Identifying Consumer Information Processing Strategies

7/30/2019 Identifying Consumer Information Processing Strategies

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used to classify natural types of processing strategies. Empirical resultsevidence a considerable amount of multistage information acquisition as wellas six clear-cut search strategies: processing by attributes, two different typesof brand processing, paired comparison processing, mixed search andrandom processing.

INTRODUCTION

Over the last decade consumer information seeking and processing hasemerged as an important field of inquiry. Bettman (1979) has illustrated that acognitive approach provides a very promising mode of explaining consumerbehavior. Some methods (e.g. information display board, recording of eyefixation) have been developed in order to capture the process of informationacquisition in brand choice. Numerous empirical studies have used theseprocess tracing methods to describe and explain consumer informationprocessing (cf. Bettman and Jacoby 1976; Biehal 1980; Capon and Burke

1980; Jacoby et al. 1976; Jacoby, Szybillo and Busato-Schach 1977; Lussierand Olshavsky 1979; Payne 1976; van Raaij 1977).

Despite the theoretical and empirical interest in this area methodologicalcontributions are still rather limited. This applies both to methods of processdata collection as well as to the analysis of such data once collected (Bettman1979). The second of these two points will be discussed here. Problems ofdata collection are not considered. See Bettman (1979) Jacoby et al. (1976)or Russo (1978) for a description of process tracing techniques. In the courseof this article new methods for analyzing information acquisition process datawill be presented. The advantages of the methods proposed will bedemonstrated using experimental data from the brand choices of 360consumers. Despite the fact the data was collected by a kind of informationdisplay board, the data analysis methods are equally applicable to acquisitionprocess data collected by other techniques.

THEORETICAL BACKGROUND

In consumer information processing theory, multiattribute decision processesare described by means of choice heuristics. Consumers are held to usedifferent kinds of heuristics when comparing and choosing brands. Bettman

(1979, pp. 179-185) provides a detailed overview of important heuristics. Twoaspects of these heuristics are of particular relevance for the problemsdiscussed here: the form of processing implied and the existence of phased(multistage) strategies. Some heuristics (e.g. linear compensatory,conjunctive or disjunctive) assume processing by brand. That is to say,consumers choose a brand, collect information on several attributes and thenevaluate that brand. Then they choose a second brand and so on. Otherheuristics (e.g. lexicographic, elimination by aspects) imply processing byattributes. This means that all brands are compared according to a singleattribute, followed by a comparison based on a second attribute and so forth.Finally, yet other heuristics (e.g. additive difference, attribute dominance)

assume a paired comparison as a special kind of attribute processing. In thisform of processing, two brands are directly compared for their different


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attributes in an XYX-manner (Russo and Rosen 1975). Subsequently, anotheralternative can be selected and compared with the previous winner.

There is additionally evidence to suggest that some consumers make use ofother forms of unstructured (random) or mixed processing (Bettman and

Jacoby 1976; Bettman 1979; Capon and Burke 1980). Phased or multistagestrategies are an important kind of mixed processing. To simplify complexchoices, consumers may use different kinds of heuristics in the course of theirdecision process. For example, an elimination by aspects rule might be usedto eliminate some brands in the first decision phase and a linearcompensatory or an additive difference rule applied in the second phase tomake comparisons within this smaller set of acceptable brands (Bettman1979; Bettman and Park 1980; Biehal 1980; Raju and Reilly 1980). Thechoice heuristics mainly refer to internal processing, but they also implyspecific search strategies (Einhorn and Hogart 1981). The form of processingadopted and the single stage or multistage nature thereof are important

characteristics of these information acquisition strategies (Bettman 1979;Biehal 7980; Svenson 1979).

METHOD

State of Research

The existing techniques for information acquisition data analysis weredeveloped mainly by Bettman, Jacoby and Payne in the mid-1970s (cf.Bettman and Jacoby 1976; Bettman and Kakkar 1977; Jacoby et al. 1976;Payne 1976). In order to describe the strategies of individual consumers suchtechniques concentrate on the sequence of information acquisition responses.An adjacent pair of acquisition responses is referred to as a 'transition'.Therefore if "n" pieces of information are required, there are "n-1" transitions.These transitions can be divided into four types. Type 1 (the same attributeand brand), Type 2 (the same brand, different attributes), Type 3 (differentbrands, the same attribute) and Type 4 (different brands and attributes).Depending on the proportion of these transition types, particularly of Types 2and 3, in the acquisition sequences, these can be grouped into three or moreacquisition patterns (cf. Bettman and Kakkar 1977; Capon and Burke 1980)

With regard to the important theoretical aspects of acquisition strategies,these traditional methods have two significant shortcomings: firstly, the formof processing cannot be described with sufficient accuracy in terms of theproportions of the four transition types. -Transitions of Type 4 are required forshifts to other attributes or brands, even highly structured (e.g. linearcompensatory, lexicographic) sequences. The occurrence of Type 4transitions, caused by the structure of the underlying heuristic. cannot belimited to somewhat infrequent shifts. In paired comparison processing,alternating sequences of transition Types 3 and 4 are almost typical (vanRaaij 1977). The normalization proposed by Bettman et al. (Bettman andJacoby 1976; Bettman and Kakkar 1977) must thus be regarded as an

inadequate solution to the problem of structured shifts. Secondly, the aboveclassification being based on full-sequence proportions, the traditional


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concepts imply that information acquisition is of a single stage nature.Bettman and Jacoby (1976) tried to include multistage processing, but theirattempt was limited to a weak identification of paired end comparisons.Changes in search strategies are usually analyzed by breaking individualsequences into two halves and then comparing the first and second halves

(Biehal 1980; Svenson 1979; van Raaij 1977).

Analysis of Triple Transitions

To overcome the limitations inherent in the existing techniques, as outlinedabove, the author has developed a set of improved methods for the analysisof acquisition process data. First of all, the concept of transitions must beextended to a sequence of three acquisition responses. For a detaileddescription see Hofacker (1983a). Thus, 'n' units of information acquired form'n-2' triple transitions. The concept of triple transitions requires a largernumber of transition types than does the traditional concept of paired

transitions. There are 3x3x3=27 possible variations of brands and attributeswithin each triple transition. These variations are divided into five categories:Categories I and III constitute those variations embracing either one or threebrands (attributes). Category II consists of all triads of acquisition responseswhich embrace two alternatives or attributes. This category can be subdividedinto those transitions which embrace

- twice the same and then another brand or attribute (Category IIa),

- first one and then twice another (Category IIb), and

- one, then another, and then the first again (Category IIc) [Jacoby et al.(1976) propose a different concept of triple transitions. They do not divideCategory II into subcategories. As a consequence Category II consists of 18of the 27 possible variations of brands and attributes. The resulting transitiontypes then being too heterogeneous for interpretation.].

25 transition types can be derived from the five transition categories of brandsand attributes. The complete classificatory matrix is shown in Figure 1. Ineach matrix _ell the transition pattern is depicted graphically in addition to anindication of its type.

With regard to the objective of describing different forms of processing ininformation acquisition, the matrix can De partitioned into two triangularmatrices along the main diagonal, which contains unstructured elements. Theupper triangle consists mainly of processing by attributes, the lower mainly ofprocessing by brands. The problem involved in categorizing pairedcomparisons concerns Types 6 and 7. If one of these transition types occurs,the transitions bordering on it have to be examined as to whether a pairedcomparison sequence exists. If this is indeed the case, type 6 is recoded asType 26 and Type 7 as Type 27. Otherwise the initial classification remainsunaltered. Similar problems can arise when distinguishing between

unstructured shifts and those caused by structured processing.


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FIGURE 1

CLASSIFICATORY MATRIX OF THE ANALYSIS OF TRIPLE TRANSITIONS- TRANSITION TYPES AND GRAPHIC PATTERNS

Normally, a diagonal shift is regarded as unstructured A transition type isrecoded as a structured shift only in the case of the same processingstructure (by attributes, alternatives or paired comparison) being valid on bothsides of the diagonal shift. Only Types 11 and 12, both clearly being pairedcomparisons, can be excluded from this category. The precise differentiationof transition Types 6, 7, 9, 10, 18, 19, 20 and 21 is indicated in brackets in theclassificatory matrix (cf. Fig. 1).

Five strategy indices can be calculated from the above 33 transition types.These indices refer to the proportion of

- processing by brands,

- processing by attributes,

- paired comparison processing,

- structured shifts, and of

- unstructured (random) processing.

The first two indices differ but slightly from the corresponding indices of thetraditional concept of paired transitions. Differences exist merely in themethod of calculation, for each triple transition consists of two transition partsthat both individually correspond to the types of the paired concept. Tocalculate the proportion of processing by brands, all transition types whichcontain parts of a search by brands and which are not paired comparisons areadded together Type 25 consists of two vertical parts and thus is enteredtwice in the sum. The proportion can finally be computed as the ratio of thisaddition to twice the number of all transitions. The index for processing byattributes is obtained in a similar manner

The proportion of paired comparisons is computed by the sum total of Types11, 12, 26 and 27 being divided by the total number of transitions for eachsubject Transition Types 28 to 33 provide the computational basis for theindex of structured shifts. These six types are added together and thendivided by twice the number of all transitions Its counterpart, namely the indexof unstructured processing, is comprised as a residual of all transition partswhich can be neither allocated to paired comparisons to processing by brandsor attributes, nor to structured shifts. The sum of these parts (diagonal shiftsand reaccesses) is divided by twice the number of the transitions of anindividual subject.

Identifying Changes in Acquisition Sequences
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The second improvement made in comparison to existing techniquesconcerns the multistage character of information acquisition sequences. Withthe method of analysis presented in this article, not only the informationacquisition process as a whole can be described, but rather in the calculatingof the five indices of the triple transition analysis the process can be checked

for changes in the acquisition strategy used It is assumed that multistagestrategies consist of at least two internally relatively homogeneoussubstrategies (Bettman 1979; Bettman and Park 1980; Svenson 1979). Abreak occurs between these substrategies, leading to a change in thetransition indices at the level of information acquisition.

An algorithm based on the notion of a moving average (Biehal 1980) wasdevised to identify this change. A vector of Xm=n-2' types of triple transitionswas first constructed from the 'n' acquisition responses of a subject, asdescribed above. A window of width 'k' moves along this transition vector. Thefive strategy indices of the triple transition analysis can be calculated for each

position of the window, i.e using a window width of 6 first of all for thetransitions 1 to 6 and then for the transitions 2 to 7 and so forth. For "m"transitions and with a window width of 'k', a result of 'm-k+l' vectors of strategyindices is arrived at. The extent of strategy change is then given by thedifference between the index vectors of adjacent window positions. These 'm-k' distances can be quantified according to a Minkowski-supremummetric(e.g. Duran and Odell 1974; Hartigan 1975). That is to say, the distancecorresponds to the index with the greatest absolute difference. If the distanceis zero, then one can safely say that no strategy change has taken place. Ifthe measure of distance is, however, greater than zero, then the processualstructure of information acquisition has changed.

If, however, all distances greater than zero were to be interpreted as thebeginning of a new phase, then the actual extent of multistaging would bevastly overestimated. Even abrupt strategy changes are smoothed by themoving average down to continuous changes, whereby the smoothing factoris dependent on the width of the window. Furthermore, it is to be assumedthat empirical information acquisition processes do not correspond completelywith the theoretical models, but rather that accidental interruptions (short orminor strategy changes), which must be eradicated in the search fortheoretically relevant strategy changes, are superimposed over these. With

the aid of an analysis of the differences that occur, continuous and oscillatingchanges are collected into one typical borderline. Minor changes in theinformation acquisition sequence caused by bias effects can be corrected bymeans of minimal cut-offs of both the distance between adjacent phases aswell as to the relative length of each phase. Any requirements made of theminimum length run the danger of confusing short adjacent phases, that,however, differ radically in their form of processing. Therefore it would seemadvisable to make careful use of the minimum length Parameter (e.q. < 20 E).

Clustering Acquisition Strategies

As a result of the moving average algorithm the single stage processes andthe individual phases of the multistage processes can be characterized by the


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five strategy indices of the triple transition analysis. In order to facilitateinterpretation the separate phases are grouped by means of a cluster analysisinto typical processing strategies in information acquisition. Unlike previousconceptualizations (Bettman and Jacoby 1976; Capon and Burke 1980)classification is not made here by explicitly predetermining threshold values in

a discrimination net. Rather, empirical types are identified in the totality ofphase-related data.

The classification can be accomplished by means of a two stage clusteranalysis. Prior to the actual analysis principle components are calculated fromthe correlated strategy indices. Following this, the first step of the clusteranalysis involves a hierarchical classification using the algorithm proposed byWard (1963). The next step entails an optimization of the preliminaryclassification arrived at by the hierarchical procedure via an iterativerelocation algorithm (Hartigan 1975; Sneath and Sokal 1973). Bothprocedures use a quadratic measure of distance to produce spheric clusters

of minimal variance. On account of the data processing facilities available, theauthor made use of subprograms of the CLUSTAN 1C Package (Wishart1975); other computer programs for cluster analysis should, however, beequally usable A description of the computer programs used in the methods ofanalysis presented in _his article is to be found in Hofacker and Piesold(1983).

SUBJECTS AND PROCEDURE

Information acquisition data from an empirical study on product decisionmaking is used to demonstrate the results obtainable using the improvedmethods of data analysis developed. The study was conducted in the Rhein-Main area of West Germany in the summer of 1981 as part of a largerresearch project. The subjects were 360 female consumers, all mothers ofbabies. They represented a full sample of the mothers of births registered atthe Frankfurt and Offenbach Registry Offices in December 1980 and in May1981.

The subjects had to choose one brand from amongst several brands of babydiapers. Product information was presented via a brand x attribute matrix inthe form of a product test by the "Stiftung Warentest", the German consumer

product testing agency. The of information acquisition responses wererecorded by means of a process tracing technique specially designed for fieldstudies using questionnaires. This process methodology, a synthesis ofinformation display board and verbal protocol, is discussed in Kaas andHofacker (1983). The main differences between the method of the presentstudy and those used in previous studies are the larger sample size (360subjects instead of a few dozen), the use of non-student subjects, thepresentation of information by means of a product test and the use of aquestionnaire-based technique to collect acquisition data See also Kaas'article (1983) for a more detailed description of the study.

RESULTS


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The results of the methods of analysis introduced above concern thesimilarities and differences of strategy indices in paired and triple transitionanalyses, the frequency of single and multistage information acquisitionprocesses and the strategy types found by means of cluster analysis.Furthermore, the analytical procedure suggested here enables statements to

be made both on the frequency of strategy types in single stage andmultistage processes as well as on the combination of certain substrategies.These results cannot be elaborated on in this paper for reasons of space butare discussed in Hofacker (1983b).

New and Traditional Strategy Indices

The strategy indices of the traditional paired concept can be compared at thelevel of full sequence data with the extended concept of triple transitions. Themean values of the transition indices are displayed in Table 1. [The originalproportions of transition Types 2 and 3 in the paired concept are computed

without any kind of normalization. Owing to very short acquisition sequences(less than three responses) the sample size in the triple transition analysis isreduced from 360 to 356 subjects.]

TABLE 1

STRATEGY INDICES OF PAIRED AND TRIPLE TRANSITION ANALYSIS

As expected, the proportion of processing by brands, processing by attributesand of unstructured processing is lower in the triple concept because of thetwo additional indices. However, the extent of the change is interestingWhereas a slight difference exists for the brand index, the search by attributesis lessened considerably and the proportion of random processing is reducedto less than half. T-Tests for dependent samples show significant differencesat the level of p < Q.001 for the three indices. In this manner the criticismlevelled against the paired transition concept is supported by empiricalevidence. Indeed, the consumer's information acquisition processes containquite a considerable proportion of paired comparisons and structured shifts,which are not treated as separate indices in the paired concept, but, moreoverconfound the remaining indices. The index of random processing seems to bemost effected by this error.

Apart from the difference in mean values, the strategy indices of bothconcepts are highly correlated. The Pearson coefficients for processing bybrands and by attributes are r=0.99 and r=0.95 respectively. The convergenceof random processing is somewhat smaller (r-0.77). These results show that,especially in the case of brand and attribute processing, the new tripletransition concept is highly comparable with the traditional paired transitionconcept. See Hofacker (1983a) for a more detailed presentation anddiscussion.

Single Stage and Multistage Processes

Not only the exact description of the form of processing in information
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acquisition but also the frequency of strategy change is of interest. Using thedata mentioned above the moving average algorithm provided stablesolutions as to the number of phases over a wide interval of variableparameters. This holds true for a window width of 4 to 8 transitions, for aminimum distance between adjacent phases of 0.1 to 0.4 and for a relative

minimum length of the separate phases of 0 % to 20 8. With regard to theinterpretability of the number and content of strategy phases a window widthof 6 and a minimum distance of 0.4 without a determined minimum length waschosen. The frequence of single stage and multistage processes ininformation acquisition is given in Table 2.

TABLE 2

PERCENTAGE OF SINGLE STAGE AND MULTISTAGE PROCESSES

More than half of the consumers retain a strategy once chosen without

making any substantial changes to it. [The number of subjects with atransition vector smaller or equal to the chosen window breadth is, at 17.7 %,not exactly small. The methodological artefact thus determined should,however, not be accorded great weighting, for there is only a slight probabilityof multi-staging in such short sequences.] In the case of the other subjects,the form of processing in information acquisition changes. The two or threestage forms of processing constitute by far the greatest part of thesemultistage strategies. Four and more stage strategies are extremely rare. Thefrequency distribution agrees in size with that in previous studies, which found60-80 E of the information acquisition processes to be single phase and 20-40% to be multiphase (cf. Lussier and Olshavsky. 1979, pp. 161-163; Barbour1981, pp. 6-8).

Types of Processing Strategies

The single stage processes and the strategy phases of the multistageacquisition processes are characterized by the five indices of the tripletransition analysis. On this basis they can be condensed into strategy typesby means of the cluster analysis described above. The total 573 phases areclassified by the cluster analysis. Applied to this da;a both Ward's (1963)hierarchical algorithm as well as the relocate algorithm show a distinct

increase of inner-cluster variance when moving from 6 to 5 clusters. Becauseof this clear elbow in the increase of error variance, the number of 6 strategytypes was used as the basis of all further analysis. The cluster's frequenciesand the mean strategy indices' of the clusters are given in Table 3. [Thefollowing process of error variance within the cluster was found for therelocate procedure when using a 10 cluster start configuration obtained fromthe hierarchical algorithm: 9CL=15.22; 8C,=17.43; 7CL=30.34; 6CL=31.295CL=70.11; 4CL=112.02; 3CL=214.19; 2CL=260.62. Almost identical resultswere obtained with a relocate procedure using a random start configuration.]

TABLE 3

PHASES OF INFORMATION ACQUISITION: TYPES OF PROCESSING
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STRATEGIES

The six strategy classes are significantly different at the level of the fivetransition indices (MANOVA: Wilks Lamda p < 0 001). The dominance ofindividual strategy indices in the clusters is a very important finding. The

classes clear-cut and compelling structure is particularly noteworthy becausethis structure is solely determined by the distribution of density in the space ofthe transition indices. It is not determined by arbitrary models, as was thecase in previous analyses (Bettman and Jacoby 1976; Capon and Burke1980; Jacoby et al 1976).

The first four clusters can be assigned to certain choice heuristics on thebasis of the form of processing. In Cluster 1 the proportion of processing byattribute is very high and the proportion of structurally-determined shifts notinconsiderable. This corresponds to processing by lexicographic or eliminationby aspects heuristics. Cluster 2 exhibits a very high proportion of brand

transitions and a low proportion of structurally-determined shifts, which pointsto the use of a linear compensatory heuristic. In Cluster 3 processing bybrands is predominant, but the proportion of structurally-determined shifts isalso considerable. Such short sequences of brand processing are to beexpected when conjunctive or disjunctive heuristics are used. Cluster 4contains paired comparisons in the information acquisition process whichcorresponds to the additive difference or the attribute dominance rule. Thestrategy phases collected in Cluster S cannot be allocated to one dominantstrategy. In this mixed type there are considerable proportions of processingby brands, by attributes and random processing. A further analysis of Cluster5 showed in almost all cases frequently changing, short sequences ofdifferent forms of processing. Bettman and Jacoby (1976) labelled thisphenomenon feedback processing. Finally, in strategy cluster 6 all informationacquisition phases are collected in which unstructured (random) processingpredominates.

SUMMARY AND DISCUSSION

In consumer information processing theory a row of choice heuristics arediscussed. The use of certain heuristics is precipitated in the informationacquisition process. which can then be recorded by means of process tracing

techniques. Traditional methods for the analysis of information acquisitiondata only allow a quite crude description of information acquisition strategiesto be made. The new methods presented above provide a more detailedpicture of the acquisition processes and thus enable a more exactinterpretation of the data to be undertaken.

The comparison of the strategy indices of paired and triple transition conceptsdemonstrated the advantages of the new technique for the description of theform of processing used in information acquisition. Despite the differences inmean values, there was a strong convergence of the proportions ofprocessing both by brand and by attribute, which ensures the comparability of

both analysis techniques.
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The new methods not only offer a better description of the form of processingbut also of the dynamic change of information acquisition processes. Themoving average algorithm is an attempt to identify the theoretically expectedchanges between homogeneous strategy phases in empirical informationacquisition data. Problems arise because of both smoothed and minor

changes in information acquisition sequences. Precisely these problems weresolved by the use of a quite complex computer program (cf. Hofacker andPiesold 1983). The results of the moving average algorithm show a highproportion of multistage information acquisition processes that could not beadequately considered in previous studies (e.g. Biehal 1980).

The strategy types discovered by means of the cluster analysis providecriteria for judging the suitability of the new analytical techniques. The naturalstrategy clusters are clearly delineated from one another with respect to theform of processing used. Furthermore, four clusters contain informationacquisition sequences theoretically to be expected when specific choice

heuristics are used. These four clusters contain more than 70 % of all phases.

Despite the promising results obtained with the new methods of analysis aconcluding evaluation is not possible as yet. Further knowledge can only begained by applying the new methods to data from other studies with differencesamples, different products and different process tracing techniques.

Two substantial implications present themselves. Both for empirical researchinto and for the theory of consumer information processing. First of all, greaterconsideration must be paid to the multistage character of informationacquisition and processing. This is particularly the case for theoretical modelsin which the almost exclusive presence of two stage processes composed ofelimination and selection phases has been assumed (Bettman 1979; Lussierand Olshavsky 1979; Raju and Reilly 1982). Secondly, in future empiricalinvestigations other characteristics should be included in the description of thephases of multistage strategies. Such features as depth and content ofinformation acquisition would come into question. At the same time this wouldprovide an opportunity to improve on the methods of analysis presented here.

REFERENCES

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Identifying Consumer Information Processing Strategies