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Group & Organization Management
2014, Vol. 39(3) 300 –325
© The Author(s) 2014
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DOI: 10.1177/1059601114522321
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Article
Mechanisms Linking
Transformational
Leadership and Team
Performance: The
Mediating Roles of Team
Goal Orientation and
Group Affective Tone
Nai-Wen Chi1 and Jia-Chi Huang2
Abstract
Extending previous research on transformational leadership (TFL), the
present study explores the mechanisms that explain the relationship
between TFL and team performance. Drawing on the three-stage model of
TFL (Conger & Kanungo, 1998), we theorize that TFL predicts high levels of
team performance through shaping team goal orientation and group affective
tone. To test the hypotheses, we use data collected from managers and
members of 61 research and development teams and use the partial least
squares analysis to test hypotheses. The results show that TFL positively
predicts positive group affective tone through team learning goal orientation
but negatively predicts negative group affective tone via team avoiding goal
orientation. Finally, we find that positive group affective tone is positively
associated with team performance, whereas negative group affective tone is
negatively associated with team performance.
1National Sun Yat-Sen University, Kaohsiung, Taiwan
2National Chengchi University, Taipei, Taiwan
Corresponding Author:
Nai-Wen Chi, Institute of Human Resource Management, National Sun Yat-Sen University,
70, Lienhai Rd., Kaohsiung 80424, Taiwan.
Email: nwchi@mail.nsysu.edu.tw
522321GOMXXX10.1177/1059601114522321Group & Organization ManagementChi and Huang
research-article2014
Chi and Huang 301
Keywords
transformational leadership, team goal orientation, group affective tone,
team performance
Team leaders play a key role in promoting, developing, and enhancing team
effectiveness (Burke et al., 2006). Therefore, it is important for researchers
and practitioners to explore the effects of leadership behaviors on team per-
formance (Mathieu, Maynard, Rapp, & Gilson, 2008). Among the several
types of leadership behaviors discussed within the leadership literature,
transformational leadership (TFL) has become one of the most popular top-
ics (Bass & Avolio, 2000; G. Wang, Oh, Courtright, & Colbert, 2011). TFL is
a type of leadership behavior in which leaders articulate a shared vision of the
future, stimulate followers intellectually and show individual consideration
to followers (Bass, 1985). Consistent with the TFL theory, recent meta-anal-
yses have documented that TFL is positively associated with team perfor-
mance outcomes (Burke et al., 2006; G. Wang et al., 2011). However,
researchers have called for more research to examine the processes through
which TFL predicts team performance (G. Wang et al., 2011). Clarifying the
“black box” of such processes is important because it describes how and why
the effect of TFL unfolds within teams (Dionne, Yammarino, Atwater, &
Spangler, 2004).
Previous research has mainly applied two distinct theoretical rationales to
explain how TFL influences team performance. The first concerns enhancing
members’ motivations to achieve team-level goals, such that transformational
leaders influence team motivational states—for example, team potency
(Bass, Avolio, Jung, & Berson, 2003; Schaubroeck, Lam, & Cha, 2007) and
team empowerment (Dionne et al., 2004; Jung & Sosik, 2002)—by commu-
nicating the importance of team goals and motivating members to achieve
these goals. The second mechanism is enhancing members’ positive reactions
toward the team—for example, team cohesion (Dionne et al., 2004; Jung &
Sosik, 2002; Pillai & Williams, 2004) and team social identification (Kark,
Shamir, & Chen, 2003)—which suggests that transformational leaders are
able to inspire team members by creating positive attitudes and reactions
toward the team.
Although scholars have provided promising evidence that partially clari-
fies how TFL predicts team performance, several plausible mechanisms are
worthy of further investigations. First, in addition to motivating followers,
transformational leaders also align team goals with individual goals (Burns,
1978). Therefore, it is plausible that transformational leaders influence team
performance by shaping shared goals within teams (Colbert, Kristof-Brown,
302 Group & Organization Management 39(3)
Bradley, & Barrick, 2008). Second, the importance of the emotional compo-
nent in the TFL processes has been highlighted within the leadership litera-
ture (Bass, 1985; Humphrey, 2002); it is also possible that TFL leads to better
team performance by shaping shared affect within teams (Gooty, Connelly,
Griffith, & Gupta, 2010; Menges, Walter, Vogel, & Bruch, 2011; Pirola-
Merlo, Härtel, Mann, & Hirst, 2002). As transformational leaders set chal-
lenging goals for team members, it is also imperative that they motivate
members to pursue such goals by instilling positive moods while coping with
negative moods within teams (Antonakis & House, 2002; Berson, Shamir,
Avolio, & Popper, 2001; Humphrey, 2002). However, to our knowledge, no
TFL study has integrated the two mechanisms within one study.
To address the aforementioned research questions, we seek to advance the
TFL literature in several ways. First, we used the three-stage model of char-
ismatic leadership (Conger & Kanungo, 1998; Connelly, Gaddis, & Helton-
Fauth, 2002) to integrate both the shaping of shared goals and affect
mechanisms into the TFL-team performance linkage. This model describes
how charismatic/transformational leaders motivate team members to achieve
team performance goals through three sequential stages: (a) Stage 1: criti-
cally evaluating the team situations, (b) Stage 2: formulating positive team
goals while inhibiting negative team goals, and (c) Stage 3: instilling positive
affect while reducing negative affect within teams when pursuing the team
goals. Thus, we believe that the three-stage model can be a useful framework
to clarify the mechanisms linking TFL and team performance.
Second, as transformational leaders might motivate team members by
stressing the negative aspects of the current situation and the positive aspects
of new goals (Conger, 1999), it is plausible that TFL reduces members’ nega-
tive behaviors and encourages team members to pursue positive team goals
(Dragoni, 2005). Thus, we include both positive and negative aspects of
team-level goal orientation (i.e., the aggregate level of team member orienta-
tion to pursue certain goals; Porter, 2005) in our framework. Finally, prior
research has mainly investigated how TFL predicts team performance through
the creation of shared positive moods (Menges et al., 2011; Pirola-Merlo
et al., 2002). However, it is plausible that transformational leaders help team
members to cope with shared negative moods while pursuing team goals
(Humphrey, 2002; McColl-Kennedy & Anderson, 2002). Thus, both positive
and negative group affective tone (i.e., a team-level aggregate of team mem-
bers’ positive or negative moods; George, 1990; Sy, Cote, & Saavedra, 2005)
were added into the proposed model. By integrating team goal orientation
and group affective tone into the existing TFL literature, we are able to extend
the research boundaries pertaining to these areas and highlight their impor-
tance within the TFL processes.
Chi and Huang 303
Theory and Hypotheses
According to Bass (1985), TFL includes four sets of behaviors: (a) idealized
influence: leaders use charismatic behaviors that cause followers to respect
and admire them; (b) inspirational motivation: leaders motivate followers by
providing them with appealing and inspiring goals; (c) intellectual stimula-
tion: leaders encourage followers to pursue innovative thoughts and learn
new ways to solve problems; and (d) individual consideration: leaders iden-
tify and pay attention to follower needs. In the present study, we argue that
TFL leads to better team performance by shaping team shared goals (i.e.,
team goal orientation) and team shared affect (i.e., group affective tone).
These arguments can be explained by Conger and Kanungo’s (1998) three-
stage model.
The three-stage model describes three sequential stages through which
transformational leaders influence team performance. In Stage 1, transforma-
tional leaders critically evaluate the team situations by identifying the short-
comings of the status quo and evaluating team members’ abilities, needs, and
satisfaction levels (Conger, 1999). These evaluations lead to Stage 2, which
is the formulation and articulation of shared goals. In Stage 2, transforma-
tional leaders make clear distinctions between the status quo and the newly
idealized goals. On one hand, leaders highlight negative aspects of the status
quo and articulate the current situations as negative and intolerable, thereby
increasing members’ intentions to improve negative situations. On the other
hand, leaders highlight positive aspects of the new goals by articulating the
goals as attractive and challenging, but attainable (Conger & Kanungo,
1998), thereby increasing members’ motivations to pursue the positive goals
(Conger, 1999).
In Stage 3, transformational leaders encourage and motivate team mem-
bers to achieve the shared goals by instilling positive affect in teams (e.g.,
pride, determination, and optimism) and coping with members’ negative
affect (e.g., nervousness, frustration, and pessimism) (Humphrey, 2002; X.
H. Wang & Howell, 2010). Thus, as positive team goals are shared and nega-
tive team goals are inhibited during the goal-pursuing processes, members
are more likely to experience high levels of positive affect and low levels of
negative affect, leading to better team performance (Connelly et al., 2002).
Moreover, leaders often use risk taking and role modeling, as well as demon-
strating unconventional expertise, to show how the goals can be achieved
(Conger & Kanungo, 1998). These behaviors might influence team perfor-
mance as well. Based on the perspective of the three-stage model, we expect
that transformational leaders might influence team performance indirectly
via enhancing team positive goal orientation and positive group affective
304 Group & Organization Management 39(3)
tone, while reducing team negative goal orientation and negative group affec-
tive tone. In addition, transformational leaders’ behaviors lead to higher team
performance directly. Our conceptual model is presented in Figure 1.
The Mediating Roles of Team Goal Orientation and Group
Affective Tone
Based on the three-stage model, we expect that TFL influences team goals,
which, in turn, predict team members’ affective states. Several researchers
have proposed similar arguments that supports above propositions. For
example, Berson et al. (2001) suggested that transformational leaders elicit
positive emotions in teams (i.e., positive group affective tone) by setting
challenging goals and convincing team members that achieving the goals will
be beneficial in the future. In addition, Humphrey (2002) proposed that trans-
formational leaders reduce negative emotions in teams (i.e., negative group
affective tone) by helping members to cope with the obstacles and frustration
in the goal-pursuing processes. As such, it is plausible that TFL is positively
related to positive group affect and negatively associated with negative group
affect based on shaping different types of team goal orientation.
Team goal orientation refers to the aggregate level of dispositional goal
orientation among team members (LePine, 2005; Porter, 2005) that can be
“cued” by strong situational factors such as leadership (Bunderson &
Sutcliffe, 2003). Team goal orientation has three dimensions: (a) team learn-
ing goal orientation (TLGO), (b) team avoiding goal orientation (TAGO),
and (c) team proving goal orientation (TPGO). TLGO refers to team
Figure 1. Conceptual model of the present study.
Chi and Huang 305
members’ shared tendencies to develop competence by acquiring new skills
and learning from experience. TLGO leads members to react positively to
new team events, explore new ways to perform the tasks, and help each other
when facing challenging situations (LePine, 2005; Porter, 2005). TAGO
refers to the aggregate levels of team members’ tendencies to avoid negative
competence judgments from others. In high TAGO teams, the collective goal
is to avoid mistakes and negative judgments rather than to perform well.
When facing challenging team tasks, members in high TAGO teams are more
likely to experience negative reactions and tend to engage in self-protective
behaviors, such as withdrawing their efforts from the team and hindering task
engagement (Mehta, Feild, Armenakis, & Mehta, 2009; Porter, 2005). Finally,
TPGO reflects the aggregated levels of team members’ tendencies to demon-
strate their performance and gain favorable judgments from others. TPGO
leads members to engage in activities that exhibit their individual capabilities
and thereby create the perception of competition within teams (Mehta et al.,
2009).
Although team goal orientation is conceptualized as a three-dimensional
construct, Pieterse, van Knippenberg, and van Ginkel (2011) argued that
team goal orientation can be studied without necessarily incorporating all
dimensions, as they are independent. Based on Pieterse et al.’s argument, we
include only TLGO and TAGO in our theoretical model, because transforma-
tional leaders are more likely to encourage the former as a means to over-
come the challenging team situations (Sosik, Godshalk, & Yammarino,
2004), while discouraging the latter to avoid inhibiting effective team inter-
actions (Mehta et al., 2009). In terms of TPGO, as transformational leaders
emphasize collective goals rather than individual goals and inspire followers
to transcend their self-interests and act on behalf of collective interests (Chi
& Pan, 2012), it is unlikely that TFL will lead to a collective tendency to
demonstrate individual performance for gaining favorable judgments from
other members. Thus, we exclude TPGO from our proposed model.
In the present study, we expect that TFL will positively predict TLGO for
two reasons. First, transformational leaders encourage team members to
acquire new skills or improve their current skills to meet job requirements
(Sosik et al., 2004). In turn, members perceive the acquisition of knowledge
as important to the team and develop collective tendencies to acquire new
skills, thereby predicting high levels of TLGO. Second, transformational
leaders challenge team members’ thinking processes and promote creative
ideas to improve the current conditions (i.e., intellectual stimulation). These
behaviors motivate teams to engage in actions such as trying new methods
and generating new ways to perform tasks (Bass, 1985; Chi & Pan, 2012),
predicting high levels of TLGO.
306 Group & Organization Management 39(3)
By contrast, teams with high TLGO continually engage in self-learning
and self-improvement in teams (Bunderson & Sutcliffe, 2003). When high
LGO teams face problems or difficulties, members enjoy the learning pro-
cesses stemming from problem solving and persist in accomplishing chal-
lenging tasks, leading them to react positively to new challenges (Porter,
2005). Therefore, teams with high LGO are more likely to have high levels of
positive group affect when they are involved in team tasks. Thus, we propose
the following hypothesis:
Hypothesis 1: TLGO positively mediates the positive relationship
between TFL and positive group affective tone.
Conger and Kanungo’s (1998) three-stage model also proposes that trans-
formational leaders constantly evaluate current environments and critically
point out any negative aspects of teams. By identifying negative team condi-
tions, transformational leaders can motivate team members to eliminate
obstacles and improve the status quo (Connelly et al., 2002), thereby reduc-
ing shared negative emotions in teams (Humphrey, 2002). As teams with high
TAGO tend to engage in actions such as evading task responsibilities and
overemphasizing the avoidance of failure (Mehta et al., 2009), transforma-
tional leaders work to inhibit such team tendencies (i.e., TAGO) by display-
ing the following behaviors. First, transformational leaders engage in role
modeling behaviors and take on greater job responsibilities to influence team
members (i.e., idealized influence; Bass, 1985; Chi & Pan, 2012; Conger,
1999). In this way, team members may come to understand that avoiding
tasks is inappropriate. Second, by exhibiting inspirational motivation, trans-
formational leaders display optimistic attitudes regarding teams’ future suc-
cess (Schaubroeck et al., 2007), which inspires team members to devote
greater effort and not focus on the possibility of failure (Bass, 1985).
However, teams with high TAGO tend to form passive attitudes toward
task completion and engagement (Mehta et al., 2009). Members in high
TAGO teams are very sensitive to negative stimuli and feel pessimistic about
negative performance information; this characteristic facilitates the self-pro-
tective processes that hinder team efforts toward goal achievement (Mehta et
al., 2009). Such negative team tendencies also signal that no member wants
to take on greater responsibility to attain team performance goals, thereby
heightening negative feelings such as nervousness, anxiety, and frustration
(Cole, Walter, & Bruch, 2008). As a result, TAGO should be positively asso-
ciated with negative group affective tone. Taken together, although TAGO
positively predicts negative group affective tone, we expect that TFL will
Chi and Huang 307
lead to low levels of negative group affective tone by negatively predicting
TAGO. Thus, the following is proposed:
Hypothesis 2: TAGO negatively mediates the negative relationship
between TFL and negative group affective tone.
The Relationship Between Group Affective Tone and Team
Performance
As theorized above, TFL differentially predicts positive/negative group affect
by facilitating TLGO and inhibiting TAGO. Group affective tone reflects
team members’ affective reactions toward current team conditions (George &
King, 2007), which also influences team members’ subsequent motivations
and behaviors in pursuit of their performance goals (Chi, Chung, & Tsai,
2011; George, 1996).
Based on the information processing function of positive affect, it is theo-
rized that positive group affective tone can enhance divergent thinking and
facilitate exchanges of ideas (Rhee, 2007) to generate useful solutions and
meet team performance goals (George, 1995). Moreover, positive group
affective tone can foster members’ helping behaviors to overcome task prob-
lems (George & Brief, 1992) as well as make members feel more confident
and optimistic about their future performance (Gibson & Earley, 2007).
Consistent with our arguments, Chi et al. (2011) also found a positive asso-
ciation between positive group affective tone and team performance. Based
on the above theoretical arguments and empirical evidence, we propose the
following hypothesis:
Hypothesis 3: Positive group affective tone is positively related to team
performance.
In contrast, negative group affective tone is more likely to facilitate inter-
personal conflict and reduce team cohesion (Jordan, Lawrence, & Troth,
2006), which are both detrimental in terms of team performance (Mehta et
al., 2009). Rhee (2007) also theorized that negative group affective tone
inhibits positive social interactions and morale building in teams, which
might reduce members’ motivations to pursue higher team performance.
Moreover, the results a study by Cole et al. (2008) indicated that negative
group affective tone is negatively related to team performance. Based on the
above theoretical and empirical arguments, we propose the following
hypothesis:
308 Group & Organization Management 39(3)
Hypothesis 4: Negative group affective tone is negatively related to team
performance.
Method
To meaningfully test our proposed model, we chose research and develop-
ment (R&D) teams as our sample because R&D team members need to set
clear project scheduling goals. Therefore, their team goal orientations may
influence their interpersonal interactions as well as performance progress.
Our sample was composed of 61 R&D teams (team leaders n = 61; members
n = 263) from 32 Taiwanese high-technology firms involved in the following
industries: information technology related industries (e.g., semiconductors,
integrated circuit design, and optoelectronics; n = 15), electronic communi-
cations (n = 6), research and development institutes (n = 3), computer sys-
tems (n = 2), and others (n = 6). The tasks performed by these teams include
basic research (i.e., performing basic research to create broad-based new
applications; n = 7), project-based (i.e., performing project-based research
that solves particular problems; n = 14), new product development (n = 21),
technical service (n = 12), and product improvement (n = 7). To examine
whether the task type influences the relationships among the study variables
(Keller, 2006), we performed one-way ANOVA. The results showed that all
study variables (TFL, TLGO, TAGO, positive and negative group affective
tones, and team performance) did not differ across teams with different task
types (F values ranged from 0.32-1.07; all ps > .10). Thus, the task type of
R&D teams should not influence our findings.
The data collection procedure was as follows. First, we introduced the
research purposes to the R&D executive for each firm to obtain their support
for data collection. R&D executives from the firms that agreed were invited
to randomly choose two to three teams per firm. We sent questionnaires to the
assigned contact persons for each firm, who distributed the questionnaires to
the chosen team leaders and team members. During the data collection pro-
cess, we attempted to reduce the potential for common method variance
(CMV) in two ways. First, to avoid issues related to social desirability
(Podsakoff & Organ, 1986), we provided a self-addressed, stamped envelope
for participants to enclose and mail their completed questionnaires; we also
emphasized that all responses would be kept confidential to reduce respon-
dents’ evaluation apprehension (Podsakoff, MacKenzie, Lee, & Podsakoff,
2003). Second, we collected data from multiple sources to reduce the influ-
ence of same source bias (Podsakoff & Organ, 1986): Team members were
asked to rate team leaders’ TFL, team goal orientation, and group affective
tone, whereas team leaders rated team performance.
Chi and Huang 309
In total, 80 team questionnaires (including 80 leader questionnaires and
400 member questionnaires) were mailed out and 61 team questionnaires
were returned (including 61 from team leaders and 263 from team members),
resulting in a valid response rate of 76%. To ensure the representativeness of
members’ opinions regarding the team-level variables, we treated the team
data as valid only when more than two thirds of each team’s members and
their leader responded to the questionnaires (Huang, 2010).
Team member were predominantly male (64%) and about 82% were
between 26 and 35 years old (M = 31.08, SD = 5.19). Most participants (84%)
possessed at least an undergraduate degree. Team size and team longevity
were 4.57 persons (SD = 2.52) and 7.29 years (SD = 5.06), respectively.
Finally, most team leaders were male (89%) and the mean age for team lead-
ers was 36.11 years old (SD = 4.86).
Measures
Following Brislin (1980), we translated the original version of the question-
naire into Chinese, then asked two bilingual foreign-language experts to
translate from Chinese to English. Finally, three organizational-behavior
scholars reviewed the translation for appropriateness.
TFL. Bass and Avolio’s (2000) 20-item Multifactor Leadership Questionnaire
(MLQ 5X) was used to measure team leaders’ TFL behaviors. Team members
were asked to evaluate their leaders’ leadership behaviors on a 7-point scale
(1 = strongly disagree to 7 = strongly agree). This scale captures the four
dimensions of TFL, including idealized influence (e.g., My leader acts in
ways that build my respect for him or her), inspirational motivation (e.g., My
leader emphasizes the importance of having a collective sense of mission),
intellectual stimulation (e.g., My leader seeks differing perspectives when
solving problems), and individualized consideration (e.g., My leader helps
followers to develop their strengths).
Past studies have indicated that the correlations among these four dimen-
sions are high (Chi & Pan, 2012; Liao & Chuang, 2007). In the present study,
we also found a high degree of shared variance among the four dimensions
(r = .64-.75; p < .01). Therefore, we followed Colbert et al. (2008) and per-
formed a second-order confirmatory factor analysis (CFA) to determine
whether the four dimensions were nested under an overall TFL factor. The
results of the second-order CFA indicate that the data fit the model well
[Comparative fit index (CFI) = .95, Normed fit index (NFI) = .94, Non-
normed fit index (NNFI) = .95, Standardized root mean square residual
(SRMR) = .07]. Based on the results of the second-order CFA and other
310 Group & Organization Management 39(3)
researchers’ approaches (Chi & Pan, 2012; Colbert et al., 2008), we com-
bined the scores of the four dimensions to form an overall TFL score and then
tested the within-group agreement on team members’ TFL scores to deter-
mine the suitability of aggregation to the team level (see “Data Aggregation”
section). Cronbach’s α for this scale was .95.
Team goal orientation. We used VandeWalle’s (1997) nine-item scale to mea-
sure team members’ goal orientation, which includes five items to measure
TLGO (e.g., I am willing to select a challenging work assignment that I can
learn a lot from; I enjoy challenging and difficult tasks at work where I’ll
learn new skills), and four items to evaluate TAGO (e.g., I’m concerned
about taking on tasks at work if my performance would reveal that I had low
ability; I would avoid taking on a new task if there was a chance that I would
appear rather incompetent to others). Responses were made on a 7-point Lik-
ert-type scale (1 = strongly disagree to 7 = strongly agree).
In the team goal orientation literature, several scholars have aggregated
individual members’ dispositional goal orientation to team level to form the
scores of team goal orientation (e.g., Huang, 2010; LePine, 2005; Pieterse
et al., 2011; Porter, 2005). Stewart (2003) and Porter (2005) have provided
theoretical explanations for such an approach. Individuals in teams function
similarly to genes—the latter combine to form the tendencies an individual
possesses, while the dispositions of the former within a team form the tenden-
cies a team possesses. Thus, it was deemed useful and appropriate to aggre-
gate individual members’ dispositional goal orientation into the team level to
determine the ways that team tendencies influence team functions. We also
followed this approach to form team goal orientation scores. To examine the
appropriateness of aggregating data, we examined the within-group agree-
ment on TLGO and TAGO (see “Data Aggregation” section).
To demonstrate the distinction between TLGO and TAGO, we conducted
a principal-axis factor analysis with Promax rotation. The results revealed
two factors, explaining 69% of the total variance explained in the items, with
item loadings as expected. Cronbach’s alphas for TLGO and TAGO were .89
and .85, respectively.
Positive and negative group affective tones. To fit with the operationalization of
group affective tone used in past studies (Chi, Chung, & Tsai, 2011; George,
1990, 1995; Tsai, Chi, Grandey, & Fung, 2012), we assessed team members’
positive and negative moods using Watson, Clark, & Tellegen’s (1988) Posi-
tive and Negative Affect Schedule (PANAS). In addition, we asked team
members to evaluate the extent to which each of a list of adjectives described
their mood states at team meetings during the past week (e.g., Tsai et al.,
Chi and Huang 311
2012); responses were made on a 5-point Likert-type scale (1 = very slightly
or not at all to 5 = extremely). We also tested the within-group agreement on
team members’ positive and negative moods to determine the suitability of
aggregation to the group level (see “Data Aggregation” section). Cronbach’s
alphas for the positive and negative moods were .93 and .91, respectively.
Team performance. We used Edmondson’s (1999) five-item scale to measure
team performance (e.g., The quality of work provided by this team is improv-
ing over time; Critical errors occur frequently in this team [reverse scored]).
Team managers were asked to rate team performance on a 7-point Likert-type
scale (1 = strongly disagree to 7 = strongly agree). Cronbach’s alpha for the
team performance scale was .86.
Control Variables
As larger teams may be able to obtain greater resources, making them
more effective and thereby influencing team interactions (Chi, Huang, &
Lin, 2009; George, 1996; Stewart, 2006), we included team size as a con-
trol variable (Mteam size = 4.57 persons; SD = 2.52). In addition, team lon-
gevity may also influence the ways teams communicate and interact with
each other, which might also, in turn, influence group affective tone and
team performance (Chi et al., 2011; Stewart, 2006). Thus, we measured
team longevity by averaging each member’s response and treating it as
another control variable (Mteam longevity = 7.29 years, SD = 5.06) in the sub-
sequent analyses.
CFA and Discriminant Validity
As our data are multilevel in nature, we followed Dyer, Hanges, and Hall’s
(2005) approach to conduct a series of multilevel CFAs. We first tested the
proposed five-factor model at the individual level (i.e., TFL, TLGO, TAGO,
positive and negative group affective tones). The CFA results show that the
proposed five-factor model fit the data better, χ2(1117) = 3,387.83; CFI = .94,
NFI = .90, IFI = .94, SRMR = .07, RMSEA = .08, than a one factor model,
χ2(1127) = 10,763.26; CFI = .82, NFI = .79, IFI = .82, SRMR = .15,
RMSEA = .18. We also tested a three-factor model, such that all TFL items
loaded on the first factor, all team LGO and positive group affective tone
items loaded on the second, and all team AGO and negative group affective
tone items loaded on the final one. This approach also produced a worse fit-
ting model than the proposed model, χ2(1124) = 5,109.42; Δχ2(7) = 1,712.59,
p < .01; CFI = .89, NFI = .86, IFI = .89, SRMR = .11, RMSEA = .12.
312 Group & Organization Management 39(3)
Before aggregating the individual responses to the team level, it was nec-
essary to ensure that the factor structure was consistent across individual and
team levels (Dyer et al., 2005). Thus, we performed a multilevel CFA that
included the proposed five factors at both the individual and team levels. The
results show that the proposed five-factor model provided an acceptable fit to
the data, χ2(2293) = 7,636.06; CFI = .88, NFI = .85, IFI = .88, SRMR = .10,
RMSEA = .12, supporting the multilevel structure of our data. The fit indices
of the multilevel CFA are below the ideal levels, which might be due to the
small sample size at the team level (n = 61; Dyer et al., 2005). Despite this
limitation, the factor loadings were statistically significant (p < .01), suggest-
ing acceptable convergent validity (Bagozzi, Yi, & Phillips, 1991). In addi-
tion, the results show that the 95% confidence interval (CI) around the
correlations among these factors did not include 1.0, which supports the dis-
criminant validity of the study variables (Anderson & Gerbing, 1988). Thus,
we proceeded to examine the appropriateness of aggregating individual
members’ responses to the team level.
Data Aggregation
To examine the appropriateness of data aggregation pertaining to TFL,
TLGO, TAGO, positive and negative group affective tone scores, we calcu-
lated the inter-rater agreement (rwg), intra-class correlation coefficient,
ICC(1), and reliability of group mean, ICC(2), for these variables (Bliese,
2000; James, Demaree, & Wolf, 1984). The results show that the mean rwg
values for TFL, TLGO, TAGO, positive and negative group affective tones
were .99, .86, .83, .93, and .95, while median rwg values for TFL, TLGO,
TAGO, positive and negative group affective tones were .99, .91, .89, .95,
and .97. Following LeBreton and Senter’s (2008) suggestion, we also calcu-
lated the range of rwg values of these variables using uniform, triangular, and
skewed distributions; the results show that rwg values ranged from .70 to .99,
suggesting a high level of inter-rater agreement on their responses.
Moreover, the ICC(1) values for TFL, TLGO, TAGO, positive and nega-
tive group affective tones were .17, .12, .23, .21, and .12 (F values ranged
from 1.52-2.40, all ps < .05), respectively. These values indicate that signifi-
cant between-group variance exists for all study variables (Bliese, 2000).
Finally, the ICC(2) values were .52 for TFL, .43 for TLGO, .55 for TAGO,
.58 for positive group affective tone, and .40 for negative group affective
tone. Although these fell below the conventionally accepted value of .70
(Bliese, 2000), LeBreton and Senter (2008) have suggested that relying solely
on ICC(2) values to justify aggregation can lead to erroneous decisions. In
addition, Chen and Bliese (2002) proposed that data aggregation should be
Chi and Huang 313
supported by high rwg values and a significant between-group variance, that
is, ICC(1) values. Based on these suggestions, we decided to aggregate team
members’ responses to the team level, as the results showed high rwg values
and a significant between-group variance in terms of all study variables.
Data Analysis Strategy
In the group and team literature, Sosik, Kahai, and Piovoso (2009) have sug-
gested that the partial least squares (PLS) data analytical technique is a pow-
erful means for team research because PLS (a) can test multivariate structural
models with a limited sample size, (b) can be applied to develop theory in
early stages of research, and (c) can use the bootstrapping technique to deter-
mine the 95% CIs of the path coefficients, providing more accurate findings.
As we had a relatively small sample size at the team level (n = 61) and a large
number of paths to be estimated, we followed Sosik et al.’s (2009) suggestion
to use the PLS approach to test our hypotheses. In addition, as PLS does not
provide fit indices for evaluating the model fitness, we used LISREL 8.54 for
our CFAs and to evaluate the model fitness among several alternative models.
After evaluating the model fitness, we used PLS to test the hypotheses.
Results
Means, standard deviations, and correlations among the study variables are
presented in Table 1. As shown in Table 1, TFL was positively related to team
LGO (r = .52, p < .01), but negatively related to team AGO (r = −40, p < .01).
In addition, team LGO was positively related to positive group affective tone
(r = .63, p < .01), while team AGO was positively related to negative group
affective tone (r = .37, p < .01). Finally, TFL, team LGO, and positive group
affective tone had positive associations (r = .58~.68, all ps < .01), while team
AGO and negative group affective tone had negative associations (r =
−.56~−.60, all ps < .01) with team performance.
Testing of Alternative Models
Following James, Mulaik, and Brett (2006), we compared the fit indices of
the three alternative models to determine the final model for testing our
hypotheses. The proposed model was specified based on the hypotheses.
Moreover, we expected that TFL would have a direct and positive effect on
team performance, even when we controlled for the mediating effects of team
shared goals and affect. Thus, we included one direct path from TFL to team
performance. It is plausible that transformational leaders shape group affect,
314 Group & Organization Management 39(3)
which in turn facilitates team goal orientations (George, 1995; Gibson &
Earley, 2007). Thus, we tested the first alternative model by specifying posi-
tive and negative group affective tones as the first set of mediators and TLGO
and TAGO as the second set of mediators. In addition, we also added three
direct paths from TFL to TLGO, TAGO, and team performance. It is plausi-
ble that the two mechanisms act as parallel mediating processes rather than
sequential processes. Thus, we included the second alternative model by
specifying positive and negative group affective tones, TLGO, and TAGO as
parallel mediators in the TFL-team performance relationship. In addition, we
also added one direct path from TFL to team performance. The fit indices of
all alternative models are presented in Table 2. As shown in Table 2, the pro-
posed model fit the data better (CFI = .96, NFI = .95, IFI = .97, SRMR = .06,
RMSEA = .12) than the alternative models (Δχ2 = 10.25~10.34, Δdf = 0, all
ps < .05). Therefore, we used the proposed model during PLS analyses to test
our hypotheses.
Hypotheses Testing
Following Sosik et al.’s (2009) suggestions, we used the PLS approach to esti-
mate the path coefficients, corresponding t values for significance testing, and
95% CIs of the proposed model. Specifically, we generated coefficients and
CIs using the bootstrapping procedure with 1,000 re-samples, with 61 cases for
each sample. The coefficients for each path are presented in Figure 2.
Table 1. Means, Standard Deviations, Inter-Correlations, and Coefficient Alphas.
Variables Mean SD 1 2 3 4 5 6 7 8
1. Team Longevitya7.29 5.10 —
2. Team Sizeb4.57 2.52 −.08 —
3. TLGO 5.51 .46 −.06 .01 .89
4. TAGO 3.82 .74 .14 −.06 −.51** .85
5. Transformational
Leadership
5.21 .46 −.05 .21 .52** −.40** .95
6. PGAT 3.10 .50 .14 .17 .63** −.35** .51** .93
7. NGAT 1.62 .36 −.13 .15 −.48** .37** −.41** −.32** .90
8. Team Performance 5.11 .87 −.07 .27* .65** −.56** .68** .58** −.60** .86
Note. Cronbach’s alpha coefficients are presented in boldface on the diagonal; n = 61.
TLGO = team learning goal orientation; TAGO = team avoiding goal orientation; PGAT =
positive group affective tone; NGAT = negative group affective tone.
aIn years.
bIn persons.
*p < .05. **p < .01.
Chi and Huang 315
As shown in Figure 2, the results of PLS analyses indicate that TFL was
positively associated with TLGO, β = .52, p < .01; 95% CI = [.34, .70]. In
addition, TLGO was also positively related to positive group affective tone,
β = .54, p < .01; 95% CI = [.30, .77]. The Sobel test (1982) indicates that the
indirect effect of TFL on positive group affective tone via TLGO was signifi-
cant (Z = 3.44, p < .01). Thus, Hypothesis 1 was supported. In terms of
Hypothesis 2, the results reveal that TFL was negatively related to TAGO, β
= −.40, p < .01; 95% CI = [−.12, −.68], while TAGO positively predicted
negative group affective tone, β = .26, p < .05; 95% CI = [.04, .48]. Moreover,
the Sobel test (1982) shows a significant indirect effect of TFL on negative
group affective tone through TAGO (Z = −2.00, p < .05). Thus, Hypothesis 2
also received support.
Moreover, the PLS results show that positive group affective tone was
positively related to team performance, β = .27, p < .01; 95% CI = [.10, .43],
while negative group affective tone was negatively associated with team per-
formance, β = −.43, p < .01; 95% CI = [−.57, −.29]. Therefore, Hypotheses 3
and 4 were also supported. Finally, after controlling for the effects of team
goal orientation and group affective tone, TFL still positively predicted team
performance, β = .31, p < .01; 95% CI = [.17, .45].
Additional Analysis
To enhance the validity of our hypothesized model, we performed additional
PLS analysis whereby we randomly selected half of members from each team
Table 2. Fit Indexes Among Alternative Models.
Measurement models χ2df Δχ2Δdf CFI NFI IFI SRMR RMSEA
Proposed model 14.80 8 — — .96 .95 .97 .06 .12
Alternative Model 1 25.14 8 10.34 0 .91 .89 .92 .09 .19
Alternative Model 2 25.09 8 10.25 0 .90 .88 .91 .10 .19
Note. The chi-square difference was compared based on the value of the proposed model. (a)
Proposed model: the model was specified based on our proposed hypotheses; (b) alternative
Model 1: we specified positive and negative group affective tone as the first set of mediators
and TLGO and TAGO are the second set of mediators. In addition, we also added three
direct paths from TFL to TLGO, TAGO, and team performance; (c) alternative Model 2: we
specified positive and negative group affective tone, TLGO, and TAGO as parallel mediators
in the TFL-team performance relationship. In addition, we also added one direct path from
TFL to team performance. TLGO = team learning goal orientation; TAGO = team avoiding
goal orientation; TFL = transformational leadership; CFI = comparative fit index; NFI =
normed fit index; NNFI = non-normed fit index; SRMR = standardized root mean square
residual. *p < .05.
316 Group & Organization Management 39(3)
as sources of for TFL, positive and negative group affective tone measures
and the other half of team members as sources for TLGO and TAGO. The
PLS results showed that our findings were substantially the same, indicating
that the same source variance did not adversely or significantly change our
findings.
Discussion
Over the last decade, researchers have begun to explore the mechanisms
through which transformational leaders influence the performance of teams
(G. Wang et al., 2011). Using the three-stage model as the backbone, we theo-
rized and found that TFL predicts team performance differentially via diverse
facets of team goal orientation and group affective tone. The present findings
contribute to the leadership, goal orientation, and group affect literature in the
following ways.
Theoretical Implications for Leadership Research
Although scholars have explored the shared goals and shared affect pro-
cesses within the TFL-team performance link, they tested each process
separately (Colbert et al., 2008; Gooty et al., 2010; Menges et al., 2011; X.
H. Wang & Howell, 2010). In addition, past studies did not investigate how
Figure 2. Path coefficients from PLS analyses.
Note. The path coefficients and corresponding t values for significance testing were generated
through the bootstrapping procedure with 1,000 re-sampling with 61 cases for each sample.
PLS = partial least squares.
†p < .10. *p < .05. **p < .01.
Chi and Huang 317
different types of shared team goals and affect explain the TFL-team per-
formance relationship. The current study represents a step forward through
the integration of both processes into one study, which provides a more
complete picture of how and why TFL is associated with team performance
(Whetten, 1989).
In particular, the current study shows that TFL was positively related to
positive team goals (i.e., TLGO), which, in turn, positively predicted positive
group affect; however, TFL was negatively related to negative goal orienta-
tion in teams (i.e., TAGO), which, in turn, was negatively associated with
negative group affect. These findings indicate that TFL not only was nega-
tively associated with negative group affect by predicting low levels of teams’
negative goal tendencies (e.g., avoiding responsibilities and negative compe-
tence judgments from others) but also was positively associated with positive
group affect by predicting high levels of teams’ positive goal tendencies (e.g.,
learning from experience and challenges).
Theoretical Implications for Goal Orientation Research
The present study contributes to the goal orientation literature by highlight-
ing the roles of TFL and group affective tone within the nomological network
of team goal orientation (Whetten, 1989). Although several scholars have
indicated that leadership is one important situational antecedent of team goal
orientation (Dragoni, 2005; Mehta et al., 2009), to our knowledge, the pres-
ent study is one of the first to explicitly test this proposition. In our findings,
TFL was positively related to TLGO, but negatively related to TAGO, sup-
porting Dragoni’s (2005) argument that leadership behavior is an important
antecedent of team goal orientation.
Moreover, the current study shows that different types of team goal orien-
tation are associated with different dimensions of group affective tone.
Specifically, TLGO was positively related to positive group affective tone,
whereas TAGO was positively related to negative group affective tone. These
findings indicate that teams with high LGO enjoy developing new skills and
achieving challenging goals, thereby predicting high levels of positive affect
in teams (e.g., enjoyment and interest), while high AGO teams are very sensi-
tive to negative stimuli and feel pessimistic about difficult tasks, thus predict-
ing high levels of negative affective reactions in teams.
Finally, previous studies on team goal orientation have predominantly
been conducted within laboratory settings using student samples or con-
ducted in individualistic cultures (e.g., LePine, 2005; Mehta et al., 2009;
Pieterse et al., 2011). In the present study, we test the effects of team goal
orientation using a sample consisting of R&D teams within a collectivistic
318 Group & Organization Management 39(3)
culture (i.e., Taiwan). Work teams in organizations face more complex and
dynamic tasks than those faced by student teams in laboratory settings. In
addition, collectivistic cultures might heighten the impact of team members’
behaviors, as members tend to place a high value on group harmony and soli-
darity (Hofstede, 1997). Our findings are similar to previous findings that
indicate that the effects of team goal orientation can be generalized to real
teams and across different cultures. Future research can further explore
whether other types of cultural values (e.g., uncertainty avoidance; Hofstede,
1997) amplify or buffer the effects of different team goal orientation dimen-
sions (e.g., TAGO). Taken together, the present study contributes to the goal
orientation literature by adding substantive antecedents, consequences, and
boundary conditions to the nomological network of team goal orientation
(Whetten, 1989).
Theoretical Implications for Group Affect
Over the last decade, researchers have begun to explore how leader behaviors
influence group affect (Chi et al., 2011; Humphrey, 2002; Menges et al.,
2011; Pirola-Merlo et al., 2002). However, past studies have mainly applied
the emotional contagion perspective to explain how leader behaviors influ-
ence group affective tone (e.g., Chi et al., 2011). The present findings suggest
that TFL predicts different group affective tone differentially through TLGO
and TAGO, indicating that transformational leaders are able to facilitate posi-
tive or reduce negative affective experiences in teams by altering their collec-
tive tendencies toward certain goals.
Practical Implications
The findings of the current study offer several practical implications to help
managers more effectively manage teams. First, our results suggest that TFL
is positively related to learning goal orientation and positive affect in teams,
but is negatively associated with avoidance goal orientation and negative
affect in teams. As such, team leaders can use TFL behaviors to encourage
followers to learn new ways to solve problems as well as create inspiring
visions and inhibit negative team actions to better communicate team goals
and manage group affective tone.
Second, it will be useful for organizations to increase team leaders’
transformational behaviors through the selection and training practices.
For instance, managers with high conscientiousness and extraversion are
likely to excel as team leaders because these personality traits tend to be
associated with TFL behaviors (Chi et al., 2011; Judge & Bono, 2000).
Chi and Huang 319
Therefore, applicants’ conscientiousness and extraversion should be taken
into consideration when selecting the team leaders. Moreover, training
courses on TFL skills can also be useful; organizations can design training
activities such as role playing and scenario simulations related to the TFL
style, which can assist in the development of these skills (Barling, Weber,
& Kelloway, 1996).
Finally, our findings also indicate that positive group affective positively
predicts team performance whereas negative group affective tone associates
with low levels of team performance. Therefore, organizations should pay
attention to the team member selection during the team composition pro-
cesses. For example, selecting team members with positive affectivity and
extraversion are useful to facilitate positive group affective tone (Chi et al.,
2011; George, 1990). In contrast, organizations should not select members
with high levels of negative affectivity because team members’ negative
affectivity is positively related to negative group affective tone (George,
1990; Tsai et al., 2012).
Research Limitations
There are several limitations of the present study that should be noted. First,
although we applied a multiple-source design such that team managers rated
team performance, all TFL measures were supplied by the same source (i.e.,
team member ratings). To address this issue, we followed Podsakoff et al.
(2003) and ensured that team members’ responses were kept confidential,
which helped reduce potential CMV problems associated with respondents’
evaluation apprehension and social desirability issues. Moreover, the split-
sample analysis suggests that the relationships among TFL, team goal orien-
tation, and group affective tone were not adversely influenced by the CMV
problem.
Second, as the data for all study variables were collected at the same point
in time, it is impossible to make causal inferences based on the cross-sec-
tional research design. However, because team managers occupy a powerful
position that signals appropriate goals and behaviors within teams (Salancik
& Pfeffer, 1978), it is less reasonable to argue that team goal orientation
influences team managers’ TFL behaviors. However, to provide a more rigor-
ous test of the proposed model, we encourage future researchers to reexamine
our model using a longitudinal research design.
Finally, although we asked team members to indicate how they felt at the
team meetings during the past week to ensure that we measured team mem-
bers’ mood states rather than affective traits (George, 1990; Tsai et al., 2012),
we did not use the same 1-week time frame to measure TFL and team
320 Group & Organization Management 39(3)
performance. This approach limits our ability to make causal inferences and
needs to be addressed in future research. We encourage future researchers to
use the same week-based time frame for all measures or even the daily study
design to reexamine our proposed model.
Directions for Future Research
To further extend the current findings, we propose some possible directions
for future research. First, although the proposed mechanisms linking TFL
and team performance were supported, we did not control for other types of
leadership behaviors, such as transactional leadership (Burns, 1978) or abu-
sive leadership (Tepper, 2000). Thus, it is possible that abusive or transac-
tional leadership positively predicts negative group affective tone through
TAGO whereas TFL positively predicts positive group affective tone
through TLGO. Future researchers can test and compare the differential
mediating processes among the different types of leadership behaviors.
Fourth, we did not include other types of situational or organizational fac-
tors that might substitute for the effect of TFL, such as rewards outside
leader’s control, staff support, or spatial distance between leader and fol-
lowers. Future researchers could include these characteristics and examine
the unique effects of TFL (Keller, 2006).
Second, the present findings also indicate that TFL had a direct positive
association with team performance, even controlling for the mediating effects
of team goal orientation and group affective tone. This finding suggests that
transformational leaders might be associated with team performance via
other unexamined processes. Future researchers can explore whether TFL
predicts team performance through the shared cognition mechanism, such as
shaping shared mental models (Chi et al., 2011; George, 1996).
Acknowledgment
We thank Wei-Tze Lee for her great assistance in data collection.
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) disclosed receipt of the following financial support for the research,
authorship, and/or publication of this article: This current study was supported by the
National Science Council of Taiwan (Grant NSC 99-2410-H-004-010-MY3).
Chi and Huang 321
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Author Biographies
Nai-Wen Chi is an assistant professor in the Institute of Human Resource Management
at National Sun Yat-Sen University, Taiwan. His primary research is focused on
group affect, emotional labor, team composition, and strategic human resource man-
agement. His work has been published in Journal of Applied Psychology, Journal of
Vocational Behavior, Journal of Organizational Behavior, Personnel Psychology,
Work & Stress, Group & Organizational Management, Applied Psychology: An
International Review, Journal of Occupational and Organizational Psychology,
Journal of Business and Psychology, and British Journal of Industrial Relations.
Jia-Chi Huang is a professor of organizational behavior and human resource man-
agement in the Department of Business Administration at the National Chengchi
University, Taiwan. His research interests include team composition, team manage-
ment, goal orientation, and strategic human resource management. His work appears
in Academy of Management Journal, Journal of Management, Human Relations,
Human Resource Management, International Journal of Human Resource
Management, and other outlets.
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