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Influence of Reinforcement Contingencies and Cognitive Styles on Affective Responses: An Examination of Rolls’ Theory of Emotion in the Context of Consumer Choice Gordon R. Foxall1 and Mirella Yani-de-Soriano Cardiff Business School Cardiff University This paper examines Rolls’ (2005) propositions that emotional responses can be systematically related to environmental contingencies and that individual differences are related to emotional responses. In addition, consumer situations, defined functionally in terms of the reinforcement pattern they uniquely portray, as proposed by the behavioral perspective model (BPM) of consumer choice are predictably associated with patterns of self-reported pleasure, arousal, and dominance (Mehrabian & Russell, 1974). Rolls’ argument that individual differences influence conditionality and emotionality is examined via hypotheses from the theory of adaptive–innovative cognitive style (Kirton, 1976, 2003). The results confirm that affective response to consumer environments is consistently predicted by the modeled pattern of operant contingencies, but not the expected relationship between cognitive styles and affective responses. jasp_823 2508..2537 Among psychologists who propose that emotional responses are linked systematically to operant contingencies, Rolls (2005) perhaps made the relationship most explicit by defining emotionality as the direct outcome of reinforcement contingencies. According to Rolls, emotions are “states elicited by rewards and punishers, that is, by instrumental reinforcers” (p. 11). Moreover, he emphasized the role of conditioning in emotionality by suggesting a two-stage process in which stimuli first reinforce the emitted behavior that generated them (i.e., instrumental or operant behavior) and, through Pavlovian association, elicit emotional responses within the brain. The selection of particular molar patterns of behavior relies, in turn, on the neurophysiological responses to different behavioral consequences that determine whether such outcomes of behavior are reinforcing or punishing. Rolls (2005, p. 14; Rolls, 2008, Chapter 3) presented a schema in which emotions are related to particular patterns of reinforcement contingency on the basis of consideration of how affective responses would be distributed along posited axes of positive and negative reinforcement and punishment. 1 Correspondence concerning this article should be addressed to Gordon Foxall, Cardiff Business School, Cardiff University, Aberconway Building, Colum Drive, Cardiff CF10 3EU, Wales, UK. E-mail: [email protected] 2508 Journal of Applied Social Psychology, 2011, 41, 10, pp. 2508–2537. © 2011 Wiley Periodicals, Inc. REINFORCEMENT AND EMOTION 2509 In earlier work, we also sought to provide a means of relating emotional responses to contingencies of reinforcement in the context of economic choice and have done so on the basis of a model of consumer behavior that refines the concepts of reinforcement and punishment in terms of their applicability to the economic choices of consumers in affluent, marketing-oriented socioeconomic systems (Foxall, 1990/2004, 1997; Foxall & Greenley, 1999; Foxall & Yani-de-Soriano, 2005). Moreover, it has drawn on the theory of environmental psychology that was proposed by Mehrabian and Russell (1974; also see Mehrabian, 1980), who made a case for pleasure, arousal, and dominance being the basic dimensions of emotion of which other emotional states are derivative (Foxall, 2005). This work has been conducted in two cultural (including linguistic) milieus; namely, England and Venezuela. A primary aim of the current paper is to extend this research further culturally by reporting an additional study, conducted in Wales, in order to examine the generalizability of the underlying model of consumer choice. This makes possible interesting comparisons between our own framework and that proposed by Rolls (2005), which suggest additional lines of investigation. Specifically, Rolls’ (1999) theory of emotion is sensitive to the possibility that individual differences in personality and cognition may influence rate and nature of conditioning and, by extension therefore, affective responses to consumer environments. In also drawing attention to the role of individual differences in the meditation of emotion and behavior, Rolls (2005) paid particular attention to the claim that extraversion–introversion and neuroticism influence conditionability, hence susceptibility to environmental contingencies and emotionality. This reflects Eysenck and Eysenck’s (1968, 1985) argument that introverts are more easily conditionable and show greater arousal by means of external stimuli than extraverts. The possibility also arises that introverts are more sensitive to punishment and frustrative nonreward than are extraverts (Gray, 1970). Extraverts may also show greater sensitivity to reward learning. The two hypotheses that emerge are that introverts are more susceptible to conditioning and that extraverts are more susceptible to positive, rewarding stimuli; while introverts are more sensitive to aversive stimulation. It has also been suggested, however, that extraverts may perform less well at vigilance tests (i.e., where the individual must detect stimuli that occur with low probability), and may perform better when arousal is high (Matthews & Gilliland, 1999). Other research has indicated that the amygdalae of extraverts are more intensely activated by representations of happy faces than are those of introverts (Canli, Silvers, Whitfield, Gotlib, & Gabrieli, 2002). Rolls (2005) noted that this evidence “supports the conceptually important point . . . that part of 2510 FOXALL AND YANI-DE-SORIANO the basis of personality may be differential sensitivity to different rewards and punishers, and omission and termination of different rewards and punishers” (p. 33). We test this proposition in the context of Kirton’s (1976, 2003) adaption–innovation theory of cognitive style, the measurement of which correlates significantly with a number of the dimensions of personality adumbrated by Rolls; notably, extraversion–introversion, flexibility, and tolerance of repetitive tasks. The Behavioral Perspective Model We explore Rolls’ (2008) first proposition to the effect that emotional responses may be systematically related to reinforcement contingencies by means of an empirical analysis of consumer behavior. This involves the development of an enhanced framework for relating patterns of operant contingency to emotional responses in settings marked by economic decision making. This, in turn, requires that we seek to relate reward contingencies more effectively to consumer behavior in particular by taking into consideration the peculiar reward structure of human economic choice. Such economic behavior is, for instance, shaped and maintained by two sources of reinforcement: utilitarian, which relates to the functional consequences of acquiring and using products and services; and informational, which refers to the symbolic outcomes of buying and consuming conspicuous goods. In addition, the extent to which the consumer behavior setting encourages or inhibits particular choices is an element in economic decision making that should be related to its subjectively emotional and as its intersubjectively observable outcomes. We present a model of consumer behavior, the behavioral perspective model (BPM; Foxall, 1990/2004) in which these elements of an operant explanation are systematically related both to overt behaviors and to the emotions of pleasure, arousal, and dominance (PAD) that Mehrabian (1980; Mehrabian & Russell, 1974) put forward as basic affective dimensions of human performance. Earlier research has supported the general pattern of contingency–affective relationships we propose (Foxall & Greenley, 1999; Foxall & Yani-de-Soriano, 2005). In this paper, we subject the BPM–PAD framework to further appraisal. The analysis of human economic behavior requires certain emphases that, although they are not absent from Rolls’ (2005) theory of emotion, must be taken into consideration explicitly if this mode of functioning is to be understood fully. Economic behavior is, by definition, an instrumental activity: It is rewarded by the outcomes it produces or attracts. Sometimes, the rewards of such behavior are associated with its recurrence, in which case they are REINFORCEMENT AND EMOTION 2511 known as reinforcers. Other times, they are associated with its reduction or cessation, in which case they are known as punishers. First, economic behavior is reinforced by two classes of reward: those that derive from the functional utility of possessing and consuming a good, and those that derive from the status considerations inherent in these processes. Since economic goods overwhelmingly provide both utilitarian and informational reinforcement rewards, albeit in varying combinations, our first question is how the resulting pattern of reinforcement influences economic choice. This suggests, at least in the context of human economic behavior, a rather more complicated typology of contingency–affective interactions than that proposed by Rolls (2005). In addition, the economic actor’s history of reinforcement and punishment interacts with the stimuli that compose the current behavior setting to generate discriminative stimuli that signal the probability of utilitarian and informational rewards or punishers contingent upon the performance of particular responses. The consumer situation that results from the interaction of learning history and behavior setting determines the scope the actor has for performing a given response; that is, its probability of occurrence. Second, economic behavior is always punished, as well as reinforced (Alhadeff, 1982). Sometimes, these influences on learning history occur simultaneously, but on occasion (e.g., as in the case of compulsive consumption), one or the other occurs only after a delay. Combining these effects yields the model portrayed in Figure 1. This figure applies the reasoning advanced thus far to the analysis of consumer behavior. The patterns of contingency proposed by the BPM have been validated for a wide range of economic behavior and have been shown to be relevant to the theoretical analysis of purchase and consumption; adoption and diffusion Utilitarian reinforcement Consumer behavior setting Consumer situation Utilitarian punishment Behavior Consumer situation Learning history Informational reinforcement Informational punishment Figure 1. Summative behavioral perspective model. 2512 FOXALL AND YANI-DE-SORIANO High utilitarian reinforcement Low utilitarian reinforcement High informational reinforcement ACCOMPLISHMENT ACCUMULATION Low informational reinforcement HEDONISM MAINTENANCE Figure 2. Operant classes of consumer behavior. of new products2; environmental conservation; marketing management; empirical investigation of brand and product choice and consumer loyalty; matching and maximization analyses in behavioral economics; elasticity of demand; as well as that of consumers’ affective responses to which reference has been made (Foxall, 1996, 2002; Foxall & Greenley, 1999; Foxall & Yani-de-Soriano, 2005; Foxall, Oliveira-Castro, James, & Schrezenmaier, 2007). There are two further theoretical developments that are now possible. First, it is possible to derive an exhaustive matrix of functionally defined consumer behaviors—actually, four operant classes of consumer behavior— which depend for their definition on whether utilitarian reinforcement and informational reinforcement are relatively high or relatively low. The labels attached to these classes (Figure 2) are intuitive, but arbitrary. They suggest the kind of behaviors likely to be associated with the patterns of reinforcement depicted, but at this stage in the absence of any empirical evidence that consumer behaviors of the sort described can actually be reliably linked to these patterns. Second, by combining the idea of consumer behavior setting scope— again, dichotomously operationalized as relatively open versus relatively closed—it is possible to derive the matrix of contingency categories shown in 2 Many writers refer to the diffusion of innovations, but we have chosen to use Kirton’s (2003) work, partly because of its sound, precise approach. He argued against using innovation as a synonym of new, but instead argued that cognitive (problem-solving) style implies that there is a difference within new—a continuum from adaptive to innovative. Foxall (1995) supported this contention with clear data, showing that the more adaptive buy more products that are adaptive new, while innovators prefer innovative new. This is opposed to the view that early buyers are all innovators, which Foxall’s data also challenge. So, these collective studies leave us with the position that cognitive affect (referring to what the individual wants, backed by incentive-based motivation) leads to the decision that the individual needs more clothes or another iron. While all consumers may seek something new, adaptors and innovators will search for their product differently and will look for a different new. For instance, in research that separated new products into the more continuous (i.e., incremental-based changes) and the more discontinuous (i.e., more radical changes), those purchasing the former were adaptive, while those purchasing the latter were more innovative (Foxall, 1995). This interpretation is supported by Drucker (1974), who uses the terms doing better and doing differently, a brilliant distinction of deep-seated preference that is entrenched in Kirton’s work and, in the marketing sphere, well supported by Foxall’s style involvement model of consumer innovation (Foxall & James, 2009). REINFORCEMENT AND EMOTION 2513 BEHAVIOR SETTING SCOPE Open Closed CC2 ACCOMPLISHMENT Fulfillment CC1 Status consumption CC4 HEDONISM Inescapable entertainment CC3 Popular entertainment CC6 ACCUMULATION Token-based consumption MAINTENANCE Mandatory consumption CC5 Saving and collecting CC8 CC7 Routine purchasing Figure 3. The behavioral perspective model contingency matrix (CC = contingency category). Figure 2. This, in turn, suggests a means of classifying consumers’ emotional responses to environments of purchase and consumption according to the pattern of contingencies that govern each of the eight consumer situations depicted in the matrix in Figure 3. Pleasure, Arousal, Dominance, and Approach–Avoidance Mehrabian and Russell (1974) proposed the argument that emotional reactions to environmental settings are fundamentally of three kinds: pleasure, arousal, and dominance. They proposed the theory that physical or social stimuli in the environment directly affect the emotional state of an individual, thereby influencing his behavior in it. The three PAD dimensions constitute a common core of human emotional response to all stimuli, regardless of the number of modalities of sensation involved, which mediate approach–avoidance behavior (Mehrabian, 1980). Measures of these dimensions are factorially orthogonal (independent of each other) so that any level of one may be accompanied by any level of the other two (Russell & Mehrabian, 1977). 2514 FOXALL AND YANI-DE-SORIANO Pleasure–displeasure is a feeling state that can be assessed readily with self-report (e.g., semantic-differential measures) or with behavioral indicators (e.g., smiles, laughter) and, in general, positive versus negative facial expressions. Arousal–nonarousal is a feeling state varying along a single dimension ranging from sleep to frantic excitement. Arousal is most directly assessed by verbal report or with behavioral indicators such as vocal activity (positive and negative), facial activity (positive and negative expressions), speech rate, and speech volume. Dominance–submissiveness is a feeling state that can be assessed from verbal reports using the semantic-differential method. It is assumed that there is an inverse relationship between dominance and the judged potency of the environment. Behaviorally, dominance is measured in terms of postural relaxation; that is, body lean and asymmetrical positioning of the limbs. An individual’s feeling of dominion in a situation is based on the extent to which he feels unrestricted or free to act in a variety of ways (Mehrabian & Russell, 1974). Research has shown systematic relationships between the emotional variables and consumer behavior. Pleasure, arousal, and dominance have been shown to mediate more overt consumer behavior, such as desire to affiliate with others in the setting, desire to stay in or escape from the setting, and willingness to spend money and consume (Donovan & Rossiter, 1982; Donovan, Rossiter, Marcoolyn, & Nesale, 1994; Foxall, 1997; Foxall & Yani-de-Soriano, 2005; Gilboa & Rafaeli, 2003; Mehrabian & de Wetter, 1987; Mehrabian, 1979; Mehrabian & Riccioni, 1986; Mehrabian & Russell, 1975; Russell & Mehrabian, 1976, 1978; Van Kenhove & Desrumaux, 1997; Tai & Fung, 1997). Donovan and Rossiter introduced the Mehrabian– Russell (1974) model in retail settings. The focus of their research was on store atmosphere; that is, on within-store variables that affect shopping behavior (Kotler, 1973–1974). They found that pleasure was a very powerful determinant of approach–avoidance behaviors within the retail environment, including the tendency of the consumer to spend beyond his original expectations. In the same manner, arousal could increase time spent browsing and exploring products in the store and the willingness to interact with sales personnel. They mentioned bright lighting and upbeat music as stimuli that induce arousal. However, they concluded that the third variable, dominance, did not relate well to in-store behaviors. In line with their previous findings, Donovan et al. (1994) measured only pleasure and arousal of shoppers during the shopping experience, and their results confirmed that pleasure could predict consumer behavior, such as extra time spent in the store and overspending. Arousal did not predict overspending, which failed to support Donovan and Rossiter’s (1982) study, but could predict underspending in unpleasant store environments, which was not found in the earlier study. These and other previous studies REINFORCEMENT AND EMOTION 2515 BEHAVIOR SETTING SCOPE Open Closed CC2 ACCOMPLISHMENT Fulfillment CC1 Status consumption CC4 HEDONISM Inescapable entertainment CC3 Popular entertainment CC6 ACCUMULATION CC5 Saving and collecting Token-based consumption CC8 MAINTENANCE Mandatory consumption CC7 Routine purchasing Figure 4. Hypothesized and observed patterns of reinforcement and pleasure, arousal, and dominance. have focused mainly on understanding the role of the three emotional dimensions—particularly pleasure and arousal—in shaping consumer choice; however, the role of dominance has been ignored or controlled (Biggers, 1981; Biggers & Rankins, 1983; Yani-de-Soriano & Foxall, 2006). The link with contingency-shaped consumer behavior stems from the possibility that pleasure, arousal, and dominance reflect utilitarian reinforcement, informational reinforcement, and consumer behavior setting scope, respectively; that is, that Mehrabian and Russell’s (1974) verbal measures of the three emotional variables they posit are typically reports of the eliciting stimuli and rewarding consequences of behavior that the BPM summarizes in these ways (Foxall, 1994, 1997). This has been borne out by three empirical investigations: one involving students and two involving adult consumers. In the case of the consumer studies, one was conducted in England and the other in Venezuela, with the instruments translated into Spanish. In each case, the pattern of results was as shown in Figure 4. The pleasure means were higher, rather than lower, wherever the model indicated that utilitarian reinforcement would be greater rather than smaller (as shown by upper-case 2516 FOXALL AND YANI-DE-SORIANO vs. lower-case typeface). The arousal means were higher, rather than lower, wherever the model indicated that informational reinforcement would be greater rather than smaller. Finally, the dominance means were higher for open, as opposed to closed, settings. In addition, in the present study, we test the assumption laid by the BPM that behavior will be expected to increase with the total quantity and quality of reinforcement available to reinforce it, as well as with the degree of openness of the setting. Therefore, approach–avoidance scores for accomplishment and hedonism are expected to be significantly higher than those of accumulation and maintenance. Moreover, approach–avoidance scores for the open settings in accomplishment and hedonism (status consumption and popular entertainment) are expected to be higher than those scores for closed settings in the same operant classes (fulfillment and inescapable entertainment), as well as for all remaining contingencies: savings and collecting, token-based consumption, routine purchasing, and mandatory consumption. Hence, we formulated the following hypotheses for testing: Hypothesis 1. Mean pleasure scores for Contingency Categories (CCs) 1, 2, 3, and 4 will each be greater than will those of CCs 5, 6, 7, and 8 (i.e., pleasure hypothesis). Hypothesis 2. Mean arousal scores for CCs 1, 2, 5, and 6 will each be greater than will those of CCs 3, 4, 7, and 8 (i.e., arousal hypothesis). Hypothesis 3. Mean dominance scores for CCs 1, 3, 5, and 7 will each be greater than will those of CCs 2, 4, 6, and 8 (i.e., dominance hypothesis). Hypothesis 4. Mean approach–avoidance (aminusa) scores for CCs 1, 2, 3, and 4 will each be greater than will those of CCs 5, 6, 7, and 8 (i.e., approach–avoidance hypothesis). Hypothesis 5. Mean approach-avoidance (aminusa) scores for CCs 1 and 3 will each be greater than will those of CCs 2, 4, 5, 6, 7, and 8 (i.e., approach–avoidance in open settings hypothesis). Adaptive–Innovative Cognitive Style Adaption–innovation theory (Kirton, 1976) posits that individuals differ in the way in which they solve problems, make decisions, and process information between two polar cognitive styles. The extreme adaptor attempts characteristically to solve problems within the framework within which they REINFORCEMENT AND EMOTION 2517 arise, applies tried and tested rules of thumb in order to bring about a resolution, and proceeds cautiously by generating and considering a relatively small range of possible solutions. The extreme innovator, by contrast, is more likely to review the framework within which a problem has arisen, comparing it with other possible frameworks, and prefers to generate numerous ideas for the problem’s resolution. He or she is likely to produce relatively novel, and even outlandish solutions. Adaptors have a stronger need for structure in their problem solving; while innovators have a stronger need for a less externally defined range of options within which to pursue outcomes. Adaption–innovation correlates positively with extraversion– introversion, as well as with flexibility, tolerance of ambiguity, self-esteem, and sensation-seeking (Kirton, 2003). Innovators, therefore, should prefer relatively open settings; reporting higher levels of PAD for such environments; and to be willing to spend a longer time there than do adaptors. Adaptors, by contrast, should prefer relatively closed settings on all of these criteria. It must be borne in mind that adaptors and innovators do not form categorical distinction: They represent extreme cognitive styles that are arrayed on a continuum. However, it is usual in forming hypotheses to treat them as dichotomous, with the understanding that the interpretation of empirical results must take into account the continuous nature of adaption– innovation. Hence, we propose the following: Hypothesis 6. Innovators will score more highly on pleasure, arousal, dominance, and behavior (approach–avoidance) than will adaptors in more open settings. Hypothesis 7. Adaptors will score more highly on the affective and behavioral (approach–avoidance) variables than will innovators in more closed settings. These hypotheses have been formulated in terms consistent with Rolls’ (2005) theory and the empirical evidence he adduces for it, both of which have been described. However, our reason for selecting adaption–innovation as a means of testing these hypotheses is that Kirton’s (2003) approach leads to rather different expectations from those made by Rolls. Indeed, Kirton’s theory leads to the expectation that there will be no correlation between affect and cognitive style. Kirton (2003) distinguished cognitive effect from cognitive affect. Cognitive effect consists of cognitive style, cognitive level, and the process of decision making and problem solving. Cognitive effect is posited as undertaking detailed problem solving and is composed of two elements: cognitive style, which is the characteristic manner in which an individual solves problems; and cognitive level, which is an individual’s 2518 FOXALL AND YANI-DE-SORIANO potential cognitive capacity (i.e., his or her level of intellectual operation). Style and level are themselves orthogonal. The distinction between cognitive effect and cognitive affect lies in the fact that whereas cognitive effect is concerned with undertaking the operations of problem solving, cognitive affect is involved in the selection of problems to be solved. These, in turn, motivate a further cognitive element, cognitive resource, which provides the skills and knowledge required to solve a particular problem and is driven by learning, and so includes coping. Emotion as a component of cognitive affect, therefore, has an influence on problem solving, but is not a direct part of problem solving. Emotion provides criteria for the achievement of a solution to a problem, which is satisfying or emotionally fulfilling. Cognitive affect contributes to motivation by playing a role in the determination of which problems will be selected, and it exercises an integrative role between emotion and the cognitive processes involved in the assimilation of past experience, insight, and reason. Kirton’s (1976) theory does not lead to the prediction of a correlation between cognitive effect and cognitive affect. There is no correlation, for instance, between cognitive style and affective attitudes any more than there is between cognitive style and cognitive level. On this basis, one would expect Hypotheses 6 and 7 to be disconfirmed. Method Sample and Procedure Data were collected from a convenience sample of British consumers. A total of 120 participants (75 females, 45 males) completed questionnaires at five different points in the city of Cardiff, including a major UK university, a health service institution, and three small business firms. The questionnaires were administered in person, allowing for greater control of the data-collection process, which resulted in 120 usable responses. Data were generated from diverse individuals who first completed the Kirton (2003) Adaption–Innovation Inventory and provided demographic information, and then rated eight situated consumer behaviors on Mehrabian and Russell’s (1974) PAD measures, thereby providing data on 960 consumer situations. The respondents received £10 cash (approximately $15 US) for their efforts. The participants ranged in age from 20 to 59 years (M = 35.6 years). Half of the respondents held a university degree. In addition, 43% had professional jobs, 25% had clerical jobs, 7% had technical jobs, and 25% were students. Both instruments were pretested (N = 10), and these respondents REINFORCEMENT AND EMOTION 2519 were not included in the main sample. As a result of the pretest, the description of some consumer situations was changed. Measures Affect. Mehrabian and Russell’s (1974) scales for measuring the three basic emotions of pleasure, arousal, and dominance were used without modification. Each scale consists of six bipolar items. The eight consumer situations were rated on a 9-point scale ranging from 1 (maximal displeasure/ minimal stimulation/least dominance) to 9 (maximal pleasure/maximal stimulation/maximal dominance). This yielded a range from 6 to 54. Behavior. Approach and avoidance behaviors were measured using six of Mehrabian and Russell’s (1974) eight behavioral measures. Approach was measured with the following questions: “How much time would you like to spend in this situation?”; “Once in this situation, how much would you enjoy exploring around?”; and “To what extent is this a situation in which you would feel friendly and talkative to a stranger who happens to be near you?” Avoidance was measured with the following questions: “How much would you try to leave or get out of this situation?”; “How much would you try to avoid any looking around or exploration in this situation?”; and “Is this a situation in which you might try to avoid other people, avoid talking to them?” Approach responses were scored from 0 (minimal approach tendency) to 7 (maximal approach tendency), yielding a range from 0 to 21. Avoidance responses were scored from 0 (minimal avoidance/escape tendency) to 7 (maximal avoidance/escape tendency), yielding the same range. A composite measure of behavior, defined as the difference between the mean scores of approach and avoidance, was computed to compare with Mehrabian and Russell’s (1974) single construct of approach–avoidance. Scores on this measure, termed aminusa (i.e., approach minus avoidance), ranged from -21 to +21. Adaption–innovation. The Kirton (2003) Adaption–Innovation Inventory (KAI) requires that respondents indicate on a 5-point scale how adaptive or innovative their characteristic style of responding would be in each of 32 situations. The scale ranges from 1 (most adaptive) to 5 (most innovative). Thus, the total scores theoretically range from 32 to 160, and the midpoint is 96. In practice, the respondents score within a narrow range of about 45 to 150, with midpoints ranging closely around 95 to 97. Stimuli Descriptions of consumer situations employed as stimuli in the study are presented in Figure 5. These situations have been appraised by panels of 2520 FOXALL AND YANI-DE-SORIANO BEHAVIOR SETTING SCOPE Closed Open CC2 ACCOMPLISHMENT You are playing roulette in an exclusive casino. Around you, there are lots of people playing and enjoying themselves. CC4 CC1 You are showing off your new Mercedes Benz sports car to your family and friends. You are at a party. You hear your favorite music. Around you, people are talking in a lively manner. CC3 HEDONISM You are at the cinema watching documentaries and advertisements while you wait for the beginning of the film you went to see. CC6 CC5 ACCUMULATION You are collecting “loyalty points” when you buy at a certain supermarket. You are reading your statement indicating how many points (or money vouchers) you have accumulated to date. You are saving up to buy an expensive item. Each week, you deposit cash in your savings account. You are reading your bank statement indicating how much you have saved to date and the amount of interest added to your account. CC8 MAINTENANCE You are waiting in an air terminal for your flight to be called. You understand you will be there for some time. CC7 You are doing your weekly shopping at a large supermarket. You go around the supermarket with your shopping cart, placing products in it Figure 5. Consumer situations used as stimuli for each contingency category. expert judges and employed effectively in earlier research (Foxall, 1997, 1999; Foxall & Greenley, 1999; Foxall & Yani-de-Soriano, 2005). Results Psychometric Properties of Measures Factor analysis and Cronbach’s alpha were applied to evaluate the unidimensionality and reliability of the affective and behavioral constructs. The results of these analyses are shown in Table 1. According to previous research (Donovan & Rossiter, 1982; Mehrabian & Russell, 1974; Soriano & Foxall, 2002), the six pleasure items, the six arousal items, and the six dominance items were expected to indicate three underlying dimensions. Similarly, the three approach items and the three avoidance items were expected to indicate one underlying dimension. The analysis yielded three factors with eigenvalues greater than 1, explaining 63% of the total variance. Varimax converged in five interactions and REINFORCEMENT AND EMOTION 2521 Table 1 Factor Analyses and Reliability Tests of the Affective and Behavioral Measures Affective Behavioral Factor 1: Pleasure Factor 2: Dominance Factor 3: Arousal Satisfied–unsatisfied (R) .830 .260 .154 Melancholic–contented .829 .183 .123 Annoyed–pleased .815 .226 .209 Despairing–hopeful .809 .072 .227 Happy–unhappy (R) .804 .223 .223 Relaxed–bored (R) .775 .297 .186 Controlling–controlled (R) .226 .763 .169 In control–cared for (R) .226 .763 .169 Influenced–influential .205 .755 .038 Submissive–dominant .303 .747 .191 Autonomous–guided (R) .009 .706 .077 Awed–important .185 .466 .156 Dull–jittery .197 .029 .762 Frenzied–sluggish (R) .229 .142 .744 Stimulated–relaxed (R) .044 .164 .725 Calm–excited .042 .020 .710 Aroused–unaroused (R) .315 .201 .695 Sleepy–wide awake .398 .244 .677 Item Factor 1: Approach–avoidance Enjoy exploring (AP) .772 Time spent (AP) .719 Feel friendly (AP) .690 Avoid looking (AV) -.675 Avoid others (AV) -.665 -.566 Try to leave Explained variance 41% 12% 10% Cronbach’s a .93 .83 .85 47% Cronbach’s a (AP) .74 Cronbach’s a (AV) .64 Note. The items are sorted by size. R = item was reverse-scored prior to statistical analyses; AP = approach; AV = avoidance. 2522 FOXALL AND YANI-DE-SORIANO extracted dimensions labeled as Pleasure (Factor 1), Dominance (Factor 2), and Arousal (Factor 3). All items show high loadings (exceeding .60) and loaded on the appropriate factor. The results show that Pleasure accounts for most of the variance with 41%, followed by Dominance with 12%, and Arousal with 10%. The scree test/plot and estimated communalities also lent support to the three-factor structure. For the behavioral measures, the analysis yielded one factor (labeled Approach–Avoidance) with eigenvalues greater than 1, accounting for 47% of the total variance. All items show high loadings (exceeding .60) except for “Try to leave” with .57, but still way above the 0.40 threshold. All three approach items correlated positively with approach–avoidance, while all three avoidance items correlated negatively, as expected. The internal consistency reliability coefficient was well above the 0.70 threshold for all of the affective factors. Because of negative average covariance among items, the behavioral factor cannot yield a single alpha coefficient. The consistency reliability coefficient for approach was greater than 0.70, while for avoidance it was slightly lower (0.64). Relationship Between Emotions and Consumer Behavior The means, standard deviations, and correlation coefficients of the constructs are presented in Table 2. As expected, pleasure, arousal, and dominance were positively and significantly correlated with approach, while they were negatively and significantly correlated with avoidance. All of the affective variables were positively and significantly correlated with the composite Table 2 Means and Correlation Coefficients for Affective and Behavioral Variables 1. 2. 3. 4. 5. 6. Pleasure Arousal Dominance Approach Avoidance Aminusa M SD 1 2 3 4 5 36.72 33.20 30.58 8.75 6.89 1.85 11.54 9.29 8.91 4.92 4.65 8.27 — .50 .50 .45 -.48 .53 — .38 .55 -.34 .52 — .33 -.32 .38 — -.49 .87 — -.86 Note. N = 960. Aminusa = approach minus avoidance. The correlations were significant at p < .01, two-tailed. REINFORCEMENT AND EMOTION 2523 Table 3 Multiple Regression Analyses for Variables Predicting Approach, Avoidance, and Aminusa Model Approach = Arousal + Pleasure + Dominance Avoidance = Pleasure + Arousal + Dominance Aminusa = Pleasure + Arousal + Dominance F(3, 956) p Adj. R2 170.00 .00 .35 104.68 193.65 .00 .00 b p t VIF .43 .20 .07 .00 .00 .03 .73 .64 .73 1.40 1.60 1.40 .37 .13 .09 .00 .00 .00 .64 .73 .73 1.60 1.40 1.40 .33 .33 .09 .00 .00 .00 .64 .73 .73 1.60 1.40 1.40 .25 .38 Note. N = 960. Aminusa = approach minus avoidance; VIF = variance inflation factor. behavioral measure aminusa. All correlations between the affective and behavioral variables were in the 0.30 to 0.50 range, which is what was expected in this type of research (Sirkin, 1999). Moderate correlations (0.50) exist among pleasure, arousal, and dominance, indicating a level I of collinearity. However, this level of collinearity is not unacceptable on the basis of the conventional cutoff values of tolerance (T) greater than .01 and variance inflation factor (VIF) less than 10 (Hair, Anderson, Tatham, & Black, 1998; Mason & Perreault, 1991). Table 3 shows that no tolerance value fell below .64, nor did VIF exceed 1.60. Moreover, this level of collinearity is normally seen in behavioral studies in marketing research, especially when the predictors are multi-item composite scales (Mason & Perreault, 1991), which indicates that the interrelations found are not sufficiently high to breach Mehrabian and Russell’s (1974) assumptions of orthogonality of the three affective variables. Table 3 also shows that, collectively, pleasure, arousal, and dominance explain 35% of the variance in approach, 25% of the variance in avoidance, and 38% of the variance in aminusa. Pleasure has the greatest influence on the prediction of the depen- 2524 FOXALL AND YANI-DE-SORIANO dent variables, followed by arousal and dominance, except for approach, in which it is surpassed by arousal. Patterns of Affect and Behavior by Contingency Category Figure 4 shows the means and standard deviations of the affective and behavioral measures for each of the eight contingency categories and the hypothesized relationships. One-way ANOVA tests were applied to assess the assumption that the means of pleasure, arousal, dominance, approach, avoidance, and approach–avoidance (aminusa) will be different for each of the eight CCs, as predicted by the BPM. The results show that mean scores of pleasure, arousal, dominance, approach, avoidance, and aminusa for each of the eight BPM’s contingency categories (CCs) differed significantly beyond the .01 level: pleasure, F(7, 952) = 62.12, p < .01, h2 = .31; arousal, F(7, 952) = 55.27, p < .01, h2 = .29; dominance, F(7, 952) = 106.75, p < .01, h2 = .44; approach, F(7, 952) = 106.18, p < .01, h2 = .44; avoidance, F(7, 952) = 14.64, p < .01, h2 = .10; and aminusa, F(7, 952) = 58.02, p < .01, h2 = .30. For all the variables except avoidance, eta squared represents a large effect size, according to Cohen’s (1988) classification of effect size (d; Kinnear & Gray, 2008, p. 322). The results of the ANOVAs were followed by post hoc tests of the pleasure, arousal, dominance, and approach–avoidance hypotheses. The results of these analyses are shown in Table 4. Pleasure. Hypothesis 1 (the pleasure hypothesis)—which stated that mean pleasure scores for Contingency Categories (CCs) 1, 2, 3, and 4 would each exceed those of CCs 5, 6, 7, and 8—is accepted. The pleasure mean of CC 1 is the largest and is greater than those of CCs 5, 6, 7, and 8. The pleasure mean of CC2 is greater than those of CCs 6, 7, and 8, but not significantly greater than that of CC5. The pleasure mean of CC3 is greater than those of CCs 7 and 8, but not significantly greater than those of CCs 5 and 6. The pleasure mean of CC4 is greater than that of CC8, but not significantly greater than those of CCs 5, 6, and 7. Arousal. Hypothesis 2 (the arousal hypothesis)—which stated that mean arousal scores for CCs 1, 2, 5, and 6 would each exceed those of CCs 3, 4, 7, and 8—is accepted. The arousal mean of CC1 is the largest and is greater than those of CCs 3, 4, 7, and 8. The arousal mean of CC2 is greater than those of CCs 3, 4, 7, and 8. The arousal mean of CC5 is greater than those of CCs 4, 7, and 8, but not significantly greater than that of CC3. The arousal mean of CC6 is greater than those of CCs 4, 7, and 8, but not significantly greater than that of CC3. Dominance. Hypothesis 3 (the dominance hypothesis)—which stated that mean dominance scores for CCs 1, 3, 5, and 7 would each exceed those of REINFORCEMENT AND EMOTION 2525 Table 4 Significant Differences Among the BPM’s Contingency Categories for Pleasure, Arousal, Dominance, and Aminusa M Pleasure CC1 CC2 CC3 CC5 CC6 CC7 CC4 CC8 CC1 CC2 CC3 CC5 CC6 CC7 CC8 CC4 CC1 CC5 CC3 CC7 CC6 CC2 CC4 CC8 CC1 CC3 CC2 CC7 CC8 CC6 CC5 CC4 46.81 40.62 39.63 38.38 36.55 34.50 33.76 23.48 Arousal 41.12 39.55 34.86 34.53 32.33 28.18 28.19 26.82 Dominance 40.58 36.34 33.88 31.53 28.19 28.13 24.75 21.20 Aminusa 8.28 7.93 5.53 2.10 -1.57 -1.65 -1.80 -3.99 Contingency category (CC) 1 2 3 5 6 7 4 8 * * * * * * * 1 * * * * 2 * * * 3 * * * 5 * 6 * 7 * 8 4 * * * * * * 1 * * * * * * 5 * * * 3 * * * 7 * * * 6 2 4 8 * * * * * * * 1 * * * * * 3 * * * * 2 * * * * 7 * * 8 * * 6 * 5 4 * * * * * * * * * * * * * * * * * * * * Note. Aminusa = approach minus avoidance; BPM = behavioral perspective model. *p < .05. 2526 FOXALL AND YANI-DE-SORIANO Table 5 Means of Pleasure, Arousal, and Dominance for Adaptors and Innovators by Contingency Category (CC) Pleasure Arousal Dominance Adaptors Innovators Adaptors Innovators Adaptors Innovators M SD M SD M SD M SD M SD M SD CC1 47.26 4.89 45.90 5.46 41.05 6.96 41.25 6.63 40.70 6.98 40.35 7.45 CC2 40.75 7.52 40.37 8.47 39.25 7.41 40.12 7.41 27.91 6.87 28.56 7.63 CC3 39.85 11.11 39.22 10.85 34.91 10.49 34.76 10.79 33.41 6.01 34.78 7.01 CC4 33.76 10.42 33.76 11.48 26.97 7.36 26.51 7.24 24.82 6.33 24.61 4.98 CC5 38.61 9.55 37.95 11.03 34.70 6.59 34.22 6.57 36.05 7.25 36.90 7.99 CC6 37.72 9.11 34.29 10.22 32.66 6.98 31.68 7.32 28.92 6.67 26.78 6.60 CC7 34.86 8.94 33.80 9.44 28.65 8.40 27.27 8.03 31.65 6.64 31.32 6.59 CC8 23.77 9.06 22.90 11.94 28.30 7.51 27.98 9.58 21.49 5.57 20.63 7.06 Note. Adaptors, N = 80; Innovators, N = 40; Total, N = 120. CCs 2, 4, 6, and 8—is accepted. Each of the dominance means for CCs 1, 3, 5, and 7 is greater than each of those for CCs 2, 4, 6, and 8. Approach–avoidance. Hypothesis 4 (the approach–avoidance hypothesis)—which stated that mean scores for approach–avoidance for CCs 1, 2, 3, and 4 would each exceed those of CCs 5, 6, 7, and 8—is generally supported. Except for CC4 being less than CC7, each of CCs 1, 2, and 3 is greater than each of CCs 5, 6, 7, and 8. Approach–avoidance and setting scope. Hypothesis 5 (the approach– avoidance for open settings hypothesis)—which stated that mean scores for approach–avoidance for CCs 1 and 3 would each exceed those of CCs 2, 4, 5, 6, 7, and 8—is accepted. CCs 1 and 3 are the highest means, with each greater than CCs 2, 4, 5, 6, 7, and 8 (though in the right direction, the difference is not statistically significant for CC3 > CC2). Adaptive–innovative cognitive style. Means and standard deviations for each CC of pleasure, arousal, and dominance for adaptors and innovators are shown in Tables 5 and 6. ANOVA results (Table 7) indicate that there was no relationship among the affective, behavioral, and cognitive style measures. Therefore, Hypotheses 6 and 7 are rejected. The results are in line with those obtained in studies conducted in England and Venezuela, and strengthen the expectation that pleasure, arousal, and dominance vary as predicted with utilitarian reinforcement, REINFORCEMENT AND EMOTION 2527 Table 6 Means of Approach, Avoidance, and Aminusa for Adaptors and Innovators by Contingency Category (CC) Approach Adaptors Avoidance Innovators Adaptors Aminusa Innovators Adaptors Innovators M SD M SD M SD M SD M SD M SD CC1 12.76 4.38 13.03 3.98 4.69 3.86 4.35 4.07 8.08 7.04 8.68 7.23 CC2 11.72 5.16 12.56 3.68 7.20 5.21 5.07 4.11 4.52 9.09 7.49 6.84 CC3 12.94 4.00 12.88 4.92 5.11 4.64 4.73 4.50 7.82 8.06 8.15 8.75 CC4 4.89 3.06 4.78 3.26 8.70 4.23 9.12 4.55 -3.81 5.81 -4.34 6.20 CC5 5.30 2.81 5.66 2.99 7.06 4.21 7.54 4.51 -1.76 5.50 -1.88 5.60 CC6 5.80 2.94 4.73 3.02 6.94 4.46 7.37 4.82 -1.14 5.94 -2.63 6.42 CC7 9.47 3.72 8.88 3.68 6.70 3.95 8.07 4.85 2.77 6.70 0.80 7.76 CC8 7.46 2.74 6.78 3.85 8.51 3.92 9.34 5.43 -1.05 5.27 -2.56 8.37 Note. Adaptors, N = 80; Innovators, N = 40; Total, N = 120. Aminusa = approach minus avoidance. Table 7 ANOVA for Affective and Behavioral Measures by KAI Scores Variable Affective Pleasure Arousal Dominance Behavioral Approach Avoidance Approach–avoidance df F h2 p 1 1 1 2.03 0.44 0.02 .00 .00 .00 .16 .51 .90 1 1 1 0.32 0.00 0.12 .00 .00 .00 .57 1.00 .73 Note. KAI = Kirton (2003) Adaption–Innovation Inventory. 2528 FOXALL AND YANI-DE-SORIANO informational reinforcement, and consumer behavior setting scope. So, moreover, does behavior. Table 8 shows that the results reported in this paper corroborate those found in earlier studies in England and Venezuela. Table 8 contains mean scores for pleasure, arousal, dominance, approach, avoidance, and aminusa for the studies in Wales (W), the first and second Venezuelan studies (V1 and V2), and the study conducted in England (E). The values are rounded in the table in order to facilitate at-a-glance impressions of the general similarity of the results of the various studies to one another and to the hypothesized patterns and of the exceptions where they arise. It is interesting, for example, that in many cases, the Welsh and the English studies have similar means. The results of the ANOVAs testing the pleasure, arousal, and dominance hypotheses are, overall, consistent throughout all of the studies (Foxall, 1997; Foxall & Greenley, 1999; Foxall & Yani-de-Soriano, 2005): for pleasure, CCs 1, 2, 3, and 4 > CCs 5, 6, 7, and 8; for arousal, CCs 1, 2, 5, and 6 > CCs 3, 4, 7, and 8; and for dominance, CCs 1, 3, 5, and 7 > CCs 2, 4, 6, and 8. The pattern of Welsh mean differences is broadly similar to those for England (Foxall, 1997; Foxall & Greenley, 1999) and Venezuela (Foxall & Yani-de-Soriano, 2005): V1 pleasure, CCs 1, 2, 3, and 4 > CCs 5, 6, 7, and 8, except for CC 4 = CCs 5 and 6; V1 arousal, CCs 1, 2, 5, and 6 > CCs 3, 4, 7, and 8, except for CCs 5 and 6 = CC3; V1 dominance, CCs 1, 3, 5, and 7 > CCs 2, 4, 6, and 8; V2 pleasure, CCs 1, 2, and 3 > CCs 6, 7, and 8; CC4 > CC8, but < CCs 6 and 7; V2 arousal, CCs 1, 2, 5, and 6 > CCs 3, 4, 7, and 8; V2 dominance, CCs 1, 3, 5, and 7 > CCs 2, 4, 6, and 8; E pleasure, CCs 1, 2, 3, and 4 > CCs 5, 6, 7, and 8, except for CC4 = CCs 5 and 6; E arousal, CCs 1, 2, 5, and 6 > CCs 3, 4, 7, and 8; E dominance, CCs 1, 3, 5, and 7 > CCs 2, 4, 6, and 8, except for CC7 = CC6. Moreover, the results of the factor analysis reported here are consistent with previous findings (Soriano & Foxall, 2002): pleasure scores ranging between .78 and .83; arousal scores ranging between .71 and .76, with the exception of the item awed–important, which ranged between .47 and .49 and was also consistent with previous results; and dominance scores ranging between .68 and .76. The percentage of variance explained is also in line with previous studies. Cronbach’s alpha scores are all consistent with previous studies (Soriano & Foxall, 2002). The results of multiple regression analysis are in line with previous research; that is, main effects are apparent for all three affective variables for each of the dependent variables: approach, avoidance, and aminusa. The percentage of variance explained is also in line with previous results. However, only for approach, arousal has a higher beta (b) than that of pleasure, which is not consistent with previous findings (Foxall, 1997; Foxall & Yani-de-Soriano, 2005). 47 41 40 34 38 37 35 23 52 47 47 41 41 38 37 23 50 44 43 29 44 36 34 22 47 40 43 38 34 33 30 23 41 40 35 27 35 32 28 28 45 42 35 31 38 38 32 28 45 44 27 25 42 38 26 23 41 41 32 27 39 36 27 28 E 41 28 34 25 36 28 32 21 40 28 40 27 37 30 38 23 46 31 41 23 40 28 39 21 W V1 V2 Dominance 14 13 10 5 8 9 7 5 W V1 V2 42 13 17 29 12 15 34 13 13 27 5 6 37 5 9 29 5 6 34 9 9 27 7 7 E Approach 13 11 13 5 6 10 10 9 E 5 6 5 9 7 7 7 9 3 4 5 11 8 9 8 9 E E 9 6 7 4 3 8 -7 -4 1 -4 0 2 -3 2 -7 0 W V1 V2 Aminusa 5 6 8 14 6 7 6 11 7 5 8 8 12 10 -4 -6 7 10 -2 1 9 8 -2 -2 9 8 2 1 12 8 -2 -2 W V1 V2 Avoidance Note. Aminusa = approach minus avoidance; W = Wales; V1 = Venezuela, Study 1; V2 = Venezuela, Study 2; E = England. CC1 CC2 CC3 CC4 CC5 CC6 CC7 CC8 W V1 V2 W V1 V2 E Arousal Pleasure Mean Scores of Affective and Behavioral Variables by Contingency Category in Four Studies Table 8 REINFORCEMENT AND EMOTION 2529 2530 FOXALL AND YANI-DE-SORIANO Discussion Reward Contingencies and Emotion The research described here extends the analysis of contingency and emotion to the realm of economic behavior, employing a more sophisticated conception of contingency for this domain by separating the effects of utilitarian and informational reinforcement, and by relating the pattern of reinforcement defined in these terms to emotional responses. Studies conducted within quite different cultural contexts found evidence for the pleasure, arousal, dominance, and approach–avoidance hypotheses and confirmed the explanatory value of the BPM as a framework for understanding the relationship between emotional responses and contingencies of reinforcement. Our findings indicate a more sophisticated relationship between operant contingencies and emotion than attested by either Rolls’ (2005) theory or previous work that has confirmed the BPM. They suggest an alternative pattern of linkages between contingencies and affect to that proposed by Rolls. The further explanation of the findings presented here with respect to operant contingencies and affective response draws upon and extends Rolls’ two-step portrayal of the roles of instrumental and classical conditioning in the generation of emotion. These are bold claims and require elaboration. Rolls’ (2005) scheme for relating contingencies of reinforcement and varieties of emotion provides a systematic arrangement of the causes and consequences of behavior, which incorporates both operant and Pavlovian conditioning, and which has produced a synthetic framework for the integration of a large volume of empirical results. A possible shortcoming of this scheme lies in its relative insensitivity to contingencies that impinge specifically on complex human behaviors, such as purchase and consumption. The distinction between utilitarian reinforcement and informational reinforcement is essential to the comprehensive depiction of economic choice of this kind. The patterns of reinforcement defined by the various combinations of these sources of contingency can now be related consistently to consumer behaviors such as brand and product choice in the marketplace (Foxall et al., 2007) and to reported consumer emotions (Foxall, 1997). The inclusion of the scope of the consumer behavior setting further enhances the possibility of relating contingencies of reinforcement to patterns of consumer emotionality. The present study adds to and strengthens the body of cross-culturally accumulated empirical results that predictably relate contingencies to actions and affect. The outcome is a more detailed account of context and emotion, one that presents a fine-grained indication of the situational determination of choice. REINFORCEMENT AND EMOTION 2531 The BPM contingency matrix (Figure 3) represents a comprehensive functional depiction of the contextual factors that control human economic responding. Each of the eight contingency categories defines the interaction of three contextual variables: utilitarian reinforcement, informational reinforcement, and behavior setting scope. Each of these uniquely defined categories is predicted to induce a correspondingly distinctive pattern of reported emotional responses in consumers. The present study confirms, in a third cultural milieu, the predicted relationships between contingencies and affective response. Therefore, we believe the BPM/PAD framework to be the most promising means available of mapping environmental contingencies and consumer affect. This work also confirms the greater relevance to the explanation of complex human behavior of the ideas of the pattern of reinforcement and the contingency category, as opposed to the notion of the schedule of reinforcement, which has limited applicability outside the operant laboratory. This is not to detract in any way from the framework advanced by Rolls (2005), which is not specifically intended to apply to human economic choice of the kind considered here. Indeed, Rolls’ integration of operant and Pavlovian conditioning into an explanation of the causal role of environmental contingencies in the inauguration of emotional response suggests a mechanism by which behavioral experience results in a learning history that influences subsequent responding. Figure 6 proposes how this relationship might be depicted. These considerations have important implications for elaboration of the BPM (Foxall, 2007). In order to appreciate the implications of this reasoning and the findings with respect to the relationship of the CCs and emotional response for the BPM, we need to distinguish the reinforcing consequences of behavior—that is, the environmental stimuli that influence the rate at which similar behavior is subsequently enacted—from the rewarding effects of the emotions elicited by the contingencies that relate these environmental events to the behavior that produced them. The behavioral perspective model portrays behavior as the outcome of the current behavior setting (the discriminative stimuli and motivating operations that signal the reinforcing and punishing consequences of action) and the consumer’s learning history. Until now, specific factors that shape a learning history, the record of previous behavior, and its reinforcing and punishing consequences have not been specified, but it is now possible to locate them, at least in part in the emotional responses elicited in the process of reward generation. This is suggested in Figure 6 by the feedback loop that links emotion and learning history. The implication of this conjecture is that the three emotional responses—pleasure, arousal, and dominance—have explanatory value in a theory of human economic choice, which inheres in their contribution to the repetition of rewarded behavior (i.e., reinforcement) 2532 FOXALL AND YANI-DE-SORIANO Figure 6. Relationship of reward, emotion, and choice. SD = discriminative stimuli; MO = motivating operations; LH = learning history; R = response; SR/A = reinforcing and aversive consequences; P = pleasure; A = arousal; D = dominance; Accomp = Accomplishment; Hed = Hedonism; Accum = Accumulation; Maint = Maintenance; Cl = closed consumer behavior setting; Op = open consumer behavior setting. Source: Foxall (2011, p. 83). and the diminution or extinction of behavior that meets with aversive consequences (i.e., punishment). Individual Differences, Contingency, and Emotion The absence of any difference between adaptors and innovators in their reporting of their affective reactions to the consumer situations investigated is of interest. It may suggest methodological limitations of the KAI in this particular context. Adaption–innovation is not in itself a measure of extraversion– introversion, but one that correlates with this dimension of personality. Perhaps direct measures of extraversion–introversion and other dimensions of personality would yield results more in line with theoretical expectations. It is surprising, however, that the element of cognitive style identified by adaption– innovation theory is not successful in discriminating between adaptors’ and innovators’ perceptions of open and closed behavior settings. The failure to find a correlation may be covered by the adaption–innovation theory. REINFORCEMENT AND EMOTION 2533 Kirton (2003) makes clear that there is a profound distinction to be made between cognitive effect (to which adaption–innovation belongs) and cognitive affect (to which pleasure, arousal, and dominance belong). There is no theoretical reason to expect a correlation between these domains, which refer to conative functions (behavioral disposition) and affective (feeling) spheres of human operation, respectively. However, there may be scope here for further empirical investigation. The relationship between adaption–innovation and extraversion– introversion is interesting. If the measurement of extraversion–introversion (E-I) concentrates on the difference in arousal between E and I, then the correlation may be expected to be low. Since E-I lies within the sphere of cognitive affect, it is not related to adaption–innovation theory, something that is supported by the present results. If an E-I measure captured better the basic learning theory link—that is, that extroverts aim for greater reinforcement, while introverts attempt to minimize punishment—then the correlation might well be higher. Indeed, since innovators are seen as more risk-taking and adaptors as more prudent, a study that incorporates a direct measure of E-I, such as the Eysenck E-I Inventory, which includes a strong element of impulsiveness, might produce results more in line with the present hypotheses. In other words, Rolls’ (2005) theory and the empirical evidence consistent with it has not been called into question by the findings reported here: One dimension of cognitive style associated with E-I has been shown not to covary with a measure of emotional response; but there are good theoretical reasons for this and other opportunities to investigate the relationship empirically. One possibility that requires further investigation is that adaption–innovation theory is generic, while Rolls’ (2005) theory is more specific. If so, then complementary roles may yet be found for the theories. An effective general theory ought to be capable of resolving issues to which a more specific theory does not attend, but which remain of importance to overall understanding. Adaption–innovation theory, and research that it has stimulated into the adoption and diffusion of new consumer products and practices (Foxall, 1995; Foxall & James, 2009), also suggests theoretical developments for the behavioral perspective model. The absence of evidence for the expected relationship between adaptive–innovative cognitive styles and responses to the scope of consumer behavior settings may yet prove consistent with adaptors’ and innovators’ differential perceptions of novelty, and the importance to each of opportunities for behavioral variety and stimulation based on the arousing capacity of physical and social environments. The assumption that led to the formulation and testing of Hypotheses 6 and 7—that adaptors, who are predicted in Kirton’s (2003) theory to be apparently impervious to boredom, differ emotionally and behaviorally from innovators, who need the constant stimulation of change—would then 2534 FOXALL AND YANI-DE-SORIANO require some reformulation. Similarly, theory and research might need to accommodate differences in the ways in which adaptors and innovators (including, of course, all gradations of the individual differences represented by these polar terms) perceive and value utilitarian reinforcement and informational reinforcement. It is even feasible that each of these categories of contingency could be subdivisible insofar as adaptive and innovative consumers embody varying consumption histories. Such possibilities would certainly be consistent with the findings of research on consumers’ diverse preferences for novelty and change with respect to new products and services. The results as they stand, however, suggest that the contingencies themselves exert the predominant influence on consumers’ affective responses to differing environments of purchase and consumption. There is, nevertheless, a need for further research to elucidate the relationships among emotion, individual cognitive differences, and contingency-shaped behavior. As has been said previously, there is a major case for conceptual clarification. Nowhere is this more obvious than in the area of the genetic basis of the behaviors in question. Kirton’s (2003) work also emphasizes the impact of genetic factors on adaptive–innovative cognitive style. The search for an inherited component of adaption–innovation (Van der Molen, 1994) is justified by the work of Cloninger (1986, 1987), who related the production of the monoamine neuromodulators dopamine and norepinephrine in the promotion of extreme innovative and adaptive behaviors, respectively. Risk-taking and novelty-seeking are closely associated with chromosome 11 and, in particular, with one of its genes, D4DR, which is associated with the production of a protein that is a dopamine receptor (e.g., Ridley, 1999). 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