growing, researchers have examined the influence of

“attitude towards the environment” (ATE) from different perspectives. Several studies

emphasize on attitude towards environmental issues and the awareness of green

products (Savita & Kumar, 2010, p. 90).

In a survey about the Europeans ATE, 79% of the French consumers stated that pro-

tecting the environment is important to them personally. Only 56% of the German

respondents agreed to this statement (Eurobarometer, 2008, p. 11).

A focus of research is put on the demographic, social and cultural variables which

affect the attitude of consumers towards environmental issues (Savita & Kumar,

2010, p. 90). Research seems to agree that environmental awareness and attitude

are significantly impacted by cultural and socio-economic characteristics (Drozdenko

et al., 2011, p. 108 - 109; Hamid, Ghafoor, & Shah, 2012, p. 112).

2 The actual influence of the consumers attitude on its behavior is discussed in research intensively.

There are a number of theories that have been put forth to explain the process by which attitudes pre-dict behavior (Cherian & Jacob, 2012, pp. 119 - 120). For example the functional theory of attitudes points out that attitudes “serve a function for the person” and they are “determined by person’s mo-tives” (Keelson & Polytechnic, 2012, p. 272).

14

Even if many studies found little or no relationship between demographic characteris-

tics and attitudes towards the environment and actual behavior (Schwepker &

Comwell, 1991, p. 95), the majority of research promotes the influencing characteris-

tics of demographics on environmental attitudes.

Balderjahn (1988, p. 53) showed that consumers with a positive environmental atti-

tude are more willing to buy green products and is supported by further studies.

Follows & Jobber (2000, pp. 739 - 741) found a weak but significant relationship be-

tween environmental attitude and the purchase of green products.

Studies by Brown & Haris (1992, p. 231), Tikka, Kuitunen, & Tynys (2000, p. 12) and

Aydin & Çepni (2010, p. 2718) have shown significant differences between men and

women in environmental attitudes. Women showed more positive attitudes towards

the environment compared to men. In addition women were more likely to purchase

green product because they believe green products are better for the environment

(Mainieri, Barnett, Vaidero, Unipan, & Oskamp, 1997, p. 190).

The results of a study by Datta (2011, p. 128) among Indian consumers suggest that

a strong and significant correlation exists between pro-environmental concern and

green buying behavior. Participants with a high environmental concern showed a

more positive attitude towards green products (AGP).

A 2010 study with 887 Portuguese consumers, aged over 18 indicates “that certain

environmental and demographic variables are significant in differentiating between

the ‘greener’ consumer group and the other segments” (Finisterra do Paço &

Raposo, 2010, p. 429). Their findings show that people in the age range between 25

and 34 and between 45 and 54 have a more positive environmental attitude. Further,

demographic factors with a significant and positive influence on the ATE and green

products are e.g. a high level of education, qualified jobs and high incomes

(Finisterra do Paço & Raposo, 2010, p. 435).

A study by Shobeiri, Omidvar, & Prahallada (2007, p. 28) also provided evidence of

the nationality as influencing factor. Between Iranian and Indian students exist signif-

icant differences between the level of environmental awareness and the environmen-

tal attitude. Furthermore, among the Iranian and Indian groups gender and the type

of school significantly influence the environmental awareness.

15

Another study by Straughan & Roberts (1999, p. 559) found a higher environmental

sensitivity among younger participants. The demographic variables ‘age’ and ‘sex’

showed to have a significant influence on environmental attitudes. According to their

findings income does not influence the interviewees’ ATE.

2.5. Intention to Purchase Green Products

According to Blackwell, Miniard & Engel (2006, p. 472) purchase intention can be

defined as “what consumers think they will buy”. To examine the purchase intentions

of consumers is of high importance for marketing as it allows to predict the demand

for a product and consequently to plan the production and strategy for the product.

The idea is to forecast the consumers future behavior based on their past (purchase)

behavior, even if the products previously purchased are different compared to those

future ones. Another direct approach is to ask consumers what they actually intend to

buy.

In a 2012 study on the attitude towards eco-friendly labeling found a significantly pos-

itive relationship between the consumers attitude towards eco-friendly products and

the prediction of the consumers intention to buy the green products (Purohit, 2012, p.

160).

According to a study in Switzerland, the purchase of green products is strongly facili-

tated by a positive consumer ATE (Tanner & Kast, 2003, p. 883). A study in India

showed that the majority of the respondents in a survey were aware of environmental

issues and stated that they intend to buy green products in the future (Chitra, 2007,

p. 181).

A study on the desire of consumers to purchase green products revealed a significant

influence of personal characteristics such as lifestyle, personality, environmental

knowledge and decision making on the purchase intention. Further influencing fac-

tors were product characteristics and external factors such as social interaction and

word of mouth (WOM) (Hasan, Sumarwan, & Suharjo, 2012, p. 185)

Still, the findings of several studies among different consumer groups in different re-

gions and nations vary. According to a study on Malaysian consumers by Rahbar &

Wahid (2010, p. 323) there is no significant relationship between consumers’ envi-

ronmental awareness and purchase behavior. Research in Pakistan supports the

Malaysian findings (Hamid et al., 2012, p. 112).

16

According to Young, Hwang, Mcdonald, & Oates (2010, p. 209) a gap between the

consumers’ attitude and their actual behavior can be noted. Even if 30% of consum-

ers state to be concerned about the environment, they do not translate this attitude

into purchasing behavior. This assumption is supported by a survey conducted

among Europeans (see chapter 2.4). Even if the ATE in France (79%) and Germany

(56%) is different, the same study found similar purchase intentions for green prod-

ucts in Germany and France. 77% of the French respondents, and 76% of the Ger-

mans stated that they are ready to buy green products. At the same time only 19% of

the French and 18% of the German respondents have already bought green products

(Eurobarometer, 2008, p. 28).

In addition to the attitude, the purchase intention for green products is influenced by

personal characteristics (D’Souza, Taghian, & Khosla, 2007, p.77). Consumer de-

mographics are a part of personal characteristics. demographic variables alone are

insufficient to profile green consumers, but they provide an idea of a common set of

factors that can provide useful information to marketers in describing green market

segments. An Australian study indicates that demographic variables like ‘age’, ‘gen-

der’, ‘income’ and ‘education’ are associated with the consumer’s understanding of

environmental issues and eco-labeling (D’Souza, Taghian, Lamb, et al., 2007, p.

373). These findings are supported by Soonthonsmai (2001) (cited in Chen & Chai,

2010, p. 30) as their results show positive correlations between ‘age’ and ‘income’.

As this thesis aims to examine the differences in French and German consumer atti-

tudes and WTP for green products, the demographic variable ‘nationality’ is of high

interest as well.

According to a study by Mainieri, Barnett, Vaidero, Unipan, & Oskamp (1997) (cited

in Cherian & Jacob, 2012, p. 119), it is assumed that inconsistencies in the attitude

behavior relationship result from generalized views applied by several studies. There-

fore literature recommends to measure a specific behavioral aspect and to direct the

attitudes measured to a specific environmental issue such as purchasing green prod-

ucts (Cherian & Jacob, 2012, p. 120).

This thesis will therefore focus on a specific purchasing situation and will measure

the influence of social demographics, ATE and green products as well as green

product purchase intention on the WTP for a green product.

17

2.6. Green Prices and Willingness to Pay

Even if consumer interest in green products is constantly growing, environmentally

preferable products do not necessarily result in a wide customers’ acceptance or

market share. Compared with competing products or services, green products face

several challenges even if they perform well. An important factor are the higher pro-

duction cost of green products due to higher cost for green raw materials, equipment,

production processes, and recycling leading to a heavy green price premium (Wang

et al., 2008, p. 2; Tiwari, Tripathi, Srivastava, & Yadav, 2011, p. 22). The American

Marketing Association defines price as “the formal ratio that indicates the quantities

of money goods or services needed to acquire a given quantity of goods or services”

(AMA, 2013c) and is an important variable of the (green) marketing mix presented in

chapter 2.3.

According to survey by the European Commission (Eurobarometer, 2009, p. 51) the

importance of price when making a purchase decision differs between European

consumers. For the target markets of this thesis, France and Germany, the im-

portance of price is relatively high. 48,5% of the German, and 47,8 % of the French

respondents state that price is an important purchase criteria. For 40,4% of the

French and 36,9% of the Germans respondents price is a very important criteria for

making purchasing decisions. Only about 2% of both countries’ respondents state

that price is not an important decision criteria.

As price is in general an important purchasing criteria for French and German con-

sumers, the actual willingness of a consumer to pay a certain price in order to acquire

a product or service is of high interest. The consumers’ WTP can be defined as the

maximum price a given consumer accepts to pay for a product or service (Le Gall-

Ely, 2009, p. 92).

Figure 1 shows that when making a purchase decision, the consumer has a refer-

ence price (RP) margin in mind, against which he compares the price of a product or

service offered. The RP enables the consumer or buyer to judge whether a proposed

offer at a given price is good or bad. The WTP on the other hand allows the consum-

er to express, in monetary terms, the perceived value he gains, or he will gain in the

future, by buying a product or service at a price above his reference price (Le Gall-

Ely, 2009, pp. 93 - 94). Kotler, Keller & Bliemel (2009, p. 590) point out as well, that

price and quality determine the perceived additional value for the consumer. Conse-

18

quently the price for a (green) product or service has to reflect its quality perfor-

mance.

Figure 1: Willingness to Pay (WTP)

Source: El-Gally, 2009, p. 95

To measure the consumers’ WTP is useful for several reasons. The WTP informs

directly about how much some goods or services are valued. Consequently, it pro-

vides indications for the pricing of these goods or services (Breidert, Hahsler, &

Reutterer, 2006, p. 1). In addition the WTP allows relative comparisons as well as

rankings of the desirability of goods and services (Hole & Kolstad, 2011, p. 446). The

Organisation for Economic Co-operation and Development (OECD) defines WTP as

“The stated price that an individual would accept to pay for avoiding the loss or the

diminution of an environmental service” (OECD, 2010). This definition reflects the

motivation of consumers to pay for a green product expecting an additional value in

terms of environmental safety. Consequently it is of high interest for the marketers of

green products like natural refrigerants, to know if consumers are willing to pay for

green products in general and to understand the motivation for their decision to buy

or to refuse green products.

A recent study by GfK about the green buying behavior of consumers in the United

States reveals, that even if the environmental awareness and green behavior of the

is wide spread, the WTP a premium for green products is decreasing. According to

the authors of the study, the reason for the decreasing WTP is that “while terms like

organic and recyclable have strong positive resonance, they are often associated

with higher prices. Understanding consumers’ triggers and the limits of their commit-

19

ment to green action is essential for marketers and researchers alike.” (GfK, 2012, p.

2).

The majority of studies, however, found a significantly higher WTP for green products

among consumers with high environmental awareness and interest in green prod-

ucts.

A study by Drozdenko et al.(2011, p. 110) among 398 home owners in the US

showed that consumers are willing to pay for green products. The willingness of con-

sumers to pay a price premium for green products cannot be generalized but de-

pends on the product category. Demographic variables such as ‘gender’ and ‘in-

come’, the consumers reference price for a certain product category as well as the

perceived additional benefit from purchasing a certain green product.

According to a study among Chinese consumers by Shen (2012, p. 93) the WTP for

green products is significantly higher for those consumers who regard environmental

conservation as important and who believe that purchasing green products is good

for the environment. The results of the study furthermore indicate that socio-

demographic characteristics such as income, gender, age and education are im-

portant factors that influence the WTP of consumers. Younger consumers in China,

with a high education and income seem to be more willing to pay for green products.

The study further reveals that the WTP varies among different product groups. In ad-

dition, the author highlights the importance of further research as the results might

depend on the cultural background of the participating consumers.

A study on the WTP of French consumers for roses that have a reduced carbon foot-

print showed that consumers were willing to pay an average price premium of 4,09€

compared to the mean price of 1,40€ for a rose with normal carbon footprint

(Michaud, Llerena, & Joly, 2013, p. 323). The results show “that consumers value

positively the environmental characteristics of a private good even when they do not

benefit from associated private advantages” (Michaud et al., 2013, p. 324).

Plassmann, Hamm, & Sahm (2009, p. 331) showed that the price is not a key buying

criteria for German consumers of green food products. Their results indicate that

consumers are willing to pay a price premium of 45% for green food compared to

conventional products.

20

2.7. Green Pricing Strategies

A growing majority of consumers is interested in green products (Ottmann, 2011, p.

9). However, as the price is still a main purchase criteria for the majority of consum-

ers (Eurobarometer, 2009, p. 51) the pricing strategies for green products have to

attract green consumers on the one hand and compete with ordinary products on the

other (Wang et al., 2008, p. 3). In general it has to be pointed out that the pricing of

green products is a relatively new area of research and the factors that influence the

consumers’ WTP are not well known. Nevertheless, research seems to agree that

generalizations of the pricing of green products are not satisfying but have to be

made on an industry level or even a product category perspective. Therefore it is of

high importance for the marketers of natural refrigerant applications to gain

knowledge and understand the consumer behavior influencing the decision to buy

this kind of green product category (Drozdenko et al., 2011, p. 106) and to know if

the target customers can afford premiums (Tiwari et al., 2011, p. 22). This knowledge

is of high importance as pricing mistakes can have a negative impact on consumers’

responses and can harm firms tremendously. The degree to which consumer react to

pricing mistakes depend on customer characteristics and situational factors (Rohani

& Nazari, 2012, p. 152).

As already mentioned in chapter 2.5. of this thesis, the price of a product has to refer

to its performance and value for the consumer or in other words reflect its price-

performance ratio. According to Simon & Fassnacht (2009, p. 34) there is an ideal-

typical path illustrating the low price, medium price and premium price positions (fig-

ure 3) reflecting the perceived relative performance and price of a product or service.

Deviations of this ideal-typical path lead to a position of advantage (high perceived

relative performance / low perceived relative price) or a position of overreaching (low

perceived relative performance / high perceived relative price). The decisive factor for

the positioning is the perception of the consumer.

21

Figure 2: Ideal-Typical Price Positions

Source: own figure based on Simon & Fassnacht, 2009, p. 34

The position of natural refrigerants in the matrix (figure 2) is not clear so far, but re-

search indicates that the prices for green products in general are perceived as pre-

mium priced as these products cost more due to disadvantages in economies of

scale and use of higher-quality components (Cabinet Maker, 2008, p. 20; Mishra &

Sharma, 2012, p. 37; Straughan & Roberts, 1999, p. 560) and are therefore posi-

tioned to serve niche markets (Peattie & Crane, 2005, p. 362). Bybee (2010, p. 85)

points out that “the perception of green being more expensive is a major downfall in

persuading consumers to switch to environmentally-friendly buying behavior”. Mar-

keters therefore have to know the perceived performance from a consumer point of

view and align the price with the benefits their products are offering (Bala & Green,

2007, p. 22) in order to avoid being perceived as expensive.

A further challenge for marketers of green products is to avoid making the price, de-

spite its importance for consumer decisions, the main decision criteria. Instead con-

sumers should consider the value of an offering in terms of quality and personal rele-

vance (Bertini & Wathieu, 2010, p. 86). Marketers have to identify optimal pricing

strategies in order to sway consumers’ buying behavior from standard to environ-

mentally-friendly products. In order to achieve this goal it is appropriate to change

from an initial cost approach to a life cycle cost approach when pricing environmen-

22

tally-friendly products. A life cycle cost approach, which has to be clearly communi-

cated to the consumers, might show that green products are not necessarily more

expensive than standard products and consumers do not have to make a choice be-

tween being economical or being green. Positive attributes of green products that are

not always calculated into their price are that, among other benefits, they last longer,

are lowering usage or consumption and increase efficiency, (Bybee, 2010, p. 85). In

addition Dhanda & Murphy (2011, p. 42) point out that transparency in the pricing of

green products is very important as it is highly valued by consumers.

2.8. Model and Hypothesis Definition

Following the existing research on the consumers’ WTP for green products, a model

(figure 3) which explains the consumers WTP was established.

Figure 3: Model and Hypotheses

Source: own figure

Research agrees (see chapter 2.4) that demographic variables ‘gender’, ‘income’,

‘education’ and ‘age’ have an influence on an individual’s ATE (Straughan and Rob-

erts 1999, p. 559; Finisterra do Paço & Raposo, 2010, p. 435; Shobeiri, Omidvar, &

Prahallada, 2007, p. 28; Drozdenko et al., 2011, p. 108 - 109; Hamid, Ghafoor, &

Shah, 2012, p. 112). The effect of these demographic variables varies between the

product category and the country in which the study is conducted. Therefore, and as

a main objective of this thesis is to compare the WTP for green products among

German and French consumers, the demographic variable ‘nationality’ was included

in the model.

23

Consequently, and on the basis of the desk research conducted, the first hypotheses

established are:

H1a: There is a significant and positive relationship between gender and attitude

towards the environment

H1b: There is a significant and positive relationship between income and attitude

towards the environment

H1c: There is a significant and positive relationship between age and attitude to-

wards the environment

H1d: There is a significant and positive relationship between nationality and attitude

towards the environment

H1e: There is a significant and positive relationship between education and attitude

towards the environment

Furthermore, existing studies find significant relationships between consumer ATE

and AGP (Savita & Kumar, 2010, p. 90;). Following these findings, this thesis aims to

examine if a positive relationship between consumers attitude towards environment

and green products exits. Therefore the following hypothesis (H2) was established:

H2: There is a significant and positive relationship between attitude towards the

environment and attitude towards green products

As presented in chapter 2.5, several studies revealed that consumers with a positive

AGP are more likely to purchase these products (Purohit, 2012, p. 160; Tanner &

Kast, 2003, p. 883) Nevertheless various studies also found significant differences in

the environmental attitude of consumers and their AGP and actual purchase inten-

tion. To examine whether this attitude behavior gap can be found among German

and French consumers for the given purchase decision tested in this thesis, hypothe-

sis 3 was established:

H3: A significant and positive relationship exists between attitude towards green

products and the intention to purchase green products

Various studies on the consumers WTP a price premium for green products found a

higher WTP among consumers with a positive environmental attitude, interest in

green products and a general intention to purchase green products (Drozdenko et al.

2011, p. 110; Shen 2012, p. 93). In order to test whether consumers with the inten-

24

tion to purchase green products are actually willing to pay a premium price, the fol-

lowing hypothesis is established:

H4: There is a significant and positive relationship between the intention to pur-

chase green products and the WTP a price premium for green products.

As the presented, existing studies already revealed that the presented constructs on

the ATE and green products as well as the purchase intention interact with each oth-

er.

Even if some studies found no significant relationship between the environmental

attitude of consumers and their intention to purchase green products (Rahbar &

Wahid 2010, p. 323; Hamid et al., 2012, p. 112; Young, Hwang, Mcdonald, & Oates

2010, p. 209), hypothesis 5 is tested, following some contrasting studies that found

significant relationships between the environmental attitude of consumers and their

intention to purchase green products (Chitra, 2007, p. 181) and their WTP

(Balderjahn, 1988, p. 53).

H5: There is a significant and positive relationship between the attitude towards

the environment and the intention to purchase green products

In order to test if a relationships exists between the environmental attitude and the

WTP a premium for green products, hypothesis 6 was formulated and will be tested.

H6: There is a significant and positive relationship between the attitude towards

the environment and the WTP a price premium for green products

Furthermore a relationship between attitude towards the environment and WTP is

assumed. In order to verify this assumption, hypothesis 7 will be verified.

H7: There is a significant and positive relationship between the attitude towards

the environment and the WTP a price premium for green products.

As presented in chapter 2.6, socio-demographic variables influence the understand-

ing and the AGP (D’Souza, Taghian, Lamb, et al., 2007, p. 373). To test these find-

ings for the proposed model in this thesis, the following hypothesis was formulated:

H8a-e: There is a significant and positive relationship between the demograph-

ic variables (gender, income, age, nationality, education) and the atti-

tude towards green products.

25

Furthermore existing research indicates a significant influence of demographic varia-

bles on the intention to purchase green products (Hasan, Sumarwan, & Suharjo,

2012, p. 185;D’Souza, Taghian, & Khosla, 2007, p.77). Following these findings and

in order to test the proposed model, hypothesis 9 is established.

H9a-e: There is a significant and positive relationship between the demograph-

ic variables (gender, income, age, nationality, education) and the inten-

tion to purchase green products.

In order to test if the demographic variables gender, age, nationality, income and ed-

ucation have an influence on the consumers WTP a premium for green products, hy-

pothesis 10 was established.

H10a-e: There is a significant and positive relationship between the demograph-

ic variables (gender, income, age, nationality, education) and the WTP

a price premium for green products.

26

3. METHODOLOGY

3.1. Research Approach

The aim of this thesis is to compare the French and German consumers’ WTP a price

premium for products sold in ‘green supermarkets’ using refrigeration systems based

on natural refrigerants. To answer the research questions as formulated in chapter

1.2. of this thesis, an inductive or deductive research approach has to be chosen

(Saunders, Levis & Tornhill, 2007, p. 124). By following an inductive approach, the

research builds on the general theory of obtaining and analyzing data in order to

build a new theory. This approach is appropriate for qualitative research (Saunders et

al., 2007, p. 126). A deductive research approach is based on existing knowledge

and theories in the research topic. The existing theories are applied to the specific

research question in order to formulate a hypothesis. Within the specific research, the

formulated hypothesis is confirmed or rejected (Bryman & Bell, 2011, p.11-12). This

thesis follows a deductive approach applying the existing theories on consumers’

WTP for green products to the specific comparison of the French and German con-

sumers’ WTP for products from supermarkets using a green refrigeration system.

According to Robson (2002) (cited in Saunders et al., 2007, p. 124 - 125) there are

five steps to describe the deductive research process:

(1) Deducting a hypothesis (or hypotheses) from the theory

(2) Expressing the hypothesis in operational terms which propose a relationship

between two specific concepts or variables

(3) Testing this operational hypothesis

(4) Examining the specific outcome of the inquiry (it will either tend to confirm the

theory or indicate the need for its modification)

(5) Modifying (if necessary) the theory according to the findings

As this thesis will use a deductive approach to test the formulated hypotheses de-

rived from the established theory, several important criteria that characterize this ap-

proach will be presented in the following. To begin with, it is important to highlight that

a deductive approach aims to explain causal relationships between variables. To test

the relationship between two variables, formulated in a hypothesis, quantitative data

will be collected. Saunders et al (2007, p. 488) point out that a deductive approach

might also use qualitative data, but to apply a quantitative research method is more

27

common. This is due to the fact that a qualitative research method focuses on words

rather than numbers as well as on specific situations or people and concerns the un-

derstanding and meaning of phenomena. The focus of a quantitative method is on

collecting numerical data to explain a theory that wants to be observed. As this thesis

aims to research the consumers’ purchasing behavior, or more precisely, the con-

sumers’ WTP, a quantitative strategy is more appropriate as it allows to include a

large sample size. The amount of data collected within a deductive approach, using

quantitative data, is usually larger due to the higher number of respondents involved

and the results are usually derived from a numerical calculation (Wiid and Diggines,

2009, p. 86). To collect a large quantity of data ensures a high validity and reliability

of the results. Furthermore it is of high importance to operationalize concepts to en-

sure the measurability of facts quantitatively. As problems can be better understood if

they are reduced to the most simple elements, reductionism is a further important

characteristic. Finally, it is important to select samples of sufficient numerical size.

This will allow to make generalizations about human social behavior (Saunders et al.,

2007, p. 125).

3.2. Methods to Measure the Consumers’ Willingness to Pay

To accurately estimate consumers’ WTP for a product or service is critical for devel-

oping new products, conducting value audits and formulating as well as implementing

competitive product and pricing strategies (Miller, Hofstetter, Krohmer, & Zhang,

2012, p. 172).

According to Breidert, Hahsler & Reutterer (2006, p. 3) several methods to measure

WTP exist. The authors differentiate the different approaches in two main categories,

namely revealed and stated preferences. Revealed preferences, obtained from price

responses, can be examined through market data or executing experiments, such as

laboratory or field experiments as well as auctions. Stated preferences, are prefer-

ence data derived from surveys and can be classified in direct surveys such as ex-

pert judgments or customer surveys as well as indirect surveys. Indirect surveys ap-

ply methods like conjoint-analysis or direct choice analysis. Miller et al. (2012, p. 173)

distinguish the several approaches to measure the WTP between those that measure

the WTP directly or indirectly and whether they determine consumers actual WTP or

hypothetical WTP. By applying a direct approach, the consumer is asked directly to

state its WTP for a specific product by using for example open ended questions. The

28

indirect approach, for example a choice-based conjoint analysis, calculates the WTP

on the basis of consumers’ choices among several product alternatives and a none

choice option.

To measure the WTP, several concepts were taken into consideration, namely open

ended questions, dichotomous choice and bidding games. Payment cards are a fur-

ther method, but are more appropriate for face-to-face surveys (London Economics,

2011, p.8). The author of this thesis decided to select the contingent valuation meth-

od (CVM) as appropriate tool to measure the consumers’ WTP for natural refrigerant

technologies. The CVM is a direct survey method measuring the consumers hypo-

thetical preferences in terms of the amount they are willing to pay for the specific

benefits of a product. The method also allows to measure the non-technology-related

attributes of a product, focusing exclusively on its environmental attributes. In addi-

tion the CVM allows to assess the (monetary) value of a product, by framing the WTP

question in the context of a contingent (hypothetical) market or purchase situation for

the product (Sadri, Mackeigan, Leiter, & Einarson, 2005, p. 1217).

The CVM is an evaluation technique based on a questionnaire, measuring the con-

sumers WTP or willingness to avoid (WTA) with respect to a specific good. Originally

CVM has been used to measure the value of non-marketed goods such as environ-

mental goods or resources. By following this approach a hypothetical market situation

is defined and the respondents are asked to specify their WTP’s (or WTA’s). In the

following the focus is put on CVM in relation to eliciting monetary preferences (Hovat,

1999, p. 4). The approach allows to examine the value obtained by reducing green

house gas emissions as a result of purchasing and using a green product.

In general a CVM study can be undertaken face-to-face, by telephone or e-mail

(Homburg, 2012, p. 264). This thesis is using e-mail as survey method as it offers the

advantage of spreading the survey to an unrestricted extent with regard to place and

time and consequently to reach a high number of potential respondents. In addition

the cost for an online survey are comparably low. Even if research indicates that the

mean WTP in internet surveys is slightly lower compared to face-to-face surveys,

internet surveys can be seen as reliable (Lindhjem & Navrud, 2011, p. 1634).

The executed CVM in this thesis follows the approach proposed by Holvat (1999, p.

4), starting with providing the participants with information about the environmental

resource as well as the product in focus. Natural refrigerants offer the main ad-

29

vantage to reduce energy consumption and green house gas emissions dramatically.

The idea of this approach is, that the information provided familiarize the respondents

about the product to be evaluated. This will increase the possibility for valid and relia-

ble answers regarding the consumers WTP and allows to answer the formulated re-

search question 2 to a satisfying extend: “Are consumers with a positive attitude to-

wards purchasing green products willing to pay a price premium for products from

supermarkets using natural refrigerants?”

In addition a series of follow up questions are asked to the participants of the survey.

This allows to confirm the given WTP and provides socio-economic background in-

formation, used to examine their relationship with the stated WTP. This additional

information about the consumers’ background are required to answer research ques-

tion 3: “Which factors influence consumer with a positive environmental attitude to

actually pay a price premium for products from supermarkets using natural refriger-

ants ?”, and research question 4: “Are there differences in the amount and the factors

that influence the willingness to pay a price premium for products from supermarkets

using natural refrigerants between French and German consumers?”

As research generally indicates that consumers with a positive ATE tend to have a

higher intention to buy green products, it is of interest to examine the environmental

attitude of French and German consumers as well. Research also seems to agree

that environmental awareness and attitude are significantly impacted by cultural and

socio-economic characteristics (Drozdenko et al., 2011, p. 108 - 109; Hamid,

Ghafoor, & Shah, 2012, p. 112). Therefore a couple of questions regarding the con-

sumers ATE in general and towards green products in detail will be asked before the

CVM questions. These question will allow to answer the following research question

1: “Are consumers with an environmental friendly attitude ready to buy green prod-

ucts?”.

The key outcome of the CVM survey is an estimate of the mean or median WTP

across the sample of people surveyed. This result can be obtained from an analysis

of descriptive statistics. The mean and median have quite different implications: The

mean is more appropriate if the WTP analysis targets for example a cost benefit

analysis in order to analyze if the mean benefits are higher than mean cost. The me-

dian provides the WTP amount for the majority of individuals (London Economics,

2011, p. 14).

30

If the sample of respondents is representative of the target population, this estimate

can be aggregated to obtain an estimate for the total population. In addition to de-

scriptive statistics, correlations and bivariate regression analysis of the respondents’

bid function are also undertaken. The regression analysis helps to fully understand

the relationship between WTP values and socio-economic, demographic and other

variables. The bid function can be estimated using either probit, logit or tobit regres-

sion models (London Economics, 2011, p.14).

3.3. Critics of the Methods to Measure WTP

The main criticism to be found in research when measuring WTP targets the gap be-

tween stated intentions and actual behavior when it comes to a purchase decision.

One main problem identified lies in the way social desirability (SD) bias distorts

measures of consumers’ intentions (Carrington, Neville, & Whitwell, 2010, p. 140).

Research shows that the phenomena of social desirability can influence WTP re-

sponses in CWM and has consequently be considered (Laughland, Musser, &

Musser, 1994, p. 29). Social desirability can be defined as a meaningful, goal-

directed behavior aimed at obtaining the approval of others, or in other words “a ten-

dency to give a favorable picture of oneself”, leading to a tendency to give answers

that make the respondents of a survey look good (Laughland et al., 1994, p. 29 - 30).

The problem of the social desirability bias can be addressed and potentially solved by

guaranteeing confidentiality to the participants (Sandhu, Ozanne, Smallman, &

Cullen, 2010, p. 358). Participants tend to feel that their anonymity is better safe-

guarded in an online survey rather than in face-to-face, and telephone interviews or

identifiable questionnaires (Laughland et al., 1994, p. 35).

Furthermore direct approaches, like the CVM, are criticized for generating less relia-

ble results than indirect approaches. Indirect approaches that can be applied are bid-

ding games and dichotomous choice methods. Within bidding games the participant

is asked whether he would pay a certain amount for a product. If the answer is “yes”,

another question with a higher price is stated until the participant answers with “no”.

Dichotomous choice formats are comprised of two questions. Following this ap-

proach, the first question refers to whether or not the consumer is willing to contribute

to a decrease in greenhouse gas emission) by buying a green product alternative. If

this question is answered with “yes”, then a second question is asked whether the

WTP is equal to an amount specified.

31

However, neither a direct nor an indirect method to measure WTP is fully proofed.

According to Miller et al. (2012, p.173), many studies have shown that both ap-

proaches can generate inaccurate results, as both measure consumers’ hypothetical,

rather than actual WTP. Consequently a hypothetical bias is given which describes

that no consequences are associated with the individual’s response. A hypothetical

bias is also found for the applied CVM in this thesis, as the CVM is hypothetical in

both payment and provision of the good. Consequently the measured WTP reflects a

stated WTP but no actual WTP (Barnard & Mitra, 2010).

3.4. Survey Design

To test the hypotheses formulated in chapter 2.7, an online survey was selected as

the adequate tool. A survey strategy is commonly associated with the deductive ap-

proach proposed in chapter 3.1. It allows to answer to questions of ‘who’, ‘what’,

‘where’, ‘how much’ and ‘how many’. In addition, a survey allows to collect a large

amount of quantitative data which is easily comparable as a standardized question-

naire is used. By applying descriptive and inferential statistics, the obtained data can

be analyzed and allows to examine correlations between variables (Saunders et al,

2007, p. 144).

The survey outline was developed only after the model and hypothesis had been

formulated. As the number of questions in an (online) questionnaire is limited, so as

to not test the participants’ patience, the survey focuses on a number of key question

(Saunders et al, 2007, p. 144 – 145). Therefore and in line with the proposed re-

search model (figure 3) the survey consists of six sets of questions:

Socio-economic characteristics

Socio-economic characteristics of the participants such as ‘gender’, ‘age’, ‘nationali-

ty’, ‘education’ and ‘income’ are essential to draw conclusions on the individual fac-

tors that influence the consumes’ ATE, AGP, their purchase intention and WTP for

these products. As questions about income’, age and education might be seen as

very personal it is proposed to state these questions at the end of the survey. This is

due to the fact that participants seem to be more tense and anxious at the beginning

of the survey and may try to find excuses for not continuing the survey (Mitchell &

Jolley, 2012, p. 308).

32

Social desirability questions

To measure the impact of social desirability on interviewee behavior, six items based

on the proposed scale by Kemper, Beierlein, Bensch, Kovaleva, & Rammstedt (2012,

pp. 25 -26) were chosen. The six items (appendix 1) measure the gamma factor of

social desirability and are based on the social desirability scale proposed by Stöber

(1999) and Stöber & Wolfradt (2001). Respondents have to answer questions such

as “It has happened that I have taken advantage of someone in the past” or “In an

argument, I always remain objective and stick to the facts.” The original scale by

Stöber asks the respondents to state whether an item is either “right” or “wrong”. In

this way results can be labeled either ‘1’ or ‘0’. For the items used in this thesis, a

point scale was chosen so as to label the participants’ responses from 1 to 6

(Kemper et al., 2012, pp. 25 -26; Tran, Stieger, & Voracek, 2012, p. 872). For the

items 2, 4 and 6 the following scale was applied: ‘6’ for "applies completely" and ‘1’

for “does not apply at all”. The items 1, 3 and 5 were coded inversely, meaning that

‘6’ was coded “does not apply at all” and ‘1’ "applies completely".

Attitude towards the Environment

The majority of questionnaires start with an introduction of the research topic. There-

fore the survey designed for the purpose of this thesis begins (after social desirability

questions) with general questions on the participant’s attitude towards the environ-

ment (Fink, 2006, p. 32). The answers to these questions will help to test hypothesis

2, which states that a positive attitude towards environmental issues influences the

AGP. The participants of the survey were asked to rate eight items regarding their

ATE, with a multiple-item scale as given response option to express their level of

agreement (appendix 1). Items such as “A clean environment provides me with better

opportunities for recreation” (Ryan & Spash, 2008, p. 29), “Environmental problems

have a direct effect on my daily life” (Eurobarometer, 2008, p. 13) or “The increasing

deterioration of the environment is a serious problem” (Chen & Chai, 2010, p. 38)

were selected based on recent studies on environmental concerns3 and attitudes to-

wards the environment.

Compared with a single scale, the multiple-item scale offers the advantages of im-

proved validity, improved reliability, increased level of measurement as well as in-

3 According to Takács-Sánta (2007, p. 26) environmental concern can be considered as an environ-

mental attitude. Consequently items regarding environmental concern reflect the attitude towards the environment as well.

33

creased efficiency in data handling. As scaling method a Likert scale was selected.

The Likert scale consists of a series of statements, with each statement followed by a

number of response alternatives ranging from “strongly agree” to “strongly disagree”

(Monette, Sullivan & DeJong, 2011, p. 350). The Likert scale was selected as it is an

appropriate instrument to measure attitudes (DeVellis, 2012, p. 93). The number of

response alternatives was selected to be 6. Research indicates that 5 – 7 is the most

common number of alternatives. 6 offers the advantage to be an even number avoid-

ing a middle scale position that occurs when five response alternatives can be se-

lected. Participants tend to have a tendency towards the middle as it is seen as neu-

tral (Molhotra, 2006, p. 88). The challenge when formulating statements (items) in the

framework of a survey is to ensure that it is formulated in a way that it retrieves in-

formation on an attitude and that it is done without being overly mild or overly ex-

treme (DeVellis, 2012, p. 94). Furthermore, negatively formulated questions were

reversed in coding.

Attitude Towards Green Products

In order to test hypotheses 2 and 3 of the proposed model to explain the consumer’s

WTP for green products, the AGP, was measured by nine items (appendix 1). The

selected items measure the respondent’s evaluation of the environmental perfor-

mance of green products e.g. “green products are good for the environment”, their

general performance “green products are of good quality/high performance” and their

performance compared to conventional products “green products are of better quali-

ty/have a better performance than conventional products”. Furthermore the respond-

ents were asked to rate green products with regard to whether they “are reasonably

priced”. To predict the consumer’s evaluation of the environmental impact of green

products, they were asked to rate items e.g. as follows: “green products will improve

the standard of living of future generations”. The response options were given on a 6

point Likert scale between “strongly agree” and “strongly disagree”.

Intention to Purchase Green Products

As the intention to purchase green products is assumed to be influenced by a posi-

tive ATE and AGP, and furthermore to have an influence on the consumers WTP,

eight items to measure the intention to purchase green products were added to the

questionnaire. As was accomplished for other questions on the consumers attitude,

the respondents were asked to evaluate the presented items on a six point scale be-

34

tween “strongly agree” and “strongly disagree”. The items focused on the consumer’s

general attitude towards the purchase of green products by way of the following

question “buying green products generally benefits the consumer” The short term

intention to purchase a green product was addressed as follows: “It is very likely that

I will purchase a green product within the next weeks”. Furthermore consumer con-

cerns about the price were taken into consideration for measuring the purchase in-

tention. The item “Green products would probably be too expensive for me” was cod-

ed in reverse. All items are found in appendix 1.

Consumers Willingness to Pay for Green Products

To measure the WTP, the CVM method was chosen as an appropriate tool. To this

end participants were asked to read information about natural refrigerants and their

impact on the environmental performance of a supermarket. In the following the re-

spondents were requested to participate in a hypothetical purchasing situation. It was

assumed that respondents make a regular purchase of 10€, in the supermarket of

their choice. It is of high importance to consider that the way questions about prices

are asked may influence the answers given. Some research indicates that asking

respondents a single, direct and open-ended question might not produce reliable an-

swers. Instead, it is better to state explicit price questions and ask respondents to

answer “yes”, “no” or “don’t know” (Auger & Devinney 2007, p. 378). In line with this

approach, by referring to the provided information on the environmental performance

of natural refrigerants, the respondents were asked to state, if they are generally will-

ing to pay a price premium for this purchase if the supermarket is using natural re-

frigerants. If the response was “No” they were asked to state the reason for their un-

willingness to pay a premium. If they indicated a WTP, they were asked to select the

amount in a dropdown list between 10€ (base price) to 20€ or more (0,25€ scaling).

In a follow up question, the respondents had to indicate the premium they are willing

to pay in percent.

To overcome the major sources of bias associated with WTP surveys, the guidelines

proposed by Fleischman Foreit & Foreit( 2004, pp. 4-5) were furthermore taken into

account. Respondents were reminded that the price increment reduces other con-

sumption and that substitutes exist for the product in question. As the level of influ-

ence (social desirability bias) may vary depending on the specific situation and con-

text, such as the product type or the purchasing situation, it is of high importance to

35

define the context of the simulated purchase decision when formulating the survey

questions. Research also indicates a higher WTP for commodity products when inci-

dental prices are displayed. However, as the aim of this study is to examine the WTP

for green products explicitly, the prices to be selected in the questionnaire showed

total values (Nunes & Boatwright, 2004, p. 465). In addition, the order of questions

was considered and chosen in a way that the respondents were not biased into nam-

ing a higher or lower maximum price than they are truly willing to pay (Fleischman

Foreit & Foreit, 2004, p. 5).

3.5. Data Collection

Before launching the final survey, a pretest was conducted in order to ensure that the

designed survey is accessible for participants not familiar with the survey topic. Fur-

thermore the relevance of the selected items and questions was secured by conduct-

ing a pretest. For the pretest, 14 individuals were asked to participate in the survey.

According to pretest participants, the questions and items were understandable. Fur-

thermore the obtained data seemed to be appropriate for the hypotheses analysis.

Therefore, no changes in the survey and question design were undertaken after the

pretest.

According to Brymen & Bell (2011, p. 175), the selection of the sampling method is

an important part in the research process, if a quantitative approach is applied. In

theory, probability and non-probability sampling are common methods. Due to a lack

of time, to avoid cost and as an online survey was selected as survey tool, non-

probability was chosen as appropriate sampling method. By applying this method,

individuals have not the same chance to be selected as they are not chosen random-

ly.

In theory, the population of the sample is comprised of respondents holding a Ger-

man or French nationality. However, as the aim of the thesis is to analyze the WTP of

consumers, it might be appropriate to define the population as Germans and French

older than15 years.

The final questionnaire was sent via email to a mailing list comprising all students at

the University of Strasbourg and the University of Erlangen-Nuremberg. In addition

the link to the survey was distributed via social networks and by private email. The

survey was online for two weeks from 2 July 2013 until 16 July 2013.

36

4. RESULTS

4.1. Demographic Data

The collected data set, generated by the conducted survey, contains 1.103 observa-

tions N. Each observation represents one survey participant. 857 individuals in the

sample are French (77,7%), 188 (17,0%) German and 54 (5,3%) have another na-

tionality (appendix 4). As this thesis aims to compare the WTP for green products

between German and French consumers, the nationality of the participants is the de-

fining factor. Therefore the socio-demographic characteristics will be analyzed in the

following with a focus on French and German participants respectively.

Gender

A majority of 805 (73,0%) out of 1.103 survey respondents are female. A cross tabu-

lation furthermore shows that in the French sample 74,4% of participants are female.

The share of female respondents in the German sample with 66,5% is slightly small-

er. Consequently, with 638 individuals (57,8%), French women represent the largest

group in the sample as a whole. Male Germans only account for 5,7% of the total

sample (appendix 4).

Age

807 (73,2%) of sample respondents belong to the age range from 15 – 24. The se-

cond largest age group (25 – 34) is comprised of 248 (22,5%) individuals. 28 (2,5%)

of the respondents are aged between 35 – 44 and twelve (1,1%) between 45 – 54.

Five (0,5 %) respondents are aged between 55 – 64 and merely three (0,3%) re-

spondents are older than 65. The predominance of responses received from inter-

viewees in the age groups from 15-24 and 25-34 is linked to the author’s strong net-

work among German and French university students with whom a request for partici-

pation in the survey developed for this thesis was shared.

A comparison of the respondents’ age between the French and German samples

reveals differences in the age of the observed nationalities. In the French sample,

more than three quarters (78,6%) of participants are aged between 15 – 24. Even if

the majority of the German sample is found in this age range as well, with 56,4%, the

share is significantly lower .The age range from 25 – 34 covers 17,2% of the French

sample and 38,8% of the German sample. As the survey was mainly conducted

among students in Germany and France, the results support the notion that French

37

students tend to be younger than German students. The distribution of older partici-

pants is very similar for both the French and German sample. In both cases they

account for less than 4% of total participants. The majority (94,9%) of ‘Other’ (nation-

alities) is found in the age range from 15 to 34 (appendix 4).

Education

The majority, 40,5% (447), of survey participants holds or is in the course of obtaining

a Master degree. 187 (17,0%) of individuals in the data set have at least a Bachelor

degree. 184 (16,7%) of the respondents state that A-levels is currently their highest

education. 104 (9,4%) hold a PhD and 30 (2,7%) have accomplished a vocational

training. 145 individuals (13,1%) stated ‘Other’ for education. This high number is

explained by the fact that students of engineering and medicine tended to state their

education separately, in ‘Other’, and not within the given more defined response op-

tions.

A comparison between the German and French samples has to also take into ac-

count the different education systems that might lead to different distributions with

respect to the highest education achieved. In France the Bachelor degree can often

be obtained as part of the Master degree program and respondents may thus not list

‘Bachelor’ as a separate degree. The majority of students in the French sample

(44,3%) stated ‘Master degree’ as their highest educational degree. In comparison,

the number of individuals with a Master degree is much smaller in the German sam-

ple (25,1%). The majority of participants in the German sample stated ‘A-levels’

(37,4%) and ‘Bachelor degree’ (33,2%) as highest education obtained. Only 12,6% of

the respondents in the French sample state ‘Bachelor degree’ as highest qualifica-

tion. The number of individuals in the data set holding a PhD is considerably higher

for the French sample, with 10,7%, compared to 0,5% in the German sample. The

number of participants which stated ‘vocational training’ as education obtained is for

both, German and French, with 2,9% (French) and 2,7% (German) comparably low

(appendix 4).

Income

The monthly net income of 388 individuals (35,2%) is below 499€. 218 (19,8%) par-

ticipants have an income between 500 – 999€. 188 (17,0%) (188) respondents earn

1.000 – 1.999€ and 80 (7,3%) between 2.000 to 2.999€. A minority of 5,3% (58) sur-

38

vey participants have an income above 3.000€ net per month. 171 respondents

(15,5%) did not indicate their monthly income.

The cross tabulation for ‘nationality’ and ‘income’ indicates, that the majority of the

French sample has a lower income than the German respondents. The monthly in-

come of 38,4% of the French respondents is between 0 and 499€, compared to

20,2% of the German sample. The majority (38,3%) of the individuals in the German

sample have a monthly income between 500 – 999€. Whereas only 15,8% of the

French sample fit into this range. The share of the characteristic value in the income

range between 1.000 – 1.999€ is similar for the German (16,5%) and French (17,2%)

sample. Even if a higher percentage in the German sample (10,6%) has a monthly

income between 2.000 and 2.999€ than in the French sample (6,4%), the differences

with regard to the total sample size are negligible. The number of individuals that did

not indicate their monthly income is higher among French respondents (17,3%) than

German respondents (8,0%). The majority of individuals in the category ‘Other’ have

a monthly income between 0 – 499€ (36,2%) and 500 – 999€ (19,0 %) (appendix 4).

4.2. Frequency Analysis and Descriptive Statistics

4.2.1. Attitude towards the Environment

An element of the conducted survey is the measurement of the consumers ATE. The

respondents rated eight items on their ATE on a scale between ‘1’ “strongly disagree”

and ‘6’ “strongly agree”. In the following, the analysis of the responses starts with a

frequency analysis to show the distribution of responses to all questions. A response

rate to each individual question will be provided (appendix 5). The aim of the fre-

quency analysis is to ensure a high level of transparency and to document the re-

sponses given in the survey. Furthermore, it allows an analysis of whether the sam-

ple is representative and supports the initial conclusions (London Economics, 2011,

p. 13).

A numerical analysis of each individual item is found in appendix 6. The mean = the

measure of central tendency; sd = the standard deviation as a measure of variability

in the data and IQR = the interquartile range, which explains the range covered by

the middle 50% of the distribution (75% minus 25%) which helps to avoid the exces-

sive influence of extreme scores (Gravetter & Wallnau, 2009, p. 108 - 112); 0% = the

minimum value; 25% = the value below which 25 % of the observations may be

found; 50% = the value below which 50 % of the observations may be found; 75% =

39

the value below which 75 % of the observations may be found; and 100% = the max-

imum value (Upton & Cook, 1996, p. 51 – 52). The mean for all eight items is higher

than 4,491, indicating that the average of all respondents at least moderately agreed

with the presented items. In addition the 25% quartile is ‘4’, indicating for the items

AE01 to AE07 that 25% of all observations mildly agree with the statements on ATE.

Conversely, this means that 75% of all observations are found above the value of ‘4’

and consequently at least mildly agree with the presented items. Furthermore the

75% quantile for all seven items (not AE04) is ‘6’. This means that 25% of the obser-

vations stated that they strongly agree with the items towards the environmental atti-

tude.

The items “The increasing deterioration of the environment is a serious problem”

(AE08) gained the highest support with a mean value of 5,404. Furthermore the item

“Environmental problems have a direct effect on my daily life” (AE01) found high

support among the majority of participants with a mean value of 4,980.

The IQR is low (between 1 and 2), which means that the middle 50% of the distribu-

tion (75% minus 25%) tend to give similar responses to the presented items. As ap-

pendix 6 shows, a single samples t-test reveals significant differences in the means

for six items between the German and the French sample (except for item AE06 and

AE08) at the 0,05 significance level.

To ensure that the ATE items measure the same effect, a factor analysis is conduct-

ed to distill appropriate items respectively. In a first step a correlation matrix for the

eight items was generated (appendix 7). The correlation matrix reveals that item ‘4’

“The big polluters (corporations and industry) should be mainly responsible for pro-

tecting the environment” is only weakly correlated with the other seven items. There-

fore item ‘4’ was no longer considered for further analysis. With the remaining seven

items a factor analysis was conducted. “A factor analysis is a data and variable re-

duction technique that attempts to partition a given set of variables into groups

(called factors) of maximally correlated variables. For metric scaled data on a large

number of variables, factor analysis generates a smaller number of variables, called

factors, which capture as much information as possible from the data set“

(Parasuraman, Grewal & Krishnan, 2007, p. 489). The purpose of the following factor

analysis is to identify items that load on the same factor and consequently measure

the same effect.

40

In a first step, a factor analysis with one factor and the null hypothesis that the model

described by the one factor, predicts the data well was estimated. The chi-square

goodness-of-fit analysis for the one factor model is 283,38, and the p-value is 3,42e-

52 (with 14 degrees of freedom). Consequently the null hypothesis has to be reject-

ed. The factor does not predict the data well from a statistics perspective. A factor

analysis with two factors only delivers slightly improved results. With three factors,

the chi-square goodness-of-fit value is 4,35, and the p-value is 0,226 (on 3 degrees

of freedom) (appendix 8). Consequently, the null hypothesis (hypothesis that three

factors are sufficient) cannot be rejected, wherefore the three factors predict the data

well from a statistics perspective. Furthermore the cumulative variance becomes

0,585 (0,54 with two factors and 0,484 with one factor). In addition, the Kaiser rule

determines that the number of factors should be ‘3’. As the variables AE05 and AE07

load on factor 1, this factor might measure the “Importance of environmental protec-

tion”. The variables AE03 and AE06 load on factor 2, measuring the “Intention to pro-

tect the environment”. Factor 3 can be labeled as “Environmental concern”, as the

variables AE01, AE02 and AE08 load on this factor.

Cronbach’s alpha is a further statistical tool, applied to measure the internal con-

sistency between different items from the same scale. The analysis permits to check

if some items are correlated and can be computed in one variable (Shiu et al., 2009,

p. 403). According to Boltz & Döring (2006, p. 216), Cronbach alpha values between

0,9 and 1,0 are an indication for high reliability (respectively internal consistency),

between 0,8 and 0,9 for moderate reliability and below 0,8 for low reliability. As

Cronbach’s alpha for the items A01, A02, A03, A05, A06, A07, and A08 is higher

than 0,8 (0,8329), the reliability between these items is sufficiently consistent (ap-

pendix 9). Consequently, for the further analysis of the proposed model to explain the

consumers WTP for green products, the seven items were summed up and divided

by the number of items in order to generate a mean ATE score (AEmean). The score

for the mean ATE of the seven selected items is 4,911, indicating that the average

individuals in the survey can be considered to have a positive ATE. The 95% confi-

dence interval for the AEmean lies between 4.862935 to 4.960988.

4.2.2. Attitude towards Green Products

As the AGP is a further criteria to be tested within this thesis, the conducted survey

collected data on the respondents’ attitude towards green products. The respondents

rated nine items on their AGP on a six point scale between ‘1’ “strongly disagree” and

41

‘6’ “strongly agree”. A frequency analysis of the participants’ responses to the items is

provided in appendix 5 and indicates that the majority of respondents have a positive

AGP. The majority of respondents strongly or moderately disagreed with item AG06

“green products are more expensive than conventional products”. But as this item

was measured as a reverse item, disagreement is counted with ‘6’. The other items

found high agreement among the respondents (appendix 5).

A numerical analysis of each individual item is found in appendix 6. The mean for the

items AG01, AG02, AG03, AG04, AG08 and AG09 is higher than 4,1, indicating that

the average of all respondents at least mildly agreed with these items. AG05 “green

products are reasonably priced” found the lowest support among the respondents

(3,004). Item AG01 “green products are good for the environment” found the highest

average support among all participants (4,8861).

An independent sample t-test found no significant differences in the means at the

0,05 significance level between the German and French samples for the items AG01

(p-value 0,52), AG02 (0,31), AG04 (0,46) and AG09 (0,17). The remaining items

showed significant differences in the mean for the German and French samples (p-

value < 0,05). Items AG03 “green products are of better quality/have a better perfor-

mance than conventional products” found significantly higher support within the

French sample (4,335) compared to the German sample (3,888). Respondents in the

German sample (3,660) tended to agree stronger with item AG05 “green products

are reasonably priced” than French respondents (2,861). Furthermore, item AG07

“green products are more expensive but have a lower performance than conventional

products” was stronger supported by German (4,457) compared to French respond-

ents (3,844). Among the French sample item AG08 “green products will improve the

standard of living of future generations” found higher support (4,588) than in the

German sample (4,324) (appendix 6).

To ensure that the selected AGP items measure the same effect, a factor analysis

was conducted. In a first step, a correlation matrix for the nine items was generated

(appendix 7). The correlation matrix reveals that item AG06 and AG07 are only weak-

ly correlated with the other seven items. But as these items are intended to measure

the respondents’ attitude towards the pricing of green products, they were kept in the

sample for the following factor analysis:

42

A factor analysis with one factor and the null hypothesis that the model described by

the one factor predicts the data well, generated a p-value below 0,00 for the chi-

square goodness-of-fit test. Consequently the null hypothesis has to be rejected, so

the factor does not predict the data well from a statistics perspective. Further tests

were performed with two, three and four factors. By choosing five factors, the null

hypothesis that five factors predicts the data well could not be rejected (p-value

0,399) (appendix 8).

The cumulative variance is 0,575. The factor analysis furthermore revealed that the

nine items measure three main effects. The items AG08 and AG09 load on factor 1,

which can be summarized as the “expected positive future impact on the environment

of green products”. Item AG01 and item AG05 load on factor 2. As these items

measure the price as well as the environmental performance of green products the

factor is difficult to label. Factor 3 measures the respondents’ quality/performance

rating for green products as the items AG02, AG03 and AG04 load on this factor.

Item AG06 and AG07 are only weakly correlated with the other items (correlation ma-

trix) and consequently do not load on one of the other factors. However, as these two

items reflect the price they will be kept in the sample for the further analysis as an

important part of the AGP. Further research also has to further measure the rele-

vance of these items for the consumers’ AGP.

Cronbach’s alpha for the nine items is 0,7418, which indicates a weak but still ac-

ceptable internal consistency of the items (appendix 9). For further analysis of the

proposed model and to explain the consumers’ WTP for green products, the nine

items loading on three factors (total of five factors) were summed up and divided by

the number of items in order to generate a mean AGP score (AGmean). The score

for the mean attitude towards green products of the nine selected items is 3,979, in-

dicating that the average individuals who participated in the survey can be consid-

ered to have a positive AGP, as they tend to mildly agree with the stated items. The

95% confidence interval for AGmean lies between 3.940075 to 4.019308.

4.2.3. Intention to Purchase Green Products

An additional criteria of the model to be tested within this thesis is the intention of

consumers to purchase green products. Therefore data on the respondents purchase

intention were collected, by asking respondents to rate eight items on a six point

43

scale between ‘1’ “strongly disagree” and ‘6’ “strongly agree”. A frequency analysis of

the participants’ response to the items is provided in appendix 5.

A numerical analysis of each individual item is found in appendix 6. The mean for the

items PI01, PI02, PI04 and PI05 is higher than at least 4,067, indicating that the av-

erage of all respondents at least mildly agreed with these items. Item PI01 “generally

speaking, buying green products is a better choice” found the highest support among

the respondents (4,318), followed by the items PI02 “if possible, I prefer buying green

products to conventional products” (4,294), PI05 “it is very likely that I will purchase a

green product within the next weeks” (4,241) and PI04 “I am ready to buy environ-

mentally friendly products even if they cost a little bit more” (4,067). The mean score

for item PI03 “buying green products generally benefits the consumer” is 3,634. The

items PI06 “I am always looking for green products when thinking about a purchase”

(3,292), PI07 “green products would probably be too expensive for me” (3,013) and

PI08 “my friends would like me to buy green products” (2,284) gained the lowest av-

erage support in the sample.

An independent sample t-test found significant differences in means at the 0,05 sig-

nificance level between the German and French samples for the items PI03 (p-value

0,00), PI07 (0,00) and PI08 (0,00). Item PI03 found higher support among the Ger-

man sample (3,957) compared to the French sample (3,546). Furthermore the Ger-

man respondents were more likely to agree with the items PI07 (3,516) and PI08

(3,053): These items PI07 (2,903) and PI08 (2,112) found significantly weaker sup-

port among French respondents.

To ensure that the items on the consumers’ purchase intention measure the same

effect and to distill appropriate items a factor analysis is conducted. Before that, a

correlation matrix for the eight “purchase items” was generated (appendix 7). The

correlation matrix reveals that item PI08 “my friends would like me to buy green

products” is only weakly correlated with the other seven items. Therefore item PI08

was not considered for further factor analysis. High correlations are found between

the item PI01 and PI02 (0,70), PI02 and PI06 (0,68), PI05 and PI06 (0,68) as well as

between PI04 and PI05 (0,65) and PI04 and PI06 (0,65).

A factor analysis with one factor and the null hypothesis that the model described

through this factor predicts the data well, generated a very small p-value (0,00) for

the chi-square goodness-of-fit test. Consequently, the null hypothesis has to be re-

44

jected, as one single factor does not predict the data well from a statistics perspec-

tive. Further tests were performed with two and three factors. With three factors, the

null hypothesis that three factors predict the data well could not be rejected at the

0,05 significance level (p-value 0,07) (appendix 8).

The cumulative variance for the three factors is 0.675. The factor analysis reveals

that the remaining seven items measure three main effects. The items PI01, PI02

and PI03 load on factor 1, which can be summarized as the “general intention to-

wards buying green products”. Item PI04 and item PI07 load on factor 2. As these

items measure the price to be considered when making a purchase decision, this

factor is labeled ‘price considerations’. Factor 3 measures the respondents’ short-

term intention to purchase green products, as the items PI05 and PI06 load on this

factor.

Cronbach’s alpha for the seven summed up items is 0,8843, indicating a high internal

consistency and thus reliability. For the further analysis of the proposed model to ex-

plain the consumers’ WTP for green products, the seven items, loading on three fac-

tors were summed up and divided by the number of items in order to generate a

mean “intention to purchase green products” value (PImean). The score for the mean

purchase intention of the seven selected items is 3,837, indicating that the average

individuals in the survey can be considered of having a positive attitude towards the

purchase of green products, as they tend to mildly agree with the stated items. The

95% confidence interval for the PImean lies between 3.771439 and 3.902656.

4.2.4. Willingness to Pay for Green Products

The key target of this thesis is to examine the consumers’ WTP a price premium for

green products. The collected data indicates

that 71,08% of the individuals participating in

the survey are willing to pay a price premium

for the products sold in the environmentally

friendly supermarket using natural refriger-

ants (figure 4). In the French sample 71,5%

of the respondents stated they are generally

willing to pay a price premium compared to

70,7% in the German sample. For the follow-

ing analysis the focus is put on the German

Figure 4: WTP Price Premium

Source: based on survey data

45

and French respondents.

WTP - Yes

The price premium the consumers are willing to pay was measured as total amount

and in percentage. The base price of a fictional purchase was 10€ and the respond-

ents were asked to state the additional amount they are willing to pay. Table 1 shows

that 746 (71,39%) participants out of the sample with 1.045 French and German ob-

servation, were willing to pay an average price of 11,64€. The WTP a price premium

in % is lower with a mean value of 12,75%, resulting in a total price of 11,28€. In ad-

dition the median has to be taken into account when analyzing the WTP of a popula-

tion, as mean and median have quite different implications: The mean is more appro-

priate if the WTP analysis targets for example a cost benefit analysis.

Table 1: Numerical Analysis WTP Total and Percentage

Source: based on survey data

However, as this thesis examines the WTP in the context of consumer choice, the

median (0,5 quantile) is of high interest as well, because it provides the WTP amount

for the majority of individuals (London Economics, 2011, p. 14). As Table 1 shows,

the median (0,5 quantile) for the respondents WTP a premium in percent is 8,75%.

This means that at least half of the respondents are willing to pay a price premium of

at least 8,75% or more. The total WTP is even higher, indicating that 50% of the indi-

viduals in the sample would pay at least 11,25€, equal to a price premium of 1,25€

(12,5%) compared to the base price of 10€.

WTP – No

299 (28,61%) out of the 1.045 French and German respondents stated that they are

not willing to pay a price premium for their purchase in an environmentally friendly

supermarket. The respondents without WTP a premium were asked to state the rea-

son for their decision. As figure 5 shows, the majority (24,7%) of the respondents that

were not willing to pay a premium argued that they expect the supermarket to be en-

vironmentally friendly anyway and do not want to have to pay for it. 23,8% of the re-

spondents stated that they try to act environmentally friendly without paying for it.

mean sd IQR 0 25% 50% 75% 100% n NA

WTP percent 12,7524 12,7728 12,5 0 3,75 8,75 16,25 87,5 746 299

WTP total 11,6377 1,3768 1,25 10 10,75 11,25 12 20 746 299

46

Figure 5: Reason for no WTP

Source: based on survey data

For 15,3% of the respondents ‘green’ is too expensive. This argument was supported

by many people stating that they would be generally willing to pay a premium but

their current situation (as a student) does not allow additional spending. To better

evaluate and judge the environmental performance of the supermarket, 12,1% of the

respondents stated that they require proof of the actual environmental performance.

As was suggested, an eco-label could provide evidence of the supermarket’s perfor-

mance.

As consumers’ skepticism towards ‘green washing’ is generally growing, it is not sur-

prising that 10,4% of the consumers stated they perceive ‘green’ to be merely a mar-

keting measure to charge higher prices from consumers. 5,8% of the respondents

indicate that they would expect the supermarket to more intensively communicate the

environmental performance of the store in order to be willing to pay a premium for its

environmental performance. Only a small minority of 3,0% stated that they do not

care about the environmental performance of the supermarket.

In addition to the predetermined answers, respondents were asked to state the rea-

son for their decision not to pay a premium in free text. Out of the various answers,

the following reasons were extracted. The majority of free text answers argue, that it

is not the consumers who have to pay for the improved refrigeration system of the

supermarket. As the supermarket saves energy and consequently costs, they see the

price increase as not justified. It merely helps the supermarket increase its profit

47

share. Other respondents stated that environmentally friendly technology should be

subsidized by the state and not the consumer. Another group of respondents was not

willing to pay a premium due to their boycott of supermarkets in general. Also, sever-

al individuals stated that they find it difficult to identify green products. Due to this un-

certainty they are not willing to pay (a price premium).

4.2.5. Social Desirability

In order to take into account the influence of social desirability biases in the respons-

es given by survey participants, data on the social desirability of respondents were

collected. The survey participants were asked to rate six general items with regard to

their respective social desirability on a six point scale between ‘6’ “applies complete-

ly” and ‘1’ “doesn’t apply at all”. The six items used are based on the proposed scale

by Kemper, Beierlein, Bensch, Kovaleva, & Rammstedt (2012, pp. 25 -26). They

measure the gamma factor of social desirability and are based on the social desirabil-

ity scale proposed by Stöber (1999) as well as Stöber & Wolfradt (2001).

As the significance of these items was already tested in previously, no further tests

were undertaken in this regard. A numerical analysis (appendix 6) reveals that the

French sample tends to show higher social desirability (SD) scores. Only item SD03

“Sometimes I only help people if I expect to get something in return” was rated signif-

icantly higher by the German respondents (4,468) than the French (3,516). The six

SD items were summed up and divided by the number of items in order to generate

the mean score SDmean. The SD mean is also higher for the French (4,216) than the

German sample (4,013). The difference in mean between the German and French

sample for the variable SDmean is significant at the 0,00 significance level.

The correlation between the SDmean score and the items for the ATE and green

products as well as the purchase intention were subsequently tested. The aim of this

particular analysis was to find items that might be influenced by the social desirability

bias. The analysis revealed that all items are only weakly correlated with the SD

mean (appendix 10). Only item AE03 “In general, I would consider myself as envi-

ronmental friendly” had a correlation higher than 0,19. As the correlation was very

weak in general and for responses that tended to support statements on the envi-

ronment and the WTP specifically, the social desirability was not considered for fur-

ther analysis.

48

4.3. Independent and ANOVA-test

To generate findings on whether there is a significant difference in average AEmean,

AGmean, PImean, WTPtotal and WTPpercent among the demographic (numerical)

variables ‘nationality’, ‘gender’, ‘age’, ‘education’ and ‘income’ at the 5% level of sig-

nificance, an ANOVA test was performed. An ANOVA permits to analyze whether two

or more means are statistically different from each other (Shiu et al, 2009, p. 722).

Furthermore an ANOVA permits to compare the difference in means for demographic

variables composed of more than two values (for example ‘income’ and ‘education’).

As one aim of this thesis is to examine if there are differences in consumer attitude,

intention and WTP with respect to the demographic variables, the means and stand-

ard deviation of variables were furthermore examined. In addition, the WTP was ex-

amined for several demographic groups in France and Germany in more detail. The

results for the demographic variables are presented as follows:

Nationality

The analysis of the French and German samples respectively showed a significant

difference in the respondents ATE and WTP at price premium (WTPtotal). The mean

ATE (AEmean) for the French sample is 4,979, which is higher than the German

mean of 4,605. As the p-value (0,00) of the F-statistic for the variable AEmean is infe-

rior to the 0,05 significance level, the null hypothesis, describing that the difference in

means is equal to zero, can be rejected. Consequently, it can be concluded that that

the difference in means for the variable AEmean is significant between the German

and French samples. There are no significant differences in the average AGP

(AGmean) for the German (4,014) and French (3,972) sample as the p-value is

above the 0,05 significance level (0,4298). The mean intention to purchase green

products (PImean) is also not significantly different (p-value 0,1623) between the

German (3,944) and French (3,814) sample. Furthermore the analysis reveals that

there is a significant difference in the French and German respondents’ WTP a high-

er (total) price. As the p-value of the t-test for the variable WTPtotal is below the

0,05% significance level (0,00), the null hypothesis is rejected. The mean WTP a

higher price was greater in the German (12,128) than in the French sample (11,531)

(see appendix 11). Interestingly the WTP a price premium in percent is not signifi-

cantly different from French to German respondents.

49

Gender

Women (4,969) and men (4,758) have both a strong positive mean ATE. The stand-

ard deviation for the male sample is slightly higher (0,932) than for the female sam-

ple. The p-value of the F-statistic is very small (0,00) and remains below the 0,05

significance level. The null hypothesis that the difference in means is equal to zero

can thus be rejected. The difference in means is consequently significant between

women and men. Both, women (4,021) and men (3,869) have a positive ATE. The

difference in the male and female mean for the variable AGmean is significant.

The purchase intention for green products is slightly higher for women (3,873) than

men (3,739). In addition women are more willing to pay a higher total price premium

(11,663) than male respondents (11,570). Women’s higher WTP for green products

(12,815) is also expressed in a higher WTP a price increase in percentage compared

to men (12,585). However, the analysis of the data set found no significant difference

between the male and female mean intention to purchase green products (PImean)

and WTP to pay a price premium neither in total nor in percent. The p-value for these

three variables was above the 0,05 significance level and the null hypothesis that the

difference in means is equal to zero could not be rejected (appendix 11). The highest

WTP was found among German females (12,196) (appendix 12).

Age

Before analyzing the differences in mean among the different age groups, it has to be

recalled that, as previously mentioned, the data set for this analysis is comprised of

95,7% respondents aged between 15 – 24 and 25 – 34 (see chapter 4.1.1). Thus,

while the results of the other age groups will be mentioned in the following, they re-

main negligible.

An analysis of the variance of the numeric variables AEmean, AGmean, PImean,

WTPtotal and WTPpercent examined the differences in means among different age

groups. Young respondents in the age range from 15 to 24 (4,879) and respondents

aged 45 to 54 showed the lowest (but still a high positive) mean ATE. The highest

positive mean ATE was found among the older respondents in the age range from 55

to 64 (5,536) as well as older than 65 (5,429). Still, as the p-value (0,12) for the F-

statistic is above the 0,05 significance level, the null hypothesis of equal means for all

age groups for the variable AEmean cannot be rejected and the true mean is conse-

quently not equal to zero.

50

The majority of all age groups shows a mildly positive AGP. The highest mean value

for AGmean is observed in the age group 55 – 64 (4,056), followed by 25 – 34

(4,025), 35 – 44 (4,008), 65+ (4,000) and 15- 24 (3,972). The lowest mean value is

found in the age group 45 – 54 (3,593). The p-value of 0,36 is above the 0,05 signifi-

cance level and the null hypothesis of equal means can consequently not be reject-

ed.

The intention to purchase green products strongly varies between the different age

groups. The p-value (0,01) is below the 0,05 significance level and the null hypothe-

sis of equal means can be rejected. Respondents aged between 35 – 44 show the

highest purchase intention (4,307), followed by people in the age range from 25 to 34

(3,998) and 55 to 64 (3,964). Respondents between 15 – 24 (3,782), 45 – 55 (3,369)

of age and older than 65 (3,643) show significantly lower mean values for the varia-

ble PImean.

The highest mean value for the WTPtotal is found in the young age groups between

25 – 34 (11,851) and 15 – 24 (11,609). Among mid aged individuals between 45 – 54

a mean WTPtotal of 11,344 was observed. Respondents older than 65 showed the

lowest WTPtotal (11,000), followed by the age ranges 55 – 64 (11,063) and 35 – 44

(11,066). The p-value (0,12) is above the 0,05 significance level and the null hypoth-

esis of equal means can consequently not be rejected.

A very high WTP in percent can be found among the respondents aged between 55

and 64 (16,875). However this value might be the result of the small sample size and

will therefore not be further highlighted. As for the WTPtotal, high WTPpercent means

are observed within the young age groups between 15 – 24 (12,794) and 25 – 34

(12,944). The p-value (0,71) for the variable WTPpercent is above the 0,05 signifi-

cance level and the null hypothesis of equal means can consequently not be rejected

(appendix 11). The highest WTP was found among German respondents aged be-

tween 15 – 24 (12,1154) and 25 – 34 (12,3021) (appendix 12).

Income

The analysis of the survey results reveals no significant differences in means of the

variables AEmean and AGmean among different income levels. The p-value for both

variables, AEmean (0,1) and AGmean (0,494) is above the 0,05 significance level

and the null hypothesis of equal means can consequently not be rejected.

51

The intention to purchase green products differs among different income levels. The

p-value of 0,01 is below the 0,05 significance level and indicates that the null hypoth-

esis of equal means can be rejected. Individuals with an income between 2.000 and

2.999€ show the highest intention to purchase green products (4,149), followed by

the income levels between 1.000 to 1.999€ (3,977). Individuals with an income above

3.000€ show the same PImean value (3,873) as the individuals in the income range

between 500 and 999€ (3,869). The lowest PImean value (3,692) is found in the low-

est income level between 0 – 499 € (appendix 11).

The difference in means for the variable WTPtotal is not equal to zero among the dif-

ferent income levels (p-value 0,03 < 0,05 significance level). The highest mean

WTPtotal is found among individuals with an income between 500 – 999€ (11,894)

and 1.000 – 1.999€ (11,826). The mean WTPtotal for the higher income groups be-

tween 2.000 – 2.999€ (11,560) and above 3.000€ (11,669) is lower than for the pre-

vious incomes. The small incomes below 499€ show the lowest mean WTPtotal of

11,442. A further analysis of the WTPtotal for the German and French samples re-

spectively reveals that the highest average WTP can be found among German indi-

viduals with an income higher than 3.000 € (12,4546) (appendix 12).

The p-value for the F-statistic on the analysis of the variance in means of the variable

WTPpercent among the different income levels is above the 0,05 significance level.

The null hypotheses of equal means can therefore not be rejected. The mean

WTPpercent is the highest in the income level between 500 – 999€ (13,763) and the

lowest for incomes above 3.000€ (11,096) (appendix 11).

Education

As the analyzed sample only comprises two observations with no high school diplo-

ma (appendix 4) this variable can be ignored for further analysis. The null hypothesis

that the difference in mean for the variable AEmean is zero for the diverse educa-

tional levels can be rejected (p-value 0,00 < 0,05). As illustrated in appendix 11, the

AEmean is very high for the variables ‘Master degree’ (4,976) and ‘PhD’ (5,103) as

well for the variable ‘Other’ (4,988). The mean ATE for ‘Bachelor degree’ is 4,875,

followed by 4,716 for the variable ‘A-levels’. Respondents that stated ‘vocational

training’ as highest education, show the lowest AEmean value (4,638).

The AGP is very positive among the groups ‘PhD’ (4,047), ‘Bachelor degree’ (4,059),

‘Master degree’ (3,972) and ‘A-levels’ (3,966). The lowest AGmean value (3,733) is

52

found for “vocational training”. As the p-value is above the significance level of 0,05,

the null hypothesis of means equal to zero cannot be rejected.

The highest mean values for the variable PImean is found among the educational

groups ‘PhD’ (4,055) and ‘Bachelor degree’ (3,922), closely followed by ‘Master de-

gree’ (3,846). Respondents with ‘A-levels’ (3,750) or ‘Other’ (3,740) as highest

achieved qualification show slightly lower mean values. The mean intention to pur-

chase green products is the lowest among individuals that are currently completing or

have completed a vocational training. As the p-value is above the 0,05 significance

level, the null hypothesis that the difference in mean for the variable PImean is zero

for the different educational levels cannot be rejected.

The difference in means for the variable WTPtotal is significant, as the p-value (0,02)

is smaller than 0,05. The educational status ‘PhD’ leads to the highest mean

WTPtotal (11,945). Among the group ‘Bachelor degree’, the WTPtotal is rather high

(11,850), followed by ‘A-levels’ (11,771) and ‘Other’ (11,669). For the group ‘Master

degree’, the mean WTPtotal is rather small (11,446). The smallest mean WTPtotal is

found for ‘vocational training’ (11,328).

The difference in mean for the variable WTPpercent is highly insignificant among the

different educational statutes and the null hypothesis that the difference in mean is

zero cannot be rejected (appendix 11).

Country-specific evaluations show that the highest total WTP was found among

German respondents with ‘A-levels’ (12,3980) and ‘Bachelor degree’ (12,2907) as

highest qualification. Interestingly, in the French sample respondents with PhDs

showed the highest WTP (11,9515) whereas a PhD in the German sample did not

lead to a higher WTP (appendix 12).

4.4. Correlation Analysis

4.4.1. Correlation Matrix

To measure the strength of a linear relationship between two or more quantitative

variables, the correlation has to be measured. The correlation between the numerical

data comprised of the mean scores for the ATE (AEmean), the mean score for the

AGP (AGmean) and the mean score for the intention to purchase green products

(PImean) was calculated. In addition the numerical variables for the mean WTP in

total (WTPtotal) and WTP a price premium in percent (WTPpercent) were analyzed.

53

Table 2: Correlation Matrix

Source: own calculation based on survey data

The correlation matrix in table 2 indicates a strong positive linear relationship be-

tween the variables AEmean and AGmean (0,4850). Furthermore a strong linear re-

lationship (0,5318) between AEmean and PImean exists. The strongest linear rela-

tionship is found between the two variables AGmean and PImean (0,6458). Between

the variables PImean and WTPtotal a low linear correlation (0,2232) and between

PImean and WTPpercent a very low correlation (0,1253) was found. The correlation

between the variables WTPtotal and WTPpercent is rather weak (0,3534), indicating

that individuals judge a price increase differently with regard to total amount and per-

centage. The relationship between the variables AEmean (0,1184) and AGmean

(0,1570) with WTPtotal (as well as WTPpercent) is very low. An initial conclusion

could be that a positive ATE and green products do not necessarily result in a higher

WTP for green products. Furthermore the results might indicate that respondents with

a positive AGP have a higher intention to purchase these products. The correlation

coefficients between the other variables are very low, as they are below 0,1 and con-

sequently indicate very weak correlations between those variables.

4.4.2. Bivariate Analysis and Pearson’s Coefficient

It is important to mention, that the correlation is not a complete description of bivari-

ate numerical data. The positive and linear relationship between the variables

AEmean and AGmean, AEmean and PImean, as well as AGmean and PImean, as

shown in the correlation matrix in table 3, can be further demonstrated by applying a

bivariate regression analysis. To this end the relationships with several combinations

of AEmean, AGmean and PImean as response variable and AEmean, AGmean,

PImean, WTPtotal and WTPpercent as explicatory variable were further tested.

A regression analysis of the explanatory variable AGmean on the response variable

AEmean generates a very small p-value for AGmean (0,00) at the 5% significance

level. Consequently this variable is highly significant. The R² is low with a value of

0,2581 and implies, that the estimated model explains 25,81% of the variance of

AEmean AGmean PImean WTPpercent WTPtotal

AEmean 1 0.4850323 0.5318320 0.08543481 0.1184982

AGmean 0.48503229 1 0.6458924 0.13785148 0.1570023

PImean 0.53183199 0.6458924 1 0.12537175 0.2232421

WTPpercent 0.08543481 0.1378515 0.1253718 1 0.3534042

WTPtotal 0.11849822 0.1570023 0.2232421 0.35340419 1

54

AEmean (table 3). This value underlines that a relationship between those variables

exists, even if the relationship is not very strong.

Table 3: Bivariate Regression Analysis

Source: own calculation based on survey data

A regression model with AGmean as response variable and AEmean as explicatory

variable generates the same results. The strongest relationship is found between

AGmean and PImean. AGmean explains 47,79% of the variance of PImean. The var-

iable AEmean explains 30,3% of the variance of PImean. Furthermore the bivariate

regression analysis reveals that the variables AEmean, AGmean and PImean explain

less than 5% of the variance of WTPtotal and WTPpercent. Consequently, these var-

iables might not be appropriate to explain the consumers’ WTP a certain price (pre-

mium).

In order to test the hypothesis H2, H3, H4, H5, H6 and H7 the Pearson’s coefficient

was calculated for the variables AEmean, AGmean, PImean, WTPYesNo, WTPtotal

and WTPpercent. The Pearson’s coefficient, allows for indicating the strength of the

relationships between these variables. A strong positive relationship is said to be

found when the Pearson coefficient is between 0,61 and 0,8. A moderate correlation

is found when the coefficient lies between 0,41 and 0,60, weak between 0,21 and

0,40 and no relationship between 0,0 and 0,2 (Shiu et al., 2009, p.555). The results

in table 4 indicate a positive relationship between all items. A strong positive relation-

ship is also found between AGmean and PImean (0,691). A positive, moderate corre-

lation is furthermore found between AEmean and PImean (0,55). In addition the ATE

(AEmean) is moderately correlated (0,508) with the purchase intention (PImean).

PImean and WTPtotal are weakly correlated (0,221). According to the Person’s coef-

ficient, all other items show no relationship between each other.

p-value R²

AEmean - AGmean < 2.2e-16 0.2581

AEmean - PImean < 2.2e-16 0.303

AGmean - PImean < 2.2e-16 0.4779

AEmean - WTPtotal 0.001542 0.0134

AGmean - WTPtotal 0.0000166 0.02462

PImean - WTPtotal 9.91e-10 0.04898

AEmean - WTPpercent 0.007299 0.01994

AGmean - WTPpercent 0.0001654 0.019

Pimean - WTPpercent 0.0006197 0.01572

55

Table 4: Pearson’s Coefficient

Source: own calculation based on survey data

In order to test whether the variables measuring consumer attitudes and purchase

intention are correlated with the general WTP, the variable WTPYesNo was coded

(Yes=2, No=1). AEmean (0,455) is weakly (almost moderately) correlated with the

general WTP (WTPYesNo). AGmean (0,349) and PImean (0,351) are only weakly

correlated with WTPYesNo (table 4).

Furthermore, the relationship between the two categorical variables WTPYesNo

(uncoded) and PastBB and the demographic variables was analyzed by applying a

Pearson’s Chi-square test. The aim of this analysis is to find out, whether the fea-

tures are stochastically independent or if they are correlated. Consequently the null

hypothesis “The characteristics are stochastically independent” is established. The

output of the Chi² test as a result of the analysis of the categorical variables ‘nation-

ality’, ‘gender’, income, ‘education’ and ‘age’ as well as the variables WTPYesNo and

PastBB is shown in table 5.

For the WTP, the p-value for ‘nationality’ (0,8295), age (0,7329) and gender (0,7948)

is above the 5% significance level. Consequently the established null hypotheses,

outlining that the characteristics are independent, cannot be rejected. Thus we may

assume that that the variables ‘nationality’, ‘gender’ and ‘age’ are not associated

with the variable WTPYesNo. Consequently, ‘nationality’, ‘age’ and ‘gender’ do not

have a significant influence on the consumer’s general WTP. As the p-value for the

variables income (0,00) and education (0,01) is below the 0,05 significance level, the

Combination of Items

Pearson

Coefficient p-value

AEmean - AGmean 0,508 0,000

AEmean - PImean 0,550 0,000

AGmean - PImean 0,691 0,000

AEmean - WTPtotal 0,116 0,002

AGmean - WTPtotal 0,157 0,000

PImean - WTPtotal 0,221 0,000

AEmean - WTPpercent 0,085 0,020

AGmean - WTPpercent 0,138 0,000

PImean - WTPpercent 0,125 0,000

AEmean - WTPYesNo 0,455 0,000

AGmean - WTPYesNo 0,349 0,000

PImean - WTPYesNo 0,351 0,000

Pearson's Coefficient

56

null hypotheses, describing the characteristics as independent, is rejected. Thus, it

can be concluded that the variables ‘income’ and ‘education’ are not associated with

the variable WTPYesNo.

Table 5: Chi2 test Demographics WTPYesNo and PastBB

Source: own calculation based on survey data

The p-values for the variables ‘nationality’ (0,00) and ‘education’ (0,02) indicate that

these variables are associated with past buying behavior for green products (table 5).

Furthermore the analysis revealed that past buying behavior and WTP are not signifi-

cantly correlated.

4.5. Multiple Regression Analysis

A regression analysis is a further important tool for statistical research. As shown in

chapter 4.3, the mutual relationship between two variables is measured with the help

of correlation or bivariate analysis. With correlation, the direction and magnitude of

the relationship between two variables is measured. At the same time a correlation

analysis cannot provide the closest estimate of a value of a dependent variable on

the basis of a given value of the independent variable. To achieve close estimates a

regression analysis is applied in the following chapters. “The target of the regression

analysis is to measure the nature and extent of the relationship between two or more

variables, thus enables to make predictions” (Jain & Aggarwal, 2008, p. 77). A multi-

ple regression analysis studies more than two variables at a time (at least three vari-

ables) in which one is a dependent variable and the others are independent variables

(Jain & Aggarwal, 2008, p. 79).

The purpose of using a regression analysis within this thesis, is to predict the output

(Yes/No) of the dependent variable WTPYesNO with the help of the other independ-

ent variables in the data set. Furthermore the aim is to predict the value (total) con-

sumers are willing to pay (dependent variable) with independent variables.

X-squared p-value* X-squared p-value*

Nationality 0,0464 0,8295 10,1754 0,006172

Age 2,7866 0,7329 14,0725 0,1697

Gender 0,0677 0,7948 0,5143 0,7733

Income 15,9384 <0,001 11,7642 0,3011

Education 16,469 0,01145 24,8052 0,01577

*Sig 0,05

WTPYesNo PastBB

Pearsons Chi2 test

57

The previous correlation tests have indicated that the demographic variables ‘gen-

der’, ‘nationality’ and ‘education’ are positively correlated with the ATE. Furthermore

the tests revealed that ATE is moderately (positive) correlated with the variable

WTPYesNo. To estimate a regression model, the demographic variables were trans-

formed from categorical into numerical variables. A regression with the above three

selected demographic variables on AEmean generates a model with a R² of 0,1923.

Consequently the estimated model explains 19,23% of the variable AEmean (table

6).

Table 6: Estimated Regression Model to Explain AEmean

Source: own calculation based on survey data

The Adjusted R² is lower than R² and has a value of 0,1907. The Adjusted R² is a

modification of R² that adjusts the number of explanatory terms in a model. Unlike R²,

the adjusted R² increases only if a new term improves the model more than would be

expected by chance. The adjusted R² can be negative, and will always be less than

or equal to R². The estimated model shows that ‘education’ increases by one unit (no

high school diploma = 1, PhD = 7), the AEmean increases ceteribus paribus (c.p.) by

0,0370. If the variable ‘gender’ increases (female = 1; male = 2) AEmean decreases

c.p. by 0,1788. Consequently, for a male respondent, the model estimates an

AEmean lower (-01788) than for women. Furthermore an increase of the variable

‘nationality’ (French = 1; German = 2) leads to a decrease c.p. of the mean of 0,3288.

‘Gender’ and ‘nationality’ are significant at the 1% significance level. ‘Education’ is

significant at the 10% significance level.

Further models were tested but did not generate higher R² values. Another criterion

for the selection of a model is the Akaike Information Criterion (AIC) and the Bayesi-

an Information Criterion (BIC). Both criteria have a preference for the model with the

smaller value, but the BIC more strongly penalizes the number of non-relevant ex-

planatory variables. According to a comparison of the AIC and BIC for the model with

nationality, education and gender as well as a model with income and age as ex-

Estimate Std.Error p-value

(Intercept) 5,3652 0,1511 0,00

Education 0,0370 0,0199 0,06

Gender -0,1788 0,0548 0,00

Nationality -0,3288 0,0666 0,00

R-squared: 0,1923

Adjusted R-squared: 0,1907

58

planatory variables the BIC and AIC for the estimated model in table 6 generated the

lowest AIC and BIC values. The AIC (1452 < 1489 < 1498) as well as the BIC (1472

< 1515 < 1532) are smaller for the model with additional explanatory variables. The

larger difference of the three models at BIC supports the argument that the infor-

mation criterion more strongly disapproves of the absorption of non-relevant explana-

tory variables. Thus the model presented in table 6 is the best model to predict the

AEmean value.

Further models to estimate the general WTP and the amount of a price premium

consumers are willing to pay were tested. The results indicate that the data collected

on demographic characteristics and consumer attitudes cannot adequately and satis-

fyingly explain the WTP. A regression model with WTPtotal as dependent variable

and ‘income’, ‘education’ and ‘nationality’ as independent variables explained less

than 5% of the variance of WTPtotal (R² = 0,473). This model could not be improved

by adding additional variables such as ‘ATE’, ‘ATP’, ‘age’ and ‘gender’ as explanatory

variables. Consequently it can be concluded that further and different variables have

to be taken into account to explain the amount a consumer is willing to pay to a satis-

fying extent.

59

5. FINDINGS & IMPLICATIONS

5.1. Findings

The analysis of the survey conducted among 1.103 individuals, thereof 1.045 Ger-

man and French respondents, revealed that 71,39% of the respondents are generally

willing to pay a price premium for products sold in an environmentally friendly super-

market using natural refrigerants. The average price consumers were willing to pay is

11,64€ (price premium of 16,4 %), compared to the reference price of 10€ for prod-

ucts sold in a conventional supermarket. Interestingly the total price respondents are

willing to pay is significantly higher than the stated price premium in percent. The

stated mean premium in percent is 12,75%, resulting in a total price of 11,28€.

Among the 28,61% of respondents that were not willing to pay a premium, 24,7%

stated that they were not willing to pay a premium as they expect the supermarket to

be environmentally friendly anyway and do not feel that they should have to pay for it.

23,8% of the respondents stated that they try to act environmentally friendly without

paying for it. Furthermore respondents stated they require evidence of the environ-

mental performance of the supermarket, for example through an institutionalized eco-

label.

In addition, the results indicate that there is a significant difference between the Ger-

man and French respondents ATE. The French sample showed a significantly (sig.

0,00) higher, respectively more positive ATE than the German sample. Between the

AGP and the intention to purchase green products, no significant differences were

found between French and German consumers. Even if the ATE is higher in the

French sample, the German sample is characterized by a significantly higher WTP.

The German sample shows an average WTP of 12,13€ (premium of 21,3%) com-

pared to 11,53 € (premium of 15,3%) in the French sample.

Within the French and German sample women have a significantly more positive

ATE, but this positive environmental attitude does not lead to significant differences

in the mean AGP, the purchase intention and the WTP.

Between the different age groups in the sample no significant differences in the ATE

and AGP and the mean WTP a price premium for green products was found. Still, it

was noted that the purchase intention does vary between the different age groups.

Young respondents (15 – 24), old respondents (over 65) as well as respondents be-

60

tween 45 and 54 show a significantly lower purchase intention than respondents

aged between 25 and 44.

Even if no significant differences in means for the ATE and AGP were measured

among different incomes, the purchase intention significantly differs. Individuals with

a monthly net income between 2.000 and 2.999€ showed the highest purchase inten-

tion, whereas people with an income below 499€ were less willing to purchase green

products. Interestingly, the highest mean WTP for the total sample was found among

individuals with a monthly net income between 500 and 999€ as well as 1.000 to

1.999€. This is significantly higher than the amount individuals with an income above

2.000€ were WTP. Furthermore the analysis revealed a significant difference (0,00)

in the general WTP (WTPYesNo) between the different income levels. The highest

general WTP was found in the high income levels above 2.000€ of monthly net in-

come.

Education has a significant influence on the respondents’ ATE as individuals holding

or maintaining a ‘PhD’ or ‘Master degree’ showed the highest positive mean scores

for this variable. Among the group ‘PhD’, the positive ATE is also expressed in the

highest WTP (11,95€) among all educational groups. Thus it was shown that a high

education has a positive and significant influence on the general WTP (WTPYesNo)

for products sold in environmentally friendly supermarkets. Respondents holding a

‘Master degree’ (74,4%), ‘A-levels’ (74,0%) and ‘PhD’ (73,1%) stated the highest

general WTP. Even if individuals with a ‘Master degree’ show a positive ATE and

high general WTP, the WTP a price premium for green products is rather low

(11,45€). The highest mean WTP is found among respondents with a Phd (11,945€)

or a Bachelor degree (11,85€) as highest educational qualification.

5.2. Test of Hypotheses and Discussion

The proposed model and the established hypotheses (chapter 2.7) suggest a positive

and significant relationship between the demographic variables ‘gender’ (hypothesis

1a), ‘age’ (H1b), ‘income’ (H1c), ‘nationality’ (H1d), and ‘education’ (H1e) with the

consumers ATE. Furthermore, the analysis of the survey results reveals a significant

and positive relationship between the variable ‘women’ and ‘ATE’. The variable

AEmean is significantly (sig. 0,00) higher for women than men and H1a is therefore

accepted. These findings are supported by many studies that have shown significant

differences between men and women in environmental attitudes with women being

61

more likely to have a positive attitude towards the environment than men (Brown and

Harris, 1992, p. 231; Tikka et al., 2000, p.12; Aydin & Çepni, 2010, p. 2718). As the

demographic variables ‘age’ and ‘income’ have no significant influence on the varia-

ble AEmean, hypotheses 1b and 1c are rejected. Finisterra do Paço & Raposo

(2010, p. 429) showed that Portuguese consumers with high incomes in the age

range from 25 to 34 and from 45 to 54 have a more positive environmental attitude.

These contradicting findings might be explained by fist, the fact that their sample was

comprised of older individuals and second the different cultural contexts in which the

surveys were conducted. The analysis showed that nationality has an influence on

the ATE as French respondents showed a significantly higher ATE and H1d is there-

fore accepted. These findings are in line with a survey conducted among European

citizens (Eurobarometer, 2008, p. 11) which revealed a higher environmental concern

among French respondents (79%) than German respondents (56%). In line with find-

ings by Aydin & Çepni (2010, p. 2718) as well as Finisterra do Paço & Raposo (2010,

p. 429), H1e is accepted as well. Individuals holding or obtaining a Master degree or

PhD showed a significantly higher and more positive ATE.

In line with findings by Savita & Kumar (2010, p. 90), Pearson’s coefficient indicates

that there is a moderate relationship between the ATE and the AGP (Pearson =

0,508; sig. 0,00). A regression analysis furthermore revealed that the two variables

are positively correlated. Consequently hypothesis 2 of the proposed model to ex-

plain the consumers WTP is accepted. In general, it is assumed that inconsistencies

in the attitude behavior relationship result from generalized views applied by several

studies and are consequently difficult to compare (Mainieri, Barnett, Vaidero, Unipan,

& Oskamp (1997), cited in Cherian & Jacob, 2012, p. 119),

As a strong and positive relationship is furthermore found between the AGP and the

intention to purchase these products (Pearson = 0,691; sig. 0,00) hypothesis 3 is ac-

cepted. The existing research also confirms a positive influence of the attitude to-

wards green and eco-labeled products on the purchase intention (Purohit, 2012, p.

160).

The influence of the consumers AGP (Pearson = 0,349; sig. 0,00) and the intention to

purchase green products (Pearson = 0,351; sig. 0,00) on the stated general WTP for

green products (WTPYesNo) was found to be rather weak. In addition, the amount

consumers were willing to pay (WTPtotal) was only weakly influenced by the AGP

62

(Pearson = 0,157; sig. 0,00) and the purchase intention (Pearson = 0,221; sig. 0,00).

As a result hypotheses 4 and 6 had to be rejected. Studies conducted in the US and

China found significantly higher WTP among consumers with a positive environmen-

tal attitude, interest in green products and a general intention to purchase green

products (Drozdenko et al. 2011, p. 110; Shen 2012, p. 93). However both studies

point out the impact of the product category on the amount customers are willing to

pay. Furthermore, customers need proof of the ecological product performance and

therefore eco-labels could play an important role in increasing the consumers WTP a

price premium.

Even if several studies found no significant relationship between the ATE and the

purchase intention, hypothesis 5 is accepted for the German and French sample, as

the results indicate a moderate to strong and positive relationship between the ATE

and the intention to purchase green products (Pearson = 0,550; sig. 0,00). Studies on

the relationship between the consumers ATE and the intention to purchase green

products in Switzerland (Tanner & Kast, 2003, p. 883) and in India (Chitra, 2007, p.

181) support these findings. Contradicting findings in a Malaysian survey conducted

by Rahbar & Wahid (2010, p. 323) and a survey in Pakistan (Hamid et al., 2012, p.

112) found no significant relationship between the ATE and the purchase intention.

However, as these studies were limited to specific groups, government employees in

Pakistan and rural areas in Malaysia respectively, the authors already highlighted the

limited transferability of these findings.

The moderate and positive relationship found between the ATE and the general WTP

(WTPYesNo) (Pearson = 0,455; sig. 0,00) allows to accept hypothesis 7. Neverthe-

less it has to be pointed out that no significant influence of the ATE on the amount of

the WTP a price premium was found (Pearson 0,116; sig. 0,00). A positive ATE con-

sequently increases the general WTP a higher price but does not necessarily lead to

a price premium being more willingly accepted.

The proposed model furthermore suggests a positive and significant relationship be-

tween the demographic variables ‘gender’ (hypothesis 8a), ‘age’ (H8b), ‘income’

(H8c), ‘nationality’ (H8d), and ‘education’ (H8e) with the consumers AGP. The analy-

sis of the survey results reveals a significantly higher AGP among women. Therefore

H8a is accepted. These findings contradict with results from Savita & Kumar (2010,

p. 96) who found similar attitudes towards environmentally friendly products among

63

men and women. This deviation might be explained by the examined product catego-

ry which is likely to generate higher support by women than men. H8b, H8c, H8d and

H8e were rejected as no significant relationships between the variables ‘age’, ‘in-

come’, ‘nationality’ and ‘education’ and the AGP was found.

In order to test the hypotheses 9a to 9e, the influence of the demographic variables

on the intention to purchase green products was analyzed. As no significant differ-

ences for the purchase intention were found among men and women, H9a was re-

jected. Respondents in the age ranges 25 – 34 and 35 – 44 showed a significantly

higher intention to purchase green products. Therefore H9b was accepted. As re-

spondents with monthly net incomes between 2.000 and 2.999€ stated a significantly

higher intention to purchase green products, compared to the incomes below 999€,

H9c is accepted. ‘Nationality’ and ‘income’ did not show significant influence on the

variable PImean and therefore hypotheses H9d and H9e were rejected.

As no significant differences in the general WTP (WTPYesNo) and the total WTP

(WTPtotal) for green products were found among women and men or varying age

groups, H10a and H10c were rejected. A study on Chinese consumers WTP for sev-

en different product categories, contrary to the findings of this thesis, found significant

differences between age and gender (Shen, 2012, p. 93). However, as cultural differ-

ences as well as product attributes strongly influence the WTP for a product (Hopkins

(2009), cited in Drozdenko et al., 2011, p. 107), these findings are difficult to compare

with the findings of this survey on the German and French consumers. The contra-

dicting findings by Drozdenko et al. (2011, p. 112), who found women in the US to be

more willing to pay for green MP3 players, differ as these product types are also seen

as fashion articles and consequently could lead to higher WTP. Food products sold in

a supermarket are considered to cover basic needs for both, men and women, and

might explain the insignificant differences in the amount respondents were willing to

pay. Even if the analysis revealed no significant differences in the general WTP a

price premium among German and French respondents, the results indicate that

German respondents were willing to pay a significantly higher mean amount than

French respondents. Hypothesis 10d is therefore accepted. As respondents holding

a Bachelor degree or a PhD (total sample) stated a significantly higher general WTP

and they were more willing to pay for a price premium for green products than groups

with other educational qualifications, H10e is accepted. The level of education has a

strong influence on the purchase behavior as “well-educated people [can] correctly

64

understand the relationship between human beings and the environment, [show]

more social responsibilities, [and are] more receptive to the concept of green con-

sumption” (Zhang, 2010, p. 178).

The results furthermore indicate a significant (sig 0,03) influence of income on the

general and total WTP, as the difference in means for WTPtotal for the different in-

come groups is significant.

Following the hypothesis tests executed in order to test the proposed model based

on the existing literature (chapter 2.7) and the estimation of several regression mod-

els to explain the general WTP (WTPYesNo) and the total amount consumers are

willing to pay (WTPtotal), a revised model to explain the consumers WTP for prod-

ucts sold in an environmentally friendly supermarket using natural refrigerants was

designed (figure 6).

Figure 6: Revised Model

Source: own figure

The revised model explains the consumers’ general WTP as a result of the consum-

ers ATE, their monthly net income and educational background. Furthermore, as was

shown, the educational background has an influence on the ATE. People with a

higher education (Master, PhD) are more likely to have a general WTP for the exam-

ined green product category. Female French respondents, with a high educational

background are likely to show the most positive ATE. The total amount consumers

are willing to pay for green products sold in an environmentally friendly supermarket

is influenced by the demographic variables ‘income’, ‘nationality’ and ‘education’.

German respondents and respondents with incomes between 500 – 999 and 1.000 –

1.999€ as well as respondents with a PhD are willing to pay the highest amount for

the examined green product category.

65

5.3. Research Questions and Implications

The aim of this thesis was to find out whether differences exist in French and German

consumers WTP for products sold in an environmentally friendly supermarket and, if

so, how these differences can be explained. The results indicate that the majority of

consumers in Germany and France is willing to pay an average price premium of

11,64€, compared to a base price of 10€, for products sold in an environmentally

friendly supermarket. Even if no significant influence of the consumers ATE on the

amount of the price premium consumers are willing to pay was found, the results

show a significant (sig. 0,00) and moderately positive influence (Pearson Coefficient

0,455) of the ATE on the general WTP. Therefore, the formulated research question

1 “Are consumers with an environmental friendly attitude ready to buy green prod-

ucts?” is supported by the results of this thesis. Consumers with an environmentally

friendly attitude are ready to buy green products. The challenge for supermarkets

using environmentally friendly refrigeration equipment is, however, to translate the

consumers’ readiness to purchase green products into an actual WTP for green

products.

Research question 2 “Are consumers with a positive attitude towards purchasing

green products willing to pay a price premium for products from supermarkets using

natural refrigerants?” was not supported by the survey results. No significant relation-

ship between a high purchase intention and the WTP, both the general WTP and the

amount of a price premium, was found. The results of the conducted survey indicate

that even if consumers have a positive ATE and AGP, they might not be willing to pay

a premium as they expect a supermarket to be environmentally friendly anyway or

want to have proof of its actual green performance. Thus, in order to maintain or in-

crease the WTP of consumers, the marketing campaigns of supermarkets should put

a stronger focus on the overall environmental performance of the company rather

than the environmental performance of specific products. As this study shows that a

high education has a positive influence on the general WTP and is assumed to have

an influence on the environmental knowledge, supermarkets should advocate the

environmental performance of their stores. In this way consumers learn to identify

which stores are environmentally friendly. This suggestion is supported by consumer

responses indicating that they expect the supermarket to proof its environmental per-

formance with an eco-label and to communicate the store’s environmental perfor-

mance more actively. Furthermore supermarkets should target women for their envi-

66

ronmental campaigns as they show a significantly higher ATE than men and are like-

ly to be more responsive to green marketing campaigns.

Research question 3 “Which demographic factors influence consumers with a posi-

tive environmental attitude to actually pay a price premium for products from super-

markets using natural refrigerants?” was answered by demonstrating that a positive

ATE (sig. 0,00), educational status (sig. 0,02) as well as income (sig. 0,00) have a

direct and positive influence on consumers’ general WTP a price premium. Further-

more the analysis revealed that nationality, gender and education of consumers have

a significant (sig. 0,00) influence on the ATE and consequently have an indirect, or

moderate influence on the general WTP for products sold in an environmentally

friendly supermarket. However, the results also show that the general WTP has no

significant influence on the amount the premium consumers are willing to pay. The

analysis revealed that only ‘nationality’, ‘income’ and ‘education’ directly and signifi-

cantly (sig. 0,00) influence the amount of price premium consumers are willing to pay.

An estimated model using multiple regression revealed that the variables collected

explain the variance of WTPtotal only to an unsatisfying extend. Consequently addi-

tional situational, product and brand specific as well as socio-economic characteris-

tics should be taken into consideration in order to explain the total WTP of consumers

more accurately.

As it is of high interest for marketers of green products in general and of technologies

using natural refrigerants in specific, to know if differences exist in t consumer behav-

ior in different (target) markets, research question 4 was formulated as follows:“Are

there differences in the amount and the factors that influence the willingness to pay a

price premium for products from supermarkets using natural refrigerants between

French and German consumers?” The survey results show that even if the average

French consumer has a more positive ATE than German consumers, the nationality

has no direct impact on the general WTP. However, German consumers (12,13€) are

willing to pay a significantly higher price for green products than French consum-

ers(11,53€). Retailers based in Germany might consequently consider charging

higher prices from consumers for products sold in a supermarket using natural refrig-

erants. This is a strong argument for the use of natural refrigerants as it allows the

retailers to charge a price premium from the consumers while improving the environ-

mental performance of their stores. In order to increase the WTP among French con-

sumers, women with a high educational background, aged between 25 – 44 should

67

be targeted through communication campaigns as they appear to be most likely to

translate their positive ATE into actual WTP.

68

6. CONCLUSION

6.1. Conclusion

This thesis aimed to compare German and French consumers’ WTP for green prod-

ucts sold in supermarkets using natural refrigerants. Therefore, a survey was con-

ducted among a total of 1.045 German and French individuals. The results demon-

strate that the general WTP for green products is directly influenced by the ATE as

well as the demographic variables ‘gender’, ‘education’ and ‘income’. The variables

‘nationality’, ‘gender’ and ‘education’ revealed a direct influence on the ATE and con-

sequently influence the general WTP indirectly. A high educational status, as well as

female sex and a French nationality have a significant and positive influence on the

ATE. However, even if women show a significantly more positive ATE, this affirmative

environmental attitude does not lead to significant differences in the mean AGP, the

purchase intention and the WTP. The results furthermore did not indicate a significant

influence of the AGP, the purchase intention and the general WTP on the level of

price premium consumers are willing to pay.

The results of the thesis indicate that French and German consumers are willing to

pay an average price premium of 16,4 % for products sold in an environmentally

friendly supermarket. However, even if French consumers are characterized by a

more positive ATE and higher general WTP compared to German respondents, the

level of the price premium they are willing to pay is significantly lower. German re-

spondents are willing to pay an average price of 12,13€ compared to the base price

of 10€ (premium 21,3%), whereas the average French premium is 15,3% above the

base price (11,53€). ‘Income’, 'nationality’ and ‘education’ revealed a significant im-

pact on the level of price premium consumers are willing to pay.

Out of the 28,61% of respondents that were not willing to pay a premium, 24,7%

stated that they were not willing to pay a premium on the basis that they expect the

supermarket to be environmentally friendly without consumers having to pay for it.

23,8% of the respondents stated that they try to act environmentally friendly without

paying for it. Furthermore, some respondents requested evidence of the environmen-

tal performance of the supermarket, for example through an institutionalized eco-

label.

69

On the basis of the results presented it can be concluded that a general WTP a price

premium for products sold in a supermarket using natural refrigerants does exist.

However, the WTP varies within the different consumer groups in a country and in

line with different cultural backgrounds (for the purpose of this thesis it was assumed

that the variable ‘nationality’ defines a particular cultural background). The results

indicate that retailers can charge premium prices for products sold in environmentally

friendly stores. However, this decision has to be supported by strong advocacy cam-

paigns that communicate the improved environmental performance to the consumers

and are adapted to the national consumer behavior.

6.2. Limitations and Further Research

The results of this thesis provide some interesting insights into the French and Ger-

man consumers’ attitudes towards the environment as well as their green purchase

behavior. However, some limitations have to be considered and will be presented in

the following:

First, the results are limited to consumers in Germany and France. Further interna-

tional comparisons are recommended to help ensure the validity of these find-

ings..Second, as 95,7% of the respondents are aged between 15-24, the results

might not be representative for consumers of all ages. Third, the results of this re-

search must be used cautiously as the study sample is mainly comprised of students.

Further research should be executed among older age groups in order to provide

more representative results. Fourth, even if the total number of 1.045 respondents

can be said to be satisfying, the German respondents are underrepresented, ac-

counting for only 17% (188) of the total sample. This limitation in terms of representa-

tiveness of German respondents have to be duly considered.

Fifth, to analyze the factors that influence the purchase behavior and WTP in more

detail, additional variables, such as the employment status (employed, unemployed,

retired), job field, place of residence (rural, urban) could be included in a future anal-

ysis. In addition, the factors that translate the general WTP into an actual WTP a

price premium should be examined through further qualitative research. The higher

WTP a price premium in the German sample cannot be explained to a satisfying ex-

tend by the factors applied in this thesis.

Sixth, the items used to measure the consumers’ attitude towards the environment

and green products as well as the consumer’s purchase intention have to be further

70

analyzed in order to provide strong evidence that these items measure the right ef-

fect. The items to measure the purchase intention showed a lack of internal con-

sistency as, for these, Cronbach’s alpha is below 0,8.

Seventh, this thesis simplified the analysis of attitudes towards the environment by

measuring one aggregated factor only. Additional factors such as attitude towards

environmental protection, environmental awareness, environmental knowledge and

environmental responsibility should be measured in future studies in order to be able

to describe the effect of environmental attitudes more in detail.

Eighth, there is a gap between what consumers state in surveys about their environ-

mental awareness and their actual purchase decisions observed at the point of sale.

This is one major limitation of the applied online CVM in this thesis as it provides a

measure of WTP that does not necessarily reflect the actual WTP. Especially online

surveys tend to generate more positive responses for the consumer’s WTP. Face-to-

face interviews with consumers conducted directly after an actual purchase or exper-

iments could circumvent this limitation. This approach would furthermore allow to

solve the problem of the stated reference price of 10€ which was chosen to represent

a small regular purchase. The WTP for a reference price of 100€, on the contrary,

might generate different results in terms of accepted price premium. A dichotomous

choice could furthermore be applied in order to deliver more reliable results on the

actual WTP.

Ninth, it is suggested that further research considers specific product categories sold

in an environmentally friendly supermarket, as consumers’ WTP a premium also de-

pends on the types and category of products. In addition, product attributes as well

as situational factors may be crucial in determining the amount consumers are willing

to pay. Product characteristics are not included in this study and should consequently

be considered in future research.

Finally the social desirability bias should be taken into account when measuring the

consumer’s WTP or ATE. The items used in this thesis to measure the SD, measure

one aspect of this bias (gamma factor) only and needs to be further supported

through additional studies.

71

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APPENDIXES

Appendix 1: Questionnaire English Version

82

83

84

85

86

(WTP Yes)

87

88

(WTP No)

89

90

91

Appendix 2: Questionnaire German Version

92

93

94

95

96

(WTP Yes)

97

98

(WTP No)

99

100

101

Appendix 3: Questionnaire French Version

102

103

104

105

106

(WTP Yes)

107

108

(WTP No)

109

110

111

Appendix 4: Nationality Cross Tabulations

Female Male Total

count 638 219 857

% within French 74,4% 25,6% 100,0%

% of Total 57,8% 19,9% 77,7%

count 125 63 188

% within German 66,5% 33,5% 100,0%

% of Total 11,3% 5,7% 17,0%

count 42 16 58

% within Other 72,4% 27,6% 100,0%

% of Total 3,8% 1,5% 5,3%

count 805 298 1103

% 73,0% 27,0% 100,0%

German

Other

Total

Gender

Nationality & Gender Cross Tabulation

Nationality

French

15 - 24 25 - 34 35 - 44 45 - 54 55 - 64 65 + Total

count 674 147 23 8 3 2 857

% within French 78,6% 17,2% 2,7% 0,9% 0,4% 0,2% 100,0%

% of Total 61,1% 13,3% 2,1% 0,7% 0,3% 0,2% 77,7%

count 106 73 4 4 1 188

% within German 56,4% 38,8% 2,1% 2,1% 0,5% 0,0% 100,0%

% of Total 9,6% 6,6% 0,4% 0,4% 0,1% 0,0% 17,0%

count 27 28 1 1 1 58

% within Other 46,6% 48,3% 1,7% 0,0% 1,7% 1,7% 100,0%

% of Total 2,4% 2,5% 0,1% 0,0% 0,1% 0,1% 5,3%

count 807 248 28 12 5 3 1103

% 73,2% 22,5% 2,5% 1,1% 0,5% 0,3% 100,0%

German

Other

Total

Age

Nationality & Age Cross Tabulation

Nationality

French

A-levels

Bachelor

degree

Master

degree

No high

school Other PhD

Vocational

training Total

count 111 108 379 2 139 92 25 856

% within French 13,0% 12,6% 44,3% 0,2% 16,2% 10,7% 2,9% 100,0%

% of Total 10,1% 9,8% 34,5% 0,2% 12,6% 8,4% 2,3% 77,8%

count 70 62 47 1 1 1 5 187

% within German 37,4% 33,2% 25,1% 0,5% 0,5% 0,5% 2,7% 100,0%

% of Total 6,4% 5,6% 4,3% 0,1% 0,1% 0,1% 0,5% 17,0%

count 3 17 21 5 11 57

% within Other 5,3% 29,8% 36,8% 0,0% 8,8% 19,3% 0,0% 100,0%

% of Total 0,3% 1,5% 1,9% 0,0% 0,5% 1,0% 0,0% 5,2%

count 184 187 447 3 145 104 30 1100

% 16,7% 17,0% 40,6% 0,3% 13,2% 9,5% 2,7% 100,0%

Other

Total

Nationality & Education Cross Tabulation

Education

Nationality

French

German

112

0 - 499 500 - 999 1000 - 1999 2000 - 2999 3000+ not specified Total

count 329 135 147 55 43 148 857

% within French 38,4% 15,8% 17,2% 6,4% 5,0% 17,3% 100,0%

% of Total 29,8% 12,2% 13,3% 5,0% 3,9% 13,4% 77,7%

count 38 72 31 20 12 15 188

% within German 20,2% 38,3% 16,5% 10,6% 6,4% 8,0% 100,0%

% of Total 3,4% 6,5% 2,8% 1,8% 1,1% 1,4% 17,0%

count 21 11 10 5 3 8 58

% within Other 36,2% 19,0% 17,2% 8,6% 5,2% 13,8% 100,0%

% of Total 1,9% 1,0% 0,9% 0,5% 0,3% 0,7% 5,3%

count 388 218 188 80 58 171 1103

% 35,2% 19,8% 17,0% 7,3% 5,3% 15,5% 100,0%Total

Nationality & Income Cross Tabulation

Income

Nationality

French

German

Other

113

Appendix 5 : Frequencies and Percentage Analysis

Frequency % Frequency % Frequency % Frequency % Frequency % Frequency %

AE01

Environmental problems have a direct

effect on my daily life. 23 2,1% 47 4,3% 59 5,3% 172 15,6% 301 27,3% 501 45,4%

AE02

I am concerned about environmental

issues such as global warming and

climate change. 23 2,1% 51 4,6% 93 8,4% 236 21,4% 330 29,9% 370 33,5%

AE03

In general, I would consider myself as

environmental friendly. 7 0,6% 12 1,1% 64 5,8% 213 19,3% 474 43,0% 333 30,2%

AE04

The big polluters (corporations and industry)

should be mainly responsible for protecting

the environment. 28 2,5% 44 4,0% 120 10,9% 304 27,6% 341 30,9% 266 24,1%

AE05

As an individual, I can play a role in

protecting the environment. 12 1,1% 33 3,0% 85 7,7% 186 16,9% 301 27,3% 486 44,1%

AE06

I take actions to protect the environment

(recycle waste, cut down energy con-sumption,

consume locally produced food, etc.) 13 1,2% 40 3,6% 66 6,0% 228 20,7% 403 36,5% 353 32,0%

AE07

Environmental protection must be given

priority over the competitiveness of the economy. 28 2,5% 57 5,2% 150 13,6% 299 27,1% 248 22,5% 321 29,1%

AE08

The increasing deterioration of the environment

is a serious problem. 9 0,8% 5 0,5% 34 3,1% 98 8,9% 294 26,7% 663 60,1%

ATTITUDE TOWARDS THE ENVIRONMENT

strongly

disagree

moderately

disagree

mildly

disagree

mildly

agree

moderately

agree

strongly

agree

114

Frequency % Frequency % Frequency % Frequency % Frequency % Frequency %

AG01 are good for the environment. 7 0,7% 14 1,3% 57 5,5% 244 23,3% 414 39,6% 309 29,6%

AG02 are of good quality/high performance. 6 0,6% 29 2,8% 139 13,3% 430 41,1% 343 32,8% 98 9,4%

AG03

are of better quality/have a better performance

than conventional products. 35 3,3% 70 6,7% 173 16,6% 290 27,8% 270 25,8% 207 19,8%

AG04 offer a good alternative to conventional products. 7 0,7% 18 1,7% 99 9,5% 267 25,6% 360 34,4% 294 28,1%

AG05 are reasonably priced. 128 12,2% 242 23,2% 317 30,3% 240 23,0% 91 8,7% 27 2,6%

AG06 are more expensive than conventional products. 336 32,2% 460 44,0% 185 17,7% 44 4,2% 13 1,2% 7 0,7%

AG07

are more expensive but have a lower performance

than conventional products. 40 3,8% 91 8,7% 240 23,0% 308 29,5% 238 22,8% 128 12,2%

AG08 will improve the standard of living of future generations. 17 1,6% 41 3,9% 115 11,0% 285 27,3% 361 34,5% 226 21,6%

AG09 will increase my own and my family’s standard of living. 37 3,5% 81 7,8% 189 18,1% 324 31,0% 226 21,6% 158 15,1%

ATTITUDE TOWARDS GREEN PRODUCTSstrongly

agree

strongly

disagree

moderately

disagree

mildly

disagree

mildly

agree

moderately

agree

Frequency % Frequency % Frequency % Frequency % Frequency % Frequency %

PI01

Generally speaking, buying green products

is a better choice. 28 2,7% 48 4,6% 156 14,9% 335 32,1% 288 27,6% 190 18,2%

PI02

If possible, I prefer buying green products

to conventional products. 42 4,0% 98 9,4% 150 14,4% 239 22,9% 253 24,2% 263 25,2%

PI03

Buying green products generally benefits

the consumer. 48 4,6% 120 11,5% 302 28,9% 340 32,5% 166 15,9% 59 5,6%

PI04

I am ready to buy environmentally friendly

products even if they cost a little bit more. 51 4,9% 97 9,3% 192 18,4% 276 26,4% 249 23,8% 180 17,2%

PI05

It is very likely that I will purchase a green

product within the next weeks. 73 7,0% 92 8,8% 145 13,9% 211 20,2% 248 23,7% 276 26,4%

PI06

I am always looking for green products when

thinking about a purchase 187 17,9% 180 17,2% 215 20,6% 194 18,6% 142 13,6% 127 12,2%

PI07

Green products would probably be too

expensive for me. 166 15,9% 250 23,9% 260 24,9% 190 18,2% 131 12,5% 48 4,6%

PI08

My friends would like me to buy green

products. 383 36,7% 258 24,7% 210 20,1% 133 12,7% 43 4,1% 18 1,7%

strongly

agreePURCHASE INTENTION ITEMSstrongly

disagree

moderately

disagree

mildly

disagree

mildly

agree

moderately

agree

115

Appendix 6: Numerical Analysis Attitude Measures

mean sd IQR 0% 25% 50% 75% 100% mean sd IQR 0% 25% 50% 75% 100% mean sd IQR 0% 25% 50% 75% 100% p-value Interpretation

AE01 4,980 1,242 2 1 4 5 6 6 5,251 0,991 2 1 4 6 6 6 3,729 1,508 1 1 4 5 5 6 < 2.2e-16 sign. diff. in means

AE02 4,730 1,251 2 1 4 5 6 6 4,817 1,201 2 1 4 5 6 6 4,399 1,390 2 1 5 6 6 6 0.0001668 sign. diff. in means

AE03 4,934 0,961 2 1 4 5 6 6 5,020 0,905 2 1 4 5 6 6 4,617 1,076 2 1 5 6 6 6 3.049 e-06 sign. diff. in means

AE04 4,526 1,223 1 1 4 5 5 6 4,599 1,183 1 1 4 5 5 6 4,186 1,313 1 1 4 5 5 6 9.419 e-05 sign. diff. in means

AE05 4,984 1,167 2 1 4 5 6 6 4,952 1,171 2 1 4 5 6 6 5,149 1,123 2 1 6 6 6 6 0.03169 sign. diff. in means

AE06 4,837 1,121 2 1 4 5 6 6 4,866 1,104 2 1 4 5 6 6 4,761 1,143 2 1 5 6 6 6 0.2513 no sign. diff. in means

AE07 4,491 1,312 2 1 4 5 6 6 4,532 1,292 2 1 4 5 6 6 4,245 1,370 2 1 4 6 6 6 0.00899 sign. diff. in means

AE08 5,404 0,907 1 1 5 6 6 6 5,418 0,892 1 1 5 6 6 6 5,335 0,981 1 1 6 6 6 6 0.2889 no sign. diff. in means

AEmean 4,861 0,764 1 1 4 5 5 6 4,932 0,727 1 1 5 5 6 6 4,553 0,826 1 1 5 6 1 6 1.784 e-08 sign. diff. in means

Items

Total sample French sample German sample

Independent samples t-test(means French and German sample)

95% significance level

Attitude towards the Environemnt Items

mean sd IQR 0% 25% 50% 75% 100% mean sd IQR 0% 25% 50% 75% 100% mean sd IQR 0% 25% 50% 75% 100% p-value Interpretation

AG01 4,8861 0,9814 2 1 4 5 6 6 4,877 0,997 2 1 4 5 6 6 4,926 0,910 2 1 4 5 6 6 0,52 no sign. diff. in means

AG02 4,3100 0,9507 1 1 4 4 5 6 4,324 0,947 2 1 3 4 5 6 4,245 0,967 1 1 4 4 5 6 0,31 no sign. diff. in means

AG03 4,2545 1,3124 2 1 3 4 5 6 4,335 1,322 2 1 3 4 5 6 3,888 1,203 1 1 3 4 4 6 0,01 sign. diff. in means

AG04 4,7579 1,0585 2 1 4 5 6 6 4,747 1,068 2 1 4 5 6 6 4,809 1,016 2 1 4 5 6 6 0,46 no sign. diff. in means

AG05 3,0048 1,2383 2 1 2 3 4 6 2,861 1,194 2 1 2 3 4 6 3,660 1,228 1 1 3 4 4 6 0,00 sign. diff. in means

AG06 2,0038 0,9418 1 1 1 2 2 6 2,043 0,959 1 1 1 2 2 6 1,824 0,838 1 1 1 2 2 6 0,00 no sign. diff. in means

AG07 3,9541 1,2805 2 1 3 4 5 6 3,844 1,270 1 1 3 4 4 6 4,457 1,208 2 1 3 5 5 6 0,00 sign. diff. in means

AG08 4,5407 1,1545 1 1 4 5 5 6 4,588 1,154 1 1 4 5 5 6 4,324 1,136 1 1 4 4 5 6 0,00 sign. diff. in means

AG09 4,1053 1,2845 2 1 3 4 5 6 4,131 1,290 2 1 3 4 5 6 3,989 1,254 2 1 3 4 5 6 0,17 no sign. diff. in means

AGmean 3,9797 0,6526 0,9 1 3,6 4,0 4,4 6 3,972 0,654 1,3 1 3 4 4,3 6 4,014 0,648 1,4 3 4 4,4 6 0,43 no sign. diff. in means

Attitude towards Green Proucts Items

Items

Total sample French sample German sample

Independent samples t-test(means French and German

sample)

116

mean sd IQR 0% 25% 50% 75% 100% mean sd IQR 0% 25% 50% 75% 100% mean sd IQR 0% 25% 50% 75% 100% p-value Interpretation

PI01 4,318 1,216 1 1 4 4 5 6 4,303 1,220 1 1 4 4 5 6 4,383 1,198 1 1 4 4 5 6 0,41 no sign. diff. in means

PI02 4,294 1,430 3 1 3 4 6 6 4,328 1,439 3 1 3 4 6 6 4,138 1,381 3 1 3 4 6 6 0,09 no sign. diff. in means

PI03 3,634 1,205 1 1 3 4 4 6 3,564 1,194 1 1 3 4 4 6 3,957 1,205 1 1 3 4 4 6 0,00 sign. diff. in means

PI04 4,067 1,385 2 1 3 4 5 6 4,048 1,395 2 1 3 4 5 6 4,154 1,341 2 1 3 4 5 6 0,33 no sign. diff. in means

PI05 4,241 1,535 3 1 3 5 6 6 4,267 1,538 3 1 3 5 6 6 4,122 1,524 3 1 3 5 6 6 0,24 no sign. diff. in means

PI06 3,292 1,621 3 1 2 3 5 6 3,282 1,596 3 1 2 3 5 6 3,335 1,737 3 1 2 3 5 6 0,70 no sign. diff. in means

PI07 3,013 1,405 2 1 2 3 4 6 2,903 1,362 2 1 2 3 4 6 3,516 1,490 2 1 2 3 4 6 0,00 sign. diff. in means

PI08 2,284 1,281 2 1 1 2 3 6 2,112 1,192 2 1 1 2 3 6 3,053 1,398 2 1 1 2 3 6 0,00 sign. diff. in means

PImean 3,837 1,081 1,57 1 3 3,86 4,57 6 3,814 1,059 1,8 1 2,3 3,8 4,1 6 3,944 1,173 1,6 1 3 3,9 4,6 6 0,16 no sign. diff. in means

Purchase Intention Items

Items

Total sample French sample German sample

Independent samples t-test(means French and German

sample)

mean sd median mean sd median mean sd median p-value Interpretation

SD01 4,245 1,451 5 4,365 1,409 5 3,697 1,519 4 0,00 sign. diff. in means

SD02 4,187 1,240 4 4,244 1,240 4 3,926 1,208 4 0,00 sign. diff. in means

SD03 3,687 1,475 4 3,516 1,457 4 4,468 1,297 5 0,00 sign. diff. in means

SD04 3,478 1,278 3 3,501 1,302 4 3,378 1,163 4 0,20 no sign. diff. in means

SD05 4,957 1,501 6 5,167 1,326 6 4,000 1,847 5 0,00 sign. diff. in means

SD06 4,741 1,097 5 4,769 1,082 5 4,612 1,578 5 0,09 no sign. diff. in means

SDmean 4,216 0,725 4,333 4,260 0,705 4,453 4,013 0,783 4,292 0,00 sign. diff. in means

Social Desirability Items

Items

Total sample French sample German sample Independent samples t-test

117

Appendix 7: Correlation Matrix Attitude towards the Environment

AE01 AE02 AE03 AE04 AE05 AE06 AE07 AE08

AE01 1 0.5865007 0.4105534 0.21052087 0.27626874 0.3506358 0.3748602 0.4927224

AE02 0.5865007 1 0.4633273 0.20933851 0.41230608 0.4636968 0.4589528 0.6004309

AE03 0.4105534 0.4633273 1 0.18129602 0.31468871 0.5721089 0.2996388 0.3831491

AE04 0.2105209 0.2093385 0.1812960 1 0.02759289 0.1217258 0.2730916 0.2185235

AE05 0.2762687 0.4123061 0.3146887 0.02759289 1 0.4671663 0.3297895 0.4558471

AE06 0.3506358 0.4636968 0.5721089 0.12172584 0.46716626 1 0.3590041 0.3666844

AE07 0.3748602 0.4589528 0.2996388 0.27309159 0.32978947 0.3590041 1 0.5019318

AE08 0.4927224 0.6004309 0.3831491 0.21852345 0.45584708 0.3666844 0.5019318 1

Correlation Matrix Attitude towards the Environment Items

AG01 AG02 AG03 AG04 AG05 AG06 AG07 AG08 AG09

AG01 1 0.4844900 0.195062274 0.37915429 0.1738492 -0.09487469 0.112456910 0.45766209 0.38642373

AG02 0.48449003 1 0.243764167 0.50776184 0.3363947 0.01686040 0.372875104 0.35416893 0.41722934

AG03 0.19506227 0.2437642 1 0.18023103 0.0434516 -0.16430139 0.002974153 0.12907500 0.22500675

AG04 0.37915429 0.5077618 0.180231032 1 0.4393272 0.06338524 0.268809448 0.43484773 0.48162211

AG05 0.17384925 0.3363947 0.043451600 0.43932722 1 0.32275804 0.287068287 0.24742181 0.34173042

AG06 -0.09487469 0.0168604 -0.164301387 0.06338524 0.3227580 1 0.224132155 0.02276149 0.05746972

AG07 0.11245691 0.3728751 0.002974153 0.26880945 0.2870683 0.22413215 1 0.07642492 0.17066720

AG08 0.45766209 0.3541689 0.129074998 0.43484773 0.2474218 0.02276149 0.076424922 1 0.71086283

AG09 0.38642373 0.4172293 0.225006751 0.48162211 0.3417304 0.05746972 0.170667198 0.71086283 1

Correlation Matrix Attitude towards Green Products Items

118

PI01 PI02 PI03 PI04 PI05 PI06 PI07 PI08

PI01 1 0.7012140 0.6325174 0.54028671 0.5142204 0.5463851 0.24210497 0.15028335

PI02 0.7012140 1 0.5770459 0.63028323 0.6273261 0.6827227 0.31230074 0.12619659

PI03 0.6325174 0.5770459 1 0.53909137 0.4586851 0.5306808 0.27505938 0.19611577

PI04 0.5402867 0.6302832 0.5390914 1 0.6512592 0.6485471 0.48492618 0.09128037

PI05 0.5142204 0.6273261 0.4586851 0.65125920 1 0.6808815 0.36002232 0.13138051

PI06 0.5463851 0.6827227 0.5306808 0.64854709 0.6808815 1 0.37432049 0.20784947

PI07 0.2421050 0.3123007 0.2750594 0.48492618 0.3600223 0.3743205 1 -0.02176704

PI08 0.1502834 0.1261966 0.1961158 0.09128037 0.1313805 0.2078495 -0.02176704 1

Correlation Matrix Purchase Intention Items

119

Appendix 8: Factor Analysis Attitude Measures

Factor 1 Factor 2 Factor 3

AE01

Environmental problems have a direct

effect on my daily life. 0.271 0.170 0.705

AE02

I am concerned about environmental

issues such as global warming and

climate change. 0.434 0.260 0.586

AE03

In general, I would consider myself as

environmental friendly. 0.226 0.472 0.388

AE05

As an individual, I can play a role in

protecting the environment. 0.494 0.344 0.127

AE06

I take actions to protect the environment

(recycle waste, cut down energy con-sumption,

consume locally produced food, etc.) 0.237 0.953 0.176

AE07

Environmental protection must be given

priority over the competitiveness of the economy. 0.489 0.201 0.294

AE08

The increasing deterioration of the environment

is a serious problem. 0.386 0.132 0.731

ATTITUDE TOWARDS THE ENVIRONMENT

4,35

0,226

chi square statistic

p-value

Factor1 Factor2 Factor3 Factor4 Factor5

AG01 are good for the environment. 0.329 0.582 0.179

AG02 are of good quality/high performance. 0.217 0.133 0.559 0.281 0.309

AG03

are of better quality/have a better performance

than conventional products. 0.116 0.116 0.590

AG04 offer a good alternative to conventional products. 0.274 0.286 0.538 0.288 0.200

AG05 are reasonably priced. 0.170 0.950 0.123 0.221

AG06 are more expensive than conventional products. 0.275 -0.151 0.309 -0.319

AG07

are more expensive but have a lower performance

than conventional products. 0.137 0.116 0.310

AG08 will improve the standard of living of future generations. 0.842 0.310

AG09 will increase my own and my family’s standard of living. 0.795 0.163 0.114 0.178 0.238

p-value

0.71

0.399

ATTITUDE TOWARDS GREEN PRODUCTS

chi square statistic

Factor1 Factor2 Factor3

PI01

Generally speaking, buying green products

is a better choice. 0.897 0.191 0.226

PI02

If possible, I prefer buying green products

to conventional products. 0.586 0.325 0.501

PI03

Buying green products generally benefits

the consumer. 0.567 0.294 0.298

PI04

I am ready to buy environmentally friendly

products even if they cost a little bit more. 0.342 0.895 0.277

PI05

It is very likely that I will purchase a green

product within the next weeks. 0.338 0.423 0.568

PI06

I am always looking for green products when

thinking about a purchase 0.351 0.369 0.717

PI07

Green products would probably be too

expensive for me. 0.118 0.422 0.243

INTENTION TO PURCHASE GREEN PRODUCTS

5,27

0,0739p-value

chi square statistic

120

Appendix 9: Cronbach’s alpha

Variable Number of

Items

Cronbach's

alpha

Attitude towards the environment

(AEmean)7 0,8329

Attitude towards green products

(AGmean)9 0,7418

Intention to purchase green

products

(PImean)

7 0,8843

121

Appendix 10: Correlation Attitude Items with SDmean

Attitude Towards

the Environment

Correlation with

SD mean

AE01 0.1897397

AE02 0.1803656

AE03 0.3016969

AE05 0.1678481

AE06 0.1946935

AE07 0.1533410

AE08 0.1722492

Attitude Towards

Green Products

Correlation with

SD mean

AG01 0.10272939

AG02 0.13739873

AG03 0.02508655

AG04 0.12736435

AG05 0.08042494

AG06 0.02776712

AG07 0.05691605

AG08 0.18481209

AG09 0.18258088

Purchase Intention

Green Products

Correlation with

SD mean

PI01 0.15935987

PI02 0.12889995

PI03 0.11205109

PI04 0.16418003

PI05 0.13971591

PI06 0.17499965

PI07 0.08474642

Correlation Attitude Items

and Social Desirability

122

Appendix 11: ANOVA test

mean sd mean sd F-value p-value*

AEmean 4,605 0,886 4,979 0,774 34,20 0,00

AGmean 4,014 0,648 3,972 0,654 0,62 0,43

PImean 3,944 1,173 3,814 1,059 2,24 0,14

WTPtotal 12,128 1,761 11,531 1,255 21,06 0,00

WTPpercent 12,955 11,515 12,708 13,040 0,04 0,84

*0,05 significance level

German French F-statistic

Nationality

mean sd mean sd F-value p-value*

AEmean 4,969 0,750 4,758 0,932 14,14 0,00

AGmean 4,021 0,643 3,869 0,667 11,18 0,00

PImean 3,873 1,064 3,739 1,121 3,21 0,07

WTPtotal 11,663 1,384 11,570 1,358 0,67 0,41

WTPpercent 12,815 12,950 12,585 12,314 0,05 0,83

Gender

Female Male F-statistic

*0,05 significance level

123

mean sd mean sd mean sd mean sd mean sd mean sd F-value p-value*

AEmean 4,879 0,822 4,986 0,732 5,148 0,859 4,881 1,055 5,536 0,376 5,429 0,000 1,74 0,12

AGmean 3,972 0,653 4,025 0,652 4,008 0,643 3,593 0,760 4,056 0,390 4,000 0,157 1,10 0,36

PImean 3,782 1,063 3,998 1,115 4,307 1,110 3,369 1,148 3,964 0,472 3,643 1,515 2,90 0,01

WTPtotal 11,609 1,354 11,851 1,519 11,066 0,781 11,344 0,896 11,063 0,657 11,000 1,414 1,75 0,12

WTPpercent 12,794 12,922 12,944 13,018 12,263 9,243 5,688 2,470 16,875 9,601 11,250 10,607 0,59 0,71

Age

15-24 25-34 F-statistic

*0,05 significance level

35-44 45-54 65+55-64

mean sd mean sd mean sd mean sd mean sd mean sd F-value p-value*

AEmean 4,956 0,774 4,794 0,869 4,941 0,815 5,065 0,735 4,810 0,892 4,896 0,783 1,87 0,10

AGmean 3,943 0,638 3,996 0,665 4,006 0,723 4,068 0,615 3,883 0,625 4,004 0,616 0.879 0.494

PImean 3,692 1,058 3,869 1,129 3,977 1,138 4,149 1,039 3,873 1,122 3,816 0,966 3,26 0,01

WTPtotal 11,442 1,182 11,894 1,641 11,826 1,475 11,560 1,252 11,669 1,616 11,622 1,286 2,50 0,03

WTPpercent 12,078 12,640 13,763 13,814 13,067 11,318 12,052 10,503 11,096 8,756 13,556 14,946 0,60 0,70

*0,05 significance level

Income

0-499 500-999 1.000-1.999 2.000-2.999 3.000+ not specified F-statistic

mean sd mean sd mean sd mean sd mean sd mean sd mean sd F-value p-value*

AEmean 4,381 1,014 4,716 0,816 4,638 1,075 4,875 0,875 4,976 0,769 5,103 0,685 4,988 0,710 4,31 0,00

AGmean 3,704 0,612 3,966 0,603 3,733 0,759 4,059 0,647 3,972 0,632 4,047 0,684 3,945 0,708 1,50 0,18

PImean 3,429 0,286 3,750 1,057 3,610 1,114 3,922 1,170 3,846 1,048 4,055 1,101 3,740 1,060 1,50 0,18

WTPtotal 10,500 NA 11,771 1,656 11,328 0,874 11,850 1,358 11,446 1,270 11,945 1,445 11,669 1,247 2,56 0,02

WTPpercent 6,250 NA 13,756 14,212 10,703 7,331 13,871 12,097 11,878 12,882 13,180 12,476 13,017 12,081 0,67 0,68

*0,05 significance level

Other

Education

No high school

diploma A-levels Vocational Training Bachelor degree Master Degree PhD F-statistic

124

Appendix 12: ANOVA WTPtotal by Nationality

mean sd F-statisctic

p-value

0,05 mean sd F-statisctic

p-value

0,05

Female 12,1960 1,6463 Female 11,5599 1,3047

Male 11,9944 1,9789 Male 11,4494 1,0993

15 - 24 12,1154 1,8585 15 - 24 11,5270 1,2363

25 - 34 12,3021 1,6790 25 - 34 11,6462 1,4015

35 - 44 11,1667 0,2887 35 - 44 11,0469 0,8476

45 - 54 11,0833 0,8036 45 - 54 11,5000 1,0000

55 - 64 10,7500 NA 55-64 11,1667 0,7638

65+ 65+ 11,0000 1,4142

A-levels 12,3980 2,2678 A-levels 11,4088 1,0198

Bachelor degree 12,2907 1,5886 Bachelor degree 11,6125 1,1590

Master degree 11,6944 1,0608 Master degree 11,4146 1,2926

No high school diploma 10,5000 NA No high school diploma NA NA

Other NA NA Other 11,6686 1,2470

PhD 11,5000 NA PhD 11,9515 1,4553

Vocational training 11,5000 0,7071 Vocational training 11,3036 0,9156

0 - 499 11,7321 1,1137 0 - 499 11,4071 1,1870

500 - 999 12,2449 1,6890 500 - 999 11,6912 1,5875

1000 - 1999 12,4211 2,2762 1000 - 1999 11,7125 1,2535

2000 - 2999 11,7833 0,7372 2000 - 2999 11,4826 1,3859

3000 + 12,4546 2,7037 3000 + 11,3707 0,8308

not specified 12,2500 2,3479 not specified 11,5656 1,1440

0,777

2,547

1,281Income 0,271

Age 0,558

0,388

0,753

0,982

0,624

0,909

German French

Gender 0,341

Age 0,566

Education 0,027Education 0,432

Income 0,682

Gender 0,534