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Matching theory predicts choices on concurrent variable ratio schedules will show maximization via exclusive choice of the richest schedule. Preliminary research has revealed patterns of brand selection data from individual consumers which show that consumer behavior exhibits both matching and maximization. In this paper, we summarize the results of a study of 80 consumers’ brand selections for nine product categories which indicate that the patterns identified in our initial research can be generalized. We discuss the implications of our findings for research in the behavioral economics of consumption in marketing-oriented economies and for the issue of what and how consumers maximize.

It is well established by marketing research that only comparatively few buyers of a product category (such as baked beans or breakfast cereals) are entirely loyal to a single brand (Heinz or Kellogg’s Frosties, for instance). Most buyers practice multi-brand purchasing over a period of, say, three months, selecting apparently randomly among a small subset (‘repertoire’) of tried and tested brands (Ehrenberg, 1988). Data on buyer behavior for ready-to-eat breakfast cereals in the US, reported by consumer panel members are typical: “The average Shredded Wheat buyer in the year buys it about 4 times in that year, and buys other brands about 37 times. About 12 million US households [buy] Nabisco’s Shredded Wheat in the year. But it is not obvious whether these households are to be thought of as Shredded Wheat customers, or as other brands’ customers who/occasionally bought Shredded Wheat” (Ehrenberg & Goodhardt, 1977, p. 1.4). Similar patterns are apparent for the vast majority of fast-moving consumer goods in steady-state markets (i.e., those with only a slight upward year-on-year trend in sales) in most affluent, consumer-oriented economies. Each brand attracts a relatively small proportion of the buyers of the product category who purchase that brand exclusively during the period under review: as that period lengthens, this proportion declines. Multi-brand purchasing is the norm to the extent that even the heaviest purchasers of a given brand buy other brands within the category much more than they buy their favorite brand over the course of say a year. The broad similarity of this pattern of choice to that found in studies of matching (Herrnstein, 1997) invites a deeper analysis (Foxall, 1999). In order to explain such patterns in terms of the decision mechanisms employed by buyers of such products, this investigation has turned to the work of behavior analysts and experimental economists who have related choices systematically to the schedule of rewards to which they lead. Most of the work of these behavioral economists has been conducted with animals such as rats and pigeons as their subjects, though similar results, which also support the basic axioms of economic analysis, have been found for human participants in token economies and field experiments concerned, for example, with energy conservation. However, with the exception of the pilot studies which led to the present investigation (Foxall and James, 2002 and Foxall and James, in press), no work to date has attempted to discover the extent to which these principles of economic behavior apply to brand choice among human consumers. Those pilot investigations proposed and implemented a means of investigation in which consumers’ actual brand expenditures are related to the relative rates of reinforcement they produce. Their results exhibit both matching and maximization and indicate that brand selection is far more sensitive to small price differentials than has generally been acknowledged in the literatures of consumer choice and marketing management. Work of this kind thus promises to answer Logue’s (2002) challenge. The present paper extends the pilot investigations of Foxall and James by considering the individual buyer behavior of 80 consumers purchasing brands of nine fast-moving consumer goods over a 16-week period. This study is meant to (dis)confirm the results of the pilot work and to provide an assessment of the generality of the pilot findings across consumers and across product categories. The pilot research identified a contradiction between the findings of marketing science and those of behavioral economics. Although each of the intellectual traditions that have come to form the discipline of behavioral economics have found much to debate (Commons, Herrnstein, & Rachlin, 1982), the principal prediction of both is that when faced with concurrent ratio schedules organisms will exhibit both matching and maximization by exclusive selection of the cheapest (richest) schedule (Green, Rachlin, & Hanson, 1983; Herrnstein & Loveland, 1975). We should expect therefore that brand choice will exhibit maximization of returns for expenditure achieved by the exclusive purchase of the cheapest brand. This seems to rule out the multi-brand purchasing that we have seen is the norm in marketing research studies. A superficial explanation of this paradox is not hard to find. The brand choice data are aggregated: though they are collected at the level of the individual buyer, he or she may make purchases on behalf of several members of a household, each of whom might be loyal to a given brand, albeit a different brand in each case. But this still does not explain why the cheapest brand (richest schedule) is not universally favored: some consumers select exclusively among the highly differentiated, heavily advertised and therefore premium-priced brands, even though such brands differ only slightly if at all in terms of physical formulation and function from retailer-label or economy brands that cost considerably less. Even buyers of the lowest-priced brands seldom purchase wholly within that subcategory: on occasion, in some cases often, they select the highest-priced brands too, sometimes on the same shopping trip. Of course, if a particular brand is offered as part of a money-off or two-for-one deal, its sales rise appreciably but only while the deal is operative and even then it attracts consumers who have previously bought that brand rather than new buyers. Once the deal comes to an end, sales levels and thus market shares return to their previous trend levels. If the findings of behavioral economics are to be shown to apply to human consumers in naturally occurring settings, there is much to investigate and explain. Hence, the first objective of the research was to explore how far behavioral economic principles formulated in the investigation of simpler systems explain brand choice in consumer markets. It was clear from the outset, however, that the research had the potential to elucidate the role of branding in marketing and consumer behavior, especially with regard to the sensitivity of consumers’ decision processes to differences in price. The conventional wisdom in marketing is that brand choices result predominantly from non-price influences on purchase decisions. The view that the typically small price differentials between brands have little or no influence on brand choice, which is also an emphasis in the economics of imperfect or monopolistic competition, is a staple of the marketing literature on branding. It suggests that, far from maximizing, in the simple sense of obtaining the greatest returns for money expended, consumers seek a wide range of satisfactions from the products they purchase which do not result in their habitually buying the cheapest brand available. The underlying assumption is that the brands that compose a product category are similar if not identical in physical formulation and function: the differences which enable marketers to charge – and consumers willing to pay – price differentials stem largely from perceived differences among brands based on the marketing techniques that comprise ‘branding’. In order to examine further these concerns, it was necessary to employ analyses which permitted: (a) the degree of substitutability and (b) the price sensitivity of brands to be ascertained. They also required further consideration of the relationships among basic, applied and interpretive behavior analysis.

Hursh (1980, p. 687) drew attention to four elements of economic analysis which he showed to be “useful for the analysis of otherwise conflicting sets of data”. Those sets of data were obtained from the experimental analysis of the consumer behaviors of non-human animals. We now to discuss how far those four tools of analysis apply to the analysis of complex consumer behavior, i.e., the choices of human consumers in the marketplace. 7.1. The scope of consumer behavior settings Hursh first concludes that, “A behavioral experiment is an economic system and its characteristics can strongly determine the results”. In the open economy, daily total consumption is independent of response level since food is made available to the organism outside the experimental period; demand is highly elastic with respect to price (response rate declines as price increases). In a closed economy, elasticity of demand is low for an essential commodity like food. We need additional concepts to understand consumer behavior in the real world. One of these, derived from the behavioral perspective model (BPM, see Foxall, 1990) is the scope of the consumer behavior setting which has proved relevant to the interpretation (and to some degree the prediction and control) of consumer behavior ( Foxall, 1996 and Foxall, 2002; Foxall and Greenley, 1998, Foxall and Greenley, 1999 and Foxall and Greenley, 2000; Foxall & Soriano, in press; Soriano & Foxall, 2002). The scope of consumer behavior settings, from the most open to the most closed, represents the range of behavioral options available to the individual in a situation, and hence to the would-be interpreter of his or her behavior. The BPM is an adaptation of the three-term contingency to incorporate the complexity of consumer choice in the environment of the affluent, marketing-oriented economy. Like the three-term contingency it specifies behaviorally antecedent stimulus conditions (the behavior setting) but elaborates the simpler concepts of discriminative stimuli, establishing operations or rules by means of the construct of behavior setting scope, the extent to which these setting elements encourage or inhibit the behavior predicted to occur in such settings. Behavior setting scope is conceptualized as a continuum from closed to open in which the former type of setting permits one or at best a very few behaviors to be enacted within its confines, while the latter type permits a whole range of often competing behaviors to be enacted. The most closed setting likely to be encountered in reality is that of the animal laboratory where the experimental subject has no alternative but to be present and where its behavioral repertoire is severely restricted to serve the purposes of the researcher (see Schwartz & Lacey, 1988). More open than this, but still toward the closed pole of the continuum, is the human operant experiment which the subject is comparatively free to leave at any time even though the social and physical pressures of the experimental space may well act against this. Toward the other end of the continuum, settings of purchase and consumption are all relatively open compared to this, but still differ from one another along a restricted continuum of closed – open consumer behavior settings (Foxall, 1990). Hence, standing in line at the bank to pay in a check takes place in a relatively closed consumer behavior setting: there is probably no alternative to being there and waiting until a teller becomes available, standing in an orderly fashion is encouraged both by the physical style of the building and by the social arrangements, deviation from the established behavior program of the setting is likely to be punished by stares or glares or, if one’s fellow customers have succumbed to the latest assertiveness training fad by more direct, and potentially socially embarrassing action. Depending on their learning histories, some customers may actually seek such social disapproval and arrange for calls on their mobile phones to come in at this time but most of us seem to be sufficiently conditioned to conform fairly closely to the behavior patterns laid down by the designers of this closed consumer behavior setting. An open consumer behavior setting encourages a wider range of alternative behaviors. In a bar, for instance, all manner or beverages and snacks may be available, there may be TV to watch, talking loudly may not be discouraged, even singing and dancing may be possible. The customer is free to leave at any time, even if only to go to another bar in the vicinity – at least far freer than he or she would be to leave the bank and find another at which to present the check. Unlike open–closed economies which comprise a dichotomous classification, open – closed behavior settings form a continuum, though they are often treated for ease of exposition and research as binary variables. More importantly, open–closed economies reflect only one element of the marketing mix – price–quantity relationships and, hence, elasticity of demand – while consumer behavior settings necessarily involve the other mix elements as well as word-of-mouth and other forms of interpersonal influence – i.e., the plasticity of demand, which is further described below. 7.2. The plasticity of demand Hursh’s second conclusion is that, “Reinforcers can be distinguished by a functional property called elasticity of demand that is independent of relative value”. We have shown in essence the importance of price elasticity of demand. Indeed, we have rescued it in the study of brand choice from the marketers who claim that price differentials as small as we have been concerned with have no impact on brand choice. The burden of our analysis is that they are in fact central to understanding consumer choice. However – and this is a second rule of interpretation drawn from the BPM analysis – patterns of reinforcement can be distinguished according to the degree of utility (functionality) and information (symbolism) they provide. Utilitarian reinforcement consists in the direct usable, economic and technical benefits of owning and consuming a product or service, while informational reinforcement inheres in benefits of ownership and consumption which are usually social in nature and consist in the prestige or status as well as the self-esteem generated by ownership and consumption. The driver of a Lada, for instance, is principally concerned with the utilitarian benefits that all cars provide: the most obvious is getting from A to B, door-to-door transportation3. Informational reinforcement, on the other hand, is more likely to involve a lifestyle statement by which the consumer seeks to convey his or her social status or to bolster esteem and/or reported feelings of self-esteem. The driver of a Mercedes or a Bentley or a Porsche clearly gets from A to B in it but, in addition, gains the social esteem and status provided by friends and acquaintances who admire these prestige products and from members of the general public who see me driving around in a socially desirable vehicle. The social status and esteem that driver is accorded are the symbolic rewards of consumption. Most products have an element of both the instrumental and the symbolic. A mobile phone not only provides communications services when and where the consumer wants them; because it is a Nokia and therefore has interchangeable colored cases, it may also signal to that consumer’s social group that he or she is “cool” (or, a year or so later, “not so cool”). These considerations reflect the plasticity of demand: the sensitivity of demand not only to price but to all four generic elements of the marketing mix and their interactions and their global influence. Penrose (1959, p. 81, note 1) quotes Alderson and Sessions: … it is essential to distinguish between what the economist has called the elasticity of demand and the more fundamental factor of plasticity. The intended difference is suggested by the common meaning of the words. ‘Elastic’ refers to something that can be stretched, and ‘plastic’ to something that can be molded. Economics long pointed out that demand can be stretched to include more units of a product by the simple expedient of reducing the price. Much less attention has been devoted to the fact that demand can often be remolded into quite different forms. The investigation of plasticity of demand has generally been left to the market analyst rather than to the economist. The re-molding of demand to make a place for new products has proceeded to spectacular extent in the United States. To make use of the innate plasticity of demand means to find ways of changing the habits and attitudes of consumers. Changing a buying habit means, among other things, making it as convenient as possible for consumers to buy the new product. Changing buying attitudes means supplying consumers with reasons for preferring the new product. Cost and Profit Outlook, Vol. 5, No. 8 (Aug. 1952)”. (italics added by Penrose.) Interesting research can, however, combine economic and marketing variables. 7.3. The substitutability of brands The third conclusion drawn by Hursh is that, “Reinforcers may interact as complements, as well as substitutes”. We benefit here from making a distinction between utilitarian and informational or symbolic reinforcement which goes beyond the usual distinction between primary and secondary reinforcers (Foxall, 1997). In the case of brands, it is inevitable that they will tend to be substitutes in so far as they are functionally similar (almost identical in terms of physical formulation), i.e., in terms of utilitarian reinforcement, and complements in so far as they are differentiated by branding, i.e., in terms of informational reinforcement or social symbolism. Branding is an attempt to reduce the perceived substitutability of brands by altering their value to the consumer on the basis of their social significance (e.g., increasing the status of their owners and users) or psychological significance (e.g., enhancing the self-esteem of those who own and use them). 7.4. The pattern of reinforcement “Finally, because reinforcers differ in elasticity and because reinforcers can be complementary, no simple, unidimensional choice rule such as matching can account for all choice behavior”. The pattern of reinforcement (the pattern of low-to-high utilitarian reinforcement and low-to-high informational reinforcement produced by buying or using a product) is an analytical category that takes the place in interpretive behaviorism occupied by that of the schedule of reinforcement in the experimental analysis of behavior. Because patterns of reinforcement differ and because informational reinforcement increases the complementarity of brands within a product category, non-price elements of the marketing mix come to the fore. This study of brand choice indicates that the multi-disciplinarity of behavioral economics can usefully be extended by the inclusion of results and perspectives from marketing research. Behavioral economics is supported by the research in that its analyses and conclusions are shown to apply to human consumers in situations of free choice; behavioral economists should appreciate, however, the conclusions of marketing researchers to the effect that most consumers are multi-brand purchasers, and that marketing considerations other than price influence choice. Marketing researchers may need to take note of the import of price differentials in brand choice. The behavioral mechanism of choice that underlies the molar patterns of consumer choice depicted here appears to be momentary maximization of benefit, a result that is consistent with melioration or overall maximization. However, the lesson of the research is that brand choice is reinforced by two sources of reward, utilitarian which derives from the functional benefits of the good, and informational or symbolic which derives from the psychological and cultural meanings which goods acquire through their participation in social interactions and, by derivation, through advertising and other means to branding. The recognition of both sources of reinforcement is the key requirement for both marketing researchers and behavioral economists. 7.5. Continuing research Present effort is concentrated on the completion of the analyses for the remaining product categories in order to make comparison across product categories (for each consumer) and across consumers. Particularly of interest are the questions whether the pattern of there being some maximizers and some non-maximizers for each product is repeated; whether, if so, the proportions of each are similar from category to category; and whether the same consumers maximize from one category to another. However, the combination of economic, psychological, and marketing variables to which the research draws attention suggest some more fundamental research, as a summary of the findings indicates. Purchasers of fast-moving consumer goods generally exhibit multi-brand choice, selecting apparently randomly among a small subset or “repertoire” of tried and trusted brands. Their behavior shows both matching and maximization, though it is not clear just what the majority of buyers are maximizing. Each brand attracts, however, a small percentage of consumers who are 100%-loyal to it during the period of observation. Some of these are exclusively buyers of premium-priced brands who are presumably maximizing informational reinforcement because their demand for the brand is relatively price-insensitive or inelastic. Others buy exclusively the cheapest brands available and can be assumed to maximize utilitarian reinforcement since their behavior is particularly price-sensitive or elastic. Between them are the majority of consumers whose multi-brand buying takes the form of selecting a mixture of economy- and premium-priced brands. The implications of this for the conceptualization of brand loyalty are intriguing.