# Impact of NGOs' undercover videos on citizens' emotions and pro-social behaviors

Romain Espinosa, Sylvie Borau, Nicolas Treich

► To cite this version:

Romain Espinosa, Sylvie Borau, Nicolas Treich. Impact of NGOs' undercover videos on citizens' emotions and pro-social behaviors. Scientific Reports, 2024, 14 (1), pp.20584. 10.1038/s41598-024-68335-5. http://dx.doi.org/10.1038/s41598-024-68335-5

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Registered Report - Stage 2:

Impact of NGOs' Undercover Videos on Citizens' Emotions and
Pro-Social Behaviors

This manuscript is a Stage-2 working paper of a Registered Report that received
In-Principle-Acceptance from Scientific Reports on November 20th, 2023 [Link to Stage-1].

Romain Espinosa*¹, Sylvie Borau², and Nicolas Treich³

¹CIRED, CNRS, France

²Toulouse Business School, IAST, France

³University of Toulouse Capitole, Toulouse School of Economics, INRAE, IAST, France

June 12, 2024

Abstract

Undercover videos have become a popular tool among NGOs to influence public opinion and generate engagement for the NGO's cause. These videos are seen as a powerful and cost-effective way of bringing about social change, as they provide first-hand evidence and generate a strong emotional response among those who see them. In this paper, we empirically assess the impact of undercover videos on support for the cause. We in addition analyze whether the increased engagement among viewers is driven by the negative emotional reactions produced by the video. To do so, we design an online experiment that enables us to estimate both the total and emotion-mediated treatment effects on engagement by randomly exposing participants to an undercover video (of animal abuse) and randomly introducing a cooling-off period. Using a representative sample of the French population (N=3,310), we find that the video successfully increases actions in favor of animals (i.e., donations to NGOs and petitions), but we fail to prove that this effect is due to the presence of primary emotions induced by the video. Last, we investigate whether activists correctly anticipate their undercover videos' (emotional) impact via a prediction study involving activists (exploratory analysis).

**Keywords:** NGOs, undercover videos, public engagement, activism, emotions, animal welfare

<footnote>*Corresponding author: romain.espinosa@cnrs.fr</footnote>
# 1 Introduction

NGOs aim to transform society by influencing the decisions of government officials, private businesses, and individuals.¹⁻⁵ However, activists typically face three major constraints in their attempts to challenge the status quo. The first is a resource constraint, as NGOs have significantly fewer resources to influence public opinion than private companies and governments. Second, NGOs face a credibility constraint, as individuals may assume that activists overstate their evidence in order to support their agenda. Last, there is an attention constraint, with NGOs often trying to raise awareness among people who are bombarded with information and for whom the issue at stake might seem more distant than other more-immediate matters.

In recent decades, technological advances have allowed activists to overcome these three constraints by directly providing evidence to the public. The development of small cameras and smartphones has facilitated the direct recording of evidence on sites to secretly gather information (i.e., undercover videos). The use of undercover videos has become a very cost-effective way of presenting unfiltered evidence to the public, and has loosened these resource constraints. Those who see these videos now have access to first-hand evidence that likely mitigates the credibility constraint, as this information is less likely to be distorted. Last, these videos often provoke strong emotional reactions among viewers, which captures their attention and generates engagement and virality on social media.

One of the objectives of undercover videos is to generate immediate public engagement for a cause, and most NGOs encourage individuals to sign a petition or donate immediately after watching the video. Petitions can help NGOs to exert pressure on decision-makers to address the situation they highlight, and donations help them to continue their work in the longer run. As these videos likely generate strong negative emotional reactions, those who see them may use petitions and donations as a way of relieving their negative emotional state. As such, NGOs can use undercover videos to generate negative emotional reactions, and so push viewers towards actions immediately afterwards to alleviate these negative emotions.

We here empirically investigate three research questions regarding undercover videos. First, whether these videos successfully generate immediate engagement among viewers. Second, whether this increased immediate engagement reflects the viewers' emotional reaction to the video: we focus here on the role of primary emotions (anger, fear, joy, surprise, disgust, and sadness), which are known to be instinctive, basic universal emotions that are experienced after a stimulus.⁶,⁷ Third, whether activists anticipate this role of emotions in the immediate engagement that follows the viewing of the video.

The assessment of the role of emotions in reactions to undercover videos presents an empirical challenge. Observational data do not allow researchers to identify the mediated treatment effect of emotions (i.e. the greater engagement due to the change in emotions), as those who are exposed to the video are affected both by the video itself (the direct treatment effect) and the change in emotions resulting from the video (the mediated treatment effect). As such, observational data cannot predict how individuals would have reacted without having been emotionally affected. We here
build an empirical model that draws from recent advances in the mediation-analysis literature.⁸⁻¹⁰ We capture the mediated treatment effect by generating exogenous variations (experimental manipulation) on the mediator for participants exposed to the treatment. Our model estimates the mediated treatment effect by assessing to which extent the total treatment effect is affected by the exogenous changes in the mediator.

We design an experiment to elicit the role of primary emotions in the engagement generated by undercover videos in the context of animal advocacy. Animal-rights activists make intensive use of undercover videos to publicly criticize intensive farming and abbatoirs.¹¹⁻¹³ While public opinion largely supports the protection of animal welfare (e.g., over 80% of EU citizens say they care about farmed-animal welfare), individuals underestimate the prevalence of intensive farming and animal suffering.¹⁴ Animal-advocacy NGOs see undercover videos as an effective way of providing information about what happens in intensive farms (that individuals cannot see) and countering the narratives put forward by the industry (e.g., via adverts). This strategy seems to have been effective, at least in the short term, as the petitions linked to these videos often attract hundreds of thousands of signatures.

In our experiment, participants are randomly exposed to an undercover video that was recorded on an intensive farm in France in 2021. In addition, subjects in the treated group are randomly assigned to a short or long version of the experiment. In the long-treatment arm, we introduce a 5-minute cooling-off period during which subjects answer a survey that is unrelated to animal welfare (covering travel preferences). Our pilot data showed that exposure to the video significantly affects individuals’ engagement in animal welfare, and generates strong negative emotions. The pilot data also showed that the cooling-off period significantly reduces the negative emotions arising from the video, which enables us to estimate the mediated treatment effect. Our main sample consists of N=3,310 participants. Ex-ante power analysis with the pilot data indicates that our experiment allows us to detect any mediated treatment effect of 3.4 percentage points or larger with a probability of at least 95%.

We analyze participants’ engagement through a charity-giving game in which individuals can give real money to four NGOs or three petitions in favor of animal welfare. Participants decide under a veil of ignorance (i.e. they ignore which NGOs will actually be selected in the charity-giving game) and report their donations for each contingency. We aggregate the giving decisions to construct a general score of pro-animal engagement (Cronbach’s alpha=0.87). Regarding emotions, we construct a negative-emotions score using self-reported information on six primary emotions: anger, disgust, fear, sadness, happiness, and surprise.¹⁵ The general score of negative emotions is given by the sum of these emotions, with the happiness score being reverse-coded (Cronbach’s alpha=0.84). Based on the theoretical framework that we describe below, we hypothesized that the video would increase engagement and that emotions would play a significant role in this greater engagement. Accordingly, we first test whether the video successfully increases engagement, i.e. whether the total treatment effect is positive ($H_0: \Delta_{TT} \le 0$). Second, we test whether the emotional load generated by the video generates greater engagement, i.e. whether the mediated
treatment effect of emotions is positive ($H_0^2$: $\Delta_M \le 0$).

Our registered analyses show a statistically significant positive effect of watching the video on donations (+3.81 percentage points in average donations, one-sided test: $p<0.001$). The two-sided 95% confidence interval indicates an average increase in donations comprised between +1.74pp and +5.93pp. However, we fail to reject the null hypothesis for the mediated treatment effect (one-sided test: $p=0.175$) and, therefore, cannot conclude that the negative emotional load plays a significant role in the video's increase in donations. Nevertheless, on the other hand, our estimates cannot rule out the possibility that emotions play a role either. The mediated effect is estimated to be positive but of limited size (average estimate: +1.27pp), and the two-sided 95% confidence interval comprises values between -1.28pp and +3.96pp. While negative emotions sharply increase after watching the video, the failure to reject the null is driven by the fact that most of the increase in donations associated with the video survives the disappearance of the negative emotions when we introduce the cool-off. Our exploratory analyses show that the video mostly generates sadness, anger, and disgust, and decreases happiness among the viewers. They further suggest that more moderate actions benefit the most from the video (i.e., a ban on intensive farms, development of plant-based menus in university canteens, and support for farmers' initiatives to improve animal welfare).

Last, we compare our results to the expectations of activists in the NGO that released the un-
dercover video used in our experiment. Understanding activists' expectations regarding the impact
of their videos is important to decipher their strategy. Activists often claim that the emotional reac-
tion only serves as a starting point and that they hope to stimulate a rational reaction with lasting
effects. We analyze in a prediction study whether activists believe that the effect of the video on
the general population will be substantially driven by emotions. We recruited 154 engaged activists
via the NGO's social-media groups for activists who were blind to the study's hypothesis, and 50
completed the prediction study. We elicit their beliefs about the emotional impact of the video using
a survey that was administrated during the data-collection period in the main experiment. We had
no hypotheses regarding the views of activists before data collection, and we therefore carry out
exploratory analyses only. The exploratory analyses suggest that activists tend to overestimate the
altruistic behaviors of the general population. The median expectation among activists anticipates
an increase in negative emotions and donations after watching the video but the emotional load is
expected to last longer than what we observe.

The remainder of the paper is organized as follows. Section 2 discusses the empirical and
theoretical background, and Section 3 introduces the methods (ethics, design, and procedures).
Section 4 presents the pilot data. Section 5 then describes the analysis plan (empirical model,
hypotheses), and Section 6 the sampling plan. Section 7 presents the results of the main experiment,
and Section 8 the prediction study with the activists. Last, Section 9 discusses the results and
concludes.
## 2 Background

In recent years, activists across various causes have used undercover videos to raise awareness about social problems. Environmental NGOs regularly publish undercover videos exposing private companies that pollute the environment (e.g., Greenpeace on deep-sea mining, and Mother Nature on ecosystem pollution). Undercover videos are not restricted to environmental NGOs, and are also used by human-rights activists to denounce human-rights violations. For example, a number of NGOs have used undercover videos to reveal official corruption in their countries (e.g., the PCUD in Guinea) and torture in prisons (e.g., Public Verdict in Russia).

Animal-advocacy groups have become one of the most-intensive users of undercover videos world-wide. Figure 1 below plots a non-exhaustive map of animal-advocacy NGOs that have published undercover videos (own data collection). NGOs using undercover videos are present in many countries (e.g., the Humane League and Peta in the United States, Animal Justice Project and Open Cages in the UK, Varkens in Nood in the Netherlands, Oikeutta Elaimille in Finland, and the Farm Transparency Project in Australia). Some NGOs are specialized in the publication of undercover videos. For instance, the US NGO “Mercy for Animals” has released 77 undercover videos in the past decade, and the US-based NGO “Animal Equality” reported 17 undercover investigations in 2021 only. [Sources: NGO's website for Mercy for the Animals, GuideStar's data for Animal Equality.] Some NGOs like Sinergia Animal have become experts in publishing these videos, and operate worldwide.
<figure><img src="image_2.png" /><figcaption>**Figure 1:** World map of animal-advocacy NGOs using undercover videos (non-exhaustive).</figcaption></figure>

Note: Original map credits: San Jose. License: CC BY-SA 3.0 via Wikimedia Commons. Modified by the authors with Keynote (v 12.2.1). [https://commons.wikimedia.org/wiki/File:World_mapimmblank_gmt.svg?uselang=en]
The release of undercover videos showing animal abuse has also become very frequent in France, with L214 being one of the most-active NGOs worldwide. The French farming industry is one of the largest in the European Union, and most chickens (about 80%) and pigs (about 95%) are raised without outdoor access, i.e., in what activists consider as intensive farms. L214 has over the past decade become the largest NGO denouncing animal abuse on French farms. Since its creation in the early 2000s, L214 has released over 100 undercover videos. These videos denounce current farming conditions and regularly create significant citizen engagement. The petitions associated with these undercover videos often obtain over 100,000 signatures (e.g., a petition to close an abattoir - 146,000; a petition for a Parliamentary investigative committee into abattoirs - 145,000; petitions against intensive farming for chicken - 167,000, and for pigs - 148,000). [Source: L214's website]

The rise of undercover videos among animal-advocacy groups likely reflects the substantial engagement that they create. However, the role of emotions in this engagement remains an open empirical question. In a recent interview (2022), the co-founder of the L214 NGO, Sébastien Arsac, declared that activists seek to stimulate engagement through two channels: “This emotion is a starting point. Any association that wants to alert and raise awareness of a serious subject will accompany the images with matching music. Then there is rationality: in our communication, we provide scientific analyses, comments from veterinarians, and we question the figures that are provided regarding the number of animals.”*

Assessing the role of emotions in the reaction to the videos is key to understanding the success of NGO campaigns. Evidence from Behavioral Sciences suggests that emotions are likely to be a major element of the greater engagement after watching the videos. A large body of research in Psychology indicates that emotions are powerful and pervasive drivers of beliefs and decisions,¹⁶ with various applications to (for example) Marketing¹⁷ and Management.¹⁸

The negative emotional state generated by the videos is likely to be driven by negative primary emotions. The Psychological literature on emotions distinguishes primary emotions such as anger or sadness from secondary emotions such as guilt.¹⁹⁻²¹ Showing animal mistreatment in intensive farms presents these animals as victims or "harmed patients",²² which probably increases the perception of their sentience and elicits negative moral emotions such as sadness towards the "patient" and anger towards the "agent".²³ These videos likely generate strong primary negative emotions, as it has been shown that many individuals respond immediately after watching the video by signing petitions, for instance. These emotions have typically been shown to be short-lived. For example, facial expressions and physiological responses fade quickly over a short period of time.¹⁶,²⁴ Previous work has also shown that the emotional response following an anger-inducing trigger disappears after 10 minutes again suggesting a short primary-emotion lifespan.²⁵ One important question is then whether the immediate engagement generated by the videos is driven by these short-lived emotions.

Two main theories have tried to explain the effect of emotions on decision-making: the affect-as-

<footnote>*Authors’ own translation. Original source: https://web.archive.org/web/20230213124740/https://www.radiofrance.fr/franceinter/podcasts/magma-le-mag-de-la-matinale/le-mag-de-la-matinale-du-lundi-29-aout-2022-5984035.</footnote>
information theory<sup>26,27</sup> and the appraisal-tendency framework.<sup>28-30</sup> While both theories acknowledge the role of emotions in judgments and decision-making, they differ in their approach to how emotions are integrated. The affect-as-information theory posits that people usually rely on their general emotional state to make judgments and decisions. In other words, this theory suggests that people may make different decisions depending on their emotional state, as it influences their perception of a situation. For instance, when watching an undercover video of animal abuse, people may be more inclined to take action due to their heightened emotional state, but this effect may fade once the emotions generated by the video disappear.

On the other hand, the appraisal-tendency framework posits that different stimuli can trigger different appraisals, such as harm or unfairness, that can subsequently trigger different emotions, such as sadness or anger, and influence subsequent judgments differently. In other words, this theory suggests that emotions result from cognitive evaluations of situations. According to this framework, people may experience anger when perceiving an injustice - while they would consider the situation unjust because they experience anger according to the affect-as-information theory. In the case of undercover videos, the appraisal-tendency theory would predict that negative emotions arise from the perceived injustice of animal abuse. In that case, increased engagement in animal welfare may persist beyond the immediate emotional load induced by the video. Emotions and appraisals tend to have a reciprocal relationship, though, each reinforcing the other,<sup>31</sup> making it challenging to identify which one initiates the cycle.

While NGOs might attempt to provoke rational reactions to the videos, they may also have an interest in producing emotional reactions. Communication based on emotions is a key tool used by NGOs, via advertising campaigns and social-media channels.<sup>32,33</sup> A number of contributions have revealed the importance of emotions in various domains of political action. For example, emotions significantly affect the donations individuals make to NGOs.<sup>34-36</sup> It is therefore possible that activists predict a positive role for emotions in the increased support for their cause, and take advantage of this effect.

However, there are also opposing arguments suggesting a limited emotional impact of undercover videos. In the field of psychology, a substantial body of theories and research, spanning from psychoanalysis to social psychology, has proposed that individuals may engage in perceptual, cognitive, and behavioral distortions as a means of shielding themselves from the potentially distressing emotional responses that could arise in the absence of these defense mechanisms.<sup>37</sup>

In the domain of animal welfare and meat-eating, a large body of behavioral research has explored the complex psychology of consumers.<sup>38,39</sup> In particular, previous work has discussed the evidence for cognitive dissonance, also known as the "meat paradox".<sup>40</sup> This paradox explains why omnivores and vegetarians have different beliefs about and attitudes towards animals<sup>41,42</sup> and the willful ignorance about the treatment and the suffering of farmed animals.<sup>43-46</sup> Even when confronted with compelling evidence, people often demonstrate resistance to assimilating new information in this domain. For instance, it has been shown that people do not seem to process relevant information (e.g., animal intelligence) when it applies to an animal that they consume (e.g, pigs).<sup>47</sup> Given these
insights, it is plausible that viewers of undercover videos employ protective strategies (e.g., by
distancing themselves from the animals, or engaging in biased information processing about their
rearing conditions) to mitigate the emotional impact they experience.

# 3 Methods

**3.1 Ethics Information**

This work received the approval of the ethics committee of the Institute for Advanced Studies in Toulouse in May 2022 (IRB approval number: 2022-04-001). It complies with all relevant ethical regulations for research with human participants. All of the participants were recruited through a representative sampling survey company called MSI and were compensated financially by the survey company for their participation in accordance with local standard rates. They received a fixed amount of money for their participation (a show-up fee) and a variable amount determined by their decisions (see the Design and Procedure section). Participants were anonymous and were selected from the survey company's database. They were randomly allocated to one of the four conditions described below. The sample is representative of the French population.

**3.2 Design and Procedure**

**Design.** The experiment is a between-subject randomized-control trial with equal randomization of participants across treatment branches. Figure SM1 in the Supplementary Materials provides a graphical representation of the course of the experiment.

We start by informing participants about the experiment, and tell them that we will ask a
series of questions about their dietary habits, and their beliefs and attitudes towards public topics.
Participants are also told that the survey will last about 10 minutes (20 minutes) if they are in
the short condition (the long condition). Importantly, all participants are told that they may be
exposed to explicit images during the study, and have the possibility of opting-out at this early
stage of the experiment. For quota purposes, i.e. to have a balanced number of men and women,
we ask participants their gender at this stage.

Participants next face a series of screens, the order of which is randomized with equal probability. On one screen, we ask participants the frequency with which they eat a series of food products (red meat, white meat, fish, eggs, dairy, vegetables, pulses, fruits, and starchy products). We further ask them whether they identify as carnist, omnivorous, flexitarian, vegetarian, or vegan. Participants are then asked to test the sound of their computer by listening to a 4-second sound of a grasshopper and say what type of sound they have just hear, from a list of five (a grasshopper, a car alarm, a meowing cat, a whistle, and a barking dog). The study ends here for participants who provide incorrect answers.

There are variations in treatment in the following screens (see Table 1 below). Half of the
participants are randomly exposed to a video, and the other half not. The video is a one-minute
and twenty-eight-second extract from a video published online by a French animal welfare NGO
(L214), and shows footage from a French pig farm in 2021. The name of the NGO is not displayed in the extract. The video contains some written comments in French. The video shows pigs' living conditions in a French intensive farm (e.g., no outdoor access, high density, stabling, sow farrowing crates, killing piglets by hitting them against a hard surface). For participants in the treated group, we record the time spent watching the video, and the number of clicks.

Second, participants are also randomized between two other treatment arms. Half are randomly assigned a short version of the survey, and now proceed to the next series of questions below. The other half are assigned to a long version of the survey, where they answer a series of questions about their travel preferences (train vs. plane). The objective of the long survey is to produce a cooling-off period to mitigate the emotional reaction generated by the video. The questionnaire about travel preferences appears in the Supplementary Materials. Overall, participants in the experiment are allocated with equal probability to one of the four conditions: no video and short survey (NOVID-SHORT), no video and long survey (NOVID-LONG), video and short survey (VID-SHORT), and video and long survey (VID-LONG).

<table><caption>**Table 1:** Treatment-assignment probabilities</caption><thead><tr><th></th><th>Without Video</th><th>With video</th></tr></thead><tbody><tr><th>Short survey</th><td>NOVID-SHORT<br>25% of the sample</td><td>VID-SHORT<br>25% of the sample</td></tr><tr><th>Long survey</th><td>NOVID-LONG<br>25% of the sample</td><td>VID-LONG<br>25% of the sample</td></tr></tbody></table>

After the treatment, we ask respondents to report their emotions. They first indicate the extent to which facial expressions of six primary basic emotions (anger, disgust, fear, sadness, happiness, and surprise) best reflect their emotional state. This face-emotion scale consists of three male and three female faces from the well-validated Radboud Faces Database for each emotion,¹⁵ which has been widely used as an alternative to verbal self-reports.¹⁹,²⁰ Participants first click on the set of faces that best match how they felt while watching the video. They then rate how well each set of faces matches their current feelings on a scale from 1 (strongly disagree) to 7 (strongly agree). The order of these sets of faces is randomized at the individual level with equal probability.

Next, all participants can donate some money to improve animal welfare. They are told that they will receive an additional amount of 1 Euro for their survey participation that they can either keep or donate (fully or partially). We introduce a list of four French NGOs and three petitions related to animal welfare to which the participants can donate (for the latter, they donate to increase the petition's visibility). They decide under a veil of ignorance and indicate how much they would like to give for each donation option knowing that only one of their seven donation decisions will be implemented and that the effective recipient of the donations was selected before data collection. The NGO list includes NGOs working for the development of plant-based menus in university canteens (Assiettes Végétales), fighting against intensive farming and the consumption of animal products (L214), fighting against the abandonment of pets and facilitating adoption
(SPA), and supporting farmers' initiatives aimed at improving farm-animal welfare (Welfarm). The three petitions are, respectively, against intensive farming, supporting plant-based meals in public canteens, and protecting animal-welfare activists who record videos in intensive farms.

Participants then reply to a series of questions on the next screens that assess their moral concerns for animals, using the questions developed in previous work.⁴⁸ We further elicit participants’ pro-meat justifications via a list of statements used in previous work,⁴⁹ and their motivation to eat meat right now to assess any potential reactance. We next ask participants who were exposed to the video whether they consider the farming practices shown in the video to be morally wrong and whether they would share the video on Twitter or Facebook if they had seen it on these social platforms. We further ask them a series of questions about their perception of the truthfulness and credibility of the video: whether they think that (i) the video is representative of animal farming in France, (ii) it was not recorded in France, (iii) it is a fake video made up by activists, (iv) the information provided in the video is not exact, (v) some pieces of information are distorted or biased, (vi) the video is not credible, and (vii) the video is not realistic.

The last part of the questionnaire includes control checks and demographic questions. We ask participants to indicate the topic of the study (i.e., diets), which we explicitly mentioned at the beginning of the survey. Second, participants in the video conditions were asked about the contents of the video (via a multiple-choice question), and whether they had seen this video before, or a similar video before. Third, all participants underwent an attention check (a simple calculation exercise). Last, we asked demographic questions (age, marital status, parenthood, political orientation, and religion).

**Outcome variables.** We consider two outcome variables to measure the effect of the different treatments. We first create a score of the propensity to engage in animal welfare. This pro-animal score is the average of the donations in cents that are made to the NGOs and petitions divided by 100 and so takes on values between 0 and 1 with higher values reflecting a larger endowment share donated to animal welfare. Second, we investigate the role of emotions by generating an aggregate negative-emotion score. We take the sum of all emotions (with happiness reverse-coded). We standardize this average figure by adding two and dividing by 36, yielding a negative-emotion score varying between 0 and 1, with higher scores reflecting more negative emotions.* This negative-emotion score aims to assess the negative emotional state of participants by considering the intensity with which they experience different primary emotions that have different valences (happiness has a positive valence and the remaining emotions have a negative valence).

**Outcome-neutral tests.** We committed to assessing the validity of our analysis by running the following outcome-neutral tests. We first committed to an estimation of the Cronbach’s alpha

<footnote>*The construction of the negative-emotion score was supported by a Principal Component Analysis. The first dimension of a PCA on emotions in the final sample explains 56.3% of the variance in emotions (65.2% in the pilot sample). It correlates positively with all emotions but with happiness (negative correlation). All correlation coefficients are larger than 0.60 in absolute value in both the pilot and final samples except for surprise in the final sample (0.40).</footnote>
figures for both outcome variables. If these are above 0.6, we committed to saying that our out-
come variables have sufficient internal validity. Second, we committed to reporting whether the
cooling-off period reduces the negative-emotion score by at least 20 percentage points (VID-SHORT
vs. VID-LONG). If not, we we committed to considering that the protocol does not generate suffi-
cient variation in emotional states to estimate the mediated treatment effect. Third, we set up an
outcome-neutral test with respect to attrition between treatments. We committed to comparing the
distributions of participants at the beginning of the survey (after gender quota, consent validation,
and sound test) and at the end of the survey (complete answers, validation of the attention checks).
We committed to considering attrition to be a concern for the validity of the experiment if the share
of participants changes by more than 10 percentage points in at least one treatment arm. If one of
these three outcome-neutral tests fails, we committed to mentionning that our estimates carry little
weight and should be interpreted with caution.

Last, we also committed to testing whether the LONG questionnaire generates *per se* changes
in donations. We committed to running a wilcoxon rank-sum test on the aggregate donation score
testing equality between NOVID-SHORT and NOVID-LONG. If the test rejects the null hypothesis
(i.e., equality) at $\alpha = 5\%$ (i.e., the difference is statistically significant) and if the absolute differ-
ence exceeds 2 percentage points (i.e., the difference is economically significant), we committed to
correcting our estimates using the estimator described in the Supplementary Materials.

**Exclusion rules.** We drop incomplete observations, including participants who did not give their consent and who did not complete the experiment. Second, we drop participants who failed the sound test (to ensure that respondents can hear the sound of the video). Third, we drop participants who failed the attention checks (they have to say that the study is about diet, answer correctly regarding the content of the video, and complete the simple calculation task). Last, we also drop participants who spent less than 90 seconds on the survey (excluding the time spent watching the videos for the individuals in the video-treatment groups).

# 4 Pilot data

We carried out an online pilot experiment on 568 French participants; this was conducted online
by the Dynata research panel company. Data collection took place from June 21st 2022 to June
24th 2022. A total of 905 participants were recruited: 442 were randomly allocated to the SHORT
questionnaire and 463 to the LONG questionnaire. Among these respondents, 9 did not give their
consent and were excluded at the very beginning of the questionnaire, and 135 were not invited
to continue with the questionnaire in order to produce a gender-balanced sample. We dropped
participants who reported the incorrect sound, who did not finish the questionnaire, and who failed
the attention checks.In the pilot study, we retained participants who did not report any sound as
this question was not mandatory when we launched the survey. In the final experiment, we will
drop all participants who do not provide the correct answer to the sound question. The final sample
consisted of 568 respondents (220 women; average age = 45.3, SD = 13). Of these, 279 completed the SHORT questionnaire, with 133 exposed to the video (VID-SHORT) and 146 to the control condition with no video (NOVID-SHORT), and 289 completed the LONG questionnaire with 136 exposed to the video (VID-LONG) and 153 to the control condition with no video (NOVID-LONG).

The pilot-study results indicate that our indicators perform well. The Cronbach's alpha's for the two outcome variables are 0.90 (donation score) and 0.89 (negative-emotion score). Figure 2 also shows that the video has a strong impact on donations and emotions. We further observe that the LONG treatment variation significantly mitigates the negative emotions that arose from watching the video, but does not affect average donations. There is a large share of zero donations: in the SHORT questionnaire: 38% of participants do not give anything when they do not see the video, and 26% after watching the video. This suggests that the treatment can have a significant impact on the extensive margin (i.e. whether to donate or not). Last, we can note that cooling-off does not affect donations in the absence of a video (NOVID-SHORT vs. NOVID-LONG: *p* > 0.477 for all of the single-donation decisions).

<figure><img src="image_3.png" /><figcaption>**Figure 2:** The differences in donations and negative emotions in the pilot study by treatment condition</figcaption></figure>

The error bars show the 95% confidence intervals. Overall sample size *N* = 568. A) The donation score is the normalized average of donations to all NGOs and petitions. B) The negative-emotion score is the normalized average of all emotions (with happiness reverse-coded).

# 5 Analysis plan

## 5.1 Empirical model

Our objective is to estimate the overall effect of undercover videos on individuals' decisions to engage (the total effect), and more specifically the role of immediate emotions in this effect (the mediated effect). The total effect can easily be estimated by comparing engagement across treatments (with vs. without exposure to the video). Assessing the role of emotions is more complex. In what follows,
we estimate the mediated (or indirect) treatment effect by building an empirical model that relies on recent advances in the field.⁸⁻¹⁰ Observational or experimental data with a single treatment variation (one control and one treated group) typically fail to correctly estimate the mediated treatment effect, as the mediator and the outcome variables are both affected by the treatment. As such, observational data cannot distinguish the changes in the outcome variable for the treated that are due to the treatment status (the unmediated treatment effect) from those that are due to a change in the mediator (the mediated treatment effect). The introduction of a cooling-off period in our experiment allows us to disentangle these two effects.

We denote by $Y_i$ the amount of money individual $i$ gives to the animal-welfare charities, and $M_i$ her emotional state. We define four potential outcomes $Y_i^{d,m}$ that depend on whether the individual saw the video ($d = 1$) or not ($d = 0$), and on her emotional state which can be high ($m = 1$) or low ($m = 0$). We further define two potential values for the mediator $M_i^d$, depending on whether the individual was exposed to the video ($d \in \{0, 1\}$). Last, we note $D_i$ the dummy variable equal to 1 if the individual is in the treated condition (with video), and 0 otherwise (no video).

Following the classic typology in causal analysis,⁵⁰ we consider four types of participants depending on their emotional reaction to exposure to the video.¹⁰ First, always takers are individuals who are always in a high emotional state ($M_i^1 = M_i^0 = 1$): we denote them by $T_i = a$. Second, never takers ($T_i = n$) are, on the contrary, always in a low emotional state ($M_i^1 = M_i^0 = 0$). Third, compliers ($T_i = c$) are assumed to have a low emotional state in the absence of the video ($M_i^0 = 0$) but a high emotional state after they watch it ($M_i^1 = 1$). Last, the canonical framework also allows for defiers ($T_i = f$): individuals who have opposite reactions to those of the compliers ($M_i^0 = 1, M_i^1 = 0$). Here defiers would be individuals who enter the experiment with a high emotional state but become more relaxed after watching the video of animal abuse. We consider it very unlikely that any participants (or only a very few of them) would feel relieved by watching a video showing animal abuse in intensive farms. We thus assume $T_i \in \{a, n, c\}$. We denote by $p_a$, $p_n$, and $p_c$ the respective shares of always takers, never takers, and compliers in the population.

The total effect of the treatment on the treated is given by:

$$ \Delta_{TT} = \mathbb{E}[Y_i^{1,M_i^1} - Y_i^{0,M_i^0} | D_i = 1] \quad (1) $$

As we have random assignment of the video, $\mathbb{E}[Y_i^{0,M_i^0} | D_i = 1] = \mathbb{E}[Y_i^{0,M_i^0} | D_i = 0]$. We can then rewrite the previous equation as:

$$ \Delta_{TT} = \mathbb{E}[Y_i^{1,M_i^1} | D_i = 1] - \mathbb{E}[Y_i^{0,M_i^0} | D_i = 0] \quad (2) $$

The total effect of the treatment on the treated can thus be estimated as the difference in average donations between the VID-SHORT and NOVID-SHORT treatments.
We define the mediated treatment effect of the video ($\Delta_M$) as the change in donations associated with the change in the emotional state conditional on seeing the video. This is:

$$ \Delta_M = \mathbb{E}[Y_i^{1,M_i^1} - Y_i^{1,M_i^0} | D_i = 1] \quad (3) $$

We can decompose this by types of individuals, which yields:

$$ \begin{aligned} \Delta_M = & p_a \times \mathbb{E}[Y_i^{1,1} - Y_i^{1,1} | D_i = 1, T_i = a] \\ & + p_n \times \mathbb{E}[Y_i^{1,0} - Y_i^{1,0} | D_i = 1, T_i = n] \\ & + p_c \times \mathbb{E}[Y_i^{1,1} - Y_i^{1,0} | D_i = 1, T_i = c] \\ \Delta_M = & p_c \times \mathbb{E}[Y_i^{1,1} - Y_i^{1,0} | D_i = 1, T_i = c] \end{aligned} \quad (4) $$

The cooling-off period aims to manipulate the mediator among the compliers who see the video. We denote participants exposed to the cooling-off period by $C_i = 1$ and those who are not by $C_i = 0$. The pilot data suggest that this cooling-off period significantly reduces participants’ emotional stress, although this does remain slightly higher than that in the absence of the video. We thus consider that the cooling-off period successfully manipulates the mediator for a share $q$ of the compliers who saw the video but does not do so for a share of $1 - q$. We assume that the treatment effect is the same for both types of compliers.

The expected difference in mean donations between participants in the VID-SHORT and VID-LONG treatments is denoted by $\delta$ and given by:

$$ \begin{aligned} \delta = \mathbb{E}[Y_i | D_i = 1, C_i = 0] - \mathbb{E}[Y_i | D_i = 1, C_i = 1] = & \\ & p_a \times (\mathbb{E}[Y_i^{1,1} | D_i = 1, C_i = 0, T_i = a] - \mathbb{E}[Y_i^{1,1} | D_i = 1, C_i = 1, T_i = a]) \\ & + p_n \times (\mathbb{E}[Y_i^{1,0} | D_i = 1, C_i = 0, T_i = n] - \mathbb{E}[Y_i^{1,0} | D_i = 1, C_i = 1, T_i = n]) \\ & + p_c \times \{\mathbb{E}[Y_i^{1,1} | D_i = 1, C_i = 0, T_i = c] \\ & \quad - (1-q)\mathbb{E}[Y_i^{1,1} | D_i = 1, C_i = 1, T_i = c] - q\mathbb{E}[Y_i^{1,0} | D_i = 1, C_i = 1, T_i = c]\} \end{aligned} \quad (5) $$

We then assume that the cooling-off variation does not affect donations directly, but rather only through the mitigation of emotions. We found empirical support for this assumption in the pilot data, where adding a cooling-off period without the video (i.e. comparing NOVID-SHORT to NOVID-SHORT) did not affect donations or emotions. The expression above simplifies to:
$$ \delta = p_c \times q \times \mathbb{E}[Y_i^{1,1} - Y_i^{1,0} | D_i = 1, T_i = c] \quad (7) $$

The mediated treatment effect is then given by:

$$ \Delta_M = \frac{\delta}{q} \quad (8) $$

Note that we provide an alternative estimator of the mediated treatment effect in the Supplementary Materials in case our experimental data show a significant change in donations between NOVID-SHORT and NOVID-LONG (see outcome neutral tests).

The parameter $\delta$ can be estimated from the data by comparing average donations in VID-SHORT and VID-LONG treatments. The parameter $q$ corresponds to the share of compliers who return to $M = 0$ after the cooling-off period. We denote $M_k^{d,c} = \mathbb{E}[M_i|D_i = d, C_i = c, T_i = k]$. The parameter $q$ can be estimated from the following equation:

$$ \frac{\mathbb{E}[M_i|D_i = 1, C_i = 0] - \mathbb{E}[M_i|D_i = 1, C_i = 1]}{\mathbb{E}[M_i|D_i = 1, C_i = 0] - \mathbb{E}[M_i|D_i = 0]} = \frac{p_a(M_a^{1,0} - M_a^{1,1}) + p_n(M_n^{1,0} - M_n^{1,1}) + p_c(M_c^{1,0} - M_c^{1,1})}{p_a(M_a^{0} - M_a^{1,0}) + p_n(M_n^{0} - M_n^{1,0}) + p_c(M_c^{0} - M_c^{1,0})} = q \quad (9) $$

as $M_a^{1,0} = M_a^{1,1} = M_a^0 = 1$, $M_n^{1,0} = M_n^{1,1} = M_n^0 = 0$, $M_c^0 = 0$, $M_c^{1,0} = 1$ and $M_c^{1,1} = (1 - q)$.

**Estimation procedure.** In the final sample, we estimate $\delta$ by calculating the difference in the average donation scores between participants in the VID-SHORT and VID-LONG treatments (i.e., $\hat{\delta} = \bar{Y}_{VS} - \bar{Y}_{VL}$),\* and $q$ as the difference in the average negative-emotion score between participants in the VID-SHORT and VID-LONG treatments divided by the difference in the average negative-emotion score of participants in the VID-SHORT and NOVID-SHORT treatments (i.e., $\hat{q} = (\bar{M}_{VS} - \bar{M}_{VL})/(\bar{M}_{VS} - \bar{M}_{NS})$). The mediated treatment effect is estimated as the ratio of these two estimates.

## 5.2 Hypotheses

We have two objectives. The first is to determine whether the undercover video successfully generates immediate engagement for the NGO's cause among viewers. In other words, we test $H_0^1: \Delta_{TT} \le 0$. If we reject $H_0^1$, we will conclude that the undercover video successfully creates engagement. The second objective is to determine the role of emotions in the changes in engagement in favor of the NGO's cause. Following the above procedure, we estimate the mediated treatment

<footnote>\*The parameter $\delta$ is equivalent to an interaction effect between the video and the long treatments in a standard linear regression.</footnote>
effect $\Delta_M$. The theoretical section suggests that emotions play a causal mediation effect in changing individual behaviors. We test this hypothesis via $H_0^2: \Delta_M \le 0$. If we reject $H_0^2$, we will conclude that emotions play a mediating role in the changes in pro-animal behaviors after seeing the NGO's video. If we fail to reject $H_0^2$, we will calculate the one-sided 95% confidence interval to estimate the upper bound for the value of $\Delta_M$.

We committed to testing the two hypotheses by looking at the lowest bounds of the one-sided 95% confidence interval of $\Delta_{TT}$ and $\Delta_M$ via bootstrap estimation ($B = 1,000$). The lower bound will be set at the 5th percentile of the bootstrap distribution. If the 5th percentile is strictly larger than 0, we will reject the associated null hypothesis. If $H_0^1$ is rejected, but not $H_0^2$, we will calculate the 95th percentile of the bootstrap distribution, which will correspond to the upper bound of the estimated $\Delta_M$. If the upper bound of $\Delta_M$ is estimated to be strictly lower than 3 percentage points, we will consider that emotions play an economically insignificant role in mediating the effect of the videos. Otherwise, we will conclude that the study is inconclusive. *

# 6 Sampling plan

We determined our sample size by assessing the precision of our estimates. We focused here on statistical power for the mediated treatment effect, as the mediation analysis requires more observations than does the estimation of the overall treatment effect. We considered that our analysis should be able to detect a mediated treatment effect of 5 percentage points or more with a probability of at least 95%, with a probability of Type-1 error equal to $\alpha = 0.05$. Figure 3 shows that the bootstrapped distribution of the estimated $\Delta_M$ using our pilot data (with $B = 1,000$ draws) is approximately normal (Shapiro p-value=0.447). Assuming that our estimate of $\Delta_M$ is normally distributed, the above decision rule for $H_0^2$ should be able to reject $H_0^2$ if the effect size is larger than 1.645 standard errors (i.e. the 5th percentile of a normal distribution). In this case, an estimated effect size of 0.05 (i.e. 5 percentage points) would lead to the rejection of $H_0^2$ if the standard error is smaller than 0.030.

Based on the pilot data, we simulated samples of different sizes using bootstrapping with replacement (assuming that the sample data are representative of the population data), and reported the precision of our estimate of the mediated treatment effect for each sample size. We simulated $S = 1,000$ samples for each sample size under consideration (with $B = 1,000$). The R code is provided in the Supplementary Materials. Note that, in this procedure, we did not use the effect size of the pilot data to compute statistical power as it would lead to a follow-up bias in study selection.⁵²,⁵³ We relied here on the estimates of the standard errors and compute statistical power for an assumed effect size of 5 percentage points.

These simulations indicated that 800 observations per treatment would yield a 100% probability

<footnote>*Note: The Stage-1 manuscript did not contain any rationale about the 3 percentage-point cut-off which was defined arbitrarily. A referee asked during the Stage-2 review for a rationale. We provide ex-post the following information: a 3 percentage-point increase in donations corresponds to a Cohen's d of 0.18 in the pilot data using the NOVID-SHORT condition as baseline. This effect size is usually considered as small (i.e., $d < 0.2^{51}$).</footnote>
of rejecting *H*₀ if the true effect size is equal to 5 percentage points. We further estimated that the smallest effect that we can detect with a 95% probability with N=3,200 observations is 3.4 percentage points.

<figure><img src="image_4.png" /><figcaption>**Figure 3:** Bootstrapped distribution of the mediated treatment effect in the pilot data (B=1,000 draws).</figcaption></figure>

# 7 Results

The experiment took place in December 2023 in accordance with GDPR regulation. Informed consent was obtained from all the study participants. In total, 3,310 participants completed the survey, and passed the attention checks (women: 52%, average age=47.8, SD=15.0).* Participants were randomly assigned to each of the four conditions with equal probability, and the final distribution of participants is displayed in Table 2. The experiment passed all the outcome-neutral tests. First, the Cronbach alphas are above the pre-specified 0.6 threshold for donations (0.87) and emotions (0.84). Second, the cool-off period reduced the emotional score by more than 20 percentage points. Third, the differences in attrition rates do not exceed the pre-specified 10-pp window. Fourth, we do not find any statistical effect of the cool-off *per se* when we compare donations in NOVID-SHORT

<footnote>*The number of participants slightly exceeds the pre-registered number of participants (N=3,200) because the survey company invited more respondents than needed to account for potential drop-outs.</footnote>
and NOVID-LONG (Wilcoxon test: p=0.141, difference in averages: 1.15pp). We thus proceed to the confirmatory analysis with the main estimator presented above.

<table><caption>**Table 2:** Distribution of participants with complete answers who passed the attention checks</caption><thead><tr><th></th><th>Without Video</th><th>With video</th></tr></thead><tbody><tr><th>Short survey</th><td>NOVID-SHORT<br>N=858<br>25.9% of the sample</td><td>VID-SHORT<br>N=794<br>23.9% of the sample</td></tr><tr><th>Long survey</th><td>NOVID-LONG<br>N=850<br>25.7% of the sample</td><td>VID-LONG<br>N=808<br>24.4% of the sample</td></tr></tbody></table>

## 7.1 Confirmatory analyses

The main results of the experiment are displayed in Figure 4. At first sight, we observe that the video increases average donations in the short questionnaire (from 16.4% to 20.2%) together with the average negative emotions (from 25.7% to 66.0%). The cool-off device is associated with a slight decrease in average donations (from 20.2% to 19.1%) and a large decrease in average negative emotions (from 66.0% to 32.2%).

<figure><img src="image_5.png" /><figcaption>**Figure 4:** Average donations and emotions across treatments</figcaption></figure>

Note: Bars display average scores per treatment. Spikes represent 95% confidence intervals.

The bootstrap distributions of the estimates for the total and mediated treatment effects are displayed in Figure 5. We rely here on our pre-registered decision rule to reject the null hypothesis, i.e., we reject the null if the 5th percentile of the bootstrap distribution of the estimator is strictly above 0. First, we can reject the null hypothesis for the total treatment effect (the 5th percentile is equal to +2.1pp), indicating that the video significantly increases donations to pro-animal actions (NGOs, petitions). The average effect (i.e., the mean of the bootstrap distribution) indicates an
increase of 3.81pp. The p-value can be defined as the share of the distribution that is below 0, i.e.,
p<0.001. Given that the average donation score is equal to 16.4% in the control group (NOVID-
SHORT), this corresponds to an increase of 23.4% in donations. The lower and upper bounds of the
two-sided 95% confidence interval are given by the 2.5th and 97.5th percentiles of the bootstrap
distribution respectively, i.e., +1.74pp and +5.93pp.

Second, we find inconclusive evidence for the mediated treatment effect. On the one hand, we do not reject the null hypothesis of null or negative mediated treatment effect ($H_0^2: \Delta_M \le 0$, p=0.175). The fifth percentile of the bootstrap distribution is equal to -0.009, falling below zero, and 17.5% of the distribution takes null or negative values. This provides evidence in favor of the null hypothesis (i.e., null or negative mediated treatment effect) and contradictory to the alternative hypothesis (i.e., strictly positive mediated treatment effect). On the other hand, we also fail to reject an economically significant effect of emotions (p=0.108). Following our registered data analysis protocol, we investigate whether we can affirm that the mediated treatment effect is economically insignificant. To do so, we scrutinize evidence suggesting that the mediated treatment effect would be larger than +3pp, i.e. the registered threshold below which we consider the mediated treatment effect to play an economically insignificant role. We find that the 95th percentile is equal to 0.035, and about 10.8% of the bootstrap distribution is above the 3pp threshold. We are thus not able to reject the hypothesis of an economically significant role of emotions either. Altogether, we find an average small positive mediated treatment effect (+1.27pp increase in donations) but we do not have statistical support to rule out a null or negative mediated treatment effect ($\Delta_M \le 0$) of emotions nor to rule out an economically-significant effect ($\Delta_M \ge 0.03$). The two-sided 95% confidence interval is equal to [-0.0128, 0.0396].

<figure><img src="image_6.png" /><figcaption>**Figure 5:** Bootstrap distributions of the total and mediated treatment effects with 1,000 draws</figcaption></figure>
## 7.2 Exploratory analyses

We now turn to some exploratory analyses that were not pre-registered. First, we explore further the total and mediated treatment effects. With the bootstrap draws, we can compute the share of the total treatment effect that could be explained by the mediated treatment effect. For each draw, we compute the ratio of the estimated mediated treatment effect over the estimated total treatment effect. When the mediated treatment effect is negative, we consider that it explains 0% of the total treatment effect. We winsorize this ratio to get maximal values at 1, which concerns about 1.4% of the estimates. Results are displayed in Figure 6. Logically, 17.5% of the distribution is at 0 because 17.5% of the bootstrap distribution for the mediated treatment effect is lower or equal to 0 (see above). About half of our bootstrap estimates report that the mediated treatment effect explains one-third of the total treatment effect or less (i.e., median estimate equal to 34.3%). At the 95th percentile, the mediated effect accounts for 84.4% of the total treatment effect. The two-sided 95% confidence interval is equal to [0.00, 0.94], suggesting a wide range of values for the potential role of emotions in the increase in donations. Note that the particularly large confidence interval is due to the fact that taking the ratio of two estimates adds another layer of uncertainty, and our study was not ex-ante-powered to address this question. Overall, we find inconclusive evidence about the mediating role of primary emotions in the video's impact on donations.

<figure><img src="image_7.png" /><figcaption>**Figure 6:** Bootstrap distributions of the share of the estimated total treatment effect explained by the estimated mediated treatment effect (1,000 draws)</figcaption></figure>

Second, we look at how the video impacted single emotions (1-to-7 Likert scales). Figure 7 shows that the video mostly increased sadness (+2.89 between NOVID-SHORT and VID-SHORT, Wilcoxon p-value<0.001), anger (+3.09, p<0.001), and disgust (+3.91, p<0.001) scores and decreased happiness (-3.65, p<0.001). To a smaller extent, the video increased fear (+0.83, p<0.001) and surprise (+0.66, p<0.001) scores. The cool-off device appears to have mitigated this change in negative emo-
tions for all emotions under consideration (for all emotions between VID-SHORT and VID-LONG: p<0.001).

<figure><img src="image_8.png" /><figcaption>Figure 7: Average emotions per treatment</figcaption></figure>

Note: Bars display average scores per treatment. Spikes represent 95% confidence intervals.

Third, we analyze the change in donations for each NGO and petition separately (NOVID-SHORT vs VID-SHORT). Average donations per treatment are displayed in Figure 8. Exploratory non-parametric tests (Wilcoxon) suggest that the video had a statistically significant impact (p<0.05) on all donations but for two NGOs (L214, p=0.052; SPA, p=0.728). The largest increases in average donations were for two NGOs, i.e., *Assiettes Végétales* (+6.67pp, p<0.001) and *Welfarm* (+6.96pp, p<0.001), and for the petition demanding a ban on intensive farms (+10.22pp, p<0.001). These exploratory results suggest that the video might have positive externalities on less radical NGOs (*Assiettes Végétales* promotes plant-based meals at university canteens, and *Welfarm* supports animal farmers in improving their practice). In our setting, the video does not bring financial benefits to the NGO that published it (i.e., L214), which is presumably due to participants' ignorance regarding its origin. However, the video seems to help the publishing NGO generate support for their demands, i.e., the ban on intensive farms. We discuss this question in more detail in the Discussion section below.

We also investigate whether the video worked at the extensive margin, i.e., whether it attracted new donors. While we cannot directly compare conditional donations due to composition effects (in-
tensive margin), we compare the shares of participants who gave strictly positive amounts (NOVID-SHORT vs VID-SHORT). We observe similar results as above: *Assiettes Végétales*, *Welfarm*, and the petition against intensive farming increase their number of donors (+9.3pp, p<0.001; +8.3pp, p<0.001; and +10.4pp, p<0.001 respectively). The two NGOs that had no statistically significant increase in average donations (L214, SPA) also have the smallest change in the share of donors (+4pp, p=0.112; and +0.5pp, p=0.870 respectively). This suggests that the video also works at an extensive margin, attracting new donors.

<figure><img src="image_9.png" /><figcaption>**Figure 8:** Average donations per treatment</figcaption></figure>

Note: Bars display average scores per treatment. Spikes represent 95% confidence intervals.

Fourth, we do not see any difference in the moral concerns across treatment groups. Moral concerns for the animals are large in all groups, including in the baseline condition NOVID-SHORT. On the 1-to-7 Likert scale, all items of the moral concerns for animals have average scores above 6 in all treatment conditions. Fifth, we observe some impact of the video on the pro-meat justifications (PMJ). We follow previous work<sup>49,54</sup> and aggregate the PMJ items into a single PMJ score (average of the 10 items, Cronbach alpha: 0.91). Participants who saw the video display lower PMJ (NOVID-SHORT: average: 0.571, CI: [0.558, 0.584], VID-SHORT: 0.538 [0.525, 0.551], Wilcoxon *p* < 0.001). However, the exploratory analysis suggests that this decrease in PMJ vanishes with the cool-off device (NOVID-SHORT: 0.571 [0.558, 0.584], VID-LONG: 0.563 [0.550, 0.576], Wilcoxon *p* = 0.213).

Sixth, we look at how participants who were exposed to the video perceived it. We analyze the answers given to the 1-to-7 Likert scales about the credibility of the video. On average, exposed participants tend to slightly disagree with the fact that the video is an exception and not representative of animal farming in France (average: 3.61 [3.53, 3.69]). They also reject the ideas that the video was recorded in another country (2.71 [2.63, 2.78]), that the video is fake (2.19 [2.12, 2.25]),
that the information revealed in the video is not accurate (2.31 [2.25, 2.38]), that the information
in the video is biased or distorted (2.37 [2.30, 2.44]), that the video is not believable (2.17 [2.10,
2.24]), or that the video is not realistic (2.20 [2.13, 2.27]). Overall, these results indicate that the
participants have a fairly positive opinion of the credibility of the video.

Last, we look at the viral potential of this video. In the treatment groups with the video, we observe mixed positions about whether people would be willing to share the video on social media. When looking at the answers on the 1-to-7 Likert scale, we observe average answers close to the middle of the scale (3.91 [3.74, 4.07] in VID-SHORT and 3.71 [3.54, 3.87] in VID-LONG). However, these averages hide a large heterogeneity in the willingness to share the video: about 31% of the participants in VID-SHORT and 36% in VID-LONG report that it is ‘not at all likely’ that they would share the video on social media. On the contrary, 26% and 23% of the participants in these two groups say that it would be ‘very likely’ for them to share the video.

8 Perception by activists

8.1 Design

NGOs are faced with considerable resource constraints in their attempts to transform society. One key issue for activists is then to focus on the most-efficient strategies to achieve their desired change. However, activists may differ from the general population in a number of dimensions (e.g., knowl-edge, concern about the topic, demographic composition, and the type of networks they have), such that their perception of the effectiveness of their campaigns might be inaccurate. Regarding undercover videos, the expected total effect on engagement and the role of emotions in reactions may well be central to the allocation of resources between different campaigns.

We here propose to investigate whether activists have accurate perceptions of the total and mediated effects of videos. To do so, we recruited 154 activists from the NGO that produced the original video used in the experiment above. These activists were recruited via posts on the NGOs' Facebook groups for activists, posted by members of the NGO themselves. [Note that the participants were recruited in two waves. A first group of participants were recruited in June 2022, and a second group in September 2023. In September 2023, participants who registered in June 2022 were asked to confirm their willingness to participate in the study. The final sample of 154 participants comprises the participants recruited in 2022 who confirmed their participation and the participants recruited in 2023.] The activist participants were blind to the study's objective, and were only told that they would be asked about their opinions on a number of strategies implemented by French animal-advocacy NGOs. Our sample is 78% female, with an average age of 41, and has been active in the NGO under consideration for 4.4 years on average.

We designed a prediction study to assess the perception of activists regarding the role of the
emotions produced by these videos.* The text of the prediction study appears in the Supplementary

<footnote>*This study received the approval of the ethics committee of the Institute for Advanced Studies in Toulouse in February 2023 (IRB approval number: reference 2023-02-003).</footnote>
Materials. In this prediction study, we ask activists to predict as accurately as possible the average donation levels and emotional scores that we obtain from the main experiment under the three conditions (NOVID-SHORT, VID-SHORT and VID-LONG). We first present activists with the design and population of the main experiment and ask a number of comprehension questions. Second, we ask them to watch the video included in the main study.* Third, we ask participants their predictions about the average emotional state and donation levels produced in the main study. Note that activists are incentivized to give accurate predictions.†

The two main variables of interest are the emotional state and donation predictions. Regarding the emotional state, we detail to the activist how we constructed the aggregated emotional score: Participants had to report the intensity of several emotions they experienced, and we computed the sum of the reported scores for negative emotions minus the score. To simplify the prediction decision, we ask activists to assess the expected average emotional score on a normalized scale (slider task) taking values between 0% (very negative emotional state) and 100% (very positive emotional state). Regarding the donation prediction, we present to the activists the list of NGOs and petitions that were presented to the representative sample together we the donation decision they faced. Activists must report the average donation score (slider task) taking values from 0% (out of the 7 associations/petitions, the participant did not donate anything to any of the associations/petitions) and 100% (out of the 7 associations/petitions, the participant systematically donated all his/her money).

## 8.2 Exploratory results

Data collection took place in January 2024 in accordance with GDPR regulation. Informed consent was obtained from all the study participants. In total, 50 activists completed the survey and gave their consent. While no outcome-neutral test was pre-registered regarding this part of the study, we underline that the limited number of observations urges for a prudent interpretation of the activists' answers. On the other hand, let us also note that this type of audience is harder to reach than representative samples of the general population. Before discussing the results, we would like to stress three methodological points. First, interpreting the absolute emotional score, at least in the baseline group, makes little sense as it might be difficult for activists to predict absolute scores on such an unfamiliar scale. Second, we discussed for the main study differences in averages across groups (donations or emotions), and the mediated treatment effect is itself a ratio of differences in averages. However, the picture is different for the prediction study as activists must predict the reactions to each treatment. Because of the Simpson paradox,⁵⁵ note that the ratio of averages

<footnote>*We decided to ask activists to watch the video themselves before reporting their beliefs about what participants in the group who had seen the video would do, as well as what participants who had not seen the video would do for reasons of external validity: when activists decide whether to release an undercover video they know what the video shows and it is with this knowledge that they predict the individuals' reactions to it. Note that we did not include NOVID-LONG as this treatment was not relevant to the prediction study.</footnote>

<footnote>†We told activists that one of them would be randomly selected at the end of the prediction study and that one of her predictions would be randomly selected. If this prediction was sufficiently close to the correct answer (within a 5 percentage-point interval around the correct answer), we would donate 100 Euros to the NGO under consideration.</footnote>
<figure><img src="image_10.png" /><figcaption>**Figure 9:** Expected average donations and emotional scores across treatments by activists</figcaption></figure>

Note: Bars display average scores per treatment. Spikes represent 95% confidence intervals.

differs from the average of ratios. Last, because of the small number of observations, we opt for discussing the median view among activists rather than the average view to limit the influence of outliers.⁵⁶ As a result, we discuss below, when relevant, ratios of averages for the main study (between-subject design) and the median of ratios for the prediction study (within-subject design).

The main results of the prediction study are displayed in Figure 9. First, concerning donations, we observe that activists largely overestimate the altruistic actions of laypeople in the control group. The median expectation among activists is for participants in the main experiment to give on average 43.5% of their endowment while they only gave 16.4%. Second, while activists correctly anticipate an increase in donations after the video, they overestimate its size (both in absolute and relative values). The median expectation among activists is that the video increased donations by 28pp (+60.3%) while it increased average donations by 3.8pp (+23%) only. As far as emotions are concerned, activists correctly guess that participants experience more negative emotions after viewing the video, but they tend to slightly underestimate the size of the difference. The median expectation among activists is that negative emotions would increase by 35pp while they increased by 40.3pp. Because activists overestimate the negative emotional score in the control group, the median expectation among activists underestimates the relative difference in negative emotions (+85.5% vs. +156.8%).

Regarding the cool-off, activists correctly anticipate that the cool-off device mitigates the effect of the video on donations but they underestimate its effect on the emotional load. On the one hand, the median expectation among activists is that the cool-off period mitigates 36.5% of the effect of the video on donations, which is close to what we observe in the main experiment (28.9%). Note however that, in absolute value, the median expectation among activists regarding the decrease in donation is larger (-7pp) than what we observe (-1.1pp). On the other hand, the median expectation
among activists is that 41.2% of the emotional effect of the video would be offset by the cool-off device against 83.9% in our data. Altogether, most of the activists anticipate a positive mediated treatment effect (76% of the activists' sample).

# 9 Discussion

Our experiment aimed at investigating the impact of undercover videos on people's propensity to support social change. From our registered analyses, we first observe that these videos are successful in generating engagement. Watching the video about animal abuse in farms significantly increases donations in favor of animals. Second, our registered analysis fails to detect a direct role of emotions in this increase in engagement but, at the same time, also fails to reject the idea of an economically significant role of emotions. Indeed, we find a 17.5% chance that the mediated treatment effect is lower or equal to 0, but also a 10.8% chance that it is larger than 3pp (our registered threshold for economic significance). An exploratory analysis suggests that the mediated treatment effect would account, at the median, for 34.4% of the total treatment effect. However, the uncertainty of this estimate is large: the two-sided 95% confidence interval is equal to [0, 0.94]. Overall, the evidence is inconclusive on the mediated treatment effect, but, should there be a mediated treatment effect of emotions, the current balance of evidence tends to indicate that it would be of limited size.

As far as emotions are concerned, we observe that the video first generates a large negative emotional load. Exploratory analyses suggest that this negative shock is mostly due to an increase in sadness, anger, and disgust and a decrease in happiness. The video is much less likely to induce fear or surprise. While increases in donations tend to survive when the negative emotions vanish, we cannot exclude that they play a significant role in the video's impact. The literature on emotions has shown that they can shape decisions via the content of the thought, the depth of the thought, and goal activation.¹⁶ In other words, the negative emotional load might lead people to think about abuses in animal farming (content), to think more about it (depth), and to motivate them to act on this issue (goal activation). This is particularly true for anger which has been shown to stimulate motivation to fight injustice and which is here significantly affected by the video.⁷,¹⁹,⁵⁷ As discussed in the theoretical section, affects can also be informative about a situation: the negative emotions that a participant feels watching the video can contribute to their change of perception of the issue at stake. In addition, while we focused on primary emotions only, these can, in turn, generate longer-term and more elaborate emotions that can survive their disappearance, e.g., so-called secondary emotions.⁵⁸ Such secondary emotions, which include guilt and shame,⁵⁹,⁶⁰ were not captured in our study. They typically involve the self-concept⁵⁸ and can persist even after the primary emotions have vanished.

Another point of caution concerns the external validity of our definition of emotions which might not be universally applicable. For instance, recent work has suggested that individuals with limited exposure to Western concepts of emotion might perceive and classify facial expressions and emotional vocalizations differently compared to Western individuals.⁶¹⁻⁶³ Additionally, the decomposition of
the emotional state into distinct emotions might also lack biological support, and they might result
from non-distinct biological systems.⁶⁴⁻⁶⁶

Relatedly, the absence of statistical evidence supporting a causal role of felt emotions on subsequent behaviors in our study might well align with recent works in psychology on anticipated emotions. Several authors have indeed challenged the common assumption that emotions directly influence behaviors and have advanced the idea that people adapt their behavior ex-ante such as to avoid anticipated negative emotions. For example, some authors have argued that emotions function more as a feedback system rather than a direct cause of behavior, suggesting that behaviors are modified based on the learning and cognitive appraisal derived from emotional feedback.⁶⁷ Similarly, some other works have found that felt emotions do not consistently translate into behaviors, emphasizing the greater influence of anticipated emotions, such as shame or guilt, on behavior.⁶⁸

Regarding donations, exploratory analysis suggests that less radical measures benefit the most from the videos (ban on intensive farms, development of plant-based menus in university canteens with *Asiettes Végétales*, support for farmers’ initiatives aimed at improving farm-animal welfare with *Welfarm*). Banning intensive animal farming is already a popular policy in France, where about 84% of the population supports a ban (IFOP, Jan. 2024). The video seems to stimulate this policy support by creating larger engagement. More moderate NGOs like Welfarm also benefit from the situation. Overall, it seems that participants do not want to stop meat consumption but support initiatives to improve the welfare of farmed animals. This result is of particular interest for the literature on the *radical flank effect*, which questions whether moderate groups benefit or suffer from the existence of more radical groups.⁴⁹ Evidence from donations suggests here a positive radical flank effect, as Welfarm, which is usually considered as more moderate than L214,⁶⁹ experiences an increase in donations. However, one important characteristic of our design is that participants ignore that the video was published by L214. In natural contexts, people are likely to know the identity of the NGO that published the video, which might change the donation patterns and might, in fine, mitigate the positive radical flank effect.

The credibility of these videos is often questioned by public officials or by the industry as a
strategy to discredit its content. For instance, in 2020, a co-owner of a duck farm where L214
recorded and published footage of animal abuse declared that "the video is false, misleading and
dishonest".* From a legal perspective, the NGO protects itself by having long unedited takes
when recording on-site in which activists put various elements to prove the date of the recording
(e.g., newspaper) and its location.† The population seems to trust these videos: participants in
our experiment show very little adherence to the idea that the video would be fake, distorted, or
unbelievable. Among the usual arguments that we identified on this topic, participants show the
highest adherence to the idea that the video is not representative of animal farming in the country.
This could result from a sincere belief in the issue but could also be part of a strategy to avoid
cognitive dissonance. Recently, we have seen L214 focusing most of its campaigns against specific

<footnote>*Link: https://web.archive.org/web/20240120074009/https://www.lefigaro.fr/flash-eco/elevage-insalubre-decanards-la-video-de-l214-est-mensongere-selon-la-coproprietaire-20200820</footnote>

<footnote>†Example: https://www.youtube.com/watch?v=IhsZcjJ7PaE</footnote>
brands (e.g., Burger King, Domino's Pizza, LDC), which can contribute to fighting against the dilution of responsibility resulting from the 'non-representativeness' argument.

Although the undercover videos seem to achieve their objective of increased engagement, we must note however that our experimental setting is highly controlled. Some factors might substantially limit the impact of the video outside of this setting. For instance, participants might self-select, and only sympathizers could view the video. While the publication of the video might help sustain engagement in the long run, we might not have the extensive margin effect that our exploratory analyses underlined. In other words, it might fail to bring new people to act for the animals. Another example: When a new video is released, the industry and the government might also react publicly, as we have discussed in the previous paragraph. This might give people counter-arguments to discount the message of the NGO. These arguments can be about the farm itself (e.g., the public veterinary services controlled the farm and did not report any transgression*) or about other dimensions (e.g., the farmer is in economic difficulty because of the publication of the NGO†). Overall, our study might therefore overestimate the effect of the videos in uncontrolled contexts, and future research is needed in this direction.

From a methodological perspective, our experiment aimed to identify the effect of videos on donations and emotions. Following the recent advances in mediation analysis, we developed an empirical strategy with two treatment variations to causally estimate the mediated treatment effect of emotions. We believe that our paper makes here two noticeable contributions to the literature. First, the model that we developed in Section 5 can easily be used in other causal mediation analyses. This model is relatively parsimonious in terms of statistical assumptions. It relies indeed on two major assumptions: the absence of defiers (which we justify in the model as the fact that no one would feel better by viewing the video), and the assumption that the group of compliers who react to the cool-off (i.e., their negative emotions vanish) and the group of compliers who do not react to the cool-off (i.e., their negative emotions persist) react in the same way to the video treatment (i.e., on expectation, they similarly increase their donations when exposed to the video). The model identifies a linear mediated treatment effect as a linear regression would capture. Second, we contribute to the literature that looks at the impact of primary emotions by showing that a five-minute cool-off might be enough to mitigate the main treatment effect on the mediator (here: how the five-minute transport survey mitigates most of the impact of the undercover video showing animal abuse on the emotional state). In our experiment, the cool-off did not impact the outcome variable (donations), but future works that would use our method can also use our alternative estimator presented in the Supplementary Materials, should the cool-off have an effect per se on the outcome variable (i.e., in the absence of the main treatment).

Another important methodological question in this type of experimental setting is the definition of the benchmark situation with which we compare the variables of interest. In the context of

<footnote>*Link: https://web.archive.org/web/20240227055314/https://www.francetvinfo.fr/animaux/bien-etre-animal/vendee-les-autorites-n-ont-trouve-aucune-non-conformite-dans-l-elevage-mis-en-cause-par-l214_2220053.html</footnote>

<footnote>†Link: https://web.archive.org/web/20240227055546/https://www.lepoint.fr/societe/cible-par-une-video-de-l214-un-elevage-en-liquidation-judiciaire-11-07-2019-2324110_23.php</footnote>
emotions, defining a *neutral affect* is difficult, if not impossible, as there is no neutral situation in terms of emotions.⁷⁰ In our case, we compared participants’ decisions and emotions to a baseline situation where they were not exposed to any video. This aims to reflect real-life situations where, for instance, participants are asked to donate to a charity on a social media platform, and some participants were previously exposed to an undercover video (by a friend liking or sharing the video for instance). Alternative experimental designs could display a neutral video in the control group such as to ensure that there is no video effect per se on the participants. The main drawback is that there is no such thing as a completely neutral video (i.e., videos that induce no primary emotions could induce boredom), and the empirical results could depend on the specific video chosen for the baseline.

Last but not least, our prediction survey brings new knowledge about how activists see the population and how they expect them to react. First, our exploratory analysis suggests that activists overestimate the general population's altruistic behaviors. Activists overestimate indeed donations in the control group and expect a larger increase in engagement after watching the video. We postulate that activists might have a distorted optimistic view of general altruism towards animals, which could result from several behavioral factors (e.g., false consensus effect,⁷¹ majority illusion,⁷² echo chamber,⁷³ projection bias⁷⁴). This distorted perception of the general population's concerns and behaviors regarding the cause an NGO is willing to defend could, in the long run, threaten the effectiveness of its campaigns. Second, activists in our survey correctly anticipate that the video generates negative emotions among participants in our main experiment. Nevertheless, they expect the emotions to last longer than what we observe. This overestimation aligns with research on affective forecasting,⁷⁵⁻⁷⁷ which refers to the process by which people predict their future emotional reactions to events. Overall, this research has shown that people often overestimate the impact and duration of their emotional reactions. This may explain what we observe here for the activists' predictions about the duration of the negative emotions among others resulting from watching a video. Here also, it is possible that activists are more sensitive to animal abuse and, because they think that they would be affected longer by the video, they expect the general population to do so. Overall, this leads activists to overestimate the role of emotions in the increased engagement in favor of animals induced by the video. However, these results should be interpreted with caution given the small number of observations and given that they are only exploratory analyses.

**Competing Interests.** This research is funded by the French National Agency for Research (ANR) under grant ANR-19-CE21-0005-01. The authors declare no potential conflict of interest. RE serves as the Director of the French CNRS research group "Animal Welfare Research Observatory" (uncompensated) and sits on the advisory board of the research group "Animal Welfare Economics Working Group" (uncompensated). NT serves as an unpaid member of the "French Sustainable Development Commission" for the French government, the scientific committee of the "Greener by Default" association, and the "Animal Welfare Research Observatory".
**CRediT author statement.** Conceptualization, Methodology, Writing - Original Draft: RE, SB, NT; Software, Investigation, Data Curation, Writing - Revision: RE, SB; Formal Analysis, Visualization, Project Administration, Funding Acquisition: RE.

**Acknowledgment.** The authors thank Sylvain Chabé-Ferret, Andrew Clark, the editor, two anonymous referees, and participants in the seminars from iRisk (IESEG, Lille) and CEE-M (Montpellier) for useful comments. Romain Espinosa acknowledges financial support from the ANR under grant ANR-19-CE21-0005-01. Sylvie Borau and Nicolas Treich acknowledge support from the ANR-Labex Institute for Advanced Study in Toulouse, and Nicolas Treich acknowledges support from ANR under grant ANR-17-EURE-0010 (Investissements d'Avenir program).

**Code and data availability.** The data and codes to replicate the paper are available here: https://zenodo.org/doi/10.5281/zenodo.10777689

**Protocol Registration.** The Stage-1 that received In-Principal-Acceptance can be found here: https://osf.io/8cg2d
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Registered Report - Stage 2:
Impact of NGOs' Undercover Videos on Citizens' Emotions and Pro-Social Behaviors

Romain Espinosa, Sylvie Borau, Nicolas Treich

June 12, 2024

**Supplementary Materials 1: Experimental design**

<figure><img src="image_11.png" /><figcaption>Figure SM1: Design of the experiment</figcaption></figure>
# Experiment

## Intro

Dear Participant,

In this study, we are going to ask you questions about your diet and food consumption as well as your beliefs, attitudes, and opinions on current topics, and some demographic questions.

The study should last about 20 minutes.

The information provided in this questionnaire will only be used for academic/scientific purposes (no commercial purposes).

You will be compensated in points, as indicated on the previous page.

## Confidentiality, Disclosure of Information, and Declaration of Voluntary Participation

You will not be asked for your name or any other information that could be used to identify you.

The information you provide will be stored anonymously on the Qualtrics account for two weeks and can never be linked to you, your real name, or your IP address. Anonymous data will initially be accessible only to the team of researchers involved in this project, then the anonymized data will be stored indefinitely on a data-sharing account for the purpose of replication and to promote reproducible science. Participation is completely voluntary. You can end your participation at any time.

If you have any questions about this study, you can contact Sylvie Borau: s.borau@tbs-education.fr

We thank you for your participation!

## Gender

○ Man (1)

○ Woman (2)

○ I don't identify as a man or a woman (3)

## Captcha
Please click on the link below.

## Risks
Disclaimer and Consent:

In the following questionnaire, you may be exposed to explicit images that may elicit unpleasant levels of emotion. These feelings are usually temporary.

○ I am aware of the explicit content of this study. (4)
**Consent** I have read this form and am aware that I am being asked to participate in a study that may contain explicit images.

I voluntarily agree to participate in this study. I can end my participation at any time.

I agree to participate in this study (2)

I do NOT agree to participate in this study (4)

**Does not consent**

As you do not wish to participate in this study, the questionnaire ends here. Thank you for your participation.

**Randomization**

The following two questions are randomized in the questionnaire. Half of the sample is exposed to these two questions at the beginning of the questionnaire; the other half at the end of the questionnaire.
**Food consumption** Please indicate how often you consume the following items:

<table><thead><tr><th></th><th>Never (1)</th><th>A few times a year (2)</th><th>A few times a month (3)</th><th>A few times a week (4)</th><th>Almost at every meal (5)</th></tr></thead><tbody><tr><td>Red meat (1)</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td></tr><tr><td>White meat (2)</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td></tr><tr><td>Fish (3)</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td></tr><tr><td>Eggs (4)</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td></tr><tr><td>Dairy products (5)</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td></tr><tr><td>Vegetables (6)</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td></tr><tr><td>Pitch/ pulses (7)</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td></tr><tr><td>Fruits (8)</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td></tr><tr><td>Starchy foods (9)</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td><td>Ο</td></tr></tbody></table>
**Diet identity** And overall, do you identify the most as a...

◯ Vegetarian (1)

◯ Vegan (2)

◯ Omnivore (3)

◯ Carnivore (4)

◯ Flexitarian (5)

**Sound** In the rest of the questionnaire, you may be exposed to a video. That is why we would
like to make sure you can listen to sounds on your device

Below, you will be asked to listen to a sound and then indicate the sound you heard.

If you do not answer the question correctly, you will not be able to continue this study.

**Sound test** Please listen to this sound clip and indicate which sound it is by selecting the correct answer below.

◯ Honking car alarm (1)

◯ Crickets (2)

◯ A cat meowing (3)

◯ A person whistling (4)

◯ A dog barking (5)

**Error sound** You have not selected the correct answer. As you cannot play sound on your device, the questionnaire ends here. Thank you for your participation.
**Intro video** On the next page, you will be exposed to a video showing animal intensive farming in France.

This investigation took place over several months and used hidden cameras.

Please watch this video carefully. Note that the video is only one minute and 20 seconds long.

**Video** Please watch the video by clicking directly on the video. You don't need to go to Vimeo to watch it.

<figure><img src="image_12.png" /></figure>

[In the LONG treatment, the questionnaire about the transportation mode is displayed here.]

**Intro emotions**

Now we would like to know how you feel right now.

We would like you to look at some pictures of faces and tell us which picture of faces best matches with how you feel right now.
**Emotions** Choice Please click on the set of faces that best matches your emotions.

<figure><img src="image_13.png" /><figcaption>Fear</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Disgust</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Surprise</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Happiness</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Sadness</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Anger</figcaption></figure>

◯ (1)

◯ (2)

◯ (3)

◯ (4)

◯ (5)

◯ (6)

**IntroEm2** Now you will see each set of faces separately, and you will rate how well each set of faces matches how you feel right now.
<figure><img src="image_19.png" /></figure>

Fear

Please indicate how strongly you feel this emotion.

◯ 1. Strongly disagree (1)

◯ 2 (2)

◯ 3 (3)

◯ 4 (4)

◯ 5 (5)

◯ 6 (6)

◯ 7. Strongly agree (7)
<figure><img src="image_20.png" /></figure>

Sadness

Please indicate how strongly you feel this emotion.

◯ 1. Strongly disagree (1)

◯ 2 (2)

◯ 3 (3)

◯ 4 (4)

◯ 5 (5)

◯ 6 (6)

◯ 7. Strongly agree (7)
<figure><img src="image_21.png" /></figure>

Happiness

Please indicate how strongly you feel this emotion.

◯ 1. Strongly disagree (1)

◯ 2 (2)

◯ 3 (3)

◯ 4 (4)

◯ 5 (5)

◯ 6 (6)

◯ 7. Strongly agree (7)
<figure><img src="image_22.png" /><figcaption>Anger</figcaption></figure>

Please indicate how strongly you feel this emotion.

◯ 1. Strongly disagree (1)

◯ 2 (2)

◯ 3 (3)

◯ 4 (4)

◯ 5 (5)

◯ 6 (6)

◯ 7. Strongly agree (7)
<figure><img src="image_23.png" /><figcaption>Surprise</figcaption></figure>

Please indicate how strongly you feel this emotion.

1. Strongly disagree (1)

2 (2)

3 (3)

4 (4)

5 (5)

6 (6)

7. Strongly agree (7)
<figure><img src="image_24.png" /></figure>

Disgust

Please indicate how strongly you feel this emotion.

◯ 1. Strongly disagree (1)

◯ 2 (2)

◯ 3 (3)

◯ 4 (4)

◯ 5 (5)

◯ 6 (6)

◯ 7. Strongly agree (7)

Charity

In addition to the points you will receive for taking part in this survey, we will give you an additional 1 Euro which will be converted into points.

You have the option of keeping this money (the points will be credited to your account) or donating some or all of this extra money.

We present below a list of associations and petitions that you can support by making donations.
For information, a donation for a petition will help increase its visibility on social networks for
example.

Note that at the end of the survey, only one of your decisions will be implemented depending on
the association or petition that we selected upstream.

Please indicate for the 7 associations or petitions below if you wish to donate money (knowing
that only one association or petition will receive the donation, the remaining 6 being hypothetical). Note that all the actions and petitions mentioned actually exist.

**Lists List of proposed French associations:**

- Vegetable Plates: An association that seeks to put more vegetarian and vegan meals in university canteens.

- L214: An animal protection association that denounces the most cruel practices of the livestock industry and seeks to develop diets without animal products

- SPA: An association that works to protect animals, mainly domestic animals (cats, dogs), for example by fighting against abandonment and encouraging adoption.

- Welfarm: An association that supports the initiatives of breeders and the agri-food industry that contribute to improving the well-being of animals on farms

**List of petitions:**

- Against intensive farming: A petition to ban intensive farming in France

- For vegetarian meals: A petition to have at least one vegetarian meal per day in all public canteens in France

- To protect activists: A petition to protect activists in France who record videos showing the mistreatment of animals in intensive farming
**Donation associations** (cents):

<figure><img src="image_25.png" /></figure>

**Donation petitions** (cents):

<figure><img src="image_26.png" /></figure>

**Confirm** I confirm the amounts indicated above: a line will be selected to be set up. The amount I indicated will be subtracted from the additional 50 cent bonus I will receive.

I confirm (1)
**Moral concern animal** Please indicate to what extent you agree or disagree with the following statements about animals:

<table><thead><tr><th></th><th>1. Totally disagree (1)</th><th>2 (2)</th><th>3 (3)</th><th>4 (4)</th><th>5 (5)</th><th>6 (6)</th><th>7. Totally agree (7)</th></tr></thead><tbody><tr><td>Animals deserve to be protected from harm (1)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Animals deserve to be treated with care and compassion. (2)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>I have sympathy for animals (3)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>I have respect for animals (4)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Harming animals is morally wrong (5)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr></tbody></table>
Promeat Please indicate to what extent you agree with the following statements.

<table><thead><tr><th></th><th>1.<br>Totally<br>disagree<br>(1)</th><th>2 (2)</th><th>3 (3)</th><th>4 (4)</th><th>5 (5)</th><th>6 (6)</th><th>7.<br>Totally<br>agree<br>(7)</th></tr></thead><tbody><tr><td>It is acceptable to eat meat because the animals killed for our consumption do not really suffer. (2)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>It is acceptable to eat meat because the animals killed for our consumption have lower intellectual capacities than humans. (7)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>It is acceptable to eat certain animals because they are bred for that purpose. (9)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>God created animals for us to eat (10)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Eating meat is healthy (11)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>It is natural to eat meat, it is written in our genes. (12)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>It's normal to eat meat (13)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>I like meat too much to stop eating it. (14)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Eating meat is necessary for good health (15)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>Eating meat may be bad for the environment, but no more so than eating vegetables or cereals. (16)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr></tbody></table>
**Morally wrong**

How **morally wrong do** you think the animal rearing conditions shown in this video are.

Please rate how morally wrong these conditions are, using the scale below, where 0 is "not at all morally wrong" and 100 is "extremely morally wrong."

<figure><img src="image_27.png" /></figure>

These rearing conditions are... ()

<figure><img src="image_28.png" /></figure>

**Sharing** If you were to see the above video on Twitter or Facebook, how likely would you be to share it?

1. Not at all likely (1)

2 (2)

3 (3)

4 (4)

5 (5)

6 (6)

7. Very Likely (7)
**Truth** To what extent do you agree with the following statements?

<table><thead><tr><th></th><th>Completel<br>y<br>disagree<br>(1)</th><th>Strongl<br>y<br>disagre<br>e (2)</th><th>Somew<br>at<br>disagree<br>(3)</th><th>Neither<br>agree<br>nor<br>disagre<br>e (4)</th><th>Somew<br>at agree<br>(5)</th><th>Strongl<br>y agree<br>(6)</th><th>Completel<br>y agree<br>(7)</th></tr></thead><tbody><tr><td>This video is<br>not<br>representativ<br>e of animal<br>framing in<br>France, it is<br>rather an<br>exception (1)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>This video<br>was not<br>recorded in<br>France but<br>in another<br>country. (2)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>This video is<br>fake (militant<br>activists<br>made it up).<br>(3)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>The<br>information<br>revealed in<br>this video is<br>not accurate<br>(4)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>The<br>information<br>revealed in<br>this video is<br>distorted and<br>biased. (5)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>This video is<br>not<br>believable<br>(6)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr><tr><td>This video is<br>not realistic<br>(7)</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td><td>○</td></tr></tbody></table>
**<u>Eat Meat -motivation</u> And to what extent do you agree with the following statement?**

" Right now, I'm motivated to eat meat "

<figure><img src="image_29.png" /></figure>

Motivated to eat meat ()

<figure><img src="image_30.png" /></figure>

**<u>Check1</u> What is the main subject of this study?**

◯ Diet (10)

◯ Artificial intelligence (11)

◯ Obesity around the world (17)

◯ Overpopulation in the world (18)

**<u>Check2</u> What did you see in the video you were exposed to? (several possible responses)**

☐ chickens that got beaten up (1)

☐ animals helping humans (3)

☐ men and women eating beef (4)

☐ humans killing cows (5)

☐ pigs being mistreated (2)
**Seen video** - And have you seen this particular video before?

Yes (1)

No (2)

**Seen this type of video** - And have you seen a similar video recently about farm animal abuse?

Yes (1)

No (2)

**Check 3** What is 12 + 40?

12 (1)

21 (3)

32 (4)

41 (5)

52 (2)

63 (8)
**Age** How old are you ?

◯ 18 (3)

◯ 75 or more (60)

**Marital status** What is your current marital status?

◯ Single (1)

◯ In a relationship, but not married (2)

◯ Married (3)

**Children** Do you have any children?

◯ Yes (1)

◯ No (2)

**Political ideology** Overall, what would be the best description of your political views?

Far left

Center

Far right

0 1 2 3 4 5 6 7 8 9 10

1 ()

<figure><img src="image_31.png" /></figure>
**Religion** Do you believe in God ?

1. No, not at all (1)

2 (2)

3 (3)

4 (4)

5 (5)

6 (6)

7. Yes, a lot (7)

**Debrief** Thank you for your participation in this study!

The objective of this questionnaire was to better understand the influence of videos denouncing animal abuse in intensive farms on the beliefs and behavior of consumers in terms of meat consumption. This is an online experiment: half of the participants were exposed to a video showing intensive farm animal abuse, and the other half were not exposed to any video. The same questions were then asked to all participants. The ultimate goal is to explore the role of emotions in consumer reactions to animal abuse and meat consumption.

Here are some online resources if you want to learn more about farm animal abuse and
advocacy:

Factory farms are multiplying all over the world:
https://www.la-croix.com/Economie/France/Greenpeace-dresse-carte-fermes-usines-France-2018-11-26-1200985667

The defense of farm animals claimed by moderate associations:
https://www.lepoint.fr/societe/la-defense-des-Animaux-de-ferme-revendiquee-par-des-associations-moderees-12-10-2018-2262473_23.php

The peaceful retirement of farm animals that have survived slaughterhouses or mistreatment:
https://www.lemonde.fr/m-perso/article/2021/02/26/rescapes-des-abattoirs-de-l-industrie-ou-de-la-maltraitement-ces-Animaux-de-ferme-flow-a-peaceful-retreat_6071343_4497916.html
## Supplementary Materials 2: Alternative estimator for the mediated treatment effect

Based on the pilot data, we assume in the Stage-1 manuscript that there is no effect of the LONG treatment *per se* (i.e., no change in donations associated with having the long questionnaire in the absence of the video). We find some empirical support for this assumption as we fail to reject the null hypotheses of equality of donations between NOVID-SHORT and NOVID-LONG in the pilot sample (Wilcoxon rank-sum test: NOVID-SHORT vs. NOVID-LONG: $p > 0.477$ for all of the single-donation decisions). However, we cannot exclude that, in the main experiment, this assumption will not hold. In the Stage-1 manuscript, we commit to running an outcome-neutral test to test this assumption on the final sample data. If we reject the null hypothesis of equality of donations on the aggregate donation score (Wilcoxon rank-sum test: NOVID-SHORT vs. NOVID-LONG) and if the difference is larger than 2 percentage points (i.e., economically significant difference), we commit to using the following alternative estimator for the mediated treatment effect instead of the estimator presented in the main manuscript.

We now relax the assumption that the LONG version of the questionnaire has no effect on donations in the absence of video. It follows that our cool-off device might have two effects on donations: a direct effect on donations and an indirect effect through the mitigation of negative emotions. The indirect effect occurs only in the VID-LONG treatment, while the direct effect occurs in both VID-LONG and NOVID-LONG treatments.

Let us start by introducing a change in notation. We now allow the potential outcome to depend on the duration of the questionnaire. We write the potential outcome $Y_{i}^{d,m,l}$ where $l = 1$ if the participant has faced the long questionnaire and $l = 0$ if she has faced the short questionnaire. We assume that the direct effect of the long questionnaire affects on average the always-takers, the never-takers, and the compliers in a similar way. Let us note $\mu$ this direct effect. Because the long questionnaire has only a direct effect for participants who are not exposed to the video (i.e., no emotional mediation), we have: $\mu = \mathbb{E}[Y_{i}|D_{i} = 0, C_{i} = 1, T_{i}] - \mathbb{E}[Y_{i}|D_{i} = 0, C_{i} = 0, T_{i}]$.

Next, we define a new mediated treatment effect $\Delta'_{M}$ which is equal to:

$$ 
\begin{aligned}
\Delta'_{M} &= \mathbb{E}[Y_{i}^{1,M_{i}^{1},0} - Y_{i}^{1,M_{i}^{0},0}|D_{i} = 1] \\
&= p_{c}\mathbb{E}[Y_{i}^{1,M_{i}^{1},0} - Y_{i}^{1,M_{i}^{0},0}|D_{i} = 1, T_{i} = c]
\end{aligned} 
\quad (10) $$

We now define a parameter $\delta'$:

$$ 
\begin{aligned}
\delta' &= \mathbb{E}[Y_{i}|D_{i} = 1, C_{i} = 0] - \mathbb{E}[Y_{i}|D_{i} = 1, C_{i} = 1] - (\mathbb{E}[Y_{i}|D_{i} = 0, C_{i} = 0] - \mathbb{E}[Y_{i}|D_{i} = 0, C_{i} = 1]) \\
&= \mathbb{E}[Y_{i}|D_{i} = 1, C_{i} = 0] - \mathbb{E}[Y_{i}|D_{i} = 1, C_{i} = 1] + \mu
\end{aligned} 
\quad (11) $$
It follows:

$$
\begin{align}
\delta' = & p_a \times (\mathbb{E}[Y_i^{110}|D_i=1, C_i=0, T_i=a] - \mathbb{E}[Y_i^{111}|D_i=1, C_i=1, T_i=a]) \tag{12} \\
& + p_n \times (\mathbb{E}[Y_i^{100}|D_i=1, C_i=0, T_i=n] - \mathbb{E}[Y_i^{101}|D_i=1, C_i=1, T_i=n]) \nonumber \\
& + p_c \times \{\mathbb{E}[Y_i^{110}|D_i=1, C_i=0, T_i=c] \nonumber \\
& \quad - (1-q)\mathbb{E}[Y_i^{111}|D_i=1, C_i=1, T_i=c] - q\mathbb{E}[Y_i^{101}|D_i=1, C_i=1, T_i=c]\} \nonumber \\
& + \mu \nonumber
\end{align}
$$

Using the fact that $\mathbb{E}[Y_i^{d,M,1}|D_i, C_i, T_i] = \mathbb{E}[Y_i^{d,M,0}|D_i, C_i, T_i] + \mu$ (i.e., for a fixed level of the mediator, the change in average donation due to the direct effect of the long questionnaire is equal to $\mu$), we obtain:

$$
\begin{align}
\delta' = & p_a \times (\mathbb{E}[Y_i^{110}|D_i=1, C_i=0, T_i=a] - \mathbb{E}[Y_i^{110}|D_i=1, C_i=1, T_i=a] - \mu) \tag{13} \\
& + p_n \times (\mathbb{E}[Y_i^{100}|D_i=1, C_i=0, T_i=n] - \mathbb{E}[Y_i^{100}|D_i=1, C_i=1, T_i=n] - \mu) \nonumber \\
& + p_c \times \{\mathbb{E}[Y_i^{110}|D_i=1, C_i=0, T_i=c] \nonumber \\
& \quad - (1-q)\mathbb{E}[Y_i^{110}|D_i=1, C_i=1, T_i=c] - q\mathbb{E}[Y_i^{100}|D_i=1, C_i=1, T_i=c] - \mu\} \nonumber \\
& + \mu \nonumber
\end{align}
$$

Simplifying the equation, we get:

$$
\begin{align}
\delta' = & - \mu(p_a + p_n + p_c) + p_c \times q \times \mathbb{E}[Y_i^{110} - Y_i^{100}|D_i=1, T_i=c] + \mu \nonumber \\
& = p_c \times q \times \mathbb{E}[Y_i^{110} - Y_i^{100}|D_i=1, T_i=c] \tag{14}
\end{align}
$$

The new mediated treatment effect is then equal to:

$$
\Delta'_{M} = \frac{\delta'}{q} \tag{15}
$$

Empirically, the parameter $\delta'$ is estimated by a difference-in-difference in the average donation levels:

$$
\hat{\delta}' = (\bar{Y}_{VS} - \bar{Y}_{VL}) - (\bar{Y}_{NS} - \bar{Y}_{NL}) \quad (16)
$$
Supplementary Materials 3: Estimation of sample size based on precision analysis

1 #Import libraries
2 library(doParallel)
3 library(MASS)
4
5 #Set parameters
6 B=1000 #Bootstrap for estimating SE
7 S=1000 #Bootstrap for estimating precision power
8 N_vec=c(700,800) #Vector of sample sizes. Observations per treatment
9
10 #Set working Directory
11 #setwd("")
12
13 #Set seed
14 set.seed(123)
15
16 #Import data
17 mydata=readRDS("Data/treatedData.RDS")
18
19 #Function to estimate SE
20 estimateSE=function(B_funct,data_funct){
21
22 #Create subdata for bootstrapping
23 data_SHORT_NOVID_funct=data_funct[zdata_funct$treatmentVideo==0 &
24 data_funct$treatmentLongSurvey==0,]
25 data_SHORT_VID_funct=data_funct[zdata_funct$treatmentVideo==1 &
26 data_funct$treatmentLongSurvey==0,]
27 data_LONG_VID_funct=data_funct[zdata_funct$treatmentVideo==1 &
28 data_funct$treatmentLongSurvey==1,]
29
30 #Store the results
31 storeDelta_M_funct=rep(NA,B_funct)
32
33 #Loop to estimate SEs
34 for(b_funct in 1:B_funct){
35
36 #Draw data with replacement
37 bootdata_SHORT_NOVID_funct=data_SHORT_NOVID_funct[sample(nrow(
38 data_SHORT_NOVID_funct),nrow(data_SHORT_NOVID_funct), replace=TRUE),]
39 bootdata_SHORT_VID_funct=data_SHORT_VID_funct[sample(nrow(data_SHORT_VID_funct
40 ),nrow(data_SHORT_VID_funct), replace=TRUE),]
41 bootdata_LONG_VID_funct=data_LONG_VID_funct[sample(nrow(data_LONG_VID_funct),
42 nrow(data_LONG_VID_funct), replace=TRUE),]
43 databoot_funct=rbind(bootdata_SHORT_NOVID_funct,bootdata_SHORT_VID_funct,
44 bootdata_LONG_VID_funct)
45
46 #Compute the mediated treatment effect
47 delta_boot_funct=mean(bootdata_funct[bootdata_funct$treatmentVideo==1 &
48 bootdata_funct$treatmentLongSurvey==0,]$proAnimals)-mean(bootdata_funct[
49 bootdata_funct$treatmentVideo==1 & bootdata_funct$treatmentLongSurvey==1,]
50 $proAnimals)
51 q_boot_funct=(mean(bootdata_funct[bootdata_funct$treatmentVideo==1 &
52 bootdata_funct$treatmentLongSurvey==0,]$emotionsAverage)-
53 mean(bootdata_funct[bootdata_funct$treatmentVideo==1 &
54 bootdata_funct$treatmentLongSurvey==1,]$emotionsAverage))/
55 (mean(bootdata_funct[bootdata_funct$treatmentVideo==1 &
```javascript
databoot_funct$treatmentLongSurvey==0,]$emotionsAverage)-
    mean(databoot_funct[databoot_funct$treatmentVideo==0 &
        databoot_funct$treatmentLongSurvey==0,]$emotionsAverage))

Delta_M_boot_funct=delta_boot_funct/q_boot_funct

#Store the result
storeDelta_M_funct[b_funct]=Delta_M_boot_funct
}

return sd(storeDelta_M_funct))
}

#Test the function
estimateSE(B_funct=B, data_funct=mydata)

#Function that estimate precision
estimatePrecision=function(N_funct, S_funct, B_funct, data_funct) {

    #Create subdata for bootstrapping
    data_SHORT_NOVID_funct=data_funct(data_funct$treatmentVideo==0 &
        data_funct$treatmentLongSurvey==0,]
    data_SHORT_VID_funct=data_funct(data_funct$treatmentVideo==1 &
        data_funct$treatmentLongSurvey==0,]
    data_LONG_VID_funct=data_funct(data_funct$treatmentVideo==1 &
        data_funct$treatmentLongSurvey==1,]

    #Store the results
    storeSE_funct=rep(NA, S_funct)

    #Simulate data
    for (s_funct in 1:S_funct) {

        #Draw data with replacement
        bootdata_SHORT_NOVID_funct=data_SHORT_NOVID_funct[sample(nrow(
            data_SHORT_NOVID_funct), N_funct, replace=TRUE), ]
        bootdata_SHORT_VID_funct=data_SHORT_VID_funct[sample(nrow(data_SHORT_VID_funct
            ), N_funct, replace=TRUE), ]
        bootdata_LONG_VID_funct=data_LONG_VID_funct[sample(nrow(data_LONG_VID_funct),
            N_funct, replace=TRUE), ]
        databoot_funct=rbind(bootdata_SHORT_NOVID_funct, bootdata_SHORT_VID_funct,
            bootdata_LONG_VID_funct)

        storeSE_funct[s_funct]=estimateSE(B_funct=B_funct, data_funct=databoot_funct)
        #print(storeSE_funct[s_funct])
    }

    return(mean(storeSE_funct, na.rm=TRUE))
}

#Test function
estimatePrecision(N_funct=100, S_funct=10, B_funct=50, data_funct=mydata)

#Function for power analysis
#B: number of random draws for the bootstrap Standard Errors
#C: number of random draws for the power analysis
estimatePrecisionParallel=function(N_funct, S_funct, B_funct, data_funct) {

    #Create subdata for bootstrapping
```
```R
94 data_SHORT_NOVID_funct=data_funct(data_funct$treatmentVideo==0 &
95 data_funct$treatmentLongSurvey==0,]
96 data_SHORT_VID_funct=data_funct(data_funct$treatmentVideo==1 &
97 data_funct$treatmentLongSurvey==0,]
98 data_LONG_VID_funct=data_funct(data_funct$treatmentVideo==1 &
99 data_funct$treatmentLongSurvey==1,]
100
101 vecSEs=foreach(i=1:S_funct,.combine='cbind',.export='estimateSE') %dopar% {
102 set.seed(i) #Set seed here for reproductibility
103 print(i)
104
105 #Draw data with replacement
106 bootdata_SHORT_NOVID_funct=data_SHORT_NOVID_funct(sample(nrow(
107 data_SHORT_NOVID_funct), N_funct, replace=TRUE), ]
108 bootdata_SHORT_VID_funct=data_SHORT_VID_funct(sample(nrow(data_SHORT_VID_funct
109 ), N_funct, replace=TRUE), ]
110 bootdata_LONG_VID_funct=data_LONG_VID_funct(sample(nrow(data_LONG_VID_funct),
111 N_funct, replace=TRUE), ]
112 databoot_funct=rbind(bootdata_SHORT_NOVID_funct, bootdata_SHORT_VID_funct,
113 bootdata_LONG_VID_funct)
114
115 estimateSE(B_funct=B_funct,data_funct=databoot_funct)
116 }
117
118 return(vecSEs)
119 }
120
121 StoreVecRes=matrix(nrow=S,ncol= length(N_vec),data=NA)
122
123 #Run the precision estimation
124 cores=detectCores() #Number of cores
125 cl <- makeCluster(cores[1]-2, setup_timeout = 0.5)
126 registerDoParallel(cl)
127 matResults=matrix(data=NA,ncol=4,nrow= length(N_vec)) #To store the results
128 rownames matResults)=N_vec
129 colnames (matResults)=c("N","Average SE","SE 2","SE 3")
130 matResults[,1]=N_vec
131 loop_counter=1
132 for(N_loop in N_vec){
133 resVecLoop=estimatePrecisionParallel(N_funct=N_loop,S_funct=S,B_funct=B,
134 data_funct=mydata)
135 matResults[loop_counter,2]=mean(resVecLoop,rm.na=TRUE)
136 matResults[loop_counter,3]=mean(ifelse(resVecLoop <=0.02,1,0),rm.na=TRUE)
137 matResults[loop_counter,4]=mean(ifelse(resVecLoop <=0.03,1,0),rm.na=TRUE)
138 StoreVecRes[,loop_counter]=resVecLoop
139 loop_counter=loop_counter+1
140 }
141 stopCluster(cl)
142
143 #Show results
144 matResults
145
146 #Minimal SE L* where 80% SE<=L*
147 L_max=1000
148 minimalSEMat=matrix(data=NA,nrow=L_max,ncol= length(N_vec)+1)
149 colnames(minimalSEMat)=c("MaxSE",N_vec)
150 minimalSEMat[,1]=seq(1:L_max)/10000
151 for(k in 1:dim(minimalSEMat)[1]){
152
```
```R
145 for(j in 1:dim(StoreVecRes)[2]){
146     minimalSEMat[k,j+1]=mean(ifelse(StoreVecRes[,j]&=TRUE)
147     }
148 }
149 minimalSEMat
150
151 minimalSEMat[122,] #Effect size of 2 percentage points
152 minimalSEMat[183,] #Effect size of 3pp
153 minimalSEMat[244,] #Effect size of 4pp
154 minimalSEMat[305,] #Effect size of 5pp
155
156 #Results
157 resultsLstar=matrix(data=NA,nrow=R length(N_vec),ncol=3)
158 rownames(resultsLstar)=N_vec
159 colnames(resultsLstar)=c("N_vec","Minimal SE 80%","Minimal SE 95%")
160 resultsLstar[,1]=N_vec
161 for(k in 1:length(N_vec)){
162     resultsLstar[k,2]=minimalSEMat[min(which(minimalSEMat[,k+1] > 0.80)),1]
163     resultsLstar[k,3]=minimalSEMat[min(which(minimalSEMat[,k+1] > 0.95)),1]
164 }
165 resultsLstar
```

<caption>Listing 1: Ex-ante power analysis</caption>
Supplementary Materials 4: Filler questionnaire - Transportation choices
## Questionnaire about transportation mode choices

Assume that you can travel privately and at your own expense. You are traveling alone from a point of departure A to a destination B. To make this journey, you have two modes of transport available to you: air and rail.

[Version T1, T3] These modes of transport are associated with two characteristics of the journey: the duration (indicated as door-to-door time) and the cost (integrating the cost of the connections between the place of departure, the station/airport and the station/airport-place of arrival).

[T2 and T4 versions] These modes of transport are associated with three characteristics of the journey: the duration (indicated as door-to-door time), the cost (integrating the cost of the connections between the place of departure/station/airport and the station/airport/place of arrival) and information on the proportion of people who choose the plane and the train.

The options are numerous, so they will be presented to you two by two.

You will have 10 choices to make. For each of them, you are invited to choose, among the 2 options, the one you prefer. There is no right or wrong answer, only your personal opinion. Please note that once a choice has been validated, it is no longer possible to go back and change it.

[Addition for T3, T4] For your information: CO2 is a greenhouse gas emitted by vehicles used in transportation. The high level of greenhouse gas emissions in the atmosphere (such as CO2) is the cause of dangerous climate changes for the planet. Many consequences have already been observed by climatologists, such as the melting of glaciers, the rise in water levels, or the increase in frequency and intensity of extreme weather events (fires, floods...).

We observe that currently, for the same distance, the plane is about 20 times more polluting per passenger than the train.

## Trip characteristics

**Mode:** long-distance mode of transport used for this trip

**Duration:** total travel time in hours, including connection time from the departure point to the station/airport and station/airport to the arrival point, as well as waiting and boarding times.

**Cost:** total cost in euros of the trip, including the cost of the connection between home and train station/airport and train station/airport and home.

[T2 and T4] **Share of passengers choosing the option:** information given as an indication of the percentage (rounded to the nearest 10%) of passengers choosing each of the two options for the trip considered.
**Here is an example**

[T1, T3 and T5 versions]

<table><thead><tr><th></th><th>Option 1</th><th>Option 2</th></tr></thead><tbody><tr><th>Mode</th><td>Plane</td><td>Train</td></tr><tr><th>Duration*</th><td>2h10</td><td>2h50</td></tr><tr><th>Cost**</th><td>100 €</td><td>50 €</td></tr></tbody></table>

* Door-to-door time

** Total cost including the cost of connections

Choosing option 1 means that you prefer the plane trip, which takes 2h10 and costs 100€.

Choosing option 2 means that you prefer the train trip, which takes 2h50 and costs 50€.

[T2 and T4 versions]

<table><thead><tr><th></th><th>Option 1</th><th>Option 2</th></tr></thead><tbody><tr><th>Mode</th><td>Plane</td><td>Train</td></tr><tr><th>Duration*</th><td>2h10</td><td>2h50</td></tr><tr><th>Cost**</th><td>100 €</td><td>50 €</td></tr><tr><th>Proportion of people choosing the option</th><td>10%</td><td>90%</td></tr></tbody></table>

* Door-to-door time

** Total cost including the cost of connections

Choosing option 1 means that you prefer the plane trip, which takes 2h10, costs 100€. The proportion of people who choose the plane for this trip is 10%.

Choosing option 2 means that you prefer to travel by train, which takes 2h50 and costs 50€. The proportion of people who choose the train for this trip is 90%.

[For all choices, block backspace].

[To insert: 10 CHOICES (non-random order of the cards)]
[T1 and T3 versions] Here is the last choice you were offered:

[For block 1 respondents]

<table><thead><tr><th></th><th>Option 1</th><th>Option 2</th></tr></thead><tbody><tr><th>Mode</th><td>Plane</td><td>Train</td></tr><tr><th>Duration*</th><td>3h20</td><td>4h10</td></tr><tr><th>Cost**</th><td>75 €</td><td>250 €</td></tr></tbody></table>

[For block 2 respondents]

<table><thead><tr><th></th><th>Option 1</th><th>Option 2</th></tr></thead><tbody><tr><th>Mode</th><td>Plane</td><td>Train</td></tr><tr><th>Duration*</th><td>2h10</td><td>4h10</td></tr><tr><th>Cost**</th><td>75 €</td><td>250 €</td></tr></tbody></table>

For this trip, what would be according to you the proportion of people (on a representative sample of the French population) to choose the plane compared to the train for this alternative (to the nearest 10%)?

[In the form of a check box (single answer) or in the form of a cursor].

<table><tr><td>Distribution of people between plane and train</td><td>0% plane 100% train</td><td>10% plane 90% train</td><td>20% plane 80% train</td><td>30% plane 70% train</td><td>40% plane 60% train</td><td>50% plane 50% train</td><td>60% plane 40% train</td><td>70% plane 30% train</td><td>80% plane 20% train</td><td>90% plane 10% train</td><td>100% plane 0% train</td></tr><tr><td>Your estimate</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></table>
Which means of transportation do you use for your **private trips** (leisure, family, etc.), **lasting at least 2 hours** (door-to-door)? For each of the proposed modes, please indicate how often you use them for your private trips.

<table><thead><tr><th></th><th>Never</th><th>1 to 3 times every 5 years</th><th>Once per year</th><th>3 to 6 times per year</th><th>Once per month</th><th>2 to 3 times per month</th><th>Once per week or more</th><th>More than 3 times per week</th></tr></thead><tbody><tr><th>Car</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Train</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Plane</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

You will now answer a series of questions in the form of lists of propositions for which you will have to give your opinion. There are no right or wrong answers, only your opinion. Choose the answers that are the closest to your opinion.

<table><thead><tr><th></th><th>Strongly disagree</th><th>Somewhat disagree</th><th>Neither agree nor disagree</th><th>Somewhat agree</th><th>Strongly agree</th></tr></thead><tbody><tr><th>Taking the plane is pleasant</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Taking the train is pleasant</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Taking the plane is seen positively by my family and friends</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Taking the train is seen in a positive light by my family and friends</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Thinking about the plane evokes positive emotions</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Thinking about the train evokes positive emotions</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>My family and friends fly regularly</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>My relatives take the train regularly</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>My family and friends have a positive view of flying</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>My family and friends have a positive view of taking the train</th><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
To what extent would you say you are concerned about the impact of your travel on climate change?
Check the box next to the statement that best describes your opinion.

☐ Not at all concerned

☐ Somewhat unconcerned

☐ Neither concerned nor not concerned

☐ Somewhat concerned

☐ Very concerned

Do you have relatives abroad?

☐ Yes

☐ No

[If yes to the previous question]: Are these relatives located in countries not accessible by train or car?

☐ Yes

☐ No
Supplementary Materials 5: Prediction survey
Hello,

Thank you very much for agreeing to participate in this survey conducted by French academics. In the following screens, we will ask you several questions about the impact of L214 videos on the French population. More precisely, the objective for you will be to predict the impact of a video showing animals' mistreatment in intensive farms on the reactions of French people. We ask you to answer as accurately as possible and to try to predict the impact of this type of video.

For those who will have answered this survey before **January 31st**: we will randomly select a participant and we will randomly select one of his predictions. If this prediction is close enough to reality (i.e. within 5 percentage points of the correct answer), we will donate 100€ to the L214 association.

The results of this study will be communicated to you once the study is completed.

The study should last about 10 minutes.

The information provided in this questionnaire will only be used for academic/scientific purposes (no commercial purposes).

**Confidentiality, Disclosure of Information, and Voluntary Participation Statement**

For the purpose of this survey, we will use the email address you provided when you registered to retrieve your responses to the registration form. Once the data is retrieved, your email address will be deleted from the database. The anonymized data will then be indefinitely stored on a data sharing account for replication purposes and to promote reproducible science. Participation is entirely voluntary. You may end your participation at any time.

If you have any questions about this study, you can contact Sylvie Borau: s.borau@tbs-education.fr

**Purpose of processing:** The information collected about you will be processed for the research team led by Sylvie Borau (Toulouse Business School, 20 Bd Lascrosses, 31000 Toulouse, France; CIRED, 45bis Av. de la Belle Gabrielle, 94130 Nogent-sur-Marne)

The CNRS Data Protection Officer can be contacted for any questions on the protection of personal data. Its contact details are as follows: Service protection des données, 2 rue Jean Zay, 54519 Vandoeuvre-lès-Nancy ; mail : dpd.demandes@cnrs.fr.

**Personal data is collected and processed for the following purpose:** an exploration of animal activism strategies.

**Recipients of the data:** Depending on their respective needs, the recipients of all or part of the data are: the research team led by Sylvie Borau. No transfer of data outside the European Union is carried out. Your personal data will be kept for a period of: 36 months (duration of the research project).

You have the following rights for the use made of your data:

- The right of opposition: you can at any time oppose the processing of your data and have the right to withdraw your consent (depending on the case)

- The right to access and rectify your data

- The right to erasure

- The right to restricted use when your data is not necessary or no longer useful –
You can exercise these rights by contacting Sylvie Borau: s.borau@tbs-education.fr
You can also contact your DPO at the following address: CNRS Data protection department
– 2 rue Jean Zay – 54519 – Vandoeuvre lès Nancy - dpd.demandes@cnrs.fr

If you believe, after contacting us, that your computer rights and freedoms are not respected,
you have the possibility of lodging a complaint online with the CNIL or by post.

Before you start answering this questionnaire, could you please fill in your email address in
order to receive the results of this study and validate your pre-registration for this study.

<figure><img src="image_32.png" /></figure>
### SCREEN 2

**Risks:** In the following questionnaire, you will view an excerpt from an L214 video that contains explicit images that may arouse unpleasant levels of emotion. These feelings are generally temporary.

I am aware of the explicit content of this study

**Consent:** I have read this form and am aware that I am being asked to participate in a study that will contain explicit images. I voluntarily agree to participate in this study. I may end my participation at any time.

I agree to participate in this study

I do NOT agree to participate in this study

### SCREEN 3

As a last step before starting the study, we would like to make sure that you can listen to sounds on your device.

Below, you will be asked to listen to a sound and then indicate the sound you heard.

**Sound test:** Please listen to this sound clip and indicate which sound it is by selecting the correct answer below.

Audible car alarm

Crickets

A meowing cat

A person who whistles

A barking dog

### SCREEN 4

**Design of the study to which participants responded.**

Please read the following information about the study design carefully. Then, you will have to answer a short comprehension test to check your knowledge.

The objective of our research is to understand the impact of L214 videos on public opinion. To do so, we created an online experiment in which a representative sample of the French population participated between Dec 4th and Dec 18th (3,300 respondents in total).

In our experiment, we randomly assigned participants to three groups.
1) In the first group, participants were not exposed to any video. We just asked them to report their emotional state. Then we presented them with a list of animal charities and petitions to which they could donate money.

2) In the second group, participants began by watching an excerpt from an L214 video. We then asked them the same questions as in the first group about their emotional state and about donations.

3) In the third group, the participants also started by watching the L214 video clip, but they then answered an 3 ~ 4 minute questionnaire on a topic unrelated to the video (they had to choose between taking a plane or a train for fictional trips). Only after having answered this questionnaire on the modes of transportation, we asked them the same questions as in the first two groups on their emotional state and on donations.

To summarize, respondents were randomly assigned to one of three groups:

## Recap table

<table><thead><tr><th></th><th>Group 1</th><th>Group 2</th><th>Group 3</th></tr></thead><tbody><tr><td>Video L214</td><td>X</td><td>Vidéo L214</td><td>Vidéo L214</td></tr><tr><td>Transportation survey</td><td>X</td><td>X</td><td>Questionnaire of 3 ~ 4 mn related to transportation</td></tr><tr><td>Emotional state</td><td>Questions relatives à leur état émotionnel</td><td>Questions relatives à leur état émotionnel</td><td>Questions relatives à leur état émotionnel</td></tr><tr><td>Donations for animals €</td><td>Donations to associations and petitions</td><td>Donations to associations and petitions</td><td>Donations to associations and petitions</td></tr></tbody></table>
----- SCREEN 5-----

## Understanding of the study design

Please answer the following question to demonstrate your understanding of the design of this study:

Which of the following statements are true? Please check all that apply.

◯ In all 3 groups, participants were exposed to a video from L214 before answering the questionnaire.

◯ In all 3 groups, participants were asked to answer questions about their emotional state.

◯ In all 3 groups, we presented a list of animal charities and petitions for which respondents could donate money.

◯ In all 3 groups, participants were asked to complete a questionnaire about transportation.

◯ In all 3 groups, respondents were asked to predict the emotional state of French people exposed to a video from L214.

----- SCREEN 6-----

## Understanding the study design (2nd chance)

It seems that you did not answer perfectly correctly. Here is the summary table of the study design as a reminder.

### Recap table

<table><thead><tr><th></th><th>Group 1</th><th>Group 2</th><th>Group 3</th></tr></thead><tbody><tr><td>Video L214 🎥</td><td>X</td><td>Vidéo L214</td><td>Vidéo L214</td></tr><tr><td>Transportation survey 📥</td><td>X</td><td>X</td><td>Questionnaire of 3 ~ 4 mn related to transportation</td></tr><tr><td>Emotional state ♥</td><td>Questions relatives à leur état émotionnel</td><td>Questions relatives à leur état émotionnel</td><td>Questions relatives à leur état émotionnel</td></tr><tr><td>Donations for animals €</td><td>Donations to associations and petitions</td><td>Donations to associations and petitions</td><td>Donations to associations and petitions</td></tr></tbody></table>
Which of the following statements are true? Please check all that apply.

◯ In all 3 groups, participants were exposed to a video from L214 before answering the questionnaire.

◯ In all 3 groups, participants were asked to answer questions about their emotional state.

◯ In all 3 groups, we presented a list of animal charities and petitions for which respondents could donate money.

◯ In all 3 groups, participants were asked to complete a questionnaire about transportation.

◯ In all 3 groups, respondents were asked to predict the emotional state of French people exposed to a video from L214.

----- SCREEN 7-----

Answers

Here are the responses regarding the study design:

◯ In all 3 groups, the participants were exposed to a video of L214 before answering the questionnaire. -> **False**, only two groups were exposed to the L214 video before answering the questionnaire, one group was not exposed to any video.

◯ In all 3 groups, the participants had to answer questions related to their emotional state -> **True**

◯ In all 3 groups, we presented a list of animal charities and petitions for which respondents could donate money -> **True**

◯ In all 3 groups, participants were asked to complete a transportation questionnaire -> **False**, only one group completed a transportation questionnaire before answering questions about emotions and donations

◯ In all 3 groups, the respondents had to predict the emotional state of the French exposed to a video of L214 -> **False**, no group had to make predictions! On the other hand, this is what we ask you to do in this questionnaire. It's your turn now!
----- SCREEN 8-----

**Purpose of the survey**

In what follows, we will ask you to predict as best as you can and as truthfully as you can the behavior of the participants in each of the three groups.

◯ I understand the purpose of the survey.

----- SCREEN 9-----

On the following page, you will discover the extract of the L214 video that the participants of the 2nd and 3rd groups watched.

The investigation of the L214 association took place over several months and used hidden cameras.

Please watch this video carefully. Please note that the video is only one minute and 20 seconds long.

----- SCREEN 10-----

VIDEO

----- SCREEN 11-----

First, we ask you to predict the responses of the participants in the **1st group** (reminder: **these participants have not seen the video**).

Regarding the emotional state, participants were asked to indicate the extent to which they felt the following emotions - with images of faces experiencing these emotions: anger, disgust, fear, sadness, surprise, and happiness. From the data collected, we created a negative emotional state indicator that is equal to the sum of the scores assigned to the negative emotions (anger, disgust, fear, sadness, surprise) minus the score assigned to happiness.

**This emotion score that we have constructed takes values between 0% (very negative emotional state) and 100% (very positive emotional state).**

**According to you, what is the average emotion score of the participants in the 1st group (no video)?**

This emotion score takes values between 0% (very negative emotional state) and 100% (very positive emotional state). A higher score reflects a stronger positive emotion.

**How confident are you in your answer? Please indicate your level of confidence on a scale from 1 = "Not at all confident" to 10 = "Extremely confident".**

<figure><img src="image_33.png" /></figure>
As for donations, participants in the experiment were given 50 cents. They could decide to keep this money (and cash it at the end of the experiment) or to donate part of it. We presented 7 donation options to the participants (4 associations and 3 petitions). The donations for the petitions were intended to increase their visibility on social networks. Participants were informed that only one of their decisions would be implemented at the end of the study but they did not know which association or petition would be chosen. Participants were asked to indicate for each association/petition how much they would like to donate if that association or petition was chosen. Participants were informed that the selected association or petition had been chosen before the experiment. From the data collected, we created a donation indicator that represents the average percentage of a participant's donation. **This donation score takes values between 0% (out of the 7 associations/petitions, the participant did not donate anything to any of the associations/petitions) and 100% (out of the 7 associations/petitions, the participant systematically donated all his money, i.e. 50 cents). A score of 40% means that the participant has given on average 40% of his money on the 7 associations/petitions considered.**

**List of proposed French associations:**

- Assiettes Végétales: An association that seeks to put more vegetarian and vegan meals in university canteens.

- L214: An association for animal protection which denounces the cruelest practices of the breeding industry and seeks to develop food without animal products

- SPA: An association that works for the protection of animals, mainly domestic animals (cats, dogs), by fighting for example against abandonment and by promoting adoptions.

- Welfarm: An association that supports the initiatives of breeders and the agri-food industry that contribute to improving the well-being of animals in breeding

**List of petitions:**

- Against intensive breeding: A petition to ban intensive livestock farming in France

- For vegetarian meals: A petition to have at least one vegetarian meal per day in all public canteens in France

- To protect activists: A petition to protect activists in France who record videos showing the mistreatment of animals in intensive farming.

**According to you, what is the average donation score of the participants in the 1st group (no video)?**

This donation score takes values between 0% (out of the 7 associations/petitions, the participant did not donate anything to any of the associations/petitions) and 100% (out of the 7 associations/petitions, the participant systematically donated all his money). A score of 40% means that the participant gave an average of 40% of his money on the 7 associations/petitions considered. A higher score reflects the fact that the participant gave more to the associations/petitions on average.

How confident are you in your answer? Please indicate your level of confidence on a scale from 1 = "Not at all confident" to 10 = "Extremely confident".

<figure><img src="image_34.png" /></figure>
----- SCREEN 12-----

We now ask you to predict the responses of **participants in the 2nd group** (recall: these **participants saw the video but did not answer the survey about transportation**).

**What do you think is the average emotion score of the participants in the 2nd group after watching the video?**

*This emotion score takes values between 0% (very negative emotional state) and 100% (very positive emotional state). A higher score thus reflects a stronger positive emotion.*

**How confident are you in your answer? Please indicate your level of confidence on a scale from 1 = "Not at all confident" to 10 = "Extremely confident".**

<figure><img src="image_35.png" /></figure>

**According to you, what is the average donation score of the participants in the 2nd group after having seen the video?**

*This donation score takes values between 0% (on the 7 associations/petitions, the participant did not donate anything to any of the associations/petitions) and 100% (on the 7 associations/petitions, the participant systematically donated all his money). A score of 40% means that the participant gave an average of 40% of his money on the 7 associations/petitions considered. A higher score reflects the fact that the participant gave more to the associations/petitions on average.*

**How confident are you in your answer? Please indicate your level of confidence on a scale from 1 = "Not at all confident" to 10 = "Extremely confident".**

<figure><img src="image_36.png" /></figure>

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We now ask you to predict the responses of the **participants in the 3rd group**. As a **reminder: these participants watched the video and then completed the transportation questionnaire before indicating their emotional state and donation choice**. On average, participants took 3 ~ 4 minutes to complete the transportation questionnaire. Only then did they report their emotional state and desired donations.

**What do you think is the average emotion score of the participants in the 3rd group after watching the video and taking the transportation questionnaire?**

*This emotion score takes values between 0% (very negative emotional state) and 100% (very positive emotional state). A higher score thus reflects a stronger positive emotion.*
How confident are you in your answer? Please indicate your level of confidence on a
scale from 1 = "Not at all confident" to 10 = "Extremely confident"

<figure><img src="image_37.png" /></figure>

In your opinion, what is the average donation score of the participants in the 3rd group after having seen the video and after having answered the questionnaire on transportation?

This donation score takes values between 0% (out of the 7 associations/petitions, the participant did not donate anything to any of the associations/petitions) and 100% (out of the 7 associations/petitions, the participant systematically donated all his money). A score of 40% means that the participant gave an average of 40% of his money on the 7 associations/petitions considered. A higher score reflects the fact that the participant gave more to the associations and petitions on average.

How confident are you in your answer? Please indicate your level of confidence on a
scale from 1 = "Not at all confident" to 10 = "Extremely confident".

<figure><img src="image_38.png" /></figure>

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Thank you for your participation in this study.

For any questions, you can contact Sylvie Borau: s.borau@tbs-education.fr