Cognitive determinants of attribute information usage

Journal of Economic Psychology 7 (1986) 95-124 North-Holland

COGNITIVE DETERMINANTS INFORMATION USAGE

Klaus G. GRUNERT U~iugrs~ryof H~en~e~~,

95

OF ATTRIBUTE

*

Stuttgart,

FRG

Received August 14, 1984; accepted February 25, 1985

The semantic network approach from the psychology of memory is used as a model of consumer information processing to derive six hypotheses about determinants of the types of attributes consumers use in a prepurchase information acquisition situation. Results from two studies using information display board tasks provide support for most of the hypotheses, but also point out methodological problems encountered when investigating memory phenomena.

The problem

It has become customary to view the consumer decision process as one of evaluating a number of alternatives on several product attributes and basing the decision on these several evaluations. Several theoretical foundations for such a view have been advanced, including psychological theories of decision and judgment (Slavic and Lichtenstein 1973), the attitude theories by Fishbein (1967), Rosenberg (19561, and Anderson (1971), and the mi~roecono~~ theory of Lancaster (1971). Not all aspects of such a multi-attribute decision process have received equal research emphasis. While the question of how the various attribute evaluations are integrated to form an overall judgment has received quite a lot of interest, the process of selecting the attributes on which the evaluations are made has not generated much research (Grunert 1980). This study addresses the latter question. * Mailing address: K.G. Grunert, lnstitut f&r Haushalts- und KonsumKkonomik, Hohenheim (530), Postfach 700562, 7000 Stuttgart 70, BRD. 0167-4870/86/$3.50

@ 1986, EIsevier Science Publishers B.V. (North-HoIland)

Universitlt

96

K.G. Grunert / Determinants

of attribute information usage

The state of the art Of course, it has been realized that the success of an attribute integration model can be tested only if the attributes have been correctly specified (Freter 1979; Myers and Alpert 1968; Wilkie and Pessemier 1973). Consequently, a lot of thought has gone into methodologies for identifying attributes which are ‘salient’, ‘important’, or even ‘determinant’. The latter expression is meant to describe the class of attributes which actually influence a given purchase decision, while the other two refer to subjective impressions of the decision maker (Alpert 1980). This implies that the methods to determine ‘salient’ or ‘important’ attributes are mainly direct questioning techniques, while determinance can be inferred from observed behavior. However, the ability to identify the attributes which were used by a consumer in a specific purchase situation, does not imply that we are also able to explain why the consumer used the selected attributes he did and not others. Theoretical explanations of the attribute selection process are rare. Apart from one theoretical model, to be described, there are only some hypotheses of limited explanatory power. These concern the number of attributes used, the use of ‘chunks’, and the use of intrinsic versus extrinsic attributes. A number of studies have shown that the number of attributes used is typically small, i.e., in the range of three to seven (Jacoby et al. 1977; Kupsch and Mathes 1977; Kupsch et al. 1978; Olson and Jacoby 1972; Raffee et al. 1976; RaffCe et al. 1979). The theoretical explanation given for this finding is usually based on Miller (1956). He showed that man’s ability to store information in short-term memory is limited to about nine unassociated items of information. The use of ‘chunks’ is a theoretical notion also adapted from Miller: if a large amount of information is integrated into a ‘chunk’, the capacity of short-term memory can be enlarged. In a consumer context this is meant to imply that consumers prefer attributes which can be viewed as ‘chunks’, i.e., from which inferences to other attributes can be made. Examples for such chunks are ‘price’, ‘brand name’, or ‘rating in product test’. These attributes are used by consumers to infer the values of other attributes like quality and reliability. Several studies support the hypothesis that consumers prefer attributes of this type (Jacoby et al. 1977; RaffCe et al. 1976; Van Raaij 1977). Three studies have investigated the .use of intrinsic uersus extrinsic

K.G. Grunert /. Determinants

of attributeinformation

usage

97

attributes (Olson and Jacoby 1972; Pincus and Waters 1975; Szybillo and Jacoby 1974). Attributes are called intrinsic when they refer to physical properties of the product. Consequently, attributes like price, brand name, and warranties are called extrinsic. The hypothesis, which was supported, is that consumers prefer intrinsic attributes over extrinsic attributes, and use the latter only if they do not feel competent to evaluate a product on its intrinsic attributes. The idea that a consumer may feel more or less competent to evaluate an attribute was taken from the only well-specified theoretical model of attribute selection, the ‘sorting-rule model’ proposed by Cox (1967). He proposes that the attribute selection is guided by two criteria: -

the ‘predictive value’ of an attribute, i.e., the degree to which a consumer believes that an attribute is instrumental in attaining a desired state or value; - the ‘confidence value’ of an attribute, i.e., inasmuch as a consumer feels competent to rate a product on that particular attribute.

Cox believes that consumers prefer attributes with a high predictive value, and among those the attributes with a high confidence value. Kupsch has tested this model empirically, using several possible combinations of these two criteria to predict attribute usage. None of these variations of the basic Cox model was successful (Kupsch and Mathes 1977; Kupsch et al. 1978; cf. also Schellinck 1983). More recently, Howard (1977) has proposed that cue selection will depend on the type and amount of previously stored product knowledge. He predicted that situational attributes will be preferred over product-specific attributes with increasing routine in buying a product. This was supported by Kaas (1982), although Tijlle (1982) found that consumers’ conceptual maps do not mirror this distinction. The notion that stored knowledge is crucial in determining which types of external attribute information are used and which are not was investigated in greater detail by Grunert (1982). He proposed a theory of consumer information processing based on the ‘semantic network’ concept developed in cognitive psychology. This approach will be presented in more detail in the next section.

98

K.G. Grunerr / determinants

of attribute information usage

Semantic network theory and attribute information usage

The use of semantic network models in a consumer context is not new. Some researchers have attempted an adaptation (e.g., Gardner et al. 1978; Kanwar et al. 1981). Also, some of the main concepts were introduced to consumer research already by Hansen (1972: ch. 6), although he did not specifically draw on network theory. The specific application envisaged here, however, namely the explanation of consumer attribute selection, has not yet been attempted. The semantic network as a theory of memory In the late sixties 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 on general knowledge of the world, things that are known without reference to a specific time or location. Things having such references were assumed to be stored in a separate ‘episodic memory’ (Tulving 1972). However, this distinction has not survived the refinement of these models. The difficulties of devising a mechanism describing how things are transferred from episodic to semantic memory made clear that one single mechanism of storage and retrieval of information is a much more feasible model of memory (Anderson and Bower 1974; Norman 1976). 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 (1968) and elaborated mainly by Anderson (1976) and Norman and Rumelhart (1973, these models propose that a person’s stored knowledge can be modelled as a system of nodes and links. Nodes refer to ‘concepts’, things that are known, and can be labelled by words used to describe such concepts, though there is not usually assumed a one-to-one correspondence between nodes and words. However, the meaning of a concept is not inferred from its label, but from its relations with other concepts. These relations are modelled by labelled 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 which are somehow related to ‘tree’, the nature of the relation being specified by the label of the link. Thus, a link labelled ‘is a’ may lead to the node ‘plant’, links labelled ‘has as

KG.

Grunert / ~eter~~~ants

ofattribute

information

usage

99

plant \

roots

4

“j, 0,

&pine

Fig. 1. Representation of the concept ‘tree’

part’ may lead to nodes for ‘leaves’, “branches’, and ‘roots’, and links labelled ‘ type’ may lead to nodes representing ‘oak’, ‘beech’, and ‘pine’ (fig. 1). Usually, at least two types of nodes are distinguished. One type represents ‘ true’ concepts, delineating classes of phenomena which have something in common. 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 frontyard 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’ (Brachman 1977). The 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 preceding section, namely: (1) concept nodes stand for a general class of phenomena, (2) reference nodes stand for a subclass of a class defined by a concept node, and (3) object nodes stand for specific instances of a class/subclass. Thus, 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 certain brand. Each such node, along with the nodes having direct associations with it, is said to constitute a plane of the network (Quillian 1968). Fig. 2 will help to visualize what these planes represent.

automobile horse power: 20 - 400

transport

top speed: 100 - 300 km/h

prestige

price: 7 000 - 100 000 ofi4

horse power: 60-f00

P 8

top speed: 130-160kmlh

9 8 a 9 m

price: 10 000 - 16 000 DM ~

horse power: 70 00 F 51

top speed: 740 km/h

$ ;i3 price: 17 000 DM IL-_JI Instrumentalities

1 Products

Attributes

Explanation of links: label

meaning

HAS INS MSH

is, has jnstruman~t far membership

Fig. 2. ~y~otb~tics~ semantic network on ‘automobifes’.

A producr concept plane contains alf the general information about a certain product class. This includes the instrumentalities of the product, and the attributes the individual believes this product usually possesses. 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. Since they delimit what a consumer will recognize as a certain product - e.g., a car - and what not, they are very much like the

K.G. Grunert / Determinants

ofattributeinformation

usage

101

‘defining attributes’ in Hansen’s (1972: ch. 6) treatment. Attribute nodes can have links among themselves, indicating that one attribute can be used to predict the other. A product reference plane represents a product subclass, where subclass membership is defined by a product’s instrumentality to attain a certain state or value. Attribute nodes in such a plane will include a specification of the range of values the attributes have to take so that the product is instrumental for the state or value. If this range is the same as the range specified in the concept plane, i.e., all known values of this attribute would be equally desirable in this context, the attribute node will be omitted from the reference plane. This follows from a general principle usually assumed to hold for semantic networks, called the ‘economy-of-storage’ principle (Norman 1979). A product object plane symbolizes information about specific brands. It contains attribute nodes specifying the values of this brands, as they were perceived by the consumer. The ‘economy-of-storage’ principle applies here as well, i.e., only those attribute values will be stored which cannot be inferred from the concept plane. Thus, in the case of automobiles, the information that it has four wheels will be stored in the concept plane, but not in the object planes. The three planes are interconnected by a special type of link called the MSH-link (MSH for ‘membership’, because the link is between sets and subsets). It links all nodes having the same substantive meaning, but different formal status, e.g., a product object node to a product reference node. Note, however, that links between attribute nodes in different planes exist only if the value ranges specified are compatible. Thus, in fig. 2, ‘horse power: 70’ is linked by a MSH-link to ‘horse power: 60-loo’, while ‘price: 17.000 DM’ is not linked to ‘price: 10.000-15.000’. The spreading-activation

theory

The network as presented till 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 new information is added and how existing information is used. This section starts with the latter problem. A very powerful mechanism which has been proposed to model information processing in a network is that of spreading activation

102

K.G. Grunert / Determinants

of attribute information usage

(Collins and Loftus 1975). 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 (cf. also Hansen 1972: ch. 6). Activation decays over time. As long as a node is activated, the activation spreads through the network along the links emanating from that node. In spreading from one node to another some activation is lost. The amount of loss is determined by the strength of the link: the stronger a link, the less activation is lost in traversing that link. Attempts have been made to measure the speed with which the spreading process occurs (Warren 1977). Learning There must be a way in which the network can interact with the environment. Till now the only such mechanism is the sensory stimulation of concepts already stored in memory. Obviously, there have to be mechanisms that allow the network to learn, i.e., add new knowledge. There are two ways in which learning in a network can occur. This is by adding new nodes or by altering the strength of links. Adding new nodes means acquiring new factual knowledge. This is hypothesized to occur according to the ‘given-new-strategy’ (Clark and Haviland 1974): incoming information is divided into what is ‘given’, i.e., already known, and what is ‘new’. The ‘given’ also indicates how and where the new information can be linked to the existing network, which is the prerequisite for any acquisition of new information. The exact process is assumed to work in the following way: new incqming information spurs the formation of nodes which reside in a temporary buffer store. Provisory new links are formed to connect the new nodes to the existing network. Whether the addition to the network becomes permanent depends on whether the activation of the new node surpasses the threshold of consciousness for a certain amount of time. This activation results not only from the sensory stimulation, but is also transferred via the provisory links, i.e., can be motivationally caused. Thus, new information about things that are motivationally prevalent at the time of perception have a higher chance of being permanently stored in memory.

K.G. Grunert / Determinants

of attribute information

103

usage

Strengthening links means that no new factual information has been received, but that some associational link between two concepts already known becomes reinforced. This is assumed to occur whenever the two nodes between which the links exist become independently activated, where the source of the activation can be sensory or motivational. The degree of strengthening is hypothesized to depend on the product of the two activations. Derivation

of hypotheses about attribute information

usage

The model presented in the preceding sections can be used to derive a number of hypotheses about consumer attribute information usage. Only hypotheses about the use of external information will be derived. Obviously, in many cases no external information search will occur at all, and a purchase decision will be based only on information already stored. This case is not discussed here, although the model allows to derive implications for this case as well. The discussion is limited to the kinds of attributes consumers will use in a prepurchase phase. Six hypotheses will be derived. They relate attribute information usage to the existence of cognitive equivalents (Hl), the discriminatory power of the attributes (H2, H3, H4), and sensory factors (H5, H6). The first hypothesis is: HI. Consumers use more frequently attributes already stored cognitive equivalents in memory.

for

which

they

have

This follows from the assumption that links to existing knowledge have to be made in order to be able to store new information. If an attribute is known to the consumer, links can easily be formed to nodes representing this attribute in reference or concept planes. These will be motivationally activated in or before a purchase situation, so that activation will spread to the new information, raising the probability that activation of the new information will pass the threshold value necessary for permanent storage. If, however, information is presented about an attribute not known to the consumer, links can be formed only to very general concepts. These will have low activation, and it becomes less likely that the new information passes the threshold value. The next three hypotheses concern the discriminatory power of attributes. An attribute can be said to have a high discriminatory power

104

K.G. Grunett / I3eterminants

ofattributeinformation

usage

if it allows the consumer to select the brand most instrumental in attaining desired states or values. Discriminatory power, thus conceived, can have several aspects, which follow from different mechanisms assumed in the model. The most general aspect follows from the motivational activation of reference planes: from the attributes basically known to the consumer those will be acquired most easily which have attribute nodes in the reference plane corresponding to the state or value which the consumer intends to attain in buying the product. Since this reference plane will receive strong motivational activation, new attribute object nodes which can be linked to reference nodes in this plane have a very high chance of passing the threshold of consciousness. Attribute nodes stored in a reference plane represent those attribute/value combinations which have been proven instrumental in attaining the state or value represented in that plane. This allows the derivation of the second hypothesis: H.2. Consumers use more frequently attributes which they believe to be instrumental in attaining desired states or values. This hypothesis seems to be so obvious that it has rarely been investigated. It corresponds to the ‘predictive value’-deter~nant in the model by Cox, and can be found also in a related model by Nakanishi (1974). One empirical study relevant in this context has been conducted by Sheluga et al. (1979). Their hypothesis, that frequency and order of selecting attribute information from an information display board (IDB) can be explained by the attribute’s contribution to total utility, could not be supported, however. The links which indicate predictive power of attributes among themselves lead to the second aspect of discriminatory power of attributes, since an attribute having such predictive power can be said to have also higher disc~~nato~ power. Whenever an attribute node in the reference plane is linked to another attribute node, it receives more motivational activation, since part of the activation received by the other node is transferred via the link. The hypothesis derived is: H3. Consumers use more frequently attributes the values of which can be used to predict the values of other attributes. This hypothesis is congruent with the empiricaI results on the usage of ‘chunks’, referred to above. As an example, Jacoby et al. (1977)

performed an TDB experiment simulating the purchase of toothpaste, where the attribute ‘brand name’, which is usually used to infer a number of quality attributes, was selected most often by the subjects. In a German replication of this study (Raffle et al. 1976), the attribute ‘overall rating by Stiftung Warentest’ was used most often. The results of many studies indicating the use of price as an indicator of quality (reviewed by Olson (1377)) can also be explained this way. The next hyputhes~s follows frum the ~e~onomy-of-storage’ principle. ff a certain att~bnte~va~u~ combination has been observed at a product for the first time, ~urr~~~nd~g nodes in all planes wih resuh. X7 however, all subsequently observed brands of this product have the same attribute/value combination, this attribute has no informationaI value, it is ‘given’ information, since it is derivable from the product’s concept-plane, and hence will not be stored. The hypothesis:

No empirical research on this hypothesis is known to the author, Van R&j (1977) has explored implications of different degrees of attribute variability for decision making, but has not reported evidence about the effects on attribute usage. The remaining two hypotheses concern sensory factors, i.e., sensory activation resulting from product attributes. Sensory activation mainly results from the product itself, while it is being used and to a lesser degree from information acquisition and product ilxspection in the prepurchase phase. Only the former cause of sensory activation will be addressed_ From the attribute nodes in a reference plan% those which have strong hnks to the product reference node wiff be most likely to spur the addition uf new object nodes because they will receive the most activation. The strength of these links is mainly determmed by product experience: the more often a particular attribute/v&e combination has been observed when using a product, the stronger the link. This implies that only those rmdes can establish strong lirrks which correspond to attributes possessed by the particular brands bought by the consumer. There may be other attributes or attribute vahres which are more instrumental, but which cannot be found at the particular brands

106

K.G. Grunert / Determinants of attribute information usage

used by the consumer. the fifth hypothesis:

They cannot

establish

H5. Consumers use more frequently usually buy possess.

strong links. This leads to

attributes

which the brands

they

The relation between product experience and information search has been investigated, but interest centered on the influence on the amount of information acquired (Ryans and Deutscher 1975). The hypothesis has importance especially in explaining brand loyalty. Since advertising usually tries to differentiate products by emphasizing attributes not possessed by competitive products (Grunert and Stupening 1981), the sensory impressions caused by attributes during product usage of a certain brand will help to ensure that this brand will also be bought in the future. An increase in strength of a link between attribute node and the product node during product use depends on sensory stimulation of the attribute node. An attribute which the brand possesses but which is not perceptible (visible, audible, tactile) causes no sensory stimulation and hence cannot establish strong links. The hypothesis: H6. Consumers use more frequently while using the product.

attributes

which are perceptible

In many cases, important functional product attributes may not be perceptible. In accord with H3 and H6, this may cause certain perceptible attributes to be used as surrogates. A well-known piece of anecdotal evidence presented by Froman (1953, cited in Cox (1967)) illustrates this. A company developed a new noiseless mixer, which failed to gain acceptance because it did not seem to have any power. H6 is also related to the observed preference for intrinsic versus extrinsic attributes: extrinsic attributes are not visible during product use and hence cannot establish strong links. However, there are products, e.g., cosmetics, where there are few intrinsic attributes visible. In this case, the links of intrinsic attributes will not be stronger than those of extrinsic attributes, and the latter will be used as well. The next section reports two studies designed to test the six hypotheses.

K.G. Grunert / Determinants

of attribute information usage

107

Two studies

Empirical investigation of these hypotheses poses a number of methodological problems stemming mainly from the fact that memory concepts have received little attention in consumer research to date. Very little work on operationalizing these concepts has been done on which empirical research could build. For this reason, the two studies to be presented now can be regarded as a first attempt to operationalize the concepts used in the six hypotheses to make them amenable to testing in a laboratory setting. Study I Methodology

Study I was designed to investigate Hl, H3, H5 and H6. The dependent variable is attribute information usage. The independent variables are existence of cognitive equivalents for, predictive power of, previous experience with, and perceptibility of attributes (Grunert and Keller 1984). Attribute information usage was measured using an information display board of the type originally proposed by Jacoby (Jacoby 1977; Weinberg and Schulte-Frankenfeld 1983). Subjects were instructed that they were to buy a certain product and that they could acquire as little or as much information as they wanted from the board in order to arrive at a decision. The board contained information concerning six brands, designated by letters A to F, and twelve attributes. Actual attribute values in the cells were covered by cards which had to be lifted by the subject. An attribute was considered to have been ‘used’ if at least one card in the line containing information about that attribute was lifted. Fig. 3 shows an example of an IDB used, as it would look like with all the cards removed. Products used and their attributes were as follows: -

Blue jeans: Style, fabric, price, color of fabric, color of stitches, country of origin, washing instructions, zipper or buttons, number of rivets, number of pockets, prewashing, brand name. - Wine: Red/white, type of grape, region of origin, vintage, quality certification, price, packaging, contents, bottled by, taste, official control number. brand name.

wei

orange

Italien

orange

Oeutschland

orange

Belgien

gelb

England

gelb

Spanien

Farbe des Absteppfadens

Herstellungsland (made in:)

display board.

Wangler

Levis

Texwood

Lois

klarkennuae

Fig. 3. Example of information

vorgewaschen

stone-washed

stone-washed

ketne

keine

Vorbehandlung

Laky

4

5

5

5

2

5

Anzahl der Taschen

5

7

9

1Knopf Reil3verschlrB

u38a%

uf 59.90

keine

1 Knopf ReitWerschld

B?i&3%

1 Knopf Rei8verschlti

63&d%

79.90

1 Knopf Rei6verschlu8

osa%

m

Anzahl der Nieten

1 Knopf Verschlu8 (Kndpfe, ReiBverschluB) ReiBverschlu8

I

1U&d%

I

blau

blau

blau

blau

blau

blau

Farbe des Stoffes

85.-

M

95.-

w

79.90

Pioneer

keine

5.

9

5 Kniipfe

la&a%

Oeutschland

at 75.50

80 I Baunwolle 20 X Po1yacry.l

M

100 I Batanwolle

Preis

100 I BauRwolle

100 X Baumwolle

98 i Baunuolle 2 g Plyurethan

100 I Bamwolle

Ilaterlal (staff)

Klassische form

F

Klassische Form

Rbhrenform

E

Rlhrenform

RRhrenfons

D

Karottenfom

C

Schnitt

B

K.G. Grunerr / Determinants of attribute informarion u.sage

_

109

Bread: Type, weight, price, form, manufacturing date, grains used, type of grain cultivation, type of dough, packaging, preservatives, nutritional value, brand name.

Existence of cognitive equivalents was measured using a free elicitation procedure. In a free elicitation task, the experimenter presents to the subject a stimulus word, in this case the name of a product, and instructs the subject to tell him everything that comes to his/her mind about that product. Subjects are instructed to state their thoughts in short phrases or in a few words, to answer as rapidly as possible, and not to censor their comments (Olson and Muderrisoglu 1979). In the perspective of network theory, this stimulus given by the experimenter causes sensory activation of the product concept in the subject’s memory network. From there, activation spreads throughout the relevant part of the network, eliciting answers whenever the activation of related concepts becomes high enough to pass the threshold of conciousness. Thus, attributes which are stored in the context of the stimulus product in the subject’s memory network, will be part of the answers elicited from the subject. The method of free elicitation was introduced to consumer research by Olson (1979; Kanwar et al. 1981), who also showed that results generated by this method are at least moderately reliable (Olson and Muderrisoglu 1979). Concerning predictive power of attributes, it was not attempted to measure this for every individual subject. Based on previous research it was postulated that the attributes brand name and price are predictive of other attributes (Jacoby et al. 1977; Olson 1977). For this reason, brand name and price were included in the information display board and were hypothesized to have a higher probability of being used. Previous experience with attributes was ascertained using a self-administered questionnaire. In an open-ended question, respondents were asked to indicate which attributes of the brands that they personally used or owned they considered as important. Perceptibility of attributes was varied by including both attributes in the information display board that are perceptible while using or consuming the product and others that are not. In addition, a further distinction was made. Some attributes are unconditionally perceptible, i.e., they can always be perceived when using a product, like style of clothing or taste of food. Other attributes are only conditionally perceptible, i.e., whether they can be perceived depends on the type of product

110

K. G. Grunerr / Determinants of attribute information wage

use. Thus, several attributes of wine are mentioned on its label, but the label may not be visible during consumption, namely when the wine is served decanted. The experiment was conducted using 25 students as subjects. The experiment started with the free elicitation task because the subject’s cognitive structure will change during the information display board and question parts of the experiment, invalidating the results from free elicitation. Subjects first were asked to give their thoughts concerning the product toothpaste. This served as a warming-up task in order to acquaint subjects with the free elicitation methodology. After that, thoughts were elicited concerning the products blue-jeans, wine, and bread. After this, the subject completed information display board tasks for these three products. After the information display board tasks, subjects completed the questionnaire. Completion of the questionnaire was put at the end of the experiment because answering of the questions in the questionnaire involves considerable cognitive processing untypical before a real purchase situation, which therefore could have biased performance in the information display board task. Results The impact of the four independent variables on the dependent variable attribute information usage was analyzed using a logit-model. Logits, defined as the log of the odds of the occurrence of a certain event, provide a convenient way to meaningfully apply the linear model to a dependent variable that is dichotomous. The unit of analysis was the attribute information usage decision, i.e., each instance where the subject had the opportunity to use or not to use a certain attribute in a decision task. 25 subjects and 3 decision tasks, each with information about 12 attributes available, results in 900 such decisions. However, 10 subjects did not have the opportunity to obtain brand name information, so that the analysis is based on 870 attribute information usage decisions. ’ The analysis was performed using the individual data. Only when individual data is used, data variation explained by a logit model can be interpreted in analogy to explained variance in a regression model. In most cases, logits are computed from the percentages in the r This manipulation was done in an attempt to replicate the finding that unavailability of brand name leads to higher information demand (Jacoby et al. 1977). With a mean of 7.0 attributes used in the no-brand name condition and 6.4 in the brand name condition, the difference is in the expected direction, although not statistically significant ( p = 0.16, one-tailed f-test).

K.G. Grunert / Determinants of attribute information usage

111

cells of a contingency table. This however tends to overstate model fit, because variation in the data was already lost during the aggregation process (Andress 1984). Also, analysis of individual data avoids the problem of beta-error specification when testing for significant deviances from the model specified (Erdfelder 1984). As a first step in the analysis, a logit model including only the main effects of the four independent variables was estimated, i.e., a model where the log-odds of using a certain attribute are explained by additive effects of the four independent variables: log[ p;/(l

-

p;)]

= a, + a,COG, + a2 EXPi + a,CHQ + u4PERZ, +u,PERZ,,

(1)

where Pi

COG,

= = = =

EXP,

=

CHU, PERI,

= =

PERZ,

=

a0 al,.

. . a5

probability of using attribute i; constant; model parameters; 1 if attribute i was mentioned in free elicitation task, 0 otherwise; 1 if attribute i was mentioned for brands owned/used, 0 otherwise; 1 if attribute i was price or brand name, 0 otherwise; 1 if attribute i is conditionally perceptible during product use, 0 otherwise; 1 if attribute i is unconditionally perceptible during product use, 0 otherwise.

Note that, because the analysis is based on individual data, empirical values for pi could be only 1 or 0. Parameter estimation was done using the program GLIM (Aitkin et al. 1985; Arminger 1983; Nelder and Wedderburn 1972), which estimates parameters using a maximum likelihood algorithm based on iteratively reweighted least squares. The results shown in table 1 indicate significant main effects for all independent variables in the expected direction, except for perceptibility of the attribute, which is insignificant. * ’ All tests of significance are based on two-tailed t-tests, which is justified parameter estimates have an approximate normal distribution. An alternative differences between deviances, which have an approximate &i-square distribution. that this leads essentially to the same results.

given that the is to test for It was verified

112

Table 1 Logit analysis

K.G.Grunerr / determinantsof attributeinformationusage

based on main effects

- study I. (N = 870;

E = 0.19.)

Variable

Parameter estimate

Standard error

P

(Constant) COG

- 0.595 1.615

0.141 0.211

< 0.001 < 0.001

0.284 0.250 0.191 0.204

-=0.001
2.059 0.858 0.145 0.088

EXP CHU PER! PER2

ns. n.s.

Before examining possible interactions between the independent variables, it was checked whether there were any product effects by including additional dummy variables for the products and for the interactions between products and the other independent variables. While there were a few significant interactions between product investigated and other variables, none of them had a clear impact on the main effects as given in table 1. Since product-specific effects are not dealt with in the theory investigated here, product could safely be omitted as an independent variable. Examining two-way interactions between the four independent variables, two significant interactions were found: between COG and EXP, and between COG and PER. Reestimating the model parameters including these two interactions yields the parameter estimates given in table 2. The interactions are both negative, indicating that the joint effect of COG and EXP is smaller than the sum of the main effects, and that the effect of COG is smaller for perceptible attributes. PERI and PER2 were combined

Table 2 Logit analysis

including

significant

interactions

- study

1. (A’ = 870;

B = 0.21.)

Variable

Parameter estimate

Standard error

P

(Constant) COG

-0.738 2.677 2.827 0.828 0.306 - 1.804 -1.138

0.151

< 0.001

0.436 0.439 0.260 0.185 0.594 0.479

io.001 -c0.001 < 0.01

EXP CHIJ PERI+Z COGxEXP COGxPER

n.s. c 0.01

< 0.05

into one variable here because both variables showed the same effects. Higher-order interactions could not be investigated due to empty cells. The complete model as specified in table 2 explains about 20% of the total deviance. The deviance in a logit-model has a similar interpretation as the residual sum of squares in an ordinary regression model (Bishop et al. 1975). The reduction of deviance due to the specified model relative to the deviance of the null-model can be interpreted in analogy to r’ in regression analysis.

A number of theoretical and methodolo~cal problems show up in this study. An obvious theoretical problem is that the four hypotheses each specify a relationship between one independent variable and a common dependent variable, without any predictions on how the four independent variables will interact. Since all four hypotheses are based on the notion of spreading activation, and since activation is assumed to be additive in the network model of consumer information processing, the effects of the four variables might also be assumed to be additive. However, closer inspection of the theory shows that storage of the general concept of an attribute in the network, also termed existence of cognitive equivalents here, is a prerequisite for the other three effects to occur. If operationalization of all variables had been successful, a zero value for COG and a unity value of any of the other explanatory variables should not occur together. Obviously, this is not born out by the data. A free elicitation procedure does not seem to be able to elicit the complete cognitive structure with regard to a stimulus. Indeed, network theory itself predicts that this will not be the case unless the stimulus is extraordinarily strong or the cognitive structure is tapped at various points using various stimuli. The way the procedure was handled in this experiment, only the more salient parts of the cognitive structure, including the more salient attributes, will be elicited. While the measured construct is thus different from the theoretical construct used in Hl, this does not seem to be too serious here, because if Hl is true as specified, it will hold a fortiori for the more salient parts of the cognitive structure, As for the other three independent variables, measurement could certainly be improved, In our attempt to measure experience with attributes, the wording ‘which attributes of your.. . do you consider as important? was probably a bit awkward. The question may have been

114

KG. Gruneri / Determinants of attribute information usage

perceived as emphasizing importance more than own experience. This may have caused a tendency to answer in a way which is consistent with the behavior just demonstrated in the information display board task. As for the chunk characteristic of attributes, it would have been desirable to find out which attributes are regarded as chunks by the subjects, instead of assuming that certain attributes are treated in this way, such as brand name and price in this case. Regarding perceptibility of attributes, which did not have an effect in this study, a more rigorous variation in perceptibility would be desirable in the IDB matrix used. Alternatively, subjects may be asked to rate the perceptibility of attributes. Use of an information display board for measuring attribute information usage is also not without problems. The widespread criticism that an information display board forces subjects to make a more rational decision and acquire more information than they would in a real purchase situation (Kaas and Hofacker 1983; Quelch 1979) is probably not too serious in this case. A more serious problem is that information usage in an IDB task is defined as lifting of a card covering the information, while information usage in the context of the network model was defined as permanent storage in memory. In the network model, temporary storage in a buffer store, caused subconsciously by the sensory impression emanating from the information, is crucial for the usage decision. An IDB of the type used here sends out sensory impressions only concerning the attribute names, not attribute values. What would be needed is some additional theorizing on how joint sensory and motivational activation of some attribute influences the decision to remove the card covering the attribute values. An idea that might be worth pursueing is to add a recall test after the IDB-task. On the other hand, use of external storage devices is not atypical for some purchase decisions. The open information display board advocated by Kaas and Hofacker (1983), where the array of information is visible and can create sensory impressions, is probably a more adequate operationalization for testing hypotheses in the context of network theory. Study II Methodology The second study was designed to take up some of the methodological improvements suggested by the first study, and to test a different set

of hypotheses, namely HI, H2, H4, and H6. The dependent variable again is attribute information usage; the independent variables are existence of cognitive equivalents, instrumentality of attributes, variability of attribute values, previous experience with attribute, and perceptibility of attribute. In accordance with the conclusion from study I, attribute information usage was measured using the open information display board proposed by Kaas and Hofacker (1983). The complete array of information is visible to the subjects when they have to prepare their choice. In order to ascertain which pieces of information are consciously processed, the method of a ‘number protocol’ is used: each cell of the information display board is assigned a three-digit random number. The subjects are instructed to voice this number aloud whenever they look at a particular piece of information. These numbers are written down by the experimenter so that the whole process of information acquisition can be traced just like in the traditional ID3 task. Kaas and Hofacker (1983) report that, after some warming-up, subjects do not experience difficulty with this kind of information acquisition. Again, the IDB contained information concerning six brands labelled by letters. Information on ten attributes was given for each brand. The products and their attributes were as follows: - Deodorant: Skin harmlessness, fue1 gas, contents, fragrance type, price, deodorizing effect, durability, type of application, number of applications, fragrance intensity. - Household cleaner: Active ingredient, phosphat, skin harmlessness, cleaning effect, price, surface protection, contents, consistency, smell, packaging. - Chocolate: Calcium, filling, consistency, proteins, flavor, price, calories, packaging, form, weight, Existence of cognitive epiuafents was measured with the same free elicitation procedure as in study 1. Data on insmmentality of attributes was ascertained using an interview procedure. Subjects were asked for which purposes they usually used the products investigated. They were then handed a list of ten attributes and were asked to indicate, on five-point rating scales, the importance of these attributes in making a brand choice, given the purpose for which they usually use the product.

116

K. G. Grunert / Determinants

of attribute information usage

Variability of attribute values was measured by including on every information display board two attributes for which the values were identical for all brands. Previous experience with attributes was ascertained by interview, using an improved technique compared to study I. Subjects first were asked which brand(s) of the product in question they usually use. They were then asked about attributes of these brands usually used by them. Perceptibility of attributes again was varied by including both attributes that are perceptible and others that are not. As the list of attributes indicates, it was taken care this time that attributes are either unconditionally perceptible or hardly perceptible at all. 36 students served as subjects. The experiment started with the free elicitation task, followed by the information display board task and the interview. Results The data were first analyzed using a logit model similar to the one used in study I. 36 subjects and 3 decision tasks, each with information about 10 attributes, results in 1080 attribute usage decisions. Omitting cases where subjects reported not to use the product in question resulted in 1016 usable attribute usage cases. The logit-model for main effects was specified in the following way: log[ p/(1

-pi)]

= a,, + a,COGi + aslN2, + aJZV.3, + a,INdi + a,IN5,. +a,VAR,

+ a,EXP, + a,PERi,

(2)

where =

probability of using attribute i; constant; a0 = model parameters; a,, . . . a8 = 1 if attribute i was mentioned in free elicitation COGi task, 0 otherwise; = 1 if instrumentality of attribute i was rated as ‘2’ on IN2; Spoint scale; IN3,, IN4,, IN5, = same for levels 3, 4, 5 of Spoint scale; = 1 if values of attribute i varied, 0 otherwise; VAR, = 1 if attribute i was mentioned for brands EXP, owned/used, 0 otherwise; = 1 if attribute i is perceptible, 0 otherwise. PERi

Pi

=

K.G. Grunert / Determinants Table 3 Logit analysis

based on main effects

of attribute information

- study II. (N = 1016;

usage

117

B = 0.10.)

Variable

Parameter estimate

Standard error

P

(Constant) COG NE2 NE3 NE4 NE5 VAR EXP PER

0.211 0.824 - 0.001 0.064 1.059 0.967 0.862 1.035 0.022

0.271 0.269 0.274 0.260 0.310 0.296 0.216 0.276 0.185

n.s. < 0.01 n.s. n.s. -=z0.001 < 0.01 < 0.001 ‘c 0.001 n.s.

The results shown in table 3 indicate significant main effects in the expected directions for all variables except perceptibility. The 5-point scale used to measure instrumentality of attributes turns out to be effectively a dichotomous variable. Scale levels 4 and 5, indicating high perceived instrumentality, increase probability of attribute usage, while the other scale levels have no effect. No significant interactions between the variables were found. The model explains 10 percent of the total deviance, which is considerably less than was obtained in study I. Two reasons may be advanced for this. Firstly, the use of open information display boards with number protocols may have led to increased measurement error. However, compared to the traditional IDB the open IDB also led to a considerable increase in amount of information use. For every attribute there was only a small percentage of subjects who did not use a single piece of information for that attribute, making parameter estimation based only on the yes/no operationalization of attribute information usage less reliable. Given the interpretation of attribute information usage in the network model, which is based on passing the threshold of consciousness for a certain minimum amount of time, it might also be questionable whether this condition is fulfilled when, e.g., just one single piece of information is consciously addressed in an open IDB. An alternative operationalization is the frequency with which pieces of information pertaining to an attribute are reported as consciously perceived according to the number protocol. Hence, a second analysis was conducted using this frequency of attribute information usage as

118

K. G. Grunerf / Determinants

of attribute information usage

Table 4 Loglinear analysis based on main effects - study II. (N = 1016; Variable (Constant) COG NE2 NE3 NE4 NE5 VAR EXP PER

Parameter estimate 0.458 0.153 0.422 0.538 0.725 0.764 0.290 0.114 -0.061

Standard

B = 0.10.) P

elT0r

0.071 0.038 0.075 0.069 0.068 0.066 0.045 0.037 0.034

< 0.001 < 0.001 < 0.001 c 0.001 < 0.001 c 0.001 < 0.001 < 0.01 n.s.

the dependent variable. To this end, a model was specified in which the individual frequencies were assumed to be Poisson-distributed and in which the explanatory variables are linear on the log-scale. This amounts to treating the individual frequencies as cell entries in a large contingency table on which a loglinear analysis is performed. Thus, the analysis is in direct analogy to the logit analysis based on individual data performed in the first step. 3 Table 4 shows the parameter estimates for the model based on main effects only. Comparing this with the results from the logit model, it is first interesting to note that the model indeed predicts a positive information acquisition frequency even for attributes where none of the hypotheses hold. Another difference is that the 5-point scale, measuring instrumentality of attributes, which reduced to a dichotomous variable in the logit analysis, seems to reduce to a 3-point scale here. Apart from this, the results are similar: all main effects are significant and in the expected direction, except for perceptibility of the attribute, which again does not have a significant impact on attribute information usage. The picture changes a little, however, when significant two-way interactions are taken into account. Three such interactions were found, which are displayed in table 5 along with the reestimated parameters. We now see that perceptibility of the attribute indeed has the predicted positive effect on information usage, but mainly for attributes whose values do 3 This model specification also solves the heteroscedasticity problem encountered with frequency data. A more common alternative would have been to transform the frequencies, e.g., take square roots, and use ordinary least squares. It was verified that this leads essentially to the same results.

K.G. Grunert / Determinants

Table 5 Loglinear analysis including significant Variable

(Constant) COG NE2-k3 NE4+5 VAR EXP PER VARxPER VAR xCOG EXP x COG

Parameter estimate 0.068 0.535 0.457 0.718 0.720 0.177 0.720 -0.410 -0.339 -0.174

interactions

of attribute information

usage

- study II. (N = 1016;

119

B = 0.11.)

Standard error

P

0.108 0.092 0.066 0.063 0.100 0.044 0.100 0.102 0.094 0.075

n.s. < 0.001 < 0.001 < 0.001 < 0.001 c 0.001 c 0.001 -= 0.001 c 0.001 < 0.05

not vary over alternatives, i.e., attributes that usually would have a low chance of being used. We also find again the significant interaction between EXP and COG which was found already in study I. A third interaction is between V’R and COG. All interactions amount to the joint effects of two variables being less than the sum of the individual effects. The complete model explains 11 percent of the deviance relative to the null-model. Discussion Both the logit and the loglinear model in study II explain only about half as much deviance as the model in study I. Since the models in study II included four of the five independent variables used in study I, the hypothesis that this is due to increased measurement error in the dependent variable seems plausible. The open information display board, while obviously closer to the concept of ‘attribute information usage’ used in the network model, and also more similar to real purchase situations, is not without problems. The main problem is probably not the board itself but the method of number protocols. This is quite unusual for the subject and requires some kind of a dual thinking mode, i.e., processing product information in the background, and voicing numbers in the foreground (or vice versa). It seems quite natural that this leads to more measurement error. An eye-fixation analysis suggests itself as a solution to this problem. Another possibility would be to try yet another method to measure attribute information usage. In the network model, once an attribute has been stored in

120

K.G. Grunert / Determinants

of attribute information usage

the network, it is automatically being used in a decision process with a weight that depends on the strength of the associative links with which the attribute is connected to the rest of the network (Grunert 1982). Thus, in a decision between unknown alternatives where object planes have to be constructed based on external information, it could be attempted to infer the weight of an attribute from the decision or judgment made. This weight could then be interpreted as a measure of attribute information usage. A simple way in which this could be accomplished is the factorial survey approach proposed by Rossi (Rossi and Anderson 1982), where subjects are presented with descriptions of judgment objects which are a random sample from a factorial universe, resulting in object descriptions whose dimensions are effectively uncorrelated. This method would also make it possible to take this type of investigation out of the laboratory. From a substantial point of view, the results of study II mostly support the hypotheses derived from the network model. The only exception again is perceptibility of attributes, which, it seems, first requires more clarification at the conceptual level. A problem that shows up again, however, is that the network model does not make clear predictions about the interactions of the various independent variables.

General discussion The two studies have shown that it is possible to investigate memory phenomena in consumer research, and that stored knowledge is important in determining consumer attribute information usage. The fact that memory phenomena have received so little attention in consumer research may be partly due to the fact that there seems to be a lack of suitable empirical methods. Analysis of reaction times was believed to give only very indirect evidence, and free elicitation procedures have received little attention in spite of the commendable efforts by Olson. In our studies, hardly any problems were encountered with the free elicitation procedure. Also, it seems that simple interview techniques are also able to measure some aspects of stored knowledge. In addition, the network model shows that it is possible to use the psychology of memory to derive hypotheses about consumer behavior that do not themselves entail memory variables.

The question which attributes consumers use in evaluating products is of obvious importance for both marketers and policy makers. Marketers want to design their advertising messages in a way so that consumers perceive and use certain attribute information. They want to design products in a way so that attributes valued by consumers are incorporated, Policy makers may want consumers to make increased use of certain attributes, e.g., pollution caused by certain products. They may also want to taifor consumer information to the attributes preferred by consumers. For all these tasks, infurmation about the determinants of consumer attribute information usage is necessary. The approach described takes some first steps towards giving insights into these determinants.

References A&kin, M.A., D.A. Anderson, B.J. Francis and J.P. Hinde, 1985. Statistical modelling in GLiM3. Oxford: Oxford University Press. Alpert, M-f., 1980. ‘Unresolved issues in identification of determinant attributes”. In: J.C. Bison fed.), Advances in consumer research, Vo$. 7. Ann Arbor‘ MI: Association for Consumer Research, pp. 83-88. Anderson, J.R., 1976. Language, memory and thou&t. Hi&dale, NJ: Erlbaum. Anderson, J.R. and G.H. Bower, 1974. Human associative memory, 2nd rev. pr. Washington, DC: Hemisphere. Anderson, N.H., 1971. Integration theory and attitude change. Psychological Review 78, 171-204. Andress, H.-J., 1984. Kreuztabellenanalyse und Analyse von Individualdaten mit GLIM. Zumanachrichten 14, 66-85. Arminger, G., 1983. Multivariate Analyse von qualitativen abhlngigen Variablen mit verallgemeinerten linearen Modellen. Zeitschrift Nr Sotiologie 12, 49-64. Bishop, Y.M.M., SE. Fienberg and P.W. Holland, 1975. Discrete multivariate analysis. Cambridge, MA: MIT-Press. Brachman, R.J., 1977. What’s in a concept: structuraf foundations for semantic networks. International Journal of Man-Machine Studies 9, 127-152. Clark, H.H. and SE Havifand, 1974. ‘Psycbo~ogicai processes as linguistic explanation‘. In: D. Cohen @xi.), Explaining Iinguistic phenomena. Washington, DC: Hemisphere. pp. 91-124. Collins,. A.M. and E.F. Loftus, 1975. A spreading-activation theory of semantic processing. Psychological Review 82.407-428. Cox, D.F., 1967. ‘The sorting rule model of the consumer product evaluation process’. in: D.F. Cox (ed.), Risk taking and information handling in consumer behavior. Boston, MA: Harvard Graduate School of Business. pp. 324-369. Erdfelder, E., 1984. Zur Bedeutung und Kontrolle des beta-Fehlers bei der inferentstatistischen Prtifung log-linearer Modelle. Zeitschrift ftir Sozialpsychologie 15, 18-32. Fishbein, M., 1967. ‘A behavior theory approach to the relations between beliefs about an object and the attitude toward the object’. In; M. Fishbein fed.), Readings in attitude theory and measurement, New York: Wiley. pp. 389-400.

Freter, H., 1979. ‘Interpretation und Aussagewert mehrdimensionaler Einstellungsmodelle im Marketing’. In: I-I. Meffert, H. Steffenhagen and H. Freter (eds.), Konsumentenverhalten und Information. Wiesbaden: Gabler. pp. 163-184. Gardner, M.P., A.A. Mitchell and J.E. Russo, 1978. ‘Chronometric analysis: an introduction and an application to low involvement perception of advertisements’. In: H.K. Hunt (ed.), Advances in consumer research, Vol. 5. Ann Arbor, MI: Association for Consumer Research. pp. 581-589. Grunert, K.G., 1980. ‘Multi-attribute models as a means for improving the effectiveness of neutral consumer information*. In: M.J. Baker and M.A. Saren (eds.), Marketing into the eighties. Edinburgft: European Academy for Advanced Research in Marketing. pp. 58-69. Grunert, K.G., 1982. I~fo~atio~sv~~r~itun~Proz~e bei der Kaufentscheidung: ein g~~ch~i~sy~ho~~~er Ansatz. Frankfurt: Lang. Grunert K.G. and A. Keller, 1984. Cognitive determinants of attribute information usage: a pilot study. Paper presented at the Annual Colfoquium of the International Association for Research in Economic Psychology, Tilburg. Gnmert, K.G. and E. Stupening, 1981. Werbung - ihre gesellschaftliche und okonomische Problematik. Frankfurt: Campus. Hansen, F., 1972. Consumer choice behavior. New York: Free Press. Howard, J.A., 1977. Consumer behavior: application of theory. New York: Wiley. Jacoby, J., 1977. ‘The emerging behavioral process technology in consumer decision-making research’. In: W.D. Perreault (ad.), Advances in consumer research, Vol. 4. Atlanta, GA: Association for Consumer Research pp. 263-265. Jacoby* J., Jf. SzybiBo and J. Busato-Schach, 1977. Info~ation acquisition behavior in brand choice situatious. Journal of Consumer Research 3,208-2X6. Kaas K.P., 1982. Consumer habit forming information ~ui~tioo* and buying behavior. Joumai of Business Research 19, 3-15 Kaas, K.P. and T. Hofacker, 1983. ‘Info~ationstafeln und Denkprotokotle - Bestandsaufnahme und Entwickhmgsmoghchkeiten der Prozehverfdgungstechniken’. In: Forscbungsgruppe Konsum und Verhalten (ed.), Innovative Marktforschung. Wurzburg: Physica. pp. 75-103. Kanwar, R., J.C. Olson and L.S. Sims, 1981. ‘Toward conceptualizing and measuring cognitive structure’. In: K.B. Monroe (ed.), Advances in consumer research, Vol. 8. Ann Arbor, MI: Association for Consumer Research. pp. 122-127. Kupsch, P. and H.D. Mathes, 1977. Determinanten der Qualitatsbeurteilung bei langlebigen Gebrauchsgiitem. Jahrbuch der Absatz- und Verbrauchsforschung 23, 233-265. Kupsch, P., P. Hufschmied, H.D. Mathes and K. Scholer, 1978. Die Struktur von QualitBtsurteilen und das Info~ationsverhaIten von Konsumenten beim Kauf langlebiger Gebrauchsgiiter. Opladen: Wesideutscher. Lancaster, K., 1971. Consumer demand. New York Cohnnbia. Miller, G.A., 1956. The magical number seven, pius or minus two. ~~cholo~~ Review 63,81-97. Myers, J.H. and M-1. Alpert, 1968. Determinant buying attributes: meaning and measurement. Journal of Marketing 32(4), 13-20. Nakanishi, M., 1974. ‘Decision-net models and human information processing’. In: G.D. Hughes and M.L. Ray feds.), Buyer/consumer information processing. Chapel Hill, NC: University of North Carolina. pp. 75-88. Nelder, J.A. and R.W.M. Wedderburn, 1972. Generalized linear models. Journal of the Royal Statistical Society, Ser. A, 135, 370-384. Norman, D.A., 1976. Memory and attention, 2nd ed. New York: Wiley. Norman, D.A., 1979. ‘Perception, memory, and mental processes’, In: L. Nilsson (ted.), Perspectives on memory research. Hillsdale, NJ: Erlbaum. pp. 121-144. Norman, D.A. and DE. Rumelhart, 1975. Explorations in cognition. San Francisco, CA: Freeman.

K.G. Grunert / Deierminanrs of attribute information usage

123

Olson, J.C., 1977. ‘Price as an informational cue: effects on product evaluations’. In: A.G. Woodside, J.N. Sheth and P.D. Bennett (eds.), Consumer and industrial buying behavior. New York: Elsevier. pp. 267-286. Olson, J.C., 1979. The importance of cognitive processes and existing knowledge structures for understanding food acceptance. Working paper no. 89, Department of Marketing, Pennsylvania State University, University Park, PA. Olson, J.C. and J. Jacoby, 1972. ‘Cue utilization in the quality perception process’. In: M. Venkatesan (ed.), Proceedings of the 3rd annual conference of the Association for Consumer Research. Chicago, IL: Association for Consumer Research. pp. 167-179. Olson, J.C. and A. Muderrisoglu, 1979. ‘The stability of responses obtained through free elicitation: implications for measuring attribute salience and memory structure’. In: W.L. Wilkie (ed.), Advances in consumer research, Vol. 6. Ann Arbor, MI: Association for Consumer Research. pp. 269-275. Pincus, S. and L.K. Waters, 1975. Product quality ratings as a function of availability of intrinsic product cues and price information. Journal of Applied Psychology 60, 280-282. Quelch, J.A., 1979. Measurement of the relative importance of product attribute information: a review of the information display approach. Journal of Consumer Policy 3, 232-245. Quillian, M.R., 1968. ‘Semantic memory’. In: M. Minsky (ed.), Semantic information processing. Cambridge, MA: MIT-Press. pp. 216-270. Raffle, H., M. Hefner, M. Scholer, K. Grabicke and J. Jacoby, 1976. Informationsverhalten und Markenwahl. Die Untemehmung 2,95-107. Raffle, H., J. Jacoby, M. Hefner, M. Scholer and K. Grabicke, 1979. ‘Informationsentscheidungen bei unterschiedlichen Entscheidungsobjekten - empirische Untersuchung mittels einer neuen Methode zur Erfassung von Entscheidungsprozessen’. In: H. Meffert, H. Steffenhagen and H. Freter (eds.), Konsumentenverhalten und Marketing. Wiesbaden: Gabler. pp. 113-146. Rosenberg, M.J., 1956. Cognitive structure and attitudinal effect. Journal of Abnormal and Social Psychology 53, 367-372. Rossi, P.H. and A.B. Anderson, 1982. ‘The factorial survey approach: an introduction’. In: P.H. Rossi and S.L. Neck (eds.), Measuring social judgements. Beverly Hills, CA: Sage. pp. 15-67. Ryans, A.B. and T. Deutscher, 1975. ‘Product class experience, dimensionality, and reliability: their relationship in a nonmetric scaling study’. In: M.J. Schlinger (ed.), Advances in consumer research, Vol. 2. Chicago, IL: Association for Consumer Research. pp. 285-294. Schellinck, D.A., 1983. ‘Cue choice as a function of time pressure and perceived risks’. In: R.P. Bagozzi and A.M. Tybout (eds.), Advances in consumer research, Vol. 10. Ann Arbor, MI: Association for Consumer Research. pp. 470-475. Sheluga, D.A., J. Jaccard and J. Jacoby, 1979. Preference, search, and choice: an integrative approach. Journal of Consumer Research 6, 166-176. Slavic, P. and S. Lichtenstein, 1973. ‘Comparison of Bayesian and regression approaches to the study of information processing in judgment’. In: L. Rapoport and D.A. Summers (eds.), Human judgment and social interaction. New York: Holt, Rinehart and Winston. pp. 16-108. Szybillo, J.J. and J. Jacoby, 1974. Intrinsic versus extrinsic cues as determinants of perceived product quality. Journal of Applied Psychology 59, 74-78. ToBe, K., 1982. Die Bedeutung von Informationsinhalten beim Kauf von Konsumgtitem. Marketing ZFP 4, 176-182. Tulving, E., 1972. ‘Episodic and semantic memory’. In: E. Tulving and W. Donaldson (eds.), Organization of memory. New York: Academic Press. pp. 381-403. Van Raaij, W.F., 1977. ‘Consumer information processing for different information structures and formats’. In: W.D. Perreault (ed.), Advances in consumer research, Vol. 4. Atlanta, GA: Association for Consumer Research. pp. 176-184. Warren, E., 1977. Time and the spread of activation in memory. Journal of Experimental Psychology: Human Learning and Memory 3, 458-466.

124

K.G. Gtunert / Determinants

of attribute information

usage

Weinberg, P. and H. Schulte-Frankenfeld, 1983. ‘Informations-Display-Mat&en zur Analyse der Informationsaufnahme von Konsumenten’. In: Forschungsgruppe Konsum und Verhalten (ed.), Innovative Marktforschung. Wiirzburg: Physica. pp. 63-74. 1973. Issues in marketing’s use of multi-attribute attitude Wilkie, W.L. and E.A. Pessemier, models. Journal of Marketing Research 10, 428-441.