the repertory grid method and preference mapping in market research: a case study on chocolate...

Food Quality and Preference

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Jean A McEwan*t and David M H Thomson Food Quality Group. Department of Food Science and Tcchnology. University of Reading. Whiteknights. PO Box 226. Rc~,ding RG(~ 2AP.

UK

The repertory grid method and preference mapping in market research: a case study on chocolate confectionery

Abstract

A procedure based on the repertory grid method was used to elicit information from twenty-six female consumers, on the intrin- sic product characteristics which they indi- vidually perceived in a range of thirty-one chocolate confectionery products. Each subject described a private list of attributes, with corresponding line scales, which she ultimately used to rate all the products. Photo- graphs of the confectionery products were used in the elicitation procedure. The result- ing product by attribute matrices, which were qualitatively and quantitatively different for each subject, were input to generalised Procrustes analysis, to yield a product space in four dimensions. Individuals' attributes were organised into thirty-seven classes and these were used to interpret the product space. It was the consensus view of this group of consumers, that attributes associated with sharing and the ability to section the product, together with differences in texture, were the main discrimination features separating the products. Other less important factors were also identified.

Using a preference mapping technique, preference ratings were successfully superimposed on the four dimensional product space. This implies that the main discriminating features were important in determin- ing preference. On the first two dimensions, a majority of this exclusively female panel showed a preference for light/airy textured chocolate, and avoided the more chunky

products. However, on the third and fourth dimensions, the main direction of preference was towards the milk chocolate products. Chocolate consumption data derived from a diary study could not be similarly related to the product space.

Keywords: constructs; elicitation; generalised Procrustes analysis; preference mapping; product space; repertory grid method

Introduction

There are a wide variety of market research procedures which could be used to elicit information about product attributes, and to measure attitudes and beliefs. These include unstructured spontaneous techniques, such as interviews and projective methods, through to highly structured methods, such as dissimilarity scaling, where appropriate statistical procedures are used to obtain product spaces (maps), and to identify salient product dimensions (Hughes 1974).

Interviews can be held at the individual level, or with a group where each member should act as a catalyst for eliciting ideas and thoughts from others (Hughes 1974). However, this type of unstructured technique has been criticised for its lack of efficiency in identifying the salient product attributes (Payne 1965). Additionally, they

* Present address: Dep~,rtment of Quality, Campden Food and Drink Research Association, Chipping Campden. Gloucestershire, GL55 6LD, UK 1" correspondence

60 McEwan and Thomson

inevitably suffer from bias due to the interviewer or dominant personalities within the group. With projective methods (Kassarjian 1974) subjects are presented with incomplete or vague product ideas, and asked to explore and expand on these. In so doing, it is expected that beliefs, attitudes and perceptions about the product will emerge (Hughes 1974).

The repertory grid or triad method (Kelly 1955; Frost & Braine 1967) is a partially structured technique which is sometimes applied in market research. In this application, a subject is typically presented with a deck of cards, with the name of a product or brand printed on each, and asked to randomly select any three products which are familiar to him. The subject then identifies so-called constructs (e.g. continua such as cheap - expensive or unsophisticated - sophisticated), which describe the way in which two products in the selected triad are similar to each other, and in the same way different from the third. All the products or brands are then rated on these continua, by the subjects. The triad procedure is repeated until the subject cannot describe any more constructs. According to Frost and Braine (1967), an average of eighteen constructs per subject is usual. For each subject, responses are then put into the form of a grid (product by construct matrix), and comparison of the constructs can, for example, be used to examine competitive products (Hughes 1974). According to Hughes (1974), the repertory grid technique is superior to the unstructured methods for two important reasons. Firstly, subjects identify their own constructs (attribute dimensions), which eliminates interviewer bias. Secondly, when the terms elicited are subsequently used in questionnaires, they are usually found to be more meaningful to other subjects than terms chosen intuitively by a researcher. However, in spite of the fact that products are rated along the various attribute dimensions, the repertory grid method, in this form, is still essentially a qualitative technique.

Information obtained by elicitation procedures, such as the repertory grid method, can often be represented in the form of a knowledge structure diagram (Olson 1981). Although these show the inter- relationships between the various constructs, they give no quantitative information as to the relative importance of each construct in relation to the product, nor do they show the relationships between products. Fransella and Bannister (1977) have used INGRID principal component analysis (Slater 1964; 1977) to examine the relationship between constructs and the objects. This procedure is essentially the same as usual principal component analysis, although in applications found by the

authors, only one construct is apparently important in each of the first two principal components. Additionally, these two principal components always account for a comparatively high percentage of the variation in the data. Such a situation is unlikely in the context of market research, as rather more than two perceptual attributes are likely to influence food preference and consumption. In fact, each dimension is likely to comprise several important attributes to separate the products (objects). In addition, there appears to be no structured way of comparing the individual principal component analysis plots, derived by Slater's INGRID method, or indeed any other of the applications of PCA reported by Fransella and Bannister (1977).

A solution to this problem was first proposed by Thomson and McEwan (1988), who recognised that the constructs elicited using the repertory grid method are analogous to the attributes obtained in free-choice profiling (Williams & Langron 1984; Williams and Arnold 1985). In both free-choice profiling and the modified repertory grid method (Thomson & McEwan 1988), a subject describes his/her own private list of product attributes (constructs), with corresponding scales. The subject then rates each product with respect to his/her own attributes. In spite of inevitable individual differences amongst the product by attribute matrices obtained, a consensus (across subject) space showing inter-product relationships can be obtained using a statistical procedure known as generalised Procrustes analysis (Gower 1975). However, since the product space is ultimately interpreted with respect to each subject's constructs, individuality is pre- served to a large extent.

Generalised Procrustes analysis (GPA) has been described in detail elsewhere (Arnold & Williams 1986). Suffice to say that it is a statistical procedure which can be used to obtain a perceptual space of objects (e.g. products or brands) in several dimensions, when the various subjects (i.e. sensory assessors or consumers) use qualitatively different attributes, and also different numbers of attributes, to profile the products. If the individual subjects rate all the products, for each attribute on their own personal list, the result will be a product by attribute matrix where the identities and the number of attributes will differ across subjects. If the continuum (scale) associated with each attribute is considered as an axis in space, the ratings assigned by an individual subject to any one product can be construed as the coordinates of that product in a multidimensional space. This quite logically leads to the concept of a configuration of all the products in multidimensional space,

and of a different configuration of the products for each subject.

In spite of the implied qualitative differences in the attributes used by the various subjects, it is assumed that subjects from a common cultural background will perceive products in a broadly similar manner (Stef- flre 1971). Hence, differences in the identities of the attributes selected, in the number of attributes and in the way in which they are scored, may be largely (but not wholly) idiosyncratic. If this is the case, then the configurations of the products yielded by the various subjects will show a measure of geometric similarity. GPA is a procedure which is used to maximise geometric similarity amongst such configurations (Gower 1975), by simul- taneously exposing them to the mathematical procedures of translation, rotation/ reflection and scaling. The result is a consensus (across subject) representation of the products in multidimensional space. In practice, between two and six complex dimensions (principal components) are usually adequate to represent the objects. These principal components are linear combinations of all the attributes used by all the subjects. However, for each principal component, only some of the original attributes make an important contribution to the separation of the products. These can be identified for each subject, by selecting either those attributes with the highest positive or negative loading coeffi- cients (Thomson & McEwan 1988), or by those which are most strongly positively or negatively correlated with each principal component (MacFie 1987). In short, a map-like representation of the products is obtained, which is interpreted at the individual level.

The repertory grid method has previously been applied in a pilot study involving student perceptions of meat and meat products (Thomson & McEwan 1988). This paper describes the application of the repertory grid method to a section of the UK chocolate confectionery market. Since the ultimate objective of any form of market research must surely be to identify the factors which determine choice of purchase within a product range, an external preference mapping procedure (Schiff- man et al. 1981; MacFie & Thomson 1984) was used in an attempt to relate the volume of the various products consumed and preference (i.e. declared liking) to the consensus chocolate product space.

Experimental procedure

Subjects Twenty-six female consumers (subjects) from High Wycombe, UK, volunteered to

Food Quality and Preference (1989) ! (2)

The repertory grid method and preference mapping 61

participate in the repertory grid experi- ment. Subjects were in the age range 20-65, and were not specifically chosen to represent a particular socio-economic group. However, all subjects were house- wives without full-time jobs outside the home.

Products

Thirty-one chocolate confectionery products (Table 1) were selected in consulta- tion with Cadbury Schweppes pie, to cover five categories of chocolate confectionery. Separate photographs of the thirty-one products were taken, each displaying the product with and without its wrapper. The photographs, and not the actual products, were used to elicit the constructs. Care was taken to ensure that each product in the photograph was as near as possible to its actual life size.

Table 1 The thirty-one chocolate confectionery products

Milk Moulded Blocks 1 Aero 2 Cadbury's Dairy Milk 3 Galaxy 4 Nestle 5 St. Michael Milk 6 Yorkie

Plain/Plain Recipe Blocks 7 Bournville Dark 8 Terry's Plain

Countline/Chocolate Biscuits 9 Drifter

10 Kit Kat 11 Twix 12 Waifer

Milk Moulded Recipe Blocks 13 Fruit and Nut 14 Toblerone 15 Whole Nut 16 Yorkie Raisin and Biscuit 17 Yorkie Roast Almond

Countline/Filled Blocks 18 Aero Chunky 19 Boost 20 Bounty (milk) 21 Caramel 22 Crunchie 23 Double Decker 24 Flake 25 Lion Bar 26 Mars Bar 27 Marathon 28 Picnic 29 Topic 30 Fry's Turkish Delight 31 Wispa

Repertory grid method

The thirty-one (N) products (photographs) were arranged into a series of fifteen triads ( ( N - 1)/2). The selection procedure for the triads was as described by Thomson and McEwan (1988). Four different group- ings of fifteen triads were made to mini- mise order effects. Subjects were presented with the first triad which comprised three photographs (a, b and c) laid in front of them, on a flat surface (usually a table). On the basis of their knowledge of the product, subjects were asked in what ways two selected products (a and b) from the triad, were similar to each other, and in the same way different from the third (c). The interviewer recorded responses (constructs) as they were elicited, and when no new constructs were forthcoming, the other two combinations (a and c vs. b; b and c vs. a) were presented. This procedure was repeated for the remaining fourteen triads. The same interviewer was used throughout and was a graduate in psychology who had previous experience in inter- viewing. She received approximately five hours" training from experienced researchers.

Subjects provided anchors representing the two extremities of each of their own constructs on a 100 mm continuous line scale. These rating scales, unique to each subject, were then used to quantify the perceptual characteristics of the thirty-one confectionery products.

Construct classification scheme

As part of the interview procedure, de- tailed definitions for each construct were obtained from the subject who described them. This information was used in the development of a construct classification scheme, which was designed to aid interpretation after statistical analysis.

The first step in the development of the classification scheme was to list all the constructs elicited; this task was performed by the experimenter. On the basis of verbal labels and definitions, similar constructs were grouped. Each group was given a class name and a definition.

Interviews and postal information

Individual interviews were conducted in the subject's home, by the same experienced interviewer. Two interviews, each lasting approximately forty-five minutes and spaced one week apart, were required to elicit the constructs. In a third interview, lasting approximately one hour, each subject defined her constructs and provided verbal anchors for each of her scales.

In the final stage of the study, each subject scored the intensity of each of her own constructs, for the thirty-one confec-

tionery products. This part was conducted by post, and did not use the photographs. Product rating booklets were completed by the subjects in two batches, one week and two weeks after completion of the elicitation procedure.

Data treatment

Using a recently developed data reduction procedure (Krzanowski 1987), each subject 's data matrix (N products by V constructs) was sequentially reduced to fifteen and then to ten constructs (where appropriate). For subjects with less than the above number of constructs, dummy (zero) variables were added to the data matrix.

Generalised Procrustes analysis (GPA) was performed on the full and reduced data sets. In each case, consensus perceptual spaces for the thirty-one products were obtained. Further GPA on the three sets of consensus scores (Williams & Arnold 1985) was used to determine if data reduction had influenced the resultant perceptual space. From this analysis, a decision was made as to which of the three data sets should be further interpreted.

For the chosen data set, vector loadings were examined for each subject to determine the important constructs (largest positive or negative loadings) on the first four principal components (PC's). Con- structs were then assigned to general construct classes as described above. A summary table was constructed to show the number of times each construct class occurred, on each of the first four principal components. Construct classes which were significant for each principal component were determined using the binomial statistic (O'Mahoney, 1986), with n = 26 (number of subjects) and p = l/c (where c is the number of construct classes).

Principal coordinate analysis on the dis- tances between each subjects' perceptual space, was used to obtain a two- dimensional subject space, and hence to identify any possible subgroups or outliers in the populations (Arnold & Williams 1986).

Consumption and preference measurement

Self-completion diaries were used to obtain consumption data for the thirty-one chocolate confectionery products. To prevent attention being focused on the particular products under investigation, subjects were asked to list their consumption of all confectionery products, except non- chocolate biscuits, chewing gum and bub- ble gum. Subjects were instructed to fill in the diary at the end of each day, by recording which products they had eaten,

Food Quality and Preference (1989) 1 (2)

62 McEwan and T h o m s o n

the sizes of the item and the amount of each which they had consumed. Each of the eleven diaries spanned a two week period, and they were sent to consumers one week in advance. Subjects were re- quested to put each completed diary in a pre-paid envelope, and to post it the day after completion.

Consumption data were tabulated for the thirty-one chocolate confectionery products. In this study, 1 gram of a product was defined as one unit of consumption. From this, unit consumption per ten week period was calculated, for twenty-two out of the twenty-six subjects, for each of the confectionery products. External preference mapping (PREFMAP) analysis (Schiffman et al. 1981; Davies & Coxon 1983; MacFie & Thomson 1984) was performed to determine if any of the perceptual dimensions, from the GPA, were related to the consumption data obtained from the diaries. The metric option of PREFMAP was chosen as it is statistically more powerful, and Huber (1975) has shown that it gives similar results to the non-metric option.

Subjects also provided preference ratings for each of the thirty-one products. These were made on 100 mm continuous line scales, labelled with 'dislike extremely' and 'like extremely' at the left and right hand poles, respectively. Preference judge- ments were made on the basis of the subject's memory of the product, and are likely to include factors other than the sensory characteristics of the product. This was deemed necessary so that these data were compatable with the repertory grid information.

Results and discussion

R e p e r t o r y gr id m e t h o d

Subjects found the triangular elicitation procedure (method of triads) used in the repertory grid method helped them to focus on the perceptual attributes of the confectionery products. Across the group, between eight and thirty-one constructs (i.e. perceptual attributes) were elicited. This is testimony in itself to the efficacy of the procedure, since experienced market researchers had previously found difficulty in getting consumers to describe the perceptual attributes of chocolate confectionery using other procedures (Todd 1986). The photographs presented the products with and without the wrapper to help in the recall process, and so can be considered as a memory trigger. Thus, it would be expected that perception 9 f important fla- your and texture characteristics would be elicited. It might prove interesting to per- form this study with product names only,

thus evaluating the effect of the photographs.

Although the descriptive labels used by the various subjects were often quite different, examination of the associated construct definitions and the verbal labels used to anchor the poles of the scales showed that some product attributes were clearly common to many of the subjects. Differ- ences in the number of constructs elicited would, of course, be expected, due to psychological differences amongst individuals. These can probably be accounted for by the fact that some subjects will genuinely perceive more or different product attributes. Some will choose to frag- ment complex attributes into simpler, but often highly correlated, components, and some will choose to describe an attribute which they have only vaguely identified using several different but, again, highly correlated constructs. Although high cor- relation amongst the perceptual attributes is essential before a meaningful multidimensional representation of the products can be obtained (Chatfield & Collins 1980), it is recognised that not all attributes elicited by a subject contribute to the derived product configuration (Krzanows- ki 1987). Hence, there is scope for sys- tematic deletion of attributes, which con-

tribute relatively little to discrimination amongst the confectionery products. By so doing, interpretation of the product spaces derived from GPA is simplified, not least because it is a vehicle through which a uniform number of constructs per subject can be obtained. Application of Krza- nowksi's variable reduction procedure to data previously collected by Thomson and McEwan (1988) gave better recovery of structure (Gains et al. 1988) than alternative methods (Jolliffe 1973; McCabe 1984).

Figures 1 and 2 reveal the effect of variable reduction on the positioning of the thirty-one products, on the first four principal components of the consensus product space. Each product number appears three times, representing the positions of the products when thirty-one, fifteen and ten constructs were used to derive the space.

On the first two principal components (Fig. 1) the three numbers associated with each product are almost superimposed, in most cases. This means that reduction of the number of constructs per subject from thirty-one to ten, has caused almost no change in the relative positioning of the products with respect to PC's 1 and 2. It also implies that virtually all of the important discriminatory data can be attributed to just ten constructs from each subject.

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Figure 1 Product space for 31, 15 and 10 constructs, after generalised Procrustes analysis: PC 1 vs. PC 2



However, there is some disparity between the three product points on PC's 3 and 4 (Fig. 2). This could have arisen because of subtle differences caused by the elimina- tion of constructs, or because these lower principal components only explain a small proportion of the variation in the data and, hence, are poorly defined. In addition a separate exercise revealed that the percentage variation and interpretation did not • alter significantly. Since the disparity be- o a. tween the three spaces is small, and be- [ cause of the previously mentioned advan- o tages in using fewer constructs, only the O product space derived from ten constructs per subject is further interpreted here. _a.

Figures 3 and 4 show the positions of the o branded products on PC's 1 to 4, of the "~ consensus space. These PC's accounted for a. 43.1%, 16.3%, 9.5% and 9.2% (total = 78.1%) of the variation in the data, respectively. The construct classification scheme used to interpret the product space is shown in Table 2, and comprises thirty- seven classes. This classification scheme was derived by grouping individual constructs which the experimenters believed to describe similar phenomena, as based on the construct descriptors, the construct definitions and the verbal labels used to anchor the poles of the construct scales. By definition, this process is subjective, but previous experimentation has demons- trated the validity of this approach (Thom- son & McEwan 1988).

A three stage process was adopted to determine the importance of the various construct classes in discriminating amongst the branded products. Firstly, an ' important' construct class was operationally defined as comprising one or more individual (subject) constructs with a positive or negative loading coefficient of greater than 0.4. These are identified with a single asterisk in Table 2. In the second stage of the process, the number of occurrences of each construct class on the first four

c principal components was tabulated . (Table 3). Finally, the binomial statistic s was used to determine whether the number t~ of occurrences per class was significantly ; ¢L

better than chance (Table 3). "~ C

Examination of Table 3 indicates that ": L

the first principal component of the product space (Figure 3) separates products on the basis of 'sections' and 'good for sharing'. This is not entirely unexpected since these two characteristics are probably the two main distinguishing features between moulded blocks (negative side of PC 1) and most countline/filled blocks (positive direction on PC 1). Cadbury's Caramel, which is one of the very few countline/filled products which is sec- tioned, fell approximately mid-way between the two groups. With such strong emphasis on the shareable/sectionable pro-

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Figure 2 Produc t space fo r 31, 15 and 10 const ruc ts , a f ter genera l i sed Procrus tes ana lys is :

PC 3 vs. PC 4

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KEY CDM - Cadbury'a Dairy Milk R & B - Raisin and Biscuit R.A. - Roast Almond

• Flake

• Turkish Delight

• W i s p s ~ A e r o Chunky

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Topic Marathon• • • Picnic

bauble necker • • • • T w I x

Boost Drifter • Mare Bar

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• Yorkiu R.A. • Tublerone

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Principal Component 2

F i g u r e 3 Consensus perceptual space for the 31 products: PC 1 vs. PC 2



ducts, there may be market opportunities for new shareable countlines. This asser- tion is supported by the rapid growth in the 'fun size' range of countline products (Cad- bury 1988). Apparent differences in texture (Classes 4 and 17), in 'percentage chocolate' (Class 34) and 'good for packed lunch/snack' (Class 36) also contribute to discrimination along PC 1.

PC 2 is very heavily dominated by texture differences ('visual texture', 'smoothness', 'light/airy' and 'texture: soft- hard'; Classes 4, 12, 13 and 17, respectively), although 'biscuit-like' (Class 22), 'percentage chocolate' (Class 34), the shareable/sectionable attributes (Classes 25 and 27) and 'attractive wrapper' (Class 1) also contribute. PC's 3 and 4 can similarly be interpreted using Table 3.

In summary, the consensus view of this group of consumers is that attributes associated with sharing/sections and texture dominate their perceptions of this

Table 2 Construct classification scheme

Class No. Construct Class * I Attractive wrapper *2 Informative label *3 Colour of chocolate *4 Visual texture 5 Shape: usual-unusual 6 Easy size to fit in mouth 7 Moreish/pleasant

*8 Milky chocolate 9 Sweet

10 Bitter I 1 Sickly

* 12 Smoothness * 13 Light/airy 14 Product thickness 15 Crumbly/flakey 16 Chewiness

* 17 Texture: soft-hard "18 Crunchy 19 Melts in mouth 20 Overall quality 21 Easy to open wrapper

*22 Biscuit-like 23 Cost 24 Value for money

*25 Sections 26 Time taken to eat

*27 Good for sharing 28 Messy to eat

*29 Treat/special occasion *30 Adult/child product 31 Eat and save some 32 Traditional product 33 Fills you up

*34 Percentage chocolate 35 Fattening

*36 Good for packed lunch/snack 37 Popular/familiar

* Important construct classes

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• • • Topic Toblerono

Yorklo R & D • Marathon • • York ie R.A.

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CDM - Cedbury'e Dairy Milk R & B - Rais in lnd Bilcuit R.A. - Roast Almond

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Prlncipll Component 4

F i g u r e 4 Consensus perceptual space for the 31 products: PC 3 vs. PC 4

range of chocolate confectionary products. The fact that PC 1 explained a large proportion of the total variation in the data (43.1%), and that this was primarily due to the distinction between products which can and cannot be shared, suggest that analyses within the two product types would be desirable (McEwan 1988). Although the distinction between the two groups seems to confirm the obvious, there was no

justification for partitioning the products a priori, when the objective was to use consumers and not confectionery experts to characterise a large part of the chocolate confectionery market.

Figure 5 shows the subject plot obtained by principal coordinate analysis (Chatfieid and Collins 1980). The axes merely reflect differences between individual subjects' product configurations in two dimensions.

Table 3 Number of occurrences of each construct class on the first four principal components

Principal Component Class PC 1 PC 2 PC 3 PC 4

I Attractive wrapper 1 3* 55 65 2 Informative label 2 2 3* 0 3 Colour of chocolate 0 0 1 3" 4 Visual texture 41 3* 3* 1 8 Milky chocolate 1 0 0 7

12 Smoothness 0 41 0 0 13 Light/airy 0 55 0 2 17 Texture: soft-hard 3* 55 2 0 18 Crunchy 1 1 3* 1 22 Biscuit-like 1 41 75 0 25 Sections 155 41 41 3* 27 Good for sharing 85 3* 3* 2 29 Treat/special occasion 1 0 2 55 30 Adult/child product 2 2 3* 2 34 Percentage chocolate 3* 4* 0 1 36 Good for packed lunch/snack 3* 2 55 1

* P<0.05 t P<0.01 ~:P<0.001



As such, separation between subjects is not necessarily related to the main principal components from the GPA. At this stage in the development of the methodology, interpretation of subject plots is limited to noting that the greater the distance between any two subjects, the greater is the difference between their product configurations. However, the extent of this difference could initially be evaluated by comparing the individual product spaces of subjects at opposite extremes of the plot (e.g. Subject 1 versus Subject 26, or Subject 8 versus Subject 16). If their product spaces were significantly different, implying differences in perception of the product range, then grouping of proximally related subjects, with subsequent re- analysis within groups, might yield important information about different consumer types. This is clearly an area for future development. The fact that there are no obvious subgroups or outliers (Arnold & Williams 1986) and that the assessor re- siduals from the GPA were very similar, suggests that there were probably no significant differences in product configurations derived by the subjects in this group. However, applying this method to a larger group of consumers might yield important information about different consumer

types (McEwan 1988; McEwan & Thom- son 1988b).

E x t e r n a l p r e f e r e n c e m a p p i n g ( P R E F M A P )

Complete sets of consumption and preference data were obtained from twenty-two of the original twenty-six subjects. For the large majority, product consumption data could not be fitted to the repertory grid consensus product space, by applying any of the four PREFMAP models (Phases I-IV). In contrast, the preference ratings of fourteen subjects were fitted using the circular ideal-point model (Phase III), and sixteen using the vector model (Phase IV). Table 4 shows the subjects who fitted the Phase III and Phase IV models. The corresponding root mean square values of 0.61 and 0.64, respectively, are satisfactory in both cases (Schiffman et al. 1981). A possible explanation for non-fitting subjects may be that the criteria used to discriminate amongst the products on the consensus space, are not the main determi- nants of preference or consumption for these particular individuals. For the preference data, inconsistent ratings may also be a contributory factor.

Table 4 Individual significance levels for the fit of preference data using the Phase III (circular ideal point) and Phase IV (vector) PREFMAP models

Subject Phase Ill Phase IV

1 - - - -

2 - - 5% 3 - - - -

4 5% 1% 5 1% 1% 6 - - - - 7 - - - - 8 1% 1% 9 1% 1%

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A = average subject - - = not fitted

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Figure 5 Subject principal coordinate analysis plot

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In this study, as more subjects' preference data fitted the vector model (Phase IV), and since examination of the F-ratios and R-squared values (Schiffman et al. 1981) indicated that no improvement occurred on moving from Phase IV to Phase III, the vector model was chosen as the most appropriate to consider. Addi- tionally, only six subjects (Subjects 5, 8, 10, 14, 16 and 18) who fitted the ideal point model had ideal points within the product space (Figures 6 and 7). Consequently, the amount of useful information that could be gleaned from the ideal point model is very limited.

Examination of Fig. 6 shows that many of the subjects have preference vectors which run in the negative directions of PC's 1 and 2. This implies preference for light/ airy products (e.g., Aero, Aero Chunky, Flake and Wispa, which are all textured chocolates) amongst this exclusively female population. Indeed the marketing strategies for many of the products in this section of the product space, are aimed at females. Only Subject 15 shows a definite preference for other countline/filled products, in spite of the fact that many of these are major brands, whilst Subject 1 shows a preference towards the chunky products (e.g. Yorkie Raisin & Biscuit). A possible


66 M c E w a n and T h o m s o n

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Preference mapping Phase Ill ideal point model and Phase IV vector model: PC 1 vs. PC 2. [Star = average subject; Circle = ideal

explanation for this is that many of these products are heavy and satiating, and hence may be more attractive to males, rather than females who seek a subtle treat.

In Fig. 7, which relates to PC's 3 and 4 of the product space, the main direction of preference is towards milk chocolate products (negative direction of PC 4), rather than plain. However, Subject 5 showed a definite preference for plain chocolate, and in fact the consumption data revealed that this was the only sort of chocolate she ate. There were a number of subjects (e.g. Subjects 4 and 9) who seemed to prefer the biscuit-like products (negative side of PC 3).

An important observation from the PREFMAP study, is the apparent diversity in preferences, even within this small group of consumers. Indeed, PREFMAP offers a potent method for segmenting consumer populations.

The fact that the' subjects' preference ratings and not their consumption data could be fitted by PREFMAP, and that consumption and preference were correlated for only three out of twenty-two subjects, emphasises the important distinction between these two phenomena (McE- wan & Thomson 1988a). As far as this study is concerned, it may be deduced that the perceptual attributes which th'e consumers used to differentiate samples are those which influence preference. Al- though the repertory grid method was very successful in this respect, the methodology must be extended in order to bridge the gap between preference and consumption.

Conclusions

As a means of eliciting the attributes which individual consumers perceive in a product, the repertory grid method offers a

rigorous, unbiased, rapid and comparatively inexpensive alternative to the group techniques normally used in market research. However, utilisation of the repertory grid method has previously been hindered because it has been difficult to rationalise the vast amount of product and contextual information which is normally obtained. By integrating the repertory grid method with GPA, consensus product spaces can be obtained. From these, the most important attributes used by consumers to discriminate amongst the products, can be readily identified. In this particular case, attributes associated with sharing and the ability to section the product, together with differences in texture, were the main discriminatory features amongst the products.

Using PREFMAP, preference ratings were successfully superimposed on the four dimensional product space. This implies that, in discriminating amongst the pro-


The reper tory gr id me thod and preference m a p p i n g 6 7

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Figure 7 Preference mapping Phase Ill ideal point model and Phase IV vector model: PC 3 vs. PC 4. [Star = average subject; Circle = ideal point; Arrow = direction of preference vector]

ducts, repertory grid method has successfully identified the factors which determine consumer preferences. Product consumption data could not be similarly related to the product space, which suggests that the methodology must be extended in order to bridge the gap between preference and consumption.

A c k n o w l e d g e m e n t s

The authors are grateful to the Agricultu- ral and Food Research Council (UK) and to Cadbury Schweppes pie for financial support .

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