Department of Business Administration,
Business and Social Sciences,
Aarhus University
Analysis of the factors influencing green brand
loyalty on the Bulgarian market
Master Thesis
Master of Science in Marketing
Student: Mariana Ditcheva
Advisor: Athanasios Krystallis
November 2011
Acknowledgements
Above all, I would like to thank to my advisor Athanasios Krystallis for his good
guidance and his advices through the entire process. I am also very grateful to my
family and my friends in Bulgaria for their great help, encouragement and support.
Further thanks to everyone who filled in my questionnaire.
Abstract
Purpose – The purpose of this study is to investigate if the different types of values,
types of costs and some psychographic characteristics of consumers influence the brand
loyalty of green brands of detergents on the Bulgarian market. Another goal is to
provide an insight for the factors that influence the intention to buy the ecological
products regarding non-consumers.
Design/methodology/approach – The research was conducted on a quantitative basis
via an online questionnaire-based survey regarding brands of ecological detergents. All
respondents were Bulgarians. The 7-item Likert scale and the Likert-type scale were
used to examine the influence of the types of values, costs and psychographic
charatcteristics on brand loyalty. The software used for analyzing the results was IBM
SPSS Statistics version 19. Principal components analysis was used to determine the
main components in the structure of costs, values and psychographic characteristics.
Then, multiple regression was used to examine what is the influence of these
components on brand loyalty in the case of customers and on the intention to purchase
in the case of non-customers of green brands of detergents.
Findings – The types of value had a positive influence on brand loyalty and,
particularly, functional value had the highest contribution to explaining it in the case of
consumers. In the case of non-consumers, altruistic value was the most influential type
of value. It turned out that the types of costs had a weaker relationship with brand
loyalty and out of the costs - only purchase effort had a statistically significant influence
in the case of consumers of ecological brands. Regarding the psychographic
characteristics, involvement and environmental concern had a positive influence on
brand loyalty. However, in the case of consumers, involvement explained the dependent
variable better and in the case of non-consumers, only environmental concern had a
statistically significant contribution. Overall, the results of the analysis were statistically
significant.
Practical implications – The result regarding functional value suggests that it could be
recommended when making materials for marketing purposes to be focused on the good
quality and performance of the ecological product. The importance of purchase effort
(which includes product availability) suggests that it would be crucial for companies to
increase the density of distribution of their ecological products in order to ease their
consumers and foster brand loyalty. Customer segmentation on the basis of involvement
could be done regarding consumers and on the basis of environmental concern –
regarding non-consumers of ecological products.
Research limitations – The main limitations were the small sample size of nonconsumers and the fact that there was limited comparability because there are few
previous similar studies.
Originality/value – As far as the researcher’s knowledge, other studies have been
conducted examining the factors influencing brand loyalty but none - in the context of
ecological products.
Keywords: Brand loyalty, Customer value, Ethical consumerism, Multiple regression,
Principal component analysis
Contents
1. INTRODUCTION ...................................................................................................................... 1
1.1. BACKGROUND .................................................................................................................. 1
1.2. RESEARCH QUESTIONS ................................................................................................... 2
1.3. DELIMITATIONS ............................................................................................................... 2
1.4. METHODOLOGY ............................................................................................................... 3
1.5. THESIS STRUCTURE ........................................................................................................ 3
2. LITERATURE REVIEW ............................................................................................................. 5
2.1. ETHICAL CONSUMERS ..................................................................................................... 5
2.2. THE THEORY OF REASONED ACTION, THE THEORY OF P LANNED BEHAVIOUR
AND NEUTRALIZATION ........................................................................................................... 5
2.3. CUSTOMER VALUE ........................................................................................................... 8
2.4. RELATIONSHIP MARKETING AND BRAND LOYALTY ................................................. 13
3. HYPOTHESES ......................................................................................................................... 18
4. METHODOLOGY .................................................................................................................... 19
4.1. RESEARCH APPROACH .................................................................................................. 19
4.2. QUESTIONNAIRE DESIGN .............................................................................................. 19
4.3. ANSWERING SCALES ..................................................................................................... 23
4.4. COMPOSITE VARIABLES ............................................................................................... 24
4.5. SAMPLE ........................................................................................................................... 25
4.6. METHODS OF ANALYSIS ................................................................................................ 25
4.6.1. STANDARD MULTIPLE REGRESSION ..................................................................... 25
4.6.2. CORRELATION ........................................................................................................ 28
4.6.3. P RINCIPAL COMPONENTS ANALYSIS .................................................................... 28
5. ANALYSIS AND DISCUSSION ................................................................................................. 34
5.1. SAMPLE CHARACTERISTICS ......................................................................................... 34
5.1.1. DEMOGRAPHICS ..................................................................................................... 34
5.1.2. BUYING BEHAVIOUR .............................................................................................. 34
5.2. RELIABILITY AND VALIDITY ........................................................................................ 36
5.3. P RINCIPAL COMPONENTS ANALYSIS ........................................................................... 38
5.3.1. STRUCTURE OF VALUES - REGARDING CONSUMERS .......................................... 38
5.3.2. STRUCTURE OF COSTS - REGARDING CONSUMERS ............................................ 39
5.3.3. STRUCTURE OF MODERATORS - REGARDING CONSUMERS ............................... 40
5.3.4. STRUCTURE OF VALUES - REGARDING NON -CONSUMERS ................................. 41
5.3.5. STRUCTURE OF MODERATORS - REGARDING NON -CONSUMERS ...................... 42
5.4. MULTIPLE REGRESSION ............................................................................................... 43
5.4.1. THE RELATIONSHIP BETWEEN PURCHASE LOYALTY AND COSTS - REGARDING
CONSUMERS ....................................................................................................................... 44
5.4.2. THE RELATIONSHIP BETWEEN P URCHASE LOYALTY AND VALUES REGARDING CONSUMERS ................................................................................................. 45
5.4.3. THE RELATIONSHIP BETWEEN INTENTION TO PURCHASE AND VALUES –
REGARDING NON - CONSUMERS ........................................................................................ 46
5.4.4. THE RELATIONSHIP BETWEEN PURCHASE LOYALTY AND MODERATORS ....... 48
5.4.5. THE RELATIONSHIP BETWEEN INTENTION TO PURCHASE AND MODERATORS –
FOR NON - CONSUMERS ...................................................................................................... 49
5.4.6. THE INFLUENCE OF BRAND COMMITMENT ON PURCHASE LOYALTY –
REGARDING CONSUMERS ................................................................................................. 50
5.4.7. RELATIONSHIP BETWEEN CUSTOMER VALUE AND TYPES OF VALUE REGARDING CONSUMERS ................................................................................................. 51
5.5. P EARSON CORRELATION .............................................................................................. 52
5.5.1. THE RELATIONSHIP BETWEEN PURCHASE LOYALTY AND CUSTOMER VALUE –
REGARDING CONSUMERS ................................................................................................. 52
5.6. SUMMARY OF THE RESULTS ......................................................................................... 53
6. CONCLUSION ......................................................................................................................... 55
6.1. F INAL CONCLUSIONS ..................................................................................................... 55
6.2. LIMITATIONS ................................................................................................................. 57
6.3. F URTHER RESEARCH ..................................................................................................... 58
BIBLIOGRAPHY ......................................................................................................................... 59
Appendix 1 - Detailed results from Principal components analysis ..................................... 67
Appendix 2 - Detailed results from Multiple regression ........................................................ 77
Appendix 3 – Questionnaire for consumers ............................................................................ 93
Appendix 4 - Questionnaire for non-consumers.................................................................. 101
Appendix 5 - Descriptive statistics ......................................................................................... 107
Appendix 6 – CD ..................................................................................................................... 112
List of tables
TABLE 1. TYPOLOGY OF CUSTOMER VALUE ............................................................................. 10
TABLE 2. HYPOTHESES ...................................................................................................................... 18
TABLE 3. BRANDS USED IN THE QUESTIONNAIRE .................................................................... 20
TABLE 4. CONSTRUCTS USED IN THE SURVEY AND THEIR SOURCES .............................. 22
TABLE 5. SOCIO-DEMOGRAPHIC CHARACTERISTICS OF THE SAMPLE ........................... 35
TABLE 6. CHRONBACH'S ALPHA FOR THE SCALES .................................................................. 37
TABLE 7. ROTATED COMPONENT MATRIX OF PCA REGARDING VALUES - FOR
CONSUMERS .................................................................................................................................. 39
TABLE 8. ROTATED COMPONENT MATRIX OF PCA REGARDING COSTS - FOR
CONSUMERS .................................................................................................................................. 40
TABLE 9. ROTATED COMPONENT MATRIX OF PCA REGADING MODERATORS - FOR
CONSUMERS .................................................................................................................................. 41
TABLE 10. ROTATED COMPONENT MATRIX OF PCA REGARDING VALUES - FOR NONCONSUMERS .................................................................................................................................. 42
TABLE 11. ROTATED COMPONENT MATRIX OF PCA REGARDING MODERATORS - FOR
NON-CONSUMERS........................................................................................................................ 43
TABLE 12. SUMMARY OF MULTIPLE REGRESSION PURCHASE LOYALTY - COSTS FOR CONSUMERS ........................................................................................................................ 44
TABLE 13. ANOVA TABLE FOR MULTIPLE REGRESSION PURCHASE LOYALTY - COSTS
-FOR CONSUMERS ....................................................................................................................... 45
TABLE 14. SUMMARY OF MULTIPLE REGRESSION PURCHASE LOYALTY - VALUES FOR CONSUMERS ........................................................................................................................ 46
TABLE 15. ANOVA TABLE FOR MULTIPLE REGRESSION PURCHASE LOYALTY VALUES - FOR CONSUMERS ..................................................................................................... 46
TABLE 16. SUMMARY OF MULTIPLE REGRESSION INTENTION TO PURCHASE VALUES - FOR NON-CONSUMER ............................................................................................ 47
TABLE 17. ANOVA TABLE FOR MULTIPLE REGRESSION INTENTION TO PURCHASE VALUES - FOR NON-CONSUMERS ........................................................................................... 47
TABLE 18. SUMMARY OF MULTIPLE REGRESSION PURCHASE LOYALTY MODERATORS - FOR CONSUMERS ........................................................................................ 48
TABLE 19. ANOVA TABLE FOR MULTIPLE REGRESSION PURCHASE LOYALTY MODERATORS - FOR CONSUMERS ........................................................................................ 48
TABLE 20. SUMMARY OF MULTIPLE REGRESSION INTENTION TO PURCHASE MODERATORS - FOR NON-CONSUMERS .............................................................................. 49
TABLE 21. ANOVA TABLE FOR MULTIPLE REGRESSION INTENTION TO PURCHASE MODERATORS - FOR NON-CONSUMERS .............................................................................. 50
TABLE 22. SUMMARY OF MULTIPLE REGRESSION PURCHASE LOYALTY - BRAND
COMMITMENT - FOR CONSUMERS ....................................................................................... 50
TABLE 23. ANOVA TABLE FOR MULTIPLE REGRESSION PURCHASE LOYALTY BRAND COMMITMENT - FOR CONSUMERS ........................................................................ 51
TABLE 24. SUMMARY OF MULTIPLE REGRESSION CUSTOMER VALUE - VALUES - FOR
CONSUMERS .................................................................................................................................. 51
TABLE 25. ANOVA TABLE FOR MULTIPLE REGRESSION CUSTOMER VALUE - VALUESFOR CONSUMERS ........................................................................................................................ 52
TABLE 26. CORRELATIONS BETWEEN PURCHASE LOYALTY AND CUSTOMER VALUEFOR CONSUMERS ........................................................................................................................ 53
TABLE 27. RESULTS FROM THE ANALYSES ................................................................................. 53
List of figures
FIGURE 1. THESIS STRUCTURE.......................................................................................................... 3
FIGURE 2. THE STRUCTURE OF THE QUESTIONNAIRE ........................................................... 20
FIGURE 3. MODEL OF RESEARCH AND FURTHER INVESTIGATION - FOR CUSTOMERS
........................................................................................................................................................... 54
1. INTRODUCTION
1.1. BACKGROUND
Building and maintaining brand loyalty has been a central topic of marketing theory in
creating sustainable competitive advantage (Gommans et al., 2001). The benefits of
brand loyalty for the companies could be great (Aaker, 2007). Loyal customers provide
marketing economies because the cost of attracting new customers could be higher than
retaining existing ones. Another important aspect is that loyal customers promote the
brand to other consumers via word-of-mouth communication about the brand. If
something is recommended by a friend, it could be much more credible than advertising
by a firm. Brand loyalty is important regarding gaining support from retailers. From the
consumers’ point of view, loyalty and routine are used to decrease the amount of time
thinking about buying some products and to have more free time to think about other
things.
Generally, maintaining brand loyalty is a big challenge for the company because it is a
tedious process to acquire a customer and to convert him so that he will buy repeatedly
from the company (Agrawal et al., 2010). That is why it would be important and
interesting to examine the factors that influence significantly brand loyalty.
Brand loyalty has been studied from different perspectives but not much research has
been done in ecological context.
In the recent years (Chen, 2010), due to the enormous amount of environmental
pollution directly related to industrial manufacturing in the world, the society has
noticed a steady increase of environmental issues (Chen, 2010 (in Chen, 2008)). More
companies want to accept the environmental responsibility which is due to the attention
of the society (Chen, 2010 (in Chen et al., 2006)). Environmental concern, nowadays,
emerges quickly as a main issue for consumers because of the global warming and a lot
of companies are trying to catch the opportunity.
One of the types of environmentally-conscious behaviour is environmental
consumerism (green buying) – buying and consuming goods which are environmentalfriendly (Manieri et al., 2001).
Because of the increased stakeholder demands, especially consumer pressure on
protecting the environment, businesses have moved beyond just addressing
environmental issues and are introducing alternatives like new products that are
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classified as green (D’Souza et al., 2007). However, it is difficult for companies to
predict consumers’ reaction towards green products with a degree of accuracy which is
necessary to enable the development of new targeting and segmenting strategies. The
key issue is in gaining an understanding of green consumers and their charachteristics.
In this survey, some psychographic characteristics of respondents are examined to
evaluate the profile of green consumers.
The environmental topic is very important but there have been no studies conducted in
Bulgaria in this area and in relation to brand loyalty which could be due to the
comparatively low level of environmental awareness in the country. According to a
survey (Miranda et al., 2009), Bulgaria has one of the lowest indexes of environmental
awareness in Europe. Therefore, it would be interesting to examine which are the
factors that influence brand loyalty and intention to buy ecological products in Bulgaria.
1.2. RESEARCH QUESTIONS
The main purpose of this study is to get a deeper insight about which perceptions of
types of values and costs and which psychographic characteristics influence the brand
loyalty towards ecological brands of detergents. Another goal is to examine the factors
that influence the intention to purchase green products regarding non-consumers of
green products. These two goals would be important for a company in order to maintain
brand loyalty and to convert the intention to buy into real buying.
The main research question is:
Which are the factors that affect brand loyalty to eco-products and intention to
purchase green products in Bulgaria?
The sub-questions are:
1. Do customer value and brand commitment affect loyalty?
2. Do different types of values affect customer value?
3. Do different types of values and costs affect loyalty in the case of consumers and
intention to purchase in the case of non-consumers?
4. Do psychographics characteristics like environmental concern and involvement
affect loyalty in the case of consumers and intention to purchase in the case of nonconsumers?
1.3.
DELIMITATIONS
The scope of this research is limited to the Bulgarian market. The Bulgarian market was
chosen due to the researcher’s accessibility and profound understanding of this market.
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Another reason is that the researcher has a thorough understanding of the background
information necessary to do the survey and could easily use networking to obtain the
sample.
Furthermore, the method which is used is snowball sampling. Hence, it is not a fully
representative sample for the consumers of ecological detergents in Bulgaria.
Moreover, this research will examine only one specific category of green products detergents. Therefore, there could be a limitation about the external validity related to
generalizability of the results to brands of different kinds of green products.
1.4.
METHODOLOGY
A single cross-sectional research design was used for the survey and quantitative
analysis was used for the obtained dataset.
The answering scale for the questions related to the types of value, costs, customer
value, brand relationships and brand loyalty is a traditional 7-item Likert scale. Data
collection was done through the software SurveyXact using an online-based
questionnaire. IBM SPSS statistics version 19 is the software which was used for
analyzing the information in the obtained dataset. The types of analysis that were
performed are Principal components analysis, Multiple regression and Pearson
Correlation. By doing it, the main factors that influence brand loyalty towards green
detergents and intention to purchase were examined.
1.5.
THESIS STRUCTURE
The thesis is divided in six parts which are illustrated in Figure 1.
Figure 1. Thesis Structure
Introduction
Literature
Review
Hypotheses
Methodology
Analysis and
Discussion
Conclusion
The introduction is the first part in which the topic and the research questions are
presented. In addition to this, there is a brief description of research methodology in this
current part. The second part presents the literature review where the main theoretical
background about brand loyalty, customer value, types of values and costs and
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relationship quality are presented. The third part includes a brief presentation of the
hypotheses. Part four describes the methodology which is used for this research. The
questionnaire design, the sampling procedure, the data collection and the types of used
analyses are presented - Principal components analysis, Multiple regression and Pearson
Correlation. An explanation of the used Likert-scale is provided. Part five presents and
discusses the results from the analysis and testing the hypotheses. The final part six
provides the key conclusions of the research, recommendations for future research and
an overview of the main limitaions of the thesis.
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2. LITERATURE REVIEW
2.1. ETHICAL CONSUMERS
There has been a steady growth in ethically-oriented markets in the recent decades
(Wheale et al., 2007). Ethical consumerism has three main components - the
environment, animal rights/welfare and human rights (Wheale et al., 2007 (in Tallontire,
2001). According to a survey of Wheale et al. (2007), there exists a scale of importance
across these ethical issues among ethical consumers. The environment turned out to be
the most important element, followed by human rights and animal rights/welfare issues.
Ethical consumer behaviour could be defined as consumption experiences, purchases
and decision making which are influenced by the ethical concerns of the consumer
(Cooper-Martin and Holbrook,1993).
A green consumer is a person who avoids products that could endanger health, make
damage to the environment, consume a disproportionate quantity of energy or waste,
use materials from threatened species from the environment, provoke unnecessary
cruelty to animals or affect badly other countries (Wheale et al., 2007 (in Elkington et
al., 1988)).
The best way to understand green consumerism is by examining each individual’s
behaviour in consumptions as a series of purchase decisions (Young et al., 2009 (in
Peattie, 1999)). The decisions may be related and guided by common values or may be
not connected and be situational.
Consumers could decrease their impact on the environment and make a difference by
their decisions to purchase. The increasing number of consumers who prefer ecofriendly products creates an opportunity for companies that use ‘eco-friendly’ or
‘environmental-friendly’ as a part of their value propositions (Datta, 2011).
A product is considered to be environmental-friendly, if it has a low environmental
impact (Datta, 2011). Eco-friendly products could also be defined as ecologically safe
products which could facilitate the long-term goal of protecting our natural habitat
(Datta, 2011 (in Manieri et al., 1997)).
2.2. THE THEORY OF REASONED ACTION , THE THEORY OF PLANNED BEHAVIOUR
AND NEUTRALIZATION
Fukukawa (2003) states that there exist two main lines of approaches in the literature on
ethical issues on marketplace - normative approaches and descriptive approaches. The
5
normative ones are based on philosophical and theoretical discourse. The descriptive
approaches use knowledge based on psychology to explain the process of decisionmaking in ethical situations on the market. The descriptive ones consider what
individuals do when they face ethical situations. There are a number of useful models of
consumer ethics in the descrptive approach. Some of them are general models - like the
Theory of Reasoned Action and the Theory of Planned Behaviour and other models are
based on specific contexts.
The Theory of Reasoned Action is considered to be one of the multi-attribute attitude
models which explain consumers’ attitudes towards an object as a function of
consumers’ perception and assessment of some key attributes or beliefs regarding the
attitude object (Schiffman et al., 2008). This theory includes three components - a
cognitive component, an affective component and a conative component. According to
the Theory of Reasoned Action intentions of people are formed as a result of attitude
towards the behaviour and subjective norms which are social norms (Ajzen, Fishbein,
1980). A subjective norm could be measured by an assessment of the consumer’s
perception about what relevant other people (like family, friends, etc.) would think of
the action. The actual behaviour of people is influenced by their intentions.
The Theory of Reasoned Action has some limitations regarding behaviour which is not
completely volitionally - controlled by people (Ajzen, 1991). The Theory of Planned
Behaviour is developed as an extension of the Theory of Reasoned Action and also
adds the component of the perceived behavioural control. The intentions are supposed
to reflect the motivational factors influencing a behaviour. However, an intention could
express in a behaviour only if the behaviour is under volitional control – if the person
can decide at his will to do or not to do the behaviour. Some non-motivational factors as
availability of resources (like time, money, etc.) represent people’s control over the
behaviour. The available resources and opportunities should influence to some extent
the likelihood of behavioral achievement. Perceived behavioural control is presumed to
be perceived self-efficacy concerning judgments about how well a person executes
certain actions to deal with some situations. Therefore, according to the Theory of
Planned Behavior, intention depends on attitude towards the behaviour, the subjective
norm and perceived behavioural control.
Fukukawa (2002) develops a framework for ethically-questionnable behaviour and adds
a fourth element influencing intentions to the Theory of Planned Behaviour - “perceived
6
unfairness.” Perceived unfairness is an additional component and refers to the extent to
which a person is motivated to correct an inbalance between companies and customers
that is perceived to be unfair. It could be related to unfairness of pricing, unfairness of
general performance of the companies or retaliation to companies. For example, buying
a counterfeit product could be perceived unethical but the genuine product could be
perceived overpriced. In this situation consumers may consider the potential to redress
this unfair balance and as a result - to lower their ethical beliefs.
The Theory of Reasoned Action and the Theory of Planned Behaviour are based on the
suggestion that consumer’s intentions are consistent with ethical judgement in the most
cases (Fukukawa, 2002). However, there are empirical evidences that there are attitude behaviour discrepancies - consumers’ ethical concerns do not always translate into
actual behaviour. This is the so - called “attitude - behaviour” gap or “values actions” gap (Young et al., 2009). For example, the results of one survey state that 30%
of UK consumers have reported a big environmental concern but they struggle to buy
green products ((Young et al., 2009 (in Defra, 2006)). Another illustration of the gap is
another research the results of which state that 46-47% of consumers held for organic
food but the actual purchase behaviour was only 4-10% of different products (Young et
al., 2009 (in Hughner, 2007).
There are two main research perspectives in the ethical consumerism literature
addressing the gap (Carrington et al., 2010). One of the streams examines the
limitations of the methodological approaches of a self-reported survey. Some authors
state that when there are researches considering ethical issues, people give answers that
are believed to be socially acceptable and overemphasize the importance of ethical
considerations in their buying behaviour i.e. social desirability bias misrepresents the
measures of intentions of ethical consumers which are inflated.
The second stream uses an approach of modelling and identifies the factors that
influence the translation of ethical attitudes in intentions for ethical purchases and
behaviour.
One of the concepts addressing the “attitude-behaviour” gap is the concept of
neutralization (Chatzidakis et al., 2006). Social norms are very important for ethical
behaviour (Chatzidakis et al., 2006 (in Davies et al. 2002)). When social norms are not
internalized to a degree that they always lead a person’s behaviour, consumers may
make coping strategies to manage with the dissonance they have. Neutralization is a
7
process with which people justify or rationalize their behaviour as a way to deal with
decision conflict and to isolate from guilt and shame (Chatzidakis et al., 2006).
Neutralization is specifically concerned with ethical reasoning processes where the
maintenance of self-esteem and self- worth is more important.
Neutralization techniques could be following unethical behaviour but they could also
precede it and make it possible. There are five techniques which are adapted in a
consumer context (Chatzidakis et al., 2006 (in Grove et al., 1989)).
1) “Denial of responsibility”- a person argues that in a certain circumstance he/she is
not personally responsible for violating the norm because there were factors in force
beyond one’s control.
2) “Denial of injury” – a person states that personal misbehaviour is not really serious
because as a consequence no one suffered directly.
3) “Denial of victim”- a person counters his/hers blame by stating that the party that
was violated deserved what happened.
4) “Condemning the condemners” – a person diverts accusations for misbehaviour by
indicating that those parties that would condemn do activities that are disapproved in
a similar way.
5) “Appeal to higher loyalties” – a person argues that violation of norms was a
consequence as an attempt to accomplish some higher order ideal or value.
Neutralization theory is a comparatively comprehensive conceptual framework which
describes which self - justification techniques could be used to defense against
dissonance and guilt of consumers when they violate internalized norms and values
(Chatzidakis et al., 2006). Therefore, it is a psychological process that could restore
equilibrium without changing the attitude.
Neutralization could be most easily applied to decision making that is less deliberative
and shows which cognitive heuristics or strategies could be used when the motivation is
to maintain the self - worth and simultaneously violate personal ethical beliefs and
values. This is most likely to happen in everyday, low - involvement contexts when
consumers have to make ethical considerations.
2.3. CUSTOMER VALUE
One of the concepts in strategic thinking states that in order to have superior
performance, a company should gain and hold a competitive advantage (Day, Wensley,
8
1988). One of the sources of a positional advantage in a company could be a superior
customer value. In order to achieve it, it would be important for a business to be
differentiated i.e. some value-adding activities to be done in a way that results in
perceived superiority regarding dimensions which are valued by customers.
There is a general agreement in literature that customer value is defined by the
perceptions of customers on the marketplace and not by the suppliers (Khalifa, 2004).
The definitions for a customer value could be separated in three categories - value
components models, benefits/costs ratios and means - ends models. If these models are
examined separately, they are incomplete because each category focuses on specific
parts of the concept and ignores others. But there also exists an overlap between them
and they do not exclude themselves.
According to the value components models, the value elements are defined as “want” or
esteem value, “worth” or exchange value and “need” or utility value (Khalifa, 2004 (in
Kaufman, 1988)). Each decision to buy includes one or a combination of these value
elements.
The means-ends models use the suggestion that customers take and use
products/services in order to achieve favorable ends (Khalifa, 2004 (in Gutmann,
1991)). The ‘means’ are the products/services and ‘ends’ are the important personal
values. Consumers get desirable or undesirable consequences from the consumption of
products/services from consuming it directly or indirectly – from the reaction of others
to one’s consumption at a later point. The means - ends models of customer value
manage to explain how the individual choice of a product/service enables the consumer
to achieve his/hers desired end states.
The means - end theory states that the decision making processes are determined by
links between product attributes, consequences of consumption and personal values of
customers (Khalifa, 2004 (in Huber, 2001)).
According to the benefits/costs ratio models, the estimation of a value is a result from a
trade-off between benefits or desired results and sacrifices or cost (Khalifa, 2004 (in
Woodruff et al., 1996)). The sacrifice includes monetary and non-monetary components
like needed time and effort to acquire the product.
According to Zeithmal (1988), there exist four types of consumer definitions of value:
- the low price;
- what the consumer wants in a product or service;
9
- the quality of the product (or service) for the paid price;
- the benefits of the consumer for all the sacrifices -“give” components.
These definitions are summarized in the definition of perceived value - “the consumer’s
overall assessment of the utility of a product based on perceptions of what is received
and what is given”.
The possible different benefits (or values) and ‘give’ components (or costs) that
influence the customer value will be described.
Distinctions of value
According to Holbrook (2006), two key distinctions of value could be defined:
1) extrinsic value (when the product serves instrumentally or functionally as a way to
a further end) and intrinsic value (when the product is like a self-justifying end).
2) self-oriented value (when a product is appreciated because of the effect it has on the
consumer) and other-oriented value (the product is valued because of the reaction
of others to it).
Considering these dimensions, the following types of customer values could be defined:
Table 1. Typology of customer value
Self-oriented
Other-oriented
Extrinsic
Intrinsic
Economic value
Hedonic value
Social value
Altruistic value
Source: Holbrook, 2006
Economic value occurs when a product serves as a way to achieving objectives of the
consumer - e.g. when appreciating efficiency or excellence. The functional value is
defined as ‘the perceived utility acquired from an alternative’s capacity for functional,
utalitarian or physical performance’ (Sheth et al., 1991). According to a survey of
Punniyamoorthy related to measuring brand loyalty for the English newspapers, there
exists a positive relationship between functional value and brand loyalty
(Punniyamoorthy et al., 2007).
Social value refers to the case when consumption experience serves as a way to
influence the responses of others and make a good impression (Holbrook, 2006).
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Hedonic value occurs when the pleasure comes from the pleasure of a consumer in
consumption experiences which are valued as ends. The definition of Sheth et al. (1991)
for emotional value is related to the perceived utility which comes from the capacity to
arouse feelings or affective states.
Altruistic value relates to a concern how my consumption would affect others and it is
considered as self - justifying - e.g. when making some ethical choice.
Costs
In order to assess the “perceived customer value” of a product, the costs for obtaining it
are usually the main concern for customers who may apply principles of costs-benefits
to evaluate the purchase (Zeithaml, 1988). The relevant costs for a consumer when
making a purchase are: monetary costs, time costs, emotional costs, learning costs,
search costs, cognitive and physical effort with social, financial and psychological risks
(Huber, 2001). Non-monetary costs include also brand image and convenience (Petrick,
2002). Therefore, the combination of monetary and non-monetary costs is equal to the
consumers’ total perceived sacrifice which affects their perceived value of the product.
Some of these costs will be viewed in more details in this research in the context of
ecological products.
1) Price
Organic products are considered to be quality - differentiated products with different
value - added characteristics (Tsakiridou et al., 2009). The producers of organic food try
to convince the consumers that their production is healthy, environmentally - friendly
produced, pesticide - free, nutritious and safe. When all these beliefs are taken into
consideration, organic products deserve to have a higher price compared to their
conventional alternatives. The willingness to pay for an organic product is the additional
price that a customer would pay for that product - above the price that a comparable
standard product has (Kalogeras et al., 2009).
According to the benefits/costs ratio models, the price/monetary cost is considered to be
an important element when estimating the customer value. The results of a research of
Dodds show that the price has a negative effect on the perceived value and willingness
to pay and affects positively the perceived quality (Dodds et al., 1991).
It turns out that often the main constraint for buying organic products is the high price
(Silverstone, 1993).
11
2) Evaluation costs
Evaluation costs include the effort and time costs which are connected with the
searching and analysis necessary to make a decision to switch (Shugan, 1980). In order
to evaluate potential alternative suppliers, time and effort is needed to collect the
information. Mental effort is required to analyse the information and reach an informed
decision.
3) Perceived risk
Sweeney and Soutar define perceived risk as “the subjective expectation of a loss”
(Sweeney et al., 1999, p.81). They suggest that only two risk dimensions should be
considered - financial and performance risk. Each risk dimension could be seen as an
expectation of a future cost which benefits for the perceived value when it is time for
the purchase. Performance risk is the suggested loss when the product or brand does
not perform as it was expected (Horton, 1976). It integrates the future quality of the
product at the time of purchase.
Only performance risk is going to be examined in this survey.
4) Effort
If there is an intensive distribution of the product and it is available in more stores, it
would reduce searching time and travelling to the stores and provide convenience (Yoo
et.al, 2000). This kind of effort is related to availability of the product.
Moderators
Barber (2009) considers the influence of environmental attitude, environmental
knowledge and involvement as variables that can predict purchase behaviour. In this
research, the relationship between these criteria and brand loyalty will be explored.
1) Environmental concern
Environmental concern is considered to be an environmental attitude (Takacs-Santa,
2007). The classical three-part concept states that attitudes have cognitive, affective and
conative dimensions.
According to the definition of Takacs-Santa, environmental concern could be narrowed
to:
- affective attitude regarding the significance of environmental problems;
12
- positive affective attitude regarding those who are affected by environmental
problems;
- negative affective attitude regarding those who cause environmental problems.
The increase of environmental concern is a device to increase pro-environmental
behaviour and to decrease environmental impact in the societies (Takacs - Santa, 2007).
2) Environmental knowledge
One of the definitions of environmental knowledge is ‘a general knowledge of facts,
concepts, and relationships concerning natural environment and its major ecosystems’
(Fryxell et al., 2003, p.48). Researches including in the environmental area, reveal that
attitudes which are not well-grounded in secure knowledge about the attitude object
have little or no impact on behaviour and decision-making (Krarup et.al, 2005).
Considering the strength of attitudes, it is stated that strong attitudes predict behaviours
and weak ones do not. The general knowledge of one person for the attitude object is
one of the factors that have an impact on the attitude strength (Davidson, 1995).
Consumers that have knowledge for the environment should be motivated to make
environmental friendly purchases, but it is difficult to verify it (Barber et al., 2009).
3) Involvement
Zaichowsky (1985) states that involvement is the degree of perceived relevance of an
object which is on the basis of inherent interests, value and needs. Consumers who are
‘highly involved’ would search actively and process information in order to make
informed selections of products and to satisfy their needs (Mittal, 1989). According to a
survey of Punniyamoorthy et al., (2007) the higher level of involvement leads to higher
level of brand loyalty.
2.4. RELATIONSHIP MARKETING AND BRAND LOYALTY
Relationship marketing could be defined as: “all marketing activities directed towards
establishing, developing and maintaining successful relational exchanges” (Dimitriadis
et al., 2010 (in Morgan et al., 1994, p.22)). Some researches suggest that the relational
exchanges could generate stronger relationships with customers which could improve
business performance (Palmatier et al., 2006). Gwinner (1998) states that the benefits
from good relationships with customers come from the fact that consumers would like
to receive things from their long-term relationship with the supplier which are above
13
just getting the main product or service. These long-term relationships are related to
customer loyalty to the product or business (Berry, 1995).
The evaluation of all relationship marketing activities is done on the basis of the
profitability of the company (Hennig – Thurau, 2002). There are two main measures in
the marketing literature that are considered to be the key relationship marketing
outcomes: customer loyalty and customer word-of-mouth communication.
Brand loyalty
There are two main approaches to the definition of customer loyalty - the behavioural
approach and the attitude-based approach (Khan, 2009 (in Jacoby et al., 1973)).
According to the attitude-based approach, customer loyalty could be defined as an
attitude (Khan, 2009 (in Fournier et al., 1997)). It requires an analysis and description of
the underlying attitudes/preference structure of the customer. The attitudinal nature
could be measured with buying intention, preference and willingness to recommend.
According to the behavioural approach, brand loyalty is defined as repeat purchase and
a behaviour which is a function of psychological processes - a result from some
psychological or situational factors (Khan, 2009 (in Jacoby, 1971)).
Smith (2003) states that the differences between customer loyalty and brand loyalty
begin to become more blurred, especially when regarding the behavioural measures of
customer loyalty.
One definition of brand loyalty is “a deeply held commitment to rebuy or patronize a
preferred product/service consistently in the future, thereby causing repetitive same brand or same brand - set purchasing, despite situational influences and marketing
efforts having the potential to cause switching behaviour” (Oliver, 1999, p.34).
This definition focuses on both approaches (Chadhuri, Holbrook, 2001). Behavioural
(or purchase) brand loyalty comprises of purchases of the brand that are repeated.
Attitudinal brand loyalty suggests some dispositional commitment regarding some
unique value that is related to the brand.
Brand loyalty has been an important focus of strategic marketing planning. It provides a
crucial basis to develop a sustainable competitive advantage (Kotler, 1984).
Khan (2009) states that brand loyalty is the customer’s decision to repurchase a brand
on a continual basis, which is expressed through intention or behaviour. It happens
because the customer thinks that the brand offers the right quality and product features
14
for the right price. Brand loyalty is an important aim and results from good marketing
programs, sales initiatives and efforts for product development.
According to Aaker (1996), brand loyalty is one of the five components of consumer
brand equity. Brand equity is a concept which is used to indicate the value of a brand
(De Palsemacker, 2007). Consumer brand equity represents the “marketing value” of a
brand. A strong brand could foster brand loyalty. It is a vehicle to building stable
relationships between the consumers and the manufacturer. Brand loyalty decreases the
marketing cost, because it is cheaper to retain an existing loyal customer than to win a
new one. Brand loyalty makes the company less vulnerable to actions of the
competition. There is more time to respond to competitive threats (De Palsemacker,
2007).
Another important aspect of brand loyalty is the role which loyal consumers have in
promoting the brand to other consumers through word-of-mouth communications about
the brand (Aaker, 2007). The recommendation of a friend could have a much higher
level of credibility than an advertisement by a company.
Brand loyalty could be also essential in gaining support from retailers. The essence of
retail is to maximize stock turnover. Brands that have a higher level of loyalty give the
retailers an assurance of turnover and increase their willingness to list these products.
Brand loyalty is also important from the consumer perspective. Consumers use loyalty
and routine to reduce the time they have to spend thinking about purchases for some
products in order to free up time to think about other things.
Some other advantages of customer loyalty are a continuous stream of profit, increase of
revenue per-customer, decrease in operating costs, increase in price premium and
increase in switching barriers (Khan, 2009 (in Reicheld, Teal, 1996)).
Relationship quality
An assumption of the relationship quality approach is that customer loyalty is
determined to a high degree by some constructs that reflect “the degree of
appropriateness of a relationship” from the point of view of the customer (Hennig –
Thurau, 1997). Relationship quality could be considered a metaconstruct which
comprises of some key components that reflect the overall nature of relationships
between customers and companies (Hennig - Thurau, 2002). There is a general
agreement that customer satisfaction, trust and commitment are the main components of
15
relationship quality (Baker et. al, 1999). These three core constructs are considered to be
interconnected rather than independent.
Customer commitment is an important concept in the development and maintaining of
marketing relationships because it is a crucial psychological force that connects the
customer to the selling company (Bansal et al, 2004). Commitment is supposed to
explain the process by which it is suggested that a consumer is loyal because he has a
favourable attitude to the brand and buys the brand frequently (Punniyamoorthy et al.,
2007). The results of a studey confirmed that there is a positive relationship between
commitment and brand loyalty (Punniyamoorthy et al., 2007).
Kim et al. (2008) views behavioural intention which is suggested in the Theory of
Reasoned Action as the most predictable of behaviours and suggests brand commitment
as a direct antecedent of brand loyalty. Brand commitment is considered to be a
behavioural intention which is held with affective and cognitive conviction.
According to the definition of Chadhuri (2001), attitudinal brand loyalty comprises of
some degree of dispositional commitment to a brand. Therefore, commitment is
suggested to be a condition which is necessary for brand loyalty (Punniyamoorthy et al.,
2007). It will be further tested in the analysis whether there is a positive direct
relationship between brand commitment and brand loyalty.
There are a lot of studies which show that trust is at the core of relationships that are
successful (Kim, 2008). Trust is the perception of “confidence in an exchange partner’s
reliability and integrity” (Morgan and Hunt, 1994, p.23). Garbarino (1999) states that
brand trust is the key issue for any long-term relationships.
Customer satisfaction is suggested to mediate customer learning from prior experience
and to explain post-buying behaviour like word of mouth, complaining and repurchase
intention (Westbrook et al., 1991).
Generally, the concept of relationship quality has been researched in the context of
services and between consumers which implies a personal and social contact between
consumers and suppliers (Dimitradis et al., 2010). The idea of brand personality
suggests that consumers attribute different descriptive personality - like characteristics
to different brands in a variety of product categories (Schiffman et al., 2007). According
to one definition, “a brand relationship is a logical extension of the idea of a brand
personality” (Dimitradis et al., 2010 (in Blackstone, 2000, p.102)). The idea of a
16
relationship between a brand and a customer is viewed as an analogue from that
combination of affective, cognitive and behavioural processes that make a relationship
between two people (Blackston, 1992). Fournier (1998) states that a brand could be a
viable relationship partner and could be an active and contributing member of the
relationship dyad. Fournier defines six main dimensions of the brand relationship
quality – love/passion, self - connection, commitment, interdependence, intimacy and
brand partner quality.
But there seems to be no consensus in literature about the exact dimensions of brand
relationship quality (Dimitriadis et al., 2010).
In this research, the construct of brand commitment as a part of the brand relationship
quality would be examined and its relationship with brand loyalty.
17
3. HYPOTHESES
The following hypotheses could be laid down based on the discussion above.
Table 2. Hypotheses
Hypothesis
The hypothesis
number
What is the underlying component structure of the values regarding consumers?
H1
What is the underlying component structure of the costs regarding consumers?
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
H13
What is the underlying component structure of the moderators regarding
consumers?
What is the underlying component structure of the values regarding nonconsumers?
What is the underlying component structure of the moderators regarding nonconsumers?
Does the lower level of costs lead to higher level of purchase loyalty?
Is there a positive relationship between types of value and purchase loyalty
regarding consumers?
Is there a positive relationship between types of value and intention to purchase
in the case of non-consumers?
Is there a positive relationship between moderators and purchase loyalty
regarding consumers?
Is there a positive relationship between moderators and intention to purchase
regarding non-consumers?
Does brand commitment have a positive influence on purchase loyalty?
Is there a positive relationship between types of value and customer value in the
case of consumers?
Does customer value influence positively purchase loyalty regarding
consumers?
In the next parts these hypotheses will be tested with the empirical studies.
18
4. METHODOLOGY
4.1. RESEARCH APPROACH
To test the hypotheses and examine the relationships, a conclusive research design was
chosen. Conclusive research is based on large representative samples and quantitative
analysis is used for the gathered data (Malhotra, 1996). It is suitable for this research
because results from it are considered to be conclusive and could be used as an output
for decision-making of managers.
Conclusive research could be descriptive or causal. For this research, a descriptive
research was done to determine the degree to which variables are associated.
More specifically, a single cross - sectional design (a subtype of a descriptive research)
was chosen because only one sample of respondents was taken from the target
population and the information was gathered only once.
The technique which was used for the research was an online questionnaire-based
survey. Some main reasons for the popularity of online surveys include (Schmidt et al.,
2006):
- the speed for creation a questionnaire, its distribution to respondents and
returning the data - Gathering of data could be quick. It is also convenient that once
gathered, the information could be easily exported to statistical programs and analysed.
This also saves time because the information does not need to be entered manually into
the statistical programs which could be rather time - consuming.
- low cost per respondent - Compared to a mail survey, there are no printing and
postage costs. The online survey tool that was used to make the current survey is
SurveyXact. Aarhus School of Business and Social Sciences provides free access to this
program to its students.
- the ability to reach a lot of people - As the present research is quantitative it was
necessary to gather data from a lot of respondents in Bulgaria. It became possible to
make the survey in Bulgaria without the researcher being physically there.
4.2. QUESTIONNAIRE DESIGN
The questionnaire was created and administered with the online survey tool called
SurveyXact. The structure of the questionnaire will be described in details below.
19
The questionnaire could be divided into the following sections, shown in Figure 2.
Figure 2. The structure of the questionnaire
Directing
Psycho-
Filter 1 Filter 1
Psycho-
Psycho-
Demo-
graphics 2
graphics 3
graphics
Filter 2
questions
graphics 1
Filter section 1
The first two questions in this section were open questions and ask the respondent to
state their opinion about a green brand and to name any green brands they know. The
third question was a multiple-choice question asking the participants whether they know
any of a given list of famous green brands in Bulgaria.
Below in Table 3 are presented all used brands in the survey.
Table 3. Brands used in the questionnaire
Brand
Type of product
Bioplam
Ecological compressed slack
Cyclus
Recycled paper
Frosch
Ecological detergents
Sodasan
Ecological detergents
Ecover
Ecological detergents
The forth question was a multiple - choice filtering question which determined the
extent (in percentage) to which respondents buy ecological detergents for household and
fabric care. Respondents who have used eco - detergents continue in the directing
questions. If the respondents did not use eco - detergents at all (100% non-ecological),
they continue to Filter section 2. For the purpose of the research, respondents that
continued in the directing questions were named “consumers” and respondents that
were directed to Filter section 2 were named “non - consumers”.
20
Directing questions
This section includes two multiple-choice question whose goal was to determine if
customers know and if they have bought any brand of a given list of brands of ecodetergents.
The third multiple-choice question asked which is the most frequent bought detergent
brand from a list of brand of eco-detegents. When a brand is chosen, it is specified that
the rest of the questions will be related to that specific brand.
The forth question in this section asked respondents how many years they have been
buying products from this brand. The last questions here were on a 7-point Likert-type
scale and asked for frequency of buying of some products of the chosen brand-e.g.
fabric softener, etc.
Psychographics section 1
For all of the questions in this section the 7-point Likert scale was used and the
respondents were asked to indicate their agreement with different statements measuring
a number of constructs. The following constructs were examined: functional value,
emotional value, social value, altruistic value, price, product availability, purchase
effort, evaluation costs, performance risk, perceived value, brand commitment, purchase
loyalty.
It should be specified that product availability was like a sub-construct of the bigger
construct purchase effort.
In Table 4 are shown the sources to all scales used in the survey.
21
Table 4. Constructs used in the survey and their sources
Construct
Source
Functional value
Sweeney et al., 2001
Emotional value
Sweeney et al., 2001
Sweeney et al., 2001 & Sanchez-Fernandez
Social value
et al., 2009
Altruistic value
Sanchez-Fernandez et al., 2009
Price
Sweeney et al., 2001 & Erdem et al., 2006
Product availability
Yoo et al., 2000
Purchase effort
Petrick, 2002
Evaluations costs
Burnham et al, 2003
Performance risk
Sweeney et al., 1999
Perceived value
Dodds, 1991
Brand commitment
Aaker et al., 2004
Purchase loyalty
Chadhuri, 2001
Personal support for environmental issues
Sparks et al., 1995
Green purchase attitudes
Mostafa, 2007
Perceived environmental knowledge
Mostafa, 2007 & Ellen, 1994
Involvement
Beatty et al, 1988
Intention to purchase
Dodds et al., 1991
Filter section 2
The first question in this section asks the respondents to indicate the most familiar brand
eco-detergent from a given list of brands. The second question here is filtering and asks
whether participants would buy from the above - chosen brand. If yes, they continue
with psychographics section 2 and if no - to Psychographics section 3. In this survey,
only the results of those respondents who answered yes in this question were examined
regarding non-consumers. The reason for this is that only the people who would buy an
ecological detergent are an object of interest.
Psychographics section 2
The questions from this section are aimed to non-customers of eco-brands and for all of
the questions, the 7-point Likert scale is used. Participants were asked to state their level
22
of agreement with statements measuring different constructs. The examined constructs
are: functional value, emotional value, social value, altruistic value, perceived value,
intention to purchase.
Psychographics section 3
The first type of questions in this section includes again the measuring of constructs
through usage of 7-point Likert scales. The following constructs are considered:
personal support for environmental issues, green purchase attitudes, perceived
environmental knowledge, involvement. The sub-constructs personal support for
environmental issues and green purchase attitudes are a part of the construct
environmental concern.
Demographics
The final section of the questionnaire includes questions about respondents’
demographics characteristics - age, education, occupation, family status and monthly
income.
The questionnaire was translated from English to Bulgarian by the researcher and a
back - translation was done by another person.
Before starting the actual survey, a smaller pilot study was done with 15 respondents.
As a result of the feedback of these respondents some minor changes were made in the
questions including adding more possible answers to some questions and changing the
wording of some questions to clarify them.
The questionnaires for consumers and for non–consumers could be viewed in
Appendices 3 and 4.
4.3. ANSWERING SCALES
Likert scale and Likert-type scale
The Likert scale is one of the most widely used scales to measure attitudes (Ary et al.,
2010). It assesses the attitude towards an object by presenting a set of statements related
to the topic and asking the respondents to indicate their level of agreement or
disagreement about each statement. The answers range from “strongly disagree” to
“strongly agree”. A numeric value is assigned to the different responses.
Likert scale is an itemized rating scale which is considered to be a part of the
noncomparative scales (Malhorta, 1996).
23
The definition of a Likert scale could be broadened depending on the judgement of the
researcher (Uebersax, 2006). There are a number of charachteristics that define a Likert
scale and if some of them are not met, it could be reffered to as Likert - type scale. If a
scale does not refer to agreement/disagreement to a subject and the anchor labels are not
bivalent and symmetrical about a neutral middle, then it could be considered as a Likert
- type scale. Therefore, the scales that are used in the survey with labels from “never” to
“At every purchase of the category” could be considered Likert - type scales.
4.4. COMPOSITE VARIABLES
In the case of consumers, the operationalization of the dependent variable purchase
loyalty (or brand loyalty) was done using two measures “I intend to keep purchasing
this brand” and “I will buy this brand the next time I buy detergents” with answers on a
7-item Likert scale from the construct of Chadhuri (2001). Purchase loyalty was
composed as a variable which is an average from those two items. Purchase loyalty, or
behavioural loyalty comprises of repeated purchases of the brand (Chadhuri et al.,
2001).
The dependent variable customer value was constructed as an average of two items “The products of this brand are very good value for money” and “The products of this
brand are considered to be a good buy” with answers on a 7-item Likert - scale, taken
from the scale of Dodds et al. (1991).
Regarding non-consumers, the dependent variable intention to purchase was composed
as an average of the following three items from Dodds et. al. (1991): “The likelihood of
purchasing them is very high”, “The probability that I would consider buying them is
very low” and “My willingness to buy them is very high”- with answers on a 7-item
Likert - scale. Intention to purchase (or behavioural intent) is in this case the
predisposition to buy this brand for the first time (Gommas et al., 2001). Behavioural
intent is an intermediary between attitude and behaviour (Gommas et al., 2001 (in
Mittal et al., 2001).
Intention to buy is not exactly the same measure as brand loyalty which is measured
with behavioural (or purchase loyalty). Brand loyalty is related to the intention to repurchase (Khan, 2009) which could not be assessed in the case of non-consumers. But
as both of them measure similar constructs, they are used for comparison between
customers and non-customers.
24
4.5. SAMPLE
The survey was done on the Bulgarian market and all respondents were Bulgarians
living in Bulgaria. As Bulgaria is the native country of the researcher, the broad
network of the researcher there could be used and the native language skills made
communication and access to information easier. Networking was used to obtain
responses. Nonprobability sampling procedure was used for the survey. More
specifically, snowball sampling was applied and respondents were asked to resend the
link for the questionnaire to their friends or people that would fill it in. In addition to
this, Facebook was used to do the survey and the link was published there. The link for
the survey was also posted in a web - forum for mothers in Bulgaria.
4.6. METHODS OF ANALYSIS
4.6.1. STANDARD MULTIPLE REGRESSION
Regression analyses are a set of statistical techniques through which the relationship
between one dependent variable and several independent variables could be assessed
(Tabachnik et al., 2007). The regression equation is:
Y'=A + B1X1+B2X2+….+BkXk
Y' – predicted value of the dependent variable
X1…Xk - the independent variables
B1…Bk - the coefficients assigned to each independent variable during regression
The goal of the regression is to reach a set of regression coefficients for the independent
variables that bring the predicted Y values from the equation as close as possible to the
Y values that are obtained by measurement. The regression coefficients are calculated to
reach two goals: they minimize deviations between predicted and obtained Y values and
they optimize the correlation between the predicted and obtained Y values. One of the
important statistics resulting from regression analysis is the multiple - correlation
coefficient, the Pearson product - moment correlation coefficient - R between the
predicted and obtained Y values:
R = ryy’
,
where
Y- each individual observed Y score
25
Y’- predicted Y score
SSy=SSreg+SSres
,
where
SSy - total sum of squares of Y
SSreg - sum of squares for regression
SSres - sum of squares of residuals
̅)2 ,
SSy=Σ(Y-Y
where
̅
Y- the mean of Y for all N cases
̅)2
SSreg=Σ(Y’-Y
SSres=Σ(Y-Y’)2
SSreg
R2 =
SSy
,
where
R2 - squared multiple correlation
Therefore, R2 is the ratio of sum of squares for regression in the total sum of squares of
Y. In other words, it is the proportion of variation in the dependent variable that is
predictable from the best linear combination of the independent variables.
The primary goal of regression analysis is to examine the relationship between a
dependent variable and the independent variables. First, it is assessed how strong is the
relationship between the dependent variable and the independent variables and then the
importance of each of the independent variables to the relationship is estimated.
Regression analyses reveal relationships among variables but do not suggest that
relationships are causal.
Regression analyses could be used with continuous or dichotomous independent
variables.
In the standard multiple regression, the independent variables are put into the equation
at the same time (Pallant, 2007).
According to Tabachnik et al., (2007), the assumptions of multiple regression are:
26
1) Sample size
The ratio cases-to-independent variables has to be substantial or it could turn out that
the solution is not meaningful (Tabachnik et al., 2007). The required sample size
depends on a number of predictors, alpha level, the desired power and expected effect
size. Some basic rules of thumb are that N≥50+8m (m - number of independent
variables) for testing the multiple correlation and N≥104+m - for the testing the
individual predictors.
The main issue here is generalizability (Pallant, 2007). If the sample is small, a result
may be obtained that does not generalize with other samples.
2) Absence of outliers among the independent variables and on the dependent
variable
Extreme cases impact too much the regression solution and affect the precision of
estimation of the regression weights (Tabachnik et al., 2007 (in Fox, 1991)). If there are
high leverage and low discrepancy, the standard errors of the regression coefficients are
too small. If there are low leverage and high discrepancy, the standard errors of the
regression coefficients are too big. And neither of these cases generalizes well to the
population values. Therefore, outliers should be rescored, transformed or deleted.
3) Absence of multicollinearity and singularity
It refers to the relationship between the independent variables (Pallant, 2007). There is
multicollinearity when the independent variables are highly correlated (r=0,9 and
above). Singularity is present when one independent variable is a combination of other
independent variables. Multicollinearity and singularity do not contribute to a good
regression model and it should be checked for them before starting.
In order to calculate the regression coefficients, an inversion of the matrix of
correlations among the independent variables should be done (Tabachnik et al., 2007).
This inversion is impossible if independent variables are singular and unstable if they
are multicollinear.
4) Normality, linearity and homoscedasticity of residuals
Some assumptions of the analysis are that the residuals (the differences between the
predicted and obtained dependent variable scores) should be normally distributed about
the predicted dependent variables and the residuals should have a straight-line
relationship with the predicted dependent variable scores – linearity (Tabachnik et al.,
27
2007). Another assumption (homoscedasticity) is that the variance of the residuals about
the predicted dependent variables should be the same for all predicted scores.
A test for these assumptions is done by an examination of the residuals scatterplots.
5) Independence of errors
According to this assumption, the errors of prediction should be independent of one
another (Tabachnik et al., 2007). Sometimes, this is violated as a function of something
related to the order of cases. This ‘something’ could be distance or time. For example,
time could be a reason for nonindependence of errors if respondents who were
interviewed earlier in the survey have more variability in response because of
interviewer inexperience with the questionnaire. Therefore, nonindependence of errors
could be a nuance factor which could be eliminated or be very interesting for the
researcher.
In this research it could be viewed as a nuance factor.
6) Absence of outliers in the solution
There could be some cases that may be poorly fit in the regression equation. They
decrease the multiple correlation (Tabachnik et al., 2007). Their examination is
informative because these cases are not well predicted by the solution. The cases which
have big residuals are outliers in the solution.
Multiple regression was used to analyse the questions related to loyalty and customer
value. It was executed using IBM SPSS Statistics.
4.6.2. CORRELATION
Correlation analysis is performed to measure the strength and direction of the linear
relationship between two variables (Pallant, 2007). The Pearson product - moment
coefficient - R which was earlier described is used for correlation. R could only have
values from -1 to 1 and the size of the absolute value gives an indication for the strength
of the relationship.
4.6.3. PRINCIPAL COMPONENTS ANALYSIS
The term ‘factor analysis’ includes different related techniques (Pallant, 2007). One of
the main distinctions is between factor analysis (FA) and principal components analysis
(PCA).
28
FA and PCA are statistical techniques which are used for a set of variables if the
researcher is trying to discover which variables in the set form coherent subsets and are
relatively independent from each other (Tabachnik et al., 2007). Variables are correlated
with each other and subsets of variables that are highly independent from other subsets
are combined into factors. PCA makes components and FA makes factors.
PCA and FA are similar in a lot of ways and researchers often use them interchangeably
(Pallant, 2007). Both of them produce a smaller number of linear combinations from the
original variables in such a way that most of the variability in the pattern of correlations
is captured. But there is a difference between them. Regarding PCA, the original
variables are transformed into a smaller set of linear combinations and all the variance
in the variables is analysed. When doing FA, a mathematical model is used to estimate
the factors and only the shared variance is analysed. Often these two approaches
produce similar results.
The goal of the researcher by using PCA is to reduce a big number of variables to a
smaller number of components, to describe (and maybe understand) in a compact way
the relationships among observed variables or to test a theory about some processes
(Tabachnik et al., 2007).
PCA will be used in this research.
Mathematically, PCA produces some linear combinations of observed variables and
each linear combination is a component (Tabachnik et al., 2007). The components
summarize the patterns of correlations in the observed correlation matrix.
According to Tabachnik et al. (2007), the assumptions for factor analysis are:
1) Sample size and missing data
Generally, correlation coefficients seem to be less reliable if estimated from small
samples (Tabachnik et al., 2007). The sample size is also dependent on the magnitude of
correlations and the number of factors. If the correlations are strong and there are a few
distinct factors, a smaller sample size is adequate.
One of the recommendations is a ratio of 10:1 - ten cases for each item to be factor
analysed (Tabachnik et al., 2007 (in Nunnally, 1978)).
If there is missing data, the missing values should be deleted or the cases - deleted or a
missing data correlation matrix is analysed (Tabachnik et al., 2007).
29
2) Normality
Assumptions regarding the distribution of variables are not in force but if the variables
are normally distributed, the solution is enhanced (Tabachnik et al., 2007).
3) Linearity
Multivariate normality suggests that there are linear relationships between pairs of
variables (Tabachnik et al., 2007). If linearity fails, the analysis is degraded because
correlation measures linear relationship and doesn’t suggest nonlinear relationship.
4) Absence of outliers among cases
Different methods should be used to detect and decrease the influence of univariate and
multivariate outliers (Tabachnik et al., 2007).
5) Absence of multicollinearity and singularity
Multicollinearity is not a problem because it is not necessary to invert a matrix.
However, singularity is a problem (Tabachnik et al., 2007).
6) Factorability of R
In order to have a factorable matrix, it should include several sizable correlations
(Tabachnik et al., 2007). If none of the correlations is more than 0,3 it is questionable
whether to use PCA because probably there is nothing to factor analyse.
7) Absence of outliers among variables
After the analysis is done, variables that are not related to others in the set are found
(Tabachnik et al., 2007). They are usually not correlated with the first few factors. A
variable that has a low multiple correlation with all other variables and low correlations
with the important factors is an outlier. Outliers are usually ignored in the analysis.
Tabachnik et al. (2007) states that the main steps in PCA are:
1) Extraction of factors
The correlation matrix (the matrix of correlations between variables) is diagonalized i.e.
it is transformed into a matrix with numbers in the positive diagonal and there are zeros
everywhere else (Tabachnik et al., 2007).
L=V’RV ,
where
30
R - correlation matrix
V - eigenvector matrix
V’- transponse matrix of V
The columns in V are named eigenvectors and the values in the main diagonal of L are
the eigenvalues.
The correlation matrix could be presented like this:
R=VLV’
R=(V√𝐿 )(√LV′) ,
where
V√𝐿 is called A
A’=√𝐿 V’
R=AA’
The correlation matrix is a product of two matrices which are a combination of
eigenvectors and the square root of eigenvalues. This is the main equation of factor
analysis.
Some of the most common extraction techniques are principal components, image
factoring, principal factors, maximum likelihood factoring, alpha factoring and
generalized least squares (Pallant, 2007).
The researcher should determine the number of factors that he/she thinks describe in the
best way the relationships among the variables. Some of the techniques that could be
used to help in the decision for the number of factors are Kaiser’s criterion and scree
test.
Kaiser’s criterion or the eigenvalue rule is one of the most common techniques. The
eigenvalue of a factor shows what is the total variance explained by that factor.
According to this rule, only the factors with an eigenvalue of 1.0 or above should be
retained for further investigation.
Another approach that could be applied is Catell’s scree test (Pallant, 2007 (in Catell,
1966)). It includes plotting the eigenvalues of the factors and examining the plot to find
a point where the shape of the curve changes its direction. The recommendation is that
31
all factors above the elbow should be retained because they contribute the most to the
explanation of the variance in the dataset.
2) Rotation of factors
Rotation is usually done after extraction to maximize high correlations between factors
and variables and minimize the low correlations (Tabachnik et al., 2007). In this way
the solution is made more interpretable without changing its mathematical properties.
There are two main ways of rotation-orthogonal and oblique. If the rotation is
orthogonal, all factors are not correlated with each other and if it is oblique-factors are
correlated.
If the rotation is orthogonal, a loading matrix is produced. It is a matrix of correlations
between observed variables and factors. The size of the loadings show the degree of
relationship between each observed variable and each factor.
The most commonly used method for orthogonal rotation is Varimax. Its goal is to
minimize the complexity of factors. It is done by maximizing the variance of factor
loadings by doing high loadings higher and low loadings lower for each of the factors.
It is achieved through a transformation matrix Λ.
AunrotatedΛ=Arotated ,
where
Aunrotated - unrotated factor loading matrix
Arotated - rotated loading matrix
Λ - transformation matrix
Λ=[
cos 𝛹
sin 𝛹
− sin 𝛹
]
cos 𝛹
The transformation matrix consists of sines and cosines of an angle Ψ.
Once the loading matrix is available, scores for factors could be predicted for each case.
B= R-1A ,
where
R-1 – the inverse of matrix of correlations
A - matrix of correlations between factors and variables
32
B - factor score coefficients for estimating factor scores from variable scores
F=ZB ,
where
F - factor scores
Z - standardized scores on variables
Interpretation and naming the factors depend on the meaning of the combination of
variables that correlate to a high degree with each factor.
PCA was used to find the underlying components structure of the values, costs and
moderators. IBM SPSS Statistics was used to perform the PCA.
33
5. ANALYSIS AND DISCUSSION
5.1. SAMPLE CHARACTERISTICS
5.1.1. DEMOGRAPHICS
The total number of valid responses given by Bulgarian consumers was 232 from which
51 respondents do not buy ecological detergents at all (the so-called ‘non-consumers’)
and 161 buy ecological detergents to some degree (the so-called ‘consumers’). The two
samples of consumers and non-consumers will be examined separately. The sociodemographic and some behavioural characteristics of the sample are presented in Table
5.
Regarding consumers, 46,6% of them - the majority were in the age group between 25
and 34, the second biggest group was from 35 to 44 – with 15,5%. Concerning the
educational level, occupation and family status - 57,8% had a master’s or higher degree,
62,1% were civil servants or private employees and 52,2% of them were singles. The
majority - 62,5% had an average income.
With reference to non-consumers, 62,7% of them were between 25 and 34 years of age.
Moreover, 51% had a master’s or higher degree, 62,7% were civil servants or private
employees and the majority - 52,9% were single. Most of them - 45,1% had an average
income.
5.1.2. BUYING BEHAVIOUR
In the case of consumers, the highest number - 30,4% reported that they used ecological
detergents in 15% of all purchases of detergents, 26,7% - in 30% of all cases and 21,7%
- in 50% of all purchases. The results are presented in Table 5.
Descriptive statistics for the rest of the variables in the survey could be found in
Appendix 5.
34
Table 5. Socio-demographic characteristics of the sample
Consumers (N=161)
Age
18 - 24
25 -34
35 - 44
45 - 54
55 - 64
65 - 74
over 75
Education
Primary school
High school
Training school
Bachelor
Master or higher
Occupation
Employer/free-lancer
Civil servant/private employee
Pensioner
Housekeeping
Student
Unemployed
Family status
Single
Married with children
Married without children
Other
Monthly personal income
Below average
Average
Above average
Types of detergents used for
household
100 % eco products
85 % eco products
70 % eco products
50% eco products
30% eco products
15 % eco products
0% eco products
Non-consumers (N=51)
Frequency
Percentage
24
75
25
17
15
4
1
14,9%
46,6%
15,5%
10,6%
9,3%
2,5%
0,6%
8
32
5
4
2
0
0
15,7%
62,7%
9,8%
7,8%
3,9%
0,0%
0,0%
2
9
13
44
93
1,2%
5,6%
8,1%
27,3%
57,8%
0
5
3
17
26
0,0%
9,8%
5,9%
33,3%
51,0%
20
100
8
6
18
9
12,4%
62,1%
5,0%
3,7%
11,2%
5,6%
3
32
1
1
9
5
5,9%
62,7%
2,0%
2,0%
17,6%
9,8%
84
60
12
5
52,2%
37,3%
7,5%
3,1%
27
16
7
1
52,9%
31,4%
13,7%
2,0%
36
99
26
22,4%
61,5%
16,1%
13
23
15
25,5%
45,1%
29,4%
5
3,1%
6,2%
11,8%
21,7%
26,7%
30,4%
0%
0
0
0
0
0
0
51
0%
0%
0%
0%
0%
0%
100%
10
19
35
43
49
0
35
Frequency Percentage
5.2. RELIABILITY AND VALIDITY
One of the main issues regarding reliability is related to the scale’s internal consistency
(Pallant, 2007). It concerns the extent to which the items that constitute to the scale
measure the same underlying construct. One of the indicators of internal consistency
that are used most often is Chronbach’s alpha. Ideally, it should be above 0,7.
The reliability of the scales for the different constructs was measured using Chronbach’s
alpha.
With reference to non-consumers, there appeared to be five constructs with a
Chronbach’s alpha below 0,7 and some items were removed from the scales in order to
increase reliability. The dropped items from these scales were: “The products of a green
brand detergent are more expensive than the average brand in the category” (from the
construct price), “Require little effort to purchase” (from the construct purchase effort),
“If I change my current preferred brand, I will not have to search very much to find a
new one” (from the construct evaluation costs), “The value of this brand to me may be
very high” (from the construct perceived value), “Trying to figure out the best product
in terms of the effects on the environment is very confusing” (from the construct
perceived environmental knowledge).
Regarding consumers, in four constructs the initial reliability was below 0,7. In a
similar way, to increase reliability, the items that were dropped were: “Products of this
brand are more expensive than the average brand in the category” (from the construct
price), “Less stores sell them, as compared to competing brands” (from the construct
purchase effort), “If I changed my previously preferred detergent brand, I would not
have to search very much to find a new one” (from the construct evaluation costs) and
“Trying to figure out the best product in terms of the effects on the environment is very
confusing” (from the construct perceived environmental knowledge).
The variable perceived value would be constructed later as a new variable for the
analysis - as an average of its items. Therefore, in order to have comparability between
the results for consumers and non – consumers, the analogous item for consumers “The
value of this brand to me is high” was also dropped.
All these mentioned items were not included in the analysis. The results for reliability before and after removing the items are presented in Table 6. After dropping the items,
all the scales had a reliability above 0,7 except purchase effort (for consumers) - 0,695-
36
which is close to the acceptable threshold of 0,7, purchase effort (for non-consumers) 0,602 and evaluation costs (for non-consumers) - 0,551.
Table 6. Chronbach's alpha for the scales
Cronbach's alpha
Construct
Functional value
Emotional value
Social value
Altruistic value
Price
Purchase effort
Evaluations costs
Performance risk
Perceived value
Brand
commitment
Purchase loyalty
(intention to
purchase)
Environmental
concern
Perceived
environmental
knowledge
Involvement
Consumers before dropping
0,945
0,955
0,850
0,873
0,278
0,606
-0,432
0,906
0,801
Consumers after dropping
0,945
0,955
0,850
0,873
0,794
0,695
0,722
0,906
0,942
Non -consumers- Non-consumers before dropping after dropping
0,909
0,909
0,882
0,882
0,775
0,775
0,940
0,940
0,417
0,728
0,569
0,602
-0,162
0,551
0,829
0,829
0,566
0,726
0,862
0,862
0,942
0,942
0,798
0,798
0,929
0,929
0,922
0,922
0,366
0,883
0,874
0,883
0,453
0,893
0,852
0,893
Content validity is related to whether the content of the manifest variables (e.g. items of
a test) is correct to measure the latent concept (e.g. attitudes) that should be measured
(Muijs, 2004). A pilot survey with 15 respondents was conducted in order to ensure
content validity. Its goal was to ensure that the questionnaire is understandable and
consistent. As a result of the feedback, some small changes were done in the
questionnaire - including adding some answers, rephrasing some questions, bolding
some words and adding some relevant photos of the eco-detergents that were mentioned
as examples – in order to improve recall.
Furthermore, another test was performed with 10 people who had already responded the
questions. They were asked for their feedback about the questionnaire and some
additional questions to find out if they oriented well and understood correctly the
questions. The results of it revealed that one of the questions seemed to be a little bit
37
confusing for them. Generally, it was expected a priori by the researcher that overall
there would be more respondents that would be non - consumers. The result could
probably be explained with the social desirability bias and/or with the above mentioned confusion in the questionnaire.
Moreover, a back-translation of the questionnaire was done to ensure that the
questionnaire has the correct meaning. In addition to this, the scales that were used were
adapted from previous studies and therefore, have been used successfully before. Their
validity has been proven in these previous studies.
5.3. PRINCIPAL COMPONENTS ANALYSIS
Principal components analysis was the one main kind of analysis used five times to test
the H1 to H5. IBM SPSS Statistics version 19 was used for all analyses.
Before doing the analyses, the suitability of the data for Principal components analysis
was estimated. In all five cases, examining the correlation matrices showed a lot of
coefficients of 0,3 and above (see Appendix 1). The Kaiser-Meyer-Oklin values were
higher than the recommended value of 0,6 (Pallant, 2007). Bartlett’s Test of Sphericity
showed p=0,00 and it reached statistical significance. Therefore, the factorability of the
correlation matrices was supported in all of the cases.
5.3.1. STRUCTURE OF VALUES - REGARDING CONSUMERS
Principal components analysis with a varimax rotation was used to explore the
underlying structure of the values regarding consumers - H1. The 15 items from the
constructs functional value, emotional value, social value and altruistic value were
subjected to Principal components analysis. As a result of the analysis, three
components (see Appendix 1A) with eigenvalues bigger than 1 were extracted,
explaining 50,7%, 18,3% and 9,3% of the variance, respectively. An inspection of the
screeplot (see Appendix 1A) showed a break after the second component. It was
decided to retain 3 components for further investigation based on the eigenvalues.
38
Table 7. Rotated component matrix of PCA regarding values - for consumers
Component
1
2
3
Perform consistently
,907
Well made
,890
Want to use products
,879
Acceptable quality
,879
Like products of brand
,876
Consistent quality
,872
Relaxed about usage of
,823
,338
,733
,396
products
Feel good of products
Good impression on others
,918
Improve perception by
,906
others
Social approval
,858
Bought by many people
,538
Environmental preservation
,884
Ethical value
,877
Ethical interest
,743
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
The rotated component matrix revealed that the first component is marked by high
loadings on the functional value, the second component has high loadings on social
value and the third component - on altruistic value. Therefore, the combination of these
three types of value represents the structure of the values in the case of consumers.
5.3.2. STRUCTURE OF COSTS - REGARDING CONSUMERS
Principal components analysis with varimax rotation was also performed on 12 items
from the scales price, purchase effort, evaluation costs and performance risk to examine
the underlying structure of costs regarding consumers - H2.
The analysis showed two components (see Appendix 1B) with eigenvalues bigger than
1, which explained 34,5% and 25,2% of the variance, respectively. According to the
screeplot (see Appendix 1B), there was a clear break after the second component.
Therefore, it was confirmed that two of the components should be retained for further
consideration.
39
Table 8. Rotated component matrix of PCA regarding costs - for consumers
Component
1
2
Reasonable price
,792
Good product for price
,701
Almost everywhere
,788
Easy to buy
,843
Little effort required to buy
,727
Much time R
,513
Afford time R
,658
Compare previous R
,749
Improper chance R
,847
Lost money R
,907
Risky performance R
,797
Worried not clean R
,828
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.
The general pattern of loadings from Table 8 showed that the first component has
highest loadings on performance risk and the second component has highest loadings on
purchase effort. In summary, the two components from the costs regarding consumers
are performance risk and purchase effort.
5.3.3. STRUCTURE OF MODERATORS - REGARDING CONSUMERS
Principal components analysis with varimax rotation was performed to explore the
structure of the moderators regarding consumers - H3. The ten items from the
constructs personal support for environmental issues, green purchase attitudes,
perceived environmental knowledge and involvement were the input variables of the
analysis. Prior to doing the analysis, the suitability of data for principal components
analysis was estimated.
Two components (see Appendix 1C) were extracted with eigenvalues above 1,
explaining 60% and 14,5% of the variance, respectively. The screeplot (see Appendix
1C) showed a break after the first component. It was decided to retain two components
based on the eigenvalues.
40
Table 9. Rotated component matrix of PCA regading moderators - for consumers
Component
1
2
Environmental concern
,798
Ethical obligation
,842
,315
Support env friendly
,843
,338
Like green
,900
Favourable attitude green
,802
Environment impact
,324
,758
awareness
Reduce waste
,747
Concern brands
,844
Care brands
,867
Important choice
,433
,694
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.
According to the rotated component matrix, the first component has higher loadings on
the constructs personal support for environmental issues and green purchase attitudes
which represent environmental concern. The second component has high loadings on
the construct involvement. Therefore, environmental concern and involvement are the
two main components that form the structure of moderators in the case of consumers
and they will be used for further analysis.
5.3.4. STRUCTURE OF VALUES - REGARDING NON-CONSUMERS
Principal factors extraction with varimax rotation was performed on the 15 items from
the scales functional value, emotional value, social value and altruistic value to explore
the structure of value in the case of non-consumers - H4.
Four components (see Appendix 1D) were extracted that have eigenvalues above 1,
which explain 47,1%, 15,5% and 10,9% of the variance, respectively. The screeplot (see
Appendix 1D) showed a break after the second component. It was decided to retain the
3 components for further investigation based on the eigenvalues.
41
Table 10. Rotated component matrix of PCA regarding values - for non-consumers
Component
1
Perform consistently N
,868
Well made N
,846
Consistent Quality N
,822
Acceptable quality N
,818
Like products of brand N
,733
Want to use products N
,661
Feel good N
,615
2
,380
,563
,330
Ethical interest N
,881
Ethical value N
,877
Environment friendly N
,856
Social Approval N
,775
Comfortable N
,500
3
,351
,560
Improve perception N
,867
Bought many people N
,828
Good impression N
,531
,641
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
The pattern of loadings in the rotated component matrix reveals that the first component
has high loadings on functional value, the second - on altruistic value and the third one
– on social value. Therefore, in the case of non-consumers the three main components
for values are the functional value, altruistic value and social value.
5.3.5. STRUCTURE OF MODERATORS - REGARDING NON-CONSUMERS
Principal components analysis was used to examine the structure of the moderators in
the case of non-consumers – H5. The eleven items from the constructs personal support
for environmental issues, green purchase attitudes, perceived environmental knowledge
and involvement were used as input variables.
As a result, two components were identified (see Appendix 1E) with eigenvalues above
1, which explained 58,7% and 15,1% of the variance, respectively. According to the
screeplot (see Appendix 1E), there was a drop after the first component. Finally, it was
decided to keep two components to investigate further.
42
Table 11. Rotated component matrix of PCA regarding moderators - for nonconsumers
Component
1
2
Like green N
,930
Favourable attitude N
,881
Support env friendly N
,792
Environmental concern N
,772
Ethical obligation N
,745
,377
Concern brands N
,408
,820
Environment impact
,490
,805
awareness N
Reduce waste N
,787
Care brands N
,765
Important choice N
,482
,675
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.
According to the rotated component matrix in Table 11, the first component has the
highest loadings on environmental concern and the second component has the highest
loadings on involvement. In sum, environmental concern and involvement are the main
components in the structure of moderators and they will be used in further analysis.
5.4. MULTIPLE REGRESSION
The multiple regression was the other main kind of analysis that was used ten times to
test H6 to H15. Multiple regressions were performed by IBM SPSS Statistics version
19.
An evaluation of the assumptions of this type of analysis was done to ensure that they
were not violated. There was an absence of multicollinearity which was determined
based on the Correlations tables (see Appendix 2), the Collinearity Diagnostics tables
and the Tolerance and VIF values from the table Coefficients. The VIF values were less
than 10 and the Tolerance values were bigger than 0,1 (Pallant, 2007). The assumptions
of linearity, normality and homoscedasticity were examined by the Residual
Scatterplots (see Appendix 2) and the Normal Probability Plots. They were not violated.
The residuals scatterplot was used to examine if there are outliers but none were found.
The existence of multivariate outliers was checked with Mahalanobis distance. In three
43
of the analyses the maximum value for Mahalanobis distance slightly exceeded the
critical value. But the maximum value for Cook’s distance in these cases was less than
1, so these cases were not supposed to cause any major problems (Pallant, 2007). It was
decided to retain these variables. Therefore, there was not a violation of the assumptions
of normality, linearity, multicollinearity and homoscedasticity.
5.4.1. THE RELATIONSHIP BETWEEN PURCHASE LOYALTY AND COSTS - REGARDING
CONSUMERS
Multiple regression was performed to examine if there is a negative relationship
between costs and purchase loyalty as a dependent variable in the case of consumers
and test H6.
As a result of the performed PCA for costs (H2), it turned out that purchase effort and
performance risk are the main components which form the structure of costs. The score
values of these two components were used as independent variables for the multiple
regression.
According to the results in Table 12, purchase effort and performance risk were able to
explain only 21,3% of the variance in purchase loyalty.
Table 12. Summary of Multiple regression Purchase
loyalty - costs - for consumers
Model
1
R
,462a
R Square
Adjusted R
Std. Error of the
Square
Estimate
,203
1,71790
,213
a. Predictors: (Constant), Purchase effort, Performance risk
b. Dependent Variable: Purchase loyalty
Results in the ANOVA table 13 indicated that F (2,158) = 21,431 and p = 0,000 which
is less than 0,05. Therefore, the model is statistically significant.
44
Table 13. ANOVA table for Multiple regression Purchase loyalty - costs - for
consumers
Sum of
1
Model
Squares
df
Mean Square
F
Sig.
Regression
126,492
2
63,246
21,431
,000a
Residual
466,284
158
2,951
Total
592,776
160
a. Predictors: (Constant), Purchase effort, Performance risk
b. Dependent Variable: Purchase loyalty
The Coefficients table (see Appendix 2A) revealed that only purchase effort has a
significant contribution to the regression with p=0,000 and standardized beta coefficient
of 0,460. Purchase effort could explain 21% of the variance in the independent variable.
Moreover, it was found out that performance risk influences negatively purchase
loyalty. The items for the costs were positively-worded which means that the higher the
result the less is the cost. Therefore, the smaller purchase effort the consumers have to
make, the higher their brand loyalty is. And with the increase of performance risk, the
purchase loyalty also increases. Therefore H6 could be only partially supported.
5.4.2. THE RELATIONSHIP BETWEEN P URCHASE LOYALTY AND VALUES - REGARDING
CONSUMERS
Multiple regression was also performed between the types of value as independent
variables and purchase loyalty as dependent variable to assess if there is a positive
relationship between them - H7.
Results from the PCA regarding types of values showed that functional, social and
altruistic value are the main components forming the structure of values. Score values of
functional value, social value and altruistic value were used as independent variables for
the analysis.
Results from the analysis indicated that the analysed model was able to explain 53,7%
from the variance in purchase loyalty. Therefore, there is a positive relationship between
the dependent variable and the independent variables and H7 is supported.
45
Table 14. Summary of Multiple regression Purchase loyalty - values - for consumers
Model
R
,733a
1
R Square
Adjusted R
Std. Error of
Square
the Estimate
,528
1,32229
,537
a. Predictors: (Constant), Altruistic value, Social value, Functional value
b. Dependent Variable: Purchase loyalty
According to Table 15, F(3,157) = 60,677 and p=0,000. Therefore, the model was
statistically significant.
Table 15. ANOVA table for Multiple regression Purchase loyalty - values for consumers
Model
Sum of
df
Mean Square
F
Sig.
60,677
,000a
Squares
1
Regression
318,271
3
106,090
Residual
274,505
157
1,748
Total
592,776
160
a. Predictors: (Constant), Altruistic value, Social value, Functional value
b. Dependent Variable: Purchase loyalty
The Coefficients table (see Appendix 2B) indicated that all of the independent variables
made a statistically significant contribution to the prediction of the dependent variable
(p=0,000 -for all of them). Functional value had the highest standardized beta
coefficient of all three of them - 0,590 and explained 34,8% of the variance in purchase
loyalty. It was followed by altruistic value with a standardized beta of 0,321 which
explained 10,3% of the variance and social value with a standardized beta of 0,294
which explained 8,6% of the variance in purchase loyalty. Therefore, functional value
had the strongest unique contribution to explaining purchase loyalty in the case of
consumers.
5.4.3. THE RELATIONSHIP BETWEEN INTENTION TO PURCHASE AND VALUES –
REGARDING NON- CONSUMERS
Standard multiple regression was performed to assess if there is a positive relationship
between the types of value and intention to purchase as a dependent variable regarding
non-consumers and to test H8.
46
According to the results from the PCA for values (H4), the main components that were
extracted were functional value, social value and altruistic value. Their score values
were used as independent variables for the regression.
Results from Table 16 indicated that altogether 29,0% from the variance in Intention to
purchase was predicted by knowing scores on functional value, altruistic value and
social value. Therefore, there is a positive relationship between the dependent variable
and the independent variables and H8 is supported.
Table 16. Summary of Multiple regression Intention to
purchase - values - for non-consumer
Model
1
R
,538a
R Square
,290
Adjusted R
Std. Error of the
Square
Estimate
,244
1,31777
a. Predictors: (Constant), Social value, Alstruistic value, Functional value
b. Dependent Variable: Intention to purchase
The ANOVA table 17 showed that F(3,47) = 6,387 and p=0,001. Therefore, the model
was statistically significant.
Table 17. ANOVA table for Multiple regression Intention to purchase values - for non-consumers
Model
Sum of
df
Mean Square
F
Sig.
6,387
,001a
Squares
1
Regression
33,272
3
11,091
Residual
81,617
47
1,737
Total
114,889
50
a. Predictors: (Constant), Social value, Alstruistic value, Functional value
b. Dependent Variable: Intention to purchase
According to the Coefficients table, (see Appendix 2C) only altruistic value made a
statistically significant contribution to the regression (p=0,000). Altruistic value had a
beta of 0,480 and explained 23% of the variance in intention to purchase. In sum,
altruistic value had the strongest unique contribution to explaining intention to purchase
in the case of non-consumers.
47
5.4.4. THE RELATIONSHIP BETWEEN PURCHASE LOYALTY AND MODERATORS
A multiple regression was performed between moderators as independent variables and
purchase loyalty as dependent variable to examine the relationship between them in the
case of consumers and test H9.
As a result of the PCA regarding moderators (H3), it turned out that environmental
concern and involvement are the main components forming the structure of moderators.
The score values of these two components were used as independent variables for the
multiple regression.
According to the results in Table 18, R Square was 0,398 which meant that altogether
39,8% of the variability in purchase loyalty was predicted by knowing scores on the
independent variables. Therefore, there was a positive relationship between the
dependent variable and the independent variables and H9 was supported.
Table 18. Summary of Multiple regression Purchase
loyalty - moderators - for consumers
Model
1
R
,631a
R Square
Adjusted R
Std. Error of
Square
the Estimate
,390
1,50333
,398
a. Predictors: (Constant), Involvement, Environmental concern
b. Dependent Variable: Purchase loyalty
Table 19 indicated that F(2,158) = 55,144 and p=0.000. Therefore, this model has
statistical significance.
Table 19. ANOVA table for Multiple regression Purchase loyalty moderators - for consumers
Model
Sum of
df
Mean Square
F
Sig.
52,144
,000a
Squares
1
Regression
235,694
2
117,847
Residual
357,082
158
2,260
Total
592,776
160
a. Predictors: (Constant), Involvement, Environmental concern
b. Dependent Variable: Purchase loyalty
The Coefficients table (see Appendix 2D) indicated that both of the independent
variables made a statistically significant contribution to the prediction of the dependent
48
variable (p=0,000 which is less than 0,05). Involvement had the strongest unique
contribution to explaining purchase loyalty - it explained 24% of the variance and had a
standardized beta coefficient of 0,490. It was closely followed by environmental
concern with a standardized beta coefficient of 0,397 which explained 15% of the
variance in the independent variable.
5.4.5. THE RELATIONSHIP BETWEEN INTENTION TO PURCHASE AND MODERATORS – FOR
NON- CONSUMERS
Standard multiple regression was performed to examine the relationship between
moderators and intention to purchase in the case of non-consumers and to test H10.
According to the results from PCA for moderators (H5), two components were
extracted - environmental concern and involvement. The score values of these two
components were used as independent variables.
According to Table 20, overall 39,0% of the variability in intention to purchase was
predicted by knowing the results of
involvement and environmental concern.
Therefore, there exists a positive relationship between the dependent variable and the
independent variables and H10 is supported.
Table 20. Summary of Multiple regression Intention to purchase - moderators - for
non-consumers
Model
1
R
,625a
R Square
,390
Adjusted R
Std. Error of
Square
the Estimate
,365
1,20788
a. Predictors: (Constant), Involvement, Environmental concern
b. Dependent Variable: Intention to purchase
Results from the ANOVA table 21 indicated that F(2,48) = 15,373 and p=0,000.
Therefore, the model is statistically significant.
49
Table 21. ANOVA table for Multiple regression Intention to purchase -moderators
- for non-consumers
Sum of
Model
1
df
Mean Square
F
Sig.
15,373
,000a
Squares
Regression
44,858
2
22,429
Residual
70,031
48
1,459
Total
114,889
50
a. Predictors: (Constant), Involvement, Environmental concern
b. Dependent Variable: Intention to purchase
According to the Coefficients table, (see Appendix 2E) only environmental concern
made a statistically significant contribution to the prediction of the dependent variable
(p=0,000). Environmental concern had a standardized beta coefficient of 0,607 and
explained 36,8% of the variance in intention to purchase.
5.4.6. THE INFLUENCE OF BRAND COMMITMENT ON PURCHASE LOYALTY – REGARDING
CONSUMERS
Multiple regression was performed to examine the influence of brand commitment on
purchase loyalty as a dependent variable regarding consumers and to test H11. The
independent variables included 5 items (Aaker et al., 2004) used to measure the
construct brand commitment.
According to the results in the Summary table, the analysed model was able to explain
40,1% of the variance in purchase loyalty. Therefore, brand commitment had a positive
influence on purchase loyalty and H11 was supported.
Table 22. Summary of Multiple regression Purchase
loyalty - Brand commitment - for consumers
Model
1
R
,634a
R Square
Adjusted R
Std. Error of
Square
the Estimate
,382
1,51291
,401
a. Predictors: (Constant), Happy, Sacrifice willing, Commitment to buy,
Postpone willing, Let down
b. Dependent Variable: Purchase loyalty
The ANOVA table 23 showed that F(5,155) = 20,796 and p=0,000 and therefore, the
model is statistically significant.
50
Table 23. ANOVA table for Multiple regression Purchase loyalty - Brand
commitment - for consumers
Model
Sum of
df
Mean Square
F
Sig.
20,796
,000a
Squares
1
Regression
237,996
5
47,599
Residual
354,780
155
2,289
Total
592,776
160
a. Predictors: (Constant), Happy, Sacrifice willing, Commitment to buy, Postpone willing, Let down
b. Dependent Variable: Purchase loyalty
The Coefficients table (see Appendix 2F) showed that there are 2 independent variables
which have a statistically significant contribution – the items “I am willing to make
small sacrifices in order to keep using this brand” (p=0,007) and “I am so happy with
this brand that I no longer feel the need to watch out for other alternatives” (p=0,000).
From them, the first variable had a standardized beta of 0,235 explaining 5,5% of the
variance in purchase loyalty and the second one had a beta of 0,407, explaining 16,5%
of the same variance.
5.4.7. RELATIONSHIP BETWEEN CUSTOMER VALUE AND TYPES OF VALUE - REGARDING
CONSUMERS
Multiple regression was performed to assess if there is a positive relationship between
types of value as independent variables and customer value as a dependent variable and
to test H12.
Results from the analysis showed that 53,0% from the variance in customer value was
predicted by knowing scores on functional value, altruistic value and social value.
Therefore, there is a positive relationship between the dependent variable and the
independent variables and H12 was supported.
Table 24. Summary of Multiple regression Customer
value - values - for consumers
Model
1
R
,728a
R Square
Adjusted R
Std. Error of the
Square
Estimate
,521
1,19304
,530
a. Predictors: (Constant), Altruistic value, Social value, Functional value
b. Dependent Variable: Customer value
51
The ANOVA table 25 showed that F(3,157) = 58,988 and p= 0,000 and therefore, the
model reached a statistical significance.
Table 25. ANOVA table for Multiple regression Customer value - values for consumers
Model
Sum of
df
Mean Square
F
Sig.
58,988
,000a
Squares
1
Regression
251,881
3
83,960
Residual
223,467
157
1,423
Total
475,348
160
a. Predictors: (Constant), Altruistic value, Social value, Functional value
b. Dependent Variable: Customer value
According to the Coefficients table, (see Appendix 2G) Altruistic value (p=0,000),
functional value (p=0,000) and social value (p=0,012) made a statistically significant
contribution to the prediction of the dependent variable. Functional value had the
highest standardized beta of 0,658 and explained 43,2% of the variance in customer
value. Altruistic value with a beta of 0,278 and social value with a value of 0,139 could
explain respectively - 7,7% and 1,9% of the same variance. Therefore, functional value
had the strongest unique contribution to explaining customer value regarding
consumers.
5.5. PEARSON CORRELATION
5.5.1. THE RELATIONSHIP BETWEEN PURCHASE LOYALTY AND CUSTOMER VALUE –
REGARDING CONSUMERS
The relationship between purchase loyalty and customer value was investigated by
using Pearson product - moment coefficient (H13).
Preliminary analyses were performed and the inspection of the scatterplot (see
Appendix 2H) revealed that there is no violation of the assumptions of normality,
linearity and homoscedasticity. There was a positive correlation between the two
variables - r=0,661, n=161 and H13 was supported.
52
Table 26. Correlations between Purchase loyalty and Customer
value - for consumers
Purchase loyalty
Pearson Correlation
Purchase
Customer
loyalty
value
1
,661**
Sig. (2-tailed)
N
Customer value
,000
161
161
Pearson Correlation
,661**
1
Sig. (2-tailed)
,000
N
161
161
Note: **. Correlation is significant at the 0.01 level (2-tailed).
5.6. SUMMARY OF THE RESULTS
The results from all analyses could be summarizes in Table 27 below.
Table 27. Results from the analyses
Dependent variable
Purchase loyalty
Purchase loyalty
Intention to purchase
Purchase loyalty
Intention to purchase
Independent
variables
costs
values
values
moderators
moderators
brand
Purchase loyalty
commitment
Customer value
values
Purchase loyalty
customer value
Note: * Indicates significance (p<0,05)
Hypothesis
R square
R
Consumers/nonconsumers
H6-partially
supported
H7-supported
H8-supported
H9-supported
H10-supported
0,213*
0,537*
0,290*
0,398*
0,390*
0,462
0,733
0,538
0,631
0,564
consumers
consumers
non-consumers
consumers
consumers
0,401*
0,530*
0,634
0,728
0,661
consumers
consumers
consumers
H11-supported
H12-supported
H13-supported
The results from the analyses could also be presented in the model in Figure 3. It is
illustrated with dotted line what other relationships could be a subject to further
research. The concept for the types of values of Holbrook (2006) is used in the model,
according to which the different types of value are economic value, social value,
hedonic value and altruistic value. The means-end model of Zeithmal (1988) relating
price, quality and value is also incorporated in the model. A part of the model of
Dimitriadis et al. (2010) which examines the relationship between relationship quality
and relational outcomes is also included in the model.
53
Figure 3. Model of research and further investigation - for customers
Types of value
Economic
Social
Hedonic
Altruistic
R2=0,530
R2=0,537
Customer value
Types of costs
Price
kjhkj
Effort
Evaluation costs
Performance risk
of green brand
Relationship
quality
- brand
commitment
Brand loyalty
R2=0,401
R2=0,213
Moderators
- environmental concern
- environmental knowledge
- involvement
54
R=0,661
R2=0,398
Relational
outcomes
WOM
6. CONCLUSION
6.1. FINAL CONCLUSIONS
After analyzing and discussing the main results in the previous chapter, some
conclusions could be made regarding the four research questions.
With regard to the first research question, the results (H11) confirmed that in the case
of consumers brand commitment had a positive influence (R2=0,401) on the brand
loyalty (which was measured with purchase/behavioural loyalty) to ecological products.
This result was statistically significant. This result was expected due to one of the
results from a previous study - a positive relationship between commitment and brand
loyalty (Punniyamoorthy et al., 2007).
As it was expected, it was found out that there is a positive relationship (H13) between
customer value and brand loyalty. Therefore, in order to foster brand loyalty, marketers
could think of some ways to improve the perception of the customer value. Some of
these ways will be discussed further because they are related to the next results.
Moving on to the second research question, the results from the analysis indicated that
in the case of consumers functional value, social value and altruistic value had a
positive influence and explained 53% of the variance in customer value (H12). All of
the independent variables had a significant contribution to the model but functional
value explained the most of the variance in customer value. The possible implications of
this result will be discussed further.
Regarding the third research question, it turns out that among consumers, purchase
effort (which includes product availability) is the only kind of cost that contributes
significantly to brand loyalty (H6) and influences it positively. Therefore, it would be
important for companies to increase the density of distribution of their ecological
products in order to ease their consumers and improve brand loyalty.
Another result, concerning values is that in the case of consumers, functional value,
social value and altruistic value have a strong positive relationship (R2=0,537) with
brand loyalty (H7) and the result is statistically significant. From them, functional value
has the highest contribution to explaining brand loyalty. This is an expected result
because according to one survey of Punniyamoorthy et al.(2007), there is a positive
relationship between functional value and brand loyalty.
55
Therefore, functional value has the highest influence on both customer value and brand
loyalty. It could be suggested that a segmentation of customers which is based on the
functional value could be done. The most important benefits for customers are a key
issue in brand and sales strategies, like product differentiation and positioning of
ecological products (Huber, 2001). It could also be proposed that it should be focused
on the functional value of the ecological product when materials for marketing purposes
are made.
Another possible implication is that, managers could use structural bonding strategy
because some researches indicate that it could influence positively the utalitarian (or
functional) value (Chiu et.al., 2004). The company could offer value - adding benefits
which are not available everywhere and are expensive or difficult to provide.
In comparison with the relationship between values and brand loyalty for consumers, in
the case of non - consumers, there is a weaker relationship (R2=0,290) between the
types of values and the intention to purchase (H8) and only altruistic value contributes
significantly to the regression.
There is one possible explanation for the difference in the strength of these relationships
in the case of consumers and non - consumers and for the fact that functional value is
the most influential construct for consumer compared to altruistic value - for nonconsumers. The reason is related to the cognitive dissonance theory. According to it a
post - purchase dissonance may appear because the products that were bought were
more expensive (Shiffmann et al., 2008). In order to reduce the unpleasant feelings
created by rival thoughts, the consumer may rationalize the decision as being wise and
try to focus on the functional value of the product.
The most influential factor - altruistic value in the case of non-consumers implies that
marketers could emphasize on the ethical value of the ecological product in different
materials for marketing purposes when the target group includes people who have not
tried the ecological product.
Considering the fourth research question, the results for consumers indicated that in
the case of consumers there is a positive relationship (R2=0,319) between moderators
and brand loyalty (H9) as it was expected. Involvement and environmental concern
contributed both statistically significant to the variance in brand loyalty but involvement
explained it better. This result confirms one of the results of a survey of
Punniyamoorthy et al. (2007) that the higher level of product involvement leads to
56
higher level of brand loyalty. Another research reveals that one of the most influential
factors on the information processing is the consumer’s involvement (Montoro-Rios et
al., 2008). When the product involvement is high, consumers consider more
advertisements and are more motivated to process the message in depth. It is suggested
by the Elaboration Likelihood Model that the central route of persuasion is more
important when there are high involvement and high frequency of purchasing and the
peripheral route of persuasion is crucial when involvement and/or purchase frequency
are lower (Montoro-Rios et al., 2008 (in Petty et al., 1983)).
Therefore, based on the results, a customer segmentation on the basis of involvement
could be done regarding consumers. Another managerial implication is that the
company could increase involvement using different activities and it could create
synergies in this way (Hennig - Thurau et.al., 2000).
In comparison, the relationship between moderators and intention to purchase (H10) in
the case of non-consumers is a little bit stronger (R2=0,390) than the relationship
between moderators and brand loyalty in the case of consumers. However, only
environmental concern contributed significantly to explaining the regression.
Considering the result, it could be suggested to make a customer segmentation on the
basis of environmental concern regarding non-consumers. Furthermore, social media
could be used for increasing the level of environmental concern.
6.2. LIMITATIONS
One of the limitations of the research was the small size of the sample for nonconsumers. The reason for this was that there were time constraints. Finding the
respondents was done through networking and it required a lot of time. There were a lot
of respondents that started filling in the questionnaire but didn’t finish it until the end
which decreased the effective response ratio to 23,8%.
Another limitation is that there were two scales that had a low internal consistency.
They were not reliable enough - measured by Chronbach’s alpha - purchase effort in the
case of consumers - 0,695 and purchase effort in the case of non-consumers - 0,602 and
evaluation costs in the case of non-consumers - 0,551.
The final limitation is that there is limited comparability because a similar study has not
been conducted before in an ecological context. As far as the researcher’s knowledge,
57
some of the results could be compared only with a previous study of Punniyamoorthy et
al. (2007) which is related to measuring brand loyalty in English newspapers.
6.3. FURTHER RESEARCH
In this research a lot of aspects of factors that could possibly influence brand loyalty of
green brands were examined. But as this research was limited regarding time and extent,
only a limited number of constructs was investigated. The ideas for further investigation
are presented with dashed lines on Figure 3. It could be further investigated in the future
some relationships with the other relational outcome – word-of-mouth (see Figure 3 the dashed lines) like the relationships between costs and word-of-mouth, between
customer value and word-of-mouth and also between costs and customer value.
Furthermore, all constructs from relationship quality - brand commitment, trust and
satisfaction, their influence on the relational outcomes and their relationship with
customer value could be examined. In addition to this, the influence of demographics on
brand loyalty and word-of-mouth could be investigated. All these relationships could be
compared for consumers and for non-consumers.
Furthermore, a research like this one could be done for other product categories.
58
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Appendix 1 - Detailed results from Principal components analysis
Appendix 1A –Detailed Principal components analysis results for the analysis of valuesregarding consumers
Table 1.1. KMO and Bartlett's Test –for the analysis of valuesregarding consumers
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
,885
Bartlett's Test of Sphericity
Approx. Chi-Square
2348,285
df
105
Sig.
,000
Table 1.2. Total Variance Explained - for the analysis of values-regarding consumers
Component
Extraction Sums of Squared
Rotation Sums of Squared
Initial Eigenvalues
Loadings
Loadings
% of
Cumulative
% of
Cumulative
% of
Cumulative
Total Variance
%
Total Variance
%
Total
Variance
%
1
7,604
50,694
50,694
7,604 50,694
50,694
6,145
40,965
40,965
2
2,748
18,320
69,015
2,748 18,320
69,015
2,959
19,726
60,691
3
1,397
9,315
78,330
1,397
9,315
78,330
2,646
17,640
78,330
4
,768
5,123
83,453
5
,589
3,929
87,382
6
,388
2,586
89,969
7
,277
1,850
91,818
8
,265
1,768
93,587
9
,220
1,466
95,053
10
,165
1,100
96,153
11
,153
1,017
97,169
12
,135
,902
98,071
13
,108
,720
98,791
14
,107
,715
99,506
15
,074
,494
100,000
Extraction Method: Principal Component Analysis.
Figure 1.1. Scree Plot - for the analysis of values-regarding consumers
67
Table 1.3. Correlation Matrix - for the analysis of values - regarding consumers
Correlation
Consistent
quality
Well made
Acceptable
quality
Perform
consistently
Like products of
brand
Want to use
products
Relaxed about
usage of
products
Feel good of
products
Bought by
many people
Improve
perception by
others
Good
impression on
others
Social approval
Ethical interest
Environmental
preservation
Ethical value
Want to
use
products
,745
Relaxed
about
usage of
products
,685
Feel
good of
products
,613
Bought
by
many
people
,254
Improve
perception
by others
,059
Good
impression
on others
,151
Social
approval
,177
Ethical
interest
,351
Environmental
preservation
,375
Ethic
al
value
,345
Consistent
quality
1,000
Well
made
,849
Acceptable
quality
,807
Perform
consistently
,800
Like
products
of brand
,738
,849
,807
1,000
,792
,792
1,000
,778
,849
,751
,726
,796
,745
,737
,711
,650
,600
,260
,197
,089
,043
,177
,129
,235
,171
,316
,299
,343
,322
,313
,360
,800
,778
,849
1,000
,776
,777
,717
,672
,253
,053
,174
,191
,280
,307
,281
,738
,751
,726
,776
1,000
,895
,849
,805
,374
,170
,225
,253
,447
,376
,396
,745
,796
,745
,777
,895
1,000
,841
,838
,295
,159
,221
,255
,447
,439
,425
,685
,737
,711
,717
,849
,841
1,000
,834
,296
,185
,180
,251
,464
,465
,473
,613
,650
,600
,672
,805
,838
,834
1,000
,278
,272
,323
,370
,511
,500
,511
,254
,260
,197
,253
,374
,295
,296
,278
1,000
,404
,347
,394
,338
,191
,236
,059
,089
,043
,053
,170
,159
,185
,272
,404
1,000
,835
,719
,350
,225
,327
,151
,177
,129
,174
,225
,221
,180
,323
,347
,835
1,000
,810
,329
,262
,315
,177
,351
,375
,235
,316
,343
,171
,299
,322
,191
,280
,307
,253
,447
,376
,255
,447
,439
,251
,464
,465
,370
,511
,500
,394
,338
,191
,719
,350
,225
,810
,329
,262
1,000
,392
,338
,392
1,000
,623
,338
,623
1,000
,408
,648
,823
,345
,313
,360
,281
,396
,425
,473
,511
,236
,327
,315
,408
,648
,823
1,000
68
Appendix 1B –Detailed Principal components analysis results for the analysis of costsregarding consumers
Table 1.4. KMO and Bartlett's Test- for the analysis of costs regarding consumers
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
,820
Bartlett's Test of Sphericity
Approx. Chi-Square
980,026
df
66
Sig.
,000
Table 1.5. Total Variance Explained - for the analysis of costs-regarding consumers
Component
Extraction Sums of Squared
Initial Eigenvalues
Loadings
% of
% of
Cumulative
Total
Variance
Cumulative %
Total
Variance
%
1
4,144
34,530
34,530
4,144
34,530
34,530
2
3,026
25,217
59,746
3,026
25,217
59,746
3
,981
8,175
67,921
4
,845
7,045
74,966
5
,679
5,661
80,627
6
,587
4,896
85,522
7
,435
3,628
89,151
8
,371
3,088
92,239
9
,292
2,433
94,672
10
,258
2,149
96,820
11
,214
1,784
98,604
12
,168
1,396
100,000
Extraction Method: Principal Component Analysis.
Figure 1.2. Scree Plot - for the analysis of costs-regarding consumers
69
Rotation Sums of Squared
Loadings
% of
Cumulat
Total
Variance
ive %
4,135
34,455
34,455
3,035
25,291
59,746
Table 1.6. Correlation Matrix - for the analysis of costs-regarding consumers
Correlation Reasonable price
Good product for
price
Almost everywhere
Easy to buy
Little effort required
to buy
Much time R
Afford time R
Compare previous R
Improper chance R
Lost money R
Risky performance
R
Worried not clean R
Reasonable
price
1,000
,659
Good
product for
price
,659
1,000
Almost
everywhere
,444
,386
Easy to
buy
,509
,402
Little effort
required to
buy
,463
,349
Much time
R
,045
-,006
Afford
time R
,066
-,051
Compare
previous R
,039
,035
Improper
chance R
-,072
-,009
Lost
money R
,020
,075
,444
,509
,463
,386
,402
,349
1,000
,721
,456
,721
1,000
,566
,456
,566
1,000
,107
,187
,119
-,038
,019
,051
-,062
-,062
,069
-,049
,017
,002
-,107
-,004
,052
-,179
-,091
,026
-,135
-,081
-,024
,045
,066
,039
-,072
,020
-,059
-,006
-,051
,035
-,009
,075
,012
,107
-,038
-,062
-,049
-,107
-,179
,187
,019
-,062
,017
-,004
-,091
,119
,051
,069
,002
,052
,026
1,000
,343
,315
,314
,372
,376
,343
1,000
,569
,414
,497
,411
,315
,569
1,000
,551
,610
,501
,314
,414
,551
1,000
,780
,598
,372
,497
,610
,780
1,000
,678
,376
,411
,501
,598
,678
1,000
,275
,390
,459
,754
,784
,637
-,125
-,066
-,135
-,081
-,024
,275
,390
,459
,754
,784
,637
1,000
70
Risky
performance
Worried
R
not clean R
-,059
-,125
,012
-,066
Appendix 1C –Detailed Principal components analysis results for the analysis of
moderators -regarding consumers
Table 1.7. KMO and Bartlett's Test - for the analysis of moderatorsregarding consumers
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity
Approx. Chi-Square
df
Sig.
,888
1275,729
45
,000
Table 1.8. Total Variance Explained - - for the analysis of moderators-regarding consumers
Component
Extraction Sums of Squared
Rotation Sums of Squared
Initial Eigenvalues
Loadings
Loadings
% of
Cumulative
% of
Cumulative
% of
Cumulat
Total
Variance
%
Total
Variance
%
Total
Variance
ive %
1
6,003
60,033
60,033
6,003
60,033
60,033
3,968
39,678
39,678
2
1,446
14,458
74,491
1,446
14,458
74,491
3,481
34,813
74,491
3
,757
7,568
82,059
4
,471
4,711
86,769
5
,313
3,127
89,897
6
,282
2,818
92,715
7
,220
2,205
94,919
8
,213
2,125
97,045
9
,155
1,547
98,592
10
,141
1,408
100,000
Extraction Method: Principal Component Analysis.
Figure 1.3. Scree Plot - for the analysis of moderators-regarding consumers
71
Table 1.9. Correlation Matrix - for the analysis of moderators-regarding consumers
Correlation Environmental
concern
Ethical obligation
Support env friendly
Like green
Favourable attitude
green
Environment impact
awareness
Reduce waste
Concern brands
Care brands
Important choice
Favourable Environment
attitude
impact
green
awareness
,588
,451
Environmental
concern
1,000
Ethical
obligation
,702
Support env
friendly
,724
Like
green
,691
,702
,724
,691
,588
1,000
,830
,763
,681
,830
1,000
,773
,693
,763
,773
1,000
,808
,681
,693
,808
1,000
,451
,466
,525
,443
,434
,320
,432
,443
,473
,401
,501
,573
,494
,439
,486
,559
,398
,335
,390
,568
72
Reduce
waste
,434
Concern
brands
,320
Care
brands
,432
Important
choice
,443
,466
,525
,443
,504
,473
,494
,398
,436
,401
,439
,335
,347
,501
,486
,390
,461
,573
,559
,568
,574
,504
1,000
,776
,543
,631
,511
,436
,347
,461
,574
,776
,543
,631
,511
1,000
,509
,618
,462
,509
1,000
,737
,673
,618
,737
1,000
,735
,462
,673
,735
1,000
Appendix 1D –Detailed Principal components analysis results for the analysis of valuesregarding non-consumers
Table 1.10. KMO and Bartlett's Test - for the analysis of valuesregarding non-consumers
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity
Approx. Chi-Square
df
Sig.
,812
599,368
105
,000
Table 1.11. Total Variance Explained Test - for the analysis of values-regarding non-consumers
Component
Extraction Sums of Squared
Initial Eigenvalues
Loadings
Rotation Sums of Squared Loadings
% of
Cumulative
% of
Cumulative
% of
Cumulative
Total
Variance
%
Total
Variance
%
Total
Variance
%
1
7,065
47,097
47,097
7,065
47,097
47,097
4,619
30,796
30,796
2
2,317
15,448
62,545
2,317
15,448
62,545
4,191
27,942
58,738
3
1,640
10,933
73,479
1,640
10,933
73,479
2,211
14,741
73,479
4
,956
6,373
79,852
5
,689
4,595
84,447
6
,516
3,441
87,888
7
,397
2,647
90,535
8
,318
2,120
92,654
9
,296
1,973
94,627
10
,210
1,397
96,024
11
,177
1,182
97,206
12
,147
,979
98,185
13
,112
,745
98,930
14
,097
,650
99,580
15
,063
,420
100,000
Extraction Method: Principal Component Analysis.
Figure 1.4. Scree Plot - for the analysis of values-regarding non-consumers
73
Table 1.12. Correlation Matrix Test - for the analysis of values-regarding non-consumers
Correlatio
n
Consistent
QualityN
Well madeN
Acceptable
qualityN
Perform
consistently
N
Like
products of
brandN
Want to use
products N
Comfortable
N
Feel goodN
Bought many
peopleN
Improve
perception N
Good
impression N
Social
Approval N
Ethical
interest N
Environment
friendly N
Ethical value
N
Acceptable
qualityN
,709
Perform
consistent
lyN
,671
Like
products
of
brandN
,649
Want to
use
products
N
,770
Comforta
bleN
,576
Feel
good
N
,550
Bought
many
people
N
-,010
Improve
perception
N
,161
Good
impression
N
,216
Social
Approval
N
,371
Ethical
interest
N
,493
Environment
friendly N
,568
Ethical
value N
,542
1,000
,792
,792
1,000
,695
,673
,540
,584
,543
,541
,371
,481
,353
,400
-,020
-,107
,152
,052
,086
,202
,142
,280
,368
,386
,385
,418
,385
,382
,671
,695
,673
1,000
,571
,463
,352
,539
-,001
,081
,190
,135
,181
,236
,223
,649
,540
,584
,571
1,000
,736
,489
,631
,112
,340
,347
,363
,445
,388
,438
,770
,543
,541
,463
,736
1,000
,765
,692
,099
,274
,310
,550
,619
,606
,609
,576
,371
,481
,352
,489
,765
1,000
,593
,002
,015
,337
,507
,537
,475
,479
,550
-,010
,353
-,020
,400
-,107
,539
-,001
,631
,112
,692
,099
,593
,002
1,000
,116
,116
1,000
,324
,614
,383
,369
,401
,156
,444
,067
,298
,004
,362
,041
,161
,152
,052
,081
,340
,274
,015
,324
,614
1,000
,548
,349
,263
,176
,224
,216
,086
,202
,190
,347
,310
,337
,383
,369
,548
1,000
,738
,497
,416
,429
,371
,142
,280
,135
,363
,550
,507
,401
,156
,349
,738
1,000
,657
,574
,615
,493
,368
,386
,181
,445
,619
,537
,444
,067
,263
,497
,657
1,000
,796
,867
,568
,385
,418
,236
,388
,606
,475
,298
,004
,176
,416
,574
,796
1,000
,864
,542
,385
,382
,223
,438
,609
,479
,362
,041
,224
,429
,615
,867
,864
1,000
Consistent
QualityN
1,000
Well
made
N
,762
,762
,709
74
Appendix 1E –Detailed Principal components analysis results for the analysis of
moderators -regarding non-consumers
Table 1.13. KMO and Bartlett's Test Test - for the analysis of
moderators - regarding non-consumers
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity
Approx. Chi-Square
df
Sig.
,802
433,242
45
,000
Table 1.14. Total Variance Explained- for the analysis of moderators - regarding non-consumers
Component
Rotation Sums of Squared
Initial Eigenvalues
Extraction Sums of Squared Loadings
Loadings
% of
Cumulative
% of
Cumulative
% of
Cumulative
Total
Variance
%
Total
Variance
%
Total
Variance
%
1
5,874
58,737
58,737
5,874
58,737
58,737
3,921
39,213
39,213
2
1,514
15,139
73,876
1,514
15,139
73,876
3,466
34,663
73,876
3
,887
8,872
82,748
4
,518
5,178
87,925
5
,408
4,079
92,004
6
,272
2,717
94,721
7
,224
2,240
96,961
8
,154
1,538
98,499
9
,100
,996
99,494
10
,051
,506
100,000
Extraction Method: Principal Component Analysis.
Figure 1.5. Scree Plot - for the analysis of moderators-regarding consumers
75
Table 1.15. Correlation Matrix - for the analysis of moderators - regarding non-consumers
Correlation Environmental
concern N
Ethical obligation N
Support env friendly
N
Like green N
Favourable attitude
N
Environment impact
awareness N
Reduce waste N
Concern brands N
Care brands N
Important choice N
Support
Environmental
Ethical
env friendly Like green
concern N
obligation N
N
N
1,000
,662
,683
,699
Environment
impact
Favourable awareness
attitude N
N
,574
,372
Reduce
waste N
,332
Concern
brands N
,492
Care
brands N
,294
Important
choice N
,489
,662
,683
1,000
,801
,801
1,000
,645
,799
,594
,677
,341
,545
,353
,352
,591
,731
,531
,594
,552
,707
,699
,574
,645
,594
,799
,677
1,000
,908
,908
1,000
,381
,332
,257
,247
,510
,412
,327
,265
,544
,452
,372
,341
,545
,381
,332
1,000
,744
,625
,448
,542
,332
,492
,294
,489
,353
,591
,531
,552
,352
,731
,594
,707
,257
,510
,327
,544
,247
,412
,265
,452
,744
,625
,448
,542
1,000
,557
,423
,374
,557
1,000
,773
,801
,423
,773
1,000
,659
,374
,801
,659
1,000
76
Appendix 2 - Detailed results from Multiple regression
Appendix 2A – Detailed Multiple regression results for the analysis Purchase loyalty –
costs regarding consumers
Table 2.1. Correlations - for the regression Purchase loyalty - costs regarding consumers
Purchase
Performance
loyalty
risk
Purchase effort
Pearson Correlation
Purchase loyalty
1,000
-,041
,460
Performance risk
-,041
1,000
,000
Purchase effort
,460
,000
1,000
Sig. (1-tailed)
Purchase loyalty
.
,303
,000
Performance risk
,303
.
,500
Purchase effort
,000
,500
.
N
Purchase loyalty
161
161
161
Performance risk
161
161
161
Purchase effort
161
161
161
Table 2.2. Residuals Statisticsa - for the regression Purchase loyalty - costs regarding
consumers
Minimum
Predicted Value
2,5585
Std. Predicted Value
-2,225
Standard Error of Predicted
,135
Value
Adjusted Predicted Value
2,6389
Residual
-4,57042
Std. Residual
-2,660
Stud. Residual
-2,681
Deleted Residual
-4,64095
Stud. Deleted Residual
-2,735
Mahal. Distance
,001
Cook's Distance
,000
Centered Leverage Value
,000
a. Dependent Variable: Purchase loyalty
Maximum
6,3336
2,020
,394
Mean
4,5373
,000
,224
Std. Deviation
,88914
1,000
,070
N
161
161
161
6,3005
3,53213
2,056
2,108
3,71352
2,132
7,414
,076
,046
4,5374
,00000
,000
,000
-,00008
-,001
1,988
,006
,012
,88525
1,70713
,994
1,003
1,73777
1,008
1,931
,009
,012
161
161
161
161
161
161
161
161
161
Figure 2.1. Scatterplot of standardised residuals
for the regression Purchase loyalty - costs
regarding consumers
Figure 2.2. Normal probability plot of the Residual
for the regression Purchase loyalty - costs
regarding consumers
77
Table 2.3. Coefficientsa - for the regression Purchase loyalty - costs regarding consumers
Unstandardized
Standardized
Coefficients
Coefficients
95,0% Confidence Interval for B
Correlations
Std.
Model
B
Error
Beta
t
Sig.
Lower Bound
Upper Bound Zero-order Partial
1
(Constant)
4,537
,135
33,513
,000
4,270
4,805
Performance
-,079
,136
risk
Purchase
,886
,136
effort
a. Dependent Variable: Purchase loyalty
Collinearity Statistics
Part
Tolerance
VIF
-,041
-,580
,563
-,347
,190
-,041
-,046
-,041
1,000
1,000
,460
6,521
,000
,617
1,154
,460
,461
,460
1,000
1,000
78
Appendix 2B –Detailed Multiple regression results for the analysis Purchase loyalty –
values regarding consumers
Table 2.4. Correlations for the regression - for the regression Purchase loyalty - values regarding
consumers
Pearson Correlation
Sig. (1-tailed)
N
Purchase loyalty
Functional value
Social value
Altruistic value
Purchase loyalty
Functional value
Social value
Altruistic value
Purchase loyalty
Functional value
Social value
Altruistic value
Purchase
loyalty
1,000
,590
,294
,321
.
,000
,000
,000
161
161
161
161
Functional value
,590
1,000
,000
,000
,000
.
,500
,500
161
161
161
161
Social value
,294
,000
1,000
,000
,000
,500
.
,500
161
161
161
161
Altruistic value
,321
,000
,000
1,000
,000
,500
,500
.
161
161
161
161
Table 2.5. Residuals Statisticsa - for the regression Purchase loyalty - values regarding
consumers
Minimum
Predicted Value
,9097
Std. Predicted Value
-2,572
Standard Error of Predicted
,105
Value
Adjusted Predicted Value
,9051
Residual
-3,15479
Std. Residual
-2,386
Stud. Residual
-2,405
Deleted Residual
-3,20563
Stud. Deleted Residual
-2,443
Mahal. Distance
,011
Cook's Distance
,000
Centered Leverage Value
,000
a. Dependent Variable: Purchase loyalty
Maximum
6,9440
1,706
,441
Mean
4,5373
,000
,201
Std. Deviation
1,41039
1,000
,054
N
161
161
161
6,9863
2,90585
2,198
2,226
2,98251
2,255
16,776
,087
,105
4,5373
,00000
,000
,000
-,00005
-,001
2,981
,006
,019
1,41052
1,30983
,991
1,003
1,34197
1,009
2,275
,011
,014
161
161
161
161
161
161
161
161
161
Figure 2.3. Scatterplot of standardised residuals
for the regression Purchase loyaltyvalues regarding consumers
79
Figure 2.4. Normal probability plot of the residual
for the regression Purchase loyaltyvalues regarding consumers
Table 2.6. Coefficientsa- for the regression Purchase loyalty - values regarding consumers
Unstandardized
Standardized
95,0% Confidence Interval
Coefficients
Coefficients
for B
Model
1
(Constant)
B
4,537
Std. Error
,104
Functional
1,135
value
Social value
,565
Altruistic value
,618
a. Dependent Variable: Purchase loyalty
Beta
t
43,539
Sig.
,000
,105
,590
10,855
,000
,928
,105
,105
,294
,321
5,408
5,912
,000
,000
,359
,412
Zeroorder
Partial
Part
Tolerance
VIF
1,341
,590
,655
,590
1,000
1,000
,772
,825
,294
,321
,396
,427
,294
,321
1,000
1,000
1,000
1,000
Lower Bound Upper Bound
4,331
4,743
80
Collinearity
Statistics
Correlations
Appendix 2C –Detailed Multiple regression results for the analysis Intention to
purchase–values regarding non-consumers
Table 2.7. Correlations - for the regression Intention to purchase – values regarding non-consumers
Intention to
purchase
Functional value Alstruistic value Social value
Pearson Correlation
Intention to purchase
1,000
,176
,480
,169
Functional value
,176
1,000
,000
,000
Alstruistic value
,480
,000
1,000
,000
Social value
,169
,000
,000
1,000
Sig. (1-tailed)
Intention to purchase
.
,109
,000
,118
Functional value
,109
.
,500
,500
Alstruistic value
,000
,500
.
,500
Social value
,118
,500
,500
.
N
Intention to purchase
51
51
51
51
Functional value
51
51
51
51
Alstruistic value
51
51
51
51
Social value
51
51
51
51
Table 2.8. Residuals Statisticsa- for the regression Intention to purchase – values regarding
non-consumers
Minimum
Maximum
Predicted Value
2,6939
5,8525
Std. Predicted Value
-2,010
1,862
Standard Error of Predicted
,190
,705
Value
Adjusted Predicted Value
2,2684
6,0998
Residual
-2,42692
2,42466
Std. Residual
-1,842
1,840
Stud. Residual
-1,896
2,178
Deleted Residual
-2,57350
3,39831
Stud. Deleted Residual
-1,952
2,273
Mahal. Distance
,064
13,345
Cook's Distance
,000
,476
Centered Leverage Value
,001
,267
a. Dependent Variable: Intention to purchase
Figure 2.5. Scatterplot of standardised residuals
for the regression Intention to purchase-values
regarding consumers
Mean
4,3333
,000
,350
Std. Deviation
,81574
1,000
,120
N
51
51
51
4,3064
,00000
,000
,009
,02690
,010
2,941
,027
,059
,87471
1,27763
,970
1,017
1,41023
1,031
2,930
,068
,059
51
51
51
51
51
51
51
51
51
Figure 2.6. Normal probability plot of the residual
for the regression Intention to purchase-values
regarding consumers
81
Table 2.9. Coefficientsa- for the regression Intention to purchase – values regarding non-consumers
Unstandardized
Standardized
95,0% Confidence Interval
Coefficients
Coefficients
for B
Model
1
(Constant)
B
4,333
Std. Error
,185
Functional
,266
,186
value
Alstruistic
,727
,186
value
Social value
,256
,186
a. Dependent Variable: Intention to purchase
Zeroorder
Partial
Part
Tolerance
VIF
,641
,176
,204
,176
1,000
1,000
,352
1,102
,480
,495
,480
1,000
1,000
-,119
,631
,169
,196
,169
1,000
1,000
Beta
t
23,484
Sig.
,000
Lower Bound Upper Bound
3,962
4,705
,176
1,428
,160
-,109
,480
3,903
,000
,169
1,374
,176
82
Collinearity
Statistics
Correlations
Appendix 2D –Detailed Multiple regression results for the analysis Purchase loyalty –
moderators regarding consumers
Table 2.10. Correlations - for the regression Purchase loyalty-moderators regarding consumers
Purchase
Environmental
loyalty
concern
Involvement
Pearson Correlation
Purchase loyalty
1,000
,397
,490
Environmental concern
,397
1,000
,000
Involvement
,490
,000
1,000
Sig. (1-tailed)
Purchase loyalty
.
,000
,000
Environmental concern
,000
.
,500
Involvement
,000
,500
.
N
Purchase loyalty
161
161
161
Environmental concern
161
161
161
Involvement
161
161
161
Table 2.11. Residuals Statisticsa - for the regression Purchase loyalty-moderators regarding
consumers
Minimum
Predicted Value
1,0213
Std. Predicted Value
-2,897
Standard Error of Predicted
,119
Value
Adjusted Predicted Value
1,0229
Residual
-5,03594
Std. Residual
-3,350
Stud. Residual
-3,386
Deleted Residual
-5,14645
Stud. Deleted Residual
-3,505
Mahal. Distance
,005
Cook's Distance
,000
Centered Leverage Value
,000
a. Dependent Variable: Purchase loyalty
Maximum
6,3287
1,476
,408
Mean
4,5373
,000
,197
Std. Deviation
1,21371
1,000
,059
N
161
161
161
6,3464
3,50640
2,332
2,349
3,55771
2,384
10,769
,084
,067
4,5378
,00000
,000
,000
-,00050
-,001
1,988
,006
,012
1,21261
1,49391
,994
1,003
1,52160
1,012
2,027
,012
,013
161
161
161
161
161
161
161
161
161
Figure 2.7. Scatterplot of standardised residuals
for the regression Purchase loyalty-moderators
regarding consumers
Figure 2.8. Normal probability plot of the residual
for the regression Purchase loyalty-moderators
regarding consumers
83
Table 2.12. Coefficientsa- for the regression Purchase loyalty-moderators regarding consumers
Unstandardized
Standardized
95,0% Confidence Interval
Coefficients
Coefficients
for B
Lower
Upper
Model
B
Std. Error
Beta
t
Sig.
Bound
Bound
1
(Constant)
4,537
,118
38,296
,000
4,303
4,771
Environmental
,763
concern
Involvement
,944
a. Dependent Variable: Purchase loyalty
Collinearity
Statistics
Correlations
Zeroorder
Partial
Part
Tolerance
VIF
,119
,397
6,423
,000
,529
,998
,397
,455
,397
1,000
1,000
,119
,490
7,939
,000
,709
1,178
,490
,534
,490
1,000
1,000
84
Appendix 2E –Detailed Multiple regression results for the analysis Intention to
purchase–moderators regarding non-consumers
Table 2.13. Correlations – for the regression Intention to purchase – moderators regarding nonconsumers
Intention to
Environmental
purchase
concern
Involvement
Pearson Correlation
Intention to purchase
1,000
,607
,147
Environmental concern
,607
1,000
,000
Involvement
,147
,000
1,000
Sig. (1-tailed)
Intention to purchase
.
,000
,152
Environmental concern
,000
.
,500
Involvement
,152
,500
.
N
Intention to purchase
51
51
51
Environmental concern
51
51
51
Involvement
51
51
51
Table 2.14. Residuals Statisticsa – for the regression Intention to purchase – moderators
regarding non-consumers
Minimum
Maximum
Predicted Value
1,4680
5,6359
Std. Predicted Value
-3,025
1,375
Standard Error of Predicted
,171
,545
Value
Adjusted Predicted Value
1,3290
5,5455
Residual
-2,25544
2,22367
Std. Residual
-1,867
1,841
Stud. Residual
-1,913
2,018
Deleted Residual
-2,36829
2,67097
Stud. Deleted Residual
-1,970
2,087
Mahal. Distance
,021
9,185
Cook's Distance
,000
,273
Centered Leverage Value
,000
,184
a. Dependent Variable: Intention to purchase
Figure 2.9. Scatterplot of standardised residuals
for the regression Intention to purchase –
moderators regarding non-consumers
Mean
4,3333
,000
,281
Std. Deviation
,94719
1,000
,085
N
51
51
51
4,3233
,00000
,000
,004
,01000
,004
1,961
,022
,039
,96266
1,18347
,980
1,010
1,25894
1,025
1,913
,043
,038
51
51
51
51
51
51
51
51
51
Figure 2.10. Normal probability plot of the
residual for the regression Intention to
purchase-moderators regarding nonconsumers
85
Table 2.15. Coefficientsa – for the regression Intention to purchase – moderators regarding non-consumers
Unstandardized
Standardized
95,0% Confidence Interval
Coefficients
Coefficients
for B
Lower
Upper
Model
B
Std. Error
Beta
t
Sig.
Bound
Bound
1
(Constant)
4,333
,169
25,620
,000
3,993
4,673
Environmental
,921
,171
concern
Involvement
,223
,171
a. Dependent Variable: Intention to purchase
Collinearity
Statistics
Correlations
Zeroorder
Partial
Part
Tolerance
VIF
,607
5,390
,000
,577
1,264
,607
,614
,607
1,000
1,000
,147
1,303
,199
-,121
,566
,147
,185
,147
1,000
1,000
86
Appendix 2F –Detailed Multiple regression results for the analysis Purchase loyalty –
brand commitment regarding consumers
Table 2.16. Correlations - for the regression Purchase loyalty-Brand commitment – regarding consumers
Purchase Commitment
Sacrifice
Postpone
Let
loyalty
to buy
willing
willing
down Happy
Pearson
Purchase
1,000
,406
,490
,493
,428
,569
Correlation
loyalty
Commitment
,406
1,000
,587
,496
,544
,557
to buy
Sacrifice
,490
,587
1,000
,594
,514
,467
willing
Postpone
,493
,496
,594
1,000
,611
,554
willing
Let down
,428
,544
,514
,611
1,000
,645
Happy
,569
,557
,467
,554
,645
1,000
Sig. (1-tailed)
Purchase
.
,000
,000
,000
,000
,000
loyalty
Commitment
,000
.
,000
,000
,000
,000
to buy
Sacrifice
,000
,000
.
,000
,000
,000
willing
Postpone
,000
,000
,000
.
,000
,000
willing
Let down
,000
,000
,000
,000
.
,000
Happy
,000
,000
,000
,000
,000
.
N
Purchase
161
161
161
161
161
161
loyalty
Commitment
161
161
161
161
161
161
to buy
Sacrifice
161
161
161
161
161
161
willing
Postpone
161
161
161
161
161
161
willing
Let down
161
161
161
161
161
161
Happy
161
161
161
161
161
161
Table 2.17. Residuals Statisticsa- for the regression Purchase loyalty-Brand commitment –
regarding consumers
Minimum
Predicted Value
2,6091
Std. Predicted Value
-1,581
Standard Error of Predicted
,135
Value
Adjusted Predicted Value
2,6132
Residual
-3,99526
Std. Residual
-2,641
Stud. Residual
-2,696
Deleted Residual
-4,16420
Stud. Deleted Residual
-2,753
Mahal. Distance
,284
Cook's Distance
,000
Centered Leverage Value
,002
a. Dependent Variable: Purchase loyalty
Maximum
7,1035
2,104
,600
Mean
4,5373
,000
,273
Std. Deviation
1,21962
1,000
,104
N
161
161
161
7,1109
3,41967
2,260
2,306
3,56005
2,339
24,148
,086
,151
4,5263
,00000
,000
,004
,01096
,003
4,969
,007
,031
1,22224
1,48909
,984
1,004
1,55129
1,010
4,632
,013
,029
161
161
161
161
161
161
161
161
161
87
Figure 2.11. Scatterplot of standardised residuals for the regression Purchase loyaltyBrand commitment – regarding consumers
Figure 2.12. Normal probability plot of the residual for the regression Purchase loyaltyBrand commitment – regarding consumers
88
Table 2.18. Coefficientsa- for the regression Purchase loyalty-Brand commitment – regarding consumers
Model
Unstandardized
Standardized
95,0% Confidence Interval
Coefficients
Coefficients
for B
Lower
Upper
B
Std. Error
Beta
t
Sig.
Bound
Bound
1
(Constant)
2,010
,282
7,119
,000
1,452
2,568
Commitment to
-,013
buy
Sacrifice willing
,222
Postpone willing
,155
Let down
-,050
Happy
,385
a. Dependent Variable: Purchase loyalty
Collinearity
Statistics
Correlations
Zeroorder
Partial
Part
Tolerance
VIF
,077
-,014
-,168
,867
-,166
,140
,406
-,014
-,010
,535
1,870
,081
,083
,093
,083
,235
,165
-,049
,407
2,746
1,878
-,536
4,642
,007
,062
,593
,000
,062
-,008
-,234
,221
,382
,319
,134
,548
,490
,493
,428
,569
,215
,149
-,043
,349
,171
,117
-,033
,288
,528
,498
,466
,502
1,894
2,006
2,147
1,991
89
Appendix 2G –Detailed Multiple regression results for the analysis Customer value –
values regarding consumers
Table 2.19. Correlations - for the regression Customer value –values regarding consumers
Pearson Correlation
Sig. (1-tailed)
N
Customer value
Functional value
Social value
Altruistic value
Customer value
Functional value
Social value
Altruistic value
Customer value
Functional value
Social value
Altruistic value
Customer value Functional value
1,000
,658
,658
1,000
,139
,000
,278
,000
.
,000
,000
.
,039
,500
,000
,500
161
161
161
161
161
161
161
161
Social value
,139
,000
1,000
,000
,039
,500
.
,500
161
161
161
161
Altruistic value
,278
,000
,000
1,000
,000
,500
,500
.
161
161
161
161
Table 2.20. Residuals Statisticsa - for the regression Customer value – values regarding
consumers
Minimum
Predicted Value
1,8814
Std. Predicted Value
-2,572
Standard Error of Predicted
,095
Value
Adjusted Predicted Value
1,9262
Residual
-3,71569
Std. Residual
-3,114
Stud. Residual
-3,149
Deleted Residual
-3,79934
Stud. Deleted Residual
-3,243
Mahal. Distance
,011
Cook's Distance
,000
Centered Leverage Value
,000
a. Dependent Variable: Customer value
Maximum
6,7209
1,285
,398
Mean
5,1087
,000
,182
Std. Deviation
1,25469
1,000
,049
N
161
161
161
6,7314
4,32192
3,623
3,707
4,52473
3,868
16,776
,161
,105
5,1051
,00000
,000
,001
,00357
,002
2,981
,007
,019
1,25585
1,18181
,991
1,004
1,21375
1,014
2,275
,016
,014
161
161
161
161
161
161
161
161
161
Figure 2.13. Scatterplot of residuals for the
regression Customer value- values regarding
consumers
Figure 2.14. Normal probability plot of the
residual for the regression Customer valuevalues regarding consumers
90
Table 2.21. Coefficientsa- for the regression Customer value –values regarding consumers
Unstandardized
Standardized
95,0% Confidence Interval
Coefficients
Coefficients
for B
Model
1
(Constant)
B
5,109
Functional
1,134
value
Social value
,240
Altruistic value
,479
a. Dependent Variable: Customer value
Std. Error
,094
Beta
t
54,333
Sig.
,000
,094
,658
12,028
,000
,948
,094
,094
,139
,278
2,547
5,080
,012
,000
,054
,293
Zeroorder
Partial
Part
Tolerance
VIF
1,321
,658
,692
,658
1,000
1,000
,427
,665
,139
,278
,199
,376
,139
,278
1,000
1,000
1,000
1,000
Lower Bound Upper Bound
4,923
5,294
91
Collinearity
Statistics
Correlations
Appendix 2H –Detailed correlation results for the analysis Purchase loyalty-Customer
value regarding consumers
92
Appendix 3 – Questionnaire for consumers
1. Could you give us your opinion about what a “green brand”?
2. Are there any green brands or green products that you know?
3. Which of the following green brands do you know?
 Frosch
 Sodasan
 Ecover
 Bioplam
 Cyclus
 None of them
This research focuses on “green” or ecological detergents, which are detergents that due to the
way they are manufactured they do not pollute the environment while at the same time they can
be used in washing as any other common detergent.
Here follow some pictures of green detergent brands that exist in various markets around the
world:
4. What type of detergents for household care and for fabric care do you usually buy?







Only ecological (100%)
Mostly ecological (85% ecological - 15% non- ecological)
More ecological and less non-ecological (70% - 30%)
Same percentage (50% - 50%)
More non-ecological and less ecological (70% - 30%)
Mostly non-ecological (85% - 15%)
Only non-ecological (100%)
93
5.





Which of the following green detergents do you know?
Frosch
Sodasan
Ecover
Other____
None of them
6. Have you ever bought any of the following green detergents, either for fabric or household
care? (tick as many boxes as you find necessary)
 Frosch
 Sodasan
 Ecover
 Other____
 None of them
7. Which is your most frequently bought green detergent brand either for fabric or household
care? (tick one box ONLY)





Frosch
Sodasan
Ecover
Other____
None of them
From now on, questions will refer to X brand, which you have mentioned as one of your
preferred brands when it comes to green detergents.
8. How many years approximately have you been buying products of this brand?
9. Please indicate which of the following products of this brand you buy frequently, where
7= I prefer this brand every time I buy a product of this category to 1=I never buy a
product of this category
Laundry powder/ liquid
Fabric softener
Dishwashing liquid/
tablets
All purpose cleaners
Window cleaner
Other
Never
1
o
o
At every
purchase
of the
category
7
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
94
SECTION Α: The following questions refer to some of the reasons according to which you
purchase this detergent brand. Please indicate the extent to which you agree with the following
statements.
The products of this brand…
Have consistent quality
Be well made
Have an acceptable
standard of quality
Perform consistently
Strongly
disagree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
Strongly
disagree
I like the products of
this brand
They make me want to
use them
I would feel comfortable
about using them
They would make me
feel good
Strongly
agree
o
o
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
This brand….
Strongly
disagree
Is bought by many
people that I know
Would improve the way
I am perceived
Would make a good
impression on other
people
Would give those who
buy it social approval
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
Strongly
disagree
Buying this brand
would have an ethical
interest for me
The environment
friendly character of
this brand is coherent
with my ethical values
Purchasing this brand
will have an ethical
value for me
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
95
To me, the products of this brand…
Are reasonably priced
Are good products for
their price
Are more expensive
than the average brand
in the category (r)
Strongly
disagree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
Strongly
disagree
I can find them almost
everywhere
Less stores sell them, as
compared to competing
brands (r)
Are easy to buy
Require little effort to
purchase
The time required to
buy them is to buy them
is too much (r)
Strongly
agree
o
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
96
Before trying the products of this brand, I though/was afraid that…
Strongly
disagree
I could not afford the
time to get the
information to fully
evaluate them (r).
Comparing the benefits
of my previously
preferred detergent
brand with the benefits
of this brand would take
too much time and
effort (r).
If I changed my
previously preferred
detergent brand, I
would not have to
search very much to
find a new one.
There was a chance that
the products of this
brand would not clean
properly (r).
There was a chance that
I would lose money (e.g.
because they would not
clean as well as my
previously preferred
detergent brand (r).
The products of this
brand were risky in
terms of how they
would perform in
household or fabric
cleaning (r).
I worried that these
products would not
clean as well as I
expected (r).
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
Overall..
Strongly
disagree
The products of this
brand are very good
value for money.
The products of this
brand are considered to
be a good buy
The value of this brand
to me is very high
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
97
SECTION B: The following questions intent to measure your attitude towards the X green brand.
Please indicate the extent to which you agree with the following statements
Strongly
disagree
I feel a commitment to
continue buying this
brand.
I am willing to make
small sacrifices in order
to keep using this brand
I would be willing to
postpone my purchase if
the products of this
brand were temporarily
unavailable.
I would stick with this
brand even if it would
let me down once or
twice.
I am so happy with this
brand that I no longer
feel the need to watch
out for other
alternatives.
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
SECTION E: Please indicate the extent to which you agree with the following statements concerning
your behavior towards X brand.
Strongly
disagree
I intend to keep
purchasing this brand
I will buy this brand the
next time I buy
detergents
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
98
SECTION F: Please indicate the extent to which you agree with the following statements
concerning your personality
Strongly
disagree
I think of myself as
someone who is
concerned about
environmental issues
I feel I have an ethical
obligation to avoid
brands and companies
that pollute the
environment.
I feel I have an ethical
obligation to support
the purchase of
environmentally
friendly products.
I like the idea of
purchasing green.
I have a favorable
attitude toward
purchasing a green
version of a product.
I am aware of the
environmental impact
of the products I buy.
I know how to select
products that reduce
the amount of waste
ending up in landfills.
Trying to figure out the
best product in terms of
the effects on the
environment is very
confusing (r).
I am very concerned
about what brands of
detergents I purchase.
I care a lot about what
brands of detergents I
consume.
Generally, choosing the
right brands of
detergents is important
to me.
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
99
SECTION G: Demographic characteristics
Age







18-24
25-34
35-44
45-54
55-64
65-74
over 75
Level of education completed





Primary school
High school
Training school
Bachelor
Masters Degree or higher
Current occupation






Employer/free-lancer
Civil servant/private employee
Pensioner
Housekeeping
Student
Unemployed
Family status




Single
Married with children
Married without children
Other
Monthly personal income
 Below average
 Average
 Above average
100
Appendix 4 - Questionnaire for non-consumers
1. Could you give us your opinion about what a “green brand”?
2. Are there any green brands or green products that you know?
3. Which of the following green brands do you know?
 Frosch
 Sodasan
 Ecover
 Bioplam
 Cyclus
 None of them
This research focuses on “green” or ecological detergents, which are detergents that due to the
way they are manufactured they do not pollute the environment while at the same time they can
be used in washing as any other common detergent.
Here follow some pictures of green detergent brands that exist in various markets around the
world:
4. What type of detergents for household care and for fabric care do you usually buy?







Only ecological (100%)
Mostly ecological (85% ecological - 15% non- ecological)
More ecological and less non-ecological (70% - 30%)
Same percentage (50% - 50%)
More non-ecological and less ecological (70% - 30%)
Mostly non-ecological (85% - 15%)
Only non-ecological (100%)
101
5. Which of the following ecological detergent brands are you most familiar with?





Frosch
Sodasan
Ecover
Other____
None of them
6. Would you consider purchasing products of this brand?
 Yes
 No
SECTION A1: The following questions refer to the general picture that you hold for the products of a green
detergent brand. Please indicate the extent to which you agree with the following statements.
To me, the products of a green detegrent brand (e.g. like the ones in the pictures above) seem to…
Have consistent quality
Be well made
Have an acceptable
standard of quality
Perform consistently
Strongly
disagree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
Strongly
disagree
I like the products of
this brand
They make me want to
use them
I would feel comfortable
about using them
They would make me
feel good
Strongly
agree
o
o
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
102
A brand like these (i.e. in the pictures above)…
Strongly
disagree
Is bought by many
people that I know
Would improve the way
I am perceived
Would make a good
impression on other
people
Would give those who
buy it social approval
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
Strongly
disagree
Buying this brand
would have an ethical
interest for me
The environment
friendly character of
this brand is coherent
with my ethical values
Purchasing this brand
will have an ethical
value for me
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
SECTION B1: The following questions refer to some other characteristics of a green detergent brand.
Please indicate the extent to which you agree with the following statements
Strongly
disagree
They seem very good
value for money.
They could be
considered to be a good
buy.
The value of this brand
to me may be very high
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
103
SECTION C1: Please indicate the extent to which you agree with the following statements concerning your
behavior towards the products of a green brand.
Having in mind the products of a green detergent brand (e.g. like the ones in the
pictures above)…
Strongly
disagree
The likelihood of
purchasing them is very
high.
The probability that I
would consider buying
them is very low (r )
My willingness to buy
them is very high
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
104
SECTION F: Please indicate the extent to which you agree with the following statements
concerning your personality
Strongly
disagree
I think of myself as
someone who is
concerned about
environmental issues
I feel I have an ethical
obligation to avoid
brands and companies
that pollute the
environment.
I feel I have an ethical
obligation to support
the purchase of
environmentally
friendly products.
I like the idea of
purchasing green.
I have a favorable
attitude toward
purchasing a green
version of a product.
I am aware of the
environmental impact
of the products I buy.
I know how to select
products that reduce
the amount of waste
ending up in landfills.
Trying to figure out the
best product in terms of
the effects on the
environment is very
confusing (r).
I am very concerned
about what brands of
detergents I purchase.
I care a lot about what
brands of detergents I
consume.
Generally, choosing the
right brands of
detergents is important
to me.
Strongly
agree
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
105
SECTION G: Demographic characteristics
Age







18-24
25-34
35-44
45-54
55-64
65-74
over 75
Level of education completed





Primary school
High school
Training school
Bachelor
Masters Degree or higher
Current occupation






Employer/free-lancer
Civil servant/private employee
Pensioner
Housekeeping
Student
Unemployed
Family status




Single
Married with children
Married without children
Other
Monthly personal income



Below average
Average
Above average
106
Appendix 5 - Descriptive statistics
Table 5.1. Descriptive statistics for the variable KnownDetergents
–for consumers
Valid
Cumulative
Frequency Percent
Percent
Percent
Valid Frosch
72
44,7
44,7
44,7
Sodasan
4
2,5
2,5
47,2
Ecover
18
11,2
11,2
58,4
Other
10
6,2
6,2
64,6
None
57
35,4
35,4
100,0
Total
161
100,0
100,0
Table 5.2. Descriptive statistics for the variable KnownGreenBrand
–for consumers
Valid
Frosch
Sodasan
Ecover
Bioplam
Cyclus
None
Total
Frequency
40
Percent
24,8
Valid
Percent
24,8
Cumulative
Percent
24,8
4
9
17
7
84
161
2,5
5,6
10,6
4,3
52,2
100,0
2,5
5,6
10,6
4,3
52,2
100,0
27,3
32,9
43,5
47,8
100,0
Table 5.3. Descriptive statistics for the variable BoughtDetergentfor consumers
Valid
Cumulative
Frequency Percent
Percent
Percent
Valid
Frosch
58
36,0
36,0
36,0
Sodasan
4
2,5
2,5
38,5
Ecover
14
8,7
8,7
47,2
Other
10
6,2
6,2
53,4
No
75
46,6
46,6
100,0
Total
161
100,0
100,0
Table 5.4. Descriptive statistics for the variable
FrequentDetergent -for consumers
Valid Frosch
Sodasan
Ecover
Other
None of them
Total
Frequency Percent Valid Percent Cumulative Percent
50
31,1
31,1
31,1
4
2,5
2,5
33,5
5
3,1
3,1
36,6
12
7,5
7,5
44,1
90
55,9
55,9
100,0
161
100,0
100,0
107
Table 5.5. Descriptive statistics for the variable Years of buying –
for consumers
Valid ,0
,1
,5
1,0
2,0
3,0
4,0
5,0
6,0
7,0
8,0
10,0
15,0
20,0
Total
Frequency Percent Valid Percent Cumulative Percent
36
22,4
22,4
22,4
1
,6
,6
23,0
1
,6
,6
23,6
43
26,7
26,7
50,3
32
19,9
19,9
70,2
16
9,9
9,9
80,1
9
5,6
5,6
85,7
9
5,6
5,6
91,3
1
,6
,6
91,9
2
1,2
1,2
93,2
1
,6
,6
93,8
6
3,7
3,7
97,5
1
,6
,6
98,1
3
1,9
1,9
100,0
161
100,0
100,0
108
Table 5.6. Descriptive statistics for the categorical variables – for consumers
Std.
N
Minimum Maximum Mean
Deviation
Skewness
Std.
Statistic Statistic Statistic Statistic Statistic Statistic Error
Consistent quality
161
1
7
5,29
1,903
-,862
,191
Well made
161
1
7
5,20
1,854
-,739
,191
Acceptable quality
161
1
7
5,27
1,805
-,884
,191
Perform consistently
161
1
7
5,11
1,847
-,630
,191
Like products of
161
1
7
5,10
1,901
-,630
,191
brand
Want to use
161
1
7
5,05
1,903
-,524
,191
products
Relaxed about
161
1
7
5,24
1,948
-,820
,191
usage of products
Feel good of
161
1
7
4,88
2,038
-,475
,191
products
Bought by many
161
1
7
3,32
1,941
,582
,191
people
Improve perception
161
1
7
2,63
1,893
,971
,191
by others
Good impression on
161
1
7
2,93
1,988
,763
,191
others
Social approval
161
1
7
3,19
2,078
,607
,191
Ethical interest
161
1
7
4,52
2,194
-,251
,191
Environmental
161
1
7
5,02
2,087
-,535
,191
preservation
Ethical value
161
1
7
4,61
2,217
-,350
,191
Reasonable price
161
1
7
4,22
1,943
,001
,191
Good product for
161
1
7
4,86
1,815
-,334
,191
price
Almost everywhere
161
1
7
3,93
2,036
,129
,191
Easy to buy
161
1
7
4,01
2,014
,117
,191
Little effort required
161
1
7
4,35
2,019
-,080
,191
to buy
Much time R
161
1,00
7,00
5,1056
1,95449
-,740
,191
Afford time R
161
1,00
7,00
4,1801
2,22455
-,161
,191
Compare previous R
161
1,00
7,00
4,6770
1,97358
-,420
,191
Improper chance R
161
1,00
7,00
4,1366
1,96054
-,133
,191
Lost money R
161
1,00
7,00
4,3230
2,01743
-,164
,191
Risky performance
161
1,00
7,00
4,7205
1,96281
-,375
,191
R
Worried not clean R
161
1,00
7,00
4,0621
2,11746
,034
,191
Good value for
161
1
7
5,00
1,810
-,481
,191
money
Good buy
161
1
7
5,22
1,735
-,588
,191
Commitment to buy
161
1
7
3,09
2,112
,638
,191
Sacrifice willing
161
1
7
3,73
2,036
,211
,191
Postpone willing
161
1
7
3,65
2,047
,179
,191
Let down
161
1
7
3,05
1,877
,616
,191
Happy
161
1
7
3,44
2,037
,374
,191
Keep buying
161
1
7
4,54
1,972
-,341
,191
Will buy
161
1
7
4,53
1,988
-,312
,191
Environmental
161
1
7
5,74
1,547
-1,123
,191
concern
Ethical obligation
161
1
7
5,22
1,767
-,735
,191
Support env friendly
161
1
7
5,21
1,762
-,660
,191
Like green
161
1
7
5,66
1,554
-,943
,191
Favourable attitude
161
1
7
5,64
1,599
-,973
,191
green
Environment impact
161
1
7
4,84
1,922
-,471
,191
awareness
Reduce waste
161
1
7
4,61
1,917
-,267
,191
Concern brands
161
1
7
4,36
1,822
-,108
,191
Care brands
161
1
7
4,46
1,827
-,118
,191
Important choice
161
1
7
4,98
1,796
-,454
,191
Valid N (listwise)
161
109
Kurtosis
Std.
Statistic Error
-,355
,380
-,458
,380
-,060
,380
-,579
,380
-,685
,380
-,855
,380
-,494
,380
-1,091
,380
-,742
,380
-,116
,380
-,560
,380
-,857
-1,344
-1,117
,380
,380
,380
-1,301
-1,102
-,905
,380
,380
,380
-1,163
-1,217
-1,286
,380
,380
,380
-,622
-1,301
-,821
-1,018
-1,081
-,988
,380
,380
,380
,380
,380
,380
-1,289
-,729
,380
,380
-,627
-,849
-1,126
-1,147
-,574
-,987
-1,001
-1,040
,605
,380
,380
,380
,380
,380
,380
,380
,380
,380
-,413
-,626
-,005
-,013
,380
,380
,380
,380
-,906
,380
-1,058
-,896
-,989
-,852
,380
,380
,380
,380
Table 5.7. Descriptive statistics for the categorical variables related to frequency of buying –
for consumers
N
Frequency
of buyinglaundry
powder
Frequency
of buyingsoftener
Frequency
of buyingdishwashing
liquid
Frequency
of buying-all
purpose
cleaner
Frequency
of buyingwindow
cleaner
Frequency
of buying other
detergents
Valid N
(listwise)
Minimum Maximum
Std.
Deviation
Mean
Statistic Statistic
7
2,72
Skewness
Kurtosis
Std.
Std.
Statistic Statistic Error Statistic Error
2,289
,979 ,191
-,664 ,380
Statistic
161
Statistic
1
161
1
7
2,27
2,049
1,356
,191
,289
,380
161
1
7
1,66
1,616
2,493
,191
5,019
,380
161
1
7
3,21
2,240
,485
,191
-1,301
,380
161
1
7
2,33
2,036
1,316
,191
,248
,380
161
1
7
2,60
2,226
1,039
,191
-,557
,380
161
Table 5.8. Descriptive statistics for the variable KnownGreenBrand –for non-consumers
Cumulative
Frequency
Percent
Valid Percent
Percent
Valid
Frosch
11
21,6
21,6
21,6
Ecover
1
2,0
2,0
23,5
Bioplam
2
3,9
3,9
27,5
None
37
72,5
72,5
100,0
Total
51
100,0
100,0
110
Table 5.9. Descriptive statistics for the variable Familiar Brands - for
non-consumers
Valid
Frosch
Other
None
Total
Frequency
12
1
38
51
Percent
23,5
2,0
74,5
100,0
Valid
Percent
23,5
2,0
74,5
100,0
Cumulative
Percent
23,5
25,5
100,0
Table 5.10. Descriptive statistics for the variable Would Buy - for
non-consumers
Valid
yes
Frequency
51
Percent
100,0
Valid
Percent
100,0
Cumulative
Percent
100,0
Table 5.11. Descriptive statistics for the categorical variables – for non-consumers
Std.
N
Minimum Maximum Mean
Deviation
Skewness
Std.
Statistic Statistic Statistic Statistic Statistic Statistic Error
Consistent QualityN
51
1
7
4,65
1,635
-,144
,333
Well madeN
51
1
7
4,86
1,470
-,147
,333
Acceptable qualityN
51
1
7
5,24
1,436
-,643
,333
Perform
51
1
7
4,43
1,526
,030
,333
consistentlyN
Like products of
51
1
7
4,33
1,621
,018
,333
brandN
Want to use
51
1
7
4,67
1,621
-,224
,333
products N
ComfortableN
51
1
7
4,75
1,623
-,153
,333
Feel goodN
51
1
7
4,33
1,669
-,019
,333
Bought many
51
1
7
2,10
1,578
1,485
,333
peopleN
Improve perception
51
1
7
2,86
1,789
,651
,333
N
Good impression N
51
1
7
3,49
1,804
-,100
,333
Social Approval N
51
1
7
3,94
1,974
-,077
,333
Ethical interest N
51
1
7
4,73
1,960
-,527
,333
Environment friendly
51
1
7
5,10
1,825
-,582
,333
N
Ethical value N
51
1
7
4,67
2,075
-,386
,333
Good value for
51
1
7
3,86
1,342
,157
,333
money N
Good buy N
51
1
7
4,59
1,388
-,283
,333
High likelihood to
51
1
7
4,24
1,750
,090
,333
buy N
Low Probabilty R
51
1,00
7,00
4,4902
1,72479
-,324
,333
High willingness to
51
1
7
4,27
1,909
,075
,333
buy N
Environmental
51
1
7
5,61
1,601
-1,239
,333
concern N
Ethical obligation N
51
1
7
4,88
1,693
-,581
,333
Support env friendly
51
1
7
5,00
1,755
-,786
,333
N
Like green N
51
1
7
5,59
1,639
-1,283
,333
Favourable attitude
51
1
7
5,67
1,532
-1,354
,333
N
Environment impact
51
1
7
3,76
1,924
,226
,333
awareness N
Reduce waste N
51
1
7
3,22
1,781
,655
,333
Concern brands N
51
1
7
3,80
1,575
,306
,333
Care brands N
51
1
7
3,49
1,690
,315
,333
Important choice N
51
1
7
4,20
1,887
-,016
,333
Valid N (listwise)
51
111
Kurtosis
Std.
Statistic Error
-,700
,656
-,231
,656
,125
,656
-,509
,656
-,143
,656
-,444
,656
-,759
-,653
1,815
,656
,656
,656
-,386
,656
-1,007
-,990
-,631
-,464
,656
,656
,656
,656
-,986
,948
,656
,656
,488
-,798
,656
,656
-,671
-1,041
,656
,656
1,197
,656
-,299
-,198
,656
,656
1,127
1,416
,656
,656
-1,026
,656
-,341
-,425
-,547
-1,078
,656
,656
,656
,656
Appendix 6 – CD
The dataset and other data regarding analyses are on a CD and are enclosed with the thesis.
112
1