Functional ingredients and food choice: Results from a dual-mode study employing means-end-chain analysis and a choice experiment

Food Policy 36 (2011) 715–725

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Functional ingredients and food choice: Results from a dual-mode study employing means-end-chain analysis and a choice experiment Michael Bitzios a, Iain Fraser b,c,⇑, Janet Haddock-Fraser a a

Kent Business School, University of Kent, United Kingdom School of Economics, University of Kent, Canterbury CT2 7NP, United Kingdom c School of Economics and Finance, La Trobe University, Melbourne, Vic 3083, Australia b

a r t i c l e

i n f o

Article history: Received 11 August 2010 Received in revised form 19 April 2011 Accepted 6 June 2011 Available online 6 July 2011 Keywords: Functional food Bread Choice experiment Means-end-chain analysis Laddering

a b s t r a c t In this paper we present the results of a choice experiment (CE) conducted to examine how the inclusion of a functional ingredient (to increase the quantity and effectiveness of fibre) affects consumer attitudes towards bread. An novel feature of the design of our CE was that it was informed by a means-end-chains (MEC) to reveal key attributes to be included in the CE. In addition, we included the Dutch eating behaviour questionnaire (DEBQ) to collect information on all participants underlying eating behaviours. We find that bread type is a major factor in determining choice, and that the inclusion of a functional ingredient yielded relatively small measures of value. We also find that there are differences in willingness-to-pay (WTP) between respondent segments and that segment membership is explained by the DEBQ information. Finally, we find that respondents have a stronger preference for a simple health statement compared to, or in addition to, the implied benefits that result from consuming a functional food product. These findings are important in informing both pricing and promotional messages for a functional bread product. Ó 2011 Elsevier Ltd. All rights reserved.

Introduction Due to advances in food technology and nutritional sciences, many new food products have been developed and marketed, offering increased health benefits and the potential to reduce the risk of diseases. Many of these new food product developments have been labelled as ‘‘functional foods’’ (Siro et al., 2008). Interestingly, there is no unique definition of what a functional food actually is. The range of definitions is in part a reflection of the difficulty of defining food that is healthy which is, in itself, a complex issue (Naylor et al., 2009; Butrriss, 2010). Despite this lack of precision about what constitutes functional food, the market globally has been estimated to be worth ten of billions of dollars (Hillman, 2000; Brockman, 2010). Furthermore, there are a number of reasons to assume why the market for functional food will grow, such as the increased emphasis on preventative health care on the part of the individual which the consumption of functional foods can help to achieve. Although consumers have accepted many forms of functional food there is evidence that consumers differ by the extent to which they buy food products with explicit functional properties. In the ⇑ Corresponding author. Address: School of Economics University of Kent Canterbury Kent, CT2 7NP UK. Tel.: +44 (0) 1227 823513; fax: +44 (0) 1227 827850. E-mail addresses: [email protected] (M. Bitzios), [email protected] (I. Fraser), [email protected] (J. Haddock-Fraser). 0306-9192/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodpol.2011.06.004

case of bakery products such as bread, there is little difficulty including functional ingredients, but whether this can be achieved in a manner which yields a product which meets consumer demands is unclear. Verbeke (2006) notes, that simply relying on consumers to adopt functional food for health benefits, if the products in question have been compromised in terms of taste, is highly unlikely. In this research we are interested in consumer attitudes and preferences for purchasing bread which includes the functional ingredient inulin. Inulin is a prebiotic functional ingredient which is non-digestible and is assumed to have a positive impact on various bacteria in the colon. Its use within the food processing industry is growing rapidly, which can be partly explained by its ability to enhance the fibre content of food whilst also being able to substitute for other ingredients. To undertake our examination of consumer attitudes toward functional ingredients we employ a new two-stage, dual-mode, process. First, we use Laddering interviews and means-end-chain (MEC) analysis to reveal key attributes consumers attach to bread. The use of MEC analysis in understanding consumer choice in relation to food is well established in the literature (e.g., Grunert and Grunert, 1995; Chema et al., 2006; Boecker et al., 2008; Barrena and Sanchez, 2009). MEC is a research method used to reveal how a consumer values product characteristics in terms of the motivation to purchase a specific product, based on personal construct theory, linking product characteristics to consequences as

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well as an individual’s preferences to motivate a purchase decision. Second, many of the attributes revealed by the MEC analysis are employed in a choice experiment (CE) to examine how consumers’ trade-off attributes of bread when making a purchase. Our CE adds to the literature examining consumer choice in relation to food, nutrition, health labels and product selection, by the mixture of attributes employed (e.g., Hu et al., 2004; Teratanavat and Hooker, 2006; Balcombe et al., 2010). Significantly, the dual-mode approach we present in this paper has enabled a CE to be developed which is informed by insights from behavioural research. Normally focus groups would be used to develop a consensual list of attributes for CE. However, the use of the MEC analysis and specifically the use of Laddering interviews enabled us to develop a richer understanding of values and motivations underlying attribute selection. Laddering interviews in combination with MEC analysis provide a robust research method to link actual product attributes with perceived consequences that result from the consumption of the product. This approach allows us understand whether or not it is simply the attributes of a product or deeper consumer motives and values that drive choice. Such insights will help inform promotional messages for the product. Thus, the adoption of Laddering and MEC provides a more structured approach than focus groups to the identification of attributes to be employed in the CE. As a result of this dual-mode approach, our paper provides an extension to the typical CE approach of employing focus groups to reveal key attributes. An additional contribution our paper makes is that our CE survey instrument includes the Dutch eating behaviour questionnaire (DEBQ). The DEBQ allows us to collect information on all participants underlying eating behaviours. Given the focus of the CE is food consumption we consider it an important design feature to understand respondents underlying eating habits. The importance of including the DEBQ in our survey instrument is demonstrated by the results we present for our CE. Finally, in terms of the CE we examine how the inclusion of a health message on a product, with and without the inclusion of a functional ingredient influences consumer preferences. Currently there is a lack of clarity over what constitutes functional food, which means that health benefits that occur in unmodified food, such as the cholesterol reducing properties of whole grains, can also been framed as functional food health benefits (Naylor et al., 2009). This lack of clarity is potentially likely to confuse consumers when attempting to make informed food choices. Thus, we aim to better understand the relationship between identified health benefits of a product, as opposed to the implied benefits that are derived from the food that that contains functional ingredients that occur naturally or added. This will enable us to better understand the importance consumers attach to particular messages about a product. By framing our research in this way our work complements that of Barreiro-Hurle et al. (2010) who examine the impact of multiple health and nutrition labels on food choice. The structure of this paper is as follows. In ‘‘Literature review’’ we briefly review the antecedent literature. We then describe in detail the various steps undertaken in the design, implementation and evaluation of our MEC. In ‘‘Choice experiment’’ we present our CE. Finally, in ‘‘Conclusions and discussion’’ we present our conclusions.

Literature review There is a large literature examining consumer attitudes towards functional food (e.g., Siro et al., 2008; Pothoulaki and Chryssochoidis, 2009). There is also related literature examining how consumers respond to information conveyed about products (e.g., Cowburn and Stockley, 2005; Grunert and Wills, 2007). Within these literatures we focus specifically on research that

has employed MEC analysis and stated preference methods relating to functional foods, and bread in particular. MEC analysis MEC analysis is based on the assumptions that respondents are aware and understand their personal motivations such that survey data will identify a link between attributes of a good and the consumer’s value set. These links are established through perceived consequences. Thus, a consumer’s utility derived from a specific product is less as a result of its attributes directly, but mostly from the functional and psychological consequences it delivers. As such, consumers’ actions generate consequences and they have ‘‘educated’’ themselves to relate these consequences with the product’s attributes, resulting in a link between attribute, as well as their personal values, consequence and value. MEC analysis and Laddering techniques have evolved to help reveal the perceptual orientations of individuals (e.g., Gutman, 1982; Reynolds and Gutman, 1988; Reynolds and Olson, 2001). MEC analysis has been widely employed to examine how the perception of food quality affects consumer food choice (Grunert, 2005). For example, Grunert and Grunert (1995) employed MEC to identify respondent perception of the meaning of ‘healthy’ in relation to bread and whether or not it is an attribute or a consequence. Employing hierarchical value maps (HVM), which shows the links between attributes, consequences and values, they identified that organic food buyers are mostly concerned about the physical attributes of the bread, including the method of production. In contrast the non-organic buyers are more concerned about price. Further analysis revealed that for organic bread consumers what matters is how the bread is produced which is perceived to yield ‘better health’, whereas for other consumers the link is from ‘production’ to ‘better taste’. Chema et al. (2006) explored possible marketing strategies for biotech functional foods for regular buyers of yoghurt and established that respondents linked functional ingredients to ‘better health’, and consumer concerns about employing a modified ingredient in food appeared to be less of an issue relative to taste. Boecker et al. (2008) also used yogurt to capture different aspects of biotechnology and how consumers respond to alternative contexts. The research described so far has used ‘soft’ laddering, where the means-end structure and choices are devised during the interview process. In contrast ‘hard’ laddering provides respondents with pre-defined choices. Barrena and Sanchez (2009) employed ‘hard’ laddering to examine consumer attitudes towards beef consumption. Beef was selected as it is a food product which has been the subject of a series of health-related food scares. Their results found different values towards the product depending on frequency of consumption, with factors relating to the consumers’ personality, self-esteem and cultural identification becoming more prevalent to frequent consumers, whereas occasional consumers focused mainly on product price. Stated preference The use of stated preference techniques such as CE and contingent valuation (CV) to examine issues associated with functional food is well established. For example, Teratanavat and Hooker (2006) used a CE to explore consumer preferences for tomato juice containing soya, which may help reduce risks of certain cancers and heart disease. They found heterogeneity in consumer preferences for the product with more than half of the sample interested in the product and willing to pay a price premium to experience its benefits. Also higher educated people, those with increased income levels, females and younger people had a preference for this product.

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Using CV, Markosyan et al. (2009) examine consumers WTP for apples with a coating that contains specific flavonoids and antioxidants. They found that respondents would pay a small price premium (i.e., 7–10%). Hailu et al. (2009) examine consumer preferences for functional foods and nutraceuticals that contain probiotics. They observe that the ‘‘mode of delivery’’ that is, the type of product used to deliver the functional benefit, matters. Siegrist et al. (2009) examine consumer willingness to buy functional food produced using nanotechnology. This study found that products produced using nanoparticulate-engineered additives yielded lower willingness to buy results than for products with functional benefits generated naturally. This finding is consist with Siro et al. (2008) who note a general dislike in the EU of food products that have been engineered to yield health benefits. In terms of bread specific research there are two relevant papers. First, Hu et al. (2004) report a CE that presented respondents with a sliced, pre-packaged loaf of bread as the vehicle product to examine consumer willingness to trade-off various attributes (i.e., health, environment and GM) associated with the bread. Employing a latent class model (LCM) they identified clear trade-offs between the risks associated with GM and the benefits associated with improved health and the environment. Hu et al. also found that these trade-offs did vary across their sample of respondents indicating preference heterogeneity. Second, Boxall et al. (2007) consider bread within a CV examining how consumers responded to information and its impact on their WTP for organic bread. They employed a trained sensory panel to help quantify differences between the bread products employed in the survey. The sensory information component of the survey revealed that the only statistically different sensory attributes were surface texture and density. There was no discernable difference with respect to taste or aroma which had been considered important attributes in determining food choice. Overall Boxall et al. found that when no information about taste was provided with the survey consumer WTP was higher when environmental information was given compared to health information. This result was reversed when taste information was provided. Thus, this implies that when a consumer does not have a sensory experience of the product, environmental benefits associated with the mode of production are greater than those associated with potential health benefits. Overall, the existing literature in this area indicates that consumers view food containing functional ingredients positively. However, these findings need to be qualified in terms of how the functional ingredient is produced and the impact it has upon the final product. There have also been efforts to examine the tradeoffs consumer will make with respect to functional ingredients, especially in relation to GM production technologies. However, the extent to which consumers differentiate between health messages compared to the inclusion of functional ingredients has not received detailed examination. The wider literature on food choice notes that consumers respond very strongly to positive health messages on food labels (Williams, 2005). In contrast, consumers understand nutritional information but appear to adjust their diet to a lesser extent. It is, therefore, an interesting question to consider if consumers respond to the inclusion of a functional ingredient in a specific product to a greater or lesser extent than a positive health message.

Laddering and MEC analysis and results Laddering implementation In line with Russell et al. (2004), the paper-and-pencil version of ‘hard’ laddering was used in this study, enabling a structured survey technique to be used for data collection. Four lists of attributes

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and physical consequences were prepared based on a review of the related bread literature (e.g., Annett et al., 2007; Kihlberg and Risvik, 2007). The psycho-social consequences list was developed as lay interpretations of the items identified from the literature, in a manner similar to Russell et al. (2004). These were provided to respondents as a means to assist them with the completion of the laddering charts. The list of values (LOV) was adopted from Kalhe et al. (2000). It consists of a validated and established set of global values and it includes values such as sense of security, sense of belonging, sense of accomplishment, self-fulfilment, being well respected, warm relationships, fun and enjoyment in life, excitement and self-respect. We also allowed subjects to use their own words to describe their feelings about the attributes or the product. This approach enabled the benefits of the more structured hard laddering approach to be used, but with some opportunity for non-constrained responses. Respondents started by selecting up to three attributes that they usually consider when buying a loaf of bread. These were then used to develop ladders as a starting point for each one of the attributes. The question that initiated the construction of ladders was; ‘‘When I buy bread, one of the most important things that I take into account is. . .’’. After the answer was given, the subsequent levels of abstraction were introduced by the preceding statement ‘‘. . .and this is important for me because. . .’’. Four levels of abstraction were chosen and respondents were required to complete between one and three charts. Respondents could fork answers (one attribute could lead up to one functional consequence, up to three psycho-social consequences and up to three values) as well as to skip levels of abstraction. The time available to complete the questionnaire was up to a maximum of 1 h. Each interview was conducted with groups of between four and eight people, with each respondent completing their own specific ladders. Respondents were asked to imagine that they were in a shop and about to make a bread choice and think of the three most important attributes that they take into account so as to decide which bread to buy. This is the same approach as employed by Russell et al. (2004). Then for each of the attributes they had to develop ladders that lead to value orientations, through functional and psycho-social consequences. A pilot survey was undertaken with a convenience sample of 15 respondents to identify and clarify issues related to the implementation of the research instrument. Following the pilot various adjustments were made to the survey instrument. The actual laddering interviews were conducted in March 2008 with a sample of 70 respondents which is consistent with previous studies employing this method (e.g., Chema et al., 2006). The sample was recruited randomly by employing an email distributed to all University of Kent staff and students. During the recruitment of participants efforts were made to ensure that the sample was not composed entirely of younger members of the population or females. The composition of the sample was 60 females and 10 males, with 60% under 30 and 55% single. Thus, although not perfectly representative of the UK population (i.e., Office for National Statistics, 2010) the sample is reasonably heterogeneous. For example, almost 20% of the sample is aged over 50, almost 50% of the sample had annual income above £20,000 per annum and the highest level of education achieved was distributed across all categories from basic school education to holding a doctorate degree. With respect to purchasing behaviour, 92% of the sample subjects buy bread once or twice per week with the majority bought in supermarkets (85%). Some 54% of participants consume bread at lunch and 30% at breakfast. Seventy percent of participants indicated an awareness of the concept of functional foods, 44% buy food products with functional properties occasionally, while the regular or very frequent functional food shoppers account for

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34% of the sample. Finally, the main source of information about food products with functional properties was the television (34%), and newspapers/magazines (33%). Laddering and MEC results Following the group interviews data analysis was conducted as follows: (i) all ladders mentioned by each individual were recorded; (ii) summary codes were developed reflecting everything mentioned; (iii) a summary implication matrix (SIM) was generated, linking the number of times each element is linked with each other element; and (iv) HVM were constructed. The development of summary codes resulted from grouping together all similar elements mentioned by each individual at the different levels of abstraction (attributes, physical consequences, psycho-social consequences and values). As an example, for the first level of abstraction, three subjects indicated ‘‘softness’’ as an important attribute, while for six subjects ‘‘density’’ of bread plays an important role. Both terms were grouped under the general attribute summary code ‘‘texture’’. The laddering identified fifteen (15) attributes that people consider when buying bread, seventeen (17) functional consequences, eleven (11) psycho-social consequences and eight (8) values. The total number of ladders produced from the analysis of the data collected using laddering was 757. The mean number of ladders produced was 11 with a range between 3 and 26, and a mode of 9. Table 1 presents a summary for the main attributes, consequences and values. The most important summary codes for each level of abstraction are listed at the top of each column. For attributes, the type of flour used for the production of bread, taste and aroma, price, sliced/unsliced, texture and perceived healthiness are important. These findings indicate that bread buyers prefer intrinsic quality notions (type of flour, texture, taste and aroma) and the associated concepts of healthiness. The reason is their purchasing motives: quality product; good health; and freshness. Also the higher attribute elicitation frequencies plus the smaller number of values suggests a tendency for a more clearly expressed need for specific bread attributes. The analysis for the construction of the HVMs was performed using the MEC Analyst Plus software (Zanoli and Naspetti, 2002). An association is illustrated in a HVM if it meets a minimum frequency requirement, namely when the number of links between two elements in the SIM goes beyond a specific value, or cut-off level. A number of different cut-off values were selected and several HVMs were produced. This enabled evaluation and presentation only of those that contained the most stable set of relations,

provided more information about meaningful cognitive associations and were relatively less troublesome to interpret. A representative HVM is presented in Fig. 1. Examining Fig. 1 we see that, type of flour is the dominant attribute followed by price, taste and aroma, and texture. At the physical consequence level, the most important area is constituted by elements related to health and product’s perceived quality. The concept of good health provides links to almost all elements being at the higher psycho-social level. Additionally, quality product holds strong associations with quality of life as well as with satisfaction – happiness concepts. The latter concept also yields very strong relations with the values fun and enjoyment in life and self-fulfilment. Another important area of the map is the one related to price. The elements included in the chain for which price is the source of, only appear in this chain and not in any other (like for example quality, as often individuals relate quality with price). This implies that people are mainly concerned with product-related attributes and, to a lesser extent, price. Finally, the taste and aroma of bread provide three, less strong, connections with freshness, pleasant experience and quality product. Nevertheless, the last concept develops a strong association with quality of life and this is why taste appears on the map. What these results imply for the development of the CE is reasonably unambiguous given the high attribute elicitation frequencies. Overall, the key attributes are, the type of flour used for the production of bread, price, texture, taste and aroma and perceived healthiness. Therefore it follows from these attributes that bread buyers prefer intrinsic quality notions and the associated concepts of healthiness. As such we can conclude that consumers have specific bread attributes in mind when making a purchase and it is these we employ in the CE. Choice experiment Selection of attributes and choice sets Our CE was designed to reveal WTP estimates associated with enhanced bread products that might include a functional ingredient. We employed a standard loaf as this is the dominant product form of bread consumed in the UK. Like Chalak et al. (2008) we used standard pre-packaged 800 g loaf of bread. Given our product choice the price attribute for our CE was simply the price for a loaf. The payment levels were chosen to be typical of current bread prices in the UK and the price range employed was determined by undertaking several visits to main UK food retailers as well as online providers to assess appropriate price ranges. Prices were checked for both in-store and premium brands. In terms of the other attributes used in the CE we explicitly employed the results of the Laddering and MEC analysis. First, we employed an attribute to describe the type of bread which, in our final CE had five levels: White, Brown, Wholegrain, 50:50, and Rye). Type here can be considered as a way in which to capture the type

Table 1 Summary of main content codes from the Laddering technique for bread. Attributes

Functional consequence

Psycho-social consequences

Values

Type of flour Taste and aroma Price Texture Healthiness Sliced/unsliced Supplier/retailer Appearance Nutritional information and Ingredients

Quality product Good health Freshness Pleasant experience Value for money Maintain weight Loyalty Financial constraints Filling

Quality of life Satisfaction – Happiness Feel relaxed Keep active Time and economic planning Keep figure – Improved image Physical well-being Improved performance Care for nature

Fun and enjoyment in life Sense of belonging Sense of accomplishment Sense of security Self-respect Self-fulfilment Warm relationships Excitement

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Cut-Off=12

SELF-RESPECT nr:33 sub:49%

TIME & ECONOMIC PLANNING nr:28 sub:41%

FUN & ENJOYMENT IN LIFE nr:61 sub:90%

SELF-FULFILMENT nr:59 sub:87%

KEEP FIGURE IMPROVED IMAGE nr:32 sub:47%

KEEP ACTIVE nr:40 sub:59%

QUALITY OF LIFE nr:55 sub:81%

SATISFACTION -HAPPINESS nr:44 sub:65%

PHYSICAL WELL-BEING nr:26 sub:38%

VALUE FOR MONEY nr:25 sub:37%

GOOD HEALTH nr:48 sub:71%

PRICE nr:32 sub:47%

TYPE OF FLOUR nr:32 sub:47%

QUALITY PRODUCT nr:56 sub:82%

PLEASANT EXPERIENCE nr:21 sub:31%

TEXTURE nr:17 sub:25%

FRESHNESS nr:29 sub:43%

TASTE & AROMA nr:25 sub:37%

Fig. 1. HVM. Note: HVM for top 5 elements from each level and a cut-off value of 12. Values in nodes are number of participants (and percentage of sample) that mentioned each element within each level of abstraction. nr = number of sample participants and % expressed as a percentage.

of flour. Second, we included an attribute describing bread texture as the MEC analysis revealed that this is an important characteristic in shaping consumers’ bread purchases. This attribute is measured as a categorical variable with three levels. Third, we have an attribute describing the bread as being sliced or unsliced. This attribute is described using three levels. Fourth, we have the method of production used to produce the grain used in making the bread. The use of this attribute is because of the importance some consumers express about the impact of agricultural production on the environment as well as the choice of technology. This variable took the form of a dummy variable. We also note that we have not included an attribute relating to aroma despite its importance as identified by the Laddering and MEC. We excluded this attribute because of the type of bread product employed in the CE. It is pre-packaged and as such does not give off the type of aroma which is alluded to in the Laddering and MEC. Finally, we introduced two attributes to examine issues related to health. We already know from the literature that consumers respond positively to health messages on food products. Thus, we employed an attribute to capture this aspect of food choice. However, we also included a separate attribute to capture if the product included a functional ingredient. The reason for including both attributes relates to the fact that it may well be the case that health

messages dominate consumer food choice relative to functional ingredient attributes. This could especially be the case for products that already provide healthy eating options. Indeed, many products such as bread can confer a health benefit without the inclusion of functional ingredients. For example, there is scientific evidence supporting the inclusion of whole grains in a healthy diet and there are a wide range of bread products on the market that include whole grains. By including both in the CE we could examine and test this conjecture. The complete set of attributes employed in our CE and their respective levels are presented in Table 2. Given our set of attributes we then employed a main effects orthogonal design to derive the choice sets. Given the number of attributes and their associated levels a complete factorial set of combinations yielded a profile of 2880 alternatives. We then reduced this to 24 alternative sets ensuring balance in the attribute levels. Next we constructed the choice cards by combining the alternatives choice sets along with a status quo option. The status quo option was defined as white lower value sliced bread. This choice reflects current market shares in relation to UK bread sales. The status quo option had fixed levels for all attributes and it appeared on all choice cards. We also included an opt-out option in the choice set so as to avoid forced choice on the part of our survey

Table 2 Attributes and attribute levels used in the CE. Attributes

Description

Levels

Type of bread Method of production Functional ingredient Sliced/unsliced Texture Health benefit Price

The different types of bread available in the hypothetical market The method of production for the main ingredient of bread A component that could potentially deliver nutritional benefits, if added The attribute indicates whether the bread is sold sliced or not The attribute shows the consistency of bread The attribute indicates whether the product promotes health The cost (in £) for buying a standard 800gr loaf of bread

White, wholemeal, brown, 50:50, rye Conventionally, organically Yes, No Medium sliced, thick sliced, unsliced Soft, firm, crunchy, springy Yes, No 0.7, 1, 1.3, 1.6, 1.9, 2.2

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respondents. Thus, each choice card had five options, a status quo, three further variations on the type of bread and the opt-out option. To avoid fatigue in the completion of the choice task we blocked our 24 choice sets into groups of six, with care taken so that each respondent faced almost the same number of different alternatives. This means that we had four final versions of the survey instrument. Each version of the survey instrument provided respondents with background information regarding functional food and ingredients. We explained that functional ingredients are food components that naturally occur in food products (e.g., Lycopene in tomatoes) or they can be added to make the food functional. We also included the following scientific definition; ‘‘food products that are satisfactorily demonstrated to affect beneficially one or more target functions of the body’’. This was followed by a plain English explanation that stated that functional foods can provide benefits to the human body in addition to nutritional value. We also stressed the difference between ‘‘healthy foods’’ (beneficial for the general state of your health) and ‘‘functional foods’’ (products that, as part of a healthy diet, promote health and help reduce the risk of certain diseases). Finally, before we asked respondents to undertake the choice tasks we provided an example of a choice card plus the description of the hypothetical choice scenario, and this is shown in Fig. 2. A point worth noting with respect to Fig. 2 is that the choice sets presented to respondents in this CE are unlabelled. We decided to take this approach as we wished our respondents to consider bread a single alternative that is composed of various characteristics.

Attitudinal variables We also collected various data on individual specific characteristics, such as socio-economic, behavioural and attitudinal data. In terms of the behavioural and attitudinal data the survey instrument included the DEBQ (i.e., van Strien et al., 1986). The DEBQ was used to collect information about participants’ underlying eating behaviours. It provides an understanding of eating patterns in three contexts: emotional, external and restrained eating. The first context, emotional eating, refers to a situation of excessive eating which is brought about by a state of confusion between an individual’s internal arousal states (i.e., anger, fear and anxiety), which normally result in a loss of appetite or hunger. The second context, external eating refers to a situation in which an individual responds to some form of food related stimuli, irrespective of their internal status with respect to hunger or satiety. Lastly, restrained eating, is a state when the conscious restrictive control associated with suppressed eating behaviour (i.e., restrained eating), may be disrupted by des-inhibition factors, such as alcohol or depression, resulting in counter regulation and overeating (van Strien et al., 1986). Survey delivery and returns We employed a single mail shot postal survey. To enable ease of completion the final design of the survey instrument consisted of six sections. In section A, after welcoming the participant and explaining the purpose of the research in the cover letter

Information about the bread contained on the packaging

Option 5 represents the opt-out option

Example Loaf of bread

Option 1

Option 2

Option 3

Option 4

Option 5

Type of bread

White

50% - 50%

Brown

Rye

I would

Grain produced Functional Ingredient

Conventionally Conventionally Organically Conventionally No

No

Yes

No

Medium

Thick

Medium

Unsliced

Texture

Soft

Firm

Soft

Crunchy

Health benefit

No

Yes

No

Yes

Price

0.70

1.90

2.20

0.70

Sliced/Unsliced

not buy any of these options

Choose one & only one option

Options 1, 2, 3 and 4 represent the hypothetical, bread products you will be asked to choose between Fig. 2. Choice card information and sample choice card. Hypothetical scenario: You are shopping and ready to buy a loaf of bread. This is a standard 800 g loaf of bread. The bread is sold in a package that presents information describing the product. The relevant information describing the bread is highlighted in bold and each one is explained as follows. Type of bread: There are five different types of bread available in the store: White bread, Wholegrain bread, Brown bread, Bread containing 50% white and 50% wholegrain flour and Rye bread. Production method of grain: The flour used for bread making has been produced by wheat or rye that is grown conventionally or organically (with fewer chemicals). Functional ingredient: If the bread product contains a functional ingredient, it is indicated simply by recording ‘‘Yes’’ on the packaging. If it is absent, a ‘‘No’’ is recorded. Sliced/Unsliced: The bread can either be unsliced, medium sliced or thick sliced. Texture: This characteristic describes the consistency of bread. That is, the bread can be soft, firm, crunchy and springy. Health benefit: If the bread product claims to potentially deliver a health benefit, it is indicated simply by recording ‘‘Yes’’. If no health benefit is claimed, ‘‘No’’ is recorded. Price: Indicates the cost for a particular type of bread. An example of how information contained on the package is summarised on the choice card is shown below.

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accompanying the questionnaire, information was provided about functional foods. Next we provided a series of warm up questions. In section C, the choice task was explained. Specific instructions were given and an example of how the choice task was provided. In section D respondents were asked to answer the choice tasks. Next in section E we introduced the DEBQ. Finally, section F asked a series of questions for collecting individual-specific information (e.g., socio-economic, attitudinal questions) as well as information on people’s feeling about the survey instrument (such as the readiness and clarity of the questionnaire). The survey instrument was distributed by post using secondclass postage to a stratified sample of 3000 UK households in May 2009. It was a single shot survey employing a simple financial incentive to induce participation (all correctly completed returns were entered into a prize draw to win one of four shopping vouchers). The stratified sample was purchased from a commercial list broker (Marketing File), the largest on-line source of direct marketing data in Europe. As the survey had four different versions the mailing list was randomly divided into four subgroups and each survey version was sent to 750 households. Each survey package contained the survey instrument along with a cover letter attached to it (in the form of a booklet). It also contained prepaid return envelopes, in which participants should enclose and send back the completed questionnaires to the researcher. No reminder postcards were sent out. The last questionnaire was received about 3 months after the survey was posted. However, most were returned within a month. The total number of respondents was 444. This corresponds to response rate of 14.8%, with the final sample size (fully completed surveys that used for the analysis) of 404 questionnaires. A summary of descriptive statistics is presented in Table 3. Table 3 shows that we have more female respondents (64%) than males (34%) (attribute takes the value ‘‘1’’ if the respondent is female). The actual proportion of females in the UK is just above 51% (Office of National Statistics, 2010). However, this is not unusual in food related surveys as females tend to be the main food shoppers. The average age of respondents is 53 years which is above the average age in the UK which is almost 40 in 2010. However, the UK population is subject to bulge with the majority of the population between 35 and 55. As such our sample average sits within this range which is desirable. In terms of households with children the sample has a figure of approximately a half, and of those that do contain children the mode is two. On the whole 29% of UK households contain children and the mode is also two. The average income of respondents (excluding non-responses) is just over £31,000 which is very close to average income in the UK. For example, average income of non-retired households was £37,600 in 2006–2007 and if we include retired households this reduces to £30,000. Thus, although the final sample is not totally representative of the UK population there is sufficient

comparability for us to treat our results as reasonably representative. However, we advise caution when drawing strong inferences for the wider population from our analysis. Model estimation In terms of data analysis there are various limited dependent models that we could employ. The analysis and results presented in this paper are based upon a LCM (Greene and Hensher, 2003). A LCM was chosen to aid our examination of heterogeneity in the choices made. The LCM is popular amongst researchers who wish to consider issues of preference heterogeneity because it is reasonable to assume that preferences are not unique to the individual, but rather a group of individuals (e.g., Hu et al., 2004; Chalak et al., 2008). The LCM identifies a discrete number of segments and within each segment preferences are assumed to be homogenous. Preference variation (heterogeneity) is between the segments. We employ a standard LCM specification which assumes a random utility model. This model has two parts, an observable deterministic component and an unobservable random component. Thus, the utility an individual n obtains from selecting alternative j in the tth choice set is

U njtjs ¼ bs X njt þ enjtjs

ð1Þ

where U is the utility obtained by individual, b is a vector of parameters of segment s, X is a vector of attributes from the CE and e is a random component assumed to be a Type 1 extreme distribution. We assume that the deterministic component of Equation (1) can be decomposed into two components. The first relates to the specific attributes of the choice made. The second captures individual specific characteristics (i.e., socio-economic and attitudinal variables). It then follows that the choice probability for an individual n, given that they belong to s, will select an alternative i from a choice set of j alternatives, for a specific choice activity is as follows:

!

0

Prnitjs ¼

ebs X nit

PJ

j¼1 e

ð2Þ

b0s X njt

Next we employ a Multinomial Logit (MNL) so as to distribute an individual n to a given class s as follows: 0

Prns ¼

eas Zn

PS

! ð3Þ

a0s Z n

s¼1 e

where Zn is a vector of individual-specific variables and as a vector of segment specific utility parameters to be estimated. Next we assume that conditional on an individual respondent being allocated to a specific segment, that the tth choice activities are independent. This then implies that conditional on a specific

Table 3 Choice experiment sample descriptive statistics. Variable

Measured

Mean

SD

Min

Max

Gender Age (Years) No. children Education level Income (£ 000’s) Work Exercise Health conscious Gluten intolerant DEBQ – emotional DEBQ – external DEBQ – restrained

0 if male; 1 if female Respondent’s age in years. There were six age group categories The number of dependent children in the household 6 levels coded 0–5: GCSE, A-level, Further education, B.A./B.Sc., M.A./M.Sc., Doctorate degree Thousands pounds per annum 1 if working, 0 otherwise 1 if participant exercises, 0 otherwise 1 if participant health conscious when buying food, 0 otherwise 1 if participant is gluten intolerant, 0 otherwise Likert scale 1–7 Likert scale 1–7 Likert scale 1–7

0.636 52.63 0.48 1.73 31.1 0.539 0.62 0.69 0.05 2.11 2.72 2.7

0.48 13.97 0.87 1.36 1.93 0.49 0.49 0.46 0.22 0.81 0.63 0.88

0 20 0 0 2.5 0 0 0 0 1 1 1

1 70 4 5 67.5 1 1 1 1 4.62 4.18 5

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M. Bitzios et al. / Food Policy 36 (2011) 715–725

segment membership, the probability that a respondent n selects an alternative i from a set of j alternatives can be shown as follows:

Prnijs ¼

T Y

Prnitjs

ð4Þ

t¼1

To estimate the LCM so as to simultaneously take account of the choice made by a respondent and the segment to which they belong we combine equations 3 and 4 as follows:

Prni ¼

S X s¼1

T Y

0

eas Zn

PS

a0s Z n

s¼1 e

t¼1

0

ebs X njt

! ð5Þ

PJ

b0s X njt j¼1 e

The term in the first bracket on the right hand side is the probability of observing any individual in segment s. The second bracket is the probability of selecting alternative i given membership of segment s. Note that if as = 0 then the LCM becomes the standard MNL. The parameters in Eq. (5) are estimated using maximum likelihood estimation. Importantly, estimation requires that the number of segments S in decided in advance. This means that it is necessary to estimate this model S times and employ statistical criteria to select the ‘‘optimal’’ number of segments. Within the literature various criteria are employed to select the optimal number of segments including the minimum Akaike Information Criterion (AIC), the minimum Bayesian Information Criterion (BIC), the modified Akaike Information Criterion (AIC3) and the Akaike Likelihood  2 ). Ratio Index (q Finally, attribute and segment specific WTP estimates are estimated as follows:

WTP ¼ 

S X s¼1

bas ps  bps

! ð6Þ

where is bas a segment-specific non-monetary coefficient and bps is the segment specific monetary coefficient on price. As WTP estimates are ratios of sums of parameters they are complex non-linear functions of the estimated parameters. Following Birol et al. (2006) we employ the Wald procedure (Delta method) in NLOGIT 4.0 to generate standard errors and associated p-values for our WTP estimates. Model results We begin our analysis by examining how our model performs as we increase the number of segments. Our results for segment selection are shown in Table 4. As would be expected the log likelihood and McFadden’s Pseudo R2 increase as we increase the number of segments until we go from four to five. If we then consider the other model selection criteria reported in Table 4 (i.e., AIC, AIC3, BIC, etc.) we can see that

the results indicated significant model improvement as the number of segments increases. Specifically, we can see that AIC and decrease (increase) until we have four segments then they increase (decrease) again. However, the rate of change between three and four segments is much smaller than compared with two to three. The BIC decreases with each additional segment added and AIC3 increases for three segments. Based on the criteria and the economic information contained in the segments of the various models and a desire to select segment parsimony we have selected the three segment model specification. For our preferred specification we have included a limited number of interaction terms to capture important trade-offs being made in the CE. Specifically, we are interested to see how survey respondents valued products that might offer a health benefit as well as a functional ingredient. We are also interested to see if the method of production is more or less important than the health benefit and functional ingredient for the consumer. In addition, we have included alternative specific constants (ASCs). One is to capture any status quo effect and the other is for the No Choice option. Our results including the segment membership equations are presented in Table 5. The first thing to observe about our results in Table 5 is that the price coefficient is the right sign and statistically significant in all segments of the LCM. Another important indication that the model is behaving consistently with economic theory is that the income coefficients in the segment membership functions are all positive and statistically significant. Considering the results in general we can see that the status quo ASC is negative in segment two indicating no bias toward the status quo. In relation to the no choice ASC we have two statistically significant coefficients, segments one and three, which are negative indicating a positive preference for the bread options provided in the CE. If we now consider the bread type attribute coefficients we observe that the signs on the bread types are a combination of positive and negative values. All bread types are preferred to the status quo in segment one, in segment two most are positive, and in segment three there appears to be no positive preference expressed for any type of bread. Thus, no one bread type is positively preferred in all segments. Next when we consider the method of production (MOP) we can see that is statistically significant for segment three of the LCM and the resulting coefficient is negative. This would indicate, at least within the context of this CE that the majority of consumers are not interested in the MOP and are happy with conventional methods of farming. In terms of results relating to the functional ingredients we find that our respondents did view this attribute positively although it is only for segment three of the LCM that we achieve a statistically significant estimate. This does indicate a willingness on the part of

Table 4 Number of segments. Model

Number parameters

LL

q2

AIC

BIC

AIC3

q 2

MNL LCM2 LCM3 LCM4 LCM5

18 43 68 93 118

3341.7 2910.9 2736.4 2701.3 2731.8

0.143 0.253 0.299 0.308 0.299

6646.3 5735.8 5336.9 5216.6 5227.6

3271.0 2743.4 2471.5 2338.9 2272.0

6791.4 6079.8 5880.8 5960.6 6171.6

0.148 0.265 0.316 0.331 0.329

LL = Log likelihood. McFadden Pseudo R2  q2 = 1  LL/LL(O). AIC = 2(LL  P). BIC = LL + (P/2)  ln(N). AIC3 = 2(LL + 3P).  2 = 1  (AIC/2LL(0)). q LL(0) = 3901.3 – the value of the log likelihood evaluated at zero.

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M. Bitzios et al. / Food Policy 36 (2011) 715–725 Table 5 Latent class model three segment regression results. Utility function variables

* ** ***

Segment 1

Segment 2

Segment 3

Coeff

p-value

Coeff

p-value

Coeff

p-value

ASC (Status Quo) Rye Whole Brown 50/50 Method of production (MOP) Functional Ingredient (FI) Unsliced Thick Slice Texture Springy Texture Firm Texture Crumbly Health Benefit (HB) Price FI⁄HB MOP⁄HB MOP⁄FI ASC (No Choice) Segment Probabilities

0.647 2.016 4.166 2.941 1.693 1.568 0.592 0.137 0.395 0.191 0.261 0.667 1.437 1.014 0.017 1.628 1.197 2.439 0.354

0.346 0.000*** 0.000*** 0.000*** 0.000*** 0.150 0.493 0.331 0.008*** 0.274 0.214 0.000*** 0.114 0.000*** 0.992 0.258 0.389 0.012**

0.688 0.459 1.132 0.387 0.609 0.225 0.193 0.086 0.395 0.668 0.750 0.693 0.591 0.661 0.216 0.157 0.278 0.139 0.304

0.010** 0.001*** 0.000*** 0.001*** 0.000*** 0.261 0.390 0.359 0.000*** 0.000*** 0.000*** 0.000*** *** 0.006 0.000*** 0.488 0.587 0.280 0.611

0.364 2.185 0.083 0.591 0.084 0.797 0.348 0.613 0.209 0.564 0.499 0.434 0.530 1.951 0.482 0.377 0.803 7.991 0.342

0.230 0.000*** 0.648 0.000*** 0.521 0.000*** 0.085* 0.000*** 0.100* 0.002*** 0.005*** 0.022** 0.006*** 0.000*** 0.060* 0.118 0.001*** 0.000***

Segment Variables Constant Exercise Health Conscious DEBQ Res DEBQ Ext DEBQ Emo Income

1.381 0.104 1.898 0.321 0.713 0.392 0.681

0.000*** 0.744 0.000*** 0.050** 0.002*** 0.073* 0.026**

0.364 0.399 0.398 0.499 0.559 0.076 0.687

0.612 0.132 0.133 0.007*** 0.044** 0.734 0.037**

Statistically significant at 10% level of significance. Statistically significant at 5% level of significance. Statistically significant at 1% level of significance.

consumers to consider buying bread that includes a functional ingredient although this only applies to a proportion of our respondents. Turning to the results with respect to whether or not the bread is sliced we see a mixture of results. No segment prefers unsliced bread whereas segment two prefers thick sliced bread. In relation to texture we find that segment two clearly dislikes soft bread which is the attribute characteristics not included in the estimation (all other attribute characteristics are positive). In contrast segment one implicitly likes soft bread as the reported texture coefficients are all negative although only one is statistically significant. In keeping with the functional ingredient results we find positive preferences on the part of our respondents for a product offering an explicit health benefit. Both segments two and three are statistically significant. We can also see that an explicit health message as opposed to the health enhancing functional ingredient has achieved marginally higher parameter estimates which, as we will shortly consider, indicates a slightly stronger preference for the health message associated with the product rather than the functional ingredient that may well be health enhancing. Finally, we have introduced three interaction terms to capture the relationship between the health message, the inclusion of a functional ingredient and the method of production. Although many of the resulting estimates are statistically insignificant we can observe that there is a negative interaction between health benefits and functional ingredient in segment three. It is only when we consider the interaction between MOP and functional ingredient do we find a positive coefficient in segment three. Next we examine our class membership results. The choice of explanatory variables to include in the segment membership was informed by those which yielded statistically significant estimates as well as allowing the model to solve. As we would expect income has a positive coefficient in each segment. Amongst the other vari-

ables included in the segment equations we see that being health conscious is positive in segment one whereas taking exercise is statistically insignificant in both segments. Also when we consider our three DEBQ variables we see that they are statistically significant and positive for Restrained Eating and Emotional Eating. In contrast external eating is negative. The next set of results we examine are the WTP estimates which are reported in Table 6.

Table 6 WTP estimates (mean, standard errors and p-values). Attributes

Rye Whole Brown 50/50 MOP FI Unsliced Thick Slice Texture Springy Texture Firm Texture Crumbly HB FI⁄HB MOP⁄HB MOP⁄FI

Segment 1

Segment 2

Segment 3

WTP

p-value

WTP

p-value

WTP

p-value

1.989 4.110 2.902 1.670 1.547 0.584 0.135 0.390 0.188

0.000*** 0.000*** 0.000*** 0.001*** 0.165 0.492 0.347 0.006*** 0.245

0.695 1.714 0.585 0.921 0.340 0.292 0.130 0.599 1.011

0.001*** 0.000*** 0.005*** 0.000*** 0.262 0.388 0.371 0.000*** 0.000***

1.120 0.042 0.303 0.043 0.408 0.178 0.314 0.107 0.289

0.000*** 0.649 0.000*** 0.520 0.000*** 0.089* 0.000*** 0.095* 0.001***

0.257 0.658

0.191 0.000***

1.136 1.049

0.000*** 0.000***

0.256 0.222

0.003*** 0.014**

1.418 2.018 1.358 0.949

0.114 0.178 0.126 0.290

0.895 0.859 0.997 0.212

0.009*** 0.011** 0.006*** 0.547

0.272 0.203 0.056 0.181

0.008*** 0.067* 0.636 0.131

The p-values reported above have been generated by employing the Wald procedure (Delta method) in NLOGIT 4.0. * Statistically significant at 10% level of significance. ** Statistically significant at 5% level of significance. *** Statistically significant at 1% level of significance.

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M. Bitzios et al. / Food Policy 36 (2011) 715–725

In Table 6 we present our point estimates of WTP. A negative sign indicates WTP and a positive sign willingness to accept (WTA). The first result of importance is the fact that the highest WTP estimates are for bread type, in particular Wholegrain bread. The importance that respondents have attached to bread type is in keeping with the results of the MEC/Laddering undertaken above. We also observe that Rye bread has positive results in segments two and three indicating a WTA on the part of respondents. That is, we would have to pay them to eat this type of bread. In terms of magnitude, our WTP results indicate the Wholegrain estimate in segment one (i.e., £4.11) is a little on the large size, whereas for segment two the WTP of £1.71. The segment two result implies a price for a loaf of approximately £2.50 which is a price that is little higher than that observed in the market for specialist wholegrain bread. The WTP estimates for the remaining types of bread are all lower, especially in segment 2 two. As such segment one appears to capture respondents who have indicated positive but high WTPs for the various types of bread used in the CE. The second important result relates to the values for the health, functional ingredient and production attributes. If we concentrate on the results for functional ingredients and compare these to the health benefit claim we observe that although consumers are WTP for both there is a stronger preference for an outright health claim as opposed to a benefit that might arise as a result of eating a functional ingredient. In addition, we can also observe that the effect of including both on a product also yields a positive WTP response. However, a product that provides only a health benefit is more highly valued than a product offering both in segments two and three. The finding indicates that consumers are willing to pay more for a product with a specific health claim, but claiming a product is healthy as well as providing further potential benefits (as a result of the inclusion of a functional ingredient) need not result in a more highly valued product. We also observe that the joint effect of indicating the method of production as well as a health benefit claim or functional ingredient also provided statistically significant WTP results. However, as we would anticipate, the resulting WTP are similar to the WTP estimates for a health benefit claim alone. Finally, if we compare the results for segment one and the others we can see that segment one has a much higher WTP for all bread types. Segment two has lower WTP estimates for all the bread type attributes but much higher estimates relating to the various texture and slice attributes. Our results regarding the MOP are probably unsurprising as the CE was generally framed within a healthy eating context. As such, the lower importance attached to MOP indicates that once we ask consumers about a selection of issues, the apparent importance of a particular aspect of a product can become far less important than might be initially assumed. Indeed, the positive coefficients in segment three for MOP, slice and texture implies that these consumers would need to be paid to eat bread produced using non-conventional technologies and with these associated attributes. Thus, segment three appears to capture a group of respondents who do not like much other than the status quo product.

Conclusions and discussion In this paper we have presented results from a dual-mode study, using a MEC/Laddering analysis to inform the choice of attributes to be used in a CE, with the objective of assessing consumer willingness to purchase and consume bread products that might contain a functional ingredient. As we have explained the market for functional foods is growing rapidly and bakery products appear to offer an obvious market opportunity for food manufacturers.

Our Laddering and MEC analysis revealed that bread consumers consider, the type of flour used for the production of bread, price, texture, taste and aroma and perceived healthiness as they key product attributes. This finding implies that bread consumers prefer intrinsic quality notions and the associated concepts of healthiness. The clarity of results from the Laddering/MEC is important for their use in the CE as it allows us to conclude that consumers have specific bread attributes in mind when making a purchase, and as such we can use these with confidence in our CE. Our main CE result is that respondents typically select bread based upon the bread type (i.e., Wholegrain, Rye, etc.). This finding supports the results of the Laddering/MEC research which revealed that consumers do consider bread type, especially in relation to flour to be an important attribute. The CE results also show that consumers are willing to pay for bread that may provide a health benefit (directly), as well as products that contain a functional ingredient, although the inclusion of a functional ingredient attracts a much lower level of interest. Thus, our CE results indicate that consumers are WTP for a bread product that contains functional ingredients but they have stronger preference for bread that offers a simple but clear health benefit. Furthermore, in common with existing research on consumer choice we do evidence of heterogeneous preferences. In addition our results provide an insight into groupings (or segments). Interestingly, when we consider what explains preferences we find that attitudinal variables have far greater power than the more conventional socio-economic variables typically employed in empirical analysis. Maybe we should not be surprised by this finding but it does raise questions about what type of data we need to collect if we are to better understand what determines choice in stated preference research. Turning to the dual-mode approach employed in this study it is useful to reflect on the success or otherwise of the analysis. First, the product which has been the subject of the research, bread, appears to lend itself to this dual-mode of analysis. This is because during the Laddering/MEC phase of the research consumers identified a reasonably coherent set of attributes, indicated by the high elicitation frequencies. This need not be the case for all subjects which are the focus of a CE. However, the fact that the Laddering/MEC did yield results of this kind does help to provide confidence in the set of attributes employed. Second, at no point did we set out to test how our CE attribute selection would differ if we employed Laddering/MEC as opposed to the more conventional focus group method. However, we would contend that the methodology used in this research has enabled a much more powerful understanding of consumer values relating to product attributes, enabling a robust CE analysis to be designed and tested. The links provided by using the dual-mode MEC and CE approach has provided us with insights into, not just WTP by attribute, but enable more informed promotional messages to be developed for the product’s target market. But, we are unable to answer definitively which if either approach is superior as a means to reveal attributes for CE. This remains an open question and such something that could be more comprehensively examined in future research. We also acknowledge that the results reported in this paper are subject to a number of limitations that can be addressed in subsequent research. First, both the Laddering/MEC and the CE samples are not truly representative of the UK population. As we have argued, both have some degree of heterogeneity and as such capture certain aspects of the wider population. However, this does mean that the results reported in the paper do need to be treated with some caution if extrapolating to wider society. Second, the CE has relied upon a single shot mail survey. This choice was as a result of budgetary limitations and it would be interesting to see if an improved sample would yield significantly different results. Third, our analysis has focused on a single food product, bread. As with so

M. Bitzios et al. / Food Policy 36 (2011) 715–725

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