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# Negative Affect Related to Door-in-the-Face Strategy

Marie-Amélie Martinie, Benjamin Bordas, Sandrine Gil

► To cite this version:

Marie-Amélie Martinie, Benjamin Bordas, Sandrine Gil. Negative Affect Related to Door-in-the-Face Strategy. Scandinavian Journal of Psychology, 2023, 10.1111/sjop.12997. hal-04343981

HAL Id: hal-04343981

https://hal.science/hal-04343981v1

Submitted on 11 Jan 2024

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Negative Affect Related to Door-in-the-Face Strategy

Marie-Amélie Martinie*

Benjamin Bordas

Sandrine Gil

Université de Poitiers, France ; CNRS (CeRCA UMR 7295)

Martinie, M.-A., Bordas, B., & Gil, G. (in press). Negative Affect Related to Door-in-the-Face Strategy. *Scandinavian Journal of Psychology*. http://doi.org/10.1111:sjop.12997
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Abstract

A full 46 years after the first study of the door-in-the-face strategy (DITF), there is still a
debate about the processes behind its effect. One relatively unexplored interpretation is the
presence of negative affect related to large request refusal. Methods. We explored negative
affect after large request refusal both explicitly (Experiment 1) and implicitly (Experiments 1
& 2). Results. Participants were in a negative state after large request refusal (Experiment 1),
and target request acceptance was a function of their emotional state (Experiment 2). Negative
affect appears to play a role in acceptance of the target request in the door-in-the-face
strategy. However, this pattern of results was only observed when affect was measured
implicitly. Conclusion. The findings shed new light on the DIFT, by taking into account the
complexity of the interaction with emotion. The reasons why negative affect occurs after large
request refusal are discussed.

Keywords: door-in-the-face, negative affect, implicit measure of affect
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Introduction

Door-in-the face (DITF) is a persuasion strategy that is well known to social psychologists working in the area of social influence and behaviour change (Cialdini, 1975; 2001). It consists in formulating a large initial request that is too costly to be accepted, followed by a smaller (target) request. It has been well established that respondents who have refused the initial large request are more likely to agree with the target request than those in a control condition to whom the target request is made directly (for meta-analyses, see Dillard et al., 1984; Fern et al., 1986; O'Keefe & Hale, 2001; Pascual & Guéguen, 2005; see Genschow et al., 2021, for a recent direct replication), regardless of communication modality (Feeley et al., 2012), including computer-based communication (Guégen, 2003). Some authors have postulated that this can be explained–albeit partially–by a negative state resulting from the DITF situation (Feeley et al., 2012; Foehl & Goldman, 1983; Goldman et al., 1984; Millar, 2002; O'Keefe & Figgé, 1997; Tusing & Dillard, 2000). The aim of the present study was thus to establish via implicit and explicit measures if and when individuals experience a negative emotional state in the DITF situation.

It has been suggested that the emotional state triggered by refusing the large request can
explain the DITF effect (e.g., Cann et al., 1975; Mauny et al., 2022; Millar, 2002; Rogers,
1976): if rejection of the first request induces a negative emotional state, acceptance of the
second request (perceived as more reasonable) may be viewed as a means of avoiding the
unpleasantness of two successive refusals. Emotion can be understood from two theoretical
perspectives that are both important and well documented (Harmon-Jones et al., 2017): a
dimensional approach (e.g., Barrett, 2006; Russel & Barrett, 1999), and a discrete approach
(e.g., Ekman & Cordaro, 2010; Izard, 2007). According to the former, two main dimensions,
namely arousal (i.e., intensity of the emotion) and valence (i.e., positive vs. negative nature of
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emotion), are sufficient to characterize emotions. According to the latter, there is a set of
discrete emotions, each with its specific function.

Few studies have empirically tested the involvement of these emotional characteristics
in the DITF effect, and the arousal component has only been tested by Terrier and Joule
(2008). Given that arousal states impair performance on complex tasks (e.g., Caparos &
Blanchette, 2017), the authors reasoned that if large request refusal produces an arousal state,
performance on a consecutive complex task should be impaired. Their results supported this
hypothesis. Concerning the valence component, some authors (Foehl & Goldman, 1983;
Goldman et al., 1984; Millar, 2002; O'Keefe & Figgé, 1997; Tusing & Dillard, 2000) have
assumed that large request refusal produces specific negative emotions such as guilt. *Guilt* is a
negative self-conscious emotion that occurs when beliefs about acceptable conduct are
infringed and the individual feels compelled to repair the transgression (Boster et al., 2016;
Carlsmith & Gross, 1969; Cryder et al., 2012; Freedman et al., 1967; Tangney & Dearing,
2003). In the DITF strategy, after declining a large request, respondents would feel guilty, and
would see acceptance of the target request as an opportunity to dispel their negative state (i.e.,
guilt arousal and reduction hypothesis). This hypothesis was tested in three studies, and
results only supported it in two studies. First, O'Keefe and Figgé (1999) observed the
presence of a feeling of guilt following the rejection not only of a large request, but also of a
small request. These results did not therefore support the hypothesis. Second, when he
manipulated the level of guilt after the refusal of the large request and reduced the level of
guilt before the small request (target request) was made, Millar (2002) observed that
compliance was only greater in the high guilt induction condition, with target request
acceptance associated with greater guilt reduction than in the control condition where
participants only received the target request. These results supported the guilt-based
explanation. Moreover, the relation between guilt induction (high vs. low) and compliance
was only mediated by guilty feeling if acceptance of the target request was associated with
high guilt reduction. Nevertheless, feelings of guilt related to turning down the large request
were measured at the end of the whole experiment, rather than immediately after refusal of
the large request. The effect of guilt related to turning down the large request was thus
inconclusive, as it may have been affected by the passage of time and/or interference from the
target request response.

Finally, another kind of research seems to suggest the involvement of a negative
emotional state in the DITF effect: just as being confronted with one's own death produces
negative affect (i.e., fear) (Lambert et al., 2014), in the framework of terror management
(Greenberg et al., 1986), the DITF effect was only observed after induction of mortality
salience (Schindler & Reinhard, 2015). In this study, participants had to write about either
their own death (existential threat condition) or dental pain (no existential threat). They were
then allocated either to a control group (target request only) or to a DITF group, where refusal
of a large request (subscribe to a newspaper for 2 years) was followed by a target request (buy
today's newspaper edition). Results showed an effect of the interaction between existential
threat and group on verbal compliance (Study 1) and behavioural compliance (Study 2). More
specifically, in the DITF group, participants were more compliant with existential threat than
without it. Moreover, in the existential threat condition, participants in the DITF group were
more compliant than those in the control group. Finally, no difference was observed between
the two groups in the absence of an existential threat. In sum, the DITF effect occurred solely
if an existential threat (i.e., death) was induced beforehand.

In summary, previous findings are both mixed and challenging. However, Schindler and
Reinhard (2015)'s results suggest that a negative state is a precondition for observing the
DITF effect, while Millar (2002)'s results suggest that negative affect mediates the DITF
effect. The present study was therefore designed to establish whether an emotional state is
involved in the DITF effect and its timecourse (reduction hypothesis), using both explicit and
implicit measures of emotion. Insofar as the role of emotion in the DITF effect is relatively
poorly documented in the scientific literature, we devised an original way of exploring the
emotional dimension by combining these two kinds of measures, as they are assumed to
capture different processes of the emotional experience. Explicit measures rely on
individuals' conscious introspection and verbalization, whereas implicit measures are
considered to monitor automatic processes and unintentional responses (for a discussion of
the challenges posed by these different types of measures in social psychology, see De
Houwer et al., 2009; Nosek, 2007). Emotional state is one factor that can shape the way we
perceive the environment in an automatic and unconscious manner. According to the mood
congruency hypothesis, people preferentially process mood-congruent (vs. mood-incongruent)
stimuli (e.g., Bower, 1981; Mayer et al., 1992; Niedenthal & Wood, 2019). This effect has
also been extended to stimuli regarded as ambiguous or not clearly interpretable. For example,
neutral faces are perceived as sadder by depressed patients (Gur et al., 1992; Leppänen et al.,
2004) or participants in a sadness-induced sample (Bouhuys et al., 1997). In a pioneering
study, Murphy and Zajonc (1993) found that characters that are ambiguous for Caucasian
people (e.g., Chinese ideograms) were categorized as representing either bad or good objects,
depending on an emotional priming condition. Therefore, as ambiguous stimuli such as
neutral material or symbols that are culturally unfamiliar are valence-free by definition, they
provide an opportunity to examine implicit affective state biases. We hypothesized that
negative affect is a precondition of the DITF effect, and that target request acceptance reduces
it. To test this hypothesis, two complementary experiments were conducted. Experiment 1
focused on participants' emotional state after refusing the large request, and Experiment 2
focused on their emotional state not only after their large request refusal but also after their
small target request response. This work was not preregistered, but for the two Experiments,
our data and research materials have been made publicly available on the Open Science
Framework and can be access at:

https://osf.io/f7nq8/?view_only=f6de1d7fbca84e0aae7e6f64fe00936b

Experiment 1

Objectives

In Experiment 1, we measured participants’ explicit and implicit emotional states before
(emotional state baseline; Affect 1) and after (Affect 2) they had responded to large or small
prosocial requests (group: large request vs. small request). Explicit emotional state was
measured with the French version (Gaudreau et al., 2006) of the Positive and Negative Affect
Schedule (PANAS; Watson et al., 1988), while implicit emotional state was measured with a
categorization task featuring ambiguous items (ideograms), the assumption being that
individuals’ emotional states would influence their information processing. We expected
participants to refuse the large request more than the small one. Hypotheses on emotional
states only concerned participants who refused the small or large requests. We assumed that
turning down a small or large request would produce negative affect. Scores on negative
emotional state measures would therefore be higher after participants had refused the request
(Affect 2) than before (Affect 1), both explicitly (i.e., affect questionnaire) and implicitly (i.e.,
categorization task, where the likelihood of categorizing the items as negative would be
greater at Affect 2 than at Affect 1). We further assumed that if negative affect contributes
to the DITF effect, and accepting small requests neutralizes it (Foehl & Goldman, 1983;
Goldman et al., 1984; Millar, 2002; O’Keefe & Figgé, 1997; Tusing & Dillard, 2000), the
effect would be specifically observed in the large request group.
Method

Participants.

Experiment 1 was conducted in accordance with the principles of the Declaration of
Helsinki, and was approved by Ethics Committee for noninterventional Research (CER-TP
2021-09-06). Written consent was obtained from all participants. The sample size was
established using G*Power 3.1 software (Faul et al., 2009) to achieve a power of 0.80 (α =
.05) to detect an effect size of *f* = .15 in an analysis of variance (ANOVA) with two groups
and two measurements correlated at .50. Based on this statistical criterion (total sample size =
90) and anticipating a loss of data owing to the online conditions of the study, we recruited a
total of 189 students.

Participants were Caucasian psychology students (174 females) who took part in this
study in exchange for course credits (M\_age = 18.6, SE\_age = 1.2). Two participants did not
complete the experiment. None of the participants had an atypical value (z score above 3) on
the self-report affect measure (PANAS). The final sample therefore comprised 187
participants randomly assigned to two groups: small request (n = 88) and large request (n =
99).

Materials.

For the item categorization task (implicit affective measure), 30 Chinese characters (i.e.,
ideograms) were selected. The ideograms each measured 200 × 182 pixels and were displayed
one by one in black against a white background. There were two boxes below each ideogram,
one containing the word *positive*, the other containing the word *negative*.

The French version (Gaudreau et al., 2006) of the PANAS (Watson et al., 1988) was
administered as the explicit affective measure. This self-report adjective checklist features 20
items, 10 of which assess positive affect (*interested, excited, strong, enthusiastic, proud, alert,
inspired, determined, attentive, and active*), and 10 negative affect (*ashamed, guilty, afraid,*
distressed, hostile, irritable, jittery, scared, upset, and nervous). Respondents rate each item on a 9-point scale ranging from 1 (Not at all) to 9 (Extremely). Internal consistency was satisfactory for both negative affect items (α = .86 for pre-experimental phase and α = .85 for postexperimental phase) and positive affect items (α = .77 for pre-experimental phase and α = .84 for postexperimental phase). We therefore summed ratings for each valence to obtain negative or positive affect scores for the pre- and postexperimental phases. Finally, to check the request manipulation (small, large), perception of the request was rated on a 9-point scale ranging from 1 (Request not at all costly) to 9 (Request extremely costly). LimeSurvey software (LimeSurvey Project Team, 2012) was used to implement the material for this experiment. It was completed online and without the researchers present.

**Design and procedure.**

Participants were informed that they would be taking part in an experiment designed to pretest experimental material. This would involve performing two categorization tasks separated by an interval of time that would be used for something else (i.e., to make a break) that had nothing to do with testing the material. The experiment therefore took place in three phases: pre-experimental (i.e., measure of Affect 1), experimental (group manipulation) and postexperimental (i.e., measure of Affect 2).

In the pre-experimental phase, participants completed the ideogram categorization task and the PANAS (Affect 1). The 30 ideograms appeared one by one in the centre of the screen. Based on their first impression, participants were asked to categorise the ideogram according to whether they represent a positive or negative object in a foreign language. To realize the categorization task, they used computer mouse to click on either *positive* or *negative* as quickly as possible. The position of these response labels was counterbalanced across phases and groups (large request vs. small request).
In the experimental phase, participants were randomly assigned to one of two groups according to their birthdate (i.e., even date: large request group; odd date: small request group). In the large request group, participants read the following message on a computer screen: "To take a break between the two stimulus categorization tasks, we submit Florian's request. Hello, my name is Florian, I am a student. I am also a volunteer at Bleu Earth, an environmental protection charity. We are currently setting up awareness initiatives in elementary schools using digital technology. We are looking for people to volunteer to carry out environmental awareness campaigns. This would involve going into a virtual classroom for 2 hours twice a week for a minimum duration of 2 years, so that a contact can be established between you, the children, and the teaching team to develop environmental awareness campaigns within the school. I am looking for volunteers. Would you agree to participate in this project for 2 hours twice a week for a minimum of 2 years?" Two words appeared under this request: *Yes* and *No*. Participants had to click on one of these two words to respond.

In the small request group, the procedure was identical, except for the request. The message went as follows: "[...] we are going to organize a conference in schools to help children learn about how they can help to protect the environment by recycling everyday objects. We will need people to help organize this conference. It would involve taking on the role of administrator in a digital classroom, admitting the children and the speakers to the digital waiting room into the conference. It would only take up one afternoon, depending on your availability. Would you be willing to participate in this project?"

In the post experimental phase, participants completed the two affective tasks again (Affect 2: categorization task and PANAS), and their perception of the request was measured.
## Results

**Data processing and analytical reasoning.**

In the main analyses we ran to test our hypotheses, we only considered participants who had refused a request. Concerning explicit emotional state, the dependent variables (DVs) were self-reported positive and negative emotional state. The independent variables (IVs) were group (small vs. large request) and timepoint (Affect 1 vs. Affect 2). The means negative and positive score were analysed in two separate linear mixed models. We reported standardized coefficients as effect sizes. Concerning the implicit emotional state, the IVs were similar, but the categorization task involved a binary DV (i.e., negative vs. positive responses). For each stimulus, the response was scored 1 if it was categorized as negative, and 0 if it was categorized as positive. We therefore analysed the likelihood of categorizing the ideograms as negative (i.e., DV = proportion of negative responses) with a logistic mixed model (Jaeger, 2008). The effect sizes were expressed as odds ratios (ORs). ORs quantify the probability of an event occurring relative to another event. For example, an OR of 2 in favour of Affect 2 would indicate that participants were twice as likely to categorize ideograms as negative in the postexperimental phase than in the preexperimental one. Analyses were performed using jamovi software version 2.2.5 (The jamovi Project, 2021). Multiple-comparison post hoc tests were used to examine significant interactions.

Before testing our hypotheses, we ran three manipulation checks: 1) request perception, 2) lower compliance with large request than with small request (N = 187), 3) absence of difference between groups in preexperimental phase on explicit and implicit affect for participants who subsequently refused the request (n = 83 in large request group, n = 60 in small request group). Perceptions of the requests and preexperimental PANAS scores were analysed with t tests, and compliance with the chi-squared test.
**Manipulation checks.**

Regarding participants’ perceptions of the requests, results revealed that the large request was perceived to be more costly (*M* = 6.78, *SE* = 1.73) than the small request (*M* = 5.36, *SE* = 2.20), *t*(185) = 4.93, *p* < .001, *d* = .72. As expected, participants were more likely to refuse the large request (83.83%) than the small request (68.18%), *χ*²(1) = 6.34, *p* < .05. Moreover, there was no difference between groups on preexperimental emotional state (Affect 1), regardless of measure (explicit or implicit). More specifically, there was no difference between groups on either negative, *t*(141) = 0.03, or positive *t*(141) = 0.41 affect scores (all *ps* > .05), and no group effect on the likelihood of categorizing ideograms as negative, *OR* = 1.01, 95% CI [0.987, 1.05], *p* > .05.

**Main analyses**

**Explicit emotional state.** In our 2 (group: large request vs. small request) x 2 (affect measurement timepoint: Affect 1 vs. Affect 2) design, with group as a between-participants factor and timepoint as a within-participants factor, we analysed negative and positive affect scores with two mixed models, with random intercepts to account for the dependency in the data. Group was contrast coded (large -0.5, small + 0.5) as well as timepoint (Affect 1 -0.5, Affect 2 +0.5).

For the negative affect score, analysis revealed an affect measurement timepoint effect, *F*(1, 183) = 10.81, *p* < .001, *β* = -.31. Whatever the group, participants reported higher negative affect at Affect 1 (before request refusal) (*M* = 2.86, *SE* = .61) than at Affect 2 (after request refusal) (*M* = 2.65, *SE* = .68). Analysis revealed neither a main effect of group, *F*(1, 245) = 1.71, nor an interaction between affect measurement and group, *F*(1, 183) = 0.01, all *ps* > .05. For the positive affect score, an affect measurement timepoint effect was also observed, *F*(1, 179) = 6.07, *p* < .01, *β* = -.20, with a higher score at Affect 1 (*M* = 1.83, *SE* =
.76) than for Affect 2 (M = 1.68, SE = .68). This analysis failed to reveal a group effect, F(1, 141) = 0.12, and there was no interaction between group and timepoint, F(1, 141) = 0.03, all ps > .05.

**Implicit emotional state.** In our 2 (group: large request vs. small request) x 2 (affect measurement timepoint: Affect 1 vs. Affect 2) design, with group as a between-participants factor and timepoint as a within-participants factor, we analysed the likelihood of categorizing the ideograms as negative with a logistic mixed model, with random intercepts to account for the dependency in the data. Group and timepoint were coded as in the previous analysis. This analysis revealed no main effect of group, OR = 0.983, 95% CI [0.899, 1.076], and no main effect of affect measurement timepoint, OR = 1.05, 95% CI = [0.969, 1.152] (all ps > .05). As expected, we observed an interaction between group and affect measurement timepoint, OR = 0.838, 95% CI [0.705, 0.996], p < .05 (see Fig. 1). Post hoc tests revealed that in the large request group, the likelihood of categorizing the ideograms as negative was 1.15% greater at Affect 2 (after request refusal) than at Affect 1 (before affect refusal) (p < .05). Contrary to our prediction, in the small request group, no difference emerged between Affect 1 and Affect 2 (p > .05).

Discussion

The aim of Experiment 1 was to implicitly and explicitly test the presence of negative
affect after the refusal of a large request versus a small request. First, the large request was
refused more often than the small request, confirming the relevance of the requests we used.
Second, affect measures yielded the expected result, but only when the measure was implicit.
We did not observe any interaction between group and affect measurement timepoint on
the explicit measure of emotional state, but there was a main effect of timepoint, in that
participants were less emotionally activated (for both negative and positive values) in the
postexperimental phase (i.e., Affect 2) than in the preexperimental one (i.e., Affect 1). This
can be explained by the fact that a state of tranquillity set in as the protocol was rolled out.

By contrast, the implicit emotional state measure revealed that in the large request
condition, the likelihood of categorizing the ideograms as negative was significantly higher
after rejecting the request than beforehand, even if the effect size was small. This difference
was not observed in the small request group. Taken together, our results suggest that
participants experienced a more negative emotional state after refusing the large request than
after refusing the small one.

Experiment 2

Objectives

The main finding of Experiment 1 raised two questions: What would have happened to
the negative affect generated by refusal of the large request had respondents subsequently
agreed to a target request? Would it have disappeared completely or only partially after target
request acceptance? The aim of the second experiment was to answer these two questions. It
also allowed us to test the emotional effects observed in Experiment 1 again (to improve the
reliability of the results) using different material in the categorization task (i.e., neutral words)
and in a different context (whereas Experiment 1 was conducted online, Experiment 2 took
place in the laboratory). As the explicit measure was found not to be sensitive to affect, it was
abandoned.
**Materials and Method**

**Procedure.**

Participants in Experiment 2 were divided into two groups. In the DITF group, their implicit emotional state was measured prior to a large request (Affect 1), after their response to this large request (Affect 2) and after their response to a target request (Affect 3). The procedure for participants in the control group was that the same as for the DITF group, except that a large request was not made. Responses to target requests and responses to categorization tasks (negative, positive) and reaction times when categorizing items as positive or negative were measured. It was expected to observe more acceptance of the target request in the DITF group than in the control group. Moreover, assuming that negative affect is a precondition for observing the DITF effect, and acceptance of the target request neutralizes this negative affect (Foehl & Goldman, 1983; Goldman et al., 1984; Millar, 2002; O'Keefe & Figgé, 1997; Tusing & Dillard, 2000), it was expected participants in the DITF group to report more negative affect at Affect 2 than in either Affect 3 or Affect 1. We predicted that they would categorize neutral words as more negative than positive and/or respond faster for negatively categorized words. These effects would not be observed among either participants who refused the target request or controls.

**Participants.**

The sample size was established using G*Power 3.1 software (Faul et al., 2009) to achieve a power of 0.80 (α = .05) to detect an effect size of *f* = .15 in an ANOVA with two groups and three measurements correlated at .50. Based on this statistical criterion (total sample size = 74), and anticipating the exclusion of some data, we created a larger sample of 99. Participants were female Caucasian psychology students, who participated in exchange for course credits. Four foreign students did not understand the instructions, one claustrophobic
student left the experiment room during Affect 1 measurement, two participants were familiar
with the DITF strategy, and three participants agreed to the large request. In accordance with
reaction time data collected with this kind of categorization task, but without applying too
strict a criterion, only extreme data suggesting that the participant did not perform the trial
correctly (e.g., adhering to speed instructions) were removed (< 300 ms and > 2500 ms). In
the case of 10 participants, almost all the reaction times were either too short or too long,
showing that they did not take the task seriously, and their data were therefore excluded from
the analyses. Finally, for Affect 1, we calculated a percentage of negatively categorized items
for each participant. Participants with atypical values at Affect 1 (z score above 3) were
excluded from the analyses (n = 4)<fnref n="1" />. The final sample therefore comprised 75 participants
(i.e., control group: n = 38; DITF group: n = 37). As in Experiment 1, a Committee for
Noninterventional Research (CER-TP 2021-09-06) approved the experimental protocol, and
written informed consent was obtained from all participants.

Materials.

Three lists of 42 words, and one list of 41 words, all neutral for young female adults,
were taken from Monnier and Syssau (2014)'s French Affective Norms. The words did not
differ on either number of letters, *F*(2, 120) = 0.64, arousal, *F*(2, 120) = 0.89, valence, *F*(2,
120) = 0.01, film frequency, *F*(2, 120) = 0.07, book frequency, *F*(2, 120) = 1.23, or
imageability, *F*(2, 120) = 0.66, all *ps* > .05, across the three lists. A different list was used for
each of the three categorization tasks (Affect 1, Affect 2, Affect 3). E-Prime (Psychology
Software Tools, 2016) was used to manage the presentation of the stimuli and record the
responses and response times.

<footnote n="1">¹ Results of subsequent analyses without the exclusion of outliers were identical.</footnote>
**Experimental design.**

The experiment was introduced to participants as a pretest of three word lists for a linguistic study. It took place in an experimental room of the laboratory. The protocol design was composed of five phases: Phase 1 corresponded to the baseline measurement of affect (Affect 1); Phase 2 corresponded to the presentation of the large request in the DITF condition (or a 40-s break in the control condition); Phase 3 corresponded to the second measurement of affect (Affect 2); Phase 4 corresponded to presentation of the target request (i.e., small request); and Phase 5 corresponded to the third measurement of affect (Affect 3), see Figure 1.

-- Insert Figure 1 about here --

In Phase 1, participants performed the word categorization task for the first time. The following instruction appeared on the computer screen: "You will see words appear one by one in the centre of the screen. These words are all nouns. Your task will be to say as quickly as possible whether the word is positive or negative for you. To do this, you will have to press one of the two keys on the keyboard. A cross will appear first, followed by the word. You will then have to give your answer. For the sake of speed, place your two index fingers on the keys of the keyboard and answer as intuitively as possible when the word appears. There is no right or wrong answer; what we are interested in is your first impression. When you are ready, press the space bar for a practice trial". The participants then performed the task. For each trial, a fixation cross appeared in the centre of the screen for 2 seconds, followed by a black screen for 500 ms. The word to be categorized was then displayed and remained on the screen until the participant pressed one of the two response keys (D or K). A black screen then appeared again for 500 ms, before the start of the next trial. The responses associated with the D and K keys and the order of presentation of the three word lists were counterbalanced
across participants. The experimenter left the experimental room at the start of the test trials, so that participants performed them on their own.

At the end of this first word categorization task, participants were instructed to open the door of the experimental room to signal that they had finished the task. The experimenter then sat down next to the participant and said, “Before repeating the word categorization task with a fresh set of words, I would like to give you some information about Bleu Earth, a charity that campaigns for environmental protection. It is currently organizing events in a vocational high school in a nearby town. It is looking for volunteers to carry out environmental protection awareness initiatives. It would involve going to the high school for 2 hours twice a week for a minimum duration of 2 years, so that contact can be established between you, the students and the teaching team to work jointly on ways of raising environmental awareness within the high school. Would you agree to participate in this project by giving up 2 hours of your time twice a week for at least 2 years?” This request took 40 s to complete. Once participants had responded to this large response, they performed the second word categorization task alone in the experimental room (Affect 2. As before, when they had completed this task, they were instructed to open the door of experimental room to signal that they had finished. The experimenter said, "If I may, I would like to return to the proposal I made earlier. The charity organizes other events, such as conferences to teach children that they can help to protect the environment by recycling everyday objects. We need people who can accompany the children to this conference. It would only take up one Thursday afternoon. Are you interested?" This second request was the target request. After participants had responded, they performed the word categorization task alone in the experimental room (Affect 3).

The protocol for the control condition was that the same as for the DITF condition, except that instead of describing the large request, the experimenter said he was going to
get some documents for the next test. He left the experimental room for 40 seconds, leaving
the participant alone in front of the screen.

Data processing.

As in Experiment 1, the results of the manipulation checks measures were first
analysed, to ensure that 1) the two groups did not differ on implicit affect at Affect 1, and 2)
compliance was greater in the DITF group than in the control group. Analyses were
performed with jamovi software version 2.2.5 (The jamovi Project, 2021). In the main
analyses (implicit emotional state), the first DV was the proportion of negative responses
(response scored 1 if word categorized as negative, and 0 if word categorized as positive).
This DV was thus a binary measure and was subjected to a logistic mixed model analysis. The
second DV was responses reaction times. As this DV was a continuous measure, we used a
linear mixed model. The IVs were group (DIFT vs. control), measurement timepoint (Affect 1
vs. Affect 2 vs. Affect 3), and target requested response (accepted vs. rejected). When we
needed to examine significant interactions further, we ran contrast analyses and multiple-
comparison *t* tests.

Results

Manipulation checks.

At Affect 1, there was no difference between groups on time taken to categorize items
as negative, t(73) = 0.10, or positive, t(73) = -0.34, all ps > .05. Moreover, the binomial
logistic regression performed on negative responses in the categorization task showed no
group effect at Affect 1, OR = 0.898, 95% CI [0.778, 1.037], ns. As expected, compliance was
higher in the DITF group (62.16%) than in the control condition (39.47%), χ²(1) = 3.86, p <
.04.
**Implicit emotional state.**

Regarding the proportion of negative responses, in the 2 (group: DITF vs. control) x 3 (affect measurement timepoint: Affect 1 vs. Affect 2 vs. Affect 3) x 2 (target request response: accepted vs. rejected) design, with group and request as between-participants factors and timepoint as a within-participants factor, we calculated a logistic mixed model with random intercepts to account for the dependency in the data for negative responses in the word categorization task. In this analysis, affect measurement timepoint was contrast coded to directly test the expected pattern. We expected negative affect to be higher after (Affect 2) than before (Affect 1) the large request, as well as after the target request (Affect 3). The contrast of interest therefore compared Affect 2 with both Affect 1 and Affect 3 (Affect 1 coded +0.408, Affect 2 coded -0.816, Affect 3 coded +0.408). The residual variance contrast compared Affect 1 (coded -0.707) with Affect 3 (coded +0.707, Affect 2 was coded 0). DITF group was coded +0.5, and the control group -0.5. Accepted request was coded +0.5, and refused request -0.5. We only report effects related to our hypotheses here (for the other effects, see Table 1). Our hypotheses would be supported if the interaction between group, target request response, and contrast of interest was significant, and not the interaction with the residual variance contrast.

-- Insert Table 1 about here --

As expected, this analysis revealed a significant interaction between group, target request response, and interest contrast, *OR* = 0.288, 95% CI [0.213, 0.387], *p* < .001 (see Fig. 3). When we broke this interaction down, we found a main effect of affect measurement timepoint in the DITF group, with an increase between Affect 1 and Affect 2, and a decrease between Affect 2 and Affect 3, *OR* = 0.632, 95% CI [0.568, 0.704], *p* < .001. This pattern of
results was 2.66% more likely if the target request was accepted rather than refused, as suggested by the significant interaction between request response and interest contrast, *OR* = 0.375, 95% CI [0.302, 0.464], *p* < .001. No other effect was significant (all *ps* > .05). In the control group, a main effect of affect measurement timepoint was also observed, showing an increase between Affect 1 and Affect 2, and a decrease between Affect 2 and Affect 3, *OR* = 0.775, 95% CI [0.699, 0.859], *p* < .001. This pattern was 1.30% more likely if the target request was refused rather than accepted, as suggested by the significant interaction between request response and contrast of interest, *OR* = 0.768, 95% CI [0.624, 0.944], *p* < .05. No other effect was significant (all *ps* > .05).

Insert Figure 2 about here

Regarding reaction times, in our 2 (group: DITF vs. control) x 3 (affect measurement timepoint: Affect 1 vs. Affect 2 vs. Affect 3) x 2 (target request response: accepted vs. rejected) design, with group and request as between-participants factors and timepoint as a within-participants factor, we used mixed model, with random intercepts to account for the dependency in the data. Reactions times for categorising items as negative and positive were analysed respectively. We used the same contrast of interest as in the previous analysis. This analysis revealed an effect of affect measurement timepoint: participants were faster to categorize items as negative at Affect 2 (*M* = 904, *SE* = 29.2) than at Affect 1 or Affect 3 considered together, *t*(4269,1) = 7.68, *p* < .001, *β* = .15. Moreover, they were faster to categorize items as negative at Affect 3 (*M* = 956, *SE* = 29.5) than at Affect 1 (*M* = 995, *SE* = 29.4) *t*(4244) = -3.56, *p* < .001, *β* = -.07. No other effect was significant (all *ps* > .05). We ran an identical analysis on reaction times for categorizing positive items. Participants were faster at categorizing items as positive at Affect 3 and Affect 1 considered together than at Affect 2 (*M* = 955, *SE* = 28.3), *t*(4658,3) = 2.78, *p* < .001, *β* = -.05. Moreover, they were faster to
categorize items as positive at Affect 3 (M = 901, SE = 28.0) than at Affect 1 (M = 957, SE =
28.0) t(4639.7) = -5.95, p < .001, β = -.11. No other effect was significant (all ps > .05).

Discussion

The aim of Experiment 2 was twofold. First, we wanted to retest the negative affect
effect observed in Experiment 1 after the large request had been turned out, by changing two
elements in order to generalize the results: the material in the categorization task, and the
context in which the experiment took place. Second, we wanted to test the hypothesis that
accepting the target request makes it possible to neutralize the negative affect resulting from
the refusal of the large request. Taken together, results supported our hypotheses on
compliance and on responses to the implicit affect measure (categorization of words as
negative), but not our hypothesis on associated reaction times. When categorizing items as
negative, participants performed faster at Affect 2 than either Affect 1 or Affect 3. Also, they
performed faster at Affect 3 than Affect 1. Reaction time did not interact with either group or
target request response. For the categorization of items as positive, we only observed a
learning effect, with participants performing faster at Affect 3 than either Affect 1 or Affect 2.

**General Discussion and Conclusion**

The aim of the present study was twofold: to test whether negative affect before
acceptance of the target request increases the DITF effect (H1), and whether the acceptance of
the target request helps to dispel this negative affect (H2). Results supported both these
hypotheses. We achieved consistent results in both experiments for the first effect, using an
implicit measure of emotional state featuring two different ambiguous materials (ideograms
and neutral words) in two different experimental settings (online and in the laboratory). The
results of Experiment 1 showed that negative affect levels were higher after refusal of the
large request than before. This pattern of results was not observed for the small request.
Negative affect therefore seems to occur in the first phase of the DITF strategy (i.e., after large request refusal).

Results of Experiment 2 were consistent with those of Experiment 1, with a higher level of negative emotional state in the DITF group before target request presentation (i.e., after large request refusal). Experiment 2 went further by revealing that in the DITF group only participants who had accepted target request were in negative emotional state beforehand. After the target request acceptance, their negative emotional state was completely dispelled. Taken together, these results suggest that 1) a negative affect before the target request's formulation is a precondition to observed DITF effect, and 2) target request acceptance in the DITF condition contributes to the neutralization of this negative affect resulting from the refusal of the large request. In the control condition, the opposite results were observed. Participants who refused the target request were in a more negative emotional state beforehand, and those who accepted it were in a less negative state beforehand. In sum, results showed an emotional congruency effect in the control group (Mayer et al., 1992), in that participants' emotional state before the target request was made influenced their response to this target request in exactly the same way. It is interesting to note that these results were obtained on the implicit measure of participants' emotional state and not on the explicit measure (Experiment 1). This distinction is not surprising, as many studies have emphasize the added value of indirect measures compared with self-report evaluative judgements, allowing experimenters to access unintentional responses rather than controlled ones (e.g., De Houwer et al., 2009), or associative processes rather than propositional ones (e.g., Gawronski et al., 2011). One alternative explanation for the apparent absence of this effect on explicit measures may be that moral and social emotions (e.g., guilt) involve relations with others (Elster, 1998). Experiment 1 was performed on line, and may therefore not have favoured the emergence of the processes involved in this type of social
emotion. Nevertheless, the DITF effect has already been observed in a computer-
communication context, so without any interaction with a requester (Guégen, 2003). This
calls into question the guilt hypothesis and not the hypothesis concerning negative affect after
turning down a large request.

The present results are consistent with previous findings (Millar, 2002; O'Keefe & Figgé, 1997; Schindler & Reinhard, 2015) suggesting that rejection of a large request triggers a negative affective state. Several mechanisms could explain the development of this negative emotional state. First, a large request refusal may introduce inconsistencies between participants' ideal behaviour and their effective behaviour (Cann et al., 1975; Rogers, 1976) or initial position on the issue (Mauny et al., 2022). According to cognitive dissonance theory (Festinger, 1957) and some of its subsequent reformulations (Aronson, 1992; Cooper & Fazio, 1984), inconsistency between behaviour and attitude, self-concept, or adherence to moral norms produces a state of psychological discomfort (i.e., dissonance state) that individuals then seek to reduce (e.g., through behaviour change). According to the DITF strategy, target request acceptance allows participants to reduce this inconsistency. Consistent with this view, Mauny et al. (2022) observed that after refusing a large request of making five platelet donations over 1 year, in the knowledge that each donation takes approximately 2 hours, only participants for whom blood donation is important agreed to donate blood (i.e., target request). However, in the classic dissonance paradigm, dissonance reduction is only observed among people with a high preference for consistency (Bator & Cialdini, 2006; Cialdini et al., 1995), whereas with the DITF strategy, compliance increases when there is a low preference for consistency (Cantarero et al., 2017). This questions the interpretation of the DITF effect in terms of cognitive dissonance. Nevertheless, Cantarero et al. (2017)'s results are not surprising, for even if turning down the large request produces negative affect, people with a
low preference for consistency are more open to changing their responses than persons with a
high preference for consistency.

Second, refusing a large request may produce negative affect because it threatens the self-image (Millar, 2002; Pendleton & Baston, 1979). In other words, people may be perceived of as being indifferent to the problems of others. Accepting the target request makes a good impression on others. Third, refusing a large request may produce negative affect because it is associated with help: the DITF situation is viewed more as a helping situation than a negotiating situation (Tusing & Dillard, 2000), so a refusal is tantamount to transgressing social responsibility norms (Bell et al., 1996; Tusing & Dillard, 2000). These internalized norms stipulate that we must help those in need, and govern our behaviour (Berkowitz & Daniels, 1963). People who transgress them are judged negatively (Brauer & Chekroun, 2005). In the DITF framework, people may therefore fear being judged negatively by requesters. This situation could make them feel uncomfortable (Terrier & Joule, 2008; Terrier et al., 2011). Consistent with this hypothesis, Terrier et al. (2011) observed that the DITF effect was stronger when the requester explained to participants that noncompliance with the large request was unacceptable. Finally, according to some authors, large request refusal produces specific negative emotions such as guilt (Millar, 2002; O'Keefe & Figgé, 1999), as the request is made by a person who works for a noble cause, and it is difficult to refuse that person a favour (Foehl & Goldman, 1983; Goldman et al., 1984).

Nevertheless, it is not because negative affect is observed after large request refusal (Experiments 1 & 2) that it is associated with refusal. It may also arise from the mere fact of having been exposed to a large request. People may feel irritated or angry that such a request has been made. Why? Because the requests are too costly to be acceptable and/or because they are prosocial requests, such as blood donation (Cialdini & Ascani, 1976; Mauny et al., 2022), promotion of road safety (Cann et al., 1975; Even-Chen et al., 1978), support of a heart
charity (Reingen, 1977), or financial donation to children's cancer centres (Howard, 2019), and are linked to some sort of threat (death or, in the present study, environmental damage). Prosocial cause has been found to moderate the DITF effect (see meta-analysis by Dillard et al., 1984). It is interesting to note that Schindler and Reinhard (2015) produced the DITF effect with a marketing cause rather than a prosocial cause, but only if an existential threat had been induced beforehand. This calls into question the nature of the cause: should it be prosocial or threatening? Further research is needed to answer this question.

The present study had several limitations. First, compliance was verbal, and did not involve any actual behaviour. Second, the implicit measure of affective state tested the valence of the emotional state, but did not identify a specific negative emotion (e.g., guilt, fear). Future studies should seek to determine whether the negative state resulting from the DITF effect is an unspecified negative state or corresponds to a discrete negative emotion.

Third, the exclusively female sample potentially restricted the generalizability of results, although the idea that women are more emotional is highly debatable (for a meta-analysis, see Else-Quest et al., 2012). Fourth, like other studies in emotion research (e.g., Leppänen et al., 2004), our results exemplify the sensitivity of implicit categorization tasks with ambiguous stimuli to affective states. We can therefore emphasise the advantages of implicit measures in experimental design, notably for assessing unconscious mechanisms or controlling response biases such as self-presentation concerns in an experimental setting (Gawronski & Hahn, 2019). Nevertheless, even though the present implicit task was intended to assess a feeling and not an attitude, there is a fierce debate about the real added value of implicit measures in social psychology studies (for a review, see Meissner et al., 2019). Fifth, the implicit task set is not a validated task per se, even if it corresponds to a great deal of work based on the congruence between the individual's state and his or her perception of the world (i.e., the congruency effect).
Despite these limitations, the present study contributes to our understanding of the link
between the DITF strategy and concomitant emotional states. Taken together, these results
show that target request acceptance seems to be related to the respondent's emotional state.
More specifically, the higher the negative affect, the higher the acceptance of the target
request in the DITF strategy. This is a new result that contributes to the understanding of this
strategy. Future research will need to replicate these results with actual behaviours and
determine the cause of this negative effect and the contributing factors.
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<figure><img src="image_2.png" /><figcaption>*Figure 1. Experimental phases of design*</figcaption></figure>
<figure><img src="image_3.png" /></figure>

*Figure 2.* Mean proportion of negative responses in the categorization task (i.e., implicit affect measure) as a function of group (control vs. DITF condition), target request response (refusal vs. acceptation) and affect measurement (Affect 1 vs. 2 vs. 3) – Experiment 2.