the influence of politeness behavior on user compliance...

Int J of Soc Robotics (2017) 9:727–743DOI 10.1007/s12369-017-0420-0

The Influence of Politeness Behavior on User Compliancewith Social Robots in a Healthcare Service Setting

Namyeon Lee1 · Jeonghun Kim2 · Eunji Kim2 · Ohbyung Kwon2

Accepted: 11 July 2017 / Published online: 9 August 2017© Springer Science+Business Media B.V. 2017

Abstract Particularly in the healthcare service domain,social robots are expected to be good assistants, advisers,or practitioners. To increase the effectiveness of healthcareservices provided by social robots, patients must complywith their requests. Research is plentiful on what makespatients comply with healthcare advice. In this paper, whichis based on Bulgurcu’s study of rationality-based beliefs,command-compliance theory, and social exchange theory,we propose a research model of compliance during interac-tion with social robots, examining beliefs about and overallassessments of the consequences of complying with robotrequests and extending the findings of previous studies to thesetting of healthcare services. We specifically investigate theperceived level of politeness in robots’ speech and gestures asa determinant of compliance intention. Using a social robot,NAO, as a provider of healthcare services, we conducted anexperiment. The results suggest that the aforementioned the-ories are useful in understanding user behaviors toward socialrobots in a healthcare service setting. Interestingly, andunlikein other settings, the perceived level of politeness of a socialrobot in a healthcare service setting negatively affects the per-ceivedbenefit of compliance, and, hence, intention to comply.

B Ohbyung [email protected]

Namyeon [email protected]

Jeonghun [email protected]

Eunji [email protected]

1 Sungkyul University, Anyang, Kyunggi-do,Republic of Korea

2 Kyung Hee University, Hoegi-dong, Dondaemun-gu, Seoul,Republic of Korea

A lower politeness level is closer to a command or strong rec-ommendation than a suggestion or causal recommendation,which is common in shopping, tourism, or convention set-tings. The findings of this study imply that polite behaviorfrom a social robot is an important factor in the compliance ofhealthcare service users. Direct speech with polite gesturesis the most effective way to increase patient compliance inwith healthcare advice provided by social robots in health-care settings. However, higher levels of politeness do notalways increase patients’ intention to comply.

Keywords Human–robot interaction · Social robot ·Compliance · Social exchange theory · Politeness theory

1 Introduction

Compliance in the healthcare context has been of interest inacademic research for the past two decades. In healthcare sit-uations, patients’ or their helpers’ compliance, which refersto the degree or extent of their conformity to the recommen-dationsmade by healthcare practitioners [20] or “the extent towhich a person’s behavior coincides with medical or healthadvice” [31], is crucial to effective treatment and healingof patients. Compliance in the medical field is an importantfactor in achieving healthcare goals. Increased compliancewith healthcare recommendations helps to achieve the goalof improving the health status of users and, consequently,their satisfaction with healthcare services [22,39,43].

In conventional healthcare settings, assistance and recom-mendations have long been provided by individual healthcarepractitioners or teams of practitioners. Many of the practicesinvolved in providing this assistance or making these rec-ommendations are highly repetitive and menial. For manyservice providers, the result is fatigue, annoyance, and dis-

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728 Int J of Soc Robotics (2017) 9:727–743

satisfactionwithwork, which decreases the quality of service[43]. Moreover, suggestions by healthcare practitioners mayinclude both recommendations and commands. For example,in order to relieve stress, a practitioner could recommend aspecific exercise or prescribe medicine at a certain dosageand regimen.

To improve this situation, robots may offer an alterna-tive. Many robots are able to take over routine, menial tasksinvolved in patient care. Social robots, for example, arephysical entities utilized in complex, dynamic, social envi-ronments that are sufficiently empowered to behave in amanner conducive to their own goals and those of their com-munity, including other robots or people. Many social robotshave interpersonal skills [35,50] that allow them to play orcommunicate with people. Examples include Paro, which isused in psychological therapy for the elderly [37], and KAS-PAR, which is used with autistic children [63]. Comparingrobots to humans in service settings, we see some benefitsfrom using robots. First of all, robots are simpler and morepredictable than humans, especially to elderly patients andchildren [7]. For these populations, following instructionsfrom a robot may be easier than following instructions froma human [14]. Second, robots make embodied interactionspossible in situations where menial tasks might otherwisebe performed by impersonal machines [18,23]. Because oftheir advantages, robots may be useful in providing hospitalpatients or their helpers with guidance such as enrollmentadvice and helping them to handle paperwork such as medi-cal prescriptions.

However, the question remains as to whether patients inhospitals will comply better with advice or recommendationscoming from a service robot than from a human source. Com-pliance issues connected with robots have been studied in thecontext of educating toddlers and older children, who benefitfrom a simple treatment approach [56,60]. Adult compliancewith instructions given by robots may not be different fromrobot–child interaction. Therefore, a very careful and politeapproach may be needed. In this study, we investigate theeffects of robots’ polite expression on patient compliancewith commands and recommendations given by a robot in ahealthcare service setting.

We specifically focus on the social robot’s interpersonalskill of showing respect and honor using polite speech andgestures, which is especially important in oriental cultures,as a strategy to encourage compliance with robot instructionsin a healthcare setting. Robots may exhibit polite behaviorusing verbal and gestural expressions. Politeness levels maydiffer according to the characteristics of the user and the sit-uation [44]. In a healthcare service setting, user complianceis more important than it would be in the context of a shop-ping mall or restaurant. While politeness may be higher orlower in other settings, we speculate that showing respect andhonor is crucial for patient compliance with recommenda-

tions or requests made by social robots in healthcare servicesettings. In this study, therefore, we examine how politenessexpressed by a social robot affects user compliance with therobot’s requests in a healthcare setting. To do this, we devel-oped a researchmodel combining findings from conventionalsocial robot research [24,67], social exchange theory [15,21],politeness theory [10,45] and Bulgurcu’s compliance model[12].

2 Related Work

Enhancing compliance with prescribed medical regimens isa serious challenge to developers of many systems such ashealthcare applications in smart phones and remote healthmonitoring systems [2]. Compliance in healthcare settings,defined as “the extent to which a person’s behavior (in termsof taking medications, following diets, or executing lifestylechanges) coincides with medical or health advice” [28], isvery crucial for successful healthcare. Noncompliant behav-ior can be found in all types of treatment and preventionsituations [13] whether themedical service is related to phys-ical or psychological wellness.

To enhance patient compliance, message givers mustchoose an effective communication channel [74]. The infor-mation given to patients should be reasonable, explicit, andcomprehensive; written information or oral instructionsmustbe given as necessary [16,27]. Information should also bepresented in terms that patients can understand [75].

Human-like social robots offer a promising technology tocommunicate with patients and to enhance patient compli-ance in that they are able to engage inmultimodal interactionsby using gestures, speech, and facial expression. Many stud-ies have considered compliance in human–robot interaction(HRI) [46,81]. Compliance studies in HRI tend to examinedirect requests from the robot to the human [4,53].

One of the important goals of human-like social robots,especially socially assistive robots, is to enhance user com-pliance with the robot’s requests. Hence, the extant literatureexplores ways to enhance user compliance with human-likerobots [24]. For example, a robot’s gaze behavior as a nonver-bal cue in social interactions enables attention and referentialcommunication [3,22,32]. Facial expression has also beenfound to increase the interaction capabilities of humanoidrobots significantly [58].

Robot appearance also affects user compliance and accep-tance [82]. Showing politeness is another way to enhancecompliance. However, regarding the role of politeness strate-gies in human–robot interaction, conflicting findings arepresented in the literature [66]. Some studies have exam-ined the positive relationship between politeness and humanwillingness to use robots [65]; however, they did not addressthe psychological constructs that interconnect polite behav-

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Fig. 1 Research model

ior and acceptance. Therefore, the relationship betweenpoliteness of social robots and human compliance requireselucidation. The effect of the perceived level of politenesson attitudes toward and compliance with the requests ofsocial robots, especially attitudes toward the consequencesof compliance or noncompliance, has not been empiricallyinvestigated. Consequently, what makes the patient complyor refuse to comply with the recommendations or commandsof human-like social robots is still a great research challenge.

3 Model and Hypotheses

3.1 Model of Compliance

Our research model, as shown in Fig. 1, explains the rela-tionship between the level of politeness of social robotsand patients’ compliance with robots’ recommendations orcommands in a hospital setting. While extant literature onhuman–robot interaction has found that features of speechand gesture to express politeness have a direct relationshipwith compliance with the requests of service robots, thismodel focuses on the process of developing compliance.Four constructs are paramount in thismodel: stimuli (human–robot interaction), cognition (perceived level of politeness),attitude (overall assessment of consequences of complyingwith the social robot’s requests), and outcomes (compliance).In this framework, using a social robot is viewed as a pro-cess of gradually increasing compliance. In our model, therelationship between cognition (perceived level of polite-ness) and attitude (perceived cost or benefit) was derivedfrom social exchange and politeness theory, the relationshipbetween attitude and outcomes (intention to comply) wasbased onBulgurcu’s compliancemodel, and the link betweenactual robot behavior and cognition came from conventionalsocial robot research. In this study, based on the theory ofplanned behavior, intention to comply refers to a robot user’sintention to follow the guidance of a robot [1].

3.2 Attitude Toward Consequences of Complying withSocial Robots

Bulgurcu’s model [12] describes motivations for compliancewith recommendations or commands that require changes toor constraints on the behavior of information system users.In the related study, the perceived benefits of complianceand the costs of noncompliance were identified as motivatorsfor user compliance. According to this model, the perceivedbenefits of compliance refer to the overall expected favor-able consequences to a user of complying with guidance, inthis case, the guidance of a social robot, while the perceivedcost of noncompliance is the overall expected unfavorableconsequences of noncompliance. In other words, the cost ofnoncompliance is the perceived physical, psychological, oreconomic harm that might come to a person if he or she doesnot comply with the robot’s requests. The perceived benefitsof compliance include intrinsic benefits, safety, and rewards,while the perceived cost of noncompliancemay include sanc-tions, intrinsic costs, and vulnerability. Consistent with thefindings of Bulgurcu et al. [12], we expect to see a posi-tive relation between the perceived benefits of complianceand the intention to comply, and between the perceived costof noncompliance and the intention to comply. Hence, wehypothesize as follows:

Hypothesis 1 The perceived cost of noncompliance is posi-tively associated with intention to comply with the requestsof social robots.

Hypothesis 2 The perceived benefit of compliance is posi-tively associated with intention to comply with the requestsof social robots.

3.3 Perceived Level of Politeness

Politeness refers to “the degree to which the service provideris perceived as being considerate, tactful, deferent, or courte-

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ous” [19]. Politeness is viewed as “a rational, rule-governed,pragmatic aspect of speech that is rooted in the human need tomaintain relationships and avoid conflicts” [10]. Estimatingthe level of politeness is cultural and subjective. The sameverbal or gestural cue may be recognized differently accord-ing to the characteristics (e.g., age, gender, personality, etc.)of the speaker, listener, and context while the conversationis ongoing. Factors affecting politeness have been consid-ered in the academic communities of linguistics and literature[10,11,44,69].

User compliance with the advice or recommendation ofa social robot is a key success factor for the use of socialrobots in situations such as those described above. This studyfocuses on the effects of the politeness level of social robotson compliance of the user of the robot, with considerationof compliance-related consequences [12]. Assessment of theconsequences of complying with social robots may includeperceived benefits of compliance and the perceived cost ofnoncompliance, and eventually intention to comply.

Moreover, politeness strategy is believed to facilitate com-munication in human interaction, as it can build rapport[64,76] andminimize the potential for conflict and confronta-tion [65]. Hence, politeness is a key element in interactionwith social robots [47,72].

According to the theory of politeness, people are morewilling to complywith the advice of a service provider if theyfeel that the provider is being polite [10]. This causality hasbeen consistently observed in studies of computer-mediatedcommunication [26]. We believe that this causality may alsobe evident in the case of social robot–customer interac-tion. Some studies have proven that this is true with childcustomers. Children readily comply with robots that makeindirect, and therefore polite, requests [36].

The concept of reciprocity is grounded in social exchangetheory [15,21]. This concept may potentially explain whyhumans react the way they do to the respectful and disre-spectful treatment that they receive from others, includinghuman-like robots. In this study, we focused on the level ofpoliteness, which has been defined as a representative pat-tern of behavior that clearly shows favor. If a user perceives aservice robot to be polite, judging the robot’s behavior favor-ably, the user may be motivated to respond with compliance.Hence, our study is based on politeness theory and socialexchange theory and extends the findings of previous studiesto the setting of healthcare services.

According to social exchange theory, lower levels ofpoliteness make the user hesitate to repay requests with non-compliance due to the possibility of criticism and retaliationfrom others. Peoplemay feel that a requestmadewith a lowerlevel of politeness is a command (if the message that therobot is trying to give is reasonable) rather than a suggestion,and hence they may perceive more psychological, physical,or economic disadvantages of noncompliance [17]. Associ-

ating lower levels of politeness with reasonable commandsmay be more likely when the robot’s gesture or speech iscourteous or serious. The associated emotional burden maybe construed as a perceived cost of noncompliance.

Likewise, people may feel it burdensome to deny requestsfrom robots (perceived as official or serious commands) thatare more likely to provide physical, psychological, or eco-nomic benefits than to deny more personal, less significantrequests. Friendly and casual gestures from robots maymaketheir requests less coercive and more likely to be ignored.This situation has been found in previous research, especiallyin the healthcare service setting [2].

In addition, commands may be associated with the cost ofnoncompliance. Research indicates that command trainingresulted in increased compliance of children [62], students[5], and patients [34]. When giving commands, they donot use gestures and voice that indicate greater politeness,because the command receiver perceives politeness as indi-cating that noncompliance will have no negative impact onthem, and hence there is no risk [29]. Moreover, commandsstated politely may be perceived more as a request than animportant advice that needs to be followed.

In our literature review, we found a contradiction betweentheories on the impact of commands on compliance andsocial exchange theory in terms of the perceived level ofpoliteness and the cost of noncompliance/ benefit of com-pliance. In this paper, we examine two ways of expressingpoliteness: through gesture and speech. Thus, we hypothe-size that:

Hypothesis 3-1The perceived level of politeness as indicatedby gesture is positively associated with the perceived cost ofnoncompliance with the requests of social robots.

Hypothesis 3-2The perceived level of politeness as indicatedby speech is negatively associated with the perceived cost ofnoncompliance with the requests of social robots.

By contrast, receiving a polite request such as a sugges-tion or advice rather than a command may imply that somesort of reward for compliance may be forthcoming. Hence,we hypothesize that:

Hypothesis 4-1The perceived level of politeness as indicatedby gesture is positively associated with the perceived benefitof compliance with the requests of social robots.

Hypothesis 4-2The perceived level of politeness as indicatedby speech is negatively associated with the perceived benefitof compliance with the requests of social robots.

3.4 Speech, Gestures, and Politeness

Human speakers show politeness by being supportive, avoid-ing threats, maintaining smooth relations, and sustaining

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Table 1 Speech features and gestures showing politeness

Higher politenesslevel

Lower politeness level

Gesture Bowing slow action Pointing fast action

Speech (words) Using honorificsIndirectsuggestions

Direct requests

successful communication [49]. For robots, politeness looksa little different; it is manifested in the various robot behav-iors that are performed by functions (e.g., bowing, pointing,or honorific speech) programmed into the robot’s system.Wedivided the various functions of human-like social robots intotwo groups based on the politeness level, as listed in Table 1.Particularly relevant to our research setting, “direct requests”is a conventional communication strategy used in healthcareservices [57].

In general, people show politeness using verbal cues andgestures. In Eastern culture, they bow or place their handson their bellies, they point with two arms rather than point-ing with a finger. Moreover, they use terms of respect andinterrogative sentences rather than commands. If the robot’sspeech and gestures successfully show the level of politenessappropriate to the user, then the user will recognize the levelof politeness as intended.

The extant literature on the impact of human likeness onrobot acceptance reported mixed results. For example, Daut-enhahn et al. [23] suggested that even though subjects maywant a robot companion to communicate in a very human-like manner, human-like behavior was less desirable. Theresults imply that there must be a mediator between politebehavior (PB) and intention to comply (IC) (and also betweennoncompliance (PCON) and the perceived benefit of com-pliance (PBOC)). In addition, it is necessary to verify thatthe (im)polite behavior as stimulus is really recognized as(im)polite. People are affected by various biases such as self-interest, stereotypes, beliefs, or expectations [61]. Severalstudies have been conducted to reduce miscommunicationor misunderstanding about human–robot interaction causedby these biases [9,41,48].

Hence, we hypothesize that:Hypothesis 5-1 Social robots’ polite behavior (verbal andgestural) is positively associated with the perceived level ofpoliteness as indicated by gesture.

Hypothesis 5-2 Social robots’ polite behavior (verbal andgestural) is positively associated with the perceived level ofpoliteness as indicated by speech.

3.5 Control Variables

In addition to determinants such as politeness level and com-pliance, we include certain control variables in this study.

Table 2 Sources of measurement items for post-survey

Construct Source Items

Perceived politeness for gesture Developed for this study 3

Perceived politeness for verbal Developed for this study 3

Perceived benefit of compliance Bulgurcu et al. [12] 4

Perceived cost of noncompliance Bulgurcu et al. [12] 4

Intention to comply Ajzen [1] 3

First, ethnicity is included as a control variable. The per-ceived level of politeness varies across cultures. Certainconversational behavior may be considered polite in one eth-nic group, but impolite in another group [51]. When it comesto gender, studies have shown that women tend to be morepolite and expect more polite expressions from others thanmen [33]. Women are more expressive in their speech andmore reluctant to accept less than satisfactory service. Inaddition, we controlled for any prior knowledge of human-like robots in hospitals or experience using kiosks to avoidthe effects of these factors in the experiment.

4 Experiment

4.1 Measurements

To improve our understanding about the compliance behav-ior of robot users according to the robot behaviors (speechand gestures) manifested in healthcare service settings, anexperiment was conducted. The pre-questionnaire included10 items: 4 demographic items (i.e., gender, age, ethnicity,address), 3 items regarding the subjects’ prior experiencewith robots, and the remaining 3 items regarding their priorknowledge of service robots. A 7-point Likert-type scalewas used for measurement of the 7 items. For the remain-ing item, “Which communication method would you prefera robot to use to inform you about the difficulties it is havingwhile accomplishing tasks?”, respondents had 3 responses tochoose from: sound–gesture expressions, sound-based com-mands, or text-based commands. This item was included totest if human–robot interaction in a service context differsfrom simple human–robot dialogue [52]. Two items wereused to determine the participants’ experience with kiosksand robots (i.e., “How often do you interact with robots?”1= never, 7= daily, and “What level of experience do youhave working with or on robots?” 1= none, 7= high).

The post-questionnaire included 23 items, 17 of whichreferred to concepts about the perceived cost of noncompli-ance, perceived benefit of compliance, intention to comply,perceived politeness for verbal communication, and per-ceived politeness for gesture. The sources of measurementitems for this post-survey are listed in Table 2.

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Table 3 Characteristics of subjects

Categories Number %

Gender

Male 66 55.9

Female 52 44.1

Age

18–19 10 8.5

20–29 77 65.3

30–39 19 16.1

40–49 9 7.6

50–59 3 2.5

Preferred role of robot

Friend 26 22.0

Secretary 5 4.2

Medical doctor 8 6.9

Assistant 72 61.0

Other 7 5.9

An additional 3 items asked participants about their opin-ions of robots with personality and emotional capabilities.In order to determine whether exposure to NAO changedtheir opinions regarding social robots, 2 items from the pre-questionnaire were repeated in the post-questionnaire: “Doyou want to comply with NAO’s command?” and “Do youthink that having a robot display an appropriate level ofpoliteness could make them more acceptable in everydayhuman life?” (1= definitely, 7= not at all).

4.2 Subjects

The experiment was conducted in March 2016. The exper-imental group consists of 118 customers of a universitymedical center who sought recommendations about men-tal health and preventing further illness. Table 3 providesdemographic information about the participants. Males andfemales made up 55.9 and 44.1% of the participants, respec-tively.

Table 4 shows the percentages of participants in the genderand age groups by preferred role of the robot. While menprefer the human-like social robot to be an assistant for them,women prefer friend-like robots relatively more than meneven though the majority of women view the robot as anassistant. Regardless of their age, only a few participantsexpect that human-like robots can fill the role of a secretaryor medical doctor.

4.3 Robot Used in the Experiment

In this experiment, NAO, an interactive, autonomous, andprogrammable human-like robot developed by Aldebaran

Robotics, was used. As shown in Fig. 2, the robot has ahuman-like appearance. It weighs 4.3 kg and stands 58 cmhigh. NAOhas 25 degrees of freedom (DOF), which includestwo arms (2× 5 DOF), a head (2 DOF), pelvis (1 DOF), leg(2 × 5 DOF), and hand (2 × 5 DOF). It can communicatewith humans by walking, talking, and recognizing faces andspeech like humans do. It has various sensors (e.g., cam-eras, microphones, and pressure sensors) and many devicesto express itself (speech synthesizer, LED lights, and 2 speak-ers). NAO can be controlled using Linux, Windows, or MacOS and be programmed using C++ modules and Python orJava script languages.

In order to perform our experiment, we imported health-care service software into the NAO robotic system. Inproviding healthcare services, NAO asks questions to iden-tify the patient’s current stress level. Then, based on thestress level, NAO recommends relevant therapies or providesinstructions to decrease the stress level. While this process isrunning, NAOmay behave differently at each step accordingto a predefined level of politeness.

The choice of (im)polite gestures and speech was inspiredby research in polite computing and social computing [77,78]. Polite computing is about how software design canfoster politeness in the interaction between users and com-puters. Several design principles are involved: respectinguser choice, disclosure, offering useful choices, and usingpolite expressions. These principles have been adopted byresearchers of human–robot interaction. According to theseprinciples, experimenters have generated numerous ideas. Inaddition, examples of different politeness strategies can befound in the SCARE corpus, a human-to-human corpus ofinteractions consisting of fifteen spontaneous English dia-logues associated with an instruction-giving task [6]. Justas the SCARE corpus data was collected using the QUAKEenvironment, a virtual reality game, the authors collectedexamples of (im)polite speech and gestures using the imagedatabase, novels, and scripts of TV dramas. We found sam-ples in the following categories: respecting user choice (e.g.,would you mind, what do you think), disclosure (e.g., myname is NAO), and offering useful choices (e.g., I can pro-vide you…). In addition, polite language such as excuse me,thank you, and please was also utilized.

4.4 Procedure

In this paper, we targeted patients seeking mental healthadvice at a healthcare center located in a university medi-cal center. Participants were encouraged to select a ribbonindicating the level of service they would receive (higheror lower politeness). Participants were not informed aboutthe meaning of the colors of the ribbons. They completeda pre-questionnaire, which included items regarding theirdemographics, their opinions about service robots, and their

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Table 4 Distribution ofparticipants by preferred role ofrobot

Preferred role of robot (frequency/%) Total

Assistant Secretary Medical doctor Friend Other

Gender

Male 43 3 4 11 5 66

% 65.1 4.5 6.1 16.7 7.6

Female 29 2 4 15 2 52

% 55.8 3.8 7.7 28.8 3.8

Age group

18–19 4 1 4 0 1 10

% 40 10 40 0 10

20–29 50 3 5 16 3 77

% 64.9 3.9 6.5 20.8 3.9

30–39 10 1 2 4 2 19

% 52.6 5.3 10.5 21.1 10.5

40–49 7 0 0 1 1 9

% 77.8 0.0 0.0 11.1 11.1

50–59 1 0 1 1 0 3

% 33.3 0.0 33.3 33.3 0.0

Fig. 2 Gestures expressing politeness level. a Higher politeness. bLower politeness

prior knowledge about service robots. After completing thepre-questionnaire, the subjects moved into the experimen-tal space to interact with NAO. To allow us to focus on therelationship between polite expression and compliance withrobot recommendations in a healthcare service setting, weinstalled the robot in a corridor in a large-scale universitypublic healthcare center. The experimental environment isshown in Fig. 3.

As participants stepped into the healthcare center toreceive services, the social robot responded as described inTable 5.

The politeness level of the statements (Step 1–Step 6)for each participant was determined by the type of ribbonthey randomly selected. According to the level selected, therobot used different speech and gestures. Participants choseto respond to the robot with “yes” or “no”. However, if aparticipant felt uncomfortable about the robot’s politenesslevel, then he or she had the option of quitting the inter-action by saying “quit”. In the experiment in this study, insteps 1–5, the robot asked questions to measure users’ stresslevels using language of different politeness levels. In step6, according to the stress level determined by the robot, therobot suggested consultation with a doctor if the stress levelwas very high (Table 1). If the stress level was intermediate,the robot suggested the participant do one of a number ofthings: listen to music, exercise, or travel, saying, for exam-ple, “You may want to listen to music to reduce stress,” or“You’d better take an hour walk per day”. Lastly, if the levelof stress was low, the robot simply provided information onthe health status of the user without any recommendations orcommands.

Though we planned for participants that quit to fill inthe questionnaire, no participants chose to quit the experi-ment. After participants had completed the interaction withthe social robot, they were asked to complete a post-questionnaire regarding their reactions to NAO’s featuresand appearance. In addition, in order to determine whetherexposure to NAO changed their attitudes toward compliancewith requests coming from robots, the post-questionnairealso asked about their perceptions and feelings regardingrobots with social capabilities. “Appendix A” shows the post-questionnaire items.

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Fig. 3 Experimentalenvironment

5 Results

5.1 Measurement Validation

The data were analyzed using PLS (Smart-PLS version 3.0)and SPSS 22.0. Descriptive statistics are shown in Fig. 4. Totest the measurement model, we assessed reliability and con-struct validities (convergent and discriminant validities). Forconvergent validity, all statistical values of Cronbach’s alphaand composite reliability exceeded the recommended thresh-old values (0.60 for Cronbach’s alpha and 0.70 for compositereliability), as shown in Table 6. An exploratory factor anal-ysis (see “Appendix B”) further verified convergence of theconstruct items. A factor analysis also demonstrated theirdiscriminant validity. Additionally, the values for the squareroot of the average variance extracted (AVE) were greaterthan the squares of the correlations between the constructs,indicating satisfactory discriminant validity of all constructs(Table 6). To assess common method bias, Harman’s single-factor test was conducted [59]. All variables were included inan exploratory factor analysis, and the first factor explainedless than 50% of the variance (34.39%), indicating that com-mon method bias is not of great concern in this study.

5.2 Model Testing

First, we conducted a one-way ANOVA test using the datafrom the pre-questionnaire to examine if users’ previousexperience or preference affect the perceived level of polite-ness. As shown in Table 7, no significant differences werefound between the two subject groups. Thus, we concludedthat differences between participants in terms of prior expe-rience using service robots had no impact and caused no biasin the results.

In addition, we conducted a correlation analysis betweenthe exogenous variables and the perceived level of polite-ness (PLP). The results are shown in Table 8. No linearitybetween the exogenous variables and the dependent variable(PLP) was evident. We therefore concluded that the exoge-nous variables had no significant impact on PLP.

5.3 Result of Path Analysis

The proposed model was tested using structural equationmodeling (SEM). SEM is a statistical analysis techniquedesigned to test conceptual or theoretical constructs. SEMconsists of a set of linear equations that simultaneously testtwo or more relationships among directly observable and/orunnamed latent variables. To conduct the SEM analysis, wechose the commonly used PLS method. Unlike conventionalLISREL, EQS, AMOS, and other structural equation mod-els, it is based on a principal component which explains thetotal variance. Unlike covariance-based structural equationmodeling, there are no constraints on sample sizes, normaldistributions of variables, or residuals. It is a useful analyticaltool for predicting causality rather than for theory verifica-tion [80]. In a PLS analysis, R2 is used instead of goodnessof fit indicators such as CFI, AGFI, and FMSEA. Examiningthe R2 score of the endogenous variable using PLS analysisallows us to assess the utility of the variables, and examiningthe structural paths can help us to evaluate the explanatorypower of the structural model.

Figure 5 shows the standardized path coefficients, t-statistics, and explained variance for each equation in thehypothesized model. As shown in Fig. 4, all paths were sig-nificantly higher than 0.05, except one path (between PCONand IC).

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Table 5 Behaviors of the robotat each step

Step Politeness Level: High Politeness Level: Low

1 GestureNAO bows

courteously right

after recognizing

that a participant has

arrived.

NAO bows in a

friendly manner

with open arms right

after recognizing

that a participant has

arrived.

Speech Good afternoon, sir (or ma’am). My name

if NAO and I can provide you with a service

to diagnose your current stress level. Would

you be willing to get diagnosed and receive

some recommendations?

Hey. I’m NAO. I can diagnose your stress

level. Do you want a diagnosis?

2 Gesture NAO shows respect

by gently putting a

hand on his chest

and lowering his

head at the same

time.

NAO makes the

request with open

arms and a tilted

pose.

Speech Please answer my questions on symptoms

with a “Yes” or “No”.

Answer my questions on symptoms with a

“Yes” or “No”.

3 Gesture NAO makes the

request courteously

with open hands

while bending at the

waist.

NAO makes the

request in a friendly

manner while

raising one arm.

Speech Dear sir (or ma’am), are you often in a

situation where you feel rushed while

you’re working?

Are you often in a situation where you feel

rushed while you’re working?


request courteously

with open hands

while bending at the

waist.

NAO makes the

request while

pointing.

Speech Dear sir, compared to others, do you feel

you have a harder time with anger

management?

Compared to others, do you get angry

easily?


request with a

careful attitude and

one hand on his

heart.

NAO makes the

request while

pointing and with an

aggressive attitude.

Speech Dear sir, do you have a hard time expressing

your emotions, preferring to keep things to

yourself?

Do you have a hard time expressing your

emotions, preferring to keep things to

yourself?

6 Gesture NAO bows courteously while placing his hands on his chest.

NAO waves

goodbye.

Speech Dear sir, we are sorry to tell you that you

are currently suffering from high stress

levels. Please have a consultation with your

doctor to ease your stress levels. Thank you

for using our service.

You have a high stress level right now. You need a consultation with your doctor. Bye.

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Fig. 4 Descriptive statistics

Table 6 Results of validity testing

Constructs Cronbach’s Alpha Composite reliability IC PB PBOC PCON PLP_G PLP_S

IC 0.921 0.950 0.864

PB 1.000 1.000 0.114 1.000

PBOC 0.945 0.960 0.654 0.187 0.858

PCON 0.895 0.927 0.334 0.210 0.289 0.761

PLP_G 0.950 0.968 0.063 −0.436 0.226 −0.081 0.909

PLP_S 0.959 0.973 −0.125 −0.670 −0.101 −0.315 0.610 0.924

IC intention to comply, PB polite behavior, PBOC perceived benefit of compliance, PCON perceived cost of noncompliance, PLP_G perceivedlevel of politeness for gesture, PLP_S perceived level of politeness for speechSquared AVE values are bolded in diagonal

Table 7 ANOVA test withpre-survey data

Constructs Level of politeness for gesture Level of politeness for speech

Higher Lower F-value Higher Lower F-value

M SD M SD M SD M SD

Q1 4.231 1.820 4.276 1.601 .018 4.183 2.391 3.938 2.208 0.320

Q2 4.227 2.140 4.252 1.693 .002 4.060 2.370 4.084 2.316 0.001

Q3 4.136 1.872 4.261 1.722 .052 4.090 2.300 4.080 2.322 0.000

Q4 4.255 1.753 4.228 1.663 .004 4.144 2.353 3.826 2.155 0.348

Q5 4.159 1.540 4.322 1.873 .258 4.211 2.297 3.979 2.334 0.291

Q6 3.883 1.579 4.375 1.768 .822 3.911 2.381 4.140 2.297 0.218

Q1: Frequency of use of kiosks in the hospital, Q2: Self-efficacy as a result of using kiosks in the hospital,Q3: Self-efficacy as a result of using human-like robots, Q4: Preference_about user interface with speech andgesture, Q5: Preference_about user interface with speech only, Q6: Preference about user interface with_textonly

Table 8 Correlation between exogenous variables and the level of politeness and determinants

Exogenous variables Gender Age Role Q1 Q2 Q3 Q4 Q5 Q6

PLP_G

Pearson Correlation −0.022 −0.071 −0.089 −0.012 −0.004 −0.021 0.006 −0.047 −0.124

Sig.(2-tailed) 0.810 0.443 0.335 0.893 0.964 0.820 0.947 0.613 0.180

PLP_S

Pearson correlation −0.084 −0.122 0.047 0.052 −0.003 0.001 0.055 0.050 −0.043

Sig. (2-tailed) 0.369 0.189 0.617 0.573 0.975 0.989 0.556 0.591 0.641

Q1: Frequency of use of kiosks in the hospital, Q2: Self-efficacy as a result of using kiosks in the hospital, Q3: Self-efficacy as a result ofusing human-like robots, Q4: Preference_about user interface with speech and gesture, Q5: Preference_about user interface with speech only, Q6:Preference about user interface with_text only

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Fig. 5 Results of path analysis.Note *p < 0.1; **p < 0.05;***p < 0.01

Firstly, we investigated how the perceived cost of non-compliance and the perceived benefit of compliance affectthe intention to comply. These factors were positively associ-ated with the intention to comply (t-value of PCON= 2.004and t-value of PBOC= 9.917); thus, Hypotheses 1 and 2were accepted. For Hypotheses 3 and 4, we investigated theinfluence of the perceived level of politeness as indicated bygesture and speech on the perceived cost of noncompliance.The results showed that the perceived level of politeness forgesture had no significant influence on the perceived costof noncompliance (t = 1.287), while the perceived levelof politeness for speech was negatively associated with theperceived cost of noncompliance (β = −0.418, t = 4.102,p < 0.001). Therefore, Hypothesis 3-1 was rejected, whileHypothesis 3-2was accepted.Moreover,we tested the impactof the perceived level of politeness on the perceived benefitof compliance. Two factors (the perceived level of politenessas indicated by gesture and speech) had significant influenceson the perceived benefit of compliance (therefore, Hypothe-ses 4-1 and 4-2 were accepted). Lastly, Hypotheses 5-1 and5-2 examined the links between polite behavior and the per-ceived level of politeness; polite behavior was found to besignificantly related to the perceived level of politeness forgesture (β = 0.430, t = 5.706, p < 0.001). Also, politebehavior had a significant impact on the perceived level ofpoliteness for speech (β = 0.670, t = 10.812, p < 0.001).Since the coefficient was positive, we can conclude that morepolite behavior resulted in a higher perceived level of polite-ness than less polite behavior (therefore, Hypotheses 5-1 and5-2 were accepted).

6 Discussion

6.1 Findings and Implications

To the best of our knowledge, this experimental study is thefirst to examine the relationship between the intention to com-ply, perceived cost of noncompliance, and perceived benefitof compliance based on the results of testing of the perceivedlevel of politeness. We developed a new research modelwhich combines the findings of conventional social robotresearch (H5-1 and H5-2), social exchange theory (H3-1and H3-2), politeness theory (H4-1 and H4-2), and Bul-gurcu’s compliance model (H1 and H2). Bulgurcu’s modelwas utilized to find the independent variables (i.e., PBOCandPCON) directly affecting compliance, while social exchangetheory and politeness theory were applied to verify the effectof the robot’s polite behavior on these variables. Althoughthe connection between polite behavior from robots and usercompliance has been an interesting research topic in the HRIcommunity [37,38,54,67], to the best of our knowledge, Bul-gurcu’s research model has not previously been utilized toelucidate compliance behavior.

The results presented herein also provide opportunity forfuture research into constructs such as the perceived level ofpoliteness, perceived cost of noncompliance, perceived ben-efit of compliance, compliance intention, and their relationto the level of politeness as gleaned from the verbal cues andgestures of a social robot. This study can be useful to organi-zations considering adopting social robots in domains otherthan healthcare services.

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From the results of the experiment, some useful impli-cations for academics and healthcare managers can beunderstood. First, the results for H1 and H2 support thoseof previous studies on the relationship between and attitudetoward compliance and intention to comply, in which a posi-tive relation is found [55]. Until now, hypotheses on attitudestoward compliance and intention to comply have not beenverified in robot-to-human communication settings.

The results of testing of H3-2 and H4-2, in which therelationship between perceived level of politeness for speechand attitude and compliance were hypothesized, suggest thatshowing politeness may decrease the intention to complywith requests from a social robot due to the perceived costof noncompliance and perceived benefit of compliance. Thisstudy gives better understanding of the reasons why a robot’sindirect suggestion accompanied by polite behavior is nothelpful in ensuring patient compliance with recommenda-tions in a healthcare setting. As with previous studies in themedical field, effective communication between doctor andpatient plays a very important role in increasing compliance.The level of politeness from doctors has been mentionedas a major factor in previous studies [70,71,73]. However,high levels of politeness do not always result in increasedcompliance. In Sinha’s study, the experiment proved theeffectiveness of varying the level of politeness according tothe gender of the patient. Also, Bickmore [8] showed thatthe level of politeness of the computer agent healthcare pro-gram had a positive impact on the long-term compliance ofprogram users and a negative impact on short-term compli-ance. These findings imply that a high level of politeness doesnot always have a positive effect on compliance. Therefore,politeness level should vary according to the user’s situation[8,70].

Our results are consistent with those of extant literaturewhich investigates patient compliance with the requests ofcaregivers, health assistants, ormedical experts. Using direct,reasonable commands has been proven more effective thanindirect, polite suggestion in terms of patient compliance[16,27]. Moreover, while studies on politeness have focusedon human-to-human relationships, this study expands thisresearch area to robot-to-human communication in orderto verify findings about human perceptions of expressedpoliteness toward humans by a robot. The results of thisstudy reveal that the rules of politeness can apply to bothhuman-to-robot relationships and human-to-human relation-ships. Moreover, the results suggest that a user’s evaluationof politeness behavior from robots may inform our under-standing of the interconnection between the perception ofpolite behavior and user compliance with the guidance andrecommendations of a social robot in the healthcare servicesetting. It is therefore necessary to apply the appropriate levelof politeness according to each user’s situation.

In testing of the perceptions of the perceived level ofpoliteness as indicated by the robot’s gestures (H3-1 andH4-1) and the relationship between this perceived level ofpoliteness and compliance, H3-1 was unexpectedly rejected,which means that the robot’s gestures did not affect the per-ceived cost of noncompliance. One explanation for this resultmay be related to the fact that themeaning of a robot’s gesturemay not be as clear as that from its speech, especially whenthe robot specifies the cost of noncompliance. This possibil-ity has been identified in other situations such as workplaces[25].

The result also suggests that the perceived level of polite-ness as indicated by gesture is positively related to the per-ceived benefit of compliance (therefore, H4-1 is accepted).The results of testing of H4-1, as well as H4-2, are consistentwith social exchange theory, which refers to the social costand reciprocity of mentoring and the process wherein men-tors and mentees evaluate costs and benefits to determine ifthe relationship is viable [45]. According to this theory, themore customers who perceive politeness on the part of therobot, themore likely theywill be to expect that the robot willbe persistently polite to them if they complywith its requests.At the same time, for customers who wonder what will hap-pen if they do not comply, they worry that any unexpectedresponses from the service robot may cause discomfort, anduncomfortable communication with the service robot mayincrease their psychological burden.

H5-1 and H5-2 were accepted in this study. Therefore, weconclude that the robot’s polite behaviorwaswell constructedand that its politeness was appropriately understood by theparticipants. These results also suggest that if social robotssuccessfully convey their intentions and gain compliance bydemonstrating polite behavior in their speech and gestures,they may be useful as service providers (in hospitals in thiscase) to support employees who are short of time, as theyare able to provide simple, effective, and highly-structuredcustomer support.

This study contributes to the research on natural languageprocessing and gestures of human-like robots. As in Kimet al. [40], verbal communication may be evaluated based onhuman–computer interaction, computer-supported collabo-rative work, and lastly, computer-mediated communication.Furthermore, the results of this study suggest that if compli-ance with requests is essential to successful customer care,then it is important to increase the perceived level of polite-ness.

With the advent of artificial intelligence and big dataanalytics, social robots may become more common and beacceptedmore quickly in various situations, includinghealth-care settings. Artificial intelligence and big data analyticsare very useful to optimize robots for use in these situationsthrough speech and gestures according to the user’s currentcontext. Accordingly, careful assessment of the acceptance

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of technology in the form of social robots is important. Rig-orous empirical studies must be conducted to improve ourunderstanding of human psychological processes of accep-tance of robots and their guidance. This study is the first,to the best of the authors’ knowledge, to focus on perceivedpoliteness based on politeness theory, social exchange theory,and the theory of compliance of Bulgurcu et al. [12].

6.2 Limitations

First, due to limited resources and the difficulties of main-taining a large sample of participants over a longer period oftime than this survey-based experiment, studies on HRI nor-mally involve a relatively small number of subjects [42,53].Even though this study’s sample size (n = 118) is relativelylarger than anyother similar studies, future studieswith largersamples must be conducted to confirm and extend the inter-pretations of the results found here.

Second, since the subjects in this study were Asian (Chi-nese and Korean), they may interpret nonverbal/verbal politebehavior differently than itwould be interpreted in other partsof the world. Webster defines politeness as “Exhibiting inmanner or speech a considerate regard for others”. There-fore, politeness is a fundamental and general social concept[77]. It can be argued that specific speech and gestures varybetween cultures; however, politeness is a common theme.Nonetheless, since the speech and gestures used in the experi-ment in this studymay be perceived as rude in another culture[67], further research focusing on a different culture must beconducted with a modified robot behavior design accordingto the norms of the culture. Future research must be extendedto non-Asian contexts in order to identify any effects of cul-tural differences and verify the results presented here.

Third, the experiment involved a social robot providingguidance services in a hospital setting. For many guidanceservices, customer participation entails no serious loss inconsideration time or convenience in order to comply withthe guidance provided. However, since the perceived costof compliance is often used in studies of compliance andinvolves warnings in critical situations about the effectsof noncompliance [12,79], it is necessary to understandand consider this perceived cost, which is not includedin this research model, in contexts where compliance andtime/effort from customers are needed, such as security-related services.

Lastly, the main focus of this study was the relationshipbetween the robot’s verbal/nonverbal polite expressions and

human compliance. Neither the appearance nor the size ofthe robot was the main focus for this study simply becausewe used only one type of robot, NAO, which has now gainedpopularity. However, since a robot’s size and appearance canaffect human impressions of it [30,68], future studies shouldalso consider these and any other relevant characteristics ofrobots.

7 Conclusion

This experimental study is the first to examine the relation-ships between the intention to comply, perceived cost ofnoncompliance, and perceived benefit of compliance withthe guidance of social robots through testing of the perceivedlevel of politeness. The findings from the study provide somevaluable information on human feelings about root-basedadvice and perceptions of the use of social robots in a hospi-tal setting. In this study, we identified significant parameters,using the verbal cues and gestures of a social robot, thatmay influence user compliance with the advice of robots in ahospital setting. These parameters will be considered in theimplementation of the hospital’s new advisory system,whichruns 24 hours a day, 7 days a week for the benefit of hospitalguests.

In healthcare service settings, human-like robots may beused as biomedical machines to determine patients’ psycho-logicalwellness. Theymay be perceived as amore acceptableway of helping customers than nonhumanoid informationtechnology. For example, many websites provide question-naires to measure wellness levels (e.g., stress, depression,fatigue), encouraging users to get help from medical health-care centers. We assert that human-like robots are superiorto websites in terms of what they do well, that is, providemultimodal services, especially using gestures and speech.The authors believe that the combination of the optimal levelof politeness and multimodality affects users’ willingness tocomply with the request to see a human assistant or doctorwho can help them cope with stress, as shown in the exper-iment in this study. Finally, assessment of financial viabilityis beyond the scope of the current study, but should be anarea for future research.

Acknowledgements This work was supported by the National Strate-gic R&D Program for Industrial Technology (10041659) and fundedby the Ministry of Trade, Industry, and Energy (MOTIE).

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Appendix A

See Table 9.

Table 9 Constructs and items

Construct/item

Perceived Cost of Noncompliance (Bulgurcu et al. [12])

PCON1: My noncompliance with NAO’s guidance would beharmful to me

PCON2: My noncompliance with NAO’s guidance wouldimpact me negatively

PCON3: My noncompliance with NAO’s guidance wouldcreate disadvantages for me

PCON4: My noncompliance with NAO’s guidance wouldgenerate losses for me

Perceived Benefit of Compliance (Bulgurcu et al. [12])

PBOC1: My compliance with NAO’s guidance would resultin benefits to me

PBOC2: My compliance with NAO’s guidance would providegains to me

PBOC3 My compliance with NAO’s guidance would befavorable to me

PBOC4: My compliance with NAO’s guidance would createadvantages for me

Perceived Level of Politeness in Gesture (developed for this study)

PLP1: NAO exhibited honorific behavior while speaking tome

PLP2: I thought NAO’s gestures were intended to show merespect

PLP3: NAO used honorific gestures while speaking to me

Perceived Level of Politeness in Speech (developed for this study)

PLP4: NAO spoke to me in honorific form

PLP5: I thought NAO respected me based on its words

PLP6: NAO used honorific speech while speaking to me

Intention to Comply (Siponen, Adam Mahmood, and Pahnila, 2014)

IC1: If NAO actually provided guidance for me in thehospital, I would comply with NAO’s guidance

IC2: I intend to recommend that others comply withNAO’s guidance

IC3: I intend to assist others in complying with NAO’sguidance

Appendix B

See Table 10.

Table 10 Results of exploratory factor analysis

Items PBOC PCON PLP_G PLP_S IC

PBOC1 0.894 0.124 0.094 −0.042 0.159

PBOC2 0.881 0.130 0.107 −0.051 0.242

PBOC4 0.856 0.082 0.117 −0.038 0.369

PBOC3 0.845 0.163 0.107 −0.078 0.300

PCON3 0.125 0.920 −0.052 −0.075 0.005

PCON2 0.137 0.885 −0.007 −0.108 0.103

PCON4 0.126 0.833 −0.089 −0.260 0.049

PCON1 0.073 0.761 0.011 −0.038 0.202

PLP_G3 0.115 −0.015 0.926 0.281 0.046

PLP_G2 0.116 −0.055 0.885 0.343 0.069

PLP_G1 0.162 −0.046 0.863 0.299 −0.022

PLP_S2 −0.093 −0.113 0.319 0.897 −0.015

PLP_S3 −0.072 −0.163 0.353 0.884 −0.006

PLP_S1 −0.028 −0.209 0.293 0.882 −0.008

IC3 0.390 0.121 0.010 −0.040 0.817

IC2 0.453 0.170 0.025 −0.008 0.782

IC1 0.503 0.139 0.056 0.044 0.653

Eigenvalue 3.756 3.119 2.758 2.753 2.077

Proportion 22.096 18.347 16.221 16.195 12.220

Cumulative proportion 22.096 40.443 56.663 72.858 85.078

The number in bold indicates that the factor loading value is greaterthan 0.5

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Namyeon Lee is presently an Assistant Professor at Sungkyul Univser-sity, South Korea, where he joined in 2014. He worked as a post-doctorat Center for Advanced Information Technology (CAITech) at KyungHee University before joining Sungkyul University until in 2013. In2007, he worked at CINDI, Department of Industrial Engineering atWayne State University to perform a project on man–machine inter-faces of ubiquitous devices. He received his MS and Ph.D. degree inManagement Information Systems fromKyung Hee University in 2008and 2013, respectively. His current research interests include big dataanalysis, text mining, and human-robot interaction.

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Jeonghun Kim is a Doctoral Candidate in Kyung Hee University,and also working for Center for Advanced Information Technology(CAITech) in South Korea. His research interests include bigdata anal-ysis, data mining and text mining.

Eunji Kim is currently working for Center for Advanced InformationTechnology (CaiTech) as a researcher. She takes part in a Human-RobotInterface (HRI) project for Korea Institute of Industrial TechnologyEvaluation and Planning as well as National Research Foundation ofKorea. Her current research interests include development of humanfriendly robot service, user behavior analysis based on big data analyticsand social network analysis.

Ohbyung Kwon is a Professor in the School of Management at KyungHee University. He received his master’s and Ph.D. degrees in MISfrom Korea Advanced Institute of Science and Technology (KAIST),and a B.A. from Seoul National University. He was working for IRSI atCarnegie Mellon University and Department of MIS at San Diego StateUniversity.Hepublished several articles in Journal ofMIS, InternationalJournal of Information Management, Decision Support Systems, etc.His research interests include human-robot interaction, smart things,big data analytics, and decision support systems.

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