Methods of Inquiry
Introduction:
The carbon label on any product describes the carbon dioxide emitted during the manufacturing, storage, transportation and consumption of the product. The purpose of the labelling is to inform consumers about the carbon footprint left by the product and helping them in making a purchase decision with a minimum contribution to global warming. Increasing concerns for climate change over a past couple of years has attracted consumer’s attention towards sustainable and low carbon products. In UK, carbon label for products is provided by The Carbon Label Company, a subsidiary of UK government created The Carbon Trust. As per International Standards Organisation, carbon labels are categorised as type-I eco-labels.
Examples of the products featuring carbon label are Tesco brand washing detergents, orange juice, potatoes & light bulbs, Walkers Crisps and Boots shampoos. Increasingly companies are adopting carbon labelling of products as a part of their corporate social responsibility and cost reduction. Eco-labelling is a promising approach to enhance the environmental performance of a product through consumer choice (Solomon 2003).
This research proposal builds on ideas from the environmental behaviour paradigm and models of consumer multi-attribute choice in a sense that one interprets environmental friendly choices as a trade-off situation between several other criteria. Earlier many researchers have focused on describing underlying factors driving attitude and decision making towards environmentally friendly products, this research will try to explore consumer behaviour in a more realistic manner, where consumers have to balance their decision with different product attributes.
The aim is to find whether environment friendliness is an important product attribute and the extent to which consumer’s decision behaviour is driven by the carbon footprints in comparison to other drivers. Further the study will look at what consumers say about their environment friendliness in choosing a product and to what extent they follow the same while making the buying decision. The findings will be useful to policy makers, manufacturers and marketers to know what importance environment friendliness play in the product attributes profile.
Literature review:
The proposer carried a literature survey to discover academic research conducted in this field of interest. However, no precise academic research on consumers’ attitude towards carbon labelling of products in UK could be found. Nevertheless, some relevant research articles have been identified, which are related to eco-labelling and environment friendly consumerism in Finland and Switzerland. By reviewing these literatures, this exploratory research has been proposed to bridge the gap related to carbon labelling and consumer attitude in UK.
Prime literatures reviewed:
(1) Horne Ralph E. (2009). Limits to labels: The role of eco-labels in the assessment of product sustainability and routes to sustainable consumption. International Journal of Consumer Studies, ISSN 1470-6423.
(2) Uusitalo L. and Rokka J. (2008). Preference for green packaging in consumer product choices – Do consumers care? International Journal of Consumer Studies ISSN 1470-6423.
(3) Sammer K. and W�stenhagen* R. (2005). The Influence of Eco-Labelling on Consumer Behaviour. Institute for Economy and the Environment (IWOe-HSG), University of St. Gallen,
Switzerland. Published in Business Strategy & the Environment. Version: Sept. 1, 2005.
There has been a growing interest towards environmental issues in ‘consumer behaviour’ and various studies have been performed to understand the aspects of such behaviour. Interestingly the choice of the consumers is revealing the fact that consumers are concerned about the environment.
In the economic and cognitive psychology traditions, it is deduced that consumers behave rationally based on their preferences and beliefs. Studies show that consumers have high preference for greener products; however, the link between consumer attitude and behavioural measure is rather weak. Even the most environment conscious customer does not choose a product on the basis of its environment aspects, the consumer trades off between different attributes of the product. So it has been difficult for policy makers and marketer whether environment friendliness is an important product attribute for consumers. (Uusitalo 2008)
It is established that consumers prefer eco friendly products; however, their buying behaviour is often inconsistent with this (Uusitalo 2008). Many people view themselves as green consumers and willing to buy products with less environmental effect. However, their buying decision is often guided by the quality/brand & price of the product and individual buying habit (OECD 2005, Pedersen & Neergaard 2006, and Gallastegui 2002, cited by Horne 2009).
As per economic theory, humans are rational and their decision depends on the maximisation of their utility. As per behavioural science consumer’s decision making is driven by multiples of factors such as personal factors, marketing mix factors, psychological factors, socio-cultural factors, social factors and situational factors. In this paper a research is proposed to understand the influence of three of the factors: price, brand and the carbon label on buying decision of UK retail consumers.
Background variables such as age, gender and education level are very weekly associated with the environment friendly attitude (Anderson and Cunningham 1972, cited by Uusitalo 2008). However, a study conducted in Finland by Uusitalo & Rokka (2008) for consumer preference for eco-packaging confirms that on an average older members and females are more likely to choose eco friendly packaging. Whereas the correlation between ‘level of education’ and ‘green preferences’ could not be established.
However, there has been some consensus between few other 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 cited by Uusitalo 2008)
In this research proposal, a study will be conducted, to explore the correlation between background variables and environment friendly behaviour in UK retail market.
Over the last 2-3 years, the concern over climate change has driven consumers towards greener products (products with lesser carbon foot print) and sustainable lifestyle (Horne 2009). Environment consciousness does not automatically lead to environment friendliness and change in purchasing behaviour (Pedersen and Neergaard 2006, cited by Horne 2009). As per a research finding by Morris (1997), eco labelling might guide consumers toward environmental friendly product is a vulnerable assumption (Horne 2009). However, the research is more than 13yrs old and during this period there has been a tremendous change in awareness on climate changes. In this research a fresh study will be carried to understand current consumer behaviour towards greener products in UK.
EU Flower Label, an eco-label, signifies a product’s kindness on the environment. Only the best products which are kindest to 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 well-funded information campaign. (European Commission 2007, cited by Horne 2009)
Research design & Method:
To conduct the research, four brands will be chosen from three different product categories. Food: Fruit juice, FMCG: Detergent and Appliances: Light bulbs. In the study it will be conveyed that the core benefit of all the three different products are same or fairly identical and focus will be given on relevant product attributes – brand, price & carbon footprint. These three product categories will provide ideal research setting as (1) these products are used daily or frequently; (2) used or consumed by all demographic irrespective of age, social status and education level; (3) the core benefits are fairly identical between products in a chosen category.
The survey will be quantitative and a discrete choice based method will be adapted. In the questionnaire a choice based conjoint analysis will be used to study the buying behaviour. The respondents will be shown different product alternatives with different attribute level composition. They will be asked to evaluate and make choices from the set of products (four alternative products with varying attributes) in each product category that they would most likely to buy. Both text and visuals will be used to present the functional attributes. Information on age, sex and education level will be collected in the beginning of questionnaire.
Why conjoint analysis: Conjoint analysis is one of the most popular methods of analysing consumer preferences. Using choice based conjoint analysis, consumers’ over all perception of utility can be divided into values or utility contributed by individual attributes, when a consumer trades off between the attributes. Conjoint analysis allows analysing consumer’s decision making process more precisely than it is possible with simple questionnaires. Rather than asking importance of different attributes of a product, in conjoint analysis products with varying attributes level are present and respondents are asked to choose a product that makes most value to them. Consumers value any product depending on different product attributes, which are the motivators for them to buy the product (Lancaster1966, cited by W�stenhagen 2005).
Many 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 is more reliable, detailed and easy to understand (Pullman, Moore 1999, SPSS 1997, cited by Kotri 2006). From the analysis for 300 types of application which are used to learn consumer needs, Anderson (1993) has concluded that conjoint analysis is the most successful method of analysis with 85% success rate (Kotri 2006).
Sample & Data collection: Random respondents will be picked up from customers entering or coming out of retails such as Tesco, ASDA, Sainsbury, Iceland and Aldi for collection of primary data. Considering the time and financial constraints, the research will be carried out in Durham and Newcastle. As the products chosen are general and respondents will be picked up randomly, it is assumed that the sample chosen will represent the UK population. This type of data collection is feasible, cost effective and quick way of collecting data. Further this will provide a realistic environment to access consumer behaviour. The required number of responses can be achieved within the time frame of MBA dissertation. To improve the data quality and dissonance, fifty percent data will be collected from customers going in and rest fifty percent from customers coming out of stores. The research will be a cross-sectional one.
The data collection will be census. Total 400 responses will be collected with equal proportion of gender and age group. That is out of 140 male/female respondents necessary care will be taken to include fairly equal percentage of teenagers(13-19), young adults (20-35), middle aged (36-50) and elderly ( 51 and above). The data collected will be fed into SPSS on daily basis and the respondent demographic will be monitored. This will help in collecting a balanced data (50 samples/ group) from all groups to get a normal distribution. As per central limit theorem the sample size should be large to get normal distribution; however, Stutely’s (1930) suggests that a normal distribution can be achieved from a sample size of 30 from each category within the overall sample (Saunders et al 2009, p. 218 ).
Saunders et al (2009) suggests for a sample size of 383 for population of 1million and 384 for a population of 10million, to get a confidence level of 95%. That is for every increase of sample size, the covered population size increases exponentially. As UK population is around 60million, the chosen sample size of 400 will be representative of whole population for a statistical inference with 90% plus confidence level.
Colour printed questionnaires with pictures of the products will be used to collect the responses. In this choice based survey, for every question respondent will be required to select a product which they will prefer to buy from the four options available. A pilot test will be carried out to ensure the content, construct & face validity and refining of the questionnaire (Saunders 2009, p. 394).
While inviting the respondents, they will be informed that this survey is a general marketing survey. Because the cognition of purpose of this survey may change their natural buying behaviour and make them over carbon conscious while taking the survey. At the end of the survey, they will be provided a leaflet explaining the purpose of the survey and the importance of carbon labelling.
Why this method of survey: This chosen method of survey is more suitable than internet based survey for the proposed research. First, this survey will bring a closer simulation of buying environment as the prospects will be invited from the customers entering and exiting from the stores. On the other hand while in an online survey the respondent mostly will be responding from the comfort of his home or office, which may not simulate the buying environment. Second, it will be easy to maintain the proportion of respondents of different age and gender groups as proposed earlier while limiting the respondents to UK residents only. Third, adequate response rate can be achieved within the set time limit. As and when necessary some extra effort and time can be made to match up with the schedule.
Questionnaire design and data analysis:
The empirical method will be quantitative and two multivariate techniques: discrete conjoint analysis and cluster analysis will be carried.
Conjoint analysis: The relative importance of selective product attributes will be estimated by conducting a choice based conjoint analysis. The result will reflect the respondent’s preference for different attributes in the product choice and average importance and mode of each attribute will be calculated. Using conjoint analysis and hierarchical Baye’s method estimates for individual level utility function will be made and respondents will be clustered into segments.
In this conjoint analysis the independent variables are brand, price and carbon footprint and the dependent variable is the subject’s preference. From the choice data collected from respondents, the utility of attribute levels and the importance of attributes will be estimated using IBM SPSS Conjoint 14.0 software. The result of discrete choice based cluster analysis will help in finding the answer for question no 1 & 3.
The following three attributes will be evaluated for all the three products using conjoint study.
1. Brand: Four alternative existing brands in the market will be chosen. So four attribute levels for brand.
2. Price: As the price attribute, the unit price of the product will be mentioned. For example the price for all the fruit juices will be presented in pounds/litre. Three attribute levels will be chosen for price.
3. Carbon footprint: The carbon footprint (CF) labelled by the manufacturer will be mentioned. Where required scaling will be done to mention the CF/unit. For example, if the CF is 200gm for a half litre pack, then it will be presented as 400gm/litre. In case of un-availability of CF for a product, an average will be calculated using CF from other equivalent brands. Three attribute levels for carbon footprint.
The selected combination of three attributes with different levels for each attribute will result in 36 profiles for each product. However, to keep the research simple and achievable, the Generate Orthogonal Design procedure will be used to create a reduced set of realistic product profiles that will be small enough to be included in the survey but large enough to assess relative importance of each attribute (SPSS Conjoint� 14.0 Documentaion 2005, p.5). For analysis the Discrete model will be used to indicate that the attribute levels are categorical and no assumptions have been made about the relationship between the attributes and the scores (SPSS Conjoint� 14.0 Documentaion 2005, p.18).
From the conjoint analysis the Utility Scores, Relative Importance and Correlations will be estimated. The Running Simulation will be used to predict the preferences of product profiles that were not rated by the subjects. This will help in designing product profiles which are most valued by consumers.
Cluster Analysis: The responses will be grouped in relation to their age, sex and education level using cluster analysis. In the cluster analysis importance of attributes for different segments will be found. The cluster analysis will be done on IBM SPSS Statistics. The TwoStep clustering algorithm will be used to find homogeneous clusters based on gender, age group and level of education. The categorical variables are Level of education, Gender and Age group of customers and the continuous variable is environment friendly shopping behaviour of customers. The result of cluster analysis will help us finding the answer for question no 2.
As proposed earlier, the survey will be conducted without revealing the purpose of the survey and in the survey the attribute carbon footprint will be presented as a label on the product (clearly noticeable). Further by asking few general questions on carbon label at the end of questionnaire, awareness on carbon label will be estimated.
Ethical Issues:
In order to maximise the quality of response the respondents will not be made cognizant of real purpose of the survey. This might raise an ethical issue, however from a teleological view it is justified. Further as proposed in the methodology at the end of the survey they will be debriefed and presented a leaflet explaining the purpose of the survey and importance of carbon label. If any of the participants wishes to know about the result of the survey, the same will be made available to them as per business school’s guidelines.
The research data will be collected anonymously and no personal data will be collected and stored, so there will not be any ethical issues in particular. For collection of data, respondents will be approached in a friendly manner; and they will be informed about the voluntary nature of the survey and the tentative time it may take to finish the survey. Their rights & dignity will be respected and protected during both pre and post survey. Respondents can withdraw from the survey without giving any reason, and they will be treated with the same respect and dignity.
Reflection:
As mentioned in the methodology the survey will be conducted in person at shopping centres to get more realistic data. However in the unlikely event of not being able to collect the targeted number of samples, which may be due to bad weather or any restriction on collection of data in those areas, then the plan-b of an internet-mediated survey method will be adopted. Online survey tools such as SurveyMonkey or SuperSurvey will be used to collect the data and the survey will be targeted to the respondent segment through Facebook. Facebook allows designing advertisements which can be targeted respondents based on geographic location, gender, age and education level of a person. And the survey will become a self-selecting sampling one. Then a snowball sampling technique can be applied so that at the end of the survey a respondent can suggest his/her friends to take the survey. However, this method of survey may lack the shopping environment of the original method and might lead to subject error.
The collected data will be analysed with conjoint multivariate analysis. Currently the proposer does not have adequate level of expertise to carry on the conjoint analysis on IBM SPSS Conjoint 14.0. As soon as the proposal is accepted the proposer will start augmenting his skills on using SPSS conjoint tools and interpreting the results with help of tutorials and documentations.
The project seems to be researchable in the given time of two and half months. The guidance of an academic supervisor will be immensely helpful in fine tuning the research paradigm and methodology. The proposer’s interest in behavioural economics and consumer psychology will drive his motivation in the topic in order to sustain attention.
Limitations and Enhancements:
An individual’s financial status also drives his buying behaviour. A wealthy person may choose an expensive product as a premium product rather because the product is an environment friendly one. In this research only three attributes are considered for study while keeping all other attributes equal to all products. However, in real life scenario it is not true. So the research can be enhanced with more product attributes to get better understanding of consumer behaviour. Further, the research can be conducted using real products to allow participants feel them while taking a decision.
Conclusion:
The purpose of this research will be to study environmental consumer behaviour through examining consumer choices between realistic product alternatives where consumers have to balance their decision based on different attributes. The research findings will be useful for developing effective marketing propositions to target the environment sensitive consumer segments. Further a balanced marketing mix can be developed as marketing strategy developers will have better understanding of consumer behaviour as per different attributes. The success of marketing mix will ultimately form an incentive tool for product developer to develop environmental friendly products. This whole process has the potential to form a positive feedback cycle. Firms will have better incentive to produce and market environmental friendly product. The increasing marketing activity based on green will improve consumer preference for green products.
References:
Denzin, Norman K. (1970). The Research Act: A Theoretical Introduction to Sociological Methods, Chicago: Aldine. International Journal of Consumer Studies ISSN 1470-6423, Centre for Design, College of Design and Social Context, RMIT University, Melbourne, Australia.
Horne, Ralph E. (2009). Limits to labels: The role of eco-labels in the assessment of product sustainability and routes to sustainable consumption. International Journal of Consumer Studies ISSN 1470-6423, Centre for Design, College of Design and Social Context, RMIT University, Melbourne, Australia.
Kotri, A. (2006). Analyzing Customer value using conjoint analysis. University of Tartu – Faculty of Economics & Business Administration Working Paper Series. 2006, Issue 46, pp. 3-33, 2006.
Sammer K. and W�stenhagen* R. (2005). The Influence of Eco-Labelling on Consumer Behaviour. Institute for Economy and the Environment (IWOe-HSG), University of St. Gallen, Switzerland. Published in Business Strategy & the Environment. Version: Sept. 1.
Saunders M., Lewis P. and Thornhill A. (2009). Research methods for business students. Prentice Hall, Fifth edition.
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W�stenhagen, Katharina S. and Rolf. (2005). The Influence of Eco-Labelling on Consumer Behaviour – Results of a Discrete Choice Analysis. Business Strategy & the Environment, Switzerland: Business Strategy & the Environment, Sept 2005.
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Carrigan, M. & Attala, A. (2001) The myth of the ethical consumer – do ethics matter in purchase behaviour? Journal of Consumer Marketing, 18, 560-577.
European Commission. (2007) “Eco-label Flower week 2006, Special Eurobarometer 275/Wave 66.3 – TNS Opinion & Social.” 2007.
OECD (2005) Effects of Eco-labelling Schemes: Compilation of Recent Studies, COM/ENV/TD(2004)34/FINAL. Organisation for Economic Co-operation and Development, Paris.
Pedersen, E.R. & Neergaard, P. (2006). Caveat emptor – let the buyer beware! Environmental labelling and the limitations of green consumerism. Business Strategy and the Environment, 15, 15-29.
Gallastegui, I.G. (2002). The use of eco-labels: a review of the literature. European Environment, 12, 316-331.
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Morris, J. (1997). Green Goods? Consumers, Product Labels and the Environment. IEA, London.
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Lancaster K. 1966. A New Approach to Consumer Theory. Journal of Political Economy 74: 132-157.
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