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How Psychological Safety Affects Team Performance: Mediating Role of Efficacy and Learning Behavior

  • Institute for Industrial Policy Studies

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This article examines the mechanisms that influence team-level performance. It investigates psychological safety, a shared belief that the team is safe for interpersonal risk taking and a causal model mediated by learning behavior and efficacy. This model hypothesizes that psychological safety and efficacy are related, which have been believed to be same-dimension constructs. It also explains the process of how learning behavior affects the team’s efficacy. In a study of 104 field sales and service teams in South Korea, psychological safety did not directly affect team effectiveness. However, when mediated by learning behavior and efficacy, a full-mediation effect was found. The results show (i) that psychological safety is the engine of performance, not the fuel, and (ii) how individuals contribute to group performance under a psychologically safe climate, enhancing team processes. Based on the findings, this article suggests theoretical and methodological implications for future research to maximize teams’ effectiveness.
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fpsyg-11-01581 July 23, 2020 Time: 17:35 # 1
published: 24 July 2020
doi: 10.3389/fpsyg.2020.01581
Edited by:
Carlos Francisco De Sousa Reis,
University of Coimbra, Portugal
Reviewed by:
Zhenxing Gong,
Liaocheng University, China
Ramayah Thurasamy,
Universiti Sains Malaysia (USM),
Sehoon Kim
Specialty section:
This article was submitted to
Organizational Psychology,
a section of the journal
Frontiers in Psychology
Received: 18 January 2020
Accepted: 12 June 2020
Published: 24 July 2020
Kim S, Lee H and Connerton TP
(2020) How Psychological Safety
Affects Team Performance: Mediating
Role of Efficacy and Learning
Behavior. Front. Psychol. 11:1581.
doi: 10.3389/fpsyg.2020.01581
How Psychological Safety Affects
Team Performance: Mediating Role
of Efficacy and Learning Behavior
Sehoon Kim1*, Heesu Lee2and Timothy Paul Connerton3
1Department of Business Administration, Seoul School of Integrated Sciences & Technologies (aSSIST),
Seoul, South Korea, 2Department of Education, Chung-Ang University, Seoul, South Korea, 3Business School
Lausanne, Chavannes, Switzerland
This article examines the mechanisms that influence team-level performance. It
investigates psychological safety, a shared belief that the team is safe for interpersonal
risk taking and a causal model mediated by learning behavior and efficacy. This model
hypothesizes that psychological safety and efficacy are related, which have been
believed to be same-dimension constructs. It also explains the process of how learning
behavior affects the team’s efficacy. In a study of 104 field sales and service teams in
South Korea, psychological safety did not directly affect team effectiveness. However,
when mediated by learning behavior and efficacy, a full-mediation effect was found. The
results show (i) that psychological safety is the engine of performance, not the fuel, and
(ii) how individuals contribute to group performance under a psychologically safe climate,
enhancing team processes. Based on the findings, this article suggests theoretical and
methodological implications for future research to maximize teams’ effectiveness.
Keywords: team psychological safety, team learning behavior, team efficacy, team effectiveness, organizational
learning, full mediation effect
Teams play a crucial role in highly effective organizations. Teams perform better than individuals
(Glassop, 2002), becoming sources for firms’ sustainable competitive advantage. Through
horizontal interaction, the knowledge gained by teams contributes to performance on an
organizational level (Edmondson, 2012). There is a growing concern about how to improve
the performance of teams in organizations. Although a large body of literature has focused on
individual motivation over decades, research to advance the understanding of team motivation
processes is insufficient (Kozlowski and Bell, 2003;Chen and Kanfer, 2006).
From the literature, physical factors such as team size and task attributes, personal factors
such as member competencies and personality, and organizational–environmental factors were
studied as antecedents of team effectiveness (TEF) (Cohen and Bailey, 1997;Mathieu et al., 2008).
However, organizations are gradually recognizing the value of psychological assets, the importance
of synergy among individuals and groups for innovation and growth in highly competitive markets
(Donaldson et al., 2011).
The concept of psychological safety appeared half a century ago in the organizational science
field, but in recent years, empirical research flourished (Frazier et al., 2017). Previous literature
has shown that psychological safety has a direct influence on work performance (Baer and Frese,
2003;Schaubroeck et al., 2011). Besides, more authors insisted that organizational support, safety
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Kim et al. Psychological Safety and Team Performance
climate, and performance are unquestionably related,
implying that psychological safety might involve benefits
that extend its influence on work engagement (Rich et al., 2010;
Christian et al., 2011).
Team psychological safety (TPS) is a shared belief that
people feel safe about the interpersonal risks that arise
concerning their behaviors in a team context (Edmondson,
2018). “Project Aristotle,” which explored over 250 team-level
variables, found that successful Google teams have five elements
in common: psychological safety, dependability, structure and
clarity, meaning, and impact of work (Google, 2015). The
findings argue that psychological safety is the most critical factor
and a prerequisite to enabling the other four elements. However,
surprisingly, despite the importance of that psychological factor,
only 47% of employees across the world described that their
workplaces are psychologically safe and healthy (Ipsos, 2012).
As Edmondson (2018) pointed out, TPS is the engine of
performance, not fuel. Various factors affect the mechanism
in the underlying process. What we need to understand is
“how” psychological safety leads to team performance. What
is necessary for identifying such mechanisms are (i) extended,
sustained research at group level and (ii) expansion of the
studies in various contexts (e.g., country and culture). Notably,
research conducted at the group level is insufficient compared
to those conducted at the individual level in psychological safety
literature. If related work continues and data accumulate, the
theoretical background to examine the incremental validity issue
at the group level will be intensified (Frazier et al., 2017).
In many cases, psychological safety has been studied in limited
regions (i.e., advanced economies in the west), and now the
research context needs to be expanded (Abror, 2017). There
is a need to verify the influence of psychological safety on
group performance, enhancing its explanatory potential and
applicability in the workplace. Additional research is needed
to determine what factors mediate the relationship between
psychological safety and group effectiveness.
Psychological safety could affect behavioral outcomes such
as team’s creativity (Madjar and Ortiz-Walters, 2009), and
both individual learning (Carmeli and Gittell, 2009;Carmeli
et al., 2009) and team learning (Edmondson, 1999;Wong
et al., 2010). Team learning behavior (TLB) is a symbolic
variable that affects TEF. TLB is the process by which members
interact, acquire knowledge and skills needed for their work,
and share information (Argote et al., 1999), and it raises the
team process level to generate performance-oriented ideas. When
members learn and improve their problem-solving skills, they
can create a competitive organization (Dyer and Nobeoka, 2000).
Despite the mediating role of learning that has been empirically
demonstrated in previous literature, it still needs to be dealt
with as a research subject when considering the significance of
learning in modern organizations.
Psychological safety has been linked to several attitudinal
outcomes as well. Another factor that drives TEF is efficacy.
Team efficacy (TE) is a member’s assessment of team ability
to perform job-related activities successfully (Walumbwa et al.,
2004). Confidence in the team’s abilities affects performance
and aligns the members’ activities on the team level (Gibson
et al., 2000;Gully et al., 2002). However, few studies have
reported the effects of psychological safety to efficacy to present
(Abror, 2017). Therefore, there is a theoretical implication to
see how efficacy mediates the relationship between psychological
safety and performance at the group level. We selected the
team’s learning behavior and efficacy as mediating variables
to understand the mechanism for creating TEF. Despite the
extensive research and empirical support for the critical role
of psychological safety, a few unclear questions remain: How
does psychological safety affect TEF? How does it affect learning
behavior and efficacy? How does learning behavior mediate the
overall relationship, and how does it affect the team’s efficacy?
Does TE mediate between psychological safety and TEF?
Our aim in this research is to contribute to the team
and psychological safety literature in three ways: (i) bring
team literature together with related theories by examining
psychological safety and learning behavior as determinants of TE;
(ii) extend the TEF model and the traditional input–process–
output (I–P–O) framework (Hackman, 1987;Cohen and Bailey,
1997) by integrating psychological safety (as contextual input),
learning behavior, and efficacy (as process and team traits) that
might stimulate TEF; and (iii) embrace TE as a possible mediator
between psychological safety and TEF creation.
Psychological safety is “a condition in which one feels (a)
included, (b) safe to learn, (c) safe to contribute, and (d) safe
to challenge the status quo, without fear of being embarrassed,
marginalized or punished in some way” (Clark, 2019). TPS is a
group variable that describes team context. In the last decade, the
concept of psychological safety started attracting attention as a
primary factor in predicting TEF.
Results from several empirical studies conducted in various
regions and countries show that psychological safety plays a vital
role in workplace effectiveness (Edmondson and Lei, 2014). The
psychological safety of individuals and their teams’ psychological
safety are different constructs (Baer and Frese, 2003). The concept
was first pioneered by Schein and Bennis (1965) in organizational
phenomena and developed by Kahn (1990) as a representative
definition of the psychological safety of an individual.
Creating a psychologically safe workplace is different from
being undisciplined or being unconditionally generous to
any process or outcome (Edmondson, 2012). Two factors—
psychological safety and accountability for performance—
identify four types of teams. In this regard, the presence of TPS
does not necessarily mean that TEF will increase automatically.
Prior research also focused on the relationship between
psychological safety and outcomes such as innovation, employee
attitudes, creativity, knowledge sharing, voice behaviors, and
communication (Newman et al., 2017). Overall, TPS is known to
have a positive association with TEF (Schaubroeck et al., 2011;
Kessel et al., 2012;Newman et al., 2017).
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FIGURE 1 | Psychological safety-accountability for performance framework. Source: Edmondson (2012: 174).
Extant literature has found positive associations between
psychological safety and learning behavior at different levels
(Newman et al., 2017). Several pieces of empirical evidence on
such relationships were found in previous literature at the team
level (Roberto, 2002;Van den Bossche et al., 2006;Stalmeijer et al.,
2007;Bstieler and Hemmert, 2010;Ortega et al., 2010;Wong
et al., 2010) and individual level.
In addition to this, the relationship between TPS and efficacy
should be confirmed. Abror (2017) argues that TPS affects group
efficacy. The author criticized Edmondson (1999) for putting
TPS and TE on the same level and argued for the need to
identify the relationship between the two factors. Recent studies
started arguing that TPS may affect group efficacy (May et al.,
2004;Roussin et al., 2016;Hernandez and Guarana, 2018). TPS
appears to have a significant effect on team behavior and goal
orientation and improves performance while affecting a team’s
efficacy (Roussin et al., 2016). The following hypotheses arise
from the above background.
H1: TPS positively affects TEF.
H2: TPS positively affects TLB.
H3: TPS positively affects TE.
Discussions on team learning arose since Argyris (1986) defined
organizational learning and discussed it as a sub-element of a
learning organization. Edmondson (1999) used the term “team
learning behavior” to distinguish the learning process from
learning outcomes.
Team learning behavior is defined as gaining and sharing
skills, knowledge, and information about work through the
interaction of members (Argote et al., 1999), an iterative team
process leading to a change (van Offenbeek, 2001). Gibson and
Vermeulen (2003) defined TLB as a process of experimentation,
reflective communication, and codification. The three elements
are interdependent and difficult to replace. Edmondson et al.
(2007) divided the perspective on team learning research
into three streams.
Previous literature has shown that there is a positive
relationship between TLB and TEF. Zellmer-Bruhn and Gibson
(2006) identified the factors that influence team learning,
team learning’s effects on task performance, and interpersonal
relationships. TLB had a positive effect on TE. Van den Bossche
et al. (2006) studied how teams build shared beliefs in a
collaborative learning environment and found that team learning
improves the perceived performance of a team.
Team learning behavior is also known to be positively
associated with the team’s efficacy (i.e., van Emmerik et al., 2011).
However, further research is needed to verify the direction of the
causal relationship between the two variables.
This study views TLB as a process variable and identifies
the relationship between TPS and TEF. Also, TLB’s mediating
role between TPS and TE would be identified to confirm its
value as a useful predictive tool. This section raises the following
H4: TLB positively affects TEF.
H5: TLB positively affects TE.
TE has a vital role in team research (Rico et al., 2011). As the
importance of creating team-based outcomes has grown, TE has
attracted the interest of researchers (Day et al., 2009).
Efficacy is the belief that an individual’s ability or competency
to perform a particular task will produce a successful outcome
(Bandura, 1986, 1997). When expanded into a group level, it
becomes group efficacy, which is the belief of group members that
they can accomplish a given task. TE is unlikely to be the sum of
individual competence and self-esteem (Bandura, 2000).
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The concept of TE, together with team resilience and
team optimism, is a representative sub-construct of positive
organizational behavior (West et al., 2009). It is an essential
antecedent predicting group performance (Werner and Lester,
2001;Gully et al., 2002;Chen et al., 2005;Tasa et al., 2007;
Porter et al., 2011;Zoogah et al., 2015). The literature supports
that efficacy coordinates group processes, such as decision-
making and team communication. The level of belief can
lead to different outcomes, even under the same conditions.
Several empirical works have proved the effect of TE on team
performance (Mathieu et al., 2008). In the TE literature, it appears
to influence TEF.
As such, we predicted that TE would activate collective
processes and impact group performance. Teams that believe
they can succeed in a given task can perform better. TE is
expected to play an indispensable role in achieving crucial tasks
that require enhanced team performance.
H6: TE positively affects TEF.
Concepts such as team performance, characteristics, and attitudes
of team members define TEF in a comprehensive way (Shen
and Chen, 2007). It is difficult to measure or give TEF one
single definition. In earlier literature of TEF, the majority of
studies defined “effectiveness” as physical outcomes. However,
it gradually expanded to the concept of team performance,
characteristics, or member attitudes (Shen and Chen, 2007). Lin
et al. (2005) insist that researchers should pay attention to various
factors simultaneously at the individual and organizational levels
to maximize performance.
Rousseau et al. (2006) summarized studies dealing with
individual-level variables that improve TEF. As noted, the
effectiveness criteria for defining a team’s performance are not
limited to the team’s physical output. In addition to productivity,
most studies adopted team member satisfaction, attitudes, and
perceived outcomes as essential measures. The most widely used
are performance and attitude aspects. In this study, TEF is
measured by a team’s perception of their performance. Team
performance is the result of a dynamic process of member
interaction. In-role behavior describes a state in which team
members play a supportive role in achieving goals (Williams
and Anderson, 1991). Surveys are common ways to measure
perceived team performance (Pearce and Sims, 2002;Pearce and
Herbik, 2004). In this study, in-role behavior will measure TEF.
Theoretical Framework and the
Moderating Role of TLB and TE
The theoretical foundation can be put on the social cognitive
theory (Bandura, 1988). According to the theory, learning is
a cognitive process taking place in a social context and could
occur purely via observation or instruction, even without direct
reinforcement. Also, one’s sense of efficacy can play a crucial
role in approaching goals, tasks, and challenges (Luszczynska
and Schwarzer, 2005). The theory adequately describes the
mechanism of how psychological safety leads to its outcome
variables and the relationship between behavioral changes and
cognitive beliefs.
Psychological safety at the group level as a model of TEF
uses some forms of the input–process–output (I–P–O) model
as a theoretical framework. The I–P–O model is an approach
that explains the mechanism of team outcome creation. It was
Gladstein (1984) and Hackman (1987) who introduced the I–P–
O model to explore the mechanism, and Cohen and Bailey (1997)
further expanded the model to the TEF model. This framework
suits the structural mediation process that involves TLB and TE as
process variables for the research. The framework is still valid in
many effectiveness research (Liu et al., 2010;Dulebohn and Hoch,
2017;Escribano et al., 2017;Mansikka et al., 2017).
Team learning behavior is known to mediate the relationship
between psychological safety and performance (Edmondson,
1999;Li and Yan, 2009;Brueller and Carmeli, 2011;Kostopoulos
and Bozionelos, 2011;Hirak et al., 2012;Huang and Jiang, 2012;
Li and Tan, 2013;Ortega et al., 2014). Sanner and Bunderson
(2013) found in their meta-analysis that TLB was a significant
mediator in a large body of literature. In this regard, we try to
confirm TLB’s mediating role in the TEF creation mechanism.
Bandura (1988) identified factors that affect efficacy. Social
persuasion is encouragement or discouragement from another
person. Also, psychological factors alter the level of efficacy.
As noted earlier in the literature review, the team’s efficacy
affects the performance (Luszczynska and Schwarzer, 2005),
and TLB affects TE.
As such, this study investigates the following mediation
effects. The hypothesized relationships of the research model (see
Figure 2) are as follows:
H7: TLB mediates the relationship between TPS and TE.
H8: TE mediates the relationship between TPS and TEF.
H9: TLB and TE jointly mediate the relationship
between TPS and TEF.
We collected samples from 16 local sales and service companies
located in 98 outlets in South Korea. The survey targeted sales,
service, and admin staff working at the front line. Under normal
working conditions, field employees providing customer service
have to work with a high level of customer orientation, with their
service level evaluated continuously. Besides, they are exposed
to complaints from dissatisfied customers and feel pressure
about their performance, resulting in significant anxiety and
stress. Therefore, fieldwork teams were considered appropriate
for the research.
A mobile survey was sent to a total of 282 teams
and 1,433 employees. Five hundred thirty-six questionnaires
were recovered (37%), and 529 valid samples were analyzed.
Frequency analysis was performed to examine the distribution of
respondents (see Table 1).
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FIGURE 2 | Research model.
TABLE 1 | Demographic characteristics of the participants.
Attributes Frequency (%) Attributes Frequency (%)
Gender Male 465 87.9 Tenure in team (months) Under 6 87 16.4
Female 64 12.1 612 80 15.1
Age Under 30 120 22.7 1324 94 17.8
3035 142 26.8 2536 59 11.2
3540 133 25.1 Above 37 209 39.5
4050 132 25.0 Team function Admin 55 10.4
Above 50 2 0.4 Sales 236 44.6
Tenure in company (years) Under 3 222 42.0 Service 238 45.0
35 132 25.0 Team size 5 149 28.2
510 121 22.9 57 253 47.8
1015 41 7.8 810 80 15.1
Above 15 13 2.5 1114 22 4.2
Title Staff 207 39.1 Above 15 25 4.7
AM 126 23.8 Education High school 109 20.6
MGR 96 18.1 Associate degree 200 37.8
AGM 73 13.8 Bachelor’s degree 206 38.9
Above GM 27 5.1 Master’s degree 11 2.1
Total 529 100.0 Doctor’s degree 3 0.6
No. of members (mean) 4.5 Member’s age (mean) 31
Respondents had the following characteristics: gender, 465
male (87.9%) and 64 female (12.1%); age, 30s group highest
(26.8% for 30–35 and 25.1% for 35–40), mean = 31; title, staff
level highest (39.1%); tenure, under 3 years (42.0%); tenure in the
team, 3 years or above (39.5%); team size, less than five members
(28.2%), mean = 4.5; team function, admin (10.4%), sales (44.6%),
and service (45.0%); and education, bachelor’s degree (38.9%).
Original measurements developed in English were translated to
Korean and reviewed by HRD professionals and a group of Ph.D.
students to ensure accuracy in the delivery of the meaning. All 27
items adopted a Likert 7-point scale, from 1 = not at all to 7 = to
a large extent. TPS consisted of seven questions by Edmondson
(1999). Sample items were as follows: “Members are criticized
when making a mistake,” “Members often ignore individual’s
opinion,” and “Members do not degrade other peoples efforts.”
Team learning behavior adopted nine items from Gibson and
Vermeulen (2003). Sample items were as follows: “The team’s
ideas and practices are introduced to other teams,” “Members
exchange ideas,” and “The team leaves documents about the
details of work.”
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For measuring TE, we adjusted six items by Riggs and Knight
(1994). Sample items were as follows: “Members have the best
work skills,” “Members have above-average ability,” “The team has
excellent performance compared to other teams.”
Team effectiveness was adapted from Williams and
Anderson (1991), with the following sample items: “Fulfilling
responsibilities given by the organization,” “Achieving the level
of task that we expect,” and “Meeting official performance
requirements” (see Table 2 and Appendix 1).
Analytical Procedure
Process Macro 3.3 was used for the mediated regression model,
and Jamovi was used for other analytical procedures,
including exploratory factor analysis (EFA) and CFA. First, the
demographic distribution was confirmed by frequency analysis.
Second, the normality of distribution was tested by descriptive
analysis. Third, EFA was carried out to test the variance. Fourth,
the confirmatory factor analysis (CFA) secured the validity
and reliability of the measurement model. Fifth, we tested the
reliability and validity of each team’s value through ICC and
Rwg tests to clear level issues. Sixth, the regression analysis
confirmed the relationships between variables. Seventh, statistical
significance was confirmed by bootstrap replications. It verified
the mediating effects and effect size within the relationships.
There is a possibility of common method bias (CMB) when
measuring constructs in the same survey. This issue can lead
to the structural underestimation or overestimation of the
coefficients (Bido et al., 2017). As criticized by Guide and Ketokivi
(2015), researchers should be careful about claiming that the issue
is cleared after conducting weak tests, such as Harman’s (1967).
In this case, EFA is considered a legitimate statistical procedure
to test CMB that supplements the weaknesses of Harman’s,
considering both the structural model and the measurement
model (Bido et al., 2017). Also, when trying to identify a
potential structure or to ensure if the measurements reflected
the construct accurately, an additional EFA procedure could
be considered, regardless of existing theoretical backgrounds
(Fabrigar and Wegener, 2012).
In this study, the maximum likelihood method and oblimin
rotation were applied to extract the factors. In the process,
variables that did not meet the criteria were removed (factor
loadings less than 0.50 and communality less than 0.40). The
results of Cronbachs αconfirmed the reliability of measurement
instruments (see Table 3).
It was confirmed that the measurements constituting the
four theoretical constructs were grouped into factors without
difficulty, and the factor loadings and construct reliability (CR)
were also found to be significant. The Bartlett test result showed
that the model had a good fit (P<0.001), and the KMO statistics
were 0.960, which is also acceptable. TPS question 2 showed
a low communality level and was further reviewed for use in
the following CFA. Finally, the model was used for CFA after
removing three questions from TPS, one from TE, two from TLB,
and one from TEF.
CFA was conducted to confirm the fit of the measurement model.
The criteria for model fit are a chi-square NC (CMIN/df ) of
5.0 and below, an absolute fitness index (SRMR) below 0.08,
an RMSEA below 0.10, and incremental fitness index, TLI,
and CFI above 0.90.
The average variance extracted (AVE) value ranged from 0.519
to 0.735, indicating that all variables met the criteria of 0.50
(Bagozzi and Yi, 1988). The internal consistency of Cronbachs
αcoefficient was found to be reliable, with all variables above
0.70 or higher (Murphy and Davidshofer, 1988). The standard
factor loadings of most items except for one item from TPS and
two from TLB were above the recommended level of 0.70 and
were significant (P<0.001) (Hair et al., 2017). All the items
in CFA were adopted, considering overall AVE (Bagozzi and Yi,
1988). From the above analysis results, the measurement model
is acceptable, showing an appropriate level of reliability.
The model fit details are as follows. From the results of
χ2= 650 (P<0.001), NC (CMIN/df ) = 3.963, TLI = 0.933,
CFI = 0.942, SRMR = 0.044, and RMSEA = 0.075, no item showed
lack in model fit criteria. The reliability analysis results are as
shown in Table 4.
Validity of the Constructs
Convergent validity and discriminant validity were verified to
confirm the validity of the construct. Convergent validity is
verified by factor loading, CR, and AVE. Convergent validity was
confirmed from the measurement model as all the constructs
TABLE 2 | Measurement items of the construct.
Variable No. Scale Source
Independent Team psychological safety 7 Likert 7-point Edmondson, 1999
Mediator Team learning behavior Experimentation 3 Gibson and Vermeulen, 2003
Reflective communication 3
Codification 3
Team efficacy 6 Riggs and Knight, 1994
Dependent Team effectiveness (in-role behavior) 5 Williams and Anderson, 1991
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TABLE 3 | Result of exploratory factor analysis.
Item Factor loading Communality Cronbach’s α
TPS_1 – – – 0.682 0.510 0.793
TPS_2 – – – 0.361 0.324
TPS_3 – – – 0.689 0.622
TPS_5 – – – 0.600 0.568
TLB _ex_1 0.621 – – – 0.787 0.922
TLB_ex_2 0.647 – – – 0.705
TLB_ex_3 0.641 – – – 0.543
TLB_com_3 0.519 – – – 0.791
TLB_cod_1 0.758 – – – 0.545
TLB_cod_2 0.847 – – – 0.696
TLB_cod_3 0.810 – – – 0.604
TE_1 – 0.885 0.787 0.925
TE_2 – 0.755 0.742
TE_4 – 0.819 0.715
TE_5 – 0.608 0.704
TE_6 – 0.622 0.727
TEF_1 – 0.621 0.713 0.916
TEF_3 – 0.864 0.813
TEF_4 – 0.886 0.728
TEF_5 – 0.618 0.760
Eigen value 4.126 3.799 3.107 2.353
Variance (%) 20.631 18.996 15.533 11.767 66.927
*Dimension reduction: Maximum likelihood in oblimin rotation. *n = 529.
were found to be higher than 0.50 in factor loading, 0.70 in CR,
and 0.50 in AVE (see Table 5).
Discriminant validity means that latent variables are
constructs that are independent of each other. If the correlation
between factors is relatively high (above 0.80 or 0.85), the
researcher can consider a more parsimonious model (Brown,
2015). The results of correlation analysis among the factors are
presented (see Table 6).
The r±2SE method was applied to verify discriminant
validity. This method adds and subtracts two standard error
range from the correlation values of each factor and checks
whether the value includes 1 in the range. The absence of 1 in
the calculation range verifies the discriminant validity (Fornell
and Larcker, 1981). The r±2SE range of correlations among all
factors did not include 1 (see Table 7).
Level Issue
This study assumes a team-level analysis. Klein et al. (1994)
collectively defined three “level issues” that arise in group-level
research, which are the level of theory, level of measurement,
and level of analysis (Klein et al., 1994). In this study, all the
questionnaires measure the team’s view based on a reference-shift
model. In the case of using the results of summed or averaged
individual responses as a team value, there are two additional
requirements as follows.
First, the group members’ responses must be consistent
and show homogeneity. Second, the variation or variance
between teams should be higher than that within a team. To
prove this, Klein and Kozlowski (2000) proposed Rwg (within-
group interrater reliability), intraclass correlation (ICC)(1), and
ICC(2). These are the methodologies that support inference for
aggregation of individually collected data. This study conducted
essential statistical procedures to resolve the level issues before
aggregating individual values into a team value.
Checking the consistency and consensus of each rater’s
answer to the question solves the problem. ICC is a standard
method used for reliability verification in multilevel studies
(James, 1982). Reliability refers to the degree of consistency that
an individual rater’s evaluation has, and there are two kinds,
ICC(1) and ICC(2). Both use analysis of variance to verify data
consistency. The usual cutoff level for ICC(1) is 0.20. ICC(2)
further supplements ICC(1). It analyzes each group’s composite
rating to verify the reliability and is acceptable at 0.60 or higher.
The ICC(1) result shows that TEF did not meet the criteria,
and TLB and TEF were not acceptable by ICC(2) baseline (see
Table 8).
Rwg is an additional verification procedure for the variables
which did not meet baseline values. Rwg, also referred to as
the within-group agreement index, checks for consistency or
reliability of lower-level data (James et al., 1993). Its baseline is
0.70 or higher (James et al., 1993;Klein and Kozlowski, 2000), but
variables with Rwg values higher than 0.50 can be aggregated as a
team’s value (James et al., 1993). Finally, a total of 104 team data
were analyzed after excluding teams with less than three members
and whose Rwg values did not meet the requirements.
Hypothesis Test
Relationship Between Variables
A process analysis was conducted to verify the effect size on direct
and indirect effects simultaneously (Hayes, 2013). By default, a
thousand resampling of the percentile bootstrapping method is
used to estimate the parameters. The absence of 0 in the 95%
confidence interval identifies statistical significance (Preacher
et al., 2007;Hayes, 2013). The analysis was carried out based on
the Hayes (2013) procedure to verify all the relationships and the
direct and indirect effects.
The direct effect of TPS on TE was not significant (H1,
β= 0.037). As expected, psychological safety activates team
processes but may not direct driver of performance (Edmondson,
2008). TPS had a positive effect on TLB (H2, β= 0.747) and had
a significant positive effect on TE (H3, β= 0.596). The result was
consistent with previous researches that a sense of safety has a
significant impact on team behavior change and performance.
Also, TLB had a positive effect on TE (H4, β= 0.317). The
learning process affected the team’s efficacy, which is the team’s
emotional response. TLB had a positive effect on TEF (H5,
β= 0.193) and was consistent with previous studies’ results that
learning improves the quality of task performance.
Finally, the positive effect on TEF of TE was confirmed (H6,
β= 0.694). In summary, TPS did not directly affect effectiveness
but had a positive effect on other variables. In the other causal
paths, positive causal relationships were identified (see Figure 3
and Table 9).
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Kim et al. Psychological Safety and Team Performance
TABLE 4 | Result of confirmatory factor analysis.
Factor Indicator Estimate Std. estimate SE t-value CR AVE Cronbach’s α
TPS TPS_1 1.000 0.705 0.809 0.519 0.793
TPS_2 0.758 0.568 0.066 11.600***
TPS_3 0.956 0.812 0.056 17.000***
TPS_5 0.928 0.772 0.060 15.400***
TLB TLB_ex_1 1.000 0.899 0.921 0.627 0.922
TLB_ex_2 0.894 0.846 0.032 27.600***
TLB_ex_3 0.805 0.734 0.038 21.100***
TLB_com_3 0.934 0.892 0.030 31.400***
TLB_cod_1 0.769 0.682 0.041 18.600***
TLB_cod_2 0.921 0.780 0.040 23.300***
TLB_cod_3 0.824 0.675 0.045 18.200***
TE TE_1 1.000 0.873 0.927 0.719 0.925
TE_2 1.033 0.864 0.038 27.300***
TE_4 0.953 0.832 0.038 25.400***
TE_5 0.894 0.836 0.035 25.500***
TE_6 1.130 0.834 0.045 25.300***
TFE TEF_1 1.000 0.844 0.917 0.735 0.916
TEF_3 1.197 0.879 0.046 26.000***
TEF_4 1.168 0.823 0.050 23.300***
TEF_5 1.160 0.881 0.045 25.900***
Model fit
Criteria Under 5.0 Above 0.90 Above 0.90 Under 0.08 Under 0.10 Interval
Result 650 164 3.963 0.933 0.942 0.044 0.075 0.0690.081
*n = 529, ***P <0.001.
Mediating Effect
For the mediating effects to be statistically significant, the
indirect effect must show significance in the relationship of
the independent variable to the dependent variable. If only
the indirect effect is significant in a proposed model, it is a
TABLE 5 | Test of convergent validity.
Item Factor loading CR AVE
Criteria Above 0.50 Above 0.70 Above 0.50
Accepted Accepted Accepted
TPS 0.714 TPS 0.809 TPS 0.519
TLB 0.787 TLB 0.921 TLB 0.627
TE 0.848 TE 0.927 TE 0.719
TEF 0.857 TEF 0.917 TEF 0.735
TABLE 6 | Correlations between dimensions.
Mean SD 1 2 3 4
1. TPS 5.748 1.249 1
2. TLB 5.044 1.177 0.728*** 1
3. TE 5.615 1.109 0.750*** 0.769*** 1
4. TEF 5.669 1.069 0.630*** 0.772*** 0.857*** 1
***P <0.001.
full-mediation effect. In a partial-mediation model, both indirect
and direct effects are significant.
The mediating effect of TLB was identified between TPS
and TEF (H7, β= 0.144). Also, the mediating role of TE was
verified between TPS and TEF (H8, β= 0.413). The effect size
TABLE 7 | Test of discriminant validity by r±2SE method.
rSE r-(2×SE) r+(2 ×SE) Including 1
TPS TE 0.750 0.026 0.698 0.802 N
TPS TLB 0.728 0.027 0.674 0.782 N
TPS TEF 0.630 0.033 0.564 0.696 N
TLB TE 0.769 0.021 0.727 0.811 N
TE TEF 0.857 0.016 0.826 0.888 N
TLB TEF 0.772 0.021 0.729 0.815 N
TABLE 8 | Test of level issue: ICC and rwg values.
Factor ICC(1) ICC(2) AVG. rwg F-value
0.20 0.60 0.50
TPS 0.427 0.770 0.843 4.340***
TLB 0.229 0.572 0.870 2.335***
TE 0.277 0.632 0.907 2.717***
TEF 0.144 0.431 0.892 1.757***
*Average team size: 4.5. *n = 471, ***P <0.001. Bold values indicate meeting
standards/cut-off criteria of ICC(1), ICC(2), and rwg.
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Kim et al. Psychological Safety and Team Performance
FIGURE 3 | Research model with regression coefficient values. ns: not significant.
was confirmed, and the upper and lower bounds of the 95%
confidence interval did not contain 0. TLB and TE showed a
double-mediation effect on the relationship between TPS and
TEF (H9, β= 0.165).
The total effect of the research model was significant
(β= 0.722). The applicability of the research model was
supported, and TE was found to have the most substantial
indirect effect. The results of the mediation effect analysis are
presented (see Table 10).
In conclusion, TPS did not directly affect TEF, but TLB
and TE indirectly influenced TEF. It also confirmed that TLB
contributed to team performance through TE. From the above,
the full-mediation and double-mediation effect were found in
the research model.
This paper explored how psychological safety influences the
team’s effectiveness through learning behavior and efficacy. We
applied two mediators in the research design to examine causal
TABLE 9 | Result of main effect analysis.
Hypothesis Path βSE tLLCI ULCI Remarks
H1 TPS TEF 0.037 0.064 0.413 (0.100) 0.153 Rejected
H2 TPS TLB 0.747 0.060 11.349*** 0.563 0.801 Accepted
H3 TPS TE 0.596 0.063 7.788*** 0.367 0.618 Accepted
H4 TLB TE 0.317 0.069 4.149*** 0.150 0.425 Accepted
H5 TLB TEF 0.193 0.060 2.513* 0.032 0.268 Accepted
H6 TE TEF 0.694 0.079 7.520*** 0.438 0.752 Accepted
*P<0.05 and ***P<0.001.
relationships. In summary, the research model was found to have
a full double-mediation effect. TPS did not have a direct effect on
the dependent variable.
First, based on social cognitive theory, we have found the
crucial roles of learning behavior and efficacy in connecting
psychological safety and TEF. The finding of team learning’s
mediation effect is consistent with previous studies (i.e.,
Kostopoulos and Bozionelos, 2011). Also, the mediating role of
TE has been confirmed. To date, little research has been done on
the mediating role of TE between psychological safety and TEF.
As discussed earlier, psychological factors and climate could alter
the level of efficacy. According to social cognitive theory, traits
such as the team’s expectations and beliefs could be affected by the
psychological factors (environment) and influencing behavior.
When members believe that they can complete a given task,
the team produces more positive results (e.g., Tasa et al., 2007;
Porter et al., 2011).
Second, the results showed that learning behavior positively
affects the team’s efficacy. The result was in line with van
Emmerik et al. (2011). This finding answers the request of
Knapp (2016) for additional research to determine if efficacy
is significantly related to learning behavior at the team level.
Learning behavior is a process that leads to a shared result
and is a link toward change in organizations. If the members
recognize excellent communication in the team, they become
more involved, and the belief in the team’s ability could
be strengthened.
Third, the results did not support one of our hypotheses that
psychological safety affects TEF. The research model supported
full mediation. This result is consistent with the claims of
Edmondson (2012, 2018). Psychological safety is the “engine,” not
“fuel” for performance. If individuals are under an atmosphere
that highly values their ideas and actions, employees can adapt
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Kim et al. Psychological Safety and Team Performance
themselves even to challenging tasks. A team’s psychological
safety promotes team learning and consequently increases the
team’s effectiveness. Also, the favorable climate promotes the
team’s efficacy and contributes to the performance of the team.
The findings of the study present important contributions
to the present knowledge in the domain. First, the
research contributes to psychological safety literature by
unfolding its little-known relationship with TE, answering
the theoretical call from Abror (2017) to examine the
relationship between the two constructs. As discussed,
we found a significant effect of TPS to TE, confirming
the mechanism of how team performance is created
through the path.
Today, there is only limited empirical evidence on the effect
of psychological safety to efficacy (Abror, 2017). The author
criticized Edmondson (1999) for putting the two variables
on the same level. Until recently, researchers have insisted
that TPS and TE are both psychological factors on the same
dimension. Therefore, the causal relationship between the two is
rarely experimented. This paper aims to ignite debates on that
theoretical discordance in the future based on the full-mediation
effect identified.
Recent studies started arguing that psychological safety might
affect group efficacy (e.g., Roussin et al., 2016;Hernandez and
Guarana, 2018). In the field of education, researchers started
reporting the relationship between psychological climate and
efficacy. When there is a respectful, collaborative, and trusting
school climate (Bryk et al., 2010;Ronfeldt et al., 2013), teachers
tend to report higher levels of efficacy and more likely to stay in
the profession (Allensworth et al., 2009;Johnson et al., 2012). The
research hinted at the theoretical implications and discussions,
moving a step forward under the workplace context.
Second, our research contributes to the current literature of
TEF by developing and exploring the two different mediating
paths, further broadening the boundaries of the studies
in human behavior.
The study extends the prevailing framework for TEF (Cohen
and Bailey, 1997) by adding empirical data. In the research model,
we added a less-proven relationship (i.e., TE as another mediator)
to a “psychological safety–team learning–effectiveness” model,
further contributing to the applicability and the expandability of
the variables as valid predictors in future team studies. To our
knowledge, little research has been conducted at a team level,
incorporating TPS, TLB, TE, and TEF.
We approached from the aspects of social cognitive theory
to explain the TEF creation mechanism that is affected
by psychological factors. Prior literature also examined the
relationship between psychological safety and other outcomes,
integrating theoretical views from social learning theory, social
identification theory, social information processing theory, or
social exchange theory (Carmeli, 2007;De clercq and Rius,
2007;Schaubroeck et al., 2011;Singh et al., 2013;Chen
et al., 2014;Liu et al., 2014;Wang et al., 2018). Our
study contributes to building concrete theoretical foundations,
enriching various angles available to decipher the complicated
phenomena under a team context.
The effect of TLB on TE also presents a new perspective.
Previous research has demonstrated that efficacy affects learning
behavior (i.e., van Emmerik et al., 2011). However, the studies
that reported learning behavior’s effect on TE are limited. This
study argues that learning behavior can be a catalyst for the
efficacy of teams.
Furthermore, our research answers Frazier et al.’s (2017) call to
continue research under the team context. Group-level research is
insufficient compared to individual-level studies, and continued
research would contribute to the robustness of related theories
(Frazier et al., 2017).
Third, the study extended the contexts where psychological
safety research takes place. Most of the research was conducted
in western countries and advanced economies (Abror, 2017).
Moreover, most of the literature dealt with limited work context
(e.g., medical, healthcare, and nursing). This research paid
attention to frontline sales and service employees in South Korea,
broadening boundaries for future empirical work.
The research results may provide several implications for
practice. First, the findings point to the vital role of safety
climate as a performance enabler in an organization. Top
management’s intense pressure can lead to extreme consequences
(Edmondson, 2018). Unconditional emphasis on psychological
safety is also undesirable. Unrestrained psychological well-
being could result in cheating and incompliance with the
group’s social constraints (Pearsall and Ellis, 2011). Leaders
should pay close attention to establishing an equilibrium that
might maximize team performance. Teams can move into a
“learning zone” when accountability for performance interacts
with psychological safety.
Second, the findings also suggest that energizing the team’s
process should be considered for enhanced performance
TABLE 10 | Total, direct, indirect effect of research model.
Hypothesis Effect Path βSE LLCI ULCI Remarks
Total TPS TEF 0.722 0.077 0.556 0.861 Full mediation
Direct TPS TEF 0.037 0.064 (0.100) 0.153
H7 Indirect 1 TPS TLB TEF 0.144 0.070 0.009 0.276 Accepted
H8 Indirect 2 TPS TE TEF 0.413 0.074 0.283 0.562 Accepted
H9 Indirect 3 TPS TLB TE TEF 0.165 0.056 0.069 0.286 Accepted
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Kim et al. Psychological Safety and Team Performance
in teams. When a safe environment is ready, members
facilitate learning from failures (Hirak et al., 2012), and
members’ feedback-seeking behavior and adaptability could be
strengthened (Gong and Li, 2019). Therefore, leaders can take
a strategy that promotes a psychologically safe climate and
stimulates interaction, regardless of external support at a team
level. Raising the team’s efficacy would be a superior strategy, too.
Regarding the limited resources and authority of team leaders,
promoting the team process can be a reliable approach.
Third, the results shed light on the importance of team
learning in an organizational context. There are limitations to a
top-down approach and centralized training. Learning at a lower
level should be stressed as a way of contributing to the firm’s
sustainability. Leaders should pay attention to approaches that
nurture the dynamic learning process that mediates psychological
safety and efficacy, finally leading to performance.
In this study, we suggest several limitations as follows. First, as
Wang et al. (2018) pointed out, it is still difficult for researchers
to infer causal relationships when there is a possible underlying
bias from research methodology. In any survey method, some
form of bias may be present that leads to the overestimation or
underestimation of coefficients or relations (Bido et al., 2017).
We collected data from multiple sources based on a single survey
followed by a statistical procedure to test the CMB issue. We
recommend future researchers of human behavior in business to
consider the ex ante approach (i.e., the time difference in data
collection) so that they can minimize the bias.
Second, longitudinal data collection would provide a stronger
theoretical foundation than cross-sectional data. The mediation
effect explained by cross-sectional data might not be fully
adequate to reveal the hidden structural relationships (Maxwell
et al., 2011). Replication of this study based on longitudinal
data collection would also be an option for future researchers,
re-simulating the findings of the study.
Third, researchers can consider a new line of methodologies
and other mediation variables. As Newman et al. (2017)
suggested, a qualitative research approach would provide a more
holistic and more profound understanding of how psychological
safety influences the outcome. With more observational
techniques, researches can provide descriptions of a vibrant and
dynamic process of a TEF creation. Several factors can influence
TEF as a mediator or a moderator. Including little known factors
in a research model would provide precious evidence about teams
in an era of rapid change.
The datasets generated for this study are available on request to
the corresponding author.
Ethical review and approval was not required for the study on
human participants in accordance with the local legislation and
institutional requirements. Written informed consent from the
patients/participants or patients/participants legal guardian/next
of kin was not required to participate in this study in accordance
with the national legislation and the institutional requirements.
SK devised the research idea, developed the research model,
and performed the analytic calculations for the manuscript. HL
and TC contributed to the final version of the manuscript and
supervised the research. All authors contributed to the article and
approved the submitted version.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Kim, Lee and Connerton. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). The
use, distribution or reproduction in other forums is permitted, provided the
original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academic practice.
No use, distribution or reproduction is permitted which does not comply with
these terms.
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TABLE A1 | Questionnaire and descriptive statistics.
Author Questionnaire Mean SD Skewness Kurtosis
Edmondson (1999) Members criticized when making a mistake* 5.254 1.045 0.682 0.536
Can bring up work problems and awkward stories
Members often ignore other people’s opinions*
Able to take risks
Cannot ask for help from other members*
Members do not degrade my efforts
My own skills and talents appreciated and utilized
Gibson and Vermeulen (2003) Actively propose new ideas for tasks 4.932 1.321 0.500 0.087
Creating a new way of doing things.
Ideas and practices often introduced to other teams
Mutual communication 5.535 1.272 1.072 0.977
Chance to express own opinions
Exchange ideas with each other
Documents the details of work 4.533 1.409 0.246 0.415
Records good ideas
Records or manages best practice
Total 5.000 1.205 0.593 0.236
Riggs and Knight (1994) Has above-average ability 5.562 1.080 0.781 0.572
Members have the best work skills
Some members can’t do their job properly*
Excellent performance compared to other teams
Can achieve more than the team’s goal
Very efficient
Williams and Anderson (1991) Fulfilling responsibilities given by the organization 5.678 1.030 0.702 0.228
Achieving the level of task that we expect
Meeting official performance requirements
Doing a key role that can improve team’s evaluation
Frontiers in Psychology | 15 July 2020 | Volume 11 | Article 1581
... Transformational leaders also indirectly influence OCB through organizational learning in the South Korean manufacturing industry [95] and leader-member exchange in Taiwan [96]. Furthermore, Kim et al. (2020) have also found a significant indirect effect of ethical leadership on OCB through team moral efficacy [97]. ...
... Transformational leaders also indirectly influence OCB through organizational learning in the South Korean manufacturing industry [95] and leader-member exchange in Taiwan [96]. Furthermore, Kim et al. (2020) have also found a significant indirect effect of ethical leadership on OCB through team moral efficacy [97]. ...
... The current findings have supported the moderating hypothesis in this study. Similarly, allocentrism [105], group size among Israeli healthcare employees [106], green dedication [86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][103][104][105][106], individualism, masculinity, and power distance have been also found to have a conditional impact on the transformational leadership-OCB relationship. Contrary to present findings, Schuh et al. (2011) have found that leader organizational identification significantly influences employee organizational identification through leader behavior [102]. ...
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Conference Paper
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Supervisors have a crucial role in ensuring the successful socialization of new organizational members, such that their autonomy and latitude in terms of deciding whether and how to support organizational newcomers are key questions. The current study proposes and tests an input– process–output model of newcomers’ socialization, in which supervisors are active decision- making and action-taking agents. The findings show that supervisors first evaluate newcomers’ ability to generate feasible ideas and promote themselves. Then, on the basis of these initial evaluations, supervisors decide whether to increase their support of newcomers’ innovation or intensify newcomers’ socialization; both choices ultimately determine their successful socialization. The test of the theoretical model uses a three-wave longitudinal sample of 325 newcomer–supervisor dyads, across a wide range of industries, along with qualitative evidence gathered from interviews with 41 supervisors.
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Teams do not always provide the diversity of knowledge, attitudes, skills and experience required to generate an innovative response to challenges or perform according to expectations. This paper synthesizes the key results of research into work teams carried out over the last decade from 1999 through 2009.To this end, we set out a brief explanatory framework for the effectiveness of work teams based on a differentiated analysis of inputs, mediators and outcomes. Our approach uses the SWOT technique, which identifies strengths, weaknesses, opportunities and threats with a view to teamwork research in the next decade. Finally, we integrate and discuss the key challenges facing the field if it is to change threats into opportunities.
The aim of this study was to investigate the relationship between the flight-related core competencies for professional airline pilots and to structuralize them as components in a team performance framework. To achieve this, the core competency scores from a total of 2,560 OPC (Operator Proficiency Check) missions were analyzed. A principal component analysis (PCA) of pilots’ performance scores across the different competencies was conducted. Four principal components were extracted and a path analysis model was constructed on the basis of these factors. The path analysis utilizing the core competencies extracted adopted an input–process–output’ (IPO) model of team performance related directly to the activities on the flight deck. The results of the PCA and the path analysis strongly supported the proposed IPO model.
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.