When the Time Seems Closer:
The Effect of Temporal Representation of
Time on Consumers’ Behavior in the
Environmental Domain
By Anouk Kruizinga S2816199
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Marketing Management, of the Faculty of Economics and Business at the University
of Groningen, Groningen, The Netherlands.
3 Preface
Before you lies the thesis that marks the end of my time as a MSc Marketing graduate at the University of Groningen. It was written from September 2018 till January 2019, and brings together the knowledge I have gained during my studies and my personal interests. It deals with two complex yet highly interesting issues; climate change and consumer psychology. The entire research process has made me realize how valuable the generation and diffusion of research is, and how much there is still to be discovered. I hope to continue this exploration in a future position, in which I can hopefully contribute to the knowledge base and actual actions towards a more sustainable society.
I would like to thank the team of the Marketing Department of the University of Groningen, who have inspired me to learn so many new things. A special thank you goes to my supervisor Dr. Mehrad Moeini-Jazani, who has been a great help in the development of this thesis. Furthermore, my sincere thanks extend to my friends and family, for listening, inspiring and keeping me going.
4 When the Time Seems Closer: The Effect of Temporal Representation of Time on
Consumers’ Behavior in the Environmental Domain
Abstract
5 CONTENTS
1 INTRODUCTION ... 7
2 THEORY ... 9
2.1 The Temporal Nature of the Environment ... 9
2.2 Psychological Distance and Temporal discounting ... 10
2.3 Temporal Discounting in the Environmental Domain ... 11
2.4 Construal Level theory ... 12
2.5 The Malleability of Time ... 13
2.6 Influencing Subjective Perception of Time ... 15
2.7 Additional Factors Influencing Behavioral Intentions and Policy Support ... 17
2.8 Current Research ... 19
3 METHODS ... 21
3.1 Design and Procedure ... 21
3.2 Participants ... 21 3.3 Measures ... 22 3.3.1 Time perception. ... 22 3.3.2 Urgency ... 24 3.3.3 Behavioral intentions ... 24 3.3.4 Policy support ... 24
3.3.5 Exploring Potential Mediators & Moderators of Our Effect ... 25
4 RESULTS ... 28
4.1 Descriptive Statistics ... 28
4.1.1 Sample characteristics ... 28
4.1.2 Overall behavioral Intentions and Policy Support ... 29
4.2 Manipulation Check: Subjective Perception of Time ... 30
4.3 Correlations between Main Variables in this Research ... 30
6
4.5 Potential Mediating Variables ... 33
4.6 Mediation Effect ... 34
4.7 Potential Moderating Factors of our Effect ... 35
5 DISCUSSION ... 36
5.1 General Discussion ... 36
5.2 Contributions and Implications ... 37
5.3 Limitations and Future Research ... 38
6 CONCLUSION ... 40
7 REFERENCES ... 41
8 APPENDICES ... 47
Appendix 1. Participants’ location (N = 396). ... 47
Appendix 2. Participants’ educational attainment per condition (in percentages). ... 48
Appendix 3. Article CNN. ... 49
Appendix 4. Measurement Items ... 52
Appendix 5. Scenario text. ... 58
7
1 INTRODUCTION
Climate change is different and inherently more widespread than any other thread we face today. Impacts on natural and human systems are felt worldwide, from rising sea levels and extreme weather conditions to the global food production. According to the latest Intergovernmental Panel on Climate Change report anthropogenic global warming is currently increasing as much as 0.3°C per decade due to past and ongoing emissions (IPCC, 2018). In order to mitigate the predicted disastrous effects climate change targets for reductions in greenhouse-gas emissions have been adapted and institutionalized across many developed and developing countries Research, however, demonstrates that it is unlikely that these targets will be met if we continue our current consumption patterns (e.g., Friedlingstein et al., 2014; Karl, Melillo, Peterson, & Hassol, 2009). In order to achieve ambitious goals in climate change mitigation efforts – such as more efficient and reduced energy use globally - significant changes in existing societal structures are immediately necessary and require the engagement of the broader audience (IPCC, 2018; Karl et al., 2009; Spence & Pidgeon, 2009;2010; Williams et al., 2012).
8 investigated the temporal distance of climate change are either conceptual, or tested individuals’ outcome perception at objectively different durations (e.g., 3 months from now vs. 1 year from now) (Hardisty and Weber, 2009; Sundblad, Biel & Arling, 2007). It is unclear at this moment, whether different visual framing techniques of an objectively equivalent duration can influence an individual’s temporal distance to climate change. For example, it is unclear if expressing time relative to a smaller or larger time duration might alter the way people subjectively perceive this duration. Hence, how distant is 5 years perceived when considering in the context of 50 years in comparison to the consideration of 5 years in the context of 6 years?
9
2 THEORY
2.1 The Temporal Nature of the Environment
The term climate change refers to long-term changes in weather condition, which are observed over several decades (the United States Environmental Protection Agency, 2017). Climate-related changes are extremely diverse, and include amongst others carbon emissions, increases in air and water temperatures, a rise in sea level, and species extinctions. These changes do not only have an effect locally and temporarily (e.g.,, in the form of natural disasters), but also have high long-term impacts on constructs such as the global food supply, fresh water availability and global safety issues (for a review see Karl et al., 2009).
The biophysical phenomena of climate change is characterized by several temporal dimensions that influence people’s psychological distance. Perhaps the most prominent temporal dimension of climate change is its extremely long-term extension into the future. To clarify, some effects of climate change are visible in the present, but many are due to happen over a long timescale. In climate change messages both policymakers and researchers regularly use a time horizon of 2050 for climate change scenarios (Caseldine, 2012), this while people on average solely think about the future as about 15 years from now, with a limited ability to imagine the future as beyond 10-20 years (Tonn et al., 2006). National polling data from the United States revealed that while people might be aware that climate change impacts are occurring now, the general public tend to see severe impacts at large scale as happening in the distant future, well after 2050 (Leiserowitz, 2005). Hence, due to the general discourse being focussed on distant future events a certain distance exist between our lives now and the future impacts of climate change. As a result of this distant future orientation in discourse people experience difficulties in assessing possible consequences of environmental changes (Moser et al., 2013; Newell, McDonald, Brewer, & Hayes, 2014), which is suggested to be in part the reason why people do not act in a sustainable matter (Energy Saving Trust, 2007; Weber, 2010;2016).
10 mitigation, given that climate change mitigation legislation is in need of long-term commitment and dedication and imposes immediate costs in order to not result in a “symbolic aspirational statement” (Lazarus, 2008, p. 1153; Weber, 2010).
2.2 Psychological Distance and Temporal discounting
Beyond the temporal characteristics of the natural phenomenon of climate change, individuals have their own response mechanisms to temporal dynamics and constraints. Literature suggests that sustainable behavior is motivated by both internal factors as well as external factors of the environment, such as an individual’s characteristics or by incentives of the environment (Shaw, McMaster & Newholm, 2016; McDonagh & Prothero, 2014; Soyez, 2012; Videras, Owen, Conover & Wu, 2012). A dominant explanation for people’s lack of sustainable actions found in the literature is their perceived psychological distance to climate change and their responses accordingly (e.g., Spence, Poortinga & Pidgeon; McDonald, Chai & Newell, 2015). Trope and Liberman (2010, p. 1) define psychological distance as the “subjective experience that something is close or far from the self, here and now.” They distinguish four different dimensions of distance: spatial (physically close vs. distant), social (i.e., involving similar vs. dissimilar others) probabilistic (i.e., likely vs unlikely to occur) and temporal (near future vs distant future) (Trope & Liberman, 2003). According to their theory of psychological distance people are egocentric, where its reference point is the self in its current location and timeframe. Therefore, people’s perceived distance to a certain outcome or event is reflected in their decisions choices. Here, for each of the four dimensions of distance generally counts: the more distant it is perceived, the less value is attained to it.
11 outcome or consequence. These reasons include factors that diminish the expected utility of an outcome, such as changing preferences or uncertainty (Frederick, Loewenstein & O’donoghue, 2002). This is especially relevant in the environmental context, given that sustainable acts sometimes entail giving up a virtue now, for rather uncertain future benefits (Veenhoven, 2008).
2.3 Temporal Discounting in the Environmental Domain
Since the beginning of this century researchers have started to explore people’s psychological distance to issues in the environmental domain. Milfont and Gouveia (2006) found that environmental degradation, or the underlying climate change trend, is generally perceived as distant in all four dimensions of psychological distance. Additionally, Gattig and Hendricks (2007) found that discounting mechanisms are constant across the different dimensions of distance. As described in existing research it seems that higher temporal distance reinforces the other dimensions of psychological distance. For example, higher temporal distance concurs with higher levels of hypothetical distance. Subsequently, these uncertainties associated with the future predictions may elicit overall feelings of pessimism on this front (Gifford et al., 2009). ). These characteristics emphasize yet again the importance of psychological distance with regard to the environment.
In their research on the psychological distance of climate change Spence, Poortinga and Pidgeon (2012) examined the link between the different dimension of psychological distance and people’s concern about, and willingness to mitigate climate change. They found that lower psychological distance on all dimension to climate changes is generally associated with higher levels of concern. More importantly, their findings show that people who felt subjectively close to climate change have a higher preparedness to act (i.e., reduce their energy use).
12 improved air quality one year from now). Not only were their preferences recorded, but additional questions were asked to measure their personal preferences between the different domains. For example, in the air quality scenario participants chose between paying [or gaining] $250 immediately or having improved [worse] air quality for the next 21 days. In these studies similar discount rates were found for financial and environmental outcomes. Yet it is worth noting that others found that temporal discounting is less pronounced for environmental risks scenarios than for the other forms of distance (i.e., social, hypothetical and physical distance) (Böhm & Pfister, 2005). One explanation grounded in literature is that people’s perceived baseline control is low for temporal distance, as in comparison to the three other dimension of psychological distance (i.e., spatial, social, and probabilistic) (Han & Gershoff, 2018; Trope & Liberman, 2010). Overall, although these studies are focused on the environmental domain, none of the examined environmental risk scenarios explicitly associated with climate change.
2.4 Construal Level theory
13 reason that consequences of near future events are generally feared more (Loewenstein & Schwartz, 2010).
Table 1. Schematic differences between, low and high construal level.
Construct Low construal High construal Selected source
Psychological distance Proximal Distal Trope and Liberman, 2003;2010
Temporal distance Present Distant past or future
Physical distance Here (local) Far away
Hypothetical distance Certain Possible
Social distance Family or friends Strangers
Cognitive factors
Representation Concrete, detailed Abstract, simple Bar-Anan et al., 2006 Evaluation of outcomes Feasibility Desirability Fujita et al., 2008 Evaluation of actions Process focus, the
´How.’
Outcome focus, the ´Why.´
Liberman & Trope, 2008
Motivational factors
Motivation Intrinsic Extrinsic Vansteenkiste. Matos, Lens &
Soenens, 2007
Goal focus Context/based General Fujita et al., 2008
Goal orientation Prevention Promotion Lee, Keller & Sternthal, 2010
2.5 The Malleability of Time
Where the common belief amongst scientists is that people have stable tendencies towards being either patient or impulsive in their intertemporal choices, a growing body of evidence reveals that choices are malleable and can be heavily influenced by their context. Research has found that choices between two alternative options are strongly dependent on what the alternative options are, the state of the decision-maker, and how the options are described (for a review see Lempert & Phelps, 2016).
14 and the relevance of perceived certainty of outcomes. Interestingly, this theory too challenges the grounds of utility theory, as their findings show that people underweight probability outcomes over those that are obtained with certainty.
In research on psychological distance, context-effects are generally being examined in relation to the domains of spatial (e.g., Han & Gershoff, 2018; Spence & Pidgeon, 2010), social (Nan, 2007), and hypothetical distance (Bent, 2017). Similar to discounting theory, much less scrutiny has been dedicated to context-effects in the domain of an individuals’ temporal distance to an event, and their subsequent intertemporal-decisions. Notably, knowledge on this topic is missing in scientific discourse on climate change judgment and behavioral intentions.
Research employing different types of framing techniques in problem areas such as the social or environmental domain reveal that time durations are not objectively constructed nor perceived (Lempert & Phelps, 2016; Monga & Bagchi, 2012; Pahl, Sheppard, Boomsma, & Groves, 2014; Spence, Poortinga & Pidgeon, 2010; Zauberman et al., 2009). This notion of subjective time perception has among others deep roots in the psychology of time (Fraisse, 1963; Sherover, 2001; Tvretzky, 1998; Zimbardo & Boyd, 2015). This notion translates into the thought that people have difficulty thinking about time as an independent construct, and therefore often misjudge the duration of an event. As Construal Level Theory proposes, people are only experiencing the present, which means any estimation or discussion with regard to the past or the future is construed in their mind. Research on intertemporal distance have tried to assess value function of different moments in time, but only a few of these researchers have attempted to map the level of subjectivity as part of this process.
15 As becomes apparent, the perception of time is an important factor in the decision making process, especially at moments when individuals have to make decisions with regard to the associated outcomes of events. Due to the fact that the effects of climate change over time are perceived as uncertain and distant, making climate change personally relevant and close is especially challenging. Decreasing this subjective distance through changing ones reference point offers possibilities for increasing their likelihood of action.
2.6 Influencing Subjective Perception of Time
Notably, one way of decreasing ones psychological distance to an event is to make the outcome seem closer in time. Generally it is suggested that when the passage of time is perceived as passing faster, people generally experience higher levels of arousal (Boven et al., Droit-Volet & Meck, 2007), which then leads a more rapid readiness for changes (Bradley, 2001; Loewenstein & Schwartz, 2010). Other studies, however, have revealed that the expected relationship is not always in place; making climate change seem more proximate may not always yield the desired goal of mitigating behavior motivation (Brügger et al., 2015, 2016; Ejelov, Hansla, Bergquist & Nilsson, 2018). As explained earlier, more concrete associations with a threatening outcome can increase levels of fear. Some research suggests that inducing fear (for the purpose of getting people to act) through messages on climate change may even be counterproductive. Researchers such as Angrilli, Cherubini, Pavese, and Manfredini, (1997), and Ejelöv and colleagues (2018) found that attention-related mechanisms, which are elicited by high-arousal or fearful messages, might activate the defensive system. In the context of two empirical studies O’Neill and Nicholson-Cole (2009) investigated the role of fearful messages in climate change communication and public engagement. Their findings show that fearful messages are generally a successful tool for attracting attention. However, fear is not a useful proxy for motivating personal engagement with the issue presented. For this reason, researchers argue that, above all, psychological distance influences which construct – either relatively high, or relatively low – come to mind most dominantly in their judgment and behavioral intentions (Brügger et al., 2015). This emphasizes the importance of the appropriate context of the message in making environmental issues and outcomes seem temporally closer.
16 judgments. Often, the size of the unit is used to make inferences about the proximity or magnitude of events. Chandran and Menon (2004) investigated the effects of temporal framing on judgments of health risks, using every day framing and every year framing. Their findings suggest that every day framing makes that risks appear more concrete and proximal than every year framing. Statistics in every day frames resulted in an increase in self-risk perceptions, concern, and anxiety about the risk, and subsequently to intentions to exercise precautionary behavior. They argue that a hazard seems more proximal in time because a day is shorter than a year. Similarly, in a study on the granularity of quantitative expressions on recipients judgment and choice, Zhang and Schwarz (2011) found that consumers have more confidence in the accuracy of messages framed in a smaller granularity (e.g., days) as opposed to those framed in bigger granularity (e.g., months or years).
Although this framing techniques, which use the reformulations of messages in terms of their granularity, definitely deserves further investigation, another interesting framing technique to examine is the use of visualizations in climate change messages. In the study of Chandran and Menon (2004) participants have to construe associations with the terms ‘1 year’ or 356 days’, but perhaps do visual techniques offer more input to form these associations. Visual framing techniques seem especially relevant when considering the high importance and potential of visuals in marketing messages (Joo, Li, Steen & Zhu, 2014).
Even though the attention to visuals in marketing messages is gaining attention, to date, there has been surprisingly little research examining the effect of visualizations on people’s subjective perceptions of time. The few studies that have employed visuals have mainly focused on the comparison of different intentions, or the value of the scale itself. For example, Kaplan, McKerchar, and Reed (2014) examined the degree to which hyperbolic models of discounting can describe students’ rating of concern and their willingness to act upon environmental degradation making use of scales from 1 – 100. Amongst others, they found that these hyperbolic models of delay discounting adequately describe self-report data. On the contrary, Burson, Larrick, and Lynch (2009) found that different designs of scales did affect people’s judgment. An increase in the ratio properties of an attribute’s scale, led to a higher weight to the value. For instance, a difference of 20 of a 100-point is given more weight than the difference of 1 on a 5-point scale. It is unknown whether this judgment has long-lasting effects, or whether these could be transferred into different domains.
17 between environmental benefits or losses reveal that environmental outcomes know a steeper discounting rate over time. So it seems that people evaluate outcomes at objectively different times in a different way. It remains unclear however, how the subjective perception of time of outcomes at objectively the same time duration are affecting individual’s behavioral intentions and policy support.
2.7 Additional Factors Influencing Behavioral Intentions and Policy Support
In literature, several different factors are identified to have a direct influence on people’s behavioral intentions and policy support in the environmental domain. The most prominent factors affecting personal beliefs discussed in literature are personal efficacy (Hornsey et al., 2015; Yoong, Bojeim, Osman & Hashim, 2018), their levels of skepticism - or perceived uncertainty - towards the issue (Gifford et al., 2009), and their personal experience (McDonald, Chai & Newell, 2015; Montreux & Barnett, 2009).
Researchers such as Yoong, Bojei, Osman and Hashim (2018) and Hornsey et al. (2015) found that an individuals’ perceived self-efficacy positively influences an individual’s behavioral intentions. Here, self-efficacy is defined as the evaluation of one’s capability and controllability (Gist, 1987). Individuals that perceive high self-efficacy believe that their actions will help to accomplish the desired goal, while individuals on low self-efficacy believe the task beyond their capability and will therefore not pursue their goals. Although one might argue that the overall baseline control for environmental issues is relatively low due to their temporal distance, Hornsey et al. (2015) found direct evidence for motivated control in the environmental domain. In their study participants who read a high-threat message reported more efficacy than those who read a climate change message that downplayed the threat. Although they display motivated control, these researchers did not examine whether this leads to subsequent changes in behavioral intentions or policy support.
18 might have been impacted by media reporting and political debate on climate change. Additionally, these authors found that at lower levels of uncertainty (or higher level of concern) individuals reported a higher preparedness to reduce their energy use.
A third variable which is shown to affect people’s views on climate change and their willingness for behavioral change is their personal experience with the issue. Spence, Poortinga, Butler and Pidgeon (2011) found that people with personal experience with flooding report a higher level of willingness to save energy as those who have never experienced flooding. Making the climate change message more personally relevant may increase certain behavioral intentions. By incorporating any of the aforementioned characteristics into message framing researchers also identify potential pitfalls associated with decreasing psychological distance, such as fear triggering the defense system (for a review see McDonald, Chai & Newell, 2014). This highlights moreover the importance of message framing, or possibly even the measurement of these emotions within the experiment.
Furthermore, previous research has revealed that certain demographic and lifestyle characteristics have an effect on people’s behavioral intentions and policy support in the environmental domain. For example, political affiliation is another factor influencing people’s willingness to act and to support climate-friendly policy measures (McCright & Dunlap, 2011; Schuldt, Rickard & Yang, 2018; Tobler, Visschers & Siegrist, 2012). Tobler, Visscher, and Siegrist (2012) found that respondents on the right wing were less will to show behavioral intentions or support climate mitigating policies. Similarly, in a messaging experiment designed to measure the influence of climate change impact departure dates Rickard, Yang, and Schuldt (2017) found a moderating effect for political orientation. Their findings showed that U.S. conservatives showed greater willingness to support climate change policy after reading about nearby impacts (i.e., in New York City vs. Singapore) and more in the distant future (i.e., in 2016 vs. in 2020). Additionally, Whitmarsh (2011) found that individual political values are a strong predictor of skepticism about climate change. Even more so than their education or knowledge on the topic. Moreover, the found evidence of indirect effects of age, gender, location and lifestyle on levels of skepticism.
19
2.8 Current Research
As emphasized throughout this chapter, bridging the temporal distance to climate change is essential for mitigating climate change. Given the importance of behavioral intentions in the context of meeting the current climate change targets that have been set, it is of high importance to examine the temporal perceptions of climate and the way these temporal perceptions could be altered. Although there is a growing body of evidence on subjective perceptions of time, research on actual techniques to manipulate this perception is currently still lacking. Reviewing literature on psychological distance has indicated that there are different ways to influence people’s reference point with regard to future outcomes. One under-researched method is the visualization of duration as a method to influence subjective estimates of duration.
The current study will focus on manipulating participant’s subjective perception of time in order to make the objective duration of the event seem subjectively shorter. Based on existing research it is expected that when people perceive the duration to the effects of climate change as closer in time they will experience higher levels of urgency and have an increased willingness to take action and to support climate change mitigation policy measures. Whereas the dominant stream of research found that events closer in time are construed at lower levels, and hence generally associated with more concrete goals, this study will also examine whether there is a correlation between people’s subjective perception of time and the level at which they are construing different tasks. These relationships are summarized in the conceptual model in figure 1.
This research is complementary to existing literature, where it will study the subjective time perception of environmental outcomes keeping all factors beyond the time-frame equal. We expect that a duration of 5 year in the context of 50 years is perceived as temporally closer, as for those who compared a 5 year duration in the context of 10 years. Similarly, we expect that 5 years in the time of 10 years seems closer when compared to those who are shown a visualization of 5 years in the context of 6 years.
20 effects of other variables – such as personal efficacy, skepticism and personal experience – on this relationship.
21
3 METHODS
3.1 Design and Procedure
The study was designed using the online survey tool Qualtrics. Once the questionnaire was designed, participants were collected through Amazon Mechanical Turk, and were given a monetary reward upon completion. To get a representative sample the title of the study was not specified to the environmental domain. Instead the title was kept more neutral: “a short marketing research survey investigating people's opinion about various daily life issues’. The experiment started with the manipulation of time perception in the first question, in which they had to visualize a day in 5 year time at different time horizons. The study had a between subject design, with three levels of the independent variable time perception. Subjects were randomly assigned to one of the three treatment conditions. After being exposed to the treatment, participants were asked the same question on urgency, behavioral intentions, policy support, personal beliefs, construal’s, and several demographic characteristics. After a general description of the participants, these constructs and their measures will be discussed in turn.
3.2 Participants
22 have no preference (1.5% indicated “other,” e.g., Green). A cross tabulation of these variables showed that these characteristics were comparable over the different group, only a small different in educational level was found. Just over half (51.5%) of the respondents in the distant condition reported that their highest degree achieved is a bachelor degree, whereas this percentage is lower in the neutral and proximate groups (40.6% and 37.5% respectively). Whereas more respondents in the neutral and proximate condition report they have attained high school, or a GED agree (30.1% and 35.3% respectively), and only 24.3% in the distant condition reports they have attained a high school or GED degree (for an overview see Appendix 2). Further analysis ruled out any effect of educational attainment on our manipulation.
3.3 Measures
3.3.1 Time perception.
Time perception is manipulated by means of a slider scale reflecting three different time durations. In the first – and main – condition (N = 136) participants were asked to indicate where they consider a day in 5 years from now on a slider scale ranging from now (0) to 50 years from now (100) (i.e., somewhere close to ‘now’)(see Figure 2).
23 A control condition (N = 133) was added in which participants were asked to indicate where they see a day in 5 years from now on a continuous slider scale from now to 10 years from now (i.e., somewhere close to the middle of the slider scale).
Figure 3. Visual of the manipulation in which time seems neutral
For exploratory purposes a third condition (N = 136) was designed, to explore whether we could actually make time seem further away in time. In this third condition participants were asked to indicate where they see a day in 5 years from now on a continuous slider scale from now to 6 years from now (i.e., somewhere towards the end of the slider scale). The visually equivalent comparison with the previous two scales would be 5,5 years. For fluency purposes 6 years is maintained.
Figure 4. Visual of the manipulation in which time seems distant
24 crucial in climate change mitigation. This article is based on real climate change issues, and was adapted from an article published by CNN October 8this year (see appendix 3), however is shortened and adapted to a five-year context for purposes of this research.
3.3.2 Urgency
In total 5 questions were included in the questionnaire to measure participants sense of urgency adopted from the past research (Park, Im, and Keil, 2008; Spence, Poortinga and Pidgeon, 2012). The items were: ‘I feel that the negative effects of climate change are of considerable urgency.’; ‘I believe that climate change has to be tackled quickly’; ‘I feel climate change will affect my life soon’; ‘I feel climate change will affect the U.S. soon.’; and ‘I feel climate change will soon affect the environment in which my family and I live.’ All of which are measured on a 1-7 Likert scale from 1 - “Not at all agree” to 7 – “Very much agree.” Internal consistency of these items is tested and is excellent (Cronbach alpha = .968). All questions have been randomized within the scale.
3.3.3 Behavioral intentions
The respondents were presented with an extensive list of possible activities measures. We developed 15 behavioral items, adapted from past studies (e.g., Gifford & Comeau, 2011; Leiserowitz, 2005; Tobler, Visschers & Siegrist, 2012). Overall the aim was to cover a wide range of behaviors from different domains and with different characteristics (e.g., costs, effort, and duration) which an individual can engage in to help protecting the environment. Simultaneously, these items were considered to be a fit for or adaptable for the United States context. These items concerned the intent to act pro-environmentally in the residential domain such as installing energy efficient windows or lowering the thermostat in winter, as well as ones in the transportation or mobility domain and general consumption behavior (e.g., reduce energy consumption). The response options for these items were 1 – “strongly disagree” to 7 – “strongly agree.” Internal consistency of these items is tested and is excellent (α = .920). All questions have been randomized within the scale. See Appendix 4 for the full list of items. 3.3.4 Policy support
25 Leiserowitz, 2006; Schuldt, Rickard & Yang, 2018; Tobler, Visschers & Siegrist, 2012), which were adapted to the United States context. For a total of 10 policies respondents were asked to indicate to which degree the support or oppose each policy (see Appendix 4 for the full list of items). These responses were recorded on a 7-point Likert scale from 1 - “strongly oppose” to 7 – “strongly support.” Also, the items for this scale have been randomized within the scale. These items together have a Cronbach’s Alpha of .905, although this is an excellent value statistics reveal that the Alpha can increase when items seven or nine would be removed. Subsequent factor analysis using oblimin rotation revealed two factors with eigenvalues above 1. The first factor (eigenvalue = 5.7, 57.1% of variance) incorporated the 8 policies external to the respondents (e.g., improved public transport). The second factor (eigenvalue = 1.4, 14.3% of variance) incorporated two policies internal to the respondent (i.e. where costs are imposed on the respondent). Items within each factor were averaged, resulting in scales of external policies (α = .926) and internal policies (α = .851).
3.3.5 Exploring Potential Mediators & Moderators of Our Effect
3.3.5.1 Mind-set: abstract versus concrete
26
3.3.5.2 Personal efficacy
Three items – adapted from Hornsey and colleagues (2015) –measured participants personal efficacy, or individual control, to climate change: “I believe my actions have an influence on climate change”; “It is hard to imagine that individuals like myself can make a difference with respect to a global phenomenon such as climate change” (reversed); and “There is little point in me taking action against climate change because so many others will not”. All items were responded to on a 7-point scale from 1 – “strongly disagree” to 7 – “strongly agree. All questions have been randomized within the scale. Overall, the scale showed adequate reliability (α = .801).
3.3.5.3 Skepticism
Skepticism is measured using items from existing literature on uncertainty and skepticism in relation to climate change (e.g., Spence Poortinga & Pidgeon, 2012). The following four items are used to measure skepticism towards climate change: ‘The seriousness of climate change is generally exaggerated in the media’, ‘It is uncertain what the effects of climate change will be’, ‘Most scientists agree that humans are causing climate change’ (reversed), and ‘I am uncertain that climate change is really happening’. All items were responded to on a 7-point scale from 1 – “strongly disagree” to 7 – “strongly agree.” All questions have been randomized within the scale. Overall, the scale showed adequate reliability (α = .789).
3.3.5.4 Personal experience
Participants were asked to indicate whether or not they had experienced each of the following natural phenomena in the past five years: flooding, severe heat waves, droughts, freak storms, and ‘other extreme weather events.’ These questions were answered on a yes (I have experienced) and no (I have not experienced) scale. Answers to these items are averaged into a personal experience score.
3.3.5.5 Demographic measures
28
4 RESULTS
4.1 Descriptive Statistics
4.1.1 Sample characteristics
In addition to the basic demographic characteristics discussed in the previous chapter we measured several other important characteristics that might be relevant to this research. As can be seen in table 2, respondents on average have experienced between two to three (M=.45, SD=.31) forms of climate change related weather events. Also, people indicated being in a fairly positive mood (M=5.-, SD=1.3) while answering the questionnaire. In addition, political orientation was measured on a 1 - ‘very conservative’ to 7 - ‘very liberal’ scale. The average political orientation is close to the middle (M=4.5, SD=1.7). A frequency table showed that 22% of the respondents indicate they are neither conservative nor liberal, 26.4% indicated to be on the conservative side of the spectrum, and 51% of the respondents indicated to be on the liberal side of the political spectrum.
Furthermore, the average household size is 2.8 people, including the respondent him- or herself, with a standard deviation of 1.5 people. In total 191 (47.2%) respondent indicated that they own or partly own their residence, 194 (47.9%) of the respondents is renting their house, 2 (0.5%) individuals are unsure, and 18 (4.4%) indicated ‘other’. Answers in this last category mainly concerned living expense free, at family of friends’ residence. Overall, these numbers are in congruence with the numbers reported by the United States Consensus Bureau, on July 1, 2018.
Table 2. Mean and standard deviations of sample characteristics
Variable Range M (SD)
Personal experience 0-1 .45 .31
Current Mood 1-7 5.0 1.4
Political orientation 1-7 4.5 1.7
29 4.1.2 Overall behavioral Intentions and Policy Support
The mean behavioral intentions of the respondents on a scale from 1 – ‘strongly disagree’ to 7 – ‘strongly agree’ is 5.2 (SD=1.1), indicating that people generally show relatively high behavioral intentions. Earlier factor analysis revealed two different constructs for policy support: external policies, which and internal policies. Each of these constructs are measured with items ranging from 1-7. The mean score of the responses to external policies of 5.7 (SD=1.2) is considerably higher than the mean support for the internal policies of 3.5 (SD=1.8). The higher standard deviations reveal that there is spread in the data with concern to policy support. Also see Table 3.
3.3.5 Additional Measures
The constructs of urgency, efficacy and skepticism are also measured on a 7-point scale. The means score of urgency of 5.3 (SD=1.7) is highest, meaning that people generally agree to perceiving certain levels of urgency with regard to climate change. This is followed by the levels of individual efficacy with a mean score of 4.7 (SD=1.5), indicating that individuals tend to agree that their actions have an influence on climate change and that they can make a difference. A mean score of skepticism of 3.0 (SD=1.5) revealed that climate change is perceived as quite uncertain and with skepticism.
Table 3. Mean and standard deviation of main constructs
30
4.2 Manipulation Check: Subjective Perception of Time
A one-way ANOVA with time frame (proximate, neutral, and distant) as independent variable and perceived temporal distance as dependent variable revealed the expected relationship. As expected, the difference between the temporally close frame (M = 49, SD = 28.3), the temporally neutral frame (M = 62, SD = 24.6), and the temporally distant frame (M = 70, SD = 21.8) is highly significant (p < .001) and in the right direction. People in the close condition had perceived 5 years from now subjectively closer than people in the neutral and distant condition. Moreover, people in the neutral condition perceived 5 years from now as subjectively closer than those who were in the distant condition, suggesting that our manipulation of subjective time has been successful.
4.3 Correlations between Main Variables in this Research
31
Table 4. Means and intercorrelations among measures
M (SD) Te m p o ra l d ista n ce Be h a v io ra l in te n tio n s Po li cy Ex te rn a l Po li cy Inter n a l Po li tica l O rienta tio n u rg en cy Co n str u a l Effica cy S k ep ticism Per so n a l Ex p er ience Temporal distance 60 (26) 1 Behavioral intentions 5.2 (1.1) -.05 1 Policy External 5.7 (1.2) .08 .69*** 1 Policy Internal 3.5 (1.7) .09 .43*** .42*** 1 Political Orientation 2.3 (0.9) -.05 .30*** .47*** .25*** 1 Urgency 5.3 (1.6) .00 .63*** .70*** .38*** .53*** 1 . Construal .44 (.28) .10* -.13** -0.5 -.04 .00 -.05 1 Efficacy 3.9 (0.9) -.04 -.13** -.11* .10* -.14** -.10** .08 1 Skepticism 3.8 (1.1) .001 .30*** -.40*** -.15** -39*** -.52*** .04 .34*** 1 Personal Experience .45 (.3) -.03 .45*** .31*** .11* .13** .33*** -.02 .04 -.05 1 *p < .05, ** p < .01, *** p < .001
4.4 The Effect of Subjective Time on Outcome Variables
32 5.4), compared to participants in the neutral condition (M = 5.0) significantly lower than those of participants in the close condition. Additionally, among our covariates, there are moderate effects of mood (b = .108, variance explained = 3,4%, sig. <.001), gender (b = .514, variance explained = 4,6%, sig <.001) and political orientation (b = .194, variance explained is = 8,6%, sig. <.001).
Policy support external. The ANCOVA statistics revealed no significant differences between the different time conditions and their mean policy support external [F(2, 391)=2.088, p = .125]. Also, the Levene’s test shows a significance level of below 0.05, indicating that the assumption of equal variances is not met. Other variables that do have an effect of this category of policy support are mood (b = .089, variance explained = 2,1%, sig. =.004), gender (b = .125, variance explained = 2,1%, sig. =.004) and political orientation (b = .168, variance explained = 19,2%, sig. <.001). Similar to the effect of these variables on behavioral intentions, we here too find that people with a more positive mood, that are female, and those that are more liberal politically oriented show higher levels of policy support.
Policy support internal. For this test the Levene’s test and normality checks were met. However, the ANCOVA statistics reveal also no significant effect of time perception on internal policy support [F(2, 396)= 0.269, p = .764). Additionally, among our covariates, there are small effects of mood (b = .130, variance explained = 1.3%, p =.022), gender (b = -.422, variance explained = 2.1%, p= .03) and political orientation (b = .175, variance explained = 5.1%, p = <.001). Additionally, skepticism (b = -,254 variance explained = 1.9%, p =.006) and efficacy (b = .041 variance explained = 3.0%, p =.001) have an effect on policy support intern.
33
Table 5. Mean and standard deviations for policy support
Construct M SD
Policy support external
Proximate 5.8 1.1
Neutral 5.5 1.3
Distant 5.7 1.2
Policy support internal
Proximate 3.4 1.8
Neutral 3.6 1.8
Distant 3.4 1.7
4.5 Potential Mediating Variables
Urgency. A one-way ANCOVA was conducted to examine how our time manipulation has influenced perceived urgency of climate change consequences, whilst controlling for the same set of covariates. Subsequent Levene’s test and normality checks were carried out, and also these assumptions were met. Results revealed that, although we observed a difference in mean scores of urgency consistent with the direction of our hypothesis, the overall effect was not significant [F(2, 391) = 1.347, p = 0.261]. Importantly, however, in the time close condition, participants’ urgency was (M = 5.4, SD = 1.5) descriptively higher than those in the neutral (M = 5.2, SD = 1.7), and far away conditions (M = 5.3, SD = 1.8). Among the covariates, mood (b = -.073, variance explained = 3.2%, =.001,), age (b = -.027, variance explained = 6.2%, p = .013), gender (b = .723, variance explained = 3.4%, p =.000), and political orientation (b = .21, variance explained = 2.1%, p = .000) were significant predictors of the urgency.
34
4.6 Mediation Effect
Although our IV was not significantly predicting our potential mediators - perceived sense of urgency and construal level - we carried out a full mediation analysis to explore the strength of these effects and variables on our dependent variables. Traditionally mediation analysis requires the IV to predict the mediator and the DV, that the mediator predicts the DV, and that the connection between the IV and the DV decreased when the mediator is controlled for (Baron & Kenny, 1986). Others have argued that it is possible to read a significant indirect effect of the IV to DV connection nonetheless (e.g., Hayes, 2009). For this reason, we decided to test the mediating effects of both urgency and construal level on the outcome variables behavioral intentions, policy support external, and policy support internal. Mediation analysis was conducting using Hayes’ (2013) PROCESS model 4. Bootstrapping of 5000 samples was used, with 95% confidence intervals.
As can be seen in figure 5, the manipulation of subjective perception of time had a direct effect on behavioral intention (b = .22, t(394) = 2.06, sig = 0.04). Although, perceived urgency (and no construal level) significantly predicted participants’ behavioral intentions, the overall indirect path did not reach significance.
Figure. 5. Parallel mediation model with outcome variable Behavioral Intentions.
35 A similar mediation model was run, this time with policy support extern as outcome variable. In these models pathway ‘a’ is identical to the previous model. In the mediation model with policy extern as outcome variable the relationship between urgency and policy support extern (b = .32, t(394) = 8.13, p = <.001) is significant, and the relationship between construal and policy support extern (b = .05, t(394) = .34, p = .74) is insignificant.
The mediation model with policy interns as outcome revealed that here too, urgency has a significant effect (b = .388, t(394) = 4.97, p = <.001), and construal has an insignificant effect (b = -.10, t(394) = -.36, p = .72).
4.7 Potential Moderating Factors of our Effect
The primary intention of this thesis was to invent and test a method to make future times seem psychologically closer and to test whether such a method would change consumers’ intention to engage in environmentally friendly behaviors and policies. Nevertheless, we also collected several relevant variables that may moderate this effect. Specifically, from variables such as personal efficacy, skepticism about climate change, as well as personal experience with climate change consequences and political orientation may well be moderating the effect of our IV on DVs. To explore this curious question, we systematically and independently examined these moderation hypotheses.
To assess whether these variables influence the relation between the time manipulation and the outcome variables Hayes (2013) PROCESS model 1 for moderation was run. Bootstrapping of 5000 samples was used, with 95% confidence intervals. In all of these models the zero was observed between the lower limit confidence interval and the upper limit confidence interval, hence these variables have no moderating effect on the relationship of the treatment on the outcome variables.
36
5 DISCUSSION
5.1 General Discussion
Our temporal distance to climate change is considered as one of the main reasons why we neglect to properly mitigate the biggest threat we are facing (Gifford, 2011). In light of this, the effects of our subjective perceptions of time on our behavioral intentions and policy support seems curious and deserves further exploration.
Previous studies have found that framing techniques can successfully influence people’s reference point, and therefore ones temporal distance to a certain event. The dominant research stream in environmental science found that when climate change is considered to be temporally closer, individuals are more likely to act and support climate change mitigation policy. Some suggest this is due to an increase in levels of urgency, others explain this behavior through Construal Level Theory, where near future events are construed at more concrete levels. With a visual framing technique this research examined the effectiveness of this technique in influencing subjective perceptions of time as well as the effects of people’s willingness to act and support policy.
The findings suggest that interventions that make distance from future times psychologically closer may be effective tools in spurring consumers to adopt environmentally friendly behaviors. Unfortunately however, we were not successful in delineating the underlying process of this effect. Although we measured the two main theoretically-driven mediators, sense of urgency and construal level, none of them was statistically mediating the effect of subjective perception of time on behavioral intentions. Nevertheless, the observed effects sizes in the mediation analysis demonstrated that it is more likely the urgency, rather than construal level, to be the ultimately mediator of our effect. Further research with larger sample sizes are required to confirm this hypothesis.
37 personal investments of sort versus not. Similarly these studies could test for the duration of the manipulation effect by randomizing the order of the measurement of the outcome variables In congruence with existing literature, the findings suggest that people who have personal experience with climate change effects and people towards the liberal side of the political spectrum are more inclined to support pro-environmentally actions and policies. This study also confirmed that people who are more skeptical towards climate change are less inclined to support mitigating actions and policies, which is in line with previous findings of Spence Poortinga & Pidgeon (2012). Surprisingly, we found that people who more skeptical about the issue show higher willingness to act on climate change. Furthermore, our findings suggest that people that perceive higher personal efficacy in climate change mitigation generally report less behavioral intentions and support for policies. This is in line with the theory of motivated control from Hornsey et al. (2015), which proposes that people who feel less in control tend to act in order to restore their sense of control.
Further analysis on the possible moderating effect of these variables revealed that the factors personal efficacy, skepticism about climate change, personal experience with climate change, and political orientation do not have an effect on the relationship between the subjective perception of time and their intentions to act and support climate change policies.
5.2 Contributions and Implications
This present study provides insights into the alteration of the subjective perception of time of climate change by means of a visual framing technique. Although a number of the proposed relationships are considered significant on a 5% significance level, it does not take away the practical significance of the observation. Based on previous literature and the findings in this study, the magnitude of the relationship between time perception and outcome variables such as behavioral intentions and policy support is considered of high importance.
38 In addition, this study provides insight for practitioners and policy makers as to how visual representation of time make future times seems closer and as result influence their subsequent behavioral intentions and policy support for climate change mitigation. The results imply that practitioners or policy makers can make use of framing techniques to increase behavioral intentions (e.g., reduction in energy use). Similarly, this visual technique could be relevant for encouragement of adaptation measures. To illustrate, ones reference point with regard to climate change impacts has an influence not only on the value of sustainable actions, but also to certain mitigation and adaptation measures connected with climate change. For example, people’s attitude towards flood insurance or the attractiveness of further technical adaptation. To illustrate, this technique could be used to make return on investment in sustainable (for example, solar panels) seem more proximate.
5.3 Limitations and Future Research
Although the subjective time perception prime has been shown to be an effective way of decreasing people’s temporal distance, it is nearly impossible to control for individual thought processes. In this research we measured two mediators as proposed in literature, to find that both sense of urgency and construal level were not statistically mediating the effect of subjective perception of time on behavioral intentions nor policy support. This could be due to the sample size, or because of the involvement of one or more other factors that were not measured in this study. Future studies should incorporate a larger and more diverse sample, amongst others to provide insight on this.
Similarly, it is worth exploring how this current framing technique has an effect on decisions of smaller magnitude in the environmental domain. In the present study the manipulation is perhaps not strong enough to elicit the level of urgency and concrete associations to lead to a major increase subsequent behavioral intentions and support of policy, and solely have an effect on t. When the scenario would address a more concrete example or issue reflecting climate change the relationships found in this study might be reflected more strongly.
40
6 CONCLUSION
Climate change is long-term, delayed, and an inherently more complex issue. Human behavior on the other hand, is fluid and can be influenced and adapt to considerable changes in short-term efforts. From a psychological perspective we have argued that subjective perceptions of time in an important element in the broader theory of psychological distance. Both concepts show extremely relevant to the environmental domain, and climate change mitigation measures. The challenges we are facing with regard to climate change are diverse of nature, and involve a wide range of actions within the current and future societal structures.
Research already revealed that consumers of today generally perceive climate change as a distant issue, where its effects are of predictive nature and generally communicated in the context of long durations. Due to this perceived distance feelings of considerable urgency – and hence sustainable actions - are lacking. If there is a desire to change this temporal distance to the environment, it is key to change the way the effects of climate change are communicated. The findings from psychology and climate change science stresses the potential of changing ones reference point to climate change through message framing.
41
7 REFERENCES
Angrilli, A., Cherubini, P., Pavese, A., & Manfredini, S. (1997). The influence of affective factors on time perception. Perception & psychophysics, 59(6), 972-982.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.
Bent, B. J. (2017). The Effects of Delay and Probabilistic Discounting on Green Consumerism. Böhm, G., & Pfister, H. R. (2005). Consequences, morality, and time in environmental risk evaluation. Journal of Risk Research, 8(6), 461-479.
Bord, R. J., Fisher, A., & Robert, E. O. (1998). Public perceptions of global warming: United States and international perspectives. Climate research, 11(1), 75-84.
Bradley, M. M., Codispoti, M., Cuthbert, B. N., & Lang, P. J. (2001). Emotion and motivation I: defensive and appetitive reactions in picture processing. Emotion, 1(3), 276.
Brügger, A., Dessai, S., Devine-Wright, P., Morton, T. A., & Pidgeon, N. F. (2015). Psychological responses to the proximity of climate change. Nature climate change, 5(12), 1031.
Brügger, A., Morton, T. A., & Dessai, S. (2016). “Proximising” climate change reconsidered: A construal level theory perspective. Journal of Environmental Psychology, 46, 125-142. Burson, K. A., Larrick, R. P., & Lynch Jr, J. G. (2009). Six of one, half dozen of the other: Expanding and contracting numerical dimensions produces preference reversals. Psychological science, 20(9), 1074-1078.
Caseldine, C. (2012). Conceptions of time in (paleo) climate science and some implications. Wiley Interdisciplinary Reviews: Climate Change, 3(4), 329-338.
Chandran, S., & Menon, G. (2004). When a day means more than a year: Effects of temporal framing on judgments of health risk. Journal of Consumer Research, 31(2), 375-389.
Droit-Volet, S., & Meck, W. H. (2007). How emotions colour our perception of time. Trends in cognitive sciences, 11(12), 504-513.
Ejelöv, E., Hansla, A., Bergquist, M., & Nilsson, A. (2018). Regulating Emotional Responses to Climate Change–A Construal Level Perspective. Frontiers in Psychology, 9.
Faralla, V., Novarese, M., & Ardizzone, A. (2017). Framing effects in intertemporal choice: A nudge experiment. Journal of Behavioral and Experimental Economics, 71, 13-25.
Frederick, S., Loewenstein, G., & O'donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of economic literature, 40(2), 351-401.
42 Fujita, K., Eyal, T., Chaiken, S., Trope, Y., & Liberman, N. (2008). Influencing attitudes toward near and distant objects. Journal of Experimental Social Psychology, 44(3), 562-572.
Gattig, A., & Hendrickx, L. (2007). Judgmental discounting and environmental risk perception: Dimensional similarities, domain differences, and implications for sustainability. Journal of Social Issues, 63(1), 21-39.
Gifford, R. (2011). The dragons of inaction: psychological barriers that limit climate change mitigation and adaptation. American Psychologist, 66(4), 290.
Gifford, R., & Comeau, L. A. (2011). Message framing influences perceived climate change competence, engagement, and behavioral intentions. Global Environmental Change, 21(4), 1301-1307.
Gifford, R., Scannell, L., Kormos, C., Smolova, L., Biel, A., Boncu, S., Corral, V., Güntherf, H., Hanyu, K., Hine, D. & Kaiser, F. G. (2009). Temporal pessimism and spatial optimism in environmental assessments: An 18-nation study. Journal of environmental psychology, 29(1), 1-12.
Han, J. J., & Gershoff, A. D. (2018). When Good Things Feel Closer and Bad Things Feel Farther: The Role of Perceived Control on Psychological Distance Perception. Journal of Consumer Psychology.
Hardisty, D. J., & Weber, E. U. (2009). Discounting future green: money versus the environment. Journal of Experimental Psychology: General, 138(3), 329.
Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication monographs, 76(4), 408-420.
Hornsey, M. J., Fielding, K. S., McStay, R., Reser, J. P., Bradley, G. L., & Greenaway, K. H. (2015). Evidence for motivated control: Understanding the paradoxical link between threat and efficacy beliefs about climate change. Journal of Environmental Psychology, 42, 57-65. IPCC, 2018: Summary for Policymakers. In: Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [V. Masson-Delmotte, P. Zhai, H. O. Pörtner, D. Roberts, J. Skea, P. R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, T. Waterfield (eds.)]. World Meteorological Organization, Geneva, Switzerland, 32 pp.
Joo, J., Li, W., Steen, F. F., & Zhu, S. C. (2014). Visual persuasion: Inferring communicative intents of images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 216-223).
Kahneman, D., & Tversky, A. (2013). Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I (pp. 99-127).
43 Karl, T. R., Melillo, J. M., Peterson, T. C., & Hassol, S. J. (Eds.). (2009). Global climate change impacts in the United States. Cambridge University Press.
Lee, A. Y., Keller, P. A., & Sternthal, B. (2009). Value from regulatory construal fit: The persuasive impact of fit between consumer goals and message concreteness. Journal of Consumer Research, 36(5), 735-747.
Lempert, K. M., & Phelps, E. A. (2016). The malleability of intertemporal choice. Trends in Cognitive Sciences, 20(1), 64-74.
Liberman, N., Sagristano, M. D., & Trope, Y. (2002). The effect of temporal distance on level of mental construal. Journal of experimental social psychology, 38(6), 523-534.
Liberman, N., & Trope, Y. (1998). The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. Journal of personality and social psychology, 75(1), 5.
Liberman, N., & Trope, Y. (2008). The psychology of transcending the here and now. Science, 322(5905), 1201-1205.
Leiserowitz, A. (2005). American risk perceptions: Is climate change dangerous?. Risk Analysis: An International Journal, 25(6), 1433-1442.
Leiserowitz, A. (2006). Climate change risk perception and policy preferences: The role of affect, imagery, and values. Climatic change, 77(1-2), 45-72.
Loewenstein, G., & Schwartz, D. (2010). Nothing to fear but a lack of fear: climate change and the fear deficit. G8 Magazine, 60-62.
McCright, A. M., & Dunlap, R. E. (2011). The politicization of climate change and polarization in the American public's views of global warming, 2001–2010. The Sociological Quarterly, 52(2), 155-194.
McDonagh, P., & Prothero, A. (2014). Sustainability marketing research: Past, present and future. Journal of Marketing Management, 30(11-12), 1186-1219.
Milfont, T. L., & Gouveia, V. V. (2006). Time perspective and values: An exploratory study of their relations to environmental attitudes. Journal of Environmental Psychology, 26(1), 72-82. Monga, A., & Bagchi, R. (2011). Years, months, and days versus 1, 12, and 365: the influence of units versus numbers. Journal of Consumer Research, 39(1), 185-198.
Mortreux, C., & Barnett, J. (2009). Climate change, migration and adaptation in Funafuti, Tuvalu. Global Environmental Change, 19(1), 105-112.
Moser, C., Stauffacher, M., Smieszek, T., Seidl, R., Krütli, P., & Scholz, R. W. (2013). Psychological factors in discounting negative impacts of nuclear waste. Journal of Environmental Psychology, 35, 121-131.
Nan, X. (2007). Social distance, framing, and judgment: A construal level perspective. Human Communication Research, 33(4), 489-514.
44 O'Neill, S., & Nicholson-Cole, S. (2009). “Fear won't do it” promoting positive engagement with climate change through visual and iconic representations. Science Communication, 30(3), 355-3.
Pahl, S., Sheppard, S., Boomsma, C., & Groves, C. (2014). Perceptions of time in relation to climate change. Wiley Interdisciplinary Reviews: Climate Change, 5(3), 375-388.
Park, C., Im, G., & Keil, M. (2008). Overcoming the mum effect in IT project reporting: Impacts of fault responsibility and time urgency. Journal of the Association for Information Systems, 9(7), 1.
Rickard, L. N., Yang, Z. J., Schuldt, J. P., Eosco, G. M., Scherer, C. W., & Daziano, R. A. (2017). Sizing up a superstorm: exploring the role of recalled experience and attribution of responsibility in judgments of future hurricane risk. Risk analysis, 37(12), 2334-2349.
Schuldt, J. P., Rickard, L. N., & Yang, Z. J. (2018). Does reduced psychological distance increase climate engagement? On the limits of localizing climate change. Journal of Environmental Psychology, 55, 147-153.
Shaw, D., McMaster, R., & Newholm, T. (2016). Care and commitment in ethical consumption: An exploration of the ‘attitude–behavior gap’. Journal of Business Ethics, 136(2), 251-265. Sherover, C. M. (2001). The human experience of time: The development of its philosophic meaning. Northwestern University Press.79.
Solomon, S., Plattner, G. K., Knutti, R., & Friedlingstein, P. (2009). Irreversible climate change due to carbon dioxide emissions. Proceedings of the national academy of sciences, 106(6), 1704-1709.
Soyez, K. (2012). How national cultural values affect pro-environmental consumer behavior. International Marketing Review, 29(6), 623-646.
Spence, A., & Pidgeon, N. (2009). Psychology, climate change & sustainable bahaviour. Environment: Science and Policy for Sustainable Development, 51(6), 8-18.
Spence, A., & Pidgeon, N. (2010). Framing and communicating climate change: The effects of distance and outcome frame manipulations. Global Environmental Change, 20(4), 656-667. Spence, A., Poortinga, W., Butler, C., & Pidgeon, N. F. (2011). Perceptions of climate change and willingness to save energy related to flood experience. Nature climate change, 1(1), 46. Spence, A., Poortinga, W., & Pidgeon, N. (2012). The psychological distance of climate change. Risk Analysis: An International Journal, 32(6), 957-972.
Sundblad, E. L., Biel, A., & Gärling, T. (2007). Cognitive and affective risk judgements related to climate change. Journal of Environmental Psychology, 27(2), 97-106.
Thaler, R. (1981). Some empirical evidence on dynamic inconsistency. Economics letters, 8(3), 201-207.
