Tang, N., Chu, J., Leong, K., & Rosenthal, S. (2020). To thine communication partner be true: The effect of presentation consistency on perceived authenticity and liking after making a first impression online. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 14(3), Article 1. doi:https://doi.org/10.5817/CP2020-3-1

To Thine Communication Partner Be True: The Effect of Presentation Consistency on Perceived Authenticity and Liking After Making a First Impression Online

Nicholas Tang, Janell Chu, Kahmun Leong, & Sonny Rosenthal

Nanyang Technological University, Singapore, Singapore


This experiment examines the effects of presentation consistency on perceived authenticity and liking in computer-mediated communication, differentiating between profile views and short online text-based conversations. The experimental design is a 2 (presentation-consistent vs. -inconsistent) x 2 (profile view vs. short conversation) between-subjects experiment using university students (N = 173) in Singapore. Results show higher perceived authenticity (η2p = .29) and liking (η2p = .16) after short conversations than after profile views without conversations. When there is only a profile view, perceived authenticity is lower when the profile photo is inconsistent with the profile text than when it is consistent (η2p = .05). We discuss these findings in terms of schema tuning, where presentation inconsistencies can be accommodated over time as individuals develop unique mental schemas about their communication partners.

Keywords: Perceived authenticity; schema theory; idealization; liking; computer-mediated communication



Individuals use online platforms to form relationships and maintain existing ones (Mesch & Talmud, 2006). However, there are questions about how truthfully people represent themselves online, for example, in the context of online dating (Gibbs et al., 2011; Toma et al., 2008). Computer-mediated communication (CMC) users often misrepresent themselves, even when they expect future offline interactions (Toma et al., 2008). When CMC users anticipate subsequent offline interactions, they especially desire indicators their partners’ self-presentations are legitimate (Walther et al., 2009). Similarly, users of internet dating sites rely on photographs as evidence to support claims found in textual profile descriptions (Ellison et al., 2006). These informational cues serve as warrants about the authenticity of the self-presentation (DeAndrea, 2014), where users regard information as more truthful when they believe the source is less able to manipulate it (Walther, 2011).

The process of idealization (Walther, 1996) can explain how perceived authenticity forms between CMC users. Individuals curate positive impressions of themselves by controlling and editing the information they disclose online (Tong & Walther, 2015; Walther, 1996). Their conversation partners form idealized impressions of them by attributing favorable personal qualities to their disclosures (Walther, 1996). This can result in increased relational intimacy in CMC compared to face-to-face (FtF) communication due to those positive attributions (Jiang et al., 2011). Yet, when CMC channels introduce nonverbal cues, such as vocal tone (e.g., through voice recordings in WhatsApp) and body language (e.g., through videoconferencing in Skype), it is more difficult for CMC users to control the content of their disclosures. Their communication partners can compare different pieces of information and draw additional inferences about them. For example, after online communication partners have their first FtF meeting, there is a decrease in interpersonal attraction (Sharabi & Caughlin, 2017) and romantic attraction (Antheunis et al., 2019). One explanation of this effect is communication partners may be different in person from how they imagined each other (Ramirez & Wang, 2008). This effect may be heightened when nonverbal cues, which often have higher warranting value than verbal cues, are the source of the expectancy violation. Thus, inconsistencies between verbal and nonverbal cues may affect the impressions individuals form of their communication partners (Weisbuch et al., 2010).

To what extent do inconsistencies in self-presentations affect perceived authenticity? Answering that question can clarify how presentation consistency affects liking, a concept focal to many studies about self-disclosure (e.g., Collins & Miller, 1994). In the first part of our literature review, we draw on concepts of intrapersonal authenticity (Wood et al., 2008) to explicate the concept of perceived interpersonal authenticity. In the second part, we review literature to explain the formation of perceived authenticity and liking in CMC, differentiating between what happens after viewing a profile versus having a short online conversation. Whereas first impressions are often based on superficial information (Walther, 1993), idealization develops over the course of online interactions. We discuss this process in terms of schema tuning (Rumelhart & Norman, 1978), which may occur when individuals adjust their mental representations of communication partners over time. The effect of presentation consistency on perceived authenticity and liking may be stronger when communication partners make judgments quickly based on superficial information. That effect may implicate the warranting value of different kinds of information and clarify aspects of the idealization process.

Impersonal, Interpersonal, and Hyperpersonal Interaction

CMC platforms—especially text-only CMC—have a relatively limited capacity to convey nonverbal information, transmitting less social information in comparison to FtF communication (Walther, 1996). According to social presence theory (Short et al., 1976), media affording fewer information cues produce a lower level of social presence, or how salient individuals sense their communication partners to be. Consequently, lower social presence leads to decreased interpersonal closeness communication partners feel for each other (Short et al., 1976).

However, the effect of cue restriction on social presence does not prevent interpersonal communication from occurring in CMC. According to the social information processing theory (Walther, 1992), online communicators are motivated to form subjective impressions of their communication partners from available verbal cues. Over time, communicators can translate social messages into verbal behavior, achieving impressions and relations comparable to when individuals communicate FtF (Walther et al., 2015).

Walther (1996) extended this argument with the hyperpersonal model, which states CMC can result in enhanced interpersonal communication, given enough communication time. The lack of nonverbal cues, channel asynchronicity, and presence of feedback mechanisms allow users to selectively self-present by controlling and editing the information they disclose online (Tong & Walther, 2015; Walther, 1996). This allows individuals to curate positive impressions of themselves, as verbal cues are easier to edit and control than nonverbal cues (Ekman & Friesen, 1969). Their conversation partners may then idealize them by over-interpreting available cues based on the limited information provided (Walther et al., 2016). This process involving the sender, receiver, and channel results in hyperpersonal communication, where levels of affection and emotion between CMC partners may exceed that between partners in a comparable FtF interaction (Walther, 1996). We draw on the concept of idealization and relate it to the process of schema tuning to explain how self-presentations in CMC may affect perceived authenticity and liking.

Idealization and Schemas

Idealization is a process of impression formation based on information communication partners have about each other. During this process, individuals tend to over-attribute favorable qualities to their partners by “filling in the blanks” with information they perceive to be congruent with what they already know (Toma & Choi, 2016, p. 395).

This process of tapping into prior knowledge to understand and interpret an unknown situation is consistent with the concept of schemas. Bartlett (1932) discovered people tend to recall generic characteristics of information cues, sometimes even characteristics that were not present. He argued people recall details based on past experiences, where those past experiences form the basis of their schemas. In brief, schemas are cognitive structures made up of broad expectations and understanding of the world that undergird any form of information processing (Rumelhart, 1980). They function as guides for individuals to recall, infer, and perceive reality (Augoustinos et al., 2014). People thus understand and process novel stimuli by tapping into their existing storehouse of knowledge, or schemas.

In a communication context, schemas act as structures people can use to inform social interactions (Cantor & Mischel, 1979; Nishida, 1999; Taylor & Crocker, 1981). We argue, in the context of CMC interactions, individuals tap into person schemas when forming impressions of their conversation partners. Person schemas are a type of schema representing trait-based models of people, which individuals form according to their experiences with others. These models, which are based on the categorization of others in terms of salient personality characteristics, may be used to predict the outcomes of social interactions with types of individuals (Augoustinos et al., 2014). This is related to prior work showing individuals can accurately predict how others will act based on the observation of “thin slices” of expressive behaviors of less than a minute (for a review, see Ambady & Rosenthal, 1992). As individuals form impressions about a communication partner, they tend to create idealized representations of them based on stereotypical impressions (Walther, 1996), which they derive from person schemas. This may lead people to have certain expectations of how their partners should be.

This idealization is less pronounced in FtF settings in part because of the “leakage” of nonverbal cues (Walther, 2007). The addition of nonverbal information leaves fewer gaps for individuals to fill in about their communication partners, which means the mental representations of them are based less on a generic notion and more on specific evidence. This, in addition to the challenge of curating a positive image of the self, explains how the presence of nonverbal cues may inhibit idealization.

Revealed and Expressed Presentations

When a CMC channel includes photos, voice recordings, or video recordings, it introduces nonverbal information senders are less able to control (Walther, 2007). Ramirez and Wang (2008) found when conversations move from CMC to FtF interactions, individuals often experience a violation of expectations, where the novel nonverbal cues contradict their mental images of their communication partners based on prior verbal information. Likewise, the introduction of nonverbal cues in CMC platforms provides more social information, allowing individuals to make additional evaluations of their communication partners. One evaluation is to what extent nonverbal cues reveal personal attributes that are consistent with prior expressed verbal self-presentations. During this evaluation process (see Ellison et al., 2006), if individuals detect inconsistency between revealed and expressed presentations, they may perceive their communication partners to be less authentic.

Perceived Authenticity

In CMC contexts, people desire indicators of authenticity based on the appearance, behavior, and attitude of their communication partners (Walther et al., 2009). These indicators help individuals develop perceived authenticity at an interpersonal level. However, research into authenticity has traditionally taken a self-evaluative perspective (Didonato & Krueger, 2010; Gilmore & Pine, 2007; Goldman & Kernis, 2002; Reinecke & Trepte, 2014; Wood et al., 2008). Those studies examined intrapersonal authenticity, or individuals’ perception their behaviors are consistent with a salient image of the self. Few studies have looked at perceptions of interpersonal authenticity and how it affects relational outcomes.

This person-centered conception of authenticity offers a useful description of authentic communication (Wood et al., 2008). One aspect of this conception, authentic living, involves emotional expression and behavior in ways consistent with individuals’ “conscious awareness of physiological states, emotions, beliefs, and cognitions” (Wood et al., 2008, p. 386). In other words, authentic living involves individuals behaving in ways true to themselves, in accordance with their beliefs and values. In the CMC context, authentic living could manifest as individuals’ decisions to present nonverbal cues (e.g., photographs of themselves) consistent with their verbal self-presentations. We adapt the concept of authentic living to define perceived authenticity more broadly, characterizing it as the evaluation of compatibility between the revealed and expressed self-presentations of others. The greater the compatibility, the greater the perceived authenticity.

When communication is limited to verbal information, individuals may form mental images of their communication partners, drawing details from the verbal information and forming idealizations based on schemas. When communication includes both verbal and nonverbal information, the same process occurs, but there is less schema-based idealization, since the nonverbal cues help fill in some of the details about the communication partner. Similar to how users on internet dating sites rely on photographs as warrants to support claims found in textual profile descriptions (Ellison et al., 2006), nonverbal cues can be more revelatory about communication partners than the self-presentations they express verbally, facilitating evaluations of authenticity (Figure 1).

Figure 1. Communication-Centered Conception of Perceived Authenticity .

Note . Communicators form expectations of their partners based on verbal information, which are then compared to observations of
their partners based on nonverbal information. The congruence between their expectations and observations
should give rise to perceived authenticity.


Perceived authenticity is important to study because it may affect more tangible relational outcomes. For example, individuals have more positive impressions of their communication partners who have “authentic” facial expressions (Ekman, 1992; Frank et al., 1993; Grandey et al., 2005). One of the more common relational outcomes appearing in the CMC literature is liking, or interpersonal attraction (Kashian et al., 2017; Ramirez & Zhang, 2007; Rubin & Step, 2000; Schiappa et al., 2005; Utz, 2010).

Some research has shown a positive relationship between perceived authenticity and liking (Liu & Perrewé, 2006). This effect is worth replicating for two reasons. First, that prior study examined perceived authenticity in a workplace setting and did not identify effects of specific communication channels. Presumably, the dominant channel in that setting was FtF, since the researchers asked participants about their colleagues. Second, that prior study employed a restricted definition of authenticity, which the current study expands. Other research has shown computer characters with inconsistent verbal and nonverbal cues linked to introversion and extroversion, such as posture and verbal style, are less likeable (Isbister & Nass, 2000). That is an effect of human-computer interaction, which we replicate in a CMC context. Thus, we predict the following:

Hypothesis 1: Perceived authenticity of another person is positively associated with liking of that person.

Hypothesis 2: Perceived authenticity of another person is higher when there is consistency between verbal and nonverbal cues than when there is inconsistency.

Hypothesis 3: Liking of that person is higher when there is consistency between verbal and nonverbal cues than when there is inconsistency.

Schema Tuning

As communication partners self-disclose, they may integrate new information about their partners, moving from general schematic representations to specific impressions of them (Augoustinos et al., 2014; Axelrod, 1973; Rumelhart, 1980; Rumelhart & Norman, 1978). This process comports with research showing when general schemas are inadequate to make sense of new information, individuals either form new schemas or update existing ones (Rumelhart & Norman, 1978). One of the ways people update their schemas is through a process called “schema tuning,” which occurs when people apply the most appropriate schema to understand a novel situation and revise it to align it more completely with the situation (Rumelhart, 1980; Rumelhart & Norman, 1978). In CMC, schema tuning may happen when there are inconsistencies among different kinds of information individuals have about their communication partners. Based on an initial impression, individuals will draw on person schemas most closely fitting their communication partners. During online interactions, they will tune the schemas to accommodate novel characteristics of their communication partners. For example, if two individuals begin a conversation in an online forum for computer gamers, their initial impressions of each other will likely reflect a “gamer” schema. Over time, they may learn things about each other that deviate from the initial schemas, which they can tune to accommodate those inconsistencies.

Figure 2. Conceptual Diagram of Perceived Authenticity After Viewing a Profile Versus Having a Short Online Conversation .

Note . Communication partners engage in schema tuning during online conversations, resulting in perceptions of their partners as
authentic independent of the evaluations made based only on the profile view.

During online interactions, inconsistencies between verbal and nonverbal information may become less salient as individuals tune schemas to make sense of their communication partners (Figure 2). Over time, individuals compile verbal and nonverbal information to form specific mental representations of their communication partners, which they can use to explain away apparent inconsistencies. This argument aligns with the observation that individuals are motivated to form positive impressions of their communication partners based on all available information (Walther, 1992). Schema tuning could thus underscore idealization, describing part of how individuals tend to over-attribute favorable qualities when constructing mental representations of their communication partners. However, given only a first impression based on limited online profile information, individuals have insufficient information for drawing an appropriate schema and engaging in schema tuning. As a result, inconsistencies between verbal and nonverbal information remain salient and may negatively affect perceived authenticity and liking. Thus, we predict the following:

Hypothesis 4: Perceived authenticity is higher after individuals have engaged in short online conversations with another person versus when they have viewed that person’s profile only.

Hypothesis 5: The effect of inconsistent verbal and nonverbal cues on perceived authenticity disappears after a short online conversation compared to viewing the profile only.

Hypothesis 6: Liking is higher after individuals have engaged in short online conversations with communication partners versus when they have viewed that person’s profile only.

Hypothesis 7: The effect of inconsistent verbal and nonverbal cues on liking disappears after a short online conversation compared to viewing the profile only.



Participants were recruited from a large research university in Singapore through undergraduate courses that offered course credit for participating in research. Those who agreed to participate were enrolled through the school’s online research participation system. The sample included 173 individuals (48 males and 125 females) ranging in age from 19 to 26 years (M = 21.10, SD = 1.76). Upon completing the experiment, participants received either partial course credit or a cash incentive of 10 Singapore dollars.


The experiment used a 2 (presentation-consistent vs. -inconsistent) x 2 (profile view vs. short conversation) between-subjects factorial design. After providing informed consent, participants were told the purpose of the study was to understand how individuals form perceptions of their online chat partners. Then they received a series of instructions: (1) create a chat profile, (2) review the profile of an assigned chat partner, (3) complete a 30-minute chat with the assigned partner, and (4) complete an online survey. All participants received the same instructions, but those in the “profile view” condition immediately completed the online survey after reviewing their chat partner’s profile. They were led to believe there would be an online interaction so they would pay the same attention to their chat partner’s profile as those in the “short conversation” group (see Walther, 1994, 1996; Walther et al., 2009).

We used the instant messaging application, Telegram, which is available for Android and iOS smartphones. The chat partner profiles were fictitious and contained a display picture of either a muscular or skinny male, along with verbal disclosures related to weightlifting. We used pictures based on a pre-test in which 143 undergraduate students rated 10 photographs on muscularity, ranging from 1 (not at all muscular) to 5 (very muscular), and attractiveness, ranging from 1 (not at all attractive) to 5 (very attractive). We selected the pictures rated most and least muscular with similar levels of perceived attractiveness. During the 30-minute chat, the researchers served as confederates posing as chat partners and adhered to a conversation script to ensure consistency across chat sessions. Each confederate had an equal assignment to all experimental conditions to account for individual variations in chat habits outside the script (e.g., the time interval between sending messages). Participants engaged in a structured self-disclosure task, taking turns responding to six predetermined questions. Disclosures by the confederates were scripted to accentuate weightlifting habits. In the fourth and final stage, all participants completed an online survey. Following that, all participants were debriefed about the nature and purpose of the deceptions we employed.


Presentation-Consistent vs. -Inconsistent

All profiles were identical except for the display picture. In the presentation-consistent conditions, the display picture showed a “muscular” male. In the presentation-inconsistent conditions, the display picture showed an “unmuscular” male. We chose weightlifting as the defining characteristic of the communication partner because it is relatively easy to distinguish between weightlifters and non-weightlifters based on their physical appearance.

Profile View vs. Short Conversation

In the profile view conditions, participants reviewed their chat partner’s profile and then completed the survey. In the short conversation conditions, participants reviewed the profile, participated in a 30-minute chat, and then completed the survey.

Dependent Variables

Perceived Authenticity

The measure of perceived authenticity included one item each from the Authenticity in Relationships Scale (AIRS; Lopez & Rice, 2006) and the Authenticity Index (AI; Kernis & Goldman, 2006). We revised those items to measure the interpersonal aspect of authenticity. We developed an additional five Likert items to measure the current conception of perceived authenticity (see Table 1). Response options ranged from 1 (strongly disagree) to 5 (strongly agree), and the scale had good reliability (M = 3.25, SD = 0.59, Cronbach’s α = .85).


We adapted the five strongest indicators of social attraction based on McCroskey and McCain’s (1974) factor analysis of the Interpersonal Attraction Scale (see Table 1). Response options ranged from 1 (strongly disagree) to 5 (strongly agree), and the scale had good reliability (M = 3.45, SD = 0.60, Cronbach’s α = .81).

Table 1. Measurement Items.

Scale / Item



Perceived Authenticity (α = .85)



I can count on my partner being who he/she really is, regardless of what setting he/she is in.



My partner is totally himself/herself when with me.



My partner presents himself/herself in an honest way.



The image my partner creates is true to his/her character.



My partner really is the way he/she portrays himself/herself.



The way my partner presents himself/herself is who I expect him/her to be.



My partner tries to impress me so that I will believe something about him/her that isn’t really true.



Liking (α = .81)



I think my partner could be a friend of mine.



I would like to have a friendly chat with my partner.



It would be easy to meet and talk with my partner.



We could establish a personal friendship with each other.



My partner would fit into my circle of friends.




Manipulation Check

To evaluate the manipulation of presentation consistency, participants responded to the question, “Was your conversation partner’s profile picture consistent with the information he/she provided?” This question was presented as the final item on the questionnaire to avoid suspicion. Response options ranged from 1 (not at all consistent) to 5 (very consistent). An independent samples t-test showed participants in the presentation-consistent condition (M = 4.36, SD = 0.84) reported greater consistency than those in the presentation-inconsistent condition (M = 2.50, SD = 1.20), t(151.83) = 11.83, p < .001. This result suggests the manipulation was successful.

Hypothesis Testing

We used univariate ANOVA to examine treatment effects of the manipulations, with focused ANOVAs to test conditional effects. Table 2 presents descriptive summaries of the experimental cells.

Our test of hypothesis 1 estimated the partial correlation between perceived authenticity and liking, controlling for the two experimental manipulations. In support of that hypothesis, the partial correlation was significant (r = .27, p < .001).

Failing to support hypothesis 2, perceived authenticity was not different between presentation-consistent and presentation-inconsistent conditions, F(1, 170) = 2.58, p = .11.

Failing to support hypothesis 3, liking was not different between presentation-consistent and presentation-inconsistent conditions, F(1, 170) = 0.01, p = .94.

In support of hypothesis 4, perceived authenticity was higher after a short conversation (M = 3.57, SD = 0.44) than a profile view (M = 2.94, SD = 0.55), F(1, 170) = 69.13, p < .001, η2p = .29.

In support of hypothesis 5, when there was only a profile view, perceived authenticity was higher in the presentation-consistent condition (M = 3.07, SD = 0.62) than in the presentation-inconsistent condition (M = 2.81, SD = 0.43), F(1, 86) = 4.93, p = .03, η2p = .05. When there was a short conversation, perceived authenticity did not differ between the presentation-consistent and presentation-inconsistent conditions, F(1, 83) = 0.03, p = .86. Figure 3 shows this interaction effect.

Table 2. Cell Sizes, Means, and Standard Deviations.


Perceived Authenticity









      Profile view







      Short conversation















      Profile view







      Short conversation















      Profile view







      Short conversation














Figure 3. The Effect of Presentation Consistency on Perceived Authenticity, Conditioned on a Profile View Versus Short Conversation.

Note. The error bars show the 84% confidence intervals, which allow for a visual evaluation of mean differences at roughly
p = .05 (Payton et al., 2003).

In support of hypothesis 6, liking was higher after a short conversation (M = 3.69, SD = 0.48) than a profile view (M = 3.21, SD = 0.62), F(1, 170) = 32.19, p < .001, η2p = .16.

Failing to support hypothesis 7, the effect of presentation consistency on liking was non-significant after both a profile view, F(1, 86) = 0.38, p = .54, and a short conversation, F(1, 83) = 0.87, p = .36.


This study examined how the consistency between verbal and nonverbal information in CMC affects individuals’ feelings about their communication partners when they form first impressions based on viewing their communication partner’s profile versus having a short online conversation with them. Outcomes of interest were perceived interpersonal authenticity and liking. Experimental results provided mixed support for seven hypotheses. The supported hypotheses have useful theoretical implications for the idealization process and the hyperpersonal model.

First, consistent with existing literature (e.g., Liu & Perrewé, 2006), results showed a positive relationship between perceived authenticity and liking. This result is intuitive, since authenticity is a person attribute, perception of which may support the formation of a positive affective response to a person. This finding also lends support to our goal of clarifying the idealization process, as liking is an oft-studied relational outcome of text-only CMC (Kashian et al., 2017; Sprecher, 2014; Walther et al., 2018), where idealization is expected to occur. This is not to say perceived authenticity directly affects liking; prior research implicates trust as a mediator of that relationship (Wickham, 2013; Feng et al., 2004). Future research can extend the current study by examining trust as a mediator of the authenticity-liking relationship.

Second, results failed to show a main effect of presentation consistency on perceived authenticity and liking. This null finding could be due to relational motives influencing the communication process (Walther, 1992). One of those motives is the need for affiliation, which Murray (1938) described as the desire to form positive relationships and connections with others. Given a strong need for affiliation, individuals might prioritize the positive evaluation of their communication partners and ascribe less importance to presentation consistency. It would be interesting for future research to consider the potential moderating role of need for affiliation.

Third, results showed, regardless of presentation consistency, perceived authenticity and liking were higher after short online conversations than after profile views. In other words, social interaction via CMC resulted in positive relational outcomes. This is consistent with research showing the exchange of self-disclosures between individuals is positively associated with them liking each other (Collins & Miller, 1994; Kashian et al., 2017), and that familiarity promotes liking in social interactions among new acquaintances (Reis et al., 2011). This finding is also related to social penetration theory, which describes the development of relational closeness through mutual self-disclosures (Altman & Taylor, 1973). Based on the current findings, one reason for this effect is the short conversation gave study participants more time and information to develop mental representations of their communication partners. This could involve schema tuning, as participants eventually accepted the skinny weightlifter, despite “skinniness” being a physical trait not intuitively linked to the “weightlifter” schema. On the other hand, given only a profile view, study participants had a basic mental representation of their communication partners, constraining the attribution of favorable qualities based on the information available.

This effect of online interaction speaks to the idealization process, as participants were motivated to form favorable impressions of their partners, regardless of their apparent inconsistencies. The introduction of nonverbal cues can disrupt the idealization process if the new information conflicts with the broader mental representation of a communication partner. Ramirez and Sumner (2015) partly attributed this effect of modality switching to a violation of expectations, which Burgoon and Le Piore (1993) previously showed can negatively affect interpersonal attraction in communication contexts. However, given enough time, individuals can accommodate apparently conflicting information by engaging in schema tuning. This suggests that individuals are inclined to regard their communication partners as authentic, aided by the process of schema tuning, regardless of inconsistencies between verbal and nonverbal information. This inclination could thus underscore or work in tandem with the idealization process, lending to the hyperpersonal states that may arise in CMC. Our findings are consistent with that argument, showing the positive effect of online interaction was independent of presentation consistency.

Finally, results showed a conditional effect of presentation consistency on perceived authenticity, which occurred after viewing a profile without a conversation. This finding clarifies the main effects of the two manipulations. First, it suggests presentation consistency affects perceived authenticity only when the available information emphasizes the inconsistency. When individuals see conflicting verbal and nonverbal information in a profile, they perceive their communication partners as less authentic than when the verbal and nonverbal information are compatible. Second, it further supports the argument that, given a short online conversation, there will be positive relational outcomes regardless of verbal-nonverbal consistency. Though, we must temper these conclusions, since results failed to show the same moderation effect on liking.


This study has several limitations. First, we used a convenience sample of undergraduates, which is not generalizable to other populations. Second, the pairing of communication partners was artificial, as in real life individuals often choose their communication partners rather than having them assigned. Third, because the confederate gave a consistent self-presentation by following a scripted conversation, it is not surprising perceived authenticity was higher after the short conversation. Fourth, all study participants were assigned a male communication partner. If there were any related gender effects, we were unable to account and control for them. Fifth, the manipulation of authenticity in this study was based on a superficial characteristic. This made it easier for study participants to evaluate the authenticity of their communication partners, but it restricted the operationalization of verbal-nonverbal consistency. It is possible for individuals to infer authenticity based on subtler nonverbal cues; thus, the observed effects may be more extreme than would occur naturally when individuals engage in CMC. Finally, the contrast of profile views and short online conversations supports conclusions about effects related to the presence of social interaction, but not those related to the degree of social interaction. It would be interesting to know at what duration a CMC exchange produces the observed effects, which the current experimental design cannot address.


This study explored the effects of presentation consistency and online interaction on perceived authenticity and liking of CMC partners. Results suggest presentation consistency matters most in the formation of initial impressions about interpersonal authenticity. Given a short online conversation, individuals like their communication partners and perceive them as authentic, even if there was verbal-nonverbal inconsistency at their first introduction.


Altman, I., & Taylor, D. A. (1973). Social penetration: The development of interpersonal relationships. Holt, Rinehart & Winston.

Ambady, N., & Rosenthal, R. (1992). Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis. Psychological Bulletin, 111(2), 256–274. https://doi.org/10.1037/0033-2909.111.2.256

Antheunis, M. L., Schouten, A. P., & Walther, J. B. (2019). The hyperpersonal effect in online dating: Effects of text-based CMC vs. videoconferencing before meeting face-to-face. Media Psychology. Advance online publication. https://doi.org/10.1080/15213269.2019.1648217

Augoustinos, M., Walker, I., & Donaghue, N. (2014). Social cognition: An integrated introduction (3rd ed.). Sage.

Axelrod, R. (1973). Schema theory: An information processing model of perception and cognition. American Political Science Review, 67(4), 1248–1266. https://doi.org/10.2307/1956546

Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge University Press.

Burgoon, J. K. & Le Poire, B. A. (1993). Effects of communication expectancies, actual communication, and expectancy disconfirmation on evaluations of communicators and their communication behavior. Human Communication Research, 20(1), 67–96. https://doi.org/10.1111/j.1468-2958.1993.tb00316.x

Cantor, N., & Mischel, W. (1979). Prototypes in person perception. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 12, pp. 3–52). Academic Press.

Collins, N. L., & Miller, L. C. (1994). Self-disclosure and liking: A meta-analytic review. Psychological Bulletin, 116(3), 457–475. https://doi.org/10.1037/0033-2909.116.3.457

DeAndrea, D. C. (2014). Advancing warranting theory. Communication Theory24(2), 186–204. https://doi.org/10.1111/comt.12033

Didonato, T. E., & Krueger, J. I. (2010). Interpersonal affirmation and self-authenticity: A test of Rogers’s self-growth hypothesis. Self and Identity, 9(3), 322–336. https://doi.org/10.1080/15298860903135008

Ekman, P. (1992). Facial expressions of emotion: New findings, new questions. Psychological Science, 3(1), 34–38. https://doi.org/10.1111/j.1467-9280.1992.tb00253.x

Ekman, P., & Friesen, W. V. (1969). Nonverbal leakage and clues to deception. Psychiatry, 32(1), 88–106. https://doi.org/10.1080/00332747.1969.11023575

Ellison, N., Heino, R., & Gibbs, J. (2006). Managing impressions online: Self-presentation processes in the online dating environment. Journal of Computer-Mediated Communication11(2), 415–441. https://doi.org/10.1111/j.1083-6101.2006.00020.x

Feng, J., Lazar, J., & Preece, J. (2004). Empathy and online interpersonal trust: A fragile relationship. Behavior & Information Technology, 23(2), 97–106. https://doi.org/10.1080/01449290310001659240

Frank, M. G., Ekman, P., & Friesen, W. V. (1993). Behavioral markers and recognizability of the smile of enjoyment. Journal of Personality and Social Psychology, 64(1), 83–93. https://doi.org/10.1037/0022-3514.64.1.83

Gibbs, J. L., Ellison, N. B., & Lai, C.-H. (2011). First comes love, then comes Google: An investigation of uncertainty reduction strategies and self-disclosure in online dating. Communication Research, 38(1), 70–100. https://doi.org/10.1177/0093650210377091

Gilmore, J. H. & Pine, B. J., II. (2007). Authenticity: What consumers really want. Harvard Business Review Press.

Goldman, B. M., & Kernis, M. H. (2002). The role of authenticity in healthy psychological functioning and subjective well-being. Annals of the American Psychotherapy Association, 5(6), 18–20.

Grandey, A. A., Fisk, G. M., Mattila, A. S., Jansen, K. J., & Sideman, L. A. (2005). Is “service with a smile” enough? Authenticity of positive displays during service encounters. Organizational Behavior and Human Decision Processes, 96(1), 38-55. https://doi.org/10.1016/j.obhdp.2004.08.002

Isbister, K., & Nass, C. (2000). Consistency of personality in interactive characters: Verbal cues, non-verbal cues, and user characteristics. International Journal of Human-Computer Studies, 53(2), 251–267. https://doi.org/10.1006/ijhc.2000.0368

Jiang, L. C., Bazarova, N. N., & Hancock, J. T. (2011). The disclosure–intimacy link in computer-mediated communication: An attributional extension of the hyperpersonal model. Human Communication Research37(1), 58–77. https://doi.org/10.1111/j.1468-2958.2010.01393.x

Kashian, N., Jang, J.-w., Shin, S. Y., Dai, Y., & Walther, J. B. (2017). Self-disclosure and liking in computer-mediated communication. Computers in Human Behavior, 71, 275–283. https://doi.org/10.1016/j.chb.2017.01.041

Kernis, M. H., & Goldman, B. M. (2006). A multicomponent conceptualization of authenticity: Theory and research. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 38, pp. 283–357). Elsevier Academic Press.

Lopez, F. G., & Rice, K. G. (2006). Preliminary development and validation of a measure of relationship authenticity. Journal of Counseling Psychology53(3), 362–371. https://doi.org/10.1037/0022-0167.53.3.362

Liu, Y., & Perrewé, P. L. (2006). Are they for real? The interpersonal and intrapersonal outcomes of perceived authenticity. International Journal of Work Organization and Emotion, 1(3), 204–214. https://doi.org/10.1504/ijwoe.2006.010788

McCroskey, J. C., & McCain, T. A. (1974). The measurement of interpersonal attraction. Speech Monographs, 41(3), 261–266. https://doi.org/10.1080/03637757409375845

Mesch, G., & Talmud, I. (2006). The quality of online and offline relationships: The role of multiplexity and duration of social relationships. The Information Society, 22(3), 137–148. https://doi.org/10.1080/01972240600677805

Murray, H. A. (1938). Explorations in personality. Oxford University Press.

Nishida, H. (1999). A cognitive approach to intercultural communication based on schema theory. International Journal of Intercultural Relations, 23(5), 753–777. https://doi.org/10.1016/S0147-1767(99)00019-X

Payton, M. E., Greenstone, M. H., & Schenker, N. (2003). Overlapping confidence intervals or standard error intervals: What do they mean in terms of statistical significance? Journal of Insect Science, 3(1), Article 34. https://doi.org/10.1093/jis/3.1.34

Ramirez, A., Jr., & Sumner, E. M. (2015). Modality switching. In C. R. Berger & M. E. Roloff (Eds.), The International Encyclopedia of Interpersonal Communication. Wiley-Blackwell. https://doi.org/10.1002/9781118540190.wbeic212

Ramirez, A., Jr., & Wang, Z. (2008). When online meets offline: An expectancy violations theory perspective on modality switching. Journal of Communication, 58(1), 20–39. https://doi.org/10.1111/j.1460-2466.2007.00372.x

Ramirez, A., Jr., & Zhang, S. (2007). When online meets offline: The effect of modality switching on relational communication. Communication Monographs, 74(3), 287–310. https://doi.org/10.1080/03637750701543493

Reinecke, L., & Trepte, S. (2014). Authenticity and well-being on social network sites: A two-wave longitudinal study on the effects of online authenticity and the positivity bias in SNS communication. Computers in Human Behavior, 30, 95–102. https://doi.org/10.1016/j.chb.2013.07.030

Reis, H. T., Maniaci, M. R., Caprariello, P. A., Eastwick, P. W., & Finkel, E. J. (2011). Familiarity does indeed promote attraction in live interaction. Journal of Personality and Social Psychology, 101(3), 557–570. https://doi.org/10.1037/a0022885

Rubin, A. M., & Step, M. M. (2000). Impact of motivation, attraction, and parasocial interaction on talk radio listening. Journal of Broadcasting & Electronic Media, 44(4), 635–654. https://doi.org/10.1207/s15506878jobem4404_7

Rumelhart, D. E. (1980). Schemata: The building blocks of cognition. In R. J. Spiro, B. C. Bruce, & W. F. Brewer (Eds.), Theoretical issues in reading comprehension: Perspectives from cognitive psychology, linguistics, artificial intelligence and education (pp. 33–56). Erlbaum.

Rumelhart, D. E., & Norman, D. A. (1978). Accretion, tuning, and restructuring: Three modes of learning. In J. W. Cotton & R. Klatzky (Eds.), Semantic factors in cognition (pp. 37–53). Erlbaum.

Schiappa, E., Gregg, P. B., & Hewes, D. E. (2005). The parasocial contact hypothesis. Communication Monographs, 72(1), 92–115. https://doi.org/10.1080/0363775052000342544

Sharabi, L. L., & Caughlin, J. P. (2017). What predicts first date success? A longitudinal study of modality switching in online dating. Personal Relationships24(2), 370–391. https://doi.org/10.1111/pere.12188

Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications. Wiley.

Sprecher, S. (2014). Initial interactions online-text, online-audio, online-video, or face-to-face: Effects of modality on liking, closeness, and other interpersonal outcomes. Computers in Human Behavior31, 190–197. https://doi.org/10.1016/j.chb.2013.10.029

Taylor, S. E., & Crocker, J. (1981). Schematic bases of social information processing. In E. T. Higgins, C. P. Herman & M. P. Zanna (Eds.), Social cognition: The Ontario symposium (Vol. 1, pp. 89–134). Erlbaum.

Toma, C. L., & Choi, M. (2016). Mobile media matters: Media use and relationship satisfaction among geographically close dating couples. In CSCW '16: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 394–404). ACM. https://doi.org/10.1145/2818048.2835204

Toma, C. L., Hancock, J. T., & Ellison, N. B. (2008). Separating fact from fiction: An examination of deceptive self-presentation in online dating profiles. Personality and Social Psychology Bulletin, 34(8), 1023–1036. https://doi.org/10.1177/0146167208318067

Tong, S. T., & Walther, J. B. (2015). The confirmation and disconfirmation of expectancies in computer-mediated communication. Communication Research, 42(2), 186–212. https://doi.org/10.1177/0093650212466257

Utz, S. (2010). Show me your friends and I will tell you what type of person you are: How one's profile, number of friends, and type of friends influence impression formation on social network sites. Journal of Computer-Mediated Communication, 15(2), 314–335. https://doi.org/10.1111/j.1083-6101.2010.01522.x

Walther, J. B. (1992). Interpersonal effects in computer-mediated interaction. Communication Research, 19(1), 52–90. https://doi.org/10.1177/009365092019001003

Walther, J. B. (1993). Impression development in computermediated interaction. Western Journal of Communication, 57(4), 381–398. https://doi.org/10.1080/10570319309374463

Walther, J. B. (1994). Anticipated ongoing interaction versus channel effects on relational communication in computer-mediated interaction. Human Communication Research, 20(4), 473–501. https://doi.org/10.1111/j.1468-2958.1994.tb00332.x

Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal Interaction. Communication Research, 23(1), 3–43. https://doi.org/10.1177/009365096023001001

Walther, J. B. (2007). Selective self-presentation in computer-mediated communication: Hyperpersonal dimensions of technology, language, and cognition. Computers in Human Behavior, 23(5), 2538–2557. https://doi.org/10.1016/j.chb.2006.05.002

Walther, J. B. (2011). Theories of computer-mediated communication and interpersonal relations. In M. L. Knapp & J. A. Daly (Eds.), The handbook of interpersonal communication (4th ed., pp. 443–479). Sage.

Walther, J. B., Kashian, N., Jang, J.-w., & Shin, S. Y. (2016). Overattribution of liking in computer-mediated communication. Communication Research, 43(3), 372–390. https://doi.org/10.1177/0093650214565898

Walther, J. B., Kashian, N., Jang, J.-w., Shin, S. Y., Dai, Y. N., & Koutamanis, M. (2018). The effect of message persistence and disclosure on liking in computer-mediated communication. Media Psychology, 21(2), 308–327. https://doi.org/10.1080/15213269.2016.1247718

Walther, J. B., Van Der Heide, B., Hamel, L. M., & Shulman, H. C. (2009). Self-generated versus other-generated statements and impressions in computer-mediated communication: A test of warranting theory using Facebook. Communication Research, 36(2), 229–253. https://doi.org/10.1177/0093650208330251

Walther, J. B., Van Der Heide, B., Ramirez, A., Jr., Burgoon, J. K., & Peña, J. (2015). Interpersonal and hyperpersonal dimensions of computer-mediated communication. In S. S. Sundar (Ed.), The handbook of the psychology of communication technology (pp. 3–22). Wiley. https://doi.org/10.1002/9781118426456.ch1

Weisbuch, M., Ambady, N., Clarke, A. L., Achor, S., & Weele, J. V.-V. (2010). On being consistent: The role of verbal-nonverbal consistency in first impressions. Basic and Applied Social Psychology32(3), 261–268. https://doi.org/10.1080/01973533.2010.495659

Wickham, R. E. (2013). Perceived authenticity in romantic partners. Journal of Experimental Social Psychology, 49(5), 878–887. https://doi.org/10.1016/j.jesp.2013.04.001

Wood, A. M., Linley, P. A., Maltby, J., Baliousis, M., & Joseph, S. (2008). The authentic personality: A theoretical and empirical conceptualization and the development of the Authenticity Scale. Journal of Counseling Psychology, 55(3), 385–399. https://doi.org/10.1037/0022-0167.55.3.385

Correspondence to:
Sonny Rosenthal
Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore
31 Nanyang Link, #03-48
Singapore 637718
Email: sonnyrosenthal(at)ntu.edu.sg

Editorial record: First submission received on May 28, 2019. Revisions received on December 22, 2019, April 5, 2020 and May 9, 2020. Accepted for publication on May 19, 2020.
Editor in charge: Alexander Schouten

About Authors

Nicholas Tang (Bachelor of Communication Studies, 2018) is a graduate from the Wee Kim Wee School of Communication and Information. His research interests include computer-mediated communication and media psychology. He currently works as a researcher in the public sector.

Janell Chu (Bachelor of Communication Studies, 2018) is a graduate from the Wee Kim Wee School of Communication and Information. She currently works in marketing and sales in a tech company.

Kahmun Leong (Bachelor of Communication Studies, 2018) is a graduate from the Wee Kim Wee School of Communication and Information. She currently works as a public relations executive.

Sonny Rosenthal is an Assistant Professor in the Wee Kim Wee School of Communication and Information. He teaches and conducts research about media psychology, environmental communication, and science communication. His work focuses on the use of social media for public engagement with science and the role of communication in encouraging proenvironmental behaviors.