Psychographic measures and sensory consumer tests: When emotional experience and feeling-based judgments account for preferences

Food Quality and Preference 21 (2010) 178–187

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Psychographic measures and sensory consumer tests: When emotional experience and feeling-based judgments account for preferences M. Kergoat a,b,*, A. Giboreau c, H. Nicod b, P. Faye d, E. Diaz d, M.A. Beetschen e, N. Gerritsen e, T. Meyer a a

Université Paris Ouest Nanterre La Défense, Department of Psychology-EA3984, 200 avenue de la République, 92001 Nanterre Cedex, France ADRIANTÒSilliker, 18 rue Saint-Amand, 75015 Paris, France c Institut Paul Bocuse, Château du Vivier, BP25 – 69131 Ecully Cedex, France d PSA Peugeot Citroën, route de Gisy, 78943 Vélizy-Villacoublay Cedex, France e Unilever Port Sunlight, Quarry Road East, Bebington Wirral CH63 3JW, United Kingdom b

a r t i c l e

i n f o

Article history: Received 31 October 2008 Received in revised form 5 June 2009 Accepted 10 June 2009 Available online 14 June 2009 Keywords: Individual differences Sensory liking Classification Seat car fabric Tactile properties

a b s t r a c t The aim of this study is to explore psychological and psychosocial individual differences in order to understand heterogeneous sensory preference clusters identified in consumer tests. We conducted two studies with 100 participants in each. Six seat car fabrics were rated on liking items. A questionnaire composed of the Affect Intensity Measure (AIM) [Larsen, R. J. (1984). Theory and measurement of affect intensity as an individual difference characteristic. Dissertation Abstracts International, 85, 2297B], the RationalExperiential Inventory (REI), the Iowa–Netherlands Comparison Orientation Scale, (INCOM) [Gibbons, F. X., & Buunk, B. P. (1999). Individual differences in social comparison: Development of a scale of social comparison orientation. Journal of Personality and Social Psychology, 76, 129–142], and the Centrality of Visual Products Aesthetics (CVPA) [Bloch, P. H., Brunel, F. F., & Arnold, T. J. (2003). Individual differences in the centrality of visual product aesthetics: Concept and measurement. Journal of Consumer Research, 29, 551–565] was used. Two clusters of preferences were characterized by the AIM and REI measures. One group, ‘‘the velvet fabrics likers”, experienced emotions more intensely than the ‘‘non-velvet likers” who in turn, appeared to rely mostly on feelings in the judgment process. We discuss the possible influence of these psychological factors on the significance of sensory input used in the evaluation process. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Sensory consumer tests often lead to distinct clusters of preferences. One big challenge in identifying liking differences, beyond the sensory attributes of a product, is to capture consumers’ sensory preference drivers and characterize them according to their preferences. Differential measures, such as socio-demographic measures or usage and attitude questionnaires are common in consumer tests, but are clearly not sufficient to understand why consumers prefer one product over another. Understanding the consumer has been examined at length in the area of Psychology and Marketing research (Haugtvedt, Kardes, & Herr, 2008). One of the well known approaches has been the introduction of personality and psychosocial individual differences as explanatory variables of consumer behavior and consumer atti-

* Corresponding author. Address: Université Paris Ouest Nanterre La Défense, Department of Psychology-EA3984, 200 avenue de la République, 92001 Nanterre Cedex, France. Tel.: +33 156 56 12 90; fax: +33 156 56 12 99. E-mail address: [email protected] (M. Kergoat). 0950-3293/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2009.06.006

tudes. The predictive value of personality traits or psychological dispositions has been investigated for decades and has resulted in the elaboration of many scales that capture individual differences mostly associated with consumption. These studies have mainly been devoted to the investigation of consumer behaviour, the decision processes related to purchasing acts (Busseri & Kerton, 1997; Jolson, Anderson, & Leber, 1981; Sojka & Giese, 2003; Vermeir, Van Kenhove, & Hendrickx, 2002; Whelan & Davies, 2006), the effectiveness of advertisement (DeBono, 1987; Haugtvedt, Petty, & Cacioppo, 1992; Snyder & DeBono, 1985; Stayman & Kardes, 1992; Zhang & Buda, 1999) as well as product consumption. Most studies that focused on a link between psychological disposition and product consumption were related to food consumption. Variety seeking has been shown to impact the purchase and use of spreads and cheese (Van Trij, Lähteenmäki, & Tuorila, 1992). In Pliner and Melo (1997), high sensation seekers chose a larger variety of new foods in a low arousal context. Goldberg and Strycker (2002) found a predictive value of the Openness to Experience dimension (NEOPI-R) for dietary alimentary behaviors; more specifically regarding consumption of fibers and avoidance of fatty meat. Self-monitoring differences associated with

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different social psychological variables were found to influence preferences for chocolate, cheese, perfume or cola drinks (DeBono, 2000; DeBono & Rubin, 1995). Finally, the Food Neophobia scale was efficient in predicting responses for novel foods (Pliner & Hobden, 1992). One significant observation is the lack of systematic studies linking individual differences to a sensory blind evaluation of the products. This lack of studies might in part be due to a recent field of investigation dedicated to the sensory research conducted in the 1980s. The purpose of our research was to identify psychological and psychosocial factors that could play a role in the elaboration of sensory preferences in consumer tests and that could help understand the underlying processes engaged in sensory evaluation tasks. In this research, we worked on consumer seat car fabric preferences. We had the choice of focusing either on general personality traits or on specific individual differences. Personality tests like the Big-Five (Costa & McCrae, 1985), address the whole personality with a limited number of traits, while more specific tendencies or dispositions are considered to be stable though addressed in motivational rather than biological terms. The notion of correspondence or compatibility (Ajzen & Fishbein, 1977) implies that one cannot predict specific behaviors from general attitude measures. In order to predict a specific behavior (intention), a specific attitude must be measured. We therefore decided to focus on specific individual differences rather than general personality traits. Because consumer preferences full many functions (Wänke, 2009) we decided to cover a large spectrum of factors including cognitive, affective, social and sensory determinants. A review of literature crossing consumer preferences and individual difference measures leads us to focus on four constructs.

In blind sensory tests when very little product information is available, the influence of feelings and emotions on consumer behaviour and consumer attitudes must be taken into account. The Affect Intensity Measure (Larsen, 1984) assesses individual differences in the typical intensity with which people experience their emotional responses. The intensity of the emotional response, applied mainly to the impact of emotional-eliciting advertisement, has been shown to mediate the individual’s attitude toward the ad (Moore & Harris, 1996; Moore, Harris, & Chen, 1995). Exposed to a positive emotional advertisement, people with high affect intensity expressed more positive attitudes toward the ad than did people with low affect intensity. The ways people experience emotion influence lifestyle and consumer preferences as well. High intensity individuals reported stronger levels of enjoyment for emotionally stimulating activities, high social interaction activities, as well as higher levels of enjoyment produced by sensory stimuli such as smelling freshly baked bread or fine scents (Moore & Homer, 2000). The search and preference for stimulating activities are based on an arousal regulation theory of affect intensity. People differ in their baseline arousal and/or reactivity to stimulation. Some people are easily overaroused and therefore look for a reduction of surrounding stimulation. On the other hand, some people need more stimulation to compensate for under-reactivity and/or lower levels of baseline arousal. Larsen (2009) regards regular experience of intense emotions as a compensatory strategy for overcoming low levels of baseline arousal. We therefore hypothesized that high intensity people would prefer higher sensory stimulating products, which have higher arousal properties like novelty, complexity or sensory intensity.

1.1. Rational-Experiential Inventory – REI

1.3. Iowa–Netherlands Comparison Orientation Measure – INCOM

Two independent processing modes operating in a parallel and interactive manner can be used while processing information in an evaluation task; rational and analytic processing which imply cognitive elaboration, or superficial processing of the information using simple decisions making rules (Eagly & Chaiken, 1993; Kahneman, 2003). These two information-processing systems have both situational and personal determining factors. Cognitive and motivational factors are central to information processing and individual predispositions determine the degree to which an individual operates in a more or less analytic or superficial processing modes. These predispositions affect people’s receptivity to, and treatment of, different kinds of information. We introduced a measure of individual processing modes due to the fact that in Central Location Tests (CLT) consumers are engaged in evaluation tasks. Depending on their processing mode (the rational versus the experiential mode) they may take into account different product criteria in order to form their judgments. Heuristically oriented consumers could express preferences based on accessible criteria like product aesthetics, whereas analytically oriented consumers could pay more attention to the product’s properties (like maintenance, strength, etc.) and therefore make more inferences about the product. We used the Rational-Experiential Inventory (REI) developed by Epstein, Pacini, Dens-Raj, and Heier (1996) to measure these individual differences in intuitive-experiential and analytic-rational thinking. This scale, which is in line with the cognitive-experiential self-theory (Epstein, 1994), measures both processing modes. The rational system was largely inspired from the Need for Cognition measure (NFC; Cacioppo & Petty, 1982) and the experiential system’s Faith in Intuition measure can be viewed as an affective counterpart to the NFC’s cognitive aspect.

Depending on the type of product, social considerations can play a role in preference expression (Dittmar, Halliwell, Banerjee, Gardarsdóttir, & Jankovic, 2007). We therefore introduced a social comparison measure (INCOM; Gibbons & Buunk, 1999). Self-evaluation, self-enhancement, and self-esteem are three underlying motivations that determine the influence of normative rules on peoples’ opinions and behaviour. Individuals with unstable self-esteem are particularly inclined to social comparison. Applying a blind sensory evaluation, the Bearden and Rose (1990) study demonstrated that cola drink ‘‘C” judged in a pre-test as being of lower quality than a second cola drink ‘‘S”, was chosen more frequently by social comparison oriented people when the lower quality product ‘‘C” had been rated as being better than ‘‘S” by the other evaluators. Even though consumers in laboratory test situations cannot make judgements based on others’ opinions, social comparison may still influence the consumers when they reject a product that they believes will be rejected by others because of its atypical characteristics. Specifically if this product fulfills social identity needs (like perfume, car, etc.).

1.2. Affect Intensity Measure – AIM

1.4. Centrality of Visual Product Aesthetics – CVPA Finally, we used the Centrality of Visual Product Aesthetics measure created by Bloch, Brunel, and Arnold (2003) to measure how visual aesthetics may influences the way a particular consumer relates to a specific product. The first contact with the product is made through the visual channel. Product appearance is considered to be a central channel for forming consumer–product relationships (Hollins & Pugh, 1990). The CVPA measure is believed to underline other consumer behavior variables like product involvement, innovation or brand

Students (N = 0)

21 1 2 0 0 0 0 0

27–64 years old (N = 24)

0 0 0 0 0 0 0 0 6 2 0 0 0 0 0 0 8 2 2 0 0 0 2 4

Employees (N = 8) Students (N = 18) Employees (N = 27)

23 1 2 0 0 0 1 0 0 0 0 0 0 0 0 0 12 1 0 0 0 0 1 3 2 1 0 0 0 1 0 2 No (N = 14)

Each study had two parts: product assessment and an individual difference questionnaire. In the first part, products were individually rated in predetermined rotated testing. It was a blind evaluation with no specific instructions about touching the products. Participants were

Everyday (N = 72) Several times per week (N = 8) Several times per month (N = 6) Never (N = 0) Everyday (N = 0) Several times per week (N = 1) Several times per month (N = 4) Never (N = 9)

2.3. Procedure

Yes (N = 86)

2.2.3. 3d-stitched fabric A rough black stitched fabric with irregular lined patterns.

Students (N = 0)

2.2.2. Woven and knitted fabrics (W&K) W&K no. 1 is smooth and silky and is composed of dark gray stripes contrasting with bright gray at the bottom. W&K no. 2 is dark with little horizontal white lines and is rather rough to the touch. W&K no. 3 is completely black with some thin rectangular patterns on it and has feels rough.

Students (N = 6)

2.2.1. Velvet fabrics Velvet no. 1 is light and dark gray with little squares on it. It is soft and spongy. Velvet no. 2 is a short-fiber velvet, in a mid-scale gray, it feels soft and looks spongy.

Women (N = 50)

Six car seat textiles provided by PSA Peugeot Citroën were tested. This set of fabrics was composed of two velvet fabrics, three woven and knitted fabrics, and one 3D-stitched fabric. The fabrics were dark shaded colors (black and gray). These products had already been recognized in previous PSA sensory consumer tests (confidential data) as clustering products (products leading to heterogeneous preferences). This point was important, as segmentation was the starting point of our research. We expected to find again such segmentation in our samples. Products were presented on a rigid material (A4 format) with foam underneath the fabric to enhance car seat perception.

20–26 years old (N = 26)

2.2. Products

27–64 years old (N = 27)

2.1.2. Study B The study included 99 French undergraduate students (ages 18– 28; mean age, M = 19.9) from the University of Paris Ouest (see Table 2). They received course credits for their participation.

20–26 years old (N = 23)

2.1.1. Study A The study included one hundred French participants (ages 20– 64; mean age, M = 32.9) from the University of Sciences in Tours (see Table 1). They received no financial compensation for their participation.

Men (N = 50)

The participants were not recruited according to specific automobile criteria (like car models or first year of car registration) as is usually done in sensory consumer tests run within the automotive industry.

Driving frequency

2.1. Participants

Car owner

2. Method

Table 1 Study A (N = 100) – Sample composition according to sex, age (age classes have been composed according to the median, Med = 27), social and professional status, car owner and driving frequency.

loyalty, and could be important in understanding consumer decision processes. The importance of aesthetic product design for the consumer is viewed on a continuum varying from ‘‘no interest” to a ‘‘determining purchase factor”. We postulate that a high visual centrality person will base his/her judgments on visual product properties. Moreover preferences based mainly on product sensory properties, justify considering the CVPA individual difference measure.

Employees (N = 24)

M. Kergoat et al. / Food Quality and Preference 21 (2010) 178–187

Employees (N = 17)

180

181

M. Kergoat et al. / Food Quality and Preference 21 (2010) 178–187 Table 2 Study B (N = 99) – Sample composition according to sex, car owner and driving frequency. Car owner

Driving frequency

Men (N = 13)

Women (N = 86)

No (N = 73)

Everyday (N = 2) Several times per week (N = 13) Several times per months (N = 7) Never (N = 51) Everyday (N = 13) Several times per week (N = 8) Several times per months (N = 2) Never (N = 3)

0 2 1 4 6 0 0 0

2 11 6 47 7 8 2 3

Yes (N = 26)

asked to rate the products on general, visual and tactile liking on a 10-point Likert scales ranging from 1 ‘‘I do not like it at all” to 10 ‘‘I like it very much”. Then, participants had to fill out a questionnaire composed of the four individual difference measures. The measures were presented in a randomized order. We told the participants that the questionnaire was our way to ‘‘get to know them a little better”. In study A, the test took place in a room at the University of Tours where participants took the test individually. The test in study B took place in a laboratory room at the University of Paris Ouest, with eight students per session, each student in a separate booth. In both studies, the sessions ran all day long, neon white lights were continually on, making the time of day irrelevant. 2.4. Individual difference measures 2.4.1. Scale structure The Rational-Experiential Inventory (REI; Epstein et al., 1996) consists of the Need for Cognition dimension (NFC; e.g. ‘‘I would prefer complex to simple problems”) and the Faith in Intuition dimension (FI; e.g. ‘‘I generally don’t depend on my feelings to help me make a decision”). We used the validated French REI version by Meyer de Stadelhofen, Rossier, Rigozzi, Zimmermann, and Berthoud (2004) to elaborate a short 10-item version. The items were rated on a five-point scale ranging from 1 ‘‘completely false” to 5 ‘‘completely true”. The Affect Intensity Measure (AIM; Larsen, 1984) consists of the Intrapersonal Positive Affect I sub-dimension (IPA I; e.g. ‘‘When I’m happy I feel very energetic”), the Intrapersonal Positive Affect II subdimension (IPA II; e.g. ‘‘My happy moods are so strong that I feel like I’m in heaven”), the Intrapersonal Negative Affect sub-dimension (INA; e.g. ‘‘The sight of someone who is hurt badly affects me strongly”) and the Reactivity to Positive Event (RTPE; e.g. ‘‘When I know I have done something very well, I feel relaxed and content

rather than excited and elated”). Items were rated on a six-point scale ranging from 1 ‘‘never” to 6 ‘‘always”. We used the 20 items from the translated version of the AIM by Onnein-Bonnefoy (1999). The Iowa–Netherlands Comparison Orientation Measure (INCOM; Gibbons & Buunk, 1999) consists of an Opinion sub-dimension (INCOM-O; e.g. ‘‘I often try to find out what others think who face similar problems as I face”) and an Ability sub-dimension (INCOM-A; e.g. ‘‘I always pay a lot of attention to how I do things compared with how others do things”). We used the French version validated by Michinov and Michinov (2001). Items were rated on a 5-point agreement scale. The Centrality of Visual Product Aesthetics (CVPA; Bloch et al., 2003) measure consists of the value sub-dimension (e.g. ‘‘A product’s design is a source of pleasure for me”), the Acumen sub-dimension (e.g. ‘‘Being able to see subtle differences in product designs is one skill I have developed over time”) and the Response sub-dimension (e.g. ‘‘If a product’s design really ‘‘speaks” to me, I feel that I must buy it”). Items were rated on a 5-point agreement scale. 2.4.2. Scale structure validity In order to check the scales’ structure validity, we performed a Principal Component Analysis (varimax rotation) on normalized data (XLSTATÒ software) for each scale, and Maximum-likelihood confirmatory factorial analyses using AMOSÒ software were run according to the factorial scale structure obtained from the PCA. Since the maximum-likelihood chi-square obtained from CFA depends on sample size, several other indexes were taken into account to gauge how well a given model fits the data. We used the Normed Fit Index (NFI), the Comparative Fit Index (CFI) and the Root Mean Square Error of Approximation (RMSEA). NFI and CFI range from 0 to 1. High values indicate a good fit with the data (NFI > .90 and CFI > .80). A RMSEA value inferior to .05 indicates good adjustment of the model to the data; models with values that range from .05 to .08 are considered acceptable, models with

Table 3 Goodness-of-fit statistics for the REI, AIM, INCOM and CVPA scales’ factor models – Study A and Study B. Scale

Model

Study

v2

df

P

NFI

CFI

RMSEA

REI

Two correlated factors

AIM

Single factor

A B A B A B A B A B A B A B

10.3 14.1 443.43 541.55 123 94 117 110 38.8 43.18 176.03 180.84 46.9 15.1

14 14 170 170 71 71 44 44 26 26 44 44 24 24

.73 .44 .0001 .0001 .0001 .03 .0001 .0001 .05 .02 .0001 .0001 .03 .58

.92 .83 .38 .36 .74 .83 .63 .68 .85 .84 .66 .60 .89 .95

1 .99 .48 .42 .86 .95 .72 .77 .94 .92 .71 .64 .94 1

.00 .00 .12 .14 .08 .05 .12 .12 .07 .08 .17 .17 .09 .00

Four correlated factors INCOM

Single factor Two correlated factors

CVPA

Single factor Three correlated factors

REI, Rational-Experiential Inventory; AIM, Affect Intensity Measure; INCOM, Iowa Netherlands. Social Comparison Orientation; CVPA, Centrality of Visual Product Aesthetics. Indices: Chi2 must not be significant, or (and) NFI (not sensitive to size sample as chi2 is) must be >.90; CFI must be >.80; RMSEA <.05 is good, between .05 and .08 is reasonable; between .08 and 10 poor, and >.10 unacceptable.

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values superior to .08 should be improved and models with values superior to .10 are unacceptable. Each original measure, except the Rational-Experiential Inventory composed of two independent measures (NFC and FI), is considered as unidimensional construct composed of several subdimensions. We therefore ran confirmatory factorial analyses testing single-factor solutions, as well as multiple-factor solutions (see Table 3). Results showed that single-factor solutions did not reach the satisfactory model level (no indexes reached an acceptable level), so we based the calculation of each participant’s scores on the sub dimension structure of the scales. For example, on the Affect Intensity Measure consumers get four scores (one score for each of the AIM’s sub-dimensions). 2.5. Statistical processing of the data All analyses were performed using XLSTATÒ software. 2.5.1. Product hedonic assessment The products’ hedonic assessments were made for entire sample as well as for each fabric preference cluster. Two-way ANOVAs were computed for each of the three liking items (general, visual, and tactile) with products and participants as independent variables. Duncan post hoc comparisons were only run on product effect. 2.5.2. Principal component analyses In order to observe on which general characteristics consumer preferences are based, Principal Components Analyses (PCA) were run on consumers’ general liking using covariance, with products in rows and consumers in columns. Performing a PCA can determine the relevance of conducting a classification analysis. A quick look at the PCA map, indicating a wide subject distribution, clearly indicates different preference clusters. 2.5.3. Classifications Before characterizing consumers according to their preferences, we needed a solid method that would segment consumer preferences. We started by conducting a hierarchical ascendant classification (HAC) based on general liking data using the Ward criteria aggregation method and the square Euclidian distances similarity/dissimilarity index. We then performed a classical K-means algorithm. Cluster numbers were obtained from the HAC, criteria trace (W) was the classification criteria and two hundred iterations were conducted. Initial partition established by using the HAC partition, facilitated convergence between both classifications. 2.5.4. Discriminant factorial analysis We conducted a Discriminant Factorial Analysis (DFA) with individual difference sub-dimensions as quantitative explanatory variables, in order to characterize the preference clusters obtained in the classification results.

Two DFA models are possible: similar or non-similar covariance matrices hypotheses. The similarity hypothesis implies a linear DFA and hypothesizing a difference between covariance matrices implies a quadratic DFA. The Box test tests the null hypothesis of covariance matrices equality. We conducted a linear DFA and repeated a DFA in the event of a significant box test, which implies a quadratic model. Wilks’ lambda tests the hypothesis of mean vector equality between the clusters. If Wilks’ lambda is significant, the preference clusters have been characterized by some of our individual difference measures. We used the confusion matrix results to evaluate the quality of our model. This classification board gives us the percentage of well-classified observations; the higher the percentage, the better the model. Finally, we used the unidimensional test of cluster mean equality results to test the hypothesis of mean equality between clusters for each individual difference measure. A weak Wilks’ lambda value implies weak intra-classes variations and therefore strong inter-classes variations which in turn implies a significant difference between clusters (univariate Wilks’ lambda is made up of values ranging from 0 and 1). Fisher’s F-test was also computed to test cluster variance differences for each individual difference measure. 2.5.5. Coefficient of determination We wanted to determine the proportion of general preference variability explained by individual difference variables. We calculated the coefficient of determination in both studies running a linear regression with individual differences as explanatory factors and general liking as the dependent variable. 3. Results 3.1. Product hedonic assessments In both studies we observed only slight differences in product appreciation. The biggest differences were observed for tactile appreciation that led to the largest discrimination between products. Participants preferred the velvet fabrics and the softer woven and knitted fabric (W&K no. 1). The grey striped W&K no. 1 proved to be the most appreciated fabric in both studies, independent of assessment type. (see Table 4). 3.2. Preference clusters The PCA indicates a two-factor model that accounts for 59.2% of the variance (study A) and 59.0% of the variance (study B). Axis one represented the soft-rough dimension of the ‘‘tactile” factor. A large dispersion of subjects on axis one indicated in the PCA maps, suggests at least two preference groups. In both studies, one preference group consists of consumers with a preference for velvet

Table 4 Seat car fabric hedonic means and Duncan post hoc comparisons on liking items for the all sample – Study A and Study B.

General liking Visual liking Tactile liking

Study Study Study Study Study Study

A F = 3.79 ; p < .003 B F = 3.49 ; p < .005 A F = 5.72 ; p < .0002 B F = 8.98 ; p < .002 A F = 16.05 ; p < .0002 B F = 37.89 ; p < .0002

Velvet no. 1

Velvet no. 2

3D-stitched

W&K no. 1

W&K no. 2

W&K no. 3

5.4ab 5.9a 4.6bc 5c 6.3a 7.5a

4.8bc 5.8ab 4.3c 4.9c 6.2a 6.8b

4.9abc 6.3a 5.1ab 6.5a 4.6b 5.2c

5.6a 6.3a 5.5a 6.5a 6.4a 6.7b

4.2c 5.8a 4c 6.1ab 4.4b 5.8c

5abc 5.2b 5.4a 5.4bc 4.8b 4d

Signification of letters next to means: a similar letter means no significant difference between the fabrics, and different letters mean a significant difference between the fabrics at p < .05.

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2 2

Study B

2 w&K n°1 1

2 1 2 1 1 1 1 2 1 2 Velvet n°1 2 1 1 2 1 1 2 1 1 22 22 n°3 W&K 1 2 2 2 2 2 2221 1 111 2 1 11 2 2 2 1 1 1 22 111 1 22 1 1 22 2222 222 1 1 1 2 Stitch 3D 2 2 1 2 1 1 1 1 2 1 11 2 1 2 2

11

Velvet n°2 1 1

W&K2n°2

Axis 2 - 25,4% of inertia

Axis 2 - 23.5% of inertia

Study A

2 2 W&K 22

2 2 2

2

2

2

2 n°3 W&K 22

2 2 2 2 2

2

2 22 2

21 2

2

22 2 2 22 2 2 2 2 2 2 2

2 222 2 2

1 1 1

1 1 1

21 11 1111 1 1 1

1

2 1 1 2

W&K n°2 2 Stitch 3D 2

n°1 1 1

1

1

1 1

1

1 1 Velvet 1 11 1 1 Velvet

1

n°1 n°2 1

1

2

1

1

2 Axis 1 - 33.8% of inertia

Axis 1- 33,6% of inertia

Fig. 1. (A and B) Principal component analysis maps on general liking and consumer clusters according to the K-means classification.

fabrics and the other group with consumers who prefer woven knitted and 3D-stitched fabrics (see Fig. 1A and B). 3.2.1. PCA – study A Factor one explains 33.8% of the variance, and factor two 23.5% of the variance. The fabrics that contributed the most to the construction of principle components were the two velvet fabrics (36.1% and 25.6%) and the W&K no. 3 (19.7%) for axis one, and the W&K no. 1 (44.9%) and W&K no. 2 (33%) for axis two. 3.2.2. PCA – study B Factor one explains 33.6% of the variance and factor two 25.4% of the variance. The fabrics that contributed the most to the principal component construction were the two velvet fabrics (34.6% and 29.7%) and to a lesser extent the 3D-stitched fabric (12.7%) for axis one, and the W&K no. 1 (41.3%) and 3D-stitched (23.8%) for axis two.

the velvet fabrics and were fond of the W&K fabrics (particularly the W&K no. 1) and the 3D-stitched fabric (see Table 6). We named cluster one the ‘‘velvet likers” and cluster two the ‘‘non-velvet likers”. The velvet likers gave the most positive evaluation to velvet fabrics, independent of the assessment, but the gray striped W&K no. 1 was also liked in both studies. Participants were more discriminative on the tactile assessment than the visual one, particularly in study B where the fabrics received similar assessments for visual liking. In study A, the non-velvet likers liked the W&K no. 3, the grey striped W&K no. 1 and the 3D-stitched fabrics the most. Participants in study B had similar preferences; the gray striped W&K no. 1 and the 3D-stitch were their favorite fabrics. The non-velvet likers did not like the two velvet fabrics at all. While the velvet likers discriminated between products using tactile assessments

3.2.3. Segmentation The HAC partition into two clusters was selected for both studies (Figs. 2 and 3). The following K-means equilibrates the number of participants for each cluster (Table 5). We found a difference in the number of consumers in each cluster between the HAC and the K-means classifications. This is not surprising since the two classifications are based on different methods (aggregation for the HAC and iteration for the K-means). 3.3. Hedonic assessment of car seat fabrics by consumer clusters In both study A and study B, participants in cluster one preferred velvet fabrics, while participants in cluster two rejected

Fig. 3. Dendogram of the hierarchical ascendant classification on general liking – Study B.

Table 5 Number of consumers per preference cluster according to the hierarchical ascendant classification and the K-means – Study A and Study B. Number of consumers Study A

Fig. 2. Dendogram of the hierarchical ascendant classification on general liking – Study A.

HAC K-means

Study B

Cluster 1

Cluster 2

Cluster 1

Cluster 2

61 50

39 50

32 41

67 58

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Table 6 Seat car fabric hedonic means and Duncan post hoc comparisons on liking items by consumer clusters – Study A and Study B.

General liking

Study A Study B

Visual liking

Study A Study B

Tactile liking

Study A Study B

Velvet likers F = 19.24; p < .001 Non velvet likers F = 15.62; p < .001 Velvet likers F = 14.71; p < .002 Non velvet likers F = 15.77; p < .001 Velvet likers F = 6.98; p < .001 Non velvet likers F = 21.98; p < .001 Velvet likers F = 2.55; p < .03 Non velvet likers F = 23.85; p < .001 Velvet likers F = 30.03; p < .001 Non velvet likers F = 2.52; p < .04 Velvet likers F = 53.71; p < .001 Non velvet likers F = 9.54; p < .001

Velvet no. 1

Velvet no. 2

3D-stitched

W&K no. 1

W&K no. 2

W&K no. 3

6.5a 4.3b 7.3a 5.1d 5.7a 3.6c 6a 4.3d 7a 5.6ab 8.4a 6.9a

6.1a 3.5c 6.9a 4.6d 5.6a 3c 6.1a 3.9d 6.7a 5.6ab 7.7a 6.1bc

3.6c 6.3a 5c 7.1a 3.8c 6.4a 5ab 7.5a 3.7b 5.5ab 4.2d 6c

5.2b 6a 5.9b 6.5ab 4.9ab 6.1a 6.1a 6.7ab 6.4a 6.4a 6.6b 6.8ab

3.6c 4.9b 5.1bc 6.4bc 3.5c 4.5b 5.7ab 6.3bc 4.1b 4.8b 5.5c 4.6d

3.6c 6.4a 4.3c 5.8c 4.4bc 6.5a 4.8b 5.8c 3.7b 5.9a 3.2e 5.9c

Signification of letters next to means: a similar letter means no significant difference between the fabrics, and different letters mean a significant difference between the fabrics at p < .05.

(preference for the softer fabrics), non-velvet likers preferences were driven mainly by the visual appreciation of the product and not by the fabric’s tactile properties. In view of the preference clusters, participants’ preferences seemed to rely more or less on distinct sensory properties. We examined the correlation between general liking, visual liking and tactile liking for both clusters in each study. The results confirmed our expectation of a higher correlation between general liking and tactile liking for the velvet likers rather than for the nonvelvet likers. Correlation between tactile and general liking was moderate for the non-velvet likers, r = .54 and high for the velvet likers, r = .82 in study A; and the difference between the two groups was significant; p < .009. A difference between the two groups was observed, to a lesser extent in study B as well, respectively r = .47 for the non-velvet likers and r = .57 for the velvet likers, but was not significant, p < .51. 3.4. Clusters and individual differences We conducted a DFA that resulted in a one axis (two a priori clusters) representation, containing the maximum of variance (100%) for both studies. 3.4.1. DFA – study A The Box test was not significant, p < .10 implying an equality of the covariance matrices (linear model). Wilks’ lambda mean vectors were not equal, F = 2.65; p < .007 which indicates that the clusters are characterized by individual difference measures. Individual difference variables were able to separate the clusters correctly at a rather high 73% (confusion matrix).

3.4.2. DFA – study B The Box test was significant, p < .009, so we ran another DFA considering a quadratic model (without covariance matrices equality). Wilks’ lambda mean vectors were not equal, F = 1.88; p < .05, and the model quality was good with 84% of the participants ranked in the correct cluster (cluster of origin). 3.4.3. Explanatory factors of preference Individual difference sub-dimensions appeared to be relevant preference cluster characterizing factors. The cluster means equality unidimensional test showed several significant characterization factors in both studies (see Table 7). The non-velvet likers tended to socially compare themselves with others (INCOM – ability) more than the velvet likers did (respectively M = 17.4 and M = 15.4), F(1, 99) = 4.21; p < .05; they tended to purchase products simply because of high aesthetic design more than velvet likers did (CVPA – response; Ms. = 8.3 and 6.8; F(1, 99) = 4.07; p < .05); and finally reacted more intensely than velvet likers to positive events (AIM – Reactivity to positive events; Ms. = 16.5 and 14.5; p < .02). These results however, were only observed in study A. In study B, the velvet likers were able to categorize and recognize well designed products (CVPA – acumen) better than non-velvet likers (Ms. = 12.9 and 10.3; F(1, 99) = 9.21; p < .003). In both studies we observed similar results for two sub-dimensions; the intrapersonal positive affect I sub-dimension and the Faith in Intuition sub-dimension. The velvet likers experienced positive emotions more intensely than the non-velvet likers. This result was significant in study A, F(1, 99) = 5.04; p < .03 (velvet likers, M = 16.8, and non-velvet

Table 7 Unidimensional test of cluster mean equality – Study A and Study B. Scales

INCOM Ability INCOM Opinion IPA I IPA II INA RTPE REI Rational REI Intuition CVPA Value CVPA Acumen CVPA Response

Lambda

F (1, 98)

F (1, 97)

p-Value

Study A

Study B

Study A

Study B

Study A

Study B

0.95 0.96 0.95 0.99 1 0.93 0.99 0.95 1 1 0.96

0.99 0.99 0.96 0.98 1 0.99 0.99 0.96 0.97 0.90 0.98

4.21 3.45 5.04 0.18 0.006 6.71 0.73 4.42 0.02 0.02 4.07

0.88 0.20 3.59 1.86 0.01 0.79 0.48 3.65 2.57 10.17 1.04

.04 .06 .02 .66 .93 .01 .39 .03 .88 .88 .04

.35 .65 .06 .17 .90 .37 .48 .05 .11 .002 .30

INCOM, Iowa Netherlands Social Comparison Orientation; IPA, Intrapersonal Positive Affect; INA, Intrapersonal Negative Affect; RTPE, Reactivity to Positive Event; REI, Rational-Experiential Inventory; CVPA, Centrality of Visual Product Aesthetics.

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likers, M = 15.2) and is a trend in study B, F(1, 98) = 3.43; p < .07 (velvet likers, M = 18, and non-velvet likers, M = 16.7). Non-velvet likers rely on their feelings during decision making processes more than the velvet likers do. This result was significant in study A, F(1, 99) = 4.42; p < .04 (non-velvet likers, M = 6.6, and velvet likers, M = 5.8) and is a trend in study B, F(1, 98) = 3.65; p < .06 (non-velvet likers, M = 7.2, and velvet likers, M = 6.6). The linear regressions indicate that the psychological individual difference factors explained about 20% of the general liking variance in both studies. More precisely, 16% was explained in study A, F(66, 599) = 1.61; p < .003; R2 = .16; and 22% in study B, F(66, 593) = 1.68; p < .002; R2 = .22. 4. Discussion The aim of the present study was to identify psychological and psychosocial individual difference variables likely to explain sensory preferences for car seat textiles. In both studies we used the same set of car seat fabrics. The individual difference variables accounted for about 20% of preference variability. This result is rather satisfactory if we consider that according to consumer research, personality traits explain 5–10% of the variance (Kassarjian & Sheffet, 1991). 4.1. Preference clusters Much like in previous research that used the same fabrics as we did, two clusters of preferences emerged in both studies. In the one cluster participants liked the velvet fabrics more and in the other cluster they preferred the woven & knitted and 3D-stitched fabrics more. We started by differentiating people according to the way they processed sensory information, using visual and tactile modalities for each cluster. For velvet likers tactile evaluation of the fabric was more important than visual appreciation of the product, whereas the non-velvet likers relied chiefly on visual liking and did not pay much attention to the fabric’s tactile properties. The one group of participants liked soft and smooth feeling products like the velvet fabrics and to a lesser extent the W&K no. 1. For these participants tactile information was an important preference driver. Although we observed a high correlation between general and visual liking for that cluster, discrimination based on visual liking was weaker than discrimination based on tactile information. This correlation was probably due to a partial equivalence of information received from visual and tactile product aspects. For the other group, the non-velvet likers, the product’s soft tactile properties were not a major factor of appreciation and fabric preference. 4.2. Cluster characterization Four individual difference measures were thought to account for sensory preferences. A majority of the scales’ sub-dimensions allowed characterizing the clusters. 4.2.1. Characteristics of non-velvet likers We presumed that for non-velvet likers, identified as more visually oriented, a product’s visual aesthetics would be a significant criterion when evaluating products. That was found to be true, but only in study A, where non-velvet likers were able to chose a product only because of its beautiful design (CVPA – response). Preference for highly aesthetic products could be linked to social considerations where product consumption and possession would be a way to enhance self-identity. The CVPA measure is highly correlated with materialism measurements (Richins & Daw-

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son, 1992). In the possession-defined success dimension of materialism measurements, possessions other than being evidence of success are a way of enhancing one’s desired self-image. The higher score for social comparison orientation observed for the nonvelvet likers in study A as well as the moderate but significant correlation between the social comparison orientation scale and the CVPA measure (r = .30) are in line with that assumption. We also observed a higher score of non-velvet likers on the Reactivity to Positive Events sub-dimension of the Affect Intensity Measure only in study A. In other words, participants who preferred woven and knitted as well as 3D-stitched fabrics, declared reacting to positive events with more enthusiasm. One reason for the discrepancies between the results in study A and B could be that the study B sample, unlike the study A sample, was composed only of young people. There was also a significant age difference between the two clusters in study A. The non-velvet likers were younger than the velvet likers. Predisposition to conformity is age-dependent; younger people tend to be less self-confident and are more sensitive to social conformity. This age effect could also explain to a certain extent, the younger group’s (nonvelvet likers) higher enthusiasm about positive events; this characteristic being more representative of younger people. The use of feeling-based judgments during decision making processes was a factor that characterized non-velvet likers in both studies. Linking psychological factors to product preference is not that obvious. We could however postulate that a correlation between intuitive information processing and the use of visual sensory input could be a significant determinant of preferences. Vision is not necessarily the dominant sensory information used in evaluating a product (Schifferstein, 2006) but remains the first connection between the consumer and the product. Vision is immediate, implicit, and does not require a specific cognitive or physical effort. As McCabe and Nowlis (2003) pointed out that the involvement of a given sensory modality in collecting information partially depends on object exploration allocation efforts. Haptic activity is judged more costly than simple visual perception of the object, as it requires more physical energy (Jones & O’Neil, 1985) and deliberate goals. Thus the preference for an intuitive mode of processing, or in other words a superficial treatment of the information, could to a certain extent explain the dominance of the visual modality for non-velvet likers when evaluating car seat fabrics. 4.2.2. Characteristics of velvet likers Velvet likers compared with non-velvet likers could be characterized as paying much less attention to social appearances and to what others might think of them. This assumption is based on the differences between the two groups observed in study A on the CVPA response sub-dimension and the INCOM sub-dimension. These results however, were not observed in study B. The velvet likers in the second study appeared to be more capable than the non-velvet likers of categorizing and recognizing products with highly aesthetic designs (CVPA – acumen). Ability (acumen subdimension) and purchase decision (response sub-dimension) do not necessarily rely on the same processes. High response levels to highly aesthetic products could be one of the impulse behavior underpinnings (Rook, 1987). When purchase decisions based on design criteria seem to be based on primary affective reactions to stimuli, the acumen used to categorize and recognize products should be based on cognitive reasoning. CVPA’s authors (Bloch et al., 2003) also noted that a consumer may highly value product aesthetics but may not necessary display high acumen levels. The main dimension of velvet likers’ characteristics was their propensity to experience their positive emotions more intensely. People that experience intense emotions are supposed to reach

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higher levels of stimulation that would compensate for low baseline arousal (Larsen, 2009). The importance of tactile modality for velvet likers could therefore be interpreted as a means of increasing their arousal level. Neither visual product properties nor context evaluations in laboratory settings represent high sources of stimulation. The evaluation task of fabrics in grey/black colors is rather monotonous and cannot be regarded as being highly stimulating. Tactile information could therefore represent a supplementary source of stimulation for people needing higher stimulation to compensate for lower activation levels. The preferences of individuals for whom the tactile fabric properties are important, must be driven by the most pleasant and hedonic tactile property, which is the softness of the fabric. Why did the non-velvet likers in study A score higher than the velvet likers on the Reactivity to Positive Events sub-dimension of the Affect Intensity Measure? This result seems to contradict the other results we presented above, but a deeper look at the correlations between the AIM sub-dimensions indicates no correlation between the RTPE sub-dimension and the other AIM sub-dimensions (Intrapersonal Positive Affect I and II, and the Intrapersonal Negative Affect). Several factors could explain this lack of correlation. Translation mistakes or cultural differences could provide a partial explanation, but the distinction between reactivity and intensity in the Affect Intensity Measure (Bryant, Yarnold, & Grimm, 1996) could constitute a more relevant explanation. The authors made a conceptual distinction between the emotional reactivity construct and the emotional intensity construct. An individual can initially be disturbed by negative events or surprised by positive events but may subsequently need to use different coping strategies in order to reduce the high negative or positive intensity of these feelings. Thus, reactivity can be viewed as a predisposition for emotional response whereas intensity represents the strength of one’s experienced emotions. 4.2.3. Preference cluster summary The clusters identified in both studies were characterized best by the intensity of consumers’ experienced emotions and their propensity to rely on feeling-based judgments. We proposed an interpretation of these results considering the significance of the visual and tactile modalities in fabric preferences. Velvet likers experience high intensity emotions and by extension generally search more stimulation. The tactile properties of the products represented a supplementary source of information in low arousal contexts. The non-velvet likers’ intuitive and superficial way of processing information may explain their preferences. These preferences are based mainly on the visual properties of the products, the latter being less costly in terms of sensory processing. The functional attitude approach (Maio & Olson, 2000; Wänke, 2009) hypothesizes that peoples’ attitudes (preferences) serve specific needs. We can therefore explain the positive attitudes toward velvet fabrics expressed by people who experience emotions intensely as driven by a higher need for stimulation. We can also assume that people who focus on visual product characteristics may have a stronger need to make quick heuristic decisions. 5. Conclusion These exploratory studies provide a conclusive search of psychological and psychosocial factors able to differentiate people and identify judgmental processes according to sensory preferences in blind sensory tests. The clusters were in line with those observed in previous consumer tests, even when our hypotheses were tested on samples not recruited according to consumer criteria. This tends to support a possible generalization of the results on

consumer targets. Future research should however check generalization potential, as well as examine possible replication of the results using other textile products (Kergoat et al., in preparation). The diversity of our samples across studies in terms of observed variables like age, sex, or familiarity with the product was one of the study’s limitations. As pointed out in the discussion, a majority of the results obtained in study A were not replicated in study B. We believe this was due in part to age range differences in the samples. The difference in participants’ characteristics across both studies, make interpretation of the results more difficult and must therefore be considered with some caution. Subsequent studies on that issue should control these parameters that are likely to impact preferences. The individual difference approach characterizes consumers according to their sensory preferences for seat car textiles. We believe that beyond this goal, the individual difference approach can be viewed as a first step towards a comprehension of consumer sensory preferences and the underlying processes involved in sensory preferences, independent of the consumption sensory area (i.e. food or non-food). We considered both the individual difference approach as well as the functional attitude approach as a way to understand consumers’ needs. We were therefore able to pinpoint and understand some underlying processes of consumer sensory preferences present during sensory evaluation tasks. Identifying consumer needs is a key for understanding consumption preferences and the individual difference approach is an effective tool to identify these needs. Selected individual difference measures however, must be integrated into a reliable and strong theoretical framework, otherwise interpretation will be poor and difficult. Examining the significance of sensory input for consumers in sensory tests could be interesting since we found that sensory input can possibly be linked to affective processes. Even though we examined a classic approach in the consumer research area, its application in the sensory field is still rather limited. A more systematic investigation should be conducted. This investigation should take into account the type of products evaluated and the significance of the sensory input involved in sensory evaluations. We believed consumers could rely on social consideration to express their preferences for car seat fabrics; automobiles being evident visible objects with social identity implications (Steg, 2005). These implications appear to be less obvious for non durable goods or current food preferences, even though we know that food consumption has real social implications as well. Individual differences, like the Affect Intensity Measure, should have a cross-product application since the search of stimulation can be foreseen whatever the type of product and/or as soon as product properties vary in terms of arousal. While seat car fabrics are not representative of all products, our study access more general psychological processes involved in a consumer sensory evaluation task, and highlights the individual difference approach as a relevant tool for food sensory researchers. References Ajzen, I., & Fishbein, M. (1977). Attitude–behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888–918. Bearden, W. O., & Rose, R. L. (1990). Attention to social comparison information: An individual difference factor affecting consumer conformity. Journal of Consumer Research, 16, 461–471. Bloch, P. H., Brunel, F. F., & Arnold, T. J. (2003). Individual differences in the centrality of visual product aesthetics: Concept and measurement. Journal of Consumer Research, 29, 551–565. Bryant, F. B., Yarnold, P. R., & Grimm, L. G. (1996). Toward a measurement model of the affect intensity measure: A three-factor structure. Journal of Research in Personality, 30, 223–247. Busseri, M. A., & Kerton, R. R. (1997). Beyond control? Understanding consumer behavior using a measure for consumer locus of control. Consumer Interests Annual, 43, 40–45.

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