The purpose of the study was to assess whether and how communication medium (i.e. face-to-face vs text-based instant messaging) moderates the processes through which two contrastive, discrete emotions (i.e. anger and compassion) influence dispute resolution tactics and relational outcomes.
A total of 254 participants formed same-sex negotiation dyads to resolve a dispute between roommates either face-to-face (FtF) or through computer-mediated communication (CMC) in two experimental conditions (high vs low responsibility) designed to induce anger and compassion. Multi-group structural equation modeling procedures were used to assess four actor–partner mediation models that predict both intrapersonal and interpersonal effects of anger and compassion on relational outcomes through dispute resolution versus facework tactics.
Results showed that anger had a significant indirect effect on relational outcomes through competitive (i.e. power-based and face-threatening) tactics in FtF negotiations but not CMC, whereas compassion had a significant indirect effect on relational outcomes through both competitive and cooperative (i.e. power-based, interest-based, face-threatening and face-enhancing) tactics in CMC but not FtF negotiations.
The study extends existing scholarship on emotion in negotiations by demonstrating the moderating effects of communication medium and sheds insights on why communication technology should be considered for resolving emotion-laden disputes.
Negotiation is a ubiquitous social activity through which individuals who perceive incompatible goals attempt to resolve their differences and reach agreements and shared understanding (Roloff et al., 2003). When a dispute arises, emotions are inevitable. In the past two decades, a sizable body of research has examined the effects of emotions, particularly anger, on negotiation behaviors and outcomes (for a review, see Hunsaker, 2017). It was noted that about 30% of the published studies involved computer-mediated negotiations where the anger expresser was the computer (Hunsaker, 2017), with the rest involving human negotiators in either face-to-face (FtF) or computer-mediated settings. Although researchers using e-negotiations speculate how their findings about the effects of emotions may differ in FtF settings (e.g. Dorado et al., 2002; Jang and Bottom, 2022; Shao et al., 2015), little research has empirically assessed such differences.
Inspired by a generation of work on computer-mediated communication (CMC), research on e-negotiations has demonstrated that some characteristics of CMC, such as the lack of nonverbal cues, relative anonymity of the parties and reduced social presence, make it more difficult for negotiators to build rapport (Morris et al., 2002), develop trust (Naquin and Paulson, 2003) or reap higher profit (Stuhlmacher and Citera, 2005). Negotiators in CMC were found to communicate positive affect less frequently, use more distributive tactics and forcing behaviors, make fewer concessions and are less likely to reach an agreement than those in FtF negotiations (Galin et al., 2007; Giordano et al., 2007; Johnson and Cooper, 2009). Nevertheless, scholars noted that using CMC can also be advantageous to negotiation outcomes (Friedman and Belkin, 2013). For example, the greater social distance and lower incidence of emotional expressions in CMC can reduce emotional intensity and facilitate integrative agreements (Henderson, 2011; Williams and Bargh, 2008). These conflicting findings suggest that whether the use of information technology helps or hinders negotiation processes and outcomes depends on how it interacts with other contextual variables (Nadler and Shestowsky, 2006). A recent study showed that the effects of anger on deal-making negotiation outcomes were qualified by both the media richness of communication channels and the levels of anger intensity (Yun and Jung, 2022). To date, little research has examined how communication mediums may interact with discrete emotions to influence negotiation tactics and outcomes in a dispute resolution context. As the proliferation of communication technologies has made CMC an increasingly popular option for resolving conflicts, especially after the COVID-19 pandemic forced many individuals and organizations to make CMC an essential part of organizational and interpersonal communication, a study that examines the effects of emotions through different communication channels on dispute resolution tactics and outcomes can provide valuable insights for practitioners.
The current study seeks to advance existing scholarship on emotion in negotiations by examining whether and how communication medium (face-to-face vs instant messaging) moderates the processes through which two contrastive, discrete emotions (i.e. anger and compassion) influence dispute resolution tactics and relational outcomes. In doing so, it also seeks to reconcile the inconsistent findings in the CMC literature by examining emotion as a boundary condition to better understand when and how communication technology should be used for resolving emotion-laden disputes.
Anger and compassion in negotiations
As one of the basic emotions that are universally experienced across cultures (Ekman, 1999), anger is by far the most studied emotion in conflicts and negotiations because of its prevalence and impact (for reviews, see Hunsaker, 2017; Van Kleef and De Dreu, 2010). The detrimental effects of anger on negotiations have been well-documented, such as causing increased competitive behaviors, rejection of ultimatum offers, fewer integrative tactics, smaller joint gains, fewer concerns for the counterpart’s interests and less desire to work together in the future (e.g. Allred et al., 1997; Liu and Zhu, 2021). Although researchers have identified many factors that moderate the influence of anger on negotiations, such as time pressure (Van Kleef et al., 2004), power (Overbeck et al., 2010; Sinaceur and Tiedens, 2006), culture (Adam and Shirako, 2013; Liu, 2009), perceiver’s motivation (Van Kleef et al., 2004), type of negotiation situations (Adam and Brett, 2015), moral judgment (Dehghani et al., 2014) and self-regulation (Jäger et al., 2017), the intrapersonal effect of felt anger on competitive tendencies is largely supported across cultures and negotiation contexts, especially for high-power negotiators (e.g. Liu, 2009; Overbeck et al., 2010).
However, anger’s interpersonal effect was less conclusive. Anger was found to have positive effects on negotiation outcomes, such as signaling power and dominance and eliciting greater concessions from negotiation counterparts (e.g. Van Kleef et al., 2004). Such interpersonal effect of anger on the counterpart’s yielding tendencies was mainly supported in deal-making negotiations, particularly for low-power negotiators (e.g. Butt and Choi, 2010; Overbeck et al., 2010; Wang et al., 2012), negotiators with poor alternatives (Sinaceur and Tiedens, 2006) and when anger is expressed by counterparts from a different culture (Ramirez-Marin et al., 2022). However, a recent study showed that expressed anger did not yield significant concessions from the counterpart when negotiations were conducted via instant messaging (Jang and Bottom, 2022). Similarly, another study showed that the positive effect of expressed anger on the counterpart’s concession-making was reduced when communication channels were less rich in non-verbal cues (e.g. text and emoticon), compared with richer ones (voice and video); participants also reported more satisfaction and desire for future interaction when negotiating with leaner media (Yun and Jung, 2022).
To date, the moderating effect of communication channels on the influence of anger has not been examined in a dispute resolution context. Studies of online disputes found that expressed anger lowered settlement rates between buyers and sellers on eBay, especially when disputants reciprocate face-attacking messages or angry feelings (e.g. Brett et al., 2007; Friedman et al., 2004). Researchers have sought to explain the effects of anger by examining cognitive mediators, such as perceived toughness (Sinaceur and Tiedens, 2006; Van Kleef and De Dreu, 2010), the intention to compromise or yield (Ramirez-Marin et al., 2022), attentional focus and cognitive exhaustion (Shao et al., 2015) and motivation for information search (Rees et al., 2020). Although these studies have contributed to a nuanced understanding of the role of anger in negotiations, the communication process through which anger interacts with communication channels to influence negotiators’ economic and psychological outcomes has been under-explored. The current study seeks to fill the void by providing such an assessment in a dispute resolution context.
On the other hand, compassion, a positive emotion that stems from a similar cognitive appraisal process as anger (see Weiner, 1995), was sometimes examined in contrast to anger in negotiations (e.g. Allred et al., 1997; Zhang et al., 2014). While anger tends to arise when one judges another person to be responsible for a negative event, compassion results from the perception that the other person is not responsible because of uncontrollable external circumstances (Liu and Wang, 2010). Ample research has documented the positive effects of compassion on conflicts and negotiations, such as its association with higher concern for others, integrating, compromising and obliging conflict styles (Zhang et al., 2014), trust toward the counterpart, cooperative intentions (Liu and Wang, 2010) and perception of greater self-other similarity (Oveis et al., 2010). However, few studies have provided empirical support for its positive effects on the actual negotiation tactics and outcomes. Empathy was even found to have a detrimental effect on negotiation outcomes in the context of competitive deal-making negotiations when it is conceptualized as involving emotional resonance but without cognitive understanding or perspective taking (e.g. Galinsky et al., 2008; Longmire and Harrison, 2018).
The definition of compassion has been controversial. While anger is considered a basic emotion, compassion is often used interchangeably with sympathy and empathy. Luciano et al. (2020) argue that the behavior of perspective-taking (i.e. the ability to “be in the other person’s shoes”) is “the core ability in empathy and compassion” (p.282; emphasis in original). In a similar vein, Goetz et al.’s (2010) empirical review supports an “emotion-family approach” to compassion that emphasizes the shared appraisal process of compassion, sympathy and pity that motivates patterns of behavior toward ameliorating the negative situation (e.g. distress or suffering) for another individual. In a study that examined the interpersonal effects of sympathy in negotiations, Shirako et al. (2015) defined compassion as synonymous with sympathy, focusing on the concern for another’s needs and welfare, rather than emotional resonance. They found that sympathy improved the counterpart’s (and hurt one’s own) distributive outcomes and increased both parties’ integrative outcomes when sympathy appeals were made by negotiators in a vulnerable situation. Although these findings were replicated in both CMC and FtF negotiations, the dependent measures were limited to economic outcomes in deal-making negotiations. The behavioral process through which such effects took place was largely ignored. We are currently ill-informed about how compassion may influence dispute resolution tactics and relational outcomes, let alone whether such effects may be amplified or mitigated by communication mediums.
Communication scholars have contributed substantially to the theoretical understanding of the process through which emotions influence conflicts and negotiations. Zhang et al. (2014), for example, extended the face negotiation theory by linking emotions, such as anger and compassion, with self-construal and face concerns as an additional explanation for culture’s effect on conflict styles. Liu and Zhu’s (2021) study further showed that disputants’ anger had a significant indirect effect on relational outcomes through dispute resolution and facework tactics in FtF dispute negotiations. Specifically, anger was found to reduce one’s own and the counterpart’s desire to continue the relationship by increasing the importance that disputants placed on power goals, which prompted them to use more competitive and less cooperative tactics. While the path models received support from both Chinese and American cultures, we know little about (a) whether compassion has the opposite effects on dispute resolution processes and outcomes and (b) whether the effects of anger and compassion are amplified or mitigated when disputes are resolved FtF versus via CMC. The current study seeks to extend this line of research by providing such assessments.
Traditional dual concern models of conflict tend to classify conflict behaviors based on individuals’ predispositions, such as concerns for self versus other (Pruitt and Rubin, 1986), concern for production versus people (Blake and Mouton, 1964), or assertiveness versus cooperativeness (Thomas and Kilmann, 1976). The current study follows the “IRP” framework of three types of dispute resolution tactics – interests (i.e. integrating the underlying interests of both parties), rights (i.e. relying on mutually acknowledged, objective or independent standards or regulations) and power-based tactics (i.e. forcing conciliation based on the relative power difference of the two parties; Ury et al., 1988) because it has proven useful for categorizing the specific strategies used for dispute resolution (e.g. Tinsley, 2001). Inspired by Zhang et al.’s (2014) work that links emotions with face concerns, the study also examines two types of facework tactics – face-threatening and face-enhancing tactics (Goffman, 1967; Liu and Zhu, 2021) to account for the effect of emotions on disputants’ relational outcomes. The study first hypothesizes as follows:
Disputants’ anger will have an indirect, negative effect on both their own and their counterpart’s relational outcomes, whereas their compassion will have the opposite effects, which will be mediated by their dispute resolution (rights-, power- and interests-based) and facework (face-threatening and face-enhancing) tactics.
Emotion and communication channels in negotiations
Despite a growing body of knowledge about e-negotiations in the past two decades, there remains a lack of clear understanding about the effect of communication channels on negotiation performance, as these studies are grounded in competing theories and yield conflicting findings. One stream of CMC theories, such as the media richness theory (Daft and Lengel, 1986), “cues-filtered-out” theory (Culnan and Markus, 1987) and social presence theory (Short et al., 1976), suggest that communication media (e.g. FtF, telephone, IM and email) differ in the amount of information they allow individuals to communicate, which affects the quality of communication. Specifically, face-to-face communication offers individuals access to a wide range of aural (e.g. quality, pitch and volume of the voice), visual (e.g. facial expressions, eye contact, gestures, proximity and physical appearance) and nonverbal (e.g. laughter, pauses) communication cues and is therefore the richest communication medium affording individuals with the most social presence. In CMC where individuals rely on text messages to encode and decode meaning, the limited aural, visual and nonverbal access makes it difficult for individuals to develop personal ties with each other. CMC may also promote de-individuation (i.e. loss of identity for the individuals), which can result in diminished concern for morality and a reduced sense of responsibility for one’s actions (Barkhi et al., 1999). Taken together, CMC may set a context for a greater amount of negative and deregulated behaviors.
However, another stream of theories, such as the social information processing theory (Walther, 1992) and the hyperpersonal model of CMC (Walther, 1996) suggest the opposite. These theories contend that humans have a need for affiliation when communicating with others; individuals can adapt to leaner media and benefit from CMC over time. For example, a CMC environment allows individuals to plan and edit their messages before sending them out and engage in more selective information sharing (Walther, 1992). Communicators in a CMC environment may even develop electronic paralanguage to transmit socio-emotional cues (Griessmair and Koeszegi, 2009) and establish relational communication quickly, especially when anticipating future interaction (Walther, 1996). The greater social distance in CMC may also enable parties to be more detached from others’ influence and engage in more creative problem-solving, take longer to provide more thoughtful responses and arrive at more objective and rational solutions (Fujita et al., 2008).
Although the first theoretical approach seems to focus on the costs of CMC, whereas the second one focuses on its benefits, a deep dive into the literature reveals some consistent patterns regarding the role of communication channels in negotiations. In line with the first approach, negotiators were found to reciprocate more forcing behaviors (e.g. threats, demands, intimidation) in FtF negotiations than CMC, especially when conflict is escalated (Dorado et al., 2002; Giordano et al., 2007). Visual access, or the immediate presence of non-verbal cues in FtF interactions, was found to amplify emotional experiences (Johnson and Cooper, 2009; Williams and Bargh, 2008), whereas physical barriers placed between negotiators were found to reduce the use of dysfunctional contentious tactics and increase the likelihood of integrative solutions (Carnevale and Isen, 1986). As nonverbal cues are considered unconscious and therefore trusted more than verbal messages in conflict situations, the reduction of social cues in anger expression through CMC is considered to compromise the perception of dominance, causing conflict parties to focus more on the substantive content of the messages, rather than relational content (Shell, 1995). A recent study found that most participants avoided the instruction to express anger in a negotiation through instant messaging, even when incentivized to do so (Jang and Bottom, 2022). It is likely that the mediated environment makes the strategic display of anger less appealing because of the fewer social cues that can be used to express it and the potentially greater effect on damaged relationships (Deffenbacher et al., 1996).
Not only are the detrimental intrapersonal effects of felt anger on negotiation behaviors and outcomes mitigated by communication media, but the positive interpersonal effects of expressed anger were also found to be weaker or non-significant in a mediated environment. For example, Yun and Jung (2022) found that anger expressed through text- and emoticon-based communication channels made the counterpart concede less than when anger was expressed through richer communication channels (e.g. video and voice). However, due to reduced access to nonverbal cues associated with angry expressions, such as heightened voice, angry face and aggressive gestures, negotiators in text-based and emoticon-based negotiations reported greater satisfaction than those that negotiated through video or voice channels. Taken together, these studies suggest that the greater spatial and temporal distance in CMC may de-intensify negotiators’ angry feelings and reduce their urge to reciprocate competitive tactics compared with FtF negotiations.
While anger has been universally recognized as a basic emotion, with relatively unequivocal nonverbal signals, such as the furrowed brow, clenched fist and raised voice, nonverbal displays of compassion are generally less discernible and the evidence for distinct expressions of compassion has been mixed (Goetz et al., 2010). Using the EmotionID software to analyze facial expressions, Baránková et al.’s (2019) study found that during the most compassionate moment, there was an increased likelihood of tightened lower eyelid, slightly pulled down upper eyelid, pulled lip corners and head tilted to the right. In addition, other nonverbal cues, such as eye contact, tone of voice and posture can all add to the richness of the content for communicating rapport (Jap et al., 2011). Nevertheless, in a study that used photographs of facial expressions to assess recognition of non-basic emotions, including compassion, over 80% of the participants interpreted the faces as communicating a different emotion than the predicted emotion (Widen et al., 2011). Kanovský et al. (2020) found that the compassion expression resembles the facial expression of sadness to some extent, confirming a similar conclusion made by Ekman and Cordaro (2011). The elusiveness explains why research on the role of compassion in negotiation has focused on the effect of felt compassion, rather than expressed compassion (e.g. Liu and Wang, 2010; Shirako et al., 2015). For the same reason, whether there are fewer or more channels of communication media may make less difference for the communication of compassion than for anger.
Research on the positive effects of sympathy or compassion on negotiation outcomes has received support from both CMC and FtF negotiations (Allred et al., 1997; Shirako et al., 2015), although the mechanisms through which such effects occurred have been under-explored. As explained in the previous section, the influence of emotions on negotiation tactics and outcomes has been explained through the lens of negotiators’ interaction goals: While competitive goals were found to mediate the influence of anger on negotiation tactics and outcomes (e.g. Liu and Zhu, 2021), research suggests that cooperative goals can mediate the influence of compassion on behaviors and outcomes (Liu and Wang, 2010). Although research has yet to empirically assess how emotions interact with communication media to influence negotiation processes and outcomes, scholars have developed theories to account for boundary conditions of CMC, such as task or partner familiarity or communication orientation. For example, based on meta-analyses of how channel attributes of communication media influence negotiation outcomes, Swaab et al. (2012) proposed a communication orientation model (Swaab et al., 2012). Their analyses concluded that (a) the presence of communication media led to high-quality negotiation outcomes only for negotiators with a neutral orientation; (b) more channels had limited or no effect on cooperative negotiators; and (c) more channels even hurt communicators’ outcomes for negotiators with a non-cooperative orientation. In a similar vein, recent research showed that when communicators are sufficiently familiar with one another, the need for synchronous communication channels may be alleviated (Geiger, 2020).
These findings provide a helpful theoretical basis for advancing theories on emotion in negotiation by examining whether communication mediums can moderate the effects of emotions on negotiation. When anger arises, as negotiators place more importance on competitively oriented interaction goals, more visual, vocal or synchronous channels have a greater chance to transmit cues of domination, leading to spirals of competitive behaviors. In dispute resolution, this means that angry disputants are more likely to increase the use of power-based, rights-based and face-threatening tactics and less likely to use interests-based on face-enhancing tactics in FtF negotiations than CMC; consequently, the negative effect of anger on negotiation outcomes can be more pronounced in FtF negotiations.
On the other hand, nonverbal displays of compassion are generally less discernable than those of anger in FtF interactions. More channels of communication in FtF negotiations may not enhance the effects of compassion, whereas in a mediated environment, using language to express compassion, give face, acknowledge the constraints faced by the counterpart and show understanding, has been found to have a significant positive effect on dispute resolution outcomes (Brett et al., 2007; Maaravi et al., 2019). In addition, when compassion arises, as negotiators place more importance on cooperatively oriented interaction goals (e.g. meet both parties’ needs, exchange information, gain trust, build a positive relationship; see Liu and Wang, 2010), they can adapt themselves to fewer channels by using (para)linguistic means to build rapport and develop trust. Not only are the costs of fewer channels offset, but the benefits of CMC, such as the ability to take more time to craft thoughtful verbal messages, can even enhance the effects of compassion.
Research has shown that in situations of impasse, the inhibited ability to create rapport in CMC may even result in better outcomes for clients and less unethical behaviors in the process than FtF interactions (Jap et al., 2011). Although this seems to contradict the conventional wisdom that FtF meetings are more appropriate for handling ambiguous situations that involve conflicting interpretations, such findings suggest that the greater spatial, temporal and psychological distance in CMC allows individuals to be more intentional in their goal pursuit and make better and more rational decisions. Similarly, a recent study shows that more visual access, such as access to self-viewing videos in CMC, may cause less positive experiences and outcomes in interpersonal workplace conversations because it gives individuals a greater chance of detecting any incapacity or errors that are not up to par (Shin et al., 2023). Given the low accuracy in recognizing compassion based on nonverbal cues (e.g. Widen et al., 2011), text-based CMC, such as instant messaging, may allow individuals to focus more on language use to express compassion, which was found to have a significant effect on negotiation outcomes (Maaravi et al., 2019). Taken together, we hypothesize (see Figure 1 for a conceptual model):
Communication medium will moderate anger’s indirect, negative effect on both disputants’ own and their counterpart’s relational outcomes through the mediation of dispute resolution (power-based, rights-based and interests-based) tactics (H2a) and facework (face-threatening and face-enhancing) tactics (H2b), such that anger’s negative indirect actor and partner effects will be more pronounced in FtF than in CMC.
Communication medium will moderate compassion’s indirect, positive effect on both disputants’ own and their counterpart’s relational outcomes through the mediation of dispute resolution (power-based, rights-based and interests-based) tactics (H3a) and facework (face-threatening and face-enhancing) tactics (H3b), such that compassion’s positive indirect actor and partner effects will be more pronounced in CMC than FtF.
Method
Participants and procedures
Participants were 254 Chinese college students (106 men, 142 women and 6 unidentified) recruited from three major universities in Eastern and Southern China, with an average age of 20.52 (SD = 1.89). Participants were recruited through in-class announcements, word of mouth and solicitation messages on WeChat, the primary social media platform for Chinese, which also has an instant messaging function. Their participation was voluntary, but they were allowed to either receive a small amount of extra credit from their course instructors or 20 RMB. Upon arrival at the research site, participants were instructed to read and sign a consent form before they completed a series of tasks. They were debriefed about the manipulation of emotion upon completing the study.
Participants were randomly paired up to form same-sex negotiation dyads. They were then randomly assigned to one of two bargaining roles (Pan vs Sun) in one of four (i.e. 2 [high vs low responsibility] × 2 [FtF vs instant messaging]) conditions to resolve a dispute between two college juniors sharing a two-bedroom apartment. After reading the negotiation scenario, participants responded to a pre-negotiation questionnaire assessing their cognitive and emotional responses. After completing the negotiation role-play, they reported negotiation outcomes, use of dispute resolution and facework tactics and perceived satisfaction and desire to continue the relationship as roommates in a post-negotiation questionnaire.
Negotiation scenario and experimental manipulation
The negotiation scenario was adapted from a negotiation exercise “College Town Apartments” developed by Northwestern University’s Dispute Resolution Research Center (also see Liu and Zhu, 2021). It describes a dispute between two college juniors who share an apartment and are notified by their landlord to pay a late fee charge. Participants across both bargaining roles and all the experimental conditions received identical background information, such as the location and rent of the apartment, how the two parties became roommates and the current arrangement for rent payment (i.e. Pan sends a single check for the rent to the landlord after Sun gives him or her a check for half the rent).
Following prior studies (e.g. Allred et al., 1997), anger and compassion were induced by manipulating participants’ attribution of responsibility for perceived negative behaviors. Therefore, role-specific information concerning the relational history of the two parties was designed to elicit self-serving biases that cast the counterpart’s behavior in a negative light. For example, Pan is led to believe that Sun is never in the apartment, cannot seem to do anything on time, is impossible to reach and fails to give him/her half the rent before the due date; therefore, Pan only paid for his/her own half of the rent. On the other hand, Sun is led to believe that Pan is around all the time, with his/her stuff everywhere and that he/she put a check for half the rent on Pan’s messy desk two days before it was due; therefore, Sun was surprised that Pan did not send in his/her money. Both parties were led to believe that the other party was responsible for the late fee. To further establish an emotion-laden dispute, the scenario describes an initial face-to-face conversation that ended up being interrupted by a telephone ring: Pan was told that Sun came home after midnight as usual, beat his/her door loudly and started to shout and accuse Pan for the late fee issue, whereas Sun was told that Pan was avoiding the confrontation, took a long time to open the door and started to shout about his/her precious sleep time being wasted before a mid-term exam the next morning. The conversation was interrupted by their mutual friend who was calling Pan.
In the low responsibility condition that was designed to induce compassion, participants were provided additional mitigating information about uncontrollable, extraneous circumstances that accounted for the counterpart’s negative behavior. For example, Pan learned from their mutual friend that Sun had a very tough time in the past two months (e.g. breaking up with his/her girl/boyfriend, losing his/her teaching assistantship, working in a restaurant for long hours), which explained why he/she was seldom available. While Pan was talking on the phone, Sun found the envelope he/she left for Pan buried under piles of old newspapers and junk mails on Pan’s desk, with the check still inside, as well as a box of Prozac, an anti-depressant medicine and a note “rent due now!!!” on the wall. Participants in the high responsibility condition that was designed to induce anger did not receive such mitigating information. The manipulation, therefore, focused on altering participants’ perception of how responsible the other party was for negatively perceived behavior.
Participants were asked to prepare for a subsequent negotiation with the other party to resolve the dispute either face-to-face or through text-based instant messaging through WeChat. All negotiation scenarios and questionnaires were translated into Chinese and back-translated into English for linguistic equivalence. A pilot study was conducted to assess Chinese participants’ perceived realism of the scenarios. Results from a one-sample t-test showed that realism (M = 5.08, SD = 0.92) was significantly above the midpoint of the seven-point scale (4.00), t(62) = 43.95, p < 0.001.
Instrumentation
Data transformation. The core variables in this study were all measured using a magnitude scale (i.e. a scale not bound at the upper end), with 0 meaning “not at all” and 100 as a modulus or standard distance meaning “moderately x”. For example, for a measure of anger, participants are told that 0 means “not angry at all,” and 100 means “moderately angry”; they are asked to use any number from 0 or higher to indicate the degree of their anger. All values exceeding 1,000 were set to 1,000 to eliminate outliers (see Fink and Chen, 1995). Compared with scales that have few response alternatives, such as Likert scales, such measurements are considered to allow more precision in determining the functional forms of the variables when assessing hypotheses (see Fink, 2009). These measures, as expected, were positively skewed, which could violate the assumptions of many statistical analyses unless transformed. Transformation procedures introduced in prior studies (e.g. Fink and Chen, 1995; Miller, 1988; Zhu et al., 2016) were performed to approximate a normal distribution. Specifically, the following functional formula was used for data transformation: Y* = (Y + k) λ, where Y is the original variable, k is a constant, λ is the power value (λ ≠ 0) and Y* is the transformed variable. After transformation, skewness values were < |1.00| for the vast majority of the measurement items, with only 4 out of 26 items measuring dispute resolution tactics exceeding 1 (maximum skewness = 1.61). Principal component analyses were performed on measurement items comprising each of the scales, and the factor scores were used for subsequent analyses.
Manipulation check. A manipulation check was conducted to assess whether participants in the high responsibility condition reported attributing greater responsibility to the counterpart, and more anger and less compassion than those in the low responsibility condition. Following prior studies (Allred et al., 1997), attribution of responsibility was measured by multiplying a measure of perceived negativity of the other party’s behavior, measured using three items (e.g. “I consider Sun’s behavior as rude and adversarial”; Cronbach’s α = 0.75) and a measure of perceived responsibility of the other party for such behavior, also measured using three items (e.g. “I hold Sun personally responsible for the situation”; Cronbach’s α = 0.72). Four items assessed feelings of anger, annoyance, madness and irritation prior to negotiation (Cronbach’s α = 0.91), and four items assessed feelings of sympathy, compassion, empathy and understanding (Cronbach’s α = 0.87). Independent samples t-tests showed that manipulation had a significant effect on all of the measures, with participants in the high responsibility condition perceiving greater negativity, t(252) = 3.02, p = 0.003, Cohen’s d = 0.38, attributing greater personal responsibility to the counterpart, t(252) = 4.12, p = 0.000, Cohen’s d = 0.52 and feeling more anger, t(252) = 3.71, p = 0.000, Cohen’s d = 0.47 and less compassion, t(252) = −8.63, p = 0.000, Cohen’s d = −1.08, than those in the low responsibility condition. Manipulation proved very successful.
Dispute resolution and facework tactics. Following prior literature (Liu and Zhu, 2021), 27 items were used to assess three types of dispute resolution tactics and two types of facework tactics. Sample items include the following:
“I questioned about who should be responsible for the situation” (rights-based tactic; Cronbach’s α = 0.79);
“I used threats to gain an upper hand in the negotiation” (power-based tactic; Cronbach’s α = 0.87);
“I tried to understand the concerns and emotions behind my partner’s position” (interests-based tactic; Cronbach’s α = 0.82);
“I criticized my partner in the negotiation” (face-threatening tactic; Cronbach’s α = 0.79); and
“I acknowledged the legitimacy of my partner’s position or arguments” (face-saving tactic; Cronbach’s α = 0.86).
Negotiation outcomes. Six items measured negotiators’ satisfaction and desire to continue the relationship with the counterpart (Liu and Zhu, 2021). Sample items include the following:
“I am pleased with the outcome of the negotiation;”
“I am satisfied with the way we negotiated;” and
“Based on my experience with Sun/Pan in the negotiation, I would like to continue our roommate relationship.”
Results from an exploratory factor analysis showed that the factor structure of the six items was unidimensional. Cronbach’s α was 0.95.
Results
Owing to the non-independent nature of dyadic data, the hypotheses were evaluated using two-group structural equation modeling analyses with AMOS 26, and the method of estimation was maximum likelihood. Four path models predicting the indirect effects of anger and compassion on actor’s and partner’ relational outcomes in both FtF negotiations and CMC were assessed to see whether some of the relationships would be moderated by a communication medium (see Figures 2–5).
Mediation model with compassion as IVs and dispute resolution tactics as mediators
Mediation model with compassion as IVs and dispute resolution tactics as mediators
Model 1 predicted anger’s indirect effect through the mediation of power-, rights- and interests-based dispute resolution tactics. The mediated path model received support from negotiations conducted both FtF and via CMC (see Figure 2): χ2 (130, 40) = 46.84, p = 0.212, CMIN/DF = 1.17, RMSEA = 0.04, CFI =0.96. Standardized regression coefficients of all the relationships in the path model in both FtF negotiations and CMC are summarized in Table 1, along with model comparison statistics. Results from bootstrapping moderated mediation procedures (see Hayes, 2013) showed that anger’s indirect, negative actor effect on relational outcomes was significantly mediated by power-based tactics in FtF negotiations, bFtF = −0.11, SE = 0.07, p < 0.05 (CI: −0.27, −0.01) but not in CMC, bCMC = −0.00, SE = 0.02, p > 0.05 (CI: −0.07, 0.02). H2a received partial support.
Regression coefficients of anger’s effects on FtF negotiations vs CMC
| FtF (n = 78 dyads) | CMC (n = 49 dyads) | comparison | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Independent and dependent measures | βSun | SESun | βPan | SEPan | βSun | SESun | βPan | S EPan | χ2Sun | χ2Pan |
| Model 1 | ||||||||||
| Anger → power | 0.38*** | 0.17 | 0.20+ | 0.14 | 0.06 | 0.14 | −0.07 | 0.10 | 6.11* | 2.93+ |
| Anger→ rights | 0.18 | 0.16 | −0.03 | 0.14 | −0.20 | 0.14 | 0.28* | 0.12 | 4.47* | 2.38 |
| Anger→ interests | 0.00 | 0.14 | 0.02 | 0.12 | −0.06 | 0.14 | 0.20 | 0.10 | 0.11 | 0.66 |
| Power → RelationA | −0.27*** | 0.08 | −0.43*** | 0.12 | −0.29** | 0.13 | −0.31** | 0.16 | 0.35 | 0.30 |
| Rights → RelationA | 0.15 | 0.09 | 0.33*** | 0.12 | −0.04 | 0.12 | 0.04 | 0.12 | 0.90 | 4.15* |
| Interests → RelationA | 0.44*** | 0.10 | 0.47*** | 0.12 | −0.04 | 0.13 | 0.41*** | 0.15 | 9.29** | 0.49 |
| Power → RelationP | −0.13 | 0.09 | −0.31** | 0.11 | −0.10 | 0.15 | −0.15 | 0.14 | 0.11 | 6.07* |
| Rights → RelationP | 0.15 | 0.10 | 0.26** | 0.11 | −0.24* | 0.14 | −0.06 | 0.11 | 5.74* | 4.57* |
| Interests → RelationP | 0.00 | 0.11 | 0.13 | 0.11 | −0.05 | 0.14 | 0.53*** | 0.13 | 0.09 | 6.07* |
| Model 3a | ||||||||||
| Anger → threats | 0.53*** | 0.14 | 0.13 | 0.13 | 0.18 | 0.13 | 0.07 | 0.12 | 8.14** | 0.30 |
| Threats → RelationA | −0.29** | 0.10 | −0.23* | 0.15 | −0.36** | 0.15 | −0.31* | 0.14 | 0.24 | 0.04 |
| Threats → RelationP | −0.14 | 0.12 | −0.20+ | 0.12 | −0.24+ | 0.12 | −0.36** | 0.10 | 0.31 | 0.59 |
| Model 3 b | ||||||||||
| Anger→ enhance | −0.09 | 0.15 | 0.13+ | 0.11 | 0.07 | 0.12 | −0.08 | 0.12 | 0.93 | 1.67 |
| Enhance → RelationA | 0.72*** | 0.08 | 0.75*** | 0.08 | 0.45*** | 0.12 | 0.60*** | 0.12 | 0.70 | 1.14 |
| Enhance → RelationP | 0.12+ | 0.09 | 0.18* | 0.10 | 0.11 | 0.17 | 0.45*** | 0.10 | 0.01 | 2.07 |
| FtF (n = 78 dyads) | CMC (n = 49 dyads) | comparison | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Independent and dependent measures | βSun | SESun | βPan | SEPan | βSun | SESun | βPan | S EPan | χ2Sun | χ2Pan |
| Model 1 | ||||||||||
| Anger → power | 0.38*** | 0.17 | 0.20+ | 0.14 | 0.06 | 0.14 | −0.07 | 0.10 | 6.11* | 2.93+ |
| Anger→ rights | 0.18 | 0.16 | −0.03 | 0.14 | −0.20 | 0.14 | 0.28* | 0.12 | 4.47* | 2.38 |
| Anger→ interests | 0.00 | 0.14 | 0.02 | 0.12 | −0.06 | 0.14 | 0.20 | 0.10 | 0.11 | 0.66 |
| Power → RelationA | −0.27*** | 0.08 | −0.43*** | 0.12 | −0.29** | 0.13 | −0.31** | 0.16 | 0.35 | 0.30 |
| Rights → RelationA | 0.15 | 0.09 | 0.33*** | 0.12 | −0.04 | 0.12 | 0.04 | 0.12 | 0.90 | 4.15* |
| Interests → RelationA | 0.44*** | 0.10 | 0.47*** | 0.12 | −0.04 | 0.13 | 0.41*** | 0.15 | 9.29** | 0.49 |
| Power → RelationP | −0.13 | 0.09 | −0.31** | 0.11 | −0.10 | 0.15 | −0.15 | 0.14 | 0.11 | 6.07* |
| Rights → RelationP | 0.15 | 0.10 | 0.26** | 0.11 | −0.24* | 0.14 | −0.06 | 0.11 | 5.74* | 4.57* |
| Interests → RelationP | 0.00 | 0.11 | 0.13 | 0.11 | −0.05 | 0.14 | 0.53*** | 0.13 | 0.09 | 6.07* |
| Model 3a | ||||||||||
| Anger → threats | 0.53*** | 0.14 | 0.13 | 0.13 | 0.18 | 0.13 | 0.07 | 0.12 | 8.14** | 0.30 |
| Threats → RelationA | −0.29** | 0.10 | −0.23* | 0.15 | −0.36** | 0.15 | −0.31* | 0.14 | 0.24 | 0.04 |
| Threats → RelationP | −0.14 | 0.12 | −0.20+ | 0.12 | −0.24+ | 0.12 | −0.36** | 0.10 | 0.31 | 0.59 |
| Model 3 b | ||||||||||
| Anger→ enhance | −0.09 | 0.15 | 0.13+ | 0.11 | 0.07 | 0.12 | −0.08 | 0.12 | 0.93 | 1.67 |
| Enhance → RelationA | 0.72*** | 0.08 | 0.75*** | 0.08 | 0.45*** | 0.12 | 0.60*** | 0.12 | 0.70 | 1.14 |
| Enhance → RelationP | 0.12+ | 0.09 | 0.18* | 0.10 | 0.11 | 0.17 | 0.45*** | 0.10 | 0.01 | 2.07 |
Notes:
RelationA = actor’s relational outcome; RelationP = partner’s relational outcome; Sun and Pan are two members of the negotiation dyad; +p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001
Model 2 predicted compassion’s indirect effect through the mediation of the power-, rights- and interests-based tactics. The mediated path model also received support (see Figure 3): χ2 (130, 36) = 36.09, p = 0.465, CMIN/DF = 1.00, RMSEA = 0.00, CFI = 1.00. Table 2 summarizes the standardized regression coefficients along with model comparison statics. Results from bootstrapping moderated mediation procedures further showed that compassion’s indirect, positive actor effect on relational outcomes was mediated by both power-based tactics in CMC, bCMC = 0.06, SE = 0.04, p < 0.05 (CI: 0.01, 0.11), and interests-based tactics in CMC, bCMC = 0.13, SE = 0.07, p < 0.05 (CI: 0.02, 0.28), but by neither power-based tactics in FtF negotiations, bFtF = −0.00, SE = 0.03, p > 0.05 (CI: −0.06, 0.07), nor interests-based tactics in FtF negotiations, bFtF = 0.04, SE = 0.04, p > 0.05 (CI: −0.04, 0.13). Moreover, compassion’s indirect, positive partner effect on relational outcomes was significantly mediated by interests-based tactics in CMC, bCMC = 0.08, SE = 0.04, p < 0.05 (CI: 0.01, 0.18), but not in FtF negotiations, bFtF = 0.02, SE = 0.03, p > 0.05 (CI: −0.03, 0.08). H2b received substantial support.
Regression coefficients of compassion’s effects on FtF negotiations vs CMC
| FtF (n = 78 dyads) | CMC (n = 49 dyads) | comparison | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Independent and dependent measures | βSun | SESun | βPan | SEPan | βSun | SESun | βPan | S EPan | χ2Sun | χ2Pan |
| Model 2 | ||||||||||
| Compassion → Power | 0.04 | 0.14 | −0.04 | 0.12 | −0.19 | 0.14 | −0.30* | 0.12 | 1.35 | 1.66 |
| Compassion → Rights | 0.14 | 0.13 | 0.05 | 0.12 | −0.17 | 0.15 | −0.11 | 0.15 | 2.93+ | 0.77 |
| Compassion → Interest | 0.15 | 0.11 | 0.11 | 0.10 | 0.25* | 0.14 | 0.39*** | 0.12 | 0.50 | 2.80+ |
| Power → RelationA | −0.27** | 0.08 | −0.40*** | 0.12 | −0.26* | 0.13 | −0.26* | 0.16 | 0.18 | 0.50 |
| Rights → RelationA | 0.11 | 0.09 | 0.31*** | 0.12 | −0.02 | 0.12 | 0.05 | 0.12 | 0.58 | 3.60+ |
| Interests → RelationA | 0.43*** | 0.10 | 0.46*** | 0.12 | −0.08 | 0.13 | 0.34* | 0.15 | 10.69*** | 0.93 |
| Power → RelationP | −0.14 | 0.09 | −0.31** | 0.11 | −0.11 | 0.15 | −0.13 | 0.14 | 0.00 | 0.97 |
| Rights → RelationP | 0.11 | 0.09 | 0.26** | 0.11 | −0.21+ | 0.14 | −0.06 | 0.11 | 4.50* | 4.63* |
| Interests → RelationP | 0.06 | 0.11 | 0.13 | 0.11 | −0.01 | 0.15 | 0.53*** | 0.13 | 0.18 | 6.11* |
| Model 4a | ||||||||||
| Compassion → Threats | −0.02 | 0.13 | 0.06 | 0.11 | −0.23** | 0.16 | −0.20* | 0.14 | 1.36 | 2.17 |
| Threats → RelationA | −0.30** | 0.10 | −0.23* | 0.15 | −0.36** | 0.12 | −0.31* | 0.15 | 0.24 | 0.04 |
| Threats → RelationP | −0.14 | 0.12 | −0.20+ | 0.13 | −0.24+ | 0.14 | −0.36** | 0.13 | 0.31 | 0.59 |
| Model 4b | ||||||||||
| Compassion → Enhance | 0.10 | 0.11 | 0.17 | 0.09 | 0.34** | 0.12 | 0.49*** | 0.12 | 1.38 | 5.86* |
| Enhance → RelationA | 0.71*** | 0.07 | 0.75*** | 0.11 | 0.44*** | 0.14 | 0.61*** | 0.12 | 0.70 | 1.12 |
| Enhance → RelationP | 0.12 | 0.08 | 0.18* | 0.10 | 0.11 | 0.17 | 0.45*** | 0.10 | 0.01 | 2.07 |
| FtF (n = 78 dyads) | CMC (n = 49 dyads) | comparison | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Independent and dependent measures | βSun | SESun | βPan | SEPan | βSun | SESun | βPan | S EPan | χ2Sun | χ2Pan |
| Model 2 | ||||||||||
| Compassion → Power | 0.04 | 0.14 | −0.04 | 0.12 | −0.19 | 0.14 | −0.30* | 0.12 | 1.35 | 1.66 |
| Compassion → Rights | 0.14 | 0.13 | 0.05 | 0.12 | −0.17 | 0.15 | −0.11 | 0.15 | 2.93+ | 0.77 |
| Compassion → Interest | 0.15 | 0.11 | 0.11 | 0.10 | 0.25* | 0.14 | 0.39*** | 0.12 | 0.50 | 2.80+ |
| Power → RelationA | −0.27** | 0.08 | −0.40*** | 0.12 | −0.26* | 0.13 | −0.26* | 0.16 | 0.18 | 0.50 |
| Rights → RelationA | 0.11 | 0.09 | 0.31*** | 0.12 | −0.02 | 0.12 | 0.05 | 0.12 | 0.58 | 3.60+ |
| Interests → RelationA | 0.43*** | 0.10 | 0.46*** | 0.12 | −0.08 | 0.13 | 0.34* | 0.15 | 10.69*** | 0.93 |
| Power → RelationP | −0.14 | 0.09 | −0.31** | 0.11 | −0.11 | 0.15 | −0.13 | 0.14 | 0.00 | 0.97 |
| Rights → RelationP | 0.11 | 0.09 | 0.26** | 0.11 | −0.21+ | 0.14 | −0.06 | 0.11 | 4.50* | 4.63* |
| Interests → RelationP | 0.06 | 0.11 | 0.13 | 0.11 | −0.01 | 0.15 | 0.53*** | 0.13 | 0.18 | 6.11* |
| Model 4a | ||||||||||
| Compassion → Threats | −0.02 | 0.13 | 0.06 | 0.11 | −0.23** | 0.16 | −0.20* | 0.14 | 1.36 | 2.17 |
| Threats → RelationA | −0.30** | 0.10 | −0.23* | 0.15 | −0.36** | 0.12 | −0.31* | 0.15 | 0.24 | 0.04 |
| Threats → RelationP | −0.14 | 0.12 | −0.20+ | 0.13 | −0.24+ | 0.14 | −0.36** | 0.13 | 0.31 | 0.59 |
| Model 4b | ||||||||||
| Compassion → Enhance | 0.10 | 0.11 | 0.17 | 0.09 | 0.34** | 0.12 | 0.49*** | 0.12 | 1.38 | 5.86* |
| Enhance → RelationA | 0.71*** | 0.07 | 0.75*** | 0.11 | 0.44*** | 0.14 | 0.61*** | 0.12 | 0.70 | 1.12 |
| Enhance → RelationP | 0.12 | 0.08 | 0.18* | 0.10 | 0.11 | 0.17 | 0.45*** | 0.10 | 0.01 | 2.07 |
Notes:
RelationA = actor’s relational outcome; RelationP = partner’s relational outcome; Sun and Pan are two members of the negotiation dyad. +p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001
Model 3 predicted anger’s indirect effect through the mediation of face-threatening and face-enhancing tactics. The mediated path model including both sets of tactics failed to produce good fit due to multicollinearity. Therefore, the mediators were assessed separately but presented in the same figure (see Figure 4). The path model with face-threatening tactics as the mediator produced good fit, χ2 (54, 12) = 13.29, p = 0.349 CMIN/DF = 1.11, RMSEA = 0.03, CFI = 0.99. The model with face-enhancing tactics as the mediator produced an acceptable fit, χ2 (54, 12) = 21.57, p = 0.043 CMIN/DF = 1.80, RMSEA = 0.08, CFI = 0.96. Standardized regression coefficients from both models are summarized in Table 1, along with model comparison statics. Results from bootstrapping moderated mediation procedures showed that anger’s indirect, negative actor effect on relational outcomes was mediated by face-threatening tactics in FtF negotiations, bFtF = −0.18, SE = 0.11, p < 0.05 (CI: −0.42, −0.02), but not in CMC, bCMC = −0.05, SE = 0.04, p < 0.05 (CI: −0.16, −0.01). Moreover, anger’s indirect, negative partner effect on relational outcomes was also mediated by face-threatening tactics in FtF negotiations, bFtF = −0.13, SE = 0.08, p < 0.05 (CI: −0.31, −0.01), but not in CMC, bCMC = −0.03, SE = 0.03, p < 0.05 (CI: −0.11, −0.01). Face-enhancing tactics did not mediate anger’s actor or partner effect on relational outcomes. H3a received partial support.
Model 4 predicted compassion’s indirect effect through the mediation of face-threatening and face-enhancing tactics. The mediators were also assessed separately due to multicollinearity and presented together in Figure 5. The model with face-threatening tactics as the mediator produced good fit, χ2 (54, 12) = 13.77, p = 0.315 CMIN/DF = 1.15, RMSEA = 0.03, CFI = 0.98, so did the model with face-enhancing tactics as the mediator, χ2 (54, 12) = 20.61, p = 0.06, CMIN/DF = 1.72, RMSEA = 0.08, CFI = 0.97. The standardized regression coefficients are summarized in Table 2, along with model comparison statistics. Results from bootstrapping moderated mediation procedures showed that compassion’s indirect, positive actor effect on relational outcomes was mediated by both face-threatening tactics in CMC, bCMC = 0.09, SE = 0.04, p < 0.05 (CI: 0.02, 0.17) and face-enhancing tactics in CMC, bCMC = 0.31, SE = 0.09, p < 0.05 (CI: 0.13, 0.50), but by neither face-threatening tactics in FtF negotiations, bFtF = −0.01, SE = 0.04, p > 0.05 (CI: −0.06, 0.08), nor face-enhancing tactics in FtF negotiations, bFTF = 0.05, SE = 0.09, p > 0.05 (CI: −0.13, 0.22). Moreover, compassion’s indirect, positive partner effect on relational outcomes was also mediated by both face-threatening tactics in CMC, bCMC = 0.07, SE = 0.03, p < 0.05 (CI: 0.02, 0.14) and face-enhancing tactics in CMC, bCMC = 0.19, SE = 0.06, p < 0.05 (CI: 0.07, 0.32), but by neither face-threatening tactics in FtF negotiations, bFtF = −0.00, SE = 0.03, p > 0.05 (CI: −0.06, 0.07), nor face-enhancing tactics in FtF negotiations. bFtF = 0.03, SE = 0.05, p > 0.05 (CI: −0.09, 0.13). H3b received full support.
Discussion
Despite the ubiquity of emotion-laden disputes in everyday life and the prevalent use of CMC to resolve conflicts, our understanding of whether and how communication media may exacerbate (enhance) or mitigate (impede) the detrimental (positive) effects of emotions in dispute negotiations remains limited. The current study seeks to fill the void by comparing the influence of two contrastive discrete emotions, anger and compassion, on behavioral tactics and relational outcomes in dispute resolution across two modes of communication: FtF and CMC (i.e. instant messaging). A major contribution of the study is the evidence that the paths through which anger and compassion influence disputants’ behaviors and relationships varied significantly depending on the communication medium. While the effects of anger on competitive (i.e. power-based and face-threatening) tactics were significant in FtF negotiations, they were non-significant when disputants negotiated via CMC; while the effects of compassion on both competitive (i.e. power-based and face-threatening) and cooperative (i.e. interests-based and face-enhancing) tactics were significant in CMC, they were non-significant in FtF negotiations. Whereas competitive tactics reduced disputants’ desire to continue their relationship in the future, cooperative tactics helped maintain relational ties. These results contribute to dispute resolution theory, scholarship on emotion in negotiations, the literature concerning e-negotiations and CMC theories in general. The results also have practical implications for resolving disputes online, especially for those with an existing relationship.
Contributions of findings to theory
Although there is abundant research on the effects of anger in negotiations, positive emotions, such as compassion (used interchangeably with sympathy), received far less attention. Research either examined compassion’s effects on negotiation outcomes in deal-making negotiations without considering processes (e.g. Allred et al., 1997; Shirako et al., 2015), or compassion’s effects on behavioral intentions or conflict styles without considering outcomes (e.g. Liu and Wang, 2010; Zhang et al., 2014). The current study is one of the few that seeks to advance our theoretical understanding of when and how compassion influences dispute resolution behaviors and relational outcomes.
The study found that compassion had a non-significant effect on all three types of dispute resolution (power-, rights- and interests-based) tactics and both types of facework (face-threatening and face-enhancing) tactics in FtF negotiations. However, compassion had a significant effect on the use of both competitive (power-based and face-threatening) and cooperative (interests-based and face-enhancing) tactics in dispute resolution via CMC. These findings help to contextualize why research on felt compassion in FtF negotiations has been sparse. Compared to anger, compassion in conflict situations is not as easily detectable as in FtF negotiations; the fuller access to aural, visual and nonverbal channels in FtF negotiations may even compromise compassionate negotiators’ ability to transform the interaction from an emotion-laden dispute to a productive conversation, as nonverbal cues associated with competing goals are readily communicated in FtF encounters as well. On the other hand, the greater social distance in CMC allows disputants to fully take advantage of the benefits of CMC, such as taking longer to respond, being more thoughtful in preparing a response and focusing less on power or threats but more on the underlying interests of both parties. These findings help connect dots in theorizing the role of compassion in negotiations while specifying the conditions in which the influence is more pronounced.
These findings also contribute to the existing CMC literature by demonstrating that the use of CMC is particularly helpful for resolving emotion-laden disputes. Although earlier CMC theories suggest that fewer communication channels generally make it difficult for individuals to develop relational ties, the study showed that the reduced media richness in the CMC environment can help mitigate the detrimental effects of anger and enhance the positive effects of compassion on disputants’ desire to continue working with each other in the future.
For angry negotiators, the fuller access to aural, visual and nonverbal cues in the FtF negotiations facilitates communication of a competitive action tendency through increased competitive (power-based and face-threatening) tactics. On the other hand, the mediated environment allowed angry negotiators to be less susceptible to the counterpart’s anger, less impulsive and better able to resist the temptation of reciprocating competitive tactics.
In addition, although the findings provided substantial support for the dispute resolution theory (Ury et al., 1988) and face theory (Goffman, 1967), they also generated new insights concerning the role of rights-based dispute resolution tactics. Specifically, consistent with existing theories, the findings showed that power-based and face-threatening tactics not only reduced disputants’ own, but also their counterpart’s satisfaction and desire to continue the relationship with each other. On the other hand, interests-based and face-enhancing tactics facilitated positive relational outcomes for both parties. These effects were by and large observed in both FtF negotiations and CMC. However, the effects of rights-based tactics were mixed. Although negotiation scholars generally consider rights-based tactics to be competitively oriented (Ury et al., 1988), for one of the dispute parties, rights-based tactics had a positive effect on both disputants’ own, and their counterpart’s relational outcome in FtF negotiations but not CMC. In addition, anger’s positive effect on rights-based tactics was more pronounced in CMC than FtF negotiations, and rights-based tactics were found to have a significant, negative effect on the counterpart’s relational outcomes in CMC, but not FtF.A deeper dive into the dispute literature reveals that scholars noted that a right focus may be somewhat less damaging to the relationship in certain conditions and “power approaches […] may be more contentious if there is direct interaction” (Lytle et al., 1999, p. 39). In an emotion-laden dispute situation where disputants are expected to be competitive, the use of rights-based tactics may signal a focus on rational reasoning and constructive problem-solving, which may positively violate the counterpart’s negative expectations, and therefore, lead to greater satisfaction in FtF negotiations. However, in a CMC environment where disputants may expect less emotional expressions, increased use of rights-based tactics can be perceived as lacking empathy and in turn lead to less satisfaction and hurt the relationship. The assertion that power-based tactics may be more contentious and damaging to disputants’ relationships than rights-based tactics, especially in FtF negotiations, received empirical support. On a theoretical level, the study shows that multiple streams of knowledge can be advanced by examining how communication medium interacts with discrete emotions to influence dispute resolution processes and outcomes.
Contributions of findings to practice
On a practical level, the study confirmed the use of CMC as an effective means to resolve disputes, especially when the disputants have an existing relationship and are emotionally invested in the situation. Specifically, to combat the detrimental effect of anger on increased competitive (i.e. power-based and face-threatening) tactics, disputants should consider the following options: First, when face-to-face interaction is inevitable, disputants should consider combining rights-based tactics (i.e. using objective and fair standards that are mutually acknowledged, such as rules, regulations, contracts or law) and interests-based tactics (i.e. exchanging information about each other’s needs, concerns, priorities, preferences, etc.) to both avoid appearing weak and transform the conflict into a productive conversation toward a mutually beneficial or acceptable agreement.
Second, when angry feelings arise, disputants are naturally prone to using aggressive language to express their frustration and attack the other party’s face. When possible, disputants should consider using an electronically mediated communication medium, such as instant messaging or emails, to resolve the dispute. The greater spatial, temporal and social distance makes it easier for disputants to refrain from such unproductive emotional expressions and instead focus more on gaining an improved mutual understanding. It should be noted that the findings of the study are consistent with recent research that suggests that more visual access to social cues, such as the presence of a self-viewing screen in a video chat, does not always lead to more positive experiences (Shin et al., 2023), and less social cues in a mediated environment may help mitigate misbehaviors of negotiators that develop too much rapport (Jap et al., 2011). Consideration of communication channels should be linked to the goals that negotiators pursue, as well as the expected outcomes of emotional experiences (Tamir and Ford, 2012).
Third, despite the prevalence of anger in negotiations, the study showed that when disputants were able to consider external circumstances in evaluating the conflict situation and the counterpart’s behaviors, they experienced compassion, which increased the chance of having productive, mutually satisfying dispute negotiations in a CMC environment. This is consistent with prior research that found sympathy appeals to be effective in achieving integrative agreements (Shirako et al., 2015). On one hand, disputants should consider proactively sharing information that helps to dispel the other party’s misunderstanding, including uncontrollable circumstances and vulnerabilities, as doing so may help reduce the counterpart’s anger and even induce the counterpart’s compassion toward themselves. On the other hand, they should also seek to validate their preexisting knowledge about the situation and avoid making fundamental attribution errors about the counterpart. Using interests-based tactics to exchange information regarding the underlying needs and concerns can help accomplish this goal, too.
Limitations and directions for future research
Despite the contributions, the study has a few limitations that should be addressed in future research. First, the study was conducted with a Chinese sample. Although theoretical path models that explain the effects of emotions in face-to-face negotiations have generally received support across cultures despite the fact that the Chinese culture places more emphasis on emotional restraint and harmony (e.g. Liu and Wilson, 2010; Liu and Zhu, 2021), future research should replicate the study with a Western sample and explore the extent to which the divergent effects of anger and compassion as moderated by communication media are culturally universal or specific. Second, dispute resolution and facework tactics were measured using self-reports. Although behavioral coding and self-report measures of negotiation strategies were found to be equally effective in predicting joint gains and satisfaction (Vogel et al., 2017), future research should replicate the study by coding the actual negotiation interaction between the disputants to capture the dynamic negotiation processes. Third, the study used a dispute resolution scenario between college students in a laboratory setting, which limits the ecological validity of the study. Future research should replicate the study in additional dispute resolution contexts, as well as deal-making negotiation situations, with non-student samples or through alternative research methods, such as case studies of real-world disputes, to see whether the moderating effects of communication media are context-specific or generalizable to diverse negotiation situations.
The author would like to thank Xiaohua Wang and Jiandong Zhang for their help with data collection for this study.
