香京julia种子在线播放

One way to reduce climate changing CO₂ emissions is to change our travel behavior, in particular to fly less. Although flights are “only” responsible for around 4% of global emissions (Klöwer et al., 2021), this figure is noteworthy when you consider that only a relatively small proportion of the world’s population, mainly from wealthy countries (e.g., Switzerland), flies. For example, only 11% of the total world population flew in 2018 (Gössling and Humpe, 2020). Hence, in Europe, for example, flights cause 14% of transport-related emissions, which is far more than trains (0.4%). In addition, aviation infrastructure also causes more emissions than that of rail transport (European Environment Agency, 2021). Therefore, especially in Europe, which is an area with dense air traffic, switching from plane to train could significantly reduce emissions.

However, literature shows that behavioral change is anything but easy (e.g., see Halpern, 2015; Sheeran and Webb, 2016; Thaler and Sunstein, 2021). In addition, the planned change involves a problem that can be illustrated using the scenario of “Ben,” who has to plan a business-related conference trip. While planning this trip (from Zurich to Florence), Ben carefully weighs up his travel options. One of them is a train journey through beautiful landscapes at a relatively low price. However, its duration of almost 6 h seems too long. After all, “time is money.” Ben therefore opts for the other option, a direct flight of just over an hour. On the day of the trip itself, however, when Ben finally arrives at the conference center in Florence, he realizes that the time it took him to get to Zurich airport, go through security, board the plane, wait for his luggage in Florence and take the bus to the city center, in addition to the flight itself, added up to a total travel time that was surprisingly close to that of the train journey. What happened to Ben is referred to as “time cost neglect” (Pan et al., 2024). But what annoys Ben most about his realization is that he should have been aware of these additional time costs; after all, it was not his first flight. This situation is not unlike US consumers not taking VAT (sales taxes) into account when buying groceries, as the prices of the goods on the shelves are shown without VAT. However, if those consumers are asked about the amount of tax to be added, they can state them fairly accurately for a whole range of products. Interestingly, when the VAT is stated in advance, it causes consumers to shop differently, and the demand for those goods decreases (Chetty et al., 2009). The question we now ask ourselves is whether the explicit addition of (implicitly) known missing information (i.e., the additional time cost) at the time of booking would have changed Ben’s (or anyone else’s) decision and thus led to less plane travel overall (and therefore less demand for this “good” or service)? Early indications that this could be the case were provided by Tversky and Kahneman’s (1981) famous “Asian disease” problem, or more precisely from its critics.

Kahneman and Tversky (1986) used this problem to illustrate that people do not always act according to the principles or basic assumptions formulated in “rational choice” theories (e.g., von Neumann and Morgenstern, 1953; Savage, 1954), such as descriptive invariance. Descriptive invariance assumes that the way in which identical (choice) options (e.g., yogurt A and yogurt B) are described linguistically or framed (e.g., whether yogurt A is 90% fat-free or contains 10% fat) should not influence people’s decisions (i.e., choosing yogurt A over yogurt B). They illustrated the violation of the principle by presenting participants with two versions of the problem (see Box 1), each giving participants a choice between a program with a “certain” outcome (A or C) and a program with a “risky” outcome (B or D).

A closer look at the problem reveals that the options or programs in each version have the same expected value (“EV”; 200 in version 1, 400 in version 2) and that the number of lives that can be saved in both versions is also the same [in version 1, 200 (out of a total of 600) people are saved; in version 2, 400 (out of a total of 600) people die].

Consequently, from a rational point of view, participants should therefore be indifferent in both versions regarding their choice between the two options or, if not, at least deviate in the same direction in their choice (i.e., always choose the “risky” option).

However, Tversky and Kahneman found that the way the options or programs were “framed” in the two versions—either as gaining/saving lives (gain framing) or losing lives (loss framing)—caused people to choose differently. That is, in the gain framing (version 1), 72% of participants chose program A, the certain option, while the opposite was observed in the loss framing (version 2): Here, 78% of participants chose program B, the risky option. Accordingly, gain framing makes people risk-averse, while loss framing makes people risk-seeking. Although these results are not consistent with rational choice theories, they can be well predicted by Kahneman and Tversky’s (1979) prospect theory, which uses a (neutral) reference point to categorize outcomes into gains or losses relative to that point. The resulting concave curve for gains or convex curve for losses results in the behavioral pattern observed above (e.g., risk aversion for gains). These “risky-choice framing” effects have not only been replicated many times (e.g., Kühberger, 1998; Levin et al., 1998) but have also been applied to everyday problems.

In this context (i.e., everyday problems) it was found that depending on how the problem is described, people can be encouraged to engage in certain (e.g., putting on sunscreen) or risky activities (e.g., going for a mammography or prostate screening), the problem at hand is described differently (whether the use of this method is morally/ethically right is another question, but needs to be considered). In this sense, for example, Detweiler et al. (1999) found that depending on whether the message on a flyer distributed at the beach about the use of sunscreen was described as a gain or a loss (e.g., “Protect yourself from the sun and you will help yourself stay healthy” or “Expose yourself to the sun and you risk getting sick,” p. 191), beachgoers intention (and also actual use) to use sunscreen with a sun protection factor (SPF) of 15 and higher was higher in the gain framing, especially if they had not previously planned to use it.

However, as authors such as Kühberger (1995); see also Mandel (2001, 2014), Kühberger and Tanner (2010), Tombu and Mandel (2015), and Gigerenzer (2018) have noted—and what is crucial for our work here—is that while in Tversky and Kahneman’s (1981) “Asian disease” problem in the risky options (programs B and D) all information is explicitly stated, this is not the case in the certain options (programs A and C). For example, program A says nothing about what happens to the remaining 400 lives. In this respect, however, it has been argued that people rightly assume (as in our case with air travel or VAT) that the remaining 400 people would lose their lives (Mandel, 2014), and accordingly it should be obvious by these simple mathematical calculations that, for example, program A and C are identical (called: proof by arithmetic argument, see, for example, Mandel, 2014). But, Kühberger (1995) has shown that if the missing information “400 people will die” (in program A) or “200 people will be saved” (in program C) is added to certain options, that is, made explicit (see also Mandel’s experiment 3), the effect demonstrated by Tversky and Kahneman (1981) disappears. There are many reasons why missing information could change the result. One of these is that, for example, the sentence “200 will be saved” is not understood as meaning that exactly 200 people will be saved, but rather that at least that many people will be saved (e.g., Mandel, 2014). Note that omitting part of the information in the risky options, as Reyna and Brainerd (1991) did (e.g., “2/3 probability that no people will be saved”) also changes the results (see also Kühberger and Tanner, 2010).

Hence, the explicit mention of missing information in the adapted versions of Tversky and Kahneman’s (1981) “Asian disease” problem influenced people’s decisions in a way that made them more similar across different versions (i.e., there was no preference for one program over another anymore). Let us come back to the plane-versus-train problem. It is important to note that in this regard both choice options (plane and train) presumably lie in the loss quadrant of prospect theory, as it is about “lost time,” and that they are both certain (here we momentarily overlook the possibility of delays and strikes for both modes of transport). The difference between the two options is that all time costs are listed for the train option, whereas this is not the case for the other option, the flight. In this case, only the time for the flight itself (approx. 1 h) is listed, but not the additional time costs incurred (approx. 4 h; missing information) for traveling to the airport, checking in, etc.

Now, the psychology of attention or, more specifically, the salience theory by Bordalo et al. (2012); see also Bordalo et al. (2022) may give us a clue as to why the explicit presentation of missing information as in Tversky and Kahneman’s (1981) “Asian disease” problem might change decisions. Consider thereby the following quote from Bordalo et al. (2012), p. 1255: “the decision maker evaluates lotteries¹ by focusing on, and weighting more, their most salient states.” Applied to Ben’s situation, the most salient state is the available information of a short flight of about an hour. Consequently, this information is disproportionately weighted in his decision (see also Dertwinkel-Kalt et al., 2017 or the focus illusion by Kahneman et al., 2006). The presentation of the missing information could now lead to the information of the short flight becoming less salient, which reduces its weighting and thus also the decision in favor of the plane.

Although the Asian disease problem and its criticisms cannot be mapped one-to-one to our problem, we expect different decision-making behavior or choices (plane or train) depending on the completeness of the information provided. More specifically, the explicit mention of the (implicitly known) missing information should lead to the plane option becoming less salient, as its originally perceived advantage (short travel time) is reduced by the perception of the actual travel time, which should lead to the train being chosen more frequently.

In our case, we present the missing information not only verbally (i.e., with words), but primarily visually, for example, through a segmented visual timeline of the entire journey and the use of simple graphic elements (e.g., a train pictogram). This dual-modal approach is based on proven findings: visual information is better retained than purely verbal information [Paivio and Csapo, 1973; see also, for example, Osman and Thornton (2019) or Kühne et al. (2023) for the improved impact of labels as a supplement to purely textual nutritional information for healthier and more sustainable food choices]. Similar benefits (e.g., retention, understanding, decisions) can also be observed with pictograms. For example, the risk of reduced fitness to drive can be better assessed (and thus a more informed decision made) if the verbal information is supplemented by corresponding pictograms (Emich et al., 2014).

What is new about our work? First, testing the effect of displaying missing information in the appropriate decision context (i.e., switching from plane to train). Second, the mainly visual presentation of the missing information. Third, testing the effects through a real (randomized) experiment. And last but not least, testing the influence of other factors such as destination, travel distance and price structure in this context.

Study 1 examines, among other things, how the travel itinerary presentation form (standard vs. comprehensive) affects the choice of the mode of transport (plane or train). More specifically, a standard itinerary that only shows flight or train travel time is compared to a comprehensive itinerary that includes door-to-door travel time.

In addition to repeating the key results from Study 1, the aim of Study 2 was to examine selected results from Study 1 in more detail and to expand on the results. As can be seen in Figure 3B of Study 1, there is a greater difference between plane and train travelers in the factor “time effort (travel time)” than in the “number of changes.” This could indicate that these two factors do not cover the same underlying concept. Therefore, we also manipulate the number of changes in Study 2. Furthermore, we are interested in whether we can observe the same results as in Study 1 in relation to a longer trip. Last but not least, we are interested in how certain price structures (absolute vs. relative differences; see Kahneman and Tversky, 1984) affect the choice between plane and train.

Studies 1 and 2 showed that presenting a trip through a comprehensive itinerary encouraged participants to make more sustainable travel choices (i.e., preferring the train more often). By implementing comprehensive itineraries in existing booking platforms (e.g., booking.com), these platforms could contribute to more sustainable travel. Not wanting to rely solely on booking platforms to implement this intervention, the question arises whether there are other ways of encouraging participants—employees of companies—to choose the train more often. In Study 3, we explore whether introducing a company guideline which indicates that traveling by plane does not save (much) time would be an effective option.

We were able to show, that when implicitly known information such as “total travel time for flights also includes the time for security checks, etc.” is made explicit, the preference of mode of transport changes (i.e., fewer plane travels). Moreover, the results found were seen regardless of whether the information was made explicit, through the (visual) presentation of comprehensive travel itineraries (Studies 1 and 2) or through the simple (written) mention of a travel guideline (Study 3).

What underlying mechanism could explain the results? In this regard, we postulate selective attention, which brings implicit information into consciousness and thus enables its semantic processing (e.g., Treisman, 1964; Treisman et al., 1974). This allows “times for security checks, etc.” or more generally the attribute “additional time” to be considered in decision-making. Note that decision-making involves evaluating all relevant attributes (e.g., price, time effort) of the options (e.g., plane, train), weighing them up properly and then choosing the option with the highest expected value (EV). Hence, if attributes—as in the case of implicitly known attributes—are not attended, they are not considered when calculating the expected value of the option. Wright (1974) found that product evaluations and therefore product choice are often based on the few attributes that attract attention. Research in this area has furthermore shown that “the way people screen product information is related to the benefits they are seeking” (see Haley, 1971, p. 8). For example, consumers who want the benefit of caries prevention in a toothpaste pay particular attention to information about the product feature fluoride, while consumers who are looking for the taste of a toothpaste are more likely to look for features such as mint flavor (Haley, 1968). A similar observation can be made in Study 1, for example, regarding participants’ preference to travel by plane or train. When choosing the plane, time seems to be the decisive factor, while when choosing the train, convenience is the decisive factor (Ratneshwar et al., 1997). However, we also noted that such attention effects appear to be short-lived. The more time passes before a decision (plane or train) is made, the more the advantage of the formerly attended attribute disappears. The participants seem to have turned their attention to other attributes in the meantime, so that the attribute “additional time” is no longer included in the calculation of the EV. However, research still needs to be conducted into exactly how attention works. For example, findings from studies that have investigated how humor in advertising maintains attention could be helpful here (Goodrich et al., 2011).

In addition to a closer look at the role of attention, the question of whether information about attributes such as “additional time” may be deliberately avoided or ignored with regard to the decision must also be investigated. Although research on the topic of “information avoidance,” defined as “any behavior intended to prevent or delay the acquisition of available but potentially unwanted information” (Sweeny et al., 2010, p. 341), is still in its infancy, various authors (Sweeny et al., 2010; Gigerenzer and Garcia-Retamero, 2017; Golman et al., 2017) have shown its impact on people’s decisions. Thereby, information avoidance seems to be influenced by factors such as whether the information is a threat to one’s current beliefs or social norms (Hart et al., 2009; Foust and Taber, 2025). Since these factors are also important for choosing a more sustainable mode of transport, future research should investigate whether information avoidance also plays a role in scenarios like ours, and if so, what the driving factors are.

What might be potential study limitations? First, the content of the scenarios—a work-related conference trip as an employee of a medium-sized company. Such a work-related scenario could lead participants to be more sensitive to time efficiency, which—compared to a leisure travel scenario—could lead to a different response to the information on total travel time. Future studies should therefore investigate whether the observed results can also be generalized for other travel scenarios (e.g., leisure, visiting family). Second, the study sample which consisted primarily of university students residing in Switzerland—a cohort that is young, female and likely to be environmentally-friendly as in, for example, Meyer (2015); or Kirby and Zwickle (2021). This contrasts with the typical European business traveler (Cats, 2025; Schmalz et al., 2021), who, according to Cats (2025), is in his early 40s, male and less environmentally friendly. This is because the business traveler only takes the train for around a third of business trips in Europe. The fact that the nudge led to a substantial additional shift (i.e., toward the train)—even in Studies 1 and 2, where environmental issues were never mentioned—suggests that sudden awareness of the hidden time costs, rather than pre-existing environmentally-friendlier behavior, is responsible for the effect. However, future field studies with real business travelers and real booking data would be required to verify to what extent the effect exceeds the effect generated with our student sample. Third, hypothetical (via experiments) rather than actual travel preferences were measured. Although experimental results are often valid approximations of real-world behavior (Hainmueller et al., 2015; Mullinix et al., 2015; Stroud and Van Duyn, 2020), eliciting actual preferences can confirm the effectiveness of our manipulation in the real world. Fourth, the effect of price was only examined in scenarios where plane travel was more expensive than train travel. In this context, it was found that the effectiveness of the manipulation disappeared when flights were very cheap. Future research could also investigate scenarios where plane travel is cheaper than train travel. This could support the generalizability of the results of our manipulation. Lastly, the focus of our studies was on analyzing the impact of the itinerary presentation form on mode of transport. Although we also examined factors such as time and cost, our samples (although already quite large) would have been too small to include these factors in an extended analysis and examine their interaction with the itinerary presentation form on mode of transport. However, it would be interesting to investigate in future studies whether, for example, people for whom saving time is most important are more influenced by the indication of total travel time.

Before we end with a conclusion, we would like to point out the general significance of the results of these studies. Every day we make choices with incomplete information. For example, when we book a hotel room in Switzerland, we are told the price but not the tourist tax, which must be paid additionally, on arrival at the hotel. Even if the consequences in this example are less far-reaching, they are still important in other cases. For example, if we have to decide on the treatment of a serious illness, where doctors provide information on the success rate and side effects, but not, for example, on the recovery time. In both cases, this could mean that our choice would be different than if we had been explicitly provided also with the implicitly known information.

To conclude, our studies have shown that simple manipulations (e.g., changing the standard itinerary presentation form) have a major impact on people preferring a more environmentally friendly mode of transport. Hence, by implementing these insights (e.g., by booking platforms), an important contribution could be made to reducing CO₂ emissions and thus the climate crisis.