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International Journal of Social Robotics manuscript No.
(will be inserted by the editor)Eﬀect of Robot’s Whispering Behavior on People’s Motivation
Kayako Nakagawa · Masahiro Shiomi · Kazuhiko Shinozawa · Reo
Matsumura · Hiroshi Ishiguro · Norihiro Hagita

Received: date / Accepted: date

Abstract This paper presents the eﬀect of a robot’s
whispering behavior on people’s motivation. Here, “whispering behavior” consists of a whispering cue and asmall voice, which provides a natural sense of physicalproximity in a context of conﬁdentiality, thus increasing intimacy. A laboratory experiment was conducted
to investigate this eﬀect. In the experiment, a robot requested the participants to perform an annoying taskthat involved writing as many equations in a 9x9 multiplication table as possible. The result showed that thewhispering cue improved task performance as measured
by the number of written equations and writing time.
The small voice, however, had no eﬀect. Furthermore,
to investigate the eﬀectiveness of a robot’s whispering behavior on recommendations, we conducted a ﬁeld
trial in a shopping mall. The results showed the eﬀectiveness of whispering on recommendations, suggestingthat whispering behaviors are useful for various services
that aim to build motivation, such as advertisements,
sales promotions, and encouragement to study.
Keywords Communication robot · Whispering
behavior · Motivation improvement · Behavior change ·Persuasion

1 Introduction
In human-human interaction (HHI), our attitudes andbehaviors are aﬀected by various kinds of information
K. Nakagawa · M. Shiomi · K. Shinozawa · R. Matsumura ·H. Ishiguro · N. hagita
Intelligent Robotics and Communication Labs., ATR, 2-2-2
Hikaridai Keihanna-Sci. City, Kyoto, Japan.
Tel.: +81-774-951454
Fax: +81-774-951401
E-mail: kayako nkgw@atr.jp

on others, including gender, personality, gestures, social
touching, and physical distance. Recently, many studies
have focused on such social cues in the ﬁeld of persuasion and behavior change. The research focus of thisﬁeld is expanding from HHI to human-computer andhuman-robot interactions (HRI). For example, it wasreported that people often treated computers as social
partners, and their personality and gender inﬂuenced
people’s decisions [1]. Fogg deﬁned the advantages ofcomputers for advertisement as “captology”: computers as persuasive technology [2].
Robots, like computers, can use such persuasive techniques for changing behaviors. Moreover, we believethat in some cases, physical existence more eﬀectively
inﬂuences people’s attitudes and behaviors than a virtual existence. Previous works reported that a robot
with a physical presence aﬀects human decision making more greatly than a screen agent in the real world[3]. Some researchers are using real robots for advertisements in such environments as shopping malls [4][5].
These works, however, focused on the robot’s presence
itself without revealing how robots should act to aﬀect
people’s behavior.
In HHI, the social relationship with a persuader is
an important factor in decision making [6], and thisrelationship is greatly aﬀected by physical information.
For example, close distance indicates a familiar relationship and thus creates familiarity eﬀectively [7]. Therefore, interaction between a human and a robot that creates such proximity and a familiar situation might beeﬀective in changing behavior. From this perspective,
we focus on a whispering situation between a humanand a robot (Fig. 1), which reﬂects a typical persuasive
physical cue used for persuasion in HHI that is often
observed in conversations between close friends. To reproduce such a whispering situation, we have designed

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Kayako Nakagawa et al.

tigate a whispering cue that could entrain people to joina whispering situation. Based on the results, we clariﬁed the eﬀect of the combination of a whispering cue
and a small voice. To investigate the eﬀectiveness of therobot’s whispering behavior in an actual application,
we conducted a ﬁeld trial where a robot recommends a
speciﬁc shop in a shopping mall.

2 Related Work

Fig. 1 A robot is whispering to a woman

a robot’s whispering behavior that consists of a whispering cue and a small voice.
Whispering behavior naturally encourages proximity and feelings of familiarity. Past studies have reported that nearness and familiar feelings increase persuasive eﬀects [8][9][7]. Therefore, such a whispering
behavior might be eﬀective when a robot is trying tochange people’s behavior. On the other hand, in unfriendly relationships, close distance sometimes brings
negative eﬀects [7]. It remains unclear whether a robot’s
whispering behavior positively aﬀects attitudes and changes people’s behaviors.
Behavior change encompasses a variety of positivechanges, for example, doing homework, quitting smoking, and increasing the amount of exercise. Fogg classiﬁed behavior changes using 3 x 5 matrix [10]. The matrix consists of the temporal dimension (done one-time,
has a duration, and lasting change) and the dimensionof how change is made (doing familiar/unfamiliar behavior, increasing/decreasing behavior intensity or duration, and ceasing a behavior). McGuire argued that
persuasion involved the phases of attitude change andbehavior change [11]. In this study, we focus on “improving motivation” as enhancing attitudes and increasing behavior, for example, doing homework or exercises.
An approach based on this factor would be useful invarious services where robots are expected to support
people’s behavior changes.
In this paper, we discuss how a robot’s whispering
behavior improves people’s motivation, 1 which is an
important factor in various types of behavior changes
associated with enduring and resistant attitudes [13].
First, we conducted a preliminary experiment to inves1

This paper is an extended version of previous work [12],and additionally contains the results of a preliminary experiment, a report on a ﬁeld trial in a shopping mall, and moredetailed discussions

2.1 Physical existence and robot’s appearance
Many HHI studies have investigated the eﬀects of appearance, such as high-status clothing, attractive facial
features, and conventional looks, on impressions and behavior changes [14][15][16][17]. In HRI, Kanda et al. investigated the diﬀerences in physical existence between
adults and two kinds of humanoid robots [18]. Siegel et
al. reported the eﬀects of robot gender on persuasion[19].
Some studies have revealed the advantages of physical existence. For example, Kidd et al. reported thata robot is considered more attractive and trustworthy
than a CG agent [20]. Powers et al. compared a robot
and a CG agent and concluded that the robot is viewed
as more social, more reliable, and more capable [21].
Moreover, robots have been reported to more greatly
aﬀect not only impressions but also behavior than CG
agents. Shinozawa et al. investigated whether people
tend to take advice from a robot more than a CG agent[3]. Bainbridge et al. investigated how people respondto a request to throw a book by comparing humans,
agents, and robots [22].

2.2 Physical distancePhysical distance is an important factor associated withrelationships among people. For example, a forwardbent posture and proximity eﬀectively create more sympathy in others than verbal information [9][23]. Proximity is related to familiarity in interactions [7][24].
Touching another lightly is also eﬀective not only forfamiliarity but also for persuasion [25][26]. On the other
hand, proximity to unfamiliar people causes uncomfortable impressions [7].
Based on these works, we believe that proximity iseﬀective for behavior change in HRI, and thus we focused on a whispering situation that naturally produces
close distances and familiar relationships.

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Eﬀect of Robot’s Whispering Behavior on People’s Motivation

3

conversational partner in natural HRI studies. We useda corpus-based speech synthesis method to generatespeech [32].

3.2 Participants
The participants were 24 university students (12 men
and 12 women, average age 20.6, S.D. 1.2) who wererecruited on the web, with no consideration given to
their academic majors.
(a) Overview(b) Link mechanisms

Fig. 2 Desktop sized Robot: “Robovie-mR2”

2.3 Speech style
HHI studies have revealed that speech styles inﬂuence
impressions and aﬀect behavior changes. McGuire reported that such voice properties as loudness and speed
aﬀect persuasion [11]. Siegman concluded that those
who speak relatively quickly with short, silent pauses
are seen as having more favorable attributes than those
who speak more slowly [27]. Pope et al. reported thatsilent, ﬁlled pauses and speech hesitations are negatively correlated with listener attraction to speakers[28].
Moreover, the context of conﬁdential talks with arobot promoted interaction with children [29]. HHI studies have shown that the interaction of self-disclosureand sharing secrets improved familiarity [30] [31]. Basedon such research, we are interested in exploring how
a robot’s whispering behavior can create situations of
conﬁdential talk.
In the paper, we investigate our hypothesis that a
robot’s whispering behavior that creates proximity in a
conﬁdential context is eﬀective for behavior change.

3.3 Conditions
We conducted a within-participant experiment with three
conditions:– Gesture condition: making a whispering gesture– Speech condition: speaking to ask the subject tolend an ear– Gesture & speech condition: performing both gesture and speech as described above
We designed the whispering gesture as the robot
moving both hands to its mouth from a standing posture, and the speech as the robot uttering this phrase:“Could I have your ear?”

3.4 Evaluation
After the interaction, the participants answered a questionnaire to evaluate the robot’s whispering behavior
on a 1-7-point scale, where seven represents the mostpositive impression, four is neutral, and one is the mostnegative. The items are shown below.
Q1. Did the robot ask you to lend an ear?
Q2. Did the robot want to have a conﬁdential talk?

3.5 Procedure
3 Investigation of Whispering Cue
Since we did not know how a robot should act to entrain
people in whispering situations, we investigated appropriate whispering cues for such situations in a preliminary experiment.

The participants sat in front of the robot and communicated with it. After the greeting, the robot behaved depending on each condition. The participants answeredquestionnaires after each session. The order of the three
conditions and the gender ratio in each condition werecounter-balanced.

3.1 Robot
3.6 Results
We used “Robovie-mR2,” an interactive humanoid robot(Fig.2) that has four degrees of freedom (DOFs) in its
arms, three in its head, and four in its eyes. It stands
42 cm tall. Such a robot is often used as a table-style

Figure 3 shows the questionnaire results. The score averages of Q1 were 2.38 (gesture), 5.63 (speech), and5.92 (gesture & speech); the standard error (S.E.) of

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Kayako Nakagawa et al.
Table 1 Conditions

Normal volume
Small volume

Without
whispering cue
Condition 1
Condition 3

Withwhispering cue
Condition 2
Condition 4

recognized as a whispering cue. Therefore, we adopted
the behavior where the robot brought both hands to
its mouth and asked, “Could I have your ear?” as its
whispering cue for our experiment.

(a) Q1. Impression of asking to lend an ear

4 Experiment
In Section 3, we deﬁned the behavior of the whisperingcue for our study. Next, to investigate the eﬀect of arobot’s whispering behavior, we conducted a betweenparticipants experiment that consisted of the whispering cue and a small voice.

4.1 Experimental design(b) Q2. Impression of conﬁdential talkFig. 3 Average and standard error (S.E.) of preliminary experiment’s results

each condition was 0.34, 0.38, and 0.37, respectively.
The averages of Q2 were 2.54 (gesture), 4.63 (speech),and 5.96 (gesture & speech), with the S.E. of each condition 0.31, 0.40, and 0.22, respectively.
We conducted a one-way repeated measures ANOVA.
The results showed that the impression of asking to lend
an ear were signiﬁcantly diﬀerent between conditions(F (2.46) = 36.85, p < .01), and multiple comparisonsrevealed signiﬁcant diﬀerences: gesture and speech condition > gesture condition (p < .01), and speech condition > gesture condition (p < .01). Moreover, the impression of having a conﬁdential talk were signiﬁcantly
diﬀerent between conditions (F (2.46) = 34.95, p < .01),
and multiple comparisons revealed signiﬁcant diﬀerences:
gesture and speech condition > speech condition (p <.01), speech condition > gesture condition (p < .01),
and gesture and speech condition > gesture condition(p < .01).
These results suggest that the behavior of the gesture and speech condition is the one most likely to beThis study investigates the eﬀect of the robot’s whisperon improving people’s motivation, so we adopted a task
whose degree of eﬀort could be objectively measured.
Accordingly, we designed a simple scenario in which therobot introduced itself and requested an annoying task:
writing equations in a 9x9 multiplication table (1x1=1,
1x2=2...9x9=81).
The robot requested participants to perform thetask with/without a whispering cue and in a normal/small
voice depending on the condition. The conditions’ details are shown in Table 1. Based on the results of thepreliminary experiment, the whispering cue consistedof a gesture where the robot brought both hands to its
mouth and asked, “Could I have your ear?” However,the participants themselves decided whether to lean toward the robot’s mouth.
Figure 4 shows scenes from the experiment. The following ﬂow was used in the experiment’s scenario.
Robot: Nice to meet you! I’m Robovie.
Participant: Hello.
Robot: I’d like to tell you about this experiment. Pleasewrite equations in a 9x9 multiplication table on thepaper on that desk (pointing).
Participant: I see.
Robot: Oh, I have a request.
Robot: (Bringing both arms to its mouth) Could I havea word? *1
Robot: (Waiting for a request trigger from the operator)
Please write as many equations as possible.*2

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Eﬀect of Robot’s Whispering Behavior on People’s Motivation(a) Greeting

5(b) Explanation of the task

Fig. 5 Experimental settings(c) Could I have your ear?

(d) Whispering

havior, in which the robot said, “Please write as manyequations as possible.”
In conditions 2 and 4, the robot gave a whisperingcue. If the participant bent his/her body toward therobot within two seconds, the operator sent the trigger
for the next behavior. If the participant did not move,the operator sent the trigger two seconds after saying,“Oh, I have a request,” as in conditions 1 and 3.

4.2 Hypotheses(e) Please go ahead.

(f) Starting the task

Fig. 4 Scenes from experiment (condition 4)

Participant: OK.
Robot: (Returning both arms to their home positions)*1
Robot: Please start writing. Let me know when you areﬁnished.
*1: Only conditions 2 and 4*2: The robot uses normal volume (around 60 dB) under conditions 1 and 2. The robot uses a small volume(around 30 dB) under conditions 3 and 4.
The trigger for the robot’s behavior was sent by theoperator in the Wizard of Oz method [33], since we
did not use speech or behavior recognition. The timingof sending the trigger in each condition was set up as
follows.
In conditions 1 and 3, the robot did not give a whispering cue. Two seconds after saying “Oh, I have a request,” the operator sent the trigger of the next be-

We hypothesized that the requests made with whispering cues would improve task performance (the numberof written equations and the writing time) over thatby the requests without whispering cues because thewhispering situation encourages intimacy and creates
warmth. Moreover, we assumed that the combinationof whispering cues and small voices is more eﬀective for
improving task performance because the latter createsa situation of conﬁdentiality. Based on this viewpoint,
we made the following predictions:Hypothesis 1: The robot’s whispering cues will increase
the number of written equations and the writing time.
Hypothesis 2: The robot’s small voice will increase thenumber of written equations and the writing time.

4.3 Procedure
Participants were ﬁrst given a brief description of theexperiment and asked to sign a consent form. They sat
on chairs in front of the robot, which was placed on atable at a distance of 75 cm. We based this distance onknowledge of “personal distance,” which is the distancewhen acquaintances talk [7]. After interaction with therobot, participants answered a questionnaire.

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Kayako Nakagawa et al.

Fig. 6 Average and S.E. of number of written equations

Fig. 7 Average and S.E. of writing time

The task and the questionnaire took ﬁve and ten
minutes, respectively. The experiment was run in an
isolated space with no outside distractions. The experimenter left the experimental space before the interaction started.

authority to make participants complete the task compared with a human. Moreover, the instruction of thetask “Write as many equations as possible” indicatedthat the participants could terminate the task wheneverthey wanted, and thus many participants terminatedthe task at will before completing it.

4.4 Measurement
To evaluate the eﬀects of each factor, we measured thenumber of written equations and the writing time inseconds. The task was writing equations in a 9x9 multiplication table, so the maximum number of equations
was 81. After completing the task, subjects also answered a questionnaire that measured their enjoymentof the task, their enjoyment of the robot, and the clarity of the robot’s pronunciation. All items were rated
on a 1-7-point scale, where seven represents the mostpositive impression, four is neutral, and one is the mostnegative.

4.5 Participants
The participants were 40 university students (29 males
and 11 females) who were randomly assigned to thefour conditions; 10 participants were assigned to eachcondition.

5 Results
Few of the subjects actually wrote all possible equations. The reason might be that the request to performthe task was made by the robot, and the robot had less

5.1 Veriﬁcation of predictions: number of equations
and writing time
The results are given in Fig.6 and Fig.7. The averages of
the number of equations were 65.1 (with cue x normal
voice), 66.9 (with cue x small voice), 46.9 (without cue
x normal voice), and 46.2 (without cue x small voice);standard error (S.E.) of each condition was 8.57, 6.21,
7.11, and 7.82, respectively. The averages of the writing time were 225.10 (with cue x normal voice), 243.80(with cue x small voice), 163.00 (without cue x normal
voice), and 158.70 (without cue x small voice); S.E. ofeach condition was 28.86, 28.79, 23.49, and 20.83, respectively.
To test our hypotheses, we conducted a two-way(cue x volume) ANOVA for the number of written equations and the writing time. There was a signiﬁcant difference in cue factor (number of equations: F (1, 36) =6.76, p < .05, writing time: F (1, 36) = 8.19, p < .01).
Therefore, hypothesis 1 was supported. In other words,
the whispering cue improved the participant’s motivation to carry out the annoying task.
However, there was no signiﬁcant diﬀerence in volume factor or in the interaction eﬀects. Therefore, hypothesis 2 was not supported.

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Eﬀect of Robot’s Whispering Behavior on People’s Motivation

Fig. 8 Average and S.E. of task enjoyment

7

Fig. 10 Average and S.E. of clarity of robot’s pronunciation

6 Field Trial Using a Whispering Behavior

Fig. 9 Average and S.E. of enjoyment of robot

5.2 Analysis of questionnaire results
We analyzed the diﬀerences in the participants’ ratings:
how enjoyable was the task (Fig. 8), how enjoyable was
the interaction with the robot (Fig.9), and the clarity of
the robot’s pronunciation (Fig. 10). The result of a twoway (cue x volume) ANOVA for task enjoyment showedthat there was no diﬀerence in cue factor (F (1, 36) =.01, p = .93) and only a marginal diﬀerence in volume
factor (F (1, 36) = 4.01, p < .1). There was no diﬀerencein the enjoyment factors of the robot (cue: F (1, 36) =.9, p = .46, volume: F (1, 36) = .04, p = .21) or the
clarity of the robot’s pronunciation (cue: F (1, 36) = .85,
p = .36, volume: F (1, 36) = .03, p = .85).

The laboratory experiment revealed that a robot’s whispering behavior increased the motivation of the people
with whom it interacted. We also investigated this contribution to social robots that work in real environments by developing a simple application in combination with a social robot to demonstrate one possible useof whispering behavior.
We believe that one realistic application is recommending items to customers in an advertising context.
We chose such an application for two reasons: the potential for future applications and the possibility of using the whispering behavior. In the future, using robots
as shop assistants may be possible; in fact, past studies have conducted ﬁeld trials in which a social robot
worked in a shopping mall to help people, and they revealed the eﬀectiveness of the robot [34][35]. In suchenvironments, robots would also engage in advertising
to increase the desire to buy items, to raise interest inthem, and to increase motivation to look at them. We
were concerned that the volume of whispering might
be too small to convey the robot’s advertising messages
due to the environmental noises in the shopping mall;
however, our laboratory experiment showed that the
robot’s whispering behavior with a normal voice was
suﬃciently eﬀective for improving motivation. Therefore, it was reasonable to expect the whispering behavior of the robot to contribute to the eﬀect of advertisement in a shopping mall.
This ﬁeld trial investigated how the whispering behavior contributes to shop recommendations in a real
shopping environment. We prepared in cooperation witha shopping mall a simple coupon printing system, where
the robot oﬀers a coupon to customers to recommend

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Kayako Nakagawa et al.

command to the printer to print it. As in the laboratory
experiment, a human operator performs in place of the
speech recognition function.
We obtained permission to record video and sensor
data from the mall authorities. The experimental protocol was reviewed and approved by our institutional
review board.

6.2 Conditions
We conducted a between-participants factorial design
with one factor: a whispering behavior that combined
the whispering cue and a voice at a normal volume.
We did not investigate the eﬀectiveness of the factor ofvolume in the trial, since the experiment in Section 4
did not show any signiﬁcant results for volume factor.
Consequently, the conditions were limited to two:
1. With whispering behavior condition: the robot uses
the whispering behavior when it recommends theshop.
2. Without whispering behavior condition: the robot
does not use the whispering behavior when it recommends the shop.

Fig. 11 Robot interacting with customers

a particular shop. Mall customers can use the printed
coupon for a discounted lunch in the mall.

In both recommendation conditions, the robot uses
the same script to recommend the shop: “I recommend(shop name), which is located right behind you.” The
only diﬀerence between the conditions is whether the
robot uses the whispering behavior.
The experiment was conducted over two weekdays.
In each condition, the experiment lasted the three hours(11 am to 2 pm) when many customers would take theirlunch.

6.1 System Settings and Methodology for
Recommendations
6.3 Measurement
The ﬁeld trial was conducted inside a mall located neara train station. The robot was placed in a main corridor.
The mall’s visitors are mainly families, couples, andfriends, all of whom could freely interact with the robot.
Figure 11 shows the environment where we installed
the robot. We adjusted the height of the robot stand to
120 cm to allow it to interact with various visitors, suchas children. We placed an A7-sized printer (Brother,
MW140BT type-F) next to the robot with which it
could print coupons.
We used the same robot with the same control mechanism as in Section 3 for the advertising robot. In this
scenario, the robot ﬁrst greets customers and asks them
if they want to hear a shop recommendation. If theywant to listen to the recommendation, the robot starts
it. If they want to print the coupon, the robot sends a

To evaluate the eﬀectiveness of the whispering behavior for advertising, we measured the number of people
who printed coupons. Even if the same person printed
coupons more than once, we only counted it as once.

6.4 Hypothesis
We hypothesized that recommendations with the whispering behavior would increase the number of printed
coupons more than simple recommendations. Based on
this consideration, we made the following prediction:
Hypothesis 3: A robot using the with-whispering behavior will increase the number of printed coupons beyond
that achieved by a robot without such behavior.

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Eﬀect of Robot’s Whispering Behavior on People’s Motivation9

all customers who interacted with the robot bent toward the robot during the robot recommendations.
Table 2 shows the results for the number of printed
coupons in the trials. We veriﬁed the diﬀerences in thenumber of people under both conditions with a Chisquare test. The results reveal statistically signiﬁcantdiﬀerences (χ2 (1) = 4.332, p < .05, φ = 0.362) and indicate that the rate of people who printed coupons in the
with-whispering behavior condition exceeded that in
the without-whispering behavior condition. Thus, theresults support our prediction.

7 Discussion(a) The robot giving a whispering cue toa woman(b) The woman leaning toward therobot
Fig. 12 Overhead views of a whispering interaction

7.1 Contribution and implementation of whispering
behavior
The number of equations and the writing times weresigniﬁcantly high in the whispering cue condition, eventhough the participants found the task boring in eachcondition (the values of the items are around the middle). Furthermore, there were no signiﬁcant diﬀerences
for the task enjoyment between the conditions; the whispering cue did not decrease the enjoyment of the tasks,
even though the participants spent a long time on them.
These results show that whispering cues by robots eﬀectively encouraged motivation. The approach of encouraging motivation can be applied to various importantbut monotonous and boring tasks: homework, rehabilitation, and so on.
For the whispering cue in our experiment, the robot
requested participants to bend their ears closer to therobot. For many communication robots, actively moving their mouths near people’s ears is diﬃcult for technical and safety reasons. However, since the whisperingcue in our experiment is easy to implement, the behavior can be applied to various robots.

Table 2 Field trial results

Number of people
who printed coupons
Number of people
who did not print couponsWithwhispering
behavior

Without
whispering
behavior

10/13(77%)

8/20(40%)

3/13(23%)

12/20(60%)

6.5 Results
In the trials, 13 people used the system in the withwhispering behavior condition, and 20 used it in the
without-whispering behavior condition. Fig. 12 showsa customer bending toward the robot during its recommendation in the with-whispering behavior condition;

7.2 Why did the robot’s whispering behavior improvetask performance?
7.2.1 Eﬀect of distanceIn the experiment, we often observed scenes where participants leaned forward to listen to the robot’s request.
Such bending behavior, which creates nearness between
the robot and participants, has been reported to be effective for behavior change [24].
We classiﬁed the distance between participants androbots into two categories when the robot is whispering: close (0-45 cm) and personal (45-120 cm) [7]. Table 3 shows the classiﬁcation results. A two-way (cueThe final publication is available at Springer via
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Kayako Nakagawa et al.

Table 3 Distance between robot and participantWithout
whispering cue
NormalSmallvolume volume
Personal
distance
Close
distanceWithwhispering cue
NormalSmallvolume volume

9

8

1

0

1

2

9

10

x volume) ANOVA using the arcsine transformation
method indicated signiﬁcant diﬀerences for the cue factor (χ2 (1) = 24.5, p < .01). The volume factor andthe interaction eﬀect were not signiﬁcant. Therefore,
whispering cues created proximity between the robots
and the participants, and they made the participantscome closer to the robots. There might be other factors
causing proximity. For example, the participants could
not hear the robot’s speech well. However, the resultsof Experiment 1 showed that there was no statisticallysigniﬁcant diﬀerence in the clarity of the robot’s pronunciation. That is, at least there was no bias due to theclarity of the robot’s pronunciation between conditions.
We also analyzed the number of written equations
and the writing time using a one-way (classiﬁed distance: close and personal) ANOVA. The results showeda signiﬁcant diﬀerence in the distance factor (numberof written equations: F (1, 38) = 4.86, p. < 05, writingtime: F (1, 38) = 6.60, p. < 05), indicating that proximity is eﬀective for behavior change. They also indicatedthat other behaviors that trigger close distances, e.g.
hugging and hand beckoning, might improve the eﬀectof behavior change.
7.2.2 “Foot-in-the-door” eﬀect
One reason why whispering cues aﬀect motivation might
be that the behavior elicited a “foot-in-the-door” effect [36]. The foot-in-the-door eﬀect is a famous persuasion/selling technique that achieves agreement toa large request by ﬁrst getting agreement to an easy,smaller one. When the robot makes an easy request,“Could I have a word?”, the subjects would be more
receptive to the subsequent request, i.e. writing equations. Moreover, we believe that the whispering behavior elicits not only a “foot-in-the-door” eﬀect that affects cognitive processes but also a psychological eﬀect,
for example, a closer relationship and warm feeling.
Thus, the “foot-in-the-door” eﬀect might be one of thefactors that improve the participants’ motivation.
We compared the task performances among the participants in the without whispering cue condition: those
who were not asked to lend an ear. There were two participants who were classiﬁed at a close distance in the

without whispering cue and small voice condition; these
two wrote 72 and 76 equations and spent 262 and 254
seconds writing equations, respectively. These scores
were higher than the averages of the other participantsin the small volume condition who were classiﬁed ata personal distance (number of equations: 39.2, writingtime: 133.88). The results of a one-way ANOVA showeda diﬀerence for the number of equations (F (1, 8) =4.34, p < .1) and a signiﬁcant diﬀerence for writing time(F (1, 8) = 13.71, p < .01). Even though the number ofsamples is quite small, these results are consistent withthe analysis of distance and suggest that the close distance caused by a whispering context greatly inﬂuenced
people’s behavior.
7.2.3 Other factors
Although we had predicted that a small-volume voice
would enforce the context of self-disclosure and sharing secrets in Experiment 1, the results did not show
this eﬀect. The reason might be that the participant
was alone with the robot in the experimental space andthus there was no necessity to speak in a small voice inorder to create the situation of sharing secrets. Consequently, a small-volume voice might be eﬀective in thecase where there are other people in the same space.
We were also concerned that proximity might havecaused discomfort, but the results indicated that the
robot’s whispering did not cause negative eﬀects on therelationship between the human and the robot. The reason might be that the participants themselves could
decide whether they leaned toward the robot at a close
distance when the robot tried to whisper to them. The
design of interaction might have decreased the discomfort caused by unexpected proximity. On the other hand,
we did not investigate the eﬀect of close distance in thecase where the robot actively changed the distance tothe people. Such investigations would clarify eﬀective
situations of robot’s whispering behavior on improving
motivation.

7.3 Applications of whispering behavior
We conducted a ﬁeld trial where a robot gave recommendations using whispering behavior, and the results
showed the eﬀectiveness of whispering behavior on people’s behavior changes in a real environment. We believethat the whispering behavior is also eﬀective for services
in education and healthcare, where people have to perform important but monotonous tasks. As an example
of an application for whispering behavior, we have already developed a counseling robot for lifestyle diseases

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http://link.springer.com/article/10.1007%2Fs12369-012-0141-3

Eﬀect of Robot’s Whispering Behavior on People’s Motivation11

in the healthcare ﬁeld that provides health information
and advice [37].

8 Conclusion

As mentioned in 7.1, the whispering behavior wedesigned can be implemented for various robots. Thisbehavior is natural and reasonable because a child whowants to whisper to an adult probably asks her to lend
her ear and makes her lean over, like the robot’s behavior in our experiment. Accordingly, the whispering
behavior we used is useful and practical for various services.

We focused on the eﬀect of the physical interaction ofrobots on behavior change. Physical interaction is one
advantage real robots have over computers. Accordingly, we considered a whispering situation that is observed between people who are familiar with each other.
To investigate the eﬀectiveness on behavior change in
people, we conducted laboratory experiments and a ﬁeld
trial where a robot gave recommendations in a shoppingmall.
The results of the laboratory experiment show thatwhispering cues signiﬁcantly improve participants’ motivations to perform an annoying task. This indicatesthat the whispering cue eﬀectively improved participant motivation. But using a small voice had no eﬀect
on task performance. We also evaluated the eﬀect of
the whispering behavior on advertisement in a shopping mall. The results show that the whispering behavior signiﬁcantly increases the rate of people who ask forcoupons of the shop recommended by the robot and iseﬀective even in such a loud environment. Our experimental results indicate that a robot’s whispering behavior can improve people’s motivations and be a key
behavior for successful robot recommendation services.
Experiments using other indications, such as the quality of tasks or changing decisions, might provide further
insights about the range of the eﬀect of whispering behavior.
This paper presented the eﬀect of a robot’s whispering behavior on people’s motivation. On the other
hand, a robot’s eﬀective behaviors for improving people’s motivations are not limited to whispering behaviors. For example, we previously reported the eﬀects
of a robot’s motion with emotional presentation andits active touching on behavior change [42][43]. We willcontinue to investigate the physical behaviors made by
robots that inﬂuence people’s behavior changes.

7.4 Limitations
Since our comparisons are based on a case study withan existing robot, Robovie-mR2, the robot generality islimited. The appearance of the robot we used might give
positive impressions, and a robot whose appearance
makes a negative impression might decrease the eﬀect
on behavior changes. However, robots that provide services and communicate with people are designed withfriendly and familiar appearances [38][39][40]. Therefore, we believe that the ﬁndings of our study oﬀer
important knowledge for researchers interested in behavior change using interactive robots and that they
are useful for designing robot behaviors for persuasive
situations.
The eﬀect of whispering behavior is also related withthe contexts of self-disclosure and sharing secrets. Previous works reported that a robot’s self-disclosure improved familiarity [34], and sharing secrets with a robot
increased the interaction duration [41]. The robots usedin those studies were quite diﬀerent in appearance andsize from the robot used in our experiment, so the ﬁndings of those studies support the assumption that whispering behavior with other types of robots would be
eﬀective for behavior change.
The experiments were conducted only in Japan, so
there might be cultural diﬀerences in the impressions ofa robot’s whispering behavior. On the other hand, thereasons why we hypothesized the eﬀect of whispering
behavior were based on western works of psychology on
using proximity and self-disclosure for creating friendliness and familiarity. Therefore, we assume the resultsof our work provide useful knowledge for designing arobot’s behavior across diﬀerent cultures. Of course,
the appropriate scripts and acceptable situations andplaces for a robot’s whispering behavior would be different across cultures, so minor adjustments might beneeded in actual use.

Acknowledgements We thank the staﬀ of the Asia andPaciﬁc Trade Center Co., Ltd. for their kind cooperation.
We also thank Yoshifumi Nakagawa, Seiji Tachibana, Hiroshi
Atsuta, Guan Wenjiao, and Tong Mingyu of the Kiban-PP
project at Osaka University for their help. This research was
supported by the Ministry of Internal Aﬀairs and Communications of Japan.

References
1. B. Reeves and C. Nass. The Media Equation: How People
Treat Computers, Television, and New Media Like Real
People and Places. University of Chicago Press, 1996.
2. B. J. Fogg. Persuasive Technology: Using Computers toChange What We Think and Do. Morgan Kaufmann,
2002.

The final publication is available at Springer via
http://link.springer.com/article/10.1007%2Fs12369-012-0141-3

12
3. K. Shinozawa, F. Naya, J. Yamato, and K. Kogure. Differences in eﬀect of robot and screen agent recommendations on human decision-making. International Journalof Human-Computer Studies, Vol. 62, No. 2, pp. 267–279,
2005.
4. H. M. Gross, H. J. Boehme, Ch. Schroeter, St. Mueller,
A. Koenig, Ch. Martin, M. Merten, and A. Bley. ShopBot: Progress in developing an interactive mobile shopping assistant for everyday use. In IEEE International
Conference on Systems, Man and Cybernetics, pp. 3471–
3478, 2008.
5. M. Shiomi, T. Kanda, D. F. Glas, S. Satake, H. Ishiguro,
and N. Hagita. Field trial of networked social robots in a
shopping mall. In Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems,
2009.
6. H. C. Kelman. Processes of opinion change. The Public
Opinion Quarterly, Vol. 25, No. 1, pp. 57–78, 1961.
7. E. T. Hall. The hidden dimension. Anchor Books, 1990.
8. M. Argyle and J. Dean. Eye-contact, distance and aﬃliation. Sociometry, Vol. 28, No. 3, pp. 289–304, 1965.
9. R. F. Haase and D. T. Tepper. Nonverbal componentsof empathic communication. Journal of Counseling Psychology, Vol. 19, No. 5, pp. 417 – 424, 1972.
10. B. J. Fogg. Behavior Wizard: A Method for Matching
Target Behaviors with Solutions. PERSUASIVE, pp.
117–131, 2010.
11. W. J. McGuire. Handbook of Social Psychology, chapter
Attitudes and Attitude Change, pp. 233–346. Random
House, New York, 1985.
12. M. Shiomi, K. Nakagawa, R. Matsumura, K. Shinozawa,
H. Ishiguro, and N. Hagita. “could I have a word?”: Eﬀects of robot’s whisper. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems,
2010.
13. R. E. Petty and J. T. Cacioppo. Communication and
Persuasion: Central and Peripheral Routes to Attitude
Change. Springer, September 1986.
14. L. Bickman. The eﬀect of social status on the honesty ofothers. Journal of Social Psychology, Vol. 85, pp. 87–92,
1971.
15. L. Bickman. The social power of a uniform. Journal ofApplied Social Psychology, Vol. 4, pp. 47–61, 1974.
16. S. R. Pallak. Salience of a communicator’s physical attractiveness and persuasion: a heuristic versus systematic
processing interpretation. Social Cognition, Vol. 2, pp.
158–170, 1983.
17. S. R. Pallak, E. Murroni, and J. Koch. Communicatoreﬀectiveness and expertise, emotional versus rational appeals, and persuasion: A heuristic versus systematic processing interpretation. Social Cognition, Vol. 2, pp. 122–
141, 1983.
18. T. Kanda, T. Miyashita, T. Osada, Y. Haikawa,and H. Ishiguro. Analysis of humanoid appearancesin human-robot interaction. IEEE Transactions on
Robotics, Vol. 24, No. 3, pp. 725–735, 2008.
19. M. Siegel, C. Breazeal, and M. I. Norton. Persuasive
robotics: The inﬂuence of robot gender on human behavior. In Proceedings of the IEEE/RSJ International
Conference on Intelligent Robots and Systems, pp. 2563–
2568, 2009.
20. C. D. Kidd and C. Breazeal. Eﬀect of a robot on user
perceptions. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.
3559–3564, 2004.

Kayako Nakagawa et al.
21. A. Powers, S. Kiesler, S. Fussell, and C. Torrey. Comparing a computer agent with a humanoid robot. In Proceedings of the ACM/IEEE International Conference on
Human-Robot Interaction, pp. 145 – 152, 2007.
22. W. A. Bainbridge, J. Hart, E. S. Kim, and B. Scassellati. The eﬀect of presence on human-robot interaction.
In IEEE International Symposium on Robot and Human
Interactive Communication, pp. 701–706, 2008.
23. A. Mehrabian. Inference of attitude from the posture,orientation, and distance of a communication. Journalof Counseling and Clinical Psychology, Vol. 32, pp. 296–
308, 1968.
24. D. Byrne, C. R. Ervin, and J. Lamberth. Continuity
between the experimental study of attraction and reallife computer dating. Journal of Personality and Social
Psychology, Vol. 16, pp. 157–165, 1970.
25. N. Gueguen. Touch, awareness of touch, and compliance with a request. Perceptual and motor skills, Vol. 95,
No. 2, pp. 355–360, 2002.
26. J. D. Fisher, M. Rytting, and R. Heslin. Hands touchinghands: Aﬀective and evaluative eﬀects of an interpersonal
touch. Sociometry, Vol. 39, pp. 416–421, 1976.
27. A. W. Siegman. Nonverbal Behaviors and Communication, chapter The Telltale Voice: Nonverbal Messages of
Verbal Communication, pp. 351–434. Lawrence Erlbaum,
1987.
28. B. Pope and A. W. Siegman. Ambiguity and verbal
ﬂuency in the TAT. Journal of Consulting Psychology,
Vol. 30, pp. 239–245, 1966.
29. T. Kanda, R. Sato, N. Saiwaki, and H. Ishiguro. Friendly
social robot that understands human’s friendly relationships. In Proceedings of the IEEE/RSJ International
Conference on Intelligent Robots and Systems, pp. 2215–
2222, 2004.
30. N. L. Collins and L. C. Miller. Self-disclosure and liking:A meta-analytic review. Psychological Bulletin, Vol. 116,
No. 3, pp. 457 – 475, 1994.
31. H. T. Reis and P. Shaver. Handbook of Personal Relationships, chapter on Intimacy as an Interpersonal Process,pp. 367–389. Wiley & Sons, Chichester, England, 1988.
32. H. Kawai, T. Toda, J. Ni, M. Tsuzaki, and K. Tokuda.
XIMERA: A new TTS from ATR based on corpus-based
technologies. In Proceedings of Fifth ISCA Workshop on
Speech Synthesis, pp. 179–184, 2004.
33. D. Dahlback, A. Jonsson, and L. Ahrenberg. Wizard of oz
studies-why and how. Knowledge based systems, Vol. 6,
No. 4, pp. 258–266, 1993.
34. T. Kanda, M. Shiomi, Z. Miyashita, H. Ishiguro, and
N. Hagita. A communication robot in a shopping mall.
Transactions on Robotics, Vol. 26, pp. 897–913, October2010.
35. H.-M. Gross, H. Boehme, Ch. Schroeter, S. Mueller,
A. Koenig, E. Einhorn, Ch. Martin, M. Merten, and
A. Bley. Toomas: interactive shopping guide robots in everyday use - ﬁnal implementation and experiences fromlong-term ﬁeld trials. In IROS’09: Proceedings of the2009 IEEE/RSJ international conference on Intelligent
robots and systems, pp. 2005–2012. IEEE Press, 2009.
36. J. Freedman and S. Fraser. Compliance without pressure:
The foot-in-the-door technique. Journal of Personality
and Social Psychology, Vol. 4, pp. 195–202, 1966.
37. O. Sugiyama, K. Shinozawa, T. Akimoto, and N. Hagita.
Inheritance of personal factors among multi-robot healthcare system. In Proceedings of Workshop on Ubiquitous
Networking Robotics: an Approach for Human-Robot Interaction In The IEEE/RSJ International Conference onIntelligent Robots and Systems, 2010.

The final publication is available at Springer via
http://link.springer.com/article/10.1007%2Fs12369-012-0141-3

Eﬀect of Robot’s Whispering Behavior on People’s Motivation
38. T. Kanda, H. Ishiguro, M. Imai, and T. Ono. Development and evaluation of interactive humanoid robots.
Proceedings of the IEEE (Special Issue on Human Interactive Robot for Psychological Enrichment), Vol. 92,
No. 11, pp. 1839–1850, 2004.
39. Y. Hosoda, S. Egawa, J. Tamamoto, K. Yamamoto,
R. Nakamura, and M. Togami. Basic design of humansymbiotic robot emiew. In Proceedings of the IEEE/RSJ
International Conference on Intelligent Robots and Systems, pp. 5079–5084, 2006.
40. S. Kato, S. Ohshiro, H. Itoh, and K. Kimura. Development of a communication robot ifbot. In IEEE International Conference on Robotics and Automation, pp.
697–702, 2004.
41. T. Kanda, R. Sato, N. Saiwaki, and H. Ishiguro. A
two-month ﬁeld trial in an elementary school for longterm human-robot interaction. IEEE Transactions on
Robotics (Special Issue on Human-Robot Interaction),Vol. 23, No. 5, pp. 962–971, 2007.
42. K. Nakagawa, K. Shinozawa, R. Matsumura, H. Ishiguro,
and N. Hagita. Emotional motion modiﬁcation method
using subtle expressions for humanoid robots. The Transaction of Human Interface Society, Vol. 12, No. 3, 2010.
(in japanese).
43. K. Nakagawa, M. Shiomi, K. Shinozawa, R. Matsumura,
H. Ishiguro, and N. Hagita. Eﬀect of robot’s active touchon people’s motivation. In 6th ACM/IEEE International
Conference on Human-Robot Interaction(HRI2011), pp.
465–472, 2011.

13

munication Science Laboratories. Since then, he has
been conducting research for building a robot that cancommunicate with humans naturally and smoothly. He
moved to ATR in May 2003.
Reo Matsumura received his M.E. degree from Osaka University, Osaka, Japan, in 2008. He is a Ph.D.
student at Osaka University and an Intern Researcher
at the Network Robot Laboratory, Intelligent Robotics
and Communication Laboratories, Advanced Telecommunications Research Institute International (ATR),
Kyoto, Japan. His research interests include humanoid
robotics, environment recognition, and motion planing.

Hiroshi Ishiguro received his Ph.D. degree in engineering from Osaka University, Japan in 1991. He was
Research Associate (1992-1994) in the Graduate Schoolof Engineering Science at Osaka University and Associate Professor (1998-2000) in the Department of Social Informatics at Kyoto University. He was also Visiting Scholar (1998-1999) at the University of California, San Diego, USA. He was Associate Professor (20002001) and Professor (2001-2002) in the Department ofComputer and Communication Sciences at Wakayama
Kayako Nakagawa received her M.E. degree from OsUniversity. He then moved to Department of Adapaka University, Osaka, Japan, in 2009. She is a Ph.D.
tive Machine Systems in the Graduate School of Enstudent at Osaka University and an Intern Researcher
gineering Science at Osaka University as a Professor
at the Interaction System Laboratory, Intelligent Robotics(2002-2009). He is currently Professor of Department
and Communication Laboratories, Advanced Telecomof Systems Innovation in the Graduate School of Engimunications Research Institute International (ATR),
neering Science at Osaka University (2009-) and Group
Kyoto, Japan. Her research interests include humanLeader (2011-) of Hiroshi Ishiguro Laboratory at the
robot interaction and human interface.
Advanced Telecommunications Research Institute International (ATR). His research interests include disMasahiro Shiomi received the M.E. and Ph.D. detributed sensor systems, interactive robotics, and angrees in engineering from Osaka University, Osaka, Japan,
droid science.
in 2004 and 2007, respectively. From 2004 to 2007, he
was an Intern Researcher at the Intelligent Robotics
Norihiro Hagita received the B.E., M.E., and Ph.D.
and Communication Laboratories (IRC) and is currently
degrees in electrical engineering from Keio University ina Researcher at IRC at the Advanced Telecommunica1976, 1978, and 1986. In 1978, he joined Nippon Teletions Research Institute International (ATR) in Kyoto,graph and Telephone Public Corporation (Now NTT).
Japan. His research interests include human-robot inHe was engaged in developing handwritten characterteraction, interactive humanoid robots, networked robots,recognition. He was a visiting researcher in the Departand ﬁeld trials.
ment of Psychology, University of California, Berkeley
in 1989-90. He is currently Board Director of ATR and
Kazuhiko Shinozawa is a Group Leader, Interaction
ATR Fellow, director of Social Media Research LabSystem Laboratory, Intelligent Robotics and Commuoratory Group and Intelligent Robotics and Communication Laboratories, Advanced Telecommunications
nication laboratories. His major interest is networked
Research Institute International (ATR). He received
robotics, human-robot interaction, pattern recognition,
the B.E. and M.E. in Electrical Engineering from Keioand data-mining technology. He is a member of IEEE,
University in 1988 and 1990, respectively. In 1990, he
the Information Processing Sociery of Japan, and thejoined the NTT Human Interface Laboratories, where
Japanese Sociery for Artiﬁcial Intelligence, The Instihe was engaged in solving optimization problems andtute of Electronics, Information and Communication
predicting weather radar images by using artiﬁcial neuEngineers(IEICE) and The Robotics Society of Japan.
ral networks. In 1998, he moved to the NTT Com-