The perceptual mapping procedure we propose differs from existing procedures in that the brands and attributes to be judged are determined by individual respondents. Because a respondent is likely uncertain about judgments of unknown brands and/or irrelevant attributes, the resulting data may be less valid. However, our procedure requires an additional step in the data collection procedure, namely, the selection of brands and attributes, which may induce respondent fatigue and have a negative impact on data quality. To examine the relative advantages of various procedures, we therefore conduct an experiment to compare three alternative methods, including both respondents’ evaluations of the task and the perceptual maps obtained.
We compare three data collection procedures for multiattribute perceptual mapping: (1) our proposed procedure with idiosyncratic sets of brands and attributes, (2) the procedure of Steenkamp et al. (1994) with idiosyncratic attribute sets but a fixed brand set, and (3) traditional multiattribute ratings with fixed sets of brands and attributes. For the latter two procedures, to determine brand and attribute sets in advance, we conducted a small-scale preliminary study to identify brands and attributes that consumers typically select with our proposed procedure. To select the brands, we also examined actual market shares. In addition, the number of brands and attributes should be feasible and comparable to regular perceptual mapping studies. Therefore, we selected 10 brands and eight attributes for the traditional procedure; to increase generalizability, we also replicated our study using two different product categories, supermarket chains and car brands.
The data were collected through an Internet survey conducted with the CentERpanel of Tilburg University, The Netherlands. The panel is representative of the population of Dutch households, and our total sample size was 1,224 subjects. To prevent cross-task and order effects, we applied a 3 (data collection procedures) × 2 (product categories) between-subjects design, with respondents randomly assigned to one of the six treatments. Sample sizes per cell ranged from 182 to 227. In the overall sample, 48% of respondents were men; 33% were younger than 35 years and 31% were 65 years or older.
The main judgment task consisted of multiattribute ratings of brands. In a brand-by-brand approach, the respondents rated all attributes on a 10-point scale with labeled end points. For tasks with fixed brand and/or attribute sets, the order of these brands and attributes was random across respondents, but the order of attributes remained constant across the different brands for each individual respondent. Because the sets of brands and/or attributes were not determined in advance for some experimental cells, we developed two judgment tasks to select the respondent-specific brands and attributes. First, each respondent indicated, “Which supermarkets do you know? Please think of supermarkets where you do your grocery shopping as well as supermarkets which you do not visit usually.” Second, for the attributes, they revealed, “Which attributes of supermarkets do you consider to be important when deciding where to shop?” Similar questions featured the other product category, car brands. Such free elicitation tasks are fast and yield relatively many attributes (Bech-Larsen and Nielsen 1999) and have been recommended by Breivik and Supphellen (2003) and Steenkamp and Van Trijp (1997). We limited the number of self-generated brands and attributes to a maximum of 10 and eight, respectively, to match the sizes of the fixed brand and attribute sets. Respondents also had to label the end points (1 and 10) for each self-generated attribute then considered the brands one by one to rate each of them on the attributes.
At the end of the online questionnaire, respondents viewed a list of items associated with task evaluation dimensions: attribute relevance, brand knowledge, judgment certainty, task simplicity, and task attitude (see Appendix II). For each task evaluation dimension, we included three or four items, one or two of which were reverse coded, and used a seven-point scale, from 1 = totally disagree to 7 = totally agree. The order of the items was random across subjects. All five scales indicate sufficient internal consistency, with Cronbach’s alpha values between 0.67 and 0.83. Therefore, we averaged the items to obtain the overall measures of the five evaluation dimensions.
Results: evaluation of judgment tasks
For some respondents, the number of brands (attributes) was fixed to 10 (eight), but other respondents self-selected either the attributes or both brands and attributes. People in the respective treatments groups mentioned an average of 6.3 supermarket chains and 8.6 car brands with 4.7 and 4.2 attributes for supermarkets and automobiles, respectively. The number of attributes elicited is lower than in studies by Steenkamp and Van Trijp (1997:8.62) and Bech-Larsen and Nielsen (1999: 9.53). We speculate that this could be due to product category effects or to differences in wording of the elicitation task.
The data collection procedures differ significantly according to measures of the relevance of the attributes (Table 1). For both product categories, the attributes in idiosyncratic sets appear more relevant, and relative evaluations of brand knowledge and judgment certainty depend on the product category. For supermarkets, the respondents indicate much greater knowledge of the idiosyncratic brand sets than for the fixed sets. In this sense, our proposed procedure considerably outperforms existing procedures. Certainty in judgments, which combines attribute relevancy and brand knowledge, improves as we move from a procedure with all fixed sets, to only fixed brand sets, to idiosyncratic brand and attribute sets. For car brands though, we find no significant differences in brand knowledge or judgment certainty across the three procedures.
For both product categories, task simplicity is similar across the three procedures. It thus appears that the burden of the brand and attribute elicitation tasks balances out the effort differences that mark the three attribute rating tasks. The task attitude measures indicate that the respondents actually enjoy procedures with one or two idiosyncratic sets better than the procedure with all fixed sets, especially for the supermarket category.
Thus with the exception of the relevance of the attributes, the three procedures do not differ substantially for judgments of car brands. Perhaps subjects are familiar with many automobile brands (as indicated by the higher brand knowledge ratings for the fixed brand sets, compared with those for supermarkets), but they lack detailed or diagnostic information (i.e., relatively low ratings on attribute relevance and judgment certainty). As a consequence, task simplicity and task attitude worsen for all respondents who judge automobiles instead of supermarkets, irrespective of the data collection procedure.
Results: comparison of perceptual maps
For each product category, we analyze four datasets, which represent evaluations of the fixed brand set obtained with each of the three procedures, plus evaluations of all brands self-selected by at least 10 people for the procedure using idiosyncratic brand sets. This latter criterion yields a set of 21 supermarkets and 28 automobile brands. For the supermarket data, the free brand elicitation method surprisingly excluded one of the supermarkets from the fixed set. We had selected this supermarket (A&P) on the basis of its market share in the Netherlands (about 4%), yet it did not appear well known among our respondents. The explanation may be regional, in that A&P has attained very high market share in a small region of the country but remains poorly known in other areas. We deleted any non-informative respondents, including those who rated only one brand or one attribute or rated all brands exactly the same across all attributes. The final number of subjects per dataset ranges between 141 and 220 (Table 2). For each dataset, we obtained solutions for one to eight dimensions with generalized canonical correlation analysis as described in the model section. Using the relative changes in model fit (RI and VAF), we selected four-dimensional solutions for both product categories. In Table 2, we provide the model fit for each analysis and the similarity (congruence coefficient) between solutions.
For both product categories, the model fit (RI and VAF) is clearly highest for the dataset with idiosyncratic brand and attributes sets, limited to 10 brands from the fixed sets. Apparently, the ratings provided by respondents who select the brands and judged them on self-selected attributes are most consistent across subjects.
To compare perceptual maps, we use the congruence coefficient, which reveals the similarity of interpoint Euclidean distances in two configurations. Lorenzo-Seva and ten Berge (2006) show that congruence values in the range 0.85–0.94 correspond to fair similarity, and higher values indicate near equivalence. For our proposed fully idiosyncratic procedure, the relative position of the set of 10 brands is barely affected, whether the rest of the brand set is analyzed simultaneously or not, as indicated by the congruence coefficients of 0.95 and 0.91, for supermarkets and car brands, respectively. The same result holds for the maps derived with the two procedures using fixed brand sets, with congruence coefficients of 0.91 and 0.94. However, with congruence coefficients between 0.76 and 0.90, we see that the correspondence between the procedures using idiosyncratic brand sets versus those with fixed brand sets is considerably lower. For example, inspection of the four-dimensional coordinates shows that in the maps based on the fixed sets, the points corresponding to the Albert Heijn and Aldi supermarkets are separated from one cluttered group of all other supermarkets. Using idiosyncratic sets, Albert Heijn and Aldi remain separated but the clutter of supermarkets disappears and store-specific positions can be identified. Hence, whether the brand set is fixed or idiosyncratic has a substantial impact on the resulting perceptual map.
Our newly proposed approach offers clear advantages with respect to the data-collection task. The attributes of the idiosyncratic sets appear more relevant than those in the fixed attribute sets. In addition, the brands of the idiosyncratic sets are at least as familiar (automobiles) or more familiar (supermarkets) to respondents than those in fixed brand sets. Thus respondents enjoy the judgment task more and are more certain about their judgments. Furthermore, the resulting perceptual maps are minimally affected by the rating procedure applied, and the relative position of the core set of 10 brands is similar across all solutions. However, the procedure with idiosyncratic brand sets offers the advantage of revealing the relative positions of a larger set of brands.