Talk and Let Talk: The Effects of Language Proficiency on Speaking Up and Competence Perceptions in Multinational Teams

Abstract

Collaboration within multinational teams necessitates the adoption of a common language, typically English, which often leads to significant differences in language proficiency across members. We develop and test a multilevel model of the effects of language proficiency within multinational teams. An experimental study of 51 teams (102 American and 102 Chinese participants) revealed that, at the individual level, members with higher levels of language proficiency were more likely to speak up, which led to more positive perceptions of their competence. At the team level, greater dispersion in language proficiency across members was associated with less accurate competence recognition, which, in turn, led to lower overall team performance. Moreover, communication medium moderated these relationships, such that the effects of language proficiency were more potent in face-to-face than in computer-mediated teams. We discuss the implications of these findings for future research and for managing participation, competence, and technology in multinational teams.

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Group & Organization Management
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Article
Talk and Let Talk: The
Effects of Language
Proficiency on Speaking
Up and Competence
Perceptions in
Multinational Teams
Huisi (Jessica) Li1, Y. Connie Yuan1,
Natalya N. Bazarova1, and Bradford S. Bell1
Abstract
Collaboration within multinational teams necessitates the adoption of
a common language, typically English, which often leads to significant
differences in language proficiency across members. We develop and test a
multilevel model of the effects of language proficiency within multinational
teams. An experimental study of 51 teams (102 American and 102
Chinese participants) revealed that, at the individual level, members with
higher levels of language proficiency were more likely to speak up, which
led to more positive perceptions of their competence. At the team level,
greater dispersion in language proficiency across members was associated
with less accurate competence recognition, which, in turn, led to lower
overall team performance. Moreover, communication medium moderated
these relationships, such that the effects of language proficiency were
more potent in face-to-face than in computer-mediated teams. We discuss
the implications of these findings for future research and for managing
participation, competence, and technology in multinational teams.
1Cornell University, Ithaca, NY, USA
Corresponding Author:
Huisi (Jessica) Li, Johnson Graduate School of Management, Cornell University, Sage Hall,
Room 201F, Ithaca, NY 14853-0001, USA.
Email: HL922@cornell.edu
756734GOMXXX10.1177/1059601118756734Group & Organization ManagementLi et al.
research-article2018

2 Group & Organization Management 00(0)
Keywords
team, language proficiency, speaking up, competence perception, computer-
mediated communication
Multinational teams are becoming an increasingly prevalent and important
part of the global economy (Kozlowski & Bell, 2013). These teams, often
enabled by technology, allow organizations to bring together the diverse
capabilities needed to accomplish a variety of critical tasks (Connaughton &
Shuffler, 2007; Wilson & Doz, 2012). Although multinational teams offer
numerous benefits, they also face some significant challenges. In particular,
the different national languages spoken by team members can create barriers
to effective communication and decision making (Harzing, Köster, & Magner,
2011). Multinational teams typically adopt a common language, most often
English, which can lead to significant differences in language proficiency
between native and nonnative speakers.
Although language and communication processes are critical to informa-
tion processing within teams (Harzing & Feely, 2008), differences in lan-
guage proficiency have received limited research attention within the teams
literature (Brannen & Doz, 2012; Harzing et al., 2011). However, several
recent studies, most of which have been qualitative or descriptive in nature,
have provided initial evidence that asymmetries in language proficiency may
have important effects within multinational teams, including contributing to
subgrouping or clustering (Hinds, Neeley, & Cramton, 2014), shaping status
and power dynamics (Neeley, 2013; Neeley & Dumas, 2016), and undermin-
ing trust formation (Tenzer, Pudelko, & Harzing, 2014). There remain, how-
ever, many important, yet unexplored, questions about whether and how
language differences influence important team processes and outcomes.
In the current study, we use a multilevel, experimental approach to exam-
ine the effects of language proficiency on information exchange within mul-
tinational teams. To leverage their broad pool of resources, multinational
teams must not only encourage information sharing but also develop an accu-
rate understanding of what knowledge and competence exist within the team
(Ren & Argote, 2011; van Knippenberg, De Dreu, & Homan, 2004).
Extending expectation states theory to multinational collaboration (Berger,
Wagner, & Zelditch, 1985; Berger, Webster, Ridgeway, & Rosenhotz, 1986),
we argue that at the individual level, language proficiency may influence the
extent to which an individual speaks up within a team, which in turn may
shape how other team members perceive his or her competence. We extend
these relationships to the team level by examining how the dispersion of lan-
guage proficiency across the members of a team influences patterns of

Li et al. 3
speaking up, and how these patterns influence the recognition of competence
within the team and team performance. Moreover, given that multinational
teams often rely on communication technology (Mathieu, Maynard, Rapp, &
Gilson, 2008), we examine these relationships in teams operating either face-
to-face or through computer-mediated communication to determine whether
communication medium moderates the effects of language proficiency on
competence judgment and performance within multinational teams.
This study makes several important theoretical and practical contribu-
tions: First, it offers fine-grained insights into the effects of language profi-
ciency, which has received only minimal attention within the teams literature.
As Hinds et al. (2014) stated, “Despite the central role of a lingua franca in
global work, a gap remains in our understanding of how language affects
work and workers in international organizations” (p. 536). We address this
problem by examining whether and how language proficiency adds a unique
layer of complexity to team dynamics and effectiveness, above and beyond
culture. Specifically, this study aims to examine the impact of language pro-
ficiency on both the team-level and the individual-level processes of speak-
ing up, competence perceptions, and task performance.
Second, this study helps extend expectation states theory into the domain
of both multinational and computer-mediated teams (e.g., Bazarova & Yuan,
2013; Yuan, Bazarova, Fulk, & Zhang, 2013). In doing so, it helps address
our currently limited understanding about the processes through which mul-
tinational teams develop competence recognition. Research on expectation
states theory has mainly studied preexisting (i.e., gender, age, race, and edu-
cation) and task-related characteristics (i.e., task-relevant statements; Berger
et al., 1986). Recent work, however, increasingly recognizes the role of
dynamic and interactive factors in forming expectations and competence rec-
ognition (Liao, Bazarova, & Yuan, in press; Treem, 2012). Still, factors that
are unique and prevalent in multinational teams and computer-mediated
teams, such as language proficiency and communication technology, need to
be better understood. This study examines the role of language proficiency
and speaking up in competence perception and recognition, and also how
their influence varies across face-to-face and computer-mediated teams.
Finally, this study contributes to our understanding of the role of media,
such as face-to-face and text-based computer-mediated chat, in multinational
team collaboration (Connaughton & Shuffler, 2007). It is crucial to explicitly
examine the contextual influence of the communication medium when exam-
ining the impact of language proficiency on multinational collaboration as
these interactions often involve computer-mediated communication. The
conceptual model proposed in this study is presented in Figure 1 and dis-
cussed below.

4 Group & Organization Management 00(0)
Theoretical Framework and Hypotheses
Language in Multinational Teams
Companies are increasingly stipulating common language policies to expand
their global reach and to facilitate team collaboration (Crystal, 2007; Neeley,
2013). When team members possess different native languages, common lan-
guage proficiency, defined as the ability to communicate information in a cer-
tain language to fulfill a social function (Jones, 1975), naturally varies among
team members, potentially resulting in barriers that undermine team processes.
For instance, team members with lower levels of language proficiency may
avoid speaking English and making speech mistakes due to performance anxi-
ety or job insecurity (Neeley, Hinds, & Cramton, 2012). They may also risk
losing status, become less influential, lower their anticipation of career
advancement, and even feel demotivated and inferior (Neeley & Dumas,
2016; Neeley et al., 2012). Thus, although intended to increase efficiencies of
teamwork, common language mandates may inadvertently introduce ineffi-
ciencies and even lead to losses in productivity (Neeley et al., 2012).
In multinational teams, the challenge in realizing the performance benefits
of a broader knowledge and competence pool lies in encouraging information
sharing (van Knippenberg et al., 2004) and facilitating competence recogni-
tion within teams. Adequate information sharing and accurate competence
recognition do not occur automatically (Pieterse, van Knippenberg, & van
Dierendonck, 2013). Differences in language proficiency among team mem-
bers may prevent some from contributing actively to discussions, and thus
disrupt competence recognition and team performance. In the following
Figure 1. Theoretical model.

Li et al. 5
section, we consider how language proficiency may influence the tendency
of team members to speak up, and how this relationship may differ depending
on whether a team operates face-to-face or through computer-mediated
communication.
Language Proficiency and Speaking Up: Communication
Medium as a Moderator
In face-to-face teams, low language proficiency may inhibit one’s speaking
up due to various factors, such as the restriction of language ability, self-
censoring, and being offered fewer opportunities to speak up. First, there are
cognitive costs associated with processing a foreign language (Takano &
Noda, 1993) and expressing oneself with restricted linguistic resources
(Wang, Fussell, & Setlock, 2009). Low language proficiency slows down
communication, and makes it more challenging and frustrating for both
native and nonnative speakers (Takano & Noda, 1993; Wang et al., 2009).
Second, individuals with lower language proficiency might avoid speaking
up because they are afraid to make mistakes, fear discrimination due to their
accent (Gluszek & Dovidio, 2010), worry about having to defend their posi-
tion in their nonnative language, or assume others will not appreciate their
input (Neeley et al., 2012). In organizational settings, due to performance
anxiety or job insecurity, many nonnative speakers, even those who are con-
fident and vocal in their native language, are reluctant to speak up in English
(Neeley et al., 2012). Finally, individuals with lower language proficiency
might be allocated fewer opportunities to talk because intergroup biases may
lead other team members to view their contributions as less valuable (Hinds
et al., 2014). Based on the above reasoning, we expect that individuals with
higher levels of language proficiency will speak up more often than individu-
als with lower language proficiency.
However, the relationship between language proficiency and speaking up
may be influenced by whether a team operates face-to-face or through com-
puter-mediated communication. Under the realm of computer-mediated com-
munication, a wide range of communication media may be used, such as chat
(i.e., instant messaging), email, and tele- or video conferencing (Baltes,
Dickson, Sherman, Bauer, & LaGanke, 2002). In this study, we chose to
focus on chat, a nonanonymous, synchronous, text-based form of computer-
mediated communication, for three reasons: First, we argue that differences
in the availability of important cues in face-to-face versus text-based com-
munication may have implications for the effects of language proficiency on
individual and group processes in multinational teams. In particular, the
restriction in social cues in a text channel can relax some of the cognitive and

6 Group & Organization Management 00(0)
social constraints that prevent members with low language proficiency from
speaking up. Second, the majority of studies to date on computer-mediated
communication have used synchronous text-based systems (Baltes et al.,
2002), so we adopted chat to provide a common baseline for comparison.
Third, synchronous text-based communication is prevalent in organizations,
and thus is practically important (Charlier, Stewart, Greco, & Reeves, 2016).
By reducing social cues, text-based computer-mediated communication
may influence members’ participation rate through two related effects: equal-
ization (Dubrovsky, Kiesler, & Sethna, 1991) and empowering (Amichai-
Hamburger, McKenna, & Tal, 2008). The equalization effect emphasizes
reductions in social status differentiation, which should lead to more equal
participation from all group members (Dubrovsky et al., 1991; Kiesler &
Sproull, 1992; Weisband, 1992). Whereas face-to-face team members tend to
conform to the expected social order determined by language proficiency
levels (Hinds et al., 2014; White & Li, 1991), differences in social attributes
matter less in computer-mediated communication due to the absence of con-
textual and nonverbal cues (Dubrovsky et al., 1991; Kiesler & Sproull, 1992).
In particular, text can help reduce nonnative speakers’ communicative inef-
ficiencies (e.g., lack of fluency or a heavy accent), which are most often
associated with stigma and self-censoring (Gluszek & Dovidio, 2010).
Therefore, the relaxed social constraints of a text chat can stimulate more
contributions from members with low language proficiency by reducing
social status differentiation due to differential language proficiencies.
Text-based computer-mediated communication also has an empowering
effect (Amichai-Hamburger et al., 2008), which refers to decreased appre-
hension in revealing one’s authentic self in the absence of visual and audio
cues (Baltes et al., 2002; Bargh, McKenna, & Fitzsimons, 2002). Lowered
social expectations and reduced risks of social sanctions make people feel
more comfortable to be themselves in computer-mediated communication,
which can encourage more open and active group participation (e.g., High &
Caplan, 2009). This could be particularly helpful for individuals with low
language proficiency, who may feel empowered in this channel compared
with face-to-face communication in which they tend to worry about making
mistakes and being judged for language incompetence.
Finally, text-based chat enables different interactional norms from face-
to-face conversations (Herring, 1999) that can further help people with
lower language proficiency to speak up in group discussions. Whereas face-
to-face speakers are expected to take turns in an orderly fashion with mini-
mal gap and overlap, computer-mediated communication exchanges are
characterized by conversational discontinuity, gaps, and overlaps within
turn sequences (Herring, 1999). Individuals with low language proficiency

Li et al. 7
can take advantage of these interactional possibilities offered by text-based
chat. They can participate in overlapping exchanges and easily jump into a
conversation rather than having to wait for a turn to speak. Taken together,
this evidence suggests that text-based computer-mediated communication
may serve to reduce the gap in speaking up between members with high and
low language proficiency. Thus, we expect language proficiency will have a
greater influence on speaking-up behavior in face-to-face teams than in
computer-mediated teams.
Hypothesis 1a (H1a): Communication medium moderates the relation-
ship between individuals’ language proficiency and speaking up, such that
the relationship is stronger (more positive) in face-to-face teams than in
text-based computer-mediated teams.
At the team level, varying levels of language proficiency among team
members may manifest itself in different language proficiency dispersions,
which, in turn, might affect speaking turn dispersion. Woolley, Chabris,
Pentland, Hashmi, and Malone (2010) defined speaking turn dispersion as
“the variance in the number of speaking turns by group members” (p. 688),
and provide evidence that more effective teams tend to exhibit a more equal
distribution of speaking turns (Engel, Woolley, Jing, Chabris, & Malone,
2014). In contrast, less effective teams tend to have a few people who domi-
nate the conversation. What is less clear, however, is what factors influence
the variability of speaking turns in teams. In the context of multinational
teams, we focus on language proficiency as a potentially important predictor
of members’ participation in group discussions (Neeley, 2013). The language
proficiency within a team can be characterized by two ways: mean and dis-
persion. Language proficiency mean refers to the average level of language
proficiency among team members, whereas language proficiency dispersion
refers to the variation or standard deviation of language proficiency across
the members of the team. Thus, low language proficiency dispersion indi-
cates that members are relatively consistent in their language proficiency,
whether it is low, average, or high. In contrast, high language proficiency
dispersion indicates a wider range of language proficiencies across team
members.
Within a face-to-face team, increased variability in language proficiency
is likely to lead to greater speaking turn dispersion because the constraints
associated with language proficiency are experienced unequally among
group members. That is, when team members all have equally low language
proficiency (i.e., low language proficiency dispersion), they are likely to
have equal chances of speaking up in a team, thus resulting in low speaking

8 Group & Organization Management 00(0)
turn dispersion. Similarly, if all members have equally high language profi-
ciency (i.e., low language proficiency dispersion), the team is also likely to
have low speaking turn dispersion. In contrast, when team members possess
varying levels of language proficiency (i.e., high language proficiency dis-
persion), their experience of constraints associated with language proficiency
(e.g., cognitive costs, self-censoring, and restricted opportunities to talk) will
also vary. Accordingly, this should lead to greater differences in speaking
turns across team members, resulting in high speaking turn dispersion. Thus,
in face-to-face teams we expect language proficiency dispersion to positively
influence speaking turn dispersion, over and above the mean level of lan-
guage proficiency within the team. In contrast, the equalization and empow-
ering effects of text-based computer-mediated communication, as described
earlier, may reduce the salience and impact of differences in language profi-
ciency, thus weakening the relationship between the dispersion of language
proficiency and the speaking turn dispersion within a group. Thus, we pro-
pose that language proficiency dispersion will have a stronger influence on
speaking turns when teams communicate face-to-face than when they use
text-based computer-mediated communication.
Hypothesis 1b (H1b): Communication medium moderates the relation-
ship between dispersion of language proficiency and speaking turn disper-
sion, such that the relationship is stronger (more positive) in face-to-face
teams than in text-based computer-mediated teams.
Speaking Up and Competence Perception and Recognition
It is important to understand the dynamics of speaking-up behaviors in a
team because speaking up may influence competence perception and recog-
nition. Competence perception and competence recognition are substan-
tively different, as competence perception captures others’ subjective
impressions of a focal person’s competence level, while competence recog-
nition reflects the accuracy of others’ competence evaluations measured
against an objective benchmark of actual competence level (Yuan et al.,
2013). In this study, as detailed below, they are conceptualized at different
levels: At the individual level, we are theoretically interested in the percep-
tions of an individual’s competence, because people make decisions accord-
ing to their perceptions regardless of their accuracy; at the team level, we are
theoretically interested in the ability of the team as a whole to accurately
recognize competence, because inaccuracy in competence recognition will
result in inadequate usage of a team’s intellectual resources and potentially
undermine team performance.

Li et al. 9
Competence perception is important at the individual level because per-
ceived experts, regardless of their actual level of competence, can have
greater influence on decision making relative to other members and may
enjoy more favorable career consequences. Previous work has drawn on
expectation states theory to understand the role of interaction processes in
shaping competence perceptions within teams (e.g., Littlepage, Robison, &
Reddington, 1997). According to this theory, team members draw on a vari-
ety of cues to develop expectations about others’ competence and contribu-
tions (Berger et al., 1985; Berger et al., 1986). These expectations are
heuristic, and affect performance evaluation and social influence in a team
(Kalkhoff & Thye, 2006). They are based on cues—the social information
and salient observations drawn from group interactions. They can be obvious
or subtle, conscious or unconscious, and categorical (e.g., gender, race, and
occupation) or task related (e.g., task-relevant statements; Berger et al.,
1986). Among categorical cues, speech cues appear to be particularly impor-
tant. For example, speech speed, volume, and hesitancy are often used as
cues in competence perceptions (Berger et al., 1986). We propose that the
number of times each member speaks up may represent a salient cue that
shapes expectation states in team interactions. If individuals are differenti-
ated in terms of a competence cue, in this case the number of times they speak
up, perceptions of their competence should be differentiated accordingly
(Berger et al., 1985; Berger et al., 1986). Thus, we propose the following
hypothesis:
Hypothesis 2a (H2a): A group member’s speaking up is positively related
to others’ perceptions of his or her competence.
At the team level, competence recognition is essential for teams to achieve
top performance (Ren & Argote, 2011), which is especially important for
multinational teams that offer a broader pool of information and competence.
While even culturally homogeneous teams often have difficulties recogniz-
ing and utilizing competence (e.g., Bottger & Yetton, 1988; Littlepage,
Schmidt, Whisler, & Frost, 1995), multinational teams face even bigger chal-
lenges due to cultural stereotypes and intercultural miscommunication (Yoon
& Hollingshead, 2010). Hence, at the team level of analysis, we focused on
competence recognition.
Expectation states theory also helps understand how speaking turn disper-
sion may relate to competence recognition at the team level, which is concep-
tualized as overall accuracy of all members’ competence evaluations, or the
consistency between all members’ perceived and actual competence levels.
Speaking up is a highly observable cue that will influence perceptions of an

10 Group & Organization Management 00(0)
individual’s competence. When the speaking turn dispersion in a team is low,
or in other words when every member in the team talks roughly the same
number of times, speaking up as an expectation-inducing cue becomes undif-
ferentiated and less salient. Instead, team members can base their competence
judgments on cues, such as the content of one’s speech, which better reflect
members’ actual competence levels. In contrast, when the speaking turn dis-
persion is high, the number of times a member speaks up will become a very
salient competence cue. When competence judgments are formed more on the
basis of heuristic cues, such as speaking turns, rather than the actual content of
discussion, actual competence is less likely to be recognized. Thus, we expect
that teams with greater dispersion in speaking turns experience greater diffi-
culty in accurately recognizing competence within the team.
Hypothesis 2b (H2b): Speaking turn dispersion is negatively related to
competence recognition within the team.
When speaking turns are more equally distributed within a team, all mem-
bers have an opportunity to contribute their knowledge to the team discus-
sion, which should result in higher quality team solutions. For example,
teams are more creative when all members have opportunities to express their
views and when they feel comfortable doing so (Edmondson, 1999). A more
even distribution of speaking turns should also enhance overall team effec-
tiveness by promoting competence recognition, which can in turn further
enhance knowledge sharing within groups (Yuan, Fulk, & Monge, 2007).
Teams that exhibit greater specialization and coordination of expertise tend to
perform better and make more effective decisions (Hollingshead, Brandon,
Yoon, & Gupta, 2011; Lewis & Herndon, 2011; Ren & Argote, 2011). Thus,
we expect that competence recognition serves as a mechanism through which
speaking turn dispersion affects team performance.
Hypothesis 3 (H3): Competence recognition mediates the negative rela-
tionship between speaking turn dispersion and team performance.
Language Proficiency and Team Performance: A Moderated
Mediation Model
Taken together, we lay out the full model from language proficiency to the
outcomes at both the team (i.e., competence recognition and team perfor-
mance) and individual levels (i.e., competence perception) to provide a com-
prehensive depiction of the role of language proficiency in multinational
teams. At the individual level, as argued above, we expect that individuals

Li et al. 11
with higher language proficiency will be more likely to speak up, which will
in turn lead to more positive perceptions of their competence. In addition, we
expect that the positive relationship between language proficiency and speak-
ing up will be stronger in face-to-face teams than in computer-mediated
teams. Accordingly, we propose a first-stage moderated mediation model
(Edwards & Lambert, 2007), in which communication medium moderates
the effect of language proficiency on perceived competence level via speak-
ing-up behavior. Specifically, we expect the positive indirect relationship
between language proficiency and competence perceptions via speaking up
to be stronger in face-to-face than in computer-mediated teams.
Hypothesis 4 (H4): The indirect relationship between one’s language pro-
ficiency and others’ perceptions of his or her competence via speaking up
is moderated by communication medium, such that the positive indirect
relationship is stronger in face-to-face teams than in text-based computer-
mediated teams.
Similar to the individual level, where speaking up serves as the link
between one’s language proficiency and perceived competence, at the team
level we propose that speaking turn dispersion acts as a mechanism that
explains the relationship between a team’s language proficiency dispersion
and competence recognition, which in turn eventually leads to team perfor-
mance. Thus, the effects of language proficiency dispersion on ultimate team
performance go through two stages, namely speaking turn dispersion and
competence recognition (as illustrated in Figure 1). Below, we develop two
hypotheses that focus on the mechanisms and boundary conditions for how
language proficiency dispersion influences (a) competence recognition and
(b) team performance.
At the team level, we expect that greater dispersion of language profi-
ciency will lead to greater speaking turn dispersion, which will in turn exhibit
a negative relationship with competence recognition. As argued earlier, we
also expect communication medium to moderate this relationship, such that
the positive relationship between language proficiency dispersion and speak-
ing turn dispersion will be stronger in face-to-face teams than in text-based
computer-mediated teams. Therefore, we propose a first-stage moderated
mediation model (Edwards & Lambert, 2007), in which communication
medium moderates the influence of language proficiency dispersion on com-
petence recognition via speaking turn dispersion. Specifically, we expect the
indirect relationship between language proficiency dispersion and compe-
tence recognition via speaking turn dispersion will be stronger (or more nega-
tive) in face-to-face than in computer-mediated teams.

12 Group & Organization Management 00(0)
Hypothesis 5 (H5): The indirect relationship between language profi-
ciency dispersion and competence recognition via speaking turn disper-
sion is moderated by communication medium such that the indirect
relationship is stronger (or more negative) in face-to-face teams than in
text-based computer-mediated teams.
Finally, at the team level we examine the indirect effect of language profi-
ciency dispersion on team performance through competence recognition. We
chose to test competence recognition as the mediator for two reasons: First, as
reviewed above, existing research shows that it is a strong predictor of team
performance. Second, there is also likely to be a direct relationship between
language proficiency dispersion and competence recognition, not functioning
through speaking turn dispersion. According to expectation states theory
(Berger et al., 1985; Berger et al., 1986), team members may draw from a
focal person’s language proficiency as a status and performance expectation
cue to infer his or her competence level. When this cue is differentiated among
members and salient in teams, language proficiency dispersion could directly
hinder competence recognition in a team. Again, we propose a first-stage
moderated mediation model (Edwards & Lambert, 2007), in which communi-
cation medium moderates the influence of language proficiency dispersion on
team performance via competence recognition. Specifically, we expect the
indirect relationship between language proficiency dispersion and team per-
formance via competence recognition to be stronger (or more negative) in
face-to-face teams than in computer-mediated teams.
Hypothesis 6 (H6): The indirect team-level relationship between lan-
guage proficiency dispersion and team performance, via competence rec-
ognition, is moderated by communication medium, such that the indirect
relationship is stronger (or more negative) in face-to-face teams than in
text-based computer-mediated teams.
Method
Sample and Procedure
Participants in this study were 204 graduate students (51 teams) from differ-
ent fields of study at a university in the northeastern United States. Our par-
ticipants were recruited from the university-managed participant pool of
current students. The data used in this study were part of a larger dataset.
Participants were invited to sign up for our study via email. Each group con-
tained four members; two positions were reserved for non-Asian American

Li et al. 13
participants, and two were reserved for Chinese participants. Only those
Chinese students who had lived in the United States for less than 5 years were
eligible to participate in this study, which was consistent with other studies of
intercultural communication (e.g., Bazarova & Yuan, 2013), because length-
ier socialization in a Western country could change their communication
styles. The age of the participants ranged from 20 to 50, with a mean of 25.63
(SD = 4.36), and 106 (52%) were male. The means and SDs of the age of
Americans were 24.34 and 3.28, respectively (n = 102), and those of the
Chinese were 26.82 and 5.09, respectively (n = 102). Individuals were com-
pensated US$20 for their participation in the study. As an additional stimulus,
participants were informed prior to the experiment that each member of the
five top-performing teams would receive a US$30 gift certificate to a popular
local restaurant.
Each team was randomly assigned to either a face-to-face or a computer-
mediated condition, which we informed the participants about when they
arrived at our laboratory. When members of the computer-mediated condition
arrived, they were put into four separate rooms without meeting each other in
person, whereas members of the face-to-face teams were put into the same
room. In the computer-mediated communication condition, the experimenters
also took a headshot photo of each team member who was referred to as
“Member 1,” “Member 2,” “Member 3,” or “Member 4” in the online chat
system. Participants’ first and last names and a photograph corresponding to
their member number were displayed in a shared online document that was
open and visible to all group members throughout the discussion. To parallel
this in the face-to-face condition, members of the face-to-face teams were
placed at a round table, with the labels “Member 1,” “Member 2,” “Member
3,” and “Member 4” on the tabletop; group members’ names, along with their
member numbers, were written on a whiteboard visible to the group.
We used an intellective problem-solving task that consisted of four ques-
tions. We first asked our participants to complete the four questions individu-
ally, and told them that all questions had correct answers. Then, we collected
their individual answers, after which they worked as a group to reach a col-
lective decision for each question. We did not, at any point, tell them the
correct answers or anyone’s scores (or answers). Consistent with prior
research (Woolley et al., 2010), the problem-solving task used in the current
study required logic reasoning and was not reading intensive. In a pilot study
with 52 participants, we found no significant difference in performance on
the task between Chinese and American students. See the appendix for a
sample task question. Upon finishing the group task, each participant indi-
vidually completed an online questionnaire assessing ratings of other mem-
bers’ language proficiencies and competence levels.

14 Group & Organization Management 00(0)
Measures
Unless otherwise noted, all items were measured on a 7-point Likert-type
scale (1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly
agree).
Team performance. As there were correct answers to the four questions that
the teams completed, a team’s performance score was the number of ques-
tions that a team solved correctly.
Language proficiency. We used five items adapted from the Interagency Lan-
guage Roundtable (2014) to assess language proficiency. The Interagency
Language Roundtable scale was standardized and validated over the years by
government agencies with the assistance of the Educational Testing Service,
and has a long history of wide use in academia (e.g., second language teach-
ing and testing researchers), U.S. government (e.g., the American Council on
the Teaching of Foreign Languages), and private organizations (e.g., health
care providers). Each participant was rated by his or her team members. The
scale tends to be used by professional evaluators in more systematic and
time-consuming evaluations than in a laboratory experiment setting, thus we
adapted it to be context relevant, within a reasonable length, and easily acces-
sible to our participants, while choosing items that collectively capture the
domain of the construct. Sample items included “This person had trouble
finding the right words to express him/herself (reverse-coded)” and “This
person mispronounced/misspelled a lot of words (reverse-coded).” Cron-
bach’s alpha was .77.
We used group members’ judgments to assess one another’s language pro-
ficiency for three reasons: First, prior research has found others’ evaluations of
language proficiency to be reliable (Cucchiarini, Strik, & Boves, 2002). In the
current study, we found adequate interrater agreement among members’ rat-
ings of the same focal person’s language proficiency, as rwg.j = .82. Second,
very high correlations have been found between others’ ratings and objective
language proficiency criteria, such as speech rate, mean length of utterances,
phonation/time ratio, and the duration and number of pauses per minute (e.g.,
Cucchiarini, Strik, & Boves, 2000; rs between .77 and .91). Finally, this
approach is widely used in language and communication characteristics
research (e.g., Gluszek, Newheiser, & Dovidio, 2011). To verify that partici-
pants’ ratings of each other’s language proficiency levels were not biased by
task performance, we hired two professional English-as-a-second-language
(ESL) teachers, both of whom had more than 10 years of experience in English
instruction and evaluation, to rate participants’ language proficiency levels.

Li et al. 15
Correlating their ratings with participants’ ratings, the results showed that par-
ticipants’ ratings of their team members’ language proficiencies were unlikely
to be influenced by the members’ task competence and performance.1
Language proficiency dispersion. Consistent with the recommendations of Rob-
erson, Sturman, and Simons (2007), we used the standard deviation of mem-
bers’ language proficiencies as an index for the dispersion of language
proficiency in a team. Standard deviation outperforms other representations
of variation in group members’ responses, such as awg, rwg, and average
deviation index, when there is an interaction effect, and is also relatively easy
to calculate and to understand relative to other measures of dispersion (Rob-
erson et al., 2007).
Speaking up. Group discussions in the face-to-face condition were video
recorded and then transcribed. Conversations in the computer-mediated com-
munication condition were automatically archived by the chat system. To
measure speaking up, in both conditions, we used the transcripts to count the
number of times each member of the team contributed to the team discussion.
To facilitate between-team comparisons and interpretation of results, we
divided this number by the total number of speaking-up instances in a team
to obtain a percentage for each team member.
Speaking turn dispersion. Consistent with Engel et al.’s (2014) operationaliza-
tion, we used the standard deviation of the speaking-up variable within each
team as the measure of speaking turn dispersion.
Competence perception. Consistent with previous research (e.g., Thomas-
Hunt & Phillips, 2004), competence perceptions were measured using the
mean of other group members’ rankings of a focal person’s task competence
within the team (1 = most expert, 4 = least expert). To simplify interpretation
of the results, we then reverse coded this measure, so that a higher score indi-
cates a higher competence perception. Before calculating each member’s
score, we tested the interrater agreement among members’ perception ratings
to the same focal person’s competence. The average Cohen’s Kappa was .27
(p < .05; Cohen, 1968), indicating a moderate degree of agreement among
team members (Altman, 1991; Landis & Koch, 1977).
Competence recognition. Consistent with Littlepage et al. (1997), we opera-
tionalized competence recognition as Spearman’s rank correlation between
rankings of team members’ perceived competence and their rankings of
actual competence (determined by individual task scores). An individual’s

16 Group & Organization Management 00(0)
perceived competence was captured as the other three group members’ rat-
ings of the focal person’s competence ranking (e.g., Member A was rated by
B, C, and D; B rated by A, C, and D, etc.). An individual’s actual competence
was a rank score of the individual’s actual score from working on the task
individually. We reverse coded both, so that higher scores indicate a higher
level of actual and perceived competence. The competence recognition score
for each group is the Spearman correlation coefficient of 12 pairs of ranking
scores (with three pairs of scores for each of the four members). Using Spear-
man’s rank correlation is appropriate because, unlike regression, Spearman’s
correlation does not require independence or homoscedasticity of observa-
tions. In addition, the nested structure of the data does not influence Spear-
man’s rank correlation. In theory, competence recognition (as the correlation
between members’ actual and perceived competence) ranges from −1 to 1,
with −1 indicating total inaccuracy and 1 indicating perfect accuracy. In the
current sample, the competence recognition of the teams ranged from −0.71
to 0.92 (M = 0.05, SD = 0.39), demonstrating considerable variability across
teams. In 20 (of 51) teams, this correlation was negative. The median rank
order correlation is .09.
Control variables. In the individual- and cross-level analyses, we controlled
for group members’ gender (0 = female and 1 = male), age, and citizenship (0
= American, 1 = Chinese), which is also often a proxy for cultural back-
ground to ensure that the effects were due to language proficiency not other
characteristics. Moreover, we measured the most widely examined cultural
dimensions of collectivism and individualism using eight self-report items for
each (Triandis & Gelfand, 1998), which allowed us to further demonstrate
the effect of language proficiency over and above that of culture. Example
items measuring collectivism and individualism were, respectively, “If a
coworker gets a prize, I would feel proud” and “I’d rather depend on myself
than on others.” Cronbach’s alphas for collectivism and individualism were
.70 and .76, respectively. We also controlled for actual competence levels,
measured by individual task scores (i.e., the number of questions an individ-
ual solved correctly), to isolate the effects of speaking up on competence
perceptions, over and above actual competence.
Moreover, we controlled for speech content quality, measured by other
team members, to isolate the effects of speaking up on competence percep-
tions, above and beyond the quality of contribution. As one’s speech content
quality naturally influences others’ perceptions of one’s competence level,
especially in multinational teams (Yuan et al., 2013), controlling for it allows
us to assess more clearly the unique effect of speaking up as our core con-
struct. Sample items were “He or she was thoughtful when making an

Li et al. 17
argument” and “He or she was capable of showing the logical connections
among the different parts of his or her arguments” (adapted from de Vries,
Bakker-Pieper, Siberg, van Gameren, & Vlug, 2009). Cronbach’s alpha was
.77. Before calculating each individual’s score, we verified the adequate
interrater agreement among members’ ratings to the same focal person’s
speech content quality, as rwg.j = .87.
In the team-level analyses, we controlled for the time (in minutes) each
team took to finish the task. To test the effects of language proficiency disper-
sion and speaking turn dispersion on other team-level variables, we con-
trolled for the mean level of language proficiency, following the
recommendation of Roberson et al. (2007). To test the effect of competence
recognition on team performance, we also controlled for the mean level of
actual competence, and the mean and dispersion (i.e., standard deviation) of
speech content quality.2
Analytic Strategy
Our data contained a hierarchical structure in which the individual-level
(Level 1) variables were nested within teams (Level 2). Hierarchical linear
modeling was performed using Mplus 7.2 (Muthén & Muthén, 2012). This
method partitions the variances of Level 1 outcome variables into within- and
between-group components, and then explores how Level 1 and Level 2 pre-
dictor variables can help explain these variances. Level 1 variables included
individual’s language proficiency, speaking up, competence perception, and
relevant control variables discussed above. Level 2 variables included team
performance, communication medium (i.e., face-to-face vs. computer-medi-
ated communication), language proficiency dispersion, speaking turn disper-
sion, competence recognition, and relevant control variables.
To test random slope models (i.e., models in which the relationship
between Level 1 variables varies across teams) for H1a and H4, we used the
raw scores of the Level 1 predictor (i.e., language proficiency), which results
in statistically equivalent models as using grand-mean-centered random
slope models (Enders & Tofighi, 2007; Snijders & Bosker, 1999). We did not
use group mean centering after careful considerations and following Snijders
and Bosker’s (1999) recommendation that
one should be reluctant to use group-mean-centered random slope models
unless there is a clear theory (or empirical clue) that not the absolute level of Xij
(i.e., one’s actual language proficiency in this case3) but rather the relative
score (Xij – X.j) (i.e., one’s language proficiency compared to group mean) is
related to Yij (i.e., one’s speaking up). (p. 88)

18 Group & Organization Management 00(0)
Nevertheless, similar cross-level interaction (i.e., H1a) and cross-level
moderated mediation (i.e., H4) results were obtained when we used group-
mean-centered language proficiency (cf. Hofmann, Griffin, & Gavi, 2000).
Finally, although it is difficult to estimate precise effect sizes in cross-level
models, we report Snijders and Bosker’s (1999) overall pseudo R2, which
estimates the proportional reduction of errors owing to predictors. Further
details of model specification procedures are presented in the next section.
Results
Table 1 displays means, standard deviations, and bivariate correlations among
all study variables. At the team level, competence recognition was positively
related to team performance (r = .46, p < .01) and negatively related to speak-
ing turn dispersion (r = −.32, p < .05). At the individual level, citizenship was
unrelated to actual competence or speech content quality (r = .09 and r =
−.12, p > .05). Citizenship was related to language proficiency and speaking
up, such that scores were lower for Chinese participants than for American
participants (r = −.43, p < .01; r = −.40, p < .05, respectively). Speaking up
and speech content quality were positively related to others’ perceptions of
one’s competence (r = .32 and .37, respectively, p < .01).
H1a predicted that communication medium moderates the relationship
between language proficiency and speaking up, such that the positive rela-
tionship would be stronger in face-to-face teams than in text-based com-
puter-mediated teams. To test this hypothesis, we estimated the cross-level
moderating effect of communication medium (i.e., face-to-face and com-
puter-mediated communication) on the relationship between language pro-
ficiency and speaking up. As seen in Table 2, in support of H1a, the
multilevel modeling results demonstrated a positive effect of face-to-face
communication (vs. computer-mediated communication) on the random
slope between language proficiency and speaking up (b = 2.61, SE = 1.31,
p < .05). To establish the nature of this interaction, we performed simple
slopes analysis (Aiken & West, 1991). In computer-mediated teams, lan-
guage proficiency was not significantly related to speaking up (b = .15, SE
= 0.09, p = .13), whereas in the face-to-face condition, language profi-
ciency was significantly and positively related to speaking up (b = .35, SE
= 0.14, p < .01). Following Cohen, Cohen, West, and Aiken’s (2003) rec-
ommendations, we plotted this interaction at the two values of communica-
tion medium. As shown in Figure 2, in the face-to-face condition the
positive relationship between language proficiency and individual speaking
up was stronger.

19
Table 1. Descriptive Statistics, Correlations, and Reliabilities for Study Variables.
Team-level variablesaM SD 1 2 3 4 5 6 7 8 9
1. Team performance 1.88 0.79
2. Mean actual competence 1.13 0.48 .00
3. Task time (in min) 46.73 18.5 .10 .18
4. Speech content quality mean 4.93 0.47 .26 −.04 .02
5. Speech content quality dispersion 0.64 0.31 .15 −.07 .08 −.25
6. Communication mediumb0.47 0.50 .09 −.26 −.46** .31* −.01
7. Language proficiency mean 5.59 0.51 .25 −.10 −.23 .43** −.02 .55**
8. Language proficiency dispersion 0.67 0.29 −.06 −.09 .01 −.11 .18 .17 −.26
9. Speaking turn dispersion 9.68 3.87 −.10 −.06 .08 .10 −.04 .23 .12 .02
10. Competence recognition 0.02 0.40 .46** .01 −.21 .00 .12 −.08 .09 −.21 −.32*
Individual-level variablescM SD 1 2 3 4 5 6 7 8 9
1. Competence perception 2.50 0.86
2. Gender (0 = F, 1 = M) 0.48 0.50 .15*
3. Age 25.58 4.45 −.04 .13
4. Citizenship (0 = United States, 1 = Chinese) 0.50 0.50 −.07 .00 −.28**
5. Collectivism 5.15 0.42 .05 −.05 −.08 −.06 (.70)
6. Individualism 4.62 0.49 .02 −.08 .01 .01 .05 (.76)
7. Actual competence 1.13 0.96 .08 .13 .01 .09 −.01 .09
8. Speech content quality 4.93 0.78 .37** −.02 .20 −.12 .12 .08 .10 (.77)
9. Language proficiency 5.59 0.81 .12 −.04 .14* −.43** .07 −.08 −.08 .44** (.77)
10. Speaking up 24.44 9.05 .32** .14 .13 −.40* .03 −.01 −.01 .34** .28**
Note. Numbers in parentheses are Cronbach’s alphas.
an = 51.
bN = 204.
c0 = computer-mediated communication, 1 = face-to-face.
*p < .05. **p < .01, two-tailed tests.

20
Table 2. Results of Multilevel Regression (N = 204).
Variable Speaking up Speaking up
Competence
perception
Competence
perception
Level 1 controls Estimate SE Estimate SE Estimate SE Estimate SE
Gender (0 = F, 1 = M) 2.36 1.33 2.52 1.34 .18 0.13 .18 0.13
Age −.07 0.13 −.02 0.13 −.02 0.01 −.02 0.01
Citizenship (0 = United States, 1 = Chinese) −5.81*** 1.53 −4.82** 1.54 −.07 0.13 −.03 0.13
Collectivism −.84 0.96 .23 0.22 −.15 0.08 −.11 0.08
Individualism −.17 0.90 .44 0.50 −.10 0.09 −.07 0.09
Actual competence .07 0.59 .04 0.06 .01 0.05 .01 0.05
Speech content quality .38*** 0.09 .38*** 0.08
Level 1 main effects
Language proficiency 2.22*** 0.62 −.14 0.08 −.12 0.08
Speaking up .02** 0.01 .03* 0.01
Level 2 main effect
Communication medium (CM)a−15.3* 7.68 −.13 0.08 .42* 0.21
Cross-level interaction
Language proficiency × CM 2.61* 1.31 2.38* 1.32
Akaike (AIC) 1,308.34 1,312.00 1,798.26 1,887.32
Bayesian (BIC) 1,340.49 1,357.01 1,871.26 1,993.50
Sample-size-adjusted BIC 1,308.82 1,312.67 1,801.56 1,892.11
Pseudo R2.18 .23 .22 .22
Note. Coefficients listed in the table are unstandardized. AIC = Akaike information criterion; BIC = Bayesian information criterion.
a0 = computer-mediated communication, 1 = face-to-face.
*p < .05. **p < .01. ***p < .001, one-tailed tests.

Li et al. 21
In H1b, we argued that communication medium moderates the team-level
relationship between language proficiency dispersion and speaking turn dis-
persion, such that the positive relationship would be stronger in face-to-face
teams than in text-based computer-mediated teams. To test this hypothesis,
we estimated the team-level moderating effect of communication medium
(i.e., computer-mediated communication and face-to-face) on the relation-
ship between language proficiency dispersion and speaking turn dispersion.
As seen in Table 3, failing to support H1b, the interaction between team
members’ language proficiency dispersion and communication medium on
speaking turn dispersion was not significant (b = 4.38, SE = 4.28, p > .05).
H2a predicted that speaking up in a team would be positively related to
other members’ competence perceptions. To test H2a, we estimated the rela-
tionship between one’s speaking up and perceived competence. As seen in
Table 2, in support of H2a, speaking up was positively related to others’ per-
ceptions of a team member’s competence (b = .02, SE = 0.01, p < .01).
In H2b, we predicted that at the team level, speaking turn dispersion would
be negatively related to competence recognition within the team. To test H2b,
we estimated the relationship between speaking turn dispersion and compe-
tence recognition. As seen in Table 3, in support of H2b, speaking turn dis-
persion had a negative effect on competence recognition (b = −.03, SE = 0.02,
p < .05).
H3 stated that competence recognition would mediate the negative rela-
tionship between speaking turn dispersion and team performance. To test H3,
Figure 2. Cross-level interaction in which communication medium moderates the
effect of language proficiency on speaking up.
Note. FTF = face-to-face; CMC = computer-mediated communication.

22 Group & Organization Management 00(0)
Table 3. Results of Team-Level Regressions (N = 204).
Speaking turn
dispersion
Competence
recognition
Team
performance
Variables Estimate SE Estimate SE Estimate SE
Controls
Language proficiency mean −.53 1.57 −.06 0.15 .15 0.30
Mean actual competence .48 1.33 .01 0.12 .11 0.24
Task time .04 0.04 −.01 0.00 .01 0.01
Speech content quality mean .12 0.14 .28 0.29
Speech content quality
dispersion
.34 0.18 .30 0.39
Main effects
Language proficiency
dispersion
−.87 2.33 −.37 0.21 .08 0.45
CMa2.87 1.75 −.15 0.17 .11 0.35
Speaking turn dispersion −.03* 0.02 −.01 0.03
Competence recognition .78* 0.35
Interaction
Language proficiency
dispersion × CM
4.38 4.28
R2.11 .29* .28*
Adjusted R2.00 .13* .08*
Note. CM = communication medium.
a0 = computer-mediated communication, 1 = face-to-face.
*p < .05. **p < .01. ***p < .001.
we estimated the indirect relationship between speaking turn dispersion and
team performance via competence recognition, following the procedures of
Hayes (2008). In support of H3, there was a negative indirect effect of speak-
ing turn dispersion on team performance via competence recognition
(Estimate = −.02, 95% bias-corrected 5,000 time bootstrap confidence inter-
val (CI) = [−.07, −.00], p < .05), while controlling for task time, communica-
tion medium, the mean level of members’ actual competence, and the mean
and dispersion of speech content quality.
In H4, we argued that the indirect relationship between language profi-
ciency and competence perceptions via speaking up would be moderated by
communication medium, such that the indirect relationship would be stron-
ger (or more positive) in face-to-face teams than in computer-mediated
teams. To test H4, we estimated the indirect relationship between language
proficiency with perceived competence via speaking up in both the com-
puter-mediated communication and face-to-face conditions using Bauer,

Li et al. 23
Preacher, and Gil’s (2006) method, which is for multilevel mediation models
and accounts for team membership. Our model reflects Edwards and
Lambert’s (2007) first-stage mediated moderation model. That is, speaking
up mediated the relationship between language proficiency and perceived
competence, and communication medium moderated the path from language
proficiency to speaking up.
The proposed conditional indirect relationship was significant (Estimate
= .11, SE = 0.06, 95% bias-corrected 5,000 time bootstrap CI = [.00, .21], p
< .05), such that the indirect effect of language proficiency on competence
perceptions was higher in the face-to-face condition (Estimate = .18, SE =
0.03, 95% bias-corrected 5,000 time bootstrap CI = [.04, .32], p < .05) than
in the computer-mediated communication condition (Estimate = .07, SE =
0.03, 95% bias-corrected 5,000 time bootstrap CI = [.01, .14], p < .05), sup-
porting H4.
H5 stated that the indirect relationship between language proficiency dis-
persion and competence recognition via speaking turn dispersion would be
moderated by communication medium, such that the negative indirect rela-
tionship would be stronger in face-to-face teams than in computer-mediated
teams. To test H5, we estimated the conditional indirect effect of language
proficiency dispersion on competence recognition through speaking turn dis-
persion under different communication media (i.e., face-to-face or computer-
mediated communication). This model reflects Edwards and Lambert’s
(2007) first-stage moderation model. Using the bootstrapping method of
Preacher, Rucker, and Hayes (2007), we found that the indirect effect of the
interaction of language proficiency dispersion with communication medium
on competence recognition through speaking turn dispersion was not signifi-
cant (Estimate = −.12, 95% bias-corrected 5,000 time bootstrap CI = [−.51,
.04], p > .05), failing to support H5. For competence recognition, the indirect
effect of language proficiency dispersion via speaking turn dispersion was
not significant in either the face-to-face condition (Estimate = −.06, 95%
bias-corrected 5,000 time bootstrap CI = [−.27, .06], p > .05) or the computer-
mediated communication condition (Estimate = .07, 95% bias-corrected
5,000 time bootstrap CI = [−.03, .34], p > .05). Using the Preacher and Hayes
(2008) method, the model explained significant variance (R2 = .27, p < .05),
suggesting that the overall model was significant, but the conditional indirect
effect was not. This unexpected finding might be due to a power issue stem-
ming from the small sample size (i.e., 27 computer-mediated teams and 24
face-to-face teams), as this is a conditional (i.e., interaction) effect that gener-
ally requires greater power to detect.
Our final hypothesis, H6, predicted that the indirect relationship between
language proficiency dispersion and team performance via competence

24 Group & Organization Management 00(0)
recognition would be moderated by communication medium, such that the
negative indirect relationship would be stronger in face-to-face teams than in
computer-mediated teams. To test H6, we estimated the conditional indirect
effect of language proficiency dispersion on team performance through com-
petence recognition under different communication media (i.e., face-to-face
or computer-mediated communication). Our model reflects Edwards and
Lambert’s (2007) first-stage moderated mediation model, and we used the
bootstrapping method of Preacher et al. (2007). The indirect effect of the
interaction of language proficiency dispersion with communication medium
on performance through competence recognition was significant. The pro-
posed conditional indirect relationship was also significant (Estimate = −.75,
SE = 0.50, 95% bias-corrected 5,000 time bootstrap CI = [−1.81, −.12], p <
.05). Specifically, the indirect effect was significant in the face-to-face condi-
tion (Estimate = −.61, 95% bias-corrected 5,000 time bootstrap CI = [−1.40,
−.13], p < .05) but not in the computer-mediated communication condition
(Estimate = .14, 95% bias-corrected 5,000 time bootstrap CI = [−.31, .64], p
> .05), supporting H6. Using the Preacher and Hayes (2008) method, the
model explained significant variance (R2 = .24, p < .01).
In sum, the results showed that at the individual level, team members with
higher language proficiency were more likely to speak up, which in turn
increased other team members’ perceptions of their competence. The indirect
relationship between language proficiency and competence perceptions via
speaking up was moderated by communication medium, such that the posi-
tive indirect relationship was stronger in face-to-face teams than in text-based
computer-mediated teams. At the team level, greater dispersion of language
proficiency across a team, above and beyond mean language proficiency
level, led to greater difficulty in recognizing competence within the team and
lowered overall team performance. Moreover, the indirect team-level rela-
tionship between language proficiency dispersion and team performance, via
competence recognition, was moderated by communication medium, such
that the negative indirect relationship was stronger in face-to-face teams than
in computer-mediated teams.
Discussion
Theoretical Implications
The potential implications of language proficiency have received only mini-
mal attention within the literature on work groups and teams. Yet, our study
showed that language proficiency adds a unique layer of complexity to mul-
tinational team dynamics and effectiveness, above and beyond the impact of

Li et al. 25
ethnic background, culture, and actual competence levels. In the current
study, language proficiency influenced both the team-level and the individ-
ual-level processes of speaking up, competence perception and recognition,
and task completion, above and beyond cultural values (which were included
as controls in the analyses). Our findings help explain past research that has
reported impoverished and silenced discussions in board meetings within
multinational corporations that switch to English as the working language
(Piekkari, Oxelheim, & Randøy, 2015). Our finding that speaking up medi-
ates the relationship between language proficiency and competence percep-
tion helps explain why nonnative speakers in multinational teams often fail to
adequately communicate their professional competence (Piekkari, Vaara,
Tienari, & Säntti, 2005) and go through “the subjective experience of a
decreased professional regard” (Neeley, 2013, p. 476).
We also found that when members base competence recognition on lan-
guage proficiency cues, team performance suffers. What might seem like a
harmless (at least to members with high language proficiency) judgment ten-
dency at the individual level is detrimental to the effectiveness of the entire
team. According to our findings, substantial language proficiency dispersion
across team members makes it difficult for them to form accurate interpersonal
perceptions of competence, which likely contributes to the organizational fac-
tions induced by language proficiency asymmetries (Hinds et al., 2014), and
the common social divisions between native speakers and nonnative speakers
within groups (Steyaert, Ostendorp, & Gaibrois, 2011). As collaboration via a
common language becomes a reality in more and more work teams, our find-
ings suggest that greater attention needs to be devoted to the effects of language
on team dynamics and performance. While language has been the omitted vari-
able in most studies of multinational teams, we recommend taking language
into consideration explicitly, at least as a control variable, to avoid model mis-
specification, biased results, and inconsistent cross-study comparisons.
This study also has the potential to contribute to expectation states theory
in two ways: First, it is one of the few studies that has tested the theory in the
context of intercultural collaboration. Our results show that, regardless of
actual competence, drawing on language and speaking up as performance
expectation cues could potentially hinder competence recognition and team
performance. Second, the current study also extends expectation states theory
to computer-mediated teams in that the results showed that, in both face-to-
face and computer-mediated teams, members from different national back-
grounds all highly value other members’ speaking up. Our research addresses
the currently limited understanding about the processes through which multi-
national teams develop accurate competence recognition.

26 Group & Organization Management 00(0)
Finally, this study contributes to our understanding of the role of commu-
nication media, such as face-to-face and text-based computer-mediated chat,
in multinational team collaboration. As discussed earlier, few existing studies
have explored how language proficiency may influence competence judg-
ments in intercultural collaboration. Even fewer studies have considered how
these effects may differ across various types of communication media.
Consistent with previous arguments that a text-based computer-mediated
communication creates possibilities for compensation and adaptation by
reducing cognitive load, lowering social risks, and motivating efforts
(Amichai-Hamburger et al., 2008; Herring, 1999; Walther, 1996; Walther &
Burgoon, 1992), our results support the idea that reduced social cues in com-
puter-mediated communication relax constraints and help individuals with
low language proficiency to speak up. This study contributes to the growing
stream of research within both the team and international business literatures,
and offers a critical reflection on the interplay between language and technol-
ogy use in organizations.
Limitations and Future Research Directions
It is important to note a few limitations of the current study: First, more defin-
itive inferences about the causal effect of language proficiency on speaking
up could be made through future studies that manipulate language profi-
ciency levels. However, we found that, in both face-to-face and computer-
mediated teams, participants’ ratings of language proficiency correlated
significantly with the ratings of professional ESL (English as second lan-
guage) teachers who reviewed only the first half of the team discussions. In
addition, there was a nonsignificant correlation between members’ ratings of
language proficiency and competence perception (as shown in Table 1).
Together, these findings suggest that it is unlikely that participants’ ratings of
their team members’ language proficiency were skewed by the members’
competence and performance. Future research could also examine the possi-
bility of a reciprocal and dynamic relationship between language proficiency
and speaking up over time, in which speaking up among nonnative speakers
increases their language proficiency over time, which may in turn influence
future speaking up. But within the time frame examined in the current study,
the direction of the relationship is likely to be from language proficiency to
speaking up. Moreover, failing to support for H5, our results showed that the
indirect effect of the interaction of language proficiency dispersion with com-
munication medium on competence recognition through speaking turn dis-
persion was not significant. This may indicate that other team processes
mediate the effect of language proficiency dispersion on team competence

Li et al. 27
recognition, such as grouping or clustering (Hinds et al., 2014), status and
power dynamics (Neeley, 2013; Neeley & Dumas, 2016), and trust formation
(Tenzer et al., 2014). This finding may also indicate that language proficiency
dispersion and its interaction with communication medium had a direct effect
on competence recognition that did not function through speaking turn dis-
persion. In teams with high language proficiency dispersion, where language
proficiency serves as a salient status and performance expectation cue, com-
petence recognition should be less accurate. Lending support to this reason-
ing, the direct negative effect of language proficiency dispersion on
competence recognition was significant (Estimate = −.44, 95% bias-corrected
5,000 time bootstrap CI = [−.78, −.11], p < .05).
Second, to strengthen the external validity of this study, data should be
collected from employees working in multinational teams. Nevertheless,
the lab setting utilized in the current study offered a number of advantages,
including being able to control for alternative explanations and to record
conversations. Also, testing the current hypotheses with a student sample
likely yields more conservative results: First, in this study, all participants
are university students who are functional in English, familiar with working
with people from different national backgrounds, and generally identify
with the norm of valuing diversity and inclusion. Second, in workplace
multinational teams, power dynamics may be more salient, and there may
be more at stake, both for the individuals and for the team. Accordingly, the
effects of language proficiency may be even more pronounced in organiza-
tions, where there is likely to be not only greater dispersion among mem-
ber’s language proficiencies but also higher risk of social sanction for
language incompetence and stronger status differentiation. Future research
could study how these factors shape the effects of language barriers on
work team processes.
Third, future research should consider teams at various points along the
virtuality continuum, as work teams are rarely exclusively face-to-face or
exclusively communicating with text. Future research should also examine the
effects of other types of communication media, such as email and tele- or
video conferencing. These technologies might have different effects from the
text-based chat examined in this study, depending, for example, on task-tech-
nology fit (Maruping & Agarwal, 2004). In tele- or video conferencing, the
cognitive and social constraints of low language proficiency might be higher
than in text-based chat, thus the relationship between language proficiency
and speaking up might more closely mirror what we observed in the face-to-
face teams. In contrast, email might help equalize the participation of mem-
bers with different levels of language proficiency, because of the lowered
cognitive constraints and social cues for status differentiation. Future research

28 Group & Organization Management 00(0)
should also examine the increasingly common mixed-mode form of commu-
nication (e.g., multinational teams using different media for different tasks).
Finally, some related and important questions remain unanswered. For
example, although our findings reveal that speaking up (i.e., “quantity”) has
a significant effect on competence perceptions above and beyond the effect
of speech content (i.e., “quality”), they also make it clear that both the quan-
tity and quality of speech are important in determining how others evaluate
someone’s competence. Accordingly, future research may explore the bound-
ary conditions that shape the relative importance of these two factors. For
example, characteristics of both the task (e.g., whether there are objectively
correct answers) and team members (e.g., expertise, functional diversity, and
cultural backgrounds) may determine whether the quantity or quality of
speech plays a greater role in determining competence perceptions. Another
future research direction is to understand how perceptions of competence
evolve over time, and how past experiences of working together and judging
one another’s competence transfer to future group collaborations. Finally,
future research should explore the potential cross-level effects of the lan-
guage and speaking turn variables. For example, individuals with higher lev-
els of language proficiency may be more likely to speak up, or even dominate
group conversations, when there is significant dispersion of language profi-
ciency within a team. Similarly, speaking up may have a greater influence on
perceptions of an individual’s competence when speaking turns are more
unevenly distributed within a team. We were unable to detect these effects
within our data, but a more systematic examination of these effects would
benefit our understanding of how to effectively manage participation and
competence recognition in multinational teams.
Practical Implications
Our study offers insights into how organizations can proactively respond to
the growing utilization of multinational teams and harvest the performance
benefits of their talents. In many organizations, the official language is given
without any explicit strategies for managing people from different linguistic
backgrounds. The support available to help both native and nonnative speak-
ers raise awareness of language-related issues, develop effective team com-
munication, or support organizational language mandates is often weak or
nonexistent. Beyond providing language training to nonnative speakers or
recruiting only employees with higher levels of language proficiency, which
might be practically difficult to implement, we offer some suggestions to
leaders and members of multinational teams that may be more immediately
actionable.

Li et al. 29
First, managers should make sure that high and low language proficiency
members are given equal chances to speak up and contribute. They could
gently remind members who tend to dominate group conversations to be
more cognizant of how broader contributions might benefit team perfor-
mance. They should also encourage members with low language proficiency
to speak up during group discussions. Managers could actively solicit the
input of members with low language proficiency through text messages,
emails, or other communication media that allow them more time to compose
and reflect on their responses. To encourage participation, managers should
also cultivate a psychologically safe (Edmondson, 1999) and inclusive
(Nishii, 2013) team climate. Second, managers should consciously invest
time and effort in accurately recognizing each member’s competence, and be
cautious not to let differences in speaking up unduly bias their perceptions. In
particular, they should remind team members to draw on the quality rather
than the quantity of speaking up as cues to develop expectations about others’
competence.
Members of multinational teams, both native and nonnative speakers of
English, need to recognize that their active participation is important for the
team to benefit from their talents. Team members with low language profi-
ciency or a tendency to withhold opinions at meetings also need to realize
that if they do not speak up, they will be seen as less competent, which can
have adverse career implications (Littlepage & Mueller, 1997). To contribute
more during team meetings, members with low language proficiency may
contribute more actively through other channels, such as one-on-one discus-
sions, emails, reports, and presentations (Bazarova & Yuan, 2013). Team
members who tend to dominate group conversations could practice distilling
key messages into concise points and active listening skills. All members of
multinational teams need to contribute to inclusive meetings to advance team
competence recognition and performance.
Human resource practitioners can also play a role in helping to facilitate
active contribution, inclusion, and appreciation in multinational teams. When
composing multinational teams, for example, it may be important to select
employees who possess an adequate level of language proficiency, cultural
sensitivity, and flexibility in communicating with people. Information tech-
nology (IT) departments in organizations might also provide multinational
teams with computer-mediated communication technologies to facilitate task
performance. For tasks that require information sharing and logic reasoning,
text-based computer-mediated communication may help lower cognitive
constraints, mask status differentials, and reduce the risks of social sanctions,
thereby equalizing the distribution of speaking turns (Baltes et al., 2002). It
should also be noted that text-based computer-mediated communication may

30 Group & Organization Management 00(0)
not be an appropriate fit for all types of tasks, as previous research has found
that the effect of using communication technology depends on the type of
task being performed and decision processes (Goodhue & Thompson, 1995;
Maznevski & Chudoba, 2000). For example, emotion-laden negotiations and
relationship-building tasks may be better carried out face-to-face than via
computer-mediated chat. Emerging technologies are also being introduced
that can visualize team members’ differences in speaking turns in real time,
thus stimulating reflection and inviting more equal participation (Leshed,
Cosley, Hancock, & Gay, 2010). IT departments can also provide multina-
tional or virtual teams with knowledge management technologies for team
members to exchange ideas, document knowledge, and solve problems. Such
tools have been found to provide virtual spaces or “virtual water coolers,”
which help overcome knowledge sharing challenges and encouraging spon-
taneous communication (Ellison, Gibbs, & Weber, 2015). They may also help
build trust, identification, psychological safety, and perceived proximity
(Ellison et al., 2015). Organizations could use these computer-mediated com-
munication technologies to facilitate information sharing, accurate compe-
tence recognition, and task accomplishment in multinational teams.
Conclusion
This research provides insight into the effects of differences in language pro-
ficiency, a largely ignored yet increasingly important phenomenon in global
organizations. Our findings demonstrate that language proficiency influences
the extent to which individuals speak up within a team, which may in turn
influence how other team members perceive their competence. We also extend
these relationships to the team level, and reveal that the language proficiency
dispersion across a team influences the recognition of competence within the
team and overall team performance. Moreover, differences in language profi-
ciency are more salient when team interactions occur face-to-face than through
text-based computer-mediated communication. This study underscores the
challenges members of multinational teams face when adopting a common
language, and highlights the need for future research to more explicitly con-
sider language proficiency configurations among team members.
Appendix
Sample Task Question
Quantum, a restaurant, is open for business every Monday through Saturday
but is closed Sundays. Lunch is the only meal served on Mondays, Tuesdays,
and Thursdays. Dinner is the only meal served on Wednesdays, Fridays, and

Li et al. 31
Saturdays. The restaurant’s floors are polished, and its plants are watered
only on days that Quantum is open for business, according to the following
policies:
 Plants are watered 2 days each week, but never on consecutive days
and never on the same day that floors are polished.
 Floors are polished on Monday and 2 other days each week, but never
on consecutive days and never on the same day that plants are watered.
If dinner is served on a day that plants are watered, which of the following
must be true?
A. Plants are watered on Tuesday.
B. Floors are polished on Thursday.
C. Plants are watered on Wednesday.
D. Floors are polished on Wednesday.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publi-
cation of this article.
Notes
1. Both teachers were blind to our hypotheses and rated the participants’ language
proficiency levels independently, using the same scales that the participants had
used in the study. They were instructed to focus on the first half of the video
recordings of face-to-face teams or the first half of the transcripts of group dis-
cussions of computer-mediated teams, which aimed to ensure that their evalua-
tions of the participants’ language proficiency were not influenced by ultimate
performance on the task. After they each spent around 6 hr to rate six face-to-
face teams and six computer-mediated teams, we calculated interrater reliability
(Cronbach’s α = .87). After confirming that both professional raters had high
agreement in their evaluations, Rater 1 spent an additional 16 hr and finished
rating the remaining teams. Given the high interrater reliability between the
two professional raters, we only compared Rater 1’s ratings with those of the
participants, and found that they correlated significantly with each other, which
suggests that it is unlikely that participants’ ratings of their team members’
language proficiencies were influenced by the members’ task competence and
performance.

32 Group & Organization Management 00(0)
2. We also tested models with word count and number of thoughts conveyed by
participants as control variables. Number of thoughts was the mean number of
thought units calculated by two trained independent coders (interrater reliability
= .998). At both the individual and the group levels, speaking-up variables (i.e.,
individual-level score, group mean, and group SD) were moderately correlated
with word count and number of thoughts, which were themselves highly inter-
correlated (r > .95, p < .05). For example, group mean word count and group
mean number of thoughts were correlated at .97, but they only correlated .35 and
.34 with group mean speaking turns (p < .05). For all hypotheses, controlling for
word count or number of thoughts did not change our results, and the effects of
word count or the number of thoughts were not significant. This suggests that
it is the frequency of speaking up, not simply talking more or expressing more
thoughts, that matters for the individual and team processes under consideration.
3. Texts in the parentheses were added by the authors.
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Associate Editor: M. Travis Maynard
Submitted Date: September 12, 2016
Revised Submission: November 17, 2017
Acceptance Date: December 30, 2017
Author Biographies
Huisi (Jessica) Li is a PhD student in Management and Organizations at Cornell
University. Her research interests include social hierarchy (e.g., power and status),
diversity and inclusion in teams, and cross-cultural communication and negotiation.
Y. Connie Yuan is an associate professor of Communication at Cornell University.
Her research interests include intercultural communication, the adoption and usage of
information and communication technology in organizations, and knowledge man-
agement through the development of social capital.
Natalya N. Bazarova (Ph.D, Cornell University) is an associate professor of
Communication and Director of Social Media Lab at Cornell University. Her research
interests include social media communication and personal relationships in dyads,
groups, and networks, with a special focus on self-disclosure and privacy, well-being,
and prosocial behaviors.
Bradford S. Bell is an associate professor of HR Studies in the ILR School at Cornell
University. He received his doctorate degree in Organizational Psychology from
Michigan State University. His research focuses on learning and development, work
groups and teams, and virtual work arrangements.