The pervasiveness of negotiation in real life has promoted research exploring diverse factors that impact negotiation outcomes. One factor that has garnered significant attention from scholars is the emotions that are experienced and expressed in negotiation settings (Barry et al., 2004; Kopelman et al., 2006; Druckman and Olekalns, 2008; Jang and Bottom, 2022). Among diverse emotions, the expression of anger has been extensively studied in relation to the strategic use of emotions for achieving better deals (Kopelman et al., 2006; Tng and Au, 2014). Whether anger expression helps or hurts in negotiation can be a complex issue as negotiation performance can be measured either by economic (Pietroni et al., 2008; Adam and Brett, 2018) or psychological (Kopelman et al., 2006) outcomes. Prior research has reported that anger expression can elicit larger concessions from a negotiation partner leading to higher economic gain for the party who shows anger (Van Kleef et al., 2004a,b; Sinaceur and Tiedens, 2006; Jang and Bottom, 2022). When overall satisfaction with the negotiation process and outcome (Van Kleef et al., 2004a,b) and desire for future interaction with the same negotiation partner (Van Kleef and Côté, 2007; Pietroni et al., 2008; Van Kleef and De Dreu, 2010) were measured to understand psychological aspects of negotiation experience, anger display by a negotiation partner was reported to invoke negative emotional experiences, reducing the other party’s desire for future interaction and overall satisfaction with the negotiation process and outcome.

Notwithstanding an enhanced understanding of the role of anger display in negotiation settings, more nuanced aspects of anger expression have not yet been adequately investigated. We expect that exploring different communication channels employed for anger display can offer valuable insights into how anger expression could lead to different outcomes as a function of its carrier.

While face-to-face communication is the traditional, dominant manner of human interaction and is preferred for many reasons (Warschauer, 2013), it has some limitations, such as the necessity for the participants to be in the same place and time for synchronous communication (Chen and Tseng, 2016). Computer-mediated communication (CMC) is a viable alternative to face-to-face interaction since it transcends geographical and temporal limitations. CMC refers to all communications that use electronic devices as a medium and is a broader concept encompassing various types of technical tools, including text messages [Short Message Service(SMS)] via cell phone, VoIP (e.g., Skype or other voice call functions of messenger applications such as Telegram), and video calling such as Skype and Zoom (Kirwan, 2016).

Given the unique characteristics of each channel, CMC channels can be broadly grouped into three categories: text, auditory, and visual (refer to Geiger, 2020, Figure 3). As we are interested in emotional communication, in particular, we focus on communication tools that convey messages using (1) written texts, (2) spoken language along with auditory cues (e.g., vocal tone and volume), or (3) both verbal language and visual cues (e.g., facial expression and bodily movement), and can enable synchronous communications between two parties on par with traditional face-to-face interactions. Even though email is a widely used text-based channel, it is not included in our research due to its asynchronous nature.

First, SMS, which refers to a text messaging service of cell phone, smartphone, or internet applications (e.g., WhatsApp, Facebook Messenger, and Instagram direct message), might be one of the most familiar and frequently used channels due to its convenience. However, the text-based channels are limited, in that they cannot deliver auditory and visual non-verbal cues that face-to-face interactions can (Walther, 2015). Along with the common use of SMS, the use of emoticons is rapidly growing in personal as well as professional messages (Kaye et al., 2016). Despite its massive popularity and critical role in communicating one’s feelings, there are only a limited number of academic studies on its functions. The existing definitions of an emoticon emphasize the use of typographical symbols and the representation of one’s emotion (e.g., Rezabek and Cochenour, 1998; Huffaker and Calvert, 2005; Walther, 2015). The characteristics of an emoticon can play a complementary role of non-verbal cues when negotiators use text-based channels. In the absence of facial expressions and other visual cues, emoticons can substitute visual cues, or more specifically, facial expressions, in online communication despite their fictionality (Crystal, 2001; Walther and D’Addario, 2001; Zhou et al., 2017). Therefore, when using a text-based channel for emotional communication, emoticons can be utilized as a supplementary tool or in replacement of textual contents.

Given that texting and calling are the original ways of communication for cell phone users, they remain the most common modes (Laursen, 2012; Kemp, 2017). However, despite the use of the same device, the two communication methods differ significantly, as phone calls convey more non-verbal cues such as tone of voice, pitch, pauses, and breathing, all of which facilitate listening and understanding (Hopper, 1992). However, an auditory channel still lacks visual non-verbal cues that a video channel can offer. Video calls can be a viable alternative to face-to-face interaction since it does not require people to be in the same place simultaneously; yet it enables people to see each other and use their own voices. Video calling offers most of the benefits of face-to-face communication but is still not the same, as the non-verbal cues delivered through the video channel could suffer from reduced clarity, lack of vividness, or delay in message exchange due to technical issues.

Despite these differences among CMC channels, relatively fewer attempts have been made to differentiate these channels, especially in negotiation research (Sprecher, 2014; Schweinsberg et al., 2022). In the present study, communication channels for emotional expression are categorized into four different groups: Text refers to channels where emotional messages (“I’m angry”) are expressed via written text. Emoticon describes a communication method where emotion is displayed through images mimicking human facial expressions rather than typed words. Voice refers to channels in which emotion is expressed via spoken words carrying non-verbal cues related to vocal expressions (e.g., volume or tone). Video refers to video conferencing channels (e.g., Skype or Zoom), where emotions can be expressed and observed with visual cues such as a facial expression or bodily movement.

According to media richness theory (Daft and Lengel, 1986), these four CMC channels − Text, Emoticon, Voice, and Video − differ in their information richness, more specifically their capacity to deliver non-verbal cues. Non-verbal cues are trusted more than verbal messages in conflict situations because non-verbal behavior is considered unconscious (Ting-Toomey, 1999; Afifi, 2007) and seen as a reflection of less controlled and indeliberate processes by observers (Knapp et al., 1978). In the context of emotions, non-verbal cues were found to serve as a facilitator for emotional exchange and to strengthen the content of the message (Rezabek and Cochenour, 1998; Walther and D’Addario, 2001). Considering the importance of non-verbal cues in communications and the diversity of communication methods within the CMC category, it is noteworthy to compare CMC channels differing in non-verbal cue availability in a negotiation context.

As reviewed earlier, previous negotiation research has shown that anger display can result in positive economic outcomes such as more concession from negotiation partner in distributive negotiation and negative psychological/relational outcomes such as reduced satisfaction or future intention to negotiate with the same partner. In this research, we attempt to test these anger effects under different communication channels (see Figure 1). First, we expect to replicate the previous finding that anger expression negatively affects the psychological experiences of the perceiver compared to no anger display. Next, we would like to examine whether this negative relationship will be stronger when a richer communication channel is used for anger display. For example, compared to a negotiator who gets an angry text message, a negotiator who observes a partner’s anger expression through video call where anger is expressed through multiple cues including heightened voice, angry face, and aggressive gesture would report more negative relational outcomes, such as lower satisfaction about negotiation or lower intention for future interaction. Thus, we come to our first set of hypotheses:

A well-established finding on the outcome of anger expression in negotiation is that people tend to make a larger concession to the person who is seen angry. Anger expression is usually perceived as a sign of aggression, incivility, and hostility, and unsuitable in most organizational contexts (Geddes and Callister, 2007). Yet during negotiations, observers perceive anger expression as a sign of toughness (Tiedens et al., 2000; Tiedens, 2001) and dissatisfaction with the offer, and tend to make more concessions to avoid an impasse (Van Kleef et al., 2004a; Pietroni et al., 2008). In line with existing studies, we expect that when negotiators perceive a partner’s anger expression, they will make a larger concession to reach an agreement. Additionally, the amount of concession will differ by communication channel such that the more non-verbal cues are employed for displaying anger, the stronger will be the anger effect on the amount of concessions. Our next set of hypotheses is as follows.

In order to understand the psychological mechanisms underlying the hypothesized channel effects, we need to trace back to prior research on functions and consequences of emotions from the perspective of interpersonal relationships (Frijda and Mesquita, 1994; Keltner and Haidt, 1999; Pietroni et al., 2009; Lerner et al., 2015; Olekalns and Druckman, 2015). First, it has been argued that emotional display tends to induce complementary or reciprocal emotional reactions from its observer, which are instrumental for responding to important social events (Keltner and Haidt, 1999). Second, the display of one’s emotion communicates valuable information to both oneself (Schwarz and Clore, 1983) and other people (Oatley and Johnson-Laird, 1987) by revealing one’s inner emotional state (Ekman, 1993). Lastly, emotions can serve as incentives or obstacles for others’ social behavior (Klinnert et al., 1983).

More recently, Emotions as Social Information (EASI) model proposed by Van Kleef (2009) is being widely applied to organizational and social phenomena including negotiation, leadership, and group decision making. It posits that emotions function as social information and influence counterparts via non-verbal (facial expression, body posture, or tone of voice) and/or verbal (language) channels, through an affective and inferential mechanism. The model formulates an individual’s overall decision-making process by utilizing emotions as an information source. It categorized the subsequent behaviors into two types: undesired and desired behaviors.

The affective mechanism describes the affective reactions of a perceiver who observes others’ emotional expressions. One’s emotional display can influence a perceiver’s attitude and behavior through several pathways, one of which concerns the affective reactions induced by emotional contagion, or the proclivity of catching others’ emotions (Hatfield et al., 1994). Emotional experiences via this process are referred to as reciprocal emotional experiences. For example, when an individual observes others’ non-verbal displays of emotions, such as facial, vocal, and postural expressions, those displays are mimicked; they produce congruent emotional states through afferent feedback processes (Hawk et al., 2012). These experiences through emotional contagion can influence one’s judgment and decision (Forgas, 1995). In addition to this is complementary emotional arousal (Keltner and Haidt, 1999), where complementary emotions are provoked when an individual observes certain emotions in others. Experiencing fear after observing another’s anger expression is an example of this complementary emotional experience (Dimberg and Öhman, 1996).

According to this affective reaction mechanism, when negotiators observe counterparts’ anger expression, they would experience reciprocal emotions by means of emotional contagion. The experience of anger as a response to counterpart’s anger expression falls into the affective reactions path of EASI model. Given the critical role of non-verbal displays during this contagion process, the extent to which the observer’s anger experience in response to others’ anger displays will increase with the richness of the communication channel employed. Specifically, when more non-verbal cues are provided signaling one’s anger, an observer will experience a greater level of anger than when less non-verbal cues are available, which will in turn negatively affect psychological outcomes in negotiation. This leads us to our next hypotheses on the mediating role of perceiver’s anger experience in the proposed channel effect.

Emotions as Social Information model proposes that an inferential mechanism is activated when an individual tries to interpret the meaning from others’ affective expressions. Since people experience a particular emotion based on their appraisal of the current situation (Frijda et al., 1989; Lazarus, 1991; Scherer et al., 2001), observing a particular emotion in another person offers useful information about how they interpret the given situation (Van Kleef et al., 2011). An individual can engage in making a judgment on a counterpart’s emotional expression as a part of inference processes.

The distinct appraisal patterns of discrete emotions (Manstead et al., 1989; Smith et al., 1993) can provide resourceful information about the expresser (Van Kleef, 2009; Hareli and Hess, 2010). For instance, we can infer one’s inner state (Ekman, 1993), one’s orientation toward others (Hess et al., 2000), and appraisal of the situation (Manstead and Fischer, 2001) from displayed emotions. Thus, the negotiator’s evaluation of the authenticity of the counterpart’s emotional expression can be a process of information gathering for the inferences about the counterpart’s intentions based on the emotional expression. These inference processes can also affect the observer’s subsequent attitudes and behavior (Wang et al., 2012). When negotiators perceive their partner’s anger display as a genuine signal of inner state, they will infer that the partner is not satisfied with the situation and adjust their behavior accordingly such as offering larger concessions to their partner to avoid an impasse (Tng and Au, 2014). By contrast, when the negotiators infer that the anger display is inauthentic, they will assume that the counterparts are deliberately expressing anger as a way of bluffing to maximize benefits. In this context, the negotiator will feel unjustly treated (Allred et al., 1997) and take actions to prevent future injustice (Van Dijk et al., 2008). Thus, when lower-level authenticity is experienced, it will lead to less cooperative behavior such as smaller concessions.

To address how the expression of anger via different communication channels affects both economic (e.g., concession: objective measurement of negotiation outcome in a distributive negotiation setting) and psychological (e.g., desire for future interaction and satisfaction with negotiation process) outcomes, this study employed a scenario method. Five different conditions in which anger expression by the negotiation partner is delivered through four different channels or anger is not explicitly expressed (i.e., neutral) were created and study participants were randomly assigned to one of these conditions. Participants’ responses to their negotiation partner’s anger display were collected and analyzed to test the channel effect. Study 2 is a post-hoc study to see if the differential effects of communication channels found in Study 1 could be due to differences in the level of perceived anger expression intensity, a possible alternative explanation. A low versus the intense level of anger expression during negotiation under the same video channel was manipulated in order to test the effect of anger expression intensity while holding the channel effect constant. All participants in Study 1 and Study 2 were recruited online and were compensated for their participation when the survey was successfully completed.

For statistical power, we used G*Power (Faul et al., 2009) analysis with medium effect size, f² = 0.0625 at significance level α = 0.05 with a power of 0.80. The results recommended a sample size of 197 (Study 1) and 128 (Study 2) for each study. For both studies, more than the minimum sample size requirement was collected. It assures the test of possible three-way interaction effect by adding participants’ gender in the analysis (Dawson and Richter, 2006).

Study 2 was designed to test a possible alternative explanation for Study 1 findings. The fact that richer channels conveying more non-verbal cues reinforced the anger effect on negotiation outcomes made us curious to know whether this finding could be possibly explained by the level of anger intensity perceived by participants. Besides an effort to provide an alternative explanation for the phenomenon, it also answers the call for future research exploring the anger intensity in negotiation (Hunsaker, 2017). To separate the intensity effect from the channel effect, Study 2 attempts to examine the role of anger expression intensity in isolation while controlling for the channel effect. A single communication channel with affluent non-verbal cues (i.e., video) was employed and the intensity of anger expression was manipulated by corresponding instructions to the actors, who played the role of negotiation partner in the video clip.

This research contributes to the literature in several ways. First, in addition to confirming the prior findings regarding the effect of anger expression on negotiation, this study extends previous research by proving that the way anger is expressed − more specifically, channel selection for emotional communication − can bring out meaningfully different outcomes in negotiations. While previous studies on anger expression in negotiation did not systematically and fully examine the role of diverse communication channels, our research demonstrates that the effect of anger expression can vary by channel. The findings not only highlight the importance of communication channel selection for effective negotiation in the era of multiple computer-mediated communication methods available but also highlight the reason why we need to take communication methods into consideration in academic research for a more accurate understanding of the role of emotions in negotiation.

Second, this research attempted to systematically differentiate between CMC channels in the context of negotiation research. By focusing on the difference in non-verbal cue availability, four different channels in CMC were chosen: text, emoticon, voice, and video. As the channel shifted progressively from text to video, more non-verbal cues were displayed as suggested by media richness theory, and their potential effects on delivering anger expression were examined. Despite some insignificant effects, depending on the outcome variables, the results revealed that the effect of anger expression differs between text-based channels and non-verbal-based channels. The results confirmed that anger is more effectively delivered to the counterpart via richer channels where non-verbal cues, especially auditory cues, are readily available.

Third, the EASI model was empirically examined by testing two mechanisms (affective and inferential) using mediation analyses. The anger effects on two relational negotiation outcomes were significantly stronger when anger was displayed via richer channels (voice and video) and this channel effect proved to be mediated by the perceivers’ anger experience following anger display by their partner. In other words, participants who observed their counterpart’s anger expression through voice message or video experienced more intense anger as a reaction and this explained why they reported lower satisfaction and lower desire for future interaction. The channel effect of anger expression on concession as an economic outcome variable was expected to be related to the perceived authenticity of anger expression, which helps participants infer the true intention behind anger display. Yet, we found no evidence supporting the inferential mechanism in this research. Still, our attempt to combine media richness theory with the EASI model to hypothesize and empirically test the communication channel effects made an important step forward to a better understanding of the role of media.

Fourth, this research not only examined the communication channel effect but also tried to separate it from the intensity effect. In Study 1, we were not able to disregard the possibility that communication channels varying in their capacity to deliver non-verbal cues may impact the observer’s perception of anger intensity. To test the possibility that the difference between channels can be fully explained by the intensity effect, in Study 2, we tried to tease them apart by manipulating anger expression intensity in a single channel. We found that anger intensity mattered in the case of the desire for future interaction, but did not influence the other two outcome variables, satisfaction, and concession. This implies that the role of the communication channel in the anger effect cannot be simply attributable to the perceived intensity of anger.

The findings of this research provide some useful insights for practitioners in the field of negotiation. First, exploring the channel effect is a timely endeavor, considering that due to the pandemic, computer-mediated communication has become an inseparable part of our lives. This study would provide meaningful guidance for negotiators in choosing a communication channel. The same message (“I’m angry”) can be delivered via different channels using language/vocabulary (text), vocal expressions, and facial expressions/bodily postures. Given that different outcomes are expected depending on the communication channel employed, practitioners should be aware of the importance of channel selection when entering a negotiation.

Additionally, this research suggests that CMC channel selection should be done not only for convenience but also with careful consideration of negotiation goals and type (one-time vs. long-term). In other words, strategic selection of communication channels is crucial for attaining goals. Using the channel with affluent non-verbal cues such as video calls can be effective when the economic gain is the focus of the negotiation and negotiators would like to use anger to win more concessions from their partner. However, if the negotiators care about long-term relationships rather than short-term gains, anger displays through rich channels should be reconsidered because of the possibility of negative psychological consequences. Thus, strategic channel selection needs to be aligned with the purpose of the negotiation.

Lastly, negotiators should take into account that their decision-making process in negotiations is being influenced by affect (Cristofaro, 2020). In other words, field practitioners are required to understand the impact of making a hasty or emotional decision and regulate their emotional states when making a decision (Côté et al., 2013). Thus, negotiation skill training provided by an organization needs to cover emotional regulation, expression as well as recognition to cultivate a more emotionally versatile negotiator (Sharma et al., 2020). This caveat also aligns with the trend shifting from bounded rationality to bounded emotionality (Cristofaro, 2017) – the interactional relations between (un)conscious mental process and affective states are generating a new framework regarding rationality (Cristofaro, 2019).

This research is not without limitations. First, in spite of significant differences between text-based channels (text, emoticon) and non-verbal channels (voice, video), not all the hypothesized differences between channels were found to be significant. Specifically, there were no differences between text versus emoticon and voice versus video. Anger expression via text versus emoticon could be perceived similarly in that both channels rely on text message format. What was more surprising is that the participants did not show any difference in their perception of and reaction to anger between voice and video. In particular, contrary to our expectations, the amount of concession was greatest when anger was delivered through voice, not video. This is somewhat contradictory to our assumption that more non-verbal cues will make the anger display stronger. Would vocal expression of anger be sufficient for conveying an angry message and additional non-verbal cues like facial/bodily expression are just redundant? Or does it have something to do with our confederates’ lack of acting skills? If their facial expressions were perceived as less natural and inauthentic compared to their vocal expressions, anger display in the voice condition might have exerted more influence. Further research should be undertaken to compare and contrast the roles of vocal cues versus visual cues.

In addition, our study design where five different conditions were compared inevitably created a large number of hypotheses testing several group comparisons by contrast codings. This raises the issue of applying p-value correction before testing this multitude of hypotheses. Yet there is no firm consensus on using corrected p-values among statisticians. Some scholars insist that not performing adjustments would increase the possibility of type I error (e.g., Curran-Everett, 2000) while others argue that p-value corrections are not necessary (e.g., Rothman, 1990; Matsunaga, 2007; Althouse, 2016). Researchers defending the latter perspective touch on possible problems (Rubin, 2017a). One of the problems of conducting p-value correction is that it will increase the likelihood of type II error in return (Matsunaga, 2007). It accompanies a decrease in statistical power as well (Rothman, 1990). The opponents of the p-value corrections approach contend that type I error is dissipated to overall hypotheses rather than being localized to one or several hypotheses (Matsunaga, 2007; Rubin, 2017b). While we acknowledge both views are valid, we followed the no-adjustment approach as well as the other social science studies (Ding et al., 2020; Diachenko et al., 2021; Thériault et al., 2021) by conceding the potential problems of the taken approach.

Second, this research employed controlled experimental study designs to systematically compare the effect of different communication channels, which inevitably brings about the issue of ecological validity. Given the synchronous characteristics of the negotiation, inducing anger via pre-recorded voice and video may not be the most ideal method. Additionally, the techniques used in this study cannot be considered as reproducing full online interaction, in that only anger expression was presented via different channels while other contents were delivered via text message. Future research must develop more sophisticated experimental designs and methods incorporating the synchronous aspect of negotiation so that interactions with a negotiation partner proceed naturally while other factors are under control. Relatedly, the negotiation tasks given to the participants do not reflect the entire process of actual negotiation. The task was intentionally designed to reach an agreement while other prior research considered the possibility of impasses (Yip and Schweinsberg, 2017; Adam and Brett, 2018). Moreover, while most negotiation research used multi-issue tasks, the negotiation task used in this research dealt with a single issue, for the sake of simplicity. Considering these aspects, a more natural negotiation process should be devised in future research.

Third, the participant sample was recruited from South Korea, known as a hierarchical (Hofstede, 1980) and high-context (Ting-Toomey, 1985) culture where indirect communication styles (Morrison et al., 1994) are more common. The cultural characteristics of our sample might have affected the way of negotiating in this study and its results. Thus, to ensure the generalizability of our findings, further research needs to be conducted in different cultural settings such as western countries where using low-context (Hall, 1976) and egalitarian cultures (Hofstede, 1980) such as the United States.

Fourth, further attempts to explore the inferential mechanism of the EASI model are needed. We measured perceived authenticity of emotional expression as one potential mediator reflecting people’s inference about the true intention of anger expression, but the mediation analysis was not significant. Other variables, such as inferences regarding the counterpart’s reservation price or toughness, need to be considered to further investigate the inferential mechanism explaining the anger display effect in a negotiation setting.

Next, even though there were a few attempts to explore the effect of anger intensity in negotiation (Adam and Brett, 2018; Venkiteswaran and Sundarraj, 2020), they are relatively in a nascent stage despite the importance of the intensity factor in understanding the effect of anger expression more accurately. In this research, the confederates expressed anger in three different levels by changing their vocal tone/volume and facial expression, but participants in our pilot study failed to discern moderate-intensity anger from high-intensity anger. Since the video channel can deliver visual cues in addition to auditory cues, abundant non-verbal cues available could have made it difficult to clearly distinguish different intensity levels. For this reason, other communication channels can be easier for intensity manipulation. Considering the vocabularies used in expressing anger in the anger manipulation technique (Yun et al., 2020), future research might be able to use a different channel for successful intensity manipulation.

Sixth, while anger expression is the focus of this study, other emotions, such as guilt, anxiety, and happiness, are worth testing in the future as discrete emotions might elicit different outcomes in negotiation. It would be interesting if similar channel effects are observed when other emotions are expressed via different channels.

Lastly, in this research, only the steady-state emotion of participants was considered. An individual’s affective experience changes over time (Weiss and Cropanzano, 1996). Given the changing nature of emotional states, transitional emotions need to be included in future research. By exploring how an individual’s emotional experiences and expressions change over time during negotiation, more interesting findings can accrue.