MORALS, VALUES, AND ENVIRONMENTALLY SIGNIFICANT BEHAVIOR
By
Summer Lee Allen

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Sociology – Doctor of Philosophy
2017

ABSTRACT
MORALS, VALUES, AND ENVIRONMENTALLY SIGNIFICANT BEHAVIOR
By
Summer Lee Allen
This dissertation uses a multiple-paper format to explore the relationships among
environmentally significant behaviors (ESBs) at the individual and household levels, value
orientations, and moral intuitions. Forty years of scholarship provides a nuanced understanding of
the drivers, processes, and consequences of a range of ESBs; yet, there remain emergent and
underexplored behaviors and predictors. In Chapter Two, I report the results of a between-subjects
experiment investigating how values, moral intuitions, and varying psychological distances in
messages about biodiversity loss influence subjects’ choice of donating to a local, national, or
international arm of a biodiversity conservation charity. In Chapter Three, I report on a betweensubjects experiment examining how water conservation behavioral intentions and donation to an
environmental charity are influenced by psychological distance (i.e., spatial and temporal distance
from a drought), values, and morals. Chapter Three also introduces a new water conservation
behavior instrument. In these two chapters, I use ordinary least squares, logistic, and zero-inflated
negative binomial regression techniques. I directly observe monetary donations as a relatively novel
outcome measure reflecting advances in online experimentation methods and capturing an important
fundraising mechanism for environmental causes. I also introduce psychological distance as a novel
potential predictor of environmentally significant behaviors.
Research streams of this decades-long duration can benefit from occasional reviews and
systematization to improve their coherence and lead to a more organized accumulation of
knowledge. To this end, in Chapter Four, I report an analysis of a sample of the environmentally

significant behavior/pro-environmental behavior literature. My review identifies the differences in
and consequences of the performance of individual versus scaled outcome measures as well as a
variety of psychosocial predictors, and the employment of our most-used theoretical approaches.

ACKNOWLEDGMENTS

I extend my most sincere thanks and appreciation to my Committee Chair, Aaron
McCright, for his attention to detail, patience, and belief in process. I also thank Tom Dietz and
Sandy Marquart-Pyatt for their continuous support, encouragement, and funding opportunities.
Kelly Millenbah generously provided perspective and career advice in addition to her academic
expertise.

iv

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................................ vii
LIST OF FIGURES ....................................................................................................................... ix
CHAPTER 1: INTRODUCTION ................................................................................................... 1
Environmentally Significant Behavior/Pro-Environmental Behavior ........................................ 2
Values ......................................................................................................................................... 4
Morality....................................................................................................................................... 7
Psychological Distance ............................................................................................................... 8
This Research .............................................................................................................................. 9
REFERENCES ......................................................................................................................... 14
CHAPTER 2: EXPLAINING MONETARY SUPPORT FOR CHARITABLE
CONSERVATION PROGRAMS ................................................................................................ 21
Introduction ............................................................................................................................... 21
Methods..................................................................................................................................... 24
Participants............................................................................................................................ 24
The experiment ..................................................................................................................... 25
Analytic techniques ............................................................................................................... 27
Results ....................................................................................................................................... 28
Discussion ................................................................................................................................. 32
APPENDICES .......................................................................................................................... 34
APPENDIX A: Supplementary Tables ................................................................................. 35
APPENDIX B: Survey.......................................................................................................... 39
APPENDIX C: Experimental Messages, Donation Distribution Graph, and Brandt Test
Results ................................................................................................................................... 50
REFERENCES ......................................................................................................................... 56
CHAPTER 3: WATER CONSERVATION IN THE HOME AND WALLET ........................... 61
Introduction ............................................................................................................................... 61
Previous Scholarship................................................................................................................. 62
Methods..................................................................................................................................... 65
Participants............................................................................................................................ 65
Procedures ................................................................................................................................. 67
Measures ............................................................................................................................... 71
Analytic methods .................................................................................................................. 75
Results and Discussion ............................................................................................................. 76
Performance of the water conservation instrument .............................................................. 76
Predicting self-reported water conservation behaviors ......................................................... 78
Predicting water conservation behavioral intentions ............................................................ 80
v

Predicting donations to a water conservation program ......................................................... 82
Performance of psychological distance ................................................................................ 84
Conclusion ................................................................................................................................ 85
APPENDICES .......................................................................................................................... 87
APPENDIX A: Nested Regression Models .......................................................................... 88
APPENDIX B: Message Slides ............................................................................................ 92
REFERENCES ......................................................................................................................... 94
CHAPTER 4: MEASURING PRO-ENVIRONMENTAL BEHAVIOR ................................... 100
Introduction ............................................................................................................................. 100
Sampling process ................................................................................................................ 101
Sample description .............................................................................................................. 103
Measuring Individual Pro-Environmental Behaviors ............................................................. 105
Measuring General Pro-Environmental Behavior with Scales ............................................... 108
Comparing Specific and General Behavior Measures ............................................................ 110
Consequences, Suggestions, and Conclusions ........................................................................ 113
APPENDIX ............................................................................................................................. 119
REFERENCES ....................................................................................................................... 128
CHAPTER 5: CONCLUSIONS ................................................................................................. 132

vi

LIST OF TABLES

Table 1: Unstandardized coefficients (and robust standard errors) from zero-inflated negative
binomial regression predicting choice to donate and donation amount ................................ 30
Table 2: Unstandardized coefficients (and standard errors) from multinomial logistic regression
model predicting donation target (n=479; pseudo r2=0.08) ................................................. 31
Table 3: Selected socio-demographic sample characteristics ....................................................... 36
Table 4: Variable names, coding, means, and standard deviations .............................................. 37
Table 5: Odds ratios (and standard errors) from ordered logistic regression model predicting
donation target (n=479; pseudo r2=0.05) ............................................................................. 38
Table 6: Brant test of parallel regression assumption ................................................................... 55
Table 7: Selected socio-demographic sample characteristics of total sample and comparison of
means of for california and michigan sub-samples (ca – mi) ............................................... 67
Table 8: Water conservation behavior items and descriptive statistics (low likelihood=1 to very
high likelihood=5)................................................................................................................. 72
Table 9: Factor loadings and eigenvalues from principle component factor analysis of water
conservation instrument items with pro-max rotation .......................................................... 78
Table 10: Unstandardized coefficients (and standard errors) from ols regression of self-reported
water conservation behaviors on a range of theoretical and socio-demographic predictors 80
Table 11: Unstandardized coefficients (and standard errors) from ols regression predicting
intentions to perform water conservation behaviors on a range of theoretical and sociodemographic predictors ......................................................................................................... 82
Table 12: Logistic and ols regressions (and standard errors) predicting donation to california
drought program and amount of donation ............................................................................ 84
Table 13: Unstandardized coefficients (and standard errors) of nested ols regression models of
routine water conservation behavioral intentions ................................................................. 89
Table 14: Unstandardized coefficients (and standard errors) of nested ols regression models of
once-and-done water conservation behavioral intentions ..................................................... 90
Table 15: Unstandardized coefficients (and standard errors) of nested logistic regression models
of donation behavior ............................................................................................................. 91

vii

Table 16: Predictor performance in selected models of specific behaviors................................ 112
Table 17: Predictor performance in selected models using pro-environmental behavior scales 113

viii

LIST OF FIGURES

Figure 1: News story for control condition ................................................................................... 51
Figure 2: News story for low social distance/low temporal distance condition ........................... 51
Figure 3: News story for high social distance/low temporal distance .......................................... 52
Figure 4: News story for low social distance/high temporal distance condition .......................... 52
Figure 5: News story for high social distance/high temporal distance condition ......................... 53
Figure 6: Distribution of the donation amount variable................................................................ 54
Figure 7: Histogram of donation amounts .................................................................................... 70
Figure 8: Current condition slide .................................................................................................. 93
Figure 9: Forecast condition slide ................................................................................................. 93
Figure 10: Literature review selection process ........................................................................... 103

ix

CHAPTER 1: INTRODUCTION

Environmentally significant behaviors at the individual and household levels are two
important factors in sustaining the availability and quality of critical nature-derived resources
such as energy (e.g., Poortinga, Steg, and Vlek 2004), water (e.g., Dolnicar, Hurlimann, and
Grün 2012), and ecosystems (e.g., Harshaw, Sheppard, and Lewis 2007). Scholars suggest that
individual and household behaviors could yield savings approaching 48% of current residential
energy use in the EU (Fonseca et al. 2009) or 31% of U.S. CO2 emissions (Dietz et al. 2009).
We have over forty years of research that give us a nuanced understanding of the drivers,
processes, and consequences of environmentally significant behavior. Nevertheless, with greater
understanding have come further questions. In this dissertation, I address several of these.
Different phrases have been interchangeably used to refer to a suite of related concepts.
Environmental concern originated in the 1970s and represents the oldest and broadest umbrella
concept. It is still used today. Environmental concern encompasses values, attitudes, beliefs,
knowledge, worldviews, identities, policy preferences, self-reported behaviors and behavioral
intentions. This is a convenient shorthand; however, it is imprecise. Clearly not all of these
concepts require concern. Nor are they homogenous. Each of these drivers and outcomes has
different predictors and behaves differently in various contexts. Consequently, they do not fit
together well in any real analytical sense. More recently, Dietz (2003) has suggested the phrase
environmental decision-making as shorthand for the concepts held under environmental concern,
plus the cognitive and affective processes that leads to decisions and actions relevant to the
environment.

1

Scholars have also examined behaviors, specifically. We see use of environmental
behavior and then pro-environmental behavior to more precisely identify actions that affect, or
have the intention to affect, the environment. The latter term recognizes that environmental
behavior could have negative consequences and so delineates those actions that are intended to
be “friendly” to the environment. In 2000, Stern introduced a definition of environmentally
significant behavior that pays specific attention to the impacts of the behavior on natural
resource quality or quantity.
In this dissertation, I use the phrase environmentally significant behavior when framing
or discussing my work as a whole. It is the term that most closely fits my intended research.
When I refer to the larger body of literature I follow Dietz using environmental decision-making
as shorthand. However, the majority of scholars in this area still use pro-environmental behavior
as their preferred term, so when directly discussing their work (i.e., in Chapter 4), I will use their
chosen terminology.

Environmentally Significant Behavior/Pro-Environmental Behavior
Individual- and household-level environmentally significant behaviors (ESBs) have been
of interest to scholars since the early years of environmental sociology (e.g., DeFronzo and
Warkov 1979; Klausner 1977). Scholars studied behaviors such as those deemed harmful to
recreational environments (e.g., littering and vandalism); the relationships between lifestyles and
water or energy allocation; participation in and opposition to the Environmental Movement; and
efforts to avoid pollution or other technological hazards (i.e., environmental racism-related
behaviors). Now scholars also consider resource conservation behaviors, consumption practices,
recycling, transportation choices, and technology acceptance (Attari et al. 2010; Dietz 2014;

2

Guagnano, Stern, and Dietz 1995; Steg and Vlek 1997; Stern et al. 2010). These changes have
emerged as environmental consciousness has spread more widely among the public and as
environmental sociology has progressed in its sophistication. Of particular interest, we have
developed better methods to quantify resource use and to estimate the potential impacts of
various environmentally significant behaviors (e.g., Dietz et al. 2009; York, Rosa, and Dietz
2003).
Predicting ESBs has proven to be a highly complex endeavor. There are contextual
factors such as the geophysical environment or an area’s energy-delivery system to consider.
Social-psychological and demographic factors such as attitudes, beliefs, values, moralities, social
identities, ideologies, and knowledge are both directly and indirectly influential, and individual
cognitive factors such as preferred information processing strategies and heuristics work to
facilitate or constrain the various ESBs (Dietz, Fitzgerald, and Shwom 2005; Gifford and Nilsson
2014; Stern 2014). Hundreds of studies of ESBs have focused upon the performance of these
demographic and social-psychological predictors (Bamberg and Möser 2007). Demographics
have typically explained only a small amount (between 5% and 15%) of the variation in ESBs
with age, sex, educational attainment, and political ideology being the most consistent predictors
(Barr 2007; Dietz, Kalof, and Stern 2002; Dietz et al. 1998; Gifford and Nilsson 2014). Among
social-psychological influences, Gifford (2011) has identified at least 30 different psychological
predictors alone. Factors such as worldview (e.g., the New Ecological Paradigm), value
preferences (e.g., Schwartz’s continuum, post-materialism, or cultural theory), perceptions of
risk, attitudes toward the environment generally or an ESB specifically, and knowledge about
environmental issues are all predictors of ESBs. Social-psychological predictors demonstrate

3

better predictive abilities with somewhere between 20% and 35% of variation typically
explained.
Scholars test these predictors mostly atheoretically, but also use a few meso-level
theories. The best supported of these are the Norm Activation Model (Schwartz 1977), ValuesBeliefs-Norms theory (Stern et al. 1999), the Theory of Planned Behavior (Ajzen 1991), and
combinations of these three (e.g., Kaiser, Hübner, and Bogner 2005).
Despite the breadth of this work on ESBs, we still have many questions regarding the
best predictors of specific individual behaviors. Our field could also benefit from more
systematic examination of promising predictors that have received insufficient or no attention
lately. Here, I build from existing studies to examine one concept that has received extensive
though unsystematic examination (values) and one concept that has received almost no attention
in recent work (morals) in the context of a directly observed ESB: monetary donation to an
environmental cause.

Values
Examination of the role of values in environmental decision-making shows that values
influence behaviors more indirectly than do intentions, attitudes, and some beliefs (Dietz,
Fitzgerald and Shwom 2005). For instance, in a large, cross-national study using the Theory of
Planned Behavior extended with Schwartz values (Schwartz 1994) and post-materialist values
(Inglehart and Flanagan 1987), Oreg and Katz-Gerro (2006) find that both positively influence
attitudes toward recycling, frequency of driving a car, and environmental citizenship, as well as
personal norms regarding the performance of these behaviors, but values are not seen to have any
direct effects on these behaviors. Instead, values guide attention to and accessibility of these

4

knowledges, beliefs, and attitudes (Best and Mayerl 2013; Steg et al. 2014). This action is
highlighted in Values-Beliefs-Norms theory-based studies (e.g., Stern et al. 1999), where values
are established as specific precursors to knowledge about potential consequences and beliefs
about responsibility. We also see that values combine with other social psychological factors
(e.g., norms or beliefs about personal control) and situational contexts (e.g., availability of
recycling bins or household-level energy use data) to lead to behavioral intentions (Steg et al.
2014; Dietz, Fitzgerald, and Shwom 2005).
Within environmental sociology, the Rokeach/Schwartz approach to conceptualizing and
measuring values receives the most empirical support. The cultural theory/grid-group approach
(Douglas and Wildavsky 1983) has been criticized for being a reflection of political ideology
(CITES), while the post-materialism approach does not adequately explain variation in the
Global South (CITES). Building on the work of Rokeach (1979), Schwartz (1994:21) has
developed a continuum of behavior-motivating value orientations that he defines as “desirable
trans-situational goals, varying in importance, that serve as guiding principles in the life of a
person or other social entity.” Schwartz and his colleagues have identified 12 cross-cultural
values, though the subtypes are usually collapsed to 10 in research: Power, Achievement,
Hedonism, Stimulation, Self-Direction, Universalism (Social), Universalism (Environmental),
Benevolence, Tradition, Conformity, Security (Personal), and Security (Social).
The literature indicates that a few specific values are related to environmental decisionmaking (Stern and Dietz 1994; Stern, Dietz, Kalof and Guagnano 1995; Steg and deGroot 2012).
Stern and Dietz (1998) created a reduced-item values scale based on these Schwartz values that
relate to environmental decision-making. They re-labeled the resulting factors for ease of use.
The Universalism (Environmental) items are called “Biospherism,” the Universalism (Social)

5

and Benevolence items are called “Social Altruism,” and the Hedonism and Power items are
called “Egoism” (Dietz, Fitzgerald, and Shwom 2005). More recent research suggests that
Hedonism and Egoism should be treated separately, however, as they have different theoretical
implications (Steg and deGroot 2012). Biospherism is concern for the ecosystem as a whole and
generally shows positive and significant influence on ESB-related attitudes and norms.
Altruism, concern for fellow humans as a group, shows a positive and significant effect on
environmentally significant behaviors that focus on social well-being. Egoism is concern for
self, possibly extended to immediate family. It demonstrates a significant negative influence on
environmentally significant behavior. Finally, Hedonism is concern for feeling better now. It is
an emotion-focused drive that also shows impact on environmentally significant behavior,
though the direction of influence varies based on the behavior in question and how good it makes
the respondent feel (Steg, Perlaviciute, van der Werff, and Lurvink 2014).
Research on environmental values in the past decade has largely been conducted outside
of the United States, predominantly in Western Europe and the British Commonwealth.
Examination of a large sample of these studies indicates that values in roughly 50% of these
recent empirical studies are Schwartz-derived, though this percentage appears to be closer to
30% in the United States. The NEP scale (Dunlap et al. 2000) is also popular, with roughly 20%
of studies using it as a measure of values rather than as the “primitive beliefs” Dunlap and
colleagues characterize their work as being. In the U.S., the prevalence of NEP use is closer to
50%. The other half of U.S. studies use either more obscure conceptualizations or the authors
create their own measures. Recent values work in the United States seems to focus on the role of
values in explaining choices between sustainable practices at the household, farm, and
community levels, and explaining climate change belief and policy acceptability, while

6

international studies are explaining conservation and sustainability practices, and cross-cultural
studies tend toward validation of models developed in the U.S. or Western Europe.
My dissertation brings both Schwartz-derived values—the most empirically supported
and globally prominent conceptualization—and the NEP scale to bear on water conservation and
donation to an environmental cause, where they have as yet received minimal attention.

Morality
Morality “guides human action beyond the immediate gratification of desires and the
momentary demands of the situation” (Haidt 2008:68), “consists of a reasonably coherent set of
notions of what is right and what is wrong” (Luckmann 2003:276), and has affective as well as
cognitive significance (Stets 2010). Thus, like values, morals provide trans-situational guidance.
But, unlike values, they work through emotional judgment about right and wrong and are based
on socially agreed narratives about our roles, place, and purpose in the world (Smith 2003).
Understanding moral systems, their origins, how they transform over time, how they are
reproduced in social institutions, and how they are acted upon may be important for deeper
understanding of social action and organization (Hitlin and Vaisey 2010). Durkheim, Weber,
and Marx dedicated much energy to explorations of morality (Durkheim and Coser 1997; Gerth
2007; Shilling and Mellor 1998). Though there was a lull in the study of morality after Parsons’
structural-functional explanations fell into disfavor (Abend 2008), cultural sociology took up the
task of explaining moral systems, conceiving them as culturally derived “toolkits” (Swidler
1986) rather than socially imposed behavioral imperatives. A major weakness of this approach,
however, is the (sometimes implicit) assumption that human behavior is, at heart, explained by

7

rational choice theory (Smith 2003). We know this is only part of the explanation (Stets 2005)
and that, as mentioned, emotion also plays a major role (Lamont 2009).
Recent scholarship indicates that morals impact worldviews, beliefs, attitudes, and
behavioral intentions (Firat and Hitlin 2012; Graham, Haidt, and Nosek 2009; De Groot and Steg
2009; Hofmann et al. 2014; Lamont 2009). Given that morals are related to values (Hitlin 2007)
and other aspects of decision-making like heuristics and information processing (Schwartz and
Bilsky 1990; Haidt 2008; Wikstrom 2010), morals may be useful in an examination of ESBs.
Studies focused on environmental topics suggest that the majority of people in affluent industrial
countries view ESBs as being in the moral domain (Fransson and Garling 1999). They also
suggest that, because of the visceral, emotional nature of moral beliefs, they may be able to
transform behavioral goals like reduced car-driving from difficult “shoulds” to a more “feels
good” frame as individuals behave according to a moral imperative (Abrahamse et al. 2009;
Lindenberg and Steg 2007; Steg et al. 2014). In this dissertation, I introduce moral intuitions as
a potential predictor of ESBs as an initial exploration of a potentially valuable research thread.

Psychological Distance
Construal Level Theory, also termed psychological distance, is not a focus of this
dissertation but it is a theoretical frame that I use for my messaging experiments in Chapters Two
and Three. This theory suggests that our attitudes, judgments, and subsequent choices of objects,
people, groups, and issues varies by the social, temporal, spatial, or hypothetical distance we
construe between our self and my mental representation of the object of attention (Trope and
Liberman 2010). Oversimplified, the more abstract an object of attention is for us the less we
identify with it and thus are less likely to spend our time, energy, or resources upon it. As related

8

to environmental decision-making, psychological distance may be a useful theoretical frame to
illuminate attitudinal or behavioral differences in response to global environmental issues,
between groups, or where planning for future consequences is important.
To date, the environmental decision-making literature has not used Construal Level
theory. There have been several studies of climate change concern that point to the usefulness of
this frame for global issues (e.g., Uzzell 2000) and non-environmentally focused researchers
have just begun to examine the impact of Construal Level theory on consumer behaviors
(Liberman, Trope, and Waslak 2007). This dissertation provides the first introductions of
psychological distance to the issues of combatting biodiversity loss and enhancing water
conservation.

This Research
In this research, I address the roles of values and morals on two types of ESBs (water
conservation behavior, and monetary donations to a water conservation organization and to a
biodiversity conservation organization) as well as the foundational issue of measuring proenvironmental behaviors (PEBs). My research questions are:
1. How do psychological distance, values, and morals interact to influence
charitable donation behavior? (Chapters 2 and 3)
2. How do psychological distance and values influence self-reported home water
conservation behaviors and behavioral intentions? (Chapter 3)
3. How have PEBs been conceptualized and operationalized, and what
ramifications do these have for our theoretical and empirical understanding of
PEB? (Chapter 4)

9

Chapter Two considers Research Question One. I conducted an experiment using the
Amazon Mechanical Turk (AMT) capability for recruitment and Qualtrics for survey design and
delivery. Experiments using AMT are faster and more cost-effective to conduct (Litman,
Robinson, and Rosenzweig 2015). AMT samples tend to be more female, more educated, lower
income, and more liberal/Democrat than a representative U.S. sample (Paolacci and Chandler
2014) but AMT samples compare favorably to other pay-for-sample services like GfK (Berinsky
et al. 2012) and are superior to university-student convenience samples in diversity and
representativeness. Experiments also allow me to access actual behavior instead of self-reports
or intentions.
In Chapter Two, I address two key issues facing conservation organization fundraising:
more accurately identifying and engaging likely donors and selecting projects where their
personnel and resources may be most efficacious. I add to the very limited literature on personal
characteristics of donors to conservation charities by comparing subjects who choose to give to
local versus national versus international/global programs. I use Construal Level Theory (i.e.,
psychological distance) to partially explain donation choice, focus on relatively durable and
trans-situational donor characteristics (motivating values and moral intuitions), and employ
actual donation as the outcome variable. I do this with a 2x2 factorial design experiment that
manipulates social and temporal distance. The results of zero-inflated negative binomial
regression and multinomial logistic regression analyses indicate that temporal distance influences
spatial target of donation, while motivating values and moral intuitions weakly influence
subjects’ choice to donate, amount donated, and spatial target of donation.
Chapter Three addresses Research Questions One and Two. As in Chapter Two, I use the
AMT capability and Qualtrics. This experiment explores the effect of a psychological distance

10

message about the California drought on water conservation behavior and donation to a water
conservation charity. The study considers two complementary approaches to addressing water
scarcity issues: promoting individual water conservation behaviors and supporting organizations
that work on water conservation issues at a broader social and political scale. I introduce a
refined behavioral measurement instrument that incorporates the concept of behavioral plasticity
as I examine the experimental effects of temporal and spatial psychological distance and known
environmental decision-making predictors on subjects’ self-reported home water conservation
behaviors, behavioral intentions, and donation to a water conservation program. I use the
Values-Beliefs-Norms model to guide model building with ordinary least squares and logistic
regressions for model analysis. Results support the validity of my instrument, confirm the VBN
model as relevant to home water conservation behaviors, and document that the effects of
psychological distance become statistically insignificant with the introduction of psychosocial
factors into the models. Secondary analyses suggest, however, that there may be value in further
investigation with this psychological distance concept.
In Chapter Four, I undertake a systematic review and analysis of the of the PEB literature
for implications for the study of PEBs and environmental decision-making. Over the last five
decades, scholars across academic disciplines have studied individual pro-environmental
behaviors as important components of such things as mitigating pollution, resource conservation,
and anthropogenic climate change. Yet there is inconsistency in how pro-environmental
behavior is measured and limited understanding of the consequences of those measurement
choices. In a review of a sample of the pro-environmental behavior literature, I examine how we
have been measuring pro-environmental behaviors and the implications of those measurement. I
conclude with recommendations for future measurement of pro-environmental behaviors.

11

By addressing these research questions, my dissertation makes at least five key
contributions. First, my examinations of values and morals simultaneously will give wider
sociology some of the first empirical evidence of how these concepts may operate together. This
will be true on two levels: the specific manner in which the operationalizations I have chosen
(Schwartz values and Moral Foundations) work together, as well as the theoretical relationships
between values theory and moral foundations theory.
Following from this more general input on the relationships between values and morals, a
second contribution of my dissertation is the introduction of both Schwartz values and morals
into the discussion of donation to environmental causes. These will potentially provide
important, more stable predictors of these behaviors than currently exist in the literature.
Third, my dissertation introduces the concept of behavioral plasticity to the water
conservation literature. Behavioral plasticity is how perceived difficulty to act constrains the
behavioral responses across individuals, behaviors, and contexts. Understanding this dimension
of ESBs allows interventions such as policy, education, and incentive programs to be more
precisely targeted to those who have not yet performed the ESBs of interest. Fourth, my
examination of the individual- and household-level PEB literature will help scholars target
identified gaps, more clearly specify the contributions of their work, and tie their work into a
more coherent whole. This will enable others to explicitly build upon their work and leverage
relevant aspects for related research. In addition, my analysis of the implications of my
theoretical approaches to date, will clarify some perhaps unseen assumptions about ESBs and
potentially open new avenues of theoretical exploration. My dissertation’s fifth and final
contribution is to provide the first evidence of how psychological distance interacts with
Schwartz values and morals, both in the specific contexts I have chosen and more generally.

12

Globalization has extended the range of possible ESBs to include those that impact national,
regional, and international issues across a broader span of time (e.g., the immediate crisis of a
drought to the looming devastation of species extinctions). These expanded options may
uncover limits to how “trans-situational” values and morals may be.

13

REFERENCES

14

REFERENCES

Abend, Gabriel. 2008. “Two Main Problems in the Sociology of Morality.” Theory and Society
37(2):87-125.
Abrahamse, Wokje, Linda Steg, Robert Gifford, and Charles Vlek. 2009. “Factors Influencing
Car Use for Commuting and the Intention to Reduce It: A Question of Self-Interest or
Morality?” Transportation Research Part F: Traffic Psychology and Behaviour
12(4):317-324.
Ajzen, Icek. 1991. “The Theory of Planned Behavior.” Organizational Behavior and Human
Decision Processes 50(2):179-211.
Allen, Summer, Thomas Dietz, and Aaron M. McCright. 2015. “Measuring Household Energy
Efficiency Behaviors with Attention to Behavioral Plasticity in the United States.”
Energy Research and Social Science 10:133-140.
Attari, Shahzeen Z., Michael L. DeKay, Cliff I. Davidson, and Wändi B. de Bruin. 2010.
“Public Perceptions of Energy Consumption and Savings.” Proceedings of the National
Academy of Sciences of the United States of America 107(37):16054-16059.
Bamberg, Sebastian and Guido Möser. 2007. “Twenty Years after Hines, Hungerford, and
Tomera: A New Meta-Analysis of Psycho-Social Determinants of pro-Environmental
Behaviour.” Journal of Environmental Psychology 27(1):14-25.
Barr, Stewart. 2007. “Factors Influencing Environmental Attitudes and Behaviors: A U.K. Case
Study of Household Waste Management.” Environment and Behavior 39(4):435-473.
Berinsky, Adam. J., Gregory. A. Huber, and Gabriel. S. Lenz. 2012. “Evaluating Online Labor
Markets for Experimental Research: Amazon.com’s Mechanical Turk.” Political
Analysis 20(3):351-356.
Best, Henning and Jochen Mayerl. 2013. “Values, Beliefs, Attitudes: An Empirical Study on
the Structure of Environmental Concern and Recycling Participation: Values, Beliefs,
Attitudes.” Social Science Quarterly 94(3):691-714.
DeFronzo, James and Seymmy Warkov. 1979. “Are Female-Headed Households Energy
Efficient: A Test of Klausner’s Hypothesis among Anglo, Spanish-Speaking, and Black
Texas Households.” Human Ecology 7(2):191-197.
deGroot, Judith I.M. and Linda Steg. 2009. “Morality and Prosocial Behavior: The Role of
Awareness, Responsibility, and Norms in the Norm Activation Model.” The Journal of
Social Psychology 149(4):425-449.

15

Dietz, Thomas, Amy Dan, and Rachael Shwom. 2007. “Support for Climate Change Policy:
Social Psychological and Social Structural Influences.” Rural Sociology 72(2):185–214.
Dietz, Thomas, Amy Fitzgerald, and Rachael Shwom. 2005. “Environmental Values.” Annual
Review of Environment and Resources 30:335-372.
Dietz, Thomas, Gerald T. Gardner, Jonathan Gilligan, Paul C. Stern, and Michael P.
Vandenbergh. 2009. “Household Actions Can Provide a Behavioral Wedge to Rapidly
Reduce U.S. Carbon Emissions.” Proceedings of the National Academy of Sciences
106(44):18452-18456.
Dietz, Thomas, Linda Kalof, and Paul C. Stern. 2002. “Gender, Values, and
Environmentalism.” Social Science Quarterly 83(1):353-364.
Dietz, Thomas, Paul C. Stern, and Gregory A. Guagnano. 1998. “Social Structural and Social
Psychological Bases of Environmental Concern.” Environment and Behavior 30(4):450471.
Dietz, Thomas. 2003. “What is a Good Decision? Criteria for Environmental DecisionMaking.” Human Ecology Review 10(1):33-39.
Dietz, Thomas. 2014. “Understanding Environmentally Significant Consumption.”
Proceedings of the National Academy of Sciences 111(14):5067-5068.
Dolcinar, Sara, Anna Hurliman, and Bettina Grun. 2012. “Water Conservation Behavior in
Australia.” Journal of Environmental Management 105:44-52.
Douglas, Mary and Aaron Wildavsky. 1983. Risk and Culture: An Essay on the Selection of
Technological and Environmental Dangers. Berkeley, CA: University of California
Press.
Dunlap, Riley E., Kent D. Van Liere, Angela G. Mertig, and Robert E. Jones. 2000. “New
Trends in Measuring Environmental Attitudes: Measuring Endorsement of the New
Ecological Paradigm: A Revised NEP Scale.” Journal of Social Issues 56(3):425-442.
Durkheim, Emile and Lewis A. Coser. 1997. The Division of Labor in Society. Free Press.
Firat, Rengin and Steven Hitlin. 2012. “Morally Bonded and Bounded: A Sociological
Introduction to Neurology.” Pp. 165–99 in Biosociology and Neurosociology, vol. 29,
edited by W. Kalkhoff, S.R. Thye, and E.J. Lawler. New York: Emerald Group
Publishing Limited.
Fonseca, Paula, George Markogiannakis, Anibal de Almeida, Casper Kofod, and Nicolai
Feilberg. 2009. “Characterization of the Household Electricity Consumption in the EU:

16

Potential Energy Savings and Specific Policy Recommendations.” Proceedings of the
European Council for an Energy Efficient Economy. Summer 2009.
Fransson, Niklas and Tommy Garling. 1999. “Environmental Concern: Conceptual Definitions,
Measurement Methods, and Research Findings.” Journal of Environmental Psychology
19:369-382.
Gerth, Hans. H. and C. Wright Mills. 2007. From Max Weber: Essays in Sociology. New York:
Routledge.
Gifford, Robert and Andreas Nilsson. 2014. “Personal and Social Factors That Influence ProEnvironmental Concern and Behaviour: A Review.” International Journal of Psychology
49(3):141-147.
Gifford, Robert. 2011. “The Dragons of Inaction: Psychological Barriers that Limit Climate
Change Mitigation and Adaptation.” American Psychologist 66(4):290-300.
Graham, Jesse, Jonathan Haidt, and Brian A. Nosek. 2009. “Liberals and Conservatives Rely on
Different Sets of Moral Foundations.” Journal of Personality and Social Psychology
96(5):1029-1046.
Guagnano, Gregory, Paul Stern, and Thomas Dietz. 1995. “Influences on Attitude-Behavior
Relationships: A Natural Experiment with Curbside Recycling.” Environment and
Behavior 27(5):699-718.
Haidt, Jonathan. 2008. “Morality.” Perspectives on Psychological Science 3:65-72.
Harshaw, Howard W., Stephen Sheppard, and John L. Lewis. 2007. “A Review and Synthesis
of Social Indicators for Sustainable Forest Management.” Journal of Ecosystems and
Management 8(2):17-36.
Hitlin, Steven and Stephen Vaisey. 2010. Handbook of the Sociology of Morality. DE: Springer
Verlag.
Hitlin, Steven. 2007. “Doing Good, Feeling Good: Values and the Self ’s Moral Center.” The
Journal of Positive Psychology 2(4):249-259.
Hofmann, Wilhelm., Daniel C. Wisneski, Mark J. Brandt, and Linda J. Skitka. 2014. “Morality
in Everyday Life.” Science 345(6202):1340-1343.
Inglehart, Ronald and Scott C. Flanagan. 1987. “Value Change in Industrial Societies.”
American Political Science Review 81(04):1289-1319.
Inskeep, Benjamin D. and Shahzeen Z. Attari. 2014. “The Water Short List: The Most Effective
Actions U.S. Households Can Take to Curb Water Use.” Environment: Science and
Policy for Sustainable Development 56(4):4-15.

17

Kaiser, Florian G., Gundula Hübner, and Franz X. Bogner. 2005. “Contrasting the Theory of
Planned Behavior With the Value-Belief-Norm Model in Explaining Conservation
Behavior.” Journal of Applied Social Psychology 35(10):2150-2170.
Klausner, Samuel Z. 1977. “Energy and the Structuring of Society.” Pp. 261–306 in Human
Behavior and Environment, edited by S. Klausner. New York: Springer.
Lamont, Michèle. 2009. The Dignity of Working Men: Morality and the Boundaries of Race,
Class, and Immigration. Cambridge, MA: Harvard University Press.
Liberman, Nira, and Yaacov Trope. 2008 “The Psychology of Transcending the Here and
Now.” Science 322(5905):1201-1205.
Lindenberg, Siegwart and Linda Steg. 2007. “Normative, Gain, and Hedonic Goal-frames
Guiding Environmental Behavior.” Journal of Social Issues 63(1):117-137.
Litman, Leib, Jonathan Robinson, and Cheskie Rosenzweig. 2015. “The Relationship between
Motivation, Monetary Compensation, and Data Quality among US- and India-Based
Workers on Mechanical Turk.” Behavior Research Methods 47(2):519-528.
Luckmann, Thomas. 2003. “Transformations of Religion and Morality in Modern Europe.”
Social Compass 50(3):275-285.
Oreg, Shaul and Tally Katz-Gerro. 2006. “Predicting Proenvironmental Behavior CrossNationally Values, the Theory of Planned Behavior, and Value-Belief-Norm Theory.”
Environment and Behavior 38(4):462-483.
Paolacci, Gabriele and Jesse Chandler. 2014. “Inside the Turk: Understanding Mechanical Turk
as a Participant Pool.” Current Directions in Psychological Science 23(3):184-188.
Poortinga, Wouter, Linda Steg, and Charles Vlek. 2004. “Values, Environmental Concern, and
Environmental Behavior: a Study into Household Energy Use.” Environment and
Behavior 36(1): 70-93.
Rokeach, Milton. 1979. “From Individual to Institutional Values: With Special Reference to the
Values of Science.” Pp 70-81 in Understanding Human Values, edited by M. Rokeach.
Free Press.
Schwartz, Shalom H. 1994. “Are There Universal Aspects in the Structure and.” Journal of
Social Issues 50(4):19-45.
Schwartz, Shalom H. 1977. “Normative Influences on Altruism.” Advances in Experimental
Social Psychology 10: 221-279.

18

Schwartz, Shalom H. and Wolfgang Bilsky. 1990. “Toward a Theory of the Universal Content
and Structure of Values: Extensions and Cross-Cultural Replications.” Journal of
Personality and Social Psychology 58:878-891.
Shilling, Chris and Philip A. Mellor. 1998. “Durkheim, Morality and Modernity: Collective
Effervescence, Homo Duplex and the Sources of Moral Action.” The British Journal of
Sociology 49(2):193-206.
Smith, Christian. 2003. Moral, Believing Animals: Human Personhood and Culture. New
York: Oxford University Press.
Spence, Alexa, Wouter Poortinga, and Nick Pidgeon. 2012. “The Psychological Distance of
Climate Change: Psychological Distance of Climate Change.” Risk Analysis 32(6):957972.
Steg, Linda and Charles Vlek. 1997. “The Role of Problem Awareness in Willingness-toChange Car Use and in Evaluating Relevant Policy Measures.” Traffic and Transport
Psychology. Theory and Application 1997:65-75.
Steg, Linda and Judith I.M. De Groot. 2012. “Environmental Values.” p.81-92 in The Oxford
Handbook of Environmental and Conservation Psychology, edited by S. Clayton. New
York: Oxford University Press.
Steg, Linda, Gerta Perlaviciute, Ellen van der Werff, and Jesper Lurvink. 2014. “The
Significance of Hedonic Values for Environmentally Relevant Attitudes, Preferences, and
Actions.” Environment and Behavior 46(2):163-192.
Steg, Linda, Jan W. Bolderdijk, Kees Keizer, and Goda Perlaviciute. 2014. “An Integrated
Framework for Encouraging Pro-Environmental Behaviour: The Role of Values,
Situational Factors and Goals.” Journal of Environmental Psychology 38:104-115.
Stern, Paul C. 2014. “Individual and Household Interactions with Energy Systems: Toward
Integrated Understanding.” Energy Research and Social Science 1:41-48.
Stern, Paul C., and Thomas Dietz. 1994. “The Value Basis of Environmental Concern.”
Journal of Social Issues 50:65-84.
Stern, Paul C., Gerald T. Gardner, Michael P. Vandenbergh, Thomas Dietz, and Jonathan M.
Gilligan. 2010. “Design Principles for Carbon Emissions Reduction Programs.”
Environmental Science and Technology 44(13):4847-4848.
Stern, Paul C., Thomas Dietz, Linda Kalof, and Gregory A. Guagnano. 1995. “Values, Beliefs,
and Proenvironmental Action: Attitude Formation toward Emergent Attitude Objects.”
Journal of Applied Social Psychology 25:1611-1636.

19

Stern, Paul C., Thomas Dietz, Troy D. Abel, Gregory A. Guagnano, and Linda Kalof. 1999. “A
Value-Belief-Norm Theory of Support for Social Movements: The Case of
Environmentalism.” Human Ecology Review 6(2):81-96.
Stern, Paul, Thomas Dietz, and Gregory Guagnano. 1998. “A Brief Inventory of Values.”
Educational and Psychological Measurement 58(6):984-1001.
Stern, Paul. 2000. “Toward a Coherent Theory of Environmentally Significant Behavior.”
Journal of Social Issues 56(3): 407-424.
Stets, Jan. 2010. “The Social Psychology of the Moral Identity.” p389-406 in Handbook of
Sociology of Morality, edited by S. Hitlin and S. Vaisey.
Swidler, Ann. 1986. “Culture in Action: Symbols and Strategies.” American Sociological
Review 51:273-286.
Trope, Yaacov, and Nira Liberman. 2003. “Temporal Construal.” Psychological Review
110(3): 403-410.
Uzzell, David L. 2000. “The Psycho-Spatial Dimension of Global Environmental Problems.”
Journal of Environmental Psychology 20(4):307-318.
Wikstrom, Per-Olof. 2010. “Explaining Crime as Moral Action.” p211-229 in Handbook of
Sociology of Morality, edited by S. Hitlin and S. Vaisey.
York, Richard, Eugene A. Rosa, and Thomas Dietz. 2003. “STIRPAT, IPAT and ImPACT:
Analytic Tools for Unpacking the Driving Forces of Environmental Impacts.” Ecological
Economics 46(3):351-365.

20

CHAPTER 2: EXPLAINING MONETARY SUPPORT FOR CHARITABLE
CONSERVATION PROGRAMS

Introduction
The current scale and pace of biodiversity loss and ecosystem change is unprecedented
(MEA 2005; UNEP 2014), prompting public warnings about “the sixth extinction” (Kolbert
2014) and driving campaigns to name our current geological epoch as “The Anthropocene”
(Waters et al. 2016). Sustained efforts to conserve species and ecosystems have never been so
consequential, increasing pressure on conservation organizations to expand existing projects and
embark on ambitious new ones. Americans gave approximately $10.5 billion to environmental
and animal welfare organizations1 in 2014 (The Giving Institute 2015) for conservation missions
and programs spanning from local to international or global in focus (Aldashev and Verdier
2010). Maintaining, if not increasing, this level of financial support will be crucial for the
effectiveness and expansion of conservation efforts in the near future.
Conservation organizations can partially manage this financial uncertainty by more
accurately identifying and engaging likely donors (Land Trust Alliance 2016) and by selecting
those local, national, and/or international/global projects where their personnel and resources
may be most efficacious. This study examines some likely predictors of donation both to a
conservation organization and between its programs. I do this within an experiment
investigating how framing the problem of biodiversity loss influences such donation.

1

Public donation data does not include a specific category for “biodiversity conservation.” Rather, such donations
fall within categories for environmental and animal welfare organizations, which are typically conflated in donation
reports (Bennett, 2003).

21

While the literature on predictors of philanthropic donation remains sparse and mixed
(Bekkers and Wiepking 2011; Bennett 2003) I draw insights from the robust interdisciplinary
literature on environmental decision-making. Briefly, many studies document the strong
influence of values on environmental decision-making, often indirectly through related beliefs,
attitudes, and intentions (Dietz, Fitzgerald, and Shwom 2005). The Rokeach/Schwartz approach
to conceptualizing and measuring personal values—based upon the work of Milton Rokeach
(1979) and Shalom Schwartz (Schwartz and Bilsky 1990)—receives the most empirical support,
documenting how the influence of values is trans-situational and typically persists across the life
course. Many studies find that biospherism, altruism, and hedonism are positively related to proenvironmental decisions, and egoism is negatively related to the same (Stern and Dietz 1994;
Stern, Dietz, Kalof and Guagnano 1995; Steg and deGroot 2012). I expect similar relationships
in this study.
Recent research suggests that another set of trans-situational characteristics also may be
relevant for understanding environmental decision-making. An emerging body of work finds
that moral intuitions influence a range of attitudes, worldviews, beliefs, and behavioral intentions
(De Groot and Steg 2009; Firat and Hitlin 2012; Graham, Haidt, and Nosek 2009; Hofmann,
Wisneski, Brandt, and Skitka 2014; Lamont 2009). Moral Foundations Theory, which identifies
five general moral intuitions (harm, fairness, in-group loyalty, authority, and purity) is the
leading approach for conceptualizing and measuring moral intuitions (Graham, Haidt and Nosek
2009). While the majority of residents in affluent industrial countries place environmental
decision-making in the moral domain (Fransson and Garling 1999) no empirical studies examine
which specific moral intuitions are related to environmental decision-making. As such, I include
trans-situational moral intuitions in my analyses largely for exploratory purposes.

22

Perhaps as important as examining how potential donors’ characteristics influence
donation behavior is investigating how the framing of biodiversity loss influences the same.
Environmental scientists and conservation advocates may emphasize the effects of local
biodiversity loss to nearby communities or the effects of global biodiversity loss, perhaps to
remote communities in developing countries. They also may highlight current problems or focus
on ones in the near or distant future. Yet, we do not fully understand how such variation in
emphases may affect donation behavior, or how individuals’ values or morals may moderate the
influence of such framing on donation behavior.
I draw insights from Construal Level Theory (Liberman and Trope 2008; Trope and
Liberman 2010) which argues that “psychological distance”—conceptualized as the spatial,
social, and/or temporal distance between individuals and targets of interest—is consequential for
many attitudes, beliefs, and behaviors. While a few scholars have applied Construal Level
Theory to examine concern and behavioral intentions about climate change (a problem that
clearly spans spatial, social, and temporal scales) (Spence and Pidgeon 2010; Spence et al. 2012;
Uzzell 2000), the theory receives limited attention in the broader environmental decision-making
literature. In line with Construal Level Theory, I expect that low psychological distance
(emphasizing the current effects of biodiversity loss to people like the potential donors) will be
positively related to donation to local conservation programs, while high psychological distance
(emphasizing the future effects of biodiversity loss to people unlike the potential donors) will be
positively related to donation to international conservation programs.

23

Methods
Participants
I conducted a 2x2 full factorial experiment with a control condition within an online
Qualtrics questionnaire to participants recruited via Amazon Mechanical Turk (AMT). (See
Appendix B for the full questionnaire). The two experimental conditions were low versus high
social distance (people in industrial societies versus people in pre-industrial societies) and low
versus high temporal distance (now versus in future decades). AMT is an online labor market in
which “requestors” advertise tasks that “workers” then complete for pay. Samples derived from
AMT are more diverse than are the typical convenience samples of university students used in
social-psychological experiments (Weinberg et al 2014) and they compare favorably to samples
derived from professional survey organizations such as Knowledge Networks/GfK (Berinsky et
al. 2012).
I recruited 491 American adults over the age of 18 to participate in my experiment. I
paid subjects $1.00 for completing the experiment, which took approximately 11 minutes and 40
seconds on average. The actual samples analyzed range from the 487 subjects who selected a
donation amount to the 479 subjects who identified their preferred donation target. While my
convenience sample is more demographically, socially, and geographically diverse than are the
traditional experiment recruitment pools of university undergraduates, it is more male, younger,
white, and highly educated than would be a representative sample of the U.S. general public
(U.S. Census Bureau 2016). (See Table 3 in Appendix A.)

24

The experiment
My experiment had four experimental conditions and a control condition described
below. After giving consent to participate in this study, subjects answered a series of questions
designed to measure their value orientations, moral intuitions, and general environmental beliefs.
Subjects then were randomly assigned to one of five conditions. All subjects read a brief news
story about the effects of biodiversity loss that was created specifically for this experiment. (See
Appendix C for all five fabricated news stories). Subjects in the control condition received a
news story about the social and economic costs of biodiversity loss in general. Subjects in the
low social distance/low temporal distance condition received a similar news story modified to
emphasize the effects of biodiversity loss to people in industrial societies now, and those in the
high social distance/low temporal distance condition received a news story on the effects of
biodiversity loss to people in pre-industrial societies now. Subjects in the low social
distance/high temporal distance condition received a news story on the effects of biodiversity
loss to people in industrial societies in future decades, and those in the high social distance/high
temporal distance condition received a news story on the effects of biodiversity loss to people in
pre-industrial societies in future decades. After reading their assigned news story, subjects
answered two comprehension check questions and a manipulation check question.
Subjects then completed a small number of questions that measure selected demographic
and social characteristics. On a subsequent page, subjects were notified that for completing the
questionnaire they had earned a bonus of $1.00, equivalent to their participation pay. I gave
them the option to donate between $0.00 and $1.00 of their bonus to The Nature Conservancy.2
Subjects first identified whether they did not want to donate their bonus. If they did want to

2

I chose The Nature Conservancy because it is relatively apolitical, one of the largest conservation advocacy
organizations, and its programs range from local to regional/national to international/global in scope.

25

donate their bonus they were prompted to choose between a local, national, or international
conservation program. Subjects choosing to donate then indicated how much of their bonus they
wanted to donate by inputting a numeral between 1 and 100. On the final page, I thanked
subjects for their participation and debriefed them about my research objectives.

Variables
Appendix B contains all of the questionnaire items that I used to create all of the
variables employed in my analyses. Appendix A contains Table 4, which displays the name,
description, coding, mean, and standard deviations of all of the variables employed in my
analyses. I created my outcome variables from my final two questionnaire items. Donation
amount ranges from 0 to 100. Subjects’ preferred target of their donation is measured with an
ordinal variable, donation target, where “no donation”=0, “local”=1, “national”=2, and
“international”=3.
I measured value orientations using a revision of Schwartz’s main instrument (Schwartz
1994) with additional hedonism items as suggested by Steg, Perlaviciute, Van der Werff, and
Lurvink (2012). Subjects indicated the importance (from “not at all”=1 to “extremely
important”=7) of several brief value statements as a guiding principle in their lives. Calculating
the average of responses to similar items, I created five values scales using alpha scaling:
altruism/biospherism (Cronbach’s α = 0.83), hedonism (α=0.77), self-interest (α=0.53),
traditionalism (α=0.67), and openness to change (α=0.46).
I measured moral intuitions using the Moral Foundations Questionnaire (Graham, Haidt,
and Nosek 2009), which consists of two questions assessing the kinds of moral intuitions people
use when making decisions about right and wrong. Subjects indicated the relevance (from “not

26

at all relevant”=1 to “extremely relevant”=6) of several brief moral considerations when
deciding whether something is right or wrong, and they expressed their judgment (from “strongly
disagree”=1 to “strongly agree”=6) about several statements. Calculating the average of
responses to similar items, I created five moral intuition scales using alpha scaling: prevention of
harm (α=0.76), fairness (α=0.71), in-group loyalty (α=0.77), respect for authority (α=0.79), and
purity (α=0.86).
I accounted for general environmental beliefs with a short (5-item) version of the New
Ecological Paradigm scale (Dunlap, Van Liere, Mertig, and Jones 2000), which averages the
responses (“strongly disagree”=1 to “strongly agree”=7) to similar items about the humanenvironment relationship: new ecological paradigm (α=0.84). I also employed five demographic
and social variables as statistical controls in my analyses. Two are dummy variables: female for
gender (“male”=0; “female”=1) and white for race (“non-white”=0; “white”=1). Age is
measured with five categories: “18-29”=1 to “60 and over”=5. Education is measured by the
highest degree earned: “less than high school diploma or equivalent”=1 to “doctoral degree”=7.
Income is measured as approximate yearly household income: “less than $25,000”=1 to
“$100,000 or more”=5.

Analytic techniques
My analyses explained three key outcomes: (a) whether or not subjects donate to the
conservation organization; (b) how much donors actually donate; and (c) donors’ preferred
spatial target of their donation (local, national, or international). My count variable donation
amount contains approximately 60% zeroes. Not only is this variable zero-inflated and
consequently non-normal, but the ratio of its variance (1002.88) to its mean (18.74) indicates

27

overdispersion. For these reasons, to explain my first two outcomes I employed zero-inflated
negative binomial (ZINB) regression, which is most appropriate for this data structure (Long
1997). ZINB regression yields two different models—a logit model predicting that subjects
make no donation and a model predicting donation amount among the subgroup of donors—
under the theoretical assumption that these two processes can be modeled independently. To
explain my third outcome about the spatial target of donation, I used multinomial logistic
regression. Zero-inflation violates the proportional odds assumption of ordered logistic
regression, as indicated by diagnostic test results displayed in Appendix C. Multinomial logistic
regressions treat the donation target variable as categorical rather than ordinal.3
Finally, to explore whether or not subjects’ values or morals may moderate the influence
of my experimental messages on donation behavior, I created interaction terms between my
experimental dummy variables and my values and morals variables (e.g., social distance X
respect for authority). Since these analyses are exploratory, I briefly summarize them in a
footnote below.4 I performed all analyses with Stata 14.1.

Results
I included two comprehension checks in my survey to assess subjects’ attentiveness. The
first item requested a brief summary of their assigned message. In that open-ended question,
approximately 99% of subjects accurately described their assigned message. The second item

3

Results of an ordered logistic regression model (Displayed in Table 5 in Appendix A) are essentially the
same as reported in my multinomial logistic model.
4
Exploration of the interactions between my experimental effects and values and morals measures indicate that
hedonism values and respect for authority moral intuitions negatively moderate temporal distance messaging
effects on donation behavior.

28

asked subjects to identify a fact in the message, and approximately 88% of subjects did so
correctly. These results suggest that my messaging was clear.
Table 1 displays the results of the two ZINB regression models, with the first column
containing the logistic regression coefficients explaining the likelihood of being in the “no
donation” category and the second column containing the ZINB regression coefficients
explaining the donation amount of donors. Briefly, neither experimental dummy variable
influences either making a donation or not or the donation amount. Consistent with many studies
of environmental decision-making, altruism/biospherism values decrease the likelihood of being
in the “no donation” category (i.e., increase the likelihood of making a donation):and traditional
values increase the likelihood of not making a donation. Only one predictor (in-group loyalty
moral intuition) helps to explain the donation amount. Having a moral intuition for your own
group’s well-being decreases donation amounts among those who did donate. None of the
demographic and social controls influence donation behavior.

29

Table 1: Unstandardized coefficients (and robust standard errors) from zero-inflated negative
binomial regression predicting choice to donate and donation amount
Logit Model Predicting
No Donation

ZINB Model Predicting
Donation Amount

Experimental Effects
High temporal distance
High social distance

-0.34 (0.21)
-0.02 (0.21)

0.16 (0.09)
-0.06 (0.10)

Values, Morals, and Environmental Beliefs
Altruism/Biospherism value
Hedonism value
Self-Interest value
Traditional value
Openness to Change value
Harm moral intuition
Fairness moral intuition
In-Group Loyalty moral intuition
Authority moral intuition
Purity moral intuition
New Ecological Paradigm

-0.54 (0.14)***
0.12 (0.13)
-0.11 (0.10)
0.36 (0.13)**
0.01 (0.14)
-0.01 (0.17)
0.10 (0.19)
-0.19 (0.17)
0.04 (0.18)
0.02 (0.14)
-0.19 (0.10)

0.04 (0.08)
0.02 (0.07)
-0.07 (0.05)
-0.01 (0.07)
-0.06 (0.07)
0.13 (0.09)
-0.04 (0.08)
-0.27 (0.08)**
0.16 (0.09)
0.01 (0.06)
0.04 (0.05)

Demographic and Social Characteristics
Female
White
Age
Educational attainment
Income

0.08 (0.21)
0.09 (0.24)
-0.13 (0.09)
-0.05 (0.09)
0.04 (0.11)

-0.04 (0.11)
0.05 (0.12)
0.04 (0.04)
-0.04 (0.05)
0.09 (0.05)

Constant

2.82 (0.97)**

Sample size

311

3.52 (0.59)***
176

Wald Chi-Square = 46.56, p<0.001
*p<.05, **p<.01, ***p<.001

Table 2 displays the results of the multinomial logistic regression model explaining the
likelihood of subjects donating to local, national, or international conservation programs versus
not donating. As expected by Construal Level Theory, exposure to news stories emphasizing
high temporal distance in the effects of biodiversity loss increases the likelihood of donating to
international conservation activities versus not donating. Consistent with the existing literature,
altruism/biospherism values increase the likelihood of earmarking a donation to local
30

conservation programs versus not making a donation at all, and traditional values decreases the
likelihood of the same. Having a moral intuition for respecting authority—which taps
preferences for duty, conforming to rules, and obeying orders—decreases the likelihood of
donating to international conservation activities versus not donating at all. No demographic or
social variable influences the choice of donation target.

Table 2: Unstandardized coefficients (and standard errors) from multinomial logistic regression
model predicting donation target (n=479; Pseudo R2=0.08)
Local
National
International
______________________________________________________________________________
Experimental Effects
High temporal distance
High social distance

0.22 (0.29)
0.01 (0.29)

-0.26 (0.34)
-0.07 (0.35)

0.75 (0.30)*
0.29 (0.31)

Values, Morals, and Environmental Beliefs
Altruism/Biospherism value
Hedonism value
Self-Interest value
Traditional value
Openness to Change value
Harm moral intuition
Fairness moral intuition
In-Group Loyalty moral intuition
Authority moral intuition
Purity moral intuition
New Ecological Paradigm

0.87 (0.22)***
-0.17 (0.19)
0.15 (0.15)
-0.52 (0.19)**
-0.25 (0.19)
0.14 (0.26)
-0.32 (0.26)
0.30 (0.26)
0.18 (0.26)
-0.18 (0.21)
0.08 (0.15)

0.42 (0.25)
-0.21 (0.22)
0.21 (0.18)
-0.21 (0.24)
-0.08 (0.22)
0.07 (0.30)
-0.21 (0.31)
0.06 (0.30)
0.18 (0.31)
-0.01 (0.24)
0.30 (0.17)

0.37 (0.22)
-0.03 (0.19)
0.12 (0.14)
-0.16 (0.19)
0.16 (0.21)
0.12 (0.27)
0.10 (0.27)
0.09 (0.25)
-0.61 (0.27)*
0.08 (0.21)
0.16 (0.16)

Demographic and Social Characteristics
Female
White
Age
Educational attainment
Income

-0.14 (0.29)
0.37 (0.36)
0.04 (0.14)
0.19 (0.14)
0.10 (0.14)

0.21 (0.35)
-0.10 (0.40)
0.16 (0.16)
-0.05 (0.16)
0.01 (0.17)

-0.30 (0.32)
-0.36 (0.34)
0.14 (0.14)
0.09 (0.14)
-0.11 (0.16)

Constant
-3.80 (1.43)**
-4.50 (1.71)** -4.16 (1.56)**
______________________________________________________________________________
*p<0.05, **p<0.01, ***p<0.001

31

Discussion
This study offers the following scholarly contributions, each of which point to promising
avenues for future research. First, I applied Construal Level Theory, which is optimally suited
for explaining the effects of distance gradients, to the understudied issue domain of support for
biodiversity conservation. While some conservation efforts are more clearly local (e.g.,
protecting a specific stream or small forest or re-introducing a species to an isolated island),
many others span wider areas and cross political boundaries (e.g., reducing the effects of air and
water pollution on a region-wide habitat or fighting the global trafficking of endangered species)
(Visconti et al. 2016). My results suggest that conservation organizations’ decisions to vary their
priorities across the spatial scale (from local to national/regional to international/global) may not
substantially affect who chooses to donate to them or how much is donated. While a message
emphasizing a high temporal distance in the effects of biodiversity loss did increase the
likelihood of donating to an internationally focused program, no other experimental messages
directly influenced any other donation behavior analyzed.
Second, I add to my scholarly understanding of how relatively durable, trans-situational
characteristics (i.e., values and moral intuitions) influence environmental decision-making.
Research in the philanthropy literature finds that most donors select donation targets on the basis
of how well the mission and projects of the latter match their personal values (Aldashev and
Verdier 2010). Indeed, philanthropic management services recommend that conservation
organizations carefully attend to the values of current and potential donors (Network for Good
2015). My results confirm two robust findings of many other studies in the wider environmental
decision-making literature. Briefly, humanistic and biospheric altruism is positively related to

32

financial support for a conservation organization, while traditional values are negatively related
to the same.
Since few studies have examined the influence of moral intuitions on environmental
decision-making, I offer caution when interpreting the performance of the moral intuitions
variables. Nevertheless, my results do appear consistent with key findings in the wider
environmental decision-making literature (e.g., McCright, Xiao, and Dunlap 2014). That is,
moral intuitions revealing preferences for traditionalism and conservatism (in-group loyalty and
respect for authority) are negatively related to financial support for a conservation organization.
Future research should continue to examine the influence of durable, trans-situational
characteristics, such as personal values and moral intuitions, on support for biodiversity
conservation.
Third, rather than examine behavioral intentions, I employed a relatively novel method to
measure actual donation behavior. As such, I offer one of the first empirical studies of financial
support for biodiversity conservation. While the amount available to donate was small, it
nevertheless was equivalent to the money subjects received for their participation. They likely
perceived this not as trivial but as at least somewhat consequential. Thus, this method seems
appropriate for studying actual environmental decision-making, and I urge other scholars to
utilize this method to further investigate actual (and not just intentional) financial support for
biodiversity conservation.

33

APPENDICES

34

APPENDIX A
Supplementary Tables

35

Table 3: Selected socio-demographic sample characteristics
Gender
Race
Age

Female
White
Aged 18-29
Aged 30-39
Aged 40-49
Aged 50-59
Aged 60 or older
Mean

Educational Attainment“less than high school diploma
or equivalent”=1 to “doctoral degree”=7
Household Income“less than $25K”=1 to
“$100K and more”=5 2.11

36

3.42
1.05

Percentage
44.9
75.1
40.9
33.0
13.9
7.9
4.3
Standard Deviation
1.14

Table 4: Variable names, coding, means, and standard deviations
_______________________________________________________________________
_______
Variable
Coding
Mean
Standard Deviation
Donation Amount
positive integer
between 0 and 100
18.74
31.67
Values
Altruism/Biospherism
“not at all important”=1
to “of supreme importance”=7
6 items
5.40
1.11
Hedonism
same as Altruism
3 items
5.53
1.04
Self-Interest
same as Altruism
3 items
4.38
1.27
Tradition
same as Altruism
3 items
5.61
1.06
Openness to Change
same as Altruism
3 items
5.33
0.96
Moral Intuitions
Prevention of Harm

6 items
Fairness
6 items
In-group Loyalty
6 items
Authority
6 items
Purity
6 items

“not at all relevant”=1 to
“extremely relevant”=6,
and “strongly disagree”=1
to “strongly agree”=6
4.73

0.86

4.64

0.80

3.59

0.99

3.85

1.00

3.47

1.24

same as Prevention of Harm
same as Prevention of Harm
same as Prevention of Harm
same as Prevention of Harm

General Environmental Beliefs
New Ecological Paradigm “strongly disagree”=1 to
“strongly agree”=7
5-items with 2 reversed
5.28
1.31
______________________________________________________________________________

37

Table 5: Odds ratios (and standard errors) from ordered logistic regression model predicting
donation target (n=479; Pseudo R2=0.05)
Odds Ratios
(Standard Errors)
______________________________________________________________________________
Experimental Effects
Temporal distance (biodiversity loss in 10yrs)
Social distance (biodiversity loss affects others)

1.60 (0.31)*
1.10 (0.22)

Values, Morals, and Environmental Beliefs
Altruism/Biospherism value
Hedonism value
Self-Interest value
Traditional value
Openness to Change value
Harm moral intuition
Fairness moral intuition
In-Group Loyalty moral intuition
Authority moral intuition
Purity moral intuition
New Ecological Paradigm

1.58 (0.22)**
0.90 (0.11)
1.13 (0.11)
0.81 (0.10)
1.00 (0.13)
1.08 (0.19)
0.93 (0.16)
1.11 (0.19)
0.80 (0.14)
0.99 (0.14)
1.16 (0.12)

Demographic and Social Characteristics
Female
White
Age
Educational attainment
Income

0.91 (0.19)
0.90 (0.21)
1.13 (0.11)
1.07 (0.10)
1.00 (0.10)

Cut 1
2.82 (1.00)
Cut 2
3.58 (1.00)
Cut 3
4.25 (1.00)
______________________________________________________________________________

38

APPENDIX B
Survey

39

Q2 Values: Please indicate how important each of the following is AS A GUIDING
PRINCIPLE IN YOUR LIFE. Use the following scale where 1 represents “Not At All
Important” and 7 represents “Of Supreme Importance”.
Not At
Of
All
Supreme
2
3
4
5
6
Important
Importance
1
7
m

m

m

m

m

m

m

m

m

m

m

m

m

m

a world at
peace

m

m

m

m

m

m

m

enjoying
life

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

a varied life
family
security

protecting
the
environment
respecting
authority
being
curious
Equality
selfdiscipline

40

Q3 Values: Please indicate how important each of the following is AS A GUIDING PRINCIPLE
IN YOUR LIFE. Use the following scale where 1 represents “Not At All Important” and 7
represents “Of Supreme Importance”.
Not At
Of
All
Supreme
2
3
4
5
6
Important
Importance
1
7
m

m

m

m

m

m

m

m

m

m

m

m

m

m

being
influential

m

m

m

m

m

m

m

an exciting
life

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

gratification
unity with
nature

honoring
parents and
elders
social
justice
pleasure
respecting
the earth
wealth

41

Q4 Moral Perspectives: Please read the following sentences and indicate your disagreement or
agreement.
Strongly Moderately Somewhat Somewhat Moderately Strongly
Disagree Disagree
Disagree
Agree
Agree
Agree
Compassion for
those who are
suffering is the
most crucial virtue
When the
government
makes laws, the
number one
principle should
be ensuring that
everyone is treated
fairly
I am proud of my
country's history
Respect for
authority is
something all
children need to
learn
People should not
do things that are
disgusting, even if
no one is harmed
It is better to do
good than to do
bad
One of the worst
things a person
could do is hurt a
defenseless animal
Justice is the most
important
requirement for a
society

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

42

Q5 Moral Perspectives: Please read the following sentences and indicate your disagreement or
agreement.
Strongly Moderately Somewhat Somewhat Moderately Strongly
Disagree Disagree
Disagree
Agree
Agree
Agree
People should be
loyal to their
family members,
even when they
have done
something wrong

m

m

m

m

m

m

Men and women
each have
different roles to
play in society

m

m

m

m

m

m

I would call some
acts wrong on the
grounds that they
are unnatural

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

It can never be
right to kill a
human being
I think it's morally
wrong that rich
children inherit a
lot of money
while poor
children inherit
nothing
It is more
important to be a
team player than
to express oneself
If I were a soldier
and disagreed with
my commanding
officer's orders, I
would obey
anyway because
that is my duty
Chastity is an
important and
valuable virtue

43

Q6 Moral Perspectives: When you decide whether something is right or wrong, to what extent
are the following considerations relevant to your thinking? Whether or not . . .
Not At
Not
Slightly Somewhat
Very
Extremely
All
Very
Relevant Relevant Relevant Relevant
Relevant Relevant
someone suffered
emotionally
some people were
treated differently
than others
someone's action
showed love for his or
her country
someone showed a
lack of respect for
authority
someone violated
standards of purity
and decency
someone was good at
math
someone cared for
someone weak or
vulnerable
someone acted
unfairly

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

44

Q7 Moral Perspectives When you decide whether something is right or wrong, to what extent
are the following considerations relevant to your thinking? Whether or not . . .
Not At
Not
Slightly Somewhat
Very
Extremely
All
Very
Relevant Relevant Relevant Relevant
Relevant Relevant
someone did
something to betray
his or her group
someone conformed
to the traditions of
society
someone did
something disgusting
someone was cruel
someone was denied
his or her rights
someone showed a
lack of loyalty
an action caused
chaos or disorder
someone acted in a
way that God would
approve of

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

m

45

Q8 Views about Nature: Please read the following sentences and indicate your disagreement or
agreement.
Strongly
Disagree

Moderately
Disagree

Somewhat
Disagree

Neither
Disagree
nor
Agree

Somewhat
Agree

Moderately
Agree

Strongly
Agree

The socalled
“ecological
crisis”
facing
humankind
has been
greatly
exaggerated

m

m

m

m

m

m

m

If things
continue on
their present
course, I
will soon
experience a
major
ecological
catastrophe

m

m

m

m

m

m

m

Humans are
severely
abusing the
environment

m

m

m

m

m

m

m

The balance
of nature is
strong
enough to
cope with
the impacts
of modern
industrial
nations

m

m

m

m

m

m

m

The earth is
like a
spaceship
with very
limited
room and
resources

m

m

m

m

m

m

m

46

Q9 It is important to us that you CAREFULLY and COMPLETELY read the following news
story. Please FULLY CONSIDER the message IN ITS ENTIRETY. I want to know how well
you understand the news story, so I will ask you some important questions about it afterwards.
Q15 Please clearly describe the MAIN POINT of the news story's message. The more complete
your answer, the easier it will be for us to know how well you understood it.
Q16 The CURRENT rate of biodiversity loss is ____________ than the rate of biodiversity loss
in previous eras.
m much lower
m slightly lower
m about the same
m slightly higher
m much higher
Q17 Please read the following sentences and choose the ONE that you think is the most accurate.
m Biodiversity loss is a problem that does affect people like me.
m Biodiversity loss is already a serious problem right now.
m Biodiversity loss is a problem that mainly affects people very different from me.
m Biodiversity loss will mostly be a serious problem in the future.

47

Q18 What is your sex?
m Male
m Female
Q19 How old are you as of today?
m 18-29
m 30-39
m 40-49
m 50-59
m 60 or older
Q20 Are you Hispanic, Latino, or Chicano?
m No
m Yes
Q21 What race/ethnicity do you identify with MOST?
m Arab-American or Middle Eastern
m Asian or Asian-American
m Black or African-American
m Indian/South Asian or Indian-American
m Native American/American Indian or Alaska Native
m Native Hawaiian or other Pacific Islander
m White
m Other (please specify) ____________________
Q22 What is the highest degree you have earned?
m 12th grade or less
m high school diploma or GED
m associate's degree
m bachelor's degree
m master's degree
m professional degree (e.g., law or medicine)
m doctorate degree
Q23 What is your approximate yearly income?
m $0-$24,999
m $25,000-$49,000
m $50,000-$74,999
m $75,000-$99,999
m $100,000 and up

48

Q24 In what ZIP code is your home located? (enter your 5-digit ZIP code; for example, 00544 or
94305)
Q25 Please enter your Mechanical Turk Worker ID Number. Be sure to check it for accuracy as
this will be needed to pay you!
Q26 One Final Question For completing this survey, you have earned a bonus of $1.00! This
bonus is IN ADDITION TO the $1.00 you have already earned. You have the option to donate
some of your bonus to The Nature Conservancy, a biodiversity conservation organization
founded in 1951. I will deduct the amount you select from your bonus and donate it to The
Nature Conservancy. Your donation will be used to support efforts in conserving nature and
preventing further loss of biodiversity. You have the option to specify whether you want your
donation to support local, national, OR international biodiversity conservation efforts. (I will use
the ZIP code you provided to identify the nearest local office of The Nature
Conservancy.) Considering the article about biodiversity loss you read earlier, would you like to
donate some of your bonus to The Nature Conservancy?
m I prefer NOT to donate
m I prefer to donate to LOCAL efforts
m I prefer to donate to NATIONAL efforts
m I prefer to donate to INTERNATIONAL efforts
Q27 How much of your bonus would you like to donate to The Nature Conservancy? Please
enter an amount between 0 and 100 cents.

49

APPENDIX C
Experimental Messages, Donation Distribution Graph, and Brandt Test Results

50

Figure 1: News story for control condition

Figure 2: News story for low social distance/low temporal distance condition

51

Figure 3: News story for high social distance/low temporal distance

Figure 4: News story for low social distance/high temporal distance condition

52

Figure 5: News story for high social distance/high temporal distance condition

53

0

.05

Density

.1

.15

Figure 6: Distribution of the donation amount variable

0

20

40
60
donation amount

54

80

100

Table 6: Brant Test of parallel regression assumption
Variable
All
Social far
Time far
Altruism/Biospherism
Hedonism
Wealth/Power
Traditional
Openness to Change
New Ecological Paradigm
Prevention of Harm
Fairness
In-Group Loyalty
Respect for Authority
Purity
Sex
Age
Ed
Income
White

chi2
42.71

p>chi2
0.205

df
36

0.71
1.00
9.55
0.68
0.25
4.38
3.09
0.85
0.24
1.87
1.10
5.89
1.37
1.31
0.36
1.93
1.03
2.44

0.702
0.607
0.008
0.712
0.883
0.112
0.213
0.654
0.888
0.392
0.578
0.053
0.505
0.519
0.835
0.381
0.597
0.295

2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2

A significant test statistic provides evidence that the parallel regression assumption has been
violated.

55

REFERENCES

56

REFERENCES

Aldashev, Gani, and Thierry Verdier. 2010. “Goodwill Bazaar: NGO Competition And Giving
To Development.” Journal of Development Economics 91(1):48–63.
Bekkers, Rene. 2010. “Who Gives What And When? A Scenario Study of Intentions to Give
Time and Money.” Social Science Research 39(3):369–381.
https://doi.org/10.1016/j.ssresearch.2009.08.008
Bekkers, Rene, and Pamala Wiepking. 2011. “Who Gives? A Literature Review of Predictors
of Charitable Giving Part One: Religion, Education, Age and Socialisation.” Voluntary
Sector Review 2(3):337–365.
Bennett, Rene. 2003. “Factors Underlying The Inclination to Donate to Particular Types of
Charity.” International Journal of Nonprofit and Voluntary Sector Marketing 8(1):12–
29.
Berinsky, Adam. J., Gregory. A. Huber, and Gabriel. S. Lenz. 2012. “Evaluating Online Labor
Markets for Experimental Research: Amazon.com’s Mechanical Turk.” Political
Analysis 20(3):351-356.
deGroot, Judith I.M. and Linda Steg. 2009. “Morality and Prosocial Behavior: The Role of
Awareness, Responsibility, and Norms in the Norm Activation Model.” The Journal of
Social Psychology 149(4):425-449.
Dietz, Thomas, Amy Fitzgerald, and Rachael Shwom. 2005. “Environmental Values.” Annual
Review of Environment and Resources 30:335-372.
Dunlap, Riley. 2008. “The New Environmental Paradigm Scale: From Marginality To
Worldwide Use.” The Journal of Environmental Education 40(1):3–18.
Dunlap, Riley E., Kent D. Van Liere, Angela G. Mertig, and Robert E. Jones. 2000. “New
Trends in Measuring Environmental Attitudes: Measuring Endorsement of the New
Ecological Paradigm: A Revised NEP Scale.” Journal of Social Issues 56(3):425-442.
Ein-Gar, Danit, and Liat Levontin. 2013. “Giving From a Distance: Putting the Charitable
Organization at the Center of the Donation Appeal.” Journal of Consumer Psychology
23(2):197–211. https://doi.org/10.1016/j.jcps.2012.09.002.
Firat, Rengin and Steven Hitlin. 2012. “Morally Bonded and Bounded: A Sociological
Introduction to Neurology.” Pp. 165–99 in Biosociology and Neurosociology, vol. 29,
edited by W. Kalkhoff, S.R. Thye, and E.J. Lawler. New York: Emerald Group
Publishing Limited.

57

Fransson, Niklas and Tommy Garling. 1999. “Environmental Concern: Conceptual Definitions,
Measurement Methods, and Research Findings.” Journal of Environmental Psychology
19:369-382.
Graham, Jesse, Jonathan Haidt, and Brian A. Nosek. 2009. “Liberals and Conservatives Rely on
Different Sets of Moral Foundations.” Journal of Personality and Social Psychology
96(5):1029-1046.
Haidt, Jonathan. 2008. “Morality.” Perspectives on Psychological Science 3:65-72.
Hitlin, Steven. 2007. “Doing Good, Feeling Good: Values and the Self ’s Moral Center.” The
Journal of Positive Psychology 2(4):249-259.
Hofmann, Wilhelm., Daniel C. Wisneski, Mark J. Brandt, and Linda J. Skitka. 2014. “Morality
in Everyday Life.” Science 345(6202):1340-1343.
Kolbert, Elizabeth. 2014. The sixth extinction: An unnatural history. New York: A&C Black.
Lamont, Michèle. 2009. The Dignity of Working Men: Morality and the Boundaries of Race,
Class, and Immigration. Cambridge, MA: Harvard University Press.
Land Trust Alliance. 2016. “Fundraising.” Retrieved March 19, 2017, from
https://www.landtrustalliance.org/topics/fundraising.
Liberman, Nira, and Yaacov Trope. 2008 “The Psychology of Transcending the Here and
Now.” Science 322(5905):1201-1205.
Long, J. Scott. 1997. “Regression Models for Categorical and Limited Dependent
Variables.” Advanced Quantitative Techniques in the Social Sciences New York: SAGE
Publications.
McCright, Aaron. M., Chenyang Xiao, and Riley Dunlap. 2014. “Political Polarization on
Support for Government Spending on Environmental Protection in the USA, 1974-2012.”
Social Science Research 48:251-260.
Millenium Ecosystem Assessment. 2005. “Ecosystems And Human Well-Being: Biodiversity
Synthesis.” Retrieved June 3, 2017, from
http://www.millenniumassessment.org/documents/document.354.aspx.pdf.
Network for Good. 2015. “Getting Started.” Retrieved March 19, 2017, from
http://www.networkforgood.com/getting-started/.
Rachlin, Howard, and Bryan A. Jones. 2008. “Altruism Among Relatives And Non-Relatives.”
Behavioural Processes 79(2): 120–123. https://doi.org/10.1016/j.beproc.2008.06.002.

58

Rokeach, Milton. 1979. “From Individual to Institutional Values: With Special Reference to the
Values of Science.” Pp 70-81 in Understanding Human Values, edited by M. Rokeach.
Free Press.
Schwartz, Shalom H. 1994. “Are There Universal Aspects in the Structure and.” Journal of
Social Issues 50(4):19-45.
Schwartz, Shalom H. and Wolfgang Bilsky. 1990. “Toward a Theory of the Universal Content
and Structure of Values: Extensions and Cross-Cultural Replications.” Journal of
Personality and Social Psychology 58:878-891.
Spence, Alexa, and Nick Pidgeon. 2010. “Framing And Communicating Climate Change: The
Effects Of Distance And Outcome Frame Manipulations.” Global Environmental
Change 20(4): 656–667. https://doi.org/10.1016/j.gloenvcha.2010.07.002.
Spence, Alexa, Wouter Poortinga, and Nick Pidgeon. 2012. “The Psychological Distance of
Climate Change: Psychological Distance of Climate Change.” Risk Analysis 32(6):957972.
Steg, Linda and Judith I.M. De Groot. 2012. “Environmental Values.” p.81-92 in The Oxford
Handbook of Environmental and Conservation Psychology, edited by S. Clayton. New
York: Oxford University Press.
Steg, Linda, Gerta Perlaviciute, Ellen van der Werff, and Jesper Lurvink. 2014. “The
Significance of Hedonic Values for Environmentally Relevant Attitudes, Preferences, and
Actions.” Environment and Behavior 46(2):163-192.
Stern, Paul C., and Thomas Dietz. 1994. “The Value Basis of Environmental Concern.”
Journal of Social Issues 50:65-84.
Stern, Paul C., Thomas Dietz, Linda Kalof, and Gregory A. Guagnano. 1995. “Values, Beliefs,
and Proenvironmental Action: Attitude Formation toward Emergent Attitude Objects.”
Journal of Applied Social Psychology 25:1611-1636.
The Giving Institute. 2015. “Giving USA 2015 Press Release - The Giving Institute.”
Retrieved August 31, 2016, from
http://www.givinginstitute.org/?page=GUSA2015Release.
Trope, Yaacov, and Nira Liberman. 2003. “Temporal Construal.” Psychological Review
110(3): 403-410.
United Nations Environment Programme. 2014. “Rapidly Growing Middle Class Presents
Challenges and Opportunities for the Environment and Health in Asia and the Pacific.”
Retrieved June 3, 2017, from
http://www.unep.org/geo/sites/unep.org.geo/files/documents/asia_and_the_pacific_fact_s
heet.pdf.

59

U.S. Census Bureau. 2016. “Population Estimates, July 1, 2016, (V2016).” Retrieved March
15, 2017, from //www.census.gov/quickfacts/.
Uzzell, David L. 2000. “The Psycho-Spatial Dimension of Global Environmental Problems.”
Journal of Environmental Psychology 20(4):307-318.
Visconti, Piero, Mikel Bakkenes, Daniele Baisero, Thomas Brooks, Stuart HM Butchart, Lucas
Joppa, Charles Rondinini. 2016. “Projecting Global Biodiversity Indicators Under
Future Development Scenarios.” Conservation Letters 9(1):5–13.
https://doi.org/10.1111/conl.12159.
Waters, Colin N., Jan Zalasiewicz, Colin Summerhayes, Anthony Barnosky, Clement Poirier,
Agnieszka Gałuszka, and Charles Jeandel. 2016. “The Anthropocene is Functionally
and Stratigraphically Distinct from the Holocene. Science 351(6269), aad2622.
Weinberg, Jill, Jeremy Freese, and David McElhattan. 2014. “Comparing Data Characteristics
and Results of an Online Factorial Survey Between a Population-Based and a
Crowdsource-Recruited Sample.” Sociological Science 1:292–310.
https://doi.org/10.15195/v1.a19.
Wikström, Per-Olof. H., and Robert Svensson. 2010. “When Does Self-Control Matter? The
Interaction Between Morality and Self-Control in Crime Causation.” European Journal
of Criminology 7(5):395–410. https://doi.org/10.1177/1477370810372132.

60

CHAPTER 3: WATER CONSERVATION IN THE HOME AND WALLET

Introduction
In 2014 and 2015, locations as dispersed and diverse as Detroit, Israel, and Kyrgyzstan
experienced water scarcity-related conflict. The devastating consequences of these disputes
included dehydration and water-borne illness, agricultural failures, reduced industrial capacity,
decreases in the abundance of wildlife and ecosystem services, and property devaluation (Water
Project 2017). In addition to these immediate health, economic, and environmental problems
(Moe and Rheingans 2006; Postel 2000) water scarcity disputes create policy debates for those
places currently experiencing or anticipating such conflict. These debates center around
infrastructure improvements, water restrictions, equitable use, and access rights with other
jurisdictions (e.g., Rijsberman 2006).
The wide range of water uses and users yields an equally broad range of approaches to
addressing water scarcity. Most methods focus on one user community such as agriculture,
industry, or households. Recommended interventions are typically efficiency or curtailment
focused. Efforts are split between incentivization and the creation of constraints to achieve the
chosen intervention within a particular user group. Two common and complementary
approaches are to promote home water conservation and to support organizations that work on
water conservation issues at broader social and political levels (Datta et al. 2015). This paper
provides insights on both of these approaches while making several other contributions.
First, I introduce a revised measurement instrument that improves how we measure water
conservation behaviors by incorporating the concept of behavioral plasticity (Dietz, Gardner,
Gilligan, Stern, and Vandenbergh 2009). This will enable a more detailed inspection of where

61

and to whom home water conservation behaviors programs might best be targeted. Second, I use
the Values-Beliefs-Norms theoretical framework to consider the effects of psychosocial
environmental concern predictors (e.g., value orientations, personal norms, and environmental
worldview) on self-reported home water conservation behaviors and water conservation
behavioral intentions. Finally, I report the results of an experiment investigating the effects of
psychological distance (e.g., temporal and spatial distance from a water scarcity situation) on
donations to a California drought-focused water conservation charity. These results give more
detail to our understanding of who donates to environmental causes and why.

Previous Scholarship
Home water conservation behavior
The environmental decision-making literature suggests that home water conservation
behavior has two dimensions, indoor and outdoor, and that it consists of six primary types of
behavior: showering, clothes-washing, running faucets, bathing, irrigation, and use/installation of
water-saving devices (e.g., low-flow showerheads and toilets, drought-tolerant plantings/grass)
(Dolnicar, Hurlimann, and Grün 2012; Jorgensen, Graymore, and O’Toole 2009). All six types
are included in the refined water conservation behavior instrument which follows. To investigate
these behaviors, I select from psychosocial, contextual, and demographic variables that
demonstrate significant effects in previous research.
Psychosocial predictors of increased home water conservation include stronger proenvironmental attitudes and beliefs (generally measured with the New Ecological Paradigm scale
[R. Dunlap 2008]) (Mayer, DeOreo, and AWWA Research Foundation 1999; Willis et al. 2013)
holding personal norms about water conservation, and the awareness of consequences of water

62

scarcity (Corral-Verdugo, Bechtel, and Fraijo-Sing 2003; Lute, Attari, and Sherman 2015).
Relevant contextual factors that predict individual water conservation in the home include
drought conditions and attendant water restrictions (Trumbo and O’Keefe 2005; Willis, Stewart,
Giurco, Talebpour, and Mousavinejad 2013). Demographic variables of relevance include older
age, higher education, middle income, and female gender all predicting increased water
conservation behavior. For this research, I include all of these variables.

Donation to environmental efforts
Where the environmental decision-making literature gives us insights into the predictors
of direct water conservation by an individual, we go to the charitable donation literature to better
understand monetary support for water conservation causes. This latter literature suggests that
women are more likely to make donations in general, while men, when they do donate, make
larger contributions; greater educational attainment is positively associated with larger donation
amounts; and younger individuals are more likely to donate to environmental/animal causes than
are older people; (Bekkers 2010; Bekkers and Wiepking 2011; Gittell and Tebaldi 2006;
Sargeant 2014; Sargeant, Ford, and West 2006; Wiepking and Bekkers 2012).
Though the Giving Institute (2015) indicates that individual Americans gave $10.5 billion
for environmental and animal welfare causes in 2014, characterizations of individual donors to
these causes is limited and mixed. One large, cross-sector study finds that social and
demographic characteristics that predict donations in general do not help predict donations to
environmental/animal organizations specifically (Wiepking 2010) but other studies indicate that
younger, wealthier, more educated individuals are more likely than older, poorer, and less
educated donors to give to environmental causes and organizations (Bennett 2003). In a very

63

recent study, Clements et al. (2015) found that reporting higher levels of personal values such as
biospheric altruism and pro-environmental beliefs as measured by the New Ecological Paradigm
increased the likelihood of an individual donating to an environmental effort.

Role of psychological distance
Finally, my contribution to understanding donations to conservation efforts is the
inclusion of psychological distance, conceptualized here as the temporal, spatial, or social
distance between donors and recipients. Studies that consider psychological distance (i.e.
Construal Level Theory [Liberman and Trope 2008; Trope and Liberman 2010]) suggest that
individuals are more willing to donate to an organization when they are psychologically distant
from the population in need (Bekkers 2010; Ein-Gar and Levontin 2013). Related social
distance research that does not explicitly use Construal Level Theory also suggests that altruistic
and pro-social behavior varies inversely with the amount of social distance an individual
perceives (e.g., Rachlin and Jones 2008). To date, psychological distance has received limited
empirical attention in the environmental decision-making literature, with the few existing studies
focused upon climate change (Spence and Pidgeon 2010; Spence et al. 2012; Uzzell 2000).
However, as the average citizen’s connection to the wider world has increased through
traditional and social media, and with environmental programs increasingly raising funds in the
United States for their international and global efforts, the psychological distance between
potential donors and an environmental issues may emerge as an influential predictor of donation
behaviors.

64

Methods
For this research, I use a cross-sectional, between-subjects, random assignment
experiment. I controlled spatial distance by recruiting from only two states – Michigan and
California - representing those not affected and affected by a drought, respectively. I
manipulated temporal distance in my experimental message (drought effects now versus in ten
years). The experiment was embedded in an online Qualtrics questionnaire for which subjects
received payment.

Participants
I recruited 405 American subjects over the age of 18 years for a survey-based experiment
investigating the influence of psychological distance on donations to a California water
conservation program. The sample is comprised of a subsample from California (201 subjects)
and another from Michigan (204 subjects). Though 405 were recruited, only 375 completed the
water conservation behaviors and intentions portion with 367 completing the donation behavior.
Consequently, sample sizes vary slightly in each model.
I recruited my subjects through the Amazon Mechanical Turk (AMT) service and
delivered the survey using Qualtrics. AMT is an online labor market in which “requestors”
advertise tasks which “workers” then complete for pay. In this case, I paid subjects $1.00 for
completing the experiment. Practically speaking, experiments using AMT are faster and more
cost-effective to conduct than traditional experiments. AMT samples tend to be more female,
more educated, lower income, and more liberal/Democrat than a representative U.S. sample, but
AMT samples compare favorably to other pay-for-sample services like GfK (Berinsky, Huber,
and Lenz 2012). AMT samples are also more diverse and closer to national representation than

65

are the more typical university student convenience samples (Weinberg, Freese, and McElhattan
2014). While this convenience sample is more demographically and socially diverse than are the
traditional experiment recruitment pools of university undergraduates, it is more male, younger,
white, and highly educated than would be a representative sample of the U.S. general public
(U.S. Census Bureau 2016). Table 7 shows selected socio-demographic characteristics of the
full sample as well as differences in the means of the two subsamples. The Michigan subsample
is more male, more white, and has a higher rate of home-ownership than the California
subsample. The California subsample has more reporting of personal norms around water
conservation as well as more awareness of the consequences of water conservation behaviors. I
conducted my analyses using the full sample.

66

Table 7: Selected socio-demographic sample characteristics of total sample and comparison of
means of for California and Michigan sub-samples (CA – MI)
Characteristics

Total Sample
Percentage
48.9
73.8

Female
White
Age
Aged 18-29
Aged 30-39
Aged 40-49
Aged 50-59
Aged 60 or older
Homeowner
Aware of local water restrictions

42.2
33.6
10.8
9.6
3.7
42.8
20.2

Mean
Educational attainment
“12th grade or less”=1 to “doctorate degree”=7
3.38
Household income
“less than $25K”=1 to “$100K and more”=5
2.29
Altruism personal values
“not at all important”=1 to “of supreme importance”=7 5.32
Awareness of consequences
“strongly disagree”=1 to “strongly agree”=7
4.19
New Ecological Paradigm
“strongly disagree”=1 to “strongly agree”=7) 5 items 5.25
Personal Norms
“strongly disagree”=1 to “strongly agree”=7
5.02

Subsamples Compared
t-statistic
df
-2.65**
403
-5.47***
403
-1.78
403

-4.02***
16.34***

402
403

SD
1.05

1.05

403

1.19

1.52

401

0.98

-0.08

403

1.59

11.07***

403

1.28

0.08

403

1.21

5.84***

403

Procedures
Water conservation behaviors instrument
One of my intentions with this paper is to help improve how we measure water
conservation behaviors through incorporating the concept of behavioral plasticity (Dietz et al.,
2009) into a new instrument (Allen, Dietz and McCright 2015). Attending to behavioral
plasticity—how the perceived difficulty to act constrains behavioral responses across
individuals, behaviors, and contexts – allows us to collect more precise self-reported behavior
and behavioral intention data, hopefully limiting the ever-present gap between these and

67

observed behavior data. This instrument uses modified response categories to identify the homes
in which water conservation behaviors are already performed, or those that have not yet
performed the behavior but may in the future. It aids in sorting out those homes where water
conservation behaviors cannot be performed because of structural constraints. As Allen, Dietz,
and McCright (2015) note, looking at behaviors that are already performed may help us
understand the roles of structural and social psychological factors, traditionally the more stable
influences in pro-environmental behavior.
Another goal is to provide a replicable set of home water conservation behavior items
that reflects our best understanding of which actions have the most potential for proenvironmental impact. To this end, I took items primarily from Inskeep and Attari (2014). I
group these behaviors after distinctions made in Inskeep and Attari (2014) and in Allen, Dietz,
and McCright (2015) as “routine” and “once-and-done”. Routine behaviors include those
actions that a subject performs on a regular and frequent basis (e.g., bathing, running a faucet).
Once-and-done behaviors include installation of water-saving devices (e.g., low-flow toilets and
showerheads) or replacement of less-conserving appliances (e.g., dishwashers and clothes
washers). Because of the small number of outdoor behaviors, I do not include the
indoor/outdoor dimension in my analyses, but do use it for display purposes in the relevant table.
In all, the items in this instrument include installation and replacement of water-related
appliances and fixtures such as dishwashers, rain sensors, and faucets, taking shorter showers,
not flushing after every toilet use, and use of the garbage disposer. Table 8 displays the full
instrument and a description of my question stem and coding follows in the Measures section.

68

Donation experiment
California, at the time of data collection, was experiencing an extended drought state of
emergency that resulted in widespread residential water use restrictions (USGS 2017). Because
of the multi-year nature of the drought and the resulting inconveniences of the water restrictions,
Californians had no real expectation as to when drought effects might be alleviated. In my
experiment, I took advantage of this uncertainty by presenting subjects with information labeled
as current (2016 - low temporal distance) or as a forecast (2026 - high temporal distance).
Subjects were randomly assigned to one of two conditions designed to test the messaging effects
of a fabricated informational presentation slide illustrating the severity, change, and impacts of
the California drought. The message slides are available in Appendix B.
To consider the effects of spatial distance, I recruited two samples, one from California
and the other from Michigan. In contrast to California, Michigan has had no issues with water
availability in the recent past, rendering drought conditions more abstract than the very real daily
considerations they are for Californians. In psychological distance terms, this geographic
removal from a subject in Michigan provides a natural spatial distance correlate.
Before being presented with the stimulus, subjects answered a series of questions
designed to measure their overall value orientations, their general environmental beliefs, and
their personal norms about home water conservation and water scarcity. All subjects then
answered two open-ended questions about the slide’s message and implications. Subjects
answered a series of questions regarding their own home water conservation behaviors, followed
by standard socio-demographic questions.
After answering the final socio-demographic item, subjects were thanked for their
participation and notified that they had earned a bonus of $1.00 (equivalent to their participation

69

pay). They were then given the option to donate some, all, or none of their bonus to the Pacific
Institute, an independent nonprofit that works on freshwater issues and has a specific California
drought program. Subjects indicated their desired donation amount by selecting an amount
between $0.05 and $1.00 in 5 cent increments from a drop-down menu. Figure 1 indicates the
distribution of donation amounts with 60% of subjects choosing not to donate and 11% of
subjects choosing to donate exactly half their bonus (i.e. 50 cents). The total amount donated
was $59.05.

0

50

Number of Donations
100
150

200

250

Figure 7: Histogram of donation amounts

0

.2

.4
.6
Donation Amount

70

.8

1

Measures
Water conservation behaviors instrument measures
Table 8 displays the items used in my water conservation behavior instrument, the
percentage of respondents reporting they are already performing the behavior, the percentage
reporting they cannot perform the behavior, and the mean score on the likelihood of performing
the behavior in the future. I derived items from a review of the literature, but primarily from
Inskeep and Attari’s (2014) review of the most efficacious home water conservation behaviors.
The question stem asked: “Taking into consideration the costs and inconvenience for each action,
how unlikely or likely are you personally to perform each of the following behaviors?” and
response options were comprised of a range of where “very unlikely” =1 and “very likely” =4, “I
already do this” =5 and “I can’t do this” =6. Calculation of the mean intention includes those
who fully intend, i.e., are already performing, the behavior.
Before conducting my analysis, I created indices reflecting the intersections of the selfreported behavior/behavioral intentions and routine/once-and-done dimensions for a total of four
indices – self-reported routine behaviors, self-reported once-and-done behaviors, routine
behavioral intentions, and once-and-done behavioral intentions. For each subject, I derived their
mean response to the group of items, accommodating subjects who did not respond to all items.
I then conducted these analyses using standard OLS regression techniques.

71

Table 8: Water conservation behavior items and descriptive statistics (low likelihood=1 to very
high likelihood=5)
Behavioral Item

Percentage
Already
Performing

Indoor Routine
Take a shower instead of a bath
Only wash a full load of clothes (or adjust the water level
to the load size) whenever I wash clothes
Reduce the time faucet is running while brushing teeth
or washing hands
Take shorter showers
When taking a bath, fill tub only half full
Don’t pre-rinse dishes before putting them in the
dishwasher
Stop using water with the garbage disposer
Reduce how often I flush my toilets
Outdoor Routine
Water outdoor plants in the morning
Don’t water my lawn and/or garden or use collected
rainwater to water them
Indoor Once-and-Done
Inspect for and fix plumbing leaks
Replace my clothes washer with a water-efficient one
Replace my current showerheads with low-flow
showerheads
Replace my dishwasher with a water-efficient one
Replace my current toilets with low-flow toilets
Install toilet tank water-saving inserts
Install faucets labeled as water-conserving
Replace my outdoor plants and grass with native or
low-water- use plants
Outdoor Once-and-Done
Install a drip irrigation system for my outdoor plants
Install an outdoor sensor that detects whether it has
rained recently

72

Percentage
Cannot
Perform

Mean
Likelihood
of Performing
(for Eligible
Subjects)

77.4

1.7

4.6

72.4

2.0

4.5

66.2
44.7
30.2

0.7
1.0
11.1

4.4
3.9
3.5

21.2
14.4
35.2

15.8
18.6
3.5

3.1
2.8
3.3

36.6

17.8

3.9

28.9

20.0

3.6

42.6
37.6

13.1
19.3

4.1
3.9

35.8
30.4
27.4
18.8
18.8

13.1
30.9
22.2
17.3
19.0

3.7
3.8
3.5
3.2
3.4

17.0

24.4

3.2

10.2

28.0

2.9

2.7

21.0

2.3

Donation experiment measures
In addition to the water conservation behaviors instrument above and the temporal and
spatial conditions inherent in my sample selection and experimental condition assignments, I
measured psychosocial characteristics associated with the VBN model of pro-environmental
behavior - value orientations, general environmental beliefs, personal norms around water
conservation and scarcity, awareness of the consequences of water scarcity, and standard sociodemographics.
I used a revision of Schwartz’s value orientation survey (Schwartz, 1994) with additional
hedonism items as suggested by Steg, Perlaviciute, Van der Werff, and Lurvink (2012) to
measure value orientations. The question stem asked subjects to “indicate how important each of
the following is as a guiding principle in your life.” Response options ranged from “not at all
important” =1 to “extremely important” =7. There were three items for each value orientation,
which I then summed into scales. Cronbach’s alphas were as follows: altruism (a= 0.80),
hedonism (a= 0.76), egoism (a= 0.47), tradition (a= 0.61), and openness to change (a= 0.54). I
accounted for general environmental beliefs with a short (5-item) version of the New Ecological
Paradigm scale (Dunlap, Van Liere, Mertig, and Jones, 2000) which averages the responses
(“strongly disagree”=1 to “strongly agree”=7) to similar items about the human-environment
relationship: new ecological paradigm (α=0.84).
Personal norms around saving water were measured using a 7-option scale where
“strongly disagree” =1 and “strongly agree” =7. I asked subjects to read a series of statements
and indicate their disagreement or agreement. I measured personal norms using three items: “I
should conserve water”, “I always save water even when it’s a big effort” and “I feel guilty if I
don’t conserve water” again creating a summative scale (Cronbach’s α=0.69).

73

Using the same 7-option response scale where “strongly disagree” =1 and “strongly
agree” =7, I measured awareness of the consequences of water scarcity along four levels of
psychological distance – consequences for the subject, consequences for the subject’s family,
consequences for the subject’s community, and consequences to the more general environment.
I used the item “Drought and water scarcity impact my choices and personal freedom” for
awareness of consequences to the self and I measured awareness to family with the item “My
family suffers when there is a drought”. Awareness of consequences to community was
measured as “The effects of water scarcity and drought on my community are worse than I
realize”, and I used the statement “Water scarcity has a large negative impact on the
environment” for measuring awareness of consequences to the environment. These items were
then combined into a summative index (Cronbach’s α = 0.87).
I also included items ascertaining subjects’ home-ownership status, either owning,
renting, living in a home someone rents for them, or living in a home someone else owns and to
whom they do not pay rent (e.g., living with parents). For my analyses, I collapsed these
categories into a dichotomous own variable (does not own =0, owns =1). I also employed five
demographic and social variables as statistical controls in my analyses. Two are dummy
variables: female for gender (“male”=0; “female”=1) and white for race (“non-white”=0;
“white”=1). Age is measured with five categories: “18-29”=1 to “60 and over”=5. Education is
measured by the highest degree earned: “less than high school diploma or equivalent”=1 to
“doctoral degree”=7. Income is measured as approximate yearly household income: “less than
$25,000”=1 to “$100,000 or more”=5.

74

Analytic methods
My analyses answer five questions:
(1) Does the proposed instrument perform as expected?
(2) What characterizes those who already perform home water-saving behaviors?
(3) What characterizes those who intend to perform home water-saving behaviors?
(4) What predicts who is likely to donate for water conservation? and
(5) What predicts how much a donor will donate for water conservation?
To answer these questions, I first examined the predictors of self-reported home water
conservation behaviors and then predictors of water conservation behavioral intentions. I then
examined predictors of subjects’ donation decisions, using logistic regression to understand
which variables contribute to the donation/no donation decision and, after separating out those
subjects who chose to donate, I conducted OLS regression to predict donation amounts. Again,
because of the small number of outdoor behaviors, I do not include the indoor/outdoor dimension
in my analyses, but do use it for display purposes in the relevant table.
As a secondary analysis, I performed nested regressions on each of the four water
conservation indices, donation choice, and donation amount in order to more closely examine the
performance of psychological distance in these models. These are available in Appendix A. I
used the VBN theoretical model to build each set of regressions. I conducted all analyses using
Stata 14.2.

75

Results and Discussion
Performance of the water conservation instrument
This water conservation behaviors instrument differentiates current behavior from
intentions and allows for the inability of some respondents to perform some behaviors. First, I
discuss the performance of the instrument with descriptive statistics. Then I follow with an
analysis of the predictors of current behavior and behavioral intentions (Tables 10 and 11).
These yield results consistent with the general patterns of previous research and suggest that the
instrument has predictive validity.
Table 8 displays the descriptive statistics for the water conservation behaviors instrument.
I see that in this sample, the most common water conservation behavior is taking a shower
instead of a bath (77% of respondents), while the least common is installing an outdoor sensor
that detects whether it has rained recently (3% of respondents). Replacing a dishwasher with a
water-efficient one, installing a drip irrigation system for outdoor plants, and replacing outdoor
plants and grass with native or low-water-use plants are the most constrained behaviors, with
between 24% - 30% of respondents reporting they could not perform each of these behaviors.
For those who could perform them but have not yet done so, the most likely future behaviors are
taking a shower instead of a bath (mean=4.6) and only washing a full load of laundry or
adjusting water levels to match load size (mean=4.5), while the least likely future behavior is
installing an outdoor water sensor that detects whether it has rained recently (mean=2.3).
Overall, outdoor behaviors are the most constrained behaviors in my sample. This may be a
reflection of my sample’s rate of homeownership and housing types (i.e. if they do own, owning
apartments or townhomes).

76

I also examine the dimensionality of my water conservation items. A principal
components analysis of the variables followed by a promax rotation yields four factors with
eigenvalues greater than 1. Table 9 reports the factor loadings and eigenvalues from this
analysis. The factor loadings suggest that the items could be sorted into four factors, which I
describe as once-and-done behaviors (factor 1), outdoor routine behaviors (factor 2), indoor
routine behaviors 1 (factor 3), and indoor routine behaviors 2 (factor 4). The indoor/outdoor
dichotomy is not perfectly clean in this sample, though the pattern is nonetheless clear.
Likewise, the split of routine indoor behaviors into two factors still indicates the routine nature of
the behaviors, though the differences between the two sets of behaviors is not obvious. Two
items loaded on both factor 1 and factor 2. “Install a drip irrigation system...” and “Install an
outdoor sensor...” both relate to both capital improvements and outdoor actions. Two items
failed to load on any factor, “When taking a bath, fill tub only half full” and “Inspect for and fix
plumbing leaks”. Overall, this pattern seems to match the kinds of time and financial constraints
and difficulties involved in performing these routine versus once-and-done behaviors. It also
suggests support for the indoor/outdoor division.

77

Table 9: Factor loadings and eigenvalues from principle component factor analysis of water
conservation instrument items with pro-max rotation
Behavioral Item
Replace my current toilets with low-flow toilets
Install toilet tank water-saving inserts
Replace my clothes washer with a water-efficient
one
Replace my current showerheads with low-flow
showerheads
Install faucets labeled as water-conserving
Replace my dishwasher with a water-efficient
one
Replace my outdoor plants and grass with native or
low-water- use plants
Install a drip irrigation system for my outdoor
plants
Install an outdoor sensor that detects whether it has
rained recently
Don’t water my lawn and/or garden or use collected
rainwater to water them
Water outdoor plants in the morning
Only wash a full load of clothes (or adjust the water
level to the load size) whenever I wash clothes
Take a shower instead of a bath
Reduce the time faucet is running while brushing
teeth or washing hands
Reduce how often I flush my toilets
Take shorter showers
Stop using water with the garbage disposer
Don’t pre-rinse dishes before putting them in the
dishwasher
When taking a bath, fill tub only half full
Inspect for and fix plumbing leaks
Eigenvalues

Factor 1

Factor 2

Factor 3

Factor 4

0.8346
0.7541

0.0050
0.0103

-0.0089
-0.0609

0.0613
0.1135

0.7542

0.0191

0.2320

-0.1431

0.7585
0.7672

-0.1872
0.1704

0.1016
-0.0683

0.1317
-0.0466

0.7643

-0.0765

0.1771

0.0546

0.5751

0.3992

-0.0949

-0.0245

0.4395

0.5926

-0.0884

-0.112

0.5440

0.4184

-0.2653

0.0091

0.2303
-0.0329

0.5139
0.7353

0.0513
0.2056

0.1378
-0.0081

0.1316
-0.0048

0.0577
-0.0154

0.7037
0.7057

-0.0435
-0.0413

-0.0459
-0.2393
-0.0092
0.0897

0.0161
0.3307
0.0155
-0.1218

0.7820
-0.0341
0.3947
0.0009

-0.0245
0.6436
0.4103
0.7788

0.1688
-0.1292
0.2279
6.7463

-0.0584
0.3469
0.2937
2.5922

-0.0900
0.3493
0.3922
1.222

0.7758
0.2594
-0.0878
1.0679

Predicting self-reported water conservation behaviors
I further assess the construct validity of my water conservation behaviors instrument by
demonstrating that well-known predictors of environmentally significant behavior influence my
subjects’ water conservation behaviors and behavioral intentions, even while controlling for
subjects’ demographic and social characteristics. The results in Table 10 are generally congruent

78

with the Values-Beliefs-Norms theoretical model, offering evidence that my instrument has
construct validity. Personal norms have a statistically significant positive effect on both routine
and once-and-done behavior scale scores. Awareness of water restrictions has a similarly
positive effect on these scale scores. Noticing the difference in performance of awareness of
consequences, a knowledge factor, and awareness of water restrictions, an external context and
behavioral expectation, also leads us to view a subject’s knowledge of water restrictions acts as a
social norm in this sample. Thus, the statistical significance of these variables suggests that a
more normative approach to incentivization of water conservation behaviors, rather than one
based on increasing knowledge of impacts should be efficacious.
Additionally, the performance of the demographic and social variables aligns with the
results of recent studies. Other than age, none of the remaining variables have consistent effects
across the two categories of behaviors. This finding is similar to previous research documenting
that race (McCright and Dunlap 2015; Clements et al. 2014a; Clements et al. 2014b) income, and
education (O’Connor et al 1999; McCright and Dunlap 2015) either have no effect or mixed
effects, especially when controlling for environmental beliefs. Nevertheless, my results also
show face validity in the performance of race and income as statistically significant on once-anddone behaviors. I might expect older subjects and those with more income and homeownership
experience to be more likely to make capital investments in appliances and facility upgrades in
their homes.

79

Table 10: Unstandardized coefficients (and standard errors) from OLS regression of self-reported
water conservation behaviors on a range of theoretical and socio-demographic predictors
Self-Reported Routine
Water Conservation Behaviors

Self-Reported Once-and-Done
Water Conservation Behaviors

Values, Beliefs, and Norms
Altruism
Awareness of Consequences
New Ecological Paradigm
Personal Norms
Awareness of Water Restrictions

0.02 (0.02)
-0.02 (0.01)
0.01 (0.01)
0.07 (0.01)***
0.08 (0.04)*

0.02 (0.02)
0.00 (0.01)
-0.00 (0.01)
0.04 (0.02)**
0.12 (0.040**

Socio-Demographic Characteristics
Female
Age
White
Educational attainment
Income
Home ownership
Michigan residence
Constant

0.03 (0.03)
0.02 (0.01)**
0.05 (0.03)
-0.03 (0.01)*
0.02 (0.01)
-0.02 (0.03)
0.03 (0.04)
-0.10 (0.10)

0.00 (0.03)
0.03 (0.01)**
0.13 (0.04)***
-0.02 (0.01)
0.04 (0.01)**
-0.02 (0.03)
0.01 (0.04)
-0.34 (0.11)**

Adjusted R2
N
*p<.05, **p<.01, ***p<.001

0.16
375

0.19
375

Predicting water conservation behavioral intentions
Table 11 reports the effects of key variables from OLS regression models predicting
water conservation behavioral intentions. Similar to the self-reported behaviors findings,
personal norms and awareness of water restrictions, along with altruistic value orientation, are
typically the strongest predictors of behavioral intentions. Thus, the results in Table 11 provide
additional evidence that my instrument has construct validity. Personal norms and altruism have
a statistically significant positive effect on both behavioral intention categories, while awareness
of water restrictions influences the more costly behavior category. I suggest that altruism
appears as significant here where it did not in the previous models of self-reported behavior
because of the more abstract nature of behavioral intentions. Previous VBN-based research
80

documents that value orientations have more influence before the introduction of specific
attitudes, norms, and constraints (Dietz, et al. 2005). I also suggest that in these models, in
addition to their action as a social norm, awareness of water restrictions act as a marker for
subjects’ understanding of what actions would be appropriate responses to water restrictions.
Subjects may not view their curtailment-focused routine behaviors as having an especially large
impact on water conservation in the home when compared to efficiency measures such as
appliance upgrades. In other words, though they are aware of the restrictions, subjects may not
think that their routine behaviors have much impact.
As expected, the remaining demographic, social, and political variables have no
consistent effects on behavioral intentions across the two models, but a few of the sparse effects
warrant brief attention. For routine behavioral intentions, being older is the only significant
socio-demographic predictor, while income effects once-and-done intentions as does being
white. Again, these results are in line with previous literature, suggesting that the instrument
performs as expected. Overall, these results suggest that home water conservation behavioral
studies can be expected to conform to the traditional models and predictor patterns that work for
other environmentally significant behaviors.

81

Table 11: Unstandardized coefficients (and standard errors) from OLS regression predicting
intentions to perform water conservation behaviors on a range of theoretical and sociodemographic predictors
Routine Water Conservation
Behavioral Intentions

Once-and-Done Water
Conservation Behavioral
Intentions

Construal Level Theoretical Predictors
Temporal distance (forecast message)
Spatial distance (Michigan residence)

0.03 (0.07)
-0.02 (0.10)

0.06 (0.09)
0.01 (0.13)

Values, Beliefs, and Norms
Altruism
Awareness of Consequences
New Ecological Paradigm
Personal Norms
Awareness of Water Restrictions

0.11 (0.04)**
-0.03 (0.03)
0.03 (0.03)
0.22 (0.04)***
0.13 (0.10)

0.20 (0.06)***
0.06 (0.04)
-0.02 (0.04)
0.14 (0.05)**
0.35 (0.13)**

Socio-Demographic Characteristics
Female
Age
White
Educational attainment
Income
Home ownership

0.10 (0.07)
0.04 (0.02)**
0.01 (0.08)
-0.07 (0.04)
0.04 (0.03)
-0.01 (0.08)

0.09 (0.09)
0.02 (0.02)
0.39 (0.11)**
-0.07 (0.05)
0.15 (0.04)**
0.10 (0.10)

Constant

1.97 (0.25)***

Adjusted R2
N
*p<.05, **p<.01, ***p<.001

0.23
375

0.72 (0.33)*
0.24
375

Predicting donations to a water conservation program
Table 12 displays results from my logistic and OLS regressions on donation behaviors.
The logistic model uses my entire sample to predict donation choice, while I predict donation
amounts using only those subjects who chose to donate. My psychological distance conditions –
controlled spatial distance and experimentally assigned temporal distance – did not show
statistical significance in my final models. I discuss my secondary analyses to better understand

82

why this is so below, but essentially, we see the strong effects of psychosocial variables as is
typically with environment-related studies.
In the logistic model of my subjects’ choice to donate or not to donate, two predictors
have statistically significant effects. NEP and routine behavioral intentions each increase my
subjects’ likelihood of donating for water conservation. As noted, NEP has been documented as
a positive influence of pro-environmental donation behavior (Clements, et al. 2015). Routine
behavioral intentions are the closest intentional match to donation behavior in this experiment.
Donation as I have organized it here, is a low-cost, low-risk behavior with minimal external
constraints. Routine behaviors are similarly low-cost and low-risk and generally low-constraint.
Intentionality, rather than an actual behavior, further adds to the lack of perceived constraint.
Thus, the congruence between this intention measure and the measured donation behavior makes
sense. Regarding prediction of the donation amount, my model does not perform particularly
well, though I do see two variables with statistically significant influence. Table 12 again shows
NEP as significant and positive as is income; the latter intuitively correct as well as documented
in previous literature (Wiepking 2010).
Predicting donations to an environmental cause is a relatively new undertaking.
Donation may be a factor of environmentally relevant beliefs, measured here by the NEP;
however, income influences the amount of donation but not the actual choice to donate. Given
the relatively small amount of variance explained by this set of variables, further research is
needed to identify more reliable predictors or models for monetary support of environmental
causes.

83

Table 12: Logistic and OLS regressions (and standard errors) predicting donation to California
drought program and amount of donation
Logit Model Predicting
Donation

OLS Model Predicting
Donation Amount

Experimental Effects
Temporal distance (forecast message)
Spatial distance (location)

-0.06 (.23)
-0.66 (.34)

Values, Beliefs, and Norms
Altruism
Awareness of Consequences
New Ecological Paradigm
Personal Norms
Awareness of Water Restrictions

-0.21 (.14)
-0.01 (.09)
0.39 (.11)***
-0.08 (.13)
0.46 (.33)

0.01 (.03)
0.01 (.02)
0.06 (.03)*
-0.03 (.03)
-0.04 (.08)

Socio-Demographic Characteristics
Female
Age
White
Educational attainment
Income
Home ownership

0.06 (.24)
0.10 (.06)
0.21 (.29)
-0.18 (.12)
-0.03 (.11)
-0.20 (.26)

0.03 (.05)
0.01 (.01)
0.06 (.06)
-0.04 (.03)
0.06 (.03)*
0.05 (.06)

Behavioral Intentions
Routine behavior intentions
Once-and-done behavior intentions
Self-reported routine behavior
Self-reported once-and-done behavior

0.97 (.33)**
-0.27 (.22)
-1.49 (.84)
0.16 (.69)

0.08 (.08)
-0.05 (.05)
-0.19 (.20)
0.10 (.16)

-4.06 (1.27)**
0.08

-0.28 (.31)

Constant
Pseudo R2
Adjusted R2
N
*p<.05, **p<.01, ***p<.001

367

0.07 (.05)
0.03 (.08)

0.04
149

Performance of psychological distance
In my analyses, psychological distance, the frame of my experiment, failed to reach
statistical significance for any of my models. And in my secondary analyses, nested regressions
to isolate when variables’ influence overtake my spatial and temporal distance conditions,
neither the fixed spatial distance measure of Michigan residence, nor the manipulated temporal

84

distance measure of forecasted drought effects showed influence on self-reported behaviors or on
the amount of donation a subject chose if they chose to donate at all. Spatial distance did,
however, show statistically significant positive influence on both types of behavioral intentions
and on a subjects’ choice to make a donation. For behavioral intentions, the effects of spatial
distance are overcome when awareness of the consequences of water scarcity is introduced to the
model. And, in line with Construal Level Theory, being spatially distant from the drought (i.e. a
Michigan resident) increased a subject’s likelihood of donating to water conservation
organizations. It is not clear from this data whether that is a factor of greater concern about the
drought or of greater preference for donating to an organization instead of specific victim groups.
However, the spatial distance effect is overcome when NEP beliefs are added in the model
suggesting that it is the concern about the drought rather than donation target preference.

Conclusion
The goals of this paper were to evaluate a new measurement instrument through
consideration of the effects of traditional psychosocial environmental concern predictors on selfreported home water conservation behavior and on water conservation behavioral intentions, and
to report the results of an experiment on the effects of psychological distance on monetary
donation to a water conservation program. I did this by answering five questions: (1) Does the
proposed instrument perform as expected? (2) What characterizes those who already perform
home water-saving behaviors? (3) What characterizes those who intend to perform home watersaving behaviors? (4) What predicts who is likely to donate to a water conservation charity?
and (5) What predicts how much a donor will donate to the water conservation charity?

85

Given these results, I suggest that this water conservation behavior instrument is suitable
for replication. My factor analyses and descriptive statistics suggest construct validity, while my
regression results document predictive performance in line with research on other, similar proenvironmental behaviors as noted in the literature. Thus, the suggestion from the regression
results is that we should expect results from water conservation behavior studies to conform to
our existing models of other pro-environmental behaviors and their predictor patterns. To follow
this thread, research could be undertaken using other well-known models of pro-environmental
behavior such as the Theory of Planned Behavior (Ajzen 1991).
Psychological distance also deserves further study, especially in the context of donation
behavior. Though not statistically significant in the final models, the performance of spatial
distance is enough to be intriguing, especially with regard to how it affects donation choice –
through organizational preference or levels of concern. In all, these results point out that there is
more work to be done to understand the drivers behind donations to environmental efforts.
Clearly, the explanatory power of my donation models could be improved, perhaps with deeper
consideration of subjects’ perceptions of the donation receiver and donation in general, and more
complete survey of subjects’ external constraints.
Overall, greater understanding of home water conservation behaviors, intentions, and
constraints to these as examined here will provide better information for policy debates and
water scarcity-alleviation programs. More specific characterization of donors could lead to
bigger, more, and more effective pro-environmental efforts.

86

APPENDICES

87

APPENDIX A

Nested Regression Models

88

Table 13: Unstandardized coefficients (and standard errors) of nested OLS regression models of
routine water conservation behavioral intentions

Forecast Message
Michigan Residence

Model 1

Model 2

Model 3

0.00
(0.07)
-0.18*
(0.07)

-0.00
(0.07)
-0.18*
(0.07)
0.26***
(0.04)

-0.01
(0.07)
-0.18*
(0.07)
0.23***
(0.04)
0.07*
(0.03)

-0.00
(0.07)
-0.09
(0.08)
0.20***
(0.04)
0.06
(0.03)
0.06*
(0.03)

4.12***
(0.12)
400
2.81*
0.01

2.71***
(0.22)
400
20.49***
0.13

2.57***
(0.23)
400
16.73***
0.14

2.36***
(0.28)
400
14.44***
0.14

Altruism
NEP
Awareness of Consequences
Personal Norms
Female
Age
Income
White
Homeowner
Awareness of Water Restrictions
Routine Intentions
Once-and-Done Intentions
Routine Already
Once-and-Done Already
Constant
N
F
Adj R2

89

Model 4

Table 14: Unstandardized coefficients (and standard errors) of nested OLS regression models of
once-and-done water conservation behavioral intentions

Forecast Message
Michigan Residence

Model 1

Model 2

Model 3

-0.02
(0.10)
-0.27**
(0.10)

-0.03
(0.10)
-0.27**
(0.10)
0.34***
(0.05)

-0.03
(0.10)
-0.27**
(0.10)
0.33***
(0.06)
0.01
(0.04)

-0.03
(0.09)
-0.12
(0.11)
0.30***
(0.06)
-0.01
(0.04)
0.10**
(0.04)

3.78***
(0.17)
375
3.52*
0.01

2.00***
(0.31)
375
17.77***
0.12

1.98***
(0.32)
375
13.30***
0.12

1.63***
(0.34)
375
12.32***
0.13

Altruism
NEP
Awareness of Consequences
Personal Norms
Female
Age
Income
White
Homeowner
Awareness of Water Restrictions
Routine Intentions
Once-and-Done Intentions
Routine Already
Once-and-Done Already
Constant
N
F
Adj R2

90

Model 4

Table 15: Unstandardized coefficients (and standard errors) of nested logistic regression models
of donation behavior

Forecast Message
Michigan Residence

Model 1

Model 2

-0.05
(0.21)
0.43*
(0.21)

-0.05
(0.21)
0.43*
(0.21)
0.04
(0.11)

-0.08
(0.22)
0.48
(0.26)
-0.16
(0.13)
0.03
(0.08)
0.32**
(0.10)

-1.01
(0.36)
367
4.10
4.13
0.13
0.01

-1.22
(0.69)
367
0.12
4.26
0.24
0.01

-2.05*
(0.80)
367
11.03
15.87
0.01
0.03

Altruism
Awareness of Consequences
NEP
Personal Norms
Female
Age
Income
White
Homeowner
Awareness of Water Restrictions
Routine Intentions
Once-and-Done Intentions
Routine Already
Once-and-Done Already
Constant
N
Wald chi2
LR chi2
Prob > chi2
Pseudo R2

91

Model 3

APPENDIX B

Message Slides

92

Figure 8: Current condition slide

Figure 9: Forecast condition slide

93

REFERENCES

94

REFERENCES

Ajzen, Icek. 1991. “The Theory of Planned Behavior.” Organizational Behavior and Human
Decision Processes 50(2):179-211.
Allen, Summer, Thomas Dietz, and Aaron M. McCright. 2015. “Measuring Household Energy
Efficiency Behaviors with Attention to Behavioral Plasticity in the United States.”
Energy Research and Social Science 10:133-140.
Bekkers, Rene. 2010. “Who Gives What and When? A Scenario Study of Intentions to Give
Time and Money.” Social Science Research 39(3):369–381.
https://doi.org/10.1016/j.ssresearch.2009.08.008.
Bekkers, Rene, and Pamala Wiepking. 2011. “Who Gives? A Literature Review of Predictors of
Charitable Giving Part One: Religion, Education, Age and Socialisation.” Voluntary
Sector Review 2(3):337–365.
Bennett, Robert. 2003. “Factors Underlying the Inclination to Donate to Particular Types of
Charity.” International Journal of Nonprofit and Voluntary Sector Marketing 8(1):12–
29.
Berinsky, Adam. J., Gregory. A. Huber, and Gabriel. S. Lenz. 2012. “Evaluating Online Labor
Markets for Experimental Research: Amazon.com’s Mechanical Turk.” Political
Analysis 20(3):351-356.
Clements John M, Aaron M. McCright, and Chenyang Xiao. 2014. “Green Christians?: An
Empirical Examination of Environmental Concern within the U.S. General Public.”
Organization and Environment 27:85-102.
Clements John M, CHenyang M. Xiao, and Aaron M. McCright. 2014. “An Examination of the
‘Greening of Christianity’ Thesis among Americans, 1993-2010.” Journal for the
Scientific Study of Religion 53:373-391.
Clements, John M., Aaron M. McCright, Thomas Dietz, and Sandra T. Marquart-Pyatt. 2015.
“A Behavioural Measure Of Environmental Decision-Making For Social
Surveys.” Environmental Sociology 1(1):27-37.
Corral-Verdugo, Victor, Robert Bechtel, and Bianca Fraijo-Sing. 2003. “Environmental Beliefs
and Water Conservation: An Empirical Study.” Journal of Environmental Psychology
23(3):247–257.
Datta, Saugato, Juan Jose Miranda, Laura De Castro Zoratto, Oscar Calvo-González, Matthew
Darling, and Karina Lorenzana. 2015. “A Behavioral Approach to Water Conservation.”

95

Retrieved from http://wwwwds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2015/06/02/090224b082ee780
e/1_0/Rendered/PDF/A0behavioral0a0ence0from0Costa0Rica.pdf.
Dietz, Thomas, Amy Fitzgerald, and Rachael Shwom. 2005. “Environmental Values.” Annual
Review of Environment and Resources 30:335-372.
Dietz, Thomas, Gerald T. Gardner, Jonathan Gilligan, Paul C. Stern, and Michael P.
Vandenbergh. 2009. “Household Actions Can Provide a Behavioral Wedge to Rapidly
Reduce U.S. Carbon Emissions.” Proceedings of the National Academy of Sciences
106(44):18452-18456.
Dolcinar, Sara, Anna Hurliman, and Bettina Grun. 2012. “Water Conservation Behavior in
Australia.” Journal of Environmental Management 105:44-52.
Dunlap, Riley. 2008. “The New Environmental Paradigm Scale: From Marginality to
Worldwide Use.” The Journal of Environmental Education 40(1):3–18.
Dunlap, Riley E., Kent D. Van Liere, Angela G. Mertig, and Robert E. Jones. 2000. “New
Trends in Measuring Environmental Attitudes: Measuring Endorsement of the New
Ecological Paradigm: A Revised NEP Scale.” Journal of Social Issues 56(3):425-442.
Ein-Gar, Danit, and Liat Levontin. 2013. “Giving From A Distance: Putting The Charitable
Organization At The Center Of The Donation Appeal.” Journal of Consumer Psychology
23(2):197–211. https://doi.org/10.1016/j.jcps.2012.09.002.
Gittell, Ross, and Edinaldo Tebaldi. 2006. “Charitable Giving: Factors Influencing Giving in
U.S. States.” Nonprofit and Voluntary Sector Quarterly 35(4):721–736.
https://doi.org/10.1177/0899764006289768.
Inskeep, Benjamin D. and Shahzeen Z. Attari. 2014. “The Water Short List: The Most Effective
Actions U.S. Households Can Take to Curb Water Use.” Environment: Science and
Policy for Sustainable Development 56(4):4-15.
Jorgensen, Bradley, Michelle Graymore, and Kevin O’Toole. 2009. “Household Water Use
Behavior: An Integrated Model.” Journal of Environmental Management 91(1):227–
236. https://doi.org/10.1016/j.jenvman.2009.08.009.
Liberman, Nira, and Yaacov Trope. 2008 “The Psychology of Transcending the Here and
Now.” Science 322(5905):1201-1205.
Lute, Michelle L., Shahzeen Attari, and Steven Sherman. 2015. “Don’t Rush to Flush.”
Journal of Environmental Psychology. https://doi.org/10.1016/j.jenvp.2015.06.003.

96

Mayer, Peter, William B. DeOreo, and AWWA Research Foundation (Eds.). 1999. Residential
end uses of water. Denver, CO: AWWA Research Foundation and American Water
Works Association.
McCright, Aaron M. and Riley E. Dunlap. 2015. “Comparing Two Measures of Social
Movement Identity: The Environmental Movement as Example.” Social Science
Quarterly. doi:10.1111/ssqu.12148.
Moe, Christine L., and Richard Rheingans. 2006. “Global Challenges in Water, Snitation and
Health.” Journal of Water and Health 4(SI):41–57.
O’Connor, Robert E., Richard J. Bord, and Ann Fisher. 1999. “Risk Perceptions, General
Environmental Beliefs, and Willingness to Address Climate Change.” Risk Analysis
19(3):461-471.
Pacific Institute. n.d.. “Water Conflict Chronological List.” Retrieved December 22, 2015, from
http://www2.worldwater.org/conflict/list/.
Postel, Sandra. L. 2000. “Entering an Era of Water Scarcity: The Challenges Ahead.”
Ecological Applications 10(4):941–948. https://doi.org/10.2307/2641009.
Rachlin, Howard, and Bryan A. Jones. 2008. “Altruism Among Relatives and Non-Relatives.”
Behavioural Processes 79(2):120–123. https://doi.org/10.1016/j.beproc.2008.06.002.
Rijsberman, Frank. R. 2006. “Water Scarcity: Fact or Fiction?” Agricultural Water
Management 80(1):5-22.
Rokeach, Milton. 1979. “From Individual to Institutional Values: With Special Reference to the
Values of Science.” Pp 70-81 in Understanding Human Values, edited by M. Rokeach.
Free Press.
Sargeant, Adrian. 2014. “A Retrospective - Charitable Giving: Towards a Model of Donor
Behaviour.” Social Business 4(4):293–323.
https://doi.org/10.1362/204440814X14185703122847.
Sargeant, Adrian, John B. Ford, and Douglas C. West. 2006. “Perceptual Determinants Of
Nonprofit Giving Behavior.” Journal of Business Research 59(2):155–165.
https://doi.org/10.1016/j.jbusres.2005.04.006.
Schwartz, Shalom H. 1977. “Normative Influences on Altruism.” Advances in Experimental
Social Psychology 10: 221-279.
Spence, Alexa, and Nick Pidgeon. 2010. “Framing and Communicating Climate Change: The
Effects of Distance and Outcome Frame Manipulations.” Global Environmental Change,
20(4):656–667. https://doi.org/10.1016/j.gloenvcha.2010.07.002.

97

Spence, Alexa, Wouter Poortinga, and Nick Pidgeon. 2012. “The Psychological Distance of
Climate Change: Psychological Distance of Climate Change.” Risk Analysis 32(6):957972.
Steg, Linda, Gerta Perlaviciute, Ellen van der Werff, and Jesper Lurvink. 2014. “The
Significance of Hedonic Values for Environmentally Relevant Attitudes, Preferences, and
Actions.” Environment and Behavior 46(2):163-192.
Stern, Paul C., Thomas Dietz, Troy D. Abel, Gregory A. Guagnano, and Linda Kalof. 1999. “A
Value-Belief-Norm Theory of Support for Social Movements: The Case of
Environmentalism.” Human Ecology Review 6(2):81-96.
The Giving Institute. 2015. “Giving USA 2015 Press Release - The Giving Institute.”
Retrieved August 31, 2016, from
http://www.givinginstitute.org/?page=GUSA2015Release.
Trope, Yaacov, and Nira Liberman. 2003. “Temporal Construal.” Psychological Review
110(3): 403-410.
Trumbo, Garrett W., and Craig O’Keefe. 2005. “Intention to Conserve Water: Environmental
Values, Reasoned Action, and Information Effects Across Time.” Society and Natural
Resources, 18(6):573–585. https://doi.org/10.1080/08941920590948002.
U.S. Census Bureau. 2016. “Population Estimates, July 1, 2016, (V2016).” Retrieved March
15, 2017, from http://www.census.gov/quickfacts/.
U.S. Geological Survey. 2017. “Is the drought over?” Retrieved September 7, 2017, from
https://ca.water.usgs.gov/data/drought/.
Uzzell, David L. 2000. “The Psycho-Spatial Dimension of Global Environmental Problems.”
Journal of Environmental Psychology 20(4):307-318.
Weinberg, Jill, Jeremy Freese, and David McElhattan. 2014. “Comparing Data Characteristics
and Results of an Online Factorial Survey Between a Population-Based and a
Crowdsource-Recruited Sample.” Sociological Science 1:292–310.
https://doi.org/10.15195/v1.a19.
Wiepking, Pamala. 2010. “Democrats Support International Relief and the Upper Class Donates
to Art? How Opportunity, Incentives and Confidence Affect Donations to Different
Types of Charitable Organizations.” Social Science Research 39(6):1073–1087.
https://doi.org/10.1016/j.ssresearch.2010.06.005.
Wiepking, Pamala, and Rene Bekkers. 2012. “Who Gives? A Literature Review of Predictors
of Charitable Giving. Part Two: Gender, Family Composition and Income.” Voluntary
Sector Review 3(2):217–245.

98

Willis, Rachelle M., Rodney Stewart, Damian P. Giurco, Mohammad Reza Talebpour, and
Alireza Mousavinejad. 2013. “End Use Water Consumption in Households: Impact of
Socio-Demographic Factors and Efficient Devices.” Journal of Cleaner Production 60,
107–115. https://doi.org/10.1016/j.jclepro.2011.08.006.

99

CHAPTER 4: MEASURING PRO-ENVIRONMENTAL BEHAVIOR
Introduction
Scholars across disciplines have studied pro-environmental behaviors—actions that
affect, or have the intention to affect, the environment in a “friendly” way—for five decades,
gaining more nuanced understanding of the factors that contribute to environmental decisionmaking. We have studied the relationships between lifestyles and water or energy allocation;
participation in and opposition to the environmental movement; and efforts to avoid pollution or
other technological hazards (i.e. environmental racism-related behaviors). Scholars also consider
resource conservation behaviors, consumption practices, recycling, transportation choices, and
technology adoption (e.g., Attari et al. 2010; Dietz 2014; Guagnano, Stern, and Dietz 1995; Steg
and Vlek 1997; Stern et al. 2010).
The behaviors that favorably impact the quality or availability of critical natural resources
and services occur at a range of scales. Those at the individual level show importance to
sustaining the availability and quality of energy (e.g., Poortinga, Steg, and Vlek 2004), water
(e.g., Dolnicar, Hurlimann, and Grün 2012), and ecosystems (e.g., Harshaw, Sheppard, and
Lewis 2007). Scholars also suggest that individual behaviors, specifically those in the home,
could yield savings approaching 48% of current residential energy use in the EU (Fonseca et al.
2009) or 31% of U.S. CO2 emissions (Dietz et al. 2009).
As we study these pro-environmental behaviors as performed by individuals, we have
increased our measurement and analytical sophistication. Initial studies of littering, for example,
used simple newspaper counts and descriptive statistics in the 1970s. Today, we calculate the
most impactful behaviors, deliver surveys with response options that accommodate respondent
explanations of their self-reported behaviors, and use causal modeling to compare explanatory

100

models. And while analysis is assisted by concurrent advances in statistical mathematics and
software development, data is improved through enhanced understanding of question
formulation, item choice, and response options. In this paper, I report on an analysis of a sample
of the empirical pro-environmental behavior (PEB) literature with special attention to how we
measure these behaviors and the theoretical implications of those choices. My primary analytical
questions while conducting this review were (1) Are there identifiable patterns to how we are
conceptualizing and studying these pro-environmental behaviors? (2) When we are interested in
measuring pro-environmental behavior, does the literature show a difference in predictor
performance if we choose a specific, representative behavior versus using a behavioral scale as
the outcome variable? And (3) If there is a difference, how do the predictors perform
differently?
To answer these questions, I first describe and discuss the patterns visible in theoretical
frame, question stem, and response option choices within this sample. Then, to see whether
predictor performance varies by outcome variable (i.e. general scales versus items about one
behavior type), I narrow the sample to those models using either the Norm Activation
Model/Values-Beliefs-Norms model, or the Theory of Planned Behavior model and identify
significant predictors and their performance patterns. Finally, I suggest avenues for future
measurement improvements.

Sampling process
I began this examination with four recent, well-cited reviews by senior researchers of
pro-environmental behavior (Dietz, 2014; Dietz, Fitzgerald, and Shwom, 2005; Gifford and
Nilsson, 2014; Steg and Vlek, 2009). I extracted all references clearly relevant to individual or

101

household-level PEBs5. I excluded review papers, theoretical pieces, and methodological articles
as not empirical; meta-analyses in favor of the original research; studies with children as
subjects; and studies where the outcome was an attitude rather than a behavior or behavioral
intention.
These four initial articles yielded approximately 400 unique references. I further
narrowed those 400 papers by deriving the average number of citations per year since their
publication. I used Google Scholar’s citation count for total citations and divided that number by
years since publication date. I gathered citation numbers and calculated the citations per year in
December 2016. If a piece received ten or more citations per year on average, I included it in my
analysis. This narrowed my sample to 150 articles and chapters. Further review revealed that
fifty-one additional pieces belonged to one of my excluded categories, yielding a final sample of
ninety-nine articles and chapters (see Appendix A for full listing).
In reviewing these pieces6, I focused upon models rather than papers. Many include
more than one experiment or investigation or outcome variable. For example, Kaiser and
Gutscher (2003) included six models of specific behaviors as well as a model examining a
general PEB scale. Thus, one paper contributed seven models. Within these ninety-nine articles,
there were 182 models of pro-environmental behavior.

5

I include household-level behaviors in this discussion of individual behaviors because surveys are generally
completed by a representative individual within a home rather than reflecting the experiences of all household
members. In this way, these surveys are more correctly considered measures of individuals.
6
	I coded publication year, number of citations, country of all authors’ affiliation, theories used, type of study
design, sample location, sample design, sample size, unit of analysis, analytical methods, outcome type,
outcome behavior, whether the outcome measure is a single item or composite, and predictor variables. For
behaviors used in NAM, VBN, and TPB models, I also coded question stems, items used, and response
options.	

102

Figure 10: Literature review selection process

Sample description
The ninety-nine reviewed works span the decades from 1975 until 2012, with the most
productive decades being the 1990s (forty-five articles) and the 2000s (thirty-seven articles).
Within this sample, there are 182 PEB models. Forty-two (22%) of the models address
recycling, thirty-two (17%) focus on transportation choices, and twenty-nine (15%) engage with
“general pro-environmental behavior.” Another twenty-nine (15%) address consumer behaviors.
Twenty-four models (13%) consider energy saving behaviors, eighteen (9%) focus upon littering
or pollution-related behavior, seven (4%) model water conservation, one model examines
planting native vegetation while another one focuses upon composting.
U.S. scholars produced 43% of the 182 models, with the Netherlands the next largest
source at 15%. More than 25% of the U.S.-produced models focused upon recycling, with
consumer behaviors, “pro-environmental behavior,” and littering/pollution each accounting for
around 20% of those U.S. models. Conversely, scholars in the Netherlands focused their work

103

predominantly upon transportation (42% of their twenty-nine models) and energy savings (28%).
Other countries represented in this sample are the UK, Canada, France, Australia, Germany,
Switzerland, and Spain.
Surveys are the most frequently used data collection means in this sample with selfreported behaviors the most prevalent outcome type. Of the 182 models, 124 use a self-reported
behavior measure for the outcome and twenty used a directly observed behavior. The remaining
models used behavioral intentions as the outcome measure. The most-used analytic method is
multiple regression (eighty-five of the 182 models), with structural equation modeling the next
most prevalent. The majority of studies in this sample are atheoretical (seventy-six of 182).
When a theory is tested, it is typically the Theory of Planned Behavior (TPB), Schwartz’s Norm
Activation Model (NAM), or a combination of these two7. The TPB was used in thirty-nine of
the 182 models, NAM in another thirty-nine, and Values-Beliefs-Norms (VBN) in sixteen
models. Almost all VBN-based models were produced in the United States with Schwartz
values the most used values measure. Overall, there is very limited variable replication in the
sample except within articles by the same author(s). Similar models are tested, as mandated by
authors’ theoretical choices, but the tested measures are rarely replication.

7

TPB is a heavily researched, well-supported framework for predicting behavioral intentions. It postulates that
attitude toward a behavior, perceived behavioral control, and subjective norms are the most important predictors of
behavioral intentions. Often, scholars append other factors to TPB to test their relative efficacy (Ajzen 1991; Smith
and McSweeney 2007). VBN is an extension of Schwartz’s model of pro-social behavior, the Norm Activation
Model (Schwartz 1977). VBN suggests that values lead to awareness of consequences of a situation and ascription
of responsibility to take some action, which then lead to activation of a personal norm to behave in a certain way
(Stern and Dietz 1994; Stern 2000). VBN has been used to good effect in environmental and pro-social studies for
over twenty years with scholars typically appending new factors onto the model. Recently, scholars have also begun
testing TPB and VBN together (Kaiser et al 2005; Bamberg and Möser 2007). In general, the two models seem to
work well with VBN factors more distant from behavioral intentions than are the attitudes and norms used in the
TPB model.

104

Measuring Individual Pro-Environmental Behaviors
We can draw conclusions about how we are conceptualizing these pro-environmental
behaviors from examining our measurement instruments. To do this, I reviewed each model for
items chosen, question stems, response options, and theories used. Question stems, available
responses, and theoretical frames were largely consistent across behavior types (recycling,
energy conservation, consumer behaviors, and transportation).
Recycling items and question stems are straight-forward, asking whether or not a
respondent performs recycling behavior, “Do you…” or “How likely are you to…”. Variation
occurs in materials indicated (e.g., newspaper, glass, or composting) and location of recycling
activity (e.g., curbside or at a recycling center). Asking respondents questions in these ways
have specific consequences. “Do you...?” implies current behavior, yet is not specific. This
increases the likelihood of a socially desirable response as the respondent may define “current”
in a broader way than the researcher intends. Likewise, asking “How likely are you to...” leaves
significant room for a socially desirable answer or an answer that does not fully encompass
situational constraints. In both cases, these question stems do not create conditions for accurate
self-reporting by the respondent.
Twenty of the forty-two recycling models are atheoretical, but among those employing
specific theory there is a wide range of traditional and novel models used. NAM/VBN is the
most prevalent with TPB a close second; however, there are also studies using identity theory,
self-determination theory, and Rogers’ protection motivation theory. Theoretical models used
can tell us about the dominant conceptualizations of a pro-environmental behavior (PEB).
NAM/VBN are models of pro-social/altruistic behavior while TPB assumes a goal-focused
process balancing social norms, perceived behavioral control, and attitudes toward the behavior.

105

Understanding these frames gives insight into how researchers conceptualize the behaviors. In
the case of recycling, we see from the majority choice of NAM that recycling is largely an
altruistic behavior to U.S. investigators. If they use TPB or another theory it is in conjunction
with the NAM model. Reviewed research conducted in other countries use either TPB –
suggesting a conceptualization of recycling as a goal-oriented action – or a less well-known
theory of personal benefit (e.g., self-determination theory).
Energy conservation items range from the very general, self-reported “participate in an
energy program with your utility” to directly observed changes in energy use. Routine items
such as using energy saving light bulbs are also present. This behavior type is the most directly
observed due to engagement with energy utilities and their monitoring capabilities. Question
stems here are “Do you…”, “Have you…”, or “How likely are you to…”. As with recycling,
these question stems either leave room for respondents to interpret the implied timeframe to their
own benefit or they measure an attitude toward the specific energy conservation behavior.
Twelve of the twenty-four energy conservation models are atheoretical, with the
remaining models using NAM or TPB in an even split. Thus, energy conservation studies are
split in their conceptualization between pro-social/altruistic and goal-oriented choice. Yet in
these models, we see personal norms, values, and external constraints exerting the most influence
suggesting that the NAM/VBN model would be a better choice for this category of behavior.
Consumer behaviors include a wide range of items. We see information searches,
purchasing based upon attributes such as “green”-ness or “eco-friendly”-ness, and more specific
items such as “buying organic food” and “using unbleached paper”. Question stems include “Do
you…”, “How often do you…”, and “How likely are you to…”. The “Do you...” and “How

106

likely are you to...” stems remain problematic; however, the “How often do you...” formulation
ensures that both researcher and respondent are using a similar time-scale.
Fourteen of the twenty-nine models in this category are atheoretical with the majority of
the remaining using NAM/VBN. There are no directly observed models of consumer behavior
in this sample. The dominance of the NAM/VBN model in these investigations suggests that
scholars are thinking about resource expenditures as pro-social activities rather than economic
substitution dilemmas, an equally viable possibility.
Transportation items are split between use of alternative transportation and habits when
operating a personal vehicle (e.g., limiting speed when on the freeway). As with consumer
behaviors, question stems in surveys include “Do you…”, “How likely are you to…”, and “How
often do you…”. Nineteen of the thirty-two transportation models are atheoretical. Ten use TPB
either alone or in tandem with NAM. The majority of studies use TPB to investigate this topic,
indicating a lean toward goal-driven conceptualization. It is also worth noting that only three of
the thirty-two models in this sample come from U.S. researchers, perhaps an indicator of cultural
influence on research topic choice.
Response option choice and question stem choice have not changed much over the past
decades. For all studies, response options correspond to the question stems, varying between the
dominant binary choice (yes/no) and a 5, 6, or 7-point Likert-type scale ranging from “rarely” to
“frequently”. Behavioral intention outcomes are measured with the same Likert-type scales but
with labels gauging likelihood (e.g., “not likely” = 1 to “very likely” = 7). Also of note, response
options used do not accommodate “cannot” answers. New work that includes behavioral
plasticity - the extent to which perceived difficulty to act constrains behavioral responses across

107

individuals, behaviors, and contexts – in response option formulation is, however, providing
examples of how this may be accomplished (e.g., Allen, Dietz, and McCright, 2015).
Response options for question stems that ask “How often...” are split between the more
subjective “rarely” to “always” scale and specifying an arbitrary time period (i.e. “How often in
the past 5 years…”).
Overall, a question stem such as “How often in the past (X period)...” appears to be more
optimal for investigating individual behavior types. This avoids timeframe disconnect between
researcher and respondent, avoids slipping into attitudinal measurement, and allows for a more
tailored timeframe for each item. Shorter timeframes may be more appropriate for gauging
strength of habits or how routinized a behavior is for a respondent while a five or more year
timeframe may be appropriate for considering capital improvements and larger investments such
as cars. Differing timescales according to the underlying research question – how routine is a
consumer behavior or how durable is a behavioral commitment to buying a fuel-efficient car –
would allow us to understand a respondent’s responses with greater precision and mitigate some
social desirability influence.
Among reviewed response options, even numbered Likert-type scales have the most
theoretical support (e.g., Croasmun and Ostrom 2011) and facilitate inclusion of behavioral
plasticity. I discuss the extensive use of NAM/VBN and TPB models below.

Measuring General Pro-Environmental Behavior with Scales
I now turn to examining scales of “general pro-environmental behavior”. Twenty-nine
models used a general pro-environmental scale as the outcome measure with six of these asking
for behavioral intentions.

108

The scales range from three items to forty-five items in length. Most scales include
multiple items measuring the same behavior in different ways, essentially creating subscales.
Included behavior types are water conservation (e.g., shower vs bath), waste avoidance (e.g.,
saving plastic bags), transportation (e.g., driving at lower speeds on the freeway), recycling (e.g.,
participating in curbside recycling programs), consumer behavior (e.g., buying organic food),
political actions (e.g., voting based on pro-environmental stance), hazardous substance/pollution
avoidance (e.g., not using chemical pesticides), energy conservation (e.g., cutting back on
heating and air conditioning), and self-education about environmental issues. Not all scales
include the full range of these behaviors, however.
As with the specific PEBs, question stems for these scales are either frequency-based
(e.g., “How often have you…” or “In the past twelve months…”) or simple reports (e.g., “I…”)
and use 5-point to 7-point Likert type response options, or simple binary options, respectively.
Seventeen of the twenty-nine models in this category are atheoretical. Of the remaining, five use
NAM/VBN and the rest are split between TPB and Geller’s active caring hypothesis (Geller
1995).
Examining how we have conceptualized PEBs in scale creation, we generally elect not to
impose a theoretical frame. Altruism (including active caring) seems to be a more common view
of PEB motivation. Where scholars have provided measures of dimensionality for their scales,
most report uni-dimensionality regardless of their scale’s composition. The mix of behaviors
included in these scales indicates that behavior types are considered roughly equivalent with
regard to pro-environmentalism and accessibility. The General Ecological Behavior scale
(Kaiser 1998) of thirty-eight items, for example, is reported by Kaiser and colleagues as unidimensional despite including the entire range of PEB categories discussed above.

109

Comparing Specific and General Behavior Measures
One of the goals of this paper is to examine whether the literature show a difference in
predictor performance if we choose a specific, representative behavior versus using a behavioral
scale as the outcome variable. While we assume that using scales in survey research improves
the likelihood of an accurate self-report and better accommodates a respondent’s situational
constraints, it is also more time and attention consuming for the respondent and potentially more
expensive for researchers. Thus, if the literature indicate that comparable results may be had
from using a specific behavior instead of a mix, we may be able to simplify our data collection.
For this comparison, I selected studies that use directly observed or self-reported
behaviors8 and either the NAM, VBN, or TPB model (thus, 29 models of specific behaviors and
6 models of general PEB scales). I then identified the significant predictors in each empirical
model. I observed the patterns for specific behavior types (recycling, energy conservation,
consumer behaviors9, and transportation) and for those studies using the general PEB scales.
Referring to Table 16, recycling in this sample is typically conceptualized as an altruistic
behavior and is modelled in seven of the ten instances with NAM. When NAM is used by itself
or in tandem with the TPB, personal norms always appear as significant predictors. When TPB
is used to predict recycling behavior, including mixed models with NAM, perceived behavioral
control (PBC) is significant in four of the five cases. Behavioral intentions are significant only
when NAM items are not examined or controlled. Past behavior also shows significance when it
is included.
8

Previous research indicates that behavioral intentions and actual behaviors have differing prediction patterns,
largely because actual behavior must incorporate a range of constraints in order to be accomplished. In this way,
intentions can be considered indicators of support rather than commitment to act or an action. In the rest of this
paper, I focus on behavior (i.e., directly observed and self-reported behaviors). This has the effect of limiting the
models analyzed.
9
Political behaviors are almost exclusively measured as intentions, except within the general scales, so I do not
report on this category.

110

Energy conservation behavior is modeled in this subsample always by NAM or its variant
VBN, indicating a typical conceptualization as an altruistic behavior. Personal norms are
significant in three of the five models here. Values appear as significant predictors when tested.
External constraints almost always show as significant predictors with this topic. Consumer
behaviors are also measured largely by the NAM or VBN. Personal norms are always
significant when included. Environmental concern is significant in two models, and PBC is
significant when included. Additionally, transportation models behave largely according to the
theory chosen, with behavioral intentions and personal norms significant in TPB and
NAM/VBN models respectively.
Overall, we see that patterns in prediction of specific behaviors are clear; variables
modeled by theory as closest to the behavior of interest (i.e. personal norms for NAM/VBN and
behavioral intentions for TPB) perform as expected. Specific behaviors require predictors to be
equally specific, so personal norms appear as statistically significant when they are about the
behavior of interest rather than a general pro-environmental norm. Similarly, past behaviors are
significant when they are specific. Demographics show the most influence in energy
conservation models, but are not significant when psychosocial factors are introduced.
Table 17 displays information for models using the PEB scales. We see that these
models are equally split between NAM and TPB and significant predictors reflect the expected
outcomes: Behavioral intentions are exclusively significant in TPB models while values and
norms are significant for NAM/VBN models. We also see that psychosocial factors remain the
dominant predictors. Demographics do not show statistical significance in these models, either.

111

Table 16: Predictor performance in selected models of specific beh aviors

112

Table 17: Predictor performance in selected models using pro-environmental behavior scales

These results are not surprising and confirm the value of these approaches to
understanding specific PEBs. They also suggest that these behaviors types are all motivated
similarly, being predictable with the same models with the same predictor patterns. This points
to these PEBs, and the scales of these PEBs, being potentially interchangeable as predictors of
general environmental activity and concern.

Consequences, Suggestions, and Conclusions
Taken in total, this review offers several insights and related implications of which we
should be cognizant and which lead to three further future articles. First, in choosing atheoretical
models, we decline to hypothesize why selected predictors operate in specific ways against
specific outcomes. Often, we are solution-focused, more interested in testing whether or to what
extent a specific variable or intervention influences a behavior; however, these are still
opportunities to add to our collective theorizing about such questions as categorizing PEBs,

113

common predictors across types of PEBs, and the underlying psychosocial reasons why certain
predictors perform as they do.
When we do use theory to guide our investigations, we use two well-known frames –
NAM and TPB – and receive very consistent results. These models work well. Perceived
behavioral control and behavioral intentions and personal norms are important in environmental
decision-making. We should now turn more attention to those factors that better explain those
very consistent predictors. What else predicts respondents’ perceived behavioral control in
cases of recycling? What develops a respondent’s personal norms around a specific behavior?
This might require a move to using less well-known theories in tandem with TPB. For instance,
we can imagine that theories about identity would explain a portion of a respondent’s personal
norms, and we could consider that habit and goal-framing affect perceived behavioral control.
Allen and Marquart-Pyatt (in press) also provide an example of ways to consider external
constraints and their influence on perceived behavioral control. If these ideas show significance,
we would uncover more detailed and intervention-accessible variables for predicting our
behaviors of interest.
Relatedly, in using TPB and NAM/VBN we have established the importance of both
altruistic and utility/goal motives to the broad range of PEBs, but have potentially neglected
other influences. For example, Steg and colleagues have been advocating for consideration of
hedonism as both a motivating value and a behavioral frame (L. Steg, Perlaviciute, van der
Werff, and Lurvink, 2014; Lindenberg, S and L. Steg 2007) and habit is a useful though underexplored (in this sample) influence.
As we consider improvements to our measurement instruments, in addition to the
suggestions discussed above, we should consider that choosing items that are easy for many

114

respondents in North America and Western Europe (e.g., recycling glass, buying organic) limits
the benefits of some studies. If, instead, we update items to more difficult behaviors we may
receive better indications of respondents’ levels of concern as indicated by the amount of effort
they are willing to expend to behave pro-environmentally in the face of constraints. With the
absence of these constraints in our models, Attitude-Behavior-Constraints theory10 (Guagnano,
Stern and Dietz, 1995) suggests that we are measuring only half of the behavioral equation,
especially given our demonstrated preference for attitude-driven models (i.e., NAM/VBN and
TPB). Perhaps a better question is how far attitudes and their psychosocial antecedents can drive
more difficult behaviors in the presence of varying types of constraints. Measuring actual or
self-reported behavior means we are studying environmental decision-making in the space
between intention and action, where external constraints can have their strongest effects. Of
course, we also need to know whether measuring constraints is too hard? If not, whether there is
there a minimal set of constraints we should include? And how we might measure constraint
difficulty? We are also interested in knowing whether causal modeling could lend more
explanation to the study of environmental decision-making. Additionally, better items for
gauging a respondent’s dedication to pro-environmental behavior among a range of constraints
might be comfort-related energy conservation items, specialty food purchasing, hazard/pollution
avoidance, and alternative transportation choices.
Studying more difficult or less common yet more environmentally impactful behaviors
could provide useful insights into behaviors that research (e.g., Inskeep and Attari 2014; Dietz et

10

The Attitudes-Behavior-Constraints model considers the interaction between internal behavioral factors such as
attitudes and knowledge and external constraints that ease or make more difficult the behavior of interest (Guagnano
et al., 1995). Guagnano and colleagues theorize that in instances where there are strong constraints on behavior
internal factors will not be significant, whereas in cases of weak constraints, those internal factors will be significant
predictors of the behavior.

	
115

al 2007; Gardner and Stern 2008) tells us more individuals should be practicing. Our
measurement of PEBs, in both general and specific formats, could benefit from greater attention
to these impact-oriented items. Stern (2000:408) offered a useful definition of impact in his
definition of environmentally significant behavior. He suggests that environmentally significant
behavior is a behavior that “changes the availability of materials or energy from the environment
or alters the structure and dynamics of ecosystems or the biosphere itself.” Scholars have not
explicitly adopted this definition, remaining instead with the “eco-friendly” focus of proenvironmental behavior, but Stern’s focus upon impact over intention for behavioral
measurement seems very important. Several studies exist that demonstrate how we might
calculate impact. Three excellent examples are Dietz, Gardner, Gilligan, Stern, and
Vandenbergh (2009); Gardner and Stern (2008); and Inskeep and Attari (2014), which calculate
or discuss the most impactful energy and water conservation measures individuals can take in
their daily lives. These items also have the benefit of being up-to-date. With changes in
electronics, product supply chains, recycling capabilities, and transportation options, some items
may be obsolete as representing impactful pro-environmental behaviors.
Further, as discussed, Stern means any behavior that increases or decreases the naturally
occurring levels of any material, being, or energy; or that changes a natural system at any level,
should be considered “environmentally significant” and thus of scholarly and policy interest.
Stern’s definition implies that the significance of such behaviors is assigned by expertise and not
by lay-people. It privileges viewing nature as a resource depot over a waste sink.
When we add his exemplar categories to our considerations, more questions emerge.
Stern (2000:409) sets out four types of ESBs: “activism,” “non-activist public sphere” behaviors,
“private sphere environmentalism,” and “other” in which he points to influencing organizations.

116

With these categories, he muddies his definition. The definition and his own discussion
emphasize impact over intent, yet all of these categories could be considered intent-focused.
Activism success is dependent upon accumulation of political or economic or social leverage.
Petitions and policy support, examples of “non-activist public sphere” behaviors, are even further
removed from direct impact. Stern places these actions in his list of ESBs because they
presumably indirectly impact policy that then indirectly impacts the resources or dynamics of an
environmental system. Yet, the act of signing one’s name to a petition may or may not have any
effect. It may instead be merely a signal of concern or intent. Private sphere environmentalism
depends upon accurate knowledge of the impacts of various behaviors to be impact-oriented,
which most individuals lack, and organizational impact is very similar to activism in its reliance
upon some significant leverage accumulation for impact. Clearly behaviors that directly affect
resource availability and ecosystem dynamics should be considered significant, but how to
classify those with indirect impacts is less clear.
With this definition, Stern intends to emphasize behaviors that have a detectable impact
on natural systems and resources in a concrete, biophysical way. His categories blur this line.
Thus, I conclude that Stern wants to articulate behaviors that are directly engaged with the
biophysical world AND those that create the social contexts in which those direct engagements
are facilitated or constrained. If this is true, it requires a reformulation of the definition and
example categories. However, within this sample of the literature, there was very limited factor
analysis to support in-depth discussion of dimensionality. Nor was there real empirical evidence
within this sample for differentiating environmentally significant behaviors into types beyond the
indoor/outdoor and routine/once-and-done that recent work explores.

117

The results of this investigation suggest three further avenues of research and harken
back to my initial comments about understanding and systematizing our ESB literature. An
article addressing the dimensionality and typology of ESBs, an article on measurement and
analytic techniques, and a paper on possible theoretical approaches to understanding ESBs are all
suggested here. Each will need some accompanying empirical data to demonstrate the derivation
of effects of these different perspectives.

118

APPENDIX

119

Articles Reviewed

Aarts, Henk, and A. P. Dijksterhuis. 2000. “The automatic activation of goal-directed
behaviour: the case of travel habit.” Journal of Environmental Psychology 20: 75–82.
Abrahamse, Wojke and Linda Steg. 2011. “Factors Related to Household Energy Use and
Intention to Reduce It: The Role of Psychological and Socio-Demographic
Variables.” Human Ecology Review 18: 30–40.
Abrahamse, Wokje, Linda Steg, Robert Gifford, and Charles Vlek. 2009. “Factors Influencing
Car Use for Commuting and the Intention to Reduce It: A Question of Self-Interest or
Morality?” Transportation Research Part F: Traffic Psychology and Behaviour
12(4):317-324.
Abrahamse, Wojke, Linda Steg, Charles Vlek, and Talib Rothengatter. 2007. “The Effect of
Tailored Information, Goal Setting and Feedback on Household Energy Use, Energy
Related Behaviors and Behavioral Determinants.” Journal of Environmental Psychology,
27: 265–276.
Allen, James, and Jennifer L. Ferrand. 1999. “Environmental Locus of Control, Sympathy, and
Pro-Environmental Behaviour: A Test Of Geller's Actively Caring Hypothesis.”
Environment and Behaviour 31:338–353.
Arnocky, Steven, Mirella Stroink, and Teresa DeCicco. 2007. “Self-Construal Predicts
Environmental Concern, Cooperation, and Conservation.” Journal of Environmental
Psychology 27:255–264. doi:10.1016/j.jenvp.2007.06.005.
Bagozzi, Richard P., and Pratibha Dabholkar. 1994. “Consumer Recycling Goals and Their
Effect on Decisions o Recycle: A Mean-End Chain Analysis.” Psychology and
Marketing 11: 313-340.
Baldassare, Mark and Cheryl Katz. 1992. “The Personal Threat of Environmental Problems As
Predictor of Environmental Practices.” Environment and Behavior 24:602–616.
Balderjahn, Ingo. 1988. “Personality Variables and Environmental Attitudes As Predictors of
Ecologically Responsible Consumption Patterns.” Journal of Business Research 17:51–
56.
Bamberg, Sebastian and Peter Schmidt. 2003. “Incentives, Morality or Habit? Predicting
Students’ Car Use for University Routes With the Models of Ajzen, Schwartz and
Triandis.” Environment and Behavior 35:264–285.

120

Becker, Lawrence J. 1978. “Joint Effect of Feedback and Goal Setting on Performance: A Field
Study of Residential Energy Conservation.” Journal of Applied Psychology 63:428–433.
Berger, Ida E. 1997. “The Demographics of Recycling and The Structure of Environmental
Behavior.” Environment and Behavior 29:515-531.
Berger, Ida. E. and Ruth M. Corbin. 1992. “Perceived Consumer Effectiveness and Faith In
Others as Moderators of Environmentally Responsible Behaviors.” Journal of Public
Policy and Marketing 11:79-89.
Black, J Stanley, Paul C. Stern, Julie T. Elworth. 1985. “Personal and Contextual Influences on
Household Energy Adaptations.” Journal of Applied Psychology 70(1): 3-13.
Boldero, Jennifer. 1995. “The Prediction of Household Recycling of Newspapers: The Role of
Attitudes, Intentions, and Situational Factors.” Journal of Applied Social Psychology
25:440–462.
Brandon, A. Lewis. 1999. “Reducing Household Energy Consumption: A Qualitative and
Quantitative Field Study.” Journal of Environmental Psychology 19:75–85.
Bratt, Christopher. 1999. “The Impact of Norms and Assumed Consequences on Recycling
Behaviour.” Environment and Behaviour 31:630–656.
Chen, Mei-Fang, and Pei-Ju Tung. 2010. “The Moderating Effect of Perceived Lack of
Facilities On Consumers' Recycling Intentions.” Environment and Behavior 42:824–844.
Cialdini, Robert B., Raymond Reno, and Carl Kallgren. 1990. “A Focus Theory of Normative
Conduct: Recycling The Concept of Norms to Reduce Littering in Public
Places.” Journal of Personality and Social Psychology 58:1015–1026.
Clark Christopher, Matthew J. Kotchen, Michael R. Moore. 2003. “Internal and External
Influences on Proenvironmental Behavior: Participation in a Green Electricity Program.”
Journal of Environmental Psychology 23:237-46.
Corraliza, Jose A., and Jaime Berenguer. 2000. “Environmental Values, Beliefs and Actions.”
Environment and Behavior 32:832–848.
Davies, Janette, Gordon R. Foxall, John Pallister. 2002. “Beyond The Intention–Behaviour
Mythology: An Integrated Model of Recycling.” Marketing Theory 2:29–113.
Davis, Jody. L., Jeffrey D. Green, and Allison Reed. 2009. “Interdependence with The
Environment: Commitment, Interconnectedness, and Environmental Behavior.” Journal
of Environmental Psychology 29:173–180.
Derksen, Linda and John Gartrell. 1993. “The Social Context of Recycling.” American
Sociological Review 58:434-442.

121

Ebreo, Angela, James Hershey, and Joanne Vining. 1999. “Reducing Solid Waste. Linking
Recycling to Environmentally Responsible Consumerism.” Environment and Behavior
31:107-135.
Ellen, Pam S., Joshua Lyle Wiener, and Cathy Cobb-Walgren. 1991. “The Role of Perceived
Consumer Effectiveness in Motivating Environmentally Conscious Behaviors.” Journal
of Public Policy and Marketing 10:102-117.
Fujii, Satoshi, and Ryuichi Kitamura. 2004. “What Does a One-Month Free Bus Ticket Do to
Habitual Drivers? An Experimental Analysis of Habit and Attitude Change.”
Transportation 30:81–95.
Fujii, Satoshi, Tommy Garling, and Ryuichi Kitamura. 2001. “Changes In Drivers’ Perceptions
And Use of Public Transport During a Freeway Closure: Effects of Temporary Structural
Change On Cooperation In a Real-Life Social Dilemma.” Environment and Behavior
33:796–808.
Gamba, Raymond J. and Stuart Oskamp. 1994. “Factors Influencing Community Residents’
Participation In Commingled Curbside Recycling Programs.” Environment and Behavior
26:587-612.
Garling, Tommy, Satoshi Fujii, Anita Garling, and Cecilia Jakobsson. 2003. “Moderating
Effects of Social Value Orientation on Determinants of Proevenvironmental Intention.”
Journal of Environmental Psychology, 23:1–9.
Gatersleben, Birgitta, Linda Steg, and Charles Vlek. 2002. “The Measurement and
Determinants of Environmentally Significant Consumer Behaviour.” Environment and
Behavior 34(3):335-362.
Granzin, Kent L. and Janeen Olsen. 1991. “Characterizing Participants in Activities Protecting
The Environment: A Focus on Donating, Recycling, and Conservation Behaviors.”
Journal of Public Policy and Marketing 10:1-27.
Gregory, Gary, and Michael Di Leo. 2003. “Repeated Behaviour and Environmental
Psychology: The Role of Personal Involvement and Habit Formation in Explaining Water
Consumption.” Journal of Applied Social Psychology 33:1261–1296.
Grob, Alexander. 1995. “A Structural Model of Environmental Attitudes and Behaviour.”
Journal of Environmental Psychology 15:209–220.
Grunert, Suzanne. C., and Hans Jorn Juhl. 1995. “Values, Environmental Attitudes and Buying
of Organic Foods.” Journal of Economic Psychology 16:39-62.

122

Guagnano, Gregory, Paul C. Stern, and Thomas Dietz. 1995. “Influences on Attitude-Behavior
Relationships: A Natural Experiment with Curbside Recycling.” Environment and
Behavior 27(5):699-718.
Harland, Paul, Henk Staats, and Henk Wilke. 1999. “Explaining Pro Environmental Behavior
By Personal Norms and The Theory of Planned Behavior.” Journal of Applied Social
Psychology 29:2505–2528.
Heath, Yuko, and Robert Gifford. 2002. “Extending the Theory of Planned Behaviour:
Predicting The Use of Public Transportation.” Journal of Applied Social Psychology
32:2154–2185.
Holbert, R. Lance, Nojin Kwak, and Dhavan Shah. 2003. “Environmental Concern, Patterns of
Television Viewing, and Pro-Environmental Behaviors: Integrating Models of Media
Consumption and Effects.” Journal of Broadcasting and Electronic Media 47:177–196.
Hopper, Joseph R., and Joyce McCarl Nielsen. 1991. “Recycling as Altruistic Behavior:
Normative and Behavioral Strategies to Expand Participation In a Community Recycling
Program.” Environment and Behavior 23:195–220.
Hunecke, Marcel, Anke Blobaum, Ellen Matthies, and Rainer Hoger. 2001. “Responsibility and
Environment: Ecological Norm Orientation and External Factors in The Domain of
Travel Mode Choice Behavior.” Environment and Behavior 33:830–852.
Joireman, Jeffrey A., Terell P. Lasane, Jennifer Bennet, Diana Richards, and Salm. Solaimani.
2001. “Integrating Social Value Orientation and The Consideration of Future
Consequences Within The Extended Norm Activation Model of Pro-Environmental
Behaviour.” British Journal of Social Psychology 40:133–155.
Kaiser, Florian G. 2006. “A Moral Extension of The Theory of Planned Behaviour: Norms and
Anticipated Feelings of Regret In Conservationism.” Personality and Individual
Differences 41:71–81.
Kaiser, Florian G., and Heinz Gutscher. 2003. “The Proposition of a General Version of The
Theory of Planned Behaviour (TPB): Predicting Ecological Behaviour.” Journal of
Applied Social Psychology 33:586–603.
Kaiser, Florian G., and Todd A. Shimoda. 1999. “Responsibility as a Predictor of Ecological
Behavior.” Journal of Environmental Psychology 19:243–253.
Kaiser, Florian G., Michael Ranney, Terry Hartig, and Peter A. Bowler. 1999. “Ecological
Behavior, Environmental Attitude, and Feelings of Responsibility for The
Environment.” European Psychologist 4:59–74.
Kaiser, Florian G., Sybille Wölfing, and Urs Fuhrer. 1999. “Environmental Attitude and
Ecological Behaviour.” Journal of Environmental Psychology 19:1–19.

123

Karp, David Gutierrez. 1996. “Values and Their Effect on Pro-Environmental
Behavior.” Environment and Behavior 28:111–133.
Klöckner Christian, and Ellen Matthies. 2004. “How Habits Interfere with Norm-Directed
Behaviour: A Normative Decision-Making Model for Travel Mode Choice.” Journal of
Environmental Psychology 24:319-27.
Knussen, Christina, Fred Yule, Julie MacKenzie, and Mark Wells. 2004. “An Analysis of
Interventions to Recycle Household Waste: The Roles of Past Behaviour, Perceived
Habit, and Perceived Lack of Facilities.” Journal of Environmental Psychology 24:237–
246.
Lynne, Gary, C. Franklin Casey, Alan Hodges, and Mohammed Rahmani. 1995. “Conservation
Technology Adoption Decisions and The Theory of Planned Behaviour.” Journal of
Economic Psychology, 16:581–598.
Mannetti, Lucia, Antonio Pierro, and Stefano Livi. 2004. “Recycling: Planned and SelfExpressive Behaviour.” Journal of Environmental Psychology 24:227–236.
McCalley, L.T., and Cees J. H. Midden. 2002. “Energy Conservation Through ProductIntegrated Feedback: The Roles of Goal-Setting and Social Orientation.” Journal of
Economic Psychology 23:589–603.
McCarty, John A. and L.J. Shrum. 1994. “The Recycling of Solid Wastes: Personal Values,
Value Orientations, and Attitudes About Recycling as Antecedents of Recycling
Behavior.” Journal of Business Research 30:53-62.
Minton, Ann, and Randall L. Rose. 1997. “The Efects of Environmental Concern on
Environmentally Friendly Consumer Behaviour: An Explanatory Study.” Journal of
Business Research 40:37–48.
Mobley, Catherine, Wade M. Vagias, and Sarah L. DeWard. 2010. “Exploring Additional
Determinants of Environmentally Responsible Behavior: The Influence of Environmental
Literature and Environmental Attitudes.” Environment and Behavior 42:420–447.
doi:10.1177/0013916508325002.
Nilsson, Maria, and Rikard Küller. 2000. “Travel Behaviour and Environmental Concern.”
Transportation Research Part D 5:211–234.
Nordlund, Annika M., and Jorgen Garvill. 2002. “Value Structures Behind Pro-Environmental
Behavior.” Environment and Behavior 34:740–756.
Oom do Valle, Patricia, Efigenio Rebello, Elizabeth Reis, Joao Menezes. 2005. “Combining
Behavioural Theories to Predict Recycling Involvement.” Environment and Behaviour
37:364–396.

124

Oreg, Shaul and Tally Katz-Gerro. 2006. “Predicting Proenvironmental Behavior CrossNationally Values, the Theory of Planned Behavior, and Value-Belief-Norm Theory.”
Environment and Behavior 38(4):462-483.
Osbaldiston, Richard, and Kennon Sheldon. 2003. “Promoting Internalized Motivation for
Environmentally Responsible Behavior: A Prospective Study of Environmental
Goals.” Journal of Environmental Psychology 23:349–357.
Oskamp, Stuart, Maura J. Harrington, Todd C. Edwards, Deborah L. Sherwood, Shawn M.
Okuda, and Deborah Swanson. 1991. “Factors Influencing Household Recycling
Behavior.” Environment and Behavior 23, 494–519.
Poortinga, Wouter, Linda Steg, and Charles Vlek. 2004. “Values, Environmental Concern, and
Environmental Behavior: A Study into Household Energy Use.” Environment and
Behavior 36(1): 70-93.
Raymond, Christopher M., Gregory Brown, and Guy Robinson. 2011. “The Influence of Place
Attachment, and Moral and Normative Concerns on The Conservation of Native
Vegetation: A Test of Two Behavioural Models.” Journal of Environmental
Psychology 31:323–335.
Scannell, Leila, and Robert Gifford. 2010. “The Relations Between Natural and Civic Place
Attachment and Pro-Environmental Behavior.” Journal of Environmental
Psychology 30:289–297.
Schultz, P. Wesley, and Lynette C. Zelezny. 1998. “Values and Proenvironmental Behaviour. A
Five-Country Study.” Journal of Cross-Cultural Psychology 29:540–558.
Schultz, P.Wesley. 1999. “Changing Behavior With Normative Feedback In- Terventions: A
Field Experiment on Curbside Recycling.” Basic and Applied Social Psychology 21:2536.
Schwepker, Charles H., and T. Bettina Cornwell. 1991. “An Examination of Ecologically
Concerned Consumers and Their Intention to Purchase Ecologically Packaged
Products.” Journal of Public Policy and Marketing 10(2):77–101.
Scott, David, and Fern K. Willits. 1994. “Environmental Attitudes and Behavior: A
Pennsylvania Survey.” Environment and Behavior 26:239-260.
Staats, Henk, Paul Harland, Henk A.M. Wilke. 2004. “Effecting Durable Change: A Team
Approach to Improve Environmental Behaviour in The Household.” Environment and
Behaviour 36:341–367.
Steel, Brent S. 1996. “Thinking Globally and Acting Locally? Environmental Attitudes,
Behavior, and Activism.” Journal of Environment Management 47:27-36.

125

Steg, Linda. 2005. “Car Use: Lust and Must. Instrumental, Symbolic and Affective Motives for
Car Use.” Transportation Research A 39:147–162.
Stern, Paul C. 1999. “Information, Incentives, and Proenvironmental Consumer Behavior.”
Journal of Consumer Policy 22:461–478.
Stern, Paul C., and Thomas Dietz. 1994. “The Value Basis of Environmental Concern.”
Journal of Social Issues 50:65-84.
Stern, Paul C., Thomas Dietz, and Linda Kalof. 1993. “Value Orientations, Gender, and
Environmental Concern.” Environment and Behavior 25:322–348.
Stern, Paul C., Thomas Dietz, Linda Kalof, and Gregory A. Guagnano. 1995. “Values, Beliefs,
and Proenvironmental Action: Attitude Formation toward Emergent Attitude Objects.”
Journal of Applied Social Psychology 25:1611-1636.
Tabernero, Carmen, and Bernardo Hernández. 2011. “Self-Efficacy and Intrinsic Motivation
Guiding Environmental Behavior.” Environment and Behavior 43:658–675.
Tanner, Carmen. 1999. “Constraints on Environmental Behaviour.” Journal of Environmental
Psychology 19:145–1E7.
Tanner, Carmen, and Sybille Wölfing Kast. 2003. “Promoting Sustainable Consumption:
Determinants of Green Purchases by Swiss Consumers.” Psychology and Marketing,
20:883–902.
Tarrant, Michael A., and H. Ken Cordell. 1997. “The Effect of Respondent Characteristics on
General Environmental Attitude-Behavior Correspondence.” Environment and
Behavior 29:618–637.
Taylor, Shirley, and Peter Todd. 1995. “An Integrated Model of Waste Management Behavior.
A Test of Household Recycling and Composting Intentions.” Environment and Behavior
27:603–630.
Tertoolen, Gerard, Dik Van Kreveld, and Ben Verstraten. 1998. “Psychological Resistance
Against Attempts to Reduce Private Car Use.” Transportation Research A 32:171–181.
Thøgersen John, and Folke Ölander. 2003. “Spillover of Environment-Friendly Consumer
Behavior.” Journal of Environmental Psychology 23:225-36.
Thøgersen, John, and Alice Grønhøj. 2010. “Electricity Saving in Households—A Social
Cognitive Approach.” Energy Policy 38:7732–7743. doi:10.1016/j.enpol.2010.08.025.

126

Thøgersen, John, and Folke Ölander. 2002. “Human Values and The Emergence of a
Sustainable Consumption Pattern: A Panel Study.” Journal of Economic
Psychology 23:605–630.
Thøgersen, John. 1999. “The Ethical Consumer. Moral Norms and Packaging Choice.” Journal
of Consumer Policy 22:439–460.
Thøgersen, John. 2006. “Understanding Repetitive Travel Mode Choices in a Stable Context: A
Panel Study Approach.” Transportation Research Part A 40:621–638.
Tonglet, Michele, Paul S. Phillips, and Adam D. Read. 2004. “Using The Theory of Planned
Behaviour to Investigate The Determinants of Recycling Behaviour: A Case Study From
Brixworth, UK.” Resources, Conservation and Recycling 41:191–214.
Urien, Bertrand, and William Kilbourne. 2011. “Generativity and Self-Enhancement Values in
Eco-Friendly Behavioral Intentions and Environmentally Responsible Consumption
Behavior.” Psychology and Marketing 28:69–90. doi:10.1002/mar.20381.
Van Houwelingen, Jeannet H., and W. Fred Van Raaij. 1989. “The Effect of Goal-Setting and
Daily Electronic Feedback on In-Home Energy Use.” Journal of Consumer Research,
16:98–105.
Vaske Jerry J., and Maureen M. Donnelly. 1999. “A Value Attitude-Behavior Model Predicting
Wildland Preservation Voting Intentions.” Society and Natural Resources 12:523-37.
Verplanken, Bas, Henk Aarts, Ad Van Knippenberg, and Carina van Knippenberg. 1994.
“Attitude Versus General Habit: Antecedents of Travel Mode Choice.” Journal of
Applied Social Psychology 24:285–300.
Verplanken,Bas, Henk Aarts, Ad Van Knippenberg, and Anja Moonen. 1998. “Habit Versus
Planned Behaviour: A Field Experiment.” British Journal of Social Psychology 37:111–
128.
Vining, Joanne, and Angela Ebreo. 1990. “What Makes a Recycler? A Comparison of
Recyclers and Nonrecyclers.” Environment and Behavior 22:55-73.
Vining, Joanne, and Angela Ebreo. 1992. “Predicting Recycling Behavior from Global and
Specific Environmental Attitudes and Changes in Recycling Opportunities.” Journal of
Applied Social Psychology 22:1580-1607.
Webster, Frederick E., Jr. 1975. “Determining the Characteristics of the Socially Conscious
Consumer.” Journal of Consumer Research 2: 188-196.

127

REFERENCES

128

REFERENCES

Ajzen, Icek. 1991. “The Theory of Planned Behavior.” Organizational Behavior and Human
Decision Processes 50(2):179-211.
Allen, Summer, Thomas Dietz, and Aaron M. McCright. 2015. “Measuring Household Energy
Efficiency Behaviors with Attention to Behavioral Plasticity in the United States.”
Energy Research and Social Science 10:133-140.
Allen, Summer, and Sandra T. Marquart-Pyatt. In Press. “Workplace Energy Conservation at
Michigan State University.” International Journal of Sustainability in Higher Education.
Attari, Shahzeen Z., Michael L. DeKay, Cliff I. Davidson, and Wändi B. de Bruin. 2010.
“Public Perceptions of Energy Consumption and Savings.” Proceedings of the National
Academy of Sciences of the United States of America 107(37):16054-16059.
Bamberg, Sebastian and Guido Möser. 2007. “Twenty Years after Hines, Hungerford, and
Tomera: A New Meta-Analysis of Psycho-Social Determinants of pro-Environmental
Behaviour.” Journal of Environmental Psychology 27(1):14-25.
Croasmun, James T., and Lee Ostrom. 2011. “Using Likert-Type Scales in the Social
Sciences.” Journal of Adult Education 40(1): 19.
Dietz, Thomas, Amy Fitzgerald, and Rachael Shwom. 2005. “Environmental Values.” Annual
Review of Environment and Resources 30:335-372.
Dietz, Thomas, Gerald T. Gardner, Jonathan Gilligan, Paul C. Stern, and Michael P.
Vandenbergh. 2009. “Household Actions Can Provide a Behavioral Wedge to Rapidly
Reduce U.S. Carbon Emissions.” Proceedings of the National Academy of Sciences
106(44):18452-18456.
Dietz, Thomas. 2014. “Understanding Environmentally Significant Consumption.”
Proceedings of the National Academy of Sciences 111(14):5067-5068.
Dolcinar, Sara, Anna Hurliman, and Bettina Grun. 2012. “Water Conservation Behavior in
Australia.” Journal of Environmental Management 105:44-52.
Fonseca, Paula, George Markogiannakis, Anibal de Almeida, Casper Kofod, and Nicolai
Feilberg. 2009. “Characterization of the Household Electricity Consumption in the EU:
Potential Energy Savings and Specific Policy Recommendations.” Proceedings of the
European Council for an Energy Efficient Economy. Summer 2009.

129

Gardner, Gerald. T. and Paul C. Stern. 2008. “The Short List: The Most Effective Actions US
Households Can Take to Curb Climate Change.” Environment: Science and Policy for
Sustainable Development 50(5): 12-25.
Geller, Edward S. 1995. “Actively Caring for the Environment: An Integration of Behaviorism
and Humanism.” Environment and Behavior 27(2): 184-195.
Gifford, Robert and Andreas Nilsson. 2014. “Personal and Social Factors That Influence ProEnvironmental Concern and Behaviour: A Review.” International Journal of Psychology
49(3):141-147.
Guagnano, Gregory, Paul Stern, and Thomas Dietz. 1995. “Influences on Attitude-Behavior
Relationships: A Natural Experiment with Curbside Recycling.” Environment and
Behavior 27(5):699-718.
Harshaw, Howard W., Stephen Sheppard, and John L. Lewis. 2007. “A Review and Synthesis
of Social Indicators for Sustainable Forest Management.” Journal of Ecosystems and
Management 8(2):17-36.
Inskeep, Benjamin D. and Shahzeen Z. Attari. 2014. “The Water Short List: The Most Effective
Actions U.S. Households Can Take to Curb Water Use.” Environment: Science and
Policy for Sustainable Development 56(4):4-15.
Kaiser, Florian. G. 1998. “A General Measure of Ecological Behavior.” Journal of Applied
Social Psychology 28(5): 395–422. https://doi.org/10.1111/j.1559-1816.1998.tb01712.x.
Kaiser, Florian. G., and H. Gutscher. 2003. “The Proposition of a General Version of The Theory of
Planned Behavimy (TPB): Predicting Ecological Behaviour.” Journal of Applied Social
Psychology 33: 586–603.
Lindenberg, Siegwart, and Linda Steg. 2007. “Normative, Gain and Hedonic Goal Frames
Guiding Environmental Behavior.” Journal of Social issues 63(1): 117-137.
Poortinga, Wouter, Linda Steg, and Charles Vlek. 2004. “Values, Environmental Concern, and
Environmental Behavior: a Study into Household Energy Use.” Environment and
Behavior 36(1): 70-93.
Schwartz, Shalom H. 1977. “Normative Influences on Altruism.” Advances in Experimental
Social Psychology 10: 221-279.
Smith, Joanne R. and Andreè McSweeney. 2007. “Charitable Giving: The Effectiveness of a
Revised Theory of Planned Behavimy Model in Predicting Donating Intentions and
Behaviour.” Journal of Community and Applied Social Psychology 17(5):363-386.
Steg, Linda. 2005. “Car Use: Lust and Must. Instrumental, Symbolic and Affective Motives for
Car Use.” Transportation Research Part A: Policy and Practice 39(2):147–162.

130

Steg, Linda, and Charles Vlek. 2009. “Encouraging Pro-Environmental Behaviour: An
Integrative Review and Research Agenda.” Journal of Environmental
Psychology 29(3):309-317.
Steg, Linda and Charles Vlek. 1997. “The Role of Problem Awareness in Willingness-toChange Car Use and in Evaluating Relevant Policy Measures.” Traffic and Transport
Psychology. Theory and Application 1997:65-75.
Steg, Linda, Gerta Perlaviciute, Ellen van der Werff, and Jesper Lurvink. 2014. “The
Significance of Hedonic Values for Environmentally Relevant Attitudes, Preferences, and
Actions.” Environment and Behavior 46(2):163-192.
Stern, Paul C., and Thomas Dietz. 1994. “The Value Basis of Environmental Concern.”
Journal of Social Issues 50:65-84.
Stern, Paul C., Gerald T. Gardner, Michael P. Vandenbergh, Thomas Dietz, and Jonathan M.
Gilligan. 2010. “Design Principles for Carbon Emissions Reduction Programs.”
Environmental Science and Technology 44(13):4847-4848.
Stern, Paul. 2000. “Toward a Coherent Theory of Environmentally Significant Behavior.”
Journal of Social Issues 56(3): 407-424.

131

CHAPTER 5: CONCLUSIONS

Predicting environmentally significant behaviors and explaining environmental decisionmaking generally are highly complex endeavors. Nevertheless, we have attempted these for over
forty years. In beginning this dissertation, I focused on explaining the relative and interrelated
influence of morals and values on donations to environmental causes and on water conservation
behaviors. In the course of my research, results indicated that morals may not add explanatory
power to environmental decision-making models, at least as tested here.
In Chapter Two, I reported the results of a between-subjects experiment conducted via
Qualtrics and AMT. I investigated how varying the psychological distance in messages about
biodiversity conservation influenced subjects’ choice of actually donating to a local, national, or
international arm of a biodiversity conservation organization. I also examined how moral
intuitions from Moral Foundations Theory and key concepts in the VBN model predicted such
donation behavior. The results indicated that temporal distance influences spatial targets of
donation, while motivating values and moral intuitions weakly influence subjects’ choice to
donate, amount donated, and spatial target of donation.
In Chapter Three, I created a new survey instrument to measure self-reported water
conservation behaviors and behavioral intentions that accounts for behavioral plasticity, or the
extent to which perceived difficulty to act constrains behavioral responses across individuals,
behaviors, and contexts. Using this new instrument, I conducted another between-subjects
experiment that examined how water conservation behavioral intentions and an observed water
conservation behavior (donation to an environmental organization) were influenced by
psychological distance (i.e., spatial and temporal distance from a drought) and values. Results

132

support the validity of a new instrument, confirm the VBN model as relevant to home water
conservation behaviors, and document that psychosocial factors such as values and norms
subsume the effects of psychological distance on donation to a water conservation program.
In Chapter Four, I reported a review of the PEB literature and an analysis of the resultant
meanings for the study of PEBs and environmental decision-making. I examined how we have
been measuring pro-environmental behaviors and the implications of those measurement
methods. My results suggest improvements in question stem and response option formulations,
item choices, and expansion of our theoretical repertoire that could lead to deeper understanding
of the motivations behind PEBs.
Through this dissertation, my planned scholarly contributions were five-fold. I hoped to
give wider sociology some of the first empirical evidence of how values and morals may operate
together, both in the specifics Schwartz values and Moral Foundations, as well as the theoretical
relationships between values theory and moral foundations theory. I planned to introduce both
Schwartz values and morals into the discussion of donation to environmental causes, providing
important, more stable predictors of these behaviors then currently exist in the literature. I aimed
to introduce the concept of behavioral plasticity to the water conservation literature. I also
hoped to provide the first evidence of how psychological distance interacts with Schwartz values
and morals, both in the specific contexts I have chosen and more generally; and finally, I wanted
to help scholars target identified gaps in PEB research, more clearly specify the contributions of
their work, and to clarify some perhaps unseen assumptions about ESBs and potentially open
new avenues of theoretical exploration.
My findings suggest that there is overlap in the conceptualizations of Moral Foundations
and Schwartz value orientations. For example, Chapter Two reported the similarities between

133

the Respect for authority moral foundation and Schwartz’s Traditional value orientation. This
raises the question of defining motivating values versus moral foundations. If morals are
conceptualized as intuitive judgments and values are internalized enough to be both transsituational and enduring, we need to differentiate these two theories to facilitate greater crossdiscipline collaboration with psychologists.
There is also likely significant overlap between moral foundations and personal norms.
Ajzen’s recommended formulation of personal norms items focuses upon respondents’ gut
feelings and sense of guilt. This is quite similar to Moral Foundations Theory in attempting to
access the subjects’ intuitive response to a particular behavior or topic. I suggest that moral
intuitions fall somewhere between the generality of a value and the specificity of a norm.
Further study and theorization is needed to clarify these relationships.
I introduced behavioral plasticity to the water conservation literature in Chapter Three.
My reported evidence suggests the instrument is suitable for replication and future research
should test this. In addition to more easily sorting and characterizing respondents by past
behaviors, behavioral intentions, and eligibility for performing a behavior, including these
modified response options would partially address a point from Chapter Four about greater
consideration of external constraints in our models of environmental decision-making.
Future research should also consider more exploration of the role of psychological
distance in environmental decision-making. In Chapters Two and Three, psychological distance
did not appear to exert any significant influence on my studied behaviors. However, my
secondary analysis in Chapter Three suggests that it may play a role in certain situations or under
certain constraints. Regarding specific interactions with values or moral intuitions, there do not
appear to be any of note.

134

My review of pro-environmental literature yielded a robust characterization of our
literature and several avenues for future enhancement of our research. By identifying our
theoretical tendencies, under-explored frames, and presenting ideas for measurement item
improvement, Chapter Four identifies assumptions to go beyond, gaps that could be filled, and
clear ways to contribute to knowledge accumulation. We should commit to theorizing, expand
our theoretical repertoire past NAM/VBN and TPB, and consider the implications of researching
easy behaviors. We can include more impactful behaviors in our studies, use more causal
analysis, longitudinal studies, and panel surveys. We can also expand our sample draws to
developing and emerging economies. Any of these will expand our literature in fruitful and
important ways.

.

135