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Supporting Sustainable Food Consumption: Mental Contrasting with Implementation Intentions (MCII) Aligns Intentions and Behavior


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With growing awareness that sustainable consumption is important for quality of life on earth, many individuals intend to act more sustainably. In this regard, interest in reducing meat consumption is on the rise. However, people often do not translate intentions into actual behavior change. To address this intention-behavior gap, we tested the self-regulation strategy of Mental Contrasting with Implementation Intentions (MCII). Here, people identify and imagine a desired future and current obstacles standing in its way. They address the obstacles with if-then plans specifying when, where, and how to act differently. In a five-week randomized controlled experimental study, we compared an information + MCII intervention with an information-only control intervention. As hypothesized, only MCII participants’ intention of reducing their meat consumption was predictive of their actual reduction, while no correspondence between intention and behavior change was found for control participants. Participants with a moderate to strong intention to reduce their meat consumption reduced it more in the MCII than in the control condition. Thus, MCII helped to narrow the intention-behavior gap and supported behavior change for those holding moderate and strong respective intentions.
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fpsyg-07-00607 April 27, 2016 Time: 13:27 # 1
published: 29 April 2016
doi: 10.3389/fpsyg.2016.00607
Edited by:
Caroline Braet,
Ghent University, Belgium
Reviewed by:
Eva Kemps,
Flinders University, Australia
Lien Goossens,
Ghent University, Belgium
Laura S. Loy
Specialty section:
This article was submitted to
Eating Behavior,
a section of the journal
Frontiers in Psychology
Received: 26 January 2016
Accepted: 12 April 2016
Published: 29 April 2016
Loy LS, Wieber F, Gollwitzer PM
and Oettingen G (2016) Supporting
Sustainable Food Consumption:
Mental Contrasting with
Implementation Intentions (MCII)
Aligns Intentions and Behavior.
Front. Psychol. 7:607.
doi: 10.3389/fpsyg.2016.00607
Supporting Sustainable Food
Consumption: Mental Contrasting
with Implementation Intentions
(MCII) Aligns Intentions and Behavior
Laura S. Loy1,2*, Frank Wieber2,3, Peter M. Gollwitzer2,4 and Gabriele Oettingen4,5
1Media Psychology Division, School of Communication, University of Hohenheim, Stuttgart, Germany, 2Social Psychology
and Motivation Division, Department of Psychology, University of Konstanz, Konstanz, Germany, 3Centre for Health
Sciences, School of Health Professions, Zurich University of Applied Sciences, Winterthur, Switzerland, 4Motivation Lab,
Psychology Department, New York University, New York, NY, USA, 5Educational Psychology and Motivation Division,
Department of Psychology, University of Hamburg, Hamburg, Germany
With growing awareness that sustainable consumption is important for quality of life on
earth, many individuals intend to act more sustainably. In this regard, interest in reducing
meat consumption is on the rise. However, people often do not translate intentions
into actual behavior change. To address this intention-behavior gap, we tested the self-
regulation strategy of mental contrasting with implementation intentions (MCII). Here,
people identify and imagine a desired future and current obstacles standing in its way.
They address the obstacles with if-then plans specifying when, where, and how to
act differently. In a 5-week randomized controlled experimental study, we compared
an information +MCII intervention with an information-only control intervention. As
hypothesized, only MCII participants’ intention of reducing their meat consumption
was predictive of their actual reduction, while no correspondence between intention
and behavior change was found for control participants. Participants with a moderate
to strong intention to reduce their meat consumption reduced it more in the MCII
than in the control condition. Thus, MCII helped to narrow the intention-behavior gap
and supported behavior change for those holding moderate and strong respective
Keywords: sustainable consumption, meat consumption, intention-behavior gap, behavior change intervention,
mental contrasting, implementation intention
Sustainable consumer behavior aims to enable a good present and future quality of life on
earth through a wise use of resources (Jackson, 2005;UNDESA, 2010;UNEP, 2012). Among
dietary choices, consuming less meat qualifies as sustainable behavior change, because plant-based
products require less land, fossil fuel, and water resources. They cause lower pollution as well as
greenhouse gas emissions than meat products (Jungbluth et al., 2000;Leitzmann, 2003;Pimentel
and Pimentel, 2003;FAO, 2006;Goodland and Anhang, 2009;Popp et al., 2010;UNEP, 2010;IPCC,
2014;Tilman and Clark, 2014;Westhoek et al., 2014).
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Loy et al. Supporting Behavior Change Intentions
For a growing number of individuals, reducing meat intake
has become a means to consume more sustainably (Kalof et al.,
1999;White et al., 1999;Fox and Ward, 2008;Vinnari and Tapio,
2009). Next to environmental protection, other reasons speak for
and are named by individuals for reducing meat consumption
as, for example, animal-ethical aspects (Pluhar, 2010), health
aspects (ADA, 2009), or political and social aspects such as
global nutrition security (Leitzmann, 2003;UNEP, 2010; for an
overview see Ruby, 2012). However, despite the rising interest in
reducing meat intake (Povey et al., 2001;Grunert, 2006;Vermeir
and Verbeke, 2006;Cordts et al., 2013), many people experience
great difficulties preventing them to change their nutritional
routines (Lea and Worsley, 2001;Lea et al., 2006).
Thus, the question arises what kind of intervention could
support individuals to overcome these personal difficulties in
order to reduce their meat intake. Many theoretical models of
behavior change and derived interventions assume intentions
(i.e., “self-instructions to perform particular behaviors or to
obtain certain outcomes”; Webb and Sheeran, 2006, p. 249)
as immediate predictors of behavior (Fishbein and Ajzen,
2010). However, although forming the intention “I want to
reduce my meat consumption!” is a necessary first step to
behavior change, it may not suffice. Several mechanisms can
interfere between intention formation and behavior change, as
for example following established habits, giving in to temptations,
missing good opportunities for action, or simply forgetting about
intentions (Gollwitzer and Sheeran, 2006;Ji and Wood, 2007;
Wood and Neal, 2009). Getting started to reduce meat intake
can, for example, be hindered by a lack of knowledge on plant-
based alternatives or by regularly cooking with other people who
insist on having meat. Staying on track to eat differently can be
threatened by giving in to temptations such as seeing a delicious
meat dish on the menu of a restaurant. These mechanisms
result in the so-called intention-behavior gap (Sheeran, 2002):
formulated intentions do not correspond to action; the strength
of agreement with the statement “I want to change my behavior”
is not or weakly correlated with the degree of subsequent behavior
The intention-behavior gap is illustrated by a meta-analysis
on the determinants of pro-environmental behavior (Bamberg
and Möser, 2007), in which intentions to perform a behavior
accounted only for 27% of variance in actual behavior. Moreover,
this gap has also been found for interventions aiming to change
participants’ intentions. In a meta-analysis of experimental
studies (Webb and Sheeran, 2006), a medium-to-large change in
intention resulted only in a small-to-medium change in behavior.
The present study suggests a self-regulation approach,
focusing on how individuals can effectively guide their behavior
toward goal attainment (e.g., Karoly, 1993). One strategy
to narrow the intention-behavior gap consists in forming
implementation intentions (IIs; Gollwitzer, 1993, 1999, 2014).
Here, individuals make if-then plans specifying when, where,
and how to act (“If I encounter situation X, then I will perform
behavior Y”) in order to implement a certain intention (“I intend
to achieve goal Z”).
As a consequence of forming IIs, individuals should easily
recognize the anticipated situation when it arises and then initiate
the linked action immediately, efficiently, and without requiring
a conscious intent to act in the critical moment (e.g., II effects
are still evident when the critical cue is presented subliminally
or when the respective goal is activated outside of awareness;
Sheeran et al., 2005;Bayer et al., 2009). In line with these
assumptions, studies confirmed that the mental representation of
the anticipated situation becomes highly accessible (Aarts et al.,
1999;Wieber and Sassenberg, 2006;Webb and Sheeran, 2007;
Achtziger et al., 2012) and the link between situation and action is
strengthened (Brandstätter et al., 2001;Webb and Sheeran, 2007,
2008;Bayer et al., 2009;Mendoza et al., 2010). These cognitive
consequences of forming IIs are rather stable over time (Papies
et al., 2009).
Meta-analyses showed that IIs have medium to large effects
on goal attainment (Gollwitzer and Sheeran, 2006;Adriaanse
et al., 2010b;Bélanger-Gravel et al., 2013;Chen et al., 2015).
They help overcome typical problems of goal striving such as
getting started with the intended behavior as well as staying on
track when encountering obstacles (for summaries see Gollwitzer
and Sheeran, 2009;Gollwitzer et al., 2010a,b;Gollwitzer and
Oettingen, 2011).
Regarding consumption-related intentions, IIs have
supported individuals to drink less alcohol (e.g., Armitage,
2009;Chatzisarantis and Hagger, 2010;Armitage and Arden,
2012;Hagger et al., 2012), quit and prevent smoking (e.g.,
Armitage, 2007b;Conner and Higgins, 2010), or eat healthier
(e.g., De Nooijer et al., 2006;Armitage, 2007a;Knäuper
et al., 2011;Chapman and Armitage, 2012). Interestingly, II
interventions to reduce consumption of certain foods seem to
benefit from personalization. Adriaanse et al. (2009) found that
IIs specifying personally relevant critical cues for unwanted
snacking in the if-part reduced unhealthy snacking behavior
(Study 2), while IIs specifying experimenter-defined cues in the
if-part did not (Study 1).
One way to support individuals’ efforts to find and select
personally relevant II cues is to precede IIs with mental
contrasting (MC; Oettingen, 2000, 2012;Oettingen et al., 2001).
Here, individuals identify a personal wish or goal (e.g., holding
one vegetarian day a week), identify and imagine the most
positive future outcome of goal attainment (e.g., contributing to
a healthy environment), and identify the main personal obstacle
currently impeding their goal attainment (e.g., not knowing any
meat-free recipes).
Mental contrasting is shown to affect behavior by changing
non-conscious cognitive and motivational processes (summary
by Oettingen, 2012). In line with this assumption, MC was
observed to increase the strength of individuals’ implicit mental
association between the wished-for future or goal and the obstacle
as well as between the obstacle and the instrumental means
to overcome the obstacle, given that chances of reaching the
future are high. These mental associations in turn predicted
goal pursuit (Kappes et al., 2012;Kappes and Oettingen, 2014).
In addition, individuals with high chances of realizing the
future who performed MC (versus relevant control groups)
implicitly categorized their present reality as an obstacle and
more readily detected an obstacle toward goal attainment as
such (Kappes et al., 2013). Moreover, MC affected implicit and
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Loy et al. Supporting Behavior Change Intentions
explicit indicators of motivational processes. For example, when
chances of success were high, MC increased individuals’ implicit
(systolic blood pressure) and explicit (self-reported) energization,
which in turn supported the desired behavior changes (e.g., stress
coping; Oettingen et al., 2009a;Sevincer et al., 2014).
Mental contrasting has helped individuals to reach a variety
of wishes and goals including success in academic performance
(Gollwitzer et al., 2011), finding integrative solutions in a
negotiation task (Kirk et al., 2011), helping behavior (Oettingen
et al., 2010), and exercising (Johannessen et al., 2011;Sheeran
et al., 2013). Regarding consumption-related goals, MC has
supported people to eat fewer calories (Johannessen et al., 2011),
initiate a reduction of cigarette consumption (Oettingen et al.,
2009b), and manage dieting behavior of individuals with Type II
diabetes (Adriaanse et al., 2013; summaries by Oettingen, 2012;
Oettingen and Schwörer, 2013).
Complementing MC instructions with II instructions
results in the intervention strategy of mental contrasting with
implementation intentions (MCII; overviews by Oettingen, 2012,
2014;Oettingen et al., 2013). This combination appears especially
well suited to support behavior change, because MC helps to
identify important personal obstacles for behavior change that
can then be addressed by IIs. Individuals can link an obstacle
as situational cue in the if-part to specific actions to overcome
the obstacle in the then-part. MC per se implicitly connects the
obstacle to an effective instrumental response so that people can
master the obstacle more easily (Kappes et al., 2012). However,
adding an explicitly formulated plan of how to overcome the
obstacle in the form of an II additionally benefits their goal
pursuit and helps to translate intentions into actual behavior
Mental contrasting with implementation intentions has been
applied to facilitate behavior change in several domains including
exercise (Stadler et al., 2009), physical capacity of chronic back
pain patients (Christiansen et al., 2010), and the promotion
of healthy consumption in terms of eating more fruits and
vegetables (Stadler et al., 2010), and snacking less (Adriaanse
et al., 2010a). In the latter study, female participants were
randomly assigned to one of three intervention conditions:
MC, II, or MCII. In support of the effectiveness of combining
MC and II, MCII participants reported being more successful
at diminishing their unhealthy snacking 1 week after the
intervention than participants who engaged in MC or II alone.
Individuals in both conditions involving MC experienced more
clarity about the critical obstacles for a healthier diet. This clarity
in turn was related to success in reducing their habit (Adriaanse
et al., 2010a, Study 2).
The present research examines whether MCII can support the
translation of individuals’ intentions to consume less meat into
actual behavior. We think of MCII as a well-suited technique to
narrow individuals’ intention-behavior gap. In order to test this
assumption, we conducted a 5-week longitudinal randomized
controlled experimental study, in which participants reported
their daily meat consumption in food diaries. We compared
the effectiveness of two different intervention formats: (1) the
classical information intervention in an information-only control
condition (see e.g., Luszczynska et al., 2007;Stadler et al.,
2010), and (2) the self-regulation intervention MCII in an
information +MCII condition. We hypothesized that intentions
predict behavior change better in the MCII condition than the
control condition; in other words, the intervention condition
(control vs. MCII) should moderate the intention-behavior
The study was advertised on the campus of a German university
as a study on meat consumption. In the announcement, we
indicated that we looked for participants who were no vegetarians
and were proficient in the German language. To address our
research question, we draw on the data of 60 members of
the university (n=45 female) with a mean age of 22.3 years
(SD =4.25; range 18–49 years).1Most of them were students of
various majors (psychology major n=29). They received either
financial compensation or class credit.
Study Design
We compared the effectiveness of two interventions in sup-
porting an intended reduction in meat consumption: an infor-
mation-only control intervention and an information +MCII
intervention. Participants filled in diaries on their meat
consumption in the week before the intervention (Baseline
Diary), the week after the intervention (Follow-up 1 Diary), and
the 4th week after the intervention (Follow-up 2 Diary). The
differences in meat consumption served as dependent variables.
Furthermore, we analyzed the strength of intention to reduce
meat consumption at baseline as predictor of behavior change
and intervention condition (control vs. MCII) as a moderator of
the intention-behavior relation.
The university’s ethics committee approved the study. We used
a fixed randomization to assign participants to the experimental
conditions. To enhance standardization, we held all meetings
with participants in the same laboratory and used written
instructions. Each participant followed a study schedule of
5 weeks including two laboratory meetings.
Baseline Measurement
Experimenter 1 conducted the first laboratory meetings, during
which she was left unaware of condition. She informed
participants about the study procedure and let them sign the
consent form. Furthermore, participants answered Baseline-
Questionnaire 1 containing variables we used to characterize
the sample and to conduct randomization checks. Next to
demographic variables, we asked participants to estimate their
past average meat consumption (see Berndsen and Van der Pligt,
1Accounting for the concerns raised by Simmons et al. (2011), we consider it
relevant to disclose that our study additionally included a third intervention
condition. This third condition did not address this article’s research question and
did not affect our hypothesis and results. It was run for educational purposes.
Information can be found in the Supplementary Material.
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Loy et al. Supporting Behavior Change Intentions
2005). Participants indicated how many days a week they usually
eat meat at breakfast, lunch, dinner, and as snacks, and then
specified the average weight of meat consumed for each meal
(none, less than 50 g, 50–100 g, 100–150 g, 150–200 g, 200–250 g,
250–300 g, and more than 300 g). Our application of the Self-
Report Index of Habit Strength (Verplanken and Orbell, 2003)
contained 12 items. One example was: Eating meat is something
I do without thinking; 1 (doesn’t apply at all) to 7 (fully applies).
Cronbachs αwas 0.91 and we computed an average score for each
Before leaving, participants received a paper-based 7-day diary
on their meat consumption. They started to use it right after the
first meeting in Week 1 of the study schedule (Baseline Diary;
see Bolger et al., 2003, for an overview on diary methods). Seven-
day periods are recommended for measuring food consumption,
because of possible systematic variations over the course of a week
(De Castro, 1994;Wolper et al., 1995). Adapting an approach
from Berndsen and Van der Pligt (2005) for measuring meat
intake, each day included a column for breakfast, lunch, dinner,
and snacks (hence, each 1-week diary contained 28 items). We
asked participants to indicate for each of these categories the
weight of meat consumed (none, less than 50 g, 50–100 g,
100–150 g, 150–200 g, 200–250 g, 250–300 g, and more than
300 g). Examples of seven typical dishes and the respective
weight were given as orientation (e.g., sausage about 100–120 g).
We additionally encouraged participants to consult the weight
information on packaging. Using the midpoint of the weight
categories (e.g., 75 g for the category 50–100 g), we calculated a
score for the average amount of meat consumed per day.
One week later, three different experimenters, who were
unaware of content and hypotheses of the study, had the second
laboratory meetings with the participants. Participants answered
Baseline-Questionnaire 2, which contained a measure of the
strength of intention to reduce meat consumption (i.e., the
assumed predictor of behavior change). The measure contained
four items following recommendations by Fishbein and Ajzen
(2010): I intend to eat less meat in the coming weeks; I would like
to reduce my meat consumption in the coming weeks; I will eat
less meat in the coming weeks; and I will try to consume less meat
in the coming weeks; 1 (doesn’t apply at all) to 7 (fully applies).
Cronbachs αwas 0.98 and we computed an average score for each
We provided the interventions during the second meeting.
All participants read a text providing information about meat
consumption. The information text was drawing on an article and
a book written by a renowned German nutritionist (Leitzmann
and Keller, 2010;Leitzmann, 2011). It not only addressed
environmental consequences of meat consumption but also
other arguments for reduced consumption that have been
identified in research on individuals’ reasons for eating less
meat (e.g., Leitzmann and Keller, 2010;Ruby, 2012). These
further arguments referred to ethical, health, and social/political
2Further measures beyond the scope of this article’s research question are not
explicated here. They did not affect our hypothesis and results. Information can
be found in the Supplementary Material.
aspects of meat consumption including references to scientific
In the MCII condition, written instructions additionally led
them through the MCII procedure. The MCII material was
constructed similar to standard MCII interventions (Stadler et al.,
2009, 2010;Oettingen et al., 2013). We explained the steps of
MC with examples and then asked participants to write down
their own personal thoughts regarding each step. In Step 1,
we asked participants to determine a personal goal regarding
their meat consumption (e.g., halving meat consumption or
holding one vegetarian day a week). In Step 2, they had to
state positive outcomes they related to attaining this goal (e.g.,
reducing environmental impact), then identify the best outcome,
and imagine events and experiences associated with this best
outcome. In Step 3, we asked them to identify and write
down obstacles of present reality hindering goal achievement
(e.g., habitual meat consumption in specific situations, lack
of knowledge on alternatives), then identify their two main
obstacles, and imagine events and experiences associated with
these two main obstacles. Finally in Step 4, we suggested the
formulation of IIs following Stadler et al. (2009, 2010). Two types
of IIs were explained with examples. In the strategy to overcome
the obstacle, participants should consider IIs specifying a behavior
to overcome the two main obstacles which they had identified
during MC in Step 3 (e.g., If I come home after sports with an
appetite for meat, then I will cook with only half the amount of
meat but more vegetables). In the strategy to prevent the obstacle
from occurring, we suggested to explicate a situation to prevent
the obstacle in the if-part, followed by a respective response in
the then-part (e.g., If I write down my shopping list before going
to the supermarket on Saturday, then I will look up a vegetarian
recipe). We asked participants to formulate four personal IIs: for
each of their two main obstacles they should write down one
obstacle overcoming II and one obstacle preventing II. Thus, in
sum, individuals wrote down (1) a personal goal, (2) positive
outcomes of goal achievement, the best outcome, and respective
associations, (3) obstacles of goal achievement, their two main
obstacles, and respective associations, and finally (4) four IIs
addressing their two main obstacles (see Supplementary Material
for the exact wording).
Dependent Variables
At the end of the second meeting, participants received two
further 7-day diaries identical to the Baseline Diary, one for Week
2 starting on the day after the intervention (Follow-up 1 Diary),
one for Week 5 starting 3 weeks after the intervention (Follow-
up 2 Diary). Altogether, we computed three meat consumption
scores in g/day (Baseline Diary, Follow-up 1 Diary, and Follow-
up 2 Diary) as well as two meat reduction scores in g/day (Meat
Reduction Score 1: Baseline Diary – Follow-up 1 Diary; Meat
Reduction Score 2: Baseline Diary – Follow-up 2 Diary). In
Weeks 3 and 4, participants did not fill in any diaries. We asked
3The decision not to restrict the text to environmental outcomes of meat
consumption only was based on the recommendation by Tobler et al. (2011)
that information interventions on sustainable food consumption should take both
environmental and other arguments for sustainable consumption (e.g., health
arguments) into account.
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Loy et al. Supporting Behavior Change Intentions
participants to drop their diaries anonymously in a provided
Additional Variables
Finally, participants received a link to an online questionnaire
containing additional variables at the end of Week 5. This
questionnaire served to rule out alternative explanations
that differences between conditions regarding participants’
experiences of the study rather than the MCII intervention
caused possible differences in meat consumption. We asked
participants the following additional items: How sincerely did
you answer the questions in the study? 1 (not at all) to 7
(very much), and Participating in the study was interesting
for me! 1 (doesn’t apply at all) to 7 (fully applies). Moreover,
we assessed perceived experimenter demand to reduce meat
consumption (see Adriaanse et al., 2010a): How much did
the research staff conducting the study want you to reduce
your meat consumption? 1 (not at all)to7(very much).
In order to assess participants’ reasons behind reducing meat
consumption, we additionally included the following four
questions: How important do you find health aspects/animal-
ethical aspects/environmental aspects/and social and political
aspects of meat reduction? 1 (not at all)to7(very much).
Participant Flow and Missing Values
All 60 participants handed in their Baseline Diary after Week
1 and returned to the second meeting that included the
intervention. Then, n=58 handed in their Follow-up 1 Diary
covering Week 2, and n=55 their Follow-up 2 Diary covering
Week 5; n=56 answered the final online questionnaire at
the end of Week 5. For each follow-up diary as well as the
online questionnaire, we performed a 2 (Attrition: response vs.
non-response) ×2 (Condition: control vs. MCII) contingency
table chi-square test, which revealed no differential attrition for
the conditions, χ2s(1) 2.07, ps0.492. A missing value
analysis showed that item non-response was below 5% for all
variables; Little’s Missing Completely At Random (MCAR) Test
was not significant. It can therefore be assumed that missing
variables occurred at random (Little and Rubin, 2002). For the
sake of statistical power and in order not to exclude individuals
due to few missing items, we applied maximum likelihood
estimation. We generated a second data set including expectation
maximization (EM) estimated values for all item non-response,
but not wave non-response data (see Schafer and Graham, 2002).
Results are reported for this data set and the n=55 participants
completing the study.
Data Analyses
Descriptives and Randomization Check
Before the intervention, participants reported on average a
moderate habit strength (M=3.96, SD =1.20, range 1.50–
6.14) and a moderate intention to reduce meat consumption
(M=4.05, SD =1.80, range 1–7). More specifically, our study
comprised participants whose strength of intention was moderate
to strong (i.e., 61.7% of participants reported a strength of
intention 4.00 on our scale ranging from 1 to 7) as well as
participants whose strength of intention was rather weak (i.e.,
38.3% <4.00). Reported past consumption was M=110 g/day
(SD =60.1, range 14–257); consumption as assessed by the
Baseline Diary was M=97 g/day (SD =47.7, range 11–207). All
t-tests used to compare the conditions on these baseline variables
as well as age were non-significant, ts(53) 1.72, ps0.091.
A chi-square test for gender did not reveal any difference between
conditions either, χ2(1) =0.34, p=0.746. Hence, we can assume
a successful randomization. Average meat reduction scores in our
sample were M=36 g/day (SD =39.8, range 57 to 139) at
Follow-up 1 and M=38 g/day (SD =40.9, range 32 to 161)
at Follow-up 2. Table 1 displays the average intention to reduce
meat consumption as well as meat consumption levels at baseline,
Follow-up 1, and Follow-up 2, differentiated for the interventions
and including meat reduction scores.
Reduction of Meat Consumption
Participants in the control condition reduced meat consumption
at Follow-up 1 (i.e., in Week 2), t(27) =3.66, p=0.001,
d=0.63, as well as Follow-up 2 (i.e., in Week 5), t(27) =3.62,
p=0.001, d=0.63. So did participants in the MCII condition
at Follow-up 1, t(26) =5.90, p<0.001, d=1.09, as well as
Follow-up 2, t(26) =6.60, p<0.001, d=1.18. Whereas MCII
participants did not have a significantly higher reduction of their
meat consumption than the control participants at Follow-up 1,
t(53) =1.79, p=0.079, d=0.49, they did so at Follow-up 2,
t(53) =2.19, p=0.033, d=0.61. However, it has to be noted
that the higher reduction in the MCII group did not result in
lower consumption levels in an absolute sense in the follow-up
TABLE 1 | Means and standard deviations for the intention to reduce meat consumption, meat consumption levels, and meat reduction in g/day.
Variable Control MCII t
Baseline intention M(SD) 3.81 (1.97) 4.29 (1.59) 0.98
Baseline consumption M(SD) 86.7 g (45.4) 108.5 g (48.3) 1.72
Follow-up 1 consumption M(SD) 60.3 g (39.7) 63.2 g (34.9) 0.29
Follow-up 1 reduction 1M(SD) 26.4 g (38.2) 45.2 g (39.1) 1.79
Follow-up 2 consumption M(SD) 59.7 g (42.4) 58.2 g (38.2) 0.14
Follow-up 2 reduction 1M(SD) 27.0 g (39.4) 50.3 g (39.6) 2.19
n28 27
MCII, mental contrasting with implementation intentions.
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TABLE 2 | Linear regression of meat reduction on condition (Control vs. MCII), intention, and the interaction term.
Variable βSE t p LLCI ULCI
Follow-up 1
Condition (Control vs. MCII) 15.82 9.90 1.57 0.116 3.06 35.69
Intention 6.082.59 2.34 0.023 0.78 11.10
Condition ×Intention 14.81∗∗ 5.19 2.85 0.006 4.39 25.23
Follow-up 2
Condition (Control vs. MCII) 19.50 10.20 1.91 0.062 0.99 39.99
Intention 7.86∗∗ 2.52 3.12 0.003 2.80 12.92
Condition ×Intention 10.485.06 2.07 0.044 0.32 20.64
MCII, mental contrasting with implementation intentions. Betas are unstandardized coefficients. Condition was coded 0 =Control, 1 =MCII. Condition and Intention were
then mean centered. LLCI, lower level 95% confidence interval, ULCI, upper level 95% confidence interval. p<0.05, ∗ ∗ p<0.01.
FIGURE 1 | Simple slopes for intention to reduce meat consumption differentiated for intervention condition (control vs. MCII) in the 1st week (A) and
4th week (B) after the intervention. Meat reduction values of this display were calculated for weak intention (M1SD), moderate intention (M) and strong
intention (M+1SD). MCII, mental contrasting with implementation intentions.
measures, as the meat consumption scores in the MCII group
were comparatively higher at baseline (although not statistically
MCII’s Impact on the Intention-Behavior Relation
Next, we tested our prediction that the intention-behavior
consistency differs between the control and the MCII
intervention. In a first step, we determined the correlation
between participants’ intention of reducing meat consumption
and their actual meat reduction. For information +MCII
participants, intention correlated with behavior change at
Follow-up 1 (r=0.54, p=0.003) and Follow-up 2 (r=0.53,
p=0.004). In contrast, for information-only control participants,
intention correlated with behavior change neither at Follow-up 1
(r= −0.06) nor at Follow-up 2 (r=0.14, ps0.490).
In a second step, we conducted two moderated linear
regression analyses using the SPSS-Macro PROCESS by Hayes
(2012). We regressed meat reduction at Follow-up 1 as well as
Follow-up 2 on condition (control vs. MCII), intention, and the
Condition ×Intention interaction term. Condition and intention
were mean centered (see Aiken and West, 1991;Hayes, 2013), and
95% confidence intervals (CI) for the estimates were computed
through 1,000 bootstrapped samples. We report unstandardized
beta coefficients as recommended by Whisman and McClelland
At Follow-up 1, the model explained 20.1% of variance
in meat reduction, F(3,51) =4.83, p=0.005 (see Table 2).
We observed the predicted Condition ×Intention interaction
effect, p=0.006. In the control condition, intention did not
predict the reduction in meat consumption, p=0.745, but
it did in the MCII condition, B=13.62, SE =3.71, 95%
CI [6.16,21.08], t(53) =3.67, p<0.001 (see Figure 1). To
determine the intention level necessary for an additional effect
of the MCII intervention on meat reduction, we reversed
our analysis with condition as predictor and intention as
moderator. We applied the Johnson-Neyman technique to
determine the value along the continuum of the moderator
intention at which the effect of the predictor condition
transitions from statistically non-significant to significant (Hayes
and Matthes, 2009;Hayes, 2013). We found that the effect
of the MCII-intervention transitioned from statistically non-
significant to significant at a intention level of 4.60 on
our scale ranging from 1 to 7, B=24.03, t(53) =2.26,
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Loy et al. Supporting Behavior Change Intentions
Running the same analyses for Follow-up 2, the model
explained 21.9% of variance in meat reduction, F(3,51) =4.69,
p=0.006 (see Table 2). Again, we found the hypothesized
Condition ×Intention interaction effect, p=0.044. In the
control condition, intention did not predict the reduction in
meat consumption, p=0.403, but it did in the MCII condition,
B=13.20, SE =3.90, 95% CI [5.37,21.03], t(53) =3.38,
p=0.001 (see Figure 1). The additional effect of the MCII-
intervention on meat reduction transitioned from statistically
non-significant to significant at an intention level of 4.27,
B=22.17, t(53) =2.03, p=0.048. In line with our
hypothesis, MCII supported the translation of intentions into
behavior at Follow-up 1 and Follow-up 2. Individuals with a
moderate to strong intention to reduce their meat consumption
reduced it more in the MCII condition than the control
Even though we successfully randomized participants to
conditions and baseline meat consumption did not significantly
differ between both groups, the descriptive difference between
the two groups might still appear relevant. To test the stability of
our pattern of results, we thus ran a matching analysis regarding
baseline meat consumption (Van Casteren and Davis, 2007). This
analysis resulted in two smaller datasets (n=23 per condition)
with M=96.04 (SD =44.38) in the control condition and
M=98.04 (SD =43.54) in the MCII condition. We repeated our
moderated regression analyses for these matched subgroups. For
Follow-up 1, we observed the Condition ×Intention interaction
effect, p=0.024. In the control condition, intention did not
predict the reduction in meat consumption (p=0.716), but
it did in the MCII condition, B=11.70, SE =3.63, 95%
CI [4.38,19.02], t(44) =3.23, p=0.002. For Follow-up 2,
the Condition ×Intention interaction effect did not reach
significance, p=0.307. Still, the pattern remained equivalent: in
the control condition, intention did not predict the reduction in
meat consumption (p=0.431), but it did in the MCII condition,
B=9.45, SE =3.05, 95% CI [3.30,15.60], t(44) =3.10, p=0.004.
Additional Variables
In order to rule out the alternative explanation that participants
in the two conditions had experienced the study differently, we
compared participants’ answers in the final online-questionnaire
(n=54, see Table 3). There were no differences between
the MCII and control condition in perceived experimenter
demand by research staff members, whether participating
was perceived as interesting, and sincerity of participating,
ts(52) 1.82, ps0.075. Also with regards to reasons for
reducing meat consumption, there were no differences in the
importance participants attributed to the respective aspects
between conditions, ts(52) 1.05, ps0.300. Together, these
findings suggest that the additionally assessed variables cannot
account for the observed differences in the reduction in meat
consumption between conditions. Interestingly, environmental
aspects of reducing meat consumption (M=5.74, SD =1.36)
received a higher rating of importance compared to social and
political (M=5.13, SD =1.74), animal-ethical (M=4.69,
SD =1.80), and health aspects (M=4.24, SD =1.69),
ts(53) 3.03, ps0.004.
We observed that the self-regulation intervention strategy of
MCII (Oettingen, 2012;Oettingen et al., 2013) translated
participants’ intentions to reduce meat intake into actual
behavior change. In line with our hypothesis, participants’
intentions of reducing their meat consumption in the MCII
condition were more predictive of their actual reduction than
those in the information only control condition. This result
supports our hypothesis that MCII narrows the intention-
behavior gap. Participants supported with MCII reduced their
meat consumption in correspondence with their prior intentions
already in the 1st week after the intervention and sustained the
intention-consistent consumption level 4 weeks later. By applying
MCII to sustainable eating behavior in the form of reducing meat
intake, our study extends prior research on the MCII intervention
in other domains such as studying (Duckworth et al., 2011, 2013;
Gawrilow et al., 2013), exercising (Christiansen et al., 2010),
relating to one’s partner (Houssais et al., 2013), or negotiating the
sale of a car (Kirk et al., 2013).
MCII Puts Behavior in Line with
In addition, our study shows that the responsiveness to the
strength of the goal intentions reported for the II technique
(i.e., only strong goal intentions benefit from making if-then
plans; Sheeran et al., 2005) is also valid for MCII as a
combined intervention. As our study comprised participants
whose strength of intention was moderate to strong (i.e., 61.7%
of participants reported a strength of intention 4.00 on
our scale ranging from 1 to 7) as well as participants whose
strength of intention was rather weak (i.e., 38.3% <4.00),
we were able to identify whether a minimum strength of
intention was necessary for MCII to reduce meat intake more
than information only control instructions. We observed that
MCII participants who expressed a moderate or strong intention
reduced their meat consumption more than participants in
the control condition, whereas those who reported a weak
intention did not. These results extend prior findings that
demonstrated MCII effects for participants with moderate to
strong intentions. In a study by Adriaanse et al. (2010a),
for example, participants on average reported a strength of
intention of 5.49 on a 7-point Likert scale with only 9.8%
of the participants reporting an intention lower than 4.00.
Together, these findings highlight the importance of moderate to
strong intentions for MCII to support the attainment of desired
Limitations and Future Research
Several limitations of our study have to be noted, which lead us
to suggestions for future research. First, the convenience sample
from a student population limits the generalizability of the
results. Participants’ baseline level of meat consumption was only
about half the amount of the German average (BMELV, 2010).
Hence, future research should aim at replicating our findings in
more diverse samples.
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Loy et al. Supporting Behavior Change Intentions
TABLE 3 | Means and standard deviations of additional variables in the control and MCII condition.
Variable Control MCII t
Demand by research staff M(SD) 4.22 (1.63) 3.78 (1.85) 0.94
Interestingness of participation M(SD) 5.85 (1.17) 5.78 (1.28) 0.22
Sincerity of participation M(SD) 6.59 (0.75) 6.22 (0.75) 1.82
Importance environmental aspects M(SD) 5.78 (1.48) 5.70 (1.27) 0.20
Importance social/political aspects M(SD) 5.07 (1.86) 5.19 (1.64) 0.23
Importance animal-ethical aspects M(SD) 4.48 (1.95) 4.89 (1.65) 0.83
Importance health aspects M(SD) 4.48 (1.78) 4.00 (1.59) 1.05
n27 27
MCII, mental contrasting with implementation intentions.
Second, we had chosen a sample size in the range of prior
similar studies (e.g., Adriaanse et al., 2010a). Still, it might be
appropriate to extend the sample size of future studies in order
to (a) be able to detect small effect sizes more reliably, and (b)
to decrease the likelihood of baseline group differences despite
Third, although it is a strength of our study to at least
include two follow-up measures, previous MCII studies provided
evidence for the stability of effects on behavior change for up to
2 years (see Stadler et al., 2010). Accordingly, the inclusion of
long-term follow-ups would be a valuable extension.
Fourth, we cannot unequivocally determine overall effect sizes
of the MCII and control intervention per se on meat reduction,
as no control group without any intervention was included. The
lack of such a control group arose out of the primary focus
to investigate differences between the classic information only
intervention and the self-regulation focused MCII intervention.
In future research, therefore, it would be worthwhile to include
another control group receiving no intervention at all in order
to determine and control for a possible meat reduction over
time that is due to systematic study-unrelated variations in meat
consumption (e.g., elicited by a food scandal that generated a
lot of media attention). Still, the lack of such a control group
does not affect our hypothesis and results on differences between
the information +MCII group and the information-only control
Fifth, we suspect it was not only the provided information
and MCII that contributed to changes in consumption but also
asking participants to keep a diary, as doing so qualifies as
a form of self-monitoring (see e.g., Zepeda and Deal, 2008).
In support of this assumption, a review of health behavior
change interventions identified self-monitoring as a particularly
strong type of intervention (Michie et al., 2009). This is also
consistent with our finding that participants in the information-
only condition reduced their meat intake and with the feedback
provided by some participants in our study, who stated that
observing their own behavior this closely had been interesting as
well as surprising as it revealed a higher consumption level than
they would have expected. Thus, future research could vary the
degree of self-monitoring to detect its distinct influence.
Sixth, the used diary measure could be improved. In
general, diary approaches have the advantage to minimize biases
compared to retrospective reports of consumption for a whole
week (Schwarz, 1999). Whereas paper-based diaries are easiest to
use for participants (Bolger et al., 2003), they bear the limitation
of not tracking time of access. Future studies might thus provide
insights into participants’ reporting behavior by using online
food diaries (see for example Järvelä et al., 2006) or experience
sampling technology (Bolger et al., 2003). Although participants
might experience regularly accessing online food diaries or
entering data on electronic devices as additional burden, such
computer-based approaches seem a promising route for future
research. Although compliance with regards to diary entries in
the present study cannot be taken for granted, the high level of
reported sincerity in participating, as well as the low amount of
missing values and of diaries which were not returned, speak for a
high quality of the data collected. It has also been pointed out that
diary data are vulnerable to distortions through social desirability.
As more objective measures in the context of assessing food
intake, photographic diaries have been proposed (see for example
Zepeda and Deal, 2008;Martin et al., 2009;Staiano et al., 2012)
which could be a further valuable extension in future research.
However, they increase the effort for participants and are costly.
The finding that perceived demand from research staff did not
differ between the MCII and control condition supports the
assumption that social desirability does not explain the observed
differences between conditions in the present study.
Seventh, our measure of meat consumption is limited by
us providing weight categories in contrast to demanding exact
weight information from participants. However, by giving
examples and encouraging participants to consult weight
information on packages, participants should have been able to
report rather precisely, how much meat they had eaten.
Finally, we do not know how the participants who consumed
less meat substituted their meat intake. Future research could
thus extend the diary to other types of food.
Food choices have been claimed to be among the most relevant
areas for a transformation toward a more sustainable society, and
animal products are named as specifically important “because
more than half of the world’s crops are used to feed animals,
not people. Land and water use, pollution with nitrogen and
phosphorus, and (greenhouse gas) emissions from land use
and fossil fuel use cause substantial environmental impacts”
(UNEP, 2010, p. 80). For example with regard to climate
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Loy et al. Supporting Behavior Change Intentions
change, Hedenus et al. (2014) calculated that, under current
trends of the worldwide consumption of animal products, food-
related greenhouse gas emissions may increase to a carbon
dioxide (CO2)-equivalent emission level until 2070 which is
likely to be larger than the total CO2-equivalent emission level
compatible with meeting the hoped for 2C limit of temperature
rise. Westhoek et al. (2014, p. 196) report that halving the
consumption of animal products in the European Union “would
achieve a 40% reduction in nitrogen emissions, 25–40% reduction
in greenhouse gas emissions and 23% per capita less use of
cropland for food production.” Apart from that, these dietary
changes are also expected to lower various health risks (Tilman
and Clark, 2014;Westhoek et al., 2014). Hence, supporting
individual intentions to attempt dietary changes of their meat
consumption seems an important societal goal, specifically in
light of the difficulties individuals report with acting on their
intentions (Lea et al., 2006).
Our study shows that MCII can be applied as a strategy
to support behavior change. MCII empowered individuals
with moderate and strong intentions to reduce their meat
consumption to translate their intention into behavior. Our
research complements a growing body of evidence confirming
that MCII is a useful intervention approach in helping people
to attain their desired futures. Hence, we suppose that MCII
should also be useful for facilitating other sustainable consumer
behaviors (e.g., other food purchasing or eating behaviors such
as choosing a regional/seasonal diet or organic products, but
also mobility choices or energy use), which individuals intend to
perform but perceive as difficult to implement.
In line with previous findings, the present study suggests that
people who engage in the self-regulation strategy of MCII
change their behavior in the service of solving societal problems.
Regarding the challenge to consume more sustainably, MCII
helped individuals who were motivated to reduce their meat
intake to realize an actual behavior change. We thus showed that
MCII aligns behavior change to individuals’ respective intentions
and narrows the intention-behavior gap. Future studies using
MCII may aim at supporting sustainable behavior not only
pertaining to meat intake but also to other pressing issues of
responsible consumption.
LL was involved in formulating the research question, designing
the study, collecting and analyzing the data, and writing the
article. FW was involved in formulating the research question,
designing the study, and writing the article. PG and GO were
involved in interpretating the data, writing the article, and
revising it critically for important intellectual content. All authors
were responsible for drafting and approving the final manuscript.
Lastly, all authors agree to be accountable for all aspects of
the work in ensuring that questions related to the accuracy or
integrity of any part of the work are appropriately investigated
and resolved.
We thank Patrick Fissler, Siegmar Otto, and the members of
the Social Psychology and Motivation Lab at the University of
Konstanz for their helpful comments on earlier versions of this
manuscript. Furthermore, we thank Sigmar Papendick, Jochen
Mauch, Wanja Wolff, Christine Grund, and Katherina Giese for
their support with conducting the study.
The Supplementary Material for this article can be found
online at:
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
The reviewer LG and handling Editor declared their shared affiliation, and the
handling Editor states that the process nevertheless met the standards of a fair and
objective review.
Copyright © 2016 Loy, Wieber, Gollwitzer and Oettingen. This is an open-access
article distributed under the terms of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction in other forums is permitted, provided
the original author(s) or licensor are credited and that the original publication in this
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or reproduction is permitted which does not comply with these terms.
Frontiers in Psychology | 12 April 2016 | Volume 7 | Article 607
... In the recent years, a growing number of studies have tested the impact of II interventions to increase healthy eating -or decrease unhealthy eating (Adriaanse et al., 2009(Adriaanse et al., , 2011Chapman & Armitage, 2010, 2012Knäuper et al., 2011;Loy et al., 2016;Nooijer et al., 2006;Stadler et al., 2010). II interventions for healthy eating are if-then plans which specify the where, when, and how of goal striving (e.g., "If I am tempted to snack at work, then I will eat more fruits!") ...
... This is relevant for the many informational campaigns and behavioural interventions which rely on a 'kitchen-sink' approach stating all the benefits from some behavioural change both to the individual and the society. Emphasising both environmental and health co-benefits in climate policy applications have been especially advocated in the context of changing food consumption (Tobler et al., 2011;Karlsson et al., 2020) and in the context of II interventions (Loy et al., 2016). ...
Full-text available
There is a debate about whether framing motivations as personal or planetary benefits - or both - is more effective at encouraging sustainable actions and promoting positive behavioural spillovers. In a pre-registered online longitudinal experiment, we randomly allocate n = 1242 respondents to either a control condition, or to one of three novel, interactive implementation intention interventions framing the benefits of a vegetarian diet in terms of either personal health, or planetary health, or both personal and planetary health. We ask respondents to choose between real vegetarian or non-vegetarian foods. We then ask them to donate part of their money to a charity. We finally measure their food choices three days and two months after the interventions. Compared to the control group, we find that participants assigned to any of the behavioural interventions are twice as likely to choose a vegetarian option. We find no statistically significant differences in the proportions of vegetarian options across the three experimental conditions. We find evidence of a positive behavioural spillover on the donations to charity amongst participants exposed to combined personal and planetary health. Three days after the interventions, participants allocated to this combined frame still reported to eat more vegetarian meals than in the control group. Such carryover effects, however, did not persist two months after the interventions. Overall, our research offers new insights about framing behavioural interventions to motivate sustainable actions and their potential behavioural spillovers.
... A more active approach in helping individuals break their meat-eating habits is to encourage them to experiment with meat reduction actions. Previous studies testing action-planning interventions, often in the form of implementation intentions, have been successful in reducing participants' meat intake [8,12]. ...
... Perhaps most importantly, the OPTIMISE intervention asked individuals to plan daily actions to break their meat-eating habits. Action-planning after goal-setting has also been found to be effective in other studies that tested meat reduction interventions [8,12]. Taken together, our results suggest that formally guiding individuals through the whole self-regulation process (goal-setting, self-monitoring, and action-planning with regular reflection) is more effective in the short term than prompting individuals to selfmonitor alone and relying on natural self-regulation, in line with other health behaviour research [15][16][17]. ...
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Purpose A reduction in meat intake is recommended to meet health and environmental sustainability goals. This study aimed to evaluate the effectiveness of an online self-regulation intervention to reduce meat consumption. Methods One hundred and fifty one adult meat eaters were randomised 1:1 to a multi-component self-regulation intervention or an information-only control. The study lasted 9 weeks (1-week self-monitoring; 4-week active intervention; and 4-week maintenance phase). The intervention included goal-setting, self-monitoring, action-planning, and health and environmental feedback. Meat intake was estimated through daily questionnaires in weeks 1, 5 and 9. The primary outcome was change in meat consumption from baseline to five weeks. Secondary outcomes included change from baseline to nine weeks and change in red and processed meat intake. We used linear regression models to assess the effectiveness of all the above outcomes. Results Across the whole sample, meat intake was 226 g/day at baseline, 118 g/day at five weeks, and 114 g/day at nine weeks. At five weeks, the intervention led to a 40 g/day (95%CI − 11.6,− 67.5, P = 0.006) reduction in meat intake, including a 35 g/day (95%CI − 7.7, − 61.7, P = 0.012) reduction in red and processed meat, relative to control. There were no significant differences in meat reduction after the four-week maintenance phase (− 12 g/day intervention vs control, 95% CI 19.1, − 43.4, P = 0.443). Participants said the intervention was informative and eye-opening. Conclusion The intervention was popular among participants and helped achieve initial reductions in meat intake, but the longer-term reductions did not exceed control. Trial registration NCT04961216, 14th July 2021, retrospectively registered.
... Furthermore, the effects of providing information (e.g. about the consequences of meat consumption or dietary recommendations) were enhanced when consumers additionally set personal goals related to meat consumption (e.g. V. Carfora, Caso, & Conner, 2017;Loy, Wieber, Gollwitzer, & Oettingen, 2016). In contrast to effective study findings of goal setting in combination with information and education, goal setting showed to be ineffective when the education was provided via an automated computer system (Delichatsios, Friedman, et al., 2001). ...
... Our systematic review clearly shows that most studies, especially those testing interventions providing information and framing of issues of meat consumption, measured the effects of interventions on attitudes or intentions rather than actual consumption. Informing consumers about the negative consequences of meat consumption might increase consumers' awareness or even intention to eat less meat; however, they might still fail to act accordingly referring to the so-called intentionbehaviour gap (Loy et al., 2016). Although interventions providing information alone are often not effective in changing actual eating behaviour long-term, knowledge about issues seems to be the basis for consumers' actions (White, Habib, & Hardisty, 2019). ...
A reduction of meat consumption and shift to plant-based diets, especially in industrialized countries, is acknowledged as crucial for reaching climate targets, addressing public health problems, and protecting animal welfare. While scholarly research distilled drivers of meat consumption and barriers to its reduction, insights into the effectiveness of measures to initiate such a profound change in consumer behaviour are relatively scarce. This paper presents a systematic literature review on consumption-side interventions in the context of meat consumption across scholarly disciplines. Our analysis confirms that existing research predominantly assessed interventions addressing personal factors of behavioural change such as knowledge and emotions. Whether these interventions are effective depends on whether information (i) is provided on health, animal welfare or environmental effects, (ii) is emotionally or cognitively framed, and (iii) is aligned with consumers' information needs. Moreover, linking meat to living animals or to the humanness of animals activates negative emotions and, thus, reduces meat consumption. Further, increasing the visibility and variety of vegetarian dishes in food environments decreases meat-eating. Also, educational courses on how to shop and cook vegetarian food are effective in reducing meat consumption. There is less evidence on the effectiveness of interventions addressing socio-cultural factors such as social norms. Regarding future research directions, existing research mainly investigated the influence of interventions on attitudes and behavioural intentions. Hence, there is still a need for studies to assess more long-term effects of intervention measures on actual meat consumption and their potential to initiate fundamental changes in dietary habits.
... The low effectiveness of these anti-food waste messages may, nonetheless, be moderated according to social context e.g., proximity to deprived areas where food insecurity is more salient (Loy et al., 2016); community cafeteria where people can visually notice the food waste produced by others; penalties for wasted food (Reschovsky and Stone, 1994). This analysis would be an important addition to our results because we restricted our focus to individual attitude change, whereas many food waste contexts involve social gatherings with family and friends. ...
Food waste at the consumer level poses a global challenge for natural resources management and contributes to climate change. However, there is limited evidence regarding the best messaging strategies to fight household food waste. Here we experimentally test several food waste messages, aiming to identify the most effective strategies to promote individuals' willingness to tackle food waste in the future. We used participants recruited via MTurk and employed a between-subjects design. Study 1 (N = 261) tested three common prompts ("reduce waste", "don't waste" or "stop waste") and showed that only prompts to "reduce waste" were effective. Study 2 (N = 733) tested several consequences of food waste (economic, social or environmental), with none showing a significant effect on the willingness to tackle food waste. Lastly, Study 3 (N = 1459) tested whether combining different stimuli (prompts, consequences, tips for action) would produce stronger effects, but revealed mixed results, suggesting that more information is not necessarily better to help fight food waste. Implications for policy-making and intervention are discussed. These conclusions are based on self-reported measures and thus future research should further corroborate these results in field studies with an observed food waste impact.
... Digital notifications reminding individuals to monitor their red or processed meat consumption appear to be promising (Carfora et al., 2017), but more research is needed to solidify these findings. Further, creating implementation intentions (e.g., creating an intention to consume a meat-alternative in a specific circumstance) may be a reliable tool for reducing meat consumption (Loy et al., 2016;Rees et al., 2018). ...
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Transitioning toward plant-based diets can alleviate health and sustainability challenges. However, research on interventions that influence animal-product consumption remains fragmented and inaccessible to researchers and practitioners. We conducted an overview of systematic reviews, also known as a meta-review. We searched five databases for reviews that examined interventions that influence (increase or decrease) the consumption of animal products. We quantitatively summarised results using individual studies' directions of effect because reviews rarely reported effect sizes of primary studies. Eighteen reviews met inclusion criteria, 12 of which examined interventions intended to decrease animal-product consumption and 6 of which examined interventions intended to increase animal-product consumption. In total, only two reviews conducted quantitative meta-analyses. Across all reviews, vote counting indicated that providing information on the environmental impact of meat consumption may reduce consumption, with 10 of 11 estimates suggesting reduced consumption (91% [95% CI 62.3%, 98.4%]; p = .012). Providing information on the health consequences, emphasising social norms, and reducing meat portion sizes also appeared promising, albeit with more limited evidence. Reviews examining interventions that decreased consumption predominately focused on meat (10/12 reviews). Future reviews should conduct quantitative syntheses where appropriate and examine interventions that influence the consumption of animal products other than meat.
... Persuasion strategies can also be used in combination with other intervention strategies. For example, pairing information with prompts (Abrahamse, 2020;Carfora et al., 2017a) or a self-regulation technique of imagining a future goal (Loy et al., 2016) reduced red meat consumption more than standalone information, indicating the relevance of bundling intervention strategies. ...
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Background Protein transition, i.e. the transition from high levels of traditional meat consumption towards consuming less meat or more plant-based or alternative animal-based proteins, is highly dependent on consumer behaviour. This position paper adds to the literature by integrating the research streams on behavioural sciences and meat reducing strategies, thereby contributing to the use of behavioural science insights in developing meat reducing interventions towards a more plant-based food transition. Scope and approach Meat-reducing strategies involve substituting meat with novel proteins, consumption of less meat or consuming meat less often, and becoming a vegetarian or vegan. Based on previous literature four systematic steps for effective interventions towards behaviour change are described in view of the current literature in the specific context of meat reduction. Finally, emergent strands of future research are identified. Key findings and conclusions The four described steps compromise: (1) identifying the problem and desired behaviour change, (2) examining the main drivers of behaviour change, (3) select fitting interventions, and (4) impact assessment. Based on the meat-reducing literature the key strands for future interventions in the context of protein transition are identified. Moreover, literature gaps are defined. Resulting in an overview of systematic steps for interventions to support behaviour change in the protein transition.
Due to the negative impacts of meat consumption, finding ways to reduce individual meat intake is an urgent issue. The present study tested whether daily mobile-phone text message reminders about the animal welfare, environmental, and health consequences of meat would reduce peoples' meat consumption. The study further investigated the role of a range of potential moderators, such as values and attitudes, for the effectiveness of these reminders. Results showed a significant, unexpected increase in meat consumption over the course of the one-week intervention. Neither animal welfare, environmental, nor health reminders reduced daily meat consumption during this period. Only one moderated effect was observed. On days in which participants received reminders of the animal welfare consequences of meat consumption, those who scored higher on disgust propensity ate less red meat. Text-based interventions may have limited effects in changing people's meat consumption, but further research is needed to establish their effectiveness or lack thereof.
The environmental footprint embedded in the human diet is massive. To convey the enormity of the problem, persuasive environmental messages often report large-scale, aggregate data (such as the billions of tons of greenhouse gases released to the atmosphere annually by the beef industry.) Is this strategy effective? In five studies (total N = 1237), the environmental footprint of beef was presented to participants with either aggregate, nation-level numeric data, or with the same data scaled to the individual level. Across the studies, a clear pattern emerged: Participants who received aggregate-level (versus individual-level) data perceived less of a connection between their behavior and the environment and expressed less intention to curb their meat consumption. These data suggest that aggregate environmental figures, rather than raising urgency, may instead demotivate many message recipients.
Evidence suggests that self-regulation abilities play an important role for the job finding success of unemployed persons. We conduct a randomized controlled trial embedded in an established labor market reactivation program to examine the effect of a self-regulation training on job search input of long-term unemployed participants. Our treatment involves teaching a self-regulation strategy based on mental contrasting with implementation intentions. We find that the treatment has a positive effect on the quality of application documents as well as on the probability of participants submitting their documents on time. However, we do not find a significant treatment effect on labor market reintegration. We discuss several reasons for this null finding and conduct further exploratory analyses to learn about heterogeneous treatment effects.
Currently, there are many advocacy interventions aimed at reducing animal consumption. We report results from a lab (N = 267) and a field experiment (N = 208) exploring whether, and to what extent, some of those educational interventions are effective at shifting attitudes and behavior related to animal consumption. In the lab experiment, participants were randomly assigned to read a philosophical ethics paper, watch an animal advocacy video, read an advocacy pamphlet, or watch a control video. In the field experiment, we measured the impact of college classes with animal ethics content versus college classes without animal ethics content. Using a pretest, post-test matched control group design, humane educational interventions generally made people more knowledgeable about animals used as food and reduced justifications and speciesist attitudes supporting animal consumption. None of the interventions in either experiment had a direct, measurable impact on self-reported animal consumption. These results suggest that while some educational interventions can change beliefs and attitudes about animal consumption, those same interventions have small impacts on animal consumption.
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The data includes measures collected for the two experiments reported in “False-Positive Psychology” [1] where listening to a randomly assigned song made people feel younger (Study 1) or actually be younger (Study 2). These data are useful because they illustrate inflations of false positive rates due to flexibility in data collection, analysis, and reporting of results. Data are useful for educational purposes.
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In this chapter, the authors focus on one of the more cognitive mediators of behavior, dealing with the extent to which implementation intentions lead to successful execution of an intended behavior. From this viewpoint, automatic processes such as those discussed in the preceding chapters (particularly Chapters 5 and 6) are sometimes an unwanted source of interference that must be dealt with for a desired behavior to transpire. The chapter is framed as a lesson on how to overcome such interferences by creating the most efficacious forms of intentions, though a by-product of such efforts is a better understanding of the "miraculous" translation of intentions into actions. Among the phenomena discussed is the greater effectiveness of intentions that have more specific antecedent conditions (the "if" in intentions couched in "if-then" form).
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.
Fantasy realization theory states that when people contrast their fantasies about a desired future with reflections on present reality, a necessity to act is induced that leads to the activation and use of relevant expectations. Strong goal commitment arises in light of favorable expectations, and weak goal commitment arises in light of unfavorable expectations. To the contrary, when people only fantasize about a desired future or only reflect on present reality, expectancy-independent moderate goal commitment emerges. Four experiments pertaining to various life domains supported these hypotheses. Strength of goal commitment was assessed in cognitive (e.g., making plans), affective (e.g., felt attachment), and behavioral terms (e.g., effort expenditure, quality of performance). Implications for theories on goal setting and goal striving are discussed.
The present experiment investigated cognitive and behavioral effects of planning (i.e. forming implementation intentions) on goal pursuit during the performance of mundane behaviors. Participants received the goal to collect a coupon halfway the hall from the lab to the cafeteria. Later, they were also given the task to go from the lab to the cafeteria. Thus participants had to attain a new goal by interrupting a mundane behavior. Some participants enriched their goal with implementation intentions, others did not. Results showed that participants who formed implementation intentions were more effective in goal pursuit than the control group. Importantly, the data suggest that the effects of planning on goal completion are mediated by a heightened mental accessibility of environmental cues related to the goal completion task. Copyright (C) 1999 John Wiley & Sons, Ltd.
The life cycle and supply chain of domesticated animals reared for food account for about half of all human-caused greenhouse gases (GHG). Emissions from livestock respiration are part of a fast cycling biological system, where the plant matter eaten was itself developed through the conversion of atmospheric carbon dioxide into organic compounds. The extra emissions from landuse for livestock and feed comes to around 2,672 million tons of CO 2e, while livestock generates 37% of human-induced methane. Livestock-related GHGs could be managed by governments through the imposition of carbon taxes, in which case leaders in the food industry and investors would search for opportunities that such carbon taxes would help create. Large organic-food companies might find these opportunities particularly appealing and such companies could establish subsidiaries to sell meat and dairy analogs, possibly exclusive of meat or dairy products.