“It may be that most consumers forget the attribute-based reasons why they chose or rejected the many brands they have considered and instead retain just a summary attitude sufficient to guide choice the next time.”
This is how Dolnicar and Rossiter conclude their paper on the low stability of brand-attribute associations. Evidently, we need to be very careful how we ask the brand image question in order to get test-retest agreement over 50%. “Is the Fiat 500 a practical car?” Across all consumers, those that checked “Yes” at time one will have only a 50-50 chance of checking “Yes” again at time two, even when the time interval is only a few weeks. Perhaps, brand-attribute association is not something worth remembering since consumers do not seem to remember all that well.
In the marketplace a brand attitude, such as an overall positive or negative affective response, would be all that a consumer would need in order to know whether to approach or avoid any particular brand when making a purchase decision. If, in addition, a consumer had some way of anticipating how well the brand would perform, then the brand image question could be answered without retrieving any specific factual memories of the brand-attribute association. By returning the consumer to the purchase context, the focus is placed back on the task at hand and what needs to be accomplished. The consumer retrieves from memory what is required to make a purchase. Affect determines orientation, and brand recognition provides performance expectations. Buying does not demand a memory dump. Recall is selective. More importantly, recall is constructive.
For instance, unless I have tried to sell or buy a pre-owned car, I might not know whether a particular automobile has a high resale value. In fact, if you asked me for a dollar value, that number would depend on whether I was buying or selling. The buyer is surprised (as in sticker shock) by how expensive used cars can be, and the seller is disappointed by how little they can get for their prized possession. In such circumstances, when asked if I associate “high resale value” with some car, I cannot answer the factual question because I have no personal knowledge. So I answer a different, but easier, question instead. “Do I believe that the car has high resale value?” Respondents look inward and ask themselves, introspectively, “When I say ‘The car has high resale value,’ do I believe it to be true?” The box is checked if the answer is “Yes” or a rating is given indicating the strength of my conviction (feelings-as-information theory). Thus, perception is reality because factual knowledge is limited and unavailable.
How might this look in R?
A concrete example might be helpful. The R package plfm includes a data set with 78 respondents who were asked whether or not they associated each of 27 attributes with each of 14 European car models. That is, each respondent filled in the cells of a 14 x 27 table with the rows as cars and the columns as attributes. All the entries are zero or one identifying whether the respondent did (1) or did not (0) believe that the car model could be described with the attribute. By simply summing across the 78 different tables, we produce the aggregate cross-tabulation showing the number of respondents from 0 to 78 associating each attribute with each car model. A correspondence analysis provides a graphic display of such a matrix (see the appendix for all the R code).
Well, this ought to look familiar to anyone working in the automotive industry. Let’s work our way around the four quadrants: Quadrant I Sporty, Quadrant II Economical, Quadrant III Family, and Quadrant IV Luxury. Another perspective is to see an economy-luxury dimension running from the upper left to the lower right and a family-sporty dimension moving from the lower left to the upper right (i.e., drawing a large X through the graph).
I have named these quadrants based only on the relative positions of the attributes by interpreting only the distances between the attributes. Now, I will examine the locations of the car models and rely only the distances between the cars. It appears that the economy cars, including the partially hidden Fiat 500, fall into Quadrant II where the Economical attributes also appear. The family cars are in Quadrant III, which is where the Family attributes are located. Where would you be if you were the BMW X5? Respondents would be likely to associate with you the same attributes as the Audi A4 and the Mercedes C-class, so you would find yourself in the cluster formed by these three car models.
Why am I talking in this way? Why don’t I just say that the BMW X5 is seen as Powerful and therefore placed near its descriptor? I have presented the joint plot from correspondence analysis, which means that we interpret the inter-attribute distances and the inter-car distances but not the car-attribute distances. It is a long story with many details concerning how distances are scaled (chi-square distances), how the data matrix is decomposed (singular value decomposition), and how the coordinates are calculated. None of this is the focus of this post, but it is so easy to misinterpret a perceptual map that some warning must be issued. A reference providing more detail might be helpful (see Figure 5c).
Using the R code at the end of this post, you will be able to print out the crosstab. Given the space limitation, the attribute profiles for only a few representative car models have been listed below. To make it easier, I have ordered the columns so that the ordering follows the quadrants: the Mazda MX5 is sporty, the Fiat 500 is city focus, the Renault Espace is family oriented, and the BMW X5 is luxurious. When interpreting these frequencies, one needs to remember that it is the relative profile that is being plotted on the correspondence map. That is, two cars with the same pattern of high and low attribute associations would appear near each other even if one received consistently higher mentions. You should check for yourself, but the map seems to capture the relationships between the attributes and the cars in the data table (with the exception of Prius to be discussed next).
|Mazda MX5||Fiat 500||Renault Espace||BMW X5||VW Golf||Toyota Prius|
|High trade-in value||4||3||0||36||41||4|
|Good price-quality ratio||11||20||15||7||30||21|
|Value for the money||9||7||12||8||24||10|
Now, what about Prius? I have included in the appendix the R code to extract a third dimension and generate a plot showing how this third dimension separates the attributes and the cars. If you run this code, you will discover that the third dimension separates Prius from the other cars. In addition, Green and Environmentally Friendly can be found nearby, along with “Technically Advanced.” You can visualize this third dimension by seeing Prius as coming out of the two-dimensional map along with the two attributes. This allows us to maintain the two-dimensional map with Prius “tagged” as not as close to VW Golf as shown (e.g., shadowing the Prius label might add the desired 3D effect).
The Perceptual Map Varies with Objects and Features
What would have happened had Prius not be included in association task? Would the Fiat 500 been seen as more environmentally friendly? The logical response is to be careful about what cars to include in the competitive set. However, the competitive set is seldom the same for all car buyers. For example, two consumers are considering the same minivan, but one is undecided between the minivan and a family sedan and the other is debating between the minivan and a SUV. Does anyone believe that the comparison vehicle, the family sedan or the SUV, will not impact the minivan perceptions? The brand image that I create in order complete a survey is not the brand image that I construct in order to make a purchase. The correspondence map is a spatial representation of this one particular data matrix obtained by recruiting and surveying consumers. It is not the brand image.
As I have outlined in previous work, brand image is not simply a network of association evoked by a name, a package, or a logo. Branding is a way of seeing, or as Douglas Holt describes it, “a perceptual frame structuring product experience.” I used the term “affordance” in my earlier post to communicate that brand benefits are perceived directly and immediately as an experience. Thus, brand image is not a completed project, stored always in memory, and waiting to be retrieved to fill in our brand-attribute association matrix. Like preference, brand image is constructed anew to complete the task at hand. The perceptual frame provides the scaffolding, but the specific requirements of each task will have unique impacts and instability is unavoidable.
Even if we attempt to keep everything the same at two points in time, the brand image construction process will amplify minor fluctuations and make it difficult for an individual to reproduce the same set of responses each time. However, none of this may impact the correspondence map for we are mapping aggregate data, which can be relatively stable even with considerable random individual variation. Yet, such instability at the individual level must be disturbing for the marketer who believes that brand image is established and lasting rather than a construction adapting to the needs of the purchase context.
The initial impulse is to save brand image by adding constraints to the measurement task in order to increase stability. But this misses the point. There is no true brand image to be measured. We would be better served by trying to design measurement tasks that mimic how brand image is constructed under the conditions of the specific purchase task we wish to study. The brand image that is erected when forming a consideration set is not the brand image that is assembled when making the final purchase decision. Neither of these will help us understand the role of image in brand extensions. Adaptive behavior is unstable by design.
Appendix with R code:
library(plfm) data(car) str(car) car$freq1 t(car$freq1[c(14,11,7,5,1,4),]) library(anacor) ca<-anacor(car$freq1) plot(ca, conf=NULL) ca3<-anacor(car$freq1, ndim=3) plot(ca3, plot.dim=c(1,3), conf=NULL)