DOI: 10.5061/dryad.3j9kd51tc
title: Evaluating the usefulness of Protection Motivation Theory for predicting climate change mitigation behavioral intentions among a US sample of climate change deniers and acknowledgers

description: Background: This paper summarizes data from 7 studies that used Protection
 Motivation Theory (PMT) to guide climate messaging with the goal of
 increasing climate-mitigating behavioral intentions.  Together,
 these studies address 5 research questions. 1) Does PMT predict behavioral
 intentions in the context of climate change mitigation?  2) Does
 PMT work similarly for climate change deniers vs believers? 3) Are the
 effects of threat and efficacy additive or multiplicative?  4)
 Does adding measures of collective threat and efficacy improve the model
 accuracy for a collective problem like climate change? 5) Can threat and
 coping appraisals – and ultimately behavioral intentions – be shifted
 through climate messaging?   Methods: Seven online
 experiments were conducted on US adults (N = 3,761) between 2020 and
 2022.  Participants were randomly assigned to a control condition
 or to one of several experimental conditions designed to influence threat,
 efficacy, or both.  Participants indicated their belief in
 climate change, ethnicity, gender, and political orientation. 
 They completed measures of personal threat and efficacy, collective threat
 and efficacy, and behavioral intentions.  Results: Multiple
 regressions, ANCOVAs, and effect sizes were used to evaluate our research
 questions. Consistent with PMT, threat and efficacy appraisals predicted
 climate mitigation behavioral intentions, even among those who deny
 climate change. Different interactions emerged for climate deniers and
 acknowledgers, suggesting that in this context threat and efficacy are not
 just additive in their effects (but these effects were small). Including
 measures of collective threat and efficacy only modestly improved the
 model. Finally, evidence that threat and efficacy appraisals can be
 shifted was weak and inconsistent; mitigation behavioral intentions were
 not reliably influenced by the messages tested.  Conclusions: PMT
 effectively predicts climate change mitigation behavioral intentions among
 US adults, whether they deny climate change or acknowledge it. Threat
 appraisals may be more impactful for deniers, while efficacy appraisals
 may be more impactful for acknowledgers.  Including
 collective-level measures of threat and efficacy modestly improves model
 fit. Contrary to PMT research in other domains, threat and efficacy
 appraisals were not easily shifted under the conditions tested here, and
 increases did not reliably lead to increases in behavioral intentions.
description: The data file is an aggregate of 7 studies conducted between the
 summer of 2020 and spring 2022.  Studies are numbered chronologically from
 earliest to latest.  The studies used different manipulations and tested
 different hypotheses (described below). All used Protection Motivation
 Theory to inform messaging designed to influence threat appraisals,
 efficacy appraisals, and climate change mitigation behavioral intentions. 
 All studies used identical measures of PMT constructs (with the exception
 of Study 1, which used the same items but a different response scale for
 measures of threat).  The items used to measure severity, vulnerability,
 performance efficacy, and response efficacy appraisals were taken from
 previous research (Bostrom, Hayes, & Crosman, 2019).  Each study
 also included measures of collective severity, vulnerability, performance
 efficacy, and response efficacy.  In all studies,
 participants provided informed consent and were randomly assigned to
 experience a manipulation of some kind (described below).  Participants
 then completed questions that measured our main dependent variables, and
 provided demographic information.  In all studies, participants also
 responded to an attention check question (a factual question about the
 condition they were assigned to).  Participants who failed the attention
 check question were removed from the data set (N = 181, or 4.5%). This
 resulted in a final sample size of 3761.
 Manipulations Study 1:  India. This study
 evaluated whether reading about the swift recovery of natural systems
 during the COVID lock-down would increase both personal and collective
 response efficacy for climate change mitigation behaviors. We reasoned
 that reading about natural systems’ recovery would increase participants’
 sense that reducing carbon-emitting behavior would make a difference, and
 also that large numbers of people could and would change their behavior. 
 Participants were randomly assigned to read an article about dramatic air
 quality improvements in two Indian cities (with before and after lockdown
 pictures), or a travel blog about the same cities (with after pictures
 only). Study 2: Prospection 1.  This study evaluated
 whether imagining a positive or negative future impacted threat and
 efficacy evaluations. We hypothesized that imagining a positive future
 would increase response efficacy, as things that we imagine seem more
 likely (cite simulation heuristic). We hypothesized that imagining a
 negative future would increase vulnerability and severity assessments, for
 the same reason. We did not have distinct hypotheses for personal vs
 collective levels of the variables.  After reading a brief description
 outlining current environmental challenges, participants were randomly
 assigned to one of four conditions.  The positive prospection condition
 was asked to imagine that we had successfully addressed our environmental
 challenges, and asked to describe their neighborhood 30 years in the
 future.  The negative prospection condition was asked to imagine that
 environmental challenges had continued to worsen, and were also asked to
 describe their neighborhood 30 years in the future. Two control conditions
 either wrote about current events in their neighborhood, or did no writing
 task. (These two groups were identical and ultimately were
 combined.) Study 3: Racial Disparity 1.  This study
 examined whether reading about the racial disparities in climate change
 impacts had different impacts on White people versus People of Color
 (POC). We hypothesized that White people who read that communities of
 color are disproportionately impacted by climate change might have lower
 estimates of vulnerability and severity, relative to a control condition.
 We predicted the opposite for POC:  reading that their communities are
 more impacted by climate change would increase vulnerability and severity
 ratings. We did not have distinct hypotheses for personal vs collective
 levels of the variables. We recruited roughly equal numbers of
 White-identifying and POC-identifying participants, who were randomly
 assigned to read an article about climate change’s disproportionate
 impacts on communities of color or a control article about climate
 change’s disproportionate impacts on coastal communities.
 Study 4: Scientist 1. We examined whether reading about
 scientists making accurate predictions would affect participants’
 vulnerability and severity perceptions. We contrasted reading about
 scientists’ COVID-19 predictions to reading about climate predictions. 
 COVID-19 provided a recent, vivid, and tangible example of a highly
 disruptive threat that everyone had been undeniably affected by. It also
 provided a recent, vivid, and tangible example of scientists making
 accurate predictions, as well as showing the value of science-informed
 public policy and science-based solutions (vaccines). We hypothesized that
 the COVID article would increase climate threat and efficacy assessments
 more than an article focused on climate change among climate deniers, as
 climate change is so politically polarized in the US.  We hypothesized
 that both articles would increase threat and efficacy assessments among
 those acknowledging the reality of climate change.  Participants in our
 study were assigned to one of three conditions — reading an article about
 accurate epidemiologist predictions, reading an article about accurate
 climatologist predictions, or reading no article at all.
 Study 5: Scientist 2.  This follow-up to Study 4 was identical,
 except that it included pre-measures of belief in climate change as well
 as the PMT variables.  This created a within-subjects design to boost
 statistical power.  Data included in the merged data set came from the
 measures collected after reading the articles, to make this data as
 similar as possible to the other studies. Study 6:
 Racial Disparity 2.  Study 6 was a straight replication of Study 3 with a
 larger sample size. Study 7: Prospection 2.  As in
 Study 5, this study used a prescreen survey of PMT variables to create a
 within-subjects design. We again hypothesized that imagining a positive
 future would increase efficacy, while imagining a negative future would
 increase threat assessments. Participants read a brief paragraph outlining
 the challenge of climate change and our potential to respond to it. They
 were then randomly assigned to see either a positive image (e.g. solar
 panels; positive prospection), a negative image (e.g. drought-stricken
 field; negative prospection), or both (order counterbalanced; positive +
 negative prospection). Participants were asked to imagine the future
 depicted and write about what it would be like to live in it. A control
 condition skipped this task.
description: # Evaluating the Usefulness of Protection Motivation Theory for Predicting
 Climate Change Mitigation Behaviors Among a US Sample of Climate Change
 Deniers and Acknowledgers
 [https://doi.org/10.5061/dryad.3j9kd51tc](https://doi.org/10.5061/dryad.3j9kd51tc) This data is the aggregation of 7 studies conducted between the summer of 2020 and spring 2022. All studies used Protection Motivation Theory as a framework for evaluating messages about climate change designed to increase mitigation behavioral intention.  The studies used different manipulations to influence threat, efficacy, or both. All studies contained one or more control conditions. ## Description of the data and file structure The data files were created in SPSS but exported to a CSV file to make it accessible to those without an SPSS license. Each row corresponds to a single participant.  Missing data is indicated with a blank field.  Metadata detailing the meaning of each variable and value labels is available in the CSV file “MetadataforEvaluatingPMTforPredictingCCMitigation”. Copies of the surveys used to collect data can be found in the compressed folder “SurveysforEvaluatingPMTforPredictingCCMitigation”. ## Code/Software SPSS syntax files used to generate the analyses reported in the paper are included.