Carbon Labelling

CARBON LABELLING IN RETAIL GROCERY INDUSTRY - A study on consumer attitude and behaviour

Nitai Chand Patra

Mail: [email protected]

MBA Full Time

Ustinov College

Word Count: 14995

Date: 6th Sept 2010

Dissertation submitted as part requirement for the degree of Master in Business Administration of the University of Durham, 2010.

CARBON LABELLING IN RETAIL GROCERY INDUSTRY

Declaration

“This dissertation is the result of my own work. Material from the published or unpublished

work of others, which is referred to in the dissertation, is credited to the author in question

in the text. The dissertation is 14995 words in length. Research ethics issues have been

considered and handled appropriately within the Durham Business School guidelines and

procedures.”

Nitai Chand Patra 2


Abstract:

This research looks into - UK retail consumer attitude and behaviour towards carbon

labelling, with a view at providing practical recommendations for the enhancements of

carbon label’s effectiveness. Using the Theory of Planned Behaviour (TPB) framework (Ajzen

1991) an extended model for carbon labels has been developed to explain consumer

behaviour in the context of retail grocery. The model has been tested with structural

equation modelling. Next, consumer behaviour has been studied by analysing relative

importance of the carbon footprints in comparison to other major product attributes such

as brand and price. Further, effectiveness of traffic signal based carbon labels has been

evaluated. The empirical study was quantitative and was based on discrete choice based

conjoint analysis. Using orange juice as the instrument, from the responses of 208 UK

participants, consumer behaviour was studied. The choice based approach brings new

insights and empirical evidence on the carbon label.

No comprehensive research has been carried so far to understand the attitude and

behaviour of consumers towards carbon labelling. The study presented in this dissertation

systematically evaluates various factors influencing consumer behaviour towards the use of

carbon labels as a decision making tool. Results show that consumer’s knowledge, ease of

locating, interpreting and comparing the carbon footprints are major predicators of using

the carbon label as a decision making tool. Social norms and influence from others such as

family, friends and environmental groups have positive influence on intention of using

carbon labels, but the influence does not get translated to environmental friendly purchase

behaviour.

Further, results imply that consumers’ attitude differs from behaviour and there is no

association between eco-friendly behaviour and age, income and education. Further, it has

been found that the integration of traffic light label with the present label can enhance the

effectiveness of carbon labels. Based on these findings, some of the topical

recommendations are: print carbon labels on more products, integrate traffic light label

with present carbon label, present carbon labels next to the price and communicate more

about the labels.

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Contents

2.1. CHOOSING THE RIGHT FRAMEWORK ......................................................................................................................... 14 2.2. CONCEPTUALISING A FRAMEWORK FOR CARBON LABELS ................................................................................................... 17

2.2.1. Attitude - Intention .............................................................................................................................. 18 2.2.2. Subjective Norm – Intention ............................................................................................................... 19 2.2.3. Perceived behavioural control - Intention ........................................................................................... 19 2.2.4. Perceived behavioural control - Behaviour ......................................................................................... 20 2.2.5. Comparison amongst SN, PBC & ACL .................................................................................................. 20 2.2.6. Attitude towards carbon labels ........................................................................................................... 20 2.2.7. Attitude-Behaviour gap: ...................................................................................................................... 21 2.3.1. Motivating Factors: ............................................................................................................................. 21 2.3.2. Role of communication ....................................................................................................................... 23 2.3.3. Awareness Level .................................................................................................................................. 24 2.3.4. Proposal of TLS based carbon label ..................................................................................................... 25

3.1. PART-I GENERAL QUESTIONNAIRE .......................................................................................................................... 27 3.2. PART-II STRUCTURAL EQUATION MODELLING AND QUESTIONNAIRE DEVELOPMENT .................................................................. 27

3.2.1 Structural Equation Modelling (SEM) ................................................................................................... 27 3.2.2. Questionnaire Development ............................................................................................................... 29 3.3.1. Conjoint analysis ................................................................................................................................. 31 3.3.2. Conjoint Questionnaire Development ................................................................................................. 32 3.3.3. Traffic Light Conjoint Questions .......................................................................................................... 34

3.5. DATA COLLECTION & DESCRIPTION ........................................................................................................................ 36 4.1.1. Reliability and validity analysis ........................................................................................................... 38 4.1.2. Test of normality ................................................................................................................................. 39 4.2.1. What does carbon label represent? .................................................................................................... 40 4.2.2. Attitude towards carbon labelling (ACL) ............................................................................................. 41 4.2.3. Subjective norm score (SN) distribution .............................................................................................. 41 4.2.4. Perceived behavioural control score (PBC) distribution ..................................................................... 42 4.2.5. Intention score (INT) distribution ....................................................................................................... 42 4.2.6. Behaviour score (BEH) distribution ..................................................................................................... 43

4.4. CONJOINT ANALYSIS RESULT ................................................................................................................................. 46 4.4.1. CONJOINT ANALYSIS ........................................................................................................................................ 46

4.4.2. Traffic light conjoint analysis .............................................................................................................. 47 4.5. WILCOXON SIGNED RANKS TEST (BEH – INT) ......................................................................................................... 48 5.1. HYPOTHESIS TESTING ......................................................................................................................................... 51

5.1.12. Consolidated hypothesis testing results ............................................................................................ 58 6.1. RECOMMENDATIONS .......................................................................................................................................... 62

6.1.1. Direct influence on behaviour ............................................................................................................. 62 6.1.2. Influence on habits that control behaviour ......................................................................................... 63 6.1.3. Influence on the convenience of using carbon labels .......................................................................... 63

6.3. CONCLUSION ................................................................................................................................................... 67

APPENDIX ................................................................................................................................................... 77

A1. SAMPLE QUESTIONNAIRE ..................................................................................................................................... 77 A2. PRODUCT DIRECTORY ......................................................................................................................................... 86 A4.PRE-SURVEY RESULT ............................................................................................................................................ 87 A5. GENDER ........................................................................................................................................................ 88 AGE ................................................................................................................................................................... 89

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INCOME ............................................................................................................................................................... 90 EDUCATION ........................................................................................................................................................... 91 ATTITUDE TOWARDS CARBON LABELS (ACL) .................................................................................................................... 92

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List of Tables

Table No. Description Page no.1 Questionnaire 302 Coding 313 Demographic profile of respondents 374 Reliability and validity statistics 385 Score Interpretation 406 Model fitness indices of proposed model 447 Model fitness indices of extended model 459 Regression weights 4510(a) Effects on INT 4610(b) Effect on BEH 4611(a) Conjoint analysis 4611(b) Attribute utility from traffic light conjoint analysis 4712 Wilcoxin signed rank test results 4813 Attribute level utility from two conjoint analysis 5614 Consolidated hypothesis testing result 57

List of illustrations

FIGURE 1: A SAMPLE CARBON LABEL........................................................................................................... 10

FIGURE 2: THEORY OF PLANNED BEHAVIOUR (AJZEN 1991).........................................................................15

THE FIGURE 3: THE FINAL MODEL DEVELOPED FROM SEM...........................................................................45

Figure 13: NAT Proposed by Schwartz 1977......................................................................66

Contents of the attached CD

1. Complete data collected in this study.

2. Analysis of responses for individual questions.

Acknowledgement

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I would like to take this opportunity to express my gratitude to all who have helped me in

completion of this dissertation. Firstly, my heartfelt gratitude to my supervisor, Miss Mary I

Mundel, with her guidance only the dissertation has been successful. Secondly, I offer my

regards to my family members and friends to encourage and motivate me throughout the

study. Finally, I thank all the participants and other individuals, who have extended their

support.

Thank you all.

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1. Introduction

Grocery consumption contributes to almost one third of the total environmental impact and

emissions arising from EU economies (European Commission 2007). Researchers have

confirmed that, most of the consumer value environment friendliness and products derived

from ethical sources. However, consumers’ buying behaviour is often found to be

inconsistent with their attitude (Uusitalo 1990). It would be interesting to investigate how

companies’ ethical and social responsibility will pay off and the growing concerns of

environment get translated into a widespread purchase of eco-friendly products. The

objective of this dissertation is to contribute to the understanding of eco-friendly shopping

behaviour by examining in the context of carbon labelling and confirming the factors

affecting effectiveness of the label.

Bronwen Jones expressed that carbon literacy is increasing. Many people now understand

direct emissions from their air travels, using cars instead of public transports or household

energy consumption. And it won’t be very long before consumers understand indirect

emissions from their consumption of goods and services (Berry et al. 2008). Food miles, a

term coined by Tim Lang, Professor of Food Policy, City University, London in early nineties

represents the distance travelled by a product between points of production and

consumption. In recent past the concept of food miles became a strong marketing tool for

UK’s National Association of Farmers. In 2006, the association launched a campaign “Local

food is miles better”. Kemp et al. (2009) in their research on UK consumers’ attitude &

behaviour towards Food miles have reported that 21.5% consumers preferred not buying

products, which have travelled a long distance such as from New Zealand & Kenya.

In the use of the term food miles, it is the assumption that the longer a product has

travelled, the larger is its effect on the environment. Saunders et al. (2006) during life cycle

assessment of some products found that, for products those are transported from far New

Zealand, the green house gases emitted due to transportation are higher. However, the

total environmental impacts of those products are far less than the similar products

produced in the UK (Kemp et al. 2009). Therefore, the concept of food miles has some flaws

in the assumption. Further, with the rise in concern of global warming, the concept of food

miles has now evolved to the carbon label. In food miles only a product’s carbon emission

from transportation and distribution were matters of concern, whereas in carbon labels, the

sum total of green house gases emitted during production, transportation, distribution and

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consumption are considered. Carbon labels delineate a bigger picture of a product’s impact

on environment. Additionally, in 2002, World Summit for Sustainable Development in

Johannesburg, some leaders suggested for life cycle assessment of products and proposed

adoption of tools and policies for sustainable production and consumption (UN 2002).

The Carbon Label conveys the volume of the carbon footprints (carbon dioxide and other

green house gases) generated by a product or service during its complete life cycle starting

with raw materials to end user’s consumption and disposal. The Carbon Label is developed

by the Carbon Trust, a non profit organisation and the leading authority for reduction of

carbon in UK.

In November 2008, the UK government passed the legislation and adopted the Climate

Change Act, which sets a target of reducing green house emission levels to 80% below 1990

levels by 2050. Further an interim target of 34% reduction has been set for2020 (Carbon

Trust 2010). The Carbon Trust has been established by UK government in 2001 with a vision

to take UK towards a low carbon economy and help achieve the set targets in Climate

Change Act.

The two primary purposes of carbon trust are, firstly, to inform consumers about the

environment impact of the products and services they consume and help them in choosing

an environment friendly product, secondly, to help the businesses in measuring the carbon

emission of the products at every step of their supply chain, thus eventually helping

businesses to explore and evaluate the cost and energy saving opportunities by reducing

waste and enhancing efficiency in the production and distribution of the product.

Additionally, companies displaying carbon labels are committed to reducing the carbon

footprint of the respective products in a span of two years.

Some of the pioneering companies in carbon labelling are Tesco, Walkers, Boots and

Innocent Drink, who have printed carbon footprint on their own branded products to inform

consumers on the volume of carbon generated during the products' life cycle. These

companies work with Carbon Trust to calculate the carbon footprint of their selected

products range.

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Figure 1: A sample carbon label

Now many more products are displaying a carbon label. E.g. Kingsmill breads, Walkers

Crisps, Tesco brand washing detergents, orange juice, potatoes & light bulbs, and Boots

shampoos. Appendix-A2 (page-85) presents a list of such companies and products. The

volume of green house gasses emitted is displayed on the label in gm or kg or tonnes. The

lesser the carbon emission of a product, the more environment friendly is the product. For

example, a product with a carbon footprint of 1000gm/unit is comparatively eco-

friendlier/greener than a product with a carbon footprint of 1200gm/unit.

As per the Carbon Trust (2010), the display of the carbon footprints on the products

enhances brand reputation and sales appeal. Further, as per the corporate social

responsibility (CSR) agendas, increasingly many companies are displaying the carbon

footprint of the products. Tesco, the leading supermarket in the UK measures and displays

the full carbon footprint of more than 500 of its products as one of its CSR- initiatives (Tesco

CSR 2010). Tesco CSR manager expressed “We will …begin the search for a universally

accepted measure of the carbon footprint of every product we sell … [to] enable us to label

all our products so that customers can compare their carbon footprint as easily as they can

currently compare their price or their nutritional profile.” (Leahy 2007)

To understand why consumers act as they do, marketers and policy makers require

understanding of more than just the attitude (Ramayah et al. 2009). Understanding the

underlying beliefs, values and other influencing factors that manifest towards the attitude

and intention will help in developing effective marketing proposition and environmental

policies. In order to design effective campaigns and policies; it has been a continuous

endeavour by marketers, environmentalists and government agencies to understand

consumers’ environmental friendly behaviour.

As the concept of carbon labelling recently gained momentum, not much research has been

done so far, so the number of publicly available literatures on carbon labelling is limited.

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Few of the pertinent works were as such. Berry et al. (2008) conducted eight focus groups

with UK consumers to understand their perception on carbon labelling and recommended

some enhancements. Boots Plc and Tesco have referred to quantitative surveys with their

customers on carbon labels during 2007 in their CSR; however, the reports are not available

on public domain. Upham et al. (2009) tried assessing the public perception on carbon labels

in UK via three focus groups. All these studies were focused on consumer perception, but

none tried evaluating the attitude-behaviour link and factors influencing such behaviour and

the role of communication in such behaviour and hence the study was an attempt towards

filling that gap.

Berry et al. (2008) and Upham et al. (2009) recommended use of traffic light system for

carbon labels, this study evaluated the proposal. Vanclay et al. (2009) have studied

Australian consumers’ behaviour towards traffic light based carbon labels in a non-intrusive

way by monitoring sales at a convenience store. Vanclay et al. performed a comprehensive

study exploring the role of communication in the effectiveness of altering consumer

behaviour in the context of carbon labelling. This study extends Vanclay et al. study on the

role of communication in UK retail settings.

Theoretically, this study builds on ideas that environmental friendly shopping behaviour is

influenced by a number of factors and consumer’s environmental choice is a trade-off

between several choice criteria. Therefore this study not only tries to describe the

underlying attitudes, values and intention towards socially responsible buying behaviour,

but also tries to explore environmental friendly buying behaviour in a more realistic choice

situation, where consumers have to balance their purchase decision over various product

attributes. The aim is to evaluate the extent to which consumers value carbon footprint in

their buying behaviour compared with other major attributes, in a situation where the

purpose of carbon labels is made explicit to the consumers.

Therefore, the following were the objectives of this research:

i. To find the percentage of buyer population is aware of carbon labelling.

ii. To develop a model explaining various factors affecting consumer’s environment

friendly shopping behaviour by extending the theory of planned behaviour model

with structural equation modelling.



iii. To evaluate the extent to which consumer value carbon labels in their product

choices in comparison to other important product attributes. This examines the

importance of carbon labels and its position in the hierarchy of major influencing

factors of consumers’ decision making process.

iv. To explore, demographic variables such as age, sex, education and income level and

their effect on behaviour towards carbon labels.

v. To examine, the effect of integration of traffic light signals with current labelling.

The empirical method employed was quantitative and the research was segregated into two

parts. In the first, using the TPB framework and structural equation modelling a model was

developed describing factors affecting consumers’ eco-friendly buying intention and

behaviour. In this part, consumers past behaviour, intention and attitude have been studied

based on self reporting questionnaires. However, as expressed by Fisher (1993), the human

tendency is to present oneself in the best possible light. So measuring consumer behaviour

from stated preference is a vulnerable method. In order to mitigate this short coming, in the

second part, consumer behaviour towards carbon labelling has been studied using discrete

conjoint analysis. In this method respondents were presented with various combinations of

attributes such as the price, brand and carbon footprint of a product. Based on respondent’s

product choice their behaviour was estimated. The possible impact of consumer

demographics such as age, gender, education level and income level are examined as an

extension.

Household consumptions make up to 45% of average UK consumers’ total carbon footprint.

These averages to 5 tonnes per person per year and around 300 metric tonnes per year for

total UK population (Berry et al 2008). So a step towards understanding consumer

perception towards carbon labelling will be immensely helpful in reducing the emission and

controlling climate change. This dissertation adds to the body of literature by evaluating

factors those influence consumers’ use of carbon labels in their purchase decisions.

Additionally, it suggests some enhancements for improving influence of carbon labels on

purchase decisions.

The remaining part of the dissertation is organised as follows. Section-2 presents the

development of the research hypotheses and research model from literature review.

Section-3 elaborates on the research design, method, questionnaire development, data



collection and sample description. Section-4 presents the final verified model and statistical

results derived for testing of hypotheses proposed under section-2. Section-5 elaborates on

the result and testing of the hypotheses followed by additional discussion on the model and

extensions of research. Section-6 submits the recommendations to various stakeholders

followed by research limitations, recommendations for further researchers and conclusion.



2. Literature review

2.1. Choosing the right framework

Attitudes play an important role in affecting behaviour and influencing decision making.

They strongly influence factors such as what products to buy, where and when. The decision

making process is complex and various factors influence them on various levels. There are

several theories explaining the relation between attitude and behaviour. Staats (2004) has

recommended use of Theory of Reasoned Action (TRA), Theory of Planned Behaviour (TPB)

and Norm Activation Theory (NAT) in the context of environmental behaviour.

In the economic and cognitive convention, it is assumed that consumers behave rationally,

in the sense they act consistently as per their preferences and beliefs. Most consumer

behavioural studies are based on this assumption and employ variations of attitude-

behavioural models such as TPB (Ajzen 1991) or TRA (Ajzen & Fishbein 1975). These models

have clearly demonstrated that attitude can influence or predict behaviour (intention).

However, the relation between attitude and behaviour has been found to be varying to a

large extent on the context of application (Ajzen & Fishbein 1980).

The TRA model postulates that behaviour is the result of three main factors: attitude

towards the behaviour, subjective norm and behavioural intention. Attitude and subjective

norms are postulated to influence intention, which in turn results in behaviour. TPB (Ajzen

1988, 1991) is an extension of TRA and includes an additional component of perceived

behavioural control. According to TPB, the immediate determinant of an individual’s

behaviour is his/her intention to perform the task. And the determinants of intention are

attitude, subjective norm and perceived behavioural control.



Figure 2: Theory of Planned Behaviour (Ajzen 1991)

Attitude is explained as a person’s general evaluation of an object or behaviour. Further it

can be described as an individual’s favourable or unfavourable evaluation of performing the

behaviour. Schwartz (1992) explained that attitude is a set of beliefs about an object or act

which may translate into intention of carrying out the act. Therefore, the more favourable is

an individual’s attitude towards behaviour, the stronger will be the intention to perform the

behaviour.

Subjective norm is explained as an individual’s perception towards the social pressure (from

family, friends and society at large) to perform or not perform the particular behaviour and

normative beliefs are its antecedent. If a consumer perceives that significant others approve

(disapprove) his behaviour, then he is more likely (less likely) to perform the intended

behaviour. A responsibility towards environment can also be linked with subjective norm; as

it is expected that consumers play an active role in solving environmental problems by

choosing environmentally friendly products or way of life (Uusitalo 1990).

Perceived behavioural control (PBC) is the measure of an individual’s perception on the

ability to perform the behaviour in the context and control beliefs are its antecedent.

Follows and Jobber (2000) classified it as perceptions of convenience. They have postulated

that inconvenience and additional efforts on the part of a consumer, towards purchasing a

green product, act as a deterrent for consumers to adopt a green practice. In the early

version of TPB (Ajzen 1988) there were no direct link between PBC and behaviour. However,

after meta-analysis of TPB model, Ajzen (1991) proposed that PBC can affect behaviour

directly. Ajzen argued that the increased feeling of control or convenience would increase



the extent to which an individual would be willing to exert additional effort in order to

successfully perform the particular behaviour.

Intention is explained as the immediate determination of acting in a certain way. All the

factors discussed flow through intention and then influence actual purchasing behaviour of

a consumer. Intention has been found to be a good predictor of behaviour. Bagozzi et al.

(1990) has proposed that, in an attitude-behaviour relationship, intention is influenced by

the level of effort needed to exhibit the behaviour.

The underlined assumption for TPB is that people behave rationally in normal condition.

Conner & Armitage (1998) expressed that, this assumption sets limitations to the model, as

people often act spontaneously or habitually. Sutton (1998) review of nine meta-analysis of

TRA & TPB model, found that attitude, subjective norm and PBC account for only 40-50% of

variances in behavioural intention. And the power of these three variables of TPB are low

regarding prediction of behaviour and indicated that different studies found additional

variables affecting behaviour and intention depending on the context of study.

Nevertheless, researches confirm that extended models can help achieving better

predictability of the relationships (Rokka & Uusitalo 2008).

Further, Sutton (1998) found that only 19-38% of variances in behaviour can be explained by

intention and Armitage & Conner (2001) found that (meta-analysis of 185 TPB based

independent studies) TPB could explain only 27-39% of variances in behaviour and

intention. The lower prediction power of the TPB model regarding behaviour has raised

scepticism of the model, as a major part of the behaviour is still unexplained. This implies

that, the TPB model cannot satisfactorily explain the difference between the intention and

behaviour or why consumers behave differently than they originally intended.

Additionally, the attitude concept in TPB is meant for a general evaluation of behaviour, that

is, the attitude is just a decision criterion which guides such behaviour. Many researchers

found that, in the social dilemma character of pro-environmental behaviour, moral

dimension plays an important role. However, these moral dimensions are not covered in

TPB’s attitude. Schwartz (1977) norm activation theory (NAT) helps this issue by explaining

the personal obligation to perform a specific behaviour using situational and personality

trait activators (Harland 2007). However, I felt that without understanding the general

attitude & behaviour, exploring moral dimensions would be less useful.



Furthermore, behavioural models such as TPB rely on self reports, which suggest

vulnerability of data, mostly because of presentation biases due to social desirable

responses. This threatens the validity and reliability of the models (Armitage & Conner

2001).

Although there are some limitations of TPB as discussed, as the carbon labelling is an

emerging concept, and currently no framework exists for evaluation of its effectiveness, I

considered this framework as a stepping stone in the process of development of a

comprehensive framework. Further in order to achieve desired validity and reliability of the

model and minimise error, necessary statistical tests and structural equation modelling

were employed for the analysis.

Eco-friendly buying behaviour is a complex phenomenon and there will be several factors

influencing the behaviour. Many researchers have adopted the TPB to examine consumer’s

behaviour and factors influencing such behaviour in the environmental friendly

consumerism context. Some of the useful works are Ho (2002) for waste recycling behaviour

of Singaporean household, Cheung et al. (1999) for waste paper recycling behaviour, Oliver

& Lee (2010) for buying hybrid cars in Korea, Tsay (2010) green products in Taiwan, Gupta &

Ogden (2009) for green consumerism in USA and Della Lucia et al. (2007) for organic coffee

buying behaviour in Brazil (Ramyah et al. 2009).

Staats (2004) expressed that the TPB has been chosen by many researchers for investigating

specific environmental behaviour. And it has been proven to be useful over other models

such as TRA, in understanding the behaviour with the contribution of perceived behavioural

control. Following the footsteps & recommendations of previous researchers, TPB was

chosen to systematically explore various influencing factors of behaviour towards carbon

labels. Further, this was a confirmatory research, and the TPB provided the necessary

support to develop the right framework thereafter.

2.2. Conceptualising a framework for carbon labels

Following the TPB model, in the proposed model attitude toward the carbon labels (ACL)

and subjective norm towards carbon labels (SN) are positioned as indirect predictors of

behaviour towards the use of carbon labels (BEH). Intention is the immediate determinant

of behaviour. Perceived behavioural control (PBC) has both direct effect and indirect effect

(via intention) on BEH.



Figure 3: Proposed model for carbon label.

Researchers have found that the three factors discussed in TPB have varied degrees of

influence on intention, which ultimately influences behaviour (Armitage & Conner 2001).

Ajzen (1991) confirmed the same that the effects of individual factors are situational. In

certain case some factors might have very low or insignificant influence. In this research, it

was hypothesised that all the three factors of TPB have a significant influence on behaviour

via intention.

2.2.1. Attitude - Intention

As discussed earlier, attitude towards behaviour reflects an individual’s positive or negative

evaluation towards performing the particular behaviour. So, a favourable attitude will lead

to a stronger intention of performing the behaviour. In their research on recycling

behaviour, Tonglet et al. (2004) found that positive attitude towards recycling is the most

significant predictor of recycling behaviour, and has the strongest correlation with the

intention. Further, Armitage and Conner (2001) from meta-analysis of 115 studies reported

positive relation between attitude and intention. Consequently, it can be expected that an

individual’s positive or negative attitude towards carbon labels would influence individual’s

intention of using carbon footprints during the purchase of any products or services

accordingly. Thus the following hypothesis (H1) was derived:

H1. There is a positive relationship between attitude towards carbon labels and intention

towards using carbon labels as a decision making tool.



2.2.2. Subjective Norm – Intention

Conner and Armitage (2001) discussed that several authors have considered the subjective

norm to be the weakest component and have deliberately removed them from analysis.

However, Trafimow and Finlay (1996) from analysis of 30 studies found that individual’s

attitude to behaviour relationship were primarily driven by subjective norm.

Further, Uusitalo (1989) suggested that favourable attitude towards environmental friendly

products alone cannot predict behaviour, if the social norms and individual’s awareness of

such norms are weak. One’s perception of how friends, relatives and society at large

behave, what they feel about one’s environmental friendly behaviour and social benefits of

such behaviour encourage one to behave favourably or unfavourably. If people important

to a person expect the person to choose environment friendly products then it might

translate to green consumerism. Further, various incentives while buying greener products

(E.g. green club card points by Tesco) will further strengthen the subjective norms. So, it

implies that the subjective norms related to environment supportiveness, incentives from

the retailers and society’s expectations exert a positive influence on an individual’s intention

of using carbon labels. Thus the following hypothesis (H2) was proposed:

H2. The subjective norms have a positive influence on consumer’s intention of using carbon

labels as a decision making tool.

2.2.3. Perceived behavioural control - Intention

Pro-environment PBC measure is operationalised by combining statements related to

amount of effort required in performing the specific environment related act. The easier

and convenient specific environment behaviour is to perform, the higher will be the

intention of performing the behaviour (Dahab et al. 1995, Ajzen 1991, Uusitalo 1989,

Armitage and Conner 2001). Consequently, if an individual perceives the use of carbon

labels as easy & convenient and there are convenience & enough products with carbon

labels to choose from, then the individual has a higher control over the act. Therefore, it

implies that higher is the PBC towards carbon labels the stronger will be the intention of

using them in purchase decision making. Thus the following hypothesis (H3) was derived:

H3. There is a positive relationship between perceived behavioural control towards carbon

labels and the intention towards using carbon labels as a decision making tool.



2.2.4. Perceived behavioural control - Behaviour

Ajzen (1991) argued that under conditions where the behavioural intention alone could

account for a small variance in behaviour; PBC would act directly on behaviour and predict

behaviour. This is based on the rationale that the increased feeling of behavioural control

will influence the subject to exert additional effort, in order to successfully execute the

behaviour. As at this point of the study, the intention-behaviour relation was unknown, so

following Ajzen (1991), it was proposed that perceived behavioural control has positive and

direct influence on behaviour towards carbon labels. Thus the following hypothesis was

proposed:

H4. There is a positive relationship between perceived behavioural control towards carbon

labels and the behaviour of using carbon labels as a decision making tool.

2.2.5. Comparison amongst SN, PBC & ACL

Contrary to Uusitalo (1989) as discussed in section-2.2.2, page.19, Armitage and Conner

(2001) mentioned that many meta-analyses suggest that the subjective norm is the weakest

predictor of intention. Even some authors have removed subjective norm from their

analysis. This reflects lesser importance of normative factors as determinants of intention.

So, it is hypothesised here that subjective norms have lesser influence on intention of

considering carbon labels as one of the decisive factors. Thus the following hypothesis (H4)

was derived:

H5. The subjective norm has comparatively lesser influence on intention than attitude and

perceived behavioural control have, for considering carbon labels as a decision making tool.

2.2.6. Attitude towards carbon labels

Tesco CSR (2010) reported that, in their carbon label survey most of the consumers who

were aware of carbon label expressed positive attitude towards the labels (no supporting

result in the report). Berry et al. (2008) focus group research reported that 59% of the

participants expressed positive attitude towards eco-labels and were interested to know

how their purchase decisions may impact the climate change. Though, no quantitative

attitude measure had been carried out, the previous researches indicate that consumers

have an overall positive attitude towards carbon labels. Thus the following hypothesis (H5)

was derived:



H6. Overall, consumers have a positive attitude towards carbon labels.

2.2.7. Attitude-Behaviour gap:

It is well known in social psychology and consumer behaviour literatures that there is a

substantial gap between consumer intention and their behaviour (Young et al. 1998). It has

been intriguing topic for study how attitude influences behaviour via intention in different

contexts. Young et al. expressed that the empirical evidences suggest that most of the time

the intention provides a biased (under or over) estimation of behaviour or purchase

propensity. Further, there are various other factors influencing the intention to behaviour

estimation, such as habit and impulse buying or unplanned shopping. Kemp et al. (2009)

research on motivational factors in UK supermarket purchases found that 8% of

respondents expressed that they bought a product or brand because they usually buy that.

Next, marketers and consumer psychologist agreed that a large proportion of supermarket

purchases is impulsive or unplanned. Phillips and Bradshaw (1993) found that almost 50%

purchases are impulsive, whereas Bellenger et al. 1978 found it to be 38.7% (Jones et al.

2003). It indicates that there is a significant influence of impulse buying on behaviour.

Schiffman and Kanuk (1994) mentioned that intention is a good predicator of behaviour.

However, Sutton (1998) review of various studies found that only 19-38% of variances of

behaviour are explained by intention. All these previous empirical evidence suggests that

there might be a similar gap between intention and behaviour in the context of carbon

labelling. It could be because of strong attitude and social norms, consumers have the

intention of considering carbon footprint while shopping, but their pro-environmental

intention is not efficiently converted into green purchase behaviour. Thus the following

hypothesis (H6) was derived:

H7. Consumer intention to consider the carbon footprint as a decision making tool

substantially differs from actual behaviour.

2.3.1. Motivating Factors:

Staats (2004) expressed that consumer’s pro-environmental behaviour can be understood

only if the knowledge of the social dilemma is applied simultaneously. Pro-environmental

behaviour is explained as the behaviour that is relatively favourable towards the

environment in comparison to another behaviour that serves the same primary purpose or



function (e.g. using public transportation instead of using a personal car). Staats referred

social dilemma as the tension between the individual interest and collective or social

interest. As per the economic and cognitive theories, this social dilemma drives a person’s

trade-off situation between pro-environmental and non pro-environmental options. An

individual sacrifices his own interest in some degree in favour of the interest of the

community. E.g. a consumer buys a pro-environment product at £1.10, while he had the

choice to buy a comparatively less environment friendly product with equal other product

attributes at £1.00. Here the consumer sacrificed £0.10 in favour of interest of environment

and this is the trade-off.

It is established that consumers hold high regards for eco-friendly products; however, their

buying behaviour are often inconsistent with their values (Uusitalo 2008). Many people

consider themselves as environment conscious consumer and express willingness to buy

products with minimum environmental effects, however, the link between their intention

and behaviour is weak. However, their purchase decision is often guided by other factors

such as brand, quality, price and individual buying habit (Horne 2009). Even the most

environment conscious customers do not choose a product, only considering a product’s

environment aspects, they trade-off between several attributes of the product. So it has

been difficult for marketers and policy makers to determine whether environment

friendliness is an important product attribute for consumers. (Uusitalo 2008)

In the economic and cognitive psychology convention, it is accepted that consumers behave

rationally based on their preferences and beliefs. Further, studies confirm that, consumers

have high preferences for eco-friendly products; however, the link between attitude and

behaviour is weak. Customers make trade-offs in their everyday purchasing, for example, on

price versus quality/utility or availability. Nevertheless, sustainability trade-offs such as

choosing an eco-friendly product is often complicated.

Consumer behaviour varies a great deal depending on the product and its function. While

making purchase decision customers evaluate perceived value using extrinsic cues such as

price, promotion, brand & label information and intrinsic cues such as quality, specifications

and physiological characteristics (Zeithaml 1988). Further, Zeithaml expressed that extrinsic

cues are used more often when there is low product differentiation or there is a difficulty in

evaluating the quality of products.



In their research, Kemp et al. (2009) found that price and brand are the two major

motivating factors for UK supermarket consumers. During a pre-survey of this research, 80

UK supermarket buyers were asked to rank various product attributes. Among the extrinsic

cues, the price and brand received the highest ranking with relative importance 82.56% and

70% respectively. The results are consistent with the findings of Kemp et al (2009). The

detailed results of Kemp et al. (2009) and the pre-survey are presented in appendix-A4,

page.87.

Rokka & Uusitalo (2008) expressed that environmental friendliness is an important product

attribute and worth evaluating its position in consumer multi-attribute motivation models.

The urgency for controlling climate change and consumer’s role in that suggest that carbon

label should be added into consumer choice models as a relevant product attribute.

Considering Zeithaml’s (1988) extrinsic cues, a comparison among the price, brand and

carbon labels was an interesting experiment to learn consumers’ decision making process.

Environment consciousness does not automatically lead to pro-environment behaviour

(Horne 2009). According to research finding by Morris (1997), eco labelling might guide

consumers toward environmental friendly product is a weak assumption (Horne 2009).

According to the economic theory (Lancaster 1966), humans are rational and their decision

depends on the maximisation of their utility. So it implies that, price and brand will maintain

the leading position as prime motivators in decision making process and carbon label will be

next to them. Thus the following hypothesis (H8) was derived:

H8. Price and brand would maintain their leading positions as prime motivators followed by

carbon label.

2.3.2. Role of communication

Over the last 2-3 years, the concern over climate change has driven consumers towards

greener products (products with a lesser carbon footprint) and sustainable lifestyle (Horne

2009). Although there is a widespread concern about the environment damages, many

consumers fail to understand the damages caused by their own consumption. The eco-

labels such as carbon labels play an important role in informing consumers the

environmental consequences of their purchase decisions. Bech-Larsen (1996) expressed that

one of the possible reasons for the attitude behaviour gaps is that not too many products



carry any eco-labels. Another possible reason Uusitalo (1989) expressed that it is a general

tendency that consumers undervalue their marginal contributing to the problem.

Berry et al. (2008) expressed that until now, the climate related product information have

been used as a brand distinguishing factors mainly to appeal the already climate conscious

consumers, rather than as a mainstream evaluation tool. Further, carbon labelling is just a

beginning to fill this gap. Moreover, once the link among the carbon labels, products and

environment impact explained, a majority of the focus group participants expressed positive

interest and engagement with carbon labels. Moreover, they suggested further research to

assess the role of carbon labels and to find out whether communicating carbon impact of

products make any difference in consumer behaviour, and to know if they make use of the

tool.

Vanclay et al. (2009) found that the carbon labels have potential to become mainstream

toolkit if consumers are reminded of the importance & use of labels at the time of purchase.

In their study in an Australian convenience store, the customers were presented with

leaflets on carbon label, explaining its significance and role in climate change. The result

shows that there was an overall 6% decrease in sales of high carbon emitting products and a

4%increase of low carbon emitting products. It implies that, communication plays an

important role in consumer pro-environment behaviour.

According to behavioural science, consumer’s decision is driven by several factors such as

personal, marketing mix, psychological, socio-cultural, social and situational. The

communication of importance of carbon label at the time of shopping can act as a strong

social marketing mix and/or social and/or situational and drive consumers to use the carbon

label as an important product evaluation tool. The findings suggest that there is a high

probability that UK consumers would also act eco-responsibly and use the carbon labels as a

product evaluation tool if communicated adequately and given enough options. Thus the

following hypothesis (H8) was derived:

H9. When informed about the significance of carbon footprint and labelling, consumers use

carbon labels as an important tool in their buying decision.

2.3.3. Awareness Level

EU Flower Label, an eco-label, signifies a product’s kindness on the environment. Only the

best products which are kindest to the environment are entitled to carry the label. In a



study of awareness of EU Flower Label, it was found that nearly 48% of 24,000 respondents

didn’t know about the label, despite a well-funded information campaign. (European

Commission 2007, cited by Horne 2009)

In a survey conducted in Tesco, half the respondents responded that they understand what

carbon label represents (Tesco CSR 2010). As this study was limited to Tesco consumers only

and numbers of respondents in the survey were also unknown, a review of this finding will

be helpful to confirm the percentage of population in general is aware of carbon labelling.

Thus the following hypothesis (H9) was derived:

H10. Half of the retail consumers are aware of carbon labelling.

2.3.4. Proposal of TLS based carbon label

Focus group research by Berry et al. (2008) and Upham et al. (2009), indicate that the most

popular label format would be traffic lights (where green indicates ‘low-carbon’, amber

‘medium-carbon’ and red ‘high-carbon’). Some participants expressed that the current

numerical presentation of the carbon footprint, does not make much sense for them, as

they do not know what value is good, bad or optimum. Further some participants had the

misconception that the presence of carbon label suggests that the product is environment

friendly. However, it is a fact that the presence of carbon labels do not confirm environment

friendliness, it only informs the amount of green house gas emitted. The findings of these

studies imply that, the present carbon label is not effective enough and integration of TLS

can improve its effectiveness.

The main advantages of TLS are that: they are simple, consumers are already familiar with

such labels, and they are intuitive in nature. When consumers are presented with complex

product choices with lots of product information, they are less able to make informed

purchase decision. And in ideal shopping scenario, it is a fact that consumers are

continuously bombarded with a large amount of product information starting from brands,

nutritional values, price, source, carbon footprints, organic, fair-trade and many more. All

these information makes the decision making process further complex. Black and Rayner

(1992) confirmed the consumers struggle when they are presented with lots of nutritional

information. It implies that simplification of product information will be beneficial.

Lang (2006) mentioned that in many researches it has been found that consumers find it

easier to follow the traffic light system in comparison to other labelling formats (Fraser et al.



2009). In their research on the effectiveness of traffic light labels on food products, Fraser et

al.(2009) found that participants avoided foods with red lights from a mixed basket of food

products. Further they expressed that the use of label information has been observed to

alter overall food purchase behaviour.

On the other hand, Verbeke (2005) expressed that although consumers may prefer simple

label formats on the pack of products, however, it does not mean they behave to it in the

manner it was indented (Fraser et al. 2009). Because when it comes to using of the label,

the taste, brand, price and other attributes may override the purpose of the label and

making it ineffective. Further, TLS alone is ineffective in delivering the complete information

and lack of clarity. What is good and how is it compared? However, a focus group

participant expressed “I think the traffic lights are the best because it is really clear and

obvious. I know it doesn’t give stats, figures and numbers, but I don’t think we understand

the stats, figures and numbers anyway.” (Berry et al. 2008)

Consequently, the combination of TLS and current carbon label can be effective and address

all the concerns as it provides necessary information & ease of interpretation. All these

findings suggest that the TLS based carbon labelling format would be more effective in

influencing consumer decision making in a realistic choice environment. This leads to the

final hypothesis (H11) of this study:

H11. Integrating traffic light signals with the present carbon label would enhance

effectiveness of the carbon label.



3. Research Design & Method

This section presents the development of the questionnaire, discussions on the adopted

quantitative and cross-sectional methods of research, analysis technique, data collection

and description. Structural Equation Modelling (SEM) was adopted for testing and analysis

of relationships between different elements of the model. Choice based conjoint analysis

was used to calculate the part-worth utility of different product attributes.

The questionnaire was developed in four parts. Part-I contained a general question on

carbon labelling followed by a detailed explanation about carbon labels and guidance for

interpretation. Part-II contained questions related to TPB model, Part-III contained

questions related to conjoint analysis and conjoint question for evaluating usefulness of

traffic lights with the carbon labels. Part-IV contained demographic questions and open

ended feedback.

3.1. Part-I General Questionnaire

A short qualitative survey (20 respondents) was conducted with few volunteers and

supermarket customers to find what consumers think the carbon label represents. The

responses were classified and categorised into five definitions (one of which was the correct

definition). In the development of the final questionnaire, for the question what carbon

label represents, these five definitions, along with “I do not know” and “others” options

were used.

3.2. Part-II Structural Equation Modelling and Questionnaire development

This part discusses structural equation modelling and questionnaire development for testing

of the proposed model and related hypotheses. The constructs for measurement were

attitude, behaviour, intention, subjective norm and perceived behavioural control. These

elements, like other psychological constructs are latent and cannot be observed directly.

3.2.1 Structural Equation Modelling (SEM)

“SEM is a powerful statistical technique that combines measurement model or

confirmatory factor analysis (CFA) and structural model into a simultaneous statistical test”

(Loon 2008). SEM is a multivariate statistical modelling technique, which allows researchers



to examine more than one relation simultaneously using multiple regression equations

concurrently, whereas the regression analysis looks at only one relation or equation at a

time. Since no factor exists alone and often there are complex interactions among the

factors in attitude-behavioural studies, use of SEM is more realistic, effective and efficient.

SEM is more versatile than other multivariate analysis as it allows simultaneous analysis of

relationships between variables (Maxwell 2009, Loon 2008). Further, SEM takes potential

measurement errors into account, while regression does not.

The factor analysis can be performed in two ways: Exploratory factor analysis (EFA) and

Confirmatory factor analysis (CFA). In EFA no prior restrictions are set and the relation

between the observed and latent variables are explored. Whereas in CFA, the researcher

needs to specify the factors, expected relations and pattern of indicating factor loadings,

which is generally done based on previous research findings. SEM is a confirmatory method,

as the modeller is required to define the relationship between variables. The results

obtained from EFA are exploratory in nature and are often unreliable; use of CFA provides

better and reliable results (Maxwell 2009). In this study, a CFA using SME had been adopted

to test the proposed model.

SEM employs covariance analysis method for estimation. The goodness-of-fit tests are used

to determine whether the research model is consistent with the variance-covariance

pattern in the data. Further SEM specifications and criteria help the researcher in

determining an optimal model from a set of competing models. Though SEM is a relatively

new technique developed during 1970s, it has been used extensively in the study in

psychology, sociology, biological sciences and market research (Golab 2003).

Few of the several advantages of SEM over the linear-in-parameter statistical methods

presented by Golob (2003) are:

i. Test of the overall model by considering multiple equations, rather than just

computing the coefficients individually.

ii. Modelling of intervening or mediating variables.

iii. Direct, indirect and total effects are important distinguishing features of SEM. The

direct effects are the regression weights, referring one variables direct effect on

another variable along the specified path as per the model. Indirect effects represent

the sum of all effects between two variables that involve some intervening variables.



General regression models compute only the direct effect of one variable on

another; however, SEM provides information on the total effect, which includes both

direct and indirect effects.

iv. Testing of coefficients across multiple groups in a sample.

v. As most of the behavioural research data are non-normal, use of SEM is very

effective in those cases.

vi. Separation of measurement errors from specification errors.

Therefore, considering all these benefits of SEM, in this research SEM was used to test the

models.

3.2.2. Questionnaire Development

Pro-environmental attitude measure is typically operationalised by blending statements

concerning a variety of environmental issues (Follows & Jobber 2000). Samuelson and Biek

(1991) argued that a significant correlation between attitude and behaviour could only be

obtained if both attitude and behaviour measures correspond to a specific issue or object.

Hines et al. (1987) found that the attitude-behaviour relationship is weaker when general

attitude towards the environment operationalised instead of specific environment related

behaviour. So in this case as the behaviour is related to a specific act of considering carbon

footprint while shopping, the attitudinal measure should relate to carbon labelling. As no

pre-validated scales were available for measuring attitude-behaviour relationship towards

carbon labels, a scale was designed and made operational.

The questionnaire was developed following the literatures on environmental behaviour and

previous application of TPB such as Ajzen 1991 and Tonglet et al. 2004. In order to develop

an effective scale multiple pilot tests were performed. In the initial rounds, a wide variety of

questions with a mix of negatively worded questions were presented to increase the validity

of responses. However, many respondents reported fatigue and dropped out. Further, in

the reliability analysis lower reliability of scale (Cronbach alpha) were discovered. To

increase the reliability of the scale, multiple changes were made and all the questions were

made positively worded to increase participation and decrease fatigue.

The final questionnaire for measuring elements of the model contained 20 items. According

to the model, the questions broadly fell into five categories as presented in the table-1

below.



Table-1: QuestionsAttitude towards carbon label (ACL) (Reliability coefficient > 0.7) ACL1: I would prefer to buy products with low carbon footprints. ACL2: I am most likely to pay a marginally higher price for an eco-friendly product. ACL3: I consider the carbon footprint as a major product attribute in my purchase decision. ACL4: The carbon label provides satisfactory information about a product’s impact on environment. ACL5: I appreciate retailers’/manufacturers’ initiative for carbon labelling of products.

Subjective Norm(SN) (Reliability coefficient > 0.7)I choose/ will choose a low carbon emitting product because:

SN1: Climate change is a global concern and a collective responsibility.SN2: Some retailers are providing additional incentives. (e.g. Tesco Green Club Card Points)SN3: People who are important to me expect me to use low eco-friendly products.SN4: I am contributing to a higher purpose.

Perceived Behavioural Control (PBC) (Reliability coefficient > 0.7)PBC1: It is convenient to compare carbon footprints on products.PBC2: There are reasonable options to choose a low carbon footprint product.PBC3: I know where to look for the carbon label on the products.PBC4: I know how to interpret the carbon label on a product.PBC4: The higher price of eco-friendly products does not abstain me from buying them.

Intention(INT) (Reliability coefficient > 0.7)Considering my last five shopping trip intention,

INT1: I had intentions for considering carbon footprint while buying products.INT2: I had intention of comparing the carbon footprints of the products before buying them.INT3: I intended to buy at least one product with a comparatively lower carbon footprint.

Behaviour (BEH) (Reliability coefficient > 0.7)In the course of last five shopping trips,

BEH1: I did consider the carbon footprint of products while buying them. BEH2: I have compared the carbon footprints of products before buying them.BEH3: I have bought some products with a comparatively lower carbon footprint.

3.2.3. Instrument



As recommended by Ajzen (1991), a seven point bipolar scale was used for measurement.

Krosnick (2010) has expressed that bipolar scales perform best with seven points, whereas

unipolar scales performed best with five. For each question, the respondents were asked to

choose an option from a Likert-type scale with choices “Strongly Agree” to “Strongly

Disagree”. The following coding was used for statistical analysis, where a higher score

represents more favourable rating towards the concept being measured.

Table-2: Coding

Answer Value Answer Value

Strongly Agree 3 Moderately Disagree -1Agree 2 Disagree -2Moderately Agree 1 Strongly Disagree -3Undecided 0

3.3. Part-III Conjoint analysis and Questionnaire development

There were two sets of the conjoint questionnaire prepared. First part was to measure

individual attribute utility and second was to test effectiveness of traffic light based carbon

labels.

3.3.1. Conjoint analysis

Conjoint analysis is one of the most popular market research tools to study consumers’

product preference and simulate consumer choice (Kuhfeld 1994). Green and Srinivasan

(1978) promoted conjoint analysis as a very powerful tool for obtaining information about

the effect of different product attributes on purchase intention. Every product possesses

some attribute such as price, brand, organically produced, environmental impact,

guarantee, colour and so on. While making a purchase decision typically consumers do not

have the option of best of all desired attributes, this is especially true where price is also an

attribute. So the consumers make a trade-off in the purchase decision (Kuhfeld 1994,

Tormod 2001). For example, decision making process of buying a luxury car. While the car

might provide the desired comfort and safety, the trade-offs might be between price,

mileage and maintenance cost. Conjoint analysis facilitates analysing these trade-offs

happening during the decision making process.

The two popular types of conjoint analysis are: Adaptive Conjoint Analysis (ACA) and Choice-

based/Discrete Conjoint Analysis (CBC). CBC is preferred academically and is widely used for



pricing and brand value studies, whereas, ACA is preferred for larger marketing focused

works (Dobney 2010). In this research CBC was used to divide consumers’ over all

perceptions of utility into part-utility contributed by individual attributes, when a consumer

trades off between the attributes.

Conjoint analysis facilitates analysis of consumers’ decision making process more precisely

than it is possible with simple questionnaires (Tormod 2001). Rather than asking importance

of different attributes of a product, in conjoint analysis products with varying attributes

level are presented and respondents are asked to choose a product that makes the most

value to them. Consumers value any product depending on different product attributes,

which are the motivators for them to buy the product (Lancaster 1966).

Studies have confirmed that in comparison to many other methods such as rank ordering of

product attributes and multi dimensional measuring, the results obtained from conjoint

analysis are more reliable, detailed and easy to understand (Pullman & Moore 1999, SPSS

1997). Anderson (1993) has analysed 300+ types of applications, which are used to learn

consumer needs and concluded that with an 85% success rate, conjoint analysis is the most

effective method of analysis (Kotri 2006).

Additionally, the choice-bases conjoint tasks are easy for the respondents in comparison to

other methods, because the subject just needs to select a product which he/she would most

likely buy. Further the response validity is high as it mimics the real life trade-off situations.

Several studies have demonstrated the close correspondence of the predicted conjoint

analysis result and observed real life market result (Louviere 1988). Considering all the

discussed benefits, discrete conjoint analysis was chosen for data analysis.

3.3.2. Conjoint Questionnaire Development

This part was the CBC questionnaire, where the respondents were presented and asked to

choose a product from alternatives with varied combinations of attributes. Packaged orange

juice was used to illustrate the argument because (a) it is a daily or frequently used grocery

product; (b) it is used or consumed by everyone, irrespective of age, social status and

education level and (c) the core benefits are fairly identical between products in the chosen

category. The purpose was to take core benefit of the product as given and focus on: brand,

price and carbon footprint.



In any conjoint analysis, the selection of attributes and the number of levels is very

important (Rokka et al. 2008). Ideally maximum of six attributes should be considered in

order to avoid any misleading result (Green et al. 1990). In this research following three

attributes of packaged orange juice, each with three levels was used for the conjoint study.

To avoid any confusion and facilitate easy comparison, all the product options were

presented in 1liter package.

a. Brand: Three brands: Tropicana, Del Monte and Princes were used as three attribute

levels for brand. The brands were chosen considering their wide availability across

UK stores and recognisability.

b. Price: The price/litre was used as the price attribute. Three price levels were

assigned by calculating the average price of various orange juices in the UK market

(£0.80, £1.00 and £1.20).

c. Carbon footprint: The carbon footprints of the products were mentioned in gram.

Three levels were assigned by calculating the average carbon footprint of packaged

orange juices in the market (960gm, 1000gm and 1200gm).

The selected combinations of brands (3), price (3) and carbon footprints (3), resulted in 27

(3*3*3) profiles or combination of orange juice. However, to make the simulation more

realistic few combinations were eliminated such as a Tropicana with a price tag of £0.80 and

Princes with a price tag of £1.20. The rest 25 profiles were used for the study.

The conjoint questionnaire was designed and administered using online SurveyAnalytics

conjoint module, as it provided the most cost effective way (a small monthly rental) of

performing conjoint analysis in comparison to SPSS Conjoint or Sawtooth Conjoint tools

(paid long term licences). A total of 10 conjoint choice sets (each included three of the 25

product profiles develped) were presented to each respondent by using a random sampling

of profiles. The respondents were asked to choose one profile which he would most likely

buy given that those three were the only available options and the price, brand and carbon

footprint associated with the respective profiles were the only available information for

decision making. Following (Figure-4) is a sample conjoint choice set.

Figure 4: Sample conjoint questionnaire



The brand was presented as a picture and price & carbon footprints were mentioned

numerically below for the corresponding choice. A complete questionnaire set has been

attached as appendix (A1, page.76).

3.3.3. Traffic Light Conjoint Questions

On the questionnaire, this section was an extension of previous conjoint section and the

purpose was to study whether an inclusion of traffic lights into carbon labels enhances

carbon label’s effectiveness in influencing consumer behaviour. In order to study that, every

brand demonstrated earlier was made associated with a fixed carbon footprint and a traffic

light. Only two attributes, brand and price were considered.

a. Brand: The same three brands Tropicana, Del Monte and Princes were used as three

attribute levels for brand. However, now the brands were associated with a fixed

carbon footprint. In the earlier conjoint question, the carbon footprint was also an

attribute, but in this it was fixed with the brand. During the pilot test, it was found

that Tropicana is the most preferred brand, followed by Del Monte and Princes. So in

order to study the influence of traffic lights, we added red light (1200gm carbon

footprint) to Tropicana, yellow (1000gm carbon footprint) to Del Monte and green

(960gm carbon footprint) to Princes. The idea was to observe whether the red light

traffic symbol on Tropicana juices can diminish its acceptability and green light on

Princes juice can improve its acceptability.

b. Price: The price/litre was used as the price attribute. Same three price levels were

assigned as used earlier (£0.80, £1.00 and £1.20).



The three brands and three price levels led to total nine product profiles. A total of 6

conjoint choice sets (each included three product profiles from the nine) were presented to

each respondent by using a random sampling of profiles. The respondents were asked to

choose one from the three profiles in every conjoint choice set. That is, the respondents

were asked to choose one profile which they would most likely buy given that those were

the only three available options and the price and brand (carbon footprint) in the respective

profiles were the only available information for decision making. Following is a sample

choice set.

Figure 5: Sample traffic light conjoint questionnaire

The traffic signals were presented next to the product and price was mentioned in

numerical. The complete questionnaire set has been attached as appendix (A1, page.76).



3.4. Part-IV Concluding Questionnaire

At the end of the questionnaire, demographic information such as age, sex, income level

and education level were collected, and respondents were asked for an open ended

question to express their comment or provide feedback on the survey or carbon labelling.

All the questions in the survey (except the optional comment question) were closed-ended

to make the questionnaire simple and effective.

3.5. Data Collection & Description

The survey was conducted online, self-administered and the subjects were invited through

different consumer forums and groups on Facebook. The invitation along with the link for

the survey was posted on the wall of 50 forums and groups. This method helped to reach a

diverse demographic across UK with a quick turnaround. The respondents were

heterogeneous and broadly represent the UK retail consumers. This was a self-reported

research. Although the self-report does not assure validity of responses all the time, the

methodology and analysis used ensures better real life simulation than general

questionnaire based attitude surveys. The data were collected between 15th July and 10th

August 2010. A total of 208 responses were received and used for subsequent analysis.

Schreiber et al. (2006) acknowledged that the sample size of a survey is dependent on the

normality of data and the proposed statistical estimation methods. The generally agreed

practice is that to get 10 participants for every free parameter or item in the questionnaire

(Loon 2009). Further, Garver & Mentzer (1999) and Hoelter (1983) have suggested a sample

size of 200 for SEM to provide sufficient power of analysis (Loon 2009). Steven (1996)

suggested a sample size of at least fifteen times of the number of observed variables

(Golob2003). The sample size of 208 in this survey confirms all these suggestions and

practices as there were 20 items and five observed variables.



Demographic profile:

Table-3 : Demographic profile of respondentsDemographics Frequency PercentageGender Male 71 34.1% Female 137 65.9%Education Level Primary 1 0.5% Secondary 34 16.3% Diploma 33 15.9% Bachelors 41 19.7% Masters & Above 61 29.3% Others/Preferred not to say. 38 18.3%Age 19 & younger 9 4.3% 20 – 35 126 60.6% 36 – 50 34 16.3% 51 or older 19 9.1% Preferred not to say 20 9.6%Annual Income 0 – 15,000 44 21.2% 15,001 – 30, 000 41 19.7% 30,001 – 45,000 26 12.5% 45,001 or more 19 9.1% Do not know/ Preferred not to say 78 37.5%

Majority of the participants were female and aged between 20 and 35. In the real life

situation, this is the most active consumer group. However, education and income wise the

sample was well distributed.



4. Results

The results of all statistical analysis performed in the research are presented in this section

(4). Detailed analyses of the results, hypothesis testing and recommendations have been

presented in the next section (Section- 5, p.50).

4.1.1. Reliability and validity analysis

Fornell and Larcker (1981) have suggested that before performing SEM, tests for reliability

and validity should be performed. Taking these into consideration, the scales and constructs

were tested using SPSS for reliability and validity.

The table-4 below presents the measures of reliability, item loading, average variance

extracted (AVE), mean score, standard deviation and scale range for all the variables in the

model.

Table-4: Reliability & validity statistics

Factors No of items

Reliability Coefficient

Loading AVE Mean SD Scale range

Attitude(ACL) 5 0.808 ACL1 ACL2ACL3ACL4ACL5

0.7200.7700.8380.7130.721

0.556 0.9981 1.05 -3 to 3

Subjective norm(SN)

4 0.714 SN1SN2SN3SN4

0.7210.6810.7210.827

0.555 1.1394 1.02 -3 to 3

Perceived behavioural control(PBC)

5 0.835 PPC1PBC2PBC3PBC4PBC5

0.8020.7430.8090.8240.695

0.618 0.3606 1.24 -3 to 3

Intention(INT) 3 0.929 INT1INT2INT3

0.9500.9410.918

0.889 0.8607 1.38 -3 to 3

Behaviour(BEH) 3 0.912 BEH1BEH2BEH3

0.9290.9410.898

0.853 -0.0533 1.55 -3 to 3

All the factor loadings are significant at 0.05 levels. For items please refer to Table-1, page-

30.



Reliability

Reliability is assessed on two levels: construct reliability and item reliability (Hair et al.

1998). For construct reliability, the Cronbach alpha coefficient should be above 0.7, which

confirms that all the items under a factor are measuring the same underlying construct

(Pallant 2007 p.95). In this study all the scales had optimum Cronbach alpha coefficients

(Table-4, page38). For item reliability, Fornell and Larcker (1981) have suggested loading of

greater than 0.7 for all the items. Loading is the correlation between an individual item and

the measured factor. The scores of all individual items under a factor were averaged to find

the score for that factor and then the loading were estimated. All the item loadings in the

questionnaire do satisfy this condition, except for Q.7 and Q.14, which were very close to

0.7.

Validity

For convergent validity, Fornell and Larcker (1981) have suggested that all the individual

items loading should be greater than 0.7 and average variance extracted (AVE) should be

greater than 0.5. “Convergent validity assesses the degree to which dimensional measures

of the same concept are correlated” (Nusair and Hua 2010). All the constructs in the model

achieve the suggested criteria, so the convergent validity is achieved. (For result table-4,

page-38)

4.1.2. Test of normality

Normality tests (descriptive and graphical) were performed using SPSS. According to the

results, none of the factors were normally distributed. A detailed normality test is presented

in appendix-A5, p. 89. Though normal distribution is an essential criterion for regression

analysis, Shimizu et al. (2006) has supported the use of SEM with non-normal samples.

Further, Golob (2003) has expressed that the robustness of maximum likelihood estimation

of SEM and the correlation factors developed for non-normal data confirm that SEM can be

used with discrete choice variables, ordinal data and with truncated and censored variables.



4.2. Distribution of responses

The scores of all individual items under a factor were averaged to obtain the score for that

factor. The table-5 below summarises the interpretation used in this dissertation.

Table-5 : Score InterpretationScore Interpretation Score Interpretation3 Strongly Positive -1 Moderately Negative2 Positive -2 Negative1 Moderately Positive -3 Strongly Negative0 Neutral

The distributions of scores for different constructs are presented in this section.

4.2.1. What does carbon label represent?

Figure 6: Response distribution of “What does carbon label represent?”

129

7

49

5 2

115

0

20

40

60

80

100

120

140

Others I don’t know. Fair trade. Eco-friendly. Organically Produced.

Something that is

recyclable.

Amount of green house gases left by this product during its life

cycle.

No of respondents

A majority of respondents (115 out of 208) chose the correct definition of carbon label. 49

respondents chose that carbon labels represents that the product is eco-friendly. Whereas,

only 29 respondents opted for do not know option.



4.2.2. Attitude towards carbon labelling (ACL)

Figure 7: Score distribution of ACL

1 1 1 15

1 1 4 4 713

7

2114 14 15

20

1116

9 12

311

5 29

0

5

10

15

20

25

-2.6 -1.8 -1.6 -1.4 -1.2

-1

-0.8 -0.6 -0.4 -0.2

0

0.2 0.4 0.6 0.8

1

1.2 1.4 1.6 1.8

2

2.2 2.4 2.6 2.8

3

No of respondents

27 respondents had a negative attitude towards carbon labels, while 13 were neutral. Rest

168, respondents expressed a positive attitude towards carbon labelling. The mean attitude

score was 0.99≡ 1, which shows an overall moderately positive attitude towards carbon

labels. Further, the above graph (Figure-7) shows that most of the responses were on the

positive side. 51 respondents expressed positive or very positive attitude towards carbon

labels.

4.2.3. Subjective norm score (SN) distribution

Figure 8: Score distribution of SN

1 2 3 6 2 2 116 8 14 15

2917 16 20 23 18

8 2 5010203040

No of respondents

17 respondents responded negatively on the subjective norms for use of carbon labels,

while 16 respondents were neutral. Remaining 175 respondents expressed positively

because they consider carbon labels as a decision making tool. Further, the above graph

(Figure-8) shows that most of the responses were on the positive side. The mean score of

SN was 1.14, confirming an overall positive score.



4.2.4. Perceived behavioural control score (PBC) distribution

Figure 9: Score distribution of PBC

2 3 2 26 5 6 8 7

11 9 12

22

10 10 127 9 8

13 15

5 84 5 3 4

0

5

10

15

20

25-3

-2.2

-2

-1.8 -1.6 -1.4 -1.2

-1

-0.8 -0.6 -0.4 -0.2

0

0.2 0.4 0.6 0.8

1

1.2 1.4 1.6 1.8

2

2.2 2.4 2.6

3

No of respondents

73 respondents expressed negatively about the perceived behavioural control. 22

respondents expressed neutrality towards PBC. Whereas remaining 113 respondents

expressed positive PBC. The mean score for PBC was 0.3606, which confirms overall

moderately positive PBC. The frequency distribution graph (Figure-9) suggests that PBC

score was widely distributed.

4.2.5. Intention score (INT) distribution

Figure 10: Score distribution of INT

9 9 4 2 2 4 5

35

718

39

12 1524

11 614

0

10

20

30

40

50

-3 -2.33 -2 -1.67 -1 -0.67 -0.33 0 0.33 0.67 1 1.33 1.67 2 2.33 2.67 3

No of respondents

35 respondents expressed that they had no intention of considering carbon labels for their

purchase decisions, whereas 35 respondents expressed neutrality. The remaining 138

respondents expressed that they had some intentions of considering carbon label in their

purchase decisions, while 31 respondents expressed strong intentions. The mean intention

score was 0.8607, which shows an overall moderately positive intention of respondents for

considering carbon label as one of the deciding factors in their purchase decisions.



4.2.6. Behaviour score (BEH) distribution

Figure 11: Score distribution of BEH

102 2

26

8 513 12 11

32

125

16 167

19

6 2 405

101520253035

No of respondents

89 respondents expressed negative behaviour. 32 respondents expressed neutrality. Only

87 respondents expressed that they have considered carbon footprint of products in their

last five shopping trips. Only twelve participants expressed strong positive behaviour. The

mean behaviour score is -0.0533 ≡ 0. The overall behaviour score of participants in the

survey reflects neutral behaviour in using carbon labels as a decision making tool.



4.3. Structural Equation Modelling Output

Structural equation modelling using Amos-18 was used to estimate the measurements and

model fit for proposed model. Garver and Mentzer (1999) have suggested that in SEM the

indicators of a model fit are the Chi-square normalized by degrees of freedom (χ2/df),

goodness of fit index (GFI), adjusted goodness of fit index (AGFI), non-normed fit index

(NNFI), comparative fit index (CFI) and root mean squared error (RMSEA).

A low or non-significance χ2 signifies good fit, as it confirms that there is no difference

between actual and predicted matrices. Χ2 is sensitive to sample size, so for larger samples,

Kline (1998) suggested that Chi-square normalized by degrees of freedom (χ2/df) should not

exceed 3 for good model fit (Loon 2008). Garver and Mentzer (1999) have recommended

that GFI, AGFI, NNFI and CFI should exceed 0.9 and RMSEA should not exceed 0.08 (Loon

2008). Following these recommendations, the fitness of the proposed model was evaluated.

Goodness of fit of proposed model:

Table-6 : Model fitness indices of proposed modelCriteria Recommended value Obtained value Fitnessχ2/df <=3 (p<0.05) 13.29/2 Not achieved.GFI >0.9 0.9757 Achieved.AGFI >0.9 0.8180 Not achieved.NNFI >0.9 0.9786 Achieved.CFI >0.9 0.9815 Achieved.RMSEA <0.08 0.1651 Not achieved.

The goodness-of-fit indicators indicate that the proposed model was not completely fit as all

the minimum requirements of a model fit were not achieved. To improve the fitness, the

estimates were recalculated with modification indices. Then AMOS recommended an

additional possible path and relation between ACL and BEH. By evaluating the

recommendation, the modified model was established.



The Figure 3: The final model developed from SEM.

Table-7 : Model fitness indices of extended modelCriteria Recommended value Obtained value Fitnessχ2/df <=3 (p<0.05) 0.0217/1 (p<0.001) AchievedGFI >0.9 1.0000 AchievedAGFI >0.9 0.9994 AchievedNNFI >0.9 1.0000 AchievedCFI >0.9 1.0000 AchievedRMSEA <0.08 0.0000 Achieved

A χ2 value of 0.0217, df=1, p<0.001 of the modified model confirms that the sample data fits

the model, i.e. there is no difference between actual values and predicted values. Further,

all other indicators (Table-7) confirm the goodness-of-fit of data with the extended model.

Regression weights:

The table-8 presents the regression weights of different paths in the final model.

Table-8: Regression weights

Estimate Std. Estimate S.E. C.R. P

INT <--- ACL .1923 .1464 .1083 1.7757 .0758

INT <--- PBC .3508 .3163 .0768 4.5668 ***

INT <--- SN .4279 .3169 .1044 4.0974 ***

BEH <--- INT .2672 .2381 .0654 4.0864 ***

BEH <--- ACL .3390 .2299 .0916 3.7019 ***

BEH <--- PBC .5226 .4199 .0783 6.6784 ***

***p< 0.001.



The R-square value for intention (INT) is 0.4741. This suggests that, the predictors of

intention: attitude, subjective norm and perceived behavioural control can explain 47.41%

of the variances in intention. The R-square value for behaviour (BEH) is 0.6004. This value

suggests that, the predictors of behaviour: attitude, intention and perceived behavioural

control can explain 60.04% of the variances in behaviour.

In the final model (Figure-12), attitude has direct effects on behaviour and also indirect

effects via intention. Similarly, PBC has both direct & indirect effect on BEH, whereas, SN has

only indirect effect. The total effects represent the sum of direct and indirect effects. Tables-

9 & 10 present direct, indirect and total effects of different variables on INT & BEH

respectively.

Table-9: Effects on INTSN PBC ACL INT

INT (Direct effects) .4279 .3508 .1923 .0000

INT (Indirect effects) .0000 .0000 .0000 .0000INT (Total effects) .4279 .3508 .1923 .0000

Table-10: Effects on BEH

SN PBC ACL INTBEH (Direct effects) .0000 .5226 .3390 .2672

BEH (Indirect effects) .1143 .0937 .0514 .0000

BEH (Total effects) .1143 .6163 .3903 .2672

4.4. Conjoint Analysis Result

4.4.1. Conjoint analysis

The relative importance of attributes and average utility estimates for each attribute level

assessed using conjoint analysis are presented in the table-11(a) below.

Table-11(a): Attribute utility from conjoint analysis

AttributeRelative Importance

Attribute level wise utility

Brand 4.34%Tropicana Del Monte Princes2.237 2.383 2.151

Price 44.66%£0.80 £1.00 £1.203.530 2.788 1.136

Carbon footprint

50.99%960gm 1000gm 1200gm3.759 1.947 1.027



All the effects were statistically significant, yielding x2 with probabilities less than 0.01.

The results indicate that carbon footprint is an important product attribute in consumer

choice, contributing to 50.99% of overall utility of the attributes. Further from the attribute

level wise utility of carbon footprint it can be observed that 960gm carbon footprint

received highest attribute level utility followed by 1000gm and 1200gm. This clearly

indicates that respondents preferred products with low carbon footprint, which in turn

reflects consumers’ choice for low carbon emitting products and their overall pro-

environment behaviour.

Price received a relative importance of 44.66%, which is the second highest of all attributes.

Further from the utility scores, it can be observed that respondents preferred the least

expensive product. £0.80 received the highest utility score, followed by £1.00 and £1.20.

Brand received the least utility score of 4.34%. And the three levels of brand attribute have

fairly equal utility scores. This reflects relatively less importance of brand while choosing an

orange juice and respondent’s preference for the three brands were reasonably equal.

4.4.2. Traffic light conjoint analysis

Table-11 (b): Attribute utility from traffic light conjoint analysis



Brand 46.77Tropicana Del Monte Princes1.336 2.250 3.812

Price 53.23%£0.80 £1.00 £1.203.816 1.913 0.998

All the effects were statistically significant, yielding x2 with probabilities less than 0.01.

In the traffic light based conjoint analysis, with a relative importance of 53.23% the price

attribute was the most important attribute in consumer product choice followed by brand

with a relative importance of 46.77%. In this analysis the carbon footprint attribute was

made associated with every brand with a traffic light label. Here again, respondents

preferred the least expensive product, as the £0.80 attribute level received the highest

utility score among other price levels. And in brand attribute levels, Princes brand received

the highest utility level followed by Del Monte and Tropicana respectively.



4.5. Wilcoxon Signed Ranks Test (BEH – INT)

This test was performed to see whether there is any difference in behaviour and intention.

The table-12 (a & b) present the results of Wilcoxon Signed Ranks test between behaviour

and intention.

Table-12 (a) Ranks N Mean Rank Sum of RanksBEH - INT Negative Ranks 127(a) 83.91 10656.50

Positive Ranks 26(b) 43.25 1124.50Ties 55(c) Total 208

a BEH < INT, b BEH > INT, c BEH = INT

Table-12 (b)Test

Statistics BEH - INTZ -8.690(a)Asymp. Sig. (2-tailed) .000

a Based on positive ranks. b Wilcoxon Signed Ranks Test

Wilcoxon Signed Ranks Test revealed a statistically significant difference between intention

(INT) and behaviour (BEH), Z=-8.68, p=0.000, with a large effect size (r=0.42). There are 127

negative ranks, 26 positive ranks and 55 ties.

All the results presented in this section are discussed in detail in the analysis chapter

(section-5).



4.5. Results at a glance

Table-8: Regression statisticsEstimate Std. Estimate S.E. C.R. P

INT <--- ACL .1923 .1464 .1083 1.7757 .0758INT <--- PBC .3508 .3163 .0768 4.5668 ***INT <--- SN .4279 .3169 .1044 4.0974 ***BEH <--- INT .2672 .2381 .0654 4.0864 ***BEH <--- ACL .3390 .2299 .0916 3.7019 ***BEH <--- PBC .5226 .4199 .0783 6.6784 ***


Table-11 (a): Attribute utility from conjoint analysis



Brand 4.34%Tropicana Del Monte Princes2.237 2.383 2.151

Price 44.66%£0.80 £1.00 £1.203.530 2.788 1.136

Carbon footprint

50.99%960gm 1000gm 1200gm3.759 1.947 1.027

Table-11(b): Attribute utility from traffic light conjoint analysis

Attribute Relative Importance


Brand 46.77Tropicana Del Monte Princes1.336 2.250 3.812

Price 53.23%£0.80 £1.00 £1.203.816 1.913 0.998




5. Analysis and Hypothesis Testing

5.1. Hypothesis Testing

Using the statistical results presented in the chapter-4, this section elaborates the testing of

the hypotheses proposed in literature review (section-2).

5.1.1. Testing H1. There is a positive relationship between attitude towards carbon labels

and intention towards using carbon labels as a decision making tool.

The ACL->INT regression estimate (0.1923, p=0.0758) suggests that the attitude-intention

relation is insignificant. However, this is against Armitage and Conner (1998) meta-analysis,

which reports significant attitude-intention relationships (mean r=0.49, N=115).

Nevertheless, the discrepancy can be explained in the light of an experiment and the

extended model.

In the experiment, the model was tried without the attitude-intention path. SEM could not

achieve the desired model fit. The model fitness results indicate the presence of attitude-

intention association, though weak. Hence, H1 is supported. In the extended model, SEM

suggested a new path between attitude and behaviour. The relation between attitude and

behaviour (r=0.3390, p=.000) is significant. Ajzen (1991) argued that in circumstances where

the intention-behaviour relationship is not strong enough, some factors may act directly on

behaviour. Depending on the context, they may act together or in different combinations. In

this model, attitude is acting directly upon behaviour as intention-behaviour relation is weak

(0.2672, p=0.000).

All these results indicate that consumer attitude towards carbon labels is not being able to

develop a strong intention of using the carbon labels. Two of the plausible explanations

could be as such. First, consumers do not feel morally responsible for their consumption and

perceive that their contribution towards climate change is marginal or insignificant. Second,

consumers may not have the right means or behavioural controls to convert their attitude

to intentions.



5.1.2. Testing H2. The subjective norms have a positive influence on consumer’s intention of using carbon labels as a decision making tool.

The SN-> INT regression estimate 0.4279 with p=0.000, suggests that subjective norm has a

positive and significant influence on intention. Hence H2 is supported. The relation between

SN and INT suggests that social pressure or normative beliefs have influence on one’s

intention for considering carbon labels while shopping.

The SN was estimated by asking questions such as participants’ responsibility towards

climate change (SN1), perception of retailers’ incentive on green products (SN2), approval or

disapproval of people important to the participant (SN3) and purpose (SN4). All the items

had significant (>0.7) individual loading (correlation) on SN. It implies that when consumers

feel that they are responsible for climate change, society would appreciate their eco-

conscious behaviour and retailers would provide additional incentives, they significantly

intent to consider carbon labels in their purchase decision.

5.1.3. Testing H3. There is a positive relationship between perceived behavioural control

towards carbon labels and the intention towards using carbon labels as a decision making

tool.

The PBC->INT regression estimate 0.3508 with p=0.000, suggests that perceived behavioural

control has a positive and significant influence on intention. Hence the H3 is supported.

The relation between PBC and INT implies that consumers’ perception of control over their

behaviour has a significant influence on their intention. The consumers who perceive that

comparing carbon footprints is easy and there are enough products with carbon labels, have

a significant intention of using carbon labels as a decision making toolkit. Those consumers,

who perceive that comparing carbon footprints is a difficult task and there are not many

options to perform the task, do not have an intention of considering carbon labels as a

decision making toolkit.



5.1.4. Testing H4. There is a positive relationship between perceived behavioural control towards carbon labels and the behaviour of using carbon labels as a decision making tool.

The PBC->BEH regression estimate 0.5226 with p=0.000, suggests that perceived

behavioural control has positive influence on behaviour. Hence, H4 is supported. This

relation is in-line with Ajzen’s (1991) argument that, when prediction of behaviour from

intention is hindered, PBC facilitates the behavioural intention into action and predicts

behaviour directly. In the case of carbon labels, clearly the prediction of behaviour from

intention is low (r=0.2672), so the PBC facilitates the intention to action and acts directly

upon behaviour.

The result implies that, increased feeling of control and convenience of using carbon labels

will increase the extent to which consumers are willing to exert additional efforts on using

the labels as a decision tool.

5.1.5. Testing H5. The subjective norm has comparatively lesser influence on intention than

attitude and perceived behavioural control have, for considering carbon labels as a decision

making tool.

The SN->INT regression estimate (0.4279, p=0.000) is greater and significant than the

regression estimate of both ACL->INT (0.1923, p=0.0758) and PBC->INT (0.3508, p=0.000).

This suggests that subjective norm has higher influence on intention than attitude and

perceived behaviour control. Hence, H5 is not supported.

This result along with the covariance result of ACL<->SN (0.7935) and SN<->PBC (0.7658),

imply that normative belief and retailers’ incentive can affect intention, attitude and

perceived behavioural control. That is, when the social pressure is strong and incentives are

high, consumer intention grows accordingly, along with strengthened attitude and mental

preparedness to overcome the perceived difficulty of using carbon labels.

Contrary, Armitage and Conner (2001) meta-analysis found that SN is the weakest predictor

of INT. However, Armitage and Conner (2001) argued that, many studies failed to get the

right measure between SN-INT because of use of single item scale instead of multi-item

scales for SN. Trafimow and Finlay (1996) meta-analysis reported that there are significant

numbers of studies where the subjective norm was the primary driver of behavioural



intention. So, these discussions imply that the relation between SN and INT found in this

study is valid.

5.1.6. Testing H6. Overall, consumers have a positive attitude towards carbon labels.

For 168 out of 208 (80%) respondents, the attitude score was positive. Further, 113

respondents had an attitude score higher than the mean attitude score (0.99). Thirteen

respondents had a neutral score and remaining twenty seven have a negative score. This

result indicates that majority of participants were positive about carbon labels. Hence, H6 is

supported.

Despite this overall positive attitude, some participants expressed extremly negative feeling

towards carbon labelling. One participant expressed that “ There is no point of England

making such a fuss over the carbon footprint because no other country bigger than England

is doing much to help. We really are just going around with a dustpan and brush after an

earthquake”.

Another respondent commented “what is the point in buying a low carbon footprint crisp,

while I drive miles to get them, rather using public transports and air travel thousands of

miles for holidays”. These comments imply that, though consumers in UK are positive

towards carbon labels, they believe that the relative contribution from their use of carbon

labels in alleviating climate change is insignificant, so carbon labels are not worth enough

for their attention.

5.1.7. Testing H7. Consumer intention to consider the carbon footprint as a decision making

tool substantially differs from actual behaviour.

A Wilcoxon Signed Ranks Test revealed a statistically significant difference between

intention (INT) and behaviour (BEH), Z=-8.68, p=0.000, with a large effect size (r=0.42). The

ranks suggest that out of 208 samples, in 127 cases BEH < INT, 26 cases BEH > INT and 55

cases BEH = INT. This reflects the inconsistency between INT and BEH. Further, the INT ->

BEH regression estimate was 0.2672, P=0.000, shows that the influence of intention on

behaviour is weak. So the intention is not a good predictor of behaviour. The mean scores

(Table-4, p.34) suggest that participants had a moderately positive intention of considering

carbon footprint of products in their shopping but in their actual shopping, they did not

exhibit the same. So there is a significant difference between consumer intention and

behaviour. Hence H7 is accepted.



Young et al. (1998) expressed that, in consumer psychology tradition it is a known fact that,

there is always a considerable difference between consumers’ intention and subsequent

behaviour. Uusitalo (1989, 1990) expressed that studies confirm that consumers have a high

attitude towards eco-friendly products, but the consistency between the attitude and

behaviour is bleak. The finding of this study on carbon labels implies the same.

Nevertheless, during SEM analysis the model was tested without intention mediating

between the ACL, SN, PBC and BEH. However, the required goodness-of-fit of the model

could not be achieved. This suggests that intention has a role in predicting the behaviour in

case of carbon labels, though the influence is bleak.

5.1.8. Testing H8. Price and brand would maintain their leading positions as prime

motivators followed by carbon label.

According to the conjoint analysis result (Table-11(a) on page.49), the carbon footprint had

the highest relative importance (50.99%) followed by price (44.66%) and brand (4.34%). It

implies that carbon footprint was the higher motivating factor in the study. Hence the

hypothesis is not supported.

The result indicates some bias. Because Kemp et al. (2009) and the pre-survey results

suggest that price and brand are primary motivating factors (Appendix-A3 and A4, p.86).

The possible reasons for the brand receiving such a low importance could be the use of

packaged orange juice as the research instrument. As it is a very common product and the

brands used in the experiment have significant market presence, so participants didn’t pay

much importance to the brand. The attribute level utilities of all the brands also suggest the

same, as all the brands received almost equal utility score (Table-11(b) on page.49:

Tropicana-2.237, Del Monte-2.383 and Princes-2.151).

Next, price received significant importance but next to carbon label. The possible

explanation could be as follows. Firstly, as the respondents were aware of the research

purpose, they were more conscious of the carbon footprint. This kind of inclination has been

reported in many psychological experiments. Secondly, as the price levels of the products

were comparatively low (£0.80, £1.00 and £1.20) and participants’ income levels suggest

that most participants can afford the price variation, so participants paid a comparatively

higher importance to the footprint.



The discrepancy can be further explored in the light of one respondent’s comment “I do

compare the carbon footprints for similar products provided, when they are costing the

same. However, if the cost of one is lower than the other, then I do not think for the carbon

footprint.” There were two more similar comments. This implies that, for many consumers

price will remain the most important motivator. Nevertheless, the carbon label can also

become an important motivator if the price difference between products is marginal and

brand differentiation is bleak.

5.1.9. Testing H9. When informed about the significance of carbon footprint and labelling,

consumers use carbon labels as an important tool in their buying decision.

The insignificant mean behaviour score (-0.0533) suggests that currently on an average

carbon label is not an important tool in the consumer decision process. However, in the

conjoint research, participants exhibited pro-environmental behaviour by giving carbon

footprints the highest relative importance. In the beginning of the experiment, the

importance of carbon labels and the way of interpreting the labels were explained to the

participants in a simple manner using examples, which is believed to be the prime reason

for the carbon footprint’s high relative importance score. This implies that if consumers are

informed or reminded of carbon labels, during their shopping, they consider it as an

important attribute. Hence H9 is supported.

One respondent commented “In fact, this survey helped me to understand the

representation of carbon footprint. Furthermore, this gave me a feel of basics on which I

should purchase the product.” There were four other comments on the similar line of

thought. Another respondent expressed “Now I have better understanding of carbon labels,

and I will consider them henceforth. However, in order to sustain the good intention,

regular reinforcement is required.”

The findings imply that regular reinforcement through communication at the right time and

place can increase the influence of carbon labels and help achieve the purpose of carbon

labels.

5.1.10. Testing H10. Half of the retail consumers are aware of carbon labelling.

In the response to the question “What does carbon label represent?” 115 out of 208

participants chose the right and complete definition. This shows that almost the half of the



participants were aware of the meaning of carbon label. Hence the H9 is supported.

However, there are chances of guess work and possibilities of hoodwink as the survey was

administered online, participants can easily do a web-search to find the right answer and

hence the H9 could also be inconclusive.

A significant number of participants (49) chose the option “Eco friendly”. Though currently,

carbon labels do not confirm the eco-friendliness of a product, many consumers perceive

that the presence of the label signifies products’ eco-friendliness. The similar misconception

is also reported by Berry et al. (2008). In the focus group, many participants perceived the

products or services with carbon labels are green or environmentally safe.

5.1.11. Testing H11. Integrating traffic light signals with the present carbon label would

enhance effectiveness of the carbon label.

Table-13 Attribute level utility from two conjoint analysisAttribute level wise utilityTropicana Del Monte Princes

First conjoint analysis 2.237 2.383 2.151Second conjoint analysis with TLS 1.336 2.250 3.812All the effects were statistically significant, yielding x2 with probabilities less than 0.01.

In the first conjoint experiment, all the three brands received an almost similar utility score

(Ref: Table-13). However, in the second conjoint analysis, there is a clear differentiation in

the utility scores. The Princes brand which was assigned a green label received the highest

utility score (3.812), followed by Del Monte which was assigned with a yellow label. The

Tropicana brand was assigned with red traffic label and received the least utility score

(1.336). This implies the influence of traffic light label systems on consumers’ behaviour.

Participants expressed distinctive support to the environment friendly product, due to the

presence of a green or yellow label. Hence, H11 is supported.

Vanclay et al. (2009) research on traffic light based carbon labels in Australia, confirms

similar findings. Sales for products labelled green in the experiment increased by 4%.

Whereas, the sales for products with black labels (black label represented the carbon villains

in the research) decreased by 6%. Further, no significant change in sales of products with

yellow label was reported.

Hence, traffic light integrated carbon labels are more effective than present carbon label.



5.1.12. Consolidated hypothesis testing results

Table-14 below presents the consolidated results of hypothesis testing.

Table-14: Consolidated hypothesis testing resultsNo Hypothesis Statistics ResultH1 ACL +ve related to INT B=0.1923, p=0.0758 SupportedH2 SN +ve related to INT B=0.4279, p=0.000 SupportedH3 PBC +ve related to INT B=0.3508, p=0.000 SupportedH4 PBC +ve related to BEH B=0.5226, p=0.000 SupportedH5 SN-INT < ACL-INT or PBC-INT Above statistics Not supportedH6 Most UK consumer +ve ACL 80% participants Supported

H7 INT differs from BEHWilcoxon Signed Ranks, Z=-8.68, p=0.000

Supported

H8Among Price (P), brand (B) and Carbon footprint(C), P and B are leading motivators.

P= 44.66%, B= 4.34%, C= 50.99%

Not supported

H9 Communication can improve BEH H7 statistics SupportedH10 Half the consumers aware of carbon label 115 out of 208 Supported

H11Inclusion of TLS in current carbon label will enhance effectiveness

Table-13Supported



5.2. Additional Discussion

This section (5.2) discusses additional findings from the SEM and association of background

variables with carbon label related behaviour.

5.2.1. The model

Among the various factors influencing intention and behaviour, perceived behavioural

control has the highest influence on behaviour (r=0.5226) and second highest on intention

(r=0.3508). In this research, the items measuring PBC, tried to access whether consumers

are comfortable with locating & interpreting carbon labels and comparing carbon footprint

of products. The significant association between PBC and purchase behaviour suggests that

consumer behaviour is largely influenced by their knowledge about the carbon labels, and

then followed by attitude. Whereas, the intention is majorly influenced SN, and then

followed by PBC.

Direct and Indirect effects: As discussed earlier the direct & indirect effect results are one of

the distinctive features of SEM. The results (Table: 12) suggest there is no direct effect of SN

on BEH. However, SN has indirect effects on BEH (0.1143, p=0.000). This suggests that

consumers’ normative beliefs do not directly lead to pro-environment behaviour.

Next the results suggest that ACL has both direct (0.3390) and indirect (0.0514) effect on

BEH. In the proposed model as per TPB (Ajzen 1991) there is no direct path between ACL

and BEH. However, the direct effect is statistically significant. The results indicate that ACL,

SN and PBC are responsible for intention and ACL, PBC and INT lead to behaviour. Further

these effects suggest that, intention is fully mediating SN towards behaviour related to

carbon labels and partially mediating ACL and PBC.

The developed model suggests that environmental consequences and subjective norms

have a significant influence on purchase intention. However, SN has minimal influence on

behaviour as discussed. Pickett-Baker and Ozaki (2008) also stated the same that, the values

and beliefs about environmental issues and consequences have no direct link with

environmentally responsible behaviour. Staats (2004) expressed the same that the link

between environment concern and pro-environment behaviour is generally weak.



Finding of Follows and Jobber (2000) research on environmental responsible purchase

behaviour supports the results of this study. According to their model, behaviour is

influenced by intention and intention is influenced by environmental consequences and

individual consequences. Environmental consequence refers to various environmental

impacts of a purchase decision; whereas the individual consequence refers to measures of

convenience, range of product sizes, cost and efforts to follow certain behaviour. The study

found that individual consequence (B=0.63, t=8.57) has marginally higher influence on

purchase intention than by environmental consequences (B=0.55, t=7.49). Follows and

Jobber’s individual consequence measure is equivalent to PBC in this study. The findings of

this study matches with Follows and Jobber’s study that, PBC has higher influence on

behaviour than attitude and subjective norm have.

Some of the other studies where the PBC individual consequence found to have a significant

influence on behaviour are as follows. Domina and Koch (2002) in a study on textile

recycling behaviour found that the convenience and closeness of drop-off centres is closely

related to recycling behaviour (Ramayah et al. 2009). Therefore, the results of this research

supported with the finding of above cited researches, confirm that the convenience of using

carbon labels is the greatest predictor of use of carbon labels as a decision making tool.



5.2.2. Discussion on background variables

Uusitalo (2008) expressed that background variables such as age, gender and education

level are very weekly associated with the pro-environment attitude. However, a study

conducted in Finland by Uusitalo & Rokka (2008) for consumer preference for eco-packaging

confirms that on an average older member and females choose eco-friendly packaging.

Further, there has been some consensus between researchers that environmental friendly

consumers tend to occupy certain demographic characteristics as highly educated,

knowledgeable, relatively high income and more likely to be female and younger (Carrigan

and Attala 2001, De Pelsmacker et al. 2005 and Uusitalo 2008).

The table-15 below presents the statistical results in relation to exploration of difference of

carbon label associated behaviour across various background groups.

Table-15: Results of association of background variablesGrouping variable

Groups Test performed Test result

Gender

Gender: Male, FemaleBEH: Continuous to seven groups with equal range. Chi-Square

X2=7.511, df=6, p=0.276

Age

Gp1, n=9: 19 or younger, Gp2, n=126: 20 -35 yrs, Gp3, n=34: 36-50 yrs, Gp4, n=19: 51 & above

Kruskal-WalisX2=0.664, df=3, p=0.882

Income

Gp1, n=44: £15,000 or less, Gp2, n=41: £15,001 - £30,000, Gp3, n=26: £30,001 - £45,000, Gp4, n=19: £45,001 or more


Education

Gp1, n=1: Primary, Gp2, n=34: Secondary, Gp3, n=33: High school diploma, Gp4, n=41: Bachelors, Gp5, n=61: Masters & above


All the p-values are greater than 0.05, which suggest that there is no significant difference in

the carbon footprint associated behaviour across different demographic groups. This implies

that, in their carbon label related communications, the retailers, manufacturers and

marketers should pay equal attention to all the demographic groups, rather than on any

specific groups. Further during the development of eco-friendly products, manufacturers

should pay equal attention to the needs of all demographic groups, as there is no significant

variance in their green behaviour.



6. Recommendations, Limitations and Conclusion

6.1. Recommendations

Based on the findings of this study, this section (6.1) provides recommendations for

stakeholders of carbon label. Staats (2004) in his work on Pro-environmental Attitudes and

Behavioural Change has suggested three distinctive ways of intervening consumers’

environmental behaviour: (i) Direct influence on behaviour, (ii) Influence on habits that

control behaviour and (iii) Influence on the convenience of performing pro-environmental

behaviour. My recommendations are based on these three topical suggestions.

6.1.1. Direct influence on behaviour

In this intervention strategy consumer behaviour will be influenced directly by making eco-

unfriendly products off-shelves. It is to give the consumers simply the right options, not just

the information of buying a fair product from environment perspective. Many consumers

perceive that, it is the retailer's responsibility to sell only eco-friendly products and

government’s responsibility to regulate the same. This will be one of the harshest, but the

most effective strategy.

6.1.1.1. Role of government and policy makers: Currently there is no legislation providing

any guidelines on the carbon emission of products. Such legislation can bring a change.

Those products which do not follow the regulations will not be eligible for selling in stores.

Further, based on the study, I recommend that, Staats (2004) suggestions could be

implemented, by levying a carbon tax or surcharge on products not complying with

regulations.

6.1.1.2. Role of retailers: All the retailers have some kind of buying criteria such as price,

quality and fair-trade, for choosing the products, they offer. The inclusion of carbon

footprint as a selection criterion could be effective and the study presented in this

dissertation confirms that the effort towards the end is worthy.

6.1.1.3. Difficulties: While the implications of the recommendations are interesting,

unfortunately, it is not an immediate solution, because doing a life cycle assessment and

determining a limiting carbon footprint for a wide range of products is time consuming. It

takes about twenty years to carbon label 50,000 products, average range in a typical



supermarket (Berry et al. 2008). This study further highlights the fact that retailers’ and

government’s direct intervention can alienate consumer interest.

6.1.2. Influence on habits that control behaviour

The findings of this research confirm that there is a huge difference between consumer

attitude, intention and behaviour. Staats (2004) expressed that habit plays a great role for

this difference. Although most consumers have a positive attitude, as they do not have the

habit of comparing the carbon footprints, they fail to use carbon labels during decision

making. Generally, consumers look for price and ingredients and make their purchase

decision. Nearly all consumers compare prices. Therefore, if carbon footprints are presented

next to price, there is a greater chance that consumers will compare the carbon footprints.

With time, it can become a habit and can bring positive change in behaviour.

Further the findings suggest that when consumers are reminded or familiarised with the

labels at the time of shopping, they use the labels as a decision making tool (section 5.1.9).

Currently, there are hardly any practices to remind consumers about carbon labels at the

point of decision making. If retailers make in-store communication on the carbon label

through banners, point of sale display and leaflets, there can be a positive influence on

consumer habit and behaviour.

Furthermore, the findings in section 5.1.2 and 5.1.5 confirm a significant relation between

retailer’s incentives and intention of considering carbon footprints. If retailers continue

providing additional incentives for green products, consumers will develop a strong habit of

considering the carbon label as a decision tool.

6.1.3. Influence on the convenience of using carbon labels

Staats (2004) expressed that generally eco-conscious behaviour is not impeded by negative

attitudes but rather by weak positive attitudes combined with a lack of perceived

behavioural control. The results of this study confirm that there is a strong association

between PBC and behaviour (section 5.1.4). Though there is a positive attitude towards

carbon labels, so far, the use of carbon labels has been sporadic, as most consumers lack the

behavioural control.

Therefore, in-order to increase consumer’s PBC, this paper recommends the following

actions:



6.1.3.1. Integration of traffic lights with the present carbon labels: In order to enhance the

effectiveness of carbon labels, traffic lights should be added to the present label format.

Based on the findings (section 5.1.11), the traffic light integrated carbon label is more

effective and convenient than simple carbon labels and increase consumer behavioural

control.

6.1.3.2. Over the channel communications: Advertisements or presentations on

interpretation of carbon labels through web and television can enhance consumer

knowledge on the carbon label. The higher familiarisation will increase the convenience of

using the carbon labels during decision making and in effect, will increase perceived

behavioural control.

6.1.3.3. Legislation to print carbon labels on the products: The carbon label is an emerging

concept, and currently very few products have carbon labels on them. In this study, half the

participants expressed that there are not enough options with carbon labels to perform the

comparison. Therefore, legislation to print the label on all products can make an astounding

difference. Because more products with carbon labels will enhance consumers behavioural

control of using carbon labels as a decision making tool.

6.1.3.4. Difficulties: In the implementation of TLS, there would be the standardisation

problem. Presently, TLS is used in the nutritional context, which is supported by the

international consensus on daily nutritional allowances. However, currently there are no

standards for the carbon emissions of different products. So, in future the manufacturers

and the Carbon Trust need to work together to develop a scale for various products.

Further, the labelling need not to be done at once, manufacturers can be given deadlines, in

order to allow them enough time to measure the carbon footprint of their products and

display on the package. Moreover, it will be better to start with prioritising products and

services with large carbon footprints. Although it is a time consuming process, the efforts

will pay off as it would provide the desired convenience (PBC) of using carbon footprints.



6.2. Reflection and suggestions for future research

1. The use of orange juice in this research as an instrument, limits the generalisation of

the findings, as consumers may not have similar attitude and buying behaviour

towards other products with carbon labels. So further research with a mixed variety

of products will be beneficial for generalisation of the findings.

2. Although there is wide support for TPB framework for evaluating consumer attitude-

behaviour, Davies et al. (2002) and several other authors have expressed that TPB

has limited ability to explain eco-friendly consumerism in great detail and have

suggested considering additional factors and variables. Schafer (1986) attitude-

behaviour model suggests that an individual’s attitude is influenced by his beliefs,

values, personal needs and current behaviour.

Belief can be defined as the knowledge or information a person assumes to be true

about a particular thing. Whereas values can be defined as a person’s feeling about

what is desirable or undesirable. Personal needs in this context can be explained as

per a person’s expectation from his eco-friendly behaviours in terms of rewards,

support for ego and understanding of environment. Individuals usually have a

positive attitude towards an object which rewards them, so an understanding of

people’s expectation from their green buying behaviour will be very useful to

develop the desired attitude.



Further, as discussed earlier only the 19-38% of the variances in intention to

behaviour is explained by TPB (Sutton 1998), consideration of intervening factors

such as habits and expected consequences of behaviour can bring more insights on

effectiveness of carbon labelling.

3. As discussed earlier TPB does not consider moral dimensions, which is considered to

explain the social dilemma in eco-conscious behaviour. The norm activation model

supports a framework to evaluate the moral dimensions. Further studies with these

factors along with TPB model can be beneficial.

(Figure-13 NAT proposed by Schwartz 1977)

4. The research findings are based on data from the self-reported survey, which do not

provide validity all the time. A non-intrusive study can enhance the quality and

validation of data.

5. The environment friendly attitude and behaviour of consumers’ varies region and

store wise. In certain part of UK, there is increased concern and interest for use of

eco-friendly products. Additionally, some of the high street stores promote green

consumerism. A further research on geographic distribution of the eco-friendly

consumers can be beneficial for development of effective policies, actions and



events to impart positive influence on consumer attitude and behaviour towards

carbon labelling. The present research can be extended with a relatively larger

sample size, evenly distributed across various regions of UK and stores to perform

the suggested research.

6.3. Conclusion

The purpose of this research was to investigate enhancements for carbon labels and to

study environmental consumer behaviour in a realistic setting, where consumers have to

balance their decision based on various attributes. The key conclusion is that: on-time

communication, more product options with carbon label and integration of traffic light

system with current label can enhance the effectiveness of carbon labels.

I hope the research findings will be useful in developing effective marketing propositions to

target a wide audience and influence their dormant environment sensitive attitude towards

green products. The success of the communications will ultimately become an incentive tool

for product developers to develop more eco-friendly products. This whole process has the

potential to form a positive feedback loop. Firms will have a better incentive to produce and

market environment friendly product (as discussed in chapter-1, page-9). The increasing

marketing activity based on carbon labels will increase consumers’ perceived behavioural

control and in turn will influence consumer behaviour towards carbon labels and eco-

friendly products.

The findings suggest that carbon labelling is already a part of consumers’ decision making

tool and has a great potential for influencing eco-conscious behaviour. However, it is still an

evolving concept, and it will be too early to decide the effectiveness of these labels. Findings

indicate that, a significant result can be achieved by carbon labelling some more of the

products sold in retails. Great responsibility lies with the key stakeholders such as carbon

trust, retailers, manufacturers and policy makers to work out the strategy, prioritise the

products and develop a scale, in order to have a positive impact on environment through

consumer buying behaviour.

There has been a growing interest towards understanding consumer behaviour in

environmental issues and various studies have been performed to understand the aspects

of such behaviour. Interestingly in this study, the choice of the participants reveals that

consumers are concerned about the environment. However, the findings also suggest that



we cannot rely completely on the rise of pro-environmental consumerism alone. Policy

makers, retailers, manufacturers and government have a critical role in setting higher

standards and ensuring that majority of products are carbon labelled, traffic light signal is

integrated with current format, and consumers are informed about carbon labels.



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Appendix

A1. Sample Questionnaire

Hello,

The climate change has been a global concern and as per Carbon Trust (2006), 45% of green house gasses generated in UK are from what people buy and use. The present survey is a part of an academic research (Durham Business School) to understand the current attitude and behaviour of consumers towards eco-friendly consumerism. You are invited to participate in our survey.

This survey includes simple questions, and does not require any specific knowledge or skills. It takes approximately 10 minutes to complete the questionnaire. Your participation in this study is completely voluntary. You can withdraw from the survey at any point. We are interested in your personal opinions regarding green consumerism. There are no correct or incorrect responses; we are merely interested in your personal point of view. Your survey responses will be strictly confidential and data from this research will be reported only in the aggregate.

If you have any questions or comments about the survey or the procedures, you may contact Nitai Patra by email [email protected] or can fill the feedback form at the end of this survey. Please click Continue to proceed to the survey. Thank you. We appreciate your participation.

What does the Carbon Footprint label represent?1. I don’t know.2. Fair trade.3. Eco-friendly.4. Organically Produced.5. Something that is recyclable.6. Amount of green house gases left by this product during its production and transport.7. Other __________________________________________________

Carbon footprint represents the total carbon emitted during the production, transportation and consumption of a product. Many products now-a-days carry a carbon label. E.g. Tesco brand washing detergents, orange juice, potatoes & light bulbs, Walkers Crisps and Boots shampoos. The lesser the carbon emission of a product, the more eco-friendly is it. For example, a product with a carbon footprint of 1000gm/unit is comparatively more eco-friendly/greener than a product with a carbon footprint of 1200gm/unit. Please consider this definition for answering the following questions.

Please select a suitable option in the context of your retail grocery/departmental shopping such as day-to-day shopping in stores such as Tesco/ Boots/ Iceland/ Asda/ Lidl/ M&S etc. There are no correct or incorrect responses; we are merely interested in your personal point of view.

Strongly Agree

Agree Moderately Agree

UndecidedModerately Disagree

Disagree Strongly Disagree

I would prefer to buy products with low carbon footprints. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




I am most likely to pay a marginally higher price for an eco-friendly product. ❏ ❏ ❏ ❏ ❏ ❏ ❏



Strongly Agree




I consider the carbon footprint as a major product attribute in my purchase decision. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




The carbon label provides satisfactory information about a product’s impact on environment.

❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




I appreciate retailers’/manufacturers’ initiative for carbon labelling of products. ❏ ❏ ❏ ❏ ❏ ❏ ❏

I choose/ would choose a low carbon emitting product because:

Strongly Agree




Climate change is a global concern and collective responsibility. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




Some stores provide additional incentives. (e.g. Tesco Green Club Card Points) ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




People who are important to me expect me to use low eco-friendly products. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




I am contributing to a higher purpose.❏ ❏ ❏ ❏ ❏ ❏ ❏



Please choose an option which you would most likely to buy, considering that these are the only choices and information available for you. Further consider that all other functional attributes are same for all the products.





Please select a suitable option in the context of your retail grocery/departmental shopping.

Strongly Agree




It is convenient to compare carbon footprints on products. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




There are reasonable options to choose a low carbon footprint product. ❏ ❏ ❏ ❏ ❏ ❏ ❏



Strongly Agree




I know where to look for the carbon label on the products. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




I know how to interpret the carbon label on a product. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




The marginally higher price of eco-friendly products does not abstain me from buying them.

❏ ❏ ❏ ❏ ❏ ❏ ❏

In the course of last five shopping trips,

Strongly Agree




I did consider the carbon footprint of products while buying them. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




I have compared the carbon footprints of products before buying them. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




I have bought some products with a comparatively lower carbon footprint. ❏ ❏ ❏ ❏ ❏ ❏ ❏





Considering my coming three months shopping intention,

Strongly Agree




I had intentions for considering carbon footprint while buying products. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




I had intention of comparing the carbon footprints of the products before buying them.

❏ ❏ ❏ ❏ ❏ ❏ ❏

Strongly Agree




I intended to buy at least one product with a comparatively lower carbon footprint. ❏ ❏ ❏ ❏ ❏ ❏ ❏

Which best describes your gender?1. Male2. Female3. Prefer not to say



Which best describes your age?1. 19 or younger2. 20 - 353. 36 - 504. 51 or older5. Prefer not to say

Which best describes your current annual family income?1. £15,000 or less2. £15,001 - £30,0003. £30,001 - £45,0004. £45,001 or more5. I dont know.6. Prefer not to say.

Which best describes your education level?1. Primary2. Secondary3. High school diploma4. Bachelors5. Masters & above6. Prefer not to say7. Other ______________________________

Please use this space if you have any questions/comments/feedback on this survey.



A2. Product Directory

Company Product description

Walkers All varieties of standard crisps sold in single packets

Tate and Lyle 1kg bag of granulated cane sugar

Tesco Range of toilet paper and kitchen roll

Tesco Milk: Skimmed, Semi-skimmed, Whole

Tesco Range of own brand laundry detergent

Tesco Range of chilled and long life orange juice

Tesco Range of light bulbs

Tesco / MMUK Jaffa Oranges / soft fruit

PepsiCo Quaker oats and Oat so Simple

Morphy Richards Range of Irons

Allied Bakeries Kingsmill wholemeal, white and 50:50 loaves

British Sugar A range of white granulated sugar - British Sugar - B2B

British Sugar A range of white granulated sugar - Silver Spoon - B2C

Levi Strauss A one off promotional bag

Haymarket Magazines – Marketing and ENDS report

Continental Clothing A range of over 800 t-shirts and other cotton apparel

Continental Clothing Woven bags (USA and Japan) and t-shirt internet retailing service

Marshalls Complete range of 2,500 paving products

Mey Selections Scottish honey and shortbread

Sentinel Central heating cleaning fluid

Stalkmarket Biodegradable, disposable catering serving packaging

Aggregate Industries 3 varieties of paving products - Bradstones

Source: Carbon Trust, web: http://carbon-label.com/individuals/product.html Accessed on 11th July 2010.



A3. Kemp et al. (2009) survey result

The following table summarises the findings of the research of Kemp et al. (2009). The table presents purchase motivating factors reported by respondents in revealed preference survey as primary, secondary or tertiary factors they considered.

Factor Primary n (%) Secondary n (%) Tertiary n (%)Price 63 (25) 10 (4) 1 (0.4)Brand 59 (23.5) 10 (4)Portion size 50 (12) 5 (2)Freshness 26 (10.4) 11 (4.4) 1 (0.4)Only option 24 (9.6) 2 (0.8)Usual/preferred choice 20 (8) 5 (2)Country of origin 11 (4.4) 3 (1.1)Quality 6 (2.4) 4 (1.6)Organic 4 (1.6)Fair trade 2 (0.8)Others 6 (2.4)Total 251 (100) 50 (20) 2 (0.8)

n=number of respondents, % of total sample in bracket.

A4.Pre-survey result

Attribute Relative ImportanceDurability 87.69%Quality 86.92%Reliability 83.70%Price 82.56%After sales support/ Guarantee/ Warranty 80.00%Special offer/ discount/ promotions 78.95%Design/ Look/ Technology 78.08%Nutrition values 75.13%Brand 70.00%Return policy 62.00%Recyclable/ Environment friendly 56.92%Genetically Modified 56.67%Organically produced 55.90%Fairly traded 51.54%Air miles 47.37%Suitable for vegetarian 46.67%Not tested on animals 42.82%



A5. Gender

Statistics

N Valid 208Missing 0

Mean .3413Mode .00Std. Deviation .47531Variance .226Skewness .674Std. Error of Skewness .169Kurtosis -1.561Std. Error of Kurtosis .336

Frequency Percent Valid PercentCumulative Percent

Valid .00 137 65.9 65.9 65.91.00 71 34.1 34.1 100.0Total 208 100.0 100.0

Gender1.501.000.500.00-0.50

Freq

uenc

y

200

150

100

50

0

Histogram

Mean =0.34Std. Dev. =0.475

N =208



Age

Statistics


Mean 2.1106Mode 2.00Std. Deviation .97420Variance .949Skewness -.256Std. Error of Skewness .169Kurtosis .677Std. Error of Kurtosis .336


Valid .00 20 9.6 9.6 9.61.00 9 4.3 4.3 13.92.00 126 60.6 60.6 74.53.00 34 16.3 16.3 90.94.00 19 9.1 9.1 100.0Total 208 100.0 100.0

Age5.004.003.002.001.000.00-1.00

Freq

uenc

y

125

100

75

50

25

0

Histogram

Mean =2.11Std. Dev. =0.974

N =208



Income

Statistics


Mean 1.3462Mode .00Std. Deviation 1.33528Variance 1.783Skewness .598Std. Error of Skewness .169Kurtosis -.852Std. Error of Kurtosis .336


Valid .00 78 37.5 37.5 37.51.00 44 21.2 21.2 58.72.00 41 19.7 19.7 78.43.00 26 12.5 12.5 90.94.00 19 9.1 9.1 100.0Total 208 100.0 100.0

Income5.004.003.002.001.000.00-1.00

Freq

uenc

y

80

60

40

20

0

Histogram

Mean =1.35Std. Dev. =1.335

N =208



Education

Statistics


Mean 3.0625Mode 5.00Std. Deviation 1.79127Variance 3.209Skewness -.563Std. Error of Skewness .169Kurtosis -.969Std. Error of Kurtosis .336


Valid .00 38 18.3 18.3 18.31.00 1 .5 .5 18.82.00 34 16.3 16.3 35.13.00 33 15.9 15.9 51.04.00 41 19.7 19.7 70.75.00 61 29.3 29.3 100.0Total 208 100.0 100.0

Edu6.004.002.000.00

Freq

uenc

y

60

40

20

0

Histogram

Mean =3.06Std. Dev. =1.791

N =208



Attitude towards Carbon Labels (ACL)

Statistics

N Valid 208 Missing 0Mean .9981Mode .40Std. Deviation 1.04950Variance 1.101Skewness -.282Std. Error of Skewness .169Kurtosis .198Std. Error of Kurtosis .336


Valid -2.60 1 .5 .5 .5-1.80 1 .5 .5 1.0-1.60 1 .5 .5 1.4-1.40 1 .5 .5 1.9-1.20 5 2.4 2.4 4.3-1.00 1 .5 .5 4.8-.80 1 .5 .5 5.3-.60 4 1.9 1.9 7.2-.40 4 1.9 1.9 9.1-.20 7 3.4 3.4 12.5.00 13 6.3 6.3 18.8.20 7 3.4 3.4 22.1.40 21 10.1 10.1 32.2.60 14 6.7 6.7 38.9.80 14 6.7 6.7 45.71.00 15 7.2 7.2 52.91.20 20 9.6 9.6 62.51.40 11 5.3 5.3 67.81.60 16 7.7 7.7 75.51.80 9 4.3 4.3 79.82.00 12 5.8 5.8 85.62.20 3 1.4 1.4 87.02.40 11 5.3 5.3 92.32.60 5 2.4 2.4 94.72.80 2 1.0 1.0 95.73.00 9 4.3 4.3 100.0Total 208 100.0 100.0



ACL4.002.000.00-2.00

Freq

uenc

y

25

20

15

10

5

0

Histogram

Mean =1.00Std. Dev. =1.049

N =208

Observed Value420-2

Expe

cted

Nor

mal

2

1

0

-1

-2

-3

Normal Q-Q Plot of ACL



Subjective Norms (SN)

StatisticsN Valid 208

Missing 0Mean 1.1394Mode 1.00Std. Deviation 1.02141Variance 1.043Skewness -.770Std. Error of Skewness .169Kurtosis .701Std. Error of Kurtosis .336


Valid -2.25 1 .5 .5 .5-2.00 2 1.0 1.0 1.4-1.50 3 1.4 1.4 2.9-1.00 6 2.9 2.9 5.8-.75 2 1.0 1.0 6.7-.50 2 1.0 1.0 7.7-.25 1 .5 .5 8.2.00 16 7.7 7.7 15.9.25 8 3.8 3.8 19.7.50 14 6.7 6.7 26.4.75 15 7.2 7.2 33.71.00 29 13.9 13.9 47.61.25 17 8.2 8.2 55.81.50 16 7.7 7.7 63.51.75 20 9.6 9.6 73.12.00 23 11.1 11.1 84.12.25 18 8.7 8.7 92.82.50 8 3.8 3.8 96.62.75 2 1.0 1.0 97.63.00 5 2.4 2.4 100.0Total 208 100.0 100.0



SNE4.002.000.00-2.00

Freq

uenc

y

30

20

10

0

Histogram

Mean =1.14Std. Dev. =1.021

N =208

Observed Value420-2

Expe

cted

Nor

mal

3

2

1

0

-1

-2

-3

Normal Q-Q Plot of SNE



Perceive Behavioural Control (PBC)


Missing 0Mean .3606Mode .00Std. Deviation 1.24326Variance 1.546Skewness -.102Std. Error of Skewness .169Kurtosis -.435Std. Error of Kurtosis .336


Valid -3.00 2 1.0 1.0 1.0-2.20 3 1.4 1.4 2.4-2.00 2 1.0 1.0 3.4-1.80 2 1.0 1.0 4.3-1.60 6 2.9 2.9 7.2-1.40 5 2.4 2.4 9.6-1.20 6 2.9 2.9 12.5-1.00 8 3.8 3.8 16.3-.80 7 3.4 3.4 19.7-.60 11 5.3 5.3 25.0-.40 9 4.3 4.3 29.3-.20 12 5.8 5.8 35.1.00 22 10.6 10.6 45.7.20 10 4.8 4.8 50.5.40 10 4.8 4.8 55.3.60 12 5.8 5.8 61.1.80 7 3.4 3.4 64.41.00 9 4.3 4.3 68.81.20 8 3.8 3.8 72.61.40 13 6.3 6.3 78.81.60 15 7.2 7.2 86.11.80 5 2.4 2.4 88.52.00 8 3.8 3.8 92.32.20 4 1.9 1.9 94.22.40 5 2.4 2.4 96.62.60 3 1.4 1.4 98.13.00 4 1.9 1.9 100.0Total 208 100.0 100.0



PBCE3.002.001.000.00-1.00-2.00-3.00

Freq

uenc

y

40

30

20

10

0

Histogram

Mean =0.36Std. Dev. =1.243

N =208

Observed Value420-2

Expe

cted

Nor

mal

3

2

1

0

-1

-2

-3

Normal Q-Q Plot of PBCE



Intention (INT)


Missing 0Mean .8607Mode 1.00Std. Deviation 1.37901Variance 1.902Skewness -.899Std. Error of Skewness .169Kurtosis 1.061Std. Error of Kurtosis .336


Valid -3.00 9 4.3 4.3 4.3-2.33 1 .5 .5 4.8-2.00 4 1.9 1.9 6.7-1.67 2 1.0 1.0 7.7-1.00 2 1.0 1.0 8.7-.67 4 1.9 1.9 10.6-.33 5 2.4 2.4 13.0.00 35 16.8 16.8 29.8.33 7 3.4 3.4 33.2.67 18 8.7 8.7 41.81.00 39 18.8 18.8 60.61.33 12 5.8 5.8 66.31.67 15 7.2 7.2 73.62.00 24 11.5 11.5 85.12.33 11 5.3 5.3 90.42.67 6 2.9 2.9 93.33.00 14 6.7 6.7 100.0Total 208 100.0 100.0



INT3.002.001.000.00-1.00-2.00-3.00

Freq

uenc

y

60

50

40

30

20

10

0

Histogram

Mean =0.86Std. Dev. =1.379

N =208

Observed Value420-2

Expe

cted

Nor

mal

2

1

0

-1

-2

Normal Q-Q Plot of INT



Behaviour (BEH)

Statistics


Mean -.0533Mode .00Std. Deviation 1.54712Variance 2.394Skewness -.087Std. Error of Skewness .169Kurtosis -.886Std. Error of Kurtosis .336


Valid -3.00 10 4.8 4.8 4.8-2.67 2 1.0 1.0 5.8-2.33 2 1.0 1.0 6.7-2.00 26 12.5 12.5 19.2-1.67 8 3.8 3.8 23.1-1.33 5 2.4 2.4 25.5-1.00 13 6.3 6.3 31.7-.67 12 5.8 5.8 37.5-.33 11 5.3 5.3 42.8.00 32 15.4 15.4 58.2.33 12 5.8 5.8 63.9.67 5 2.4 2.4 66.31.00 16 7.7 7.7 74.01.33 16 7.7 7.7 81.71.67 7 3.4 3.4 85.12.00 19 9.1 9.1 94.22.33 6 2.9 2.9 97.12.67 2 1.0 1.0 98.13.00 4 1.9 1.9 100.0Total 208 100.0 100.0



BEH3.002.001.000.00-1.00-2.00-3.00

Freq

uenc

y

50

40

30

20

10

0

Histogram

Mean =-0.05Std. Dev. =1.547

N =208

Observed Value420-2-4

Exp

ecte

d N

orm

al

3

2

1

0

-1

-2

Normal Q-Q Plot of BEH


Documents

Carbon Labelling