B - FOXALL,G.(2015) - Operant Behavioral Economics

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Operant Behavioral EconomicsGordon R. Foxall*

Cardiff Business School, Cardiff University, Cardiff, Wales, UK

The aims of this special issue are to introduce managerial economists to a school of behav-ioral economics they may not yet have encountered by way of accounts by leading re-searchers of its basic tenets, methods, and applications. This overview introduces thepapers by setting them in the context of the development of operant behavioral economics.Copyright © 2015 John Wiley & Sons, Ltd.

It is pleasing when disciplines, whose initial develop-

ment trajectories are as diverse as those of operant

psychology and microeconomics, demonstrate an

af nity that promotes both their interaction and their

mutual benet. The potential became obvious when

behavioral psychologists began to consider and

compare the ways in which micro-economists and

behavioral psychologists study behavior (Hursh,

1978, 1980, 1984; Lea, 1978). Around the same

time, some economists were seeking an experimental

analysis of their subject matter (e.g., Castro and

Weingarten, 1970; Smith, 1982). The contingencies,

likely to bring about a productive collaboration, werebeing assembled. This does not mean that the interac-

tion is seamless or without controversy – who would

want that? – but that two intellectual communities are

able to learn from one another and to grow as a

result.

The resulting research program, operant behavioral

economics, contributes positively to the objectives of

both disciplines by bringing theoretical perspectives

and novel methodologies to bear on each of its

constituents. With some simplication, while micro-

economics permits the conceptualization of behavior

as the allocation of scarce resources among competingends, operant psychology (or behavior analysis, as it is

more usually known) conceives behavior in terms of

the allocation of a number of responses that the

individual can emit among alternative reinforcing out-

comes (Staddon, 1980; Kagel et al ., 1995; Foxall and

Sigurdsson, 2013). The term ‘operant ’ denotes behav-

iors that operate on the environment to generate conse-

quences, which are followed by changes in the rate at

which the behaviors are performed: those conse-

quences that are followed by an increase in response

rate are known as reinforcers and those followed by

a reduction in rate, as punishers (Skinner, 1953). The

‘three-term contingency’, which provides the basic

explanatory device of operant behaviorism, takes theform SD⟶R⟶Sr , where a discriminative stimu-

lus, SD, is an element of the environment in the

presence of which a response, R, has been rewarded

by the appearance of another environmental element,

Sr , which because of its ‘strengthening’ effect on the

behavior is known as a reinforcer. When the occur-

rence of a response can be accurately predicted from

the appearance of the discriminative stimulus, it is said

to be under stimulus control, and this will be

maintained as long as the response is followed from

time to time by the reinforcer. (For greater detail on

the subtleties of operant psychology and its philosoph-ical implications for the explanation of behavior, see

inter alia Baum, 2006; Moore, 2008; Rachlin, 2014.)

The opportunity for collaboration is clear from the

conceptual harmonies shown by the two disciplines:*Correspondence to: Cardiff Business School, Cardiff University,

Cardiff, Wales, UK. E-mail: [email protected]

Copyright © 2015 John Wiley & Sons, Ltd.

MANAGERIAL AND DECISION ECONOMICS

Manage. Decis. Econ. (2015)

Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI: 10.1002/mde.2712


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The central components of microeconomic

analysis nd analogues in the operant analysis

of choice: economic commodities correspond

to reinforcers (‘they are both classes of things

whose contingent presentation will maintain

behavior ’; Lea, 1978, p. 443); price corre-

sponds to a schedule parameter, since both in-

dicate how much of a limited exchangeresource must be given up in order to obtain

the reinforcer; and money is equivalent to the

number of responses upon which delivery of

the reinforcer is contingent. Thus the econo-

mist ’s demand curve, which relates the quan-

tity of a good which is bought to price, is

analogous to a function relating the quantity

of reinforcements obtained and the number of

responses required to obtain them … (Foxall,

1990/2004, p. 83).

As the papers in this special issue demonstrate, op-erant behavioral economics represents more than the

simple application of microeconomics to behavioral

psychology. Behavioral economists have developed

and applied economic analysis, broadened the range

of human activities to which economics is applied,

brought the rigor of experimental analysis to bear

where appropriate, and developed novel methodologi-

cal tools for both economics and psychology. At the

same time, operant behavioral economics has come

to provide a standpoint from which the assumptions

of neoclassical microeconomics can be subjected to

criticism and, if necessary, adjusted. Most of the pa-

pers contribute both to the conceptualization of oper-

ant behavioral economics and to either its empirical,

methodological, and practical implications or the ex-

tension of its eld of application. Despite these over-

laps, they can be broadly classied from an editorial

point of view into three groups. First are those papers

(by Hursh & Roma; Baum; Rachlin; and Ainslie) that

make important conceptual and substantive contribu-

tions to the foundations of operant behavioral econom-

ics. Second are those (by Jarmolowicz, Reed,

DiGennaro Reed and Bickel; Hantula; Roma, Hursh,and Hudja; Fagerstrom, Aksnes, and Arntzen; and

Fagerstrom, Ghinea, and Sydnes) that are principally

concerned with applications and extensions: to both

managerial concerns and to methodological sophisti-

cation. Third come those (by Sigurdsson, Hinriksson,

and Menon; Menon and Sigurdsson; and Oliveira-

Castro, Cavalcanti, and Foxall) that are concerned

with consumer behavior analysis, which combines op-

erant behavioral economics with marketing science

(Foxall, 2001, 2002). I should like to introduce each

of these categories with some brief considerations,

which may be especially useful to managerial econo-

mists encountering operant behavioral economics for

the rst time.

FOUNDATIONS

Background

Behavioral economics in this tradition has extended its

reach beyond conventional realms of economic behav-

ior such as labor and consumption, to enhance the

analysis of animal behavior and welfare, anti-social

responding, addiction and health, marketing and orga-

nizational management, and other applied areas. A

central theme is the matching of relative response rates

to relative rates of reinforcement, which is closely re-

lated to the phenomenon of hyperbolic temporal

discounting. Underlying much of this work is the

pioneering contribution of Herrnstein (1961, 1970,

1997) which showed that when, in an experimental sit-

uation, an animal is presented with two operanda, A

and B, which might be keys to peck or levers to press,

each of which delivers a reinforcer on its own variable

interval (VI) schedule, the animal matches its relative

response rates to the relative rates of reinforcement ac-

tually obtained. A schedule programs the relationship

between reinforcers and responding. An interval

schedule requires that some minimum time must elapse before a response receives reinforcement. On

a variable interval or VI schedule, the time varies be-

tween the delivery of reinforcers. In the case of the

concurrent VI schedules used in matching experi-

ments, the schedules are in operation simultaneously

or ‘concurrently’, and independently, providing rein-

forcement after different durations provided at least

one response has been made since previous reinforce-

ment. See, for example, Catania (1997). Hence, the

basic matching relationship is as follows:

Ra = Ra þ Rbð Þ ¼ r a = r a þ r bð Þ (1)

where Ra is the number of responses on operandum A,

Rb is the number of responses on operandum B, r a is

the number of reinforcements obtained on A, and r bis the number of reinforcements obtained on B.

Baum ’s (1974, 1979) presentation of the General-

ized Matching Law introduced some subtleties to the

analysis of matching phenomena that were invaluable

contributions to the investigation of behavior as eco-

nomic. As Baum points out in his paper in this issue

G. R. FOXALL

Copyright © 2015 John Wiley & Sons, Ltd. Manage. Decis. Econ. (2015)

DOI: 10.1002/mde


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(‘Driven by Consequences: The Multiscale Molar

View of Choice’), this law takes the form (exemplied

in the context of, say, a pigeon responding by pecking

on two keys):

log B1

B2¼ slog

r 1

r 2þ logb (2)

where B1 and B2 are the times expended in peckingkeys 1 and 2, respectively, r 1 and r 2, the rates of food

delivery as a result of pecking keys 1 and 2, respec-

tively, s is sensitivity to variation in the food ratio

(the schedules of reinforcement), and b indicates bias

resulting from factors other than food rate. The inter-

pretation of s as indicating the degree of substitutabil-

ity of alternative reinforcers (such as competing

brands that belong to the same product class) has

proved instructive in assessing the substitutability,

complementarity, and independence of products en-

countered in the market place.

The tendency toward matching exhibited in so

much animal and human behavior has implications

for understanding and studying choice, conceived as

the relative rate of responding on different alterna-

tives; well expressed by de Villiers and Herrnstein,

1976, (p.1131), who say, ‘our position is that choice

is merely behavior in the context of other behavior,

not a distinctive psychological process of its own’.

The underlying behavioral mechanism that accounts

for matching, what Herrnstein (1982); Herrnstein and

Prelec (1991) calls melioration, is the selection of the

more pro

table or less costly of two choices currentlyavailable, leading to the maximization of local or im-

mediate reinforcement (as opposed to global maximi-

zation of a series of choices: Herrnstein, 1990). This

tendency has implications for the study of akrasia

(weakness of will) in everyday experience as well as

in its extreme form, substance and process or behav-

ioral addiction. Melioration is the basis of the prefer-

ence reversals seen in individuals opting at time t 1for the larger of two rewards, even though it is avail-

able long after the other, but switching at t 2 to the

choice of the smaller reward, which is available sooner

once this becomes temporally closer.More generally, matching suggests that the value of

a reinforcer is inversely proportional to its delay, that

is, as the delay becomes shorter, the value increases

dramatically. This is the essence of hyperbolic

discounting. Whereas, in exponential discounting,

the larger-later reward (LLR) is always preferable to

the smaller-sooner reward (SSR), regardless of time

elapsed, in hyperbolic discounting, there is a period

during which the SSR is so highly valued (as a

function of the time remaining to its possible realiza-

tion having become so short) that it is preferred to

the LLR (Ainslie, 1992). This is clearly not because

of its objective value, which is by denition less than

that which can be obtained through patience, but be-

cause the time remaining to its possible realization is

now so short that it is preferred to the later but larger

reward.

Papers

In ‘Behavioral economics and the analysis of con-

sumption and choice’, Steven Hursh and Peter Roma

present the fundamentals of the operant paradigm

and its relevance to the behavioral economics pro-

gram. In particular, they note points of convergence

between the disciplines that comprise the paradigm

and remark specically on the role of the allocation

of behavioral resources among alternative reinforcers.

They provide a primer for an approach to behavioral

economics that is able to inform both the pure science

of behavior and managerial and policy decision-

making. The central component of this approach is

the representation of the value consumers attach to

products by the use of an exponential function to cap-

ture the non-linearity of demand curves, the exponen-

tial model of demand (Hursh and Silberberg, 2008).

Essential value is shown to be a useful means by

which to ‘categorize differences between commodi-

ties, differences between individuals toward similar

commodities, and differences in the value of commod-ities across different contexts of available alternatives

and disincentives’, all of which have obvious echoes

in managerial concerns such as the identication of

criteria for market segmentation and innovation. An

interesting development is the capacity of

questionnaire-based methods of investigation to yield

demand curves of this form, which promises to en-

hance the applicability of behavioral economics to

managerial concerns, a theme that is taken up in a later

paper by Roma, Hursh, and Hadja (‘Hypothetical pur-

chase task questionnaires for behavioral economic as-

sessments of value and motivation’).Behavior analysis is an intellectually progressive

research program: its theoretical debates and its empir-

ical ndings are stimulating and informative. In Baum

(1973), William Baum argued that the central compo-

nent of operant psychology is a sequence of responses

that is related correlatively to a sequence of

reinforcers. His seminal work in the derivation of the

Generalized Matching Law was noted previously.

Central to this work is the distinction between a

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molecular view of operant behavior, which attempts to

relate each individual response to its specic control-

ling consequences, and the molar view, which empha-

sizes sequences or patterns of behavior and

reinforcers. Baum has argued forcefully for the latter

(e.g., Baum, 2002; see also Baum, 2012; Baum, 2013).

In his paper in this issue, ‘Driven by consequences:

The Multiscale Molar View of choice’, Baum empha-sizes that behavior is to be conceptualized as choice,

that temporal limitations mean that alternative be-

haviors must compete. Whereas the conventional

way of looking at responses and their reinforcing

consequences takes the molecular perspective that

examines each individual response in relation to its

immediate outcomes, the multiscale molar view en-

visions behavior as a sequence of activities over

time that covaries with a series of consequences,

also temporally extended, that induces or drives the

behavior. Choice may be viewed in this perspective

as the relative allocation of time between alternative

behavior patterns (Baum and Rachlin, 1969). Within

this conceptualization of choice, Baum discusses

matching in terms of substitutability, foraging, as well

as impulsivity and self-control in the context of

temporal discounting.

Howard Rachlin, in his paper, ‘Social cooperation

and self-control’, also emphasizes the need to compre-

hend behavior as a sequence of activities extended in

time. A problem arises in the delineation and measure-

ment of altruism in behavior analytical terms: as

Rachlin and Lacey (2011), (p. 25) put it, ‘

The object of behavior analysis is to identify reinforcers of acts.

But an individual altruistic act apparently has no rein-

forcer; if it did, it would not be altruistic. Altruism thus

seems to defy behavioral analysis’. However, in ‘So-

cial cooperation and self-control’, Rachlin demon-

strates the usefulness of measuring altruism by

means of social discount functions, just as self-control

has been measured by delay discount functions. The

understanding of behavior as temporally extended

suggests that activities must be considered as patterns

of behavior. Social cooperation and self-control may

be selected as patterns of behavior in a procedure anal-ogous to natural selection in evolutionary biology. By

arguing that such behaviors as social cooperation and

self-control be thought of as sequences of choice over

time, Rachlin’s analysis goes beyond the basis on

which a great deal of matching research is founded,

namely that of individually considered selections of

the more highly-valued alternative. There are impor-

tant implications of this reasoning for managerial

decision-making in the contexts of both social policy

and the marketing and demarketing of commodities

likely to lead to addiction.

George Ainslie’s paper (‘The Cardinal Anomalies

that led to Behavioral Economics: Cognitive or Moti-

vational?’) provides a bridge between operant behav-

ioral economics and other schools of behavioral

economic thought, such as that of Kahneman and

Tversky (1984). Ainslie refers to the cognitive schoolas that which revealed anomalies in the microeconom-

ics of choice, arising from the phenomena of cognitive

framing that are the subject of Kahneman and

Tversky’s Prospect Theory, while the anomalies re-

vealed by the motivational school of operant behav-

ioral economics inhere in the phenomena of

hyperbolic temporal discounting and intertemporal

bargaining, notably analyzed in terms of

picoeconomics (Ainslie, 1992). The integrative poten-

tial of Ainslie’s contribution here is that the founda-

tions of framing effects generally are to be found in

motivational considerations. His paper has implica-

tions not only for the conceptual bases of behavioral

economics but for the intertemporal bargaining that

lies at the heart of many of the practical applications

of operant behavioral economics.

APPLICATIONS AND EXTENSIONS

Background

Consideration of intertemporal bargaining is particu-larly pertinent to the applied aspects of operant behav-

ioral economics such as health and addiction (Rachlin

2000). Ainslie (1992) speaks of the problem of

‘akrasia ’ or weakness of will by reference to separate

interests that are in conict: one is concerned with

our gaining long-term benet such as engaging in pro-

ductive work and the other with short-term pleasures

like undemanding amusement. One’s experience as

the locus of this clash of interests is often marked by

a sturdy resolve to undertake the more rewarding ac-

tivity, followed by a lapse into the other, just as it be-

comes available, followed by regret, further resolution, and perhaps inevitable relapse. This cycle

is characteristic of addiction but it also marks many

everyday switches of preference involved in less ex-

treme behavior. What is so preferable when we make

our plans is edged out by an alternative that is initially

unthinkable but of immense value as it hooves into

sight. Even though we know fully well that the activity

that we were determined to undertake when we set out

will bring greater benet, the fact that it is delayed

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while the less benecial can be obtained immediately

raises the value of the latter sharply till it exceeds the

current worth of the other. An intriguing facet of

Ainslie’s approach is the possibility that, by ‘bun-

dling’ together the combined benets of a series of

later-appearing rewards and comparing these in toto

with the immediate benet of a current less valuable

choice, it is possible to overcome the temptation tomake a sub-optimal decision (i.e., to exercise ‘will-

power ’ or ‘self-control’). Hence, picoeconomics has

implications for the role of cognition and metacogni-

tion in relationships between neuro-behavioral deci-

sion systems and the place of agency in

understanding their interaction.

Some of these implications are taken up by Ross

(2009), who denes the situation in economic terms

by reference to two rewards (a and b) available at dif-

ferent times such that a is, for example, taking a short

vacation starting in a week [t 1], and b is, for instance,

starting a two-year course of study for a higher degree,

[t 2]. Looking well into the future, the person’s utility

function indicates that b is preferable to a. At this

point, the person discounts the future rather gently.

However, as the time for the vacation comes closer,

the person’s utility function indicates a preference for

a over b. Ross (2012) models the various

picoeconomic interests in two ways depending on

whether these interests are conceived as acting syn-

chronously or diachronically. In the rst case, they

may be seen as subagents that have either conicting

utility functions or divergent time preferences. Agentswith conicting utility functions may be modeled in

terms of a Nash equilibrium game among these agents.

Modeling the behavior of subagents whose time pref-

erences diverge adverts to the sub-personal level of

neurophysiology in which a hyperbolic time prefer-

ence emerges from ‘competition between steeply

exponentially discounting “limbic” regions and more

patient (less steeply exponentially discounting)

“cognitive” regions’ (Ross, 2012, p. 720). This

picoeconomic portrayal depends heavily on the nd-

ings of a key experiment in neuroeconomics based

on functional magnetic resonance imaging scans of humans choosing between SSR and LLR (McClure

et al ., 2004). In the process of scrutinizing immediate

rewards, participants activated brain regions that in-

volve emotion, namely medial orbitofrontal cortex,

medial prefrontal cortex/pregenual cingulate cortex,

and ventral striatum. However, while examining

long-term payoffs, they activated areas of the lateral

prefrontal cortex (implicated in higher cognitive func-

tioning), and part of the parietal cortex related to

quantitative reasoning. In his modeling of

picoeconomic conict in terms of diachronically

appearing multiple selves, Ross (2012) speculates

briey about the cognitive demands of such a por-

trayal: each subagent is portrayed as temporarily in

control of the person’s behavior, with its own utility

function and incomplete knowledge of the other, al-

though its utility is constrained by the investmentsmade by earlier-appearing agent(s).

Papers

The idea that the more rational and more irrational as-

pects of human decision-making are respectively

governed by separate but functionally integrated brain

regions has a considerable history. However, in the

competing neuro-behavioral decision systems model

(Bickel et al ., 2007; Bickel and Yi, 2008; Jarmolowicz

et al ., 2013), the view that behavior results from the

interaction of two competing neural systems has

recently become highly developed. This work

incorporates both operant behavioral economics and

neuroeconomics, and is especially relevant for its

applicability to health issues. The competing neuro-

behavioral decision systems hypothesis identies the

mainsprings of suboptimal behavior as the exaggera-

tion of the reinforcing effects of the consequences of

behavior and an inordinately strong desire for immedi-

ate satisfaction. In behavioral economics terms, these

are described in terms of the elasticity of demand for

the reinforcer (following from its excessive valuation)and the discounting of future rewards over present

consumption (reecting impulsivity). The link between

the two is found in the neurophysiological effects of

the reinforcer on behavior. In ‘The behavioral and

neuroeconomics of reinforcer pathologies: Implica-

tions for managerial and health decision making’,

David Jarmolowicz, Derek Reed, Florence DiGennaro

Reed, and Warren Bickel argue that while behaviors

that embody ‘reinforcer pathologies’ are inconsistent

with rational choice theory, they are consistent with

behavioral economics of demand and discounting.

Hence, they note that people who suffer from rein-forcer pathologies make particular choices between

commodities they consume and social behaviors

where the latter result in less intense or delayed

gratication.

The study of experimental matching and foraging

in the context of consumption owes much to the

pioneering work of Hantula (e.g., Smith and Hantula,

2003; Hantula and Bryant, 2005; Hantula et al .,

2008). Matching is the predicted outcome when the



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behavioral options produce qualitatively equivalent

returns. A reduction in the rate of responding on

one schedule when another is adjusted to provide

less demanding payoffs in terms of the number of

responses required for each reinforcement (or more

generous payoffs for similar response rates) is

known as behavioral contrast. In their paper

‘Matching and behavioral contrast in a two-optionrepeated investment simulation’, Donald Hantula

and Charles Crowell describe a series of investment

simulations in which matching eventuated when

markets provided equivalent returns but, as expected,

when one market no longer provided returns, behav-

ioral contrast resulted. A third simulation, in which

the returns were again made equivalent, restored

matching, but a fourth phase, in which the condi-

tions that predict contrast were again enacted, re-

stored behavioral contrast in a less robust fashion.

This work extends previous research, conrming that

adults exhibit behavioral contrast and that options are

valued relatively rather than absolutely. This research

is directly relevant to nancial management and, in

particular, investment decision-making.

Roma, Hursh, and Hadja in ‘Hypothetical purchase

task questionnaires for behavioral economic assess-

ments of value and motivation’, present evidence for

the ef cacy of questionnaire methods in testing the

Exponential Model of Demand, which was discussed

in Hursh and Roma ’s expository paper. This paper

presents an interesting step in operant behavioral eco-

nomics in that it tests the possibility that self-report questionnaires – specically, the hypothetical pur-

chase task (HPT) questionnaires developed by Jacobs

and Bickel (1999) – yield data on buyers’

purchase/consumption intentions for commodities of-

fered at various prices. The method is impressive in

view of the dif culties inherent in many questionnaire

techniques of obtaining reliable estimates of demand

from consumers’ responses to hypothetical scenarios

(e.g., Foxall, 2005). The results indicate that HPT

questionnaires provide a valid and reliable means of

estimating demand that is consistent with the results

of more conventional applications of the ExponentialModel of Demand based on data for actual consumer

behavior. Moreover, the HPT approach goes beyond

previous methods of consumer research such as

Gabor ’s (1988) buy-response analysis that established

only whether consumers would be willing to buy at all

at specied prices without enquiring how much they

would buy at each price. The methodological develop-

ments presented in this paper provide an enhanced ba-

sis for market research, as well as academic inquiry.

Asle Fagerstrom, Dag Aksnes, and Erik Arntzen, in

‘An experimental study of intertemporal choices: The

case of customer relationship management ’, explore

intertemporal choice in the context of managers having

to prioritize short-term and long-term projects in the

context of customer relationship management. The re-

sults indicate a tendency to work on smaller problems

with more immediate payoffs rather than larger taskswith more delayed outcomes. The ndings suggest a

framework for understanding better the role of tempo-

ral discounting in managerial situations, and the impli-

cations of technological drift. Asle Fagerstrom,

Georghita Ghinea, and Lars Sydnes, in ‘How does

probability impact consumers’ choice? The case of on-

line reviews’, investigate the role of probability

discounting in consumers’ evaluations of online

reviews. Participants were offered two choices of ship-

ping scenario: one in which positive customer reviews

were available but prices were higher and the other in

which there were no reviews but prices were lower.

The authors employed a titration procedure to deter-

mine the point at which consumers switched from

one buying choice to the other in order to assess the im-

plications of online reviews for intentions to buy. The

shopping microworlds employed in the study were

programmed using MediaLab™, which is proving a

sound means of investigating consumer choice in

experimental situations (Fagerstrom et al ., 2009).

CONSUMER BEHAVIOR ANALYSIS

Background

Interest in applying operant behavioral economics to

understand better the behavior of consumers and

marketers has led to the development of consumer

behavior analysis. At the heart of this approach is the

behavioral perspective model (BPM), an elaboration

of the three-term contingency that renders it appropri-

ate for the investigation and interpretation of

consumer behavior in complex marketing systems

(Foxall, 1990/2004, 2010). As Figure 1 shows, themodel portrays consumer behavior as the outcome of

two kinds of reinforcing consequence: utilitarian rein-

forcement consists in the functional benets of acquir-

ing, owning, and consuming products and services,

while informational reinforcement consists in the so-

cial benets of these activities. Consumer behavior is

also subject to utilitarian and informational punish-

ment: all consumer behavior is, after all, simulta-

neously reinforced, by the utilities provided by goods

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and their consumption, and punished, by the surrender

of purchasing power and the incapacity to purchase

other commodities (Alhadeff, 1982).

The essence of the model is the consumer situation,

which comprises the interaction of the stimuli that

compose the current consumer behavior setting (dis-

criminative stimuli and motivating operations) and

the consumer ’s learning history, the behaviors she

has previously enacted and their utilitarian and infor-

mational consequences. Of particular explanatory sig-

nicance is the consumer situation consumer behavior

link: consumer behavior is a function of the consumer

situation, where the consumer situation is the interac-

tion of a learning history and a consumer behavior set-

ting. Moreover, at the initiation of any particular

instance of consumer behavior, the only relevant be-

havioral consequences (reinforcement and punish-

ment) are those that have occurred in the past whensimilar behaviors were enacted within similar situa-

tions. The consumer behavior setting comprises the

discriminative stimuli and motivating operations that

form the physical and social milieux of an act of pur-

chase or consumption.

Papers

The remaining papers are concerned with examining

propositions derived from the BPM. That by Valdimar

Sigurdsson, Hinrik Hinriksson, and Vishnu Menon,

‘Operant behavioral economics for E-mail marketing:An experiment based on the Behavioral Perspective

Model testing the effectiveness of motivating opera-

tion, utilitarian and informational reinforcement ’, de-

scribes the model and examines the explanatory value

of some of its key components in the context of e-mail

marketing. Motivating operations are another kind of

pre-behavioral stimulus, additional to discriminative

stimuli: a motivating operation is an element of the en-

vironment that enhances the relationship between a

response and its reinforcing consequences (Michael,

1993). Motivating operations have been discussed in

the literature of behavior analysis and consumer choice

by Fagerstrom, (Fagerstrom, 2010; Fagerstrom et al .

(2010)) and Fagerstrom et al . (2011)). The paper by

Sigurdsson, Hinrik, and Menon incorporates motiva-

ting operations along with utilitarian and informational

reinforcement in an examination of the suitability of

the BPM as a guide to managerial decision making in

the sphere of email marketing. (For further background

to this work, see Sigurdsson et al ., 2013a, 2013b). The

implication of their work is that the effectiveness of

email marketing is a function of managerial activity

with respect to motivating operations and utilitarian

reinforcement.

Vishnu Menon and Valdimar Sigurdsson (‘Con-

joint analysis for social media marketing experimenta-

tion: Choice, utility estimates, and preferenceranking’) note that there is a close af nity between

the individual level of analysis favored by behavior

analysis, and the individualized marketing strategies

and tactics that personal communication makes

available via social media. The authors argue that

personalized marketing interventions can be tailored

as a result of the analyses that behavioral economics,

allied with conjoint analysis, provides. Their investi-

gation is particularly interesting in the context of the

BPM, insofar as it discusses the nature of price as it

enters into both utilitarian and informational sources

of reinforcement.Research in consumer behavior analysis, which has

employed Hursh’s approach to the determination of

demand elasticity as essential value (Hursh and

Silberberg, 2008; Hursh & Roma, 2015), has shown

that the inclusion of utilitarian and informational rein-

forcement as independent variables in the exponential

demand model increases the explained variance for

consumption behavior (Oliveira-Castro et al ., 2011;

Yan, Foxall & Doyle, 2012a, 2012b). The possibility

Figure 1. Summative behavioral perspective model.



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that consumers maximize some combination of

utilitarian and informational reinforcement, which is

an implicit assumption of the BPM, was tested by

Jorge Oliveira-Castro, Paulo Cavalcanti, and Gordon

Foxall (‘What consumers maximize: Brand choice as

a function of utilitarian and informational reinforce-

ment ’) by means of a Cobb–Douglas utility function.

Data from a consumer panel were used for thispurpose and demonstrate that consumers indeed

maximize bundles of utilitarian and informational

reinforcement. The results indicate a high degree of

intertemporal consistency for each of the four product

categories investigated. This consistency in the pro-

portions of utilitarian and informational reinforcement

for brands within product categories is of managerial

practicality because it can be used to accommodate

the functional and social benets required by con-

sumers to new brands in the course of their develop-

ment, and to retail operations. The methodology

employed also allows the total amount of utility ob-

tained per consumer to be estimated. Insofar as the to-

tal utility obtained was shown to be specic to each

product, the opportunity arises for marketers to take

consumers’ preferences more closely into consider-

ation at all stages of product development, introduc-

tion, and post-launch marketing.

CONCLUDING COMMENTS

In embracing microeconomics, operant psychology

has conrmed the relevance of neoclassical analysis

to the comprehension of complex behavior in experi-

mental and natural settings. ‘Behavioral economics’

is often a banner beneath which orthodox economic

theory is criticized on the basis that it is inconsistent

with this or that of psychological or sociological

approach. Operant behavioral economics, somewhat

by contrast but not uncritically, demonstrates the rele-

vance and usefulness of microeconomic theory to our

understanding of behavioral choice that has already

been well-researched from a particular psychological

perspective. At a time when the ‘economics and psy-

chology movement ’ is capturing the imaginations of

scholars from both disciplines, the exploration of oper-

ant behavioral economics is particularly apposite. I am

deeply grateful to the authors and reviewers who have

contributed to a collection of papers on operant behav-

ioral economics which, I trust, will be of value to both

behavior analysts and managerial economists.

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