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Article

Environmentally Motivated Travel Reduction: The Effects of Availability, Herding Bias, and Self-Monitoring

by
Shiva Ghorban Nejad
1,* and
Håvard Hansen
2
1
The Norwegian School of Hotel Management, University of Stavanger, 4036 Stavanger, Norway
2
Emeriti Marketing, 4370 Egersund, Norway
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(2), 691; https://doi.org/10.3390/su13020691
Submission received: 9 December 2020 / Revised: 8 January 2021 / Accepted: 10 January 2021 / Published: 12 January 2021
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Abstract

:
Asking consumers to consume less, or to travel less, does not necessarily make them do so and is, therefore, a key challenge for public policy-makers and green marketers. In this paper, we scrutinize the effect of intuitive decisions/judgments as well as personality differences on consumers’ intentions to consume less. More specifically, we study how the availability heuristic, herding biases, and self-monitoring influence environmentally motivated consumption reduction (EMCR) in a tourism context. Drawing on EMCR and a heuristics-and-biases approach, we present an experimental study designed to test how availability (high vs. low information availability), herding behavior (herd present vs. no herd), and high vs. low self-monitoring influence consumers’ intentions to travel less in the future. The results suggest that the existence of a herd influence environmentally motivated travel reductions. Also, high self-monitors are more capable of sensing the eco-friendly direction of the public opinion in general, and adjust their behavior to align with this, than individuals with a lower self-monitoring tendency. Finally, there is a positive interaction between herding bias and self-monitoring, while we found no significant effect of the availability heuristic on the environmentally motivated travel reduction. The implications of the results and future research avenues are discussed.

1. Introduction

To reach the UN Sustainable Development Goals, vast efforts directed toward changing the behavior of the 21st century consumers have been initiated by both policy-makers, corporations, and researchers. Within the area of consumer research, a significant stream of new knowledge has increased our understanding of pro-environmental aspects, for example, of the eco-friendly consumer [1], the short-comings of the models usually applied to explain sustainable behavior [2,3], communication strategies to nudge consumers in an eco-friendly direction [4], or the introduction of new explanatory models and concepts like the New Ecological Paradigm [5,6], and the SHIFT framework (e.g., [7]). Moreover, fruitful studies of how consumers relate to sustainability issues have led researchers to identify different types of eco-friendly or sustainable behavior, where one example is Stern’s [8] categorization of environmental activism, non-activist behavior in the public sphere, private sphere environmentalism, and other significant environmental behaviors. A general conception of pro-environmental behavior is that any behavior that does as little harm to the environment as possible or even benefits it [9,10], whether it is performed in public or in the private domain, in the natural or the built world [11], falls under the definition of pro-environmental or sustainable behavior. Previous studies have taken a broad approach to the study of such behaviors, for example, how consumers replace one kind of consumption behavior with another one [12], or how and why consumers adopt new pro-environmental behaviors [6]. Stern [8] suggested that causal variables that explain pro-environmental behaviors all fall into a quartet consisting of attitude variables, contextual factors, personal capabilities, and habits or routines. In 2010, Andalucía reported that in the 27 EU countries, 20 percent of CO2 emissions were caused by household consumption [13]. Drawing on this, Egea and de Frutos [14] argued that neither new practices like recycling nor new technology that leads to more eco-efficient products or product substitutions have given the results needed, largely because they are outbalanced by a substantial growth in consumption levels. A more promising pathway, they suggested, is to reduce the consumption levels that drive environmental degradation in the first place [14,15]. In this paper, we build on this approach to sustainable consumer behavior and suggest that the greatest environmental impact results not from choosing the more sustainable of items offered, or from disposing of used items in a sustainable way, but from not buying the product, item, or service at all. Stated differently, our focus is on increasing the understanding of how to get consumers to consume less, and more specifically, we study how environmentally motivated consumption reduction (EMCR) is influenced by information availability (availability heuristic), herding bias, and self-monitoring. This implies that we set our scope on two of Stern’s [8] groups of behavioral drivers, contextual factors and personal capabilities, respectively, and on a type of behavior that would sort under his private-sphere environmentalism umbrella.
Environmentally motivated consumption reduction, shortened as EMCR, simply represents reduction in consumption for the sake of saving the environment [14]. Prior research has concentrated on the factors that support EMCR, such as anti-consumption [16,17], voluntary simplicity or downshifting [18], the effect of EMCR on consumers’ well-being [19,20], as well as barriers to EMCR, such as counter-arguing [21]. It is also known that voluntary simplicity, or downshifting, is a lifestyle that is averse to a materialistic lifestyle and contrasts mass consumption [18] and that consumption reduction also stems from borrowing products from others, renting out products we own ourselves, or other variations of collaborative consumption [22,23,24]. However, while these are examples of environmental activism and non-activist behavior in the public sphere primarily motivated by values and attitudes, more recent work suggests that consumption reduction decisions might be influenced by heuristics and biases [19]. An interesting question from this perspective is how the general consumer can be influenced to consume less without being persuaded by influencing his or her values and attitudes. For our study, the context of travel and tourism, and how to turn consumers toward a future with less traveling, is especially important for several reasons.
First, a growing pressure on the world’s resources corresponds with the global growth of tourism, a trend that significantly contributes to environmental damage [25,26]. Second, this growth mainly arises due to what can be called luxurious consumption, as a large part of world tourism is based on experiential or hedonic motives, where consumers’ wish for leisure activities and experiences are the driving force behind their traveling. Although tourism promotes the economy at the destinations, the large number of tourists in an area has often had negative impacts on the environment [25,27,28], and over-use of the available resources [29,30], sometimes to a point where the environmental loads caused by tourists are higher than the environmental loads caused by locals themselves [31]. Even though the increasing tourism activity is found to have negative environmental impacts, relatively little research has scrutinized the antecedents of EMCR within a tourism context. While studies have focused on issues like waste reduction in tourism destinations, restaurants, and hotels [32,33] or reduced water consumption and towel use [34,35], we argue that in addition to knowledge on how to behave sustainable at the destination, there is a need for more knowledge on how to get people to reduce the amount of traveling in general. New research has revealed that hearing pro-environmental arguments actually makes consumers less likely to have a pro-environmental or sustainable behavior [36], which suggests that individuals do not like to do what they are asked or told to do. This means that asking consumers to consume more environmentally friendly, or asking them to travel less, does not necessarily make them do so. To our knowledge, no study has focused on the effects of heuristics and biases on environmentally motivated consumption reduction in a tourism context. The next paragraphs outline the specifics of our study in more detail.

2. Theoretical Background and Hypotheses Development

2.1. The Availability Heuristic and Environmentally Motivated Travel Reduction

The availability heuristic concerns an individual’s tendency to judge an event by “the ease with which instances come to mind” [37]. More easily said, when information about a particular event or outcome receives one’s attention, it temporarily increases the availability of this information, which influences judgments and decisions [37,38,39]. For instance, as information about the outcome of a plane crash is much more available in memory than the statistical probability of a plane crash, most people will overestimate the danger of flying. A large body of research shows that our judgments are influenced by information most readily available at the time a judgment or decision is made, and the availability heuristic has been studied within managerial/business decision making [40,41,42], predictive judgments under uncertainty [43,44], and the area of intuition and decision-making [45,46]. In a consumer context, the effect of availability heuristics has been studied within consumers’ online purchase intentions [47], price perceptions [48], and consumers’ judgments [49] to name a few. Thus, Taylor’s suggestion that “one’s judgements are always based on what comes to mind” [50] seems to be in place.
Most of our individual behaviors and intentions are controlled by the fast, automatic system of the brain, or what Kahneman [38] calls System 1. When people make biased evaluations due to the availability heuristic, it occurs due to an automatic, subconscious, and natural tendency to rely disproportionately upon the most readily available data, in line with system 1 processing. According to Marteau [51], “effective strategies for consumption reduction involves mainly targeting this system”, and in this study we argue that readily available reasons to reduce traveling will influence future traveling more than less vivid information about the consequences of traveling. When suggesting this, we draw on the experiences from previous episodes that have received considerable attention and made their way into consumer judgments. One example is the 14 percent decline in visitors to Paris after the 2015 terrorist attacks. Another is the 18 percent drop in visitors to Brussels after the 2016 terrorist attack at Zaventem airport [52]. Although the airport was shut down for weeks, and a drop thus natural due to visitors not being able to travel to Brussel by air, the decline in arriving tourists continued for months after the airport was opened again and was first recovered in November the same year [53]. While governmental security measures probably made Zaventem the safest airport on the globe in the weeks and months following the attack, this information was not emphasized to the same degree as information on the horrors of the attacks that was broadcast shortly after it took place, information still vividly available in memory. In the same manner, we can speculate that our newly acquired information on the consequences of the COVID-19 pandemic, with death tolls looming in a large number of cities and countries, the pandemic dominating the news worldwide for months, and popular travel destinations being shut down and borders closed for travelers, are more available pieces of information than the long-term negative consequences climate change will have on a large number of these same destinations. Following this line of thinking, our first hypothesis suggests that:
Hypothesis 1 (H1).Easily available information that calls for less future traveling will have a stronger effect on environmentally motivated travel reduction than hardly available information.

2.2. Herding Bias and Environmentally Motivated Travel Reduction

Herding bias occurs when individuals draw decision-related conclusions by simply replicating the behavior of others [54]. This is also known as the Bandwagon effect, or jumping on the bandwagon, and when consumers observe that (a group of) others make a particular choice, or conduct one specific behavior instead of another, they imitate these actions and behaviors instead of making their own elaborate decisions [55,56]. Borrowed from the social psychology literature, herding behavior is a kind of concurrent social behavior that is the outcome of interactions with other individuals in a group [57].
Herding behavior has been largely studied in financial behavior among stock market investors but also within consumer behavior where it has typically been studied in relation to product adoption, purchase intentions, and product evaluations (e.g., [54,58,59,60]). Raafat, Chater, and Frith [57] further suggested that media might influence the public by generating mass herd behavior that is unnoticeable to the individuals. However, earlier scholars believed that also small shocks might influence mass herd behavior and usher large behavioral shifts [56]. Moreover, individuals are more likely to jump on the bandwagon when they are urged to make decisions under uncertainty [56,58].
Observing others making a decision lead the observer to imitate this behavior and make a similar decision [60]. According to Salazar et al. [61], this implies that their choice or decision outcome could have been different if the decision was made in pure isolation. Salazar et al. [61] studied the effect of different types of influence on the intentions to buy sustainable products and found a positive effect of the existence of the herd on the likelihood of buying sustainable and environmentally friendly products. This viewpoint leads us to theorize that the existence of a herd increases the likelihood of consumer’s planned travel reduction and intentions. Accordingly:
Hypothesis 2 (H2).Information that calls for less future traveling will have a stronger effect on environmentally motivated travel reduction if it portrays the existence of a herd than if no such herd exists.

2.3. Self-Monitoring and Environmentally Motivated Travel Reduction

As previously mentioned, that we need to change our ways of behavior to hamper the ongoing climate change seems to be commonly accepted. This implies that the world population largely agrees that we need to reduce our individual carbon footprints and that we all have an individual responsibility to contribute to reaching the UN two-degree global warming goal. However, as we have also mentioned earlier, agreeing to something and actually behaving in accordance with it are often two different things. Accordingly, we argue that individuals who monitor and adapt their behavior to the expectations of the social situation they are in are also more likely to align their actions to these commonly accepted pathways for pro-environmental behavior. Self-monitoring is a personality trait that distinguishes between an individual’s ability or motivation to express, present themselves, and behave in accordance with situational cues to create a desired impression [62]. Stated differently, they tailor their actions in instant situations to either impress others or not refrain from making a bad impression. Based on self-monitoring theory, high self-monitors tailor their behavior to match up the situation they are in and can more easily adapt and perform or not perform a behavior when conditions change. In contrast, low self-monitors represent their real personality regardless of which situation they are in, and there is no appropriate or inappropriate manner for low self-monitors except being themselves [62].
In the context of consumer behavior, self-monitoring has been studied within advertising [63,64], materialism and product involvement (e.g., [65,66]), decision-making [67], consumption behavior, and purchase intentions [68,69,70]. The central promise of self-monitoring theory suggests that individuals of the high self-monitoring kind are more prone to adapt to socially accepted trends and situations, for example, the need to reduce our carbon footprints. Their low self-monitoring companions, on the other hand, find it more difficult to adapt to different circumstances. One explanation is that high self-monitors are significantly influenced by external features as drivers of behavior, while internal features are more prominent motivators. Building on these assumptions, we argue that when consumers’ self-monitoring is higher, they are more likely to reduce their consumption owing to the generally held expectation that we all do our share in pursuing climate and environmental protection.
Hypothesis 3 (H3).High self-monitors will be more inclined to embrace environmentally motivated travel reduction than low self-monitors.

2.4. The Interaction between Herding Behavior and Self-Monitoring

In an early study on the effects of self-monitoring in consumer behavior, Becherer and Richard [71] concluded that the behavior of low self-monitors is largely driven by personality, while among high self-monitors situational factors are more likely to guide behavior. This corresponds well to later studies, for example, Oh et al.’s [72] research on counter-productive work behaviors. They found that when the behavior of high self-monitors is visible to others, they will adopt behavior that makes them look good to others. This is in line with the status enhancement motive [73] and suggests that when appearing in public (e.g., when confronted with a crowd), high self-monitors are more prone to adapt their behavior than when operating in the private sphere [72]. Following this, we suggest that the influence of herding bias on travel reduction will differ between people with different levels of self-monitoring. This assumption rests on the inherent theoretical nature of these two variables, where we argue that people who are intrinsically motivated to adapt their behavior to the expectations and behaviors of members of a social group are more receptive to information on the behaviors and expectations of others to enable this behavioral adaptation. Thus, observing the behavior of a herd, high self-monitors should be more motivated to follow this behavioral direction than individuals who do not make contextual behavioral adaptations to the same extent.
Hypothesis 4 (H4).There is an interaction effect between herding bias and self-monitoring on environmentally motivated travel reduction.
The presented hypotheses are summarized in Figure 1.

3. Method

3.1. Data Collection and Sample

To test our hypotheses, we constructed a scenario-based experiment with a 2 (high/low availability) × 2 (No herd present/herd present) × 2 (high/low self-monitoring) between-subjects factorial design. Four hundred twenty-nine participants between the age of 18 and 64 (56.4 percent female, mean age 41 years) were recruited from a national consumer panel to participate in our web-based experiment, each being randomly assigned to one of the four experimental cells made up by the availability and herding conditions. Detailed information on the participants is summarized in Table 1. On signing in to the web-based, online study, participants were welcomed and asked to read a cover story that drafted a possible scenario for the future of tourism.
The data were collected in the spring/summer of 2020, in the midst of the national COVID-19-instigated shut-down. Half of the respondents were given a story describing how the current COVID-19 pandemic, and the travel restrictions and travel reductions that have been brought about, were likely to require changes in future travel behavior compared to what we have done in the past. This was the high availability condition, and the COVID-19 pandemic focus was chosen because the COVID-19 situation had been the number one news story for months, without any other news story receiving anywhere near the same level of attention. In other words, with most national borders closed, most airlines grounded, and cross-national travels either impossible or associated with quarantine restrictions, the effects of the pandemic on traveling were vivid and top-of-mind in consumer memory. In the low availability condition, the respondents were given a story about the climate change situation and how the future of tourism might be affected by the need to reduce the carbon footprint. The results of climate change are less vivid in the respondent’s memory for several reasons. First, very few people have personally experienced the devastating results climate change may cause in the future, and while people dying from the pandemic happens here and now, with updated national and international death tolls reported on newspaper cover pages every day, the results of climate change are expected to unfold slowly in the coming decades. In other words, while most people could see, and also feel the consequences of a global pandemic themselves, the consequences of climate change are more disconnected from the individual.
The cover story also contained the herding bias manipulation, where half the respondents were given information on studies that showed how a large number of consumers would reduce the number of future travels (herd), or that no such indications existed (no herd). The scenario story was of equal length in all conditions, and full texts can be found in Appendix A. In terms of validity, we ensured internal validity by allocating subjects randomly to the experimental cells. External validity was strengthened by random selection of participants from a national consumer panel.

3.2. Measures

After reading the scenario, all subjects were asked to complete a short questionnaire measuring their thoughts on future travel reduction. To capture the variables not experimentally manipulated, we employed a combination of self-constructed items and measures adapted from existing literature. To assess respondents’ level of self-monitoring, we employed the thirteen-item Revised Self-monitoring Scale developed and validated by Lennox and Wolfe [62]. We used a Norwegian translation of the scale that has previously been employed and validated in a Norwegian sample. Planned travel reduction was measured with a self-constructed single item that reads “In the future I plan to reduce my number of leisure travels”. All scales were in a 7-point Likert-type format with anchors Strongly disagree (1) and Strongly agree (7). All measures are reported in Appendix B. In line with previous applications of the scale, the items for self-monitoring were summarized into an index (α = 0.83) (e.g., [62]), and then split into a low and high group based on a median split (e.g., [74]).

4. Results

Hypotheses Testing

The mean scores for planned travel reduction across all experimentally manipulated conditions are reported in Table 2. To test the hypotheses, an analysis of variance (ANOVA) was conducted with the three independent variables availability, herding, and self-monitoring as fixed factors and planned travel reduction as the dependent variable. Hypothesis one suggested a positive main effect of availability on planned travel reduction but was not supported. However, hypothesis two was supported as we found a significant main effect of herding on planned travel reductions (F = 4.29, p = 0.039).
There was also a significant main effect of self-monitoring on planned travel reduction (F = 13.66), which supported our hypothesis three. Finally, a significant two-way interaction was found between herding and self-monitoring (F = 5.95, p = 0.015), and the specific pattern of this interaction was consistent with hypothesis four: high self-monitors are more receptive to information on the behaviors and expectations of others to enable their behavioral adaptation such as reducing their traveling.
We did not have any expectations or hypotheses for other interactions among the variables, nor were these statistically significant. Table 3 presents the results of the ANOVA test, and Figure 2 portrays the interaction between herding and self-monitoring.

5. General Discussion and Conclusions

Motivating consumers to consume less, or travel less, is both theoretically and practically challenging. Of the four hypotheses presented in this paper, only three received empirical support and our discussion primarily focuses on these. First, we found that the existence of a herd influenced environmentally motivated travel reductions (H2). This adds to our knowledge on how herding bias mechanisms can be applied in attempts to nudge or push people in the direction of more eco-friendly behavior. For researchers attempting to follow Hüttel et al.’s [19] suggestion that heuristics and biases are fruitful pathways to shift consumption levels in a more eco-friendly manner, our results add support to this idea in general. They also suggest that public policy makers and green marketers alike could benefit from building their pro-environmental campaigns on the existence of herds adopting a consumption reduction lifestyle, rather than the usually communicated details on climate change challenges, the importance of waste reduction, etc.
Second, we found that high self-monitors are more likely to reduce the level of future travels than their low self-monitoring companions (H3). It is important to stress that this does not imply that we think consumers will travel less just because they score high on this particular personality trait. What we argue is that high self-monitors are more capable of sensing the direction of the public opinion in general and adjust their behavior to align with this than individuals with a lower self-monitoring tendency. This should mean that any pro-environmental campaign or activity that receives support in a larger part of the public will find an easier way to the attention of high self-monitors. If we see this in combination with herding, our fourth hypothesis suggested an interaction between the two (herding and self-monitoring), which was supported by the data. This implies that any actor attempting to influence future consumption levels can draw on basic textbook adoption and diffusion theory (e.g., [75]). Classical diffusion theory suggests that the diffusion of ideas, e.g., the idea of consuming or traveling less, is first adapted by innovators and early adopters before the early and late majorities enter the stage. However, the snowball is first getting seriously large when these majorities are on the move. Therefore, to get the idea of reduced travel and consumption spread in a society, public policy makers and marketers alike can focus on getting the innovators and early adopters on board, and then play the herding card to recruit the larger majorities. When the high self-monitoring individuals in these majorities sense what is on the move, they are likely to adopt this new ecological idea faster than low self-monitors, and these two may therefore end up in different phases of the diffusion process.
Previous research on herding bias [54] has found that herds affect consumers differently, depending on who the herd is. For self-monitoring, one assumption is that high self-monitors are driven by status enhancement motives [73], which should imply that when high self-monitors are exposed to a high-status herd, they would be more inclined to adopt the behavior of this group than low self-monitors. This is also in line with Attari et al. [76,77], who suggested that climate change advocates themselves must lead the way by reducing their own carbon footprints. For which consumption and travel reduction is one possibility. If high-status members among these advocates are given the role of a herd, it can be assumed that they will influence high self-monitors more than if a herd associated with less status is established. For the low self-monitors that are less affected by situation and more by personality, other persuasion strategies might be more effective, although there is a main effect of herding on consumption reduction that signals a general effect of herding that also applies to these consumers.
Nevertheless, our results lend support to the idea that if the travel/consumption reduction policy or lifestyle gets a foothold among consumers, it can be further strengthened by parading the herds that have adopted it.
These suggestions and implications all find their support in the more automatic System 1 way of processing [38], and while there are numerous alternative ways to influence consumers through attitudes, values, characteristics, and policies, our study have attempted to offer some insights on how to approach consumption reduction by insights into heuristics, biases, and personality differences. The mixed results imply that not all behavioral heuristics are equally applicable to the area of consumption reduction strategies, which leaves room for future studies to extend this field even more.

Limitations and Avenues for Future Research

In our study, all participants were recruited from one country only, and also a country with a rather high standard of living. This implies that reducing consumption levels, while still keeping a reasonably convenient lifestyle, would be easier in societies like this than the ones characterized by less affluent consumers. Thus, the motivation to reduce consumption levels and travel activities may be affected differently by our suggested measures in the latter kind of consumer groups. On the other hand, to achieve the UN Sustainable Development Goals, targeting the “big spenders” like the ones who participated in this study might prove more efficient than consumers whose consumption levels are lower in general. For example, according to Gössling and Humpe [78], “50 percent of all air travel emissions can be attributed to only one percent of the world’s population”, and if our sample is not representative of the world population as a whole, it nevertheless offers insights into the ones that should be a natural target group for any strategy aimed at more eco-friendly behavior. However, we suggest that our study should be replicated and extended also in other countries and consumer groups, and that it should be accompanied by the inclusion of other insights from behavioral economics, behavioral public policy, and cognitive heuristics and biases. By so doing, there might be interesting and important knowledge to be established on how to motivate consumers to consume less and travel less.

Author Contributions

Conceptualization, formal analysis, investigation, methodology, writing—original draft, writing—review and editing, S.G.N. and H.H. Both authors have read and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study, as it is not possible to identify any of the subjects.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are yet not publicly available due to them being part of an ongoing research project.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Manipulation conditions translated from Norwegian.

Appendix A.1. Condition 1: High Availability vs. Herd

The last months have shown all of us the vulnerability of a global society with extensive travelling between countries and continents. The rapid spread of the coronavirus, and the devastating results of this pandemic, are of great future concern not only for our way of living, but also for the mere survival of a large amount of people around the globe. And while what we have witnessed this spring is scary enough, the health experts claim that the corona crisis is just a pre-warning of what we can expect in the future. Because of this, and to reduce the possibility of spreading a new and even more dangerous virus should it emerge, we will probably have to restrain our future travel behavior, especially international and long-haul travels. Recent studies also indicate that the general population agrees to voluntary changes in future travel behavior. Studies from Norway show that 64 percent of those asked say they will now reduce their amount of international travelling, and a similar European study from 8 different countries reported that 67 percent of the participants will reduce international and long-haul travels in the future.

Appendix A.2. Condition 2: Low Availability vs. Herd

The last decades have shown all of us how vulnerable our global climate is to extensive travelling between countries and continents. The rapid increase in greenhouse gasses, and the devastating results on the climate, are of great future concern not only for our way of living, but also for the mere survival of a large amount of people around the globe. And while what we have witnessed so far is scary enough, the climate experts claim that the current climate changes are just a pre-warning of what we can expect in the future. Because of this, and to reduce the possibility of global warming increasing out of control, we will probably have to restrain our future travel behavior, especially international and long-haul travels. Recent studies also indicate that the general population agrees to voluntary changes in future travel behavior. Studies from Norway show that 64 percent of those asked say they will now reduce their amount of international travelling, and a similar European study from 8 different countries reported that 67 percent of the participants will reduce international and long-haul travels in the future.

Appendix A.3. Condition 3: High Availability vs. No Herd

The last months have shown all of us the vulnerability of a global society with extensive travelling between countries and continents. The rapid spread of the coronavirus, and the devastating results of this pandemic, are of great future concern not only for our way of living, but also for the mere survival of a large amount of people around the globe. And while what we have witnessed this spring is scary enough, the health experts claim that the corona crisis is just a pre-warning of what we can expect in the future. Because of this, and to reduce the possibility of spreading a new and even more dangerous virus should it emerge, we will probably have to restrain our future travel behavior, especially international and long-haul travels. At present, there is no clear information on whether Norwegians are inclined to voluntarily change their future travelling behavior and reduce the amount of international traveling. Some studies exist, but not to the extent that any conclusions can be drawn. Similarly, the few studies from other European countries have not addressed this particular question, leaving us with few insights on what the future might look like.

Appendix A.4. Condition 4: Low Availability vs. No Herd

The last decades have shown all of us how vulnerable our global climate is to extensive travelling between countries and continents. The rapid increase in greenhouse gasses, and the devastating results on the climate, are of great future concern not only for our way of living, but also for the mere survival of a large amount of people around the globe. And while what we have witnessed so far is scary enough, the climate experts claim that the current climate changes are just a pre-warning of what we can expect in the future. Because of this, and to reduce the possibility of global warming increasing out of control, we will probably have to restrain our future travel behavior, especially international and long-haul travels. At present, there is no clear information on whether Norwegians are inclined to voluntarily change their future travelling behavior and reduce the amount of international traveling. Some studies exist, but not to the extent that any conclusions can be drawn. Similarly, the few studies from other European countries have not addressed this particular question, leaving us with few insights on what the future might look like.

Appendix B

The list of measurement items (translated from Norwegian).

Appendix B.1. Planned Travel Reduction

  • In the future I plan to reduce my number of leisure travels.

Appendix B.2. Self-Monitoring

  • In social situations, I have the ability to alter my behavior if I feel that something else is called for.
  • I have the ability to control the way I come across to people, depending on the impression I wish to give them.
  • When I feel that the image I am portraying isn’t working, I can readily change it to something that does.
  • I have trouble changing my behavior to suit different people and different situations.
  • I have found that I can adjust my behavior to meet the requirements of any situation I find myself in.
  • Even when it might be to my advantage, I have difficulty putting up a good front.
  • Once I know what the situation calls for, it’s easy for me to regulate my actions accordingly.
  • I am often able to read people’s true emotions correctly through their eyes.
  • In conversations, I am sensitive to even the slightest change in the facial expression of the person I’m conversing with.
  • My powers of intuition are quite good when it comes to understanding others’ emotions and motives.
  • I can usually tell when others consider a joke to be in bad taste, even though they may laugh convincingly.
  • I can usually tell when I’ve said something inappropriate by reading it in the listener’s eyes.
  • If someone is lying to me, I usually know it at once from that person’s manner of expression.

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Sustainability 13 00691 g001 550
Figure 1. Research model portrayed.
Figure 1. Research model portrayed.
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Figure 2. Estimated marginal means of planned travel reduction.
Figure 2. Estimated marginal means of planned travel reduction.
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Table
Table 1. Sample size, mean age, and gender distribution across experimental conditions.
Table 1. Sample size, mean age, and gender distribution across experimental conditions.
Experimental CellsNMean Age% of Gender (M/F)
Herd/High availability10741.942.1/57.9
Herd/Low availability10741.551.4/48.6
No herd/High availability1064242.5/57.5
No herd/Low availability10940.438.5/61.5
Self-monitoring
Low Self-monitors21641.648.6/51.4
High Self-monitors21341.238.5/61.5
Table
Table 2. Mean scores and standard deviations across experimental conditions.
Table 2. Mean scores and standard deviations across experimental conditions.
Dependent VariableHerdAvailabilityM (SD)
Planned Travel ReductionHerdLow
High		4.01 (1.77)
4.17 (1.92)
No HerdLow
High		3.60 (1.83)
3.91 (1.96)
Table
Table 3. Hypotheses test results: analysis of variance (ANOVA).
Table 3. Hypotheses test results: analysis of variance (ANOVA).
Main EffectsF-Valuep-Value
Availability0.725n.s.
Herding4.2970.039
Self-monitoring13.6630.000
Interaction Effects
Herding × self-monitoring5.9520.015
Herding × availability0.526n.s.
Self-monitoring × availability0.009n.s.
Herding × availability × self-monitoring0.255n.s.
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Ghorban Nejad, S.; Hansen, H. Environmentally Motivated Travel Reduction: The Effects of Availability, Herding Bias, and Self-Monitoring. Sustainability 2021, 13, 691. https://doi.org/10.3390/su13020691

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Ghorban Nejad S, Hansen H. Environmentally Motivated Travel Reduction: The Effects of Availability, Herding Bias, and Self-Monitoring. Sustainability. 2021; 13(2):691. https://doi.org/10.3390/su13020691

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Ghorban Nejad, Shiva, and Håvard Hansen. 2021. "Environmentally Motivated Travel Reduction: The Effects of Availability, Herding Bias, and Self-Monitoring" Sustainability 13, no. 2: 691. https://doi.org/10.3390/su13020691

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