Download Influence of Reinforcement Contingencies and Cognitive Styles on

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). This, too, is consistent with Rolls’ (1999, 2005, 2008) theory of
genetic predisposition. However, there is considerable scope for further work
at both the neurophysiological and behavioral levels in order to reconcile the
operation of these forces. Kirton’s argument is consistent with the results that
we have presented with respect to the relationship between emotion and
cognitive style in the context of consumer environments. The resulting
situation calls for further conceptual and theoretical advances, as well as
further empirical research.
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