اقتصاد رفتاری انتخاب نام تجاری مصرف کننده : الگوهای تقویت و حداکثرسازی مطلوبیت

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. Based on the analysis of buying patterns of 80 consumers for 9 product categories, the paper examines the continuum of consumers so defined and seeks to relate their buying behavior to the question of how and what consumers maximize.

Within marketing science, the analysis of brand choices for fast-moving consumer goods, based on aggregate data, shows that most individuals tend to purchase a variety of brands within a product category. More specifically, such results indicate that, in steady-state markets: (a) only a small portion of consumers buy just one brand on consecutive shopping occasions, that is, few consumers remain 100% loyal to one brand; (b) each brand attracts a small group of 100%-loyal consumers; (c) the majority of consumers buy several different brands, selected apparently randomly from a subset of existing brands; (d) existing brands usually differ widely with respect to penetration level and not so much in terms of average buying frequency (i.e., how often consumers buy it during the analysis period); and (e) brands with smaller penetration levels (or market shares) also tend to show smaller average buying frequency and smaller percentages of 100%-loyal consumers (i.e., “double jeopardy”). These results have been replicated for some 30 food and drink products (from cookies to beer), 20 cleaning and personal care products (from cosmetics to heavy cleaning liquids), gasoline, aviation fuel, automobiles, some medicines and pharmaceutical prescriptions, television channels and shows, shopping trips, store chains, individual stores, and attitudes toward brands (cf. Dall’Olmo Riley et al., 1997, Ehrenberg, 1972, Ehrenberg et al., 1990, Ehrenberg and Scriven, 1999, Goodhardt et al., 1984 and Uncles et al., 1995). So sure are the relationships involved that a mathematical model has also been developed to describe such regularities, the Dirichlet Model (e.g., Ehrenberg et al., 1990), which has been used to predict the market insertion of new products (Ehrenberg, 1993), to analyze the effects of promotions (Ehrenberg, 1986 and Ehrenberg et al., 1994), and to evaluate patterns of store loyalty (Ehrenberg and England, 1990, Keng and Ehrenberg, 1984, Sharp and Sharp, 1997 and Uncles and Ehrenberg, 1990). Nonetheless, despite the wide replication of such patterns, which have been raised by some authors to the status of “empirical generalizations” in marketing (e.g., Uncles et al., 1995), little is known about the variables and the underlying behavioral mechanisms that influence and explain consumers’ brand choices. The marketing literature is not forthcoming, for instance, about the factors responsible for shaping the subset of the brands that compose a product category among which consumers choose in practice (their “consideration sets”) and what Ehrenberg calls the “repertoire” of such brands actually purchased (their “purchase sets”). It is a basic axiom of modern marketing thought that sales are produced not simply by price acting alone, any more than by product attributes, or advertising and other promotional means, or distribution effectiveness acting singly, but by a combination of all four of these influences on demand that constitute the “marketing mix.” As marketing science has developed as a separate discipline, it has de-emphasized the influence of price on demand (the principal focus of the economist’s purview) and stressed the non-price elements of the marketing mix, notably the promotional activity involved in brand differentiation (De Chernatony and McDonald, 2003, Jary and Wileman, 1998 and Watkins, 1986). Behavioral economics, partly because of the stress it has placed on the economics of animal responding in experimental situations, where the sole reliable analogue of the influences on consumer demand ruling in the market place relates to price, has necessarily followed the reasoning and methodology of the economist rather than the marketing scientist. The non-price marketing mix has, therefore, not featured in the research program of behavioral economics. The assumption that consumers maximize utility in some way or other—a preoccupation of the economics approach—is, nevertheless, common in the marketing literature. Krishnamurti and Raj (1988), for example, state that “the consumer chooses that alternative which maximizes his (or her) utility,” although they recognize that this is a latent or unobservable utility which is assumed rather than tested (cf. Rachlin, 1980). Based on this maximization assumption, one could expect consumers to choose the cheapest brands that offer the attributes and characteristics that they are looking for. Although the price of different brands is certainly one variable that is expected to influence brand choice, as exemplified by the literature on the effects of promotions (e.g., Ehrenberg, 1986, Ehrenberg et al., 1994 and Bell et al., 1999), empirical evidence showing that consumers tend to maximize when choosing across brands was not available before recent research on the behavioral economics of brand choice (Foxall and James, 2001, Foxall and James, 2003 and Foxall and Schrezenmaier, 2003). In this paper, we extend this research from the analysis of single cases to that of panel data for some 80 consumers purchasing 9 product categories, examining in detail the relationship between price and quantity demanded in relation to the functional and symbolic attributes of brands which influence the composition of consumers’ consideration and purchase sets. 1.1. Previous research Foxall (1999a), Foxall and James, 2001 and Foxall and James, 2003, and Foxall and Schrezenmaier (2003) adopted techniques refined in choice experiments in behavioral economics and behavior analysis to investigate brand choice. Three types of analysis were used: matching, relative demand, and maximization. 1.1.1. Matching analysis The results of choice experiments with nonhuman animals in behavior analysis gave support for the development of the matching law, which in its simplest form asserts that organisms in choice situations match the relative distribution of responses to the relative distribution of the reinforcers they obtain (Herrnstein, 1961 and Herrnstein, 1970). In its more general form, the generalized matching law (Baum, 1974 and Baum, 1979) states that the ratio of responses between two alternatives is a power function of the ratio of reinforcers, that is, equation(1) View the MathML source where B represents responses, R represents reinforcers, and the subscripts 1 and 2, choice alternatives. The parameter b, obtained from the intercept of the linear log–log formulation of the law, is a measure of biased responding between the alternatives, usually related to asymmetrical experimental factors such as differences in response cost between the alternatives. The parameter s, the slope of the linear log–log formulation, is interpreted as a measure of sensitivity in response distribution with changes in reinforcer distribution, which indicates that the individual favors, more than predicted by precise matching, the richer (s>1) or poorer (s<1) schedule of reinforcement. In behavioral economics, the parameter s can also be used as an estimate of the level of substitutability of the reinforcers in the situation, in which case there is evidence suggesting that it should be equal or close to 1 for substitutable commodities, and negative for complementary commodities (cf. Baum and Nevin, 1981, Foxall, 1999a and Kagel et al., 1995). Foxall and James, 2001 and Foxall and James, 2003 applied this type of analysis to data obtained from consumers’ brand choice. Consumer choice was analyzed for brands that were substitutes, non-substitutes and independent, for 1-, 3-, and 5-week periods. Matching and maximization analyses were based on relative measures of price paid and amount bought, which considered the relation between the amount paid for (or amount bought of) the preferred brand and the amount paid for (or amount bought of) the other brands in the consumer repertoire. As predicted, substitute brands showed matching whereas independent brands showed some evidence of anti-matching. Their results also showed some evidence that consumers tend to maximize the amount they pay in relation to the amount they buy within their brand repertoire by purchasing the cheapest brand (although they sometimes also bought some more expensive brand). Similar results have also been reported by more recent research (cf. Foxall and James, 2003 and Foxall and Schrezenmaier, 2003). 1.1.2. Relative demand analysis Whereas matching analysis relates the actual amount of a reinforcer obtained to the actual amount of behavior expended in obtaining it, an understanding of consumer decision making in the face of competing sources or reinforcement offered at a variety of programmed behavioral costs or prices requires a different kind of analysis. Matching analysis plots the quantity obtained of a commodity as a positively accelerating function of the amount paid for it. By contrast, the sensitivity of the quantity demanded of a commodity to its ruling market price is expressed by economists in terms of the demand curve. One of the assumptions underlying the demand curve is that as the unit price of a commodity increases, its consumption will decrease (Madden et al., 2000). This is demonstrated when demand curves plotted on logarithmic coordinates show consumption to be a positively decelerating function of unit price. The sensitivity of quantity demanded to price is expressed in economic terms as “price elasticity of demand” which at its simplest relates the percentage change in amount consumed to the percentage change in price (Houston and McFarland, 1980; see also Hursh, 1980 and Hursh and Bauman, 1987). In an attempt to incorporate some of the features of naturalistic marketing settings involving consumer choices among competing brands whose relative prices might influence selection decisions, Foxall and James (following Kagel et al., 1980) employed relative demand analysis which presents the relative amounts of brands A and B as a function of their relative prices. Their results, albeit for a restricted sample of individual consumers and covering a small number of product categories, found downward-sloping demand curves which indicated a degree of price sensitivity on the part of the buyers investigated (Foxall and James, 2001 and Foxall and James, 2003. 1.1.3. Maximization analysis Analyses to reveal whether the observed consumer behavior was maximizing returns on price expended were undertaken following procedures developed by Herrnstein and Loveland (1975), Herrnstein and Vaughan (1980). On conc ratio schedules,1 there is a fixed probability of reinforcement for each response, which can be expressed as the reciprocal of the schedule parameter. Concurrent VR30 VR60 refers to response alternatives which have respective reinforcement probabilities of 1/30 and 1/60. On ratio schedules, the probability of reinforcement is independent of response rate (something not true of VI schedules where the probability of reinforcement is inversely proportional to rate of responding). Although most research on matching and maximization has been undertaken in laboratory settings which incorporate VI schedules, VR schedules are more probable in naturalistic settings ( Herrnstein, 1982, Herrnstein and Loveland, 1975, Herrnstein and Prelec, 1991, Herrnstein and Vaughan, 1980 and Vaughan and Herrnstein, 1987). Faced with conc VR40 VR80 schedules, the individual’s maximal probability of reinforcement is obtained by responding exclusively on the VR40 schedule. Matching theory makes the same prediction for conc VR VR schedules, claiming that maximization is under these circumstances a special case of matching (cf. Rachlin, 1980). Previous research, subject to the limitations of scope noted above, confirmed that consumers tend to maximize by generally purchasing the least expensive brand available within their consideration set ( Foxall and James, 2001 and Foxall and James, 2003). 1.2. Research issues Taken together, these results indicate that, within their repertoire of brands, consumers show price sensitivity, maximizing (most of the time), and matching (which refers to the relation between the amount they spend and the amount they buy). Based on such findings, one can predict that consumers will buy, more often than not, the cheapest brand among those that they usually buy, although one still does not know why they usually buy a certain set of brands and not others. The fact that consumers tend to buy the cheapest brand within a restricted set of brands rather than the cheapest of all brands available in the product category indicates that not all brands are perfect substitutes for the others. Even though they may be functionally equivalent for the consumer, the brands are not entirely equivalent, that is, consumer preferences reflect more than functional utility. This additional source of utility is usually rationalized in the marketing literature as stemming from rather nebulous “branding” considerations. Branding is not, however, a quantifiable construct and an important objective of the research reported here was to clarify its basis as an extra-functional source of reinforcement. Although research to date is indicative that the principles and methods of behavioral economics can be usefully applied to consumer brand purchasing, there is clearly need for a more extensive investigation of a larger, systematically-selected sample of consumers purchasing a wider range of products in order to ascertain how far previously reported results are generalizable. It is necessary to take into greater consideration the differences between the typical consumption patterns of laboratory subjects which can be shown to be sensitive to price (or its analogue) and those of consumers in supermarkets who are subject to a much wider spectrum of choice under the influence of the entire array of marketing mix variables available to retailers. For example, an expectation of demand analysis as it is employed in the behavioral economics literature is that when consumers choose between qualitatively identical reinforcers which vary in terms of the unit prices that must be paid for them, the brand with the lower or lowest unit price will be exclusively chosen (Madden et al., 2000). This is the prediction of both matching and maximization theories with regard to choice on conc VR VR schedules. However, research in these theoretical traditions typically takes place within laboratory settings that restrict choice to two alternatives, one or other of which must be selected at any choice point. Consumer brand choice is more complicated than this in that numerous choices are usually available to the consumer within a given product category, more than one of which may be selected on a single shopping occasion (Foxall and Schrezenmaier, 2003). A source of difference among brands, related to this and other aspects of consumer choice, stems from the distinction between utilitarian and informational benefits offered by different brands, as proposed by the Behavioral Perspective Model (Foxall, 1990, Foxall, 1994, Foxall, 1996, Foxall, 1997 and Foxall, 1998). According to this proposal, the behavior of the consumer can be explained by the events that occur before and after the consumer situation, which influence directly the shaping and maintenance of consumer behavior in specific environments. The consumer situation, in turn, is defined as the intersection between the consumer behavior setting and the consumer learning history. The consumer behavior setting—a supermarket, a bookstore, or a rock concert—includes the stimuli that form the social, physical and temporal consumer environments. As purchase and consumption are followed by different consequences in different settings, the events in the setting become predictive of such consequences, building a learning history that relates elements of the setting to different consequences. According to the proposal, antecedent events present in the consumer behavior setting signal the possibility of three types of consequences: utilitarian reinforcement, informational reinforcement, and aversive events. One major characteristic of economic behavior is that it involves both aversive and reinforcing consequences, for one has to give away money or rights (i.e., loss of generalized reinforcers) in order to get products or services (i.e., reinforcing events). Utilitarian reinforcement consists in the practical outcomes of purchase and consumption, that is, functional benefits derived directly (rather than mediated by other people) from possession and application of a product or service. It is reinforcement mediated by the product or service and refers to consequences associated with increases in the utility (i.e., use value) for the individual (“pleasant”) obtained from the product or service. The utilitarian, most obvious, consequence of owning a car, for example, is to be able to go from one place to the other, door to door, not depending on other people’s time schedules and avoiding being exposed to weather conditions, as usually happens when one uses public transportation. Informational reinforcement, on the other hand, would be symbolic, usually but not exclusively mediated by the actions and reactions of other persons, and would be more closely related to the exchange value of a product or service.2 It does not consist in information per se but in feedback about the individual’s performance, indicating the level of adequacy and accuracy of the consumer’s behavior. Whereas utilitarian reinforcement is associated with the functional and economic consequences of purchasing and consuming goods or services, informational reinforcement is derived from the level of social status and prestige that a consumer obtains when purchasing or using certain goods. According to Foxall, informational and utilitarian reinforcements would be orthogonal, and most products and services would involve, in different levels or proportions, both types of reinforcement. Then, according to this analysis, the person who drives a Jaguar© or Bentley© gets, in addition to door-to-door transportation (utilitarian), social status and approval from friends and acquaintances who see that car as a prestigious product, and from the general public that sees him or her driving around in a socially desirable car. The social status and prestige received are the informational, symbolic, consequences that the consumer obtains, which are usually related to branding or the level of brand differentiation of the product (cf. Foxall, 1999a). The specific combination of utilitarian and informational reinforcement made available by purchase or consumption of a particular product is known as the “pattern of reinforcement” controlling these responses. Foxall and James, 2001 and Foxall and James, 2003 argued that pattern of reinforcement influences consumers’ brand choices and that it is a key to understanding what consumers maximize. Different consumers might, for example, select brands belonging to different levels of informational reinforcement, some buying mostly highly differentiated whereas others buy relatively undifferentiated brands. The differences in patterns of brand choice, including the set of brands that constitute each consumer’s brand repertoire, may be a consequence of individual differences in responsiveness to different types of benefits. This idea gains even more force when we consider that branding is usually related to price, higher-differentiated brands being more expensive than less differentiated ones, and that consumers have different income levels. Then, individual buying patterns may be predominantly related, for example, to minimizing costs, maximizing utilitarian reinforcement, maximizing informational reinforcement, or to particular combinations of these. If this is so, consumers may differ with respect to price responsiveness related to informational and utilitarian benefits. The research reported here tested predictions arising from these considerations using data from a consumer panel. Panel data are especially valuable for longitudinal studies because changes in purchasing behavior can be monitored very accurately by continuous measurements (Crouch and Housden, 2003). Furthermore, diary panel data are considered to be very precise and less susceptible to errors than those obtained through consumers’ reporting their past behavior in surveys (Churchill, 1999). Hence, they are particularly valuable when collecting multifarious information on variables such as price, shopping occasion, brand name, and so on. The special significance of this research technique for the present research lies in the fact that the data were obtained non-experimentally, by electronically tracking real consumers spending their real discretionary income. The two main purposes of the investigation were as follows. First, in order to ascertain the generalizability of earlier research findings to consumer behavior in marketing-dominated contexts, three analyses were undertaken in order to determine whether the brands in question were in fact close substitutes (matching analysis), whether brand choice was sensitive to price differentials (relative demand analysis), and whether consumers could be said to maximize returns (maximization analysis). Second, in order to gauge consumers’ responsiveness to price and non-price marketing mix elements, the brands of 9 food product categories were ranked according to their informational and utilitarian levels. The proportion of purchases made by each consumer at each brand level was computed, which served as basis for grouping consumers according to the level of brands they bought most. To test for differences in price responsiveness, price elasticities for consumer groups and individual consumers were compared.

As predicted by both matching theory and maximization theory, we have confirmed that choice on conc VR VR schedules exhibits both matching and maximizing. However, the examination of consumer choice in naturalistic environments raises a number of complications for behavior analysis and behavioral economics that are not evident from the experimental analysis of choice. While the realities of consumer behavior in affluent, marketing-oriented economies have implications for behavioral economics, the techniques of analysis which behavioral economics makes available to the marketing researcher also elucidate the nature of brand choice in the market place. A common assumption in aggregate studies of consumer choice conducted by marketing scientists is that brands within a product category are functional alternatives and that consumers will include a brand within their repertoire or purchase set only if it embodies the physical and functional benefits that are common to all members of that category (Ehrenberg, 1972 and Ehrenberg, 1993). This proposition is seldom supported by empirical evidence. Although the discovery of matching on conc VR VR schedules is both expected and perhaps in some respects trivial, it is important for the sort of analysis we have undertaken in that it confirms that the alternative brands considered are indeed substitutes in the assumed sense. The very-nearly perfect matching that we have found is a characteristic of choices that are near-perfect substitutes (Kagel et al., 1995). Another common assumption in the marketing literature is that price plays a relatively small part in the determination of consumer choice: brands that are highly differentiated by advertising command a premium but the consumer is generally portrayed as relatively insensitive to such differentials. Non-price elements of the marketing mix (i.e., promotional tactics, brand attributes, and distribution strategies) are thought to be more influential than price factors for affluent consumers operating within marketing-oriented economies (Foxall, 1999b). The relative demand and maximization analyses, which were intended to shed light on the sensitivity of consumer demand to price differentials among competing brands, present an equivocal impression of the relationship between market prices and quantity demanded. While Fig. 3 and Fig. 4 indicate the expected relationship, denoted by downward-sloping relative demand curves, the evidence for the remaining product categories is mixed. The maximization analysis suggests that consumers are in some respects sensitive to price levels when making decisions about how much of a brand to buy relative to other brands in the consideration set. However, the interpretation of the data summarized in these figures and in Fig. 5 and Fig. 6 must include the phenomenon of single shopping trip multi-brand purchasing. Although a consumer may exhibit economically rational price sensitivity by purchasing the cheapest brand in her consideration set, her general sensitivity to price may be confounded by her purchasing a premium-priced alternative at the same time. Hence, our results are equivocal on the question whether consumer brand choice is sensitive to price. The consequent need to attend to non-price elements of the marketing mix led us to the analyses of price elasticity of demand which take into consideration both the utilitarian (functional) and informational (symbolic) benefits gained by consumers from the brands they purchase and use. The evidence is that consumers choose their repertoire of brands on the basis of the informational and utilitarian level of reinforcement programmed by the brands. This is likely to be related, among other things, to their budgets, which we were not able to take into consideration. However, it is also of marketing significance in that it provides opportunities for the partitioning (segmentation) of markets. There do seem to be clearly definable segments based on combinations of the utilitarian and symbolic benefits of purchase and consumption and the cost minimization. These factors encourage consumers to choose brands within a given range defined in terms of these variables. Most purchasing takes place within a fairly narrowly defined range and consumers who switch out of that range generally move only to an adjacent range. Consumer groups, classified on the basis of the informational/utilitarian level of the brands they buy mostly, show different responsiveness to changes in prices, with extreme groups showing the lowest levels of responsiveness (possibly for different reasons). Price elasticities can be decomposed into intra-brand and at least two types of inter-brand elasticities, informational and utilitarian, according to the type of reinforcing events that influence consumer choice. Intra-brand elasticity can be interpreted as a measure of responsiveness to the aversive consequences of giving up money (Alhadeff, 1982). Therefore, choice patterns can be interpreted as being determined by different combinations of the tendencies to avoid aversive consequences, maximize informational reinforcement and maximize utilitarian reinforcement. A pattern that minimizes financial loss, showing minimum responsiveness to informational attributes and some to utilitarian ones, seems to characterize choices of consumers in Group 1. The responsiveness to informational and utilitarian attributes related to changes in price seems to be an inverse function of how much of these the consumer obtains regularly. So, the results showed increasing responsiveness to informational reinforcement from Group 6 (who obtain higher levels of it) to Group 2 (who obtain lower levels of it). The same was observed for utilitarian attributes, for those groups buying lower levels of utilitarian attributes (Groups 1, 3, and 5) showed higher responsiveness to this aspect of the brands than those that buy higher levels of utilitarian attributes more regularly (Groups 2, 4, and 6). Elasticity coefficients can be interpreted as measures of consumer “satiation” level, since the less frequently consumers purchase a given reinforcing dimension the higher their responsiveness to that dimension. In the case of intra-brand elasticity this tendency is probably related to available budget. The only exceptions were obtained for consumers that buy the least differentiated brands most of the time, for whom elasticity coefficients seem to reflect a pattern of buying the cheapest products in the category. From the point of view of the Behavioral Perspective Model, the analysis has demonstrated that relatively high and low utilitarian and informational reinforcement can be used to classify consumer behavior even within the narrow range represented by fast-moving consumer goods. In previous analyses, these variables, along with the relative openness of the consumer behavior setting, have been employed to categorize broader patterns of consumer behavior. Within that categorization, the purchase of food products is classified in terms of low utilitarian and low informational reinforcement in a relatively open setting. That categorization is meaningful when buying fast-moving consumer goods is compared with buying and using other kinds of product and service (Foxall and Yani-de-Soriano, 2004), but the demonstration of this paper is that these structural elements of the consumer situation also provide means of classifying consumer behavior within those broader categories. The results for the elasticities of demand, especially those for intra-brand, inter-utilitarian and inter-informational elasticities, suggest that the explanatory variables investigated are far from the only influences on brand choice. Nevertheless, along with the results for the inter-group elasticities of demand which provide somewhat stronger evidence of a link, they indicate that utilitarian and informational reinforcement have distinct effects on brand choice and that they may form the basis of the partitioning of markets and strategies of market segmentation.