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LETTER
Probing the Link between Biodiversity-Related Knowledge
and Self-Reported Proconservation Behavior in a Global Survey
of Zoo Visitors
Andrew Moss1, Eric Jensen2, & Markus Gusset3
1Chester Zoo, Chester CH2 1LH, UK
2Department of Sociology, University of Warwick, Coventry CV4 7AL, UK
3World Association of Zoos and Aquariums (WAZA) Executive Office, 1196 Gland, Switzerland
Keywords
Aquarium; behavior change; environmental
education; knowledge; zoo.
Correspondence
Andrew Moss, Chester Zoo, Caughall Road,
Upton by Chester, Chester CH2 1LH, UK.
Tel: +44-1244-389739.
E-mail: a.moss@chesterzoo.org
Received
30 July 2015
Accepted
13 January 2016
doi: 10.1111/conl.12233
Abstract
Many environmental communication interventions are built on the assump-
tion that increased knowledge will lead to changes in proenvironment behav-
iors. Our study probes the link between biodiversity-related knowledge and
self-reported proconservation behavior, based on the largest and most inter-
national study of zoo visitors ever conducted. In total, 6,357 visitors to 30 zoos
from 19 countries around the globe participated in the study. Biodiversity un-
derstanding and knowledge of actions to help protect biodiversity were signif-
icantly related, but only 0.6% of the variation in knowledge of actions to help
protect biodiversity could be explained by those same respondents’ biodiversity
understanding. Biodiversity understanding was only the sixth most important
variable in significantly predicting knowledge of actions to help protect biodi-
versity. Moreover, biodiversity understanding was the least important variable
of those that were significantly related to self-reported proconservation behav-
ior. Our study indicates that knowledge is a real, but relatively minor, factor in
predicting whether members of the public – zoo visitors in this case – will know
about specific proenvironment behaviors they can take, let alone whether they
will actually undertake such behaviors.
Introduction
The need to actively conserve the world’s biodiversity has
arisen primarily because of people (Mascia et al. 2003; St
John et al. 2010; Schultz 2011; Heberlein 2012a; Moon
& Blackman 2014). The threats that animal species face
in the wild such as habitat loss, climate change, over-
harvesting and human–wildlife conflict are all affected
by the needs and desires of people. A first-order pri-
ority to address the global decline in biodiversity is to
change modern societies and human behavior. This much
is well known, but achieving such transformation is an
extremely complex and multifaceted undertaking (e.g.,
Jensen & Wagoner 2009).
The most commonly used method to attempt to in-
fluence proconservation behavior change has almost cer-
tainly been education. Indeed, it has been argued that the
“ultimate aim of education is shaping behavior” (Hunger-
ford & Volk 1990, page 8). Furthermore, environmental
education’s goal has been defined as the “development of
environmentally responsible and active citizens” (Hines
et al. 1987, page 1). These are worthy aims, but previous
research indicates that the link between increased knowl-
edge (via education) and behavior change (of all types) is
weak at best (Schultz 2011). Yet, organizations seeking to
promote proconservation behaviors continue to invest in
educational programming at a level far beyond other so-
cial interventions. To further investigate the relationship
between knowledge and behavior change, our study fo-
cuses on the world’s zoos and aquariums (from here on
referred to as “zoos”), environmental education providers
with global biodiversity conservation aims.
Vast numbers of people around the world visit zoos.
In a recent study, Gusset & Dick (2011) reported that
Conservation Letters, February 2016, 0(0), 1–8 Copyright and Photocopying: C2016 The Authors. Conservation Letters published by
Wiley Periodicals, Inc. 1
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the
original work is properly cited.
Conservation knowledge and behavior change A. Moss et al.
Figure 1 Relationship between respondents’ biodiversity
understanding and knowledge of actions to help protect
biodiversity (both measured on 10-point scales).
more than 700 million visits are made to zoos each year.
Could this large cohort of people exposed to environmen-
tal education outputs be persuaded to undertake more
proconservation behaviors? If so, zoos could be power-
ful catalysts for global action to help protect biodiversity.
Many zoos have already made strategic commitments to
providing public education to visitors, on topics includ-
ing environmental and conservation education (Moss &
Esson 2013). At regional and global levels, zoo accredita-
tion bodies also support the education of visitors, via their
own mission statements, strategic statements, or collabo-
ration in global biodiversity conservation initiatives. For
example, the World Association of Zoos and Aquariums
(WAZA), the unifying organization for the world zoo and
aquarium community, has a publically stated educational
vision that aims for zoos “to engage with visitors [ ...] to
encourage conservation-sensitive behaviors that support
biodiversity conservation” (Barongi et al. 2015, page 49).
In 2010, governments agreed to the Strategic Plan
for Biodiversity 2011–2020, which is aimed at halting
and eventually reversing the loss of biodiversity on
the planet (www.cbd.int/sp/default.shtml). To build
support and momentum for this urgent task, the
United Nations General Assembly declared 2011–2020
the United Nations Decade on Biodiversity. There
are five strategic goals and 20 ambitious targets, col-
lectively known as the Aichi Biodiversity Targets
(http://www.cbd.int/sp/targets/default.shtml). Their
purpose is to inspire broad-based action in support of
biodiversity over this decade. Target 1 of strategic goal
A states that “by 2020, at the latest, people are aware of
the values of biodiversity and the steps they can take to
conserve and use it sustainably.” This is an educational
target; that is, the goal is to increase knowledge regarding
biodiversity and the actions to help protect it. It is also
a target that zoos can already claim some success in
achieving. Moss et al. (2015) surveyed almost 6,000
visitors to 26 zoos from 19 countries around the globe
and found that aggregate biodiversity understanding
and knowledge of actions to help protect biodiversity
both significantly increased over the course of zoo
visits (also see Moss et al. 2014). Other studies have
shown significant aggregate improvements in children’s
understanding of conservation biology stemming from
zoo visits (Jensen 2014; Wagoner & Jensen 2015) as
well as proconservation learning trajectories (Wagoner
& Jensen 2015). Clearly, such biodiversity knowledge
gains are viewed as an important outcome. Yet, how
would success in achieving Aichi Biodiversity Target
1’s awareness and knowledge goals actually translate
into the social and behavior change required to achieve
greater biodiversity conservation?
To begin addressing this question, we need to consider
how behavior change operates, starting at the individual
level. We begin by exploring where knowledge might fit
within some of the most widely used behavior change
models. The most commonly used models are probably
the theory of reasoned action (Fishbein & Ajzen 1975)
and its later incarnation, the theory of planned behavior
(Ajzen 1991). Both models identify attitudes and social
norms as key influences on a person’s intention to
behave in one way or another. The models assume
that individuals evaluate the potential outcomes of
performing a particular behavior or not, making rational
decisions based on available information (St John et al.
2010). The theory of planned behavior adds an extra
component, perceived behavioral control. Put simply,
this refers to an individual’s evaluation of how difficult
2Conservation Letters, February 2016, 0(0), 1–8 Copyright and Photocopying: C2016 The Authors. Conservation Letters published by
Wiley Periodicals, Inc.
A. Moss et al. Conservation knowledge and behavior change
Table 1 Tests of fixed effects factors from generalized linear mixed model
output on respondents’ knowledge of actions to help protect biodiversity
FdfP
World region 6.473 4,646 <0.001
First visit to this zoo 0.304 4,646 0.581
First visit to any zoo 7.962 4,646 <0.050
Zoo member or season ticket holder 1.876 4,646 0.171
Gender 21.115 4,646 <0.001
Local to area or visitor 2.138 4,646 0.144
Watched TV nature shows in last 12 months 0.415 4,646 0.520
Member of environmental group 14.834 4,646 <0.001
Number of zoo visits in last 12 months 0.497 4,646 0.481
Age 25.372 4,646 <0.001
Years of formal education 9.535 4,646 <0.050
Biodiversity understanding 91.967 4,646 <0.001
Number of people in visiting group 1.013 4,646 0.314
Significant effects are in bold.
a behavior will be to complete, again based on available
information. Knowledge does not feature as part of the
decision-making process in either model, but there is
an implicit assumption that individuals will need to
“know” something about the behavior in order to initiate
a rational decision-making process.
The transtheoretical model of behavior change (cf.
Prochaska & Velicer 1997) holds that people progress
through a linear series of stages based on their moti-
vation to pursue behavior change (Heimlich & Ardoin
2008). This is a model that has been mainly used with
health-related behaviors such as smoking cessation, but
Dierking et al. (2004) applied the model to a zoo exhibit
that was specifically designed to encourage proconserva-
tion behaviors. While they found that the model was of
some use in documenting the stages of intended behav-
ior change, they concluded that for a suite of related be-
haviors (such as the range of proconservation behaviors),
the model was less effective than when applied to specific
health behaviors.
The responsible environmental behavior model (Hines
et al. 1987) stresses the importance of “personality
factors” in predicting behavioral intentions, such as the
person’s attitudes toward the behavior and evaluation
of her or his ability to successfully carry out the behav-
ior (“locus of control”). This model explicitly includes
knowledge – knowledge of the issues and knowledge
of action strategies – as another predictor of behavioral
intentions. Superficially at least, these two knowledge
components (issues and actions) appear similar to the
two knowledge components of Aichi Biodiversity Target
1. However, it is worth noting that the intention to
behave should not be conflated with actual behavior.
Webb & Sheeran (2006) found that these two variables
are not necessarily as closely linked as one might expect,
reporting that a medium to large change in intention is
associated with a small to medium change in behavior.
In recent years, relatively simplistic models of human
cognition and behavior have gained favor among orga-
nizations targeting proconservation social change. At the
leading edge of this trend are mechanistic approaches
such as “social marketing.” As the name implies, this is
a framework that applies commercial marketing tech-
niques such as the selection of target audiences, the
defining of the barriers and benefits in relation to the
behaviors in question, then implementing a variety of
“tools” to alter behaviors that are problematic for con-
servation (Heimlich & Ardoin 2008).
Models such as these are implying a causal relation-
ship between knowledge and behavior. In social stud-
ies of science, this relationship has been shown to be
highly fraught. The term “knowledge-deficit model” has
come to represent the presumption that increased scien-
tific knowledge will yield concomitant increases in sup-
port for scientific positions and proscience behavior. In
the 1980s, for example, U.K. scientists and scientific in-
stitutions focused on perceived deficits in public scien-
tific literacy, or “public understanding of science.” As the
Royal Society’s (1985, page 9) report states, “Better pub-
lic understanding of science can be a major element in
promoting national prosperity, in raising the quality of
public and private decision making and in enriching the
life of the individual.” This “public understanding of sci-
ence” model was dominant in European science policy
discourse for 15 years. The “recurrent elements” of this
model included “a concern at the ‘scientific ignorance’ of
the populace, a consequent desire to create a ‘better in-
formed’ citizenry and an enthusiasm for making science
more accessible” (Irwin 1995, page 10).
However, high-profile scientific controversies, such as
mad cow disease and genetically modified crops, occa-
sioned a reassessment in European science policy. Under-
pinned by science studies scholarship from Brian Wynne
(1996), Alan Irwin (1995), and others, this reassess-
ment highlighted the limitations of the knowledge-deficit
model of public understanding of science. Increasingly,
the concept of a two-way “dialogue” or “public engage-
ment” gained support. The term “public engagement” has
since increasingly replaced the earlier concept of “pub-
lic understanding of science” in governmental rhetoric
(Irwin 2006) and in science communication discourse
(Holliman & Jensen 2009). Although the knowledge-
deficit model has been debunked following decades of
social scientific research (e.g., Wynne 1996), it remains
remarkably persistent in scientifically based communica-
tion approaches across a wide spectrum of particular do-
mains, including conservation. Yet, there have not been
Conservation Letters, February 2016, 0(0), 1–8 Copyright and Photocopying: C2016 The Authors. Conservation Letters published by
Wiley Periodicals, Inc. 3
Conservation knowledge and behavior change A. Moss et al.
any large-scale assessments of the relationship between
knowledge and behavior in the domain of public engage-
ment with conservation. To investigate how critiques of
the knowledge-deficit model may apply to conservation
efforts, our study probes the link between biodiversity-
related knowledge and self-reported proconservation be-
havior using surveys conducted with people entering
zoos around the world.
Methods
This study is part of a larger repeated measures, survey-
based evaluation of educational impacts of visits to
zoos around the world. This analysis focuses on previsit
data in order to assess the relationship between key
outcome variables for zoo visitors prior to encountering
any educational impacts from their visit (Moss et al.
2015). Previsit surveys were designed to measure our
three dependent variables (biodiversity understanding,
knowledge of actions to help protect biodiversity, and
self-reported proconservation behavior) and to assess
the potential impact of several independent variables on
these dependent variables. The two knowledge-related
dependent variables were operationalized with open-
ended questions. To measure biodiversity understanding,
we asked respondents to list anything that came to mind
when they thought of biodiversity (space for up to five
responses provided). To measure knowledge of actions
to help protect biodiversity, we asked respondents to
think of an action they could take to help save animal
species (space for up to two responses provided). To
assess proconservation behavior, we asked respondents
the closed-ended question whether, if they have listed an
action above, they have done it in the last month (yes,
no, or not sure). In addition to the three dependent vari-
ables, data relating to a number of independent variables
(both categorical and continuous) were collected (for
detailed methods and summary statistics, see supporting
information and Moss et al. 2015).
In short, the survey was designed to be printed by par-
ticipating institutions, distributed on paper by staff mem-
bers and self-administered by respondents. Potential sur-
vey respondents – visitors ࣙ10-year-old – were selected
using systematic sampling (every nth visitor) or on a
continual-ask basis (once one survey response was com-
pleted, the next visitor to cross an imaginary line was se-
lected as the potential next respondent). Surveys were
conducted from November 1, 2012 to July 31, 2013.
Thirty WAZA member organizations (from 19 countries
around the globe) participated, two of which sampled
visitors at more than one institution. The total number
of valid surveys received across participating institutions
was 6,357. We do not claim that these zoo visitors are
representative of the global general population, as this is
not relevant for this study. Here, we focus our attention
on the internal relationships between variables within
the sample, not on inferring characteristics of the general
population.
Any refusals to participate in the research were
recorded on a refusals log that contained easily observ-
able visual information about the person (gender, appar-
ent age, and apparent ethnicity) plus the stated reason
for refusal. From this we calculated a mean refusal rate
across participating institutions of 46.2%. The basic de-
mographics of those visitors who chose not to participate
in the survey were not different from the study sample.
The qualitative data from the two knowledge-related
dependent variables were subjected to content analyses
(for details, see supporting information) to provide quan-
titative data suitable for statistical analyses. Once quan-
tified, we used Tobit regression and generalized linear
mixed models (GLMM) with independent variables as
fixed effect factors and participating institutions as a (cat-
egorical) random effect factor. Tobit regression was used
to account for the potentially censored nature of the two
knowledge-related dependent variables. For the GLMM,
the restricted maximum likelihood method was used to
estimate variance components. Wald tests of exogene-
ity were used to uncover any endogeneity within the
two knowledge-related dependent variables. All statisti-
cal tests were two-tailed, had a significance level of Pࣘ
0.05 and were conducted with either IBM SPSS Statistics
21 or Stata 14.
Results
Bivariate analysis of knowledge-related
variables
We first analysed the direct relationship between our two
knowledge-related variables. There was a close relation-
ship between biodiversity understanding and knowledge
of actions to help protect biodiversity (Figure 1). While
this relationship was statistically significant (F=97.330,
P<0.001), only 0.6% of the variation in knowledge of
actions to help protect biodiversity could be explained
by those same respondents’ biodiversity understanding
([Tobit] R2=0.006). On average, there was a 0.271 point
increase in knowledge of actions to help protect biodi-
versity for each one-point increase in biodiversity under-
standing. Both variables were measured on a 10-point
scale. No significant endogeneity was found in either
variable (biodiversity understanding: Wald χ2=0.030,
P=0.863; knowledge of actions to help protect biodiver-
sity: Wald χ2=0.510, P=0.473).
4Conservation Letters, February 2016, 0(0), 1–8 Copyright and Photocopying: C2016 The Authors. Conservation Letters published by
Wiley Periodicals, Inc.
A. Moss et al. Conservation knowledge and behavior change
Figure 2 Visualization of generalized linear mixed model
parameter estimates for significant fixed effects factors (see
Table 1) in relation to respondents’ knowledge of actions to help
protect biodiversity.
Table 2 Tests of fixed effects factors from generalized linear mixed model output on respondents’ self-reported proconservation behaviour
FdfP
World region 0.474 4,330 0.796
First visit to this zoo 1.131 4,330 0.252
First visit to any zoo 0.179 4,330 0.672
Zoo member or season ticket holder 0.610 4,330 0.435
Gender 2.397 4,330 0.122
Local to area or visitor 2.236 4,330 0.135
Watched TV nature shows in last 12 months 4.373 4,330 0.037
Member of environmental group 10.324 4,330 0.001
Number of zoo visits in last 12 months 2.134 4,330 0.144
Age 3.997 4,330 0.046
Years of formal education 2.128 4,330 0.145
Biodiversity understanding 3.910 4,330 0.048
Knowledge of actions to help protect biodiversity 55.878 4,330 <0.001
Number of people in visiting group 4.787 4,330 0.029
Significant effects are in bold.
Predictors of knowledge of actions to help
protect biodiversity
We next analysed the relative impact of a range of inde-
pendent variables (including biodiversity understanding)
on knowledge of actions to help protect biodiversity.
Seven of the independent variables significantly pre-
dicted respondents’ knowledge of actions to help protect
biodiversity (Table 1). On a 10-point scale, knowledge of
actions to help protect biodiversity was lower in respon-
dents from Central and South America (-0.908) and Asia
(-0.708) compared to respondents from other regions;
increased by 0.089 with each additional year of formal
education; and decreased by 0.153 with each additional
year of age (Figure 2). Female respondents scored 0.270
higher than their male counterparts. First-time zoo
visitors scored 0.278 lower than those who had visited
zoos previously. Members of a nature, conservation, or
environmental group scored 0.346 higher than those
who were not.
Respondents’ biodiversity understanding was also in-
fluential (Figure 2), with each one-point increase seeing a
corresponding 0.269 increase in respondents’ knowledge
of actions to help protect biodiversity. However, biodi-
versity understanding was only the sixth most important
variable in significantly predicting knowledge of actions
to help protect biodiversity.
Predictors of self-reported proconservation
behavior
Finally, we analysed the relative impact of a range of
independent variables (including biodiversity under-
standing and knowledge of actions to help protect bio-
diversity) on self-reported proconservation behavior. Six
of the independent variables were significantly related
to respondents’ self-reported proconservation behavior
(Table 2): whether the respondent has watched any TV
Conservation Letters, February 2016, 0(0), 1–8 Copyright and Photocopying: C2016 The Authors. Conservation Letters published by
Wiley Periodicals, Inc. 5
Conservation knowledge and behavior change A. Moss et al.
nature shows in the last 12 months (0.224); whether
the respondent is a member of a nature, conservation, or
environmental group (0.358); the age of the respondent
(0.072); and the respondent’s visiting group size (0.087).
Respondents’ biodiversity understanding (0.066) and
knowledge of actions to help protect biodiversity (0.236)
were both also influential, especially the latter. Biodi-
versity understanding, however, was the least important
variable of those that were significantly related to self-
reported proconservation behavior.
Discussion
From these findings, we can broadly conclude two things.
First, biodiversity understanding is related to knowledge
of actions to help protect biodiversity, but it is by no
means the strongest predictive variable. Second, our two
knowledge-related variables are linked to respondents’
self-reported proconservation behavior. But again, they
were not the only predictive variables. In short, there
is a relatively weak link between biodiversity-related
knowledge and self-reported proconservation behavior
in our sample.
We found that the two components of Aichi Biodiver-
sity Target 1 – biodiversity understanding and knowledge
of actions to help protect biodiversity – are related. This
much is true, but our expectation was to actually find a
stronger relationship between the two knowledge-related
variables. This was perhaps due to our naivety in assum-
ing that the two strands of knowledge regarding biodiver-
sity (essentially, what it is and how you protect it) would
essentially be complementary. That is, by understanding
biodiversity as a concept, we assumed that respondents
would be interested in, and have knowledge of, the ac-
tions that would help protect it. What we actually un-
covered was more complex. By including the whole suite
of independent variables available to us from the survey
data, we constructed a model where biodiversity under-
standing was only one of eight significant predictive vari-
ables; in fact, it was only the sixth most important predic-
tor, with participants’ region of origin being the strongest.
We also found that our two knowledge-related vari-
ables were predictors of respondents’ self-reported pro-
conservation behavior with, as might be predicted, in-
creasing knowledge of actions to help protect biodiversity
being the more influential of the two. But surprisingly,
knowledge of actions to help protect biodiversity was not
the strongest predictor – participants’ membership in an
environmental group was more important. And, biodi-
versity understanding was in fact only relatively weakly
linked with self-reported proconservation behavior. Our
analysis shows the importance of nonknowledge factors
such as cultural differences across world regions, educa-
tion, age, and gender.
Exploring where knowledge might fit within some of
the most widely used behavior change models – theory
of reasoned action, theory of planned behavior, transthe-
oretical model of behavior change, and responsible envi-
ronmental behavior model – shows that these models are
implying a causal relationship between knowledge and
behavior, albeit mediated by factors such as perceived
control and social norms. European science policy and
science communication practice discussions have shifted
sharply away from this now-debunked knowledge-deficit
model in recent years (Jensen & Holliman 2016). Yet,
many environmental communication interventions, in-
cluding those in zoos, are built on the assumption that
increased knowledge will lead to changes in proenviron-
ment behaviors. Our study indicates that knowledge is
a real, but relatively minor, factor in predicting whether
members of the public – zoo visitors in this case – will
know about specific proenvironment behaviors they can
take, let alone whether they will actually undertake such
behaviors.
Furthermore, it has been demonstrated that, when
asked, people will often cite society-level causes for en-
vironmental problems (such as capitalism), but only be
able to quote individual-level behaviors to attempt re-
dress. This can leave individuals with a sense of pow-
erlessness and pessimism over the perceived benefits of
such behaviors (Kenis & Mathijs 2012). Moreover, indi-
vidualizing problems that have their roots in much larger
social structures may create a situation in which procon-
servation campaigns have localized success, while failing
to contribute to changes needed at the society level (e.g.,
Jensen & Wagoner 2009). For example, it could be ar-
gued that governmental lobbying and legislative change
would be more effective in the long term at addressing
many conservation issues than targeting individual con-
sumer knowledge and behavior. An alternative explana-
tion highlighted by Heberlein (2012b, page 62 ff) per-
tains to the “specificity principle.” That is, broadly framed,
general attitudes (e.g., biodiversity understanding in our
case) do not tend to translate into specific behaviors (e.g.,
specific proconservation actions in our case). However,
more specific attitudes pertaining to particular behaviors
tend to be much more likely to translate into actions, es-
pecially when underpinned by relevant values, affective
orientation, and social norms.
At this point, we must acknowledge some of the limi-
tations of our study. The most problematic is the measure
of proconservation behavior itself. Namely, we relied
on respondent self-report (Webb & Sheeran 2006). Of
course, the most valid way to measure proconservation
behavior is to do just that – measure it directly. However,
6Conservation Letters, February 2016, 0(0), 1–8 Copyright and Photocopying: C2016 The Authors. Conservation Letters published by
Wiley Periodicals, Inc.
A. Moss et al. Conservation knowledge and behavior change
for many of the behaviors in question, this is a practical
impossibility. Therefore, we believe that improved, more
detailed, and more verifiable behavioral self-report mea-
sures, measured over a longer timeframe, are the next
logical step for this research methodology. We must also
be cautious in defining any causal relationships within
this study, not only in the origin of cause but also in the
direction. That is, a change in respondent behavior could
have conceivably caused a change in related respondent
knowledge, rather than the other way round.
We conclude that interventions with the goal of pro-
moting proenvironment social change should not assume
that education is the only means of reaching that goal.
As Jensen & Wagoner (2009) have argued, there are a
host of mechanisms for social change that institutions
such as zoos and individuals could target in their ef-
forts to create a more environmentally sustainable so-
ciety. Indeed, individual-level behavior change may be
a problematic default focus from the outset. For exam-
ple, the long-understood tendency toward overproduc-
tion and uncontrolled consumption within capitalism is
undoubtedly one of the major sources of global unsus-
tainability and threats to the long-term survival of life on
earth. If such fundamental structural factors as an unsus-
tainable consumption-based economy are left unchecked,
individualized behavior change may not be sufficient to
turn the tide. Therefore, we believe that efforts to address
the undoubtedly important individual level of knowledge
and behavior should be supplemented by interventions
that target the structural threats to global biodiversity.
Acknowledgments
We are indebted to the WAZA member organizations that
conducted the surveys. We are grateful to the following
Chester Zoo staff that assisted on this project: S. Baci-
galupo, R. Pearson, M. Esson, K. Brankin, V. Small, and L.
Myers. Financial support for this project was gratefully re-
ceived from the MAVA Foundation. The manuscript ben-
efitted from comments provided by A. Chhatre, B. Smith,
and three anonymous referees.
Supporting Information
Additional Supporting Information may be found in the
online version of this article at the publisher’s web site:
Supporting Information
References
Ajzen, I. (1991). The theory of planned behavior. Organ.
Behav. Hum. Decis. Process.,50, 179-211.
Barongi, R., Fisken, F.A., Parker, M. & Gusset, M., editors.
(2015). Committing to conservation: the world zoo and aquarium
conservation strategy. World Association of Zoos and
Aquariums, Gland, Switzerland.
Dierking, L.D., Adelman, L.M., Ogden, J., Lehnhardt, K.,
Miller, L. & Mellen, J.D. (2004). Using a behavior change
model to document the impact of visits to Disney’s Animal
Kingdom: a study investigating intended conservation
action. Curator,47, 322-343.
Gusset, M. & Dick, G. (2011). The global reach of zoos and
aquariums in visitor numbers and conservation
expenditures. Zoo Biol., 30, 566-569.
Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention, and
behavior: an introduction to theory and research.
Addison-Wesley, Reading, MA.
Heberlein, T.A. (2012a). Navigating environmental attitudes.
Conserv. Biol.,26, 583-585.
Heberlein, T.A. (2012b). Navigating environmental attitudes.
Oxford University Press, New York, NY.
Heimlich, J.E. & Ardoin, N.M. (2008). Understanding
behavior to understand behavior change: a literature
review. Environ. Educ. Res.,14, 215-237.
Hines, J.M., Hungerford, H.R. & Tomera, A.N. (1987).
Analysis and synthesis of research on responsible
environmental behavior: a meta-analysis. J. Environ. Educ.,
18,1-8.
Holliman, R. & Jensen, E. (2009). (In)authentic science and
(im)partial publics: (re)constructing the science outreach
and public engagement agenda. Pages 35-52 in R.
Holliman, E. Whitelegg, E. Scanlon, S. Smidt, J. Thomas,
editors. Investigating science communication in the information
age: implications for public engagement and popular media.
Oxford University Press, Oxford, UK.
Hungerford, H.R. & Volk, T.L. (1990). Changing learner
behavior through environmental education. J. Environ.
Educ. 21, 8-21.
Irwin, A. (1995). Citizen science: a study of people, expertise and
sustainable development. Routledge, London, UK.
Irwin, A. (2006). The politics of talk: coming to terms
with the ‘new’ scientific governance. Soc. Stud. Sci.,36, 299-
320.
Jensen, E. (2014). Evaluating children’s conservation biology
learning at the zoo. Conserv. Biol.,28, 1004-1011.
Jensen, E. & Holliman, R. (2016). Norms and values in UK
science engagement practice. Int. J. Sci. Educ., Part B:
Commun. Public Engagement,6, 68-88.
Jensen, E. & Wagoner, B. (2009). A cyclical model of social
change. Cult. Psychol.,15, 217-228.
Kenis, A. & Mathijs, E. (2012). Beyond individual behaviour
change: the role of power, knowledge and strategy in
tackling climate change. Environ. Educ. Res.,18, 45-65.
Mascia, M.B., Brosius, J.P., Dobson, T.A. et al. (2003).
Conservation and the social sciences. Conserv. Biol.,17,
649-650.
Moon, K. & Blackman, D. (2014). A guide to understanding
social science research for natural scientists. Conserv. Biol.,
28, 1167-1177.
Conservation Letters, February 2016, 0(0), 1–8 Copyright and Photocopying: C2016 The Authors. Conservation Letters published by
Wiley Periodicals, Inc. 7
Conservation knowledge and behavior change A. Moss et al.
Moss, A. & Esson, M. (2013). The educational claims of zoos:
where do we go from here? Zoo Biol., 32, 13-18.
Moss, A., Jensen, E. & Gusset, M. (2014). Zoo visits boost
biodiversity literacy. Nature,508, 186.
Moss, A., Jensen, E. & Gusset, M. (2015). Evaluating the
contribution of zoos and aquariums to Aichi Biodiversity
Target 1. Conserv. Biol.,29, 537-544.
Prochaska, J.O. & Velicer, W.F. (1997). The transtheoretical
model of health behavior change. Am. J. Health Promotion,
12, 38-48.
Royal Society. (1985). The public understanding of science. Report
of a Royal Society ad hoc Group endorsed by the Council of
the Royal Society. The Royal Society, London, UK.
Schultz, P.W. (2011). Conservation means behavior. Conserv.
Biol.,25, 1080-1083.
St John, F.A.V., Edwards-Jones, G. & Jones, J.P.G. (2010).
Conservation and human behaviour: lessons from social
psychology. Wildlife Res., 37,658-667.
Wagoner, B. & Jensen, E. (2015). Microgenetic evaluation:
studying learning as it develops. Pages 293-310 in G.
Marsico, R.A. Ruggieri, editors. Reflexivity and psychology.
Information Age Publishing, Charlotte, NC.
Webb, T.L. & Sheeran, P. (2006). Does changing behavioral
intentions engender behavior change? A meta-analysis
of the experimental evidence. Psychol. Bull.,132, 249-
268.
Wynne, B. (1996). May the sheep safely graze? A reflexive
view of the expert–lay knowledge divide. Pages 44-83 in S.
Lash, B. Szerszynski, B. Wynne, editors. Risk, environment &
modernity: towards a new ecology. Sage, London, UK.
8Conservation Letters, February 2016, 0(0), 1–8 Copyright and Photocopying: C2016 The Authors. Conservation Letters published by
Wiley Periodicals, Inc.
