Assessing determinants of organic food consumption using data from the German National Nutrition Survey II

Food Quality and Preference 28 (2013) 60–70

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Assessing determinants of organic food consumption using data from the German National Nutrition Survey II Carlos Padilla Bravo a,⇑, Anette Cordts a, Birgit Schulze b, Achim Spiller a a b

Department of Agricultural Economics and Rural Development, Georg-August-University Goettingen, Platz der Goettingen Sieben 5, 37073 Goettingen, Germany Institute of Agricultural Economics, Christian-Albrechts-University Kiel, Olshausenstraße 40, 24118 Kiel, Germany

a r t i c l e

i n f o

Article history: Received 31 August 2011 Received in revised form 22 August 2012 Accepted 27 August 2012 Available online 3 September 2012 Keywords: Organic food Partial least squares Purchasing motives Germany

a b s t r a c t The organic food industry is continuously growing worldwide. Critical for sustaining this expansion is an adequate understanding of consumer behaviour. However, reported results of the numerous studies on factors determining consumer decisions are not very consistent. Therefore, we develop a comprehensive causal model to analyse data from 13,074 German consumers gathered through the representative German National Nutrition Survey II (Nationale Verzehrsstudie II). The findings indicate that altruistic motives are the major factors affecting consumer attitude and purchasing behaviour, making socio-demographic variables appear less important. Implications for the organic food industry and recommendations for further research are derived. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction The organic food industry is continuously growing worldwide, with Germany as one of the most important organic markets in Europe (Sahota, 2009). Sales volume in Germany increased from 4.60 to 5.80 billion Euros between 2006 and 2009 (Agrarmarkt Informations-Gesellschaft, 2010). To sustain this tendency over time, a clear understanding of the factors influencing consumer behaviour is critical. According to Gracia and de Magistris (2008) numerous studies have addressed a wide range of topics in several organic food markets. Perhaps most numerous are those studies trying to elucidate the role that psychographic, socio-demographic and economic factors play in consumer choice (e.g. Chen, 2007; de Magistris & Gracia, 2008; Haghiri, Hobbs, & McNamara, 2009; Honkanen, Verplanken, & Olsen, 2006; Magnusson, Arvola, Hursti, Åberg, & Sjödén, 2003; Michaelidou & Hassan, 2010; Schifferstein & Oude Ophuis, 1998; Squires, Juric, & Cornwell, 2001; Tarkiainen & Sundqvist, 2005; Zanoli & Naspetti, 2002). However, reported results in this field vary considerably across studies. For example, some researchers argue that altruistic aspects, such as environmental awareness, animal welfare and fair trade, are the most important reasons determining organic food consumption (Chen, 2007; Durham & Andrade, 2005; McEachern & Willock, 2004; Michaelidou & Hassan, 2008, 2010; Tarkiainen & Sundqvist, 2005). For others, individual aspects, such as health concerns, ⇑ Corresponding author. Tel.: +49 (0) 551 394825; fax: +49 (0) 551 3912122. E-mail addresses: [email protected] (C. Padilla Bravo), [email protected] (A. Cordts), [email protected] (B. Schulze), [email protected] (A. Spiller). 0950-3293/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodqual.2012.08.010

nutrition, food safety, food taste and product freshness, are major determinants (Chen, 2009; Haghiri et al., 2009; Magnusson et al., 2003; Makatouni, 2002; McEachern & McClean, 2002; Mondelaers, Verbeke, & van Huylenbroeck, 2009; Padel & Foster, 2005; Zanoli & Naspetti, 2002). In addition, there is also empirical evidence reporting that neither altruistic considerations nor egoistic aspects determine organic purchasing behaviour (Li, Zepeda, & Gould, 2007). Instead, search costs, dietary patterns and awareness of organic food labelling are reported to be strong predictors of organic food purchases (Li et al., 2007). Reasons for these inconsistencies may be differences in the sample size and representativeness, regional focus, type of assessed products, market development or measurement. In previous studies, sample size varies between 100 and 1600 respondents. As far as we know, the only study using a larger sample size (above 10,000 observations) analyses a covariance-based structural equation model and reports that healthrelated, nutritional and quality aspects are the main psychographic determinants of organic food purchase in Germany (Buder, Hamm, Bickel, Bien, & Michels, 2010). This investigation analyses household panel data and thus provides very good behavioural data in a shopping context. Wier, O’Doherty Jensen, Andersen, and Millock (2008) using household panel data with a smaller sample size (n = 1165) report similar results in the Danish market. Most studies, however, are survey-based and thus have to rely on stated buying behaviour, which is likely to be biased by phenomena such as wishful thinking and social desirability, among others. With a sample of 13,074 German consumers surveyed in the German National Nutrition Survey II (NVS II), this study differs from previous investigations with respect to sample size and measurement of purchasing behaviour. Compared to other

C. Padilla Bravo et al. / Food Quality and Preference 28 (2013) 60–70

survey-based studies, a much larger data set is used, which is representative for the German population aged above 18 (see Section 3.1). Second, compared to other surveys using stated purchasing behaviour, our behavioural measure is much more differentiated, asking how often a person would choose the organic alternative when shopping for 12 different food categories. Thus, the measure of organic food shopping frequency is probably more reliable compared to measures used in previous survey-based studies. By contrasting our results with the results of household panel data studies using recorded purchasing behaviour, we investigate whether survey data providing a more differentiated measure of stated behaviour can lead to comparably reliable results. We consider this point very important, since household panel data (i) often are not easy to obtain, or very costly; (ii) do not necessarily provide attitudinal information of respondents in the panel or provide aggregated information that does not allow an individual-based analysis. Thus, it is important to develop methods for survey-based studies. In this attempt, however, we are restricted to the selection of variables compiled by the researchers of the NVS II. Therefore, no specific behavioural theory can be tested; instead, we develop a theoretical model of determinants of organic food purchase based on a comprehensive literature review and estimate it using partial least squares (PLS) analysis. 2. The organic food purchase decision-process The question of how behaviour is determined by people’s values and attitudes has been a centre of interest in consumer research for decades (Ajzen & Fishbein, 1970, 2005). Organic food consumption is not an exception in this context. Several quantitative studies testing different behavioural theories1 as well as qualitative studies have tried to determine the main drivers of organic food consumption. In the following subsections we summarise the current state of knowledge regarding the determinants of organic food choice and consumer behaviour, and develop the hypotheses for the empirical analysis. 2.1. Attitude towards organic food Several authors have found that consumer attitudes towards organic food can predict purchase intention (see Chen, 2007; De Magistris & Gracia, 2008; Dean, Raats, & Shepherd, 2008; Honkanen et al., 2006; Michaelidou & Hassan, 2008, 2010; Saba & Messina, 2003; Tarkiainen & Sundqvist, 2005). The direct effect of attitude on consumer purchasing behaviour, on the other hand, has seldom been investigated. One of these rare studies (see Smith & Paladino, 2010) suggests that the effect of attitude on behaviour is not significant. Considering that the attitude–intention relationship has been frequently investigated, and that the number of studies focusing on the attitude–behaviour relationship is scarce, we want to shed light on the latter issue. Therefore, assuming that the perceived importance of organic food on the consumer’s food purchasing decisions is a good proxy for the attitude towards organic food purchase, we hypothesise that: H1. The perceived importance of organic food has a positive impact on actual purchasing behaviour. 2.2. Price The high price premium has been identified as one of the main obstacles to an increase in organic food consumption (Aertsens 1 According to Aertsens, Verbeke, Mondelaers, and van Huylenbroeck (2009) the most frequently used theoretical approaches to model organic food choice are the theory of planned behaviour (Ajzen, 1985, 1991), and the values theory of Rokeach (1973) and Schwartz (1992).

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et al., 2009; Buder et al., 2010; Durham & Andrade, 2005; Fotopoulos & Krystallis, 2002).2 Therefore, we hypothesise: H2. The perceived price importance when shopping negatively influences (a) the perceived importance of organic food and (b) consumer purchasing behaviour. 2.3. Organic purchasing motives Several purchasing motives have been identified in the organic food market as influencing organic food consumption.3 In this section we focus on those variables which relate to the aspects included in the NVS II. 2.3.1. Healthiness Health concerns are reported in the literature as affecting organic food purchase. Studies using household panel data conclude that the perceived health-related variables contribute to explain organic food choice (Buder et al., 2010; Wier, O’Doherty Jensen, Andersen, & Millock, 2008). Similarly, various survey-based studies using cross sectional data reveal that health considerations are significant factors determining organic food consumption (e.g. Bruhn, 2002; Chen, 2009; Durham & Andrade, 2005; Gracia & de Magistris, 2008; Haghiri et al., 2009; Magnusson et al., 2001, 2003; Wier, O’Doherty Jensen, Andersen, & Millock, 2008).4 Findings reported in qualitative studies are also in line with this (e.g. Baker, Thompson, Engelken, & Huntley, 2004; Makatouni, 2002; Padel & Foster, 2005; Zanoli & Naspetti, 2002). Based on this information, we propose the following hypothesis: H3. Consumers who put strong emphasis on healthiness when shopping for food (a) perceive a higher importance of the organic attribute and (b) are more likely to buy organic food. 2.3.2. Altruistic aspects There is empirical evidence suggesting that altruistic reasons also play an important role in organic food purchase decisions. For example, concern about the environment and animal welfare, consumers’ political attitudes and social aspects influence the demand for organic food (Bruhn, 2002; Chen, 2007; Durham & Andrade, 2005; Gracia & de Magistris, 2008; Haghiri et al., 2009; Magnusson et al., 2003; Wier, O’Doherty Jensen, Andersen, & Millock, 2008).5 In the same way, qualitative studies report similar results (e.g. Baker et al., 2004; Makatouni, 2002; Padel & Foster, 2005; Zanoli & Naspetti, 2002). In the particular case of Germany, consumers seem to be interested in animal welfare and show a willingness to pay for this type of attribute (Zander & Hamm, 2010). Therefore, we hypothesise that: H4. Altruistic motives positively affect (a) the perceived importance of organic food and (b) organic purchasing behaviour.

2 Other authors who address this issue are Hughner, McDonagh, Prothero, Shultz, and Stanton (2007), Magnusson, Arvola, Hursti, Åberg, and Sjödén (2001), McEachern and Willock (2004), Michaelidou and Hassan (2010), Padel and Foster (2005), RoitnerSchobesberger, Darnhofer, Somsook, and Vogl (2008), Wier and Calverley (2002) and Zanoli and Naspetti (2002). 3 An extended literature review on this topic is provided by Aertsens et al. (2009), Hughner et al. (2007) and Yiridoe, Bonti-Ankomah, and Martin (2005). 4 Other studies that address this issue include McEachern and McClean (2002), McEachern and Willock (2004), Mondelaers et al. (2009), Roitner-Schobesberger et al. (2008), Schifferstein and Oude Ophuis (1998), Verhof (2005) and Wier and Calverley (2002). 5 This issue is also addressed by McEachern and McClean (2002), McEachern and Willock (2004), Michaelidou and Hassan (2008, 2010), Mondelaers et al. (2009), Roitner-Schobesberger et al. (2008), Schifferstein and Oude Ophuis (1998) and Wier and Calverley (2002).

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2.3.3. Convenience in food purchasing and handling6 Convenience is a relevant factor affecting consumers’ choice behaviour. Occasional and non-users of organic food pay great attention to convenience issues as a purchase criterion (Fotopoulos & Krystallis, 2002; Zanoli & Naspetti, 2002). The importance of convenience aspects related to food purchase (e.g. availability of organic food, availability/location of shopping venues) has been found to have a negative impact on consumers’ attitudes towards organic food (Chen, 2007) and purchasing behaviour (Bruhn, 2002; Li et al., 2007). The negative impact of convenience related to food purchase may be given by the non-ubiquity of organic food in general, which increases the search costs for organic food (Li et al., 2007). We further assume that other convenience aspects such as placement within the store (inconvenient location in the point of sale) and packaging characteristics can also influence consumer purchasing behaviour. Therefore, with respect to convenience we hypothesise that: H5. The perceived importance of convenience related to availability, placement within the store and packaging characteristics negatively affects a) the perceived importance of organic food and b) consumer purchasing behaviour.

2.3.4. Exclusive foods Empirical findings suggest that consumers’ perception of organic products is to some extent comparable with the perception of regional and speciality food. For example, locally produced and specialty food is perceived as being tastier, more natural, of higher quality, fresher, healthier, more environmentally friendly and socially fair than conventional alternatives (Roininen, Arvola, & Lähteenmäki, 2006; Vanhonacker et al., 2010). Moreover, some consumer segments are willing to pay more for (Giraud, Bond, & Bond, 2005; von Alvensleben & Schrader, 1999) and purchase (Jekanowski, Williams, & Schiek, 2000) specialty or locally produced food. In line with this, Giraud (2002) states that some consumers have a positive attitude towards typical foods (e.g. origin labelled food, regional products, on farm processed food). In addition, organically produced food often is viewed by consumers as typical food (Sirieix & Schaer, 2000). Considering this, we hypothesise: H6. Orientation towards exclusive – regional or specialty – foods positively affects (a) the perceived importance of organic food and (b) consumer purchasing behaviour.

With respect to the role of nutrition in the organic food market, there is the general belief that organic food is more nutritious than conventional alternatives (Williams & Hammitt, 2000). This perception also increases the probability of purchasing organic food (Li et al., 2007). Thus, we could expect that consumers more interested in nutrition information will show a more positive attitude towards organic food and purchase it more frequently. Regarding search for nutrition information we state that: H7: Search for nutrition information positively affects (a) the organic purchasing motives, (b) the perceived importance of organic food and (c) purchasing behaviour, and negatively influences (d) the perceived price importance. 2.5. Socio-demographic variables Although socio-demographics are generally assumed to be important for predicting organic food consumption, comparisons across studies show inconsistent results in terms of size, significance and direction of the relationships. For example, RoitnerSchobesberger et al. (2008) found that organic consumers in Bangkok tend to be older than other consumer groups. In the same way, Squires et al. (2001) observe that in Denmark older consumers are more likely to consume organic food than younger consumers. However, Durham and Andrade (2005) and Magnusson et al. (2001) report the opposite7 in the USA and Sweden, respectively. Li et al. (2007) conclude that demographic variables are poor predictors of organic shopping behaviour. In contrast, attitudinal and behavioural variables are better predictors (Li et al., 2007). Engelken (2006) found for Germany that there are certain lifecycle phases, namely the young family, in which people are more inclined to switch to organic food. Ajzen and Fishbein (2005), analysing a set of empirical studies on the attitude–behaviour relationship, state that background factors such as socio-demographics seem to have mainly indirect effects on intention and behaviour through mediating constructs such as attitudes, subjective norms and perceived behavioural control. Against this background, we integrate all possible effects of socio-demographic characteristics on all the aforementioned variables in order to shed more light onto the impact of indirect versus direct effects. The large data set used here allows more insights into the importance of socio-demographic aspects in the organic food market. Fig. 1 summarises schematically the proposed research hypotheses.

2.4. Search for nutrition information Grunert and Wills (2007), based on a literature review about consumers’ perception of nutrition information, report that consumers in general are interested in nutrition information. In addition, nutrition information exerts a positive and significant impact on consumer’s food choice (Shiratori & Kinsey, 2011). There is also evidence indicating that consumers use different nutrition information sources when they wish to obtain nutrition information (Feick, Herrmann, & Warland, 1986; Grunert & Wills, 2007; Wang, Fletcher, & Carley, 1995). In addition, Drichoutis, Lazaridis, and Nayga (2005) found that there is a negative relationship between consumers’ price orientation and the use of nutritional food labels. Relating the use of nutritional labels to the broader behaviour of search for nutrition information in general, one can thus assume that the more importance consumers attach to food prices in their shopping, the less they search for nutrition information. 6 Although other items related to convenience (cooking convenience) were available in the data set used in this study, we detected in an exploratory analysis that the reliability of the construct was far below the recommended critical value. Therefore, we decided to omit this construct from further analyses.

3. Data and methodology 3.1. Data and sample In this study we use data gathered through the German National Nutrition Survey II. Conducted between 2005 and 2007, this nationwide consumption survey comprises detailed information about approx. 20,000 German speaking residents aged from 14 to 80 years who have been selected representatively. Comprehensive information on dietary and nutritional intake of 15,371 individuals has been collected in so-called diet-history interviews. Additionally, data on health and nutrition-related behaviour and socio-demographics were obtained by means of computer assisted personal interviews (CAPI). Further information regarding behaviour, attitudes and food purchase criteria was collected in a 7 For further comparisons regarding socio-demographics see Buder et al. (2010); Briz and Ward (2009), Bruhn (2002), Fotopoulos and Krystallis (2002), Hughner et al. (2007), Li et al. (2007), Loureiro, McCluskey, and Mittelhammer (2001) and Wier, O’Doherty Jensen, Andersen, and Millock (2008).

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Socio-demographic variables

H3b…..H6b

Purchasing motives

H3a….H6a

Organic food importance

H7b

H7a

H1

H2a H2b

Search for nutrition information

H7d

Stated purchasing behaviour

Price importance H7c

Fig. 1. Research model.

Table 1 Socio-demographic characteristics of the sample. Source: Own representation following data of the NVS II.

a

Variable

Frequency

Percentage

Mean

Sd

Age Age of the respondents

–

–

48.9

16.1

Gender Female Male

7113 5961

54.4 45.6

– –

– –

Women under 40 with childrena Yes No Missing

1032 6061 20

14.5 85.2 0.3

– – –

– – –

Social class Upper Upper middle Middle Lower middle Lower

2695 4020 3815 1710 834

20.6 30.7 29.2 13.1 6.4

– – – – –

– – – – –

Household size No. persons in household

–

–

2.6

1.3

Region South East Other

3868 1913 7293

29.6 14.6 55.8

– – –

– – –

Data only available for women.

self-administered paper questionnaire (Max Rubner-Institut, 2008a).8 In our study, only data from the CAPI and the paper questionnaire are considered. Furthermore, only participants aged 18 or over were included in the analysis. The whole sample analysed in this study consists of 13,074 respondents. The socio-demographic characteristics of the sample are displayed in Table 1. For the NVS II, a double stratified sampling method was applied. In a first step, stratification was based on Federal States and community size (‘BIK classification’) and a total of 500 study centres or sample points were drawn. In the next step, addresses of potential participants were randomly drawn from the resident registers of the communities or sample points stratified based on age and gender (Max Rubner-Institut, 2008a). By this approach it is theoretically assured that every German speaking resident had the same chance to be asked for participation, although due to non-response bias, an exact reflection of German population parameters could

8

Information available only in German language.

not be realised, e.g. regarding the distribution of men and women (Max Rubner-Institut, 2008b).9

3.2. Measures The dependent variable (stated purchasing behaviour) was operationalised by respondents’ reported organic buying frequency for 12 food groups (dairy, cereals, meat, vegetables, etc.). According to the frequency of choosing the organic instead of the conventional alternative (four categories, from ‘never’ to ‘(almost) always’), an index of purchasing frequency was calculated over the 12 categories and respondents finally were grouped into six categories of organic food purchasers, from almost never (majority of food categories never or rarely bought in organic quality) to very frequent (all foods bought almost always or always in organic quality). Non-users of organic food products, which build a further category, were identified by the question ‘Do you buy organic food?’ and not exposed to the frequency questions. These non-users constitute the majority (55.1%) of the sample. Fig. 2 shows the distribution of respondents according to their organic food purchase frequency. While very frequent consumers represent the smallest group among the users of organic food, occasional have the highest share. This is in line with findings from other studies (e.g. Mondelaers et al., 2009; Wier, O’Doherty Jensen, Andersen, & Millock, 2008). Measurement of the influencing variables is restricted in this study to those questions which were included in the NVS II. Therefore, we were not able to include all the relevant variables and construct dimensions reported in earlier attitudinal and behavioural studies. For example, in the NVS II, subjective norms and intention to purchase, which are core constructs in the Theory of Planned Behaviour (Ajzen, 1985, 1991), were not measured at all. In other cases, information contained in the database was used as empirical proxy for variables in our research model. Obviously, if the instruments for data collection had been designed to the specific purpose of analysing the determinants of organic food purchase, a number of variables would have been operationalised differently. The purchasing motives as included in the hypotheses are thus derived from the question ‘When shopping for food how important are the following aspects to you?’, which was followed by a list of 9 The following variables were compared with official statistics: distribution of men and women, age groups, German Federal states, marital status, school-leaving qualification, occupational activity, smoking behaviour. The biggest difference was found in the percentage of individuals with a lower secondary school qualification (Hauptschulabschluss), which is underrepresented in the NVS II data used for this study (around 7%). For the other variables the deviation is less than 4%.

C. Padilla Bravo et al. / Food Quality and Preference 28 (2013) 60–70

Repondents (%)

64

60

55.1

55 50 45 40 35 30 25 20

15.6

15

10.6

10.2

10

3.8

3.5

5

1.2

0 Non-users

Almost never

Seldom

Occasional

Quite frequent

Frequent

Very frequent

Organic purchase intensity Fig. 2. Distribution of user categories.

Table 2 Exploratory factor analysis defining nutritional information categories. Source of information

Categories of nutritional information source Mass media

Radio Advertising TV Newspaper Specialised/cooking books Specialised magazines Package labels Internet/SMS Food industry material Doctor Pharmacist Health insurance company Health/food monitoring/veterinary office Consumer service office DGEa, AIDb, other associations Workplace/colleagues Family and friends School/lectures/studies Children (school materials)

Specific media

Experts

Cronbach’s alpha Public organisations

Family/Other subjects

0.78 0.73 0.66 0.65

0.75 0.72 0.58 0.58 0.53 0.48

0.57 0.78 0.74 0.66

0.65 0.71 0.69 0.67

0.58 0.73 0.55 0.51 0.50

0.49

Question: Where do you retrieve information about nutrition from? Please indicate using the scale, how often you used the following information sources in the past year (scale from 1 = daily to 5 = not used). Note: From the 21 initial information sources two were left out due to double or improper factor loading. a DGE: Deutsche Gesellschaft für Ernährung (German Nutrition Society). b AID: aid infodienst Ernährung, Landwirtschaft, Verbraucherschutz (aid information service on nutrition, agriculture and consumer protection).

26 aspects that had to be rated on a 4-point importance scale.10 The number of items was reduced to the constructs mentioned in the hypotheses using confirmatory factor analysis. The importance of healthiness is captured using respondents’ stated frequency of paying attention to nine nutritional food components (e.g. fat, fibre, minerals, cholesterol, etc.) when buying food products (see Section 4.1: Table 3). Again, given the survey restrictions it was not possible to integrate other dimensions of health concern, i.e. we are just evaluating in this study the dimension related to the importance respondents attribute to nutritional information on the product label (for more detail about health concern dimensions see Schifferstein & Oude Ophuis, 1998). In the remainder, we will refer to this construct as ‘healthiness’. In this study we operationalised the variable ‘search for nutrition information’ using an index of respondents’ use of 21 nutritional 10

See Table 3 (Section 4.1) and Table 6 (Section 4.2) for a detailed overview of the measures.

information sources. Based on the stated frequency of use, the information sources were first reduced by means of an exploratory factor analysis (principal component analysis and VARIMAX rotation) into five broader groups: mass media, specific media, experts, public organisations, and family/other subjects; with four, five, three, three and four subcategories each, respectively (see Table 2). To build the index, we first counted the total number of sources used by each respondent within each category of sources. Then, we divided by the total number of sources assigned to each group. In this way, we have a specific intensity of use per person per type of source (media, experts, etc.). Finally, for each respondent weighted values were added. 3.3. Statistical approach Estimation of causal models with latent constructs can be performed either by covariance-based or variance-based structural equation modelling techniques (Gefen, Straub, & Boudreau, 2000;

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C. Padilla Bravo et al. / Food Quality and Preference 28 (2013) 60–70 Table 3 PLS confirmatory factor analysis on purchasing motives.a Measures b

Fibre Mineralsb Vitaminsb Carbohydratesb Proteinb Saltb Cholesterolb Fatb Caloriesb Animal welfarec Eco-packagingc Fair tradec GMO-freec Seasonalityc Availability in a nearby storec Easy to open packagingc Low weight packagingc Easily reachable in storec Regional productionc Variety and breedc Specialtiesc

Healthiness

Altruism

Convenience

Exclusiveness

0.82 0.79 0.77 0.77 0.74 0.74 0.73 0.71 0.68

4. Results 4.1. Assessing the measurement model

0.80 0.76 0.74 0.71 0.58 0.81 0.80 0.74 0.61 0.85 0.75 0.72

a Only those constructs considering more than one measurement item are displayed. b With respect to your nutrition, how often do you pay attention to? From 1 = never to 4 = (almost) always. c When shopping for food how important are the following aspects to you? From 1 = not important to 4 = very important.

Table 4 Assessment of the measurement model.

a b c d

Sarstedt, 2011; Henseler, Ringle, & Sinkovics, 2009; Reinartz et al., 2009). Analysis and interpretation of PLS models consists of two steps: (i) the assessment of the reliability and validity of the measurement model; and (ii) the assessment of the goodness of fit of the structural model (Hair et al., 2011; Henseler et al., 2009; Hulland, 1999). The statistical software SmartPLS version 2.0 M3 was used to analyse the data (Ringle, Wende, & Will, 2005).

Latent variablesa

No. items

CRAb (P0.7)

CRc (P0.7)

AVEd (P0.5)

Altruism Convenience Healthiness Exclusiveness

5 4 9 3

0.77 0.73 0.90 0.68

0.84 0.83 0.92 0.82

0.52 0.55 0.56 0.60

Only those constructs including more than one item are displayed. Cronbach’s alpha. Composite reliability. Average variance extracted.

In the measurement model we use only reflective indicators. We evaluate the reliability of the measures by checking the factor loadings of each measure on their respective latent construct (see Table 3). The majority of measure loadings are in agreement with the recommended threshold of 0.7 (Chin, 1998a; Hair et al., 2011; Henseler et al., 2009; Hulland, 1999), which supports the reliability of the indicators. We assess construct reliability through the Cronbach’s alpha (CRA) (Henseler et al., 2009; Hulland, 1999) and the composite reliability (CR) (Werts, Linn, & Jöreskog, 1974). However, CR is considered to be a better indicator of construct reliability (Baumgartner & Homburg, 1996; Chin, Marcolin, & Newsted, 1996; Hair et al., 2011; Henseler et al., 2009). No matter which particular reliability coefficient is used, the recommended threshold for a sufficient construct reliability in early stages of research is 0.7 or above (Nunnally & Bernstein, 1994). CR in this study reaches values greater than 0.7 (see Table 4). Convergent validity is assessed through the average variance extracted (AVE) (Fornell & Larcker, 1981). We found satisfactory values (greater than 0.5) of AVE for all the constructs assessed in this study. In other words, the latent variables are able to explain more than half of their indicators’ variance on average. To assess discriminant validity we use the Fornell–Larcker criterion (Fornell & Larcker, 1981). The Fornell–Larcker criterion postulates that a latent construct should share more variance with its assigned indicators than with another latent variable in the structural model (Hair et al., 2011). Table 5 shows that there is no evidence of correlation among any two latent constructs larger than the square root of AVE of these two constructs. Therefore, discriminant validity is supported, which means that all constructs in the research model are indeed measuring different concepts. 4.2. Testing the causal model

Reinartz, Haenlein, & Henseler, 2009). Given that the proposed theoretical model includes several exogenous variables and a number of direct and indirect paths, which increases model complexity, we use the PLS approach. PLS is a variance-based method that can be used in exploratory studies, is appropriate for testing complex causal models and relaxes the distributional assumptions required by covariance-based approaches (Gefen et al., 2000; Hair, Ringle, &

We use the R2 and the algebraic sign, size and significance of the path coefficients to assess the goodness of fit of the structural model (Baumgartner & Homburg, 1996; Hair et al., 2011; Henseler et al., 2009). The structural model was able to explain 46% of the variance in the perceived importance of organic food and 42% of the variance in purchasing behaviour. Previous studies in the

Table 5 Discriminant validity analysis.

a

Latent construct

1

2

3

4

5

6

7

8

1. 2. 3. 4. 5. 6. 7. 8.

0.72a 0.44 0.39 0.64 0.39 0.58 0.23 0.10

1.00 0.00 0.61 0.21 0.31 0.24 0.23

0.74 0.19 0.26 0.36 0.03 0.18

1.00 0.31 0.48 0.24 0.17

0.75 0.35 0.36 0.02

0.78 0.22 0.10

1.00 0.05

1.00

Altruism Purchasing behaviour Convenience Importance of organic food Healthiness Exclusiveness Search for nutrition info. Price importance

Diagonal values in bold correspond to the square root of AVE.

(⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄) ns (⁄) (⁄⁄) 0.05 0.16 0.00 0.22 0.00 0.02 0.03 (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄) (⁄⁄) (⁄⁄⁄) (⁄⁄⁄)

0.00 0.10 0.04 0.02 0.29 0.08 0.06 0.05

ns (⁄⁄⁄) (⁄⁄⁄) (⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄)

Price importance

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄) (⁄⁄) (⁄) ns

0.19 0.29 0.11 0.02 0.02 0.03 0.12 0.06

Exclusiveness

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄) (⁄⁄⁄) ns ns

0.34 0.31 0.14 0.00 0.04 0.03 0.02 0.00

Healthiness

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns ns (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄)

0.05 0.31 0.14 0.02 0.23 0.04 0.01 0.01

Convenience

0.20 0.25 0.20 0.01 0.01 0.03 0.05 0.08

Altruism

0.53 0.04 0.06 0.17 0.08 0.05 0.07 0.00 0.02 0.07 0.04 0.01 0.04

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄)

Importance of organic fooda

0.50 0.12 0.13 0.01 0.01 0.09 0.08 0.00 0.04 0.01 0.05 0.02 0.04 0.00 Importance of organic food Altruism Convenience Healthiness Exclusiveness Price importance Search for nutrition info. Age Gender Women under 40 with c. Social class Household size South East

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns ns (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄) ns (⁄) (⁄⁄) (⁄⁄⁄) ns

Path coefficient estimates Variables

b

Stated purchasing behavioura Table 6 Estimated direct effects.

Purchasing behaviour: Index from 1 = non-user to 7 = very frequent user; Importance of organic food: Scale from 1 = not important to 4 = very important; Altruism, Convenience, Health consciousness and Exclusiveness: Latent constructs (see Section 4.1); Perceived price importance: Scale from 1 = not important to 4 = very important; Search for nutrition info: Index from 0 = not involved at all to 5 = very involved; Age of the respondent in years; Gender: female = 1, male = 0; Women under 40 with children = 1, 0 otherwise; Social class: Index based on respondents’ level of education, profession and household income (Max Rubner-Institut, 2008c) scaled from 1 = lower class to 5 = upper class; Household size: Number of persons living in a household; Region South: 1 if respondent lives in the South of Germany, 0 otherwise; Region East: 1 if respondent lives in the East of Germany; 0 otherwise. a Core variables in the model. b (***) Significant at 0.001 level: t-value > 3.291; (**) significant at 0.01 level: t-value>2.576; (*) significant at 0.05 level: t-value > 1.960; ns: not significant.

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Search for nutrition info.

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organic food market using covariance-based SEM report R2 values between 13% and 37% for attitude towards organic food, 15% and 56% for purchase intention (Honkanen et al., 2006; Michaelidou & Hassan, 2008, 2010; Saba & Messina, 2003; Tarkiainen & Sundqvist, 2005), and 0.48 and 0.82 for purchasing behaviour (Buder et al., 2010; Saba & Messina, 2003; Tarkiainen & Sundqvist, 2005). Likewise, studies employing multiple regression analysis report R2 scores ranging from 19% to 55% for attitude, 45% to 80% for intention (Chen, 2007; Smith & Paladino, 2010) and 22% to 25% for purchasing behaviour (Smith & Paladino, 2010; Wier, O’Doherty Jensen, Andersen, & Millock, 2008). There are no empirical studies in this field employing the PLS approach to compare our results with. However, in PLS models R2 scores of 19%, 33% and 67% are considered weak, moderate and substantial, respectively (Chin, 1998b). Therefore, given the complexity of the research model and taking into account the values described above, we consider the R2 scores obtained to be acceptable. To assess the significance of path coefficients we employed the SmartPLS bootstrapping routine with 500 sub-samples and 13,074 cases. In PLS models the standardized path coefficients decline and may become non-significant when the number of indirect relationships in the theoretical model increases. In such a case, it is useful to report and interpret the total effects (Henseler et al., 2009). The total effect is the sum of direct and indirect effects of an independent variable on its related dependent variable (Shrout & Bolger, 2002). Path coefficients and significance levels of direct and total effects are presented in Table 6 and 7, respectively. We focus on the analysis of variables determining the performance of the core constructs in the model, i.e. organic food importance and purchasing behaviour. We found, as expected, that the perceived importance of organic food is a good predictor of stated purchasing behaviour (H1 is supported). The total effects of altruism (+), convenience ( ) and price importance ( ) on purchasing behaviour are higher than their direct effects. Considering total effects, altruism is the most important motive determining the perceived importance of organic food and purchasing behaviour. The total effect of exclusiveness (+) on organic food importance is greater than its total effect on purchasing behaviour, and no significant direct impact on purchasing behaviour was observed. Considering direct and total effects, importance of healthiness (+) exerts a significant but very small influence on importance of organic food. Although the direct effect on purchasing behaviour is low and not significant, healthiness significantly affects consumer behaviour when analysing total effects. This indicates evidence of a significant indirect effect through organic food importance. However, the size of the total effect remains very small. Overall, taking total effects into account, hypotheses H2, H3, H4, H5 and H6 are supported by our results. The positive effect of search for nutrition information on purchasing behaviour and importance of organic food increases when considering total effects. Additionally, the search for nutrition information significantly and positively affects the purchasing motives. On the contrary, it has no significant impact on the perceived price importance. While H7a, H7b and H7c are supported by our results, hypothesis H7d cannot be supported. Total effects show that certain socio-demographic aspects have a stronger impact on consumer attitude and behaviour than some purchasing motives. When considering importance of organic food, age (+), gender (+ for women) and social class (+) are the most important socio-demographic determinants regarding their total effects, which are higher compared to their direct effects. Total effects also reveal that the dummy variable ‘women under 40 with children’ (+), household size ( ), region South (+) and region East ( ) exert a significant but small impact on organic food importance. Concerning purchasing behaviour, the impact of gender (+) and social class (+) also increases when considering their total ef-

Purchasing behaviour: Index from 1 = non-user to 7 = very frequent user; Importance of organic food: Scale from 1 = not important to 4 = very important; Altruism, Convenience, Health consciousness and Exclusiveness: Latent constructs (see Section 4.1); Perceived price importance: Scale from 1 = not important to 4 = very important; Search for nutrition info: Index from 0 = not involved at all to 5 = very involved; Age of the respondent in years; Gender: female = 1, male = 0; Women under 40 with children = 1, 0 otherwise; Social class: Index based on respondents’ level of education, profession and household income (Max Rubner-Institut, 2008c) scaled from 1 = lower class to 5 = upper class; Household size: Number of persons living in a household; Region South: 1 if respondent lives in the South of Germany, 0 otherwise; Region East: 1 if respondent lives in the East of Germany; 0 otherwise. a Core variables in the model. b (***) Significant at 0.001 level: t-value > 3.291; (**) significant at 0.01 level: t-value > 2.576; (*) significant at 0.05 level: t-value > 1.960; ns: not significant.

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄) (⁄⁄⁄) (⁄⁄) (⁄⁄⁄) (⁄⁄⁄) 0.53 0.04 0.06 0.17 0.08 0.20 0.14 0.15 0.02 0.15 0.03 0.07 0.07 0.50 0.38 0.15 0.04 0.09 0.13 0.20 0.08 0.14 0.02 0.20 0.04 0.09 0.04 Importance of organic food Altruism Convenience Healthiness Exclusiveness Price importance Search for nutrition info. Age Gender Women under 40 with c. Social class Household size South East

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄)

Path coefficient estimates

b

Variables

Table 7 Estimated total effects.

Stated purchasing behavioura

Importance of organic fooda

0.20 0.26 0.23 0.01 0.05 0.03 0.05 0.08

Altruism

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄)

0.05 0.32 0.14 0.02 0.22 0.04 0.01 0.01

Convenience

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄) (⁄⁄⁄) ns ns

0.34 0.33 0.19 0.00 0.03 0.03 0.01 0.01

Healthiness

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄) (⁄⁄) ns ns

0.19 0.30 0.13 0.02 0.07 0.03 0.13 0.07

Exclusiveness

(⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄) (⁄⁄) (⁄⁄⁄) (⁄⁄⁄)

0.00 0.10 0.04 0.02 0.29 0.08 0.06 0.05

ns (⁄⁄⁄) (⁄⁄⁄) (⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄) (⁄⁄⁄)

Price importance

0.05 0.16 0.00 0.22 0.00 0.02 0.03

(⁄⁄⁄) (⁄⁄⁄) ns (⁄⁄⁄) ns (⁄) (⁄⁄)

Search for nutrition info.

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fects. Although the effects of age (+), household size ( ), region East ( ) and region South (+) on purchasing behaviour are statistically significant, they are small. Total effects show that the variable ‘women under 40 with children’ has no significant impact on purchasing behaviour. 5. Discussion and market implications This research model describes factors influencing consumer attitudes towards and purchasing behaviour of organic food. In the case of attitude towards organic food purchase, i.e. perceived importance of organic food, and purchasing behaviour, the explanatory power of our model reaches levels that are in agreement with values reported in previous investigations (see Section 4.2). Our findings support our hypothesis that importance of organic food is a good predictor of purchasing behaviour. Also, they are in line with those claiming that altruistic arguments determine organic food consumption (see Section 2.3.2). Indeed, we find the strongest relationship between altruistic purchase motives and consumers’ attitudes and behaviour towards organic products in our model. Thus, to increase organic food consumption in Germany, marketers should emphasise in communication campaigns the environmental benefits related to the production and consumption of organic food. The association of organic food products with other altruistic arguments (e.g. animal welfare, fair trade) may also be an interesting strategy to increase organic food consumption. Similar marketing strategies could also be implemented in other countries (e.g. Scotland: see Michaelidou & Hassan, 2008; Denmark: see Squires et al., 2001) where individuals are strongly motivated by altruistic aspects when shopping food. In the literature, another important factor is the rather egoistic motive of health promotion or at least preservation (see Section 2.3.1). The analysis of direct and total effects shows that healthiness (measured in this study as the attention paid to different nutritional food components) has no direct significant relationship with purchasing behaviour. However, there is evidence of a small significant indirect effect via importance of organic food. In other words, our healthiness construct is a poor predictor of attitude and behaviour in our research model. Unfortunately, comparisons with results reported in previous studies, which add further health-related variables to the attitudinal and behavioural analysis (e.g. statements regarding healthy life styles, health care, diet, etc.; see Schifferstein and Oude Ophuis (1998)) cannot be made here. In contrast to healthiness based on nutritional aspects, our findings reveal that search for nutrition information exerts a larger total effect on attitude and purchasing behaviour. Besides, the impact is even larger compared to other variables (e.g. price, convenience) assessed in this study. Put differently, those consumers looking actively or passively for information about nutritional issues from different sources show a more positive attitude towards organically produced food, and purchase it more frequently. Our results thus support previous empirical evidence suggesting that interest in nutrition information is an important predictor of consumer behaviour in the food market (Shiratori & Kinsey, 2011). In agreement with the literature, the importance of convenience issues exerts a negative impact on consumers’ attitude and purchasing behaviour in the organic food market. Our results suggest that the organic food industry should focus on facilitating the access (e.g. offering alternative shopping venues) to organic food products on the one hand, and consider some aspects regarding the characteristics of the packaging and the placement within the store on the other hand. This could increase the probability of attracting those consumers more focused on convenience aspects. Price remains an obstacle to the increase in organic food consumption, which is consistent with earlier empirical evidence

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(see Section 2.2). The entrance of new actors into the organic food retail sector, namely the large German discounters such as Aldi and Lidl, might modify consumer perception of organic food prices in the near future. Nevertheless, the point to which prices can be reduced and what can be considered a fair price for organic farmers and consumers is still a matter of discussion. In this context, higher per-unit costs generally arise in organic farming because of differences in the production process when compared to conventional agriculture, and further arguments such as regionality or animal welfare might increase the price gap as well. Economies of scale, i.e. the growth in size of farms and processing plants, could mitigate this effect but here some other concerns might come into play, which are discussed under the notion of the ‘conventionalisation of organic production’ (Best, 2008; Campbell & Liepins, 2001; Darnhofer, Lindenthal, Bartel-Kratochvil, & Zollitsch, 2010; Felger & Hirte 2007; Hall & Mogyorody, 2001; von Kratochvil, Engel, Schumacher, & Ulmer, 2005). Obviously, these issues will be a matter for discussion in the coming years. Communicating gourmet or regional attributes of organic food categories, if suitable, could also be beneficial for the attraction of a wider consumer segment to the organic food market. Nevertheless, the positive and significant impact of differentiating arguments such as specialties, certain breeds or varieties, and regionality, which we subsume under ‘exclusiveness’, could be also interpreted as a threat to organic marketing. Food in organic quality could be perceived by consumers to be interchangeable with regional or specialty food products in conventional quality, although the cost of production is much more elevated in the first case. Thus, the organic food industry should very clearly communicate the extra benefits of the organic quality in order to avoid competition with regional or specialty food product in conventional quality. As already indicated in Section 2.5, socio-demographic variables exert only a minor direct impact on organic food importance and purchasing behaviour, but their effects seem to be mediated by other variables. Our findings allow us to draw a general picture of the organic consumer profile in Germany: small households, women, older people, those with higher social status and living in Southern Germany choose the organic alternative with a higher frequency. However, this consumer profile does not fully match with previous empirical evidence reported for Germany (e.g. Buder et al., 2010; Bruhn, 2002), which can partially be explained by differences in methodology. The fact that the dummy variable ‘women under 40 with children’ significantly influences perceived importance of organic food but not purchasing behaviour does not fully support the lifecycle hypothesis of Engelken (2006). However, Ajzen and Fishbein (2005) point out that in some circumstances, the link between attitudes and intentions on the one hand and behaviour on the other would be influenced by aspects of behavioural control, i.e. even if a person has strong positive attitudes towards a behaviour and strongly intends to perform it, she or he might face barriers which are not easy to overcome.

6. Conclusions From this analysis using comprehensive data provided by the German National Nutritional Survey II, we can conclude that altruistic arguments are strong motivations influencing consumer attitude towards and purchasing behaviour of organic food in Germany. Therefore, marketing efforts for organic foods in Germany should pay more attention to communicating altruistic aspects (e.g. environmental and animal welfare) related to the consumption of organically grown food. In this way, the interest of frequent organic food consumers may be strengthened. Furthermore, this strategy might help to shift occasional consumers to segments where organic food consumption is higher.

Search for nutrition information contributes positively to explain attitudes and organic food purchase. To be more precise, in this study those consumers using several sources to gather information about nutrition tend to hold more positive feelings towards organic foods and buy organic more frequently. On the contrary, healthiness based on nutritional aspects appears to be a weak predictor of organic food consumption. Convenience issues are still an obstacle to increase organic food consumption. This suggests, for example, that improving availability of organic food in different distribution channels may make the task of purchasing easier for more convenience-oriented food consumers. Other strategies such as providing convenient packaging designs and an easily reachable and identifiable placement within the store may help to increase organic food consumption. Price also remains an important barrier to an increase in organic food consumption. Reducing this barrier may motivate more consumers to buy organically grown food products but, at the same time, may encourage the discussion about the conventionalisation of the organic food market. Consumers more motivated by regional aspects and food specialties tend to show more positive attitudes towards organic food and buy it more frequently. Marketers should thus highlight the association of organic production methods with local production and exclusiveness when suitable. Thereby, consumer segments which rather focus on the latter two aspects when buying food could also be attracted to organic food. Nevertheless, there also might be a threat of increase competition with local and specialty foods in conventional quality, which are offered at a lower price and eventually perceived as substitutes. Finally, socio-demographic variables seem to play a role as background factors by having an impact on those constructs determining attitude towards organic food purchase and the behaviour itself. From the analysis of total effects, we conclude that small households, women, older people, those with higher societal status and living in the South of Germany choose the organic alternative with a higher frequency.

7. Limitations and recommendation for further research Although the findings reported in this article are based on a large sample, our survey-based study has some limitations. First, data restrictions did not allow all variables of interest to be captured. Second, the dimensions of health concern were not fully captured in this study, which obviously makes the interpretation of the findings difficult. Additionally, missing constructs and scaling differences affect the comparability of the results with earlier studies. Third, the stated purchase frequency used as dependent variable in this study can be assumed to be less accurate compared to household panel data. Regarding convenience in terms of availability, it has to be taken into account that the data stem from a 2006 survey, thus we cannot completely ensure that direct inferences can be made regarding the present availability of organic food in the market. Extrapolating the results reported in this study to other organic food markets should consider the analysis of macro factors like per capita income, level of market development, consumers’ cultural background, the political dimension of organic food, consumers’ risk perception of the food industry, among others. Studies combining the use of large and representative samples with ‘individual-based’ panel data sets could give a better overview when assessing determinants of organic food consumption. When panel data are costly and not easily available for researchers, survey-based studies using large sample size appear to be also an effective research tool to evaluate consumer behaviour and its determinants. We would like to emphasise, however, that the

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use of a complete set of indicators measuring key complex variables (e.g. consumer’s health concerns) is needed in future investigations. In this way, more consistent evidence can be provided on the role different variables play on organic food purchasing behaviour. Acknowledgements The authors would like to thank the German Federal Ministry of Food, Agriculture and Consumer Protection (BMELV) that, in the framework of the Federal Scheme for Organic Farming (BÖL), sponsored this research project. The authors would like also to thank the two anonymous referees as well as the Editor for the useful suggestions and comments, which helped to improve the scientific quality of this manuscript. References Aertsens, J., Verbeke, W., Mondelaers, K., & van Huylenbroeck, G. (2009). Personal determinants of organic food consumption: A review. British Food Journal, 111, 1140–1167. Ajzen, I., & Fishbein, M. (1970). The prediction of behavior from attitudinal and normative variables. Journal of Experimental Social Psychology, 6, 466–487. Ajzen, I. (1985). From intentions to actions. A theory of planned behavior. In J. Kuhi & J. Beckmann (Eds.), Action-control: From cognition to behavior (pp. 11–39). Heidelberg: Springer. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracín, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173–221). Mahwah, NJ: Erlbaum. von Alvensleben, R., & Schrader, S. K. (1999). Consumer attitudes towards regional food products: A case study for Northern Germany. In AIR-CAT meeting reports. Consumer attitudes towards typical foods. The European food consumer (Vol. 5, No. 1, pp. 10–19). Ås, Norway. Agrarmarkt Informations-Gesellschaft (2010). AMI-Marktbilanz Öko-Landbau 2010. Germany: Bonn. Baker, S., Thompson, K. E., Engelken, J., & Huntley, K. (2004). Mapping the values driving organic food choice. Germany vs the UK. European Journal of Marketing, 38, 995–1012. Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13, 139–161. Best, H. (2008). Organic agriculture and the conventionalization hypothesis: A case study from West Germany. Agriculture and Human Values, 25. 95–10. Briz, T., & Ward, R. W. (2009). Consumer awareness of organic products in Spain: An application of multinominal logit models. Food Policy, 34, 295–304. Bruhn, M. (2002). Die Nachfrage nach Bioprodukten: Eine Langzeitstudie unter besonderer Berücksichtigung von Verbrauchereinstellungen. Frankfurt a. M: Peter Lang. Buder, F., Hamm, U., Bickel, M., Bien, B., & Michels, P. (2010). Dynamik des Kaufverhaltens im Bio-Sortiment. Schlussbericht 09OE014. Bundesprogramm Ökologischer Landbau, BÖL. Accessed 26.08.11. Campbell, H., & Liepins, R. (2001). Naming organics: Understanding organic standards in New Zealand as a discursive field. Sociologia Ruralis, 411, 21–39. Chen, M. (2007). Consumer attitudes and purchase intentions in relation to organic foods in Taiwan: Moderating effects of food-related personality traits. Food Quality and Preference, 18, 1008–1021. Chen, M. (2009). Attitude toward organic foods among Taiwanese as related to health consciousness, environmental attitudes, and the mediating effects of a healthy lifestyle. British Food Journal, 111, 165–178. Chin, W. W. (1998a). Issues and opinion on structural equation modeling. MIS Quarterly, 22, 7–16. Chin, W. W. (1998b). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research: Methodology for business and management (pp. 259–336). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (1996). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and voice mail emotion/adoption study. In J. I. DeGross, S. Jarvenpaa, & A. Srinivasan (Eds.), Proceedings of the seventeenth international conference on information systems (pp. 21–41). Ohio, USA: Cleveland. Darnhofer, I., Lindenthal, T., Bartel-Kratochvil, R., & Zollitsch, W. (2010). Conventionalisation of organic farming practices: From structural criteria towards an assessment based on organic principles: A review. Agronomy for Sustainable Development, 30, 67–81. De Magistris, D., & Gracia, A. (2008). The decision to buy organic food products in Southern Italy. British Food Journal, 110, 929–947.

69

Dean, M., Raats, M. M., & Shepherd, R. (2008). Moral concerns and consumer choice of fresh and processed organic foods. Journal of Applied Social Psychology, 38, 2088–2107. Drichoutis, A. C., Lazaridis, P., & Nayga, R. M. Jr., (2005). Nutrition knowledge and consumer use of nutricional food labels. European Review of Agricultural Economics, 32, 93–118. Durham, C. A., & Andrade, D. (2005). Health vs. environmental motivation in organic preferences and purchases. In Presented at the American Agricultural Economics Association annual meeting, Providence, Rhode Island. Accessed 26.08.11. Engelken, J. (2006). Junge Erwanchsene als Zielgruppe für den Bio-Markt: Eine theoretische und empirische Analyse. Disertation. Georg-August-Universität Göttingen. Accessed 04.04.12. Feick, L. F., Herrmann, R. O., & Warland, R. H. (1986). Search for nutrition information: A probit analysis of the use of different information sources. The Journal of Consumer Affairs, 20, 173–192. Felger, J., & Hirte, K. (2007). Konventionalisierung des Ökolandbaus – Eine Reflexion auf die Debatte. In K. Hirte, K. David, J. C. Hesshaus, C. Hohls, & J. Schütte (Eds.), Ökolandbau – mehr als eine Verfahrenslehre? (pp 70–81). Marburg: Metropolis. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 30–50. Fotopoulos, C., & Krystallis, A. (2002). Organic product avoidance: Reasons for rejection and potential buyers’ identification in a countrywide survey. British Food Journal, 104, 233–260. Gefen, D., Straub, D. W., & Boudreau, M. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4, 1–78. Giraud, G. (2002). Consumer perception of typical food products in Europe. In Presented at the 10th EAAE congress, Zaragoza, Spain. Accessed 26.08.11. Giraud, K. L., Bond, C. A., & Bond, J. J. (2005). Consumer preferences for locally made specialty food products across Northern New England. Agricultural and Resource Economics Review, 34, 204–216. Gracia, A., & de Magistris, T. (2008). The demand for organic foods in the South of Italy: A discrete choice model. Food Policy, 33, 386–396. Grunert, K. G., & Wills, J. M. (2007). A review of European research on consumer response to nutrition information on food labels. Journal of Public Health, 15, 385–399. Haghiri, M., Hobbs, J. E., & McNamara, M. L. (2009). Assessing consumer preferences for organically grown fresh fruit and vegetables in Eastern New Brunswick. International Food and Agribusiness Management Review, 12, 81–100. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19, 139–151. Hall, A., & Mogyorody, V. (2001). Organic farming in Ontario: An examination of the conventionalization argument. Sociologia Ruralis, 414, 399–422. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–319. Honkanen, P., Verplanken, B., & Olsen, S. O. (2006). Ethical values and motives driving organic food choice. Journal of Consumer Behaviour, 5, 420–430. Hughner, R. S., McDonagh, P., Prothero, A., Shultz, C. S., & Stanton, J. (2007). Who are organic food consumers? A compilation and review of why people purchase organic food. Journal of Consumer Behaviour, 6, 94–110. Hulland, J. (1999). Use of partial least squares (pls) in strategic management research: A review of four recent studies. Strategic Management Journal, 20, 195–204. Jekanowski, M. D., Williams, D. R., II, & Schiek, W. A. (2000). Consumers’ willingness to purchase locally produced agricultural products: An analysis of an Indiana survey. Agricultural and Resource Economics Review, 29, 48–53. von Kratochvil, R., Engel, A., Schumacher, U., & Ulmer, H. (2005). Die ‘‘Konventionalisierungsfalle‘‘: Ökologischer Landbau zwischen Vision und Realität. Ökologie & Landbau, 136, 38–50. Li, J., Zepeda, L., & Gould, B. W. (2007). The demand for organic food in the U.S.: An empirical assessment. Journal of Food Distribution Research, 38, 54–69. Loureiro, M. L., McCluskey, J. J., & Mittelhammer, R. C. (2001). Assessing consumer preferences for organic, eco-labeled, and regular apples. Journal of Agricultural and Resource Economics, 26, 404–416. Magnusson, M. K., Arvola, A., Hursti, U. K., Åberg, L., & Sjödén, P. (2001). Attitudes towards organic foods among Swedish consumers. British Food Journal, 103, 209–226. Magnusson, M. K., Arvola, A., Hursti, U. K., Åberg, L., & Sjödén, P. (2003). Choice of organic foods is related to perceived consequences for human health and to environmentally friendly behaviour. Appetite, 40, 109–117. Makatouni, A. (2002). What motivates consumers to buy organic food in the UK? Results from a qualitative study. British Food Journal, 104, 345–352. Max Rubner-Institut (2008a). Nationale Verzehrsstudie II: Ergebnisbericht Teil 1. Bundesforschungsanstalt für Ernährung und Lebensmittel. Karlsruhe, Germany. Accessed 24.06.201. Max Rubner-Institut (2008b). Nationale Verzehrsstudie II: Ergebnisbericht Teil 2. Bundesforschungsanstalt für Ernährung und Lebensmittel. Karlsruhe, Germany. Accessed 24.06.201.

70

C. Padilla Bravo et al. / Food Quality and Preference 28 (2013) 60–70

Max Rubner-Institut (2008c). Nationale Verzehrsstudie II: Ergänzungsband zum Ergebnisbericht, Teil 1. Bundesforschungsanstalt für Ernährung und Lebensmittel. Karlsruhe, Germany. Accessed 26.12.201. McEachern, M. G., & McClean, P. (2002). Organic purchasing motivations and attitudes: Are they ethical? International Journal of Consumer Studies, 26, 85–92. McEachern, M. G., & Willock, J. (2004). Producers and consumers of organic meat: A focus on attitudes and motivations. British Food Journal, 106, 534–552. Michaelidou, N., & Hassan, L. M. (2008). The role of health consciousness, food safety concern and ethical identity on attitudes and intentions towards organic food. International Journal of Consumer Studies, 32, 163–170. Michaelidou, N., & Hassan, L. M. (2010). Modeling the factors affecting rural consumers’ purchase of organic and free-range produce: A case study of consumers’ from the Island of Arran in Scotland, UK. Food Policy, 35, 130–139. Mondelaers, K., Verbeke, W., & van Huylenbroeck, G. (2009). Importance of health and environment as quality traits in the buying decision of organic products. British Food Journal, 111, 1120–1139. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill. Padel, S., & Foster, C. (2005). Exploring the gap between attitudes and behaviour: Understanding why consumers buy or do not buy organic food. British Food Journal, 107, 606–625. Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of research in Marketing, 26, 332–344. Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 M3. . Roininen, K., Arvola, A., & Lähteenmäki, L. (2006). Exploring consumers’ perception of local food with two different qualitative techniques: Laddering and word association. Food Quality and Preference, 17, 20–30. Roitner-Schobesberger, B., Darnhofer, I., Somsook, S., & Vogl, C. R. (2008). Consumer perceptions of organic foods in Bangkok, Thailand. Food Policy, 33, 112–121. Rokeach, M. J. (1973). The nature of human values. New York: Free Press. Saba, A., & Messina, F. (2003). Attitudes towards organic foods and risk/benefit perception associated with pesticides. Food Quality and Preference, 14, 637–645. Sahota, A. 2009. The global market for organic food and drink. In H. Willer, & L. Klicher (Eds.), The world of organic agriculture: Statistics and emerging trends 2009 (pp. 59–64). FiBL-IFOAM report: IFOAM, Bonn; FiBL, Frick; ITC, Geneva. Schifferstein, H. N. J., & Oude Ophuis, P. A. M. (1998). Health-related determinants of organic food consumption in the Netherlands. Food Quality and Preference, 9, 119–133. Schwartz, S. H. (1992). Universals in the content and structure of values: Theoretical advance and empirical test in 20 countries. In M. Zanna (Ed.). Advances in experimental social psychology (Vol. 25, pp. 1–65). San Diego, CA: Academic Press.

Shiratori, S., & Kinsey, J. (2011). Media impact of nutrition information on food choice. In Presented at the Agricultural and Applied Economics Association’s 2011 AAEA & NAREA joint annual meeting. Pittsburgh, Pennsylvania, USA. Accessed 27.06.201. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperiemntal studies: New procedures and recommendations. Psychological Methods, 7, 422–445. Sirieix, L., & Schaer, B. (2000). Consumer attitudes towards organic and regional foods: A study covering two regions of Germany and France. In B. Sylvander, D. Barjolle, & F. Actes (Eds.), Et Communications 17/1-2 (pp. 425–428). Le Mans: INRA. Smith, S., & Paladino, A. (2010). Eating clean and green? Investigating consumer motivations towards the purchase of organic food. Australian Marketing Journal, 18, 93–104. Squires, L., Juric, B., & Cornwell, T. B. (2001). Level of market development and intensity of organic food consumption: Cross-cultural study of Danish and New Zealand consumers. Journal of Consumer Marketing, 18, 392–409. Tarkiainen, A., & Sundqvist, S. (2005). Subjective norms, attitudes and intentions of Finnish consumers in buying organic food. British Food Journal, 107, 808–822. Vanhonacker, F., Verbeke, W., Guerrero, L., Claret, A., Contel, M., Scalvedi, L., et al. (2010). How European consumers define the concept of traditional food: Evidence from a survey in six countries. Agribusiness, 26, 1–24. Verhof, P. C. (2005). Explaining purchases of organic meat by Dutch consumers. European Review of Agricultural Economics, 32, 245–267. Wang, G., Fletcher, S. M., & Carley, D. H. (1995). Consumer factors influencing the use of nutrition information sources. Advances in Consumer Research, 22, 573–581. Werts, C. E., Linn, R. L., & Jöreskog, K. G. (1974). Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological Measurement, 34, 25–33. Wier, M., & Calverley, C. (2002). Market potential for organic foods in Europe. British Food Journal, 104, 45–62. Wier, M., O’Doherty Jensen, K., Andersen, L., & Millock, K. (2008). The character of demand in mature organic food markets: Great Britain and Denmark compared. Food Policy, 33, 406–421. Williams, P. R. D., & Hammitt, J. K. (2000). A comparison of organic and conventional fresh produce buyers in the Boston area. Risk Analysis, 20, 735–746. Yiridoe, E. K., Bonti-Ankomah, S., & Martin, R. C. (2005). Comparison of consumer perceptions and preference toward organic versus conventionally produced foods: A review and update of the literature. Renewable Agriculture and Food Systems, 20, 193–205. Zander, K., & Hamm, U. (2010). Consumer preferences for additional ethical attributes of organic food. Food Quality and Preference, 21, 495–503. Zanoli, R., & Naspetti, S. (2002). Consumer motivations in the purchase of organic food: A means-end approach. British Food Journal, 104, 643–653.