Naresh K. Malhotra, Arun K. Jain & Stephen W. Lagakos

This paper reviews the information overioad con-troversy and presents a methodology for investi-gating the effects of information ioad on con-sumer decision making performance. The proposedframework enables the statistical testing of spe-cific hypotheses and can incorporate several ex-tensions and refinements. The methodology is il-lustrated by re-analyzing the published data ofprevious studies; some interesting findings emergefrom the analysis. The paper concludes with somepublic policy and managerial implications of theconsumer information overioad concept.

The InformationOverloadControversy: AnAjternativeViewpoint

Introduction

IN recent years the area of consumer informationprocessing has received considerable attention from

researchers in marketing. Several studies dealing withvarious issues in consumer information processinghave been reported. Bettman (1974, 1979), Chestnutand Jaeoby (1977), Jaeoby (1974, 1977), Wilkie(1975, 1978) and Wright (1975) have summarizedmost of the previous work in this area.

A review of the literature suggests that the resultsof past research in consumer information processinghave not always been clear-cut and precise. Investi-gations in some areas have given rise to substantialcontroversy. In particular, considerable disagreementseems to exist regarding the information load para-digm (Malhotra 1982a). Although some researchershave indicated the occurrence of information overloadin their experiments (Jaeoby et al. 1974a, 1974b),their conclusions and findings have been questionedby others (Russo 1974, Summers 1974, Wilkie 1974).

Naresh K. Malhotra is Assistant Professor of Marketing, Georgia Insti-tute of Technology, and Arun K. Jain is Professor of Marketing, StateUniversity of New York at Buffalo. Stephen W. Lagakos is AssociateProfessor, Department of Bio-Statistics and Sidney Farber Cancer Insti-tute, Harvard University. The authors wish to acknowledge the helpfulcomments of two anonymous reviewers.

This paper first reviews the information overloadcontroversy. Next, a methodology using LOGITframework (Green, Carmone and Wachspress 1977)for examining the information load paradigm is pre-sented. The proposed approach is more flexible thanthe traditional methodology employed in that it ena-bles the formulation and statistical testing of alterna-tive hypotheses regarding the form of the informationload curve and the effect of concomitant variables.The proposed framework is illustrated by re-analyzingthe published data of previous studies on infomiationoverload. The paper concludes with a discussion ofsome public policy and managerial implications of theinformation overload phenomenon.

A Review of Past Research onInformation Overload

The information load paradigm is based on the prop-osition that consumers have finite limits to the amountof information they can assimilate and process duringany given unit of time. If these limits are exceeded,overload occurs and consumers become confused andmake poorer decisions. Hence, too much informationcan lead to dysfunctional performance. This conceptof information overload derives theoretical supportfrom research in human information processing (Miller1956, Quastler 1956), statistical prediction (Wherry

Journal of MarketingVol. 46 (Spring 1982), 27-37. The Information Overload Controversy: An Alternative Viewpoint / 27

TABLE 1An Overview of Published Empirical Work on Information Load in Marketing and A Summary of the

Results of Its Re-analysis

Invmti-gators Study

EXPERIMENTAL DESIGN

Expkri-mantslStimuli

ExperimentalStudy Manipulations

ORIGINAL STUDY

Anelytical Approach

Study Approach Study Major Rndings Study

RESULTS OF RE ANALYSIS

LogitParamatars

MalorFinding!

I. Jacoby,SpallarandBern ing(1975)

InstantRiceSampleSize =192

1,2 Both the number 1, Z A. Means and star- 1, 2, 3of brands and the dard deviationsnumber of attri- for Kendall'sbuies per brand coefficient ofwere veried at concordance be-four levels each. tween the pre-

dicted and elic-ited preferencerankings undeFdifferent treat-

1, 2 ment condi-tions.

B. Analysis of Vari-ance of tbe con-cordance coeffi-cients betweenthe predictedand elicitedrankings underdifferent treat-ment conditions.

PrBparsdDinnersSampleSize =192

M Jacoby et al.studies concludethat tbe con-sumer choiceaccuracy first in-creases and laterdecreases as tbe" tota l " amountof informationprovided is in-creased.

PiP!P3PdPsPBP?

PiPiP3P4Ps

pfPs

PtoPti

Pi3PI4

Pia

= - 0.7541.014

= - 0.487- - 0.487= - 0.325

0.7591.124

= - 1.0991.099

= - 0.000= - 0.511- - 0.511

1.435= 2.708= - 0.588= -50.593

0.511= - 3.833= -52.539= - 2.224= - 3.807= - 4.007= - 3.496

1.1 The probabilityof correct choiceincreased signif-icantly as thenumber of (a)attributes in-creased from 4to 16 or (b)brands in-creased from 4to 8.

1.2 No significanteffect on choiceaccuracy wasdetected by in-crease in thenumber of: (a)attributes from4 to 8 or 12 Of(b) brands from4 to 12 or IB.

2.1 The probabilitvof correct choicedoes not declinesignificantly asthe number ofla) attributes isincreased from4 to 8, or 12 orIbl brands is in-creased from 4to 8, 12, or 16.

2.2 When the infor-mation is pro-vided about 4brands on 16 at-tributes (insteadof 4), the prob-ability of cor-rect choice im-proves signifi-cantly. How-ever, anyconcomitant in-crease in thenumber ofbrands at this at-tribute levelleads to a de-cline in theprobability.

1931, 1940), and clinical prediction (Bartlett andGreen 1966, Kelly and Fiske 1951). While the basicproposition seems reasonable, the question remainswhether the occurrence of information overload in theconsumer setting has been empirically demonstrated.

In the recent past, marketing researchers have un-dertaken several studies to examine the occurrence ofinformation overload. Jacoby et ai. (1974a) in theirpioneering research systematically varied the amountof information defined in terms of the number ofproduct attributes and brands of laundry detergents atthree levels each. The subjects, randomly assigned toone of the nine treatment conditions, were presentedwith an appropriate number of brands and attributes.They were to provide importance ratings of the prod-uct attributes, rate their ideal brand and identify their

most preferred brand from among the alternatives inthe choice set. The effects of information load wereassessed by examining the number of subjects cor-rectly choosing their best brand across the treatmentconditions. Best brand was defined as the one comingclosest to the subject's ideal brand. In a follow-upexperiment, Jacoby et al. (1974b) varied the numberof brands and product attributes at four levels each toshed further light on the occurrence of informationoverload. Scammon (1977) extended the informationload paradigm by varying the number of attributes andthe format in which information about the experimen-tal stimuli (peanut butter) was provided. Subjectswere presented with information about two brands andrequested to identify the brand that was more nutri-tious and preferred for the next purchase. The effects

28 / Journal of Marketing, Spring 1982

Investi-gators Study

II. Jacobv, 3Spellerand Kohn(1974a)

III, Scammon 4(1977)

'Signlficam at a >> 0.05

EXPERIMENTAL DESIGN

Expari-mentalStimuli

LaundryDeter-gentsSampleSize =153

PeanutButterSampleSize =300

Study

3

ExperimentalManipulsUons

The numbers ofbrands and attri-butes were var-ied at three lev-els each.

The number ofattributes werevaried at threelevels each whiletheir format wasvaried at twolevels.

TABLE 1 (continued)ORIGirUL STUDY

Analytkal Approach

Study Approach Study

3. A. Chi-square anal-ysis of the num-ber of correctchoices in each

3 treatment con-dition.

B. Analysis of vari-ance af subjec-tive data ur derdifferent treat-ment conditions.

4 A. Chi-square anal- 4.1ysis of the num-ber of correctchoices in eachtreatment com-bination.

B. Analysis of vari-ance of aided re-call accuracyscores.

Major Rndings Study

3.

Neither the 4.amount nor theformat of the in-formation af-feaed subjects'brand prefer-ence/intentionto buy asjudged by sub-jective criteria.

RESULTS OF HE-ANALVSIS

LogitParamstara

0t =Pj =Pa -P* =01 =

01 -P2 =03 =04 =Ps =0? =

- 1.204- o.iai- 0.474

0.7231.224

- 1.386- 1.253- 1.558- 1.253

2.0202.811

Majornndingi

3.1 The probabilityaf correct choiceImproved signif-icantly as thenumber of attri-butes were in-creased from 2to 5.

3.2 The number ofbrands in thachoice set do notsignificantly in-fluence theprobability ofcorrect choice.

4.1 Using subjectivecriteria, subjectsmade poorer de-cisions when in-formation about8 nutrients pluscalories wasprovided in thepercentsge for-mat than whenno informationabout nutrientswas provided.

4.2 Subjects had atendency tomake batter de-cisions when In-formation about8 nutrients pluscalories wasprovided in theadjective formatusing subjectivecriteria thanwhen no infor-mation aboutnutrients wasprovided.

of information load were assessed by examining thenumber of objectively correct choices (identificationof the nutritionally superior brand) and subjectivelycorrect choices (selection of the nutritionally superiorbrand). All three published studies concluded that thesubjects suffered information overload as the amountof information provided was increased. Table 1 pre-sents a brief summary of the published empirical in-formation load studies in marketing.

While commending Jacoby et al. for addressingan important public policy issue, marketing research-ers have questioned their operational procedures andrejected their conclusions (Russo 1974, Summers1974, Wilkie 1974). Three basic reservations havebeen expressed about their studies.' First, it is pro-posed that the number of brands and the number ofinformation items (attributes) per brand are concep-

Additional issues have also been raised. However, these are rela-tively minor and may be found in the original articles. Here, only themajor issues addressed by all three critics have been emphasized.

tually as well as psychologically different dimensions.Hence, it is suggested that the total amount of productinformation should not be defined in terms of thenumber of brands times the number of items perbrand. Next, since the probability of making a correctchoice by chance alone is inversely proportional to thenumber of brands, subjects responding to smallernumbers of brands in the choice set are expected tohave a higher probability of correct choice. Therefore,when comparing the effects of the number of alter-natives on choice accuracy, the effect of chance fac-tors should be explicitly considered. Finally, the wis-dom of using weighted euclidean distances of thebrands in the choice set from the ideal brand to mea-sure correct choice was questioned given the problemsassociated with ideal point measurement. The use ofimportance weights and variation in the number ofattributes over which distances were computed furtherclouded the accuracy of Jacoby's measures.

However, Jacoby and his associates have gener-ally disagreed with the evaluation of their research

The Information Overload Controversy: An Alternative Viev/point / 29

procedures and findings (Jacoby 1977, Jacoby et al.1975). While they accept that their operationalizationof total infonnation may not be entirely appropriate,citing studies that report that the number of brands inthe choice set affects respondent's ability to make ac-curate choices, they contend that a definition of totalinfonnation must take into account the number ofbrands in the choice set. They acknowledge thatchance factors must be controlled when comparingdecision quality across the number of brands. Toovercome this problem, the researchers suggest thata correlation analysis be used or that the number ofbrands be kept constant in the experiment. The lattersuggestion, however, contradicts their propositionthat any operationalization of total information mustincorporate the number of brands. Finally, with re-spect to the appropriateness of the ideal point measureof correct choice, Jacoby (1977) contends that sinceno single approach for defining decision quality in theconsumer context is entirely satisfactory, their oper-ationalization "is as good as any."

Discussion

It is possible to resolve the disagreements with respectto a definition of total information. Total informationshould not be defined in terms of the product of thenumber of brands and items per brand. However,there is reasonable evidence (Jacoby et al. 1977, Mor-eno 1974) to suggest that a definition of total infor-mation should not be divorced from a considerationof the number of brands. What is needed is a meth-odology that would permit an examination, individ-ually as well as jointly, of tbe effects of the numberof brands and the number of items per brand on de-cision quality. Moreover, the methodology must notforce the researcher to adopt a particular definition oftotal information, yet it must be flexible enough topermit statistical evaluation of different operationali-zations of total information.

The analytical framework utilized by previous in-vestigators leaves much to be desired. The chi-squaretest employed by Jacoby et al. (1974a) and Scammon(1977) is at best a weak test. The two-way chi-squaretest employed by previous researchers merely exam-ines the independence between two classificatoryvariables (e.g., number of attributes and number ofbrands). However, in the infonnation load paradigmthe occurrence of information overload is determinedby examining the ability of consumers to make correctchoices across different treatment conditions. Hence,what needs to be tested is whether the number of cor-rect choices under different treatment conditions aresignificantly different. If under certain treatment con-ditions information overload does occur, one wouldexpect the ability of the consumers to make correctchoices under those conditions to decrease signifi-

cantly. This cannot be detected within the conven-tional chi-square framework employed by the pre-vious investigators.

The information processing strategy adopted bythe respondents may be influenced by the particularcombination of tbe number of attributes and the num-ber of brands in the choice set (Jacoby et al. 1976,Payne 1976, Wright 1975). The knowledge of suchinteractions would provide a richer understanding ofthe information load phenomenon. The use of analysisof variance (Jacoby et al. 1974a, 1974b; Scammon1977) on the number of correct choices to examinethe effect of different treatment conditions does notpermit the estimation of interaction effects. Sincethere is only one entry per cell, the various interac-tions between the leveis of number of brands and thenumber of attributes cannot be estimated within thestandard ANOVA framework.

The assertion that the determination of best brand,and hence correct choice, is subject to inaccuraciesis reasonable. Given that no single measure of choiceaccuracy is fully satisfactory, it is desirable to adoptdifferent operationalization s of the dependent vari-able. This could include self-report measures (Weitzand Wright 1979) as well as objective criteria,

Wilkie (1974) has stated that "there is little doubtthat the issue of consumer information provision willbe one of tbe major problems confronting marketingresearchers and policy makers in this decade"(p.462), Given the apparent importance of the issue anddisagreement with the finding of Jacoby et al. (1974a,1974b), it would be wise to examine the occurrenceof consumer information overload further. With thisin mind we next present a methodology for examininginfonnation load effects via the LOGIT framework.The proposed framework is then employed to re-ana-lyze the published data of Jacoby et al. (1974a,1974b) and Scammon (1977). Other applications ofthis methodology for examining consumer informa-tion processing behavior are provided by Malhotra(1982b, 1982c).

A LOGIT Approach to InformationLoad Paradigm

In the information load paradigm, the dependent vari-able of interest is tbe ability of the consumer to make"a correct choice" under different infonnation loadconditions. This can be modelled in terms of the prob-ability of making a correct choice. The probability ofcorrect choice can be estimated in several ways(Bishop, Feinberg and Holland 1975; DeSarbo andHildebrand 1980). However, in view of the binarynature of the dependent variable and the fact that theprobability of making a correct choice under each ex-perimental condition (cell) varies between 0 and 1,

30 / Journal of Marketing, Spring 1982

the linear logistic model (LOGIT) becomes a partic-ularly suited technique for analysis (Green, Carmoneand Wachspress 1977).^ The probability of making acotTect choice in each experimental cell could be cal-culated as a function of the number of brands and thenumber of attributes per brand on which informationis provided. Where information overload does occur,the probability of making correct choices should de-crease significantly.

The LOGIT analysis is a flexible approach. Inaddition to the commonly employed main effects plusinteractions and main effects-only models, other modelspurporting to capture the underlying information loadprocess may also be formulated and statisticallytested. For the sake of simplicity, consider the prob-ability of correct choice as a function of only the num-ber of attributes per brand on which information isprovided. A general model for the information loadparadigm may be represented as:

log, (1)

where,

Pi = the probability of correct choice in the i-theel! (information load condition).

Z = K if K > Ko- 0 if K < Kfl

K = the number of attributes per brand on whichinformation is provided.

Kg = critical number of attributes specified by theresearcher either on the basis of a priori ex-pectations or by looking at the data obtained.

As indicated in Figure 1, the researcher may postulatedifferent forms of information load curve. For ex-ample, the log odds of correct choice, when the num-ber of attributes per brand exceed Kg, may:

• first increase and then decrease (e.g., Figure 1-a)

• first decrease and then remain constant (e.g..Figure 1-b)

• increase at different rates (e.g.. Figure 1-c)• increase or decrease at a constant rate (e.g.,

Figure 1-d)

The hypothesized values of ^2 and pg will vary underthese conditions as shown in Figure 1 and can be sta-

A second operationalization of performance accuracy consists ofa comparison between each subject's actual preference ranking andthe ranking predicted from the ideal brand. The response variable maystill be treated as binary by comparing actual and predicted choicesfor all possible brand pairs (Wright 1972). A discussion of the in-herent advantages of LOGIT approach for analyzing binary data maybe found in Cox (1970).

FIGURE 1Some Possible Forms of Information Load Curve

Bo < - B , < 0 < 0

NO OF ATTBIDUTES

< 0 B2 > 0. B3 - a

NO. or ItTTniBUTES

tistically examined. For example, the appropriatenessof form 1-a could be investigated by testing the fol-lowing hypotheses (Cox 1970):

H,:

H,: + P3 < 0

The model (1) could be generalized to include thenumber of brands and the combination of the numberof brands and attributes in the discrete as well as con-tinuous cases.-' Using this framework, it is also pos-sible to take into account the effect of chance factors.

Checking for Chance Factors

As critics have rightly pointed out, the probability ofmaking a correct choice based on chance alone de-creases as the number of brands in the choice set in-creases. Thus it seems reasonable to adjust the prob-ability of correct choice for chance factors. Theredoes exist a natural measure for incorporating correc-tion due to chance (Fleiss 1975) as indicated below:

Pjo - Pic

1 - p .(2)

where,

j = the probability of correct choice in cell iadjusted for chance.

^Summers (1974) has pointed out that while examining informationload effects, it is desirable to account explicitly for the salience of theattributes on which information is provided and the variability of theattractiveness of the brands. Such extensions could be readily incor-porated by introducing additional terms in the model formulation.

The Information Overload Controversy; An Alternative Viewpoint / 31

™ Pjo ~ the observed probability of correct choicein cell i.

Pjp = the probability of correct choice in cell i bychance alone (1/number of brands).

A chi-square test could be performed to testwhether the observed distribution of correct choices(based on Pj ,) is different from what one would expectby chance alone (based on Pj .).'' Furthermore, chi-square analysis could also be conducted to testwhether the chance-adjusted distribution of correctchoices (based on P ) and the observed distribution(based on PjJ come from the same population. Wherethe chance effects are not significant, the Pj and P ^will be close and the resulting x statistic not signif-icant. However, the distributions based on Pj and Pj .will differ significantly (Malhotra 1982a).

A Re-analysis of Published Datafrom Previous Studies

The proposed methodology was employed to re-ana-lyze the published data of previous investigators. Thedependent variable in the re-analysis was the proba-bility that a respondent would make a correct choiceunder different information load conditions. Given thenature of the published data, the method of determi-nation and definition of correct choice adopted wasthe same as that used in the original studies. Thusinformation reported in previous studies (Table 1) onthe number of respondents making correct choicesunder each information load condition, given the totalnumber of respondents in that condition, was used toestimate the probability of correct choice as a functionof information load. Both the main effects plus inter-actions models and the main effects-only models wereestimated for all the published data from previous in-formation load studies discussed in Table 1. The es-timated parameters were statistically tested to re-ex-amine the hypotheses implied by previous investigators.In the following, results of model estimation and hy-potheses testing for each published data set are pre-sented.

Instant Rice Data

A main effects plus interactions model for the prob-ability of correct choice in each cell for the instantrice data may be formulated as:

1 - P , (3)

It may be pointed out that if P^ is greater than P^, the P, shouldbe set to equal to 0.

where :

Pj = the probability of correct choice in thei-th cell (information load condition)-(i, i = 1,2, . . . , 16)

X, = 1X2 = 1 if the number of brands in the stimuli

set is 80 otherwise

X3 = 1 if the number of brands in the stimuliset is 120 otherwise

X4 = 1 if the number of brands in the stimuliset is 160 otherwise

X5 = 1 if the number of attributes per brandis 80 otherwise

X5 = 1 if the number of attributes per brandis 120 otherwise

X7 = 1 if the number of attributes per brandis 160 otherwise

Xg to Xj6 = the interaction terms between the levelsof number of brands and number of at-tributes per brand.*

3i to P16 - the parameters to be estimated.

Alternately, a main effects model may be representedby eliminating Xg to Xi^ from (3).

Both the main effects plus interactions and maineffects-only models were estimated on the instant ricedata. The likelihood ratio test revealed that the inter-action terms of model (3) were not significant at a= 0.05. Thus only the results for the main effectsmodel are presented. A chi-square test of homogene-ity (Mendenhall and Schaeffer 1973, p. 502-508) be-tween the observed and predicted number of peoplecorrectly choosing their best brand using the maineffects-only model was not significant. This suggeststhat the observed and predicted distributions camefrom the same population. The test provides a furtherindication of the appropriateness of the main effects-only model. In the absence of interactions, differentadditive models may be indicated. The chi-square testof homogeneity could be used to test the appropriate-ness of the various models. The results of estimatingmain effects model are summarized in Table 1.

In terms of the number of brands, the results in-

Xi denotes the constant tenn similar to that employed in dummyvariable regression analysis, In the present formulation, it depicts thepresence of four brands each described by four attributes in the ex-periment.

*For example, Xg denotes the joint effect of the presence of eightbrands with each brand described in terms of eight attributes.

32 / Journal of Marketing, Spring 1982

dicate that the only significant parameter is p2- Thisparameter represents the effect of the number ofbrands on the probability of correct choice when thenumber of brands in the stimuli set is increased fromfour to eight. What is more interesting is that the pa-rameter p2 is positive. Thus in this particular exper-iment as the number of brands in the choice set in-creased from four to eight, the probability of makinga correct choice increased significantly. Furthermore,while the coefficients pg and p^ are negative, they arenot statistically significant at a = 0.05. When thenumber of brands in the choice set is increased fromfour to twelve or from four to sixteen, although theprobability of making a correct choice decreases, thedecrease in the probability is not statistically signifi-cant. The results for the number of attributes perbrand are equally illuminating. We note that the es-timated parameters Pg and p, are positive. Further-more, while p7 is statistically significant, P5 is neg-ative and not significantly different from zero. Thus,no definite conclusion can be made regarding the ef-fect of increasing tbe number of attributes on whichinformation is provided from four to eight (as indi-cated by pg) or from four to twelve (as indicated by|ig), because of the lack of statistical significance ofthe corresponding estimated parameters. However, asthe number of attributes on which information is pro-vided is increased from four to sixteen, the probabilityof making a correct choice {as indicated by p,) im-proves significantly. Thus our analysis shows that thesubjects in the Jacoby et al. study actually made betterdecisions when provided with information on sixteenattributes rather tban on only four attributes. The ef-fect of chance factors was also investigated usingFleiss' measure (1975) but was not found to be sig-nificant.

Prepared Dinners DataThe second data set analyzed in this paper is the oneobtained by Jacoby et al. (1974b) on prepared din-ners. Since the number of attributes and number ofbrands varied in this experiment was the same as thatfor the instant rice experiment, the interactions andmain effects-only models for this data set are identicalto the models proposed for the instant rice.

Both the interactions (3) and the main effectsmodel were estimated for these data. A likelihood ra-tio test indicated that the interactions were significantand so only the results of the interactions model arepresented in Table 1. Several interesting conclusionscan be drawn from the estimated parameters. First,none of the coefficients representing the number ofbrands are statistically significant. Second, the onlysignificant main effect for the number of attributes isIhat corresponding to sixteen attributes of informationper brand. This effect, represented by p,, is positive

and therefore indicates that as the number of attributeson which information is provided is increased fromfour to sixteen, the probability of making correctchoices increases. Finally, of the interaction terms,only pii, Pj4, p,5 and p,g are statistically significant.Moreover, all these coefficients have negative signsindicating a decrease in the probability of correctchoice for the corresponding cells (treatment condi-tions). The significance of 3,4, p,5 and p,6 impliesthat when consumers were provided with informationabout sixteen attributes per brand, the probability ofcorrect choice decreased as the number of brands inthe choice set were increased from four to eight, fourto twelve, and four to sixteen respectively. The sig-nificance of the interactions suggests that while pro-viding information on sixteen attributes per brandsleads to better choice making, any concomitant in-crease in the number of brands at this level of infor-mation leads to a relative decrease in the probabilityof making a correct choice. The chance effects, again,were not significant.

Laundry Detergent Data

The data on the laundry detergents experiment ob-tained by Jacoby et al. (1974a) were re-analyzed byestimating both interactions and the main effects-onlymodels. However, since the likelihood ratio test in-dicated that the interactions were not significant, onlythe results of the main effects will be presented. Themain effects model for the laundry detergent data maybe represented as follows:

log.1 -

= X (4)

where.

Xj = 1 if the number of brands in the stimuli setis 80 otherwise

X3 = 1 if the number of brands in the stimuli setis 120 otherwise

X4 = 1 if the number of items/brand is 40 otherwise

X5 = 1 if the number of items/brand is 60 otherwise

A chi-square test of homogeneity indicates that themodel (4) provides a good fit to the observed data.It can be observed from the results presented in Table1 that the estimated parameters representing the effectof increasing the number of brands in the choice set,p2 and P3, are negative but not statistically significantat a = 0.05. Thus our analysis indicates that althoughthe probability of making a correct choice decreases

The Information Overload Controversy: An Alternative Viewpoint / 33

as the number of brands in the choice set is increasedfrom four to eight and four to twelve, the resultingdecrease in the probability is not statistically signifi-cant. The effects of the increase in the number of at-tributes per brand on the probability of making correctchoice, p4 and P5, were found to be positive. It willbe observed that P4 is not statistically significant. Asthe number of attributes per brand increased from twoto four, the probability of making a correct choiceincreased but the improvement was not statisticallysignificant. However, pj is statistically significant ata = 0.05. The probability of a consumer making acorrect choice increases significantly as the numberof attributes per brand increases from two to six. Theresults show that as the subjects in this experimentwere provided with more information per brand, theyactually made better decisions. Chance factors werealso investigated and were not found to be statisticallysignificant.

Peanut Butter DataScammon (1977) used peanut butter brands to ex-amine the information load effects. As in the Jacobystudies (1974a, 1974b), the interactions and main ef-fects-only models were formulated and parametersestimated independently for the subjective standardsdata.^ Since the interactions were found to be signif-icant, only the results for the interaction model willbe discussed. Such a model may be presented as fol-lows:

1 -(5)

where,

X. = 1X2 = I if the number of nutrients about which

information is provided is 40 otherwise

X3 = 1 if the number of nutrients about whichinformation is provided is 80 otherwise

X4 = 1 if the adjective format is used0 otherwise

X5 =~ X3 * X4

'An attempt was also made to analyze the objective standards datacollected by Scanunon using the LOGIT framework. A likelihoodratio test (Mendenhall and Schaeffer 1973) indicated that the inter-action terms were significant. However, when a main-effects plus in-teractions model was estimated, the second derivatives matrix wasnoninvertible. Consequently the variances could not be estimated andthe statistical significance of the model parameters assessed. Hence,the results for objective standards data are not presented.

It will be observed from the results summarizedin Table 1 that besides the constant term p^, only theestimated parameters P3 and Pg are statistically sig-nificant. Furthermore, P3 is negative while (3 is pos-itive. The effect of providing information on eightnutrients plus calories, as compared to the controlledcondition of no nutrient information for the percent-age format, is represented by P3. However, the effectof providing information on eight nutrients plus ca-lories as compared to the no-nutrient-information con-dition under the adjective format is represented by p+ pg. The significance and negative sign of (33 sug-gests that the subjects in Scammon's study madepoorer decisions when information about eight nu-trients plus calories was provided in the percentageformat than when no information about nutrients wasprovided. Furthermore, the sum of Pj + pg is posi-tive. Thus our analysis shows that the subjects had atendency to make better decisions when provided withinformation on eight nutrients plus calories in adjec-tive format than when they were not provided withany information about nutrients.

A Comparative Analysis withPrevious Findings

The foregoing analysis provides insight into the in-formation load controversy using an alternative ana-lytical framework. Based on re-analysis of the dataobtained by Jacoby et al., three broad conclusions canbe drawn. First, as the number of attributes on whichinformation was provided to subjects increased, theprobability of making correct choice generally im-proved. It is interesting to note that in all the threeexperiments of Jacoby et al. (1974a, 1974b) as thenumber of attributes was increased from the lowestlevel to the highest, the probability of making conectchoices increased significantly. One may reasonablyconclude, therefore, that in the Jacoby experimentsthe provision of more information (in terms of thenumber of attributes) generally led to improved de-cision making. This finding is consistent with the ob-servations of Russo (1974) and Wilkie (1974). Sec-ond, the effect of increase in the number of brandsin the choice set on the probability of making correctchoice was not found to be significant. Although theprobability of making correct choice declined with anincrease in the number of brands, such an effect wasnot found to be statistically significant. Any claim inthese experiments that increasing the number ofbrands in the choice set led to dysfunctional conse-quences and hence information overload is untenable.Finally, as found in the prepared dinners data, theremay exist significant interactions between the numberof brands in the choice set and the number of attri-butes on which information is provided. This suggests

34 / Journal of Marketing, Spring 1982

the need to consider explicitly these two variables inthe design and analysis of tbe information load par-adigm. Jacoby, Speller and Kohn conclude that con-sumers "actually make poorer purchase decisionswith more information" (1974a). Based on the re-analysis using LOGIT framework the validity of theirconclusions is questionable. While their suggestionthat "providing substantial amounts of package in-formation can result in poorer purchase decisions"(1974b, p. 40) may be a reasonable proposition, it isnot supported by their reported data.

The re-analysis of Scammon's (1977) data dem-onstrated the existence of complex interactions be-tween the number of attributes and the type of format.Her chi-square and ANOVA framework could notdetect these interaction effects, and her conclusionthat "neither tbe amount nor the format of the infor-mation presented to the subjects affected their brandpreference/intention-to-buy" (p. 152) is not sup-ported by our re-analysis.

Some Public Policy andManagerial Implications

Although the findings of previous investigations maybe questioned, the concept of consumer informationoverload has some important implications in terms ofthe amount of information that should be provided toconsumers and the manner in which it should be madeavailable. In the last two decades the products fromwhich consumers must choose have grown enor-mously in quantity and complexity. Commercialsources are the core of the existing information sys-tem, yet there is evidence that consumers are skepticalabout the usefulness and truthfulness of this type ofinformation (Day 1976, Jacoby 1974, Newman andStaelin 1972). In an effort to bridge the informationgap, public policy makers have recently demonstratedheightened interest in consumer welfare. Programsaimed at providing useful information to consumersin the marketplace have been initiated by many fed-eral, state and local agencies.

Providing consumers with more information is astep in the right direction. The results of our re-anal-ysis suggest that consumers are capable of processingfairly large amounts of information. Yet the capacityof consumers to absorb and process information is notunlimited. It should be recognized that these previousstudies were conducted in an artificial environment inwhich the respondents were motivated to process theinformation provided due to the demands of the ex-periment. Research in consumer information process-ing does suggest that in many situations, the moti-vation of consumers to acquire and process informationis rather low (Bettman 1979, p. 43-72). If efforts onthe part of policy makers and consumer groups to

provide consumers with more information are to bearfruit, they should take into account not only the ca-pacity but also the motivation of consumers to processinformation.

Furthermore, the provision of information shouldnot be independent of the number of choice alterna-tives facing the consumer and the choice behaviorexhibited. The re-analysis of the prepared dinners dataset suggests that there may exist, for certain productcategories, significant interactions between the num-ber of brands in the choice set and the number of at-tributes on which information is provided. These as-pects need to be emphasized as they are typicallyignored by the policy makers (Bettman 1979, p. 294).

The format in which the information is presentedcan also affect the way in which consumers acquireand process information (Bettman 1979, p. 219-221;Bettman and Kakkar 1977; Bettman and Zins 1979).As pointed out by several researchers (Russo 1977;Russo, Krieser and Miyashita 1975), one may distin-guish between the availability and the processabilityof information. Our re-analysis of Scammon's studysuggests that adjective format, as compared to thenumerical format, facilitates the processing of infor-mation. If this finding is in fact general izable, thenimportant public policy implications follow. For ex-ample, policy makers could legislate that importantinformation be communicated to the consumers in averbal format.

The information load paradigm also has importantmanagerial implications. Unlike tbe policy maker, themarketing manager is more concerned with providingpersuasive information that favors his/her brand. Thisinformation is communicated through various pro-motional activities. Such information programs mustexplicitly recognize the limited capacity and motiva-tion of the target market to process information if themessage is to have its intended impact in the market.Even when consumers are capable, the motivation toprocess the information provided may be low. Forexample, although a re-analysis of instant rice dataindicates that the housewives in the Jacoby et al.(1974b) study were able to process information on 16attributes, the motivation of housewives to processthis much information while actually buying instantrice may be low. Hence, an informational type of ad-vertisement detailing so much product informationmay have limited impact in the market. Likewise, indeciding on a media mix, the manager needs to beaware that different media vary in the extent to whichthey facilitate the processing of large amounts of in-formation. Suppose, for example, a marketer believesthat his/her brand is, on balance, better than otherleading brands if a detailed, rational comparison weremade. If so, such information may be more effec-tively communicated using point-of-sale displays or

The Information Overload Controversy; An Alternative Viewpoint / 35

print advertisements as opposed to broadcast media.The use of point-of-sale displays or print advertise-ments as in the studies of Jacoby et al. (1974a, 1974b)would allow the consumer greater time to process theinformation, thereby decreasing the possibility of in-formation overload. Furthennore, the re-analysis ofScammon's (1977) study suggests that such infor-mation could be more effectively communicated in averbal as opposed to a percentage or numerical for-mat. A thorough discussion of public policy and man-agerial implications of information processing theoryin general may be found in Bettman (1979, p.293-342) and Wilkie (1975, 1978).

ConclusionThe concept of information overload has importantimplications both for public policy makers and man-agers. However, the results of past research in tbearea have not always produced clear-cut and precisefindings. This paper reviews the information loadcontroversy and suggests a methodological frameworkfor examining this paradigm. The methodology pro-posed and the re-analysis does help resolve some ofthe problems in investigating the effects of informa-tion load on consumers. Our re-analysis of previousstudies failed to support the conclusions of their au-thors that providing more information results inpoorer purchase decisions.

The LOGIT framework offers an attractive ap-proach for analyzing the information load paradigmand does not require the researcher to adopt a partic-ular definition of "total information." The method-ology is flexible enough to enable the researcher toexamine the effects of the number of brands, the num-ber of attributes or other variables of interest on theoccurrence of information overload. Moreover, sucheffects can be examined individually or jointly. Yetthe LOGIT framework permits the researcher tomodel various definitions of "total" information.*Furthennore, it is possible to examine complex inter-action effects. Knowledge of such effects permittedbetter interpretation of previous studies. If the re-searcher desires, additional explanatory variables,such as the salience of information provided, vari-ability of the relative attractiveness of the choice al-tematives, and individual variables can be explicitlyincorporated in the analysis to develop a richer un-derstanding of information load phenomenon.

'For example, using Jacoby et al.'s (1974a, 1974b) definition of"total" information, the probability of correct choice may be mod-elled as:

log.1 - P ,

= Pi + (NB * NA)

wbere NB = number of brands in the choice set and NA = numberof attributes per brand on which information is provided. Such amodel was estimated for all Jacoby data. The model provided a poorfit to the data collected by them.

REFERENCESBartlett, C. J. and Calviti G. Green (1966), "Clitiical Predic-

tion: Does One Sometimes Know Too Much?", Journalof Counseling Psychology. 13 (Fall), 267-270.

Bettman, J. R. (1979), An Information Processing Theory ofConsumer Choice, Reading, MA: Addison-Wesley.

(1974), "Decision Net Models of Buyer Informa-tion Processing and Choice: Findings, Problems and Pros-pects," in Buyer/Consumer Information Processing. G. D.Hughes and M. L. Ray, eds.. Chapel Hill: University ofNorth Carolina Press, p. 59-74.

and P. Kakkar (1977), "Effect of Information Pres-entation Format on Consumer Information AcquisitionStrategies," Journal of Consumer Research. 3 (March),233-240.

and Michael A. Zins (1979), "Information Formatand Choice Task Effects in Decision Making," Journal ofConsumer Research, 4 (September), 141-153.

Bishop, Y. M., S. E. Feinberg and P. W. Holland (1975),Discrete Multivariate Analysis: Theory and Practice, Cam-bridge, MA: MIT Press.

Chestnut, R. W. and J. Jacoby (1977), "Consumer Informa-tion Processing: Emerging Theory and Findings," in Con-sumer and Industrial Buying Behavior. Arch G. Woodside,

J. N. Sheth and P. D. Bennett, eds.. New York: ElsevierNorth-Holland, Inc., 119-134.

Cox, D, R. 0970), Analysis of Binary Data, 2nd ed., London:Chapman and Hall.

Day, George S. (1976), "Assessing the Effects of InformationDisclosure Requirements," Journal of Marketing. 40 (April),42-52.

DeSarbo, W. S. and D. K. Hildebrand (1980), "A Marketer'sGuide to Log Linear Models for Qualitative Data Analy-sis," Journal of Marketing, 44 (Summer), 40-51.

Heiss, J. L. (1975), "Measuring Agreement Between TwoJudges on the Presence or Absence of a Trait," Biometrics,31 (September), 651-9.

Green, P. E., Frank J. Carmone and David P. Wachspress(1977), "On the Analysis of Qualitative Data in MarketingResearch," Journal of"Marketing Research, 14 (February),52-9.

Jacoby, J. (1977), "Information Load and Decision Quality:Some Contested Issues," Journal of Marketing Research,14 (November), 569-73.

(1974), "Consumer Reaction to Information Dis-plays," in Advertising and the Public Interest, S.F. Divita,ed., Chicago: American Marketing Association, 101-18.

36 / Journal of Marketing, Spring 1982

. , R. W. Chestnut, K. C. Weigel and W. Fisher(1976), '*Pre-Purchase Information Acquisition. Descrip-tion of a Process Methodology, Research Paradigm, andPilot Investigation," in Advances in Consumer Research,Vol. 3, B. B. Anderson, ed., Chicago: Association forConsumer Research, 306-14.

, , D. E. Speller and Carol A. K. Beming (1975),"Constructive Criticism and Programmatic Research: Re-ply to Russo," Journal of Consumer Research, 2 (Septem-ber), 154-6.

-, and C. A. Kohn (1974a), "Brand ChoiceBehavior as a Function of Information Load," Journal ofMarketing Research, 11 (February), 63-69.

-, and (1974b), "Brand ChoiceBehavior as a Function of Information Load: Replicationand Extension," Journal of Consumer Research, 1 (June),33-42,

-, George J. Szybilio atid Jacqueline Busato-Schach(1977), "Information Acquisition Behavior in Brand ChoiceSituation," Journal of Consumer Research, 3 (March),209-216.

Kelly, E. L. and Donald W. Fiske (1951), The Prediction ofPerformance in Clinical Psychology, Ann Arbor: Univer-sity of Michigan Press.

Malhotra, Naresh K. (1982a), "Information Load and Con-sumer Decision Making," Journal of Consumer Research,in press.

(1982b), "Structural Reliability and Stability ofNonmetric Conjoint Analysis," Journal of Marketing Re-search, in press.

(1982c), "Multi-Stage Information Processing Be-havior: An Experimental Investigation," Journal of theAcademy of Marketing Science, in press.

Mendenhail, William and Richard L. Schaeffer (1973), Math-ematical Statistics with Applications, North Scituate, MA:Duxbury Press.

Miller, George A. (1956), "The Magical Number Seven, Plusor Minus Two: Some Limits On Our Capacity for Pro-cessing Information," Psychological Review, 63 (March),81-92.

Moreno, Nelson (1974), "The Effects of Motivation on Con-sumer Information Processing," unpublished Ph.D. disser-tation, Purdue University.

Newman, J. W. and R. Staelin (1972), "Prepurchase Infor-mation Seeking for New Cars and Major Household Ap-pliances," Journal of Marketing Research, 9 (August),249-57,

Payne, John W. (1976), "Task Complexity and Contingent

Processing in Decision Making: An Information Search andProtocol Analysis," Organizational Behavior and HumanPerformance, 16 (August), 366-87.

Quastler, Henry (1956), "Studies of Human Channel Capac-ity," in Information Theory, Proceedings, C, Cherry, ed..New York: Academic Press, 361-371.

Russo, J. Edward (1977), "The Value of Unit Price of Infor-mation," Journal of Marketing Research, 14 (May),193-201.

(1974), "More Information Is Better: A Re-evalu-ation of Jacoby, Speller and Kohn," Journal of ConsumerResearch, 1 (December), 68-72.

, J. Krieser and S. Miyashita (1975), "An EffectiveDisplay of Unit Price Information," Journal of Marketing,39 (April), 11-19.

Scammon, Debra L. (1977), "Information Load and Con-sumers," Journal of Consumer Research, 4 (December),148-55.

Summers, John O. (1974), "Less Information is Better?",Journal of Marketing Research, 11 (November), 467-8.

Weitz, Barton and Peter Wright (1979), "Retrospective Self-Insight on Factors Considered in Product Evaluation,"Journal of Consumer Research, 6 (December), 280-296.

Wherry, Robert J. (1940), "The Wherry-Doolittle Test Selec-tion Method," in Occupational Counseling Techniques,W. H. Stead and L. L. Shartle, eds.. New York: AmericanBook Company, 245-252.

(1931), "A New Formula for Predicting the Shrink-age of the Coefficient of Multiple Correlations," Annalsof Mathematical Statistics, 2, 440-51.

Wilkie, William L. (1978), "Consumer Information Process-ing: Issues for Public Policymakers," in Consumer Re-search for Consumer Policy. M, Denny and R. Lund, eds.,Cambridge, MA: MIT Center for Policy Alternatives,88-117.

(1975), "How Consumers Use Product Informa-tion: An Assessment of Research in Relation to Public Pol-icy Needs," prepared for the National Science Foundation,Grant No. GI-42037.

(1974), "Analysisof Effects of Information Load,"Journal of Marketing Research, 11 (November), 462-466.

Wright, P. L. (1975), "Consumer Choice Strategies: Simpli-fying vs. Optimizing," Journal of Marketing Research, 12(Febmaiy), 60-7.

(1972), "Consumer Judgmenul Strategies: Beyondthe Compensatory Assumption," in Proceedings, ThirdAnnual Conference, M. Venkatesan, ed., Chicago: Asso-ciation for Consumer Research, 316-324.

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