Consumer Attitudes and Behavior

From: C. P. Haugtvedt, P. M. Herr, & F. R. Cardes (2008) (Eds.), Handbook of Consumer Psychology (pp. 525-548). New York: Lawrence Erlbaum Associates.

20 Consumer Attitudes and Behavior Icek Ajzen University of Massachusetts – Amherst

Consumers are ordinary human beings who happen to be engaged in activities related to the purchase of products or services. It should come as no surprise, therefore, that the psychology of the consumer deals with the same kinds of issues as psychology in general: memory and cognition, affect and emotion, judgment and decision making, group dynamics, and the myriad of other topics covered in the psychological literature. As is evident in this volume, consumer psychologists employ the concepts, theories, and findings of psychology—and in particular of social psychology—to explain the behavior of the consumer (see Bagozzi, Gürhan-Canli, & Priester, 2002; Simonson, Carmon, Dhar, Drolet, & Nowlis, 2000). In the present chapter, I examine social psychological theory and research on the attitude-behavior relation as it applies to consumer behavior. CONSUMER BEHAVIOR There is general agreement that consumer behavior refers first and foremost to the act of buying a certain product or service. This, however, is by no means the only behavior of interest to consumer psychologists. At issue as well are search of information relevant to a purchase decision, selection of retail outlet or service provider, and other actions performed prior to, and in the service of, a purchase. Consider, for example, the act of buying a washing machine. Prior to the purchase, consumers may search for relevant information on the Web, consult friends and coworkers, read consumer magazines, and discuss the options with a spouse or partner. The information obtained may narrow the decision to a small number of manufacturers and brands. At this point, the consumer may well visit one or more local showrooms to view the different brands and consult sales representatives about prices, warranty, installation, delivery times, removal of the existing washing machine, and so forth. Finally, the consumer decides on a particular brand and places an order. Consumer psychology is concerned with all aspects of the consumer’s purchase decision, but in any given investigation we must, for practical reasons, limit our focus. We will usually select a behavior of particular interest and examine the determinants of the behavior in question. Although not always clearly recognized, every behavior involves a choice, even if the alternative is taking no action and thus maintaining the status quo (Ajzen, 1996; Ajzen & Fishbein, 1980). Nevertheless, it is useful to distinguish between behaviors that focus on a single option and behaviors that involve a choice among two or more distinct alternatives. As the washing machine example illustrates, most purchase decisions involve both types of behavior: the decision to buy or not to buy a new washing machine focuses on a single option whereas the decision to buy one brand of washing machine rather than another is a choice among multiple alternatives. However, in the final analysis, even 525

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behaviors involving multiple alternatives are ultimately reduced to a single-option decision. After going through the preliminary stages, the consumer either buys or does not buy a particular brand of washing machine. My discussion will therefore first focus on single-option behaviors, the basic unit of analysis, and then consider additional issues related to the prediction and understanding of purchase decisions that involve multiple options. SINGLEOPTION BEHAVIORS Any single instance of a behavior is an observable event that takes place in a certain context and at a given point in time. In addition, purchase behaviors are also directed at some target, usually a product or brand. It is therefore useful to think of purchase behavior as comprised of four elements: the action performed (buying, searching for information), the target at which the action is directed (the product category or brand), the context in which it is performed (Sears, online retailer), and the time at which it is performed (Ajzen, 1988; Ajzen & Fishbein, 1980). Each of a purchase behavior’s four elements can be defined at varying levels of generality or specificity. If we decided to collect data about the extent to which people search for information (action) about a particular model of Sony flat-screen TV (target) online (context) on a particular weekend (time), all elements would be defined at a very high level of specificity. In this case, the behavior is so narrowly defined as to be of little practical or theoretical significance. A more meaningful criterion might focus on, say, searching online for information about any kind of product in the next two weeks. Here, the action element (searching for information) and the context element (online) remain quite specific, the time element has been expanded to a two-week period, and the target elements have been greatly generalized to include all product categories. Alternatively, we might be interested in searching for information about automobiles in the next 6 months. In this example we are still interested in the same action (information search), but now the target is more narrowly defi ned as automobiles; the context is not limited to online search but could include visits to showrooms, consulting Consumer Reports, or reading automotive magazines; and the time element has been expanded to 6 months. The important point to be made is that observed behavior may differ depending on the particular definition we adopt. Thus, consumers may act differently when they search for automobiles as opposed to life insurance policies; and different patterns of information search may occur 6 months compared to 1 week prior to a purchase decision. Moreover, to study a broad category of behaviors, such as information search in general, we have to obtain data that generalizes the target, context, and time elements. This requires that we observe—or obtain self-reports—of information search with respect to different kinds of products, using different media, over an extended period of time. (For discussions of the logic of behavioral aggregation, see Ajzen, 1988; Epstein, 1979.) Choice Behaviors We have seen that single-option behaviors can be studied at a high level of generality. In fact, questions of theoretical significance are usually formulated at a fairly general level, whether they have to do with the decision to buy (or not to buy) a product, such as a new automobile, or with the determinants of such consumer behaviors as buying life insurance, putting money in a pension plan, using credit cards, and so forth. By comparison, questions about behaviors that involve a choice among two or more options are usually studied at a lower level of generality. Thus, we may be interested to know why people buy one brand of automobile rather than another, why they choose one type of medical treatment over another, or why they fly one airline rather than another. Here too, however, we must clearly define the action, target, context, and time elements of the behavioral


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alternatives. The decision to buy tickets on one airline rather than another can be affected by the destination (target element): A person may prefer one airline for overseas flights but another for domestic flights. Similarly, choice of insurance company may vary depending on whether we buy life insurance, automobile insurance, or property insurance. DETERMINANTS OF CONSUMER BEHAVIOR A purchase decision confronts the consumer with a host of potential challenges. Most important, perhaps, is the problem structuring that occurs prior to making a decision: becoming aware of the need for, or availability of, a new product or service; collecting information about the alternatives; identifying likely future events and other circumstances relevant to the purchase decision; and considering possible outcomes contingent on the decision (Albert, Aschenbrenner, & Schmalhofer, 1989; Peter & Olson, 1993; Slovic, Lichtenstein, & Fischhoff, 1988). After structuring the problem, the consumer needs to process the obtained information, choose a preferred course of action, and implement the decision at an appropriate opportunity. Finally, consumers can use feedback resulting from a purchase to reevaluate their decision, perhaps reversing it by returning a purchased product to the store. This information can also prove valuable for future purchase decisions. MULTIATTRIBUTE DECISION MODELS One approach to consumer behavior is grounded in behavioral decision theory (for reviews of this literature, see Goldstein & Hogarth, 1997; Shafir & LeBoeuf, 2002; Slovic et al., 1988). With its roots in economics and statistics, the starting point of this approach is a rational model of choice behavior. The decision maker is likened to an intuitive statistician who carefully considers the alternatives and makes full use of all available information in accordance with normative principles of probability and logic (Peterson & Beach, 1967). When faced with a choice among competing brands or products, consumers are assumed to first identify the attribute dimensions relevant to the decision. Each option is then evaluated on these attributes to reach a decision. Consider, for example, consumers trying to decide whether to buy a picture tube (CRT) or a flat panel (LCD) television set. Certain attributes, such as the dimensions of the display, warranty period, and location of dealer may be irrelevant as they are the same for the two products. The comparison may therefore rest primarily on such attributes as picture quality, price, reliability, and visual appeal. Imagine that in a particular consumer’s eyes, CRT sets are relatively inexpensive with proven reliability, moderate picture quality, and low visual appeal whereas LCD sets are expensive and without proven reliability, but have high picture quality and high visual appeal. To make a decision, the consumer must derive an overall evaluation of each product category in terms of the combination of attributes that characterize it. In the basic multiattribute model, this overall evaluation is assumed to be a weighted average of the subjective values or utilities associated with the individual attributes. That is, each attribute dimension is given a weight representing its subjective importance to the decision (with the restriction that weights add to one) and the product is given a value for each attribute.1 The subjective utility of each product is obtained by summing the weighted attribute values for that product, and the product with the highest subjective utility is chosen (see W. Edwards & Fasolo, 2001). Decisions Under Uncertainty In the above example, the attributes of each product were assumed to be known with certainty. Thus, the consumers knew the price, picture quality, reliability, and visual appeal of each product


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type. All they needed to do was to assign importance weights and subjective values to these attributes and then derive a weighted average. In many situations, however, the attributes or outcomes of choice alternatives are not known with certainty ahead of time. Often, the outcomes produced by a decision depend on the “state of the world” at the time the decision is made. For example, an LCD television can produce a high-defi nition picture only if the service providers transmit highdefinition programs. To take this uncertainty into account, the consumer has to judge not only the value of a high-definition display but also the likelihood that this attribute will be available. Perhaps more readily recognized are the risks and uncertainties inherent in investment decisions. The outcomes of a decision to invest $10,000 in a fi xed-interest certificate of deposit or a stock market mutual fund depend on future market conditions. Whereas the CD produces a known payoff over a given time period, the amount and probability of possible gains or losses to be expected of the mutual fund can only be estimated. Perhaps the most popular approach to the analysis of decisions under conditions of uncertainty is the subjective expected model (SEU) model (Edwards, 1954, 1955). The subjective expected utility of a Product P is defined in Equation 1, where SPi is the subjective probability that Product P will produce attribute or outcome i, Ui is the subjective utility of the attribute or outcome i, and the sum is taken over the n attributes or outcomes of Product P. The decision situation is formulated such that the available alternatives are mutually exclusive and the subjective probabilities of outcomes associated with a given product sum to one. It is assumed that a subjective expected utility is produced for each alternative product and that decision makers choose the product with the highest SEU. n

SEU ( P ) = ∑ SP iU i

(1)

i =1

Revealed Preferences Of course, individuals are not expected actually to perform the mental calculations described by multi-attribute models every time they make a decision. These models are taken not as accurate descriptions of the way in which decisions are made, but rather as ideal or normative models against which actual judgments and decisions can be compared. It is assumed that consumer decisions, like decisions in any domain, can be modeled as if the consumer were performing the stipulated calculations. Consistent with economists’ mistrust of self-reports and reliance on revealed preferences, much work on behavioral decision theory involves attempts to infer the decision process from choices among specified alternatives. Indeed, importance weights, subjective probabilities, and utilities are rarely, if ever, assessed in research with these models (Coombs, Bezembinder, & Goode, 1967).2 Applications of multiattribute decision models typically confront participants with a choice involving certain options and their possible outcomes. The decisions made are then evaluated as to whether they conform to the model’s implications. For example, in their well-known work on framing and risk aversion, Tversky and Kahneman (1981, p. 453) posed the following decision dilemma in a positive (lives to be saved) frame. Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows: If Program A is adopted, 200 people will be saved. [72%] If Program B is adopted, there is 1/3 probability that 600 people will be saved, and 2/3 probability that no people will be saved.


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In the negative (lives to be lost) frame, the same cover story was used to offer the following options. If Program C is adopted, 400 people will die. If Program D is adopted, there is 1/3 probability that nobody will die, and 2/3 probability that 600 people will die. [78%]

The values in parentheses show the percentage of participants who chose the more popular option. No attempt was made to assess the subjective probabilities or values of the different possible outcomes. The different results for the two frames were interpreted as risk aversion in the case of a positive frame and risk seeking in the case of a negative frame. A great deal of research in the past 25 years has shown that real-life decisions fall far short of the ideal assumed in the normative multi-attribute decision models. Due presumably to cognitive limitations of the human decision maker (Simon, 1955), subjective probability estimates are biased in numerous ways, deviating systematically from normative values (Kahneman, Slovic, & Tversky, 1982; Nisbett & Ross, 1980; Zwick, Pieters, & Baumgartner, 1995), and decisions often seem to follow rules that are incompatible with utility maximization (Coombs, 1975; Corfman, Lehmann, & Narayanan, 1991; Foxall, Oliveira-Castro, & Schrezenmaier, 2004; Kahneman & Tversky, 1979; Tversky, 1969). It is beyond the scope of this chapter to review the voluminous research related to models of this kind. Suffice it to say that many of the conclusions derived in the general judgment and decision-making literature—conclusions regarding hindsight biases, the effects of construct availability and accessibility, preference reversals, framing effects, and so forth—have also been shown to apply to the decisions of consumers (see Bettman, 1986 for reviews; Cohen & Chakravarti, 1990). As is true of research on human judgment and decision making in general, many studies on consumer behavior employ simple decision situations involving known outcomes (e.g., Carmon & Simonson, 1998; Coupey, Irwin, & Payne, 1998; Dhar & Nowlis, 1999; Hsee & Leclerc, 1998). For example, in the first of a series of studies on the relative attractiveness of products presented together or in isolation (Hsee & Leclerc, 1998), cordless telephones were described in terms of two attribute dimensions: Model A was said to have a maximum range of 150 feet and a 2-day battery life per recharge whereas Model B had a maximum range of 60 feet and the charge lasted for 10 days. Participants were asked to decide which of the two models they would buy. Similarly, in a program of research on choice deferral due to time pressure (Dhar & Nowlis, 1999), participants were given, among other hypothetical scenarios, a choice between two brands of binoculars, as follows. Brand name: JASON

Brand name: NIKON

Somewhat sturdy design 14X magnification Black case Price: $44

Extremely sturdy design 7X magnification Black case Price: $69

Under conditions of time pressure or no time pressure, participants were asked to indicate which of these two pairs of binoculars they would buy, and they were also given the option of buying neither and continuing their search. Whether time pressure influenced choice deferral was found to depend on the relative overall attractiveness of the options and on the extent to which the options shared common attributes.


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Clearly, the revealed preferences approach can provide valuable information about the decisionmaking process in general, as well as about decisions of particular relevance to consumer behavior. However, this approach also imposes severe limitations on the amount and kind of information obtained. The hypothetical decision scenarios are structured in artificial ways to enable testing of specific hypotheses about the underlying process. Attributes describing the available options are typically selected by the investigator because of their suitability for hypothesis testing, not because they realistically describe actual decisions confronting consumers. Participants are assumed to base their decisions on the information about the products provided by the investigator, and only on that information—an assumption that is highly unrealistic as consumers are likely to go beyond the information given (Bruner, 1973) to infer unmentioned attributes as well. In the above example, the particular attributes selected by the investigator may not be a representative set of attributes considered by consumers in actual purchase decisions. Moreover, participants in the experiment may have gone beyond the attributes provided to infer, for example, that because of their less than sturdy design, Jason binoculars are unsuitable for hiking. It stands to reason that the final decision will be based on all attributes the consumer associates with the available alternatives, not only those attributes originally listed by the investigator.3 The revealed preferences approach thus can provide information about general principles of consumer decision making, but it is not particularly useful for learning about the considerations that guide actual decisions with respect to the purchase of real-life consumer products. Another related limitation of the revealed preferences approach to consumer decision making is that the decision situations typically involve choice among two or a small number of alternative brands described in terms of the same attribute dimensions. Real consumer decisions, however, often focus on a single alternative—for example, whether or not to buy a new television set—or involve a choice between alternatives with noncompatible attribute dimension, such as a choice between buying a new television set or a new dishwasher. A different approach is needed to investigate consumer behavior in these kinds of situations. ATTITUDES As is true for the field of social psychology (Allport, 1968), the attitude construct occupies a central role in theories and research regarding consumer behavior. This construct, and in particular the expectancy–value model of attitude, offer an alternative to reliance on revealed preferences. A great deal of research in the area of consumer behavior has focused on the structure and determinants of brand attitudes or evaluations, and on persuasion and other techniques designed to change these attitudes (for a few recent examples, see Brunel, Tietje, & Greenwald, 2004; Coulter & Punj, 2004; Sengupta & Fitzsimons, 2004). Much of this work is based on the assumption that consumers’ attitudes toward competing brands are important determinants of their buying decisions. Before considering this proposition, however, we must examine several issues related to attitude theory and measurement. THE EXPECTANCYVALUE MODEL Although formal definitions vary, most theorists today agree that attitude is the tendency to respond to an object with some degree of favorableness or unfavorableness (e.g., Eagly & Chaiken, 1993; Fishbein & Ajzen, 1975; Osgood, Suci, & Tannenbaum, 1957; Petty & Cacioppo, 1986). It is the evaluative reaction to the attitude object that is considered to be at the core of a person’s attitude. Consistent with the cognitive tenor of most current theorizing in social psychology, this evaluative


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reaction is generally thought to be based on the person’s expectations or beliefs concerning the attitude object. Similar to multiattribute utility models in work on judgment and decision making, the most widely accepted theory of attitude formation describes the relation between beliefs about an object and attitude toward the object in terms of an expectancy–value (EV) model (see Dabholkar, 1999; Feather, 1959, 1982). One of the first and most complete statements of the EV model can be found in Fishbein’s (1963; Fishbein, 1967b) summation theory of attitude, although somewhat narrower versions were proposed earlier (Carlson, 1956; Peak, 1955; Rosenberg, 1956). In Fishbein’s theory, people’s evaluations of, or attitudes toward, an object are determined by their beliefs about the object, where a belief is defined as the subjective probability that the object has a certain attribute (Fishbein & Ajzen, 1975). The terms “object” and “attribute” are used in the generic sense and they refer to any discriminable aspect of an individual’s world. For example, a person may believe that Apple iPod media players (the attitude object) are popular with young people (the attribute). Each belief thus associates the object with a certain attribute. According to the expectancy– value model, a person’s overall attitude toward an object, such as a product, is determined by the subjective values or evaluations of the attributes associated with the product and by the strength of these associations. Specifically, the evaluation of each attribute contributes to the attitude in direct proportion to the person’s subjective probability that the product possesses the attribute in question. The basic structure of the model is shown in Equation 2, where A is the attitude toward the product, bi is the strength of the belief that the product has attribute i, ei is the evaluation of attribute i, and n is the number of accessible attributes (see Fishbein & Ajzen, 1975). n

A ∝ ∑ bi ei

(2)

i =1

People can, of course, form many different beliefs about a product or any other object, but it is assumed that only a relatively small number influence attitude at any given moment. It is these accessible beliefs that are considered to be the prevailing determinants of a person’s attitude. Some correlational evidence is available to support the importance of belief accessibility. The subjective probability associated with a given belief, i.e., its strength, correlates with the frequency with which the belief is emitted spontaneously in a sample of respondents, i.e., with its accessibility (Fishbein, 1963) as well as with order of belief emission (Kaplan & Fishbein, 1969); and highly accessible beliefs tend to correlate more strongly with an independent measure of attitude than do less accessible beliefs (Petkova, Ajzen, & Driver, 1995; Van der Pligt & Eiser, 1984). Furthermore, the likelihood that a given belief will be emitted in a free-response format is found to correspond to its accessibility as measured by response latency (Ajzen, Nichols, & Driver, 1995). Despite its apparent similarity to the SEU model, the EV model of attitude differs substantially from multiattribute utility maximization models in a number of important ways. One fundamental difference is that unlike formal decision theory, the attitude model makes no assumptions about rationality. Instead, it relies on the much weaker requirement of internal consistency. Attitudes are assumed to follow reasonably from beliefs about the attitude object, as described by the expectancy–value model. The more positive the beliefs, and the more strongly they are held, the more favorable should be the attitude. The source of the beliefs, and their veridicality, are immaterial in this model. Whether true or false, biased or unbiased, beliefs represent the subjectively held information upon which attitudes are based. People may hold beliefs about many objects and issues that are derived not from a logical process of reasoning but instead are biased by emotions or desires and may serve a variety of personal needs. The documentation of biases and errors in human judgments


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mentioned earlier lends support to this view. It follows that attitudes which are assumed to be based on beliefs will be similarly subjective and potentially biased. This view of attitudes can be seen clearly in work on such topics as prejudice and stereotypes (Allport, 1954), cognitive dissonance theory (Festinger, 1957), self-serving attributions (Kunda, 1987; Miller & Ross, 1975), and social judgment theory in persuasion (Hovland & Sherif, 1952; Sherif & Hovland, 1961). Measuring Beliefs and Attitudes In contrast to the revealed preference approach, work with the EV model assumes that attitudes toward products or brands (i.e., their expected utilities), as well as the beliefs on which they are assumed to be based, can be directly assessed. Any standard attitude scaling procedure (e.g., Likert or Thurstone scaling, the semantic differential, see A. L. Edwards, 1957; Fishbein & Ajzen, 1975; Green, 1954) can be used to measure a consumer’s general evaluation of a brand or product. Due largely to its ease of construction, the semantic differential (Osgood et al., 1957) is often the preferred method (e.g., Batra & Ray, 1986; Lutz, 1977; Madden & Ajzen, 1991; Mitchell & Olson, 1981). To illustrate, in a study on the effects of advertising on attitudes toward a fictitious brand of clothing (Coulter & Punj, 2004), brand attitudes were assessed by means of four 7-point evaluative semantic differential scales: like - dislike, good - bad, positive - negative, and favorable - unfavorable. The scale formed by the unweighted sum of these four evaluative scales served as a measure of attitude toward the fictitious brand of clothing, with a reliability coefficient alpha of .92 . Numerous studies have shown that attitudes towards products or services and toward other aspects of consumer behavior, such as attitudes toward ads or toward retailers, can easily and reliably be assessed in this manner. To understand the basis for these attitudes, however, we must— according to the expectancy–value model—examine the beliefs consumer hold about the product or service of interest. Many investigators rely on their own familiarity with the product or on prior research to select a set of attributes for investigation, under the assumption that these attributes are important determinants of attitudes or purchase decisions (for a few recent examples, see Batra & Homer, 2004; Hui & Zhou, 2003; Stoel, Wickliffe, & Lee, 2004). Thus, in a study on the effects of a product’s country of origin on beliefs and attitudes about the product (Hui & Zhou, 2003), college students were asked to rate, on 7-point scales, the standing of two brands of digital cassette players (Sony and Sanyo) on three attribute dimensions: reliability, workmanship, and durability. These attribute dimensions were selected because they were said to be commonly used in research on durable goods. In addition, the investigators also assessed overall attitudes toward the two brands by means of a three-item evaluative semantic differential scale. Country of manufacture was found to influence both brand beliefs and overall attitudes. The correlation between beliefs and attitudes was not reported, but structural equation analyses revealed significant path from beliefs about the three product attributes to overall attitudes. Use of the expectancy–value model requires a more systematic approach to the identification of accessible brand or product attributes. One popular approach, pioneered by marketing researchers but now popular in other social sciences as well (see Kahan, 2001; Kleiber, 2004), is the use of focus groups. Potential consumers of a product are brought together in small groups and, in a permissive atmosphere under the guidance of a moderator, discuss various aspects of the product or brand in question.4 The protocols from these discussions can be used, among other things, to identify product attributes that may guide consumer attitudes and buying decisions (Calder, 1977; Greenbaum, 1998). Consider, for example, a study on beliefs and attitudes regarding genetically modified (GM) food conducted in Belgium (Verdurme & Viaene, 2003). Two focus groups, consisting of eight to nine female participants varying in age and education but of similar cultural background, each


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discussed what the term genetically modified food evokes, their beliefs and attitudes with regard to GM food products and with respect to organic food, the benefits of GM food, as well as various other questions related to food consumption in general and to GM food in particular. A wealth of data was produced. Most relevant for our purposes, several beliefs about GM food were identified, including the belief that GM food has been genetically tampered with, is artificial or unnatural, involves cloning, is uncontrollable, is good for the Third World, and yields higher crops. The open discussion format of focus groups allows the investigator to identify various beliefs and feelings about a product or other consumer-related issue, to follow-up on thoughts expressed by groups members, and thus to obtain a comprehensive picture of the way consumers relate to a product of interest. It is then up to the investigator to distill the different ideas expressed and to extract from them the information relevant to a particular research question, such as identification of beliefs about a product’s most important attributes. The interactive nature of focus groups has, however, also serious potential drawbacks. Discussion participants are likely to influence each other (Bristol & Fern, 2003), with dominant individuals perhaps channeling and biasing the discussion in a particular direction; and self-presentational concerns can bias views expressed by participants (Wooten & Reed, 2000). An alternative individualistic belief elicitation approach has been used in the context of work with the expectancy–value model of attitude. This approach was specifically designed to identify attributes people associate with a given attitude object, such as a brand or product (see Fishbein, 1963; Fishbein & Ajzen, 1975). In formative research, participants—usually in groups but working by themselves—are given a few minutes to list the positive and negative characteristics, qualities, and attributes of a brand or product. It is assumed that only attributes highly accessible in memory are likely to be emitted. Whether obtained by means of focus groups, individual interviews, or belief elicitation, a content analysis can be performed to identify the most frequently mentioned attributes, and these attributes are then used in subsequent research (see Ajzen & Fishbein, 1980). With respect to each attribute, respondents are asked to indicate the likelihood that the attitude object possesses the attribute and to provide an evaluation of the attribute. In accordance with the expectancy–value model, the evaluations are multiplied by the likelihood ratings and the resulting products are summed (see Equation 2). Correlations of this belief-based estimate of attitude with a direct measure have generally provided good support for the EV model (Ajzen, 1974; Fishbein, 1963, see Eagly, 1993). Although also popular in the area of consumer research (Bagozzi et al., 2002), complete tests of the model in this domain have been relatively rare (but see Lutz, 1977; Mitchell & Olson, 1981). As indicated earlier, investigators often select a few product attributes in a nonsystematic manner and assume that these attributes are important determinants of consumer attitudes and behavior. That selective choice of attribute beliefs can result in misleading conclusions was shown in a study of the relative impact of attitude toward the ad and brand beliefs on brand attitudes (Mittal, 1990). It was argued that prior studies (e.g., Mitchell & Olson, 1981) had focused primarily on a brand’s utilitarian aspect to the exclusion of image-related beliefs, and that this resulted in an overestimation of the importance of attitudes toward the ad. Pilot work was conducted to elicit accessible beliefs about shampoos and wines. Image-related attributes (e.g., looks prestigious, will impress people) as well as utilitarian attributes (e.g., gets rid of dandruff, is made from good quality grapes) were elicited. In the main study, participants saw print ads for a fictitious new brand of shampoo and for a new brand of wine, they expressed their beliefs that the two brands possessed each of the utilitarian and image attributes, they rated each attribute on an evaluative scale, and they expressed their overall attitude toward the two brands and toward the ads on three-item evaluative semantic differential scales. The results showed that the direct relation between attitudes toward the ad and


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toward the brand was greatly attenuated when not only utilitarian but also image-related brand beliefs were included in the regression analysis. PREDICTION OF CONSUMER BEHAVIOR Up to this point we have focused on attitudes toward brands, products, services, or other aspects of consumer behavior. In our multiattribute and expectancy–value models it is postulated that these attitudes derive from underlying beliefs about the product’s attributes together with the subjective values of these attributes. The main focus of the present chapter, however, is the effect of consumer attitudes on actual behavior. As a general rule, it is assumed that attitudes toward available options—whether inferred from choices in the revealed preferences paradigm or measured directly—determine consumer decisions. When confronted with a choice between alternative brands or services, consumers presumably select the alternative toward which they hold the most favorable overall attitude.5 Because this assumption is virtually an article of faith, it is rarely questioned or empirically validated. The focus instead is on such factors as advertising that can influence beliefs and attitudes, and should thus have an effect on behavior. The criterion in many studies is a (hypothetical) choice between products, often fictitious, or an indication of willingness to perform a given behavior (for a few recent examples, see Arvola, Lähteenmäki, & Tuorila, 1999; Litvin & MacLaurin, 2001; Madrigal, 2001). ATTITUDES VERSUS BEHAVIOR Although intuitively reasonable, the assumption that consumer attitudes are predictive of behavior must be regarded with caution in light of extensive research on the attitude-behavior relation conducted over the past 40 years (see Ajzen & Fishbein, 2005; Eagly & Chaiken, 1993). Consider, for example, attempts to understand environmentally responsible consumer behavior. The predominant explanatory construct in this domain is an attitudinal indication of environmental concern. Unfortunately, measures of environmental concern are usually poor predictors of such environmentally responsible consumer behaviors as buying fewer packaged products, using less detergent, and using returnable containers (Balderjahn, 1988; see Gill, Crosby, & Taylor, 1986; Hines, Hungerford, & Tomera, 1987). The Principle of Compatibility To anybody familiar with current theory and research regarding the attitude-behavior relation, these negative findings come as no surprise. It is well known that attitudes can be expected to correlate with behavior only to the extent that the predictor and criterion are measured at compatible levels of generality or specificity in terms of their target, action, context, and time elements (Ajzen, 1988; Ajzen & Fishbein, 1977, 2005). General attitudes cannot be expected to be good predictors of specific actions directed at the attitude object. In the case of environmental concern—a very general attitude that specifies only a broad set of behaviors (protection) with respect to a global target (the environment)—the behavioral criterion would have to be assessed at an equally general level by aggregating over the many different actions in this behavioral category (Fishbein & Ajzen, 1974). In fact, the case for this argument in the domain of environmental behavior was made some time ago by Weigel and Newman (1976). The investigators used a multi-item scale designed to measure attitudes toward environmental quality and, 3 to 8 months later, observed 14 behaviors related to the environment. The behaviors involved signing and circulating three different petitions concerning environmental issues, participating in a litter pick-up program, and participating in a


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recycling program on eight separate occasions. In addition to these 14 single-act, single-observation criteria, Weigel and Newman constructed four behavioral aggregates: one based on petitionsigning behaviors, one on litter pick-ups, one on recycling, and one overall index based on all 14 observations. Prediction of each single observation from the general attitude measure was quite weak; the average correlation was .29 and not significant. The aggregates over occasions, based on multiple observations of single actions, showed a mean correlation of moderate magnitude with the general attitude (r = .42), while the multiple-act index over all 14 observations correlated .62 with the same attitude measure. Although Weigel and Newman examined behaviors unrelated to the purchase and consumption of products, the same argument can be made with respect to the prediction of consumer behavior. Concern for the environment should predict a measure of environmentally responsible consumer behaviors that aggregates across many different kinds of actions but not necessarily any particular action. In most consumer decision situations, however, we are interested not in understanding broad patterns of behavior but rather the purchase or use of a particular product or service, choice of one particular retailer over another, and so forth. These are relatively specific behaviors involving particular target and action elements, and sometimes context and time elements as well. While the principle of compatibility argues against reliance on general attitudes to predict specific behaviors of this kind, many investigators continue to be interested in broad attitudinal dispositions and their possible effects on specific behaviors (see Eagly & Chaiken, 1993). The MODE Model The most direct and sophisticated attempt to deal with the processes whereby general attitudes may influence performance of specific behavior was provided by Fazio (1986, 1990; Fazio, 1995; Fazio & Towles-Schwen, 1999) in his MODE model. Consistent with past work on the effects of attitudes on perceptions and judgments (see Eagly & Chaiken, 1998, for a review), the model assumes that general attitudes can influence or bias perception and judgment of information relevant to the attitude object, a bias that is congruent with the valence of the attitude. However, for this bias to occur the attitude must first be “activated.” Consistent with the logic of other dual-mode processing theories (see Chaiken & Trope, 1999) the MODE model posits that attitudes can be activated in one of two ways: in a controlled or deliberative fashion and in an automatic or spontaneous fashion. The acronym MODE is used to suggest that “motivation and opportunity act as determinants of spontaneous versus deliberative attitude-to-behavior processes” (Fazio, 1995, p. 257). When people are sufficiently motivated and have the cognitive capacity to do so, they can retrieve or construct their attitudes toward an object in an effortful manner. When motivation or cognitive capacity is low, attitudes can become available only if they are automatically activated. According to the MODE model, such automatic or spontaneous activation is reserved for strong attitudes. The stronger the attitude, the more likely it is that it will be automatically activated and hence be chronically accessible from memory. Whether activated automatically or retrieved effortfully, the general attitude is available and can then bias processing of information. Individuals who hold favorable attitudes are likely to notice, attend to, and process primarily the object’s positive attributes whereas individuals with unfavorable attitudes toward the object are likely to direct attention to its negative qualities. Such automatic biasing of information processing and judgments is more likely to be the case for strong, highly accessible attitudes than for weak attitudes. As a result, readily accessible, automatically activated attitudes are more likely than relatively inaccessible attitudes to bias the definition of the event and hence to guide performance of specific behaviors with respect to the attitude object.


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Studies that were designed to test the MODE model’s predictions concerning the attitude-tobehavior process (Berger & Mitchell, 1989; Fazio, Chen, McDonel, & Sherman, 1982; Fazio, Powell, & Williams, 1989; Fazio & Williams, 1986; Kokkinaki & Lunt, 1997) have focused on behavior in a deliberative processing mode. The results of these studies are generally consistent with the model. For example, in a study of consumer behavior (Fazio et al., 1989), participants seated at a computer first expressed their attitudes toward 100 commonly available products by pressing one of two keys labeled “like” and “dislike.” They were instructed to respond as quickly and as accurately as possible. The response latencies constituted a measure of attitude accessibility in memory. Participants then completed a questionnaire which assessed their attitudes toward the same 100 products on a 7-point evaluative scale (extremely bad–extremely good). Finally, they were shown 10 of the 100 products (a Snickers candy bar, a can of Dr. Pepper, a box of Cracker Jacks, etc.) and asked to choose five to take as a present. As expected, prediction of product choice from the 7-point attitude measure was significantly better for participants with highly accessible (low latency) attitudes toward the products (r = .62) than for participants with moderately accessible attitudes (r = .54) or relatively inaccessible attitudes (r = .51). The MODE model provides an elegant account of the processes and conditions under which general attitudes toward objects will or will not guide the performance of specific behaviors. Nevertheless, several important issues have been raised in regard to this approach. First, the assumption that only strong attitudes are activated automatically by mere observation of the attitude object has been challenged in priming research where it was found that all attitudes are activated automatically, irrespective of their strength or accessibility (Bargh, Chaiken, Govender, & Pratto, 1992; Bargh, Chaiken, Raymond, & Hymes, 1996). In his rebuttal, Fazio (1993) reexamined the priming results and concluded that they are not inconsistent with the idea that highly accessible attitudes are more likely to be automatically activated. The MODE model’s implications for attitude-behavior consistency, however, do not depend on the assumption that only strong attitudes are automatically activated. All we need to assume is that readily accessible or strong attitudes are more likely than less accessible attitudes to bias perceptions and judgments. Related to this issue, it has been suggested that the magnitude of the attitude-behavior relation may be moderated not by attitude accessibility but by other correlated factors such as certainty, amount of knowledge, or the attitude’s temporal stability. Support for the superior predictive validity of stable attitudes was provided by Doll and Ajzen (1992). Compared to second-hand information, direct experience with different video games was found to raise the accessibility of attitudes toward playing those games and to increase the temporal stability of the attitudes. The superior predictive validity of the attitude measures following direct as apposed to indirect experience could be explained better by their greater stability than by their higher level of accessibility. REASONED ACTION The SEU model of behavioral decision theory and the EV model of attitude theory both make the assumption that consumer decisions are based on the relative attractiveness of available alternatives. Although this approach to consumer behavior can produce valuable insights, it tends to lack ecological validity. We saw earlier that research relying on revealed preferences to infer decisionmaking processes typically confronts participants with artificial decisions among hypothetical products or services defined in terms of a selective set of attribute dimensions. Little information is gained about the specific considerations that actually guide the consumer’s behavior. Elicitation of accessible beliefs in investigations of attitudes toward brands, products, or services can provide ecologically valid information about perceived product attributes, and these beliefs may


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help explain consumer decisions. However, here too the decision-making situation is contextually impoverished. It is assumed that consumers consider the attributes of alternative products and base their decisions only on the relative advantages and disadvantages of the products in terms of these attributes. This approach fails to take into account other potentially important considerations associated with the consumer’s behavior, considerations that have to do with the social context in which the behavior occurs as well as potential situational constraints. An alternative approach to the prediction of consumer behavior has been gaining ground in recent years. Instead of focusing on general attitudes toward products or services, it is possible to focus instead on the specific consumer behaviors of interest. The principle of compatibility would suggest that the most relevant antecedents of a particular consumer behavior are identical to the behavior in terms of action, context, target, and time elements. Consider, for example, the purchase (action) of a Sony television set (target) in the next 6 months (time). In this example, the context element is left unspecified, perhaps because the investigator has no interest in studying where the product is purchased. Arguably the most immediate direct antecedent of this action is the intention to buy a Sony television set in the next 6 months. In fact, we saw earlier that measures of intention to buy or use a specified product are often used as substitutes of behavioral measures, presumably under the assumption that people’s intentions are good indications of what they actually do. Many studies have indeed substantiated the predictive validity of behavioral intentions. When appropriately measured, behavioral intentions account for an appreciable proportion of variance in actual behavior. Meta-analyses covering diverse behavioral domains have reported mean intention-behavior correlations of .47 (Armitage & Conner, 2001; Notani, 1998), .53 (Shepherd, Hartwick, & Warshaw, 1988), .45 (Randall & Wolff, 1994), and .62 (van den Putte, 1993). Studies in specific behavioral domains, such as condom use and exercise, have produced similar results, with intention-behavior correlations ranging from .44 to .56 (Albarracin, Johnson, Fishbein, & Muellerleile, 2001; Godin & Kok, 1996; Hausenblas, Carron, & Mack, 1997; Sheeran & Orbell, 1998). In a meta-analysis of these and other meta-analyses, Sheeran (2002) reported an overall correlation of .53 between intention and behavior. Consider just one example from the consumer behavior domain (East, 1993). The study was conducted in 1990 in the United Kingdom when the British Government sold shares to the public in 12 regional electric companies. Participants in the study indicated their intentions to apply for shares and—after the application period was closed—they reported whether they had actually applied. The correlation between intention and behavior was found to be .82. The Theory of Planned Behavior The intention to adopt a certain course of action logically precedes actual performance of the behavior. Consistent with this reasoning, social psychologists tend to view intentions as mediating between attitudes and actions (e.g., Bagozzi & Warshaw, 1990; Bentler & Speckart, 1979; Fishbein & Ajzen, 1975; Fisher & Fisher, 1992; Gollwitzer, 1993; Kuhl, 1985; Locke & Latham, 1990; Triandis, 1977). In research on consumer behavior, investigators have conceptualized this causal sequence as the belief-attitude-intention hierarchy (e.g., Follows & Jobber, 2000; Madrigal, 2001; Ogle, Hyllegard, & Dunbar, 2004). Undoubtedly the most popular models in this domain are the theory of reasoned action (Ajzen & Fishbein, 1973, 1980; Fishbein, 1967a; Fishbein & Ajzen, 1975) and its successor, the theory of planned behavior (Ajzen, 1988, 1991). Briefly, according to the theory of planned behavior, intentions to perform a given behavior are influenced by three major factors: a favorable or unfavorable evaluation of the behavior (attitude toward the behavior), perceived social pressure to perform or not perform the behavior (subjective norm), and self-efficacy in relation to


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the behavior (perceived behavioral control). In combination, attitude toward the behavior, subjective norm, and perception of behavioral control lead to the formation of a behavioral intention. As a general rule, the more favorable the attitude and subjective norm, and the greater the perceived behavioral control, the stronger should be the person’s intention to perform the behavior in question. Finally, given a sufficient degree of actual control over the behavior, people are expected to carry out their intentions when the opportunity arises. Intention is thus assumed to be an immediate antecedent of behavior. However, because many behaviors pose difficulties of execution that may limit volitional control, it is useful to consider perceived behavioral control in addition to intention. To the extent that people are realistic in their judgments of a behavior’s difficulty, a measure of perceived behavioral control can serve as a proxy for actual control and contribute to the prediction of the behavior in question (see Ajzen, 1991). A schematic representation of the theory is shown in Figure 20.1. When applied to consumer behavior, the intention of interest may be the intention to purchase a given product or service. The three major determinants of this behavior—attitudes toward buying the product, subjective norms, and perceptions of behavioral control—are traced to corresponding sets of behavior-relevant beliefs. Consistent with the expectancy–value model discussed earlier, attitude toward buying a product is assumed to be determined by accessible beliefs about the consequences of doing so, each belief weighted by the subjective value of the consequence in question. A similar logic applies to the relation between accessible normative beliefs and subjective norm, and the relation between accessible control beliefs and perceived behavioral control. Normative beliefs refer to the perceived behavioral expectations of such important referent individuals or groups as the person’s family, friends, and coworkers. These normative beliefs—in combination with the person’s motivation to comply with the different referents—determine the prevailing subjective norm regarding the purchase. Finally, control beliefs have to do with the perceived presence of factors that can facilitate or impede performance of a behavior. It is assumed that the perceived power of each control factor to impede or facilitate a purchase contributes to perceived control over this behavior in direct proportion to the person’s subjective probability that the control factor is present. In the case of a purchase decision, issues of control may be related to financial constraints or a product’s availability. It can be seen that the theory of planned behavior represents a “reasoned action” approach to consumer behavior because it assumes that intentions and behavior in this domain follow reasonably from the behavioral, normative, and control beliefs people hold about the behavior.

Figure 20.1

Theory of planned behavior.


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Virtually hundreds of studies have been conducted over the past 35 years, applying the theories of reasoned action and planned behavior in a variety of different domains. In the domain of consumer behavior, investigators have used these models to explore the purchase of familiar versus unfamiliar products (Arvola et al., 1999), environmentally responsible purchases in different cultures (Chan & Lau, 2001), clipping coupons online (Fortin, 2000), patronage of a particular retail environment (Ogle et al., 2004), mail-order purchases of apparel (Shim & Drake, 1990), shoplifting (Tonglet, 2002), and a host of other consumer behaviors. We saw earlier that intentions are generally found to be good predictors of behavior. In addition, a great number of studies conducted in the context of Bandura’s (1977) social cognitive theory have documented that self-efficacy is a good predictor of behavior (e.g., Garcia & King, 1991; Longo, Lent, & Brown, 1922; Sadri & Robertson, 1993). Further, measures of perceived behavioral control are often found to improve prediction over and above intention (Armitage & Conner, 2001; Cheung & Chan, 2000), and this is particularly true when the behavior is not under complete volitional control (Madden, Ellen, & Ajzen, 1992). Several meta-analyses of the empirical literature have provided evidence to show that intentions can be predicted with considerable accuracy from measures of attitudes toward the behavior, subjective norms, and perceived behavioral control (Albarracin et al., 2001; Armitage & Conner, 2001; Godin & Kok, 1996; Hagger, Chatzisarantis, & Biddle, 2002; Sheeran & Taylor, 1999; Shepherd et al., 1988; van den Putte, 1993). For a wide range of behaviors, attitudes are found to correlate well with intentions; across the different meta-analyses, the mean correlations range from .45 to .60. For the prediction of intentions from subjective norms, these correlations range from .34 to .42, and for the prediction of intention from perceived behavioral control, the range is .35 to .46. Finally, the multiple correlations for predicting intentions from attitudes, subjective norms, and perceived behavioral control ranged from .63 to .71. CONCLUDING COMMENTS The two major conceptual and research paradigms in consumer behavior—behavioral decision theory and the theories or reasoned action and planned behavior—may both seem to imply that consumers are assumed to be rational in their decisions and actions. This would, however, be an inaccurate reading of either approach. Although behavioral decision theory takes rational, normative models as its starting point, it recognizes that human decision making can be biased in a variety of ways and is best described as observing “bounded rationality” (Simon, 1955). In fact, the nature of biases in judgment and decision making has been at the center of research over the past 30 years. Similarly, the theory of planned behavior also does not assume a rational decision process. Human social behavior is said to be reasoned, controlled, or planned in the sense that it takes account of the behavior’s likely consequences, the normative expectations of important referents, and factors that may impede performance. As noted in the discussion of the expectancy–value model of attitudes, the beliefs people hold may be unfounded, inaccurate, biased, or even irrational. However, their attitudes, subjective norms, and perceptions of behavioral control are thought to follow spontaneously and reasonably from these beliefs, produce a corresponding behavioral intention, and ultimately result in behavior that is consistent with the overall tenor of the beliefs. It should be noted, however, that this does not necessarily imply a deliberate, effortful retrieval of information and construction of attitudes prior to every behavior. Attitudes, subjective norms, and perceived behavioral control are assumed to be available automatically as performance of a behavior is being considered (Ajzen & Fishbein, 2000).


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These ideas may help us understand several issues relevant to consumer behavior: brand loyalty, the importance of brand image, the purchase of luxury goods, and impulse buying. Brand loyalty refers to the tendency to purchase a particular brand repeatedly, to stay with a familiar brand used in the past rather than switch to a new brand, or to psychological commitment to a brand (Jacoby, 1971; Odin, Odin, & Valette-Florence, 2001; Olson & Jacoby, 1971). This tendency may appear to be unreasonable, especially when a new brand offers potential advantages, and various attempts have been made to study the determinants and consequences of brand loyalty (for recent examples, see Chaudhuri & Holbrook, 2001; Danaher, Wilson, & Davis, 2003; Kim, Han, & Park, 2001; Yi & Jeon, 2003). A related issue has been raised in work with the theory of planned behavior where it is often found that frequency of past behavior has an effect on later behavior that is often not fully mediated by the predictors in the theory of planned behavior (Ajzen, 1991; Albarracin et al., 2001; Bagozzi, 1981; Bentler & Speckart, 1979; for reviews, see Conner & Armitage, 1998; Fredricks & Dossett, 1983; Ouellette & Wood, 1998). Although various explanations for this effect can be offered (see Ajzen, 2002), the possibility cannot be ruled out that repeated purchase of a given product produces a habit or routine such that on future occasions the product is chosen almost automatically with only minimal cognitive control. A great deal of research has been devoted to the role of brand image in consumer behavior (for recent examples, see Batra & Homer, 2004; Jo, Nakamoto, & Nelson, 2003; Martinez & de Chernatony, 2004). At first glance, it might appear unreasonable for consumers to prefer brandname products over unknown or generic products with the same qualities. However, the theory of planned behavior assumes that people’s intentions and actions are guided by their beliefs about buying a product, not by the objective attributes of the product. Advertising and other exposures to a brand can provide an advantage by associating the brand with favorable attributes, resulting in a positive brand image not available to unfamiliar brands. If, for example, consumers believe that Bayer aspirin is a more effective pain reliever than a generic brand of aspirin, it is reasonable for them to develop a more favorable attitude toward buying the Bayer brand. Similar arguments apply to the purchase of “luxury goods,” i.e., goods that command a premium price because of the manufacturer’s reputation. It might be argued that it makes no sense to buy a $2,000 Rolex watch when equally accurate, durable, and attractive watches are available for a fraction of the price. This does not, however, contradict the logic of the theory of planned behavior which only assumes that the purchase of a Rolex watch is reasonable in light of the consumer’s own subjective beliefs and values associated with the purchase. Consumers may well believe that owning a Rolex watch confers high status or otherwise reflects positively on them. If they place high value on these consequences, their purchase of a Rolex watch would be quite reasonable. Alternatively, they may believe that important others think they should buy an expensive watch or that in their circle of friends this is a common purchase, and the resulting subjective norm could be an important motivating factor. Finally, buying on impulse is also an apparently unreasoned action that has attracted considerable attention (e.g., Beatty & Ferrell, 1998; Hausman, 2000; Jones, Reynolds, Weun, & Beatty, 2003; Verplanken & Herabadi, 2001). Fast, immediate reactions to the requirements of a situation are, of course, not inconsistent with a reasoned action approach. Well-established beliefs and attitudes are activated spontaneously and thus guide behavior without much cognitive effort. However, impulse buying appears to bypass reasoning, to be based more on emotions than on rational factors. Indeed, it is often argued that the theories of reasoned action and planned behavior are too rational, failing to take into account emotions, compulsions, and other noncognitive or irrational determinants of human behavior (e.g., Armitage, Conner, & Norman, 1999; Gibbons, Gerrard, Blanton, & Russell, 1998; Ingham, 1994; Morojele & Stephenson, 1994; van der Pligt & de Vries,


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1998; Verplanken & Herabadi, 2001). To be sure, much of the research conducted in the framework of these theories has devoted little attention to the role of emotion in the prediction of intentions and actions. This is not to say, however, that emotions have no place in theories of this kind. It is well known that general moods and emotions can have systematic effects on beliefs and evaluations: People in a positive mood tend to evaluate events more favorably and to judge favorable events as more likely than people in a negative mood (Forgas, Bower, & Krantz, 1984; Johnson & Tversky, 1983; Robles, Smith, Carver, & Wellens, 1987; Schaller & Cialdini, 1990). In a reasoned action approach, such effects would be expected to influence attitudes and intentions and thus to have an impact on behavior. SUMMARY Two major paradigms have provided much of the impetus for the study of consumer behavior. Choices among alternative brands, products, or services are in many ways no different from other kinds of decisions, and the methods of behavioral decision theory have thus proven valuable to the study of consumer behavior. Multiattribute decision models emphasize the importance of expected values derived from a product’s perceived attributes. However, as in other kinds of decisions, judgments underlying consumer choices are found to deviate in systematic ways from normative prescriptions. One limitation of the revealed preferences approach adopted in work on multiattribute decisions is that it provides no direct information about consumers’ beliefs and attitudes regarding real-life products or services. A more fruitful approach in this regard is found in the expectancy– value model of attitude. In work with this model, accessible beliefs about a product are elicited in a free-response format and attitudes toward the product are directly assessed. This approach makes it possible for the investigator to identify important attribute characteristics that guide consumer attitudes and behavior. In a related fashion, the theory of planned behavior provides a conceptual framework that focused on the specific behaviors performed by consumers, be they buying a given brand or product, searching for information about a product, or shopping at a given retail outlet. According to the theory, the immediate antecedent of such behaviors is the intention to perform the behavior in question. Intentions, in turn, are determined by attitudes toward the behavior, subjective norms, and perceived behavioral control. Behavioral, normative, and control beliefs, respectively, provide the basis for the formation of attitudes, subjective norms, and perceptions of behavioral control. As in many other behavioral domains, the theory of planned behavior has proven to be a useful conceptual and methodological framework for the study of consumer behavior. NOTES 1. Attribute values can interact with each other, requiring separate evaluation of each possible combination of attributes. This complication is usually disregarded because it seems to make little difference to the results (W. Edwards & Fasolo, 2001). 2. A notable exception are process tracing methods, such as think-aloud protocols developed for the study of problem solving (see Ericsson & Simon, 1980; Payne, 1994), which have also been applied to consumer decision making (e.g., Backlund, Skavér, Montgomery, Bring, & Strender, 2003). 3. See Ajzen (1977) for a similar argument in relation to research on interpersonal attraction. 4. It is now also possible to conduct focus groups online, by involving computer users in a simultaneous online discussion, or letting them contribute to the discussion sequentially over a period of time (see Sweet, 2001).


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5. Framing and other situational variations affect choices presumably because they influence beliefs about and evaluations of the available alternatives.

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