NEXT’s conference theme this year is ‘HERE BE DRAGONS’. Supposedly, the idea behind dragons relates to exploring unknown and maybe dangerous territories. In other words, companies sometimes enter uncharted waters full of unexpected surprises (i.e., the dragons). What might these unexpected surprises be, and how to deal with them?
Those who switch to the new BlackBerry Z10 will undoubtedly enjoy some nice innovations. However, adapting to this new device requires some time and effort. Are you used to returning to the home screen of your smart phone after closing an application? Not on the Z10, since BlackBerry has developed an improvement, it is now possible to switch between applications.
Any organisation (e.g., BlackBerry) will no doubt wonder how customers deal with and adapt to changes in their products or services? How do they learn? In answering these two questions, it is important to find out which product information customers employ when adapting to an innovation.
How do people know what to do in a given situation? Which sequence of steps will lead to a specific goal? How do you know, for instance, which order of actions is needed for sending a message on your smart phone?
Cognitive psychologists assume that people use so-called schemata (Eysenck & Keane, 2000). These schemata are mental representations that tell people what they can expect in a given situation. Furthermore, cognitive schemata also instruct people how to act in a specific environment.
Evidently, people use a schema when visiting a restaurant. This schema indicates which attributes, such as food and drinks, are to be expected in a restaurant. The mental schema also tells visitors that they will have to use the menu to order their food. In other words, the schema exemplifies the appropriate sequence of actions for attaining a particular goal.
But how do people learn when confronted with something new? For example, the new BlackBerry Z10, the recently launched Windows8, or a truly innovative app? Learning under such circumstances requires adapting an existing cognitive schema.
But before we take a closer look at the adaptation (or, accommodation) of an existing schema, let’s first focus on something else: the evaluability of product attributes. The reason for this is that we think that the evaluability of attributes plays an important role in accommodating a mental schema.
The evaluability of attributes
Christopher Hsee (Univerisity of Chicago) demonstrated the importance of attribute evaluability in a series of experiments (Hsee, 2000). In one of his experiments he employed the following two music dictionaries:
How much are customers willing to pay for a music dictionary? To answer this question Hsee recruited 116 students. As customers of a bookstore these students indicated the amount of money (somewhere between $10 and $50) they would like to spend on these dictionaries.
However, there are two possible scenarios:
· the two dictionaries are both available at the store,
· only one of the two books is available at the store.
Hsee called these two scenario’s joint and separate evaluation, respectively. Accordingly, Hsee created two conditions in his experiment. In the joint evaluation condition, customers evaluated the dictionaries simultaneously (i.e., the two books were presented at the same time). Other customers, however, evaluated the books separately, and thus were presented with either Dictionary A or Dictionary B. What were these customers willing to pay for the dictionaries?
The table above shows the average amount people were prepared to pay for each book. Notice the preference reversal. Specifically, Dictionary B received a higher amount of money in joint evaluation. Dictionary A, in contrast, received a higher amount of money in separate evaluation. Hsee claims that this preference reversal is related to the evaluability of the dictionaries’ attributes.
Customers in the separate evaluation scenario can easily evaluate the attribute Defects. The attribute Number of Entries, in contrast, is hard to evaluate separately, because most customers are lacking a reference point that helps them to make sense of the number of entries. Due to the absence of a reference point, customers attach little weight to this difficult to evaluate attribute (when compared to the easy to evaluate attribute Defects). According to Hsee, this difference in the evaluability between the attributes explains why customers in separate evaluations were willing to pay more for Dictionary A ($24 vs. $20 for Dictionary B).
In joint evaluation, however, the evaluability of the attribute Number of Entries is enhanced. Specifically, it is now possible to compare the two dictionaries with each other on the number of entries. As a result, the number of entries of one dictionary serves as a reference point for the other book. This reference information enables customers to evaluate the number of entries more easily, and as a result they pay more attention to this attribute. Moreover, due to this increase of attention, it looks as if the number of entries is perceived as even more important than the defects of a book. Because of the superiority of the Number of Entries attribute, customers are willing to pay more for Dictionary B ($27 versus $19 for Dictionary A).
Thus, without any reference point, it seems hard to evaluate an attribute. If such reference point -in one way or the other- is missing for an attribute, then people tend to pay less attention to the attribute.
Attribute evaluability and the accommodation of schemata
We now take a closer look at the accommodation of schemata. What exactly may be the role of attribute evaluability in adapting a schema?
Sometimes a new service or product (such as the new BlackBerry Z10) comes with innovative, but difficult to evaluate, attributes. In such a case, we hypothesize that an existing schema will not be adapted adequately.
In Hsee’s dictionary experiment, for instance, customers in separate evaluations had little knowledge about the number of entries. Due to the lack of a reference point, it was hard for them to evaluate this attribute. As a consequence, the customers paid less attention to this attribute (when compared to the easy to evaluate attribute Defects) when judging the dictionaries in a separate mode.
We claim that this also may be true when accommodating a schema: difficult to evaluate product information may be neglected by the customer and therefore not sufficiently processed. As a result, customers adapt existing schemata in such a way that doesn’t correspond to what the designer originally had in mind. That is, difficult to evaluate attributes might not be sufficiently included in the adapted schema.
Supposedly, for easy to evaluate attributes (remember ‘torn cover’ in Hsee’s experiment) customers have clear and similar reference points. When employing such reference points, people can easily process attributes, and as a result, adequately include them in their adapted schema. Importantly, this accommodated schema will more likely correspond to what the designer originally intended.
What about the new BlackBerry Z10? This product has many innovative improvements. However, we think that if such an improvement is difficult to evaluate, consumers experience difficulty in adapting their BlackBerry schema. Therefore, as a manufacturer of a product, you have to take into account the evaluability of such improvements.
Also notice that the evaluability of an attribute will depend on the availability of a reference point. Such a reference point serves as an anchor, and possibly makes the consumer’s mental schema more easily and quickly accommodated.
The easy way with difficult to evaluate attributes
Designers and marketers are very fond of their innovations (and quite rightly so!). Additionally, they put a lot of time and effort in designing and marketing these innovations. To them, though, many of the innovative attributes are easy to evaluate. Supposedly, they evaluate an attribute by employing a reference point, such as a previous model (older BlackBerry smart phone), or the product of a competitor (Apple iPhone). In other words, designers and marketers judge new innovative attributes in joint evaluation.
But do customers have a similar reference point at their disposal for evaluating innovative attributes? We think that customers often find themselves in a separate evaluation mode when evaluating these new, innovative attributes. New customers are not always capable, in contrast to designers and marketers, of linking a new product to some other product (such as an older BlackBerry model). Due to the lack of a comparison (reference point), these customers might experience difficulty in evaluating innovative attributes.
If we assume that separate evaluation is the default for customers, then it’s important to reflect upon how to design products, and how to present new, innovative product features to customers. For instance, when designing features (such as icons on a touch screen) help your customers to recall previous models or operating systems (=reference point). Such an approach to design, using reference points, may result in attributes that are more easy to evaluate for customers, and makes the accommodation of a mental schema faster and more efficient.
Without reference points it can be difficult for customers to handle new, innovative attributes. As a consequence, you might run the risk of customers neglecting these new attributes. And this would be a pity, when you consider the amount of time, talent and money many organisations invest in exploring unknown territories with their innovative products.
Note: This blog, written for Next Berlin spring 2013 is an edited version of our article (in Dutch) that first appeared on Frankwatching (http://www.frankwatching.com/archive/2013/03/29/innovatie-mentale-schemas-hoe-leren-mensen-iets-nieuws/) Post written by Stefan Gelissen and Fred Zimny
· Eysenck, M., & Keane, M. (2000). Cognitive psychology (4th ed.). East Sussex: Psychology press.
· Hsee, C.K. (2000). Attribute evaluability: Its implications for joint-separate evaluation reversals and beyond. In D. Kahneman, & A. Tversky (Eds.), Choices, values, and frames (pp. 543-577). Cambridge (UK): Cambridge University Press.
About the authors
Stefan Gelissen is data analyst and developer at Datall (www.datall-analyse.nl). He has extensive knowledge of human decision processes, and previously conducted research at Eindhoven University of Technology and Michigan State University.
Fred Zimny’s expertise is service design, service management, and service marketing. He blogs about these topics on his website Serve4impact.com