Experimental Tests of Normative Group Influence and Representation Effects in Computer-Mediated Communication : When Interacting Via Computers Differs From Interacting With Computers

Lee, Nass / NORMATIVE GROUP INFLUENCE IN CMC 349

Human Communication Research, Vol. 28 No. 3, July 2002 349–381© 2002 International Communication Association

Experimental Tests of Normative GroupInfluence and Representation Effects inComputer-Mediated CommunicationWhen Interacting Via Computers Differs FromInteracting With Computers

EUN-JU LEEUniversity of California-DavisCLIFFORD NASSStanford University

Two experiments addressed the questions of if and how normative social influence operates inanonymous computer-mediated communication (CMC) and human-computer interaction(HCI). In Experiment 1, a 2 (public response vs. private response) × 2 (one interactant vs.four interactants) × 3 (textbox vs. stick figure vs. animated character) mixed-design experi-ment (N = 72), we investigated how conformity pressure operates in a simulated CMC set-ting. Each participant was asked to make a decision in hypothetical social dilemmas afterbeing presented with a unanimous opinion by other (ostensible) participants. The experimentexamined how the visual representation of interaction partners on the screen moderates thissocial influence process. Group conformity effects were shown to be more salient when theparticipant’s responses were allegedly seen by others, compared to when the responses weregiven in private. In addition, participants attributed greater competence, social attractive-ness, and trustworthiness to partners represented by anthropomorphic characters than thoserepresented by textboxes or stick figures. Experiment 2 replicated Experiment 1, replacinginteraction with a computer(s) rather than (ostensible) people, to create an interaction settingin which no normative pressure was expected to occur. The perception of interaction partner(human vs. computer) moderated the group conformity effect such that people expressed greaterpublic agreement with human partners than with computers. No such difference was found forthe private expression of opinion. As expected, the number of computer agents did not affect partici-pants’ opinions whether the responses were given in private or in public, while visual representa-tion had a significant impact on both conformity measures and source perception variables.

Computer-mediated communication (CMC),1 a relatively new butrapidly growing form of human interaction, has created uniqueinteraction settings where feelings of public engagement are seri-

ously reduced. Because computer-mediation reveals a very limited num-ber of social cues concerning interaction partners, it is hard to keep alert

Eun-Ju Lee (Ph.D., Stanford University, 2000) is an assistant professor in the Department ofCommunication at University of California-Davis. Clifford Nass (Ph.D., Princeton Univer-sity, 1986) is a professor in the Department of Communication at Stanford University. Theauthors would like to thank the editor and three anonymous reviewers for their insightfulcomments on earlier drafts. Correspondence concerning this article should be addressed toEun-Ju Lee, Department of Communication, University of California, Davis, One ShieldsAvenue, Davis, CA 95616; email: [email protected].

350 HUMAN COMMUNICATION RESEARCH / July 2002

to other individuals’ presence behind the computer screen (Matheson &Zanna, 1988; Postmes, Spears, & Lea, 1998; Rice & Love, 1987). By creat-ing this pseudo-private communication environment, computer media-tion is believed to minimize concerns about public evaluation by allow-ing complete anonymity among users. This, in turn, likely liberates peoplefrom the norms and regulations that govern their ordinary life (Jessup,Connolly, & Galegher, 1990; Sproull & Kiesler, 1986). Indeed, some stud-ies have reported uninhibited and extreme verbal expressions such as“flaming”, supporting the notion of reduced normative social influencein CMC (Kiesler, Siegel, & McGuire, 1984; Kiesler & Sproull, 1992; Siegel,Dubrovsky, Kiesler, & McGuire, 1986).

However, not every researcher agrees on how reduced public self-awareness and anonymity affect (anti)normative social behaviors in CMC.Rejecting the notion that CMC is inherently impersonal and socially defi-cient, some researchers have attributed early observations of impersonaleffects of computer mediation to specific features of experimental set-tings, such as a limited time frame or a lack of anticipated future interac-tion (Walther, 1994). Based on the development of interpersonal relation-ships comparable, or even superior, to those observed in face-to-face (FtF)groups, CMC even has been labeled a “hyperpersonal” medium (Walther,1996). Similarly, it has been claimed that normative social influence caneven be amplified in CMC (e.g., Postmes, Spears, & Lea, 1998; Spears &Lea, 1992).

CMC AND NORMATIVE SOCIAL INFLUENCE

Among various forms of normative social influence, conformity togroup norms is particularly interesting in the context of CMC because ofthe weakened sense of “publicness.” Defined as a behavioral change thatincreases congruence between the individual and the group (Allen, 1965),conformity takes two different forms: private conformity and public com-pliance. Private conformity refers to “true” acceptance of the majorityopinion as a valid account of the environment. Because opinions sharedby many people are more likely to be correct than the opinions held by asingle individual, people tend to conform to the group opinion (Insko,Smith, Alicke, Wade, & Taylor, 1985). In private conformity, peopleinternalize the majority opinion as their own for its informational valueand it doesn’t matter whether or not their response is visible to andidentifiable by others.

Public compliance, on the other hand, is defined as the expression ofagreement with the majority opinion. For public compliance to occur, theindividual must believe that the group will know the individual’s ex-


pressed opinion. The simplest explanation for public compliance is thebasic human need for social approval (Maslow, 1948). Because one of thebest ways to get person B to like person A is to have person A expressopinions similar to those of person B (Kiesler & Kiesler, 1969), peopletend to comply with the sets of attitudes, beliefs, and behaviors of a groupin order to be accepted as one of them. In a similar vein, people have afundamental desire not to appear deviant or to risk possible negative sanc-tions from the majority (Diehl & Stroebe, 1987; Nemeth, 1986).

One of the key components that constitute the “publicness” of a com-munication context is the presence of interaction partners. To broadenthe notion of presence beyond the physical, researchers have developedthe concept “social presence” to refer to the individual’s psychologicalawareness of other people’s presence in the immediate environment. Thatis, social presence is defined as the degree of salience of the other person(or people) in the interaction (Short et al., 1976; Williams & Rice, 1983) orthe degree of tangibility and proximity of other people that one perceivesin a communication situation (McLeod, Baron, Marti, & Yoon, 1997). It isalso very close to the concept of “psychological distance,” which refers tosomeone’s feeling that his or her partner is “there” or “not there” (Rutter,1987). Computer mediation has dramatically expanded the boundary ofcommunication across time and space, but has weakened the awarenessof people with whom we communicate. In most cases, CMC participantsare physically isolated, each sitting alone in front of a computer and “talk-ing” to a person who might be thousands of miles away. The paucity ofnonverbal cues further constrains spatial proximity, while reducedinteractivity or reciprocity hinders temporal proximity (Burke &Chidambaram, 1995). Certain forms of CMC, such as Internet RelayedChat, do provide perception of temporal (or even spatial) proximity byvirtue of its immediacy (Reid, 1991), however CMC is nonetheless defi-cient in delivering a sense of “copresence” when compared to FtF.

Social presence also depends on the quantity and type of social contextcues carried over by the medium, which range from nonverbal signals(e.g., eye contact, facial expressions) and paralinguistic cues (e.g., voicetone, volume) to physical presence (e.g., touch and olfactory signals;Caporael, 1986; Rice, 1993; Shamp, 1991; Sproull & Kielser, 1986). Althoughsome CMC systems provide audio and visual information about theirinteractants (e.g., videoconferencing systems), most CMC systems arecurrently text-based and support the exchange of messages only. There-fore, perception of the identity of interactants derives almost wholly fromwhat and how one writes. Moreover, messages are delivered in a stan-dardized form such that they lose uniqueness and are less recognizableas products of a specific individual (Shamp, 1991). Combined with nar-row bandwidth, standardization of messages likely renders CMC less


“personal” and leads to less opportunity for interpersonal influence andcohesion (Hiltz et al., 1986; Perse & Courtright, 1993; Rice, 1993).

Anonymity also contributes to lowering the sense of public engage-ment in CMC. Being unidentified and thereby unaccountable likely liber-ates individuals from inner restraints that normally govern their socialbehaviors. Combined with the lack of individuating personal cues thatemphasize interpersonal difference among group members, anonymityin CMC likely lowers public self-awareness (i.e., how others perceive me;Matheson & Zanna, 1988). Consistent with this expectation, Jessup andhis colleagues (1990) found that anonymity attenuated majority pressurein a group decision-making situation. When compared with the identifi-able CMC condition, the anonymous CMC group contributed significantlymore critical and solution-questioning comments.

Deindividuation theory, defined as the loss of identity and weakeningof social norms and constraints associated with submergence in a groupor crowd (Zimbardo, 1969), provides a comprehensive psychological ac-count for antinormative group behaviors. Due to reduced motivation forself-evaluation, deindividuated group members become relatively lessconcerned about making a good impression (Cottrell, Wack, Sekerak, &Rittle, 1968) and tend to exhibit more extreme, more impulsive, and lesssocially differentiated behaviors (Diener, Fraser, Beaman, & Kelem, 1976).As discussed above, due to anonymity and the lack of personal cues, CMCparticipants likely experience a similar “deindividuation” state. Indeed,some studies have reported that people exhibit more uninhibited behav-ior, such as strong and inflammatory expressions, in CMC (Kiesler &Sproull, 1992; Reid, 1991; Siegel et al., 1986; see Lea, O’Shea, Fung, &Spears, 1992, for a different perspective). Some researchers have evensuggested the existence of “mechanomorphism” (i.e., attribution of ma-chine characteristics to humans) among CMC participants (e.g., Shamp,1991). Other findings have shown that CMC created a low-risk environ-ment for opinion deviates (or low-status individuals), equalizing theindividual’s participation in group decision-making, which can also beinterpreted as a consequence of reduced inhibitions associated with evalu-ation apprehension and consequent self-regulation (Dubrovsky et al., 1991;McGuire et al., 1987; McLeod et al., 1997; Sproull & Kiesler, 1986; Straus,1997; Valacich, Dennis, & Nunamaker, 1992).

Despite subtle differences in their emphasis, the aforementioned theo-ries predict the same phenomenon: Normative social influence will bereduced in CMC, compared to face-to-face interaction. In stark contrastwith these theories, however, the social identity/deindividuation (SIDE)model suggests the possibility of even greater conformity to group normsin CMC than in FtF (Coleman et al., 1999; Lea & Spears, 1991; Spears etal., 1990; Walther, 1996, 1997). According to the SIDE model,


deindividuation can actually amplify normative social influence, whencombined with a salient social identity. The model posits thatdeindividuating manipulations (e.g., anonymity) lead to a decreased fo-cus on personal identity and thereby increase responsiveness to situationalgroup norms (Postmes & Spears, 1998). Because physical isolation andvisual anonymity in CMC attenuate the perception of intragroup differ-ences, computer mediation may indeed reinforce group salience and con-formity to group norms (Spears & Lea, 1992).

There exists empirical evidence supporting this apparentlycounterintuitive notion. In the context of CMC, when group identity wasmade salient, deindividuated participants showed attitude polarizationtowards the group norm, while those whose individual identity was em-phasized showed attitude change away from the norm (Spears, Lea, &Lee, 1990). Similarly, while the anonymous CMC condition showed a ten-dency to conform to group norms activated from the prior task, the iden-tifiable CMC condition did not demonstrate such a tendency, suggestingthat the deindividuation actually led to greater normative influence inCMC (Postmes, Spears, & Lea, 1998). Conformity to the group norm isapparently not limited to experimentally induced situations. A more re-cent study demonstrated that students using emails as part of a coursedeveloped group norms that defined communication patterns withingroups, and that conformity to group norms increased over time (Postmes,Spears, & Lea, 2000). The research here did not impose any group iden-tity on the participants in experiment 1, with all ostensible communica-tion partners simply referred to as “other participants” instead of “othergroup members.” However, it is still possible that the participants felt in-group feelings with their partners presumably recruited from the sameclass as themselves. In the absence of other information about theirinteractants, it is possible that this subtle social context cue might havetaken on particularly great value and worked as a group identity cue(Walther, 1997). If this is the case, computer mediation should not inter-fere with the normative social influence process, and may even amplifygroup pressure.

Previous studies of CMC have focused on differences, or a lack thereof,between FtF and CMC, and thus the claim of reduced normative influ-ence in CMC was based on comparison of two contexts. The presentexperiment, however, takes a different approach by focusing on the dif-ference between the conforming responses to a group and those to anindividual. Ultimately, (even if the overall conformity level is lower inCMC than in FtF), if there is a significant difference between the waypeople respond to conformity pressure induced by multiple partners ver-sus a single partner, it would undermine the claim of “liberation fromgroup pressure” in CMC.2


In testing group influence on people’s decision-making, we assumethat conscious modification of opinion expression (i.e., public compliance)and genuine opinion change (i.e., private acceptance) can take place si-multaneously, but possibly not to the same extent (e.g., Jennings & George,1984). Because a group can also exert informational influence, a demon-stration of conformity to the group was considered insufficient to shownormative social influence (Allen, 1965; Insko et al., 1985; Trafimow &Davis, 1993). Thus, for a more stringent test of normative influence, wemeasured both public and private response to see if group influence wouldshow different patterns depending on the response situation.

Probably due to reverse demand characteristics, studies have foundthat a group of three to six members produced maximal influence on singledeviant minorities: Adding more people to the majority beyond this pointfailed to increase group pressure and subsequent conformity behavior(Barnard, 1991; Insko et al., 1985; Wilder, 1977). Thus, we comparedpeople’s conforming responses to four individuals with those to a singleindividual to test if and how group pressure operates in CMC.

H1: Individuals presented with four interactants’ unanimous opinions willmanifest greater public compliance than will individuals exposed to oneinteractant’s opinion.

H2: Individuals presented with four interactants’ unanimous opinions willmanifest greater private conformity than will individuals exposed to oneinteractant’s opinion.

The nature of the issue being decided poses certain demands on thesort of influence likely to emerge in decision-making groups. That is, in-tellectual issues with demonstrably correct answers evoke informationalinfluence, whereas judgmental issues involving evaluative preferencelikely evoke normative influence (Huang et al., 1997; Kaplan, 1989; Kaplan& Miller, 1987). Unlike informational influence that presumably leads topublic compliance with private change (Allen, 1965; Kaplan, 1989), nor-mative influence was expected to be more salient in a public situationthan in a private situation. Therefore, while we expect that group confor-mity effects will take place in both response settings, we also predict thatthe group conformity effect will be more powerful in a public setting thanin a private setting.

H3: Group conformity effects will be more pronounced when individuals’ re-sponses are seen by their communication partners, compared to when theresponses are given in private.


CMC AND VISUAL REPRESENTATION

To examine conformity behaviors in CMC, the first decision to be madewas how to represent the interactants. Unlike face-to-face interaction, CMCmust “represent” the interactants, either visually, orally, textually, etc.Rather than control for the potential effect of visual representation, wetook the opportunity to explore another intriguing question: Does it makeany difference how the interface represents the interactants?

Although the ability to free people from appearance-related biases andsubsequent discrimination is assumed to be one of text-based CMC’s mostimportant characteristics, the question of how visual representation af-fects impressions of, and behaviors toward, visually anonymousinteractants in CMC has not yet been scrutinized. A couple of studieshave reported the positive effects of dynamic computer-generated graphicspresentation (vs. text-only treatment) on persuasion (King, Dent, & Miles,1991) and on participation in computer-mediated small-group discussion(Ahern, 1993). Nevertheless, neither study addressed how the represen-tation of communicator, as opposed to the message itself, alters a recipient’sreaction to persuasive attempts.

Given that visual representation has nothing to do with the real iden-tity of the interactants, it seems unreasonable for individuals to make dif-ferent attributions about the interactants on the basis of the representa-tion. The current research seems to be in the spirit of the classic Heiderand Simmel (1944) study which showed that participants will use hu-man-like language when asked to describe the behaviors of simple geo-metric figures that moved in relationship to each other on film (e.g., “thetriangle is trying to get into the house”). However, there are two criticaldifferences that make the Heider and Simmel study not relevant. First,Heider and Simmel adduce no evidence that individuals actually attrib-uted social characteristics to the shapes. That is, there is no evidence thatindividuals did anything more than use a convenient language to de-scribe complex behaviors in that research paradigm. Second, in the Heiderand Simmel work, there was no reference to an explicit person representedby these shapes. Unlike this study, the communication partner was notidentified and described as a completely separate entity.

When there exist competing cues that directly reflect who the personbehind the screen is, such as the way they write or the topics they bringup, it appears irrational to judge the person based on how they look onthe computer screen. Therefore, the representation of interactants in CMCintroduces another provocative question: What does identity mean in vir-tual space? Malleability of identity has been considered one of the mostdistinctive features of CMC, such that people not only interact with oth-ers without revealing who they are, but also freely create their identity as


they wish (see Turkle, 1997, for a detailed documentation of how peoplein the virtual world experiment and play with their identity). Wouldpeople react differently toward their interactants based on what they looklike on the screen, even when they are aware of the discrepancy between“representation” and “real identity”? How effective would it be to strate-gically manipulate the way we look in cyberspace in order to create apositive impression?

To address this issue, we selected three different forms of visual repre-sentations: a simple textbox that displayed the message only, a stick fig-ure that showed only two poses (idling and speaking), and a fully ani-mated cartoonish character with facial expressions and body movements(see Figures 1–3). It should be noted that unlike richer media, which hasgreater channel capacity and delivers more personal information aboutinteractants, the richer characters implemented in Experiment 1 and 2 donot carry any more personal identity cues than the stick figures or eventhe textboxes. A recent study examined the possible benefits of seeingothers’ faces in CMC by presenting the participants with their photo-graphs. The researchers found that seeing a partner promoted affectionand social attraction in new, unacquainted virtual teams (Walther,Slovacek, & Tidwell, 2001). While the finding seems to corroborate themedia richness account as a viable explanation for the differences betweenFtF and CMC in terms of interpersonal perception (at least in a new col-laborative group), the manipulation of visual representation in the presentstudy is fully distinct from seeing another’s face or hearing their voice:The richer characters merely serve as a reminder of another human beingworking behind the screen, which may be easily forgotten in machine-mediated interaction.

EXPERIMENT 1

Method

Participants were 72 (36 male and 36 female) college undergraduatesenrolled in a communication class.3 They were randomly assigned in a 2(public response vs. private response) × 2 (one interactant vs. fourinteractants) × 3 (textbox vs. stick figure vs. animated character) mixed-de-sign experiment, with response condition as a within-subject factor and theothers as between-subject factors. All conditions were balanced for gender.4

As a judgment task, we used choice-dilemma items for the experiment.Choice-dilemma items, originally devised to examine the risky shift phe-nomenon in small decision-making groups (Kogan & Wallach, 1967;Moscovici & Zavalloni, 1969), pose a choice between two courses of


action—one of high risk (more rewarding but lower likelihood of at-tainment) and one of low risk (less rewarding but higher likelihoodof attainment).

Procedure

Upon arrival, the participant was told that he or she would interactwith another, or four other, participants via computer. To convince par-ticipants that this was a real-time interaction among one or more part-ners, the participant was asked to pick one of the two numbered cardsthat determined the room to which they would be assigned. In fact, bothcards had the identical number (2 for the one-partner condition and 5 forthe four-partner condition), so that all participants went to the same roomand expressed their opinions last.

Once the experimental session started, the first of eight hypotheticalscenarios was presented on the computer screen. For example:

Ms. E, a college senior, has studied the piano since childhood. She has wonamateur prizes and given small recitals, suggesting that she has consider-able musical talent. As graduation approaches, she has the choice of takinga medical school scholarship to become a physician, a profession whichwould bring certain financial rewards, or entering a conservatory of musicfor advanced training with a well-known pianist. She realizes that evenupon completion of her piano studies, success as a concert pianist wouldnot be assured.

After the participant read the scenario and proceeded to the next page,the (ostensible) interactant(s) gave their decision(s), and the participantalways went last. In the four-interactants condition, all the characters gavethe same decision, one after another at a random time interval, rangingfrom 4 to 9 seconds, as if each person had indicated his or her decisionafter reading the prior individuals’ decisions. In order to reduce suspi-cion about the purpose of the experiment, three filler scenarios were pre-sented in which there was divergence of opinion among the fourinteractants. Responses to these scenarios were not analyzed.

The participant then gave his or her recommendation, which was dis-played by the character assigned to the participant. The six response op-tions were: (a) definitely should do A, (b) should do A, (c) probably shoulddo A, (d) probably should do B, (e) should do B, (f) definitely should doB. The scale was treated as ranging from 1 to 6 in unit increments, withmaximum agreement as 6 and maximum disagreement as 1. By display-ing the participant’s decision along with the previously showninteractants’ decisions, we intended to contrast any discrepancy in opin-


ion between the participant and interactants, as well as to ensure thattheir decision was correctly recognized by the interactant(s). See Figures1–3 for examples of screen snapshots of the decision display.

After the initial decision was made the participant typed in an argu-ment to support his or her decision. Once the participant finished typing,a preprogrammed argument, ostensibly written by (one of) their partner(s),was delivered by the character on the screen (e.g., “Ms. E should think ofmusic as a hobby. She can still play piano for the fun of it while she getsher medical degree. And if she changes her mind later on, it’s easier to goback to the arts after getting scientific training than vice versa”). This pat-tern was repeated for the eight different scenarios.

When the participant was finished, he or she filled out a paper-and-pencil questionnaire. To measure private conformity, the participant wasprovided a summary of the scenarios presented during the interactionand asked to propose a course of action for each scenario. The responseswere considered private because they were neither visible to nor identifi-able by their partners. Given the possibility that participants feel pres-sure to provide responses consistent with the ones they have already pub-licly endorsed (Myers, 1982), to make participants feel more comfortableabout expressing a different opinion from their initial answer, the follow-ing statement preceded the questions: “Once people have gone throughall of the scenarios, they sometimes get a different perspective on theiranswers. Please indicate your present views of the scenarios.” For each

Figure 1. Screen Snapshot of Participant’s Decision Along With the Interactants’: FourInteractants, Textbox Condition


critical scenario, participants were asked to mark their decision on a10-point Likert scale ranging from definitely should do A (1) to defi-nitely should do B (10).

Index Construction

To control for the contaminating effects of people’s predispositionalrisk-taking tendency and to address differences in the private conformityand public compliance scales, the indices were created as follows. First,to estimate individuals’ risk-taking tendency, the conformity scores onscenarios where the cautious choice was given as the unanimous groupopinion were reversed, so that every response score indicated how riskythe decision was regardless of what the group opinion was. Second, allresponse scores were then z-transformed within each participant, indi-cating how much riskier or more cautious each decision was, comparedto his or her average risk-taking tendency as the baseline. Third, for thescenarios where the cautious decision was presented as the group opin-ion, the sign of the z-score was reversed so that positive scores indicateda move toward conformity with the group opinion, whereas negativescores indicated anticonformity. Finally, we averaged the corrected z-scores

Figure 2. Screen Snapshot of Participant’s Decision Along With the Interactants’: FourInteractants, Stick Figure Condition


of the responses across the five critical scenarios to measure public com-pliance (Cronbach’s α = .64) and private conformity (α = .67). The accept-able but relatively low reliability scores were not surprising given thateach scenario was quite different (see the Appendix for the complete listof scenarios). For example, participants might have found it easier toform an opinion about a college senior’s career path than about abusinesswoman’s political involvement.

The social presence index was comprised of the following items thathave been used in previous studies (Rice & Love, 1987; Short et al., 1976):warm, sociable, personal, and sensitive (α = .87). The participant was askedto indicate “How well does each adjective describe the other participant(s)you just worked with” on a 10-point Likert scale that ranged from de-scribes very poorly to describes very well in the paper-and-pencil posttestquestionnaire.

Although not included as part of the hypotheses, in order to furtherexplore the possible effects of character representation on conformity, threeadditional variables deemed to be important heuristic cues in mediatingsocial influence were measured: competence, social attractiveness, and trust-worthiness (Perloff, 1993). To create the indices for these variables, partici-pants were asked to indicate how well each adjective described theirinteractant(s) on a 10-point Likert scale.

The competence index measured participants’ perceptions of theirinteractant(s) with respect to intellectually desirable traits. It consisted of

Figure 3. Screen Snapshot of Participant’s Decision Along With the Interactants’: FourInteractants, Animated Character Condition


two items: intelligent and competent (α = .82). The social attractiveness in-dex measured the degree to which participants found their interactant(s)interpersonally attractive and was composed of three items: friendly,pleasant, and attractive (α = .79). Trustworthiness, which has been consid-ered as a key mediator of social influence, was an index of two items:trustworthy and reliable (α = .70).

Results

There were two parts to the data analyses. First, for the hypothesesinvolving the effects of the response situation, we ran a mixed-designANOVA with one within-subject factor (private response vs. public re-sponse) and two between-subjects factors (one interactant vs. fourinteractants and animated character vs. stick figure vs. textbox). Second,we ran two-way ANOVAs using only the between-subjects factors forpublic compliance and private conformity separately, as well as for thesource perception variables (see Tables 1 and 2 for means and standarddeviations).

The primary prediction of this experiment was that group influence,operationalized as the discrepancy between the conforming responses tofour interactants and those to one interactant, would be greater when theresponses were visible to the interactants compared to when they werenot. As predicted, a significant interaction was found between responsesituation and the number of interaction partners, F(1,66) = 5.25, p < .03, η2

= .07. To decompose this interaction, the hypotheses about public compli-ance and private conformity were then tested separately. Participants ex-pressed greater agreement with the unanimous group opinion than withthe same opinion presented by a single other individual, F(1, 66) = 15.9, p< .001, η2 = .19. However, this group effect was attenuated when the re-sponses were provided in a private situation, although the difference wasin the predicted direction, F(1, 66) = 2.43, p < .13. In addition, paired-samples t-tests showed that those who were exposed to one interactant’sopinion displayed greater discrepancy between the expressed opinionand the opinion measured in private, with private conformity higher thanpublic compliance, t(35) = -2.41, p < .03, whereas those who were con-fronted with four interactants’ unanimous opinion did not demonstratesuch disparity, t(35)= .74, p > .46. This finding is further analyzed in thefollowing discussion section.

Due to the lack of empirical basis on which the effects of visual repre-sentation in CMC could be hypothesized, we did not predict specific rep-resentation effects on conforming behaviors. Interestingly, there was asignificant main effect of visual representation on public compliance, F(2,66) = 3.78, p < .03, η2 = .10, but no such effect was found for private confor-mity, F(2, 66) < 1.00. A post hoc Scheffé test confirmed that people ex-


pressed greater agreement with the interactants’ opinion when they wererepresented by textboxes (M = .03) than by animated characters (M =-.33), p < .05. There was no interaction effect between the number andrepresentation of interactants for either conformity measure.

Although no specific effect on source perception was explicated, theperception of interaction partners could potentially serve to mediate theconformity effect predicted in the hypotheses. Not surprisingly, there wasno significant main effect for the number of interactants on source per-ception measures. On the other hand, visual representation significantlyinfluenced the perception of communication partners. That is, those whowere represented by animated characters were thought to be intellectu-ally superior to their counterparts assigned to textboxes or stick figures,F(2, 66) = 3.40, p < .04, η2 = .09 . Similarly, individuals tended to perceivetheir interactants as more trustworthy and socially attractive when theywere represented by animated characters, F(2, 66) = 3.63, p < .04, η2 = .10,and F(2, 66) = 3.15, p < .05, η2 = .09, respectively.

Furthermore, visual representation also contributed to the perceivedpresence of communication partners in the interaction, F(2, 66) = 3.50, p <.04, η2 = .10. That is, animated characters added the greatest “social pres-ence” to the invisible communication partners, followed by stick figuresand then textboxes. There was no significant interaction effect betweenthe number and representation of interactants on any of the perceptionmeasures.

Discussion

Experiment 1 investigated group influence by directly comparing con-forming responses to a single interactant with those to a group of

TABLE 1Mean Scores for Public Compliance and Private Conformity by Number and Visual

Representation of Interactants in Experiment 1

One interactant Four interactants

Representation Textbox Stick fig. Animated Textbox Stick fig. Animated

M SD M SD M SD M SD M SD M SD

Public compliance -.13 .53 -.40 .31 -.49 .34 .19 .65 .33 .47 -.17 .54

Private conformity -.03 .54 -.32 .63 -.13 .57 .05 .64 .21 .49 -.07 .63

NOTE: Table entries are z-scores corrected by individuals’ overall risk-taking tendency(N = 12 per cell).


interactants, in both public and private situations. As predicted, a signifi-cant interaction was observed between the number of interactants andthe response condition. Unanimous group opinion exerted greater con-formity pressure than an individual’s opinion when the participant’s re-sponses were (supposedly) shown to the interaction partners as opposedto when responses were given in private. This finding is noteworthy, par-ticularly given the weak manipulation of “publicness” employed in theexperiment. Participants worked under complete anonymity, so what-ever decision they made, it was impossible to be recognized by theinteractants even if there had been an opportunity to work with the sameinteractants again (Walther, 1994). Nevertheless, the mere fact thatinteractants would see the participant’s responses apparently led to dif-ferent reactions than they would have exhibited otherwise.

From a different point of view, however, this interaction might beviewed as indicating psychological reactance within the one-interactantcondition, with no corresponding tendency in the four-interactant condi-tion. As noted earlier, those who ostensibly interacted with another indi-vidual showed significantly greater private conformity (M = -.15) thanpublic compliance (M = -.34). Although it is tempting to interpret thisfinding in light of reactance theory, which predicts that individuals willresist attempted influence by choosing the opposite alternative when theyfeel their freedom of choice is threatened by someone else telling themwhat to choose (Brehm, 1966), several factors render this account less com-pelling. First, our participants were simply exposed to a statement of pref-erence of the other person, as opposed to being directed to follow the

TABLE 2Mean Scores for Source Perception by Number and Visual Representation of

Interactants in Experiment 1




Social presence 4.7 1.89 5.2 1.44 5.4 1.23 4.3 1.28 4.9 1.51 6.1 1.98

Social attractiveness 5.0 1.82 5.4 1.06 6.0 .79 4.9 1.80 5.7 1.28 5.9 1.48

Trustworthiness 6.1 1.42 6.4 1.45 7.5 1.29 6.5 1.70 7.0 1.26 7.4 1.32

Competence 7.4 1.40 7.3 1.56 8.2 1.18 7.8 1.50 6.9 2.00 8.3 1.59

NOTE: All variables were measured by 10-point interval scale (N = 12 per cell).


lead. In fact, one study reported that mere exposure to another person’spreference positively influenced the target individual’s decision, whilean explicit attempt to influence their decision resulted in rejection of theproposed decision (Brehm & Sensenig, 1966). Second, in this experiment,private conformity was operationalized as a measure of informationalsocial influence and measured after seeing another individual’s decision(or decisions) and the supporting argument. Therefore, as persuasive ar-guments theory for group-induced opinion shift posits (e.g., Burnstein &Vinokur, 1977), the argument presented after the participant expressedhis or her decision likely shifted his or her opinion closer to the positionadvocated in the argument. In this regard, the lack of opinion discrep-ancy between public and private settings within the four-interactant con-dition appears to provide additional support for the existence of norma-tive influence operating in the public setting: The normative pressure elic-ited public compliance to such a great extent that additional argumentsfailed to lead to private acceptance beyond that level. Finally, it is difficultto explain why psychological reactance did not appear in the four-interactant condition. Given that the basis for psychological reactance isthe perceived threat to individual freedom, resistance to attempted influ-ence should grow in proportion to the size of the threat, which presum-ably is greater when confronted with four people’s unanimous decisionsthan when faced with one person’s.

The evidence of group influence is more compelling given the natureof the “group” in this study. Conformity to the group norm more likelyoccurs when: (a) the group is required to reach a group consensus(McGuire et al., 1987), (b) members of a genuine group are faced with acommon task rather than simply receiving data about one another’s views(Wallach & Malbi, 1970), (c) there is an opportunity for future interactionwith the same group (Lewis, Langan, & Hollander, 1972), and (d) per-sonal costs in not yielding to the majority are high (Perrin & Spencer,1981). None of these conditions applies to Experiment 1. At the very least,no group identity was imposed on the participant at all: The interactionpartners were simply referred to as “other participants,” instead of asother “group members.” Therefore, the finding that people expressed moreagreement with multiple partners compared to a single partner convinc-ingly shows the robustness of group influence in CMC.

Another interesting finding concerns how the visual representationaffects source perception and public compliance. Surprisingly, peopleexpressed greater consent with their participants’ decisions when theywere represented by simple textboxes rather than by animated charac-ters. By contrast, people perceived their partners to be more competent,trustworthy, and socially attractive when represented by the fully ani-mated characters than less human-like forms. With regard to the effect on


public compliance, one possible explanation would be that textboxes didnot vary at all except for the color, whereas animated characters assumedclearly different appearance from each other, manifesting visible indi-vidual differences among the (ostensible) discussants. Even though thecharacters did not carry any personal information of the interactants theyrepresented, the recognizable visual differences among characters mighthave served as a reminder of the intragroup differences. In the textboxcondition, on the other hand, participants might have felt a greater senseof groupness due to the lack of visible difference between the rest of thegroup and themselves. The problem with this explanation is that it doesnot account for why this similarity-between-characters effect did not spillover onto the source perception variables, even though social identifica-tion theory predicts positively biased source perception of in-group mem-bers (e.g., Turner, 1985).

Although Experiment 1 demonstrated the persistence of normative so-cial influence in a simulated CMC setting, several questions concerningthe theoretical implications of the findings remain. First, the question ofwhy such group pressure exists needs further investigation. In general,the findings appear to be in-line with the SIDE model, which predictsincreased salience of group identity and subsequent adherence to situ-ational group norms in situations like CMC, where individuating iden-tity cues are scarce (Lea et al., 1992; Postmes & Spears, 1998). Although noexplicit group identity was imposed, it was still possible that participants(perhaps unconsciously) assumed similarity with their partners who weresupposedly recruited from the same course. That is, the very fact thatthey were attending the same university and had a common interest inthe subject of the class might have worked as an implicit in-group cue.

Another question is how “normative” was the public compliance ob-served in Experiment 1. As pointed out by other researchers (e.g., Turner,1991), the processes of normative and informational influence are oftenindistinguishable: The effect of the public situation might not be purelyone of mere compliance, as implied by the concept of normative influ-ence. Indeed, the significant interaction between the number of interactantsand response condition indicates that public compliance to the group normis not reducible to informational influence. Still, it would reassure thenormative nature of public compliance if researchers could show the linkbetween public compliance and normative concerns operating in a givencommunication situation.

Finally, with regard to the effects of visual representation on personperception, some might argue that the participants did not evaluate theirinvisible interactants, but what they saw on the computer screen. Althoughwe made it clear in the questionnaire that they were being asked to ratetheir partners (by underlining the key word), the participants nonethe-


less might have responded to the characters’ appearance, which suggestsdifferences in competence and trustworthiness.

To address the aforementioned issues, another experiment was con-ducted. Unlike previous studies on CMC that typically used FtF interac-tion as a comparison group, Experiment 2 used the same preprogrammedcomputer used in Experiment 1. This time, however, participants weretold that they would be working with a computer agent(s), exchangingsome decisions and arguments. There were several theoretical reasonsfor using a human-computer interaction (HCI) condition as a compari-son group. First, no one would identify oneself with computers and thus,no potential social identification was expected to take place between theparticipant and the influencing agents. By eliminating the possibility ofgroup identification by explicitly bestowing an out-group identity (i.e.,computer) on interaction partners (cf. Nass, Fogg, & Moon, 1996), we couldtest the validity of the SIDE model, which explains conformity to groupnorms in CMC via increased salience of group identity and its norms.Given that normative pressure to comply exists only when individualsperceive the source of influence as belonging to one’s own category(Abrams et al., 1990), no public compliance effect was hypothesized. Sec-ond, as normative social influence, by definition, does not exist outsidehuman relationships, HCI provided conditions under which individualswould feel no normative pressure to regulate their behaviors. By compar-ing individuals’ reactions to other people observed in Experiment 1 withthose to impersonal entities (i.e., computers), we were able to focus onthe normative component of group influence. Third, this experiment of-fered an opportunity to determine if the effects of visual representationfound in Experiment 1 were an artifact of participants’ evaluating thecharacters, instead of human partners. Any representation effects differentfrom what was observed in Experiment 1 would rule out the possibility, asthe present experiment directly asked how participants perceived the com-puter agents whose identity was inseparable from what they looked like.

In sum, the following hypotheses were drawn for Experiment 2, pri-marily in contrast to what was found in Experiment 1.

H4: The number of computer agents will not affect individuals’ expression ofagreement with the group opinion. That is, there will be no significant dif-ference in public compliance between individuals exposed to four agents’unanimous opinions and those presented with one agent’s opinion.

H5: The number of computer agents will not affect individuals’ acceptance ofgroup opinion. That is, there will be no significant difference in private con-formity between individuals exposed to four agents’ unanimous opinionsand those presented with one agent’s opinion.


Obviously, the distinction between public and private situation wouldbecome illusory when people are interacting with computer agents. With-out normative group pressure, no public compliance beyond true opin-ion change was anticipated.

H6: The group conformity pressure will not be more pronounced in publicthan in private. That is, there will be no significant interaction between num-ber of interactants and response condition.

Experiment 2 also allowed for a critical test of the normative nature ofpublic compliance observed in Experiment 1. That is, if normative influ-ence contributed to eliciting public compliance in the CMC situation, theoverall public compliance level would be lower in the HCI condition,where no normative influence is implicated.

H7: Individuals interacting with other humans (CMC) will show greater pub-lic compliance than will those interacting with computers (HCI). By con-trast, no such difference will manifest in private conformity.

Unlike CMC where the character representation had nothing to do withthe personal identity of interacting partners, characters directly representthe computerized agents. Therefore, we expect a stronger association be-tween the character representation and source perception in the HCI case.

H8: The effects of visual representation on the perception of interactants willbe more pronounced among those who interact with computers than amongthose who interact with other humans.

TABLE 3Mean Scores for Public Compliance and Private Conformity by Number and Visual

Representation of Interactants in Experiment 2




Public compliance -.21 .52 -.63 .25 -.35 .51 -.15 .63 -.44 .44 -.20 .42

Private conformity .08 .56 -.43 .54 .02 .56 .08 .64 -.41 .37 -.04 .37

NOTE: Table entries are z-scores corrected by individuals’ overall risk-taking tendency(N = 12 per cell).


EXPERIMENT 2

Method

Participants were 72 (36 male and 36 female) college undergraduatesenrolled in a communication class. They were randomly assigned in a 2(public response vs. private response) × 2 (one interactant vs. fourinteractants) × 3 (textbox vs. stick figure vs. animated character) mixed-design experiment. All conditions were balanced for gender.

Procedure

Procedures used in Experiment 2 were identical to those employed inExperiment 1, except that participants were told that they would interactwith one computer agent or multiple computer agents, each representinga different computer in different rooms networked to one another.5

Index Construction

The indices were created precisely as in Experiment 1. The reliabilitiesfor public compliance (α = .62) and private conformity (α = .62) wereacceptable and consistent with Experiment 1. In addition, the social pres-ence (α = .87), competence (α = .89), social attractiveness (α = .77), andtrustworthiness (α = .71) indices were all acceptable.

TABLE 4Mean Scores for Source Perception by Number and Visual Representation of

Interactants in Experiment 2




Social presence 4.9 2.54 2.3 1.15 4.7 2.07 3.7 1.28 3.5 1.48 4.5 1.04

Social attractiveness 5.2 1.58 2.7 1.29 4.7 2.31 3.4 1.69 3.8 1.31 5.4 1.12

Trustworthiness 8.3 1.03 6.0 2.22 6.3 1.39 6.2 1.08 6.2 2.66 6.5 1.27

Competence 7.8 1.72 6.8 1.80 7.3 .95 7.8 1.32 7.2 1.14 7.3 1.67

NOTE: All variables were measured by 10-point interval scale (N = 12 per cell).


Results

The key question in Experiment 2 concerns if and how group pressureto conform would operate as normative concerns about social approvalbecome irrelevant in human-machine interaction. Given that normativesocial influence is defined only in a social context, it was anticipated thatthere would be no mere compliance effect over and above the true accep-tance of group opinion. Consistent with our prediction, a 2 (public re-sponse vs. private response) × 2 (one agent vs. four agents) × 3 (textboxvs. stick figure vs. animated character) mixed-design ANOVA showedthat there was no interaction between response situation and the numberof interactants, F(1, 66) = 1.84, p < .19. When the conformity effect wasexamined in each response situation separately, no main effect for thenumber of agents was found on public compliance, F(1, 66) = 1.40, p > .24,nor on private conformity, F(1, 66) < 1. As anticipated, multiple computeragents did not elicit more conforming responses than a single agent,whether the response was shown to the interactants or not (see Tables 3and 4 for means and standard deviations).

To further examine if this null effect is attributable to the impersonalnature of interaction, 2 (CMC vs. HCI) × 2 (one interactant vs. fourinteractants) × 3 (textbox vs. stick figure vs. animated character) ANOVAswere run on the combined data from both experiments. Results showed asignificant interaction between the nature of interactants and the numberof interactants on public compliance, F(1, 132) = 4.09, p < .05, η2 = .03,confirming that normative group pressure eliciting public complianceexists in CMC but not in HCI. No corresponding pattern of interac-tion, however, was found on private conformity, F(1, 132) = 1.57, p > .21,as the number of interactants failed to exert a significant impact onprivate opinion in both CMC and HCI. The results of this analysis aredepicted in Figure 4.

Consistent with the idea that the impact of the nature of interactantswould be more pronounced on public compliance than on private con-formity, a significant interaction was found between response situationand the nature of interactants, F(1, 132) = 3.83, p < .05, η2 = .03. When thehypotheses on public compliance and private conformity were tested sepa-rately, the results confirmed that the difference between CMC and HCIwas statistically significant only for public compliance, F(1, 132) = 7.41, p< .007, η2 = .05, not for private conformity, F(1, 132) = .57, p > .45. In addi-tion, paired-samples t-tests showed that individuals who interacted withother people via computer did not display opinion changes between publicand private conditions, t(71) = 1.25, p > .21, whereas those who workedwith a computer revealed significant opinion discrepancy, with privateopinion in greater congruence with the group opinion than the publiclyexpressed opinion, t(71) = 4.00, p < .001.


TABLE 5Analysis of Variance

Sum MeanSource of variation of squares df square F η2

Overall conformity in Experiment 1Within-subjects contrasts

Response situation .17 1 .17 1.67 .03Response × Number .53 1 .53 5.25* .07Response × Representation .47 2 .23 2.33 .07Response × Number× Representation .01 2 .01 .06 .00

Residual 6.63 66 .10

Between-subjects effectsNumber of interactants 4.06 1 4.06 8.49** .11Representation of interactants 1.52 2 .76 1.59 .05Number × Representation 1.51 2 .76 1.58 .05

Residual 31.54 66 .48

Overall conformity in Experiment 2Within-subjects contrasts

Response situation 1.70 1 1.70 15.71*** .19Response × Number .20 1 .20 1.84 .03Response × Representation .18 2 .09 .82 .02Response × Number× Representation .03 2 .02 .14 .00

Residual 7.13 66 .11

Between-subjects effectsNumber of interactants .12 1 .12 .32 .01Representation of interactants 4.93 2 2.46 6.38** .16Number × Representation .03 2 .02 .04 .00

Residual 25.48 66 .39

Public compliance in Experiment 1Main effects

Number of interactants 3.75 1 3.75 15.90*** .19Representation of interactants 1.79 2 .89 3.78* .10

Two-way interactionNumber × Representation .63 2 .31 1.32 .04

Residual 15.59 66 .24

Public compliance in Experiment 2Main effects

Number of interactants .32 1 .32 1.40 .02Representation of interactants 1.64 2 .82 3.63* .10

Two-way interactionNumber × Representation .05 2 .03 .10 .00

Residual 14.94 66 .23


Not surprisingly, visual representation of computer agents indeed ex-erted extensive influences on almost all dependent variables. For publiccompliance, a significant main effect of representation was observed, F(2,66) = 3.63, p < .03, η2 = .10. A post hoc Scheffé test showed that the differ-ence between the textbox condition (M = -.18) and the stick figure condi-tion (M = -.54) was statistically significant (p < .04). The same pattern ofrepresentation effect was found on private conformity, F(2, 66) = 6.47, p <.003, η2 = .16. The results of a Scheffé test confirmed that the stick figurecondition (M = -.42) revealed a significantly lower level of public accep-tance than animated characters (M = -.07, p < .05) and textboxes (M = .08,p <.005). Neither conformity measures showed any interaction betweenthe number and the representation of agents (both ps were > .90).

Source perception measures revealed a more complicated pattern ofinteraction between the number and the representation of agents. No mainor interaction effect of representation was found on perceived compe-tence (all ps were > .17). However, people rated the single computer agentrepresented by a textbox more trustworthy than an animated character orstick figure. No such effect appeared for the multiple-agent condition,F(2, 66) = 3.64, p < .04, η2 = .10. The interaction between the number andthe representation was also complex for social attractiveness: People foundone textbox more socially attractive than four textboxes while the oppo-site pattern was true for stick figures, F(2, 66) = 7.81, p < .001, η2 = .19.

TABLE 5 ContinuedAnalysis of Variance

Sum MeanSource of variation of squares df square F η2

Private conformity in Experiment 1Main effects

Number of interactants .83 1 .83 2.43 .04Representation of interactants .21 2 .10 .30 .01

Two-way interactionNumber × Representation .90 2 .45 1.31 .04

Residual 22.58 66 .34

Private conformity in Experiment 2Main effects

Number of interactants .05 1 .05 .02 .00Representation of interactants 3.46 2 1.73 6.47** .05

2-way interactionNumber × Representation .02 2 .01 .03 .00

Residual 17.67 66 .27

* p < .05, ** p < .01, *** p < .001.


There also emerged a significant main effect such that animated charac-ters (M = 5.07) were rated the highest on social attractiveness, followedby textboxes (M = 4.30) and stick figures (M = 3.25), F(2, 66) = 5.73, p <.005, η2 = .15. A post hoc Scheffé test, however, confirmed that the differ-ence was statistically significant only between the animated charactercondition and the stick figure condition, p < .001. Finally, similar to socialattractiveness, one textbox was perceived as more socially present thanfour textboxes, while the reverse was true for the stick figures, F(2, 66) =3.46, p < .04, η2 = .10. In addition, people rated animated characters (M =4.58) and textboxes (M = 4.31) on social presence higher than stick figures(M = 2.91), F(2, 66) = 6.87, p < .002, η2 = .17. A post hoc comparison con-firmed that it was the stick figure condition that displayed significantlylower perceived social presence than the textbox condition (p < .05) andthe animated character condition (p < .004).

In order to investigate if and how the nature of interactants (human vs.computer) influenced the perception of interaction partners, a series of 2(CMC vs. HCI) × 2 (one vs. four) × 3 (textbox vs. stick figure vs. animatedcharacter) ANOVAs were conducted on each of the source perceptionvariables. Results showed that people perceived their interactants differ-ently only along the dimensions of social presence, F(1, 132) = 18.6, p <.0001, η2 = .12, and social attractiveness, F(1, 132) = 25.60, p < .0001, η2 =.16. There was no significant main effect of the nature of interactants ontrustworthiness and competence (both ps were > .24).

Discussion

The results of Experiment 2 differ in important respects from Experi-ment 1. First, the number of computer agents failed to provoke differentconforming responses in both private and public situations: That is, peopledid not succumb to “group” pressure induced by multiple computeragents, nor were they more convinced by multiple agents’ unanimoussuggestions than by a single agent’s. In addition, the lack of interactionbetween the response situation and the number of agents demonstrated

Figure 4. Conformity by Response Situation and Number of Interactants in CMC and HCI

CMC

-0.4

-0.3

-0.2-0.1

0

0.1

0.2

public private

conformity

1

4

HCI

-0.5

-0.4

-0.3

-0.2

-0.1

0

public private

conformity

1

4


that the way people react to conformity pressure imposed by computers,if any, does not vary as a function of the publicness of the situation.

Interpretation of these findings must be tempered by two qualifica-tions. First, Hypotheses 4, 5, and 6 predict null results, which is generallyproblematic. Second, the present experiment may have had limited power.While we are somewhat comforted by the significant results for the par-allel hypotheses in Experiment 1 and the significant interaction compar-ing Hypotheses 1 and 4, caution is certainly warranted in interpretingthese nonsignificant results.

A direct comparison between CMC and HCI provided additional sup-port for the notion of normative influence, as distinct from informationalinfluence, operating in CMC. As we predicted, people expressed greateragreement with human partners than with computers, but they did notshow greater private acceptance of the decisions proposed by humaninteractants—the source of information, whether it be human or com-puter, did not seem to affect the evaluation of the validity of information.On the other hand, the pressure to comply with the group norm did notextend outside CMC.

Another noteworthy finding was the main effect of the representationon both conformity measures, which can be characterized by the stickfigure agents being significantly more ineffective in eliciting conformitythan the other forms of representation. With the public vs. private dis-tinction minimized, it is not surprising to see identical effects of characterrepresentation in both response settings. Combined with the findings onsocial attractiveness and social presence, for which the stick figure condi-tion received the lowest ratings, the representation effect on conformityappears to reflect people’s dismissal of suggestions provided by less per-sonal and less likable entities. The significant, albeit small, correlationbetween overall conformity and social attractiveness lends support to thisconjecture (r = .25, p < .05). However, it is not clear why stick figuresreceived less favorable ratings than textboxes. We speculate that whilethe textboxes seemed ordinary, the stick figures were well below the qualityof characters usually found on the Web or in other media.

In addition, Experiment 2 yielded significant interactions between thenumber and the representation of interactants on all perception measuresexcept competence. The pattern can be characterized by one textbox be-ing perceived more positively than four textboxes, while the opposite wastrue for the stick figures. There seems to be no clear-cut explanation as towhy the representation effect worked in conjunction with the number ofagents. Yet, it should be noted that the effect of character representationon users’ perception of the interface is not universal, but contingent onother factors, such as the number of agents depicted on the screen.

Finally, the finding that people evaluated human interactants differ-ently from computers only on social attractiveness and social presence


merits note. Computers and human interactants were not rated differ-ently on dimensions such as competence and trustworthiness. This mayindicate that people have a clear conceptual distinction in mind betweenvarious dimensions of source perception. Humans were not more posi-tively rated across all dimensions. Second, it illustrates the commonlyheld notion of what differentiates computers from humans—the computercan be as intelligent and reliable as humans, but not possibly as friendlyand sensitive. Consequently, we can reject the argument that the repre-sentation effects found in Experiment 1 are attributable to participantsevaluating the character(s) instead of the human partner(s).

GENERAL DISCUSSION

Contrary to the popular image that computer mediation liberates peoplefrom normative concerns, Experiment 1 showed that people nonethelesssuccumbed to group pressure when interacting with other people viacomputer who they believed would see their responses. Results fromExperiment 2 confirmed that the normative influence observed in CMCstems from individuals’ awareness that they were engaged in “social”interactions. Taken together, these findings seem to support the socialidentification deinividuation (SIDE) model, which postulates that nor-mative concerns persistently dictate human behaviors in CMC, especiallywhen group identity is made salient. What renders our findings even morecompelling is that unlike typical experimental tests of the SIDE model,we did not explicitly induce any group identity among participants norclearly define the group norm to comply with, although we cannot ruleout the possibility that participants used the information as to who theirinteractants were (human vs. computer) as an implicit group-defining cue.

The lack of group effects in HCI, however, seems inconsistent with theline of research that has documented the robust tendency of people torespond to computers as if they were real people (Nass & Moon, 2000;Reeves & Nass, 1996). Successful replication of a wide array of well-knownsocial rules in HCI settings, including those involving politeness (Nass,Moon, & Carney, 1999), reciprocity (Katagiri, Nass, & Takeuchi, 2001),and humor (Morkes, Kernal, & Nass, 2000), has demonstrated that peopletreat computers as if they were social actors. According to this para-digm, normative group influence should also have been observed inthe HCI condition.

This discrepancy might be due to the unique characteristics of ourexperiment. First, unlike most previous studies on the “computers aresocial actors” paradigm, the extent to which participants felt that theywere in a “group” with the computer was crucial. Given that conformity


pressure in a judgmental task is a function of the individuals’ sense ofattachment or belonging to the group, it is not surprising that the partici-pants did not feel conformity pressure from computer agents. Thus, if wehad induced a minimum level of group feeling, for example, by tellingthe participants that they would work with computers as a group (cf.Nass, Fogg, & Moon, 1996), we might have found normative influence.Second, this experiment was the first attempt to apply the concept of“group” to the HCI setting. What does it mean to work with multiplecomputers? Do people ascribe individuality to different computers? Es-pecially when the computer agents were giving the same opinion, it wouldhave been particularly difficult to envision them as separate entities, blur-ring the distinction between one versus four.

Another interesting finding was the effect of visual representation onsource perception in CMC. Considering that the characters were arbitrarilyassigned objects to simply indicate the existence of individual entitiesworking in other places, it is surprising that users formed impressionsabout anonymous strangers based on the attributes of the characters.Therefore, this finding should be explored further. For example, wouldrandomly assigned gendered characters induce gender stereotypical per-ception about their interactants, even when the individuals know thatthe gender of the character does not necessarily match that of their part-ners (cf. Lee, Nass, & Brave, 2000)? Would the responsiveness to the rep-resentation be accentuated if individuals were allowed to choose the char-acter that represents themselves? In this regard, it would also be worth-while to examine if and how gender moderates the effects of pictorialrepresentation. Although no gender effects (main or interaction) wereobserved in this study, previous studies have shown that women are morereliant on visual cues in interaction and thus more affected by its absence inCMC (e.g., Dennis, Kinney, & Hung, 1999). Differences in how women andmen respond to variations of visual cues in CMC merit further investigation.

Two limitations of the experiments should be noted. First, in attempt-ing to separate normative social influence from informational social in-fluence, we employed the response situation as a within-subject factor.However, because we asked the same question twice and presented addi-tional information between the first and the second time of responding, itwas impossible to assess mere compliance effects against the individual’strue opinion. If we had measured participants’ private opinion in a pre-test, it would have served as the baseline information against which wecould measure: (a) how the presence of others and the unanimity of groupopinion affects the individual’s publicly stated opinion, and (b) how in-formational influence created by exposure to the unanimous group opin-ion affects private opinions. Second, the scales used to measure publiccompliance and private conformity were not the same. Although we ame-


liorated the problem by z-transforming each measure, future researchshould examine the trade-off between consistency of items and the influ-ence of response set.

In Experiment 2, we used HCI as a comparison group to CMC for sev-eral theoretical reasons. First, the claims of reduced normative social in-fluence have been mostly based on the comparison between FtF and CMC,with weakened social presence being the primary explanation for thisdifference. Thus, as a way to examine the validity of the social presenceaccount, we explored the other direction on the “social” continuum: WouldCMC be any different than a completely nonsocial interaction, in terms ofevoking normative responses? This study demonstrated that such a dif-ference does exist, indicating that computer mediation does not completelyremove “socialness” from the mediated interpersonal encounter. Second,it allowed a direct test of the “mechanomorphism” account of theantinormative behavioral tendency in CMC, which argues that whenimmersed in an interaction via computer, people likely computerize anddehumanize their invisible partners (e.g., Shamp, 1991). The significantdifference between CMC and HCI on public compliance and source per-ception measures, however, renders this explanation less plausible. Third,by employing HCI, we were able to hold the modality of interaction con-stant, as well as other potentially confounding factors, such as physicalattractiveness of human confederates. Thus, the current research suggeststhat face-to-face is not the only valuable comparison group when attempt-ing to understand processes in computer-mediated communication.

In conclusion, this study shows that even when there are no social cuesavailable in CMC, the very belief that an individual is interacting withother people elicits compliance to normative social pressure. Althoughcomputer-mediated communication seems to strip out the richness ofsocial interaction, normative group influence is remarkably robust.

APPENDIX

Choice Dilemmas Involving Unanimous Group OpinionMs. E, a college senior, has studied the piano since childhood. She has won amateur

prizes and given small recitals, suggesting that she has considerable musical talent. As gradu-ation approaches, she has the choice of taking a medical school scholarship to become aphysician, a profession which would bring certain financial rewards, or entering a conser-vatory of music for advanced training with a well-known pianist. She realizes that evenupon completion of her piano studies, success as a concert pianist would not be assured.

Mr. D, a married man with two children, has a steady job that pays him about $60,000per year. He can easily afford the necessities of life, but few of the luxuries. His father, whodied recently, carried a $40,000 life insurance policy. He would like to invest this money instocks. He is well aware of the secure “blue-chip” stocks and bonds that would pay ap-proximately 6% on his investment. On the other hand, he has heard that the stocks of a


relatively unknown Company X might double their present value if a new product cur-rently in production is favorably received by the buying public. However, if the product isunfavorably received, the stocks would decline in value.

Mr. G is a surgeon with a well-established surgical practice. He is married and has threechildren, one of which is just starting college. During a backyard session of football, heseriously dislocated his shoulder. Although the shoulder was properly reset at the time, thedislocation produced some nerve damage and he has been experiencing a great deal of painever since. An operation is available that will relieve the pain if completely successful, butthe operation also poses a risk of producing a permanent decrement in manual dexterity.The decrement in dexterity is normally inconsequential, but in his case, it could preventhim from continuing his surgical practice.

Ms. F is contemplating marriage to Mr. P, a man whom she has known for a little morethan a year. Recently, however, a number of arguments have occurred between them, sug-gesting some sharp differences of opinion in the way each views certain matters. Indeed,they decide to seek professional advice from a marriage counselor as to whether it would bewise for them to marry. On the basis of these meetings with a marriage counselor, theyrealize that a happy marriage, while possible, would not be assured.

Ms. K is a successful businesswoman who has participated in a number of civic activi-ties of considerable value to the community. She has been approached by the leaders of herpolitical party as a possible congressional candidate in the next election. Her party is aminority party in the district, though the party has won occasional elections in the past. Shewould like to hold political office, but to do so would involve a serious financial sacrifice,since the party has insufficient campaign funds. She would also have to endure the attacksof her political opponents in a hot campaign.

NOTES

1. It should be noted that this article focuses primarily on computer-mediated commu-nication among relatively anonymous strangers as opposed to acquaintances. We find itmore appropriate to test our hypotheses in computer-mediated interaction among strang-ers than among acquaintances, because normative social influence is less likely to persist insuch contexts. Thus, evidence supporting the existence of normative influence, if observed,would demonstrate the power of conformity pressure more clearly.

2. In fact, one study replicated the Asch experiment in a simulated CMC setting andfailed to find group effects: Subjects were willing to trust their own judgments and behaveindependent of other members (Smilowitz, Compton, & Flint, 1988). However, the findingthat some subjects attributed the discrepancy in their judgments to computer error suggestsanother possibility. Because the subjects could solve the conflict between their perceptionand others’ judgment by attributing the difference to an external cause (i.e., computer errorin this case), the erroneous majority did not impose strong conformity pressure (see Ross,Bierbrauer, & Hoffman, 1976, for how attribution process mediates group influence).

3. We initially recruited 82 participants. After the experiment, when asked if they thoughtthere were other participants in the experiment, a total of 10 of the participants expressedsuspicion regarding the existence of other participants. Their data were not analyzed.

4. Although some studies have demonstrated gender differences in conformity, withfemales being more susceptible to group pressure than males (see Eagly, 1983, for a review),post hoc ANOVAs confirmed that there were no main or interaction effects for gender onconformity in both Experiment 1 and Experiment 2. In Experiment 1, all F values rangedfrom .04 to .73, all p’s > .49, and all η2 < .02. Likewise, in Experiment 2, all F values fellbetween .002 and .77, all p’s > .47, and all η2 < .02. However, because gender was not oftheoretical interest in the current research, we did not include enough male and femaleparticipants to definitively rule out possible gender differences.


5. Because we did not further specify what “computer agent(s)” were, especially in termsof how autonomous and intelligent they were, participants might have made different in-ferences concerning the precise nature of their interactants. Given the current status of com-puter technology, however, it is possible that many participants believed that they wouldbe seeing preprogrammed responses. If this were the case, the HCI condition implementedhere can be conceptualized as asynchronous CMC between the unknown programmer(s)and the participant. This interpretation in fact renders the significant HCI-CMC differencein public compliance even more compelling: It would not be whether the communicationpartners are humans or computers, but whether they are “present” or not when the partici-pant expresses his or her dissent to the group opinion that determines the expression ofone’s opinion.

REFERENCES

Abrams, D., Wetherell, M., Cochrane, S., Hogg, M. A., & Turner, J. C. (1990). Knowing whatto think by knowing who you are: Self-categorization and the nature of norm formation,conformity and group polarization. The British Journal of Social Psychology, 29, 97–119.

Ahern, T. C. (1993). The effect of a graphic interface on participation, interaction, and stu-dent achievement in a computer-mediated small-group discussion. Journal of EducationalComputing Research, 9, 535–548.

Allen, V. L. (1965). Situational factors in conformity. Advances in Experimental SocialPsychology, 2, 133–175.

Barnard, W. A. (1991). Group influence and the likelihood of a unanimous majority. Journalof Social Psychology, 131, 607–614.

Brehm, J. W., & Sensenig, J. (1966). Social influence as a function of attempted and impliedusurpation of choice. Journal of Personality and Social Psychology, 4, 703–707.

Burke, K., & Chidambaram, L. (1995). Developmental differences between distributed andface-to-face groups in electronically supported meeting environments. Group Decisionand Negotiation, 4, 213–233.

Burnstein, E., & Vinokur, A. (1977). Persuasive argumentation and social comparison asdeterminants of attitude polarization. Journal of Experimental Social Psychology, 13, 315–332.

Caporael, L. R. (1986). Anthropomorphism and mechanomorphism: Two faces of the hu-man machine. Computers in Human Behavior, 2, 215–234.

Coleman, L. H., Paternite, C. E., & Sherman, R. C. (1999). A reexamination of deindividuaionin synchronous computer-mediated communication. Computers in Human Be-havior, 15, 51–65.

Cottrell, N. B., Wack, D. L., Sekerak, G. J., & Rittle, R. H. (1968). Social facilitation of domi-nant reseponses by the presence of an audience and the mere presence of others. Journalof Personality and Social Psychology, 9, 245–250

Dennis, A. R., Kinney, S. T., & Hung, Y-T. (1999). Gender differences in the effects of mediarichness, Small Group Research, 30, 405–437.

Diehl, M., & Stroebe, W. (1987). Productivity loss in brainstorming groups: Towards thesolution of a riddle. Journal of Personality and Social Psychology, 53, 497–509.

Diener, E., Fraser, S., Beaman, A. L., & Kelem, R. T. (1976). Effects of deindividuating vari-ables on stealing by Halloween trick-or-treaters. Journal of Personality and Social Psychol-ogy, 33, 178–183.

Dubrovsky, V. J., Kiesler, S., & Sethna, B. N. (1991). The equalization phenomenon: Statuseffects in computer-mediated and face-to-face decision making groups. Human-Com-puter Interaction, 6, 119–146.

Eagly, A. H. (1983). Gender and social influence: A social psychological analysis. AmericanPsychologist, 38, 971–981.

Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7, 117–140.


Heider, F., & Simmel, M. (1944). An experimental study of apparent behavior. Americal Jour-nal of Psychology, 57, 243–259.

Hiltz, S. R., Johnson, K., & Turoff, M. (1986). Experiments in group decision making: Com-munication processes and outcome in face-to-face versus computerized conferences.Human Communication Research, 13, 225–252.

Huang, W., Raman, K. S., & Wei, M. K. (1997). Effects of group support system and task typeon social influences in small groups. IEEE Transactions on Systems: Man and CyberneticsPart A – Systems and Humans, 27, 578–587.

Insko, C. A., Smith, R. H., Alicke, M. D., Wade, J., & Taylor, S. (1985). Conformity and groupsize: The concern with being right and the concern with being liked. Personality and So-cial Psychology Bulletin, 11, 41–50.

Jennings, L. B., & George, S. G. (1984). Group-induced distortion of visually perceived ex-tent: The Asch effect revisited. Psychological Record, 34, 133–148.

Jessup, L. M., Connolly, T., & Galegher, J. (1990). The effects of anonymity on group processin automated group problem solving. MIS Quarterly, 14, 312-321.

Kaplan, M. F. (1989). Task, situational, and personal determinants of influence process ingroup decision making. Advances in Group Processes, 6, 87–105.

Kaplan, M. F., & Miller, C. E. (1987). Group decision making and normative versus informa-tional influence: Effects of type of issue and assigned decision rule. Journal of Personalityand Social Psychology, 53, 306–313.

Katagiri, Y., Nass, C., & Takeuchi, Y. (2001). Cross-cultural studies of the computers aresocial actors paradigm: The case of reciprocity. In M. J. Smith, G. Salvendy, D. Harris, &R. Koubek (Eds.), Usability evaluation and interface design: Cognitive engineering, intelligentagents, and virtual reality (pp. 1558–1562). Mahwah, NJ: Erlbaum.

Kelman, H. C. (1958). Compliance, identification, and internalization: Three processes ofattitude change. Journal of Conflict Resolution, 2, 51–60.

Kiesler, C. A., & Kiesler, S. B. (1969). Conformity. Reading, MA: Addison-Wesley.Kiesler, S., Siegel, J., & McGuire, T. W. (1984). Social psychological aspects of computer-

mediated communication. American Psychologist, 39, 1123–1134.Kiesler, S., & Sproull, L. (1992). Group decision making and communication technology.

Organizational Behavior and Human Decision Processes, 52, 96–123.King, W. C., Dent, M. M., & Miles, E. W. (1991). The persuasive effect of graphics in com-

puter-mediated communication. Computers in Human Behavior, 7, 269–279.Kogan, N., & Wallach, M. A. (1967). Risky-shift phenomenon in small decision-mak-

ing groups: A test of the information-exchange hypothesis. Journal of ExperimentalSocial Psychology, 3, 75–84.

Lea, M., O’Shea, T., Fung, P., & Spears, R. (1992). “Flaming” in computer-mediated commu-nication: Observations, explanations, implications. In M. Lea (Ed.), Contexts of computer-mediated communication (pp. 89–112). Hertfordshire, UK: Harvester Wheatsheaf.

Lea, M., & Spears, R. (1991). Computer-mediated communication, de-individuation, andgroup decision-making. International Journal of Man-Machine Studies, 34, 283–301.

Lee, E. J., Nass, C., & Brave, S. (2000). Can computer-generated speech have gender? Anexperimental test of gender stereotypes. In CHI 2000 Extended Abstracts (pp. 289–290).New York: ACM Press.

Lewis, S. A., Langan, C. J., & Hollander, E. P. (1972). Expectation of future interaction andthe choice of less desirable alternatives in conformity. Sociometry, 35, 440–447

Maslow, A. H. (1948). “Higher” and “lower” needs. Journal of Psychology, 2, 433–436.Matheson, K., & Zanna, M. P. (1988). The impact of computer-mediated communication on

self-awareness. Computers in Human Behavior, 4, 221–233.McGuire, T. W., Kiesler, S., & Siegel, J. (1987). Group and computer-mediated discussion

effects in risk decision making. Journal of Personality and Social Psychology, 52, 917–930.McLeod, P. L., Baron, R. S., Marti, M. W., & Yoon, K. (1997). The eyes have it: Minority

influence in face-to-face and computer-mediated group discussion. Journal of AppliedPsychology, 82, 706–718.


Morkes, J., Kernal, H., & Nass, C. (2000). Effects of humor in task-oriented human-com-puter interaction and computer-mediated communication: A direct test of SRCT theory.Human-Computer Interaction, 14, 395–435.

Moscovici, S., & Zavalloni, M. (1969). The group as a polarizer of attitudes. Journal of Person-ality and Social Psychology, 12, 125–135.

Myers, G. D. (1982). Polarizing effects of social interaction. In H. Brandstatter, J. H. Davis, & G.Stocker-Kerichgauer (Eds.), Group decision-making (pp. 125–161). London: Academic Press.

Nass, C., Fogg, B. J., & Moon, Y. (1996). Can computers be teammates? International Journalof Human-Computer Studies, 45, 669–678.

Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers.Journal of Social Issues, 56, 81–103.

Nass, C., Moon, Y., & Carney, P. (1999). Are people polite to computers? Responses to com-puter-based interviewing systems. Journal of Applied Social Psychology, 29, 1093–1110.

Nemeth, C. J. (1986). Differential contributions of majority and minority influence. Psycho-logical Review, 93, 23–32.

Perloff, R. M. (1993). The dynamics of persuasion. Mahwah, NJ: Erlbaum.Perrin, S., & Spencer, C. P. (1981). Independence or conformity in the Asch experiment as a

reflection of cultural and situational factors. British Journal of Social Psychology, 20, 205–209.Perse, E. M., & Courtright, J. A. (1993). Normative images of communication media: Mass

and interpersonal channels in the new media environment. Human Communication Re-search, 19, 485–503.

Postmes, T., & Spears, R. (1998). Deindividuation and antinormative behavior: A meta-analy-sis. Psychological Bulletin, 123, 238–259.

Postmes, T., Spears, R., & Lea, M. (1998). Breaching or building social boundaries? SIDEeffects of computer-mediated communication. Communication Research, 25, 689–715.

Postmes, T., Spears, R., & Lea, M. (2000). The formation of group norms in computer-mediatedcommunication. Human Communication Research, 26, 341–371.

Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, televisions, andnew media like real people and places. Stanford, CA: Cambridge University Press.

Reid, E. M. (1991). Electropolis: Communication and community on internet relay chat. RetrievedJune 1, 2002, from http://www.aluluei.com

Rice, R. E. (1993). Media appropriateness: Using social presence theory to compare tradi-tional and new organizational media. Human Communication Research, 19, 451–484.

Rice, R. E., & Love, G. (1987). Electronic emotion: Socioemotional content in a computer-mediated communication network. Communication Research, 14, 85–108.

Ross, L., Bierbrauer, G., & Hoffman, S. (1976). The role of attribution processes in confor-mity and dissent: Revisiting the Asch situation. American Psychologist, 35, 148–157.

Rutter, D. R. (1987). Communicating by telephone. Oxford, UK: Pergamon Press.Shamp, S. A. (1991). Mechanomorphism in perception of computer communication part-

ners. Computers in Human Behavior, 7, 147–161.Sherif, M. (1936). The psychology of social norms. New York: Harper.Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications.

New York: Wiley.Siegel, J., Dubrovsky, S., Kiesler, T., & McGuire, T. N. (1986). Group processes in computer-

mediated communication. Organizational Behavior and Human Decision Processes, 37, 157–187.Smilowitz, M., Compton, D. C., & Flint, L. (1988). The effects of computer mediated com-

munication on an individual’s judgment: A study based on the methods of Asch’s socialinfluence experiment. Computers in Human Behavior, 4, 311–321.

Spears, R., & Lea, M. (1992). Social influence and the influence of the “social” in computer-mediated communication. In M. Lea (Ed.), Context of computer-mediated communication(pp. 31–65). New York: Harvester Wheatsheaf.

Spears, R., Lea, M., & Lee, S. (1990). De-individuation and group polarization in computer-mediated communication. British Journal of Social Psychology, 29, 121–134.

Sproull, L., & Kiesler, S. (1986). Reducing social context cues: Electronic mail in organiza-tional communication. Management Science, 32, 1492–1512.


Straus, S. G. (1997). Technology, group process, and group outcomes: Testing the connectionsin computer-mediated and face-to-face groups. Human-Computer Interaction, 12, 227–266.

Trafimow, D., & Davis, J. H. (1993). The effects of anticipated informational and normativeinfluence on perceptions of hypothetical opinion change. Basic and Applied Social Psy-chology, 14, 487–496.

Turkle, S. (1997). Life on the screen. New York: Simon & Schuster.Turner, J. C. (1985). Social categorization and the self-concept. Advances in Group Pro-

cesses, 2, 77–121.Turner, J. C. (1991). Social influence. Pacific Grove, CA: Wadsworth.Valacich, J. S., Dennis, A. R., & Nunamaker, J. F. (1992). Group size and anonymity effects on

computer-mediated idea generation. Small Group Research, 23, 49–73.Valacich, J. S., George, J. F., Nunamaker, J. F., & Vogel, D. R. (1994). Physical proximity ef-

fects on computer-mediated group idea generation. Small Group Research, 25, 83–104.Wallach, M. A., & Malbi, J. (1970). Information versus conformity in the effects of group

discussion on risk taking. Journal of Personality and Social Psychology, 14, 149–156.Walther, J. B. (1994). Anticipated ongoing interaction versus channel effects on rela-

tional communication in computer-mediated interaction. Human CommunicationResearch, 20, 473–501.

Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, andhyperpersonal interaction. Communication Research, 23, 3–43.

Walther, J. B. (1997). Group and interpersonal effects in international computer-mediatedcollaboration. Human Communication Research, 23, 342–369.

Walther, J. B., Slovacek, C. L., & Tidwell, L. C. (2001). Is a picture worth a thousand words?Photographic images in long-term and short-term computer-mediated communication.Communication Research, 28, 105–134.

Wilder, D. A. (1977). Perception of groups, size of opposition, and social influence. Journalof Experimental Social Psychology, 13, 253–268.

Williams, F., & Rice, R. E. (1983). Communication research and the new media technologies.In R. N. Bostrom (Ed.), Communication yearbook 7 (pp. 200–204). Beverly Hills, CA: Sage.

Zimbardo, P. (1969). The human choice: Individuation, reason, and order versusdeindividuation, impulse and chaos. In W. Arnold & D. Levine (Eds.), Nebraska sympo-sium on motivation (pp. 237–307). Lincoln: University of Nebraska Press.

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Experimental Tests of Normative Group Influence and Representation Effects in Computer-Mediated Communication : When Interacting Via Computers Differs From Interacting With Computers