Collative Properties and Hedonic Responses to Specialty Beer

U N I V E R S I T Y O F C O P E N H A G E N

F A C U L T Y O F S C I E N C E

Collative Properties and Hedonic Responses to Specialty Beer

Mette Duerlund Master Thesis Submitted May 29 th 2012

Academic Supervisors: Michael Bom Frøst and Davide Giacalone


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University of Copenhagen

Faculty of Science

Department of Food Science, Sensory Science Group


Academic Supervisors

Michael Bom Frøst and Davide Giacalone

Submitted May 29th 2012

Master Thesis 30 ECTS Points

Mette Duerlund

Gastronomy and Health

Student Number: dhj334

Signature

________________________________________


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Acknowledgements

A great thank you goes out to my main supervisor Associate Professor Michael Bom Frøst. Thank you for giving

me the opportunity to work with you and this project. Thank you for your constant inspiration and professional

approach which always gives me motivation and new insight. I respect and admire you.

Another thank you goes out to my co-supervisor Ph.D Candidate Davide Giacalone. Thank you for your daily

support and infinite professional helpfulness. Thank you for always making time in your schedule, for always

smiling, and for always creating a positive learning atmosphere. You rock.

Support for this work is granted by the consortium “Dansk Mikrobryg – Produktinnovation og Kvalitet” and the

Faculty of Science, University of Copenhagen. Thanks to the fragrance company Givaudan for providing the

flavours added to the beer.

Thank you to all my good friends and loving family, who has supported me all the way. A special thanks to Mia

Duerlund, Christina Magelund, and Jacco Gerritsen for their help.


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Abstract

The Danish beer market consists of a great variety of beers produced by both large breweries and craft

breweries. However information about flavouring agents in various Danish beers shows, that very few adjuncts

can be found in lagers. There might be possibilities in developing flavourful and characteristic lager beers. Eight

experimental lager beers were designed by adding different flavours. Preference and collative perception of

the beers were investigated. According to Berlyne’s collative-motivation model, stimuli are preferred for their

ability to generate arousal potential. The theory is concerned with relating hedonic value with arousal level

induced by certain stimuli that differ in collative properties. Berlyne termed these properties “collative”

because they depend on the collation between the past and the currently presented stimuli.

A total of 122 consumers participated in the beer tasting, 76 men and 46 women (18-75 years). The sensory

tasting took place at the University of Copenhagen, Department of Food Science. Consumers rated liking and

collative properties specifically developed to fit beers. Background information was collected simultaneously.

Results showed that there was a big differentiation in collative profiles amongst all beers. The properties

typicality, familiarity, and traditional were positively correlated, as were drinkability, refreshing, and thirst-

quenching. Negatively correlated to these were the attributes: complexity, novelty, confusing, and surprising.

An internal preference mapping showed that liking was scattered across all beer samples, indicating that all

beers were liked by someone, and points in the direction of different consumer segments for lager beers.

Multivariate statistics explained consumer liking by both consumer background and collative properties.

Female consumers were highly correlated to low frequency users. This segment had preferences towards

traditional, familiar, and typical beers high in drinkability, refreshing, and thirst-quenching characters. Male

consumers and people that stated “I often try new beers” and “I know a lot about beer” were all correlated.

For these people the properties complexity, novelty, confusing and surprising had the greatest impact on liking.

Complexity and familiarity both showed significant correlations with liking (R=0.267 and 0.274, respectively),

whereas novelty was almost uncorrelated to liking (R=0.082). Both complexity and familiarity showed linear or

monotonic relationships when plotted against liking. Relationship between novelty and liking showed an

inverted U-shape, and thus could be described by Berlyne’s theory on arousal where arousal potential is a

function of liking in an inverted U-shape.

Possibilities exist in developing new innovative lagers in order to reach consumer acceptability and to increase

sales and market share. The key note is to look at segments even within lager beers.


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Dansk Resumé

Det danske øl-marked består af et stort udvalg af øl produceret af både store bryggerier og microbryggerier.

Oplysninger samlet omkring smagsstoffer i danske øl viser, at der er meget få smagstilsætninger i lager øl. Der

eksisterer derfor muligheder for at udvikle smagsfulde og karakteristiske lager øl. Otte eksperimentelle lager øl

blev designet ved at tilsætte forskelllige smagsstoffer. Præference og kollativ opfattelse af øllene blev

undersøgt. I følge Berlyne’s kollative motivations-model, foretrækkes stimuli for deres evne til at genere

opstemthed. Teorien omhandler relationen mellem hedonisk værdi og opstemthedsniveau, som foresages af

stimuli, der differentierer i kollative egenskaber. Berlyne kalder disse termer ”kollative”, fordi de afhænger af

kollationen mellem fortid og nuværende stimuli.

I alt deltog 122 forbrugere i ølsmagningen, 76 mænd og 46 kvinder (18-75 år). Den sensoriske evaluering fandt

sted på Københavns Universitet, Institut for Fødevarevidenskab. Forbrugerne bedømte præference og kollative

egenskaber, som var specifikt udviklet til at passe på øl. Baggrundsinformation blev indsamlet samtidig.

Resultaterne viste, at der var stor differentiering i kollative profiler blandt de otte øl. Egenskaberne typiskhed,

genkendelighed og traditionel var positivt korreleret. Det samme var drikkelighed, forfriskende og

tørstslukkende egenskaber. Negativt korreleret til disse var egenskaberne kompleksitet, nyhedsværdi,

forvirrende og overraskende. En intern præference kortlægning viste at præference var spredt ud over alle øl,

hvilket peger i retning af forskellige forbrugersegmenter indenfor lager øl. Multivariat analyse forklarede

forbruger-præference ud fra både kollative egenskaber og baggrundsinformation. Kvindelige forbrugere var

højt korreleret med lavfrekvente brugere. Dette segment havde præferencer for traditionelle, genkendelige og

typiske øl, som samtidig viste høj drikkelighed og havde forfriskende og tørstslukkende karakter. Mandlige

forbrugere og folk, der udtalte ”jeg prøver ofte nye øl” og ”jeg ved meget om øl” var alle korreleret. For dette

segment var kompleksitet, nyhedsværdi, forvirrende og overraskende de største prædiktorer for præference.

Kompleksitet og genkendelighed viste moderate korrelationer med præference (R=0.267 and 0.274), hvorimod

nyhedsværdi næsten var ukorreleret med præference (R=0.082). Både kompleksitet og genkendelighed viste

lineære eller monotoniske forhold mod præference. Forholdet mellem nyhedsværdi og præference viste en

omvendt U-form, og kunne derfor beskrives med berlyne’s teori, hvor opstemthed er en funktion af

præference i en omvendt U-form.

Mulighederne findes i udvikling af nye innovative lager øl, med henblik på at opnå forbrugeraccept og for at

øge salg og markedsandele. Hemmeligheden ligger i at kigge på segmenter selv indenfor lagerøl.


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Table of Contents

1.0. Introduction .......................................................................................................................................................8

2.0. The Beer Market ................................................................................................................................................9

2.1. Lager Might be the New Black .................................................................................................................... 10

3.0. Theory on Arousal .......................................................................................................................................... 11

3.1. Collative Properties .................................................................................................................................... 12

3.2. MAYA Principle Theory ............................................................................................................................... 13

3.3. Research Done on the Arousal Response Curve ........................................................................................ 15

4.0. Conceptual Properties – Applied .................................................................................................................... 17

5.0. Drinkability ..................................................................................................................................................... 21

6.0. Materials and Methods .................................................................................................................................. 23

6.1. Recruitment and Participants ..................................................................................................................... 26

6.2. Questionnaire ............................................................................................................................................. 27

6.3. Background Information ............................................................................................................................. 28

6.4. Consumer Study ......................................................................................................................................... 29

6.4.1. Test Location and Experimental Conditions ........................................................................................ 29

6.4.2. Experimental Procedure ...................................................................................................................... 30

6.5. Data Analysis .............................................................................................................................................. 33

7.0. Results ............................................................................................................................................................ 35

7.1. Univariate Analyses .................................................................................................................................... 35

7.2. Spider-Web Plots ........................................................................................................................................ 38

7.3. Multivariate Statistics ................................................................................................................................. 40

7.3.1. Emotional Profiling - Principal Component Analysis ........................................................................... 40

7.3.2. Internal Preference Mapping for Liking ............................................................................................... 42

7.3.3. Partial Least Square Regression – PLSR ............................................................................................... 43

7.3.4. Correlations ......................................................................................................................................... 44

7.3.5. Background Variable Selection ............................................................................................................ 45

7.3.6. L-PLSR .................................................................................................................................................. 46

7.3.7. Curve Fit ............................................................................................................................................... 48

8.0. Discussion ....................................................................................................................................................... 49


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8.1. Context ....................................................................................................................................................... 49

8.2. Exposure ..................................................................................................................................................... 49

8.3. Attributes .................................................................................................................................................... 50

8.4. Drinkability ................................................................................................................................................. 52

8.5. Consumers .................................................................................................................................................. 53

8.6. Is Lager the New Black? .............................................................................................................................. 53

9.0. Conclusions ..................................................................................................................................................... 55

10.0. Limitations .................................................................................................................................................... 56

11.0. Perspectives .................................................................................................................................................. 57

12.0. References .................................................................................................................................................... 58

13.0. Appendices ................................................................................................................................................... 63


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1.0. Introduction

Recently great interest has been turned toward the consumers’ affective experience in the human-product

interaction. Insights in affect can be used to design desirable and pleasing consumer experiences. Companies

need to continually innovate and deliver new products, and not just any product, but products with the right

profile the first time. The beer market is a rapidly growing market, where product development, innovation,

and launching of new products occur frequently. Interesting perspectives within this area include the

consumer-beer interaction, and which underlying phenomena that take place when drinking beer.

In recent years, conceptual collative descriptors and consumer emotion attributes have been investigated, how

to develop them and how they relate to hedonic values. These terms contribute to interesting variables to

study in the arousal response curve also named the collative-motivation model. In order to reach high

acceptability and success in new products, prior research suggests an optimal balance between novelty and

familiarity. Preference for familiar things seems incompatible with the desire for new things, but we like what

we already know, and we also like what is novel. Results show that typicality and novelty can be joint factors

predicting preference and liking. People prefer typicality as long as it is not to be the harm of novelty. An

optimal combination of both aspects leads to optimal human preference. Familiarity and novelty are not

opposite sides of the same continuum and it is believed that the two dimensions reflect different cognitive

elements.

Purpose: The purpose of this thesis project is to utilize different herbal and berry flavours to manipulate in

beers, for instance wormwood, juniper beery, Perilla frutescens, and star anise; this by using extracts provided

by Givaudan. Furthermore the aim is to develop relevant collative, emotional, and ecological descriptors to be

assessed, and to conduct a consumer study with Danish consumers selected and screened by chosen variables.

Additionally the purpose is to link data sets and hereby link results with underlying psychographic traits,

behavioural variables, and demographic variables.

Problem formulation: How do different herbal flavours affect liking and collative perception in beer? What

conceptual descriptors are relevant in relation to Danish specialty beers, and how do they correlate to hedonic

liking according to a selected Danish population?

References are conducted in RefWorks using APA Style 6th edition.


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2.0. The Beer Market

The beer consumption in Denmark has decreased by 26% in the last ten years. We drink less and less beer, and

this change might be due to increased wine consumption (Danmarks Statistik, 2012). Still we see a consistent

interest in beer, and the Danish beer market consists of a great variety and diversity of beer produced by both

large breweries and small craft breweries. The number of breweries in Denmark went from 19 to 120 in the last

decade and 584 new beers were developed in the year 2010 (Bryggeriforeningen, 2011). These numbers

indicate a growing interest in beer, and especially microbreweries contribute to new and innovative styles of

beer on the market. However according to sales shares, microbreweries only take up 3.6% of the market

(Bryggeriforeningen, 2011), leaving the larger breweries such as Carlsberg and Royal Unibrew with a significant

market share. The Danish market is dominated by pale lagers, and thus the market share is mainly dependent

on the lager industry. However most experimentation occurs with ales and darker beers, and adding of special

flavoring ingredients thereby is mostly found in ales (Giacalone, Reinbach, & Bom Frøst, 2011).

Roughly put, the Danish beer market can be divided into two opposing counter-poles with the larger breweries

on one side and the microbreweries on the other side. By applying multivariate analysis on beer information,

Giacalone and colleagues (2011) depicted that the big breweries in Denmark are characterized by large

brewing size and bottom fermentation, which is mainly lager beers (Giacalone et al., 2011). Negatively

correlated to the large breweries was the adding of special ingredients. Special ingredients were on the other

hand positively correlated to local identity and top-fermentation, and hereby connected to the microbreweries

and darker and stronger beers like ales and stouts (Giacalone et al., 2011).

Information on different flavouring agents in various Danish beers has been collected from Danish breweries,

and it shows that very few adjuncts can be found in lagers. It is mainly the sharp and acidic fruity tastes which

are used in lagers, for instance lime in “Tuborg lime”, apple in “Ævleøl” from Refsvindinge, and cherry in

“Sortedam Sauvage” from Nørrebro brewery. Several more flavoring ingredients are found in other beer types

than lager such as wheat beer, light and dark ales, stouts, and porters. A general overview demonstrates that

certain flavours are commonly more used for certain types of beer. For instance dark, bittersweet and round

flavours like coffee, juniper berry, and liquorice are often added to dark beers like porters with high alcohol

contents. Examples are “Enebær Stout” from Grauballe brewery or “Beer Geek Breakfast” from Mikkeller

brewery. Sweeter and lighter notes are most commonly added to ales such as honey, orange, black current,

and elderflower for instance in “Fynsk Forår” from Ørbæk brewery or “Honey Gold” from Grauballe brewery.


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Furthermore it is popular to add special flavours to wheat beer with a more herbal and spiced note to it such as

coriander and thyme. Examples are “Bramley Wit” from Jakobsen and “Stuykman Wit” from Refsvindinge. The

above mentioned is by no means an exact classification of flavours and beer types in Denmark, but functions as

a general overview and guideline. There are many exceptions and crossovers with added flavours. However this

description gives rise to the question if certain flavours fit to certain beer types, regardless of novelty value.

2.1. Lager Might be the New Black

Michael Lewis expresses in an article published in The Scandinavian Brewer’s Review that in America the beer

industry has moved in two opposite extremes; a lighter alcohol direction and a heavier alcohol direction. The

large breweries contribute to one extreme with very light, non alcoholic beers that are flavour-less and with no

personality. The microbreweries contribute to the other extreme with dark, high alcoholic, very characteristic,

and unique beers (Lewis, 2010). Lewis writes that this shift to extremes may very well be counterproductive

and give rise to aversion from the consumers (Lewis, 2010). The two extremes leave a big gap in the middle

which easily could be filled out by well-balanced, highly flavoured, very drinkable lager beers (Lewis, 2010). It

seems that the Danish beer market also has tendencies towards this description and that possibilities exist in

developing new innovative lager beers by the microbreweries to increase sales and market share (Giacalone et

al., 2011). After all profit is a key factor for breweries and better sale and marketing lead to enhanced revenue.

The German Association of Sensory Analysis (ASAP) conducted a sensory profiling of European lager beers, and

found that lagers could be characterized by origin. Danish lagers were described as watery, German lagers as

bitter and astringent and French lagers as being sweet (ASAP, 2003). A more country-specific study on beer

identity in Denmark showed that Danish Lager beers were characterized by the attributes grainy/roasted,

carbonation, bitter, and alcohol (Mejlholm & Martens, 2006). Furthermore multivariate analysis on commercial

beers in Belgium showed that lager beer were mainly describes with attributes like bitter, hoppy and grainy,

but also undesirable notes in beer like diacetyl, sulphurous and musty, which can be striking and prominent in

neutral lager beer (Daems & Delvaux, 1997).

Above written review provides the background and argumentation for exploring the world of lager beer in this

master thesis and for choosing to design new flavored lagers that might have interesting profiles and high

acceptability. The sensory testing will be conducted on light and dark lager beers (Thy pilsner + Thy Classic)

with ABV - alcohol by volume - at 4.6% which is considered normal and average for lagers.


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3.0. Theory on Arousal

Research directed towards arousal, motivation, and attention is manifold and began years ago with continuing

development and adding of theories to it. In 1949 Hebb proposed a motivation model which suggests that

humans strive towards an optimal level of activation (Hebb, 1949; Köster & Mojet, 2007). Dember and Earl

(1957) started to analyse and research behaviours such as exploration, manipulation, and curiosity which they

classified under the overall term attention. They believe that to experimentally arouse attention in humans, it

is important to have stimuli that are incompatible with the person’s expectations. Arousing attention is a

dynamic process and involves the increase of a person’s psychological perceived complexity (Dember & Earl,

1957). The optimal level of arousal and complexity is not stable according to Dember and Earl. If a person is

presented with a stimulus higher than that person’s own optimal arousal level, it will cause a shift in optimal

arousal level. Such a stimulus can be called a pacer and is slightly more complex than optimal (Dember & Earl,

1957; Köster & Mojet, 2007).

Berlyne researched motivation and sought to know why people display exploratory and curiosity behavior. He

reinterpreted the theory by Dember and Earl in the arousal response curve where he connects liking and

hedonic value with complexity/arousal potential (Berlyne, 1970). See figure 3.1 below.

Figure 3.1: Arousal Response Curve

The arousal response curve depicts an inverted U-shape where arousal potential is a term which is meant to

cover all the stimuli properties that tend to raise arousal. This could include attributes like novelty and

complexity. The mechanism of the response curve is that a stimulus either high or low in complexity and

novelty will correspond to a point on the horizontal complexity axis in the figure. With loss of novelty or

complexity the stimulus will decrease to the left on the horizontal axis and with a gain in perceived novelty or

complexity the point will shift to the right on the horizontal axis. Liking will increase and decrease accordingly,


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and Berlyne states that the highest liking for a stimulus is when the stimulus is at the person’s optimal arousal

potential level. (Berlyne, 1970). Thus, stimuli have different arousal potential, and will lead to different effects,

dependent on the subjects own level of arousal (Köster & Mojet, 2007).

In 1968 Zajonc developed the mere exposure theory which states that the more we are exposed to a stimulus,

the more we will appreciate it (Zajonc, 1968). In general the theory explains why our appreciation for complex

things develops under the influence of repeated exposure (Köster & Mojet, 2007). Zajonc believed that

familiarity, through exposure, breeds liking, but Levy and Köster showed that this might also lead to product

boredom (Köster & Mojet, 2007). Another interpretation of the mere exposure theory might be that repetition

makes it easier for the person to cognitive process the stimuli and thus makes it more pleasant to experience

(Hekkert & Leder, 2008). Furthermore Walker (1980) introduced a similar theory suggesting that as a stimulus

becomes more familiar, it hence becomes less complex, and appreciation will thus change. In which direction

depends on the initial perceived complexity of the stimulus (Köster & Mojet, 2007). This could also be termed

the habituation effect, where arousal potential decreases as the stimulus becomes usual (Hekkert & Leder,

2008). It should be noted that the theories by Hebb, Berlyne, Dember and Earl, Zajonc, and Walker do not

specifically deal with food-related data. The theories have mainly dealt with and been applied to visual and

audio aesthetics.

3.1. Collative Properties

Berlyne’s arousal response curve has also been named the collative-motivation model, where stimuli are

preferred for their ability to generate arousal potential. There are several variables or properties that can yield

this. Berlyne classifies arousal potential properties in three classes; First the psychophysical properties which

relates to stimulus intensity - being the formal qualities of a product such as shape, colour, intensity that can be

quantified; Secondly the ecological properties which are accompanied by internal changes in the subject -

being for instance thirst, hunger, or sex drive; Thirdly the collative properties which influence the arousal

potential level via attention and motivation (Hekkert & Leder, 2008; Köster & Mojet, 2007). Precisely the

collative properties are of great relevance to this research project.

The collative motivation theory is concerned with relating hedonic value with the changes in arousal level

induced by certain stimuli that differ in collative properties. Berlyne termed these properties “collative”


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because they link the presented stimuli with prior experience when evaluated by the subject. They hence all

depend on collation or comparison between the past, i.e. knowledge and experience, and the currently

presented stimuli. They are also named collative properties in order to distinguish them from the other classes

normally tested - being the psychophysical and the ecological properties as mentioned before. Examples of

collative properties could be complexity, novelty, incongruity, ambiguity, typicality, and surprise. Since collative

properties like complexity and variety contribute most to arousal potential, they have been dominating in the

research field of product aesthetics (Hekkert & Leder, 2008).

The collative properties are all related to subjective uncertainty and hereby induce conflict in the subject -

increasing the arousal potential. Different exploration behaviors can be observed when subjects are presented

with stimuli. Specific exploration seeks to reduce arousal potential when this is too high due to uncertainty and

conflict. When this is resolved, the specific exploration ends. On the other hand there is the diversive

exploration which seeks to increase arousal level due to boredom. This is when we search for complexity and

variety, and the function of a stimulus becomes the quality of being interesting and entertaining (Köster &

Mojet, 2007). Beer drinking could for example be seen as diversive exploration.

3.2. MAYA Principle Theory

Another theory that touches upon collative properties such as complexity and familiarity is the MAYA principle

theory, first proposed by Raymond Loewy in 1951. MAYA is an acronym for Most Advanced – Yet Acceptable.

Both consumers and companies have a dualistic approach towards product innovativeness. It is an inner battle

between the two terms neophilia and neophobia, also characterized as the Omnivores Paradox (H. van Trijp &

van Kleef, 2008). Neophilia is the behavior of being excited and pleased with new and novel things, whereas

neophobia is a fear and reluctance towards novel products and a preference for more familiar items. It is

particularly seen in food contexts known as food neophobia (Pliner & Salvy, 2006). Preference for familiar

things thus seems incompatible with the desire for new things, but we like what we already know, and we also

like what is novel.

The MAYA principle has empirically been tested on aesthetic preference for different industrial designs such as

telephones and cars. Results show that typicality and novelty can be joint factors predicting preference and

liking for aesthetics. People prefer typicality as long as it not to be the harm of novelty and thus attractive


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designs comprise a thoughtful balance between novelty and typicality (Hekkert, Snelders, & Wieringen, 2003;

Hekkert & Leder, 2008).

With this said typicality and novelty are not to be seen as two ends of the same continuum, but correlation will

often be negative. The two collative attributes jointly determine aesthetic preference, but results also show

that each of them suppresses the positive effect of the other (Hekkert et al., 2003). Other collative variables

relevant to this discussion are prototypicality and familiarity. Through experience the human being develops

so-called prototypes, and these trigger reactions and responses in future situations; we thereby judge

impressions and products with a degree of prototypicality according to our past experience. (Proto)-typicality

and familiarity are clearly related and correlate substantially. They all find their preference in the ease of

processing (Hekkert & Leder, 2008). However familiarity should not be interpreted as the only attribute that

defines typicality, this could also be goodness of an example. It can be foreseen that typicality will be a stronger

predictor for preference when there is less time to process the stimuli (Hekkert et al., 2003).

It is normal that previous experience and people’s background form different platforms of what is novel,

typical, innovative, or familiar. Different expertise might also lead to differences in interpretations and

preference. It has been hypothesised that expert consumers display behaviour that searches for new stimuli

more that non-experts. This is because they hold a more conceptual structure and hence require less effort to

take in new information. Moreover people in general favour products that expresses a personality that can

match their own and such giving them identity and character (Hekkert & Leder, 2008).

It is a known fact that new product development failure rates are high (H. van Trijp & van Kleef, 2008).

Including above mentioned theory we are attracted to novel and innovative products, but at the same time we

have to master this novelty through successful interpretation. Product developers in the industry sometimes

develop complex and innovative products that are too novel to be appreciated right away (Köster & Mojet,

2007). Therefore they launch a less complex product that is immediately liked. However appreciation change

over time and so does a person’s optimum arousal and complexity cf. Berlyne’s collative-motivation model.

People often tend to forget to consider the dynamics of innovation. To bring in the before mentioned theory

on behaviour, it is known that diversive exploration sets in only after the specific exploration is completed, thus

only after the subject has resolved any uncertainty about the product. This is where most marketing people

make a mistake; they take the initial result instead of the appreciation after more exposures (Köster & Mojet,


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2007). More exposures (and hence the mere exposure theory by Zajonc) might also reduce the food neophobia

in humans and lead to increased preference (Pliner & Salvy, 2006).

3.3. Research Done on the Arousal Response Curve

Several research studies have been conducted concerning and dealing with the theory on arousal and Berlyne’s

arousal-response curve connecting preference and complexity. As mentioned before the theories by Hebb,

Berlyne, Dember and Earl, Zajonc, and Walker do not specifically deal with food-related data. Most of the

research which has been carried out in the field of arousal theory has been done on aesthetic preference; this

in particular with preference for visual aesthetics on design, or audio aesthetic and preference for music and

sound.

Through various studies it can be discussed whether or not Berlyne’s theory is valid. Different data show

conflicting results. Berlyne found inverted U-shaped relationships between preference and complexity

(Berlyne, 1970; Berlyne, 1971). However other studies show both monotonic and u-shaped functions

(Martindale, Moore, & West, 1988; Martindale & Moore, 1989; Mortensen & Frost, 2010). Martindale’s

experiment did not support Berlyne’s theory that collative properties are the most important predictors of

preference. He tested musical preference in relationship to collative, ecological, and psychophysical variables

and found monotonic functions, especially when stimulus was more meaningful. Semantic and meaningful

factors seemed to be the best predictors of preference rather than collative properties (Martindale & Moore,

1989). In another study Martindale again illustrated that typicality and meaningful properties are potent

predictors of preference, and he developed his own cognitive theory saying that the main determinants for

aesthetic preference are prototypicality and meaningfulness, and this usually in a monotonic function

(Martindale et al., 1988). Hekkert and colleagues argue that since typicality can only be seen as a predictor of

preference if stimulus is meaningful or can be categorized like real-life products, then typicality might

overshadow the arousal effect from complexity and novelty. This might explain why Berlyne’s theory of an

inverted u-shape relationship between preference and arousal level does not work for real life stimuli. They

conclude that Berlyne’s model has limited explanatory value when valid real-life products are evaluated

(Hekkert et al., 2003; Hekkert & Leder, 2008).

According to Hekkert and Leder (2008) aesthetic can be interpreted as the understanding through sensory

pleasure - being sensory pleasantness and delightness (Hekkert & Leder, 2008). With this definition it would be


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prominent to transfer the theory of collative properties into the world of food and sensory science. Levý and

Köster (2006) conducted an experiment on orange drinks where the theory by Berlyne (Berlyne, 1970) and

Dember and Earl (Dember & Earl, 1957) predicted the results. Liking and collative properties were rated for

seven orange drinks with different complexity. They found that exposure to complex drinks generates higher

liking for complex drinks, whereas exposure to simple drinks leads to boredom and loss of interest. Very

complex products functioned as pacers to change the individual optimal level of complexity (Lévy, MacRae, &

Koster, 2006). To sum up; several studies show different results and also vary a lot in aim, design, and

methodology accordingly.


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4.0. Conceptual Properties – Applied

This section will function as and provide background and argumentation for the development of response

variables in the beer questionnaire to be evaluated in this research project. Including above mentioned section,

it is essential to have coherence and argumentation for ones product to be evaluated and ones variables to be

measured. Collative properties are an eminent area of interest in this research project, and moreover it is

important to relate collative properties to specialty beer.

What conceptual properties are relevant for beer evaluation and why? This is a very central question to ask.

Hekkert and Leder (2008) write that there should be an optimal match between the product and the various

sensory messages; it should be mutually consistent and appropriate for the particular stimuli (Hekkert & Leder,

2008).

In recent years there has also been a great interest in the consumer’s affective experience and involvement in

the interaction between human and product. Desmet and Hekkert (2007) define product experience as “the

entire set of affects that is elicited in the user-product interaction, including the degree to which our senses are

gratified (aesthetic preference), the meaning we attach to the product (experience of meaning), and the

feelings that are elicited (emotional experience)”. The experience is of course shaped by the consumer’s

personality, background, values etc, and also by the product’s sensory and conceptual properties. At the same

time context will always play a role in product experience (P. M. A. Desmet & Hekkert, 2007). In a user-product

interaction various affective responses can occur, and the concept of core affect can be seen as a central

aspect. With core affect, two dimensions are combined (see figure 4.1). That is the affect dimension spanning

from unpleasant to pleasant, and the arousal dimension spanning from calm to activated. Product experience

can be seen as a change in core affect that is attributed to human-product interaction, and this can generate a

range of different responses as seen in the core affect model below (P. M. A. Desmet & Hekkert, 2007).


18

Figure 4.1: Core affect model (P. M. A. Desmet & Hekkert, 2007).

Precisely the topic of emotions which arise during food experiences has caught a lot of attention in recent

years (P. Desmet & Schifferstein, 2008; King & Meiselman, 2010). When you measure elicited emotions, it gives

new information which goes beyond just acceptance of a product. At the same time it is interesting to link

emotions and acceptance, and this might help to explain preference data, and why this data does not always

predict market success (King & Meiselman, 2010). Measuring emotions and collative properties might help in

future collaborations between sensory science and marketing, supporting product development and launching

of new products.

King and Meiselman (2010) argue that a key factor in evaluating consumer emotions associated with food

products is that the consumer is a product user. It seems that users obtain positive emotional responses to

tested products whereas non-users have more negative responses (King & Meiselman, 2010). The consumers

of this research project will hence be recruited with a requirement of being beer drinkers. In general eating

food and drinking beverages is a pleasant experience for healthy people, and Desmet (2008) also found that

pleasant emotions were reported more often than negative emotions for example satisfaction, enjoyment,

desire as opposed to sadness, anger, and jealousy (P. Desmet & Schifferstein, 2008).

Positive emotions are thus used to describe liked foods, and will also function as background information for

the evoked emotions chosen for the beer tasting questionnaire. Desmet (2008) especially found that

enjoyment was associated with alcoholic drinks where beer was described as fun, and also with pleasurable

social events like drinking wine or beer with friends and family (P. Desmet & Schifferstein, 2008). It is important

to bear in mind that dealing with different classes of food and beverages will require specific emotion terms for


19

measurement. A beer might elicit completely different emotions than would a smoothie or a chicken filet,

which is why it is essential to consider the world of beer experience when designing the ballot.

In the literature it is sometimes difficult to differentiate between collative properties and elicited emotions,

because authors classify same terms in different ways. For example the attributes interesting, curiosity, ample,

and surprising are often seen as collative properties and also often seen as emotions. To combine them all, the

term conceptual properties is used. Within recent years research on collative properties in relation to food has

caught some attention. Köster and colleagues (2008) tested drinks that varied in familiarity. They kept the

collative response variables very few, and defined arousal as novelty and complexity (Sulmont-Rossé,

Chabanet, Issanchou, & Köster, 2008). Another study by Lévy and Köster (2006) measured variables such as

marked, shattered, elaborated, fantastic, confusing, ample, and surprising. They added flavours to orange

drinks, and thus made the drinks differ in complexity (Lévy et al., 2006).

Conceptual properties have also been applied to Danish honeys in a study by Stolzenbach (2011) to show if

Danish honeys displayed sensory local uniqueness. A honey vocabulary was developed by a trained panel

consisting of 27 sensory descriptors, and also nine conceptual descriptors were evaluated but with no prior

training of a panel. The nine conceptual attributes chosen for honey were aromatic, complex, balanced,

unique, annoyed, disappointed, happy, desire, and liking. Conceptual properties were linked to sensory

descriptors in order to position the experience of the honeys. For instance elderflower flavour elicited desire,

happiness, and high liking. The study showed that attributes like aromatic, complex, and unique were not

related to liking. They were instead related to annoyed and disappointed. These findings might show

accordance to Berlyne's arousal theory, where too complex stimuli are above optimal level and hence low in

hedonic value (Stolzenbach, Byrne, & Bredie, 2011).

Frøst and colleagues have conducted research within a more gastronomic and dish/meal orientated area (Frøst

& Mielby, 2008; Mielby & Frost, 2010; Mortensen & Frost, 2010). The authors have conducted research on

expectation and surprise, perceived complexity, collative properties, and elicited emotions and related it to

liking. In one study results showed that dishes which were very classical and created as familiar were the most

liked dishes. On the other hand dishes which were rated high in curiosity, surprising, and challenging were least

liked, which was also true for the dishes created to be very novel and innovative. The study had 5 response

variables being curiosity, surprising, challenging, liking, and eat again rated on a 7-point likert scale (Mielby &

Frost, 2010). The two other studies (Frøst & Mielby, 2008; Mortensen & Frost, 2010) had respectively six and


20

eight response variables ranging between liking, surprise, curiosity, complexity, novelty, familiarity, stimulated,

and interesting. These two studies showed that familiarity had the lowest correlation to liking, and hence was

the least predictive variable for liking of a high-end gastronomic dish. Novelty, interesting, and stimulated on

the other hand were predictive of liking. Results showed a monotonic function between liking and arousal

potential. This consistent increasing of arousal might occur with very aesthetic and complex dishes, even

though people do not understand the dish. This is in accordance with Martindale’s critique of Berlyne´s arousal

theory as mentioned earlier. According to the chef Ferran Adriá from the famous restaurant El Bulli, molecular

gastronomy can be compared to arts, and this can yield many emotions and provide high aesthetics (Mielby &

Frost, 2010).

Studies mentioned all vary in design and methodology and show conflicting and different results in relation to

arousal and conceptual properties. The application of collative properties to specialty beer can contribute to

understanding how different flavours added are perceived and associated by the consumers. It was important

to develop attributes which were relevant for beer. Drinking beer will be seen upon as diversive exploration

(mentioned earlier on page 13), where the function of stimulus/beer is to be interesting and entertaining.

Variables are thus comparatively positive and relates to stimulation. Some of the same variables from earlier

studies will be used too, being the traditional collative variables novelty and complexity as defined by Berlyne.

A discussion point is how many response variables to include in order to reach a suitable and applicable

questionnaire, where consumers are pleased and gratified without getting exhausted on one hand or bored

and indifferent on the other hand.

Inclusion of the Variety seeking scale has been decided because drinking beer as mentioned can be seen as

diversive exploration, to seek amusement, diversion, and variety (Köster & Mojet, 2007). At the same time

people’s variety seeking tendency influences their food choice and liking. Furthermore the results may provide

insight into the individual consumers and help to understand market segmentation for specialty beer. The

VARSEEK-scale is domain specific hence developed specifically for food contexts. It is a reliable and cross-

validated instrument for measuring variety seeking tendency with consumers. (H. C. M. Van Trijp & Steenkamp,

1992). It is hypothesised that consumers with high variety seeking tendencies seek and like beers high in

variety, novelty and complexity, whereas people with low variety seeking tendencies will seek and like less

complex and more familiar beers.


21

5.0. Drinkability

This theory paragraph on drinkability provides the argument for inclusion of the related response variables in

the questionnaire to be used. When addressing the world of beer it is important to touch upon the concept of

drinkability. Drinkability is a widely used attribute to characterize beer and is also included in the proceeding

questionnaire for this project. It can be hard to precisely define drinkability and there are several

interpretations dependent on people’s background and understanding of the term. Drinkability is the noun for

the verb drinkable meaning suitable for drinking. In Layman’s words a session beer is a beer with high

drinkability, one that does not make you full, typically a beer low in alcohol. The term session beer is not used

in the academic research world. In Mattos and Moretti’s review (2005) they state that a beer that has high

drinkability is a beer that invites the drinker to have another glass, and that drinkability is a measure of how

enjoyable and attractive a beer is in order to be consumed in large quantities (Mattos & Moretti, 2005). Lewis

defines the concept of drinkability as leaving the consumer satisfied and pleased but at the same time willing

and able to drink more (Lewis, 2010). According to Lewis the large breweries define drinkability in a different

way than the craft breweries, hence the before mentioned gap between the breweries and beer styles. With

lighter low-alcoholic beers the large breweries define drinkability as consumption that is willingness and being

able to drink more. With darker, fuller and highly characteristic beers the microbreweries define drinkability as

preference which is the appreciation and liking of the beer (Lewis, 2010).

A problem is that there are no procedures widely accepted to scientifically measure drinkability of beer. Does

measurement of intake or stomach-emptying measure drinkability, or can participants grade drinkability on a

scale, and is this attribute to be judged affectively or descriptively? Mattos and Moretti believe that drinkability

is too subjective to be judged using scales or grades (Mattos & Moretti, 2005). In another study by Guinard and

colleagues (1998) they assigned drinkability as a thirst-quenching characteristic in beer and results showed that

the attributes thirst-quenching, refreshing, and drinkability were highly inter-related (Guinard, Souchard, Picot,

Rogeaux, & Sieffermann, 1998). The authors assumed that human subjects could score thirst-quenching

attributes on a scale which is, as they argue, a reasonable postulation if one believes they can score thirst

(Guinard et al., 1998).

Other authors do not believe that thirst-quenching character and drinkability are the same attributes and also

that preference and drinkability are not the same thing (Mattos & Moretti, 2005). However at the same time

drinkability can be seen as an indicator of consumer acceptability, which is a very relevant discussion point


22

given that the large breweries and the microbreweries understand drinkability differently. Hence can amount

consumed and preference for beer both simultaneously be indicators of consumer acceptability? Satiety on the

other hand is believed to be the opposite of drinkability where drinkability gets stronger as satiety decreases

(Mattos & Moretti, 2005). Ferkl and Curin (1979) found that adjuncts added to the beer showed to decrease

drinkability in lager beer (Ferkl & Curin, 1979; Mattos & Moretti, 2005), which is relevant results for the current

research project.

Drinkability can thus be seen upon in many ways. In this master thesis drinkability is seen as a hedonic

response as opposed to a sensory response giving reason for a large representative consumer sample.

Furthermore drinkability is defined as desire, willingness and ability to drink more. Because drinkability is

displayed as very subjective and in order to avoid any misunderstandings, questions regarding drinkability will

not be directly towards the term, but in a more precise and understandable way referring to the chosen

definition (see table 6.2 page 28). Drinkability will thus not be seen as preference but hedonic desire and ability

to drink more of the specific beer tested.

Drinkability will be classified according to Berlyne (Berlyne, 1967) as an arousal potential property being an

ecological property not a collative property. Ecological properties include internal changes that accompany

thirst, hunger, sexual appetite etc. (Köster & Mojet, 2007). In this sense questions directed towards satiety as

another ecological property could be interesting. Satiety can as before mentioned be seen as the antonym to

drinkability. Satiating is also defined as filling or extra-full and is one of the characteristics of beer in the beer

flavour wheel (M. Meilgaard, Dalgliesh, & Clapperton, 1979), and thus also a relevant attribute in describing

beer.

There are certain ethical dilemmas for the breweries by wanting to increase sales and produce beers by selling

beer with high drinkability. It can be consumed in big amounts and thus goes to counteract the public

guidelines of moderate alcohol consumption, and moderate drinking in general. Guinard and colleagues (1998)

found that US lager beers proved to be the most drinkable (Guinard et al., 1998). Experimenting with lager

beers might be a suitable way to keep alcohol content low and drinkability high, but at the same time add

some new appealing flavour characteristics to give more depth and personality to the world of lagers, which is

the largest and most profitable market segment. Furthermore the current research might show interesting

results about what collative characteristics in beer affect or correlate with drinkability.


23

6.0. Materials and Methods

The course of action in this research has been a two-step process; first stage being the design and development

of specialty beer prototypes using flavour extracts from Givaudan. Second and most time-consuming stage

being the consumer study where the beers were to be sensory evaluated. As mentioned earlier the present

research project is in collaboration with the international fragrance company Givaudan. Givaudan is one of the

global leaders in providing sustainable flavour creations (http://givaudan.com/). Several flavours were received

from Givaudan providing countless and interesting experimental combinations of additions to the beer. It was

decided to add the flavours to a base specialty beer after it has been brewed, and the chosen beers were, as

mentioned earlier, two types of lager beers: Thy Pilsner and Thy Classic from Thisted Brewery. The two beers

both have an alcohol percentage of 4.6% per volume. Thy Pilsner has the characteristic light colour and the

bitter hobby pilsner taste, whereas Thy Classic is darker and has a more aromatic bitter note to it

(http://www.thisted-bryghus.dk/). The reason for using lager beers as base beer is explained and discussed

earlier in the Beer Market paragraph page 9.

Base beers: Thy Pilsner and Thy Classic

In the laboratory the flavours were experimentally tested in the base

beer with a systematic approach. Initially all extracts were tested for

solubility in beer in their pure form, and all flavours could be

dissolved without diluting them in ethanol first. The aim and

purpose of the laboratory work was to design and create beers with

a balanced character where the added flavour was detectable, but at

Flavours from Givaudan


24

the same time did not have the single focus in the beer. The goal was to develop eight beer prototypes for

evaluation in the large sensory consumer study. At the same time some flavours were initially selected because

of a potential sensory and microbial synergy. The antimicrobial aspect of the beer has not been the scope of

this project, and prototypes were selected from a sensory point of view only. The addition of flavours was

applied directly to the beer under a ventilation hood with pipettes and with the use of good hygiene practices.

Optimal concentrations had to be identified, and dosages were increased and decreased according to taste and

sensitivity in gradual steps. Small pilot studies were carried out from day to day with convenient human

samples including supervisors, sensory science colleagues, friends, fellow students, and the designer herself in

order to get input and feedback regarding the prototypes. An optimal concentration in the beer was decided

upon after tasting and testing various dosages and shifting the type of base beer (Thy Pilsner or Thy Classic).

Eight different combinations that were thought to vary in flavour profiles and vary in perceived collative

properties such as complexity, novelty, and familiarity were selected. The beers were thus expected to be

sensory different and display different profiles. For the final consumer study concentrations were applied to

greater volumes of beer in kegs containing 19 litres. The final eight experimental beers were as following - seen

in table 6.1.

Table 6.1: The eight beers tested.

Base beer

Flavour added Amount added For 19 litre kegs

Characteristic of the pure flavour added

Thy Pilsner

Reference - -

Thy Pilsner

Perilla frutescens aromatic water

200ųl/100ml 38000ųl Fresh, pungent, bitter, chemestetic, green, grassy, herbal, apple, complex, acidic

Thy Pilsner

Lemon/lime 2ųl/100ml 380ųl Fresh, acidic, sharp

Thy Pilsner

Star Anise 14ųl/100ml 2660ųl Fennel, liquorice, tarragon

Thy Classic

Reference - -

Thy Classic

Juniper berry oil 6ųl/100ml 1140ųl Spicy, gin, pine, green, fresh, sharp, citrus

Thy Classic

Wormwood oil 20ųl/100ml 3800ųl Tart, bitter, wry, acidic, herbal

Thy Classic

Rum cocktail

Rum flavour

Blackberry

Sage

15ųl/100ml

10ųl/100ml

2ųl/100ml

2850ųl 1900ųl 380ųl

Alcoholic, harsh

Sweet, tart, bitter

Herb, earthy, savoury


25

Perilla frutescens was especially interesting to test since it is much unknown to Western people and

furthermore much unknown in beer. Traditionally Perilla is used in Chinese medicine to cure diseases, and it is

reported to have anti-microbial, anti-allergic, and anti-carcinogenic effects (Banno et al., 2004; Laureati,

Buratti, Bassoli, Borgonovo, & Pagliarini, 2010). It is often used in Asian cuisine and cooking. According to

Laureati and colleagues (2010), the Korean Perilla frutescens, which is used in this experiment, shows

refreshing, cooling, and pungent sensory properties (Laureati et al., 2010). The Perilla flavoured beer is

hypothesised and expected to be high in complexity and novelty. This also applies for the rum flavoured beer

where three flavours are combined - that is rum flavour, black berry, and sage. In general it is expected that the

more flavours added, the more complex the beer will be evaluated. The two references, the pure Thy Pilsner

and Thy Classic, are foreseen to be high in attributes such as familiarity, typicality, and traditional and low in

novelty and surprising. All experimental beers are expected to score high in drinkability since they are all based

on lager beers. However, in accordance with the theory, it is hypothesised that with adding of flavours

drinkability will vary.


26

6.1. Recruitment and Participants

Recruitment of consumers began approximately two months before execution of the actual study. It was

important to recruit broadly, and the main requirements were thus age above 18 and that the consumers were

users and liked beer cf. Collative Properties – Applied paragraph page 17-18, where the importance of the

participants being users is emphasised. The aim of the recruitment was to obtain a great span of people,

gender and age wise, in order to have a representative group of the Danish beer drinking population. Various

and different recruiting channels were used as methods of contact:

Faculty of Science

o Website (appendix 1)

o Facebook group

The Gossip Column/“Sladderspalten”

o Internal news mail at the University

Food of LIFE - Food, health, and nutrition

o Newsletter

o Facebook group

Word-of-mouth

Flyer (see picture)

Politiken – Danish Newspaper

o Sunday food section

A contact email address was set up for this sole purpose and responding and interested consumers were

divided into different age groups and gender to get a more detailed overview. Importance was again given to

recruiting broadly, including equal numbers of men and women, and having a large span in age difference.

Finally the chosen and screened participants were scheduled to come and taste experimental beers in week 10,

2012 at the University of Copenhagen, Department of Food Science. As incentive the consumers received one

70cl specialty beer and a bus ticket for public transport home. It is believed that the incentive was just enough

motivation for participation but not the sole reason, a so-called token incentive (Lawless, Heymann, &

SpringerLink (Online service), 2010).

Flyer for recruitment


27

6.2. Questionnaire

Development of a questionnaire for the consumers to answer has been done with great attendance to and

consideration for the product and the theory behind. In total there were twelve questions hence twelve

attributes to rate, which fitted into two pages and was considered and assumed a reasonable number to keep

the participants motivated and to avoid fatigue and boredom. The response variables belonged to different

classes of properties as mentioned earlier in the theory - that is the collative and ecological properties, elicited

emotions, and hedonic response. The collative properties were complexity, novelty, familiarity, typicality, and

traditional. The elicited emotions were surprising, confusing, and stimulating. The ecological properties relating

to drinkability were thirst-quenching, refreshing, and drinkability itself. Furthermore the hedonic response

variable was liking. All attributes have been carefully chosen and developed in compliance with the theory and

with regards to the product specialty beer and what properties and emotions beer can evoke. Furthermore it

was important to the designer that all words and attributes could be understood by a normal consumer, in

order for people to have a pleasant experience and to retrieve valid results without any misunderstandings.

Liking was the first response variable to rate in the questionnaire, this on a 15 point hedonic scale with the

anchors 1= not like at all, 8= neutral, 15= very much like. The rest of the response variables were hereafter

randomised using an incomplete randomisation in order to avoid carry-over-effects amongst the variable

questions (M. Meilgaard, Civille, & Carr, 2007). The collative, ecological, and elicited emotions were rated on a

15 point category likert scale with categories labelled 1= completely disagree, 8= neutral, 15= completely

agree. All scales were uni-polar scales where only one variable were judged at a time. Table 6.2 shows all the

response variables and full phrases as presented to the participants in the study. See the full questionnaire in

appendix 2.


28

Table 6.2: Response variables and full phrases with the original Danish phrase as presented to the consumers. Responses were measured on a 15 point scale with three anchors: completely disagree – neutral – completely agree.

Full Phrase

Liking How well do you like this beer? (not at all – neutral – very much) Hvor godt kan du lide øllen? (slet ikke – neutral – rigtig godt)

Complexity This beer is complex Denne øl er kompleks

Novelty To me this beer has novelty value For mig har denne øl nyhedsværdi

Familiarity This beer is familiar Denne øl er velkendt

Traditional This beer is traditional Denne øl er traditionel

Typicality This is a typical beer Dette er en typisk øl

Surprising This beer is surprising Denne øl er overraskende

Stimulating This beer is stimulating Denne øl er stimulerende

Confusing This beer is confusing Denne øl er forvirrende

Drinkability This is a beer which I could drink more of in a row Dette er en øl jeg kan drikke flere af i træk

Thirst-quenching This beer is thirst-quenching Denne øl er tørstslukkende

Refreshing This beer is refreshing Denne øl er forfriskende

6.3. Background Information

After tasting the experimental beers and answering the questionnaire, the consumers were asked to fill in

another sheet regarding personal background information (see appendix 3). The questions included

demographic variables such as gender, age, education, and income as well as behavioral variables like usage

and loyalty towards beer. They were also asked psychographic questions regarding their attitudes, values, and

interest in relation to beer. Additionally the background information sheet included the Variety Seeking Scale

(VARSEEK-scale) (H. C. M. Van Trijp & Steenkamp, 1992). Inclusion of this scale consisting of eight questions has

been decided because drinking beer as mentioned earlier can be seen as diversive exploration, to seek variety

(Köster & Mojet, 2007). It is hypothesised that consumers with high variety seeking tendencies seek and like

beers high in variety, novelty and complexity, whereas people with low variety seeking tendencies will seek and

like less complex and more familiar beers.


29

6.4. Consumer Study

As mentioned before, the large scale sensory consumer study was carried out in week 10 (March 5th-9th), 2012

at the Department of Food Science, University of Copenhagen. The experimental design structure included two

sessions per day at 17:00 and 19:00 with a total of eight sessions and a maximum of 20 people per session. One

session lasted around 45-60 minutes. A total of 122 consumers (N=122) participated in the beer tasting, 76

men and 46 women ranging from 18-75 years of age. Approximately 25% of the people recruited cancelled or

did not show for their assigned session which is thought to be high. The study was carried out following the

principles of good practice for sensory evaluation (Lawless et al., 2010).

6.4.1. Test Location and Experimental Conditions

The test site was as mentioned earlier geographically located at

the University of Copenhagen, Rolighedsvej 30, Frederiksberg. It

was easily accessible and with signs directing towards the beer

tasting. The sensory evaluation test was defined as a laboratory

test taking place in rooms prepared for the beer tasting

purposes, and where consumers had to transport themselves to

get there. With this choice of method the designers achieved

great control and high validity by being able to carefully manage

and standardize preparation, samples etc. Unfortunately this also limits a freer atmosphere in a normal context

without artificial conditions (M. Meilgaard et al., 2007). Context will be discussed later in the paper on page 49.

The test area, as seen in the picture above, could accommodate up to 20

people in one session. The consumers were seated next to each other and

were able to hear and talk to each other. Temperature and humidity were

attempted to be kept at a constant, and the area was frequently ventilated.

Each participant was provided with a spittoon, a water glass, water, a

pencil, crackers, and score sheets in a folder (see picture to the left).

Beer tasting area

Beer tasting station


30

6.4.2. Experimental Procedure

The experimental procedures before and during tastings were well standardized. The selected flavours were

added to the base beer in kegs of 19 litres as late as possible in order to maintain flavour intensity and to avoid

any off-flavours through the entire tasting week. It was considered important that the 19 litres would last all

week in order to have all consumers taste beer from the same production lot. Each experimental beer hence

had the same base material and was not replaced during the tasting week. Kegs and beer were kept in fridges

at a constant of 4˚C when not being poured.

Sample preparation took place in the sensory science kitchen

close to the assigned tasting rooms. Three researchers

worked on the project with pouring and serving of the

experimental beers. Samples were poured approximately 30

minutes prior to tasting, so everything could be ready in

time, and stored in fridges with a glass lid to avoid loss of

carbonation. The sample size was 50ml of beer, served in

transparent glasses with no masking of colour. Samples were

blind labelled with random three-digit codes conducted in

the statistical software programme FIZZ. This was to avoid bias. A randomisation scheme was also calculated in

FIZZ, and all samples were placed on randomisation sheets made for the purpose (see picture above).

Randomisation limits first sample, last sample, and carry-over effects. Furthermore even though the consumers

were seated next to each other, they did not taste the same beer at the same time and randomisation thus

prevented interacting about responses between participants. The sensory evaluation was carried out using a

completely randomised design (CRD), where all samples were randomly assigned to all consumers, and all

samples were evaluated in one session (Lawless et al., 2010). Bias due to order of sample presentation were

hereby minimized, and the CRD was seen fitting for this specific laboratory test.

Serving temperature was 7±2˚C. There is no universally agreed standard for serving temperature of beer. Some

brewer’s associations and brew masters recommend a serving temperature of around 14-15˚C to fully

appreciate all flavour notes in a beer. In published sensory experiments there is generally a lot of variability in

serving temperatures. It is essential to look at the goal of the experiment, and this research project attempts to

evaluate beer from a consumer’s point of view, and the serving temperature was thus chosen to resemble a

Randomisation sheet


31

real life situation, even though the colder temperatures inhibit the taste receptors. The beers in this

experiment were all lager beers, and they were therefore also all attempted to be served at the same

temperature.

Sample preparation in the Sensory Science kitchen

During the beer tasting the participants were advised to cleanse their palate to prepare their taste receptors

for the next sample and to a new flavour stimulus. The goal of palate cleansers are the removal of residual

material or taste from previous samples. Great consideration went into which palate cleanser works and fits

the best with tasting of beer. Under normal circumstances and in real life situations, consumers would most

probably cleanse their palate with salty chips or popcorn in between sips of beer. However this is a

standardised laboratory experiment and no published research suggests palate cleansers specific for beer.

Lucak and Delwiche (2009) found that table crackers were the only palate cleansers effective across all foods

tested in their study (Lucak & Delwiche, 2009). It was thus chosen to use neutral tasting table crackers and

water for rinsing of the mouth in order to obtain high validity in the present study.

Upon arrival the participants were seated and given an introduction speech by the responsible researcher.

Here is was essential to be smiling and welcoming and to give instructions in a precise and accurate manner

which people could easily understand. Below assimilates the general overview from the welcome speech:

“Hello everybody and welcome to beer tasting. We are very grateful that you all want to come and

taste beer in the name of science. My name is Mette and I am responsible for this beer experiment. I

will not reveal too much about the beers since you have to make up your own personal opinion. The

beer samples will come in glasses with a three digit number on it. It is important that you write this

number down onto the corresponding score sheet. Some of the attributes to be evaluated may seem

abstract to you, just remember that it is your own subjective opinion that we are looking for and that

there are no right or wrong answers. In front of you there are spittoons, water, and crackers to

cleanse your palate in between the samples. Please take your time, there is no rush. Keep in mind that

what you drink might not be the same as your neighbor is drinking.”


32

No information was given about the number of samples in order to avoid end-effects, and in general no

information was given about which beers the consumers had to taste. In this way the participants could fully

commit to their own personal opinion about the liking and collative associations of the beers. People were

asked to sign a declaration which stated that they would not drive immediately after the tasting, and after the

experiment they were given the token incentive and bus tickets for public transportation home. Due to ethical

considerations consumers either agreed to or declined photography taking.

Sensory evaluation of the experimental beers at the University of Copenhagen


33

6.5. Data Analysis

This paragraph aims to explain how data was handled and looked upon. Very little information is known prior

and data analysis has mostly been exploratory data analysis and thus seeks to visualize and summarize main

results in graphical manners. Analysis might generate ideas and new aspects for further research.

Raw data from score sheets and background information were computed in Excel and arranged in appropriate

schemes. Age, education, and income were divided into fewer categories to retrieve larger segments (see

appendix 4). With inspiration from Erikson’s psychosocial development theory, consumers were divided into

young adults, middle adulthood, and senior adulthood (Erikson, 1950; Erikson, 1985). Average intensity scores

and standard deviations for all twelve attributes were calculated for each of the beer samples and

subsequently put in tables with descending rank order. The VARSEEK scores from the background information

sheets were summed and distributed into the corresponding groupings according to van Trijp (1992) (H. C. M.

Van Trijp & Steenkamp, 1992). Question seven in the VARSEEK scale was re-coded before analysis. Distribution

of the scores was calculated in SPSS using frequency plots. Correlation numbers (R) between response variables

were also calculated in SPSS by Pearson’s correlation to see degree of correlation and if the correlations were

significant. In order to visualize the sensory profile for each individual beer, data was analysed in Panelcheck

using the application spider-web plots. A two-way ANOVA was carried out in order to see significance levels

and if the samples differed for each attribute.

Following, the raw data was ready for multivariate data analysis in Unscrambler version 10.1. Multivariate data

analysis offers tools to reveal underlying relationships in sets of various data. It is seen fitting for exploratory

data analysis since it provides powerful displays and visual tools to perform cognitive interpretation and

understanding of the data (M. Martens, Tenenhaus, Esposito Vinzi, Martens, & MacFie, 2007). Multivariate

data analysis in this research project has provided help in finding the covariance between the different data

sets, being data sets X, Y, Z. Matrix X is the collative properties; an 8 by 11 table with the beer samples as rows

and the collative properties as columns. Y data matrix is the hedonic responses (liking); an 8 by 122 table with

the beer samples as rows and the consumers as columns. Z data matrix is the background information; a 31 by

122 table with background information as rows and consumers as columns.

Principal Component Analysis (PCA) was carried out using data matrix X to get an overview of beer sample

variation and variable correlations. PCA was also applied to data matrix Y, an internal Preference Mapping


34

(MDPREF), in order to detect span in liking across the consumers and to concentrate on preference - one of the

most important aspects of the study. Partial Least Squares Regression (PLSR) was conducted to describe data in

terms of its patterns of co-variation and to reveal underlying important dimensions in the beer observations.

An external Preference Mapping (PREFMAP) was made to explain possible relationships between the collative

properties and the consumer liking of the eight beers. Also a PLSR was conducted for matrix Z and Y to

elucidate important background variables for liking.

Furthermore by using L-shaped PLSR (L-PLSR), the three blocks of data sets could be related by looking at

underlying phenomena. X data matrix (collative properties) shared no matrix dimensions with Z data matrix

(background information), but were through L-PLSR connected via Y (hedonic response). L-PLSR revealed which

product properties were preferred by which consumer types, and it made it possible to detect segments of

consumers for different beer samples. Liking was hereby predicted by simultaneously taking consumer

background and product properties into account (H. Martens et al., 2005; M. Martens et al., 2007).

Prior to multivariate analysis the data was brought to a common origin by mean-centering. Unscrambler

subtracted the mean from each sample and removed large magnitudes in data. When data contained different

measuring units or very different variables, standardization of data before analysis was applied; this for

instance was applied to data matrix Z (background) with various scales and variables to consider. Here one

variable is divided by its own standard deviation, and the standardization gives all variables the same variance

and influence. Data was validated by using uncertainty testing based on systematic cross-validation in order to

extract systematic signals from data, assess reliability, and thus give safe interpretations of plots (H. Martens &

Martens, 2000).

By computing and plotting smoothed points in the statistical software programme R, version 2.13.1, data could

be assessed with a non-linear approach and visually graphed in scatter plots by using locally-weighted

polynomial regression (Cleveland, 1979). This function allowed the researcher to see relationships between

liking and selected collative properties and to see what curve or function which had the best fit to data points.


35

7.0. Results

7.1. Univariate Analyses

Results from the VARSEEK scale showed similar tendencies as depicted by van Trijp (1992) (H. C. M. Van Trijp &

Steenkamp, 1992). Distribution of the scores is moderately skewed, and especially for food products many

consumers will exhibit medium or high variety seeking tendencies. Consumers rating 25 or lower are

categorised as low in variety seeking tendency. Consumers rating 26 to 34 are medium variety seekers and

consumers rating 35 or higher are high variety seekers. For the experimental beer study the majority of

participants were categorised as medium variety seekers (N=72). Distribution of scores in percentages from the

VARSEEK scale is seen in figure 7.1 below.

Figure 7.1: Overall distribution of the scores on the VARSEEK-scale and its descriptive statistics (N=122).

For the eight beers evaluated significance levels, mean scores, and standard deviations for the response

variables liking, complexity, novelty, drinkability, and familiarity are seen in tables below. All mean scores for all

response variables are seen in appendix 5. For liking, average scores ranged from 7.6 to 9.0 on the 15 point

category scale, which is a narrow range and shows that all beers were liked by someone, especially since the

standard deviations were relatively high and indicates that liking scores are spread out over a large range of

values. Least liked was the Perilla Frutescens pilsner beer, and most liked was the Lemon Lime pilsner beer

Sample mean: 33.42 Standard deviation 4.34 Median: 34.00


36

followed by the Juniper Berry classic beer. For liking a 2-way ANOVA showed that Lemon Lime was significantly

liked more than Star Anise, Wormwood, and Perilla Frutescens (see significance letters after samples in the

tables).

Table 7.1: Mean liking scores and standard deviations in descending order. Beer samples with the same letter behind them are not significantly different (p<0.01).

Liking

Lemon Limea 9.0 ± 4.0 Juniper Berrya 8.9 ± 3.4 Thy Pilsnera 8.8 ± 3.0 Rum Cocktaila,b 8.6 ± 4.0 Thy Classica,b 8.4 ± 3.6 Star Aniseb,c 7.8 ± 3.8 Wormwoodc 7.6 ± 4.0 Perilla Frutescensc 7.6 ± 3.7

The collative properties novelty and complexity had a larger range in mean scores than did liking. Both for

novelty and complexity the Rum Cocktail classic beer scored the highest with 9.6 and 9.3 respectively, and the

Thy pilsner reference scored the lowest with 4.1 and 5.2 respectively. Thy Classic reference and Perilla

Frutescens pilsner were also both in the low end of the novelty and complexity scales. Significance levels from

ANOVA revealed that there were significant differences for both novelty and complexity amongst the eight

beers. For novelty Rum Cocktail and Star Anise were significantly different from all the other samples. Thy

Pilsner was significantly different than all the other samples both in novelty and complexity. Perilla Frutescens

was also significantly different than the rest of the samples for complexity. The consumers thus did

differentiate the samples with regards to the collative properties.

Table 7.2 + 7.3: Mean novelty and complexity scores plus standard deviations in descending order. Beer samples with the same letter behind them are not significantly different (p<0.001).

Novelty

Rum Cocktaila 9.6 ± 4.3 Star Anisea 9.4 ± 4.8 Lemon Limeb 8.2 ± 4.5 Wormwoodb 8.1 ± 4.5 Juniper Berryb,c 7.8 ± 3.9 Perilla Frutescensc 7.1 ± 4.6 Thy Classicd 6.2 ± 3.7 Thy Pilsnere 4.1 ± 3.2

Complexity

Rum Cocktaila 9.3 ± 3.5 Juniper Berrya,b 8.8 ± 3.4 Wormwooda,b 8.7 ± 3.6 Star Anisea,b 8.6 ± 3.8 Lemon Limeb,c 8.3 ± 3.8 Thy Classicc 7.8 ± 3.5 Perilla Frutescensd 7.0 ± 3.9 Thy Pilsnere 5.2 ±3.2


37

Thy Pilsner reference was by far evaluated as the most familiar beer out of the eight beers with a mean score

of 10.9 on the 15 point scale followed by The Classic reference with an average score of 8.6. Thy pilsner also

showed to be significantly different from all the other beers in familiarity. The three least familiar beers were

Star Anise pilsner, Rum Cocktail classic, and Wormwood classic. Those three were not significantly different for

familiarity. For drinkability Thy Pilsner reference again scored highest followed by Lemon Lime pilsner and

Perilla Frutescens pilsner. Lowest drinkability had wormwood classic beer with a mean score of 6.6. See table

7.4 + 7.5 below.

Table 7.4 + 7.5: Mean familiarity and drinkability scores plus standard deviations in descending order. Beer samples with the same letter behind them are not significantly different (p<0.001).

Familiarity

Thy Pilsnera 10.9 ± 3.4 Thy Classicb 8.6 ± 3.7 Juniper Berryc 7.3 ± 3.9 Perilla Frutescensc,d 7.0 ± 4.4 Lemon Limec,d 6.6 ± 4.2 Wormwoodd,e 6.2 ± 4.4 Rum Cocktaile 5.3 ± 3.9 Star Anisee 5.3 ± 4.1

In general it was seen that standard deviations were high for all response variables, which indicates large

spreads in data. The reason for this could be the nature of the variables; they are all very subjective and

personal terms, and thus not well defined sensory descriptors like it would be with a descriptive trained panel.

The high standard deviations give reason to expect different and various preferences within the eight beers

and also provides argumentation for performing an internal Preference Mapping for liking later in this result

paragraph page 42. Collative properties relates to the consumers associations and prior experiences which is

why background information might also influence the wide spread in data values, and thus provides

argumentation for looking upon consumer segments and their related preferences.

Drinkability

Thy Pilsnera 9.6 ± 3.8 Lemon Limeb 8.2 ± 4.5 Perilla Frutescensb,c 7.7 ± 4.3 Juniper Berryb,c,d 7.6 ± 4.2 Thy Classicc,d 7.2 ± 4.3 Rum Cocktailc,d 7.1 ± 4.5 Star Anised 6.6 ± 4.3 Wormwoodd 6.6 ± 4.1


38

7.2. Spider-Web Plots

The different sensory profiles of the beers have been evaluated visually in spider-web plots (see next page).

This has been a good tool to graphically compare samples with respect to the 11 collative, ecological, and

emotional properties. Interesting could have been to conduct specific descriptive sensory analysis for the eight

beers with a trained panel, and compare these profiles with the shown collative sensory profiles. As seen the

two most prominent profiles are Thy Pilsner and Rum Cocktail with eminent and different shapes. Star Anise

displays similar shape as Rum Cocktail with peaks for surprising, novelty, and stimulating. All beers have

medium to high peaks in surprising except for Thy Classic and Thy Pilsner, which indicates that adding flavour

to lager beers will elicit surprising emotions. Lemon Lime, Perilla Frutescens, and Thy Pilsner display peaks for

drinkability, thirst-quenching, and refreshing, whereas the opposite holds for Rum Cocktail, Juniper Berry, and

Wormwood.


39


40

7.3. Multivariate Statistics

7.3.1. Emotional Profiling - Principal Component Analysis

Results from the PCA showed an overview of the beer sample variation (figure 7.2), and response variable

correlations (figure 7.3). The first two principal components accounted for 97% of the total variance relevant

for explaining sample variation. PC-1 explained 91% of the variation and PC-2 explained 6% of the variation in

data. The score plot below reflects variation in samples and depicts a large span and differentiation amongst

the eight beers. Thy Pilsner reference and Rum Cocktail were very dissimilar samples and explain the span

across PC-1. Star Anise and Rum Cocktail showed similar tendencies and are placed close to each other in the

score plot. Comparing and combining scores and loadings it is clear that Thy Pilsner, and to a less degree Thy

classic, is characterised by the attributes traditional, typicality and familiarity. These three descriptors also

highly correlate. Spanning in the opposite direction are surprising, novelty, confusing, and complexity situated

and these attributes describe the beers Star Anise, Rum Cocktail, and Wormwood. This means that these beers

were given the highest scores for those attributes. Thy Pilsner was given the lowest scores for surprising,

complexity, novelty, and confusing. A third grouping of beers is Lemon Lime, Perilla Frutescens and Thy Pilsner,

which are characterised by drinkability, refreshing and thirst-quenching. This also corresponds to the former

shown descending order for drinkability in table 7.5 page 37. Drinkability, refreshing, and thirst-quenching are

highly correlated attributes in the present study, and these results confirm the results from Guinard (1998)

(Guinard et al., 1998). The third grouping; Lemon Lime, Perilla Frutescens and Thy Pilsner, all have the same

base being pilsner. As shown in the correlation loading plot all response variables are very well explained

within the 100% ellipse except for stimulating.


41

Figure 7.2: Score plot from PCA with the eight beer samples on data matrix X (collative properties).

Figure 7.3: Correlation loading plot with response variables in data matrix X (collative properties). The inner and outer ellipses

represents 50% and 100% explained variance, respectively.

PC-1 (91%) -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

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Traditional Typicality Familiarity


42

7.3.2. Internal Preference Mapping for Liking

High standard deviations gave reason for performing an internal Preference Mapping on data matrix Y (liking);

this to visualize and detect the span in liking across the consumers and also to focus in on only preference for

the eight beer samples. The bi-plot below (figure 7.4) shows the internal correlation structure for liking. There

is a large spread in liking across all beer samples. This indicates that all beers were liked by at least someone

and that there might be clusters or segments of consumers that like different beers. Consumers close to a

specific beer sample rated high hedonic responses towards it, and consumers far away from a specific beer

sample rated low likings for that beer. For example did consumer number 100 like Thy Classic very much

whereas consumer 59 preferred Lemon Lime. The results also depict clusters of beer with all the four pilsner

types placed to the left in the bi-plot and all the four classic types to the right in the bi-plot. This further adds to

segmentation of the consumers. Principal component 1 explained 22%, and principal component 2 explained

18 % of the variance in data. When looking at PC-3 and PC-4 it showed same overall picture of a large spread in

liking. This higher noise can partly be explained by the more columns (122 consumers) in data matrix Y, and to

the fact that only one response variable “liking” is being depicted in the plot.

Figure 7.4: Bi-plot from Internal Preference Mapping of consumer liking (data matrix Y).

PC-1 (22%) -1 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

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7.3.3. Partial Least Square Regression – PLSR

An external Preference Mapping (PREFMAP) was conducted to elucidate possible relationships between the

collative properties in data matrix X and the consumer liking of the eight beers from data matrix Y; this to see

how well liking could be predicted from the collative properties measured. In total 91% of the X matrix is

explained in the PREFMAP plot below and 31% of the Y matrix is explained (figure 7.5). The beers have been

added as an identity matrix in Unscrambler and down-weighted in order to display the samples in the bi-plot

below. The plot shows that the variables drinkability, thirst-quenching, familiarity, typicality, traditional,

novelty, surprising, confusing, and complexity are significant variables and thus significant predictors of liking.

However predictors of liking might consist of combinations and differ from consumer to consumer. The loading

plot also depicts the mean preference versus the individual liking values. The mean preference is highly

correlated to the ecological properties thirst-quenching, drinkability, and refreshing. However sometimes it is

of little value to consider an average consumer; there is no such thing as an “average” consumer. The plot

shows that there are many liking values placed far away from the mean preference. Two segments of

consumers may strongly disagree and have very different preferences in beer liking. This gives argumentations

for segmentation, and the forthcoming L-PLSR will reveal important segments for liking of the eight beers.

Figure 7.5: Correlation loadings plot (X and Y) from PLS-regression (PREFMAP). Factors 1,2: X = Collative properties, Y = Hedonic

response. Response variables with a circle are significant predictors for liking.


44

7.3.4. Correlations

Results from Pearson’s correlation showed the raw correlation numbers between all response variables (see

table 7.6). Some of the highest positive correlations are seen between traditional and typicality (0.842) and

traditional versus familiar (0.788). The lowest negative correlations are between surprising and traditional (-

0.711) and surprising versus familiarity (-0.704). This is accordance with the PCA loading plot page 41. Liking

has significant correlations to all other variables except for surprising. Negatively correlated to liking are

confusing and surprising, otherwise all other variables are positively correlated to liking. High correlation

numbers are specifically seen for liking versus drinkability (0.705), stimulating (0.697), and refreshing (0.684).

Complexity and familiarity both have moderate correlations with liking, whereas novelty has a relatively low

correlation with liking. The former three mentioned collative properties (complexity, familiarity, and novelty)

will be analysed more in depth for their relationships with liking on page 48 under the section Curve Fit.

Table 7.6: Correlation numbers (R) between response variables. Bold numbers are significant (P<0.001). Underlined bold numbers are significant (p<0.05). N= 976 in each correlation coefficient.

Complexity Novelty Typicality Familiarity Surprising Confusing Traditional Stimulating Drinkability Thirst-quenching

Refreshing

Liking 0.267

0.082 0.273 0.274 -0.023 -0.299 0.216 0.697 0.705 0.482 0.684

Refreshing 0.165

0.045 0.342 0.320 -0.053 -0.318 0.272 0.596 0.715 0.679

Thirst-quenching

-0.067 -0.134 0.364 0.340 -0.180 -0.364 0.307 0.379 0.648

Drinkability -0.013

-0.138 0.448 0.444 -0.231 -0.444 0.405 0.508

Stimulating 0.506

0.347 0.061 0.051 0.246 -0.079 0.006

Traditional -0.407

-0.596 0.842 0.788 -0.711 -0.487

Confusing 0.296

0.439 -0.493 -0.552 0.521

Surprising 0.558

0.734 -0.683 -0.704

Familiarity -0.350

-0.642 0.773

Typicality -0.368

-0.569

Novelty 0.600


45

7.3.5. Background Variable Selection

PLSR for data matrix Z (consumer background) and data matrix Y (consumer liking) revealed which background

variables were important for liking of a specific beer sample. These results helped in removing noise and non

significant variables in the following LPLSR analysis. Results showed that gender, high income, favorite type-

wheat beer, “I often try new beers”, and “I know a lot about beer” were significant background variables for

liking of all eight beers. The figures 7.6 and 7.7 below show that being a female with a preference for wheat

beer is a positive predictors for liking of Lemon Lime, whereas being a male with high income, high stated beer

knowledge and high stated “I often try new beers” are positive predictors for liking of Juniper Berry. The stated

variety seeking tendencies showed no significant effect for any of the beers (numbers 28+29+30 in the figures).

Figure 7.6: Weighted regression coefficients; significant background variables for liking of Lemon Lime.

Figure 7.7: Weighted regression coefficients; significant background variables for liking of Juniper Berry.

Key: 0=Male, 1=Female, 2=Young adults, 3=Middle adulthood, 4=Senior adults, 5=Low education, 6=Medium education, 7=High education, 8=Low

income, 9=Medium income, 10=High income, 11=Low frequency, 12=Medium frequency, 13=High frequency, 14=BeerFav, 15=WineFav, 16=LiqueurFav,

17=OtherFav, 18=TypePilsner, 19=TypeClassic, 20=TypeWheat, 21=TypeAle, 22=TypeIPA, 23=TypeStout, 24=TypeOther, 25=”I often try new beers”, 26=”I

know a lot about beer”, 27=”I often drink beer I already know”, 28=Low VARSEEK, 29=Medium VARSEEK, 30=High VARSEEK


46

7.3.6. L-PLSR

L-PLSR was performed to study the co-variance between all three data matrices: to explain consumer liking by

both consumer background and collative properties. Data matrices X (collative properties) and Z (background)

shared no dimensions but was connected via data matrix Y. The correlation loading plot next page displays the

systematic co-variation from the multi-block analysis. For clarity some of the non-significant background

variables have been omitted. For example all the education variables were not important and situated close to

the origin of the plot and hence gave no information or valid interpretations; they were thus removed in the

analysis. All the red dots are individual hedonic responses from the participants and indicate that there are

various segments amongst the consumers. With respect to consumer background variation, principal

component 1 distinguished between female and male consumers and principal component 2 between young

adults (18-35 years) and senior adults (56-75+years), but also frequency and variety seeking tendency can be

interpreted from PC-2. The female consumers were highly correlated to low frequency users and to consumers

that had wine as their favourite alcohol type. This segment had preferences towards traditional, familiar, and

typical beers high in drinkability being refreshing and thirst-quenching. The female consumers had a preference

for the Thy Pilsner reference. Another segment with preferences towards the base beers with no added

flavours were the young adults, which are also positively correlated with medium frequency user and medium

variety seekers plus people that stated “I often drink beer I already know”. Their statement hence fitted

correspondingly with their rated liking for the more familiar beers Thy Classic and Thy Pilsner. Male consumers

and people that stated “I often try new beers” and “I know a lot about beer” were all positively loaded on PC-1.

They were also correlated to consumers who had beer as their favourite alcohol. For these people the collative

properties complexity, novelty, stimulating, confusing, and surprising had the greatest impact on liking. The

darker classic beer samples Rum Cocktail, Wormwood, and Juniper Berry with added flavours were rated

highest in liking for this segment. High variety seekers, senior adults, and people who preferred stout as their

favourite type of beer were all positively loaded on PC-2. This segment were situated in the top half of the plot

with all the four pilsner types, and Lemon lime and Star Anise were rated highest in liking for this group.

Data matrix X explained in total 96 % of the variation in the L-PLSR analysis compared to matrix Z with 41% and

data matrix Y with 2%. This gives sense when considering the number of variables in each data set with only 11

variables for the collative properties compared to 122 for the two other data sets. Hence the collative

properties are also placed in the outer ellipse in figure 7.8 next page.


47

Figure 7.8: L-PLSR correlation loading plot. Collative properties (dark blue), beer samples (light blue), individual consumer likings (red), background variables (green).

The inner and outer ellipses represent 50% and 100% explained variance, respectively.


48

7.3.7. Curve Fit

By computing and plotting smoothed points, data could be assessed with a non-linear approach and visually

graphed in scatter plots by using locally-weighted polynomial regression (Cleveland, 1979). This showed

relationships between liking and the selected collative properties complexity, novelty, and familiarity. It also

showed what curve and trajectory had the best fit to the data points. Results are shown in figure 7.9 below.

Both complexity and familiarity showed linear or monotonic relationships when plotted against liking with a

levelling out at high values. Relationship between novelty and liking showed an inverted U-shape, and thus

could be described by Berlyne’s theory on arousal where arousal potential is a function of liking in an inverted

U-shaped manner (Berlyne, 1970). These results will be further discussed in the forthcoming discussion

paragraph.

Figure 7.9: Relationships between liking and complexity, familiarity, and novelty.

2 6 10 14

7.5

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Novelty

Lik

ing

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Lik

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Lik

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8.0. Discussion

8.1. Context

When conducting an experiment the choice of setting and location is based on both practical and

methodological considerations. There are always constraints to consider such as time, place, and costs. The

present study was executed as a laboratory test and therefore holds a great proportion of control and

management. However it is imperative to discuss context when dealing with beer drinking. Beer drinking in

different contexts has not been included here, but awareness of this factor is most definitely in attendance.

Context and environment affect our food choice and our enjoyment of food, this being both the physical, the

cultural, and the social environment (Meiselman, Frewer, & Trijp, 2006). Studying the collative properties in

isolation tells us little about these properties in specific contexts. Drinking beer is often associated with a

particular time, a particular place, or a particular situation. This could either be in a bar at night, on a warm

summer afternoon, with a meal, or at special celebrating events, and in each of these contexts one might alter

preference and find certain types of beer appropriate or not. Contextual variables will thus play an enormous

role when talking about beer. Furthermore beer contains alcohol, and the influence of alcohol will probably

also affect the consumer’s appreciation and choice in specific situations. Socialization is another important

topic to discuss when addressing beer. Drinking beer is often associated with being in a social situation in the

company of others, most likely friends and family. In today’s society drinking alone is not considered normal or

fun. This is why the laboratory settings might dissociate drinking beer from its normal social context, and hence

lower the external validity of the research. A natural setting, for instance a bar or a beer festival, could have

perhaps provided insight into collative properties in more real life situations, but on the other hand could have

been difficult to manage and have too many confounding elements. So does one choose realism over control

or control over realism when researching beer and collative properties? Further research is needed about the

role of context and beer drinking.

8.2. Exposure

The present study aimed at explaining how different flavours added to beer affected liking and collative

perception of the beer, this according to Danish consumers. Liking results from Preference Mapping showed

that preferences were scattered, revealing a very heterogeneous consumer group, and that all eight beers


50

were liked but by different consumers. These data were based on one tasting session, and an interesting

discussion is whether or not these results would have been different if the consumers had repeated the

experiment and rated the beers over more replicates. According to Zajonc’s mere exposure theory (Zajonc,

1968) (mentioned on page 12) we will appreciate a stimulus more, the more we are exposed to it. Complex

products will become more familiar, and appreciation will thus change. This could also be called the

habituation effect, where arousal potential decreases as the stimulus becomes usual (Hekkert & Leder, 2008).

However it can be discussed if this is the aim of beer drinking. Beer drinking is seen as diversive exploration,

where the purpose is to increase arousal level and to seek amusement and variety as mentioned on page 13.

Different exploration behaviors can be observed when subjects are presented with stimuli. Specific exploration

seeks to reduce arousal potential when this is too high due to uncertainty and conflict. When this is resolved,

the specific exploration ends. On the other hand there is the diversive exploration which seeks to increase

arousal level due to boredom. This is when we search for complexity and variety, and the function of a stimulus

becomes the quality of being interesting and entertaining (Köster & Mojet, 2007). The exposure effect could be

explained by reduction of uncertainty as in the specific exploration, not the diversive exploration. Results also

showed that even the perceived complex beers, for instance Rum Cocktail, were liked by someone. However

repeated exposure might have shown a boredom effect for some of the products, which is essential knowledge

when launching new products on the market. Failure rates are high in new product development, and the

dynamics of innovation are often neglected. Repeated exposures to the beer tested could have contributed

with more knowledge and awareness in this area. On the other side it is imperative that the consumer will

enjoy the first sip of a beer and generate a buy-again situation to increase sales. This implies that the beer has

to be liked immediately and not just over time.

8.3. Attributes

Through various studies it can be discussed whether or not Berlyne’s theory is valid. Different data show

conflicting results. Berlyne found inverted U-shaped relationships between preference and complexity.

However other studies show both monotonic and U-shaped functions as mentioned on page 15. The present

research results concluded that both complexity and familiarity showed linear or monotonic relationships

when plotted against liking. Relationship between novelty and liking showed an inverted U-shape, and thus

could be described by Berlyne’s theory on arousal. This means that for novelty there is an optimum where

maximum hedonic appreciation is reached, and too much novelty will decrease liking. Why is this only


51

observed for novelty and not for complexity or familiarity? According to Berlyne’s theory the term arousal

potential overarches both novelty and complexity, and the properties thus hold the same overall definition. In

this research project the properties have been operationalized in separate attributes. However it can be

discussed how consumers interpret the collative properties. Berlyne termed these properties “collative”

because they link the presented stimuli with prior experience. They hence all depend on collation or

comparison between the past, i.e. knowledge and experience, and the currently presented stimuli, which gives

rise to various and differentiated interpretations and definitions of the properties. Results therefore depend on

previous experiences that may modify the arousal optimum of each participant. What do complexity, novelty,

and familiarity really mean? This remains to be established. Pearson’s correlations revealed that they are not

the same. They all show different and significant correlation numbers with liking. Complexity and familiarity are

significantly positively correlated with liking, with complexity at R=0.267, familiarity at R=0.274. Novelty is

almost totally uncorrelated to liking with R=0.082. Novelty also depicts lower significance level than the two

other attributes, and these results are in accordance with the curve functions. Some novelty is essential, but

too much novelty is fatal. Even though novelty and complexity correlate significantly (R=0.600), they do not

hold equal correlations to liking, and thus again proves that they are not the same. This is also graphically

depicted in the PCA plots.

It is essential to look at how the properties are characterized and described. Collative properties and elicited

emotions are very subjective terms, and for instance today’s consumers’ definition of novelty may deviate from

Berlyne’s definition. Berlyne described novelty as surprisingness and distance between expectation and

perception. Today’s consumers may interpret it as something never encountered (Sulmont-Rossé et al., 2008)

or by a completely different individual definition. In the present research, results showed a high positive

correlation between novelty and surprising at R=0.734. Furthermore the same thing can be discussed in

regards to complexity. It remains to be established how complexity is characterized and described. It can

include number of compounds, many notes, opposite simple, difficult to describe, or perhaps an elicited

personal emotion also. Complexity is not always the same as arousal, and complexity may be understood and

compared to different individual experiences hence the collation. Complexity showed high correlations to

novelty, stimulating, and surprising. Novelty however had a higher correlation to stimulating than did

complexity (see table 7.6 page 44).

The MAYA principle theory states that typicality and novelty can be joint factors predicting preference and

liking. People prefer typicality as long as it not to be the harm of novelty and thus preferred products comprise


52

a thoughtful balance between novelty and typicality. Correlation between novelty and typicality will often be

negative which is confirmed in the present results (R= -0.569), but both attributes are still positively correlated

to liking giving rise and seed to the MAYA principle theory. According to the theory typicality and familiarity will

correlate substantially which is also the case here (R=0.773). It is foreseen that typicality will be a stronger

predictor for preference when there is less time to process the stimuli. In the present study both attributes had

the exact same correlation with liking (R=0.274), which might explain that time given to evaluate the beers

were suitable, or that for beer typicality and familiarity are both strong predictors of liking regardless of time

constraints.

8.4. Drinkability

With regards to the ecological property drinkability it can, according to the results, be assigned as a thirst-

quenching and refreshing characteristic in beer. The three attributes are highly inter-related with high positive

correlation numbers. In the present study drinkability was seen as a desire and ability to drink more of the

beer. The questionnaire was also designed to fit this definition with the statement “I can drink more of this

beer” and not a direct drinkability question such as “this beer is drinkable”. However consumers may have had

different interpretation of the statement either drinkability as preference or drinkability as consumption.

Nevertheless at the same time drinkability can be seen as an indicator of consumer acceptability, which is a

very relevant discussion point given that the large breweries and the microbreweries understand drinkability

differently. Further studies are needed to explore how amount consumed and preference for beer can be

simultaneous indicators of consumer acceptability. Drinkability was positively correlated to liking (R=0.705),

but at the same time the internal preference mapping showed that preference scores were scattered,

indicating different preferences. It could have been interesting to measure satiety, which can be seen as the

antonym to drinkability (Mattos & Moretti, 2005) and its correlation to liking. Previous studies found that

adjuncts added to the beer showed to decrease drinkability in lager beer (Ferkl & Curin, 1979; Mattos &

Moretti, 2005)(Ferkl & Curin, 1979; Mattos & Moretti, 2005)(Ferkl & Curin, 1979; Mattos & Moretti, 2005)

which is relevant for the current research project. Present results showed significant differences between the

samples for drinkability with Thy Pilsner being significantly different from all the other samples. All

experimental beers were expected to score high in drinkability, since they were all based on lager beers.

However, in accordance with the theory, it is hypothesised that with adding of flavours drinkability will vary.

Did drinkability decrease when flavours were added? Thy Pilsner with no added flavours were rated highest in


53

drinkability followed by two other pilsner types. Wormwood scored the lowest in mean drinkability. It is

difficult to interpret, and standard deviation were high, indicating a wide spread and perhaps disagreement

about drinkability amongst the consumers. An internal Preference Mapping for drinkability scores only (not

displayed in this paper) confirmed that drinkability was scattered across all consumers, and all samples had

some drinkability characteristics, giving rise to different interpretation of drinkability by different consumers.

8.5. Consumers

It can be discussed what expectations the consumers had for the beer experiment. Often it is a specific kind of

consumer who joins this kind of experiment, most likely people with great interest and knowledge in beer,

which is why the results might not portray the average, normal Danish beer consumer. In regards to the

previous mentioned context factor, people were surprised at how clinical the experiment was and had

expected a more loose and relaxed atmosphere. It can also be discussed if they may have had expectations

that they should come and be very accurate in flavour identification etc. Some consumers may have been

disappointed, and hence their motivation factor decreased.

It was hypothesised that consumers with high variety seeking tendencies seek and like beers high in variety,

novelty, and complexity, whereas people with low variety seeking tendencies will seek and like less complex

and more familiar beers. It can be discussed if people find it prestigious to be very variety seeking and if this

affect their answer on the VARSEEK scale. Furthermore they might answer what they think the researcher

wants to hear. VARSEEK scores did not show to be important variables with regards to liking of the eight beers.

However the L-PLSR loading plot (page 47) showed that high variety seekers (N=45) were placed in the right

side of the plot towards attributes like complexity and novelty, together with male consumers and high

frequency users with beer knowledge. People in general prefer products which express a personality which can

match their own and such giving them identity and personality. Knowledgeable and high variety seekers thus

search for more complex and new beers than normal. It is important to know, what factors facilitate and what

factors hinder consumer acceptance of new products.

8.6. Is Lager the New Black?

Is lager beer the new black? This is a very relevant question to ask considering the present research and the

Danish beer market. The before mentioned gap between two extremes in the beer market could very well be


54

filled out with flavoured, balanced, drinkable lager beers. Results showed that all experimental lagers scored

high preference scores but by different consumers. Possibilities exist in developing new innovative lagers in

order to reach consumer acceptability and to increase sales. After all profit is a key factor for breweries and

better sale and better marketing leads to enhanced revenue. The world of lagers is the largest and most

profitable market segment, and experimenting with lager beers might also be a suitable way to keep alcohol

content low, drinkability high, and still maintain personality and appealing flavour characteristics. The key note

here is to look at segments even within lager beers, and in order to reach success in launching new lager beers,

it has to be directed and marketed towards a certain consumer segment, who will embrace this new beer.

Furthermore it can be discussed if certain flavours do fit to certain types of beer, regardless of novelty value. It

is mainly sharp and acidic fruity tastes which are used in lagers. Design of the beer also revealed that these

tastes e.g. Perilla and Lemon Lime fitted the best with the light pilsner types, whereas the deeper tastes e.g.

Wormwood and Juniper were seen best fitted with the darker classics.


55

9.0. Conclusions

Product experience can generate a range of different responses which is attributed to the human-product

interaction. When you measure collative properties and elicited emotions, it gives new information which goes

beyond just acceptance of a product. The present study aimed to explore how different flavours affect liking

and collative perception in beer. Results showed a large span and differentiation amongst the eight beers

tested. Properties like complexity, novelty, surprising, and confusing described the beers Rum Cocktail and Star

Anise the best, whereas the attributes typicality, familiarity, and traditional best characterized the reference

beers Thy Pilsner and Thy Classic. The attributes drinkability, refreshing, and thirst-quenching were highly

correlated. High correlation numbers were specifically seen for liking versus drinkability (0.705), stimulating

(0.697), and refreshing (0.684). Complexity and familiarity both had moderate correlations with liking, whereas

novelty was almost uncorrelated to liking. Both complexity and familiarity showed linear or monotonic

relationships when plotted against liking. Relationship between novelty and liking showed an inverted U-shape,

and thus could be described by Berlyne’s theory on arousal.

There was a large spread in liking across all beer samples. This indicated that all beers were liked by at least

someone and that there might be clusters of consumers who liked different beers. L-PLSR was performed to

study and explain consumer liking by both consumer background and collative properties. The female

consumers were highly correlated to low frequency users and to consumers who had wine as their favourite

alcohol type. This segment had preferences towards traditional, familiar, and typical beers high in drinkability,

refreshing, and thirst-quenching characters. Male consumers and people that stated “I often try new beers”

and “I know a lot about beer” were all correlated. For these people the collative properties complexity, novelty,

confusing, and surprising had the greatest impact on liking. The darker classic beer samples Rum Cocktail,

Wormwood, and Juniper Berry with added flavours were rated highest in liking for this segment.

Possibilities exist in developing new innovative lagers in order to reach consumer acceptability and to increase

sales. The key note here is to look at segments even within lager beers. There is no such thing as an average

consumer. In order to reach success in launching new lager beers, it has to be directed and marketed towards a

certain consumer segment, which will embrace the novel beer.


56

10.0. Limitations

Great care has been given to standardisation and avoidance of bias and confounding in the choice of method

and design. It is important to provide valid findings and results. There might have been unknown confounders

that the researcher has failed to control or eliminate. The best defence against this has been randomisation, so

that possible confounders have been evenly distributed and randomised between consumers. Bias on the

other hand involves errors in the measurement and often caused by systematic variation, which can lead to

inaccurate results (M. Meilgaard et al., 2007). Some bias in the present study might include variation in the

beer samples with regards to imprecise sample sizes or fluctuating serving temperatures. Another error that

was experienced was loss of carbonation in the beer samples due to different time lengths in between pouring

and serving of a sample.

It was attempted to develop a questionnaire that was adequate in number of questions and length plus it was

attempted to keep number of samples to a maximum of eight different stimuli. Despite these considerations

people might have experienced fatigue either physical or palate fatigue, which can lead to central scoring.

Another common problem with scaling is that people tend to use the middle section of the scale and avoid the

end-anchors.

A possible improvement of the method could have been to computerize the data so that the participants

would have typed in their ratings directly on a screen. This could speed the receiving of the results and

eliminate any data entry errors. On the other hand this could also have been a disadvantage if consumers were

unfamiliar with the technology and a shift in concentration away from the beer evaluation could very likely

occur.


57

11.0. Perspectives

It is essential when conducting research to be able to convert and adapt your academic results to usable and

applicable strategies in real life situations; especially for the world of beers, since beer drinking not only

happens in the academic world, but affects the entire population of consumers and the brew masters

responsible for developing new specialty beers. Brew masters sometimes brew new beer according to their

own nose or to other brew masters’/colleagues’ noses. These people are already masked and shaped by the

beer industry and beers developed in this regime might not reach the normal, Danish beer consumer, and

hence fail to succeed on the market. The results from this study can function as background knowledge for

advice to brew masters when designing and launching new beer.

First of all they should not be afraid to experiment with lager beers; it could create higher sales and new target

groups especially for the micro breweries. It is key to consider the target group when designing a new beer and

deciding what characteristics to give it. Women often have pilsner as their favourite type of beer and this

segment might be the new buyers of flavoured pale lagers which should have high resembles to ordinary

pilsners. It has to be high in drinkability and have a hint of something new with an added flavour, but perhaps

an already familiar flavour. If the target consumers are men, experimentation should occur with the darker

lagers such as the classic beers. Innovativeness can be more edgy here, especially if the consumers already

have a lot of knowledge about beer, and drink beer on a regular basis. However, it is imperative to keep a

balance between newness and familiarity in the beer regardless of the target segment and not to create a too

novel and unappreciated beer which will, most likely, fail on the market. Keep it simple and typical but at the

same time with an indication of something special and new.

When discussing if there is a market for lagers with flavours added, it is important to remember other factors

which influence choice. Food choice is complex and influenced by various aspects other than just sensory

quality. This could be non-sensory factors such as branding, labelling, bottle color and shape, price, reputation,

and so forth, which have great impact when deciding to buy and drink a beer. A lot of beers are sold mainly on

the breweries’ or the brew masters’ ability to market and target the beer well. A flavoured lager beer with

personality and character, that is produced by a well respected and established micro brewery, with a catchy

label that appeals to the target group has chances of great success. However further research with an

interdisciplinary approach is needed to establish the role of non-sensory factors in beer drinking and beer sales.


58

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13.0. Appendices

Appendix 1: LIFE website recruitment

Kom og smag på øl

Sensorikgruppen på KU-LIFE søger forsøgsdeltagere til forsøg om innovative øl

Projektet har til formål at undersøge oplevelse og præference af forskellige øl.

Eksperimentet tager ca. en time og vil foregå om aftenen i perioderne uge 7 (13.-16. februar) 2012

uge 10 (5.-8. marts) 2012

Du vil kunne vælge mellem dage og tidspunkter, der passer dig bedst. Forsøget udføres mandag til torsdag i uge 7

& uge 10 fra klokken 16:00 på KU LIFE, Rolighedsvej 30, 1958 Frederiksberg C.

Krav til deltagerne

Skal være over 18 år

Skal kunne lide øl

Du er kun forpligtet til at smage på øl, ikke drikke det hele, medmindre du har lyst. Samlet mængde øl vil være

omkring 50 cl øl. Du vil blive bedt om at underskrive en erklæring om, at du ikke vil køre lige efter forsøget. Vi

tilbyder gratis billet til offentlig transport hjem indenfor HT området.

Fordele for deltagerne

Du vil smage og drikke en række forskellige eksperimentelle øl

Du får én flaske specialøl som tak for deltagelse

Forsøget er en del af et større projekt kaldet Dansk Mikrobryg - Produktinnovation og Kvalitet.

Læs mere på: http://www.danishmicrobrew.com/Kvalitetsbryg.htm

Deltagelse i projektet

Har du lyst til at deltage, send en mail til [email protected] med følgende

oplysninger:

Fulde navn

Alder & køn

Vi vender tilbage til dig.

mailto:[email protected]


64

Appendix 2: Full Questionnaire

Ølprøve:_____________________________

Hvor godt kan du lide øllen?

Denne øl er traditionel

Denne øl er kompleks

Denne øl har nyhedsværdi for mig

Denne øl er overraskende

Dette er en typisk øl

Slet ikke

Neutral Rigtig godt

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


65

Denne øl er velkendt

Denne øl er stimulerende

Denne øl er forvirrende

Dette er en øl jeg kan drikke flere af i træk

Denne øl er tørstslukkende

Denne øl er forfriskende

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Helt uenig

Neutral Helt enig

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


66

Appendix 3: Background Information questionnaire

Baggrundsinformation

Fulde navn + email___________________________________________________________________________

Køn

Alder

18-25 26-35 36-45 46-55 56-65 66-75 75+

Uddannelse

Folkeskole eller tilsvarende

Læreplads eller udlært i et fag

Gymnasial uddannelse

Kort videregående

Mellemlang videregående

Lang videregående

Indkomst (kr. per måned brutto)

0-10.000 11.000 - 20.000

21.000 – 30.000

31.000 – 40.000

41.000 – 50.000

51.000 – 60.000

60.000 +

Hvor ofte drikker du øl?

1-6 gange/året 1 gang/mdr. 2-3 gange/mdr. 1 gang/uge 2-3 gange/uge Næsten hver dag

Hvad er din favorit type af alkohol? (sæt kun ÉT kryds)

Øl Vin Sprut Andet

Hvis du skal vælge, hvilken type øl er så din favorit? (f.eks. pilsner, classic, hvede, ale m.m.)

_____________________________________

Jeg prøver ofte nye øl

Meget uenig Noget uenig Hverken/eller Noget enig Meget enig

Jeg ved meget om øl

Mand Kvinde


67


Jeg drikker tit øl, jeg kender i forvejen


Når jeg spiser ude, kan jeg godt lide at prøve de mest usædvanlige ting, selvom jeg ikke er sikker på, om jeg kan

lide dem.


Når jeg tilbereder mad eller snacks, kan jeg godt lide at prøve nye opskrifter.


Jeg synes, det er sjovt at prøve nyt mad, som jeg ikke er kender.


Jeg er ivrig efter at vide, hvilken slags mad mennesker fra andre lande spiser.


Jeg kan godt lide at spise eksotisk mad.


Ting på menukortet, som jeg ikke kender gør mig nysgerrig.


Jeg foretrækker at spise produkter, som jeg er kender.


Jeg er nysgerrig omkring produkter, som jeg ikke kender.



68

Appendix 4: Background information categories for data analysis

Alder

18-25 26-35 36-45 46-55 56-65 66-75 75+

Uddannelse

Folkeskole eller tilsvarende

Læreplads eller udlært i et fag

Gymnasial uddannelse

Kort videregående

Mellemlang videregående

Lang videregående

Indkomst (kr. per måned brutto)

0-10.000 11.000 - 20.000

21.000 – 30.000

31.000 – 40.000

41.000 – 50.000

51.000 – 60.000

60.000 +

Hvor ofte drikker du øl?

1-6 gange/året 1 gang/mdr. 2-3 gange/mdr. 1 gang/uge 2-3 gange/uge Næsten hver dag

Young adults Middle adulthood Senior adults

Low education Medium education High education

Low income Medium income High income

Low frequency Medium frequency High frequency


69

Appendix 5: Mean scores for all response variables

Liking Complexity Novelty Typicality Familiarity Surprising Confusing Traditional Stimulating Drinkability Thirst-quenching Refreshing

Thy Pilsner 8.762 5.189 4.148 10.984 10.910 4.623 3.910 11.025 7.377 9.582 9.828 9.492

Thy Classic 8.443 7.836 6.180 8.295 8.557 6.508 5.918 8.500 7.844 7.164 7.885 7.516

Perilla 7.615 7.016 7.131 7.410 7.008 8.066 6.836 6.861 7.197 7.746 8.861 8.672

Star Anise 7.770 8.648 9.393 5.254 5.311 9.861 7.656 5.172 7.754 6.648 8.197 7.934

Wormwood 7.631 8.746 8.123 5.975 6.189 9.246 7.762 6.139 6.984 6.566 7.148 6.967

Rum Cocktail 8.598 9.328 9.648 5.328 5.328 10.311 7.885 5.164 8.582 7.139 7.418 8.180

Juniper Berry 8.852 8.820 7.844 6.967 7.336 8.033 6.623 6.992 8.164 7.557 8.123 7.828

Lemon Lime 8.959 8.311 8.156 5.787 6.598 8.910 6.746 6.311 8.205 8.205 9.049 9.246

Collative Properties and Hedonic Responses to Specialty Beer Mette Duerlund

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Documents

Collative Properties and Hedonic Responses to Specialty Beer