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If-then plans help regulate
automatic peer influence on
impulse buying
J. Lukas Thürmer,Maik Bieleke,Frank Wieber and
Peter M. Gollwitzer
(Author affiliations can be found at the end of the article)
Abstract
Purpose –This study aims to take a dual-process perspective and argues that peer influence on increasing
impulse buying may also operate automatically. If-then plans, which can automate action control, may, thus, help
regulate peer influence. This research extends existing literature explicating the deliberate influence of social norms.
Design/methodology/approach –Study 1 (N= 120) obtained causal evidence that forming an
implementation intention (i.e. an if-then plan designed to automate action control) reduces peer impact on impulse
buying in a laboratory experiment with young adults (students) selecting food items. Study 2 (N= 686) obtained
correlational evidence for the role of norms, automaticity and implementation intentions in impulse buying using
a large sample of high-school adolescents working on a vignette about clothes-shopping.
Findings –If-then plans reduced impulse purchases in the laboratory (Study 1). Both reported deliberation
on peer norms and the reported automaticity of shopping with peers predicted impulse buying but an
implementation intention to be thriftily reduced these links (Study 2).
Research limitations/implications –This research highlights the role of automatic social processes in
problematic consumer behaviour. Promising field studies and neuropsychological experiments are discussed.
Practical implications –Young consumers can gain control over automatic peer influence by using if-
then plans, thereby reducing impulse buying.
Originality/value –This research helps understand new precursors of impulse buying in understudied
European samples of young consumers.
Keywords Young consumers, Peer influence, Impulse buying, Implementation intentions,
Automaticity, Reflective-impulsive model
Paper type Research paper
© J. Lukas Thürmer, Maik Bieleke, Frank Wieber and Peter M. Gollwitzer. Published by Emerald
Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence.
Anyone may reproduce, distribute, translate and create derivative works of this article (for both
commercial & non-commercial purposes), subject to full attribution to the original publication and
authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
The authors thank the members of the Social Psychology and Motivation Lab at the University of
Konstanz and the members of the Motivation Lab at New York University for their helpful comments
on earlier drafts. This paper is partially based on the first author’s dissertation. Maik Bieleke and
Peter M. Gollwitzer gratefully acknowledge financial support from the German Research Foundation
(DFG) through the Research Unit “Psychoeconomics”(for 1882). The authors declare that they have
no conflict of interest. We thank Angela Whale for her help with English language editing.
This project has received funding from the European Union’s Horizon 2020 research and
innovation programme under the Marie Sklodowska-Curie grant agreement No 703042 and the
Young Scholar Fund at the University of Konstanz.
Peer influence
on impulse
buying
Received 22 May 2018
Revised 15 March 2019
9 October 2019
30 March 2020
Accepted 6 May 2020
European Journal of Marketing
Emerald Publishing Limited
0309-0566
DOI 10.1108/EJM-05-2018-0341
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0309-0566.htm
1. Introduction
Impulse buying (i.e. ad-hoc purchases at the point of sale; Rook (1987),Stern (1962); reviews
by Kalla and Arora (2011),Sharma et al. (2010) and Verplanken and Sato (2011) accounts for
much of everyday consumption and this holds especially true for young people. According
to a recent report (Thredup, 2019), US shoppers 18–24 years of agemake almost half of their
purchases (49%) on impulse with similar numbers observed in the UK (KPMG, 2016;Attest,
2018). At the same time, consumers accumulate worrisome amounts of debt at this young
age (R3, 2019). For marketing research and consumers alike, it is, therefore, key to
understand the drivers of impulse purchases among young consumers and to develop
effective means to regulate impulse buying.
Adolescents are particularly sensitive to social cues (Foulkes and Blakemore, 2016) and
are prone to avoid social risks (Blakemore, 2018), making them potentially vulnerable to the
problematic influence of peers on impulse buying behaviour. In line with this reasoning, the
presence of others of the same age and status (peers) increases impulse buying among
young consumers (Luo, 2005;Rook and Fisher, 1995). Existing accounts of this phenomenon
focus on social norms as follows: presumably, peers have favourable impulse buying norms
that encourage giving in to temptations. Accordingly, shoppers exhibit increased levels of
impulse buying behaviour in the presence of their peers (Luo, 2005). In other words, past
research has assumed that giving in to peer influence is a deliberate decision.
The present research builds on and extends this literature by taking a dual-process
perspective (Strack et al., 2006). This research proposes that the presence of peers may not
only affect impulse decisions by triggering deliberations based on norms but also via
triggering automatic processes. The presence of peers may become strongly associated with
impulse buying over time, and thus, manage to automatically trigger impulse buying. An
implication of this assumption is that forming a strong goal to be thrifty might not be
sufficient for blocking the automatic impact of peers on impulse buying. Young consumers,
thus, need additional self-regulation strategies to help them curb the automatic influence of
peers on impulse buying. One self-regulatory tool that qualifies for this purpose is forming
if-then plans (implementation intentions; Gollwitzer,2014, 1999;Thürmer et al., 2015a;
Wieber et al., 2015b), as such plans have been shown to automate the execution of goal-
directed responses (i.e. being thrifty) that can then outrun unwanted normative responses
(i.e. impulse buying). Accordingly, the present paper explores whether if-then plans in the
service of the set goal of being thrifty can indeed curb peer influence on impulse buying.
The current paper proceeds as follows: firstly, the literature on impulse buying is
reviewed, with a focus on the features of the phenomenon and the development of the
respective field of research. Secondly, the paper turns to peer influence on impulse buying
by focussing on existing evidence before developing the argument that it might also be
based on automatic processes. Thirdly, an effective means to control automatic peer
influences are introduced: prospectively planning out the pursuit of one’s set goals with
implementation intentions, a self-regulatory tool that is capable of blocking unwanted
automatic influences from affecting one’s goal striving.
2. Literature review
2.1 Impulse buying: the phenomenon and established processes
Impulse buying has been a topic of growing interest for more than 50 years now (reviews
and metaanalysis by Amos et al., 2014;Muruganantham and Bhakat, 2013;Kalla and Arora,
2011). Early approaches, taking a marketing perspective, defined impulse buying as any
unplanned purchase and accordingly focussed on specific product categories or product
features (Stern, 1962) that may increase the likelihood of impulse buying. Research then
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assumed the perspective of the consumer and investigated personal characteristics related
to impulse buying, including demographics (Kollat and Willett, 1967) and personality (Rook,
1987). Afterwards research also incorporated subjective experiences into models of impulse
buying (e.g. emotions; Weinberg and Gottwald, 1982), turning the field’s attention to the
personal experiences and emotions underlying impulse buying. Accordingly, Rook (1987,
p. 191) suggested that “impulse buying occurs when a consumer experiences a sudden, often
powerful and persistent urge to buy something immediately”(see also Piron, 1991). More
recent research lends empirical support to this reasoning (Beatty and Ferrell, 1998). For
instance, in experiments using eye-tracking, impulsive buyers were found to be highly
distracted (Büttner et al.,2014) and aroused by tempting products (Serfas et al., 2014).
Based on the deeper understanding of such personal experiences, more and more
research began to investigate the role of action control in impulse buying. For instance,
Wood (1998) used a delay-of-gratification framework to analyse the relation between
education and impulse buying, and Hoch and Loewenstein (1991) observed that impulse
buying may lead to a loss of self-control, resulting in even more impulse buying. The latest
research in this area has gone one step further by analysing the specific self-control
processes involved in impulse buying (Verplanken and Sato, 2011). For instance,
neuroscientific evidence suggests that attractive products may activate brain areas related
to impulsive decisions, which are difficult to control wilfully (Hubert et al.,2013).
Recent research moreover has identified social influence as a key driver of impulse
buying (Chuang et al.,2015). During adolescence, peers (i.e. others of the same age and
status) become an important source of social influence (Erdley et al., 2001) and it is, thus, not
surprising that peers also impact consumer decisions (Childers and Rao, 1992;Chaplin and
John, 2010), including whether to impulse buy or not (Mangleburg et al.,2004;Luo, 2005).
Especially young consumers who fear social rejection, and thus, avoid social risks
(Blakemore, 2018) are prone to impulse buying (Lin and Chen, 2012), pointing to the social
acceptance of impulse buying in this segment of consumers (Luo, 2005). In fact, among
adolescents, the presence of peers seems to increase the perceived value of a reward (Foulkes
and Blakemore, 2016). Accordingly, recent research shows that peerinfluence may even lead
to chronically enhanced levels of impulse buying (Baker et al., 2016). Even though most of
this research was conducted in the USA with adult populations, some correlational evidence
suggests that these findings may be extended to adolescent and/or European consumers
(Lins et al.,2015;Muratore, 2016). In sum, past research suggests that peers may increase
impulse buying, especially among young adults and adolescents.
Regarding the processes underlying the immediate peer influence on purchase decisions,
past research has focussed on the impact of peer norms. From this perspective, peers
increase impulse buying (Mangleburg et al.,2004;Luo, 2005) because of the activation of a
peer-norm to indulge (Luo, 2005). Peers may, therefore, signal to a consumer that it is
acceptable and desirable to impulse buy in the respective situation. Reflecting on this norm
then leads consumers to set corresponding goals (Fishbein and Ajzen, 1975) that they pursue
(Carver and Scheier, 1998;Thürmer et al.,2019). For instance, the consumer may form the
goal intention to follow his impulses and consequently will be willing to give in to tempting
products in a shop.
2.2 The automatic influence of peers on impulse buying
The present research suggests that more automatic processes may also be involved in the
peer impact on impulse buying. Consumer research has highlighted that the sudden urge to
buy a product is a key process involved in impulse buying (Beatty and Ferrell, 1998;Rook,
1987;Piron, 1991) and it seems unlikely that such “hot”emotional processes are fully under
Peer influence
on impulse
buying
the control of deliberate goals and norms. In line with this claim, Strack and colleagues
argue that consumer behaviour, in general, has reflective and automatic determinants
(Strack et al., 2006; see also MacInnis and Patrick, 2006;Verplanken and Sato, 2011).
From this perspective, impulse buying may involve a deliberate process of giving in to
welcome temptations but it may also occur when a consumer has the intention not to
impulse buy (e.g. he/she wants to be thrifty).
The reflective-impulsive-model (RIM; Strack and Deutsch, 2004;Hofmann et al., 2009)
specifies how deliberate and automatic influences on behaviour interact and, therefore, is
particularly useful for deriving hypotheses about the peer influence on impulse buying. The
model assumes that two systems concurrently affect behaviour, an impulsive and a reflective
one. The impulsive system rests on spreading activation in an associative network, which is
quick and efficient but rather inflexible. In contrast, the reflective system relies on deliberate
intentions, which is slow and resource-dependent but permits flexible behavioural control.
These systems may be misaligned (e.g. a deliberate goal to be thrifty vs a sudden urge to
impulse buy), thereby leading to self-control conflicts (Hofmann et al., 2009). Because the
impulsive system is swift and does not require many resources, it may “outrun”the reflective
system in such conflict situations, leading to behaviour that runs counter to the goals of the
reflective system. Effective self-control is, thus, required to prevent the fast and frugal
impulsive system from overriding the reflective impact of deliberate goals. According to this
self-control logic, peers may also lead a consumer to impulse buy in a more automatic fashion
despite the best intentions to be thrifty: when peers are associated with impulse buying, they
may activate the respective behaviour instantaneously, without requiring further deliberation.
Because this impulsive process is fast and not necessarily conscious, it may outrun reflective
processes based on the intention to be thrifty.
Recent consumer and motivation research supports the reasoning that social influences on
impulse buying may also operate automatically, countering set goals. Serfas et al. (2016) asked
participants to perform a simulatedshoppingtaskinasocialcontext (i.e. decide whether products
on a computer screen were on their shopping list for a dinner with friends). Eye-tracking data
showed that tempting products attracted participants’attention, even when participants had the
goal to focus on necessities only. Wieber et al. (2014, Study 2) moreover found that setting the goal
to be thrifty was ineffective with respect to declining an invitation by the experimenter to
purchase candy when the experimenter had established a social connection by imitating the
participants (mimicry). Participants initially did not know about the opportunity to purchase
tempting candy at the end of the experiment, thus creating an opportunity for impulse buying.
More generally related to the control of automatic social influence, Gollwitzer et al. (2011, Study 2)
found that goals were insufficient to control unwanted behaviour (i.e. following an inappropriate
request for help) primed outside of people’s awareness. Importantly, all of these studies measured
participants’goal commitment to testing whether the undetected social influence led to changing
their goals. None of these studies found such an effect, suggesting that goal intentions are not
sufficient to regulate implicit social influences. Finally, adolescents and even young adults may
face unique self-regulation challenges in the context of their peers (Oettingen and Gollwitzer,
2015), potentially further increasing impulsiveness in this population. The present research,
therefore, suggests that, in addition to the deliberate normative influence of peers via the
reflective route, peers may also promote impulse buying in an automatic fashion via the
impulsive route and without requiring deliberation:
H1. Peers increase impulse buying even when young consumers have the goal to be
thrifty.
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2.3 Gaining control of automatic peer influence on impulse buying
The central question for young consumers then is how they can gain control over this
automatic peer influence. One approach may be to change the context (e.g. avoid peers
altogether; Duckworth et al., 2016). However, given the central role that peers play in
adolescents’lives (Smetana et al.,2006), this is neither desirable nor feasible. Alternatively,
consumers may use their reflective system to lessen the peer impact. A first step may be
setting a goal (e.g. “I want to be thrifty!”and then exert sufficient self-control in the face of
temptation. However, merely setting goals has a moderate impact on behaviour (Sheeran
and Webb, 2016) that may not suffice to down-regulate strong consumer impulses (Hofmann
et al., 2009,2008). In particular, impulsive determinants of behaviour are likely to outrun
reflective determinants when cognitive capacity is limited, the brain area used for cognitive
control is not very active (i.e. the lateral prefrontal cortex) or when one is under time
pressure (Hofmann et al.,2007;Friese et al.,2008,2006,2016). All of these conditions may
be present when shopping with peers and when tempting products are available and mere
goals, therefore, should be insufficient to deal with the peer influence on impulse buying.
To increase the impact of goals, in a second step, a consumer may prospectively specify
concrete actions in their goals (e.g. take only what one really needs). This should reduce the
load of translating thegoal (e.g. to be thrifty)into concrete actions and, therefore, help break
the impulsive impact of peers. However, the reflective system still needs to initiate these
behaviours in critical situations (e.g. when facing an attractive product) and the impulsive
system may, therefore, still “outrun”this reflective effort (Hofmann et al.,2009).
Therefore, in a third step, a consumer would ideally recruit the impulsive system to
support the deliberative system. This alignment of both systems would then allow
delegating the initiation of the concrete, goal-directed action to the “fast and frugal”
impulsive system and thereby ensure the execution of the goal-directed action in the right
moment (Martiny–Huenger et al., 2016,2011). This is exactly what furnishing one’s goals
with if-then plans (implementation intentions; Gollwitzer, 1993,1999,2014) does. An
implementation intention spells out when, where and how to act on a set goal. Forinstance, a
consumer with the goal to be thrifty may add the implementation intention: “and when I am
in the supermarket, then I will only take items that are on my shopping list”. Implementation
intentions promote goal attainment (Gollwitzer and Sheeran, 2006), such as arriving at good
decisions (Henderson et al.,2007;Thürmer et al., 2015b;Wieber et al., 2015a), especially when
phrased as an if-then conditional (i.e. “if I encounter situation S, then I will show the goal-
directed response R”;Chapman et al.,2009). The processes underlying the strong effects of
these simple if-then plans include making the situation in the if-part (e.g. feeling the urge to
buy something) easy to detect and creating a strong associative link between this situation
and a goal-directed response in the then-part (e.g. take only items on the shopping list)
(Webb and Sheeran, 2007;Wieber and Sassenberg, 2006;Martiny–Huenger et al.,2017). This
associative if-then link helps initiate the goal-directed response within milliseconds once the
specified situation arises, even in critical populations (Brandstätter et al.,2001;Webb et al.,
2010, review and metaanalysis by Toli et al.,2016;Wieber et al., 2015b;Kersten et al.,2015).
An if-then plan, thus, delegates action control to a cue in the environment and thereby
strategically uses the impulsive system to reach set goals.
The beneficial effects of if-then planning have been demonstrated in a large range of
populations and contexts (Gollwitzer and Sheeran, 2006) such as reducing fat consumption
(Vilà et al., 2017) and increasing exercise (Bélanger–Gravel et al.,2013). Pertinent for the
present research, beneficial effects of implementation intentions have been observed in
consumer contexts, such as increasing fruit consumption (Knäuper et al., 2011), reducing
meat consumption (Rees et al.,2018), choosing more sustainable modes of travel (Bamberg,
Peer influence
on impulse
buying
2000), recycling more (Holland et al.,2006) or switching to an organic supermarket for
grocery shopping (Bamberg, 2002). Implementation intention effects have also been
observed among young adults, even when they were suffering from attention deficit and
hyperactivity syndrome (ADHD), which is associated with increased impulsivity (Gawrilow
et al.,2011;Gawrilow and Gollwitzer, 2008;Paul et al.,2007;Gawrilow et al.,2013). Research,
thus, suggests that implementation intentions may be an effective means for young adults to
regulate automatic peer impact on impulse buying.
The impulse buying and priming studies discussed above (Wieber et al.,2014, Study 2;
Gollwitzer et al., 2011, Study 2; Serfas et al., 2016) are in line with this claim: forming
additional implementation intentions helped participants curb unwanted social influences
successfully. This paper, therefore, hypothesizes that implementation intentions help young
consumers gain control over automatic peer influence on impulse buying:
H2. If- then plans reduce impulse purchases related to automatic peer influence when a
goal to be thrifty is present.
2.4 The current research
This research investigates how peers promote impulse buying and whether if-then plans
(implementation intentions) help curb this social influence. In Study 1, the role of implementation
intentions and the presence of peers in impulse buying was established experimentally among
young adults. To this end, the salience of either a peer group with strong impulse buying norms
or a peer group with weaker respective norms was increased. Participants moreover formed one
of three plans that included only the if-then format, only a helpful strategy or a helpful strategy in
an if-then format (implementation intention). Participants then worked on an impulse buying
supermarket task. Drawing on the reasoning that peer influence on impulse buying is automatic,
it was predicted that wording the helpful strategy in an if-then format (which automates
responding) would help reduce impulse buying further.
Study 2 sought to establish predictors of peer influence on impulse buying in
adolescents, a particularly vulnerable consumer group. To this end, a large sample of high
school-students was confronted with a hypothetical impulse buying situation; they then
reported their peer norms and how automatically they shop with peers. Moreover, it was
again examined whether implementation intentions to take only what one really needs
would moderate the impact of norms and automaticity on impulse buying. It was expected
that norms, as well as automaticity, would predict increased impulse buying and that
implementation intentions would curb this social influence.
3. Study 1: the causal role of automatic peer influence and implementation
intentions on impulse buying
The aims of Study 1 were twofold as follows: firstly, Study 1 sought to establish peer
influence experimentally. Two pre-tests with students were conducted to identify peer
groups that have weaker vs stronger indulgence norms. After making one of these identities
salient, participants worked on a simulated grocery shopping task providing a familiar
impulse buying situation (OnePoll, 2018). It was expected that the peer group with stronger
indulgence norms would lead to more impulse buying.
The second aim of Study 1 was to establish the causal role of automatic peer influence
and implementation intentions on impulse buying experimentally. The reasoning was as
follows: Research shows that implementation intentions are particularly effective when
using the if-then format (Chapman et al., 2009) because this particular format creates a
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strong associative situation-response link that leads to automated responding once the
specified situation is encountered (Webb and Sheeran, 2007). If peer influence on impulse
buying is based on automatic processes, as the current paper suggests, then it should best be
controlled by a self-regulation strategy that is also based on fast automatic processes (i.e. a
plan in the if-then format) and deliberate control by goals should be less effective. Study 1,
therefore, systematically manipulated the format (if-then conditional vs control) and the
content (helpful vs unrelated strategy) of the suggested strategy (plan) across three
conditions (Table 1). It was expected that, in comparison to a useless strategy, specifying a
helpful strategy should decrease impulse buying and that using an if-then format for the
helpful strategy should automate action control, and thus, further decrease impulse buying.
3.1 Method
3.1.1 Participants and design. The experiment and the pretests were conducted at a university
in southern Germany. Participants were randomly assigned to one of six conditions in a 2 (peer
influence: strong vs weak) 3 (implementation intention: if-then self-regulation strategy vs
self-regulation strategy without if-then conditional vs if-then control without appropriate self-
regulation strategy) between-subjects design. A power analysis using G*Power (Faul et al.,
2007) setting 1
b
= .80 and assuming an implementation intention effect of d=0.65
(Gollwitzer and Sheeran, 2006) yielded a minimum sample size of 95. In total, 124 university
students (34 female) with a mean age of 22.03 years (SD = 4.02) participated in return for e5or
course credit. Three participants did not complete the experiment and one indicated that she
did not take the experiment seriously, leaving N= 120 participants (32 female) for statistical
analyses. Exclusions were evenly distributed across conditions. (one peer from home/strategy
control, two peers from home/if-then control and one college peers/implementation intention).
3.1.2 Pretesting. The first author conducted a focus group discussion with 15second-
year university students. Students agreed that their peers from home and their college peers
are important to them, but that the former enjoy having a good time and to indulge (i.e. have
an indulgence norm) and the latter is becoming somewhat more aware of their growing
responsibilities and of the fact that they have to make ends meet (i.e. have a norm to be
thrifty). The first author and the students developed vignettes containing typical activities
and situations to make each group’s norm salient, as well as a shopping task that was
closely related to day-to-day purchases in this population. The vignettes and the task were
tested in a second, quantitative pilot study (N= 103 students; 68 female; mean age =
22.10 years, SD = 3.21). Participants across conditions equally made intended purchases
(home: M= 3.16, SD = 1.08, college: M= 3.02, SD = 1.02), F(1, 101) = 0.47, p= .50. However,
in line with the assumption that peers from home have a stronger indulgence norm than
Table 1.
Systematic variation
of the wording of the
self-regulation
strategy (plan) in
Study 1
Strategy content
Condition and strategy wording If-then format Useful strategy
Implementation intention:
“whenever we want to put something in our shopping cart,
then we will take only what we really need”
Strategy-control:
“we will only put things in our shopping cart
that we really need”
–
If-then-control:
“whenever we want something that we really need,
then we will put it in our shopping cart”
–
Peer influence
on impulse
buying
college peers, participants in the peers from home condition bought (non-significantly) more
items impulsively, M= 5.96, SD = 2.99, than participants in the college peers condition, M=
4.92, SD = 2.46, F(1, 101) = 3.71, p= .06, part
h
2
= 0.04.
3.1.3 Procedure. After giving informed consent, all participants learned that the study
consisted of a first task exploring participants’imagery skills and a second, independent
task on consumer behaviour. The two parts were presented as independent to ensure that
the respective group context and not the specific peer situation described (see below) caused
the observed effects.
In the first part, the peer influence was manipulated: participants either read a text
describing typical coming-home activities (e.g. meeting at a friend’s house to catch up) to
make their peers from home salient or they read a text describing typical student activities
(e.g. meeting at a fellow student’s house to study and discuss exams) to make their college
peers salient. All participants summarized the text, responded to three manipulation-check
items (e.g. “it was easy to imagine being the person described in the text”,1:not at all –5:
very much, Cronbach’s
a
= .77) and were informed that they had completed Part 1.
Part 2 was introduced as a study on consumer behaviour. Participants learned that their
task was to shop for one meal of spaghetti with tomato sauce for their peers from either home
or college. Participants then received a “training”sheet: participants all formed the goal we
want to be thrifty with our money and additionally specified a self-regulation strategy. The
wording was varied systematically (Table 1): in the implementation intention condition, an
appropriate strategy was phrased in an if-then format that is known to automate the execution
of the planned behaviour: whenever we want to put something in our shopping cart, then we will
take only what we really need. In the strategy-control condition, this helpful strategy was not in
an if-then format: we will only put things in our shopping cart that we really need.Finally,inthe
if-then control condition, the strategy was not geared towards reducing unintended purchases
but kept all of the relevant words of the implementation intention (e.g. shopping cart, really
need) using an if-then format: whenever we want something that we really need, then we will put
it in our shopping cart. Although this strategy is semantically very similar to that specified in
the implementation intention, it only supports buying what one would buy anyway (i.e.
intended purchases) and, thus, should not help reduce impulse purchases. All plans referred to
a group to increase the salience of the social context (i.e. shopping for peers from home vs
college peers). Participants read, envisioned and wrote down the respective plan. Participants
were then seated in front of a printed picture of a shopping cart with cards of a wide range of
food items placed behind it. The 34 available items represented the range of products available
in the local supermarket, including fresh produce, canned foods, snack foods and beverages.
Importantly, eight of these products could be usedtopreparepastawithtomatosauce(e.g.
pasta, tomatoes) while the remaining 26 items (e.g. apples, chocolate and rice) were unrelated to
this dish. The experimenter always arranged the items in the same order, which was modelled
on a near-by supermarket. Participants learned that they could buy items by putting them in
their cart and that they could buy as many items as they wished. Finally, participants indicated
their goal commitment (five items, e.g. “I want to achieve my goal”,
a
= .54) [1] and plan
commitment (three items, e.g. “Iwanttofulfilmyplan”,
a
=.81)onfive-point scales (1: notatall
to 5: very much), provided demographic information and indicated when they had had their last
meal; they then received an extensive debriefing.
The experimenter unobtrusively noted the shopping items placed into the cart. The
number of items in the shopping cart that was unrelated to spaghetti and tomato sauce
served as the dependent measure; the number of spaghetti-and-sauce items was checked to
ensure that participants across conditions performed that task equally well.
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3.2 Results and discussion
3.2.1 Manipulation check and preliminary analyses. Participants indicated that the group
scenarios were realistic, M= 4.13, SD = 0.77 and were sufficiently committed to their goals,
M= 4.11, SD = 0.52 and strategies, M= 3.91, SD = 0.69. Participants across conditions
bought an equal amount of ingredients for spaghetti and tomato sauce, M= 2.78, SD = 1.09,
Fs<1.30, ps>.28 [2].
3.2.2 Main analysis. To test the main hypotheses that peers increase impulse buying
counter to set goals (H1) and that if-then plans reduce this influence (H2), the number of
items unrelated to spaghetti and tomato sauce were entered in a between-subjects ANOVA
with peer influence (shopping for friends from home vs shopping for fellow students) and
implementation intention (if-then strategy vs strategy control vs if-then control) as
predictors. In support of H1, the expected main effect of peer influence emerged, F(1, 114) =
4.06, p= .05, part.
h
2
= 0.03: participants shopping for their friends from homemade more
impulse purchases, M= 3.19, SD = 2.31, than participants shopping for their fellow
students, M= 2.38, SD = 1.78 (Figure 1).
Moreover, in support of H2, the expected main effect of implementation intention
emerged, F(2, 114) = 3.40, p= .04, part.
h
2
= 0.06: in comparison to an if-then control plan,
M= 3.39, SD = 2.35, a strategy control plan led to less impulse buying, M= 2.75, SD = 1.92.
However, implementation intention participants made the fewest impulse purchases, M=
2.15, SD = 1.80. Polynomial contrasts comparing the three conditions showed a significant
linear effect, p= .01, but no quadratic effect, p= .99, suggesting that the if-then format
contributed to the implementation effect.
Finally, this implementation intention effect was not qualified by a peer influence
implementation intention interaction effect, F(2, 114) = 0.39, p= .91, suggesting
that implementation intentions were effective for both peer contexts. This finding is
in line with the result of the pretest, where students indicated that they had much
experience with this special shopping context and the available items. Apparently,
they impulse bought even when the social influence was quite weak. This may
suggest that consumers need implementation intentions even when they are not faced
with a strong social norm that runs counter to their goals (Gollwitzer and Sheeran,
2009).
Figure 1.
A number of
unplanned items
purchased by
intention condition
and peer influence
(Study 1). Error bars
represent standard
errors
0
1
2
3
4
5
College peers Peers from home
If-Then Control
Strategy Without If-Then
Implementaon Intenon (If-Then Strategy)
Mean Unplanned Items Purchased
Peer Influence
Peer influence
on impulse
buying
In sum, the implementation intention to purchase only what one needs reduced
impulse buying when facing a wide range of familiar and tempting items regardless of
whether shopping was done for one’s peers from home or college peers. The if-then
format contributed to this beneficial effect, which supports the assumption that the
control of automatic peer influence on impulse buying requires automatic action control
processes.
4. Study 2: predictors of peer influence on impulse buying
Study 2 used a scenario task to address additional questions as follows: The first aim was to
test our hypotheses in another population and with another product category. Adolescents
(i.e. young consumers between the ages of 16 and 18) may be especially affected by social
influence (Blakemore, 2018) but have largely been neglected in past research on impulse
buying. As the qualitative pre-test in Study 1 indicates, this population has a high
acceptance of impulse buying. Clothing is commonly purchased on impulse (OnePoll, 2018)
and usually for oneself. To rule out that social choices (i.e. shopping for others; Laran, 2010)
rather than impulsive buying drives the observed effects, an individual clothes-shopping
vignette-paradigm was used.
The second aim of Study 2 was to establish how norms and automaticity, as well as goals
and plans, affect impulse buying simultaneously. Participants’perceived automaticity of
shopping with peers (Gardner, 2015) and norms were assessed. The literature on social
norms commonly distinguishes between what is considered appropriate (i.e. injunctive
norms) and what people actually do (i.e. descriptive norms)(Cialdini, 2012). Study 1 assumed
that injunctive and descriptive norms are jointly present but both types of norms may affect
behaviour in different ways (Nolan et al.,2008;Jacobson et al.,2011). Therefore, measures of
what peers find appropriate (injunctive norm) and what they actually do (descriptive norm)
were included. Additionally, exploratory measures of habit strength (Wood et al., 2005)were
assessed. Finally, a plan manipulation was included to investigate how goals and plans can
curb this impact of norms and automaticity. Participants set the goal to be thrifty and either
furnished this goal with an implementation intention designed to curb impulse buying or
not.
Third, Study 2 used a larger sample that even allows detecting small effects. Although
Study 1 was sufficiently powered to detect a typical implementation intention effect of d=
0.65, it may not have been sufficient to detect a possible interaction. It was expected that
norms, as well as automaticity increase impulse buying and that implementation intentions,
reduce this impact.
4.1 Method
4.1.1 Participants. In total, 773 high school students (607 female) from southern Germany
with a mean age of 17.20years (SD = 2.81) completed the questionnaires administered in
testing sessions after a lecture. In total, 87 participants failed the attention check (see below;
68 female, M
age
= 17.96, SD = 6.12) and consequently were removed from analyses, leaving
a sample size of N= 686. Retained and excluded participants did not systematically differin
their age, t(85.31) = 1.28, p= .21 or gender,
x
2
(1) = 0.003, p= .95. A power analysis (Faul
et al.,2007) setting 1
b
= .80 indicated that the remaining sample size was sufficient to
detect a small effect.
4.1.2 Procedure. On the first page of the questionnaire, participants learned that the
study concerned the attitudes of young adults and gave their informed consent. Participants
then learned that they would read several short texts (scenarios) and would be asked to
answer a few questions about each scenario. It was emphasized that it was important to
EJM
imagine the scenarios vividly. The first scenario was about a new personal goal that
participants set. For participants in the implementation intention condition, this scenario
included the goal to save money and the if-then plan “if I want to put something in my
shopping cart, then I will take only what I really need!”In the control condition, the scenario
included the goal to save time and the if-then plan “if I want to watch a show, then I will
choose only what I really like!”Participants in both conditions were asked to imagine that
they would tell themselves the respectiveplan every morning. Both conditions were, thus, as
parallel as possible: goals in both conditions instructed participants to be thrifty but only the
experimental condition referred to saving money. All participants answered four items on
their commitment to the goal (
a
= .74) and four items on their commitment to the plan (
a
=
.82) at the bottom of the page on five-point scales (1: not at all to 5: very much)(“How
committed are you to your goal [plan]? To what extent do you care about your goal [plan]?
How dedicated are you to goal [plan]? To what extent have you chosen to be committed to
goal [plan]”? 1: not at all –5: very much; adapted from Klein et al., 2014).
The second scenario described a shopping situation (adapted from Rook and Fisher,
1995;Luo, 2005). Participants were asked to imagine that they go into town to buy socks [3].
All participants then read that their favourite store had a large sale with 50% off. In addition
to the socks, they find seven items that they really like (t-shirt, belt, scarf, hat, sweater,
trousers and jacket) among the sale items. To help participants adjust the scenario to their
own favourite store and to take into account different price points, prices for the items were
not indicated. Participants then indicated for each item if they would put it into their
shopping cart. All items beyond socks were considered impulse purchases (Luo, 2005).
To assess the correlates of impulse buying, participants were then asked to report their
peers’social norms and their perceived automaticity of shopping with peers. Regarding
social norms, participants completed three items on their injunctive norms (“my best friends
encourage me to go shopping”,“my best friends think that I should shop more”,“my best
friends like shopping”; 1: does not apply at all –5: completely applies,
a
= .68) and one item
on their friends’behaviour (descriptive norm; “How often do your best friends shop”?0:
never, 1: once a month or less, 2: at least once a week, 3: almost every day). Four exploratory
items on the importance of peers were included but these items did not form a reliable scale,
and therefore, are not considered further.
Regarding automaticity, participants completed a five-item measure (
a
= .79) of how
automatic they perceived shopping with their friends (e.g. “shopping with my friends is
something that I do automatically”1: does not apply at all –5: applies completely; adapted
from Gardner et al.,2012). As a potential further context variable, it was assessed whether
participants usually go to the same shops (“Do you usually shop in the same shops”?1:
usually in the same shops, 2: sometimes the same,sometimes different, 3: always in different
shops) and how often they shop as a measure of habit strength (“How often do you shop”?0:
never, 1: once a month or less, 2: at least once a week, 3: almost every day; “Do you shop with
your best friends”? 1: no, mostly alone, 2: sometimes with my best friends, 3: usually with
my best friends; adapted from Wood et al.,2005).
Finally, participants provided demographic information and were debriefed. At the end
of the shopping scenario, one item asked participants to selecta particular response (4) as an
attention check.
4.2 Results and discussion
4.2.1 Data analytic strategy. The primary focus was on the correlates of impulse buying,
which were evaluated by estimating generalized linear mixed-effects models (GLMMs)
using the lme4 package version 1.1–12 (Bates et al., 2015) implemented in R version 3.3.1 (R-
Peer influence
on impulse
buying
Core-Team, 2016). GLMMs simultaneously estimate fixed effects (e.g. experimental factors
and continuous predictors) and random effects (e.g. participants and items) without
requiring data aggregation. This is an advantage because it accounts for both the possibility
that some participants are more likely to impulse buy than others and the possibility that
items differ in the likelihood to be selected as impulse purchases. GLMMs, therefore, lead to
more robust and generalizable conclusions (Judd et al.,2012). Moreover, GLMMs can handle
discontinuous outcome variables, enabling analyses of each potential impulse buying an
item as a binary choice (0: item not selected, 1: item selected).
4.2.2 Correlates of impulse buying. In total, 93.6% of the participants chose the target
item (socks; correlations in Table 2) and more than half of the participants (59.6%) made at
least one impulse purchase decision. In line with expectations, the items differed
considerably in their likelihood to be selected as impulse purchases (sweater: 29.9%,
trousers: 25.8%, t-shirt: 24.9%, jacket: 21.1%, scarf: 18.4%, hat: 7.0% and belt: 5.0%). To
establish the correlates of impulse buying, all potential predictors were entered into a single
GLMM with impulse purchases as the dependent variable and all potential predictors of
impulse buying. Including all predictors in one model tests the effect of each predictor while
adjusting for all other predictors. This model, therefore, allows assessing the incremental
predictive power of each predictor beyond all other predictors in the model. In this model,
peer injunctive norms (i.e. what is considered appropriate),
b
= .215, OR = 1.24, SE = 0.086,
z= 2.482, p= .013 and the automaticity of shopping with friends,
b
= .223, OR = 1.25, SE =
0.080, z= 2.800, p= .005, predicted impulse buying. Injunctive norms represent a reflective
influence, while automaticity represents an impulsive influence. This pattern of results is,
therefore, in line with the reasoning that peers increase impulse buying via deliberate and
automatic processes.
Turning to additional context factors, reported peer behaviour (descriptive norms), the
habit strengths of shopping with friends and shopping in the same shops all did not impact
impulse buying, ps>0.13. This further supports the reasoning that injunctive norms and
automaticity are the main processes of peer influence on impulse buying.
4.2.3 Do implementation intentions curb the correlates of impulse buying?
Implementation intentions had no main effect on impulse buying, p= .751 [4]. To test
whether implementation intentions help curb the impact of peer norms and automaticity,
interaction effects of implementation intentions (yes vs no) and the two significant
predictors (injunctive norms and automaticity) were examined with GLMMs.
Implementation intentions attenuated the impact of injunctive norms,
b
=0.333, OR =
0.72, SE = 0.164, z= 2.03, p= .042: stronger injunctive norms were a highly significant
predictor of impulse buying in the control condition,
b
= 0.542, OR = 1.72, SE = 0.123, z=
4.40, p<.001, but were non-significant in the implementation intention condition,
b
= .209,
OR = 1.23, SE = 0.108, z= 1.94, p= .053. Implementation intentions also attenuated the
effect of automaticity on impulse buying,
b
=0.346, OR = 0.71, SE = 0.146, z= 2.38, p=
.018: automaticity was a highly significant predictor in the control condition,
b
= .529, OR =
1.70, SE = 0.109, z= 4.86, p<0.001, but non-significant in the implementation intention
condition,
b
= 0.183, OR = 1.20, SE = 0.097, z= 1.88, p= .060. Implementation intentions,
thus, curb the peer impact on impulse buying.
In sum, the observed pattern of impulse buying decisions supports the predictions: not
only social norms but also the automatic influence of peers promotes impulse buying.
Moreover, implementation intentions can attenuate this peer impact on impulse buying.
EJM
Variable M SD 123456789
1. Plan commitment 3.99 0.72
2. Goal commitment 3.95 0.66 0.67**
3. Automaticity 2.45 0.92 0.03 0.03
4. Injunctive norm 2.33 0.81 0.02 0.01 0.28**
5. Habit shopping with peers 2.28 1.13 0.02 0.05 0.40** 0.29**
6. Usually go to the same shop 1.73 0.84 0.03 0.02 0.14** 0.05 0.46**
7. Impulse purchases (sum other) 1.32 1.48 0.16** 0.10** 0.16** 0.16** 0.14** 0.05
8. Descriptive norm 1.13 0.44 0.03 0.01 0.23** 0.22** 0.40** 0.27** 0.11**
9. Planned purchase (socks) 0.94 0.25 0.12** 0.02 0.06 0.04 0.07 0.02 0.26** 0.10*
10. Plan condition 0.55 0.50 0.11** 0.10** 0.06 0.06 0.02 0.06 0.01 0.09* 0.00
Notes: *indicates p<0.05; **indicates p<0.01
Table 2.
Means, standard
deviations and
correlations (Study 2)
Peer influence
on impulse
buying
5. General discussion
This paper argues that peer influence on impulse buying among young consumers may
operate quite automatically and young consumers, thus, need to make if-then plans to curb
impulse buying. In line with this reasoning, automaticity emerged as a particularly strong
predictor of peer influence in both studies. Implementation intentions especially reduced
impulse buying when this plan was phrased in an if-then format (Study 1). Because
the if-then format is known to automate action control, this observation also supports the
assumption that peer influence not only operates deliberatively via norms but may also
operate quite automatically; apparently, breaking an unwanted automatic influence needs
antagonistic automatic processes (Adriaanse et al.,2009). In Study 2, self-reported
automaticity predicted impulse buying, even when accounting for habit strength and social
norms and this automatic impact was moderated by if-then plans. These results were
obtained across two different populations (adolescents and young adults), two different
tasks (laboratory shopping task and survey vignettes) and two different shopping contexts
(grocery shopping for friends and individual clothes shopping). This highlights the
pervasive nature of the automatic peer impact on impulsebuying.
5.1 The role of automaticity in peer influence and consumer behaviour
The present research indicates that peer influence on impulse buying leads to automated
responding. Other forms of social influence may also lead to relatively automatic responding
but are acquired in a different way. For instance, in a study by Nolan et al. (2008)
participants rated how other people behave (descriptive norms) as the least important factor
influencing their behaviour, but these descriptive norms were actually a better predictor
than any other variablesassessed in this study (see also Cialdini et al., 1990;Goldstein et al.,
2008). Social influence, therefore, may not only always operate vianorms that lead to setting
respective goals but also quite automatically. Study 2 suggests, however, that descriptive
norms (i.e. what peers actually do) are a weaker predictor of impulse buying than is
automaticity, potentially because automaticity is more proximal to the observed behaviour.
Under some circumstances, social influence may only operate automatically when the
respective goal is active. For instance, students looking at a picture of the library lowered
their voice, but only if they had the goal to visit the library and if their peers were quiet
inside the library (i.e. a strong descriptive norm was present; Aarts and Dijksterhuis, 2003).
This suggests that the normative influence was conditional on the set goal and this type of
conditional automaticity also seems to characterize the action control by implementation
intentions (Achtziger et al.,2012).
Beyond peer influence on impulse buying, the present research has implications for
consumer behaviour more generally. Much of what consumers do is automatic (Wood and
Neal, 2009;Dijksterhuis et al., 2005). For instance, consumers often purchase traditional
products despite their intentions to buy products that adhere to stricter ethical standards
(Belk et al.,2005), potentially because they stick to their habitual brand. This brand loyalty
is an automatic influence that is difficult to overcome with mere goals (Wood and Neal,
2009), and therefore, has much in common with the automatic impact of peers on impulse
buying in the present research. Also in line with the present research, implementation
intentions can help consumers close the gap between their ethical intentions and their
actions (Carrington et al.,2010). If-then planning, thus, is a powerful tool to improve
consumer decisions (Gollwitzer et al., 2017).
Consumer research has identified many situational factors that impact consumer
decisions such as the numbers and units used (Monga and Bagchi, 2012), how caloric
information is displayed (Parker and Lehmann, 2014), which drink sizes are offered (Sharpe
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et al.,2008) and whether consumers pay with cash or credit card (Chatterjee and Rose, 2012).
Presumably, these situational factors also affect consumer decisions outside of awareness.
The most commonly used tool by policymakers to help consumers gain control over these
influences is to regulate them externally (e.g. by prescribing certain payment methods). The
present research suggests that policymakers could also encourage consumers to form
implementation intentions to lessen the impact of situational factors via self-regulation
(e.g. to remind oneself of the cash-value of a credit card payment). Recent research has
introduced implementation intention inductions that maybe suited to target a large number
of people. These inductions include persuasive appeals (Fennis et al.,2011) or explicit
instructions via standardized phone calls (Nickerson and Rogers, 2010) and via a mobile app
(Oettingen, 2014; see also www.woopmylife.org). Even though the translation of
psychological research into large-scale psychological interventions is a complex endeavour
(Cohen and Sherman, 2014), the present research is an important step towardsempowering
consumers to make and implement rational, informed decisions (Len et al., 2006).
5.2 Limitations
There are limitations of the present research that warrant discussion. Firstly, the present
research takes a rather static view on social influence, with peers affecting one shopping
trip. In real life, consumer choices in social contexts lead to responses from these others
(Zhang, 2019), whichmay impact social relationships and life satisfaction (Brick et al.,2017).
Especially for young consumers, the enjoyment of shopping with others is key (Wenzel and
Benkenstein, 2018). Strict opposition to any impulse purchases in this group may be
perceived as anti-social or simply “not fun”, leading to social exclusion. Although these may
be extreme cases, future research should certainly attend to such dynamic processes.
Secondly, the present research focussed on simulated in-store settings. Online behaviour
such as writing a review (Motyka et al.,2018) or using social commerce platforms (Xiang
et al.,2016) may increase the social influence on impulse buying. For instance, seeing
positive recommendations online and extended browsing has been shown to increase
impulse buying (Chen et al., 2019;Zhang et al., 2018). Shopping in such an online social
environment may lead to the experience of flow, which can increase impulse buying online
(Huang, 2016). Moreover, Study 1 contained more male and Study 2 contained more female
participants. Future research should explore whether gender differences in susceptibility to
peer influence on impulse buying do exist. Finally, participants in the current research did
not make purchases in an actual store. Past research has observed if-then planning effects in
incentivized decisions (Thürmer et al., 2015b) and in naturalistic settings (Holland et al.,
2006), suggesting that the present findings may generalize to such contexts. Nevertheless,
future research should test whether implementation intentions are effective for regulating
peer impact on actual purchase decisions and online shopping behaviour.
Thirdly, the implementation intentions used in the present research contained a concrete
response (i.e. take only what one really needs). Recent research demonstrates that
implementation intentions may also promote more abstract, internal responses such as
ignoring pain or doubt (Thürmer et al.,2013,2017), weighing the pros and cons of decision
alternatives (Thürmer et al., 2015b;Wieber et al., 2015b) and even instigating deliberation in
general (Doerflinger et al.,2017). Implementation intentions may, therefore, also promote
more abstract strategies to decrease impulsive buying,such as increasing the salience of the
costs of impulse buying (Puri, 1996) or reducing emotions leading to impulse buying
(Donnelly et al.,2016). Such abstract strategies may generalize across a wide range of
contexts and can, therefore, have a large impact on day-to-day shopping behaviour.
Peer influence
on impulse
buying
Finally, the present research focusses on young consumers and it would be interesting to
explore whether the current findings generalize to consumers in general. For instance, the
impact of young children on their parents’impulse buying behaviour may be quite strong,
for instance when children forcefully demand the purchase of unplanned items such as
candy (McDermott et al., 2006). Such “pester power”should be hard to regulate because of
high emotional involvement (e.g. embarrassment) and limited cognitive resources. Few
studies have applied if-then planning to parenting (Van Osch et al.,2008) but if-then
planning has been shown to be effective when limited cognitive resources are available and
negative emotions need to be down-regulated (Schweiger Gallo et al., 2009;Wieber et al.,
2015b).
5.3 Contributions and avenues for future research
The insights from the present research open up a number of new avenues for future
research. First, the current research acknowledges the importance of social influence and of
automatic processes and thereby contributes to a thriving field of research. The present
research touches on important debates such as the free will in consumer choice (Baumeister
et al., 2008), self-related processes (e.g. consumption to reduce self-discrepancies; Mandel
et al., 2017) or goal pursuits in line with predominant mindsets (Murphy and Dweck, 2016).
A recent review moreover concludes that sharing information and opinions about products
(i.e. word of mouth) is driven by self-serving motives that people may not be aware of Berger
(2014). It, therefore, seems that not only the consequences but also the causes of social
influence may operate quite automatically. Such a model could potentially also account for
other forms of social influence such as between grandparents and grandchildren (Godefroit–
Winkel et al.,2019). Developing a dynamic model of social influence on consumer decisions
is a highly fruitful avenue for future research.
More broadly, the present research supports the RIM of consumer choice (Strack et al.,
2006;Hofmann et al.,2008). Not only did the present research find consistent support for
automatic peer influence but also for deliberate influence via injunctive norms. Moreover, by
integrating the RIM and implementation intentions (Martiny–Huenger et al.,2011,2016), the
present research shows how implementation intentions enable consumers to implement
reflective decisions. For continuing this integration, it would be highly fruitful to use direct
measures of automaticity such as the implicit association test or electroencephalography
(Friese et al.,2016) and to explore the brain regions involved in consumer choice using
functional near-infrared spectroscopy (fNIRS) or functional magnetic resonance imaging
(fMRI) (Perit et al., 2018;Marco et al., 2018). Recent studies have started using fNIRS with
physical tasks (Wolff et al.,2019,2018), opening up this methodology to applied settings.
Implementation intentions are known to impact these measures (review and meta-analysis
by Gollwitzer and Sheeran, 2006;Wieber et al., 2015b) and such studies would, therefore, be
highly promising.
Finally, current research has identified a host of potential moderators of social influence
on adolescents’impulse buying decisions. Brand loyalty has a pervasive impact on the
actual purchase of a product (Sheth and Koschmann, 2019, but see Keller, 2019) and on
consumption-related social behaviour such as word-of-mouth (Thompson et al.,2019). The
perception of and loyalty to brands, thus, likely moderates social influence effects on
purchase decisions. Moreover, technological innovations (Lowe et al.,2019;Hollebeek et al.,
2019) such as social networks (O’Leary and Murphy, 2019) arelikely to extend thedefinition
of “peers”beyond the immediate physical environment. In line with this reasoning, recent
research suggests that heavy multimedia use may increase the susceptibility to an
advertisement (Beuckels et al.,2019). It is also likely that peer influence may differ across
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product categories, such as music (Sinclair and Saren, 2019), dining (Veeck, 2018), “green”
products (Lee, 2009) and gift-giving among adolescents (Segev, 2016). The present research,
thus, opens up a host of avenues for future research.
5.4 Practical implications
With respect to applied issues, the following contributions seem most pertinent. First, young
consumers are highly prone to impulse buying and are accumulating debt at an increasing
rate. In line with past research, existing approaches to educating young consumers rely on
providing information, thus assuming that problematic impulse buying is a deliberate
process. The current research demonstrates that the impact of peers can also be quite
automatic, which suggests that merely educating young consumers may be insufficient to
attain lasting behaviour change.
The present research also suggests that if-then plans can help young consumers gain control
over problematic impulse buying behaviour. Recent research has introduced implementation
intention inductions suited to target a large number of people. These inductions include
persuasive appeals (Fennis et al.,2011), explicit instructions via standardized phone calls
(Nickerson and Rogers, 2010) and by pointing people to a mobile app (Oettingen, 2014; see also
www.woopmylife.org) that guides them to from if-then plans targeting their personal obstacles
standing in the way of realizing a wished behaviour change. Even though the translation of
psychological research into large-scale psychological interventions is a complex endeavour
(Cohen and Sherman, 2014), the present research is an important step towards empowering
young consumers to make and implement decisions of their own liking.
Finally, chronic impulse buying also referred to as compulsive buying (Ridgway et al.,
2008;O’Guinn and Faber, 1989) has drastic consequences for those affected. Compulsive
buying is a strong established tendency to engage in impulse buying and it may, thus,
require more powerful interventions than those used in the present research. One approach
is to tailor implementation intentions to a person’s subjective motivations (Adriaanse et al.,
2009;Oettingen, 2012). The motivation for compulsive buying mainly is to regulate negative
emotions pertaining to the self (Faber, 2000). As implementation intentions have been found
to regulate even chronic emotions that pertain to the self (Schweiger Gallo et al., 2009;
Thürmer et al.,2013), implementation intentions might even help compulsive buyers reduce
unintended purchases. Interestingly, recent neuroscientific and behavioural evidence
suggests that peer influence may actually decrease impulse buying among those consumers
who are highly compulsive (De Vries et al.,2018). One avenue for planning interventions
could, thus, be to develop if-then plans that remind compulsive buyers of their social
obligations. Investigating the effectiveness of such plans to reduce compulsive buying
seems to be a fruitful avenue for future research.
In sum, the present research demonstrates that analysing social influence and implementation
intentions conjointly can inform consumer behaviour in multiple new ways. Integrating these
literature is not only practically relevant but also a long-overdue contribution to planning
research (Hagger et al.,2016). This way, the present research can help young consumers make
better purchase decisions that are in line with their long-term goals.
Notes
1. Reliability for this manipulation check scale was unsatisfactory. The results should, therefore, be
interpreted with caution.
2. Box plots (McGill et al., 1978) of the dependent measure indicated two outliers (one in the
students/helpful strategy without if-then format condition and one in the friends/no helpful
Peer influence
on impulse
buying
strategy condition). Removing these cases rendered the implementation intention effect non-
significant, F(1,118) = 2.901, p= .059, but did not change the pattern of results. Entering when
participants had eaten their last meal or participant gender as a covariate did not change any of
the results reported below.
3. We also sought to manipulate the peer presence: the scenario indicated either “after school, you
wander through shops in town with your best friends”(peers present condition) or “after school,
you wander through shops in town alone”(peers absent condition). Contrary to predictions, this
minimal change in the description had no main effect (p= .831) or interaction effects with any of
the reported correlates of impulse buying (all ps>.280) or the implementation intention factor, p
= .183. As a potential explanation, participants came to the sessions with their high-school class
and they were, therefore, potentially sitting next to their friends when completing the survey.
This large peer influence may, therefore, have masked the minimal effect of the wording change.
Findings regarding peer norms and habits are consistent with this view.
4. Participants who formed a useful goal and a useful implementation intention reported greater goal
commitment (M= 3.957, SD = 0.677) than participants who did not (M= 3.841, SD =0.740),
t(698.01) = 2.250 and this was also true for plan commitment (M
II
= 4.018, SD = 0.689; M
control
= 3.881,
SD = 0.810), t(648.68) = 2.454. Adjusting for goal commitment and plan commitment did not alter the
results. Entering participant gender as a covariate did not change any of the results reported below.
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Author affiliations
J. Lukas Thürmer, Department of Psychology, Paris Lodron University Salzburg, Salzburg, Austria;
Organisational Studies, Department of Politics and Public Administration, University of
Konstanz, Konstanz, Germany and Economic and Social Psychology, Department of
Psychology, University of Göttingen, Göttingen, Germany
Maik Bieleke, Department for Psychology of Development and Education, Faculty of Psychology,
University of Vienna, Vienna, Austria and Social Psychology and Motivation, Department of
Psychology, University of Konstanz, Konstanz, Germany
Frank Wieber, ZHAW School of Health Professions, Zurich University ofApplied Sciences, Winterthur,
Switzerland and Social Psychology and Motivation, Department of Psychology, University of
Konstanz, Konstanz, Germany
Peter M. Gollwitzer, Department of Psychology, New York University, New York, New York, USA and
Social Psychology and Motivation, Department of Psychology, University of Konstanz,
Konstanz, Germany
EJM
About the authors
J. Lukas Thürmer is a Motivation Scientist and a Team Researcher, focussing on how teams best
attain their (performance) goals. Lukas earned his doctorate at the University of Konstanz where he
later served as an interim professor. A Marie Skłodowska-Curie Global Fellowship by the European
Union then allowed him to continue his research at the University of Pittsburgh and at Carnegie
Melon University. He now is an independent researcher at Paris-Lodron University Salzburg. J. Lukas
Thürmer is the corresponding author and can be contacted at: lukas.thuermer@sbg.ac.at
Maik Bieleke earned his PhD at the University of Konstanz and is now a Postdoctoral Fellow at the
University of Vienna. His research focus is on the role of self-regulation for decision-making and goal
attainment, with particular emphasis on understanding how people regulate cognitive and emotional
information processing. Fields of application include economic behaviour, academic achievement and
endurance performance.
Frank Wieber is a Professor of Health Sciences at the Zurich University of Applied Sciences and
privatdozent at the University of Konstanz. His research focuses on health behavior change. Further
research interests include the development and evaluation of interventions to promote children’s
mental health.
Peter M. Gollwitzer is a Professor of Psychology at New York University. His research domains
are motivation and volition, self and identity, and behaviour change interventions. More specifically,
he studies how people can best self-regulate goal striving so that their chances of reaching their goals
are enhanced.
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Peer influence
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