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Effects of emotional responses to certain foods on the prediction of consumer acceptance
Food Research International 112 (2018) 361–368
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Food Research International journal homepage: www.elsevier.com/locate/foodres
Effects of emotional responses to certain foods on the prediction of consumer acceptance
T
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Grazina Juodeikienea, Daiva Zadeikea, , Dovile Klupsaitea, Darius Cernauskasa, Elena Bartkieneb, Vita Leleb, Vesta Steiblieneb, Virginija Adomaitieneb a b
Kaunas University of Technology, Radvilenu rd. 19, LT-50254 Kaunas, Lithuania University of Health Sciences, A. Mickeviciaus str. 9, LT-44307 Kaunas, Lithuania
A R T I C LE I N FO
A B S T R A C T
Keywords: Empathic food Acceptability Emotional response Face-reading Attitude Behaviour
This research was focussed on the development of a methodology for the recognition of consumer preferences based on a combination of emotional, behavioural, and sensory trait information. A model of the impact of customers' sensory experiences on their attitudes towards food products and their behavioural intentions was analysed. Sensory and emotional analyses were used to describe five kinds of bread (wheat, rye, corn, wholemeal, and multigrain) and two types of chocolate (dark and milk). Acceptability and emotional response were rated by a consumer panel (n = 109) drawn from 21 to 24 age segments using the hedonic scores and a FaceReader software which detects six basic emotions (happy, sad, angry, disgusted, scared, surprised) and a neutral state were applied. For the products tested, expressions of happiness, anger, and sadness for each product were relatively high compared to the others, with the neutral state being the main expression. The chocolate products elicited the highest intensity of happiness, reducing the level of the neutral state. A different tendency was obtained during the testing of bakery goods: higher expression of the neutral state and sadness and low expression of happiness. The emotional/sensory experience model and consumer behavioural patterns comprise the method for the differentiation of products, which could be useful in the food industry as well as for the development of new methodologies for the prediction of changes in human emotional response to food related to psychological disorders.
1. Introduction Emotions are an essential part of social interaction and regulate most of the activities that humans regularly undertake, from simple conversations to business deals or food consumption. Despite there being little agreement among the various theories involved in the attempts to define emotions, they usually recognize the multi-faceted nature of an emotion. It has been agreed that emotions are short-lasting (from a few seconds to minutes) phenomena characterized by the awareness of a given situation, overt expressions and behaviours, readiness to act, and physiological changes supplemented with subjective feelings (Mulligan & Scherer, 2012). Emotions are characterized as affective state, where affect is an embodied reaction to perceived stimuli and emotions reflect the affective traits of perceived stimuli (Prescott, 2017). In recent years, new findings on the influence of emotions on behaviour have motivated renewed attention from industry and the public
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to the phenomena linked to affective interaction and individual emotions. Interest in the measurement of emotions in consumer and sensory science has increased because the measurement of liking/acceptability/ preference could not reliably predict food choice (Scherer, 2005; Thomson & Crocker, 2013). The strongest predictive strength was achieved by the combination of evoked emotions and liking (Dalenberg et al., 2014). According to the suggestion that emotional aspects are strongly linked to sensory experience and that those emotions could determine the preference for a product (Ferrarini et al., 2010), proposed characterizing products by making associations between sensory and emotional profiles as means to improve the products' position. As numerous studies have shown, one of the reasons for this is that emotions could influence the product choice (Ackert, Church, & Deaves, 2003) and eating behaviour (Macht, 2008; Wallis & Hetherington, 2009) has been examined more often, the opposite direction, i.e. food consumption influencing mood and emotion, has only recently gained attention in consumer and sensory research (Bhumiratana, Adhikari, &
Corresponding author. E-mail addresses: [email protected] (G. Juodeikiene), [email protected] (D. Zadeike), [email protected] (D. Klupsaite), [email protected] (D. Cernauskas), [email protected] (E. Bartkiene), [email protected] (V. Lele), [email protected] (V. Steibliene), [email protected] (V. Adomaitiene). https://doi.org/10.1016/j.foodres.2018.06.064 Received 10 May 2018; Received in revised form 25 June 2018; Accepted 27 June 2018 Available online 28 June 2018 0963-9969/ © 2018 Elsevier Ltd. All rights reserved.
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that could be used to enhance the interaction of computerized devices and their users. Recent studies featuring the FaceReader software (Noldus, 2013) include automated facial analysis of expressions elicited by disliked/liked food (de Wijk, Kooijman, Verhoeven, Holthuysen, & de Graaf, 2012; He, Boesveldt, de Graaf, & de Wijk, 2012) and the effects of facial emotional feedback on the readiness to use computerbased assessment (Terzis, Moridis & Economides, 2012). Face reading technologies can deliver additional information to conventional consumer acceptance tests (Danner et al., 2014). However, more research is needed to see how face reading technology performs in more complex testing procedures, such as simulated or “real life” environments. The research was focussed on the development of a methodology for the recognition of consumer preferences based on a combination of emotional and sensory trait information. A model of the impact of customers' sensory experiences on their attitudes towards food products and their behavioural intentions was analysed.
Chambers, 2014; Cardello et al., 2012; Dalenberg et al., 2014). Emotions could influence food choice dependent on mood via physiological effects that change appetite or by changing other behaviour that constrains or alters food availability (Gibson, 2006). This assumption was confirmed by findings indicating that when people experience a high degree of positive emotion, they may change their behaviour more (Cyders & Smith, 2008; Patel & Schlundt, 2001). Consumers anticipated emotions they might experience when consuming product can contribute to the understanding of behavioural intention and behaviour towards product. Moreover, it can help to develop a marketing plan for specific consumer groups as it can identify influencing factors and consumer beliefs towards a product (De Pelsmaeker et al., 2017). The broaden-and-build theory of positive emotions (Fredrickson, 2004) suggests that positive emotions promote discovery of novel and creative actions, which in turn builds that individual's personal resources. However, one should consider that there is a hedonic asymmetry when working with products and consumers emotions (Desmet, 2003). Whereas the general emotion literature tends to focus mainly on negative emotions, positive food-related emotional states have a higher prevalence or intensity than those that are negative (Cardello et al., 2012; Ferrarini et al., 2010). Since the internal feelings are frequently accompanied by changes in facial expressions, this potentially leads to new objective measures of the affective responses to foods. Attempts to measure emotions have been done in the psychology and sociology fields (Block, He, Zaslavsky, Ding, & Ayanian, 2009; Habhab, Sheldon, & Loeb, 2009) but the measurement of food-elicited emotions is more recent and not well established. We could believe that emotional responses to foods might be quite common, but obviously the question then is to what extent does the consumer's experience of such feelings in response to foods tell us more about their food choices than ratings of liking, and which of these emotion terms are the most effective for indicating this? Each emotion corresponds to a unique profile in experience, physiology, and behaviour (Mauss & Robinson, 2009). Also, a basic set of six emotions (happiness, anger, sadness, surprise, disgust, and fear) is claimed to be universal, and the nonverbal signals (facial expression and physiological reactions) of these emotions are recognized cross-culturally (Pantic & Rothkrantz, 2000). This rising attention to emotion in consumer and sensory research has led to the introduction of many emotional instruments to capture consumers' emotions elicited by food (Dalenberg et al., 2014). The studies focused not only on emotions, but also diffuse affect states such as moods (EsSense Profile), characterized by a relative enduring predominance of subjective feelings (King & Meiselman, 2010), while the conceptual profile includes a mix of emotions and abstract conceptualisations with emotional nuances (Thomson, Crocker, & Marketo, 2010). A multistep approach (EmoSemio) measuring emotional responses associated to a specific product category that can be checked or rated (Spinelli, Masi, Dinnella, Zoboli, & Monteleone, 2014; Ares et al., 2014). Several instruments have been developed, of which the Product Emotion Measurement Instrument (PrEmo) is one of the most wellknown tools (Desmet, 2003) which recently was applied in food products (Dalenberg et al., 2014). These instruments can generally be divided into explicit and implicit methods depending on how emotional associations are assessed. The implicit measurement of emotions are indirect and non-self-reported and register emotions while participants are consuming, smelling or looking at food; the explicit methods dominate investigating an intentional facial expression in relation to food (Danner, Sidorkina, Joechl, & Duerrschmid, 2014). Although implicit measurements are only limitedly applied in consumer and sensory research (Lagast, Gellynck, Schouteten, De Herdt, & De Steur, 2017). Current research mainly focus on explicit measurement of food-elicited emotions and self-reported sensory liking. Although emotions are spontaneous expressions of our states of mind, consequently, they represent an alternative source of information
2. Materials and methods The emotional input of foods to their acceptability was assessed through the following tasks: analysis of the relationships between selfreported hedonic liking and facial expressions, analysis of emotional expressions as the most valuable descriptors for explaining the hedonic quality of a product, and recognition of consumer preferences based on a combination of emotional/sensory information as well as a model of the impact the sensory customer experience has on their attitudes and preference towards the analysed food products. An emotional/sensorial experience-based questionnaire was used to assess consumer attitude (positive, negative, or neutral) and behaviour intentions (will try, recommend, or act actively) (Cardello et al., 2012) in order to test stimuli evaluation, and the emotional response to the taste and texture of different food products using the FaceReader software (Noldus, 2013) was evaluated. In the first part of the experiment, five samples of different breads [wheat, rye, corn (80% wheat flour and 20% corn flour), wholemeal wheat flour, and multigrain (80% wholemeal wheat flour and 20% cereal grains] and two samples of different chocolates (dark and milk) without additives were selected from a local supermarket. In the second part of the experiment, emotional responses in relation to consumer attitudes and behaviour towards the milk chocolate and aerated milk chocolate as more empathic products were analysed. All of the products were debranded and tested according to the scheme presented in Fig. 1. All food samples were tested in a sequential monadic way; samples were randomized and coded. Water was provided to rinse the palate between the samples, and consumers were asked to wait for a 30 s period to minimize carry-over effects. In order to assess the hedonic quality of the food products and to discover the emotional responses to different attitudes and behaviour, 109 students (27% males and 73% females) from the Kaunas University of Technology (Lithuania) between 21 and 24 years of age were involved in the experiment. The panellists were recruited by an e-mail from a student academic panel. All students consented to participate in routine tests of foods and the research was conducted as a part of an approved activity entitled “Emotional and sensory assessment of food quality”. The test took place in test room under operating conditions (constant single lighting, 20–22 °C temperature and 50–55% humidity). 2.1. Questionnaire-based test A personal interview was chosen as the most appropriate method for data collection in our testing. The respondents were asked to fill out a questionnaire with the advantage of flexibility: questions that the respondent did not understand were explained by the interviewer. Each personal interview was conducted as follows: initial information was given on how long the personal interview would take, followed by identifying and explaining the test topics. The respondents were 362
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The participants were asked to taste the whole presented sample at once, take fifteen seconds to reflect on the taste impressions, then give a signal with a hand and visualize the taste experience of the sample with a facial expression best representing their liking of the sample. For each sample, the intention facial expression from the point when the subject had finished raising their hand to give the signal until the subject started lowering the hand again) was extracted with the Observer XT 10.5 (Noldus Information Technology, The Netherlands) software in three steps: face detection, face registration and representation (Active Appearance Model), and face classification (Artificial Neural Network). Essentially, the usefulness of Observer XT is in its ability to review video in real time frame-by-frame and review/reassess the captured moments. FaceReader is essentially pre-coded and trained to mathematically analyse the direction and extent of certain facial feature movements, which are used to predict six basic expressions of emotion (happy, surprised, sad, angry, scared, and disgusted as well as a neutral state) in real time and the intensity at which these emotions are being expressed. The intensity measurements are judged on a continuous scale from 0 to 1, with 0 indicating that the emotion is not expressed and 1 indicating that the emotion is fully expressed. This software is limited in its emotional characterization. Happy is considered the only positive emotion, while sad, angry, scared, and disgusted are considered negative emotions. Afterwards, the participants were asked to rate their liking or disliking of the food sample on a 9-point hedonic rating scale from 0 (dislike extremely) to 9 (like extremely). The most intensive emotional responses obtained from the milk chocolate testing procedure were grouped based on the generated attitudes and behavioural intentions.
Fig. 1. Scheme of the main steps of a questionnaire- and computer-based analysis method for the evaluation of consumers' preferences.
provided with a questionnaire and the foods to be tested. In the questionnaire, respondents were asked to indicate the significance of the properties (appearance, flavour, odour, hardness, particle size, mouth coating) and marketing attributes such as brand and packaging of the products, using the category rating scale method. The questionnaire consisted of three blocks of questions: (i) attitude towards specific product in general and a particular test product evaluation, (ii) identification of the emotional reactions of consumers to the unknown brand test product, and (iii) identification of behaviour intentions. The five-point Likert scale from 1 (extremely disagree) to 5 (extremely agree) based on the recommendations of Martin and Stewart (2001) for the evaluation of the consumer approach to product categories was used in terms of products in general and consumer emotional reactions. A scale recommended by Andrade and Cohen (2007) was used for the evaluation of the emotional attitude towards the test object, and the statements intended to measure behaviour intention were based on the Baker and Churchill (1977) classic purchase intent scale.
2.3. Statistical analysis A multiple linear regression was performed to examine the correlation between facial expressions and the hedonic liking, and between consumer behaviour and attitude within emotional groups. Differences among means were analysed by the analysis of variance (ANOVA), Fisher's least significant difference test, and Pearson's correlations using the STATISTICA V10 (StatSoft, Inc., USA) software. The significance level was set at p < .05. 3. Results and discussion 3.1. The relationship between facial expressions and the hedonic acceptability of food After tasting each product, consumers were first asked about the acceptability of the product and then about the emotions it elicited. By interpreting the facial expressions evoked by the products, the consumers' real thoughts about a given product could be better understood, and it was possible to identify the liking/disliking emotions in an objective way. Emotional evaluation of bakery and chocolate products indicated that the tested products excite all six emotions (happy, sad, angry, scared, surprised, and disgusted) and also caused the neutral facial expression (Table 1). However, the intensity of every emotion for each product was different. The general feature of the results was that the intensity of the happy, angry, and sad emotions for each product were relatively high compared to the other emotions. The means of happy indicates that liked samples elicited more intense facial expressions of happy than neutral or disliked samples, with neutral and disliked samples at the same level. In the case of the neutral expression, more than half of all participants expressed a positive attitude towards bakery products when it was the dominant expression. Thus, neutral emotions is mainly linked to a more positive evaluation, this also was found in the research of using emoji (Jaeger, Vidal, Kam, & Ares, 2017). In this study, when recording two negative facial expressions, sad and angry, the responses of the subjects in terms of their attitude and
2.2. Testing of emotional response to products For the analysis, a high-resolution digital video camera (Microsoft LifeCam Studio, 1080p HD) was utilized for video capture. In the explicit measurement experiment, subjects were asked to rate the food samples during and after consumption with an intentional facial expression, which was recorded and then characterized by FaceReader 5.0 (Noldus Information Technology, Wageningen, The Netherlands). The software feature “continuous calibration” was used for the face model standardization. Food samples were presented in a balanced order across panellists. The video session started when the participant gave a signal that he or she was ready to taste the presented product. Each respondent was instructed to drink room temperature water between the samples. The duration of testing was approximately 30 s until the subject chewed, tasted and swallowed the product in full. Emotions and the correlated facial expressions have a quick onset, facial expressions can begin in a matter of milliseconds after an emotion-inducing stimulus. Preliminary examinations showed that instructions to give 30 s were a good compromise between leaving the participants enough time to experience the sample. 363
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Table 1 Average acceptability and emotional intensities (in %) measured during sensory analysis of breads and chocolate. Emotion Product
Neu
Hap
Sad
Ang
Sup
Sca
Dis
Acceptability score
Bread 1. Wheat 2. Rye 3. Corn 4. Wholemeal 5. Multigrain
0.630 0.650 0.728 0.705 0.630
0.086 0.076 0.069 0.107 0.041
0.197 0.228 0.156 0.153 0.257
0.055 0.058 0.037 0.026 0.057
0.010 0.008 0.006 0.005 0.006
0.002 0.003 0.002 0.002 0.002
0.007 0.002 0.002 0.002 0.007
5.6 4.9 5.6 6.3 5.2
Chocolate 6. Dark chocolate 7. Milk chocolate
0.667 0.720
0.160 0.198
0.1.1 0.122
0.028 0.021
0.006 0.019
0.001 0.001
0.001 0.001
7.1 8.0
Average: Bread Chocolate
0.664 0.694
0.078 0.179
0.198 0.132
0.047 0.025
0.007 0.013
0.002 0.001
0.004 0.001
5.5 7.6
Neu – neutral, Hap – happy, Ang – angry, Sup – surprised, Sca – scared, Dis – disgusted.
behaviour could be evaluated positively or negatively. If the attitude towards bakery products was neutral (60% of the subjects), then the behaviour intentions towards bakery products were negative. The majority of the subjects evaluated the sensory quality of the bakery products as liked or highly liked. However, if baked goods were favourably treated, the behaviour remained mainly neutral (Table 1). The initial analysis showed that an increase in the intensity of happy or sad emotions influenced the reduction of the level of neutral emotional expression. The statistical analysis showed significant differences (F(37.112) = 8.934, p < .001, respectively) in facial reactions between food samples. Strong relationships between the overall acceptability and happy or sad expressions were obtained (r = 0.9467 and r = −0.9215, respectively). The experiment showed that the taste and appearance of the chocolate are very important sensory characteristics of the product (Fig. 2). Besides this, the chocolate hardness, the size of the particles, and the melting speed are essential characteristics (score > 5) by which consumers evaluated the tested products. Regardless of whether the most intense facial expression was happy or sad, the panellists considered the chocolate positively. When facial expressions were neutral, almost all participants expressed a positive attitude and positive behavioural intention towards the chocolate. This can partially be attributed to the test situation which requires concentration and analytical thinking of the participant can probably suppress positive emotions/facial expressions to a certain degree. Further examinations in real-life situations, also if possible without directly asking the participants to rate the products, could be performed. Clustering of bread and confectionery products based on the emotional variables was similar to the clustering of products based on the acceptability (Fig. 3). A significant effect of gender (F(24.141) = 1.257, p = .0146) and food samples of emotional and hedonic interaction was found. The results of face-reading showed that there was a difference between men and women when rating emotional descriptors. In the case of interactions like happiness (Fig. 3a) and hedonic acceptability (Fig. 3b), bread differences varied by gender; while men did not discriminate their happiness score among breads, women reported significantly higher scores for happiness when consuming white breads and milk chocolate, and lower scores when consuming dark breads and dark chocolate (Fig. 3). Differences in the emotional expressions during consumption of the different groups of products were identified due to the associated concepts: sugary confectionery products evoked more intensive neutral/happy emotions, whereas bread – neutral/sad emotions. Thus, confectionery products can intensify the manifestation of emotions, whereas the conventional traditional products (e.g., bread) do not have that ability and evoke a broad spectrum of emotional responses. The few studies evaluating the relationship between declarative
Fig. 2. Sensory (A) and marketing (B) attributes for dark and milk chocolates (questionnaire-based analysis).
acceptance/preference of products and emotional reactions occurring during the consumption of different food samples showed that there was a high correlation with happiness, disgust, and liking (Danner et al., 2014; Kostyra et al., 2016). In the next section, emotional responses generated during the testing of different kinds of milk chocolate (traditional and aerated) will be analysed in relation to consumer attitudes and behaviour towards the chocolate products. 3.2. The distribution of consumer attitudes and behaviour caused by emotions generated during chocolate testing The emotional responses obtained from the milk chocolate 364
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generated attitudes and behavioural intentions according to the three most intensive facial expressions set out to the decreased intensity: I group – facial expressions “happy”, “neutral”, “sad”/”angry” (Hap/ Neu/Sad(Ang); II group – facial expressions “neutral”, “sad”, “happy” / “angry” (Neu/Hap/Sad(Ang); III group – facial expressions “neutral”, “happy”, “sad”/”angry” (Neu/Sad/Hap(Ang); IV group – facial expressions “neutral”, “angry”,”sad” (Neu/Ang/Sad). The main attitudes and behavioural intentions of respondents generated during sensorial analysis of milk chocolate products related to emotional expressions are presented in Fig. 4. After testing the milk chocolate as well as aerated milk chocolate, 48.7% of the respondents were placed in the Neu/Hap/Sad(Ang) emotional response group (I group) (Fig. 4). The attitude of all the respondents of this group towards the chocolate was favourable, and the behaviour of 87.5% of the respondents was positive. The behavioural intentions of 6.2% of the respondents were neutral, and the remaining 6.3% of the respondents had negative behavioural intentions. All of the sensual features of the milk chocolate remained acceptable to the respondents. Another part (32%) of the respondents was placed in the Neu/Sad/ Hap(Ang) emotional group (II group). All of the respondents in this group expressed a positive attitude towards the chocolate, and only 5.9% of the respondents had neutral behaviour towards the chocolate. The attitude and behaviour intentions of all the respondents were positive, and the most acceptable features were the appearance of the chocolate, hardness, melting speed, taste, and the size of particles. The Hap/Neu/Sad(Ang) emotional group (III group) contained 16.6% of the respondents. However, the attitude and behaviour of all respondents was positive, and all sensual features were acceptable. The smallest part of the respondents (2.7%) were grouped into the Neu/Ang/Sad emotional group (IV group) where the most intensive facial expression was angry (18–24%); these respondents showed a negative response and were not going to buy the product. Despite all emotions (positive and negative) being expressed by the respondents, their attitudes towards chocolate were favourable, and the behaviour of 89.5% of the respondents was positive. In addition, 97.4% of the respondents from all groups indicated that all the sensual features of the chocolate were acceptable. It has been noticed that the respondents evaluated milk chocolate positively and were willing to buy it when the most intensive facial expression was happy or sad. When
Fig. 3. Consumer gender effect on hedonic acceptability (a) and happy (emotional mode) (b). Products: 1 – wheat bread; 2 – rye bread; 3 – corn bread; 4 – wholemeal wheat bread; 5 – multigrain wheat bread; 6 – dark chocolate; 7 – milk chocolate.
(traditional and aerated) testing procedure were grouped into four groups based on the three most intensive emotions organized according to decreasing expression intensity. The emotional responses obtained during the milk chocolate testing procedure were grouped based on the
Fig. 4. The main consumer attitudes caused by emotions generated during consumption of milk (traditional and aerated) chocolate (intensities of emotional expressions are presented in percentage). 365
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6
4.0 2.0
R² = 0.0716
6.0
4 R² = 0.7211
2
0.0 0.6
0.8
1
0
0.1
6.0
0.2
0.3
0.0 0.8
Behaviour
R² = 0.7525
2.0
R² = 0.0329 1
0.1
0.15
0.2
Sad
4.0
Neutral
0.05
6.0
Behaviour
4.0
0.6
R² = 0.7423 0
0.4
6.0
0.4
2.0
Happy
Neutral
2.0
4.0
0.0
0 0.4
Behaviour
Behaviour
Behaviour
Behaviour
6.0
4.0 2.0
R² = 0.7963
0.0
0.0 0
0.1
0.2
0.3
0
0.4
0.05
0.1
0.15
0.2
Sad
Happy
Fig. 5. The relationships between attitude and the intensity of main emotional expressions for the traditional (A) and aerated (B) milk chocolates.
the most intensive facial expression was neutral, all or almost all respondents in each group expressed a positive attitude towards the chocolate, and the behavioural intentions of a majority of respondents (I group – 68.8%, II group – 72.7%, III group – 83.3%, IV group – 88.6%) were positive. The respondents of all groups evaluated the majority of the chocolate's sensory features as favourable or even very favourable. The common tendency was that when a behaviour was neutral, the attitude towards the chocolate remained positive. The data of some facial expressions was not included in the statistical analysis due to their insignificant values. Through analyses of the associations between individual expressed emotions and the attitude or behaviour, it was shown that the intensity of the neutral response has no significant relationship with behaviour (r = 0.1814–0.2676, p > .05) for all respondents (Fig. 5). The statistical analysis confirmed a strong relationship between behaviour scores and happy as well as sad emotional responses, both having the highest values. Increased expression of happiness indicated positive behaviour, and a higher expression of sadness indicated a reduced attitude and negative behaviour (Fig. 5). We noticed that the reduction in the neutral emotional response could indicate an increase in other – positive or negative – emotions (happy, sad, and angry) and vice versa. Slightly lower correlation coefficients for linear relationships were obtained for traditional milk chocolate [r = 0.8492 (happy), and r = 0.8616, p = .013 (sad), p < .05) compared to aerated (r = 0.8675 (happy) and r = 0.8924 (sad), p < .05) (Fig. 5). Hence, conceptual terms are comparable within products, whereas sensory attributes are comparable between products. The behaviour of respondents towards milk chocolate was significantly (p < .05) related to the attitude of respondents within the constructed emotional groups (I–IV). According to the statistical analysis, the emotional expressions generated for the most intensively evoked facial expressions in each group had a strong impact on the attitude and behavioural intentions towards milk chocolate (Table 2).
Table 2 Correlation coefficients of relationships between attitude and behaviour within emotion groups. Behaviour
Attitude
I II III IV a b c d
I groupa
II groupb
III groupc
IV groupd
0.7157 – – –
– 0.8322 – –
– – 0.9645 –
– – – 0.8464
Neu/Hap/Sad(Ang). Neu/Sad/Hap(Ang). Hap/Neu/Sad(Ang). Neu/Ang/Sad.
scale) as a factor and facial expressions as variables, showed a significant correlation between hedonic liking and the intensity of elicited facial expressions, especially for happy (F(35, 753) = 23,456, p ≤ .0001), sad (F(35.753) = 8.127, p ≤ .0001) and angry (F (35.753) = 11.212, p ≤ .001), but not for neutral (F(35.753) = 0.517, p > .05) (Fig. 6). The linear regressions showed high positive correlations with a coefficient of determination of 0.76–0.78 for angry and
Intensity of emoonal expression
1
NEU R² = 0.2061
0.8
HAP R² = 0.8712
0.6
SAD R² = 0.7576 ANG R² = 0.7841
0.4 0.2 0 3
3.3. Correlation between facial expressions and reported acceptability during milk chocolate testing
4
5
6
7
8
9
10
Acceptability scores Fig. 6. Regressions of facial expression intensity against reported acceptability scores (liking).
The statistical analysis with hedonic liking ratings (9-point hedonic 366
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4. Conclusions This study describes the application of positive–negative emotional scaling to the conceptual profiling of milk chocolate and outlines novel data modelling procedures used to explore sensory/conceptual relationships. Noldus FaceReader technology is sufficiently accurate to detect significant differences in facial expressions elicited by different food samples. Our important findings suggest clear relationships between certain sensory and emotional perceptions during product evaluation. Facial expressions of happy, sad, and angry were particularly effective for distinguishing the differences between the acceptability of foods. In the case of milk chocolate, the emotional responses sad, happy, and neutral were most often encountered, but the difference in comparison with bread bakery products was that the neutral emotional expression was identified as the dominant one. It is also important to note that it is uniquely related to the positive attitude of users and positive behavioural intentions. This can be explained by the higher level of satisfaction of hedonistic users (compared with the case of bread crisps). Regardless of whether the most intense facial expression was happy or sad, the milk chocolate seems to be a favourable product stimulating positive behaviour. The emotion–attitude–behaviour interface model can provide significant information for the prediction of consumer behaviour, although this needs further research. The procurement of more effective feedback from consumers may be useful in the bakery and confectionery industry, as well as in the development of new food products. Also, the set of products included in this study is not sufficiently representative of food product variation. Enlarging the food samples of different taste sets in order to cover a wider range of sensory properties is advised for future studies. In the next step, the emotional response parameters will be applied to not only predict the behaviour of consumers, but also to determine changes in product liking for individuals in order to provide an objective emotional analysis model for early diagnosis of depression disorders.
Fig. 7. The conceptual profile for unbranded traditional and aerated milk chocolates.
sad, and 0.87 for happy, and neutral emotional expression has a low R2 of 0.206. This study is in agreement with the relationship found between the acceptability of food products and the emotional response. The study of De Pelsmaeker et al. (2017) showed that including anticipated emotions during for consuming chocolate increases the predicted variance of the planned behaviour. The identity of the object (it's chocolate) and the associated concepts evoked in the mind of the individual person during the experience of a product is not just a reaction to the product itself but also to the associated concepts (e.g., comforting, relaxing), which influence positive consumer attitudes towards food product (Thomson et al., 2010). Thus, sensory characteristics, which are intrinsic to the product and therefore part of its identity, become linked with conceptualisations. As an example, Fig. 7 shows the basic conceptual profile of milk chocolate (traditional and aerated). The conceptual terms positioned towards the right have the highest scale values indicating that these conceptualisations (energetic, relaxing, attractive, kindliness) are the most prevalent. Conversely, aggressive, complicated and obligatory have the lowest scale values. To elicit specific emotional responses from the consumption of products and to determine their changes, consonance among the sensory characteristics and emotions must be established to understand the mechanisms underlying the relationship between restrained eating, stress, and food intake (Ferrarini et al., 2010; Jager et al., 2014). According to Mojet et al. (2015), implicit emotional measurements can deliver product information that is not related to product liking. High stress reactivity could lead to greater food intake (Newman, O'Connor, & Conner, 2007) or conversely, some people eat less than normal in response to stress because they lose taste sensations (Torres & Nowson, 2007). This study shows that measuring facial expressions differentiate between food samples in an explicit measuring approach. In the explicit measurement, a clear discrimination between liked, disliked and neutral-rated samples was possible on the basis of the intensity of elicited angry, sad, happy and neutral facial expressions. However, when not only comparing facial reactions elicited by samples but the individual liking ratings as well, we see a good correlation between acceptability and facial expressions (see Fig. 6). There was a high negative correlation of the sad facial expression with disliking and a high positive correlation of angry and happy with liking. No such relationships were found for the neutral and other measured facial expressions. An important point in this study, concerning the explicit experiment, is that the emotions directly elicited by the taste of the food are measured, as opposed to self-reported questionnaires, where reading of an emotion term or seeing the emoticons may be enough to elicit an emotion that was not experienced while tasting.
Acknowledgements The authors gratefully acknowledge The Research Council of Lithuania for the funding of this research [Project EMOPSYCHOSCREEN, Grant No. P-MIP-17-49]. References Ackert, L. F., Church, B. K., & Deaves, R. (2003). Emotion and financial markets. Economic Review, Q2, 33–41. Andrade, E. B., & Cohen, J. B. (2007). Affect-based evaluation and regulation as mediators of behaviour: The role of affect in risk taking, helping and eating patterns. In R. Baumeister, K. Vohs, & G. Loewenstein (Eds.). Do emotions help or hurt decision making? A Hedgefoxian Perspective (pp. 35–68). New York: Russell Sage Foundation. Ares, G., Bruzzone, F., Vidal, L., Cadena, R. S., Gimenez, A., Pineau, B., Hunter, D. C., Paisley, A. G., & Jeager, S. R. (2011). Evaluation of a rating based variant of checkall-that-apply questions: Rate-all-that-apply (RATA). Food Quality and Preference, 36, 87–95. Baker, M. J., & Churchill, G. A. J. (1977). The impact of physically attractive models on advertising evaluations. Journal of Marketing Research, 14(4), 538–555. Bhumiratana, N., Adhikari, K., & Chambers, E. (2014). The development of an emotion lexicon for the coffee drinking experience. Food Research International, 61, 83–92. Block, J. P., He, Y., Zaslavsky, A. M., Ding, L., & Ayanian, J. Z. (2009). Psychosocial stress and change in weight among US adults. American Journal of Epidemiology, 170(2), 181–192. Cardello, A. V., Meiselman, H. L., Schutz, H. G., Craig, C., Given, Z., Lesher, L. L., et al. (2012). Measuring emotional responses to foods and food names using questionnaires. Food Quality and Preference, 24, 243–250. Cyders, M. A., & Smith, G. T. (2008). Emotion-based dispositions to rash action. Psychological Bulletin, 134(6), 807–828. Dalenberg, J. R., Gutjar, S., ter Horst, G. J., de Graaf, K., Renken, R. J., & Jager, G. (2014). Evoked emotions predict food choice. PLoS One, 9(12), e115388. Danner, L., Sidorkina, L., Joechl, M., & Duerrschmid, K. (2014). Make a face! Implicit and explicit measurement of facial expressions elicited by orange juices using face reading technology. Food Quality and Preference, 32, 167–172. De Pelsmaeker, S., Schouteten, J. J., Gellynck, X., Delbaere, C., De Clercq, N., Hegyi, A., Kuti, T., et al. (2017). Do anticipated emotions influence behavioural intention and
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Contents lists available at ScienceDirect
Food Research International journal homepage: www.elsevier.com/locate/foodres
Effects of emotional responses to certain foods on the prediction of consumer acceptance
T
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Grazina Juodeikienea, Daiva Zadeikea, , Dovile Klupsaitea, Darius Cernauskasa, Elena Bartkieneb, Vita Leleb, Vesta Steiblieneb, Virginija Adomaitieneb a b
Kaunas University of Technology, Radvilenu rd. 19, LT-50254 Kaunas, Lithuania University of Health Sciences, A. Mickeviciaus str. 9, LT-44307 Kaunas, Lithuania
A R T I C LE I N FO
A B S T R A C T
Keywords: Empathic food Acceptability Emotional response Face-reading Attitude Behaviour
This research was focussed on the development of a methodology for the recognition of consumer preferences based on a combination of emotional, behavioural, and sensory trait information. A model of the impact of customers' sensory experiences on their attitudes towards food products and their behavioural intentions was analysed. Sensory and emotional analyses were used to describe five kinds of bread (wheat, rye, corn, wholemeal, and multigrain) and two types of chocolate (dark and milk). Acceptability and emotional response were rated by a consumer panel (n = 109) drawn from 21 to 24 age segments using the hedonic scores and a FaceReader software which detects six basic emotions (happy, sad, angry, disgusted, scared, surprised) and a neutral state were applied. For the products tested, expressions of happiness, anger, and sadness for each product were relatively high compared to the others, with the neutral state being the main expression. The chocolate products elicited the highest intensity of happiness, reducing the level of the neutral state. A different tendency was obtained during the testing of bakery goods: higher expression of the neutral state and sadness and low expression of happiness. The emotional/sensory experience model and consumer behavioural patterns comprise the method for the differentiation of products, which could be useful in the food industry as well as for the development of new methodologies for the prediction of changes in human emotional response to food related to psychological disorders.
1. Introduction Emotions are an essential part of social interaction and regulate most of the activities that humans regularly undertake, from simple conversations to business deals or food consumption. Despite there being little agreement among the various theories involved in the attempts to define emotions, they usually recognize the multi-faceted nature of an emotion. It has been agreed that emotions are short-lasting (from a few seconds to minutes) phenomena characterized by the awareness of a given situation, overt expressions and behaviours, readiness to act, and physiological changes supplemented with subjective feelings (Mulligan & Scherer, 2012). Emotions are characterized as affective state, where affect is an embodied reaction to perceived stimuli and emotions reflect the affective traits of perceived stimuli (Prescott, 2017). In recent years, new findings on the influence of emotions on behaviour have motivated renewed attention from industry and the public
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to the phenomena linked to affective interaction and individual emotions. Interest in the measurement of emotions in consumer and sensory science has increased because the measurement of liking/acceptability/ preference could not reliably predict food choice (Scherer, 2005; Thomson & Crocker, 2013). The strongest predictive strength was achieved by the combination of evoked emotions and liking (Dalenberg et al., 2014). According to the suggestion that emotional aspects are strongly linked to sensory experience and that those emotions could determine the preference for a product (Ferrarini et al., 2010), proposed characterizing products by making associations between sensory and emotional profiles as means to improve the products' position. As numerous studies have shown, one of the reasons for this is that emotions could influence the product choice (Ackert, Church, & Deaves, 2003) and eating behaviour (Macht, 2008; Wallis & Hetherington, 2009) has been examined more often, the opposite direction, i.e. food consumption influencing mood and emotion, has only recently gained attention in consumer and sensory research (Bhumiratana, Adhikari, &
Corresponding author. E-mail addresses: [email protected] (G. Juodeikiene), [email protected] (D. Zadeike), [email protected] (D. Klupsaite), [email protected] (D. Cernauskas), [email protected] (E. Bartkiene), [email protected] (V. Lele), [email protected] (V. Steibliene), [email protected] (V. Adomaitiene). https://doi.org/10.1016/j.foodres.2018.06.064 Received 10 May 2018; Received in revised form 25 June 2018; Accepted 27 June 2018 Available online 28 June 2018 0963-9969/ © 2018 Elsevier Ltd. All rights reserved.
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that could be used to enhance the interaction of computerized devices and their users. Recent studies featuring the FaceReader software (Noldus, 2013) include automated facial analysis of expressions elicited by disliked/liked food (de Wijk, Kooijman, Verhoeven, Holthuysen, & de Graaf, 2012; He, Boesveldt, de Graaf, & de Wijk, 2012) and the effects of facial emotional feedback on the readiness to use computerbased assessment (Terzis, Moridis & Economides, 2012). Face reading technologies can deliver additional information to conventional consumer acceptance tests (Danner et al., 2014). However, more research is needed to see how face reading technology performs in more complex testing procedures, such as simulated or “real life” environments. The research was focussed on the development of a methodology for the recognition of consumer preferences based on a combination of emotional and sensory trait information. A model of the impact of customers' sensory experiences on their attitudes towards food products and their behavioural intentions was analysed.
Chambers, 2014; Cardello et al., 2012; Dalenberg et al., 2014). Emotions could influence food choice dependent on mood via physiological effects that change appetite or by changing other behaviour that constrains or alters food availability (Gibson, 2006). This assumption was confirmed by findings indicating that when people experience a high degree of positive emotion, they may change their behaviour more (Cyders & Smith, 2008; Patel & Schlundt, 2001). Consumers anticipated emotions they might experience when consuming product can contribute to the understanding of behavioural intention and behaviour towards product. Moreover, it can help to develop a marketing plan for specific consumer groups as it can identify influencing factors and consumer beliefs towards a product (De Pelsmaeker et al., 2017). The broaden-and-build theory of positive emotions (Fredrickson, 2004) suggests that positive emotions promote discovery of novel and creative actions, which in turn builds that individual's personal resources. However, one should consider that there is a hedonic asymmetry when working with products and consumers emotions (Desmet, 2003). Whereas the general emotion literature tends to focus mainly on negative emotions, positive food-related emotional states have a higher prevalence or intensity than those that are negative (Cardello et al., 2012; Ferrarini et al., 2010). Since the internal feelings are frequently accompanied by changes in facial expressions, this potentially leads to new objective measures of the affective responses to foods. Attempts to measure emotions have been done in the psychology and sociology fields (Block, He, Zaslavsky, Ding, & Ayanian, 2009; Habhab, Sheldon, & Loeb, 2009) but the measurement of food-elicited emotions is more recent and not well established. We could believe that emotional responses to foods might be quite common, but obviously the question then is to what extent does the consumer's experience of such feelings in response to foods tell us more about their food choices than ratings of liking, and which of these emotion terms are the most effective for indicating this? Each emotion corresponds to a unique profile in experience, physiology, and behaviour (Mauss & Robinson, 2009). Also, a basic set of six emotions (happiness, anger, sadness, surprise, disgust, and fear) is claimed to be universal, and the nonverbal signals (facial expression and physiological reactions) of these emotions are recognized cross-culturally (Pantic & Rothkrantz, 2000). This rising attention to emotion in consumer and sensory research has led to the introduction of many emotional instruments to capture consumers' emotions elicited by food (Dalenberg et al., 2014). The studies focused not only on emotions, but also diffuse affect states such as moods (EsSense Profile), characterized by a relative enduring predominance of subjective feelings (King & Meiselman, 2010), while the conceptual profile includes a mix of emotions and abstract conceptualisations with emotional nuances (Thomson, Crocker, & Marketo, 2010). A multistep approach (EmoSemio) measuring emotional responses associated to a specific product category that can be checked or rated (Spinelli, Masi, Dinnella, Zoboli, & Monteleone, 2014; Ares et al., 2014). Several instruments have been developed, of which the Product Emotion Measurement Instrument (PrEmo) is one of the most wellknown tools (Desmet, 2003) which recently was applied in food products (Dalenberg et al., 2014). These instruments can generally be divided into explicit and implicit methods depending on how emotional associations are assessed. The implicit measurement of emotions are indirect and non-self-reported and register emotions while participants are consuming, smelling or looking at food; the explicit methods dominate investigating an intentional facial expression in relation to food (Danner, Sidorkina, Joechl, & Duerrschmid, 2014). Although implicit measurements are only limitedly applied in consumer and sensory research (Lagast, Gellynck, Schouteten, De Herdt, & De Steur, 2017). Current research mainly focus on explicit measurement of food-elicited emotions and self-reported sensory liking. Although emotions are spontaneous expressions of our states of mind, consequently, they represent an alternative source of information
2. Materials and methods The emotional input of foods to their acceptability was assessed through the following tasks: analysis of the relationships between selfreported hedonic liking and facial expressions, analysis of emotional expressions as the most valuable descriptors for explaining the hedonic quality of a product, and recognition of consumer preferences based on a combination of emotional/sensory information as well as a model of the impact the sensory customer experience has on their attitudes and preference towards the analysed food products. An emotional/sensorial experience-based questionnaire was used to assess consumer attitude (positive, negative, or neutral) and behaviour intentions (will try, recommend, or act actively) (Cardello et al., 2012) in order to test stimuli evaluation, and the emotional response to the taste and texture of different food products using the FaceReader software (Noldus, 2013) was evaluated. In the first part of the experiment, five samples of different breads [wheat, rye, corn (80% wheat flour and 20% corn flour), wholemeal wheat flour, and multigrain (80% wholemeal wheat flour and 20% cereal grains] and two samples of different chocolates (dark and milk) without additives were selected from a local supermarket. In the second part of the experiment, emotional responses in relation to consumer attitudes and behaviour towards the milk chocolate and aerated milk chocolate as more empathic products were analysed. All of the products were debranded and tested according to the scheme presented in Fig. 1. All food samples were tested in a sequential monadic way; samples were randomized and coded. Water was provided to rinse the palate between the samples, and consumers were asked to wait for a 30 s period to minimize carry-over effects. In order to assess the hedonic quality of the food products and to discover the emotional responses to different attitudes and behaviour, 109 students (27% males and 73% females) from the Kaunas University of Technology (Lithuania) between 21 and 24 years of age were involved in the experiment. The panellists were recruited by an e-mail from a student academic panel. All students consented to participate in routine tests of foods and the research was conducted as a part of an approved activity entitled “Emotional and sensory assessment of food quality”. The test took place in test room under operating conditions (constant single lighting, 20–22 °C temperature and 50–55% humidity). 2.1. Questionnaire-based test A personal interview was chosen as the most appropriate method for data collection in our testing. The respondents were asked to fill out a questionnaire with the advantage of flexibility: questions that the respondent did not understand were explained by the interviewer. Each personal interview was conducted as follows: initial information was given on how long the personal interview would take, followed by identifying and explaining the test topics. The respondents were 362
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The participants were asked to taste the whole presented sample at once, take fifteen seconds to reflect on the taste impressions, then give a signal with a hand and visualize the taste experience of the sample with a facial expression best representing their liking of the sample. For each sample, the intention facial expression from the point when the subject had finished raising their hand to give the signal until the subject started lowering the hand again) was extracted with the Observer XT 10.5 (Noldus Information Technology, The Netherlands) software in three steps: face detection, face registration and representation (Active Appearance Model), and face classification (Artificial Neural Network). Essentially, the usefulness of Observer XT is in its ability to review video in real time frame-by-frame and review/reassess the captured moments. FaceReader is essentially pre-coded and trained to mathematically analyse the direction and extent of certain facial feature movements, which are used to predict six basic expressions of emotion (happy, surprised, sad, angry, scared, and disgusted as well as a neutral state) in real time and the intensity at which these emotions are being expressed. The intensity measurements are judged on a continuous scale from 0 to 1, with 0 indicating that the emotion is not expressed and 1 indicating that the emotion is fully expressed. This software is limited in its emotional characterization. Happy is considered the only positive emotion, while sad, angry, scared, and disgusted are considered negative emotions. Afterwards, the participants were asked to rate their liking or disliking of the food sample on a 9-point hedonic rating scale from 0 (dislike extremely) to 9 (like extremely). The most intensive emotional responses obtained from the milk chocolate testing procedure were grouped based on the generated attitudes and behavioural intentions.
Fig. 1. Scheme of the main steps of a questionnaire- and computer-based analysis method for the evaluation of consumers' preferences.
provided with a questionnaire and the foods to be tested. In the questionnaire, respondents were asked to indicate the significance of the properties (appearance, flavour, odour, hardness, particle size, mouth coating) and marketing attributes such as brand and packaging of the products, using the category rating scale method. The questionnaire consisted of three blocks of questions: (i) attitude towards specific product in general and a particular test product evaluation, (ii) identification of the emotional reactions of consumers to the unknown brand test product, and (iii) identification of behaviour intentions. The five-point Likert scale from 1 (extremely disagree) to 5 (extremely agree) based on the recommendations of Martin and Stewart (2001) for the evaluation of the consumer approach to product categories was used in terms of products in general and consumer emotional reactions. A scale recommended by Andrade and Cohen (2007) was used for the evaluation of the emotional attitude towards the test object, and the statements intended to measure behaviour intention were based on the Baker and Churchill (1977) classic purchase intent scale.
2.3. Statistical analysis A multiple linear regression was performed to examine the correlation between facial expressions and the hedonic liking, and between consumer behaviour and attitude within emotional groups. Differences among means were analysed by the analysis of variance (ANOVA), Fisher's least significant difference test, and Pearson's correlations using the STATISTICA V10 (StatSoft, Inc., USA) software. The significance level was set at p < .05. 3. Results and discussion 3.1. The relationship between facial expressions and the hedonic acceptability of food After tasting each product, consumers were first asked about the acceptability of the product and then about the emotions it elicited. By interpreting the facial expressions evoked by the products, the consumers' real thoughts about a given product could be better understood, and it was possible to identify the liking/disliking emotions in an objective way. Emotional evaluation of bakery and chocolate products indicated that the tested products excite all six emotions (happy, sad, angry, scared, surprised, and disgusted) and also caused the neutral facial expression (Table 1). However, the intensity of every emotion for each product was different. The general feature of the results was that the intensity of the happy, angry, and sad emotions for each product were relatively high compared to the other emotions. The means of happy indicates that liked samples elicited more intense facial expressions of happy than neutral or disliked samples, with neutral and disliked samples at the same level. In the case of the neutral expression, more than half of all participants expressed a positive attitude towards bakery products when it was the dominant expression. Thus, neutral emotions is mainly linked to a more positive evaluation, this also was found in the research of using emoji (Jaeger, Vidal, Kam, & Ares, 2017). In this study, when recording two negative facial expressions, sad and angry, the responses of the subjects in terms of their attitude and
2.2. Testing of emotional response to products For the analysis, a high-resolution digital video camera (Microsoft LifeCam Studio, 1080p HD) was utilized for video capture. In the explicit measurement experiment, subjects were asked to rate the food samples during and after consumption with an intentional facial expression, which was recorded and then characterized by FaceReader 5.0 (Noldus Information Technology, Wageningen, The Netherlands). The software feature “continuous calibration” was used for the face model standardization. Food samples were presented in a balanced order across panellists. The video session started when the participant gave a signal that he or she was ready to taste the presented product. Each respondent was instructed to drink room temperature water between the samples. The duration of testing was approximately 30 s until the subject chewed, tasted and swallowed the product in full. Emotions and the correlated facial expressions have a quick onset, facial expressions can begin in a matter of milliseconds after an emotion-inducing stimulus. Preliminary examinations showed that instructions to give 30 s were a good compromise between leaving the participants enough time to experience the sample. 363
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Table 1 Average acceptability and emotional intensities (in %) measured during sensory analysis of breads and chocolate. Emotion Product
Neu
Hap
Sad
Ang
Sup
Sca
Dis
Acceptability score
Bread 1. Wheat 2. Rye 3. Corn 4. Wholemeal 5. Multigrain
0.630 0.650 0.728 0.705 0.630
0.086 0.076 0.069 0.107 0.041
0.197 0.228 0.156 0.153 0.257
0.055 0.058 0.037 0.026 0.057
0.010 0.008 0.006 0.005 0.006
0.002 0.003 0.002 0.002 0.002
0.007 0.002 0.002 0.002 0.007
5.6 4.9 5.6 6.3 5.2
Chocolate 6. Dark chocolate 7. Milk chocolate
0.667 0.720
0.160 0.198
0.1.1 0.122
0.028 0.021
0.006 0.019
0.001 0.001
0.001 0.001
7.1 8.0
Average: Bread Chocolate
0.664 0.694
0.078 0.179
0.198 0.132
0.047 0.025
0.007 0.013
0.002 0.001
0.004 0.001
5.5 7.6
Neu – neutral, Hap – happy, Ang – angry, Sup – surprised, Sca – scared, Dis – disgusted.
behaviour could be evaluated positively or negatively. If the attitude towards bakery products was neutral (60% of the subjects), then the behaviour intentions towards bakery products were negative. The majority of the subjects evaluated the sensory quality of the bakery products as liked or highly liked. However, if baked goods were favourably treated, the behaviour remained mainly neutral (Table 1). The initial analysis showed that an increase in the intensity of happy or sad emotions influenced the reduction of the level of neutral emotional expression. The statistical analysis showed significant differences (F(37.112) = 8.934, p < .001, respectively) in facial reactions between food samples. Strong relationships between the overall acceptability and happy or sad expressions were obtained (r = 0.9467 and r = −0.9215, respectively). The experiment showed that the taste and appearance of the chocolate are very important sensory characteristics of the product (Fig. 2). Besides this, the chocolate hardness, the size of the particles, and the melting speed are essential characteristics (score > 5) by which consumers evaluated the tested products. Regardless of whether the most intense facial expression was happy or sad, the panellists considered the chocolate positively. When facial expressions were neutral, almost all participants expressed a positive attitude and positive behavioural intention towards the chocolate. This can partially be attributed to the test situation which requires concentration and analytical thinking of the participant can probably suppress positive emotions/facial expressions to a certain degree. Further examinations in real-life situations, also if possible without directly asking the participants to rate the products, could be performed. Clustering of bread and confectionery products based on the emotional variables was similar to the clustering of products based on the acceptability (Fig. 3). A significant effect of gender (F(24.141) = 1.257, p = .0146) and food samples of emotional and hedonic interaction was found. The results of face-reading showed that there was a difference between men and women when rating emotional descriptors. In the case of interactions like happiness (Fig. 3a) and hedonic acceptability (Fig. 3b), bread differences varied by gender; while men did not discriminate their happiness score among breads, women reported significantly higher scores for happiness when consuming white breads and milk chocolate, and lower scores when consuming dark breads and dark chocolate (Fig. 3). Differences in the emotional expressions during consumption of the different groups of products were identified due to the associated concepts: sugary confectionery products evoked more intensive neutral/happy emotions, whereas bread – neutral/sad emotions. Thus, confectionery products can intensify the manifestation of emotions, whereas the conventional traditional products (e.g., bread) do not have that ability and evoke a broad spectrum of emotional responses. The few studies evaluating the relationship between declarative
Fig. 2. Sensory (A) and marketing (B) attributes for dark and milk chocolates (questionnaire-based analysis).
acceptance/preference of products and emotional reactions occurring during the consumption of different food samples showed that there was a high correlation with happiness, disgust, and liking (Danner et al., 2014; Kostyra et al., 2016). In the next section, emotional responses generated during the testing of different kinds of milk chocolate (traditional and aerated) will be analysed in relation to consumer attitudes and behaviour towards the chocolate products. 3.2. The distribution of consumer attitudes and behaviour caused by emotions generated during chocolate testing The emotional responses obtained from the milk chocolate 364
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generated attitudes and behavioural intentions according to the three most intensive facial expressions set out to the decreased intensity: I group – facial expressions “happy”, “neutral”, “sad”/”angry” (Hap/ Neu/Sad(Ang); II group – facial expressions “neutral”, “sad”, “happy” / “angry” (Neu/Hap/Sad(Ang); III group – facial expressions “neutral”, “happy”, “sad”/”angry” (Neu/Sad/Hap(Ang); IV group – facial expressions “neutral”, “angry”,”sad” (Neu/Ang/Sad). The main attitudes and behavioural intentions of respondents generated during sensorial analysis of milk chocolate products related to emotional expressions are presented in Fig. 4. After testing the milk chocolate as well as aerated milk chocolate, 48.7% of the respondents were placed in the Neu/Hap/Sad(Ang) emotional response group (I group) (Fig. 4). The attitude of all the respondents of this group towards the chocolate was favourable, and the behaviour of 87.5% of the respondents was positive. The behavioural intentions of 6.2% of the respondents were neutral, and the remaining 6.3% of the respondents had negative behavioural intentions. All of the sensual features of the milk chocolate remained acceptable to the respondents. Another part (32%) of the respondents was placed in the Neu/Sad/ Hap(Ang) emotional group (II group). All of the respondents in this group expressed a positive attitude towards the chocolate, and only 5.9% of the respondents had neutral behaviour towards the chocolate. The attitude and behaviour intentions of all the respondents were positive, and the most acceptable features were the appearance of the chocolate, hardness, melting speed, taste, and the size of particles. The Hap/Neu/Sad(Ang) emotional group (III group) contained 16.6% of the respondents. However, the attitude and behaviour of all respondents was positive, and all sensual features were acceptable. The smallest part of the respondents (2.7%) were grouped into the Neu/Ang/Sad emotional group (IV group) where the most intensive facial expression was angry (18–24%); these respondents showed a negative response and were not going to buy the product. Despite all emotions (positive and negative) being expressed by the respondents, their attitudes towards chocolate were favourable, and the behaviour of 89.5% of the respondents was positive. In addition, 97.4% of the respondents from all groups indicated that all the sensual features of the chocolate were acceptable. It has been noticed that the respondents evaluated milk chocolate positively and were willing to buy it when the most intensive facial expression was happy or sad. When
Fig. 3. Consumer gender effect on hedonic acceptability (a) and happy (emotional mode) (b). Products: 1 – wheat bread; 2 – rye bread; 3 – corn bread; 4 – wholemeal wheat bread; 5 – multigrain wheat bread; 6 – dark chocolate; 7 – milk chocolate.
(traditional and aerated) testing procedure were grouped into four groups based on the three most intensive emotions organized according to decreasing expression intensity. The emotional responses obtained during the milk chocolate testing procedure were grouped based on the
Fig. 4. The main consumer attitudes caused by emotions generated during consumption of milk (traditional and aerated) chocolate (intensities of emotional expressions are presented in percentage). 365
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6
4.0 2.0
R² = 0.0716
6.0
4 R² = 0.7211
2
0.0 0.6
0.8
1
0
0.1
6.0
0.2
0.3
0.0 0.8
Behaviour
R² = 0.7525
2.0
R² = 0.0329 1
0.1
0.15
0.2
Sad
4.0
Neutral
0.05
6.0
Behaviour
4.0
0.6
R² = 0.7423 0
0.4
6.0
0.4
2.0
Happy
Neutral
2.0
4.0
0.0
0 0.4
Behaviour
Behaviour
Behaviour
Behaviour
6.0
4.0 2.0
R² = 0.7963
0.0
0.0 0
0.1
0.2
0.3
0
0.4
0.05
0.1
0.15
0.2
Sad
Happy
Fig. 5. The relationships between attitude and the intensity of main emotional expressions for the traditional (A) and aerated (B) milk chocolates.
the most intensive facial expression was neutral, all or almost all respondents in each group expressed a positive attitude towards the chocolate, and the behavioural intentions of a majority of respondents (I group – 68.8%, II group – 72.7%, III group – 83.3%, IV group – 88.6%) were positive. The respondents of all groups evaluated the majority of the chocolate's sensory features as favourable or even very favourable. The common tendency was that when a behaviour was neutral, the attitude towards the chocolate remained positive. The data of some facial expressions was not included in the statistical analysis due to their insignificant values. Through analyses of the associations between individual expressed emotions and the attitude or behaviour, it was shown that the intensity of the neutral response has no significant relationship with behaviour (r = 0.1814–0.2676, p > .05) for all respondents (Fig. 5). The statistical analysis confirmed a strong relationship between behaviour scores and happy as well as sad emotional responses, both having the highest values. Increased expression of happiness indicated positive behaviour, and a higher expression of sadness indicated a reduced attitude and negative behaviour (Fig. 5). We noticed that the reduction in the neutral emotional response could indicate an increase in other – positive or negative – emotions (happy, sad, and angry) and vice versa. Slightly lower correlation coefficients for linear relationships were obtained for traditional milk chocolate [r = 0.8492 (happy), and r = 0.8616, p = .013 (sad), p < .05) compared to aerated (r = 0.8675 (happy) and r = 0.8924 (sad), p < .05) (Fig. 5). Hence, conceptual terms are comparable within products, whereas sensory attributes are comparable between products. The behaviour of respondents towards milk chocolate was significantly (p < .05) related to the attitude of respondents within the constructed emotional groups (I–IV). According to the statistical analysis, the emotional expressions generated for the most intensively evoked facial expressions in each group had a strong impact on the attitude and behavioural intentions towards milk chocolate (Table 2).
Table 2 Correlation coefficients of relationships between attitude and behaviour within emotion groups. Behaviour
Attitude
I II III IV a b c d
I groupa
II groupb
III groupc
IV groupd
0.7157 – – –
– 0.8322 – –
– – 0.9645 –
– – – 0.8464
Neu/Hap/Sad(Ang). Neu/Sad/Hap(Ang). Hap/Neu/Sad(Ang). Neu/Ang/Sad.
scale) as a factor and facial expressions as variables, showed a significant correlation between hedonic liking and the intensity of elicited facial expressions, especially for happy (F(35, 753) = 23,456, p ≤ .0001), sad (F(35.753) = 8.127, p ≤ .0001) and angry (F (35.753) = 11.212, p ≤ .001), but not for neutral (F(35.753) = 0.517, p > .05) (Fig. 6). The linear regressions showed high positive correlations with a coefficient of determination of 0.76–0.78 for angry and
Intensity of emoonal expression
1
NEU R² = 0.2061
0.8
HAP R² = 0.8712
0.6
SAD R² = 0.7576 ANG R² = 0.7841
0.4 0.2 0 3
3.3. Correlation between facial expressions and reported acceptability during milk chocolate testing
4
5
6
7
8
9
10
Acceptability scores Fig. 6. Regressions of facial expression intensity against reported acceptability scores (liking).
The statistical analysis with hedonic liking ratings (9-point hedonic 366
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4. Conclusions This study describes the application of positive–negative emotional scaling to the conceptual profiling of milk chocolate and outlines novel data modelling procedures used to explore sensory/conceptual relationships. Noldus FaceReader technology is sufficiently accurate to detect significant differences in facial expressions elicited by different food samples. Our important findings suggest clear relationships between certain sensory and emotional perceptions during product evaluation. Facial expressions of happy, sad, and angry were particularly effective for distinguishing the differences between the acceptability of foods. In the case of milk chocolate, the emotional responses sad, happy, and neutral were most often encountered, but the difference in comparison with bread bakery products was that the neutral emotional expression was identified as the dominant one. It is also important to note that it is uniquely related to the positive attitude of users and positive behavioural intentions. This can be explained by the higher level of satisfaction of hedonistic users (compared with the case of bread crisps). Regardless of whether the most intense facial expression was happy or sad, the milk chocolate seems to be a favourable product stimulating positive behaviour. The emotion–attitude–behaviour interface model can provide significant information for the prediction of consumer behaviour, although this needs further research. The procurement of more effective feedback from consumers may be useful in the bakery and confectionery industry, as well as in the development of new food products. Also, the set of products included in this study is not sufficiently representative of food product variation. Enlarging the food samples of different taste sets in order to cover a wider range of sensory properties is advised for future studies. In the next step, the emotional response parameters will be applied to not only predict the behaviour of consumers, but also to determine changes in product liking for individuals in order to provide an objective emotional analysis model for early diagnosis of depression disorders.
Fig. 7. The conceptual profile for unbranded traditional and aerated milk chocolates.
sad, and 0.87 for happy, and neutral emotional expression has a low R2 of 0.206. This study is in agreement with the relationship found between the acceptability of food products and the emotional response. The study of De Pelsmaeker et al. (2017) showed that including anticipated emotions during for consuming chocolate increases the predicted variance of the planned behaviour. The identity of the object (it's chocolate) and the associated concepts evoked in the mind of the individual person during the experience of a product is not just a reaction to the product itself but also to the associated concepts (e.g., comforting, relaxing), which influence positive consumer attitudes towards food product (Thomson et al., 2010). Thus, sensory characteristics, which are intrinsic to the product and therefore part of its identity, become linked with conceptualisations. As an example, Fig. 7 shows the basic conceptual profile of milk chocolate (traditional and aerated). The conceptual terms positioned towards the right have the highest scale values indicating that these conceptualisations (energetic, relaxing, attractive, kindliness) are the most prevalent. Conversely, aggressive, complicated and obligatory have the lowest scale values. To elicit specific emotional responses from the consumption of products and to determine their changes, consonance among the sensory characteristics and emotions must be established to understand the mechanisms underlying the relationship between restrained eating, stress, and food intake (Ferrarini et al., 2010; Jager et al., 2014). According to Mojet et al. (2015), implicit emotional measurements can deliver product information that is not related to product liking. High stress reactivity could lead to greater food intake (Newman, O'Connor, & Conner, 2007) or conversely, some people eat less than normal in response to stress because they lose taste sensations (Torres & Nowson, 2007). This study shows that measuring facial expressions differentiate between food samples in an explicit measuring approach. In the explicit measurement, a clear discrimination between liked, disliked and neutral-rated samples was possible on the basis of the intensity of elicited angry, sad, happy and neutral facial expressions. However, when not only comparing facial reactions elicited by samples but the individual liking ratings as well, we see a good correlation between acceptability and facial expressions (see Fig. 6). There was a high negative correlation of the sad facial expression with disliking and a high positive correlation of angry and happy with liking. No such relationships were found for the neutral and other measured facial expressions. An important point in this study, concerning the explicit experiment, is that the emotions directly elicited by the taste of the food are measured, as opposed to self-reported questionnaires, where reading of an emotion term or seeing the emoticons may be enough to elicit an emotion that was not experienced while tasting.
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