Linear processing in a semantic network: An alternative view of consumer product evaluation

Linear Processing in a Semantic Network: An Alternative View of Consumer Product Evaluation Klaus G. Grunen,

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The Success of the Linear Model in Explaining Consumer Product Evaluation The Multiattribute View Probably no other paradigm in consumer research has been as successful in recent years as the conceptualization of the process of product evaluation as being based on product attributes. The consumer first evaluates products on several distinct attributes, and these separate evaluations are then somehow integrated into an overall evaluation that leads to the purchase decision. Hence the multiattribute view presupposes an integration rule, a way in which the attribute evaluations are combined to form the overall evaluation. The most successful integration rule has been the linear model, where the overall evaluation is assumed to be computed as a weighted sum or average of the attribute evaluations. The basic formula is

where E = overall evaluation; e, = evaluation on attribute i; w, = weight of attribute i. This model has been used in several theories, the most important being attitude theories and certain psychological theories of AdJrrss cot~~por~d~rr~c to Klaus Postfbclt

70 0-i 63. D- 7000 Stuttgart

G. tinrnert, 70,

OF BUSINESS RESEARCH 10, 31-42 0 Elsevier Science Publishing Co., Inc.. 1982 52 Vanderbilt Ave., New York, NY 10017

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University

of’Hohenhrim,

Institute

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WestGrrrnany. (1982) 31 0148-2963/82/01031-12S2.75

32

Klaus

G. Grunert

decision and judgment that have become known under the heading “cognitive algebra.” Attitude Theories The most popular attitude theories in consumer research recently are those by Fishbein [lo] and Rosenberg [2 11. Both assume that attitude toward a product can be explained by the cognitive structure concerning it, where cognitive structure is operationalized as consisting of attribute evaluations and relative attribute weights. The exact concepts used differ, however, between the two theories. Both have been used extensively in consumer research and have proved to be quite successful. Another attitude theory using the linear model, not as popular in consumer research, has been proposed by Anderson [3]. Cognitive Algebra Psychological decision theory was rather late in adopting a multiattribute view, but during the past 20 years a considerable bulk of literature developed describing theories of how judgment can be explained by the combination of attribute information [22,23]. Here, too, the linear model was the most successful, but a large number of alternative integration models have been proposed [7, 11,24, 251. Simple Made1 or Simple Process? “Paramorphic” Representation Quite a number of researchers, especially psychologists, have been quite uneasy about the “perverse pervasiveness of linearity” [ 131. On the one hand, the model seemed intuitively much too simple to be able to explain high-level cognitive processes, even though complicated tasks associated with high responsibility, like medical judgment, were explained well by the model [ 12, 151. Also, interviewed subjects claimed that they used much more complicated integration rules. On the other hand, the linear model, while mathematically simple, might involve very complicated cognitive processes [9]. Obviously, people do not usually consciously multiply and add attribute evaluations in order to arrive at overall judgments. This led Hoffman, one of the original proponents of the linear model, to stress the ‘ ‘paramorphic ” character of the model: it describes the behavior of subjects well, just as a chemical formula may describe a substance well, but in both cases, some properties obviously remain undescribed, and alternative formulations are possible [ 141. Methodological Difficulties It has also been claimed that the success of the linear model is due to methodological artifacts. The main points advanced are [4,8, 131: if all attribute evaluations correlate positively with overall evaluation, as is usually the case in consumer studies, the linear model can easily

Consumer

Semantic

Networks

33

provide a good fit, even when the integration process is actuahy different; when measurement error occurs, the optimal function to fit the data becomes more linear, even when the integration process was not; only in cases where the various attribute values occur in very specific combinations do the linear model and its main competitors actually lead to different predictions; the use of correlation coefficients as an index of fit, which is quite common in this kind of research, can lead to misleading results. Faith in the linear model could be enhanced if it could be shown that the results it predicts can actually result from quite simple cognitive processes. The remainder of this paper develops a different approach toward explaining consumer product evaluation, based on so-called “semantic network” models developed in cognitive psychology, and shows that in certain cases it leads to an evaluation process structurally similar to the linear model. A Different Approach: The Semantic Network as a Model of Consumer Product Evaluation The Semantic Network as a Theory of Memory In the late 1960s cognitive psychologists started to develop explicit models of how information is stored in long-term memory. These models have also become known as models of “semantic memory,” because emphasis was initially on general knowledge of the world, things that are known without reference to a specific time or location, although this restriction was dropped later [2, 181. In spite of this, the term “semantic network” has survived to describe the most important class of such models, the network models. First proposed by Quillian [20] and elaborated mainly by Anderson [ 1] and Norman and Rumelhart [ 191, these models propose that the representation of meaning in memory can be modeled as a system of nodes and links. Nodes refer to “concepts,” things that are known, and can be labeled by words usually used to describe such concepts, though a one-to-one correspondence between nodes and words is not usually assumed. However, the meaning of a concept is not inferred from its label, but from its relations with other concepts. These relations are modeled by labeled links from one node to another. Thus the meaning of a node standing for the concept “tree” is derived from its links to other nodes, which stand for concepts somehow related to “tree,” the nature of the relation being specified by the label of the link. Thus a link labeled “is a” may lead to the node “plant,” links labeled “has a part” may lead to nodes for “leaves,” “branches,” and

Klaus

34

G. Grunprt

oak

leaves

FIGURE

1: Representation of the Concept “Tree.”

“roots,” and links labeled “type” may lead to nodes representing beech,” and “pine” (Figure 1). “oak,” “ Usually at least two types of nodes are distinguished. One type represents “true” concepts, delineating classes of phenomena that could be subsumed under its heading. These are also called “type-nodes.” A type-node “tree” represents the abstract concept of a tree, no matter which type or location. The second kind of node, also termed “token node,” represents specific instances of a type-node. Thus the tree standing in the front yard of my home would be represented by a token-node. Sometimes a third class of nodes is distinguished, which symbolizes subclasses or subsets of a concept. Thus “oak” is a subclass of “tree” [5]. Representation of Knowledge about Products This section outlines a network model designed specifically to represent knowledge about products. Nodes are formally classified according to the distinction made in the preceeding section: concept nodes stand for a general class of phenomena; reference nodes stand for a subclass of a class defined by a concept node; object nodes stand for specific instances of a class/subclass. If a certain node represents a product, a product concept node stands for the general product class, a product reference node for a subclass defined by a special kind of use of the product, and a product object node for a

Consumer

Semantic

35

Networks

automobile

.d horse power: 20-400

transport j/

$ top-speed: 100-300 km/h

/

prestige

z

b

price: 7000-100000 DM

transport F automobil horse power: 60-100

top speed: 130-160 km/h

price: 10000-15000 DX VW rabbit

horse-power: 70

140 km/h

price: I:KlO DX L__-_JUL NEEDS

_1 PRODUCTS

ATTRIBUTES

Explanation of links:

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"odes

FIGURE 2: Representation

of Knowledge about “Automobile.”

36

Klaus G. Grunrrt

certain brand. Each such node, along with the nodes having direct associations with it, constitutes a plane of the network [20]. Figure 2 presents these planes graphically. A product concept plane contains all the general information about a certain product class. This includes the needs that can be helped to be satisfied by the use of this product, symbolized by need nodes, and the attributes usually possessed by this product. The attributes are represented by attribute nodes, where each node represents not only the attribute itself, but also the range of values it can take; a product reference plane represents a product subclass, where subclass membership is defined by a product’s ability to satisfy a certain need. Such a plane will contain one need node and usually also some attribute nodes, which include a specification of the range of values the attributes have to take so that the product can be used to satisfy the need; a product object plane symbolizes information about specific brands. It contains the product object node and attribute nodes specifying the values of this brand as perceived by the consumer. The three planes are interconnected by a special type of link, the MSH-link, which links all nodes having the same substantive meaning but different formal status (e.g., a product object node to a product reference node). However, links between attribute nodes in different planes exist only if the value ranges specified are compatible. In Figure 2, “horse power: 70” is linked by a MSH-link to “horse power: 60-lOO”, while “price: 17000 DM” is not linked to “price: lOOOO15000.” The Spreading-activation Theory and the Evaluation Process The network as presented until now is just a static structure representing certain types of knowledge. In order to make it an information processing model, it has to be specified how the information is used. A very powerful mechanism proposed to model information processing in a network is spreading activation [6]. The basic idea is that the nodes in the network can be activated to various extents. Activation can be caused by motivation, an internal process, or by sensory stimulation, which is externally determined. If a certain need is felt, the corresponding node becomes motivationally activated. If a certain product is seen, the corresponding node becomes sensorially activated. If a node receives activation simultaneously from several sources, the resulting overall activation is assumed to be the sum of the several input activations. Activation diminishes over time if its source is no longer there. As long as a node is activated, the activation spreads through the network along the links emanating from that node, where the amount of activation

Consumer

Semutztic

Networks

37

spreading along a certain link is assumed to be proportional to the relative strength of this link as compared with the total strength of all links emanating from the node. The strength of a link is assumed to increase whenever the two nodes between which the link exists become independently activated. How much the strength increases is hypothesized to depend on the product of the two activations. If activation spreads from a node Yto a node X, the resulting activation Ax of node X is assumed to depend on the activation A y of node Y as follows:

Ax ‘Ay

LYX

E

(1

-e-LYX’*)

(2)

LYi

where Ax AY

L Z?yi a

= activation of node X = activation of node Y = strength of link from Y to X = sum of the strengths of all links emanating from node Y = scale parameter.

This notion of spreading activation allows us to model the product evaluation process within the network as follows: if a certain need is felt and a product to satisfy it is lacking, the corresponding need node becomes activated. This activation spreads to reference planes representing products suitable to satisfy the need. For simplicity, let us assume that there is only one such reference plane. From there activation will continue to spread to the object planes representing knowledge about specific brands. The amount of activation that a product object node finally receives will depend mainly on two factors: the number of MSH-links from attribute nodes in the reference plane to attribute nodes in the object plane. The degree ofsimilarity between the reference plane and the object plane determines the amount of activation received. In less technical terms, the more the information about a certain brand shows that it corresponds to what would ideally be expected from a product to satisfy the need in question, the more activation will be received by the product object node; the strength of these MSH-links. The stronger a link, the less activation will be lost in traversing it. Knowledge about an attribute value combination that has been experienced many times as useful in satisfying need has more power in transferring activation than knowledge about an

Klaus G. Grunert

38 attribute value combination degree.

that also has proven useful but to a lesser

Obviously, the amount of activation received by a product object node is taken in this model as an operationalization of product evaluation. The more activated such a node is, the more positively will a consumer rate it when questioned about the product’s ability to satisfy the need in question. This concept of product evaluation seems intuitively appealing, because it explains product evaluation by the use of experiences with products on the one hand, and the knowledge about certain brands on the other hand, while the actual process of information integration occurs without conscious cognitive algebra having to occur. This is in accord with introspective reports claiming that various attributes are indeed weighted against each other, but that no computations in the mathematical sense are actually done. The last assertion is not meant as a critique of the linear model. Of course, it may be possible that the linear model is another way of describing exactly the same process assumed in the network model. The next section compares the two approaches to find out whether this is indeed the case. Comparison

of the Approaches:

A Quasi-linear Model

The Simple Case For simplicity, assume that at the outset no information is available about any brand, so that all relevant information about brands has to be supplied prior to the evaluation task. This corresponds to the usual experimental paradigm used in judgment research. In the present context, it simplifies the analysis because we can readily assume that all links in the object planes are of the same strength. If we also assume that the links between the planes are of the same strength, the amount of activation received by a product object node depends only on the attribute nodes existing in the object planes and on the strength of the links between the attribute nodes in the reference plane and the product reference node. Figure 3 depicts such a case, assigning hypothetical activation values to the nodes. In this simplified case, the amount of activation received by a product object node can be computed as follows:

A,j=ARV

L

C

[

iCLi

(1

-eeLi’)

jll_D,j

’ i

1

(3)

Consumer Semantic Networks

39 100 product

attribute

attribute

2

I

brand 30.7

1

brand

2

11.1

FIGURE 3: Spreading Activation in a Product Evaluation Situation strength; 100 indicates activation value; a = 0.1).

(10 indicates

link

where A, AR Li

4

D,

V

= activation of product object nodej = activation of product reference node = strength of link between product reference node and attribute reference node i = number of product object planes from which a link to attribute reference node i exists = 1, if there is a link between attribute reference node i and a corresponding attribute object node in product object plane j = 0, if this is not the case = activation decay resulting from transfer of all other links (constant).

The substantial interpretation of this formula is quite easy: the amount of activation received by a product object node results additively from the need-satisfying qualities of a brand as expressed in this brand’s attribute values, where those attribute values have a relatively stronger impact on activation that discriminate better between the brand alternatives (i.e., that are not shared by a larger number of brands). Comparing this with the linear model, we reach two conclusions: 1. the formula corresponds to the most general form of the linear model, where each attribute value is treated as a dummy variable;

40

Klaus

G. Grunert

2. there is a configural effect, since the weight of an attribute value depends on the number of relevant alternatives possessing that value (because of the l/n, term). The semantic network approach toward explaining the consumer evaluation process thus provides a cognitive explanation for the success of the linear model; it shows that information processing resulting in a linear combination of attribute values can be a rather simple cognitive process. However, a linear model involving dummy variables for the attribute values is a much more general formula than the one usually used. It allows attribute weights to differ according to attribute value and hence allows for curvilinear utility functions; it corresponds in this respect to a linear model involving ideal point estimates. Some studies have proved the superiority of dummy and ideal-point models over the ordinary linear model [ 16, 171, although no improvement of fit can naturally be expected if all attribute values vary monotonically with overall product evaluation. Extensions The structural similarity of the network model and the linear model was shown for a simplified case. The network model also allows us to specify conditions where deviations from linear processing will occur. The two main sources for deviations are listed below: 1. DifSerential sensorial activation. If some attribute value causes more sensory activation than others during perception of product information, the weight of this attribute value in the evaluation process will be increased. If information about various brands is presented in different ways, as is usually the case in real situations, different weights may be used for the same attribute value for different brands. 2. Information already stored in object planes. The experimental situation where all information about available alematives has to be externally acquired is quite atypical. Usually, some information will already be stored. In this case, it can no longer be assumed that all links in the object planes have the same strength, and hence formula (3) no longer applies. The weight of the attribute values in this case depends not only on their predictive power concerning the ability of a product to satisfy a need, but also on the amount and kind of experience the consumer has with individual brands. Thus a brand that has been bought repeatedly and where numerous experiences exist will have established stronger links that result in more activation of the product object node, even when compared with another brand that may have exactly the same attribute values.

Consumer

Semantic

Implications:

41

Networks

Faith in and Caution Toward the Linear Model

The linear model has been widely used in marketing to explain product preference. Still, some uneasiness remained as to whether people can really be assumed to do cognitive algebra in evaluating products. The approach presented in this paper shows that simple activation processes in an associatively organized memory can result in an evaluation process that comes close to the linear model. Thus faith in the linear model is supported. On the other hand, the model shows as well that the linear model can only be a limiting case. Especially, the weights of attribute values can be highly unstable, depending not only on subjective importance of an attribute, but also on factors like sensory perception, brand loyalty, and attribute variability. In addition, curvilinear or irregular utility functions would appear to be the rule rather than the exception.

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