The Identification of Specific Information Acquisition Patterns in Information Display Board Tasks, and Their Relation to Demographics

ABSTRACT - Factor analysis was used to identify patterns of specific information used across four product decisions in information display board tasks. Canonical correlation analysis was then applied to identify and verify hypothesized relationships between factor scores on the specific information acquisition patterns with demographic variables as predictors. The resulting specific information acquisition patterns were consistent with anticipated types of patterns, and were shown to be significantly related to hypothesized demographic profiles.


Charles M. Schaninger and Donald Sciglimpaglia (1980) ,"The Identification of Specific Information Acquisition Patterns in Information Display Board Tasks, and Their Relation to Demographics", in NA - Advances in Consumer Research Volume 07, eds. Jerry C. Olson, Ann Abor, MI : Association for Consumer Research, Pages: 513-518.

Advances in Consumer Research Volume 7, 1980     Pages 513-518


Charles M. Schaninger, Marketing Department University of Massachusetts, Amherst, Mass. 01003

Donald Sciglimpaglia, San Diego State University, San Diego, California


Factor analysis was used to identify patterns of specific information used across four product decisions in information display board tasks. Canonical correlation analysis was then applied to identify and verify hypothesized relationships between factor scores on the specific information acquisition patterns with demographic variables as predictors. The resulting specific information acquisition patterns were consistent with anticipated types of patterns, and were shown to be significantly related to hypothesized demographic profiles.

In the past few years, a number of studies have utilized information display board approaches to examine consumer information acquisition and processing. To date, most of these studies have been descriptive in nature, establishing the use of criterion measures from such behavioral process methodologies, or examining the reliability and validity of such measures (Bettman and Jacoby, 1976; Jacoby, Chestnut, Weigl, and Fisher, 1976; Arch and Bettman, 1978; Jacoby, Chestnut, Joyer, Sheluga, and Donahue, 1978; Jacoby, Szybillo, and Busato-Schach, 1977; Bettman and Kakkar, 1977; Jacoby, Chestnut and Fisher, 1978). Most of the above studies examined non-durable goods such as cereals, toothpastes, margarines, headache remedies, etc. While the criteria examined by these studies varies, three principal types of criteria have been examined: depth of search (total number of cues drawn, number of attributes examined, number of brands examined, decision time, etc.); sequence of search based on types of transitions, and models thereof (Jacoby, Chestnut, Weigl, and Fisher, 1976), sequence of search based on a classification typology scheme developed by Bettman and Jacoby (1976); and information content (types of specific information used) (Jacoby, Chestnut, and Silberman, 1977; Jacoby, Szybillo, and Busato-Schach, 1977; Jacoby, Chestnut, and Fisher, 1978).

This paper shall focus on the usage of specific information and shall attempt to identify patterns of specific information usage and relate individual differences in such patterns to demographics.

The identification of demographic correlates to specific information usage could have important policy implications: Are disadvantages consumers successfully using unit pricing to make better product decisions? Are the elderly with health problems utilizing such nutritional information as saturated fat content, sodium content, etc.? The policy implications exist not only for packaging and unit pricing policies, but also for government dissemination of product information should a federal consumer information agency be formed.

Jacoby, Chestnut and Silberman (1977) examined consumer acquisition and use of nutritional information for margarine and cereals, and found that younger consumers tended to access more nutritional information. The rest of their study dealt with the use and comprehension of nutritional information in a descriptive sense, and did not relate it to other demographic variables. The two other studies examining specific information accessed were also descriptive and did not relate usage of specific types of information to demographic or other individual differences.

Monroe and LaPlaca (1972) reported that unit price information in actual store settings tends to be utilized most by more educated, professional, and higher income respondents rather than by those of lower socioeconomic standing who tend to make less price rational decisions.

Little other research bears directly on the use of specific information and its relationship to demographics.

With regard to the influence of demographic variables on information processing, most of the existing literature deals with either age differences (Phillips and Sternthal, 1977) or social class differences (Engel, Blackwell, and Kollatt, 1978; Robertson, 1970).

Phillips and Sternthal (1977) reviewed the existing literature on age differences in information processing concluding that older individuals are less capable of processing large amounts of information, and have difficulty distinguishing relevant from irrelevant information for unfamiliar decisions. They also conclude that due to greater market experience they will process less information than younger individuals for familiar decisions--due to their ability to distinguish relevant information and due to their stored knowledge about brands and well established search heuristics. Engel, et al. (1978) also conclude that individuals below 35, those with higher educational or occupational status, and those in the middle income categories will tend to engage in more extended (and rational) decision making.

The emerging literature on working wives (Douglas, 1976; Strober and Weinberg, 1977) suggests that working wives spend less time shopping, go to fewer stores, and hypothesized that they would be more likely to consumer convenience oriented and instant products. These findings suggest that working wives would be more familiar with such instant products and thus have a well defined set of relevant criteria as well as stored knowledge--leading to selective processing of certain brands by key attributes. For other products--i.e., durables, and other convenience items for which they would have less shopping/market experience than non-working housewives, they may tend to draw on more alternatives and on less relevant attributes. They are also likely to be more concerned with time/labor saving attributes for durables.

This study examines specific information acquisition for four product classes: Instant Coffee, Instant Lemonades, Non-dairy Coffee Creamers, and Dryers. It examines the acquisition of specific attributes (number of times a given attribute is drawn), and of specific alternatives. Factor analysis is employed to reduce the total number of attributes and alternatives for the four decisions to a more manageable number and to identify patterns of specific information acquisition.

Similarly, factor analysis is used to reduce a set of demographic variables. Canonical Correlation analysis is then employed, utilizing demographic factor scores as predictors and specific information acquisition pattern factor scores as criteria.

A set of general hypotheses is presented below, based on the literature review, and a set of more specific hypotheses dealing with the factor scores found in the first part of the results section will be presented in the results section.

Based upon Jacoby et al.'s (1977) research, it is hypothesized that younger housewives will access more nutritional information, particularly information dealing with artificial additives and weight related dietary concerns.

It is also hypothesized that higher socioeconomic status housewives will access the above types of information more, due to higher education, greater concern with nutrition, and diet consciousness.

It is hypothesized that older housewives will be more likely to draw medical health related dietary information such as sodium content, etc., due to their greater propensity to be on salt or fat restricted diets.

Both younger and higher socioeconomic status housewives are hypothesized to utilize unit price information more, based upon Monroe and LaPlaca's (1972) findings and on the greater propensity of younger housewives to adapt new types of information such as unit price into their set of salient buying criteria.

Younger and higher socioeconomic status housewives are also hypothesized to examine more information in general and to demonstrate more rational search and screening heuristics than older or lower socioeconomic housewives. This hypothesis was drawn from the findings of Phillips and Sternthal (1977) and Engel et al.'s (1978) conclusions with regard to such individuals exhibiting greater search tendencies in general, and greater information handling capacities (due to younger age and education).

Working wives are hypothesized to draw more information on labor saving features for dryers, due to their emphasis on saving time and labor. They are also hypothesized to examine size and convenience oriented features for instant beverage products and to examine larger sizes due to greater interest and experience with such products, and a tendency to use them.

Based upon smaller family sizes, and a tendency to use instant beverage products less, older housewives are hypothesized to be more likely to draw information on the smallest sizes or cheapest alternatives for such products, as well as be more brand loyal toward well established brands.

The principal purpose of this paper is to identify whether or not patterns of information acquisition exist across different decisions, to identify such patterns, and to examine whether and how such patterns are related to demographic profiles. The hypotheses presented above, and the specific ones presented in the results section were intended to demonstrate the types of linkages anticipated between these two variable sets. This is primarily an exploratory study attempting to examine the overall level of significance and nature of the relationship between patterns of specific information acquisition with corresponding demographic profiles.


One hundred twenty housewives were recruited from church groups in a medium sized midwestern city. Their ages ranged from early twenties to one lady in her eighties, with most in their thirties and forties. Each subject was offered a monetary incentive (donated to her church group at her option) to participate in this study of "how consumers shop for products."

All subjects first completed a battery of questionnaire measures, including the following demographic information: husband's and wife's age, occupation, and education; stage in family life cycle; dwelling type; home ownership; and combined family income.

After completing the paper and pencil measures, subjects were conducted in groups of three to another room and instructed in how to simulate their own shopping behavior, using a microwave information display board for instruction and practice.

Each housewife was then asked to "go shopping," and collect only that information which she normally did or would collect for four product classes: instant coffees (6 attributes, 14 alternatives), non-dairy coffee creamers (14 attributes, 14 alternatives), instant lemonades (16 attributes, 13 alternatives), and electric dryers (10 attributes, 13 alternatives). The matrix boards were similar to those used by Bettman and Jacoby (1976), with product alternatives in columns, and attributes across rows, although constructed of hard pasteboard with cues in split coin envelopes. For the three grocery items, all available package information and unit price information were included as attributes and arranged in alphabetical order. Dryer attributes all dealt with possession of various features and price.

Based on a pretest, the Bettman and Jacoby approach was modified, in that multiple versions of the same brands (differing primarily in container size or product form for the grocery items, and in particular feature combinations for dryers) were included, and a second card for each information cue remained visible in its slot after acquisition (rather than require subjects to remember and/or reaccess cues). In the pretest, subjects had been required to draw brand name as a cue, and to remember or reaccess both the content and position of cues. They were unable to do this, particularly for microwaves and dryers--and expressed great frustration toward the difficult "concentration-matching" game which resulted. Therefore, data from our first pilot session were dropped, and usable data was obtained for the remaining 102 post-pilot subjects for the four remaining experimental-task products.


Data Reduction

To determine information usage, counts were made on the number of times each attribute and each alternative were accessed for the four decisions. Thus, 100 variables were created (counts for 6 attributes, 14 coffee alternatives; 10 attributes, 13 dryer alternatives; 14 attributes, 14 creamer alternatives; and 16 attributes, 13 instant lemonade alternatives). Rao's canonical factoring was applied to these 100 created variables to reduce the data set and to identify patterns of information usage. This particular factoring method was chosen because it is a scale free procedure, it is based upon common variance, and it tests the statistical significance of the common variance explained by each factor. The varimax rotation solution was chosen because it was basically comparable to the various oblique solutions with different delta values, because only minor factor intercorrelations were observed, and to eliminate any multicollinearity problems in the criterion set for the following canonical analyses. Both attribute and alternative counts were included to allow the identification of brand loyal patterns, as well as sequential screening patterns--i.e., checking certain important attributes, and then drawing more information on alternatives with good scores on those attributes, etc. Thirty factors with eigenvalues greater than 1 were found, explaining 80.9% of the original variance. All 30 factors were significant beyond the .0001 level. Although the sample size was admittedly small for such a large factor analysis, this finding of extremely significant factors, coupled with the exploratory nature of the research and the need to reduce the criteria set tends to justify this analytical approach. Table 1 presents an interpretation of these 30 factors, summarizing the major variable loadings.



A number of types of patterns were anticipated, and encountered in the resulting factors:

1. Brand Loyal Search Patterns: Factors 5,7,11,12,24.

2. Seek National Brands, Avoid Private Labels (or vice versa): Factors 27,29,30,6.

3. Utilize Unit Price Information, Examine Brands with Lowest Unit Prices: Factors 3,6,20.

4. Size, Price, Self Calculation of Unit Price: Factors 2(?) ,6.

5. Size, Price, Buy Largest (Smallest), Cheapest: Factors 2,11,27.

6. Nutritional Information: Concern with Additives (natural ingredients), Calories, Sugar, Fat Content, Dietary Concerns; Age Related Dietary Concerns (Sodium Content, Percent Saturated Fat, etc.): Factors 4,16,17; 22,25; 18.

7. Sequential Screening Patterns: Draw Key Attributes, Select Alternatives with Best Scores, Avoid Those with Bad Scores: Factors 2,3,6 (previously assigned to categories 3,4,5); Factors 8,9,14,15,18,21,24,25.

8. Other Heuristic Search Patterns: Factor 28.

A number of factors were listed under more than one classification because they fell into two or more non-mutually exclusive categories (such as those falling into category 7 and other categories), or because differing interpretations could allow them to be assigned to somewhat similar categories (overlap of categories 3, 4 and 5).

Principal components analysis, with oblique rotation of delta = 0 was chosen to best represent the underlying demographic data structure. Table 2 presents an interpretation of the four resulting demographic factors with eigenvalues greater than one (accounting for 75.0% of the original variance). The first factor was characterized by very high loadings on both husband's and wife's age, and on stage in the family cycle. The second factor was characterized by living in a house rather than an apartment or townhouse, by home ownership rather than renting, and by moderate loadings on husband's occupation. The third factor was characterized by very high loadings on both husband's and wife's education, and by high loadings on husband's occupation and family income. This was interpreted as measuring social class. The fourth factor was characterized by a very high loading on wife's occupation, with a moderate loading On wife's education and a negative loading on husband's occupation; this factor was termed as "working wife."



A number of hypotheses drawn from the literature review and our own conceptualizations regarding the relationship between specific information acquisition patterns and demographics are presented below:

A. Hypotheses related to both age and social class, and age/social class interactions: younger and higher social class housewives are more likely to utilize:

1. Nutritional information: concern with additives, natural ingredients, weight concerned dietary concerns: Factors 4,16,17; 22,25.

2. More dryer cues, labor saving features (color-fashion) due to less product experience, greater information handling capacity, greater search tendencies: Factors 1,9,15,30.

3. Unit price information, examine brands with lowest unit prices: Factors 3,6,20.

4. Sequential Screening Patterns (heuristic modes of handling more information): Factors 2,3,6,8,9,14,15,18,21,24,25.

5. First listed brand heuristic processing: Factor 28.

B. Age Related Hypothesis: older housewives are more likely to utilize:

1. Medical/age related dietary information: Factors 18,22.

2. Brand loyal patterns, older well established national brands: Factors 5,7,13 (for coffee in particular).

3. Size, Price, smallest, cheapest alternatives avoid largest alternatives: Factors 2,-11,-19,-21,-27.

C. Working Wife Related: more likely to utilize

1. Larger sizes for instants, due to greater usage rates, tendency to shop less frequently: Factors 11,19,21,27.

2. Freeze dried coffees: due to greater usage of instants, greater acceptance of newer brands: Factors 11,24.

3. More concern with labor related dryer features: Factors 1,9,15,25,30.

D. Homeownership: likely to interact with age and social class.

1. More likely to have experience with dryers, hence selectively process certain brands: Factors 14,25,30,7,15.

Canonical Analysis

Table 3 presents the results of a canonical correlation analysis treating the four demographic factor scores as predictors of the thirty specific information acquisition factor scores. Two significant roots were obtained, and their coefficients and loadings as well as multivariate tests of significance, are presented.

An interpretation of the loadings and coefficients for the first set of canonical variates indicates a demographic pattern characterized by older, homeowning, lower social class housewives. This pattern is reverse to that dealt with in Hypotheses A1 to A5. Such housewives tend to examine fewer dryer attributes and brands (Factors 1,9,15), supporting Hypotheses A2 and D1. They are also less likely to utilize nutritional information concerned with artificial additives (and Vitamin C, carbohydrates for lemonades) (Factors 4,17) although slightly more concerned with natural ingredients and calories for lemonades (Factor 16), presenting partial support for Hypothesis Al. They were more likely to examine Size, Price, and smallest, cheapest alternatives (Factor 2) and less likely to utilize unit price information (Factor 3)--although these findings tended to represent moderator effects, contributing to the predictability of the overall relationships, but showing low loadings with the patterns. Thus, weak support is found for Hypotheses A3 and B3. They were also less likely to draw information on the largest national brands for creamers and lemonades (Factor 27), thus further supporting Hypothesis B3. They were also more likely to draw information on manufacturer/distributor for all three instant products, as well as draw more on Nescafe Instant Coffee (Factor 13), partially supporting Hypothesis B2. This finding also suggests that lower social class, older housewives tend to draw information which others might consider irrelevant, in using manufacturer's reputation as a cue.



The second set of canonical variates is also characterized by older, slightly lower class housewives--but they tend to be non-homeowners. The finding of higher coefficients than loadings for all three of the principal demographic factors suggests that the relationships involved were strongest for the oldest, non-homeowning housewives in the sample--reflecting that the moderating effects reinforce the basic findings. Such housewives tended to use unit price information less (Factor 3), again supporting Hypothesis A3. They also were less likely to access the largest Maxim alternatives (Factor 11), less likely to draw information on large cans of instant lemonade and with Folsoms Coffee (Factor 21), and drew less information on the largest sized creamer and lemonade alternatives (Factor 27), lending further support for Hypothesis B3 and supporting a modified version of Hypothesis B2. They tended to access Factor 22, indicating a concern for creamer weight related dietary information, as well as Factor 25, indicating concern with calories per serving and lowest calories per serving lemonade alternative (as well as highest price dryers). They did not tend to be more likely to access Factor 18, however, indicating only weight related dietary concerns. This tends to contradict Hypothesis BI and part of Hypothesis A1. They also score lower on Factor 14, being less likely to evidence selective brand acquisition screening, partially supporting Hypothesis A4. They were also less concerned with creamer price (consistent with Factor 22 loading), and less concerned with end of cycle signal for dryers (Factor 23). They were also less likely to draw information on Country Time or Country Pride Lemonades (Factor 12), suggesting that younger housewives were more drawn to brands with "old fashioned, natural" names.

None of the working wife related hypotheses tended to be supported. This demographic factor had low loadings on both of the significant canonical roots.


The specific information acquisition patterns identified by factor analysis were consistent with the types of patterns anticipated, and were shown to be significantly related to demographic factors. With the exception of hypotheses generated about working wives, most of the hypothesized relationships between demographics and specific patterns of information usage were supported. These relationships tended to indicate interactive relationships among the demographic factors, as well as to demonstrate some moderator effects. Specifically, it was found that:

1. Younger, higher social class, homeowners tended to draw more attributes and alternatives for dryers, to be more concerned with labor saving features, and color, and to exhibit sequential screening patterns for dryers.

2. Younger, higher social class homeowners also tended to be more concerned with artificial additives, and with Vitamin C content; but were not more likely to draw weight dietary attributes for creamers.

3. Younger, higher social class housewives were more likely to utilize unit price information, to access lowest unit price alternatives, and to exhibit sequential screening patterns.

4. Older, non-homeowning housewives were more likely to access weight dietary concern attributes and alternatives for creamers, but not more likely to access medical dietary concern such as percentage saturated fat, sodium content, etc., as hypothesized.

5. Older housewives tended to avoid newer and freeze-dried versions of instant coffee, but to access Nescafe, and to utilize manufacturer/distributor as a cue.

6. Older housewives were more likely to access price and size and to access the smallest, cheapest alternatives and to avoid the largest alternatives for the instant beverage products included.

7. No support was found for any relationships between working wives and specific information acquisition patterns, contrary to our hypotheses.

8. No consistent relationships between homeownership and specific information acquisition were found, due to its interacting with other demographic factors in different ways on the two roots examined.


Arch, David C., Bettman, James R., and Kakkar, Pradeep (1978), "Subjects' Information Processing in Information Display Board Studies," Advances in Consumer Research: Volume V, ed. H. Keith Hunt, Ann Arbor: Association for Consumer Research, 555-560.

Bettman, James R. and Jacoby, Jacob (1976), "Patterns of Processing in Consumer Information Acquisition," Advances in Consumer Research: Volume III, ed. B. B. Anderson, Cincinnati: Association for Consumer Research, 315-320.

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Jacoby, Jacob, Chestnut, Robert W., and Silberman, William (1977), "Consumer Use and Comprehension of Nutrition Information," Journal of Consumer Research, 4, 119-128.

Jacoby, Jacob, Chestnut, Robert W., Weigl, Karl C., and Fisher, William A. (1976), "Pre-Purchase Information Acquisition: Description of a Process Methodology, Research Paradigm, and Pilot Investigation," Advances in Consumer Research: Volume III, ed. B. B. Anderson, Cincinnati: Association for Consumer Research, 306-314.

Jacoby, Jacob, Szybillo, George J., and Busato-Schach, Jacqueline (1977), "Information Acquisition Behavior in Brand Choice Situation," Journal of Consumer Research, 3, 209-216.

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Charles M. Schaninger, Marketing Department University of Massachusetts, Amherst, Mass. 01003
Donald Sciglimpaglia, San Diego State University, San Diego, California


NA - Advances in Consumer Research Volume 07 | 1980

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