Product Familiarity: Critical Comments on Selected Studies and Theoretical Extensions

ABSTRACT - This paper addresses a number of conceptual and methodological issues involving product familiarity. First, some of the general conceptualization and measurement problems were discussed. Second, the studies of Marks and Olson (1981), Tan and Dolich (1981), and Johnson and Ruses (1981) were criticized for their weaknesses. Third, an eclectic cognitive view of product familiarity was introduced. And finally, recommendations for future research involving product familiarity were suggested.


M. Joseph Sirgy (1981) ,"Product Familiarity: Critical Comments on Selected Studies and Theoretical Extensions", in NA - Advances in Consumer Research Volume 08, eds. Kent B. Monroe, Ann Abor, MI : Association for Consumer Research, Pages: 156-160.

Advances in Consumer Research Volume 8, 1981      Pages 156-160


M. Joseph Sirgy, Virginia Polytechnic Institute and State University


This paper addresses a number of conceptual and methodological issues involving product familiarity. First, some of the general conceptualization and measurement problems were discussed. Second, the studies of Marks and Olson (1981), Tan and Dolich (1981), and Johnson and Ruses (1981) were criticized for their weaknesses. Third, an eclectic cognitive view of product familiarity was introduced. And finally, recommendations for future research involving product familiarity were suggested.


Product familiarity seams to be an emerging variable in the study of consumer behavior. Consumer researchers have used it to explain a number of consumer-related phenomena such as, message acceptance (e.g., Marks & Olson 1981), choice of decision rule (e.g. Parks 1976, Tan & Dolich 1981), product preference and purchase intention (e.g., Marks & Olson 1981), product satisfaction (e.g., Anderson, Engledow & Becker 1979), and new learning (Johnson & Russo 1981). Overall, the pattern of results utilizing product familiarity as an explanatory variable seems conflicting. The primary cause of this lack of clarity of the precise role of product familiarity as a determinant of consumer behavior may be due to the ambiguity displayed in the conceptualization and measurement of product familiarity.

This discussion will (1) focus on some of the conceptualization and measurement efforts revealed in the consumer behavior literature, (2) criticize the studies of Marks and Olson (1981), Tan and Dolich, (1981), and Johnson and Russo, (1981), which treat product familiarity as an explanatory variable of consumer behavior phenomena, (3) provide an eclectic cognitive view of product familiarity, and (4) suggest recommendations for future research in relation to product familiarity.

Conceptualization and Operationalization of Product Familiarity

One important issue concerning the conceptualization and measurement efforts of product familiarity is the unidimensional versus multidimensional treatment of product familiarity.

Product familiarity has been conceptualized and operationalized differently by different investigators. In the unidimensional tradition, Park (1976) measured product familiarity in terms of subjects' agreement with statements about the product. Woodruff (1972) used a free-recall method of purchase situations. Raju and Reilly (1979) employed self-reported "frequency of use, overall familiarity, and knowledge of how to select best brand" as measures of product familiarity. Anderson, Engledow, and Becker (1979) and Jacoby, Chestnut, and Fisher (1978) used a frequency of purchase measure as an indicator of product familiarity. Johnson and Russo (1981) used a global self-report rating measure - subjects were asked to rate their previous knowledge of automobiles compared to the rest of the population. Tan and Dolich (1981) measured product familiarity by the proportion of brands in the product class that one knew something about.

The main problem with these measures is that they are atheoretical. That is, they are not based on any one theory. In contrast, the multidimensional measurement of product familiarity, as established by Olson and his associates (e.g., Marks & Olson 1981, Kanwar, Olson, & Sims 1981, Olson & Muderrisoglu 1979), is based on the cognitive theory involving cognitive structures. The dimensions that Olson and his associates worked with are: (1) dimensionality which is defined as the number of activatable concepts associated with a particular domain, (2) articulation defined as the number of category representations or levels for each salient dimension in memory, and (3) abstraction defined as the degree to which salient, activatable dimensions are abstract versus concrete. Although more then three dimensions were identified as constituting product familiarity (reference was scantly made to the interrelatedness and centrality dimensions), Marks and Olson (1981) conceptually treated product familiarity only along the dimensionality and abstractness dimensions. High product familiarity was viewed to reflect high abstractness and low dimensionality and vice versa.

There are a number of problems which marks Olson's conceptual treatment of product familiarity. First, it is not clear how the dimensionality of product familiarity is different from its articulation. It seems to me that articulation denotes (or should I say connotes) the extent to which a psychological concept is encoded as symbolic or semantic. Symbolic psychological concepts can be construed as less articulated and semantic concepts as being more articulated. Also, how does dimensionality differ from interrelatedness? Furthermore, why wasn't the dimension of centrality incorporated in the main product familiarity schema? And finally but most importantly, why wasn't the certainty dimension considered at all? To me, product familiarity taps those beliefs (or "saliency weights" using Rosenberg-Fishbein's terminology) stored in memory. The degree of certainty of beliefs is construed to be extremely important as it affects psychological processes such as alternative evaluation and attitude change (see Fishbein and Ajzen 1975).

The measurement technique which was consistently used in most of Olson's studies (e.g., Marks & Olson 1981, Kanwar, Olson & Sims 1981, Olson & Muderrisoglu 1979) is the free-elicitation technique. Kanwar, Olson, and Sims (1981) have attempted to provide construct validation to the free-elicitation measure by relating it to product familiarity scores of a Rep test and a self-report "knowledge" measure. The results did not provide clear-cut support for the construct validity of the free-elicitation measure. However, Olson and his associates have continuously used it knowing fully that the measure's construct validity is yet to be demonstrated. Even Olson's latest study (Marks and Olson 1981), which will be discussed later in some detail, failed to provide the free-elicitation measure with the desired nomological validity. It should be noted that the free-elicitation technique is not different from the psychoanalytic technique of free-association which has been used extensively in the clinical psychology literature. It was fairly well recognized by Freud himself that the free-association technique had its share of problems. Subjects' responses are contaminated by perceptual cues, social desirability factors, memory factors, and other situational influences (The Standard Edition of the Complete Psychological Works of Sigmund Freud, 1953-1974).

Product Familiarity as a Determinant of Consumer Behavior.  Marks and Olson's (1981) study was primarily concerned with the effect of product familiarity on message acceptance, product preference, and purchase intention. Tan and Dolich's (1981) study focused on the role that product familiarity plays in the selection of decision rules. Johnson and Russo's (1981) study on the other hand, examined the role of product familiarity in new learning. Each of these studies will be scrutinized in the following sections.

Effect of Product Familiarity on Message Acceptance.  Marks and Olson (1981) expected that high product familiarity would induce greater cognitive responses related to support arguments, less cognitive responses related to counter-arguments, and overall fewer cognitive responses than under low product familiarity conditions. The results only showed that high product familiarity generated fewer counterarguments.

There are a number of problems in Marks and Olson's (1981) message acceptance part of the study that I can see offhand. First, even though 2/3 of their hypotheses (although not clearly stated) were not confirmed by the results, the support provided for the relationship between product familiarity and counterarguments can be due to experimental artifacts such as, experimental demand and/or nature of the sample.

Second, I was troubled by the fact that no theoretical justification was adequately provided to support the relationship between product familiarity and message acceptance in this allegedly important study in which a theory for product familiarity is advocated.

Third, one needs to realize that counterargument, among other cognitive responses such as source derogation, is a cognitive response mode designed to protect an interrelationship of existing psychological concepts. Each psychological concept has a value and a belief dimension. The belief dimension is that dimension which links one psychological concept to another (cf. Fishbein & Ajzen 1975). Product familiarity is generally represented as belief dimensions. Now, concerning the psychological dynamics involved in support and counter-arguments, the following explanation is in order. A cognitive response is usually activated by a cognitive comparison between a perceptual set and an internally evoked cognitive set. Incongruence between the two sets usually leads to counter-argumentation, source derogation, among other cognitive responses. This serves to protect the existing cognitive set from the threat of change inflicted by the perceptual set. Congruence between the two sets, on the other hand, generally leads to support argumentation among other responses and therefore serves to strengthen the existing cognitive set (Sirgy, in press a, in press b, Wright 1973). Based on this theoretical perspective, what would be the role of product familiarity in support or counter-arguments? Product evaluation (of existing attitude toward the product) is the major determinant of support or counter-argument. Product familiarity is just one component of product evaluation. Viewed from Rosenberg-Fishbein's multi-attribute attitude model, product familiarity reflects the set of saliency weights, whereas product evaluation represents the overall attitude.

Effect of Product Familiarity on Product Preference and Purchase Intention.  Marks and Olson (1981) argued that since counterargument is expected to affect attitudes, subjects who are more familiar with the product (i.e., secretaries) would be more likely than those who are less familiar with the product (i.e., students) to recommend its purchase. Although the results were consistent with the hypothesis, it can be argued that the effect could have been due to an artifact of the sample and/or experimental demands not to mention the equivocal measurement of product familiarity. Theoretically speaking, product familiarity does not independently or directly affect product preference and purchase intention but interacts with values which, in turn, interacts with perceptual elements to determine change in attitude and therefore behavior.

Effect of Product Familiarity on Decision Rule Selection.  Tan and Dolich (1981) used product familiarity along with cognitive complexity to determine their effect on the use of decision rules in consumer behavior. The results indicated that product familiarity plays a negligible role in the selection of decision rules in consumer behavior, and that cognitive complexity plays a significant role. These results are extremely interesting however not totally convincing.

First, no theoretical justification was provided for the choice of the designated decision rules in the study. One may argue that product familiarity would have played a major role in the selection of other types of decision rules such as, the maximax rule compared to the minimax or maximin rules (see Edwards & Tversky 1967, Fishburn 1964, Kaufman 1968, Raiffa 1968, White 1969).

Second, the measurement of product familiarity "through the proportion of brands in the product class that one knew something about" could be a very misleading indicator of product familiarity. It is quite conceivable that a consumer could be highly familiar with one or two brands while knowing little about other competing brands.

Third, attractiveness values were used without any differentiation between its individual components (i.e., salience weights, importance weights, evaluative weights, and determinance weights). Knowing something about, for example, salience weights, can provide us with further insights concerning the dynamics involved in decision rule selection. To exemplify, it is possible that those attributes which are highly salient are included in the final decision and therefore reflecting the usage of a partial version of the compensatory model. Also importance weights (which can be construed as a dimension of product familiarity) may have a lot to do with decision rule selection. Attributes which are more important than others may lead to the adoption of a partial compensatory model which only includes those important attributes.

Effect of Product Familiarity on New Learning.  Johnson and Russo (1981) examined the interaction of product familiarity and type of decision rule (attribute versus brand strategy) on learning of new information. The results provided support for the role of the "enrichment hypothesis." That is, product familiarity facilitates new learning. However, this main effect was qualified by an interaction effect between product familiarity and type of decision rule on learning of new information, and therefore providing some support for the "inverted-U hypothesis." That is, use of attribute strategy accompanied by high product familiarity contributed to new learning more than with the use of brand strategy.

Again, these results are very intriguing but not terribly convincing. This may be due to a number of problems: First, the enrichment hypothesis refers to the enhancing effect of internal existing information stored in memory on new learning (Chase & Simon 1973), whereas the inverted-U hypothesis refers to the enhancing or inhibiting effects of external information on new learning (see Jacoby, Speller & Kohn 1974, Jacoby, Speller & Berning 1974). Consequently, these two hypothesis should not be construed as competing since they operate at different levels.

Second, since the inverted-U hypothesis refers to the effect of amount of external information on learning, it seems to me that it should have little to do with the type of decision strategy (attribute versus brand strategy) as a suitable operationalization of the principle. A more suitable operationalization of the inverted-U hypothesis would involve amount of external information as opposed to type of decision strategy.

Third, subjects were specifically instructed to make a decision by rating each automobile. They weren't specifically asked to learn the material. The surprise recall measure cannot be treated as a reliable and valid measure of new learning. New learning under these conditions can be severely affected by the nature of the task, personality and motivational factors, and other situational influences.

Fourth, subjects were asked to rate each of the seven automobiles based on the information provided, and not prior knowledge. It seems to me that it would be quite difficult for a subject to ignore previous information by simply asking him/her to do so. It is therefore very likely that previous information was indeed a confounding variable which was not adequately controlled for.

Fifth, in the judgment task, it was assumed that by judging each automobile separately, subjects are not making comparative judgments based on some specific other car or based on the preceding car, among other alternatives.

And last but not least, the measurement problem involving product familiarity has to be pointed out. Product familiarity was measured by instructing subjects to "rate your previous knowledge of automobiles compared to the rest of the population." Besides its atheoretical overtones, this measure of product familiarity is confounded by (1) individual differences in the interpretation of "the rest of the population." (2) social desirability factors, (3) as well as personality factors such as cognitive complexity (i.e., cognitively complex subjects may have difficulty making global judgments compared to cognitively simple individuals), and (4) self-concepts related to mechanical devices (i.e., an individual who perceives himself as being mechanically inclined might rate his familiarity with automobiles as high despite his lack of knowledge in this area to avoid cognitive inconsistency).

An Eclectic Cognitive View of Product Familiarity

Figure 1 shows a cross-sectional and transverse view of a hypothetical physical space of cognitive structures. To fully understand product familiarity, one needs to describe how product familiarity is represented within an individual's cognitive structures. This position is quite similar to the one espoused by Olson and his associates, however, differs with respect to the delineation of cognitive structures and product familiarity dimensions.

From the cross-sectional view of cognitive structures of Figure 1, cognitions are basically represented as psychological concepts or "nodes." Each psychological concept which is content-specific is surrounded by a layer representing the value placed on that concept. The thickness of this layer reflects the positivity or negativity or direction of the value placed on that concept. Darker shades represent negative values whereas lighter shades represent positive values.

Some concepts are linked to other concepts through belief links. Each belief link is a content-specific psychological association and is directed (i.e., excitation travels in one direction). The thickness of a belief link is indicative of its degree of certainty. The thicker the belief dimension the higher the certainty projection.

Psychological concepts are also spatially arranged along a central-peripheral dimension. Central concepts are more abstract and more interrelated that peripheral concepts.



Peripheral concepts can be referred to as attributes; the more central concepts as schemata and scripts.

As shown in the transverse view of cognitive structures of Figure 1, psychological concepts are also arranged along an articulation dimension. Concepts which are highly articulated are usually encoded in semantic or verbal form, whereas those which are less articulated are encoded in symbolic or nonverbal form.

This eclectic view of cognitive structures has been influenced by the writings of Kelly (1955), Rokeach (1960), and Epstein (1973), Ableson (1976), Collins and Loftus (1975), and Kosslyn and Pomerantz (1977).

Now the question arises concerning the cognitive representation of product familiarity within the space of cognitive structures. The position which is taken by this author is that product familiarity is represented by those belief dimensions connecting the psychological concept of a particular product with other psychological concepts representing the product attributes.

Those belief dimensions describing product familiarity can be characterized by at least eleven dimensions: (1) content, (2) direction, (3) certainty, (4) centrality, (5) articulation, (6) interrelatedness, (7) consistency, (8) stability, (9) accuracy, (10) verifiability, and (11) frame-of-reference.

Content of a belief dimension refers to the specific content of the association between two psychological concepts. For example, the Ford Escort automobile is an economy car. The verb "is" is a content-specific belief linking the psychological concept "Ford Escort" with "an economy car." Direction of a belief dimension refers to the flow of activation from one concept to another (see Figure 1). In the above example, the flow is from the psychological concept "Ford Escort" to "an economy car" and not necessarily vice-versa. Certainty of a belief dimension refers to the strength of the link between two concepts and is represented by the layer's thickness of a given link (see Figure 1). In the above example, the belief that Ford Escort is indeed an economy car may be of high certainty or low certainty. Centrality refers to the degree of salience, importance, or abstractness of a given concept. In this case, the product concept "Ford Escort" may be closer to the periphery than to the center (see Figure 1). Articulation refers to the extent to which a psychological concept is verbal (semantic) versus nonverbal (symbolic). The "Ford Escort" as a psychological concept may be encoded more on a verbal-semantic level than a non-verbal-symbolic level and therefore is more articulated in form. Interrelatedness refers to the number of attribute concepts which a psychological schema is linked with. A highly interrelated product concept is therefore associated with many attributes. Ford Escort may be associated with only high gas mileage in a consumer's mind, and therefore is said to have low interrelatedness. Another consumer may associate Ford Escort with high gas mileage, style, safety, reliability, roominess, and patriotism. The latter consumer's product concept is said to be highly interrelated. Consistency refers to the degree to which the attributes associated with a psychological schema are consistent with one another. For example, a consumer who knows Ford Escort is economical and stylish but also as being unsafe and unreliable is holding an inconsistent set of attributes. Whereas a consumer who thinks of Ford Escort as being economical, stylish, safe, and reliable holds a consistent set of attributes. Also, the consumer who relates the Ford Escort to being uneconomical, lacking style, being unsafe and unreliable is also holding a consistent set of attributes. Stability refers to the extent to which knowledge of a particular concept is durable across time. A person who may know something about Ford Escort today but forgets it tomorrow lacks temporal stability. Accuracy refers to the extent to which the set of beliefs linking a psychological schema to a set of attributes are representative of the true state-of-affairs, or just simply having accurate perceptions. A consumer who thinks that the Ford Escort is a luxury uneconomical car has inaccurate product perceptions. Verifiability refers to the extent to which an individual feels that the beliefs associating a psychological schema (or product concept) with a set of attributes can be somehow verifiable. A person who believes that God controls one's destiny may feel that this kind of belief is unverifiable, however his belief that the Ford Escort is economical can be verified through actual experience. And finally, frame-of-reference refers to that psychological "script" which the individual is presently using during which certain associations between a psychological schema and its attributes become activated. For example, if one approaches the Ford Escort from a consumer's union frame-of-reference, a totally different set of attributes might be activated (e.g., safety, economy, reliability) compared to a shopper's frame-of-reference.

How does product familiarity differ from product evaluation? The only difference that I can see in these two phenomena is the value dimension characterizing the intensity and direction of the psychological concepts. Product evaluation takes into account the various dimensions of the beliefs and values pertaining to a product concept and its associated attributes whereas product familiarity deals with belief dimensions only. This formulation is consistent with traditional social-psychological attitude formulations (see Fishbein & Ajzen 1975).

Recommendations for Future Research

With this eclectic theoretical orientation, some recommendations for future research are suggested.

First, more work needs to be done in the measurement area. Atheoretical measurement of product familiarity should be discouraged. The efforts of Olson and his associates are a step in the right direction. However, since valid measures rest on adequate theory, theory development of product familiarity should have the first priority. More research is needed to test different theories of product familiarity.

Second, if product familiarity is truly multidimensional, research effort should not only be directed to identifying those dimensions but also to reveal how they are differentially combined to form overall familiarity. Can we use an additive model and sum over all the familiarity dimensions? Or would a multiplicative model be more appropriate? Should there be weights placed on those dimensions? And so on.

Third, hardly any research has been conducted treating product familiarity as the dependent variable or the phenomenon of interest. What are the differential effects of dispositional variables such as cognitive needs and cognitive complexity on product familiarity? What are the various situational factors which influence product familiarity? Examples may include time factors (e.g., repetition, schedule of reinforcement), communication factors (e.g., type of massage, type of source, type of communication channel), among others.

Finally, more systematic research needs to be conducted to determine the effects of product familiarity on consumer processes such as perception, awareness, attention, need recognition, information search, alternative evaluation, actual behavior, and outcome evaluation.


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M. Joseph Sirgy, Virginia Polytechnic Institute and State University


NA - Advances in Consumer Research Volume 08 | 1981

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