Running head: Help-seeking across Media Platforms
Should I Ask Over Zoom, Phone, or In-Person?
Communication Channel and Predicted vs. Actual Compliance
M. Mahdi Roghanizad*
Vanessa K. Bohns
Forthcoming in Social Psychological and Personality Science
Key words: compliance, egocentrism, helping, help-seeking, social influence, computer-mediated
*Corresponding author: Mahdi Roghanizad
Ted Rogers School of Management
350 Victoria Street
Toronto, ON M5B 2K3
Help-seeking Across Communication Channels 2
Research has found that people are much more likely to agree to help requests made in-person than those
made via text-based media, but that help-seekers underestimate the relative advantage of asking for help
face-to-face. It remains unknown what help-seekers’ intuitions about the effectiveness of richer media
channels incorporating audio and video features might be, or how these intuitions would compare to the
actual effectiveness of face-to-face or email versus rich media requests. In two behavioral and two
supplemental vignette experiments, participants expected differences in the effectiveness of seeking help
through various communication channels to be quite small, or nonexistent. However, when participants
actually made requests, the differences were quite large. Ultimately, help-seekers underestimated the
relative advantage of asking for help face-to-face compared to asking through any mediated channel.
Help-seekers also underestimated the relative advantage of asking through richer media channels
compared to email.
Keywords: compliance, egocentrism, helping, help-seeking, social influence, computer-mediated
Help-seeking Across Communication Channels 3
Should I Ask Over Zoom, Phone, Email, or In-Person?
Communication Channel and Predicted vs. Actual Compliance
Imagine you need to ask a friend or colleague for a small favor. Would you simply call them up,
or would you schedule a time to get together in person? How likely do you think they would be to say
“yes” to your request in person? What about over video call? Over the phone? Or over email? In the
current research, we examine how accurate people’s intuitions are about the effectiveness of asking for
help through various communication channels.
People ask for help nearly every day, most often from friends, family, and co-workers (Bar-Tal et
al., 1977; Deri et al., 2019). Previous research has shown that people are much more likely to say “yes” to
requests made in-person than to those made via email and other text-based media platforms (Constant et
al., 1996; Dabbish et al., 2005; Gerber & Green, 2000; Ling et al., 2005; Roghanizad & Bohns, 2017; Zhu
et al., 2016). However, help-seekers fail to anticipate how much more effective asking for help face-to-
face is likely to be than asking over email. In one study, help-seekers assumed making a request via email
would be equally as effective as making a request in-person; in actuality, asking for help in-person was
approximately 34 times more effective (Roghanizad & Bohns, 2017). Altogether, this research suggests
that people may use computer-mediated channels rather than asking in-person when seeking help, in part
because they fail to recognize such channels as suboptimal.
Thus far, however, this research has focused solely on help-seekers’ miscalibrations of the
effectiveness of text-based communication channels compared to in-person requests. It therefore remains
unknown whether help-seekers’ intuitions about other types of media channels, particularly richer media
channels that incorporate audio and video elements, might be better calibrated with reality, as well as how
requests made via such channels might actually compare to those made in-person or via text-based media.
The current experiments aim to fill these gaps in the literature by comparing help-seekers’
predictions of the effectiveness of asking for help via video, audio, email, and FtF channels to the actual
effectiveness of seeking help through each of these channels. In two naturalistic help-seeking experiments
Help-seeking Across Communication Channels 4
and two supplemental vignette experiments, we varied critical features of these help-seeking channels—
most notably, the help-seeker’s actual physical colocation with a potential helper, the ability to see the
help-seeker’s face (presence or absence of video), and real time interaction (presence or absence of
synchronous communication). This design allowed us to examine which features of the different
communication channels were most important for securing compliance with a help request, as well as
which features help-seekers believed to be most important, and therefore whether help-seekers were
aware of which communication channels would be most effective for seeking help.
Actual Compliance across Communication Channels
People agree to help requests for a variety of reasons. An individual may agree to help someone
because they empathize with their plight (Batson et al., 1981; Batson et al., 1987), want to look like a
good person (Grant & Mayer, 2009), want to feel like a good person (Grant & Gino, 2010), hope to
secure reciprocation from that person in the future (Deckop et al., 2003), hope to garner standing in their
group or organization (Flynn et al., 2006), or want to avoid the awkwardness of saying no (Bohns, 2016;
Deri et al., 2019).
The intensity of each of these factors is likely to depend on how a request is made, and may be
mitigated when requests are made through mediated channels, although this is likely to depend on the
specific features of a given communication channel. For example, it is less awkward to reject someone
when one has the time and space to find the right words to say “no” and when one does not have to say
“no” to the other person’s face. In line with this argument, Flynn and Lake (2008) found that people felt
more comfortable rejecting help requests when these requests were written out on a piece of paper and
handed to targets. Similarly, other research has found that people are much more likely to refuse emailed
help-requests than those made in-person (Constant et al., 1996; Dabbish et al., 2005; Gerber & Green,
2000; Ling et al., 2005; Roghanizad & Bohns, 2017; Zhu et al., 2016).
Yet despite research in support of the superiority of seeking help in-person in contrast to seeking
help over email, questions remain about whether any mediated help request is likely to be inferior to an
in-person request, or whether requests made via richer media channels might fare better. The
Help-seeking Across Communication Channels 5
ineffectiveness of help requests made via email could be the result of the inherent asynchronicity of email
or a result of the fact that replying via email means a target doesn’t have to say “no” to a help-seeker’s
face, both of which could be remedied by using richer, synchronous media channels that incorporate
audio and video components. On the other hand, it could be that a help-seeker’s colocation with a
potential helper is critical to the superiority of in-person requests, in which case even the richest media
channels would not be able to compare to the effectiveness of a face-to-face request.
Some research suggests that richer communication channels should be able to create the same
psychological conditions that make it difficult for someone to say “no” to a help request. For example, the
synchronicity of video and phone calls means a target must respond to a request for help on the spot,
making it more difficult to say “no”. Indeed, research finds that people tend to default to saying “yes”
when they don’t have the mental time and space to respond mindfully (Langer et al., 1978). This research
would suggest that synchronous phone and video calls, but not voice and video messages, may be as
effective as in-person requests.
On the other hand, several studies suggest the presence of a human face in mediated
communication may encourage prosocial behavior and promote trust (Riedl et al., 2014; Todorov, 2008;
Winston et al., 2002), alter interpersonal trait inferences (Knutson, 1996), facilitate the interpretation of
one’s emotions (Ekman, 1982), and increase willingness to donate (Chang & Lee, 2009; Perrine &
Heather, 2000). This body of literature suggests presence or absence of “face” may be the most important
feature for determining actual compliance with a request. Thus, video calls and messages, but not audio
calls and messages, may be as effective as in-person requests.
Still other research suggests that no mediated help-request is likely to be as effective as a request
for help made in-person. For example, construal level theory (Trope et al., 2007) suggests that the
physical distance inherent to any type of mediated communication channel is likely to result in greater
corresponding psychological distance, making it easier for a target to say “no” to a mediated request for
help in contrast to an in-person request (Trope & Liberman, 2003, 2010). Similarly, media naturalness
theory (Kock, 2004) posits that colocation is a critical feature of the “naturalness” of an interaction. The
Help-seeking Across Communication Channels 6
physical presence of the person seeking one’s help is likely to be more emotionally arousing—and thus
more difficult to say “no” to (Bohns, 2016) —than video or audio of that same person asking for help.
Predicted Compliance with Help Requests across Communication Channels
Research suggests that attempting to predict the relative effectiveness of different communication
channels is a difficult task with which help-seekers are likely to struggle. Help-seekers trying to predict
the outcome of their help requests must predict another person’s reaction to a situation that is very
different from the one they are currently in (Epley et al., 2004; Kruger et al., 2005; Trope & Liberman,
2003; Van Boven et al., 2013). Indeed, previous research has found that help-seekers are grossly
inaccurate when attempting to predict whether those they ask will agree to help them (Bohns, 2016; Flynn
& Lake, 2008).
The limited work that has tackled the question of how the accuracy of these predictions vary by
communication channel has specifically examined whether help-seekers recognize the differential
effectiveness of text-based versus in-person communication channels when asking for help (Roghanizad
& Bohns, 2017). This work has found that help-seekers fail to differentiate between communication
channels that produce vastly different compliance rates (Roghanizad & Bohns, 2017). While in-person
help-requests were 34 times more effective than those sent via email in the study noted earlier, help-
seekers thought the two communication channels would produce similar results. In the current research,
we expect help-seekers to be similarly inaccurate at predicting how help requests made via other
computer-mediated channels—namely, video and audio channels—are likely to stack up against in-person
and text-based help requests.
The Current Research
In two behavioral experiments, we randomly assigned participants to a communication channel
for seeking help. In Experiment 1, participants were assigned to either in-person, video call, video
message, audio call, or audio message; in Experiment 2, participants were assigned to either video call,
audio call, or email. In both experiments, participants were instructed to ask five friends for a small favor
via their assigned communication channel after predicting how many of their friends would comply. We
Help-seeking Across Communication Channels 7
chose to have participants solicit help from friends rather than strangers because this most closely
resembles the way help requests play out in the “real world” and organizational settings, as people are
much more likely to seek help from people with whom they have an established relationship (Bar-Tal et
al., 1977; Deri et al., 2019).
This experimental design, in which each help-seeker made requests of five targets, resulted in a
sample of targets (who determine actual compliance) that was five times the sample of help-seekers (who
determine predicted compliance). Thus, the behavioral studies reported below are well-powered to detect
differences in actual compliance, but less well-powered to detect differences in—or to give us confidence
in null effects for—predicted compliance. To remedy this issue, we report two additional vignette studies
in the Supplementary Materials that provide higher-powered replications of the patterns we observed for
participants’ predictions of compliance across these different communication channels. For these
supplemental vignette studies, we recruited samples large enough to run equivalence tests in order to test
for possible null effects (Lakens, 2017), as well as examining whether our findings were robust to
conditions in which a help-seeker has all communication media available to them, or when they approach
strangers vs. friends.
Experiment 1: Predicted vs. Actual Compliance with a Request across Face-to-Face,
Audio, and Video Channels
Participants asked five friends for a small favor through one of five communication channels that
varied by physical presence, absence or presence of face/video, and synchronicity. Before making their
requests, help-seekers first predicted how many of their friends would agree (predicted compliance). They
then kept track and reported the number of friends who actually agreed (actual compliance). This design
allowed us to look at both predicted and actual compliance by communication medium, as well as
comparing differences in predicted vs. actual compliance by communication medium, in a fairly
naturalistic help-seeking setting.
Help-seeking Across Communication Channels 8
A total of 888 participants (168 help-seekers, 94 female; 720 targets, 359 female) took part in the
study. Twenty-four of the original 168 requesters did not complete the second phase of the study (i.e., did
not report actual compliance), leaving 144 help-seekers who completed both phases of the study. A
sensitivity power analysis conducted in G*Power reveals that this sample size enabled us to detect
medium effect sizes (α= 0.05, 1- β= 80%, Cohen’s f ≥ 0.3). Attrition rates did not vary significantly by
condition, χ2 (4, N = 168) = 1.97, p = .742).
Design and Procedure
The experiment used a 5(Communication Channel: face-to-face, video call, audio call, video
message, audio message) × 2(Compliance: predicted, actual) mixed design where communication channel
was a between-subjects factor and compliance was a within-subjects factor. Video call (VC) and audio
call (AC) refer to synchronous communication utilizing video or audio features (e.g., Skype call, Phone
call), respectively, while video message (VM) and audio message (AM) refer to asynchronous
communication utilizing these features (e.g., WhatsApp, Voice message).
Participants came into the lab, were randomly assigned to one of the five communication
channels, and were informed that they would be asking five friends for help with a task through the
assigned channel (e.g., in-person, phone call, video-chat). The specific request they were instructed to
make was to ask for help proofreading a half-page passage (see Supplementary Materials for study
materials). Participants were instructed to make their requests using a specified script for consistency
across communication channels. Participants assigned to the FtF condition were instructed to approach
five friends and ask each of them in-person to proofread the provided passage. Participants in the
computer-mediated conditions were instructed to use a communication application of their choosing that
utilized their assigned communication mode (e.g. a participant assigned to video call could use Skype,
Facetime, Google Meet, etc.) in order to contact each of their five friends. Because participants would not
necessarily be able to connect with each of these friends at that moment in the lab, they were instructed to
Help-seeking Across Communication Channels 9
contact their friends within two days of completing the lab session and were given an additional five days
to wait for Yes/No responses and the returned passages.
After reading these instructions, participants were asked to imagine making this help request of
five friends, and to predict how many would agree to help them, i.e., would agree to complete the
proofreading task (predicted compliance). Requesters then answered a series of exploratory questions
based on mechanisms examined in previous research (Deri et al., 2019; Roghanizad & Bohns, 2017)
about why they thought helpers would be motivated to help (social norms, reciprocity, trust, awkwardness
of saying no, empathy, self-image) and demographic questions. The complete list of items and the results
of our analyses on these additional items are in the Supplementary Materials.
Finally, before they left the lab, participants were provided with a tally sheet to keep track of (a)
whether each friend agreed to complete the task (promised to help), and (b) whether the friend actually
completed the task (did help, i.e., actual compliance)
. Participants were asked to return the completed
tally sheet one week after the lab session.
Data and materials for all studies are available at Open Science Framework
Our data consists of five helpers nested under each individual help-seeker and the key DV of
interest for each individual helper is dichotomous (Actual Compliance: Did they help, yes or no?). Due to
the structure of the data, we used a multilevel analytic approach. Specifically, we used Generalized Linear
Mixed Modeling (GLMM) with binary outcomes to represent each of the individual helpers’ behavior
(did vs. did not help).
Our primary variable of interest was the number of people who did help, but since the pressure to say
“yes” might lead people to agree but then not follow through with a help-request, we also analyzed
promised to help vs. did help separately as an exploratory variable of interest in the supplemental
Help-seeking Across Communication Channels 10
Figure 1- Experiment 1 Predicted and actual compliance with help requests by communication medium.
We first looked at actual compliance by communication medium. We conducted a Generalized
Linear Mixed Model (GLMM) with communication medium as the independent variable and actual
compliance (yes or no) as a binary DV. As shown in Table 1, FtF was found to be significantly more
effective than any of the other channels.
DV: did help
Random factors: Intercept only
IVs2 (fixed factors)
Table 1- GLMM contrasting actual effectiveness of communication channels
Next, we looked at the accuracy of help-seekers’ predictions of compliance by communication
medium. Specifically, we examined whether help-seekers recognized the superiority of seeking help in-
person compared to computer-mediated channels. We conducted a 5(Communication Channel: FtF, VC,
VM, AC, AM) × 2(Compliance: Predicted, Actual) mixed-model ANOVA with repeated measures on the
second factor. A significant interaction emerged between Communication Channel and Compliance
In this model FtF was set as the reference category and each of the other media was individually compared with
FtF. Calculated p values are reported in Table 1. Closeness was also considered as the slope in the random effect
block, however, closeness explained almost zero variation. Consequently, only intercept was included.
4.52 4.34 4.17
FtF VC VM AC AM
Average compliance rate out of 5
Help-seeking Across Communication Channels 11
F(4,139) = 3.29, p = .013,
= .086, indicating that the accuracy of help-seekers’ predictions varied by
To unpack this interaction, we conducted four separate repeated measure ANOVAs comparing
FtF to each of the other channels on prediction accuracy (i.e., predicted vs. actual compliance rates).
Help-seekers overestimated the likelihood of receiving help significantly more when asking via any of the
mediated channels than when asking FtF (Table 2). The magnitude of error did not vary significantly
between the various mediated channels F(3,111)= 1.09, p= .357,
Media vs. FtF
FtF was contrasted with each of
the following media
Table 2- Prediction errors - FtF contrasted with other media
To examine help-seekers’ expectations of which communication channels would be most
effective, we compared the mean number of friends out of 5 participants expected would comply with
their help-requests across the five conditions. We ran a one-way ANOVA with communication channel
(FtF, VC, VM, AC, AM) as the independent variable and predicted compliance (0-5) as the dependent
variable. Help-seekers did appear to recognize that some channels would be more effective than others for
seeking help, F(4,139)= 3.85, p= .005,
To unpack this result, we conducted separate t-tests comparing predicted compliance for in-
person help-requests to each of the mediated channels. We found no difference between help-seekers’
predictions of the effectiveness of seeking help via video call (M=4.34, SD=.90), t(56)= 0.80, p=.428,
d=.21, 95% CI [-0.26, 0.60] or video message (M=4.17, SD=1.15), t(57)= 1.39, p=.170, d=.36, 95% CI [-
0.15, 0.85] and seeking help in-person (M=4.52, SD=.74). However, interestingly, help-seekers did
Help-seeking Across Communication Channels 12
anticipate a difference between seeking help via audio channels and seeking help in-person (although they
still substantially overestimated the effectiveness of audio channels). There was a significant difference
between help-seekers’ predictions of the effectiveness of in-person help-seeking as compared to an audio
call (M=3.55, SD=1.35), t(56)= 3.38, p=.001, d=.87, 95% CI [0.39, 1.54] and an audio message (M=3.78,
SD=1.22), t(54)= 2.72, p=.008, d=.73, 95% CI [0.20, 1.27]. After removing FtF data, overall, help-
seekers predicted that with-face (video) channels (M= 4.25, SD= 1.03) would be more effective than no-
face (audio only) channels (M= 3.66, SD= 1.28, F(1,113)= 7.54, p= .007,
To examine the role of the presence or absence of face, and synchronous versus asynchronous
media, on the effectiveness of mediated channels only, we removed the FtF data and employed two
dummy variables called “Sync” and “Face”. “Sync” distinguished between synchronous media (i.e. the
VC and AC conditions) and asynchronous media (i.e. the VM and AM conditions) while “Face”
designated whether a given medium was with-face (i.e. the VC and VM conditions) or no-face (i.e. the
AC and AM conditions). Incorporating these two dummy variables, a GLMM with a binary outcome (did
help) was employed. As depicted in Table 3, there were no significant differences of either face or
DV: did help
Random factors: Intercept only
IVs (fixed factors)
Face × Sync
Table 3- GLMM testing the effect of face and synchronicity
The only difference we found for actual compliance between the different communication
channels was a large difference between asking FtF and asking via any kind of computer mediated
channel (video call, video message, audio call, audio message). Regarding prediction accuracy, while
help-seekers did expect a difference in the effectiveness of asking via audio-only channels versus asking
in-person, the actual difference between these channels was larger than help-seekers expected, i.e., asking
Help-seeking Across Communication Channels 13
in person was even more effective than asking via audio channels than help-seekers expected. Finally,
although help-seekers expected asking via video channels to be as effective as asking in-person, FtF
requests were significantly more effective.
Experiment 2: Predicted vs. Actual Compliance with a Request across Email, Audio,
and Video Channels
Experiment 1 compared audio and video to in-person channels and found that FtF help-requests
were significantly more effective than requests made via rich media channels, and, further, that asking FtF
was more effective than asking via mediated channels than participants had initially anticipated. Yet,
despite their inferiority to in-person requests, requests made via rich media may nonetheless be superior
to text-based requests. Experiment 2 tests this possibility, in addition to help-seekers’ expectations of how
text-based requests are likely to fare compared to richer-media requests. This study was pre-registered at
A total of 1979 participants (490 help-seekers, 323 female; 1490 targets, 910 female) took part in
the study. One hundred ninety-one of the original 489 requesters did not complete the second phase of the
study (i.e., did not report actual compliance), leaving 298 help-seekers who completed both phases of the
study. A power analysis conducted in G*Power indicated that this sample size was large enough to detect
an effect of the same size we found in Experiment 1 for actual compliance between audio and video calls
(d= 0.397). Attrition did not differ significantly by condition, χ2 (2, N = 490) = 1.64, p = .441.
Design and Procedure
Experiment 2 was identical to Experiment 1 with the following modifications: The FtF, audio
message, and video message conditions were removed, and an email condition was added. Thus, there
were three between-subjects conditions: Email, Video Call, and Audio Call
Help-seeking Across Communication Channels 14
Since the data structure was identical to Experiment 1, we used the same analytic approach. We
began by looking at actual compliance by communication medium and found that both audio call and
video call were significantly more effective than email requests (Table 4).
DV: did help
Random factors: Intercept, Closeness
IVs (fixed factors)
Audio Call vs. Video Call
Email vs. Video Call
Email vs. Audio Call
Table 4- GLMM contrasting actual compliance across channels
To test the accuracy of help-seekers’ predictions, we conducted a 3 Communication Channel
(video call vs. audio call vs. email) ×2 Compliance (predicted, actual) mixed-model ANOVA with
repeated measures on the second factor. A significant interaction between Communication Channel and
Compliance emerged F(2,588) = 3.324, p = .037,
= .011, indicating that differences in predicted
compliance were smaller than differences in actual compliance between communication channel (Figure
2). This interaction was mainly driven by the Email condition. When this condition was removed, the
interaction was no longer significant, indicating that participants failed to fully recognize how much more
effective requests made via any rich medium (Audio or Video) would be compared to Email.
Figure 2- Experiment 2 Predicted and actual compliance with help requests by communication medium.
EM AC VC
Average compliance rate out of 5
Help-seeking Across Communication Channels 15
Finally, paired t-tests indicated that participants overestimated the effectiveness of their requests
across each mediated communication medium (Table 5).
Mean (Predicted, Actual)
SD (Predicted, Actual)
Table 5- Experiment 2 – Results of paired t-tests indicating overestimation of channel effectiveness.
While Experiment 1 indicated that no mediated channel was as effective as a FtF request,
Experiment 2 suggests that requests made via rich media communication channels are still more effective
than requests made via email. However, participants once again underestimate how much more effective
asking via richer media channels is.
In two behavioral help-seeking studies, help-seekers failed to appreciate the effectiveness of
seeking help via increasingly more naturalistic, or richer, communication channels. In Experiment 1, face-
to-face help requests were substantially more effective than audio or video requests, yet help-seekers
failed to recognize the superiority of asking for help in person. In Experiment 2, seeking help via video
and audio communication channels was significantly more effective than seeking help through email, but
help-seekers failed to fully appreciate how much more effective seeking help through these richer
channels was likely to be.
Two additional vignette studies reported in the Supplementary Materials provided additional
evidence for the inaccuracy of help-seekers’ predictions of compliance across different communication
channels. In Supplemental Experiment A, a sample of 1,003 participants read a series of scenarios in
which they imagined seeking help through a randomly assigned communication channel. Participants in
this study perceived mediated channels to be as effective for seeking help as asking FtF. In Supplemental
Experiment B (AsPredicted.com #75305, Anonymous link: https://aspredicted.org/9G4_FY2), a sample
of 402 participants rated the effectiveness of seeking help either from a friend or from a stranger
Help-seeking Across Communication Channels 16
(between-subjects) through each of four communication channels (FtF, video call, audio call, email) in a
within-subjects design. No difference was found in the pattern of predictions across channels and
closeness levels (i.e. no interaction), nor was any main effect of closeness detected. Participants in this
study predicted no differences between the effectiveness of video, audio, or email channels. Participants
did expect a FtF request to be slightly more effective than any of the mediated requests—but only
Given the large differences we observed in the effectiveness of FtF compared to mediated
requests, and rich media compared to email requests in our behavioral studies, these findings suggest that
people fail to fully appreciate the value of asking for help in-person, or in lieu of this possibility, through
the richest possible communication medium. These results corroborate previous research on mediated
versus face-to-face help-seeking. Roghanizad and Bohns (2017) found a similar prediction error in which
help-seekers expected making requests over email to be as effective as making requests in person, despite
large differences in actual compliance between the two modes of help-seeking.
The current findings also corroborate previous work that has looked at the actual effectiveness of
persuasion across different media channels—primarily text-based versus in-person communication
channels (Berry & McArthur, 1986; Brownlow, 1992; Burgoon, 1990; McGinley et al., 1975;
Scharlemann et al., 2001; Sproull & Kiesler, 1986; Willis & Todorov, 2006). While this work has also
shown that in-person influence is superior to almost any kind of mediated influence attempt, fairly little
prior research has examined people’s intuitions about the best way to persuade. Yet it is people’s beliefs
about which communication channel is likely to be most effective—not the actual effectiveness of these
communication channels—that drives behavior, e.g., how someone decides to go about asking for help.
Notably, while previous research has found that participants underestimate compliance with in-
person requests (Bohns, 2016; Flynn & Lake, 2008), in Experiment 1 we found that help-seekers were
mostly accurate about the likelihood the people they asked would comply with their requests. This may be
Help-seeking Across Communication Channels 17
because we specifically instructed participants to ask friends, and help-seekers’ predictions of friends’
compliance rates have been shown to be more accurate (Deri et al., 2019).
Taken together with the Roghanizad and Bohns (2017) studies on help-seekers’ misperceptions of
the value of asking for help in-person versus over email, the current findings paint a more complete
picture of the ways in which help-seekers discount the value of seeking help in-person. Similar to the
convenience of email, the convenience of other mediated channels may lead help-seekers to ask for help
by making a quick phone or video call, rather than walking down to a potential helper’s office. Our
findings suggest such a preference could deprive them of needed help.
Of course, there are situations in which meeting up with a potential helper is simply too costly or
inconvenient, and requesters may therefore choose to communicate with colleagues and friends through
mediated channels regardless of the advantages of FtF communication. Our studies suggest that in these
cases, there are benefits to using richer communication media, such as video and audio channels.
Interestingly, our findings do not indicate a clear advantage of video over audio-only channels.
Finally, our research dovetails with recent research examining people’s mispredictions about the
best communication channel to use to reconnect with old friends (Kumar & Epley, 2020). Together, these
complementary lines of work suggest that people may regularly choose suboptimal communication
channels to connect with others.
Not receiving needed help is costly. It therefore stands to reason that help-seekers would want to
maximize their chances of getting a “yes.” In two experiments, we found that seeking help in-person was
far superior to seeking help through any form of mediated communication channel—including seeking
help over synchronous, with-face video channels. Nonetheless, we found that richer media channels do
still offer an advantage over text-based channels. Yet, importantly, help-seekers appear largely unaware
of both these facts. These findings suggest that people may miss out on receiving needed help by asking
for it in suboptimal ways.
Help-seeking Across Communication Channels 18
Bar-Tal, D., Bar-Zohar, Y., Greenberg, M. S., & Hermon, M. (1977). Reciprocity behavior in the
relationship between donor and recipient and between harm-doer and victim. Sociometry, 293-
Batson, C. D., Duncan, B. D., Ackerman, P., Buckley, T., & Birch, K. (1981). Is empathic emotion a
source of altruistic motivation? Journal of personality and Social Psychology, 40(2), 290.
Batson, C. D., Fultz, J., & Schoenrade, P. A. (1987). Distress and empathy: Two qualitatively distinct
vicarious emotions with different motivational consequences. Journal of personality, 55(1), 19-
Berry, D. S., & McArthur, L. Z. (1986). Perceiving character in faces: The impact of age-related
craniofacial changes on social perception. Psychological bulletin, 100(1), 3.
Bohns, V. K. (2016). (Mis) Understanding our influence over others: A review of the underestimation-of-
compliance effect. Current Directions in Psychological Science, 25(2), 119-123.
Brownlow, S. (1992). Seeing is believing: Facial appearance, credibility, and attitude change. Journal of
Nonverbal Behavior, 16(2), 101-115.
Burgoon, M. (1990). Language and social influence.
Chang, C. T., & Lee, Y. K. (2009). Framing Charity Advertising: Influences of Message Framing, Image
Valence, and Temporal Framing on a Charitable Appeal 1. Journal of Applied Social Psychology,
Constant, D., Sproull, L., & Kiesler, S. (1996). The kindness of strangers: The usefulness of electronic
weak ties for technical advice. Organization Science, 7(2), 119-135.
Dabbish, L. A., Kraut, R. E., Fussell, S., & Kiesler, S. (2005). Understanding email use: predicting action
on a message. Proceedings of the SIGCHI conference on Human factors in computing systems,
Deckop, J. R., Cirka, C. C., & Andersson, L. M. (2003). Doing unto others: The reciprocity of helping
behavior in organizations. Journal of Business Ethics, 47(2), 101-113.
Help-seeking Across Communication Channels 19
Deri, S., Stein, D. H., & Bohns, V. K. (2019). With a little help from my friends (and strangers):
Closeness as a moderator of the underestimation-of-compliance effect. Journal of Experimental
Social Psychology, 82, 6-15.
Ekman, P. (1982). Emotion in the human face . Cambridge Cambridgeshire. New York.
Epley, N., Keysar, B., Van Boven, L., & Gilovich, T. (2004). Perspective taking as egocentric anchoring
and adjustment. Journal of Personality and Social Psychology, 87(3), 327.
Flynn, F. J., & Lake, V. K. (2008). If you need help, just ask: Underestimating compliance with direct
requests for help. Journal of Personality and Social Psychology, 95(1), 128.
Flynn, F. J., Reagans, R. E., Amanatullah, E. T., & Ames, D. R. (2006). Helping one's way to the top:
self-monitors achieve status by helping others and knowing who helps whom. Journal of
personality and social psychology, 91(6), 1123.
Gerber, A. S., & Green, D. P. (2000). The effects of canvassing, telephone calls, and direct mail on voter
turnout: A field experiment. American political science review, 94(3), 653-663.
Grant, A. M., & Gino, F. (2010). A little thanks goes a long way: Explaining why gratitude expressions
motivate prosocial behavior. Journal of personality and social psychology, 98(6), 946.
Grant, A. M., & Mayer, D. M. (2009). Good soldiers and good actors: prosocial and impression
management motives as interactive predictors of affiliative citizenship behaviors. Journal of
Applied Psychology, 94(4), 900.
Knutson, B. (1996). Facial expressions of emotion influence interpersonal trait inferences. Journal of
Nonverbal Behavior, 20(3), 165-182.
Kock, N. (2004). The psychobiological model: Towards a new theory of computer-mediated
communication based on Darwinian evolution. Organization Science, 15(3), 327-348.
Kruger, J., Epley, N., Parker, J., & Ng, Z.-W. (2005). Egocentrism over e-mail: Can we communicate as
well as we think? Journal of Personality and Social Psychology, 89(6), 925.
Help-seeking Across Communication Channels 20
Kumar, A., & Epley, N. (2020). It’s surprisingly nice to hear you: Misunderstanding the impact of
communication media can lead to suboptimal choices of how to connect with others. Journal of
Experimental Psychology: General.
Lakens, D. (2017). Equivalence tests: a practical primer for t tests, correlations, and meta-analyses. Social
Psychological and Personality Science, 8(4), 355-362.
Langer, E. J., Blank, A., & Chanowitz, B. (1978). The mindlessness of ostensibly thoughtful action: The
role of" placebic" information in interpersonal interaction. Journal of personality and social
psychology, 36(6), 635.
Ling, K., Beenen, G., Ludford, P., Wang, X., Chang, K., Li, X., Cosley, D., Frankowski, D., Terveen, L.,
& Rashid, A. M. (2005). Using social psychology to motivate contributions to online
communities. Journal of Computer‐Mediated Communication, 10(4), 00-00.
McGinley, H., LeFevre, R., & McGinley, P. (1975). The influence of a communicator's body position on
opinion change in others. Journal of Personality and Social Psychology, 31(4), 686.
Perrine, R. M., & Heather, S. (2000). Effects of picture and even-a-penny-will-help appeals on
anonymous donations to charity. Psychological Reports, 86(2), 551-559.
Riedl, R., Mohr, P. N., Kenning, P. H., Davis, F. D., & Heekeren, H. R. (2014). Trusting humans and
avatars: A brain imaging study based on evolution theory. Journal of Management Information
Systems, 30(4), 83-114.
Roghanizad, M. M., & Bohns, V. K. (2017). Ask in person: You're less persuasive than you think over
email. Journal of Experimental Social Psychology, 69, 223-226.
Scharlemann, J. P., Eckel, C. C., Kacelnik, A., & Wilson, R. K. (2001). The value of a smile: Game
theory with a human face. Journal of Economic Psychology, 22(5), 617-640.
Sproull, L., & Kiesler, S. (1986). Reducing social context cues: Electronic mail in organizational
communication. Management science, 32(11), 1492-1512.
Help-seeking Across Communication Channels 21
Todorov, A. (2008). Evaluating faces on trustworthiness: an extension of systems for recognition of
emotions signaling approach/avoidance behaviors. Ann N Y Acad Sci, 1124, 208-224.
Trope, Y., & Liberman, N. (2003). Temporal construal. Psychological review, 110(3), 403.
Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological
review, 117(2), 440.
Trope, Y., Liberman, N., & Wakslak, C. (2007). Construal levels and psychological distance: Effects on
representation, prediction, evaluation, and behavior. Journal of consumer psychology, 17(2), 83-
Van Boven, L., Loewenstein, G., Dunning, D., & Nordgren, L. F. (2013). Changing places: A dual
judgment model of empathy gaps in emotional perspective taking. In Advances in experimental
social psychology (Vol. 48, pp. 117-171). Elsevier.
Willis, J., & Todorov, A. (2006). First impressions: Making up your mind after a 100-ms exposure to a
face. Psychological Science, 17(7), 592-598.
Winston, J. S., Strange, B. A., O'Doherty, J., & Dolan, R. J. (2002). Automatic and intentional brain
responses during evaluation of trustworthiness of faces. Nat Neurosci, 5(3), 277-283.
Zhu, H., Das, S., Cao, Y., Yu, S., Kittur, A., & Kraut, R. (2016). A market in your social network: The
effects of extrinsic rewards on friendsourcing and relationships. Proceedings of the 2016 CHI
Conference on Human Factors in Computing Systems,