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Purpose – Identifying the factors that influence consumer spending in retail has been a challenging topic for academics and marketing managers. Changes in consumption buying situations can encourage or discourage these expenses. We propose that deviations from planned purchases are specific classes of consumer behavior and can explain expenses. Design/methodology/approach – The research project involved a field experiment in a supermarket where 372 purchases were observed for 13 weeks. Findings – The results show the importance of both the learning history and the consumer scenario in forecasting deviations from planned purchases and the importance of these deviations in explaining expenses. Originality/value – The results highlight that deviance groups are primarily responsible for spending more or less money on purchases, as well as the importance of consumer learning history and behavioral configuration in explaining behavior. This broadens the scope of the BPM, which often focuses on brand-level results.
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Rev. Bras. Gest. Neg., São Paulo, v.22, n.2, p.331-347, Apr/Jun. 2020.
REVISTA BRASILEIRA DE GESTÃO DE NEGÓCIOS ISSN 1806-4892
e-ISSN 1983-0807
© FECAP
RBGN
Revista Brasileira de Gestão
de Negócios
DOI: 10.781/rbgn.v22i2.4053
331
Recebimento:
11/25/2018
Aprovação:
11/28/2019
Editor responsável:
Prof. Dr. Helena Nobre
Avaliado pelo sistema:
Double Blind Review
Deviances from planned purchases: consumer
learning history and behavior setting
implications for consumer spending
Marcos Inácio Severo de Almeida¹
Ricardo Limongi França Coelho¹
¹Federal University of Goiás (UFG), Faculty of Business Administration, Account-
ing Sciences and Economic (FACE), Goiânia, Brazil
Rafael Barreiros Porto²
Denise Santos Oliveira²
²University of Brasilia (UnB), Business Management
Department, Brasília, Brazil
Abstract
Purpose – Identifying the factors that inuence consumer spending
in retail has been a challenging topic for academics and marketing
managers. Changes in consumption buying situations can encourage
or discourage these expenses. We propose that deviations from planned
purchases are specic classes of consumer behavior and can explain
expenses.
Design/methodology/approach – e research project involved a
eld experiment in a supermarket where 372 purchases were observed
for 13 weeks.
Findings – e results show the importance of both the learning
history and the consumer scenario in forecasting deviations from
planned purchases and the importance of these deviations in explaining
expenses.
Originality/value – e results highlight that deviance groups are
primarily responsible for spending more or less money on purchases,
as well as the importance of consumer learning history and behavioral
conguration in explaining behavior. is broadens the scope of the
BPM, which often focuses on brand-level results.
Keywords – Behavioral Perspective Model, Consumer spending,
Consumer learning history, Consumer behavior setting, Routine
purchasing.
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Rev. Bras. Gest. Neg., São Paulo, v.22, n.2, p.331-347, Apr/Jun. 2020.
Marcos Inácio Severo de Almeida / Ricardo Limongi França Coelho / Rafael Barreiros Porto / Denise Santos Oliveira
1 Introduction
When a consumer enters a supermarket,
they presumably have purchase goals regarding the
categories they plan to buy from. at consumer
can “fully match” the planned categories or exhibit
deviances, which vary by dierent levels, ranging
from minor deviations up to signicant changes
observed in actual behavior, representing dierent
degrees of deviance from planned purchases.
Consumer behavior as an investigation area
usually combines all these degrees of deviance,
describing them as unplanned purchases.
Unplanned purchases occur in situations where
the consumer buys at the point of purchase from
a product category that was not included in their
shopping list or their prior purchase intentions
(Martínez-Ruiz, Blázquez-Resuno, & Pino,
2017), possibly being motivated by an impulsive
desire or simply by them recalling the need to
purchase from the product category (Amos,
Holmes, & Keneson, 2014). ese purchases
contribute significantly to marketing results,
such as increased retailer incremental prots, and
have generated great interest from the academic
community (Gilbride, Inman, & Stilley, 2015).
Recent investigations have examined
unplanned purchasing drivers based on economic,
sociological, and cognitive psychology arguments
(Xiao & Nichonson, 2011). These studies
investigate budget deviation (Stilley, Inman, &
Wakeeld, 2010), unplanned purchasing drivers
(Mohan, Sivakumaran, & Sharma, 2013), and the
results of unplanned purchases in retail, however
they do not consider the existence of dierent
degrees of deviance from planned purchases,
nor an integrative analysis of unplanned buying
behavior. us, questions that are relevant for
a better understanding of unplanned purchases
remain unanswered, such as whether consumers
generally have high or low levels of deviations?
What factors drive major or minor deviations?
The main implication is that the literature
overemphasizes unplanned purchases as being
derived from consumers’ susceptibility to in-store
stimuli (Akyuz, 2018; Pornpitakpan, Yuan, &
Han, 2017; Memon, Kazi, Zubedi, & Ansari,
2019; Stilley et al., 2010), while other classes of
consumer behavior are neglected. What is the
impact of major and minor deviations on current
behavior, measured by consumer spending?
Behavioral and verbal protocols can be
used to observe the degrees of deviations in
purchases. As for the factors that drive larger or
smaller deviations, operant theory behavioral
studies perform an integrative analysis of
consumer behavior based on the three-term
analysis: stimulus › response › consequence.
ese behavioral studies have identied that in
addition to store stimuli, that is, a present variable,
intention-buy deviations are strengthened or
weakened by paradoxical eects of past behavior
(Sheeran, Godin, Conner, & Germain, 2017).
A base model of the operant theory of consumer
behavior was proposed by Foxall (1992, 2015,
2017), the Behavioral Perspective Model (BPM).
According to the BPM, the consumer’s learning
history with the product or buying environment
acts as a stimulus for displaying future behaviors
(Foxall, 1992, 2015, 2017).
e BPM has produced relevant empirical
results, mostly at the brand level, stressing the
importance of reinforcements to brand choice
(Oliveira-Castro, Cavalcanti, & Foxall, 2016;
Porto & Oliveira-Castro, 2013). An open
avenue derived from this approach relates to
the inuence of consumer situational elements
on actual-behavioral measures (Katona, 1974).
Morales, Amir, and Lee (2017) emphasize
the importance of actual-behavioral measures
to improve research reliability. One barely
researched measure of behavior is consumer
spending, according to a historical analysis
conducted by Wang, Bendle, Mai, and Cotte
(2015), although it is a critical variable to the
consumption phenomenon. Identied empirical
research has analyzed spending at a micro-level
for forecasting consumer spending (Carroll,
Fuhrer, & Wilcox, 1994; Carruth & Dickerson,
2003; Fornell, Rust, & Dekimpe, 2010), and at
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Deviances from planned purchases: consumer learning history and behavior setting implications for consumer spending
a transactional level, by breaking down spending
into measures such as wallet sharing or wallet size
(Jang, Prasad, & Ratchford, 2016). To analyze
whether major and minor deviations from
planned purchases inuence the measure of actual
behavior, consumer spending makes sense since
consumers are “situated” in a behavioral scenario
composed of social and physical stimuli, where the
primary product is chosen via a product or brand
(Foxall, 2005) and, consequently for spending,
a punishment is produced by the shopping
environment (Oliveira-Castro et al., 2016).
Thus, the purpose of this article is to
understand the behavior of unplanned purchases
in the retail environment from the integrative
view of the BPM. More specically, it aims to:
a) understand how consumers can be organized
into groups that reect their buying patterns
after being observed in a real retail environment;
b) verify the eect of variables of the consumer’s
learning history and the consumption scenario
on groups of deviations in planned purchases; c)
verify the eect of these groups of deviations in
planned purchases on a substantial measure of
performance and behavior at the individual level:
consumer spending. e underlying importance
of spending is that retail managers adapt their
strategies when they note that consumer spending
is declining, adjusting prices or assortment
compositions (Fornell et al., 2010).
2 eoretical Background
2.1 Unplanned purchases
Purchases previously unplanned in a
shopping list or according to pre-purchase
declarations are known as unplanned purchases.
ey consist of purchase decisions made within
the store (Martínez-Ruiz et al., 2017). According
to Gilbride et al. (2015), unplanned purchases
account for a signicant portion of the retail
market’s nancial results. us, a keen interest
of managers and researchers has been to identify
drivers of unplanned decisions within the store.
Research suggests that these purchasing
decisions are driven by characteristics of the
consumer (Iyer, 1989); geographical stimuli,
such as the geographic region, which can change
purchases from unplanned categories; social
stimuli, such as the presence of third parties in
the purchasing environment (Chomvilailuk &
Butcher, 2014); and especially inside store stimuli,
such as customer exposure to discounted prices
(Akyuz, 2018; Iyer, 1989; Pornpitakpan et al.,
2017; Stilley et al., 2010). ese stimuli can lead
to a strong impulsive buying urge, known in the
literature as impulse buying, or a simple reminder
of the need to buy from a product category.
Both are included in the context of unplanned
purchases (Amos et al., 2014).
Although the studies developed present
signicant advances to understand unplanned
purchasing behavior, the purchasing motivators
pointed out by the literature are diverse and
fragmented. An integrative analysis of this
behavior can be performed through an economic-
behavioral analysis (Xiao & Nichonson, 2011).
In an economic-behavioral analysis, unplanned
buying behavior can be understood on the basis
of a threefold contingency: stimuli > response
> consequences. Unplanned buying behavior is
stimulated by elements of the current consumer
behavior setting and the consumer’s learning
history. Further analysis of unplanned purchases
based on economic and behavioral analysis is
presented in the following section based on the
Behavioral Perspective Model (BPM) proposed
by Foxall (1992; 2017).
2.1.1 Using behavioral perspective elements
to explain unplanned purchases
e Behavioral Perspective of Purchase
and Consumption Model (BPM) is a theoretical
framework designed to investigate and interpret
consumer behavior in complex marketing systems
(Foxall, 1992). As shown in Figure 1, consumer
behavior occurs at the intersection of two elements
of the consumer situation, one from the present
(consumer behavior setting, stimuli present in the
buying scenario) and one from the past (learning
history) (Foxall, 2017).
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Marcos Inácio Severo de Almeida / Ricardo Limongi França Coelho / Rafael Barreiros Porto / Denise Santos Oliveira
5
intersection of two elements of the consumer situation, one from the present (consumer
behavior setting, stimuli present in the buying scenario) and one from the past (learning
history) (Foxall, 2017).
Figure 1. The Behavioral Perspective Model (BPM).
The section denoted by the dotted ellipse is the essence of the model. Dashed arrows indicate that the consumer
behavior setting and learning history make up the consumer situation. The elements of the consumer situation
stimulate consumer behavior, which in turn generates consequential utilitarian reinforcement/punishment or
information reinforcement/punishment.
Source: Retrieved from “The Routledge companion to consumer behavior analysis” G. F. Foxall, “Consumer
behavior analysis comes of age,” 2016, pp. 3-21.
The stimuli present in the buying scenario can be physical (in-store advertising
pieces), social (presence of sellers), temporal (store hours), and regulatory (prohibitive use of
a helmet). The learning history, on the other hand, is based on the consumers previous
experiences with the store or product and adds meanings that will mediate their future
behavior. The stimuli of the purchasing scenario, together with the learning history, will
predict the consumer’s future behavior (Foxall, 1998, 2017; Porto & Oliveira-Castro, 2013).
Based on this model, unplanned purchasing behavior will also be stimulated by consumer
learning history variables and consumer scenario variables. In the present study, these
variables are analyzed, and their effects on predictions of unplanned buying behavior are also
examined.
Consumer behavior has consequences that may be utilitarian or informative,
reinforcing or punitive. Utilitarian and/or informative consequences are programed into the
purchasing environment and communicated to consumers through marketing mix actions.
Utilitarian consequences derive from product use practices and consumer satisfaction or
dissatisfaction responses to functional aspects of the product, such as smell, taste, texture, and
cleanliness. Usage information is usually mentioned on the packaging or in the name or
product campaigns. Information related to third-party feedback, or social reaffirmation, is
usually linked to brands with a high level of prestige and social reliability. The possession of
products with a high level of informative reinforcement will present social reinforcement and
Figure 1. e Behavioral Perspective Model (BPM).
e section denoted by the dotted ellipse is the essence of the model. Dashed arrows indicate that the consumer
behavior setting and learning history make up the consumer situation. e elements of the consumer situation
stimulate consumer behavior, which in turn generates consequential utilitarian reinforcement/punishment or
information reinforcement/punishment.
Source: Retrieved from “e Routledge companion to consumer behavior analysis” G. F. Foxall, “Consumer
behavior analysis comes of age,” 2016, pp. 3-21.
e stimuli present in the buying scenario
can be physical (in-store advertising pieces), social
(presence of sellers), temporal (store hours), and
regulatory (prohibitive use of a helmet). e
learning history, on the other hand, is based on the
consumer’s previous experiences with the store or
product and adds meanings that will mediate their
future behavior. e stimuli of the purchasing
scenario, together with the learning history, will
predict the consumer’s future behavior (Foxall,
1998, 2017; Porto & Oliveira-Castro, 2013).
Based on this model, unplanned purchasing
behavior will also be stimulated by consumer
learning history variables and consumer scenario
variables. In the present study, these variables
are analyzed, and their eects on predictions of
unplanned buying behavior are also examined.
Consumer behavior has consequences
that may be utilitarian or informative, reinforcing
or punitive. Utilitarian and/or informative
consequences are programed into the purchasing
environment and communicated to consumers
through marketing mix actions. Utilitarian
consequences derive from product use practices
and consumer satisfaction or dissatisfaction
responses to functional aspects of the product,
such as smell, taste, texture, and cleanliness.
Usage information is usually mentioned on the
packaging or in the name or product campaigns.
Information related to third-party feedback, or
social rearmation, is usually linked to brands
with a high level of prestige and social reliability.
e possession of products with a high level of
informative reinforcement will present social
reinforcement and an increase of prestige,
achievement, or respect (Oliveira-Castro, Foxall,
& Wells, 2010). Such consequences will regulate
the rate of occurrence of a behavior in similar
future situations (Foxall, Oliveira-Castro, &
Schrezenmaier, 2007; Foxall, 2010; Sigurdsson,
Kahamseh, Gunnarsson, Larsen, & Foxall, 2013).
Reinforcing consequences increase the likelihood
of future repetition of a behavior, while punitive
consequences reduce that probability (Foxall,
1992, 1998, 2010).
e conguration in which the behavior
occurs may stimulate or inhibit the consumer
response. Open purchase configurations are
characterized by the freedom of the consumer in
the buying scenario. For example, inside a bar or
mall there are a variety of allowed behaviors, like
choosing dierent types of products, watching
a show, talking with other people, and entering
and leaving the premises at any time. us, the
consumer feels free to perform behaviors. In
contrast, closed purchase congurations limit
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Deviances from planned purchases: consumer learning history and behavior setting implications for consumer spending
the behavior of the consumer. For example, when
waiting in line at a bank there is no alternative if
the consumer does not wait their turn, and they
cannot enter or leave the line at will (Foxall, 1993,
2017). For example, in an open behavior setting,
such as a supermarket, where functional and social
benets are low, individuals exhibit a specic class
of behavior, i.e. maintenance, guided by routine
purchasing (Foxall & Yani-de-Soriano, 2005).
However, even this routine may be susceptible to
various dynamics, including in-store behaviors.
2.1.2 Using behavioral perspective elements
to explain consumer spending as an actual
behavioral measure of unplanned purchases
Consumer spending is an actual behavioral
measure (Morales et al., 2017). Major and minor
deviations from unplanned purchases can be
reected in this real variable. Foxall’s (1998, p.
322) denition of consumer behavior includes
“activities of buyers, former buyers, and potential
buyers from prepurchase to post-purchase,
consumption to discontinuance,” but it overlooks
this primary component of complex behavior,
which arises in-store, where individuals use the
environment as a cue to decide what to buy
(Sigurdsson, Larsen, & Fagerstrøm, 2016).
When analyzed at the individual
level, consumer spending is a measure that
reects realistic behavior that has some form of
consequence that signals consumer punishment
(Oliveira-Castro et al., 2016). e “degree” of
punishment may vary, as individuals may buy less
or more than originally planned, circumscribing
this behavior as deviating from a prescribed goal
(Moschis & Cox, 1989). is study investigates
the eect of groups of deviations in planned
purchases on this actual behavior variable, i.e.
consumer spending.
2.1.3 Framework proposal
erefore, our research model uses the
BPM as a basis for understanding unplanned
purchasing behavior in the retail environment.
Considering an open conguration, such as a
supermarket, consumer behavior results from
behavioral conguration (present) and learning
history (past), but also produces an important
output that predicts actual consumer spending:
deviations from planned purchases. Figure 2
shows our proposal.
7
2.1.3 Framework proposal
Therefore, our research model uses the BPM as a basis for understanding unplanned
purchasing behavior in the retail environment. Considering an open configuration, such as a
supermarket, consumer behavior results from behavioral configuration (present) and learning
history (past), but also produces an important output that predicts actual consumer spending:
deviations from planned purchases. Figure 2 shows our proposal.
Figure 2. Research framework of consumer spending behavior in routine purchasing
Note. Solid arrows illustrate the influences on consumer behavior expected by the Behavioral Perspective Model
(BPM) (and formalized by the extant literature), while dashed arrows incorporate our new proposed component:
consumer groups, represented by deviances from planned purchases.
Source: From “Deviances from planned purchase: Consumer learning history and behaviour setting implications
to consumer spending,” M. I. S. Almeida, R. B. Porto, & R. L. F. Coelho, 2016, p. 4
3 Method
We organized a field experiment in a supermarket located in a Brazilian city in the
central region of the country, with an estimated population of approximately 1.5 million
inhabitants, according to the latest survey conducted by the Brazilian Institute of Geography
and Statistics (IBGE). The chosen supermarket is open every day, has a total area of 8500
square meters, and includes meat, fresh produce, bakery, and beverage sections. The most
frequent age distribution in the urban area of this city is between 30 and 39 years (223,816
individuals). This age group includes 107,713 men and 116,797 women (Instituto Brasileiro
de Geografia e Estatística, 2010; 2015). The supermarket is located in a central area, near the
two main universities of this city and an expressway, which ensures sample randomization
and variability.
We dedicated the first two weeks of the experiment to pretesting techniques. First, the
survey instrument underwent expert review analyses before we decided on the final version.
Figure 2. Research framework of consumer spending behavior in routine purchasing
Note. Solid arrows illustrate the inuences on consumer behavior expected by the Behavioral Perspective Model (BPM)
(and formalized by the extant literature), while dashed arrows incorporate our new proposed component: consumer groups,
represented by deviances from planned purchases.
Source: From “Deviances from planned purchase: Consumer learning history and behaviour setting implications to consumer
spending,” M. I. S. Almeida, R. B. Porto, & R. L. F. Coelho, 2016, p. 4
336
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Marcos Inácio Severo de Almeida / Ricardo Limongi França Coelho / Rafael Barreiros Porto / Denise Santos Oliveira
3 Method
We organized a field experiment in
a supermarket located in a Brazilian city in
the central region of the country, with an
estimated population of approximately 1.5
million inhabitants, according to the latest survey
conducted by the Brazilian Institute of Geography
and Statistics (IBGE). e chosen supermarket is
open every day, has a total area of 8500 square
meters, and includes meat, fresh produce,
bakery, and beverage sections. e most frequent
age distribution in the urban area of this city is
between 30 and 39 years (223,816 individuals).
This age group includes 107,713 men and
116,797 women (Instituto Brasileiro de Geograa
e Estatística, 2010; 2015). e supermarket is
located in a central area, near the two main
universities of this city and an expressway, which
ensures sample randomization and variability.
We dedicated the rst two weeks of the
experiment to pretesting techniques. First, the
survey instrument underwent expert review
analyses before we decided on the nal version.
Members of a marketing research group conducted
an interviewer debrieng with the researchers
responsible for collecting data and scrutinized
question wording and questionnaire structure.
Subsequently, ten individuals were interviewed
preliminarily to analyze their purchases during
a eld test (and eventually discarded), to assess
problematic questions in the *survey instrument,
and the best way to approach the respondent.
Over the following 13 weeks, we collected
daily marketing variables present in the consumer
scenario, such as whether the supermarket had
any promotion for a specic product category
(meat, fruit, or vegetables) on that day, and we
invited customers of the supermarket during
weekdays and weekends to participate in the
research. e criterion used to select consumers
was their intention to purchase something as they
arrived in-store. ree trained researchers asked
permission for a brief interview (Stage 1), where
basic demographic and purchase plan information
was collected. In the case of demographic variables,
information was collected related to the latest
IBGE census characteristics, such as household
income, gender, and age. e behavioral variables
were recorded by orally identifying whether the
respondent had a shopping list or had consulted
prices before arriving at the supermarket.
Seven questions were assessed: 1) which
product categories the individuals were planning
to buy from (38 category options, presented in
separately; these variables were selected after an
evaluation in a group of similar supermarkets,
which are: Bar soap; Bean; Beer; Breads; Butter;
Cheese; Chocolate; Chocolate milk; Coffeel
Cookies; Deodorant; Detergents; Disinfectant;
Fabric softeners; Flour; Fruits; Juice; Margarine;
Meat; Milk; Moisturizer; Oils; Pasta in general;
Rice; Sanitary water; Sauces; Seasonings;
Shampoo; Soap; Soda; Sugar; Tea; Toilet paper;
Toothpaste; Vegetables; Washing powder; Water;
Yogurt); 2) if the consumer had a shopping
list; 3) if they had carried out a previous price
consultation; 4) reported household income
(10 possible values, according to the IBGE); 5)
gender; 6) age; and nally (7) if the individual was
accompanied during that shopping trip. At the
end of this brief interview, the researchers asked
the consumers to provide them with the receipt
after their payment at the counter (Stage 2). If a
given respondent did not authorize this request,
the completed questionnaire was automatically
discarded. Given the information from Stage 1
and Stage 2, the researchers could compile and
compare, after receiving the receipts, purchase
plan and actual behavior information, such as
the form of payment and the total value of the
purchase. Accordingly, additional variables were
registered, such as the day of the month, the day of
the week, and promotion days (dummy variable)
at the supermarket.
After the conclusion of the survey, Stage 3
started, where we developed a measure indicating
consumer groups. This procedure involved
a simple individual comparison between the
number of categories the individual consumers
planned to buy from and the quantity purchased
from. is approach was inspired by a cross-
buying measure developed by Kumar, George, and
Pancras (2008). Although the participants were
aware they were taking part in a research study
(Morales et al., 2017), they did not know about
this ex-post quasi-experimental manipulation.
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Deviances from planned purchases: consumer learning history and behavior setting implications for consumer spending
By comparing how many and which categories
an individual bought from with how many and
which categories they informed when they arrived
at the supermarket, we were able to establish a set
of consumer groups, based on degrees of deviance
at the category level. For example, the rst group
refers to what we classify as the Eective Planned
Purchase Group: individuals that fully match their
purchases, buying only from planned categories.
Conversely, the other groups include some form
of deviance, in respect to matching planned
categories and the amount (buying more or
less) of categories bought from. e operational
denitions of the consumer groups are shown
in Table 1 along with basic descriptive statistics.
Table 1
Descriptive analysis of consumer groups
Group name Denition N e incidence rate
in the sample
Average of the
amount planned
(std. dev.)
Average of the
amount bought
(std. dev.)
Eective Planned
Purchase Group
Fully matched and purchased only
from the categories planned 48 13.2% 1.85
(1.2)
1.85
(1.2)
Deviance 1 Fully matched and purchased from
more categories than planned 183 50.3% 2.49
(1.9)
6.11
(4.6)
Deviance 2
Partially matched or unmatched
and purchased from more categories
than planned
82 22.5% 5.26
(3.9)
9.64
(6.3)
Deviance 3
Partially matched or unmatched
and purchased from fewer categories
than planned
28 7.7% 5.85
(5.2)
3.5
(3.7)
Deviance 4
Partially matched or unmatched and
purchased from the same number of
categories as planned 23 6.3% 4.26
(2.3)
4.43
(2.4)
Total 364 100% 3.40
(3.1)
6.04
(5.2)
The final sample comprised 372
observations (individual purchases). An a priori
test, considering the number of groups and the
number of covariates in the nal model, using
a medium eect size of 0.25, a probability of
error of 5%, and a power of 95%, returned a
nal sample of 364 individuals, which reveals a
reasonably acceptable sample size for our eld
experiment.
4 Results
4.1 Descriptive characteristics of the
sample
Since one of the objectives of our study
was to arrange consumers into groups relating
to their buying patterns, the methodological
approach employed organized the individuals
into ve possible outcomes. First, there are the
ones that fully matched their initial intention and
purchased only from the categories they planned
to buy form, these individuals being classied as
the Eective Planned Purchase Group. is group
registered a 13.2% incidence rate, which is a low
rate. e remaining 86.8% incidence rate belongs
to four groups that deviated from their planned
purchases, varying, in our proposal, according
to two dimensions: a) if the individual matched
(fully, partially, or not at all) their intention; and
b) if the individual bought from more, less, or the
same categories they planned to buy from.
Hence, our nal categorization suggests
four deviance groups: Deviance Group 1 fully
matched and purchased from more categories
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Marcos Inácio Severo de Almeida / Ricardo Limongi França Coelho / Rafael Barreiros Porto / Denise Santos Oliveira
than planned; Deviance Group 2 partially
matched or did not match and purchased from
more categories than planned; Deviance Group 3
partially matched or did not match and purchased
from fewer categories than planned; and Deviance
Group 4 partially matched or did not match and
purchased from the same number of categories
as planned. is construction includes minor
deviations (Group 1) up to signicant changes
observed in actual behavior (from Group 2 to
Group 4).
Table 2 presents the measurement
approach used for the study variables, along with
means and standard deviations. e consumers
usually bought their products using cash (51.1%)
and debit cards (30.5%), on non-promotion
days (67.6%) and on weekdays (78.3%). ese
individuals were usually females (65.4%), most
with a reported household income ranging from
U$ 379 to U$ 757 (28.7%), whose shopping
trips were not accompanied (68.4%) and
mostly unplanned, since only 16.8% brought a
shopping list and 24.7% conducted a previous
price consultation. Descriptive statistics for the
quantitative variables show an average age of 40
and a mean purchase value of U$ 17.
Table 2
Descriptive analysis of the study variables
Variable Measurement approach Percentage Mean (std. dev)
Form of payment
In cash
Debit card
Credit card
Qualitative
(three levels)
51.7
30.5
17.8
51.1%
30.2%
18.7%
Reported household income
- up to U$ 156
- from U$ 157 to U$ 379
- from U$ 380 to U$ 758
- from U$ 759 to U$ 1137
- from U$ 1138 to U$ 1516
- from U$ 1517 to U$ 1895
- from U$ 1896 to U$ 2274
- from U$ 2275 to U$ 2653
- from U$ 2654 to U$ 3031
- more than U$ 3032
Qualitative
(ten levels)
3
9.6
28.7
19.6
9.8
12.1
7.2
3.6
1
8
0.3%
9.6%
29.1%
20.1%
9.9%
11.8%
6.9%
3.8%
0.8%
7.7%
Promotion day at the supermarket Dummy 32.4%
Purchase during the weekend Dummy 21.7%
Gender (male) Dummy 35.4%
Consumer accompanied during the shopping trip Dummy 31.6%
Shopping list Dummy 16.8%
Previous price consultation Dummy 24.7%
Age Quantitativea40.04
(15.76)
Amount planned
(Number of categories registered during the
research interview (Stage 1))
Quantitative 3.4
(3.1)
Consumer spending
(Total value of the purchase)
Quantitative 45.07
(45.67)
Note.
a
e quantitative variables (age, amount planned, and consumer spending) underwent logarithmic transformation
before inclusion in the inferential analyses to produce a scale-free interpretation.
Source: Retrieved from
“Deviances from planned purchase: consumer learning history and behaviour setting implications
to consumer spending,” M. I. S. Almeida, R. B. Porto, & R. L. F. Coelho, 2016, p. 8.
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Deviances from planned purchases: consumer learning history and behavior setting implications for consumer spending
The inferential results are reported in
the next two topics. Initially, we show the eect
of the independent variables on the probability
of belonging to groups other than the Eective
Planned Purchase Group, using multinomial
logistic regression. e use of this technique
contributed to the evaluation of the ve deviations
collected (dependent variables) and the eects of
the independent variables on the probability of
occurrence (Clogg, Petkova, & Haritou, 1995;
Krishnapuram, Carin, & Figueiredo, 2005) of
the purchase plan, as in the study by Kumar
et al. (2008).
Next, we unveil the eect of the deviance
groups on consumer spending using an ANCOVA,
which is used to evaluate if the dependent variable
is equal across dierent levels of the independent
variable (Akritas & Van Keilegom, 2001). Overall,
we show the set of variables that reect inuences
of the past and the present on deviances from the
eective purchase plan and what the potential
“risks” are of spending more or less money, in
comparison to a “planned” purchase.
4.2 Influences on the probability of
belonging to deviance groups
We apply the multinomial logistic
regression model technique to assess inuences on
the probability of belonging to deviance groups.
e reference category for this case is the Eective
Planned Purchase Group, who are individuals
who matched their initial intention when arriving
at the supermarket. We observe a low-moderate
eect of independent variables on the chances of
belonging to each group. e chi-square test of
the model is signicant (p ≤ 0.01), returning a
Nagelkerke R
2
of 35.4%. Table 3 shows estimates
(and standard errors) of the variables for each
comparison of the deviance groups with the
Eective Planned Purchase Group.
Table 3
Eect of the independent variables on the probability of belonging to the deviance groups
Dependent variableaIndependent variables Estimate Std. error
Deviance 1
(fully matched and purchased from more
categories than planned)
Intercept
Previous price consultation
Shopping list
Consumer accompanied
Form of payment
Weekend
Log of the amount planned
0.91***
0.83*
-1.02**
0.96**
0.45*
-0.97***
0.43
0.31
0.48
0.48
0.46
0.27
0.38
0.31
Deviance 2
(partially matched or unmatched and purchased
from more categories than planned)
Intercept
Previous price consultation
Shopping list
Consumer accompanied
Form of payment
Weekend
Log of amount planned
-1.24***
0.41
-1.16**
0.87*
0.39
-1.46***
2.01***
0.41
0.55
0.58
0.52
0.30
0.50
0.36
Deviance 3
(partially matched or unmatched and purchased
from fewer categories than planned)
Intercept
Previous price consultation
Shopping list
Consumer accompanied
Form of payment
Weekend
Log of Amount planned
-2.78***
0.36
-0.54
0.78
-0.70
-0.34
2.37***
0.60
0.70
0.69
0.64
0.39
0.58
0.45
Deviance 4
(partially matched or unmatched and purchased
from the same number of categories as planned)
Intercept
Previous price consultation
Shopping list
Consumer accompanied
Form of payment
Weekend
Log of amount planned
-2.47***
0.43
0.21
0.33
-0.29
-0.96
2.18***
0.61
0.69
0.66
0.69
0.42
0.68
0.47
Nagelkerke R
2
= 35.4%; chi square = 144.9 (p ≤ 0.01)
Note. Reference category: Eective Planned Purchase Group
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Marcos Inácio Severo de Almeida / Ricardo Limongi França Coelho / Rafael Barreiros Porto / Denise Santos Oliveira
e consumer learning history (previous
price consultation, consumer accompanied during
shop) and behavior setting (form of payment
- payment by credit card) variables exhibited
a positive eect on Deviance Group 1, while
purchasing during weekends and bringing a
shopping list to the supermarket showed a negative
eect. ese results indicate that payment by
credit card, being accompanied by someone, and
previously consulting prices stimulate consumers
to buy more products than planned, even though
they fully matched the purchases (following the
denition of Deviance Group 1).
By using the logistic probability formula
[P(X) = 1 / 1 + e
–(α + Σ βX)
], for example, if a
consumer consults the planned product price
before going to the supermarket, brings a
shopping list to it, comes with someone, and
pays using a credit card during the weekend, the
probability of belonging to Deviance Group 1 is
up to 83.3% in relation to the Eective Planned
Purchase Group. ese results show a signicant
eect of consumer learning history on changes in
planned behavior.
Purchases during the weekend and
bringing a shopping list to the supermarket
provide self-control to the consumer and, also,
have a negative eect on Deviance Group 2.
Conversely, the amount planned has a positive
eect on Deviance Group 2, Deviance Group
3, and Deviance Group 4. is means that the
more products a given consumer plans to buy, the
more they partially match or do not match their
purchases, notwithstanding the amount bought.
Consumers being accompanied while
shopping also had a small positive effect on
Deviance Group 2. Therefore, consumers’
companions stimulates them to buy more
products than planned, even though the consumer
can match the purchases or not. Applying the
example in the logistic probability formula, in
relation to the Eective Planned Purchase Group,
if a consumer raises the number of products they
plan to buy by 1%, the probability of belonging
to Deviance Group 3 or Deviance Group 4 is
39.89% and 42.80%, respectively. In turn, if
the consumer raises the amount they plan to
buy by 1%, purchases during a weekend, and
brings a shopping list to the supermarket, the
probability of belonging to Deviance Group 2
reaches 27.28%.
4.3 Inuences on consumer spending
Just as we tested whether there were any
predictors of the deviances from the Eective
Planned Purchase Group, in this analysis we are
interested in observing a possible implication for
these deviances in in-store consumer behavior, in
an open routine purchasing setting (supermarket),
which is consumer spending. Using an ANCOVA,
we tested in which deviance groups consumers
have higher spending, controlling for other
variables, namely: the amount planned, the form
of payment, the consumer being accompanied,
the day of the month, and income.
Table 4 shows that the model accounts for
53% of the eta squared, a reasonably acceptable
but moderate impact. e variables do not interact
with each other (interactions not shown in Table
4 for questions of space), which means that the
variables, alone, are responsible for the variances
in consumer spending. The most impactful
variable in relation to consumer spending is the
deviance groups (higher partial eta squared =
27%). Amount planned [F (1,352) = 120.46, p
≤ 0.01], form of payment [F (1,352) = 24.57, p ≤
0.01], consumer accompanied [F (1,352) = 9.12,
p ≤ 0.01], day of the month [F (1,352) = 6.11,
p ≤ 0.01], and income [F (1,352) = 4.88, p ≤
0.05] also have inuences on consumer spending,
though they are slightly less important. ese
results suggest that the deviance groups are the
main factor responsible for consumers spending
more or less money on purchases.
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Deviances from planned purchases: consumer learning history and behavior setting implications for consumer spending
Table 4
Eect of the deviances groups and control variables on consumer spending
Source Type III Sum of Squares df Mean Square F Eta Squared
Corrected model 223,37 9 24,82 35,50*** 0,53
Intercept 388,83 1 388,83 681,53***
Deviance groups 72,12 4 18,03 31,60*** 0,27
Amount planned 68,73 1 68,73 120,46*** 0,26
Form of payment 14,02 1 14,02 24,57*** 0,07
Consumer accompanied 5,21 1 5,21 9,12*** 0,03
Ln day of the month 3,49 1 3,49 6,11** 0,02
Reported income 2,78 1 2,78 4,88*** 0,01
Error 200,82 352 0,57
Total 4401,57 362
Corrected total 424,19 361  
Note. * p ≤ 0.1; ** p ≤ 0.05; ***p ≤ 0.01. Once the control variables are entered as covariates, the relationship between
each deviance group and consumer spending can be highlighted. Figure 2 shows that Deviance Group 1 (Mean = 3.55;
S.E. = 0.1) and Deviance Group 2 (Mean = 3.52; S.E. = 0.1) have signicantly (p ≤ 0.01) more inuence on Consumer
Spending than the Eective Planned Purchase Group (Mean = 2.80; S.E. = 0.1). Deviance Group 3 (Mean = 2.35; S.E. =
0.2) has signicantly (p ≤ 0.01) less inuence on Consumer Spending than the Eective Planned Purchase Group (Mean =
2.80; S.E. = 0.1). e Eective Planned Purchase Group and Deviance Group 4 (Mean = 2.99; S.E. = 0.2) have moderate
spending and they are not signicantly dierent.
Source: Retrieved from “Deviances from planned purchase: consumer learning history and behaviour setting implications
to consumer spending,” M. I. S. Almeida, R. B. Porto, & R. L. F. Coelho, 2016, p. 11
16
Figura 3. Médias marginais estimadas de gasto do consumidor por grupo de desvio
Como um todo, o padrão logarítmico do impacto dos grupos de desvio e covariáveis
nos gastos do consumidor, é mostrado na Figura 3. A ilustração destaca um padrão não linear
entre as covariáveis e o principal fator (grupos de consumidores) e a variável de resposta,
gastos do consumidor. As observações são distribuídas aleatoriamente entre a linha ajustada
revelando um ajuste adequado entre o modelo e os dados experimentais. Escolheram-se
variáveis independentes para influenciar moderadamente os gastos do consumidor (R2 =
53%).
Figure 3. Estimated marginal means of consumer spending by
deviance groups
Source: From “Deviances from planned purchase: Consumer learning history and
behaviour setting implications to consumer spending,” M. I. S. Almeida, R. B. Porto,
& R. L. F. Coelho, 2016, p. 11
342
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Marcos Inácio Severo de Almeida / Ricardo Limongi França Coelho / Rafael Barreiros Porto / Denise Santos Oliveira
Overall, the logarithmic pattern of the
impact of the covariates and deviance groups
on consumer spending is shown in Figure 3.
e illustration underscores a nonlinear pattern
between the covariates and the main factor
(consumer groups) and the response variable,
consumer spending. The observations are
randomly spread along the tted line, revealing
a fairly adequate fit between the model and
the experimental data. Independent variables
were chosen to moderately inuence consumer
spending (R
2
= 53%).
17
Figura 4. Impacto dos grupos de desvios e covariáveis nos gastos dos consumidores
5 Discussão e pesquisas adicionais
Do objetivo de que desvios das compras planejadas são classes específicas de
comportamento do consumidor que podem explicar os gastos do consumidor, nossa estrutura
empírica incorpora o desvio como um importante preditor de comportamento em um contexto
classificado como ambiente de compras de rotina aberta. Nesse sentido, segundo Foxall
(2000), o comportamento é uma função de experiências passadas da pessoa e do meio
ambiente. Essa suposição orientou o desenvolvimento de uma estrutura que especificava que
o comportamento do consumidor é determinado pelo ambiente, mas também controlado pelo
histórico de aprendizado do indivíduo. Os resultados de nosso modelo multinomial
corroboram essa premissa, pois as consultas de preços e a lista de compras podem desviar os
consumidores do grupo de Compra Planejada Eficaz. Isso indica um efeito importante de um
componente de pré-compra (o passado) no comportamento (o presente).
Portanto, os supermercados são exemplos definidos de ambientes abertos, onde os
mecanismos de controle dos vendedores estão ausentes. A implicação resultante é que um
comportamento inconsciente (Morales et al., 2017) pode auxiliar na interpretação da punição
(gastos) em um ambiente de varejo (Oliveira-Castro et al., 2016). Considerando os efeitos dos
grupos de desvios nos gastos do consumidor e seu efeito substancial proporcionado pelo
modelo ANCOVA, é possível formalizar o efeito de definição do consumidor: em um
Figure 4. Impact of the covariates and deviances groups on consumer
spending
Source: From “Deviances from planned purchase: Consumer learning history and
behaviour setting implications to consumer spending,” M. I. S. Almeida, R. B. Porto,
& R. L. F. Coelho, 2016, p. 12.
5 Discussion and Further Research
Based on the assumption that deviances
from planned purchases are specic classes of
consumer behavior that may explain consumer
spending, our empirical framework incorporates
deviance as an important predictor of behavior in
a context classied as an open routine purchasing
setting. According to Foxall (2000), behavior is
a function of the person’s past experiences and
the environment. is assumption guided the
development of a framework which specied
that consumer behavior is environmentally
determined, but also under the control of
individual learning history. e results of our
multinomial model corroborate this premise, as
price consultations and shopping lists can deviate
consumers from the Eective Planned Purchase
Group. is indicates an important eect of the
pre-purchase component (the past) on behavior
(the present).
Supermarkets are examples of open settings,
where control mechanisms from marketers are
largely absent. e resulting implication is that
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Deviances from planned purchases: consumer learning history and behavior setting implications for consumer spending
an unconscious behavior (Morales et al., 2017)
can assist in the interpretation of punishment
(spending) in a retail environment (Oliveira-
Castro et al., 2016). Considering the eects of
the deviance groups on consumer spending and
their substantial eect provided by the ANCOVA
model, it is possible to formalize the consumer
setting eect: in an open routine purchasing
environment, consumers exhibit an in-store
specic behavior, and organized deviances that
may explain their “focal” behavior.
e results highlight that deviance groups
are the main factors responsible for consumers
spending more or less money on purchases, as well
as the importance of consumer learning history
and behavior setting to explain behavior. is
expands the scope of the BPM, which usually
focuses on results generated at the brand level. e
consumer situation elements presented by Foxall
(1992) and others (Oliveira-Castro et al., 2016)
can also be related to an individual performance
measure in retail, i.e. consumer spending. In
practical terms, marketing managers involved
with retail decisions can interpret these elements
as internal and external factors responsible for
producing eects on consumer expenditure.
Due to the nature of the experimental
study, some limitations occur. One is the
use of only one Brazilian city, limiting the
understanding of behavior in dierent regions
and sizes of supermarket. Another is the total
period of the experiment, 13 weeks, which
does not capture seasonal periods throughout
the year. Moreover, the collection instrument,
which uses a questionnaire to collect data,
means respondents may have omitted purchase
categories, and may also be inuenced by new
product purchases through contact with other
customers, for example. Further research should
incorporate these elements in dierent scopes of
behavior setting as dened by Foxall (1992), such
as accomplishment, pleasure, and accumulation
environments.
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346
Rev. Bras. Gest. Neg., São Paulo, v.22, n.2, p.331-347, Apr/Jun. 2020.
Marcos Inácio Severo de Almeida / Ricardo Limongi França Coelho / Rafael Barreiros Porto / Denise Santos Oliveira
Appendix 1 - Questionnaire applied to respondents at the supermarket
entrance
Dear Customer,
We are researching cross-buying across product categories. I want to request the completion of
the following elds. ey deal with some personal data, your purchase intention, and learning from the
product categories oered at the supermarket.
1. What do you pretend to buy today in this supermarket:
( ) Bar soap
( ) Bean
( ) Beer
( ) Breads
( ) Butter
( ) Cheese
( ) Chocolate
( ) Chocolate milk
( ) Coee
( ) Cookies
( ) Deodorant
( ) Detergents
( ) Disinfectant
( ) Fabric softeners
( ) Flour
( ) Fruits
( ) Juice
( ) Margarine
( ) Meat
( ) Milk
( ) Moisturizer
( ) Oils
( ) Pasta in general
( ) Rice
( ) Sanitary water
( ) Sauces
( ) Seasonings
( ) Shampoo
( ) Soap
( ) Soda
( ) Sugar
( ) Tea
( ) Toilet paper
( ) Toothpaste
( ) Vegetables
( ) Washing powder
( ) Water
( ) Yogurt
2. Did you make a shopping list before coming to the supermarket? ( ) No ( ) Yes
3. Did you make any research about prices before entering the supermarket? ( ) No ( ) Yes
4. What is your family income?
( ) up to R$ 412 ( ) from R$ 412 to R$ 1.000 ( ) from R$ 1001 to R$ 2.000 ( ) from R$ 2001 to R$ 3.000
( ) from R$ 3001 to R$ 4000 ( ) from R$ 4001 to R$ 5000 ( ) from R$ 5001 to R$ 6.000 ( ) from R$ 6001 to R$ 7000
( ) from R$ 7001 to R$ 8000 ( ) more than R$ 8.001
5. Sex ( ) Male ( ) Female 6. Age
6. Will it be accompanied during the purchase?
347
Rev. Bras. Gest. Neg., São Paulo, v.22, n.2, p.331-347, Apr/Jun. 2020.
Deviances from planned purchases: consumer learning history and behavior setting implications for consumer spending
Authors:
1. Marcos Inácio Severo de Almeida, PhD in Business Administration, University of Brasilia, Brasília,
Brazil. E-mail: misevero@yahoo.com.br
ORCID
0000-0001-9493-0644
2. Ricardo Limongi França Coelho, PhD in Business Administration, Sao Paulo School of Business
Administration at Getulio Vargas Foundation, Sao Paulo, Brazil.
E-mail: ricardolimongi@ufg.br
ORCID
0000-0003-3231-7515
3. Rafael Barreiros Porto, Phd in Behavioral Sciences University of Brasilia, Brasília, Brazil.
E-mail: rbarreirosporto@gmail.com
ORCID
0000-0003-2210-7098
4. Denise Santos Oliveira, Master’s Degree in Business Administration, Federal University of Goias,
Goiânia, Brazil. E-mail: deniseadm@hotmail.com
ORCID
0000-0003-4981-119X
Contribution of each author
ContributionMarcos
Almeida
Ricardo
Coelho
Rafael
Porto
Denise
Oliveira
1.Denition of research problem  
2.Development of hypotheses or research questions(empiricalstudies)  
3.Development of theoretical propositions(theoreticalWork)
4.eoretical foundation/Literature review
5.Denition of methodological procedures  
6.Data collection
7.Statistical analysis  
8.Analysis and interpretation of data
9.Critical revision of the manuscript  
10.Manuscript Writing  
11.Other (please specify which)
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