Journal of Consumer Behaviour, J. Consumer Behav. 11: 105–114 (2012)
Published online 8 January 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/cb.386
Observing the influence of affective states on parent–child interactions and
in-store purchase decisions
JOHN NADEAU* and MATTHEW BRADLEY
Nipissing University, North Bay, ON Canada
ABSTRACT
This study examines the relationship of parent’s and children’s affective states on in-store family purchase decisions. In particular, the study
is interested in determining whether the affective state is related to the interaction strategy selected and their actual role in the decision. The
implementation of this study using observational methodology within the store environment makes this contribution valuable and unique.
Further, a major contribution of this study is the demonstration that the child’s prepurchase affective state is a salient antecedent to purchase
decisions of the parent–child dyad.
Copyright © 2012 John Wiley & Sons, Ltd.
INTRODUCTION
Children have an immense impact on the lives of their parents
and families. Of particular interest to marketers is the influence
that children have on their parents and family units as
consumers (Wilson and Wood, 2004). In previous research,
marketers have found that children play an important role in
family decision making (Thomson et al., 2007). While many
studies examine children’s role in family decision making
(Foxman et al., 1989; Ahuja et al., 1998; Lee and Collins,
2000), few studies appear to directly observe the interaction
between parent and child within the store environment
through unobtrusive observational methods (Darian, 1998;
O’Dougherty et al., 2006). Further, Heslop and Ryans (1980)
and Kumpel Norgaard et al. (2007) suggest that research
should focus on the attitudes and behavior of the parent–child
dyad rather than on the child alone. While research should
explore parents and children in a holistic manner, the potential
role of affect as an antecedent on the parent–child dyad and its
interaction during purchase decision making seems to have
been neglected.
Affect is an important consideration in consumer behavior
and is enacted at the moment of choice in decision making
through feelings that act as information to guide the decision
or judgment (Peters et al., 2006). Affect also provides common ground for judgments and decisions that allows a comparison between decision options and information (Peters
et al., 2006). As consumers, our emotional state or mood
shapes our view of the market and how we make decisions
in it (Mackie and Worth, 1989; Batra and Stayman, 1990;
Cohen and Areni, 1991). Further, there appears to be some
inertia, whereby a good mood may lead people to be more
generous (Kacen, 1994). However, the literature on affect
and consumer behavior tends to be based on self-reporting
by study participants (Batra and Stayman, 1990; Stegge et al.,
1994; Ahuja et al., 1998).
*Correspondence to: John Nadeau, School of Business, Nipissing University,
North Bay, ON P1B 8L7, Canada.
E-mail: johnn@nipissingu.ca
Copyright © 2012 John Wiley & Sons, Ltd.
Some may question the validity of exploring the influence
of affect on children’s role in family purchase decisions via
observation methodology because emotions and moods are
internal states. However, affective states lead to behaviors
that are congruent with these states (Bower, 1981). More
pronounced affective states may be more likely to influence
the family decision-making process, and these have external
expressions that are verbally or physically observable.
Further, Saarni (1984) found that younger children did not
regulate their expressive behavior but instead clearly displayed
how and what they felt. Therefore, expressive behavior is a
suitable approach to capture the affective state of consumers
within the store environment. Expressive behavior can be
observed through “touching, smiling, gazing and frequency
talking, the nonverbal and nonlinguistic components of communication” (Montemayor and Flannery, 1989, 4). Using
expressive behavior as an approximation for affective states
ensures direct observation of behavioral phenomena. This
approach is needed and addresses a major deficit within existing affect research that relies primarily on self-reporting to
assess its impact (Mangleburg, 1990; Liefeld, 1999).
The purpose of this study is to examine the relationship of
affective states on the role that children play with their
parents on in-store purchase decisions. In particular, the
study is interested in determining whether the affective state
is related to the interaction strategy selected by the child and
to the person who makes the actual purchase decision.
Further, the unique implementation of this study using
observational methodology within the store environment
enhances the value of this contribution.
LITERATURE REVIEW
The investigation into the role of affect as a salient
antecedent to the parent–child dyadic purchase decisionmaking process requires the foundations of two separate
literature bases. Therefore, this paper provides a brief review
of these two fields, namely, affect and consumer behavior
and family purchase decision making.
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J. Nadeau and M. Bradley
Affect and consumer behavior
Affect has been described as feeling state dimensions such as
a good or bad, happy or sad, and sleepy or aroused (Watson
and Tellegen, 1985). These feelings, mood, or emotions are
important because they influence consumer behavior
(Mackie and Worth, 1989; Batra and Stayman, 1990; Cohen
and Areni, 1991). Affect has been referred to as an assumed
inclination to act (Drellich, 1983). Therefore, affect has been
explicitly linked to behavior at the definitional level.
Mood has been defined as “a subtle, low-intensity, transient
background feeling state that influences thoughts and
actions” (Poon, 2001, 358), while emotions tend to be more
intense and short-term than mood (Cohen and Areni, 1991;
Forgas, 1992). Facial expressions associated with emotion
often involve involuntary muscle movements (Dimberg
et al., 2002) and are typically fairly brief, lasting between 1
and 10 seconds (Bachorowski et al., 2001). In addition, more
pronounced facial expressions have been linked to mood
states (Ekman and Rosenberg, 2005). This is important, as
short-lived expressive behavior deviating from baseline
observations would connote an emotional reaction, while
more stable and lasting expressive behavior is associated
with mood state. This study is particularly interested in mood
states within the store context because moods are induced
in a service encounter by elements such as transaction
procedure, physical settings, and dyadic interactions (Gardner,
1985, p. 287). Since these elements are temporally adjacent to
one another in a store environment, mood states may carry on
throughout the service encounter (Gardner, 1985, p. 291). In
particular, dyad analysis in parent–child research demonstrates
that affect influenced both dyad members through their
interaction (Montemayor and Flannery, 1989). However,
research exploring mood within the parent–child dyad has
not investigated the strategies of interactions employed.
There have been multiple theories posited to explain how
mood and emotions influence purchase decisions. Dynamic
affect regulation theories (Andrade, 2005) include the mood
management theory (Zillman, 1988) and the mood maintenance hypothesis (Clark and Isen, 1982). Zillman’s (1988)
position suggests that people in a negative affective state will
strive to change that state by seeking behaviors that bring about
a positive change to their mood or feelings. For example,
research on self-gifts demonstrated that people purchased
products for therapeutic reasons when they were in a negative
affective state (Mick and Demoss, 1990; Mick et al., 1992).
Clark and Isen (1982) argue that people currently experiencing
a positive affective state will avoid behaviors whose outcomes
threaten their current affective state. Static affect evaluation
theories (Andrade, 2005) are focused on the currency of the
situation and includes the affective primacy hypothesis
(Zajonc, 1980), feelings as information (Schwarz and Clore,
1983), and mood congruency (Bower, 1981). Zajonc (1980)
suggests that affect dominates the situation above cognitive
processing and directs behavior based on the current affective
state. For example, people may purchase products when they
are in a positive affective state as a reward for an accomplishment (Mick et al., 1992). Bower (1981) argues that affect
functions as a memory unit and assists with the selection of
behavior based on associations. Further, consistent with the
Copyright © 2012 John Wiley & Sons, Ltd.
mood congruency theory, positive moods increase the
likelihood of positive behavior and vice versa (Clark and Isen,
1982). While several researchers have found affective states to
influence consumer behavior (Mackie and Worth, 1989; Batra
and Stayman, 1990; Cohen and Areni, 1991), Swinyard (1993)
found that mood did not have an immediate effect on
shopping intentions. Therefore, there are some mixed results
in the literature.
Although the conceptualization and operationalization of
affective states have been approached as a unidimensional
construct on the basis of pleasantness and unpleasantness
(Stegge et al., 1994), mood has also been conceptualized in
a multidimensional manner. For instance, Watson and
Tellegen (1985) argue that mood can be described based upon
positive–negative and arousal dimensions. The multidimensional approach to measuring mood provides a richer portrayal
of the actual affective state and is supported by several
researchers (Eagly and Chaiken, 1993; Petty et al., 1997;
Ajzen and Fishbein, 2000). This study embraces the multidimensional approach to the conceptualization of mood.
Family purchase decision making
Children do exert influence on family decisions (Kaur and
Singh, 2006); however, the strength of this influence is
often dependent upon a number of factors. These factors
generally relate to the individual characteristics of the child,
the social/family context, or the purchase situation. Among
the characteristic factors, age of the child is a salient consideration. For instance, as the age of the oldest child increased,
their influence in the decision-making process also increased
(Heslop and Ryans, 1980; Ahuja and Stinson, 1993; Beatty
and Talpade, 1994; Flurry and Burns, 2005). The significance
of age is supported by the conceptualization of consumer
socialization stages. John (1999) articulated three main stages:
perceptual (3–7 years), analytical (7–11 years), and reflective
(11–16 years). The perceptual stage is characterized by limited
purchase influence and negotiation strategies, the analytical
stage by emerging strategies, and the reflective stage by a
complete repertoire of strategies (John, 1999).
The social or familial context also represents factors that
are important to understanding family purchase decision
making. For instance, children have embraced their siblings
and formed coalitions to exert influence on the purchase
decision (Thomson et al., 2007). Alternatively, children
may also form coalitions with one parent (i.e., father and
daughters; mothers and sons) to impact the decision (Lee
and Collins, 2000). There is also support to show that children
have more purchase decision influence in single-parent
families (Ahuja et al., 1998). Other social context factors
include the sex role orientation of the family and the
occupational status of the mother (Lee and Beatty, 2002).
While Lee and Beatty (2002) utilized an observation-based
methodology, the research design was experimentally based
and did not involve affective states as possible explanatory
variables for determining purchase roles.
Situational factors can help determine how influential a
child will be in the family purchase. Children are the most
influential in family purchases when they are the primary
consumer (Atkin, 1978). However, children also play an
J. Consumer Behav. 11: 105–114 (2012)
DOI: 10.1002/cb
Observing affective states and parent–child interaction during in-store purchases
important role in family product purchases (Flurry, 2007). In
addition, children tend to have more influence with less
expensive products (Foxman et al., 1989) yet also have a
say in high involvement purchase decisions (Thomson
et al., 2007). The stage in the decision-making process also
helps to describe the influence of the child on family
decisions (Ahuja et al., 1998), with more influence exerted
during the final decision stage of the process (Beatty and
Talpade, 1994; Wang et al., 2007). However, the importance
or emphasis of children’s influence in the early stages of the
decision-making process has also been noted (Swinyard and
Sim, 1987; Ahuja and Stinson, 1993).
Many parents welcome interaction from their children
during the decision-making process, as the information and
knowledge about products that children provide can be viewed
as beneficial to purchase outcomes (Thomson et al., 2007). In
support of this assertion, O’Dougherty et al. (2006) found that
parents responded favorably to requests made by children for
food items nearly half the time, while rejecting the requests
slightly more than half the time. Parents were more likely to
yield to the requests of their children when they encouraged
them to develop their own skills and competencies as
consumers (Caruana and Vassallo, 2003). In an effort to
maintain control, parents may engage their children by setting
options or establishing the consideration set for them and then
giving the decision to children (Wilson and Wood, 2004). An
observation-based study found that purchase outcomes were
more likely when the parent and child engaged in collaborative
interaction (Darian, 1998). While Darian (1998) utilized an
observation-based methodology to explore parent–child
interaction in the purchase context, the author did not
investigate the potential role of affective states. Another
observational study found that the length of time for which
parents and children engaged each other in purchase
interactions was a good predictor of children’s influence in
the purchase (Lee and Beatty, 2005).
The selection of an interaction strategy may also determine
the influence a child has in the family purchase decision. There
are nine different direct influence attempt dimensions: “ask
nicely,” “display anger,” “bargain,” “show affection,” “beg
and plead,” “just ask,” “show anger,” “cry or pout,” and
“con” (Williams and Burns, 2000). While Williams and Burns’
(2000) categories organize how children attempt to influence
purchase decisions, they do not indicate which strategies were
most effective. However, this classification has also been
utilized in the Flurry and Burns (2005) questionnaire-based
examination of children’s role in family purchases. The results
from this study showed that both positive (e.g., ask nicely) and
negative (e.g., display anger) interactions strategies can be
effectively applied to influence purchase decisions. Further,
Palan and Wilkes (1997) demonstrated that older children
were more successful when they utilized strategies that
closely resembled adult strategies. Alternatively, adolescents
were also successful when their interaction strategy was logically positioned to counter parental responses in their persuasion attempts (Palan and Wilkes, 1997).
The usage rates of these strategies can vary depending
upon the gender of the child. Manchanda and Moore-Shay
(1996) found that boys tend to restrict their interaction
Copyright © 2012 John Wiley & Sons, Ltd.
107
strategy portfolio to asking and bargaining while girls use a
broader range of strategies. In the broader context of family
purchases, female adolescents generally seem to be more
involved with purchases than male adolescents (Moschis
and Mitchell, 1986; Pettersson et al., 2004). However,
Gentry et al.’s (2003) review of gender and family consumer
behavior cautions against narrow and superficial interpretations of gender differences, encouraging researchers
instead to apply a gendered lens steeped in a historical
understanding and an emphasis placed upon the process by
which differences may result.
Overall, previous research suggests that children do have
an influence on family purchase decisions and can enact that
influence through different interaction strategies. However,
most of these findings are based on self-report research and
do not account for the salience of prepurchase affective
states, resulting in literature gaps. This paper reports on a
study that is intended to bridge these gaps, by providing
research results that are based on direct observation of parent–child interactions within the store environment, paying
particular attention to the prepurchase affective states of the
parent–child dyad.
HYPOTHESES
Expectations about affect, parent–child interaction, and the
purchase decision are based on the theoretical foundations of
affect. The primary goal of this paper is to ascertain whether
the affective state of the child is an important factor in
determining the role that the child plays in the purchase
decision. An ancillary purpose of the study is to understand
the nature of this relationship. Therefore, the hypotheses are
presented in a systematic manner that explores the possible
intervening effects of the selected interaction strategy on the
relationship of affective state and the decision role of the child.
Figure 1 presents a graphical illustration of this process.
H1: The prepurchase affective state of the parent–child
dyad is positively related to the purchase role of the child.
H2: The prepurchase affective state of the parent–child
dyad is positively related to the type of interaction strategy
employed by the child.
Figure 1. Graphical representation of hypothesis testing.
J. Consumer Behav. 11: 105–114 (2012)
DOI: 10.1002/cb
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J. Nadeau and M. Bradley
H3: The interaction strategy employed by the child is
positively related to the purchase role of the child.
H4: The interaction strategy selected by the child moderates
the relationship between the dyad’s affective state and the
purchase role of the child.
RESEARCH METHODOLOGY
To test the aforementioned hypotheses, observational data
were collected during the winter of 2008 in three different
grocery store locations in North Bay, Canada. The city is
relatively small, with a population of approximately 54,000
people characterized as 52 per cent female (48% male),
having a median age of 40.8 years, 92 per cent speaking
English most often at home, and 97 per cent being not a
visible minority (Statistics Canada, 2006). The three store
locations were selected to represent a mix of economy and
quality-based grocery stores. In addition, the selected sampling
frame ensures that families with varied socioeconomic levels
were included in the sample. To be included in the sample, a
family was required to have at least one child aged 1 to 18
years and at least one parent. The observations were made in
a nonintrusive manner as the researchers posed as shoppers
in the store. Therefore, subjects were not followed around the
store and observations were made only in specific areas of
the store. These specific areas for observation points included
fruits and vegetables, dairy, cereal, and sweets and snacks
(O’Dougherty et al., 2006).
Observations of parents and children were recorded at four
different points in time (t1 = prepurchase affect, t2 = interaction
strategy employed, t3 = purchase outcome, and t4 = postpurchase
affect). The structure of measuring the affective state was
derived from the Watson and Tellegen (1985) two-factor
structure of affect and the Stegge et al. (1994) mood condition
scale. Therefore, the structure of affect (the degree of
pleasantness–unpleasantness and the degree of arousal–sleepy)
was assessed on a scale with values from 1 to 5. The
measurement of expressive behavior was made using both
facial expressions and body language as reflections of children
and parents’ affective state (Eyberg et al., 2005).1 Positive and
negative dimensions of expressive behavior were linked to
positive and negative affect scales, allowing researchers to
observe behavioral cues and rate their affective states (Saarni,
1984). For example, positive dimension scale items include
“broad smile with teeth showing,” “enthusiastic, thank you,”
and “smiling with eye contact” while negative dimension scale
items include “negative noise emitted,” “puckered or pursed
mouth,” and “lowered brows as in a frown or as in annoyance,
disappointment” (Saarni, 1984).
The measurement of interaction strategies was based on
the Williams and Burns (2000) methodology. This approach
offered clear descriptions to identify the type of interaction
strategy employed. For example, “asks nicely” is defined as
1
To ensure interrater reliability, the observers made observations together on
the initial day to determine consistent application of observation rules.
Following each observation session, procedural checks with the observers
were conducted, and no issues were identified.
Copyright © 2012 John Wiley & Sons, Ltd.
a polite request while “just asks” is defined as a simple
request. This approach was also used because it is a recent
categorization of interaction strategies and represents a
positive and negative valence (Flurry and Burns, 2005). To
indicate order and groupings among the Williams and
Burns (2000) set of interaction strategies, a Q-sort was
conducted with 23 senior undergraduate students to create
ordinal data and enable directional analysis. As a result of
the Q-sort, the strategies were organized into positive,
neutral, and negative strategies. Flurry and Burns (2005)
had previously found directional groupings of the
strategies as either positive or negative. However, this
approach ignores intensity differences within the dichotomous
grouping (e.g., “shows affection” and “just asks”). The
positive strategies included “asks nicely” and “shows
affection,” where children’s interaction attempts through
verbal and physical cues represent positive interaction
strategies and influence family purchasing decisions (Palan,
1998). The neutral strategies included “bargains” and “just
asks” in which children use their expert or referent power
of knowledge or information to influence purchasing
decisions (Ekstrom, 2007). The negative strategies included
“displays anger,” “begs and pleads,” and “con,” where conflict
between the child and parent is positively correlated to
the number of influence attempts without parental yielding
(Ward and Wackman, 1972).
The final purchase decision maker was determined by
identifying the person who satisfied the request through
product selection. The decision rule was developed based
on a flowchart of parent–child interaction in purchase
decisions (Wimalasiri, 2004). For instance, a parent may
initiate a purchase decision process via an invitation, yet the
child makes the final decision by placing a product in the
shopping cart. Alternatively, a child could initiate the purchase
decision yet have the parent make the decision by denying the
request. A joint decision was measured if the parent and child
both came to a consensus during the interaction over a
purchase decision. If the parent and child responded with a
positive outcome for the purchasing decision, they were seen
to be in agreement (Saunders et al., 1973).
ANALYSIS AND RESULTS DISCUSSION
Observations that noted some form of interaction between the
parent and the child where a purchase decision was made were
of interest to the study (O’Dougherty et al., 2006). Therefore,
we analyzed the 106 out of a total of 164 observations utilized
in this study that had some interaction and purchase made.2
The observations were made in three different stores. Varying
traffic levels in these stores resulted in a distribution of
observations with 75 (71%) in store A, 21 (20%) in store B,
2
In the 58 observations excluded from the analysis, the prepurchase affect
measures were more negative (compared to neutral or positive) for both
parent and child. For the parents, 40 per cent were observed as portraying
negative affect on the pleasantness dimension and 50 per cent were seen to
be in a negative arousal state. For the children, 41 per cent were observed
with negative affect on the pleasantness dimension while 54 per cent were
seen to be in a negative arousal state.
J. Consumer Behav. 11: 105–114 (2012)
DOI: 10.1002/cb
Observing affective states and parent–child interaction during in-store purchases
and 10 (9%) in store C.3 Of the useable observations, 82 (77%)
were made in the sweets and snacks category, 11 (10%)
were made in the cereal category, 10 (9%) were made in the
dairy category, and 3 (3%) were made in the fruits and
vegetables category. The few incidences of dyad interaction
in categories outside the sweet and snacks category made
sensitivity testing on this variable difficult due to small cell
sizes. Store area was included in the analysis as a dichotomous
variable defined as either the sweets and snacks category or
other category.
Fifty-seven per cent of the children in the sample were
female. Seven per cent of the observed children were less
than 2 years, 38 per cent in the 3–6-year age range, 34 per
cent in the 7–11-year age range, and 21 per cent are 12 years
and older. The gender and age of the child were estimated by
the two observers. In the case of very young children (i.e.,
under 2 years of age), the use of clothing and verbal cues
were used to determine gender. While observing the age of
children can be a challenging task, Moeller et al. (2002)
showed that the task can be achieved fairly successfully
with some training and when observers are part of a team.
More of the parents in the observed family groups were
female (73%). The final purchase decision was made most
frequently by the parent (56% of the time), followed by a
joint decision (26%) and then by the children (17%). In the
few instances where multiple children were present, the
analysis focused on the most active child (i.e., initiator) in
the purchase scenario.
The affective states were based upon the Watson and
Tellegen (1985) framework involving two dimensions (i.e.,
pleasantness and arousal). On a scale of 1 to 5, from
pleasantness to unpleasantness, the observed children in the
study were assessed a mean score of 2.38, and parents were
assessed with an average value of 2.56. A paired sample t-test
of this dimension for the child and the parent revealed that the
observed prepurchase affective states were significantly
different (t = 2.75, p = 0.007). For the arousal–sleepy
dimension, the children in the study were observed on average
as 2.19 while parents had a significantly different average
value of 2.61 (t = 6.42, p = 0.000). The prepurchase and
postpurchase affective states were not significantly different
for the dimensions of arousal (children, t = 1.781, p = 0.077;
parents, t = 0.242, p = 0.809) and pleasantness (children,
t = 1.100, p = 0.273; parents, t = 1.729, p = 0.086). As
stated in the literature review, mood tends to describe affective
states that are more enduring (Forgas, 1992; Poon, 2001) while
emotional states tend to be much shorter in duration (Cohen and
Areni, 1991; Forgas, 1992). Therefore, the consistent observation of affective states for both the parent and the child in the
pre- and postpurchase situation suggests that the expressive
behaviors were enabling the observation of underlying mood
states as opposed to recording only fleeting emotional states.
The hypothesis testing was intended to examine the
relationships among the affective state of the child, the
interaction strategy employed, and the influence they have
on the purchase decision. Table 1 presents the results of the
3
The store variable was not included in the reported model due to the low
frequency of observations made in two of the three stores.
Copyright © 2012 John Wiley & Sons, Ltd.
109
hypothesis tests using PLUM (polytomous universal model)
ordinal regression analysis. This is an appropriate analysis
technique because the main independent and dependent variables are ordinal in nature and the recorded values represent
underlying continuous variables (Torra et al., 2006). The
appropriate link function for the ordinal regression models
was determined based on comparisons of the model fit statistics. The prepurchase affect states of the parents and store
area were included to assess the potential influence on the
hypothesized relationships. Additional variables of age and
gender were included because previous research had found
these variables to be important (Ahuja and Stinson, 1993;
Pettersson et al., 2004). For this analysis, the ages of the children were grouped in the three categories guided by the main
consumer socialization stages of the perceptual stage
(3–7 years), the analytical stage (7–11 years), and the reflective
stage (11–16 years) (John, 1999). There were also gender variables for children and parents.
The results demonstrate that the four hypothesized
relationships are significant overall. In addition, tests of
parallel lines were assessed with no evidence that the parallel
line model assumption was violated (Chen and Hughes,
2004). Further, goodness of fit statistics for the four models
using Pearson and deviance statistics indicate that they are
not significantly different. Therefore, the models are generally
good fitting. The pseudo R-square (i.e., Nagelkerke’s R-square
statistic) highlights the proportion of variation explained in the
dependent variable by the independent variables (Chen and
Hughes, 2004). In the four models, the pseudo R-square attains
values of moderate to relatively strong explanatory power of
the independent variables. However, it should be noted
that the model represented by H4 achieves the highest pseudo
R-square of all four tested models.
The results for H1 suggest that one variable is significantly
related to the purchase role of the child. The child’s
affective dimension of arousal–sleepy was found to be significant (Wald = 7.87, p = 0.005), and the positive coefficient
(coefficient estimate [coeff. est.] = 0.61) demonstrates that the
relationship is positive. For example, a less aroused child is
more likely than an aroused child to make a purchase decision.
This is consistent with the mood management theory (Zillman,
1988), where a less aroused child may utilize the purchase
situation as an opportunity to become more aroused.
The second hypothesis (H2) tested the relationship
between the prepurchase affective state of the child and the
choice of interaction strategies using the Williams and Burns’
(2000) set of seven interaction strategies. The neutral interaction strategies were most frequently used in 40 per cent of
the observations, followed closely by positive (36%) and
negative strategies (25%). The field notes provided insight into
the interactions that occurred between the active child and the
parent(s). For example, a positive interaction strategy was
observed as follows: “While mom reaches for milk, the boy
reaches for chocolate milk and politely asks if he can have it.
Mom says, ‘yes, only one’.” Negative interaction strategies
were also observed. For instance, “Child stops at the chocolate
and won’t budge. Dad sees that child wants the chocolate but
says no and the child is pulled from the aisle crying. As a result,
nothing was purchased.”
J. Consumer Behav. 11: 105–114 (2012)
DOI: 10.1002/cb
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Table 1. Ordinal regression model results for hypothesis testing
Hypothesis 1: Child’s prepurchase affect–purchase role
Nagelkerke pseudo R-square
Chi-square
0.261
27.6
Independent
Estimate
Child pleasantness–unpleasantness
–0.14
Child arousal–sleepy
0.61
Parent pleasantness–unpleasantness
0.04
Parent arousal–sleepy
0.41
Child age group
0.31
Child gender
0.14
Parent gender
0.08
Store area
–0.08
Hypothesis 2: Child’s prepurchase affect–interaction strategy used
Nagelkerke pseudo R-square
Chi-square
0.242
25.5
Independent
Estimate
Child pleasantness–unpleasantness
1.13
Child arousal–sleepy
–0.99
Parent pleasantness–unpleasantness
–0.33
Parent arousal–sleepy
–0.52
Child age group
–0.18
Parent gender
0.42
Child gender
–0.56
Store area
–0.11
Hypothesis 3: Interaction strategy used–purchase role
Nagelkerke pseudo R-square
Chi-square
0.288
30.9
Independent
Estimate
Interaction strategy
1.05
Child’s age group
0.64
Parent gender
0.77
–0.01
Child gender
Store area
0.48
Hypothesis 4: Child’s prepurchase affect–interaction strategy–purchase role
Nagelkerke pseudo R-square
Chi-square
0.383
43.5
Independent
Estimate
Child pleasantness–unpleasantness
1.68
Child arousal–sleepy
0.19
Parent pleasantness–unpleasantness
–0.11
Parent arousal–sleepy
1.21
Interaction strategy
1.15
Child’s age group
0.33
Parent gender
0.40
Child gender
–0.05
Store area
0.36
Child pleasant–unpleasant interaction strategy
–0.72
Child arousal–sleepy interaction strategy
0.07
Parent pleasant–unpleasant interaction strategy
0.10
Parent arousal–sleepy interaction strategy
0.74
It was expected that a relationship would exist between
the affective state and the interaction strategy such that a
positive affective state would elicit a positive interaction
strategy (i.e., “shows affection,” “asks nicely”). This hypothesis is supported for both affective state dimensions. The
positive (coeff. est. = 1.13) and significant (Wald = 10.6,
p = 0.001) estimate for the child’s pleasantness–unpleasantness
affect dimension indicates a positive relationship with the
interaction strategy selected. In other words, a more pleasant
observed affective state is related to the use of a positive
interaction strategy. This finding is consistent with the
expectations of affect–behavior congruency (Bower, 1981).
The child’s arousal–sleepy dimension is also significantly
related to the interaction strategies used (Wald = 7.65,
Copyright © 2012 John Wiley & Sons, Ltd.
d.f.
8
Std. error
0.192
0.217
0.198
0.241
0.168
0.252
0.279
0.297
Sig.
0.001
Wald
0.51
7.87
0.03
2.85
3.41
0.32
0.08
0.08
d.f.
Sig.
1
1
1
1
1
1
1
1
0.476
0.005
0.859
0.091
0.065
0.573
0.784
0.784
d.f.
8
Std. error
0.346
0.357
0.329
0.389
0.269
0.455
0.410
0.479
Sig.
0.001
Wald
10.60
7.65
0.98
1.76
0.43
0.86
1.86
0.05
d.f.
Sig.
1
1
1
1
1
1
1
1
0.001
0.006
0.322
0.184
0.514
0.353
0.173
0.824
d.f.
5
Std. Error
0.342
0.281
0.530
0.446
0.509
Sig.
0.000
Wald
9.38
5.26
2.12
0.00
0.89
d.f.
Sig.
1
1
1
1
1
0.002
0.022
0.145
0.981
0.345
d.f.
13
Std. Error
0.745
0.663
0.757
0.738
1.299
0.191
0.346
0.309
0.338
0.361
0.340
0.380
0.427
Sig.
0.000
Wald
5.08
0.08
0.02
2.70
0.79
2.97
1.31
0.03
1.16
3.99
0.04
0.07
2.99
d.f.
Sig.
1
1
1
1
1
1
1
1
1
1
1
1
1
0.024
0.780
0.882
0.101
0.374
0.085
0.253
0.860
0.283
0.046
0.834
0.789
0.084
p = 0.006). However, the negative estimate (coeff. est. =
0.99) indicates that aroused children are more likely to
engage in negative interaction strategies. While there is support
for the overall relationship between the prepurchase affective
state of the child and the interaction strategy used, the direction
of the relationship depends upon the affective dimension.
The third hypothesis (H3) examines the relationship
between the interaction strategy employed and the decision
maker of the purchase. This hypothesis involves the evaluation
of two ordinal variables: the interaction strategy used (i.e.,
positive and negative) and the final purchase decision maker
(i.e., parent, joint, child). The significance of the interaction
strategies coeff. est. is evidence of its importance to the maker
of the final decision (Wald = 9.38, p = 0.002). The negative
J. Consumer Behav. 11: 105–114 (2012)
DOI: 10.1002/cb
Observing affective states and parent–child interaction during in-store purchases
value of the coefficient (coeff. est. = 1.05) indicates that the
relationship is negative. In other words, negative strategies
are more likely to result in the parent making the final decision
whereas positive strategies are associated with the child
making the final purchase decision. The observers’ notes
helped illustrate this relationship. For example, “Child wants
apple sauce, Mom says that ‘we already have some at home,’
and the child continues to ask. As a result, nothing was
purchased (parent making the final decision not to purchase
anything).” In addition, the age of the child was found to be
significant (Wald = 5.26, p = 0.022) with a positive coefficient
(coeff. est. = 0.64). This is interpreted as older children having
a larger influence on the purchase decision; this is consistent
with previously reported findings in the literature (Ahuja and
Stinson, 1993).
The analysis of the first three hypotheses established
evidence to show a pattern of relationships. A final piece of
analysis is required to assess whether the use of interaction
strategies intervenes on the relationship between the
children’s prepurchase affective state and the role they play
in the final purchase decision. This is the purpose of the
fourth hypothesis (H4), and there is support for this model.
The pseudo R-square (i.e., Nagelkerke’s R-square statistic)
is the highest of all the models tested at 0.383, illustrating a
relatively strong level of explanatory power.
The role of the interaction strategy as an intervening
variable is important, and there are three key findings that stem
from this function. First, the results show that the child’s
pleasantness–unpleasantness affect dimension is significantly
related to the role that children have in the purchase
decision (Wald = 5.08, p = 0.024). The positive coefficient
(coeff. est. = 1.68) indicates that a less positive prepurchase
affective state is associated with a more active role in the
purchase decision for the child. This result reflects the mood
management theory as articulated by Zillman (1988), where
people in a negative mood state seek behaviors to bring about
a positive affective change.
Second, there is also an interaction effect of the child’s
pleasantness–unpleasantness affective state with the interaction strategy used (Wald = 3.99, p = 0.046) on determining
the final decision maker in the purchase scenario. This is an
important finding because the testing of H1 showed that the
pleasantness–unpleasantness affect dimension was not
significantly related to the purchase role. However, the
fourth hypothesis (H4) test’s results demonstrate that the
pleasantness–unpleasantness dimension is salient but must
be considered within the context of the interaction strategies
employed. The negative coefficient (coeff. est. = 0.72)
suggests that a more pleasant prepurchase affective state for
the child and a positive interaction strategy is related to the
child having a larger role in the purchase decision. Conversely,
less pleasant states of affect for children and more negative
interaction strategies are associated with the parent being
more likely to be the final purchase decision maker.
Third, relationships that were significant in previous
hypothesis testing are not significant when the interaction
strategy is explored as an intervening variable. The two main
effects of a child’s prepurchase arousal (Wald = .08,
p = 0.780) and the age of the child (Wald = 2.97, p = 0.085)
Copyright © 2012 John Wiley & Sons, Ltd.
111
are no longer significant. The selected interaction strategy
mediates the relationships of these two variables with the
dependent variable purchase role. These results contribute
to the literature by revealing a difference in the effectiveness
of the different child interaction strategies on the purchase
influence where no clear distinction was previously made
(Williams and Burns, 2000; Flurry and Burns, 2005).
Further, the disappearance of the main effect of the child’s
age is in contrast with previous findings where older children
played a more important role in the purchase decision (Ahuja
and Stinson, 1993).
Overall, the results demonstrate that the prepurchase
affective state of the child is an important factor in their
decision-making role. However, this influence is not direct,
as the child’s selection of an interaction strategy contributes
to the determination of the decision maker. In addition, of
particular note is the lack significance for the store area
effects (i.e., sweets and snacks versus other areas of the store)
in all four testing scenarios examining prepurchase affect in
the parent–child dyad purchase decision.
CONCLUSIONS
This study sought to enrich our understanding of the role of
prepurchase affective states of children with their influence in
family purchase decisions. The results of the study provide
confirmatory evidence to support an affect theory. For
instance, the mood congruency theory suggests that a positive
mood would lead to the selection of a behavior that has positive
associations (Bower, 1981). The results in this study provide
support, as a positive interaction strategy was utilized by
children when they were expressing more positive affect cues.
Using expressive behaviors as a method of approximation for
mood could have led to the exclusion of some children with
unexpressed or subtly expressed moods. However, the
research design was made as inclusive as possible and recorded
weakly exhibited behavior as more neutral. Therefore, children
shopping with their parent(s) were included in the study as
long as there was some interaction that led to a purchase
decision between the parent and the child. The findings of this
study are important because the observation methodology
provides a unique and rich perspective to the topic of a child’s
affect and its role in family purchases. These findings can be
used in combination with self-reported results to provide
triangulated evidence for an affect theory, particularly within
a parent–child dyad.
The examination of this relationship within the store
context provides insightful results that holistically reflect all
stages of decision making. The results of observations from
the sweets and snacks area of the grocery store are beneficial
because these purchases are more likely to be unplanned
decisions. While there was no significant store area effect
in this study, there is a greater chance in other areas (e.g.,
dairy, produce) that decisions have been made prior to arrival
at the store, thereby inhibiting the opportunity and ability to
observe a family decision process. Therefore, this study
presents robust findings about the parent–child dyad based
J. Consumer Behav. 11: 105–114 (2012)
DOI: 10.1002/cb
112
J. Nadeau and M. Bradley
upon observations from the beginning of the decisionmaking process until the decision is made.
A key finding from this study is the evidence that an
indirect relationship exists between children’s affective state
and the role that they play in the family purchase decision.
This relationship is moderated by interaction strategies.
Therefore, the role that children have in the purchase
scenario is not dictated by their affective state alone. If that
were the case, the findings from direct hypotheses testing
would suggest that the level of arousal would be the sole
salient affective dimension. However, the results of testing
interaction strategies as the intervening variable demonstrate
that the pleasantness affect dimension is the important one.
In other words, a pleasant affective state is associated with
a positive interaction strategy and a larger role for the child
in the purchase decision. Therefore, interaction strategies
can have a differential effect on purchase influence when
considering the child’s affective state, thus enriching the
literature that has not differentiated their effectiveness
(Williams and Burns, 2000; Flurry and Burns, 2005). Further,
the direct effect of the arousal–sleepy affect dimension is not
significant when the intervening relationship is considered.
The results also provide a basis for differences with
previous research. Specifically, the results of the holistic test
found that age was not a significant determinant of children’s
purchase role in contrast with previous findings (Heslop and
Ryans, 1980; Ahuja and Stinson, 1993; Beatty and Talpade,
1994; Flurry and Burns, 2005). However, in support of the
literature, there was some evidence in this study to show that
age had a positive relationship with the purchase role. This
may be because older children have more experience and
have learned which strategies are more effective for them.
There was also no change in the affective states of either
dyad member following their interaction. This finding is
contrary to the Montemayor and Flannery (1989) study,
which found that affective states influenced both dyad
members through various forms of interaction (e.g., touching,
smiling, talking). In addition, while some researchers found
support for gender as a determinant of the purchase role
for children (Moschis and Mitchell, 1986; Pettersson et al.,
2004), there is no evidence in this study to show that gender
is a salient consideration. It should be noted that this study
did not explore the strategies used by parents to interact with
their children in a purchase scenario. As Gentry et al. (2003)
suggest, an understanding of gender should be through a
gendered lens and an understanding of the process involved
should also be considered, not only the outcomes. Perhaps, in
the case of parent–child dyad within the store environment,
there are gender-based approaches to interaction that are
different but lead to the same purchase role outcome.
Although this paper reports on the results from a single
study, practitioners should heed the confirmatory findings
that the pleasantness–unpleasantness dimension of child
affect is a salient factor in purchase roles. Retail stores
targeting families may wish to encourage pleasant moods in
children by considering their tastes and interests when
designing the shopping space or selecting other elements of
atmospherics (e.g., music, lighting, colors). Marketers may
also wish to include positive interaction strategies in their
Copyright © 2012 John Wiley & Sons, Ltd.
advertisements targeted at children to model effective
methods of persuasion that can increase children’s roles in
family purchases.
Future research is needed that tests the relationship
between mood and interaction strategies using different
methods to determine more conclusively the influence that
children have on their parents’ decision-making process.
While this study has helped confirm or contrast results
from previous self-reporting–based research, perhaps a
multimethod approach could be employed to examine this
specific relationship. In addition, the results of this study
are based on observations made in particular areas of grocery
stores in a single city. Additional studies should explore
these relationships in the context of other environments.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the assistance of
Corey Swiergosz, Adam Clark, Mark Carswell, and Matt
Ryan with the pilot study and the help of Lydia Weiskopf
with the main data collection.
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