Consumer preferences for beer attributes in Germany: A conjoint and latent class approach

Journal of Retailing and Consumer Services 47 (2019) 229–240

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Consumer preferences for beer attributes in Germany: A conjoint and latent class approach

T

Stephan G.H. Meyerdinga, , Alexander Bauchrowitzb, Mira Lehbergerc ⁎

a

Department of Agricultural Economics and Rural Development, Georg-August-Universität Göttingen, Platz der Göttinger Sieben 5, 37073 Göttingen,Germany Faculty of Economic Sciences, Georg-August-Universität Göttingen, Germany c Department of Fresh Produce Logistics, Hochschule Geisenheim University, Germany b

ARTICLE INFO

ABSTRACT

Keywords: Consumption patterns Consumer segmentation Food-related Lifestyles Organic Beer Diet-related attribute labels

Despite high marketing expenses by large breweries, the traditional German beer market has been declining for many years. The development may be related to reasons such as demographic change or increased health awareness. In a changing market, it is especially important to gain a precise knowledge of these variables. The aim of this study is to identify the attributes of beer that are crucial to the purchasing process and to segment the German market for beer. For this purpose, a conjoint analysis was carried out with a subsequent latent class analysis. As a result of the latent class analysis, three consumer segments were identified. In addition to achieving results from the conjoint analysis, the segments were characterized by sociodemographic status, beerrelated questions, and results from a food-related lifestyle approach. The study sample was representative in terms of gender and age for the market of German beer drinkers (N = 484). The attributes of beer type, price, and origin were of importance for the selection of beer across all segments. Differences between the segments represented, in particular, the preferred type of beer as well as preferences for organic, calorie-reduced, and imported beer. The latter three characteristic specifications were relevant in only one of the three segments. This study provides evidence for a slowly changing German beer market.

1. Introduction Beer is the most consumed alcoholic beverage worldwide (Colen and Swinnen, 2016). Accordingly, the scientific interest and evidence on the topic of beer consumption behavior and its correlates it ever growing. As a part of the beer purchasing process, consumers make their decisions based on extrinsic attributes (e.g., price, alcohol content, and brand) as well as intrinsic attributes (e.g., aroma, bitterness, and carbonation), affected by socio-demographic attributes (e.g., gender, age, and income) (e.g. Espejel et al., 2007). Regarding extrinsic attributes, for instance, Lopez and Matschke (2012) find evidence that for US consumers, price changes lead more easily to switches among domestic beers than among foreign beer and consumers have a strong bias toward domestically-produced beers. Contrarily, in China the consumption of European beer is positively associated with importance attached to the product attributes origin (Wang et al., 2017). For the case of craft beer, Donadini and Porretta (2017), argue that white bottles have a positive and cans a negative effect on the Italian

consumers’ interest in beer. Concerning intrinsic attributes empirical evidence suggests past drinking experience effects future consumer choices (Sester et al., 2012; Aquilani et al., 2015). For instance, consumers in the US prefer homogeneous, bland-tasting beer (Choi et al., 2005). Overall, empirical evidence suggests that both extrinsic and intrinsic kinds of attributes are important for understanding consumer choices regarding beer. In general, empirical evidence on beer consumption behavior and preferences is rather abundant. However, it is difficult to generalize specific results regarding the preferred attribute value (e.g. whether domestic or foreign beer is preferred, the alcoholic content) from country to country, as beer preferences appear to be culturally specific as well as subject to change over time (McCluskey and Shreay, 2012). Recent studies focusing on the determinants of German consumer choices of beer is, however, sparse. In contrast to the world market development, the annual per capita beer consumption in Germany drastically fell from almost 150 l in the 1970s to 104 l in 2016 (Colen and Swinnen, 2016; Deutscher Brauer-Bund, 2017). Reasons for the

Corresponding author. E-mail addresses: [email protected] (S.G.H. Meyerding), [email protected] (A. Bauchrowitz), [email protected] (M. Lehberger). ⁎

https://doi.org/10.1016/j.jretconser.2018.12.001 Received 24 May 2018; Received in revised form 30 November 2018; Accepted 3 December 2018 0969-6989/ © 2018 Elsevier Ltd. All rights reserved.

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declining German beer market may be sociocultural or demographic changes in society or trends such as increased health awareness (Colen and Swinnen, 2016; Schricker, 2016). As health awareness has increased, a growing willingness to spend more money on better quality and unconventional beer has been noted, which in turn illustrates the importance of targeting customer benefits for efficient market development. Brewers’ efforts to counteract the trend are reflected in advertising expenditures of around EUR 1.4 billion between 2012 and 2014 (Schricker, 2016). In order to adapt to meet changing customer needs in the traditional German market, empirical research on consumer preferences for beer attributes and a segmentation of German beer drinkers with regard to their preferences is needed. Accordingly, the aim of the study was twofold: Firstly, we identify the importance of beer attributes in the buying process of German consumers. Here, we focus on extrinsic attributes only, as these are the attributes, which practitioners can adjust most easily. We then carry out a market segmentation based on consumer choices, and describe the identified segments with the help of sociodemographic characteristics, as well as food-related behaviors and lifestyle choices. Here we use the modified food-related lifestyle approach (FRL) (Gunarathne et al., 2017). This study is novel for the reason that we segments the market for beer drinkers in Germany by their consumption choice behavior and further describes the consumer segments by their food-related lifestyle. This allows practitioners to target consumer groups more precisely with their marketing instruments: Market segmentation provides information about the structure of the market and consists of the division of an overall heterogeneous market into more homogeneous sub-segments. Additionally, we include items for factors like ‘brand loyalty’ and ‘passion for alcohol’ in our study. This can be a way to develop a beerrelated lifestyle approach that differs from the existing food related lifestyle approaches (Bruwer and Li, 2017, 2007). Such an approach can be important for researches, practitioners and politicians alike, as beer is not only a traditional food product in Germany, but also a drug (Bloomfield et al., 2001).

Table 1 Characteristics and their specifications of beer analyzed in the choice-based conjoint analysis. Characteristics

Characteristic specifications

Characteristics

Characteristic specifications

Bottle color

Brown Green White Pils beer Export beer Weizen beer Pale beer Kölsch Lager beer EUR 0.39 EUR 0.49 EUR 0.59 EUR 0.69 EUR 0.79 EUR 0.89 EUR 0.99 EUR 1.09

Alcohol content

4.6% 4.8% 5.0% 5.2% 5.4% 5.6% Regional beer Beer from Germany Beer from abroad No statement of place Organic No label Calorie-reduced No label Gluten-free No label

Type

Price

Origin

Organic Calories Gluten

demand to be more accurately represented. Backhaus et al. (2011) formulated seven steps to complete a CBCA: For the first step, design of the stimuli, this study's selected characteristics and associated levels are shown in Table 1. The attributes chosen in the present study are the most common attributes of beer in Germany. Bottles are chosen as beer in cans is now very rare in Germany. There are different sizes of bottles, we used the 500 ml size, as it is the most common one. Prices are in Euro. In the study, the bottle colors green, brown, and white were displayed in the form of an image, with the aim of creating a more realistic representation. Sester et al. (2013) found that different attributes of beer are associated with different packaging. The beer types selected for this study reflect the six highest-turnover beers of 2016 in Germany (Dierig, 2017). Pils is the highest-yielding beer type, representing 61.34% of the market, followed by Weizen (9.70%), pale beer (8.14%), export (6.15%), Kölsch (2.48%), and lager (2.39%). Mixed beer drinks, nonalcoholic beer, and malt beer were excluded. The selected price range was EUR 0.39 to EUR 1.09 per bottle, which corresponds to actual retail prices and was intended to reflect a wide range of price segments. The alcohol content was based on the average alcohol content of the varieties listed by the German Brewers Association (Deutscher Brauer-Bund, 2014). The average alcohol content was 4.6–5.6% over all varieties. The concrete indication of beer origin is not mandatory in Germany, insofar as this does not mislead the consumer (Deutscher Brauer-Bund, 2014). For this reason, in addition to regional beer, the beer from Germany and beer from abroad, the level “no indication” was included in the attribute origin. Other beer characteristics have recently been shown to be important, such as whether the beer is an organic product, whether it is calorie-reduced, and whether it is gluten-free. The relevance of organic food retailing in Germany is constantly increasing. Sales increased by 9.8% from 2015 to 2016 (Bund Ökologische Lebensmittelwirtschaft, 2017). Caporale and Monteleone (2004) found that the acceptance and effect of organic labels on beer is dependent on customer awareness of the overall positive effect of organic products. A study by Van Kleef et al. (2008) found that there was a generally positive attitude among the participants with regard to easily detectable calorie content in foods. The relevance of gluten content in foods is also steadily increasing. Sales of gluten-free foods in food retailing and drugstores in Germany increased from 2016 to 2017 by 29.6%, leading to an increase in sales of 29.6% (Krien, 2017). Step 2 of the methodology was the design of the selection situation, for which Lighthouse Studio 9 (Sawtooth Software) was used to create an orthogonal reduced factorial design. The participants were each shown eight choice sets. To present the stimuli, the full-profile

2. Materials and Method The survey was conducted in Germany and took place in December 2017. To ensure that the sample was nearly representative of the German market for beer drinkers, quotas were set in terms of age and gender, based on the calculations of Maack et al. (2011). The survey itself, including recruitment and incentivization of the participants, was carried out by an independent market research agency (panel provider). The panel provider invites participants to take part in the survey and secures that the quotas are met. The authors cleared the data by excluding speed-liners and outliners. The questionnaire contained three main parts. The first part of the questionnaire consisted of eight choice sets for a choice-based conjoint analysis (CBCA). In second part, the statements of the modified FRL by Gunarathne et al. (2017) were evaluated. Part three gathered sociodemographic data and posed beer-related questions. Before the survey started with our questions of interest, two selection questions established that each participants had reached the minimum age for beer consumption of 16 years in Germany and at least occasionally drank beer. 2.1. Choice-based conjoint analysis CA is one of the standard methods of examining customer preferences and purchasing decisions. The CBCA observes the choices made by participants and derives their preferences. A derived partworth utility reflects the impact on product selection better than a change in the ranking or rating and requires fewer assumptions (Meyerding, 2016). In addition, the possibility of a “none” option, the possibility of not selecting any of the products presented, allows the 230

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method was chosen, in which each product profile consisted of all attributes. Design settings were eight random and no fixed tasks, we had four concepts per task (excluding the none option). We used traditional none option (no dual response). The random task generation method used was balanced overlap and the design type was the traditional full-profile choice-based conjoint design. There was no randomization of attribute order. The response type was discrete choice. A general guideline is to achieve standard errors of 0.05 or smaller for main effect utilities and 0.10 or smaller for interaction effects or alternative-specific effects. This was the case for the present study. The strength of design for this model is 1803.30995 (the ratio of strengths of design for two designs reflects the D-Efficiency of one design relative to the other). The representation of the bottle color was made graphically to reduce the cognitive effort of the respondents during processing, because a verbal statement does not represent the bottle color in reality. Due to the high number of attributes, only four stimuli and the “none” option were presented in each of the choice sets. Each participant was thus presented with a total of 32 different product profiles. It is possible that for some participants, who have strong preferences, especially for the type of beer, only one choice could satisfy such participants. For this reason, balanced overlap was chosen so that in some choice sets the attributes in different stimuli had the same level. This allowed the participant to choose a preferred level for that attribute while simultaneously focusing on other attributes. Fig. 1 shows the selection situation with an exemplary choice set as presented in the study. Step 3 constituted the specification of a utility model. The additive partial value model was chosen as the utility model because the attributes of bottle color, beer type, origin, organic, calories, and gluten have different relevance for each participant and their order cannot be predicted. In step 4, specification of a selection model, the multinomial logit-choice model was chosen to explain the selection process of the participants and estimate the resulting part-worth utilities. The logitchoice model is considered to be the most important choice model for mapping decision-making behavior in a CBCA. Data from the choicebased conjoint experiment were analyzed using the simple multidimensional logit model (MNL). In this model, the probability (Pkm ) that option Ck is picked from a choice set Cm of m options (C1, …, Cm ) , is the exponential of the utility (U ) of alternative Ck divided by the total of all exponential utilities of all options in the choice set:

Pkm = P (Ck | Cm) =

exp (U(Ck ) ) m exp(U(Cn) ) m 1

utilities. For the calculation of the part-worth utilities—the importance of attributes and the estimation of the utility values for different segments—the individual preferences of the participants were considered. By disaggregating the utility values, utility functions for each individual consumer are determined instead of the preferences of an average participant who does not exist in this form (Baumgartner and Steiner, 2009, p. 147). The basis for calculating part-worth utilities and the importance of attributes was the Hierarchical Bayes (HB) approach. The utility values of the different segments were determined by latent class analysis (LCA). 2.2. Hierarchical Bayesian approach Typically, HB assumes that the utility values of the participants are normally distributed. Based on this assumption, the selection decisions of all participants are included in the benefit assessment of an individual participant. Thus, even with very few selection decisions per participant, individual benefit values can be determined (Sattler, 2006). As a result, statistically valid conclusions about a population based on many individual observations can be made. This enables the possibility of a robust estimation of the part-worth utilities, regardless of the survey procedure (Baumgartner and Steiner, 2009, p. 158). The HB estimate has now become established as the preferred estimation method (Gieseking, 2009). The HB estimate, like the CBCA, was performed using the Lighthouse Studio 9 software. The program uses a Monte Carlo Markov Chain algorithm. With a value of 0.728 of the McFaddens R2, a very good model estimate can be assumed (Costanzo et al., 1982). The value for the RLH was 0.646 at the top of the range from 0.20 to 1.00, so it can be assumed that the result agrees very well with the empirical data. 2.3. Latent class analysis LCA is a market segmentation technique based on observable characteristics. In this study, the LCA was chosen as the method of a posteriori segmentation on the basis of the conjoint-based preference data. Heterogeneity is thus treated as a phenomenon that takes place between segments of participants but not between individual participants (Gieseking, 2009, p. 75). Latent class MNL is what Sawtooth softwares latent class procedure does, where the algorithm fits multiple groups (class) vectors of utilities that provide better fit to the data (where the fit is the likelihood of choice, so there is a dependent variable involved), and for which each participant has a continuous probability of belonging to each group (class). For more details regarding the used latent class approach see Sawtooth Software (2004).

(1)

The utility of every alternative is a linear function of the part-worth utilities (ßij) of the characteristics specifications: i=q

2.4. Food-related lifestyle

p

U(Ck | Cm) =

Xij *ßij i=1 j=1

(2)

In the mid-1990 s, with the FRL approach (Brunsø et al., 1996), an instrument was developed for measuring and segmenting attitudes on food and eating-related behavior. The FRL consists of 69 items, which can be assigned to five aspects (ways of shopping, cooking methods, quality aspects, consumption situations, and purchasing motives). The statements are measured on a 7-point Likert scale (from “completely disagree” to “completely agree”). Since then, the FRL has been tested in studies in the Asian region (Grunert et al., 2011) and especially in many European and other Western countries (Cullen and Kingston, 2009; Wycherley et al., 2008). Gunarathne et al. (2017) modified the FRL scale with the aim of adopting the instrument to the German food culture. Eleven of the items were taken directly from the FRL by Brunsø and Grunert (1995). Additional items were added that were suspected to have relevance to eating behavior. The modified and audited FRL consists of 41 items, which can be assigned to seven aspects (eating in company, pleasure and interest, novelty preferences, attending culinary events, quality aspects, passion for cooking, subjective knowledge, and cooking skills).

where ßij is the part-worth utility of specification j of a characteristic i and Xij is a dummy variable that represents the appearance (Xij = 1) or nonappearance (Xij = 0) of the specification j of characteristic i of option Ck . The parameters ßij for the main effects and the interactions were estimated for each specification of the characteristics considered in the experimental design (stimuli). One part-worth utility within each characteristic is scaled to zero to create the reference category for the other characteristic specifications. The probability of choosing an alternative depends on its utility and the benefits of the alternatives. The probabilities are not considered in absolute terms but in relation to each other (Backhaus et al., 2011). Step 5 involved an estimation of utility values, for which the maximum likelihood method was applied using the Newton-Raphson algorithm. In step 6, interpretation and implementation, for better interpretation of the data, one attribute utility was selected as the base value and set to zero. The other values were calculated accordingly (Hair et al., 2010, p. 297). Step 7 involved disaggregation of the 231

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Fig. 1. Exemplary choice set of the CBCA.

consideration of the factor loadings, the Kaiser-Meyer-Olkin-measure (KMO) of sampling adequacy, and the Bartlett test of sphericity. The factor loadings ranged between 0.404 and 0.833, which is appropriate if the sample size is taken into account (Hair et al., 2010). The high value of the KMO (0.922) indicates the usefulness of the factor analysis, as level are the 0.90s optimal. The Bartlett test of sphericity is a statistical test for the overall significance of all correlations within a correlation matrix (Hair et al., 2010, p. 92). The significance level is 0.000. To test the reliability of each factor, Cronbach's alpha was used. The values of Cronbach's alpha ranged from 0.670 to 0.946. As the lower limit is 0.60, internal consistency has been ensured (Hair et al., 2010, p. 92). The total variance explained by the nine factors was 59.39%. Detailed results of the factor analysis can be found in Table A1 in the Appendix.

To characterize the consumer segments in our study, we adapted the FRL of Gunarathne et al. (2017) to the beverage context. Here, 30 of the 41 items were included unchanged. Eleven of the 41 statements were slightly modified to suit the beverage context. In addition to the 41 items, nine additional items were added to the instrument. Five of the items served the operationalization of the aspect of brand loyalty, based on Muncy (1996). In addition, the aspect of the general attitude and opinion of the respondents on alcohol was operationalized with four further items. The items developed by Grimm et al. (2013) were described as a factor of “pleasure and intoxication.” This resulted in a total of 50 items. As in the study by Gunarathne et al. (2017), the items we used in our study were measured on a 5-point Likert scale (from “completely disagree” to “completely agree”). To verify the validity of the items with regard to the measurement model, an exploratory factor analysis with Varimax rotation was conducted. As part of the factor analysis, seven items were removed for lack of aptitude for further consideration of the study. The suitability of the data for the structure was confirmed by

3. Results A total of 552 respondents completed the survey. Exclusion criteria 232

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Table 2 Demographic characteristics of the beer consumers (N = 484). Variable

Description

n

%

Quotas set (%)

Variable

Description

n

%

Age

16 to 29 30 to 39 40 to 49 50 to 59 60 to 69 70 to 79 80 to 89 Mean Male Female Rural region Urban region

82 67 95 88 78 70 4 49.2 304 180 169 315

16.9 13.8 19.6 18.2 16.1 14.5 0.8 62.8 37.2 34.9 65.1

20.2 15.5 20.5 16.8 13.8 13.2

Education

Certificate of secondary education

80

16.5

General certificate of secondary education

166

34.3

High school graduation or equivalent

99

20.5

Technical college/university degree

136

28.1

Others < EUR 500 EUR 500–1,000 EUR 1,001–2,000 EUR 2,001–3,000 EUR 3,001–4,000 > EUR 4,000 unknown

3 8 38 117 130 87 65 39

0.6 1.7 7.9 24.2 26.9 18.0 13.4 8.1

Gender Residential area

66.0 34.0

Net household income per month

Note. Quotas based on Maack et al., (2011).

were inconsistent answers, a processing time of less than 7 min, and respondents with a Root-Log-Likelihood (RLH) value of < 0.5. The RLH can assume values between 0.20 and 1.00 in the study, where 1 means a perfect fit (Gieseking, 2009). With a value of < 0.5, a poor suitability of the empirical data with the result can be assumed. This resulted in 484 respondents included in the analysis. An overview of the sociodemographic data of respondents is given in Table 2. Overall, 62.8% of the respondents were male and 37.2% were female, which roughly corresponds to the study of beer drinkers by Maack et al. (2011). In average, the age of the respondents was 49.2 years old. The majority of respondents had a net household income between EUR 2001 and EUR 3000. Table 3 gives an overview of the answers to the beer-related questions. Regarding the location of beer drinking, the majority of the respondents indicated at home, followed by a restaurant, pub, and at a bar. If they do not buy beer in a restaurant, pub, or bar, the majority of the respondents typically buy beer at the supermarket. About 47.3% of the respondents indicated that they mainly drink the Pils type, which according to Nielsen (as cited in Strobl, 2016) accounts for 57% of the beer sales in the German food retail sector. Finally, the respondents were asked to assess their own knowledge about beer, for which 89.2% rated their knowledge as medium or better.

3.1. Attribute importance and part-worth utilities The solution of the logit-analysis was assessed by Chi2, RLH, and McFadden-R2 (also “Pseudo-R2,” “Likelihood-Ratio-Index,” and “Percent Certainty”) (Gieseking, 2009, p. 120). The value of Chi2 at 742.566 was well above the minimum requirement of 37.652 (p < 0.05) at 25 degrees of freedom, so that a significant influence on the answers of the respondents can be assumed by the composition of the attributes. The RLH indicates how well the result matches with the empirical data (Backhaus et al., 2011). With four alternatives per choice set plus the none option, the RLH can take values between 0.20 and 1.00, where 1 means a perfect match (Gieseking, 2009). With a value of 0.22, the value required for the minimum requirements is exceeded. McFaddens R2 describes the ability of the model to explain the pattern of choice in the data (Olitsky et al., 2017), which means what percentage between the zero prediction and the best possible prediction is achieved with the model. McFaddens R2 ranges from 0 to 1, where height is an indicator of model improvement over the null model (Brzoska, 2003, p. 224). Even sizes from 0.2 to 0.4 can indicate a good model estimate (Costanzo et al., 1982). For the most part, there is a significant impact on consumer choice within the levels. In this case, the t-ratios were above 1.645 (p < 0.05 level). Based on the HB algorithm, the importance of attributes and the

Table 3 Answers to beer-related questions of beer consumers (N = 484). Question Where do you mostly drink beer?

a

Where do you mostly buy beer, except for the drinks you buy in a bar/pub/ restaurant?a

Which container size do you buy predominantly, except for the drinks you buy in a bar/pub/restaurant?

a

Description ifications

n

%

Question

Description

n

%

At Home Bar Pub Restaurant Beverage market Specialty store Supermarket Discounter Kiosk Gas station Online Somewhere else I never buy beer Bottle Six-pack Crate Keg I never buy beer

397 89 164 187 280 32 327 160 30 23 5 5 4 103 170 204 3 4

82.0 18.4 33.9 38.6 57.9 6.6 67.6 33.1 6.2 4.8 1.0 1.0 0.8 21.3 35.1 42.1 0.6 0.8

Which beer type do you drink mostly?

Pils Export Weizen Pale Kölsch Lager Another sort Is not known to me Good Rather good Mediocre Rather bad Bad

248 26 129 53 23 6 34 5 91 143 198 44 8

47.3 5.0 24.6 10.1 4.4 1.1 6.5 1.0 18.8 29.5 40.9 9.1 1.7

Multiple answers possible. 233

How well do you know about beer?

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part-worth utilities for the overall model were calculated. The importance of attributes describes the relative importance of each attribute on the selection decision. The sum of the importance of attributes was always 100%. To measure the overall importance of attributes, the individual importance for each respondent is individually estimated, normalized, summed up, and divided by the number of respondents (Orme, 2010, p. 77). The beer type had a disproportionately high weight in the decision of the respondents. The next highest influence was the price, followed by the origin of the beer and the alcohol content. The influence of the remaining attributes “calories,” “bottle color,” “gluten,” and “organic” differed only marginally. The part-worth utilities describe the relative desirability of individual levels of attributes (Böhm . et al., 2015). One value was selected as the basis category and set to zero. The other attribute levels can be interpreted in relation to the basis level (category). The partworth utilities of the other attribute levels are recalculated to show the differences compared to the basis category. For example, EUR 1.09, the highest price is set as a basis category. It can easily be seen, that lower prices showed higher part-worth utilities compared to the basis category of the highest price EUR 1.09. The none option seen at the end of the table show how many utilities a product concept needs to offer to be chosen compared to not buying a beer. The part-worth utilities are shown in Table 4. Within the attribute “type,” Pils, in particular, was able to ensure a significant increase in the perceived attractiveness of a beer. Pale beer had the second highest part-worth utility. Weizen beer and export beer gave nearly similar benefits, followed by lager and Kölsch. Within the attribute “price,” the second lowest price, EUR 0.49, had a slightly higher part-worth utility than the lowest price, EUR 0.39. After that the part-worth utility sank with rising price. Within the attribute “origin,” the greatest benefit was provided by German beer in general. The partworth utility of regional beer was higher than beer that was labeled only as being German. Beer from abroad denoted the least benefit and was even less popular among the respondents than a missing indication of the origin. With the exception of the level “5.6%” (p < 0.1), none of the alcohol content levels within this attribute were significant; this was similar to the logit analysis, in which this attribute also had a low significance. This can be attributed to the small differences that already arose from the counting analysis. Here, the consideration of the segments in the LCA is particularly important because different segments of respondents who show different preferences can cause a significant effect on an aggregated level. It may be possible that there were segments in which the attribute was significant. Within the attribute “calories,” the absence of a clue had a higher part-worth utility than the hint of reduced calories. The same applied to the attribute “gluten.” Within the attribute “bottle color,” the greatest benefit was found for the color green, followed by brown. Even with the attribute “organic,” missing information provided a higher benefit than an indication that the beer was organic. To interpret the part-worth utilities of the none option one can compare the none value to the sum of part-worth utilities across the other attributes. In other words, a level from each attribute makes up a product concept. One can sum those levels (one per attribute) across attributes to obtain the total utility for a product concept. If that total utility exceeds the utility of the none concept, then it is more likely to be chosen than the none concept. In our study, the value of the none concept is comparably low, so it is easy for a beer product to be chosen. We additionally performed an interaction effect analysis and found some interactions between calorie x gluten, organic x gluten, and sort x price.

Table 4 Part-worth utilities of all attribute levels for the whole sample (N = 484).

segments. Since the log-likelihood increases with an increasing number of segments, it is not suitable for determining the ideal number of segments. For this reason, goodness-of-fit criteria such as the consistent Akaike information criterion (CAIC) and the Bayesian information criterion (BIC) are used (Bruwer and Li, 2017). The LCA was also performed with the Lighthouse Studio 9 software. The solution was calculated for a range of at least two and a maximum of five segments. The goodness-of-fit criteria are shown in Table 5. The CAIC value is one of the most widely used values for determining segment size (Sawtooth Table 5 Goodness-of-fit criteria of the LCA.

3.2. Segmentation The solution of the LCA can be determined for a different number of

234

Segments

CAIC

BIC

McFaddens R2

Chi2

Relative Chi2

2 3 4 5

11,318.63 11,185.00 11,202.80 11,243.05

11,265.63 11,105.00 11,095.80 11,109.05

0.131 0.162 0.181 0.197

1635.72 2019.40 2251.67 2461.48

30.86 25.24 21.04 18.37

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Table 6 Attribute importance for the three consumer segments.

Table 7 Part-worth utilities for the three consumer segments.

Software, 2004, p. 10). The CAIC value is lowest for the three-segment solution. BIC is best in the four-segment solution. However, the values of the three-segment solution and the five-segment solution are only marginally higher.

The biggest leap is between the value of the two-segment solution and the three-segment solution. The value of Chi2 is highest in the fivesegment solution, but the value as well as the value of the McFaddens R2 increase with the number of segments (Sawtooth Software, 2004, p. 235

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10). For this reason, consideration is given to the more significant relative Chi2. Here, the two-segment solution offers the optimum value. The value of McFaddens R2 marginally falls below the value of 0.2 reported by Costanzo et al. (1982), from which a good model estimate can be assumed. In addition to the goodness-of-fit criteria, the plausible interpretability of the segments is also important. For this reason, the results of the two-segment, three-segment, and four-segment solutions were also considered in terms of content. The three-segment solution provides the clearest separation between the segments in terms of their characteristics. Based on CAIC and content, this study adopted the solution with the following three segements: “type-focused” (41.3%, n = 200), “broad-minded” (30.8%, n = 149) and “conservatives” (27.9%, n = 135). Table 6 shows the importance of attributes, and Table 7 shows the part-worth utilities of each segment. The highest importance of the “type-focused” segment was the type of beer, with “Pils” having the highest part-worth utility. Also important was the attribute “price.” Regional beer had the highest partworth utility. The least part-worth utility was beer from abroad. Of minor importance were the bottle color and the alcohol content. Whether the beer was labeled as organic, gluten-free, or calorie-reduced was of little importance to this segment. For all three attributes, however, the part-worth utilities were higher if no label was presented. The importance of attributes related to the “broad-minded” segment was distributed similarly to the “type-focused” segment. The beer type was given the highest importance. The highest part-worth utility here was “Weizen,” followed by “Kölsch.” Also, the second most important attribute was the price. Here, the second lowest price of EUR 0.49 had a higher part-worth utility than the lowest price. Regional beer and beer from Germany had the highest part-worth utilities, followed by beer from abroad. The next highest importance was organic labeling; the label “organic” had a higher part-worth utility than the absence of such a label. In the “conservatives” segment, the type was also of the highest importance, with the highest part-worth utility for “Pils.” The second most important attribute in this segment was the origin, with the

highest part-worth utility for “regional beer.” Labeling for the attributes “calorie-reduced” and “gluten” had the greatest importance among all three segments. For both attributes, the part-worth utility was higher in the absence of labeling. The “conservatives” segment was the least sensitive to price. As can be seen by the relatively high part-worth utility of the none option, the consumers in conservative segment are also the ones who are most likely to not purchase a beer if it does not fit their preferences. The demographic attributes of the individual segments are shown in Table 8. The “type-focused” segment was the largest of the three segments and had the highest proportion of men. The “broad-minded” segment was the youngest segment. The majority said they came from an urban region, which was more than in the other segments. With 16.8% reporting a net household income of > EUR 4000, this represented the largest share among the segments. The “conservatives” segment was the oldest of the segments and also had the highest proportion of women. Table 9 gives an overview of the answers to the beer-related questions by segment. When asked where beer was mostly consumed, most people in all three segments answered “at home.” The largest portion of people who drink beer outside of the home was found in the “broadminded” segment. In all three segments, the supermarket and the beverage market represented the venues where most of the beer was purchased. The majority in the “type-focused” and the “conservatives” segments tended to buy crates, while the majority in the “broadminded” segment bought six-packs. The largest share in the “type-focused” segment (70.5%) and the “conservatives” segment (53.3%) indicated that they mostly drank the type “Pils.” In the “broad-minded” segment, the largest part (34.2%) indicated drinking “Weizen.” All segments valued their knowledge about beer as relatively high. A one-way ANOVA with Scheffe's post-hoc test was used for analysis of the factor means of the FRL to find out how the consumer segments differed regarding their values and lifestyles. Table 10 shows the results of the mean comparison. As a result of the ANOVA, it was found that there were significant differences between the segments within the

Table 8 Demographic characteristics of the different segments of beer consumers (N = 484). Type-focused (n = 200; 41.3%)

Broad-minded (n = 149; 30.8%)

Conservatives (n = 135; 27.9%)

Variable

Description

n

%

n

%

n

%

Age

16–29 30–39 40–49 50–59 60–69 70–79 80–89 Mean Male Female Rural region Urban region No qualification Certificate of secondary education General certificate of secondary education High school graduation or equivalent Technical college/university degree Others < EUR 500 EUR 500–1000 EUR 1001–2000 EUR 2001–3000 EUR 3001–4000 > EUR 4000 unknown

31 32 39 33 32 32 1 49.3 134 66 71 129 0 33 74 45 48 0 4 16 57 53 34 22 14

15.5 16.0 19.5 16.5 16.0 16.0 0.5 – 67.0 33.0 35.5 64.5 0.0 16.5 37.0 22.5 24.0 0.0 2.0 8.0 28.5 26.5 17.0 11.0 7.0

36 28 27 24 19 12 3 44.6 95 54 43 106 0 20 37 36 55 1 1 10 28 38 34 25 13

24.2 18.8 18.1 16.1 12.8 8.0 2.0 – 63.8 36.2 28.9 71.1 0.0 13.4 24.8 24.2 36.9 0.7 0.7 6.7 18.8 25.5 22.8 16.8 8.7

15 7 29 31 27 24 2 54.1 75 60 55 80 0 27 55 18 33 2 3 12 32 39 19 18 12

11.1 5.2 21.5 23.0 20.0 17.8 1.5 – 55.6 44.4 40.7 59.3 0.0 20.0 40.7 13.3 24.4 1.5 2.2 8.9 23.7 28.9 14.1 13.3 8.9

Gender Residential area Education

Net household income

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Table 9 Answers to beer-related questions by consumer segments (N = 484). Type-focused (n = 200; 41.3%)

Broad-minded (n = 149; 30.8%)

Conservatives (n = 135; 27.9%)

Question

Description

n

%

n

%

n

%

Where do you mostly drink beer?*

At Home Bar Pub Restaurant Beverage market Specialty store Supermarket Discounter Kiosk Gas station Online Somewhere else I never buy Beer Bottle Six-pack Crate Keg I never buy beer Pils Export Weizen Pale Kölsch Lager Another sort Is not known to me Good

170 36 72 69 112 18 136 78 15 16 2 1 1 38 66 94 1 1 141 18 54 13 2 4 7 1 47

85.0 18.0 36.0 34.5 56.0 9.0 68.0 39.0 7.5 8.0 1.0 0.5 0.5 19.0 33.0 47.0 0.5 0.5 70.5 9.0 7.0 6.5 1.0 2.0 3.5 0.5 23.5

81.2 26.2 36.9 43.6 60.4 5.4 70.5 27.5 6.0 1.3 1.3 2.0 1.3 22.8 39.6 35.6 0.7 1.3 23.5 2.7 34.2 18.1 13.4 – 6.0 2.0 13.4

106 14 37 53 78 6 86 41 6 5 1 1 1 31 45 57 1 1 72 4 24 13 1 2 18 1 24

78.5 10.4 27.4 39.3 57.8 4.4 63.7 30.4 4.4 3.7 0.7 0.7 0.7 23.0 33.3 42.2 0.7 0.7 53.3 3.0 17.8 9.6 0.7 1.5 13.3 0.7 17.8

Rather good Mediocre Rather bad Bad

60 75 17 1

30.0 37.5 8.5 0.5

121 39 55 65 90 8 105 41 9 2 2 3 2 34 59 53 1 2 35 4 51 27 20 0 9 3 20 2251 51 58 17 3

34.2 38.9 11.4 2.0

32 65 10 4

23.7 48.1 7.4 3.0

Where do you mostly buy beer, except for the drinks you buy in a bar/pub/restaurant?*

Which container size do you buy predominantly, except for the drinks you buy in a bar/pub/restaurant?

Which beer type do you drink mostly?

How well do you know about beer?

Note. * Multiple answers possible.

factors “Attending culinary events,” “Passion for cooking,” and “Subjective knowledge and cooking skills.” With regard to the factor “Attending culinary events,” the “broad-minded” segment differed significantly from the “type-focused” and the “conservatives” segments. However, there was no significant difference between these two segments. The means were highest in the “broad-minded” segment. There was a significant difference between the “broad-minded” and the

“conservatives” segment with regard to the factor “Passion for cooking.” In this regard, the “type-focused” segment did not differ significantly from the “broad-minded” or the “conservatives” segments. The means were highest in the “broad-minded” segment. With regard to the factor “Subjective knowledge and cooking skills,” all three segments differed significantly from each other; here too, the means were highest in the “broad-minded” segment.

Table 10 Mean values (SD) of the FRL factors for each segment. Factors

1. 2. 3. 4. 5. 6. 7. 8. 9.

Eating in company Pleasure and interest Novelty preferences Attending culinary events Quality aspects Passion for cooking Subjective knowledge and cooking skills Brand loyalty Passion for alcohol

Sample Total (n = 484)

Type-focused (n = 200) Mean/SD

3.29 4.01 3.28 2.50 3.62 3.25 3.37 2.94 3.35

3.32/0.877 4.03/0.702 3.25/1.009 2.49/0.989 3.60/0.771 3.31/1.132 3.40/0.938 2.92/1.035 3.56/0.856

a a a a a a,b a a a

Broad-minded (n = 149) Mean/SD 3.46/0.828 4.05/0.711 3.43/0.912 2.75/1.031 3.67/0.759 3.32/1.077 3.43/1.020 2.81/1.045 3.33/0.922

a a a b a a b a a

Conservatives (n = 135) Mean/SD 3.07/0.829 3.94/0.753 3.16/0.960 2.22/0.972 3.60/0.782 3.07/1.163 3.26/1.046 3.13/1.010 3.05/0.824

a a a a a b c a a

Note. Items were assessed by means of Likert scales (1 = totally disagree; 5 = totally agree). The ANOVA was carried out with the factor values. Superscripts stand for significant mean differences at the 0.05 level based on Scheffe's post-hoc testing.

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conducted the survey, and no quotas were set for the distribution of the sample in relation to regions within Germany. This makes difficult a precise interpretation of the results for the attribute “type,” because, in particular, types such as “Kölsch” and “Weizen” are popular in different regions (Deutscher Brauer-Bund, 2017). Second, the choice of the “Weizen” type may have been influenced by its visual representation. To create the greatest possible objectivity, a uniform bottle shape and color were chosen for all types. However, the bottle size and a white bottle color do not correspond to the usual look of “Weizen” in the retail setting. Third, by depicting the origin of the beer, a negative effect could have been introduced in the attributes “beer from abroad” and “no statement of place.” The depiction differs here from the real representation of a beer label, which either lacks an indication of the origin of the beer or indicates the country of origin, insofar as it deviates from Germany.

4. Discussion Our results from CA are consistent with many prior theoretical propositions and empirical evidence. For instance, we find evidence that German consumers prefer low priced beers as well as domestic ones. The latter is especially not surprising as Germany has a longstanding brewing tradition and there is a general preference of German consumers for regional products (Reid et al., 2014; Bavorová et al., 2016; Meyerding, 2016). However, some of our results are contrary to prior evidence and consequently indicate a need for further research: Our overall results from suggest that neither Organic, nor calorie-reduced and gluten-free labels are preferred to the “no label” option by German beer consumers. This is while there is an increasing health awareness in Germany (Nadine and Orth, 2018) and, in general, organic consumptions is on a raise (BÖLW, 2018). Moreover, studies on other food products clearly indicated an overall stated preference for organic products by German consumers (e.g. Meyerding, 2016; Meyerding et al., 2018a; Meyerding and Merz, 2018c). Our LCA approach is novel for the case of beer. By including items for factors such as ‘brand loyalty’ and ‘passion for alcohol’, it may be viewed as a first contribution towards the creation of beer related lifestyle approach. With a look at the practical issue of how to counteract declines in German beer sales, our results are new and of value for all practitioners in the German market who aim to target specific, more homogenous, market segments. As possible implications are abundant, we proceed to give, arguably, the most clear-cut implications for each of the three identified segments: The “type-focused” segment is the biggest segment and especially important to “Pils” breweries, which are able to sell their beer at al low price: “Pils” is by far the most preferred type of beer in this segment and consumers are very price sensitive. In contrast to the other segments, these consumers are more likely to buy at discounters and prefer to buy crates. As most discounters in Germany currently do not sell beer in crates, one way to capture new consumers in this segment would be to allow for selling “Pils” crates at discounters. The “broad-minded” segment is the youngest, highest educated, richest and most urban of the three segments. This segment is relevant especially for breweries with new and/or organic beers on the market, as these consumers are open to novelty preferences and willing to spend a little more on beer. It is the only identified market segment, where an organic label can add some value to the consumer. Beers for this segment should be bottle in white glass. Our results further suggest that consumers of this segment can be successfully targeted by marketing campaigns focusing on young, modern and urban consumers. Market campaigns with beer tastings may be especially fruitful. The “conservatives” segment is the smallest of the three segments. It is interesting for breweries with established brands on the market, especially when they have a rather “high alcoholic” type of beer in their portfolio: Consumers in this segment prefer 5.6% alcohol content and are, on average, the most brand loyal. According to our CA results, when targeting this segment, practitioners should avoid using labels referring to “calories” or “organic” and should use green bottles. In this segment, people are on average the oldest and there is the highest proportion of women and rural inhabitants. Accordingly, marketing campaigns focusing on this type of consumer may be effective. Our findings may be interpreted with some caution, as the study faces some limitation. First, an independent market research institute

5. Conclusion The results of the study showed that “type” is the attribute that has the highest importance for the consumer in the selection process of beer in Germany. The attributes “type,” “price,” and “origin” of a beer have the highest importance for the majority of consumers. What is surprising about the “origin” attribute is the fact that the majority of people prefer to sample a beer without identification of the origin, as opposed to that of a beer from abroad. Particularly interesting, however, is that we cannot detect a market segment interested in beers labeled with diet-related attributes, such as ‘gluten-free’ and ‘caloriereduced’, in our survey results. Additionally, in only one of the three market segments lies potential for organic beer sales. Accordingly, the German beer market is changing slowly and the majority of beer drinkers, still prefer traditional beer. Studies on other food products clearly indicated an overall stated preference for organic products by German consumers (e.g. Meyerding, 2016; Meyerding et al., 2018a; Meyerding and Merz, 2018c). Accordingly, further research needs to identify the reasons behind the lack of consumer interest in these labels in the case of beer. Our LCA approach is novel for the case of beer. Future research, however, is needed to test and verify our results. From a methodological point of view, ideally, future research should develop a MNL model to present the aggregate level part-worth values as in the present study and additionally controlling for the individual characteristics. A Hausman test should then be conducted to examine the independence from irrelevant alternative (IIA) of the MNL model. Then for the purpose of segmentation, the latent class model can be applied (Greene and Hensher, 2003; Shen, 2009). Greene and Hensher (2003) as well as Shen (2009) used the same latent class model as in the present study (for more technical details see Sawtooth Software, 2004). In addition, to avoid potential bias caused by excluding intrinsic attributes given the drinking experience of most respondents, future research should apply methods which allow for using both intrinsic and extrinsic attributes as explanatory variables (e.g. Meyerding et al., 2018b). Our study focuses on extrinsic attributes only. Regarding the attributes and levels in the choice design, the regional beer is also part of beer of Germany. The levels of the same attribute should be mutually exclusive. Thus, in a future study “beer from Germany” should be changed to “beer from other regions of the Germany”.

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Appendix A (See Table A1)

Table A1 Detailed results of the factor analysis. Factors and corresponding variables

Mean/SD

I. Eating in company (Cronbach's alpha: 0.670) Eating and/or drinking together with friends is an important part of my social life. When I serve a dinner to friends, the most important thing is that we are together.* The food taste is much better when I eat in good company.* II. Pleasure and interest (Cronbach's alpha: 0.861) I am very interested in food.* When it comes to food I am a real connoisseur. For me, eating is a matter that incorporates all senses of feeling, smell, taste, and sight.* When I eat, I enjoy food very much.* Good drinks and food play a major role in my life. III. Novelty preferences (Cronbach's alpha: 0.874) I like to buy and try exotic foods. I love to try recipes from foreign countries.* I only buy and eat foods that are familiar to me.* I look for various ways to prepare unusual meals.* I like to try new foods that I have never tasted before.* IV. Attending culinary events (Cronbach's alpha: 0.854) I like to visit food fairs.* I would like to attend food tastings.* I like to attend events around the topic of food and drink. I like to visit cooking classes.* I like to read food blogs on the Internet.* V. Quality aspects (Cronbach's alpha: 0.854) For me, the naturalness of food is an important factor.* I prefer fresh products over canned products.* I would pay more for food that is labeled (e.g., organic, Fairtrade, animal welfare). I prefer to buy food from my region.* I like to buy foods that have used hand-crafted production.* I prefer to buy foods that were traditionally made.* I prefer trustworthy labeled food (e.g., organic, Fairtrade, animal welfare) over foods without a seal of quality.* VI. Passion for cooking (Cronbach's alpha: 0.946) Cooking is my hobby.* Cooking brings me joy.* Cooking is a process of self-realization.* I have a passion for cooking.* I like to try new recipes.* I invest a lot of time for cooking.* I am proud to prepare own meals and self-invested recipes.* VII. Subjective knowledge and cooking skills (Cronbach's alpha: 0.858) I do not need recipes because I know by experience what combination of ingredients results in a delicious dish.* I am flexible and can make a meal out of all possible ingredients that I have at home.* I prefer to cook dishes creatively instead of sticking to a recipe. VIII. Brand loyalty (Cronbach's alpha: 0.839) I would only buy a different brand of beer than the ones I usually buy in exceptional circumstances. There are no other beer brands that are as good as the ones I usually buy. For me, the beer brand I usually buy is the best brand on the market. If my favorite beer brand sold out in one store, I would go to another store or wait for it to be available again. IX. Passion for alcohol (Cronbach's alpha: 0.817) Alcoholic drinks taste good. Alcohol lifts the mood. Alcohol helps to deal with stress/frustration. You can relax better with alcohol.

3.58/1.111 3.79/1.233 3.91/1.078 3.89/0.958 3.78/0.945 4.29/0.816 4.18/0.791 3.92/0.961 3.29/1.186 3.44/1.207 3.22/1.242 3.00/1.186 3.45/1.129 2.00/1.236 3.42/1.350 2.59/1.291 2.00/1.172 2.43/1.339 3.94/0.917 4.24/0.874 3.25/1.260 3.85/1.012 3.15/1.095 3.57/0.981 3.33/1.182 3.15/1.313 3.65/1.239 3.00/1.347 2.88/1.327 3.58/1.262 3.05/1.251 3.40/1.330 3.09/1.173 3.68/1.080 3.34/1.128 2.89/1.290 2.64/1.230 3.33/1.172 2.91/1.349 3.87/0.838 3.84/0.982 2.71/1.316 2.97/1.233

Note. * Statement was taken unchanged from Gunarathne et al. (2017). Scale from 1 (“strongly disagree”) to 5 (“strongly agree”). N = 484.

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