ArticlePDF Available

Abstract

Consumers’ existing habits are a key driver of resistance to new product use. In an initial survey to identify this role of habit, consumers reported on products that they had purchased intending to use. They also reported whether or not they actually used them. For one-quarter of the products they failed to use, consumers slipped back into old habits despite their favorable intentions. However, consumers effectively used new products when integrating them into existing habits. A four-week experiment with a new fabric refresher confirmed that habit slips impeded product use, especially when participants thought minimally about their laundry and thus were vulnerable to habit cues. However, slips were minimized when the new product was integrated into existing laundry habits. Thus, in launching new products, managers will want to consider consumer habits that conflict with product use as well as ways to embed products into existing habits.
ORIGINAL EMPIRICAL RESEARCH
Habit slips: when consumers unintentionally resist new products
Jennifer S. Labrecque
1
&Wendy Wood
1,2
&David T. Neal
3
&Nick Harrington
4
Received: 23 September 2015 /Accepted: 5 May 2016
#Academy of Marketing Science 2016
Abstract Consumersexisting habits are a key driver of re-
sistance to new product use. In an initial survey to identify this
role of habit, consumers reported on products that they had
purchased intending to use. They also reported whether or not
they actually used them. For one-quarter of the products they
failed to use, consumers slipped back into old habits despite
their favorable intentions. However, consumers effectively
used new products when integrating them into existing habits.
A four-week experiment with a new fabric refresher con-
firmed that habit slips impeded product use, especially when
participants thought minimally about their laundry and thus
were vulnerable to habit cues. However, slips were minimized
when the new product was integrated into existing laundry
habits. Thus, in launching new products, managers will want
to consider consumer habits that conflict with product use as
well as ways to embed products into existing habits.
Keywords Habits .Resistance .Action slip .Implementation
intentions
Introduction
Introducing a new product into the consumer marketplace is
fraught with challenges. Over half of all new products fail
(Andrew and Sirkin 2003), and consumer resistance to pur-
chasing and using new products contributes significantly to
this failure (see Heidenreich and Kraemer 2016). To guide the
development and marketing of new products in this rocky
landscape, managers often rely on market research to test
quantitative concepts (e.g., intent to purchase, definitely-
would-buy scores) and conduct qualitative focus groups to
understand adoption decisions.
Through this market research, managers identify con-
sumersdecisions that could influence success of a new prod-
uct. These judgments are believed to emerge from largely
explicit processes involving rational decision making and
forming preferences. For example, in Rogers(1962; Rogers
and Schoemaker 1971) classic analysis, adoption proceeds
through knowledge of the new product, attitude formation,
decision to adopt or reject, and finally implementation.
Similarly, Bagozzi and Lee (1999) outlined the decision pro-
cesses that consumers follow with respect to adoption, includ-
ing consumer goal setting (e.g., appraisal, adoption decision)
and goal striving (e.g., planning, actual adoption).
In the present article, we extend understanding of consumer
adoption to include an often-discussed but rarely documented
source of potential resistance to new products: consumer habit.
AccordingtoSheth(1981), Bthe strength of habit associated with
an existing practice or behavior is hypothesized to be the single
most powerful determinant in generating resistance to change^
(p. 275). Resistance due to habit occurs along a continuum that
This research was supported in part by Procter & GamblesCorporate
Products Research Division along with a grant from the John Templeton
Foundation. The opinions expressed are those of the authors and do not
necessarily reflect the fundersviews.
Rebecca Hamilton served as Area Editor for this article.
Electronic supplementary material The online version of this article
(doi:10.1007/s11747-016-0482-9) contains supplementary material,
which is available to authorized users.
*Wendy Wood
wendy.wood@usc.edu
1
Department of Psychology, University of Southern California, Los
Angeles, CA 90089, USA
2
Marshall School of Business, University of Southern California, Los
Angeles, CA 90089, USA
3
Catalyst Behavioral Sciences, Miami, FL, USA
4
Procter & Gamble, Boston, MA, USA
J. of the Acad. Mark. Sci.
DOI 10.1007/s11747-016-0482-9
includes more active processes at one end and more passive
inertia at the other. As an active process, people may deliberately
choose an existing, habitually-used product over a new alterna-
tive (Kleijnen et al. 2009). Resistance also occurs passively due
to slipping back into old habits (Ram and Sheth 1989). Existing
lines of research track the effects of habit on active resistance. For
example, consumersskill-based habits for an existing product
impeded transitions to a new product through an essentially ra-
tional decision process involving weighing past investments and
switching costs (cognitive lock-in, Murray and Häubl 2007;
Zauberman 2003). However, emerging research on the psychol-
ogy of habit identifies automated as well as deliberate effects on
consumer choice (Wood and Rünger 2016).
We test whether habits can generate behavioral resistance
through a relatively passive process involving automated cu-
ing of past behaviors. Specifically, we argue that automated
resistance to new products can take the form of habit slips,or
use failures that occur when consumers fall back into repeat-
ing habits that conflict with a new product. That is, despite
intending to use a new product, consumers might revert back
to old habits that are cued by everyday contexts. Habit slips
are thus defined as limited use of a new product due to auto-
matically slipping back into existing habits.
Our analysis of habit slips takes a systems perspective by
considering how a new product or service interacts with con-
sumerslifestyles. For example, despite initial excitement
about purchasing a premium cable TV package, when sitting
down to watch in the evening, viewers might fall back on
existing patterns and automatically turn to their usual chan-
nels. Habit slips might thus occur with little input from prod-
uct use intentions.To provide a strong test of the idea that
habit-based resistance does not depend on intentions, we focus
our research on products that consumers regard favorably and
purchased with an intention to use. Although our research is
post-purchase, we believe that a similar habit-slip mechanism
can impede initial purchase decisions, leading consumers to
stick with purchasing an incumbent product or service.
Given the limited evidence for automated habit slips, we
began our investigation with a survey to identify the extent to
which these do in fact impede consumerseveryday use of new
products. We then conducted a field experiment that introduced
a new laundry product and evaluated failures to use it. This
experiment allowed us to assess implicit and explicit product
evaluations longitudinally to rule out an alternative account in
which resistance is tied to forming or changing product judg-
ments. This experiment further validated the habit slip phenom-
enon by demonstrating that it was moderated by factors known
to moderate habit performance. Specifically, because con-
sumers tend to fall back on established habits when distracted
and thinking about something other than what they are doing,
we anticipated that habit slips would be greatest among partic-
ipants who thought little about doing laundry. Finally, both
studies also evaluated the efficacy of strategies to break habit-
based resistance barriers by making new products compatible
with existing habits (e.g., Ram and Sheth 1989).
Habit slips and other resistance barriers
Given that consumer resistance takes many forms, marketing
teams will want to carefully consider the landscape into which
they introduce new products (see Table 1). Consumers might
actively resist purchasing or using a new product by making
decisions. In addition, consumers might passively resist by
failing to adopt a new product without explicit negative eval-
uations or decisions not to purchase (Heidenreich and
Handrich 2015; Talke and Heidenreich 2014).
A number of active resistance barriers have been investigated
in prior research (see Kleijnen et al. 2009). Consumers may
experience resistance when they face uncertainty, either because
they lack information about the product or because the future of
the product is unclear. Resistance due to perceived product image
could arise from negative stereotypes about the product. One
example is both brandsand consumersreluctance to adopt
wines bottled with screw caps because of perceptions that this
reflects inferior quality (Garcia et al. 2007).Anumberofaspects
of new product uncertainty or risk have been investigated, in-
cluding functional, economic, social, and physical risks (Kleijnen
et al. 2009). Physical risk, or the possibility of physical harm due
to the new product, may be common for some types of products
(e.g., new food or health-related products) but should be relative-
ly rare for most products (Stone and Grønhaug 1993). In contrast,
functional or performance risk, involving concerns about wheth-
er the new product will perform reliably or interface seamlessly
with complementary products or services, either existing or
promised in the future, can cause many consumers to postpone
adoption (Antioco and Kleijnen 2010; Szmigin and Foxall
1998). This may be a particularly serious issue for novel
products that rely on co-evolving, compatible innovations
(Bucklin and Sengupta 1993). Additionally, consumers often
consider how their reference group will support their adoption
behavior, particularly for conspicuous products, and will fail to
use new products if the social risk is too high (Dholakia 2001).
Finally, economic risk, or concerns about product value, may
prevent consumers from adopting new products (Kleijnen et al.
2007). Consumers can attempt to predict the value they will
receive for the product price across several factors: acquisition
(cost-benefit ratio at purchase), transaction (experience of getting
a good deal), in-use (derived utility), and redemption (residual
value at trade-in) (Parasuraman and Grewal 2000). After
weighing the benefits against the cost, consumers may simply
decide that the advantages of the new product are not worth the
price. When consumers are faced with uncertainty as they make
decisions about a new product, they may experience frustration
and ultimately postpone adoption or reject the new product out-
right (Strebel et al. 2004).
J. of the Acad. Mark. Sci.
In addition to lack of information and uncertainty, switching
costs are another reason for consumersactive resistance. That is,
when an existing product is easy to use and familiar, but a new
product is difficult or has a steep learning curve, consumers may
forego adopting the new product to avoid the additional learning
cost (Murray and Häubl 2007). Also, consumers might choose to
stay with a well-known product in order to preserve the time and
resources associated with search and evaluation processes for a
new alternative (Zauberman 2003). In a similar way, having
repeated experience with an existing product, particularly if it is
complex, may increase consumersliking for it, and this famil-
iarity and ease of use can promote resistance to a new alternative
(Cox and Cox 2002; Kleijnen et al. 2009). For these reasons,
discontinuous or novel products tend to be evaluated less favor-
ably among consumers with familiarity in a domain (Moreau
et al. 2001).
Unlike active resistance, passive resistance is not a function of
consumersactive decision making about a new product or ser-
vice. Passive resistance might occur when consumers are simply
unaware of a new product or have no exposure to it. Consumers
also resist passively when they know about the new product but
are uninterested or judge it irrelevant to them (Joseph 2010). This
resistance can be driven by an individualsdispositional tendency
to resist change across a variety of domains (Oreg 2003)orfrom
satisfaction with the status quo (Talke and Heidenreich 2014).
Thus, consumers are unlikely to consider a new product alterna-
tive when they are not motivated to change because they are
satisfied that their current product meets their current needs or
goals (Ellen et al. 1991;Ram1987; Szmigin and Foxall 1998).
For example, although consumers saw value in moving from
primarily check-based to card-based financial transactions, they
initially failed to adopt debit cards. They were satisfied with the
performance of their existing credit cards and appreciated not
having the money immediately withdrawn from their account
(Talke and Heidenreich 2014). This inclination toward maintain-
ing the status quo is further fostered by an underlying psycho-
logical bias involving ownership (i.e., endowment effect): con-
sumers overestimate the benefits associated with their currently
Tabl e 1 Drivers of resistance to new products
Driver Description Example references Mechanism
of resistance
Physical risk Consumers decide not to adopt a new product
due to possible physical risks
Stone and Grønhaug 1993 Active
Functional risk Consumers decide not to adopt a new product
due to uncertainty about complementarity
with existing or upcoming products
Antioco and Kleijnen 2010;Szmigin
and Foxall 1998
Active
Social risk Consumers decide not to adopt a new product
because of concern about othersevaluations
Dholakia 2001 Active
Economic risk Consumers decide not to adopt a new product
because of difficulty determining its true value
or whether the price will change over time
Kleijnen et al. 2007; Parasuraman and
Grewal 2000
Active
Perceived switching costs Consumers decide not to adopt a new product
because of difficulty in learning the new product
or recognized costs to learning the new over
keeping the old
Murray and Häubl 2007; Zauberman 2003 Active
Considerable exposure to
information about or
aspects of an alternative
product
Consumers decide not to adopt a new product
because they prefer products they have
encountered repeatedly in the past based
on heightened familiarity, at least when these
new products are reasonably complex
Cox and Cox 2002;Moreauetal.2001 Active
Choosing an established habit
over a new product that
conflicts
Consumers decide not to adopt a new product
when it cannot be integrated into a well-established
pattern of use that consumers have no desire
to change
Kleijnen et al. 2009 Active
Slipping back into an established
habit for using an alternative
product
Consumers continue to use existing products rather
than adopting new ones when existing usage
habits are strong and they fall back into these
old patterns
Ram and Sheth 1989;Wasson1979 Passive
Unawareness or indifference
to new products
Consumers continue to use existing products rather
than adopting new ones when they do not know
about or think the new product is relevant for them
Joseph 2010 Passive
Preference for status quo,
disinclination to change
Consumers continue to use existing products rather
than adopting new ones when they are generally
resistant toward change or are content with their
current situation
Bagozzi and Lee 1999; Ellen et al. 1991;
Gourville 2006;Oreg2003;Ram1987;
Sheth 1981; Szmigin and Foxall 1998;
Talke and Heidenreich 2014
Passive
J. of the Acad. Mark. Sci.
used products and underestimate the benefits afforded by new
innovations (Gourville 2006).
We focus here on a novel source of passive resistance:
barriers to use due to conflicts with existing habits. As we
explain, even when consumers like a new product, they may
fail to use it and instead fall back into established habits.
Habit cuing
Habits are defined as context-response associations that people
learn as they repeatedly perform responses in stable contexts. As
habits gain strength, perception of the context cue automatically
brings to mind the associated response (Neal et al. 2012; Wood
and Rünger 2016). Habit slips thus emerge from the habit cuing
process, as consumersmindless repetition of activated behavior
patterns leads them to overlook using a new product or service.
Habits can be cued by a variety of factors, including physical
locations, times of day, and prior actions in a sequence (Ji and
Woo d 2007). Of course, consumers are not literally locked in to
performing habits, and those seeking novelty or wishing to
change might make a decision to act in nonhabitual ways.
However, even given such an intention, consumers may not have
the willpower to inhibit the habitual response in mind and choose
to do something new (Neal et al. 2013).
H1: Habit slips are as prevalent a source of consumer resis-
tance to new product use as are better-established active
drivers of resistance (e.g., perceived switching costs).
Compatibility with existing habits
Our second hypothesis is that new products that conflict with
existing habits will be more susceptible to habit slips than ones
that are compatible. When a new product conflicts with con-
sumersexisting habits, the opposing habit promotes resistance
as it continues to be cued in competition with use of the new
product. However, not all new products conflict with habits in
this way. Ones that are compatible with existing habits can take
advantage of habit cues instead of competing with them. In
support of this idea, consumers who successfully adopted a
new cooking tool also reported that it was more compatible
with their existing habits than those who failed to adopt the tool
(Ostlund 1974, Study 2). More generally, Tornatzky and
Kleins(1982) meta-analytic review revealed that successful
adoption and implementation of new products was associated
with consistency with existing habits (also Wasson 1979).
Compatibility with existing habits could reflect specific prod-
uct features. In general, consumers find new products compatible
when they completely replace an old product and can thereby be
directly integrated into a habitual behavior stream. This possibil-
ity is consistent with Ram and Sheths(1989) suggestion to
reduce habit-based resistance by integrating new products into
an existing product or activity. The successful marketing of bot-
tled water in the last decade illustrates this process. Consumers
increased purchase of bottled water has been matched with de-
creased purchases of sugar-sweetened soda, suggesting that one
is replacing the other as consumers have become aware of sodas
links to obesity (Sanger-Katz 2015). The highly similar market-
ing and packaging of these two products no doubt contributed to
consumersrelatively direct transition between them.
Consumers also might increase the compatibility of new prod-
uct use by devising strategies to incorporate it into existing rou-
tines. For example, cognitive strategies can promote new product
use by Bintegrating the innovation into the preceding activity or
product^(Ram and Sheth 1989,p. 11). A number of cognitive
strategies have been developed to harness existing mental associ-
ations by tying a product to prevailing habit cues (e.g., Adriaanse
et al. 2011). For example, linking use of dental floss to tooth
brushing proved to be an effective strategy to increase long-term
floss use (Judah et al. 2013). If consumers strategically integrate
use of a new product into an established habit in this way, they
potentially avoid slips and use a new product as intended.
H2: Consumers are more likely to engage in a habit slip
when using a new product that conflicts with an existing
habit than when using a product that can be integrated
into an existing habit.
Conditions under which habit slips occur
Our final hypothesis identifies the circumstances in which habit
cues will be most likely to impact new product use. When habit-
ual responses are automatically activated, consumers are influ-
enced less by their intentions and other guides to behavior. For
example, in behavior prediction studies, participants with stronger
habits typically repeated their past behavior with little influence
from their intentions (Gardner et al. 2011;OrbellandVerplanken
2010). A field study in a local cinema illustrates this same effect:
consumers with stronger habits to eat popcorn at the cinema
continued to eat it even when the popcorn was stale and they
reported not liking it (Neal et al. 2011). Thus, a distinguishing
feature of the automaticity of habit slips that separates them from
decisions not to use a new product is that slips depend little on
consumersnegative product evaluations or intentions.
Another distinguishing feature of habit slips is that they are
especially likely to occur when consumersdecision-making ca-
pacity is reduced by distractions and related factors in daily life.
Under such conditions, consumers should be especially likely to
slip and automatically follow cues to act habitually without con-
sidering whether they are following through on their intentions to
use a new product. This phenomenon was captured in Reasons
(1979) daily diary research, in which peoples unintended actions,
or action slips, were often part of habitual behavior sequences that
occurred when people were absentmindedly thinking about
J. of the Acad. Mark. Sci.
something unrelated to what they were doing. Laboratory
tests have since validated that habits are performed inadvertently
when thought is impeded by, for example, advanced age
or performing a secondary task (e.g., de Wit et al. 2014;Ruh
et al. 2010).
H3: Although habit slips do not depend on product evalua-
tions or intentions to use, they are more likely when
consumers use a product mindlessly and have not inte-
grated it into existing habits.
In summary, as depicted in Fig. 1, habit slips are likely to
emerge as products conflict with consumersexisting habits
(H2). Finally, slips should not occur for consumers who think
carefully about a domain or who integrate new products into
their existing habits (H3).
In light of these predictions, we conducted two studies (a
survey and experiment) to test whether habits contribute to
consumersfailure to use new products as intended.
Study 1: existing habits can impede use of new
products
The primary focus of the survey was to establish that habit slips
are a common impediment to new product adoption (H1) and to
Positive
evaluations &
intentions?
H3
Decision
not to use
Conflict with
existing
habit?
H2
No
Yes
Yes
No
No
No
Product use
Habit slip
Product use
Lack strategy to
integrate?
H3
Mindless about
use?
H3
Product use
And
Yes
Fig. 1 Process model depicting factors that determine whether consumers will use new products or slip back into their existing habits
J. of the Acad. Mark. Sci.
test whether they are more likely for new products that conflict
with or are compatible with existing habits (H2). Habit slips
were indicated by a failure to use new products because of the
tendency to fall back on existing habits. The survey also
assessed other potential impediments to new product use, in-
cluding perceived difficulty (an indicator of lock-in), cost, and
changed evaluations of the new product. In this way, we could
compare the incidence of habit slips with other reasons for
nonuse. The survey also assessed how much participants liked
the new products and their compatibility with existing habits.
Participants
One hundred fifty MTurk workers (81 women, median
age = 32 years) currently living in the U.S. completed the
online survey for $1 (selected to have a 95% or better HIT
approval rate).
Materials and procedure
Participants nominated and described two products that they
had purchased in the last 6 months, identifying one that they
used regularly and one that they intended to use regularly but in
actuality used rarely or not at all. Participants nominated their
own products in order to ensure that our findings were not
limited to specific, given product examples. Participants nomi-
nated a broad spectrum of items, including regular use of an
iPhone, slippers, a computer, jeans, and a toothbrush, and failed
use of a basketball jersey, Shake Weight, juicer, DVD cleaner,
waffle maker, and picnic basket. We conducted a number of
analyses to identify differences between the products nominat-
ed in successful versus unsuccessful categories, and no system-
atic effects emerged. Most popularly nominated (successful/un-
successful) products were: computers and electronics (27%/
18%), household products and supplies (15%/19%), and beauty
and health (14%/15%). Participants further indicated whether
or not the new product replaced a different product they had
used in the past, and if yes, named the replaced product.
Anticipated and actual product use Participants reported
how often they expected to use and actually used the products
on scales from 1 (never or almost never)to5(every day or
almost every day). Participants also reported on their frequency
of use of any old product that the new product was replacing.
Self-report behavioral automaticity index (SRBAI) To as-
sess the automaticity of product use, participants completed
the SRBAI (Gardner et al. 2012) for the old products that had
been replaced by the new one as well as for the new products
they described. Specifically, participants indicated on 5-point
scales from 1 (strongly disagree)to5(strongly agree)whether
they used each product Bautomatically,^Bwithout having to
consciously remember,^Bwithout thinking,^and Bbefore I
realize Im doing it^(alphas = .90 and .94, for regularly-
and rarely-used products, respectively).
Explanations for failure to use Participants selected which
one of the following explained their rarely-used products: (a)
habit slip: BI fell back on my old habit and did what I used to
do,^(b) cognitive lock-in: BIt was difficult to use,^BInever
really learned how to use it,^(c) attitude change: BI did not
like it,^(d) lack of motivation: BIw
asnt motivated to use it,^
BIdidnotneedit,^(e) personal limitations: BIdidnthavethe
opportunity to use it,^BIdidnt have the time to use it,^BI
forgot about it,^(f) product limitations: BIt did not work prop-
erly,^BIt cost too much to use,^no longer had the product
because BIlostit,^BI gave it to someone else,^or (g) BOther.^
Conflict with and integration into habits Participants also
rated the extent to which the product conflicted with an
existing habit and whether it changed an existing routine
(1 = not at all,5=extremely).
Results
Indicatingthat the products nominated were appropriate to test
our hypotheses, participants reported that rarely-used products
were in fact used significantly less often (M= 1.86) than those
used regularly (M= 4.58), t(146) = 29.00, p< .001.
Additionally, participants perceived greater automaticity in
products they regularly used (M= 3.86) than ones rarely used
(M=1.60),t(145) = 21.93, p<.001.
Consistent with the broader argument that new products are
often used in a context with existing habits, when the new prod-
uct replaced an existing one (see BCompatibility with established
product habits^below), the old product was moderately to
strongly habitual, and thus appropriate to create habit slips.
Specifically, participants reported that the old products that their
new product was replacing (n= 131 products) were used more
often than several times a week (M= 4.15), and that using the
previous product was moderately automatic (M
SRBAI
=3.58).
Participants who did not specifically replace an old product
may instead have replaced an existing habitual behavior.
Hypothesis 1: incidence of slips To identify the incidence of
habit slips, we first evaluated the reasons for rarely-used prod-
ucts. Habit slips (i.e., BI fell back on my old habit and did what
Iusedtodo^) accounted for 25% of the products that partic-
ipants nominated as purchased but used rarely. Specifically,
habit slips and cognitive lock-in (Bit was difficult to use^and
BI never really learned how to use it^; 27% of products), were
the most commonly selected explanations for nonuse out of
the 13 provided (see Table 2).
Hypothesis 2: compatibility with established product
habits To test the effects of compatibility with existing products,
J. of the Acad. Mark. Sci.
we compared the amount of conflict experienced for regularly-
used and for rarely-used new products. Supporting H2, regularly-
used products conflicted less with other habits (M= 1.25) than
rarely-used ones (M= 1.71), t(146) = 4.84, p< .001. However,
contrary to expectations, regularly used products were about as
likely (M= 1.98) as rarely used ones (M= 2.00) to have changed
participantsexisting routines, t(146) = 0.18, p= .859.
We further tested the effects of compatibility by evaluating
whether regularly-used and rarely-used new products completely
replaced product use in an existing habit. In support of H2, par-
ticipants were more likely to use the new product when they
integrated it into their existing habits by completely replacing a
previous product. Regularly used products were more likely to
have completely replaced a previous product (63% of partici-
pants) than rarely used ones (25% of participants), McNemar
χ
2
(1, N= 146) = 36.05, p< .001.
Discussion
This survey provides some of the first empirical evidence that
habits significantly impede new product adoption by encourag-
ing consumers to unintentionally slip back into past actions.
That is, in support of our first hypothesis, habit slips accounted
for about one-fourth of the products that participants bought
intending to use regularly but in the end used only rarely.
Suggesting the pull of habits, consumers reported moderately
strong automaticity for existing products, prior to the new pur-
chase. Habit slips were then indicated by consumers falling back
on old habits and doing what they used to do. Our results reveal
that consumers fell back into old habits with some frequency.
Their newly purchased exercise bike became a catch-all for
clothing, the fancy new purse was forgotten for the old favorite,
and who really needs a product to clean DVDs anyway?
More active resistance was also evident in participants
reports that they did not use products that were difficult in
some way. Echoing earlier research on cognitive lock-in
(Murray and Häubl 2007), consumers apparently made a ra-
tional choice not to use difficult products and instead reverted
to their existing ones in order to avoid switching costs. As an
additional barrier, some consumers changed their minds and
decided that they did not like the new products.
The utility of our behavioral-level analysis of consumer
resistance is highlighted further by the importance of the com-
patibility of a new product with existing habits. Consistent
with H2, participants were more likely to successfully use a
new product when it did not conflict with a previously
established habit. Barriers to use also were lowered when
the new product completely replaced one that participants
already used, presumably because it would then be possible
to just integrate the new item into an existing behavior stream.
We also conducted a replication study that demonstrated the
reliability of the habit slip results with a new set of consumers
(see Online Supplement). This second study also evaluated
whether participants actively tried to integrate new products into
existing habits (H3). For these ratings, participants first indicated
whether or not they had ever owned each of a variety of common
products (Swiffer Sweeper; Kindle, Nook or other E-Book read-
er; pedometer; Nintendo, PlayStation, Xbox, or other video game
system; musical instrument; non-prescription sunglasses; dental
floss), and then indicated whether they employed a strategy to
remember to use it, including integrating the new product into an
existing habit. Because every participant owned at least one of
the products and could have reported a strategy for each of the
products they owned, hierarchical regression models (reflecting
products nested within participants) were constructed to predict
product use. Consistent with H3, greater use was associated with
the strategy of integrating the new product into an existing habit:
BI made a plan to use it every time I was doing certain relevant
routines or habits,^b= 0.59, SE = 0.20, t(363) = 2.96, p= .003.
Thus, successfully integrating the product into an existing habit
helped participants to avoid slips and use the new product as
intended (see Appendix 1for full results).
Study 1 clearly established the incidence of habit slips and the
barriers to use that arose from incompatibility with consumers
current behavior. However, several aspects of our analysis could
not be tested in the survey. Although liking for the new product
was not associated with the incidence of habit slips (see Online
Supplement), the correlational design makes it difficult to rule
out factors other than slips that limited product use. Furthermore,
because we did not control for the different types of products that
participants reported on, product type could have contributed to
some of our results. For these various reasons, we conducted a
longitudinal experiment to systematically evaluate the mecha-
nisms behind habit slips. Furthermore, building on the replication
Tabl e 2 Reasons for rarely used products: Study 1
Category Explanation % of
products
Habit slip BI fell back on my old habit and
did what I used to do.^
25%
Cognitive lock-in BIt was difficult to use.^18%
BI never really learned how to use it.^9%
Attitude change BI did not like it.^10%
Lack of
motivation
BIwasnt motivated to use it.^8%
BI did not need it.^4%
Personal
limitations
BI did not have the opportunity to use
it.^
3%
BI did not have the time to use it.^4%
BI forgot about it.^6%
Product
limitations
BIt did not work properly.^5%
BIt cost too much to use.^1%
BIlostit.^2%
BI gave it to someone else.^1%
Other (participants could specify) 3%
J. of the Acad. Mark. Sci.
study, we manipulated several cognitive strategies that managers
can deploy to address this barrier to new product adoption.
Study 2: habit slips depend on compatibility of a new
product with habits
College students in this experiment trialed a new laundry
product, a fabric refresher, that augmented their existing laun-
dry habits. This domain was chosen so that it was likely that
participants would fail to use the new product due to habit
slips, given that most participants reported already having
strong laundry habits. Under these conditions, people may fail
to use a new product by falling back into old laundry patterns.
This second study also enabled us to examine three aspects of
the slip mechanism in more detail. First, following H2, we antic-
ipated that habit-related barriers to adoption could be controlled
through strategies to integrate the fabric refresher into existing
routines. We tested two different cognitive strategies. The first
strategy, which did not specify integration into an existing habit,
involved implementation intentions, or if-then plans (Gollwitzer
and Sheeran 2009). That is, some participants planned to use the
product by specifying when, where, and how they would do so.
In this condition, participants formed plans to use the refresher
under circumstances they selected (e.g., when getting dressed in
the morning). Implementation intentions promote behavioral
follow-through by increasing the accessibility of a triggering
cue, which is the if component of the plan, and creating an
automatic link to the desired action, which is the then component.
Despite the general effectiveness of this strategy, it has not been
clearly successful at changing the behavior of those with strong
habits (Maher and Conroy 2015;Webbetal.2009).
We also tested a second strategy that involved directly inte-
grating a new product into a habitual routine, and thus might
help participants overcome habit slips. Illustrating this approach,
participants consumed healthier snacks when they linked a
healthy snack food to a context in which they typically ate an
unhealthful snack (Adriaanse et al. 2011). When participants
planned in this way, they learned cognitive associations between
the habitual context and the healthy foods. Presumably, these
new associations could then automatically replace the old pat-
terns by triggering the new behavior. The current study built on
this strategy by explicitly embedding the laundry refresher into
participantsexisting laundry routines. Specifically, participants
in this condition formed a strategy of habit-cued use designed to
both inhibit the habitual behavior and remind them to use the
new laundry product in that context. That is, at the point when
they usually washed their clothes or rewore them, participants
planned to think, BDont do what you normally do, use the
laundry refresher instead.^For example, if students normally
grabbed a pair of jeans off the floor while hurrying to class, they
would now stop and think, BDont just rewear the smelly jeans,
use the fabric refresher.^This strategy of integrating use of the
new laundry refresher into participantsexisting laundry habits
was intended to facilitate use.
Second, testing H3, we evaluated the conditions under
which slips occurred. We anticipated that slips would emerge
despite participantsliking for and intentions to use the new
product. To provide a controlled, experimental test of this
claim, participants reported across four weeks their intentions
to use the product and their explicit and implicit liking for it, as
well as their actual use of it. By assessing both implicit and
explicit evaluations, we tested the variety of ways that liking
for the new product contributed to habit effects.
In short, we predicted that slips would not depend on these
indicators of liking and intention. We also anticipated that
slips would be most apparent for participants who did laundry
relatively mindlessly and thought little about it. This pattern
would echo classic action slip findings in which slips occur
during episodes of distraction and thinking about something
other than what one is doing (see Wood and Rünger 2016). In
contrast, people who carefully thought about how to do their
laundry should be less likely to inadvertently repeat old pat-
terns and should be more successful at using the new product.
In summary, our primary predictions were that participants
would avoid habit slips and use the new product primarily
when they (a) integrated it into their existing habit (habit-
cued use strategy, H2) or (b) were thoughtful about their laun-
dry (H3). By examining both of these factors together in a
single experiment, we also could test the interaction between
them. It seemed likely that, regardless of cognitive strategy,
participants who typically think about laundry should not be
vulnerable to falling back into old habits and thus should suc-
cessfully use the new product. In a sense, these participants do
not need the strategy to counteract habit slips. If this is correct,
then the analysis on product use should reveal an interaction
between strategy and thought in which the habit-cued strategy
is effective at reducing slips only for participants who think
rarely about their laundry. Furthermore, the beneficial effects
of strategy and thought on product use should not depend on
participantsliking for the product or intentions to use it (H3).
Participants
A total of 70 students (55 women, M
age
= 20.21 years) partic-
ipated for payment plus extra credit in their psychology class.
One additional student who indicated that he misunderstood
the study instructions (assessed in the second session) was not
included in the final analyses.
Procedure
Lab session 1 For a study on a new laundry product, partic-
ipants first completed a brief survey assessing demographic
information and laundry habits. Each participant then received
a free trial bottle of a fabric refresher with the following
J. of the Acad. Mark. Sci.
instructions: BFor the next four weeks, try using it whenever
you want to refresh clothes that you have already worn but
would like to wear again.^The control group received these
instructions but no additional information (n=20).
The remaining participants were provided with one of our
two strategies to promote use. So that participants implement-
ed the strategies at point of use, they were instructed to do a
sniff test when deciding whether to rewear or launder previ-
ously worn clothes.
The standard implementation intentions group was
instructed that, if the item was smelly, then they should use
the fabric refresher (n= 20). To help them plan in this way, this
group wrote down where, when, and how they would use the
sniff test and the product refresher. These instructions were
carefully constructed to parallel the implementation intentions
used in prior research, BIf situation Yoccurs, then I will initiate
goal-directed behavior X!^(Gollwitzer and Sheeran 2006,p.
82).
The habit-cued use group replaced existing laundry habits
with the refresher (n= 30). Instead of how they typically dealt
with previously worn clothes, including wearing them again
or washing them, they were instructed to do the sniff test,
inhibit their standard response, and instead use the new prod-
uct. To help them plan in this way, participants wrote down the
situations (where, when) they would typically rewear or re-
wash clothes, and how they would not do what they normally
do and instead use the new product. The inhibitory component
of this strategy is reminiscent of another habit control strategy
involving vigilant monitoring (Quinn et al. 2010). However,
in the present study, participants simultaneously inhibited the
existing habit and remembered to replace it with the new
product.
Finally, all participants completed an assessment of implicit
attitudes toward the product using the Affect Misattribution
Procedure (AMP), described below in the Measures section
(Payne et al. 2005), and reported on their initial thoughts about
it. Participants agreed to report on their product use via a web
survey every week for the next four weeks.
Weekly surveys to assess in-home product use To reinforce
the initial instructions, in the first weekly survey, participants
were reminded of their assigned strategy (no-strategy control,
implementation intentions, habit-cued use). This served as a
check of participantsunderstanding. After re-reading the in-
structions, they indicated how confident they were that the
instructions presented were what they had originally received.
As noted above, all participants correctly remembered the
strategy instructions, with the exception of one, who was de-
leted from the analyses.
Lab session 2 At the end of four weeks, participants returned
to the lab and completed the final weekly survey, gave explicit
product evaluations, and again responded to the AMP implicit
attitude measure. One participant failed to show up for the
final session, but the reported analysis included this individ-
uals responses for the earlier sessions.
Measures
Laundry habits In the first lab session, participants reported
on their existing laundry habits, specifically how often they
rewore and washed clothes when they were only slightly dirty
on scales from 1 (never)to5(almost always).
Product use In each weekly survey, participants reported the
number of times they used the product that week.
Demonstrating the validity of this measure, it was significant-
ly correlated with the grams used from the bottle(s) that par-
ticipants returned at the end of the study, r(50) = .50, p<.001.
Given that not every participant remembered to return their
bottle to be weighed, we assessed product use in the study
from participantsreports. To further check on the validity of
student reports, they indicated at the follow-up survey, after
the end of the study, how accurately they had reported product
use. A full 70% of respondents answered that their weekly
reports of product use were 90100% accurate. Only 5% of
participants reported accuracy of less than 70% (retained in
the analysis because their data did not differ from the rest of
the sample).
Thought about laundry decisions On a 5-point scale ranging
from 1 (no thought)to5(very much thought), participants
reported how much thought they typically gave to their deci-
sions to wash or rewear an item of clothing that they had
already worn. They made this judgment during the first lab
session and thus indicated how much thought they typically
gave prior to participating in the study.
Intention to use and to purchase During the initial session
before they had used the product, participants indicated on a 7-
point scale anchored by 1 (strongly disagree)and7(strongly
agree) whether they intended to use the product. During the
final session, on 5-point scales anchored by 1 (fully disagree)
and 5 (fully agree), participants indicated how likely they were
to purchase the product in the future and whether they planned
to purchase it. These purchase measures were highly correlat-
ed, r(65) = .85, p< .001, and were combined into a single
measure of intention to purchase.
Product difficulty Participants rated how easy or difficult it
would be for them to use the product from 1 (extremely
difficult)to7(extremely easy).
Perceived control Participants indicated their agreement with
whether or not using the product was completely up to them
from 1 (strongly disagree)to7(strongly agree).
J. of the Acad. Mark. Sci.
Implicit attitudes: affect misattribution procedure The
AMP relies on the principle of affect carryover, in which an
affective response to a briefly presented stimulus (e.g., a photo
of a puppy) influences the judgment of a subsequent neutral
target (e.g., inkblot, Chinese character) in an affect-congruent
manner (e.g., the neutral inkblot seems more positive when
appearing after a photo of a puppy). We adapted the AMP to
measure implicit evaluations of (a) the fabric refresher (using
images of the bottle), (b) a competing laundry product (using
images of that bottle), and (c) a neutral image (a gray box). In
this assessment, participants first saw one of these images
flashed for 75 ms in the center of the screen (fabric refresher,
a competing laundry product, or neutral image), then a blank
screen for 125 ms, followed by one of 48 unique inkblots for
100 ms. The inkblot was then covered with a mask until the
participant made their judgment whether the inkblot was
pleasant or unpleasant by pressing a key on the keyboard.
Participants completed a total of 48 trials (16 fabric refresher
primes, 16 competing product primes, and 16 neutral image
primes).
Participantsimplicit affect to the primes was assessed from
the number of times the inkblot was deemed pleasant when it
was preceded by the fabric refresher versus the neutral image.
The comparison between the fabric refresher and the compet-
ing laundry product was not used in the final analyses because
of the extremely positive existing associations participants had
with the competing product, which created a ceiling effect
across sessions for that particular control prime. Participants
completed the AMP during both the initial and final lab ses-
sions, allowing us to calculate change in positive implicit as-
sociations to the fabric refresher during the study period.
Explicit product evaluations In the final lab session, partic-
ipants rated the product on the following evaluative attributes
using 5-point scales ranging from 1 (fully disagree)to5(fully
agree): convenience, time-saving, fits lifestyle, beneficial, lik-
ing, would recommend to a friend, Facebook-worthy, appeal-
ing packaging, smells good, smells fresh, removes odors, and
freshens clothes. Because the scales reflected a single under-
lying evaluative indicator (Cronbachs alpha = .92), they were
combined into a single measure of explicit product evaluation.
Results
Overall, participants used the fabric refresher an average of
12.06 times (range 525 times) across the four-week study. If
the potential maximum use was 25 times, then many partici-
pants did not use it at every opportunity, but on average used it
only 48% of these times. In general, participants reported
strong current laundry habits, with 73% reporting a strong
habit for rewearing and/or washing their clothes. Means and
standard deviations of all variables across conditions are pre-
sented in Table 3.
Relationship between habit strength and other variables
Participants with stronger laundry habits were not more in-
volved in the product category. Thus, habit strength was un-
related to intentions to use the new product, thought about it,
or evaluations of it. Of the various factors we assessed,
strength of existing laundry habits was related only to per-
ceived control over product use, r(68) = .28, p= .019. The
full table of correlations can be found in Appendix 2, and
relevant additional measures and statistical tests are located
in the Online Supplement.
Hypotheses 2 and 3: thought and strategies to use new
product We constructed a regression model predicting total
use from (a) experimental condition (dummy coded into im-
plementation intentions vs. control; habit-cued vs. control),
(b) how much participants thought about their laundry deci-
sions, and (c) the two interactions between the two condition
variables and amount of thought. The predicted effects
emerged for habit-cued use. That is, in addition to greater
product use from the habit-cued strategy (vs. control),
Tabl e 3 Mean product use and product evaluations as a function of
experimental condition: Study 2
Measure Standard
implementation
intentions
Habit-cued
use
Control
Product use 11.05 (4.25) 13.28 (4.30) 11.17 (3.75)
Thought about laundry
a
2.30 (0.66) 2.60 (0.81) 2.55 (0.69)
Pre-study laundry loads
a
4.85 (2.78) 4.43 (2.66) 3.80 (1.85)
Intention to use
a
5.55 (1.10) 5.63 (1.10) 5.45 (1.28)
Product effectiveness
a
5.20 (1.06) 5.27 (1.31) 5.30 (1.13)
Difficulty
a
5.60 (1.35) 5.70 (1.51) 5.85 (1.39)
Perceived control
a
5.50 (1.50) 5.07 (1.91) 6.00 (1.34)
Pre use AMP
a
3.85 (8.13) 3.07 (9.53) 4.15 (5.21)
Explicit attitudes
b
3.72 (0.58) 3.92 (0.63) 3.90 (0.80)
In-study laundry loads
b
4.15 (2.11) 4.83 (3.78) 4.58 (2.97)
Freq of re-wearing
clothing
b
4.51 (1.53) 3.98 (1.71) 4.15 (1.94)
Normative beliefs
b
1.78 (2.82) 2.53 (3.16) 2.75 (2.87)
Purchase intentions
b
2.78 (0.88) 3.20 (0.99) 3.26 (1.15)
Means for product use (total number of times used across 4 weeks),
thought about laundry decisions (1= no thought, 5 = very much thought),
loads of laundry in the past month (pre-study), intention to use (1 = strong-
ly disagree, 7 strongly agree), product effectiveness (1 = strongly dis-
agree, 7 = strongly agree), product difficulty (1 = extremely difficult,
7 = extremely easy), whether using the product was completely up to
them (1 = strongly disagree, 7 = strongly agree), implicit (AMP) evalua-
tions, explicit evaluations, loads of laundry during the study, number of
times participant typically wore items of clothing between washings,
normative beliefs (number of favorable minus unfavorable comments
from others), and purchase intentions (15 scale with increasing numbers
indicating increasing favorability)
a
Measures assessed at beginning of study, n=70
b
Measures assessed at end of study, n=67
J. of the Acad. Mark. Sci.
unstandardized beta =2.55, SE =1.20,t(60) = 2.13, p=.037,
and among those who thought more about laundry decisions,
unstandardized beta =2.68, SE =1.37,t(60) = 1.95, p=.055,
the predicted interaction appeared between habit-cued use and
amount of thought, unstandardized beta = 3.90, SE =1.64,
t(60) = 2.37, p=.021.
1
However, standard implementation
intentions did not increase product use beyond the control
group instructions, and standard implementation intentions
(compared to controls) did not interact with amount of
thought, all ts < 1. In a supplementary analysis reconfigured
so that the condition variable directly compared habit-cued
with standard implementation intentions, the strategy by
thought interaction remained significant, unstandardized beta
=4.05, SE =1.69,t(60) = 2.40, p=.020.
To interpret the predicted interaction, we calculated the
simple main effects of the manipulation for participants who
gave little versus considerable thought to their laundry deci-
sions (Cohen et al. 2003). Within the control and standard
implementation intentions conditions, participants failed to
use the product unless they gave considerable thought to their
laundry, simple slope = 2.68, t(60) = 1.95, p= .055, and
simple slope = 2.83, t(60) = 1.99, p= .051, for control and
standard strategy, respectively; see Fig. 2.However,partici-
pants using a habit-cued strategy used the product frequently
regardless of their amount of thought, simple slope = 1.22,
t(60) = 1.34, p= .184. Thus, high product usage levels were
achieved both by participants who chronically thought about
their laundry, regardless of experimental condition, as well as
participants who thought little about laundry decisions but
integrated new product use into their existing laundry routine
through the habit-cued strategy.
Hypothesis 3: intentions or liking for new products To
ensure that the success of the habit-cuing strategy was
not driven by alternative mechanisms, we conducted addi-
tional analyses to rule out the influence of various factors
that could guide product use, including perceived difficul-
ty, implicit attitudes (AMP), explicit attitudes, and inten-
tions. Each of these variables was entered as a covariate in
a separate regression model containing experimental con-
dition, amount of thought, and the resulting interactions
predicting total use. The interaction of habit-cued use with
thought remained significant in all four models after in-
cluding these additional predictors, indicating that the
habit-cued strategy continued to significantly promote
use among those who thought little about their laundry,
even when other contributing factors were controlled.
Discussion
This experiment provides direct insight into the workings of
habit slips through a naturalistic trial of a new laundry product
across several weeks. As we had anticipated, participants were
most successful at using the new laundry product when they
adopted a cognitive strategy of tying its use to their existing
habits of washing or rewearing their previously worn clothes.
This habit-cued strategy integrated the new product into par-
ticipantsexisting laundry habits by (a) inhibiting the habit
and (b) substituting the new product. In these ways, use of
the new product became compatible with participantsalready
established laundry habits (H2).
A second clear finding in this experiment is that, consistent
with H3, participants slipped back into their strong laundry habits
regardless of the favorability of their intentions to use the refresher
and despite their explicit or implicit evaluations of it. This disso-
ciation from liking is a distinguishing feature of habit slips that
separates them from decisions not to use a new product. That is,
slips do not depend on consumersnegative product evaluations
or intentions. Instead, much like action slips, habit slips represent
glitches in the rational control of behaviorwhen people fail to
follow through on their intentions to use novel products.
The experiment findings also support H3 in that habit slips
occurred primarily among participants who typically did not
think about their laundry. Thus, participants were especially like-
ly to slip and fail to use the new product when they were acting in
arelatively mindless way and not monitoring their behavior.
Lack of thought left them vulnerable to falling back into old
laundry habits. This pattern is consistent with prior research on
action slips in which actions were especially likely to be captured
by habit cues when participants were distracted and not thinking
about what they were doing (see Wood and Rünger 2016).
1
The variance inflation factors (VIF) for the regression model were suf-
ficiently low to rule out concerns about multicollinearity: habit-cued use
(1.46), standard implementation intentions (1.51), and thought (1.05).
8
9
10
11
12
13
14
15
16
-1 -0.5 0 0.5 1
Total Product Use
Amount of Thou
g
ht
Control
Standard
Implementation
Intentions
Habit-Cued Use
Fig. 2 Regression model predicting use of new laundry product from
amount of thought and cognitive strategy: Experiment 2. Vertical axis
represents number of times participants used the new product during the
4-week period. Simple slopes (± 1SD) depict relations between product use
and amount of thought for each cognitive strategy. Means are unadjusted
J. of the Acad. Mark. Sci.
Forming a habit-cued strategy did not change participants
motivations to use the new product, their evaluations of it, or
the perceived difficulty of using the product. Yet this cognitive
strategy did change behavior. It seems, then, that the strategy
worked by stopping participants from acting on the habitual
response in mind.
Finally, this experiment highlights the utility of correctly
matching behavior change strategies to the mechanisms that
promote an unwanted behavior. Because habit slips are activated
by existing habits, strategies to overcome such slips most effec-
tively target the habit cuing mechanism and integrate new prod-
ucts into existing routines. Of the cognitive strategies tested in
Study 2, standard implementation intentions were relatively un-
successful. Yet, research indicates that these standard implemen-
tation plans, because they remind people of their best intentions,
are useful at changing nonhabitual behavior when participants
are at risk of forgetting their intentions (Webb et al. 2009). By
this logic, change strategies that heighten awareness of product
intentions should be most successful at altering behavior guided
by explicit evaluations. Thus, we suspect that there is no one-
size-fits-all behavior change strategy. Instead, the most success-
ful approaches will be based on a careful analysis of the psycho-
logical mechanisms that underlie an unwanted behavior.
General discussion
The current research offers one of the first empirical demon-
strations of the ways in which existing habits impede con-
sumersadoption of new products. Together, the survey and
longitudinal experiment provide clear evidence that habit is a
strong barrier to adoption.
Most prior research in this area has focused on one part of
the puzzle of new product adoption, emphasizing active forms
of resistance, especially consumersexplicit beliefs and inten-
tions that impede new product use (e.g., Claudy et al. 2015).
Relevant to the use-based resistance in the present research,
such beliefs might emerge as people actually use a product
and gain experience with it. For example, consumersinten-
tions to use a products many attractive features might become
less favorable following purchase given the recognition of
costs associated with complexity in those features (e.g.,
Meyer et al. 2008). Similarly, consumers may fail to act on
product preferences that were formed from indirect experi-
ence, given that initially abstract product construals may shift
with direct experience and the concrete experience of chal-
lenges involved in actual use of the product (Hamilton and
Thompson 2007). Clearly, changes in product use intentions
are an important reason for consumersfailures to use a new
product as planned. However, the present research demon-
strates a more passive mechanism impeding new product use
that does not depend on active intentions but instead emerges
from consumersmindless, habitual repetition of past action.
Managerial implications
Our results provide several important insights to help man-
agers understand the value provided by new products and
services and how to market them to consumers. One com-
mon way to assess value is through consumer surveys and
focusgroupstoidentifypositivefeaturesofanewproduct
that might distinguish it sufficiently to attract consumer
purchase and use. Our research indicates the importance
of conducting these investigations within the context of
consumersdaily lives. Although new products and ser-
vices might appear desirable in the abstract, ones that con-
flict with existing habits are unlikely to be used. Thus,
even after our consumers in Study 1 were favorably
impressed enough to purchase an item, they failed to use
it when their existing habits conflicted with new product
use. Accurate assessment of barriers to adoption thus re-
quires understanding the contexts in which consumers are
likely to use a new product.
Along with demonstrating the importance of evaluating
product introductions within everyday contexts in order to
understand how they fare in the stream of consumersongoing
behavior, our research suggests specific ways to integrate
products into these behavior streams. That is, we provided
evidence of several cognitive strategies involving tying a
new product to an existing behavior in order to promote adop-
tion. Our initial survey findings demonstrated that some con-
sumers already used these strategies spontaneously in order to
increase compatibility with existing habits. The experiment
demonstrated further that these strategies can be effectively
taught as new products are introduced. An especially effective
strategy involves habit-cuing, as consumers piggyback use of
a new product onto an existing one (e.g., first brush, then floss,
Judah et al. 2013).
Limitations and future research
Although our experiment focused on cognitive strategies to
promote use, product packaging may be equally successful at
reducing conflict with existing habits. These strategies involve
alterations in existing product design so that new products can
be easily integrated into established routines. For example,
U.S. consumers initially resisted tofu because it required spe-
cific cooking skills and techniques to make it palatable. Tofu
manufacturers partially addressed this barrier by packaging it
into ready-to-eat frozen desserts (Ram and Sheth 1989). A
more recent example is the availability of reusable shopping
bags that fold small enough to fit into a purse or briefcase.
These can be readily available when consumers are in the
checkout line in the grocery store. We suspect that packaging
and design compatibilities between old and new products con-
tributed to consumersreports in our initial survey that a new
product had replaced an old one. As would be expected,
J. of the Acad. Mark. Sci.
frequently-used new products were reported especially likely
to completely replace old ones in these ways.
In general, cognitive and packaging integration strat-
egies represent powerful paths to new product adoption
and avoiding the habit slips that occur when new prod-
ucts get neglected in consumersongoing habits and
routines. The broad implication of our work is not to
fight against consumerspast behavior, but instead to
enlist it as an ally to promote successful adoption of
new products.
Acknowledgments The authors thank Julia Cooperman and Kerry
Zweig for their assistance with data collection, Timothy Hayes for his
help with data analysis, and Joseph Priester and Stephen Read for their
helpful comments on an earlier version of the article.
Appendix 1: Product use strategies (replication
study)
Measures
Ownership of common items Participants indicated whether
or not they currently owned or had owned in the past seven
common products (featured in Amazons Best Selling
Products lists 2009, 2010: Swiffer Sweeper; Kindle, Nook or
other E-Book reader; pedometer; Nintendo, PlayStation,
Xbox, or other video game system; musical instrument; non-
prescription sunglasses; and dental floss).
Strategies to use new products For each product partici-
pants owned, they indicated whether or not they sponta-
neously tried to integrate the new product into an
existing habit: BI made a plan to use it every time I
was doing certain relevant routines or habits.^They also
reported on other possible use strategies: BIputitwhere
I would be sure to see it so I would remember to use it;^
BI put reminders on my calendar to use it;^BImadea
plan to use it in a certain context or at a particular time
of day;^BI asked a friend, family member, or roommate
to help remind me to use it;^BI really liked it and
wanted to use it, so I just remembered;^or indicated
an BOther^strategy.
Results
Greater use of the new product was associated with the
habit integration strategy (BI made a plan to use it every
time I was doing certain relevant routines or habits,^
b=0.59,SE =0.20,t(363) = 2.96, p=.003),aswellas
positive product evaluations (BI really liked it and wanted
to use it, so I just remembered,^b= 0.89, SE =0.17,
t(364) = 5.33, p= .003). No other strategies promoted
product use.
Appendix 2: Table of intercorrelations (Study 2)
Tabl e 4 Summary of intercorrelations (Study 2)
Measure 2 3 4 5 6 7 8 9 10 11 12 13 14
1. Product use .14 .11 .38** .16 .30* .13 .06 .41** .30* .45** .09 .03 .43**
2. Thought about laundry
a
.01 .34** .05 .13 .04 .20
t
.02 .15 .15 .12 .10 .20
3. Laundry habit strength
a
.13 .12 .06 .28* .07 .18 .09 .22
t
.14 .10 .02
4. Intention to use
a
.26* .60** .13 .11 .07 .15 .41** .37** .20 .20
5. Product effectiveness
a
.36** .22
t
.04 .06 .09 .41** .13 .01 .41**
6. Product difficulty
a
.15 .05 .07 .02 .34** .29* .07 .19
7. Perceived control
a
.14 .26* .10 .00 .15 .08 .07
8. Pre use AMP
a
.29* .58** .13 .15 .20 .28*
9. Post use AMP
b
.61** .01 .00 .03 .06
10. Change in AMP .12 .12 .18 .18
11. Explicit attitudes
b
.12 .46* .75**
12. Freq of re-wearing clothing
b
.10 .06
13. Normative beliefs
b
.40*
Measure 14 is purchase intentions, assessed at the end of the study
a
Measures assessed at beginning of study, n=70
b
Measures assessed at end of study, n=67
t
p<.10,*p<.05,**p<.01
J. of the Acad. Mark. Sci.
References
Adriaanse,M.A.,Gollwitzer,P.M.,deRidder,D.T.,deWit,J.B.,&
Kroese, F. M. (2011). Breaking habits with implementation intentions:
a test of underlying processes. Personality and Social Psychology
Bulletin, 37,502513. doi:10.1177/0146167211399102.
Andrew, J., & Sirkin, H. (2003). Innovating for cash. Harvard Business
Review, 81,7683. doi:10.1225/R0309E.
Antioco, M., & Kleijnen, M. (2010). Consumeradoption oftechnological
innovations: effects of psychological and functional barriers in a
lack of content versus a presence of content situation. European
Journal of Marketing, 44, 17001724. doi:10.1108/
03090561011079846.
Bagozzi, R. P., & Lee, K. H. (1999). Consumer resistance to, and accep-
tance of, innovations. Advances in Consumer Research, 26,218
225.
Bucklin, L. P., & Sengupta, S. (1993). The co-diffusion of complemen-
tary innovations: supermarket scanners and UPC symbols. Journal
of Product Innovation Management, 10, 148160. doi:10.1016/
0737-6782(93)90006-C.
Claudy, M. C., Garcia, R., & ODriscoll, A. (2015). Consumer resistance
to innovationa behavioral reasoning perspective. Journal of the
Academy of Marketing Science, 43,528544. doi:10.1007/s11747-
014-0399-0.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple
regression/correlation analysis for the behavioral sciences (3rd ed.).
Mahwah: Erlbaum.
Cox, D., & Cox, A. D. (2002). Beyond first impressions: the effects of
repeated exposure on consumer liking of visually complex and sim-
ple product designs. Journal of the Academy of Marketing Science,
30,119130. doi:10.1177/03079459994371.
de Wit, S., van de Vijver, I., & Ridderinkhof, K. R. (2014). Impaired
acquisition of goal-directed action in healthy aging. Cognitive,
Affective, & Behavioral Neuroscience, 14,647658. doi:10.3758/
s13415-014-0288-5.
Dholakia, U. M. (2001). A motivational process model of product in-
volvement and consumer risk perception. European Journal of
Marketing, 35,13401362. doi:10.1108/EUM0000000006479.
Ellen, P. S., Bearden, W. O., & Sharma, S. (1991). Resistance to techno-
logical innovations: an examination of the role of self-efficacy and
performance satisfaction. Journal of the Academy of Marketing
Science, 19,297307.
Garcia, R., Bardhi, F., & Friedrich, C. (2007). Overcoming consumer
resistance to innovation. MIT Sloan Management Review, 48(4),
8288.
Gardner, B., de Bruijn, G. J., & Lally, P. (2011). A systematic review and
meta-analysis of applications of the self-report habit index to nutri-
tion and physical activity behaviors. Annals of Behavioral Medicine,
42,174187. doi:10.1007/s12160-011-9282-0.
Gardner, B., Abraham, C., Lally, P., & de Bruijn, G. J. (2012).
Towards parsimony in habit measurement: testing the convergent
and predictive validity of an automaticity subscale of the self-
report habit index. International Journal of Behavioral Nutrition
and Physical Activity, 9, 102113. doi:10.1186/1479-5868-9-
102.
Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and
goal achievement: a meta-analysis of effects and processes.
Advances in Experimental Social Psychology, 38,69119. doi:10.
1016/S0065-2601(06)38002-1.
Gollwitzer, P. M., & Sheeran, P. (2009). Self-regulation of consumer
decision making and behavior: the role of implementation inten-
tions. Journal of Consumer Psychology, 19,593607. doi:10.
1016/j.jcps.2009.08.004.
Gourville, J. T. (2006). Eager sellers, stony buyers. Harvard Business
Review, 84,99106.
Hamilton, R. W., & Thompson, D. V. (2007). Is there a substitute for
direct experience? Comparing consumerspreferences after direct
and indirect product experiences. Journal of Consumer Research,
34,546555. doi:10.1086/520073.
Heidenreich, S., & Handrich, M. (2015). What about passive innovation
resistance? Investigating adoption-related behavior from a resistance
perspective. Journal of Product Innovation Management, 32,878
903. doi:10.1111/jpim.12161.
Heidenreich, S., & Kraemer, T. (2016). Innovationsdoomed to fail?
Investigating strategies to overcome passive innovation resistance.
Journal of Product Innovation Management, 33,277297. doi:10.
1111/jpim.12273.
Ji, M. F., & Wood, W. (2007). Purchase and consumption habits: not
necessarily what you intend. Journal of Consumer Psychology, 17,
261276. doi:10.1016/S1057-7408(07)70037-2.
Joseph, R. C. (2010). Individual resistance to IT innovations.
Communications of the ACM, 53(4), 144146. doi:10.1145/1721654.
1721693.
Judah, G., Gardner, B., & Aunger, R. (2013). Forming a flossing habit: an
exploratory study of the psychological determinants of habit forma-
tion. British Journal of Health Psychology, 18,338353. doi:10.
1111/j.2044-8287.2012.02086.x.
Kleijnen, M., de Ruyter, K., & Wetzels, M. (2007). An assessment of
value creation in mobile service delivery and the moderating role of
time consciousness. Journal of Retailing, 83,3346. doi:10.1016/j.
jretai.2006.10.004.
Kleijnen, M., Lee, N., & Wetzels, M. (2009). An exploration of consumer
resistance to innovation and its antecedents. Journal of Economic
Psychology, 30,344357. doi:10.1016/j.joep.2009.02.004.
Maher, J. P., & Conroy, D.E. (2015). Habit strength moderates the effects
of daily action planning prompts on physical activity but not seden-
tary behavior. Journal of Sport & Exercise Psychology, 37,97107.
doi:10.1123/jsep.2014-0258.
Meyer, R. J., Zhao, S., & Han, J. K. (2008). Biases in valuation vs. usage
of innovative product features. Marketing Science, 27,10831096.
doi:10.1287/mksc.1080.0367.
Moreau, C. P., Lehmann, D. R., & Markman, A. B. (2001). Entrenched
knowledge structures and consumer response to new products.
Journal of Marketing Research, 38,1429. doi:10.1509/jmkr.38.1.
14.18836.
Murray, K. B., & Häubl, G. (2007). Explaining cognitive lock-in: the role
of skill-based habits of use in consumer choice. Journal of
Consumer Research, 34,7788. doi:10.1086/513048.
Neal, D. T., Wood, W., Wu, M., & Kurlander, D. (2011). The pull of the
past: when do habits persist despite conflict with motives?
Personality and Social Psychology Bulletin, 37,14281437. doi:
10.1177/0146167211419863.
Neal, D. T., Wood, W., Labrecque, J. S., & Lally, P. (2012). How do habits
guide behavior? Perceived and actual triggers of habits in daily life.
Journal of Experimental Social Psychology, 48,492498. doi:10.
1016/j.jesp.2011.10.011.
Neal, D. T., Wood, W., & Drolet, A. (2013). How do people adhere to
goals when willpower is low? The profits (and pitfalls) of strong
habits. Journal of Personality and Social Psychology, 104,959
975. doi:10.1037/a0032626.
Orbell, S.,& Verplanken, B. (2010). The automatic component of habit in
health behavior: habit as cue-contingent automaticity. Health
Psychology, 29,374383. doi:10.1037/a0019596.
Oreg, S. (2003). Resistance to change: developing an individual differ-
ences measure. Journal of Applied Psychology, 88,680693. doi:10.
1037/0021-9010.88.4.680.
Ostlund, L. E. (1974). Perceived innovation attributes as predictors of
innovativeness. Journal of Consumer Research, 1,2329. doi:10.
1086/208587.
J. of the Acad. Mark. Sci.
Parasuraman, A., & Grewal, D. (2000). The impact of technology on the
quality-value-loyalty chain: a research agenda. Journal of the
Academy of Marketing Science, 28, 168174. doi:10.1177/
0092070300281015.
Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. D. (2005). An
inkblot for attitudes: affect misattribution as implicit measurement.
Journal of Personality and Social Psychology, 89,277293. doi:10.
1037/0022-3514.89.3.277.
Quinn, J. M., Pascoe, A., Wood, W., & Neal, D. T. (2010). Cant control
yourself? Monitor those bad habits. Personality and Social
Psychology Bulletin, 36, 499511. doi:10.1177/0146167209360665.
Ram, S. (1987). A model of innovation resistance. Advances in Consumer
Research, 14,208212.
Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: the
marketing problem and its solutions. Journal of Consumer
Marketing, 6,514. doi:10.1108/EUM0000000002542.
Reason, J. T. (1979). Actions not as planned. In G. Underwood & R.
Stevens (Eds.), Aspects of consciousness (pp. 6789). London:
Academic Press.
Rogers, E. M. (1962). Diffusion of innovations. New York: Free Press of
Glencoe.
Rogers, E. M., & Shoemaker, F. F. (1971). Communication of innova-
tions: A cross-cultural approach. New York, N. Y.: Free Press.
Ruh, N., Cooper, R. P., & Mareschal, D. (2010). Action selection in
complex routinized sequential behaviors. Journal of Experimental
Psychology: Human Perception and Performance, 36, 955975.
doi:10.1037/a0017608.
Sanger-Katz, M. (2015). The decline of big soda.The New York times.
Retrieved from http://www.nytimes.com.
Sheth, J. N. (1981). Psychology of innovation resistance: The less devel-
oped concept (LDC) in diffusion research. In Research in marketing
(Vol. 4, pp. 273282). Greenwich: JAI Press.
Stone, R. N., & Grønhaug, K. (1993). Perceived risk: further consider-
ations for the marketing discipline. European Journal of Marketing,
27(3), 3950. doi:10.1108/03090569310026637.
Strebel, J., ODonnell, K., & Myers, J. G. (2004). Exploring the connec-
tion between frustration and consumer choice behaviorin a dynamic
decision environment. Psychology and Marketing, 21,10591076.
doi:10.1002/mar.20037.
Szmigin, I., & Foxall, G. (1998). Three forms of innovation resistance:
the case of retail payment methods. Technovation, 18,459468. doi:
10.1016/S0166-4972(98)00030-3.
Talke, K., & Heidenreich, S. (2014). How to overcome pro-change bias:
incorporating passive and active innovation resistance in innovation
decision models. Journal of Product Innovation Management, 31,
894907. doi:10.1111/jpim.12130.
Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and
innovation adoption-implementation: a meta-analysis of findings.
IEEE Transactions on Engineering Management, EM-29(1), 28
45. doi:10.1109/TEM.1982.6447463.
Wasson, C. R. (1979). Consumer choice processes: search, or automatic
response? Journal of the Academy of Marketing Science, 7,360
373. doi:10.1007/BF02729685.
Webb, T. L., Sheeran, P., & Luszczynska, A. (2009). Planning to break
unwanted habits: habit strength moderates implementation intention
effects on behavior change. British Journal of Social Psychology,
48,507523. doi:10.1348/014466608X370591.
Wood, W., & Rünger, D. (2016). Psychology of habit. Annual Review of
Psychology, 67,289314. doi:10.1146/annurev-psych-122414-
033417.
Zauberman, G. (2003). The intertemporal dynamics of consumer lock-in.
JournalofConsumerResearch,30,405419. doi:10.1086/378617.
J. of the Acad. Mark. Sci.
... Among the articles with a psychological perspective only three of them, Labrecque et al. [50], Hess et al. [51], and Conrady et al. [16] explicitly mention laundry and laundering activities. However, a number of articles were identified that points towards more abstract psychological concepts that could guide laundry descisions. ...
... One psychological article that explicitly included laundering in the study of habits was Labrecque et al. [50]. Here, the occurrences of 'habit slips' as resistance to new products (in this case a fabric refresher) were investigated through a survey and later through a four-week experiment. ...
... Unfortunately, design interventions and information campaigns seem to have little success in changing behavior [11,12]. Regardless of available technology, habits rather than intentions seem to guide behavior [50,73]. None of the identified articles addressed this type of individual adaptation with the exception being Conrady et al. [16]. ...
Article
Today’s washing appliances are much more efficient than those of a decade ago, but the environmental benefits of this efficiency are counteracted by shifts in consumer behavior. Initiatives to reverse these shifts have often proven futile, indicating a basic lack of clarity on why we clean our clothes. This article is an explorative review with the aim of identifying dominant factors that shape how we do our laundry. The results can be used both as an introduction to laundry research in general, as well as a baseline for future interdisciplinary research. Three guiding principles are presented that describe the most influential factors underlying laundering: (1) technology changes conventions, while social context dictates technology acceptance; (2) technological solutions are often suggested to influence consumers, but individual concerns seem to override the effect of such interventions; (3) consumers are guided by social conventions, rooted in underlying psychological dynamics (e.g. moral dimensions of cleanliness). Looking at these principles it is understandable why interventions for sustainability are failing. Many interventions address only a part of a principle while disregarding other parts. For example, consumers are often informed of the importance of sustainability (e.g. “washing at lower temperature is good for the environment”), while questions of social belonging are left out (e.g. “many of your neighbors and friends wash at lower temperature”). To increase the possibility of a lasting change, it would be beneficial if instead all of the three principles could be addressed given the specific consumer group of interest.
... A tendency to slip back into old habits occurs once behavior has become controlled by cues and is less sensitive to changes in the desirability of behavioral outcomes. The cue-contingent behavioral inflexibility conferred by habits represents a challenge for attempts to change behavior via attitudes, such as campaigns by governments or companies wishing to introduce new products to consumers (Labrecque et al. 2017). However, resistance also protects desired behavioral habits against the vicissitudes of daily attitudes or counter-attitudinal persuasion attempts (Itzchakov et al. 2018). ...
... The study also revealed the nature of habits that comprise action sequences: Once the habit sequence was initiated (picking up a cigarette), it ran on to its conclusion (lighting up) even if individuals intended and expected to be able to interrupt the sequence and step outside before the final act. In a consumer context, Labrecque et al. (2017) report that a habit slip ("I fell back on my old habit and did what I used to do") was the most frequent reason given by consumers for rarely using new products they had intentionally purchased. ...
... In other words, habit slips do not occur as a function of unfavorable attitudes or lack of motivation. Habit slips are more likely to occur when an individual is distracted or acting mindlessly (Labrecque et al. 2017, Orbell & Verplanken 2010. The smokers in Orbell & Verplanken's (2010) field study reported that they found themselves lighting cigarettes indoors when they were distracted in conversation. ...
Article
Efforts to guide peoples’ behavior toward environmental sustainability, good health, or new products have emphasized informational and attitude change strategies. There is evidence that changing attitudes leads to changes in behavior, yet this approach takes insufficient account of the nature and operation of habits, which form boundary conditions for attitude-directed interventions. Integration of research on attitudes and habits might enable investigators to identify when and how behavior change strategies will be most effective. How might attitudinally driven behavior change be consolidated into lasting habits? How do habits protect the individual against the vicissitudes of attitudes and temptations and promote goal achievement? How might attitudinal approaches aiming to change habits be improved by capitalizing on habit discontinuities and strategic planning? When and how might changing or creating habit architecture shape habits directly? A systematic approach to these questions might help move behavior change efforts from attitude change strategies to habit change strategies. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... Finally, we investigated how performance on the symmetrical outcome-revaluation task relates to a self-report measure of automaticity. While the outcome-revaluation task is considered the canonical assay of habits in the field of associative learning, the fields of social and health psychology have mainly used self-reported measures to study the habit status of real-life behaviors , including teeth-flossing, unhealthy snacking (Verhoeven et al., 2012), exercise (review: Gardner et al., 2011;Ouellette & Wood, 1998) and consumer behavior (Labrecque et al., 2017). The most commonly used self-report measurement is the 12-item self-report habit index (SRHI: Verplanken & Orbell, 2003). ...
Article
The translation of the outcome-devaluation paradigm to study habit in humans has yielded interesting insights but proven to be challenging. We present a novel, outcome-revaluation task with a symmetrical design, in the sense that half of the available outcomes are always valuable and the other half not-valuable. In the present studies, during the instrumental learning phase, participants learned to respond (Go) to certain stimuli to collect valuable outcomes (and points) while refraining to respond (NoGo) to stimuli signaling not-valuable outcomes. Half of the stimuli were short-trained, while the other half were long-trained. Subsequently, in the test phase, the signaled outcomes were either value-congruent with training (still-valuable and still-not-valuable), or value-incongruent (devalued and upvalued). The change in outcome value on value-incongruent trials meant that participants had to flexibly adjust their behavior. At the end of the training phase, participants completed the self-report behavioral automaticity index – providing an automaticity score for each stimulus-response association. We conducted two experiments using this task, that both provided evidence for stimulus-driven habits as reflected in poorer performance on devalued and upvalued trials relative to still-not-valuable trials and still-valuable trials, respectively. While self-reported automaticity increased with longer training, behavioral flexibility was not affected. After extended training (Experiment 2), higher levels of self-reported automaticity when responding to stimuli signaling valuable outcomes were related to more ‘slips of action’ when the associated outcome was subsequently devalued. We conclude that the symmetrical outcome-revaluation task provides a promising paradigm for the experimental investigation of habits in humans.
... The purchase intention created because of good image of the firm in terms of doing good for society and environment was pulled back due to the attachment toward their current coffee drinking habit. Through this moderating effects of habituation on relationship of perceived quality and target consumers behaviors (i.e., purchasing), our study confirmed the results on the same issues (Labrecque et al. 2017). We went further by providing explanation for the circumstance when a part of consumers even though was favorable to new products but finally still did not buy it. ...
Thesis
Considered as a universal concept, corporate social responsibility (CSR) was actually originated from occidental perspective, making it inadequate to be imposed globally. The CSR perceived by consumers might be divergent in oriental context. Aiming to expand the understandings of consumer perceptions and their responses to CSR in oriental context, we therefore conducted our studies in Vietnam. We first conducted a qualitative study to explore CSR insights of consumers and built a Five Personas typology of consumers. Then, two scenario-based experiments in food sector and cosmetics & skincare sector allowed to test consumer reactions to CSR. The second experiment was integrated in a larger data collection used to form a structural equation model, explaining the psychological mechanisms behind consumer responses. Our findings reveal that CSR has a real impact on consumer evaluations of firm and products, whereas country-of-origin and production process turned out to have no significant impact. Given no cue on quality control, consumers still form their evaluations on product quality, which makes perceived product quality the mediator between the firm engagement in CSR and consumer responses toward the firm (brand attitude, purchase and recommendation intention). We found that consumption habituation and perceived firm motives toward CSR moderate this relationship while CSR skepticism is the mediator. Beneath the surface, some individual constructs can explain the mechanisms. We highlight consumer green values, playing the moderating role between firm green engagement and perceived product quality. Five constructs form green values including mindfulness, voluntary simplicity, internal locus-of-control, connectedness to nature, and death anxiety. We hope to expand the understandings of how consumers perceive and react towards CSR in oriental context that might be more sophisticated than the appearances.
... Whereas past work has distinguished key types of cues that activate habitual scripts (e.g., technical, spatial, and mental cues), here we focus on the dimensions of contexts and responses that underlie the performance of habits. Our perspective builds on what has been referred to as habit "stacking"-i.e., habits that reinforce one another, thus taking advantage of the automaticity learned in neighboring or overlapping contexts [21,52]. ...
Article
Full-text available
Social media habits represent one of the most common – and controversial – forms of habitual behavior in contemporary society. In this brief article, we summarize the state of research on social media habits, along with their position within the technology habit literature. First, we review the wide range of positive and negative behaviors falling under the umbrella of “social media habits.” Then, we deconstruct how a given social media habit can be viewed from four levels of analysis: platform, device, interface, and motor. Last, we anticipate how future researchers and designers will have the potential to detect (un)healthy habitual processes via digital tracking. As a whole, the article demonstrates the need to break apart the components of social media habits in order to clarify their implications for well-being.
... After eight months, participants who were stacking flossed almost 3 times more per month in comparison to the participants who did not stack. Stacking works best if the new behaviour is compatible with an existent habit [13]. ...
Article
Current dietary patterns are often sub-optimal from a health and/or an ecological perspective. Changing dietary patterns is desirable, but difficult because of the persistence of food habits. Food habits are especially strong in breakfasting. This study explores two strategies for dietary behavioural change during breakfast: stacking, where a food component is added to an existing food habit, and swapping, where one food component is replaced by another one. Ninety-one participants (72 females, 19 males) adjusted their daily breakfast habits for four weeks by either adding a healthy food component (apple) to their existing breakfast or by swapping their less- sustainable dairy product for a more sustainable plant-based product (soy milk or soy yoghurt). Participant’s choice and liking of the breakfast was monitored daily with short questionnaires, whereas other information was collected weekly using more extensive questionnaires. The results showed that both swapping and stacking strategies were equally effective during the 4-week study period (compliance>94%). During the study period liking for all three products increased initially but levelled off after 2 weeks for apples and soy yoghurt, whereas liking for soy milk continued to increase (p<0.05). All products were liked better by participants who scored relatively low on the HTAS reward and pleasure dimensions. The suitability of soy milk as breakfast component increased during the study period, whereas the suitability of the other products was either stable (apple) or decreased (soy yoghurt). The strength of the breakfast habit increased after the first week for apple and soy milk and decreased for soy yoghurt, signalling a growing integration of apple and soy milk in the existing breakfast habit. Breakfasts with apple triggered more positive emotions after 3 weeks than the two breakfasts with soy products. Four weeks after the end of the study period, voluntary compliance with the products dropped to 26% for soy milk and to 15%-18% for apple and soy yoghurt. The results suggest that a long-lasting breakfast modification requires 1) a relatively small modification whereby one item is replaced by another item that serves the same function (e.g., replacing cow milk by soy milk), 2) a breakfast item that is increasingly liked over repeated exposure, and 3) does not require additional preparation. These findings provide a good basis for further research into consumer’s food habits, how they evolve and change, to ultimately facilitate development of new sustainable food products that better fit in existing and new habits.
Article
The Status Quo Bias (SQB) describes an individual's preference to avoid changes and maintain the current situation. In today’s world, technological advances require nearly constant change within organizations. Thus, SQB can become an issue when it hinders progress. Therefore, it is crucial to understand how this effect can be reliably measured and, even more importantly, what countermeasures to employ. Prior research has focused more on individual measuring approaches and less on countermeasures. As researchers across different research fields have studied this bias, we conduct a literature review spanning different scholarly fields. This broader research focus allows us to identify four measurement approaches and 13 countermeasures along the three aspects of cognitive misperception, rational decision making, and psychological commitment of SQB. Our overview consolidates existing knowledge and will hopefully be the starting point for researchers to start combating this bias where needed. Successful and proven countermeasures can, for example, increase the acceptance and adoption of digital innovations and technology in general and thereby allow organizations to capitalize on their investments.
Article
Purpose This paper aims to investigate the effect of factors that inhibit adoption of mobile payments service in India. Design/methodology/approach Based on the extant literature on mobile payment service and other related literature, factors were identified that drive consumer resistance toward its adoption. It engaged “innovation resistance theory” framework for understanding consumer resistance. The framework addressed five categories of barriers, namely, usage, value, risk, image and tradition that lead to negative perception of innovation, and therefore, induces positive impact on its resistance. Additionally, the study considered a few lesser investigated barriers (habitual use of cash, surveillance, technology) for the study, thus extending the existing theoretical framework. Hypotheses were framed, field data were collected and then analyzed using multivariate techniques. Findings Few interesting observations were made from the study. Usage, image and value barriers hindered adoption of mobile payment service. In case of men, usage, value and image were the primary barriers. For women, usage, image, habitual use of cash and technology acted as barriers that curbed mobile payments service adoption. Additionally, except risk, tradition and surveillance barriers, relationships of all other constructs with adoption intention were moderated by gender. Research limitations/implications This research was limited to the views of the urban population in India who used mobile payments service. The results may vary across geographical contexts because of culture or socioeconomic differences. Practical implications The growth of mobile payment service has remained sluggish in India despite high levels of digitization. The study results will offer valuable insights to the Indian business managers and policymakers to identify what action plan needs to be instituted to make mobile payments service more attractive and acceptable to users. Originality/value This empirical study extended and tested the classical innovation resistance theory framework by adding three less studied barriers (surveillance, habitual use of cash and technology) in a developing nation, thus enriching the current literature on consumer resistance toward mobile payments. It also examined the moderating effect of gender on mobile payments service adoption.
Article
In the past decade, a core assumption of research on business model innovation (BMI) has been its beneficial character. However, studies have shown that potentially disrupting BMI is not immune to failure. Still, studies that investigate the causes of BMI failures are lacking. This article shifts the focus to the dark side of BMI by using a demand‐side approach, which cross‐fertilizes on the NPD research stream of passive innovation resistance. We argue that BMI, like any other type of innovation, imposes change on the customer, which endangers the status quo. As a result, passive innovation resistance evolves, potentially disrupting continuous adoption. Thus, the main goal of the current study is to investigate whether and how BMI evokes negative effects of passive innovation resistance on customers’ adoption behavior (Study 1) and to determine which marketing instruments can be used as countermeasures (Study 2). Our findings confirm that passive innovation resistance is a strong inhibitor of continuous BMI adoption. However, the detrimental effects of passive innovation resistance on continuous BMI adoption can be attenuated by employing benefit comparisons or testimonials in business model (BM) announcements. From a theoretical perspective, this study enhances the current knowledge on how stable customer predispositions affect the adoption process of BMI. By so doing, our study confirms the applicability of passive innovation resistance beyond the NPD domain but also sheds light on differences in the cause‐effect mechanism between BMI and product innovation contexts. From a managerial perspective, this study equips managers with effective countermeasures to passive innovation resistance that should reduce the probability of BMI failure.
Article
In times of rapid technological advancements, consumers often reject new products as they intentionally postpone their adoption until significant technology improvements are available. This phenomenon is commonly called consumer leapfrogging behavior. While previous studies have found vast empirical evidence for the occurrence and detrimental effects of such behavior, only a few studies have focused on investigating the nature and determinants of consumer leapfrogging. Hence, this article systematically explores and empirically validates potential determinants of consumer leapfrogging behavior by applying a multimethod approach. First, we conducted a systematic literature review to summarize the current research. Second, we applied a qualitative study to identify potential reasons for consumer leapfrogging behavior. The results show that known theoretical rationales for innovation rejection behavior tied to active and passive innovation resistance do not comprehensively account for the complex psychological processes of this behavior. Consequently, we introduce a new construct called “leap disposition” to explain consumers’ disposition to reject a new product and instead wait for a superior subsequent product generation. Third, we empirically validate and quantify the relative importance of both established constructs (i.e., active and passive innovation resistance), as well as the newly introduced leap disposition, for leapfrogging behavior within a large-scale study.
Article
Full-text available
As the proverbial creatures of habit, people tend to repeat the same behaviors in recurring contexts. This review characterizes habits in terms of their cognitive, motivational, and neurobiological properties. In so doing, we identify three ways that habits interface with deliberate goal pursuit: First, habits form as people pursue goals by repeating the same responses in a given context. Second, as outlined in computational models, habits and deliberate goal pursuit guide actions synergistically, although habits are the efficient, default mode of response. Third, people tend to infer from the frequency of habit performance that the behavior must have been intended. We conclude by applying insights from habit research to understand stress and addiction as well as the design of effective interventions to change health and consumer behaviors. Expected final online publication date for the Annual Review of Psychology Volume 67 is January 03, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
Article
Full-text available
Behavioral research shows that reasons for and reasons against adopting innovations differ qualitatively, and they influence consumers’ decisions in dissimilar ways. This has important implications for theorists and managers, as overcoming barriers that cause resistance to innovation calls for marketing approaches other than promoting reasons for adoption of new products and services. Consumer behavior frameworks in diffusion of innovation (DOI) studies have largely failed to distinctly account for reasons against adoption. Indeed, no study to date has tested the relative influence of adoption and resistance factors in a single framework. This research aims to address this shortcoming by applying a novel consumer behavior model (i.e., behavioral reasoning theory) to test the relative influence of both reasons for and, importantly, reasons against adoption in consumers’ innovation adoption decisions. Based on two empirical studies, one with a product and a second with a service innovation, findings demonstrate that behavioral reasoning theory provides a suitable framework to model the mental processing of innovation adoption. Implications for managers and researchers are discussed.
Article
Purchase and consumption behaviors in daily life often are repetitive and performed in customary places, leading consumers to develop habits. When habits have formed, environmental cues can activate the practiced responses in the absence of conscious decision making. This research tested these ideas using a longitudinal design. We predicted that regardless of their explicit intentions, consumers would repeat habits to purchase fast food, watch TV news, and take the bus. The results yielded the anticipated pattern in which participants repeated habitual behaviors even if they reported intentions to do otherwise. Intentions only guided behavior in the absence of strong habits. This study ruled out a number of artifactual accounts for these findings including that they arise from the level of abstraction at which intentions are identified, the certainty with which participants held intentions, a restriction of range in the measures, and the strategy participants used to estimate frequency of past performance.
Article
The sustained development and successful introduction of innovations continues to be of major concern for companies’ long-term success. However, empirical research points to high failure rates of innovations, indicating that most new products fail as they are rejected by consumers due to their resistance to innovation. Several studies have confirmed the importance of passive innovation resistance as dominant barrier, which has to be overcome before new product adoption can start. However, empirical evidence on how to overcome passive innovation resistance is still lacking. This study intends to address this gap by evaluating the effectiveness of marketing instruments (i.e. mental simulation and benefit comparison) to reduce negative effects of passive innovation resistance on new product adoption. The results of a scenario-based experiment (n=679) confirm high effectiveness for both instruments. However, the effectiveness varied with the type of passive innovation resistance present. More specifically, mental simulation was found to be the most effective instrument in case of cognitive passive resistance, whereas benefit comparison was found to be most effective in case of situational passive resistance. Thereby, the effect of both marketing instruments was stronger the more radical the new product was perceived. Hence, companies should assess the type of passive innovation resistance that is predominant in their target market, and align their choice of marketing instruments that accompany a new product launch to most effectively overcome passive innovation resistance. Employing such new product launch tactics should decrease initial market resistance and thus help companies in reducing innovation failure rates.
Article
This study was designed to examine the moderating influence of habit strength on daily action planning effects on physical activity and sedentary behavior. A 2 by 2 design was used with experimental factors corresponding to action planning interventions for (a) engaging in physical activity and (b) limiting or interrupting sedentary behavior. At the end of each day for 1 week, university students (n = 195) completed (a) a questionnaire about their behavior during the day and behavioral intentions for the following day and (b) a planning intervention(s) corresponding to their randomly assigned experimental condition. Action planning increased physical activity in those with weak habits but decreased physical activity in those with strong habits compared with those who did not create action plans. Action planning did not impact sedentary behavior. Action planning was a useful behavior change technique for increasing physical activity in people with weak habits, but may be iatrogenic for those with strong habits.
Article
According to dual-system theories, instrumental learning is supported by dissociable goal-directed and habitual systems. Previous investigations of the dual-system balance in healthy aging have yielded mixed results. To further investigate this issue, we compared performance of young (17-24 years) and older (69-84 years) adults on an instrumental learning task. Following the initial learning phase, the behavioral autonomy of the motivational significance of the instrumental outcome was assessed with an outcome-devaluation test and slips-of-action test. The present study provides evidence for a disrupted dual-system balance in healthy aging, as reflected in reduced outcome-induced conflict during acquisition, as well as in impaired performance during the test stage, during which participants had to flexibly adjust their actions to changes in the current desirability of the behavioral outcome. These findings will be discussed in relation to previous aging studies into habitual and goal-directed control, as well as other cognitive impairments, challenges that older adults may face in everyday life, and to the neurobiological basis of the developmental pattern of goal-directed action across the lifespan.
Article
Adoption literature has been dominated by a novelty-seeking paradigm, whereas resistance to innovation has received considerably less attention as a means to explain and predict adoption-related behaviour. The lack of a good metric to assess consumers’ predisposition to resist innovations has prevented the establishment of a common ground for empirical research and thus hampered progress to date. This article develops and empirically validates a scale to measure individual differences in consumers’ predisposition to resist innovations (hereafter, passive innovation resistance, or PIR). The proposed instrument entails a personality-specific and situation-specific measure that assesses individual differences in consumers’ predisposition to resist innovations, emerging from their inclination to resist changes and exhibit status quo satisfaction. The scale represents a measure of the generic tendency to resist innovations and thus captures the notion of a general disposition to act in a consistent way in various situations. The results of multiple studies show that the PIR scale has good psychometric properties, and its relationships with other constructs conform to theoretical expectations. Furthermore, the PIR scale explains and predicts adoption-related behaviours beyond the variance accounted for by traditionally investigated constructs such as innate innovativeness, big-five personality dimensions or demographic variables. These results clearly reveal the importance of PIR for determining adoption-related behaviour but contest a conceptualisation of constructs that tap only novelty seeking at a high level as the direct antecedent of adoption. Research that attempts to explain and predict adoption-related behaviour can benefit from taking a resistance perspective as well.
Article
Adoption literature is largely subject to a pro-change bias; researchers mainly assume that consumers are open to change and thus interested in evaluating new products. However, consumers often reject innovations without considering their potential, such that the adoption process ends before it really has begun. The present study instead argues that innovation resistance, prior to product evaluation, is a regular consumer response that must be recognized and managed to facilitate new product adoption. The authors suggest differentiating passive from active innovation resistance. While passive innovation resistance results from a consumer's generic predisposition to resist innovations prior to new product evaluation, active innovation resistance is an attitudinal outcome that follows an unfavorable new product evaluation. This study also extends extant innovation decision models by describing how passive and active innovation resistance emerge and how they affect decision-making in later stages of the process.