Teams make it work: How team work engagement mediates between social resources and performance in teams

Abstract

Ref.: Torrente, P., Salanova, M., Llorens, S., & Schaufeli, W.B. (2012). Teams make it work: How team work engagement mediates between social resources and performance in teams. Psicothema, 24, 106-112.
In this study we analyze the mediating role of team work engagement between team social resources (i.e., supportive team climate, coordination, teamwork), and team performance (i.e., in-role and extra-role performance) as predicted by the Job Demands-Resources Model. Aggregated data of 533 employees nested within 62 teams and 13 organizations were used, whereas team performance was assessed by supervisor ratings. Structural equation modeling revealed that, as expected, team work engagement plays a mediating role between social resources perceived at the team level and team performance as assessed by the supervisor.

Full text

The study of work engagement has become a popular topic
since the turn of the century (Bakker, Rodríguez-Muñoz, & Derks,
2012). Work engagement is a positive affective-motivational and
work-related psychological state characterized by vigor, dedication
and absorption (Schaufeli, Salanova, González-Romá, & Bakker,
2002). Despite its relevance in work settings, the vast majority of
scholars have focused on work engagement at the individual level,
thus ignoring the role of teams (Richardson & West, 2010). This is
even more remarkable if we consider that teams play a crucial role
in employee health and well-being (Wilson, DeJoy, Vandenberg,
Richardson, & McGrath, 2004), and productivity (Salanova,
Llorens, Cifre, Martínez, & Schaufeli, 2003). In order to ll this gap,
this study analyzes the role of team work engagement as a mediator
between social resources and team performance, as assessed by the
team supervisor, using aggregated data at the team level of analysis.
The Job Demands-Resources (JD-R) Model (Bakker &
Demerouti, 2007; Demerouti, Bakker, Nachreiner, & Schaufeli,
2001) is a heuristic and parsimonious model that posits how two
sets of employees’ working conditions (i.e., job demands and
job resources) relate with their psychosocial health and well-
being, which in turn are associated with several employee and
organizational outcomes (Llorens, Bakker, Schaufeli, & Salanova,
2006). The JD-R model has been successfully studied in different
countries as well as in different occupations such as white-collar
workers (Schaufeli & Bakker, 2004) and blue-collar workers
(Bakker, Demerouti, De Boer, & Schaufeli, 2003).
The JD-R Model assumes two independent processes in order
to explain the impact of job demands and job resources on various
work-related outcomes. The health-impairment or erosion process
posits that the presence of chronic job demands consumes energy
and effort, and may therefore undermine employee health and well-
being and lead to burnout, which in turn can lead to an increase
in psychological and somatic complaints (Hakanen, Bakker, &
Schaufeli, 2006). In contrast, the motivational process posits that
job resources foster employees’ motivation and induce positive
emotions, as is the case for work engagement. Next, this affective-
motivational state may lead to positive results for the organization,
such as a decrease in turnover intention (Schaufeli & Bakker, 2004)
and sickness absence (Schaufeli, Bakker, & Van Rhenen, 2009).
The erosion process of the JD-R Model has also been tested at
the team level of analysis by Bakker, Van Emmerik and Van Riet
(2008), whose results showed the mediating role of burnout between
job demands and resources on the one hand and performance on
the other. However, despite the fact that work engagement plays
a mediating role in the motivational process at the individual
level of analysis by linking resources to outcomes (Llorens et al.,
2006), the positive path of the JD-R Model remains to be tested
at the collective, team level. In order to analyze whether engaged
teams are also better-performing teams, we include the aggregated
perceptions of team social resources and team work engagement
as well as the supervisor-rated team performance. Following the
JD-R Model, social resources may constitute the starting point of
a virtuous process.
Psicothema 2012. Vol. 24, nº 1, pp. 106-112 ISSN 0214 - 9915 CODEN PSOTEG
www.psicothema.com Copyright © 2012 Psicothema
Fecha recepción: 10-3-11 • Fecha aceptación: 18-7-11
Correspondencia: Pedro Torrente
Facultad de Ciencias Humanas y Sociales
Universitat Jaume I
12071 Castellón (Spain)
e-mail: torrente@uji.es
Teams make it work: How team work engagement mediates between
social resources and performance in teams
Pedro Torrente1, Marisa Salanova1, Susana Llorens1 and Wilmar B. Schaufeli2
1 Universitat Jaume I and 2 Utrecht University
In this study we analyze the mediating role of team work engagement between team social resources
(i.e., supportive team climate, coordination, teamwork), and team performance (i.e., in-role and
extra-role performance) as predicted by the Job Demands-Resources Model. Aggregated data of 533
employees nested within 62 teams and 13 organizations were used, whereas team performance was
assessed by supervisor ratings. Structural equation modeling revealed that, as expected, team work
engagement plays a mediating role between social resources perceived at the team level and team
performance as assessed by the supervisor.
Cómo el engagement en el trabajo del equipo media entre los recursos sociales y el desempeño. En este
estudio analizamos el rol mediador del engagement en el trabajo en equipo entre los recursos sociales
(i.e., clima de apoyo, coordinación, trabajo en equipo) y el desempeño del equipo (i.e., desempeño in-
rol y extra-rol) tal como predice el Modelo de Demandas-Recursos Laborales. Se utilizó una muestra
de 533 empleados anidados en 62 equipos y 13 organizaciones. El desempeño del equipo fue evaluado
por los supervisores. Los Modelos de Ecuaciones Estructurales revelaron que, tal como se esperaba, el
engagement en el trabajo del equipo juega un rol mediador entre los recursos sociales percibidos por el
equipo y su desempeño evaluado por el supervisor.

TEAMS MAKE IT WORK: HOW TEAM WORK ENGAGEMENT MEDIATES BETWEEN SOCIAL RESOURCES AND PERFORMANCE IN TEAMS
107
According to the JD-R Model (Demerouti et al., 2001, p. 501),
job resources are dened as «those physical, psychological, social,
or organizational aspects of the job that may do any of the following:
(a) be functional in achieving work goals; (b) reduce job demands at
the associated physiological and psychological costs; (c) stimulate
personal growth and development». Previous research shows that
social resources can inuence work engagement at the individual
level. For instance, teachers with high levels of social resources
(i.e., innovative climate, supervisor support, and supportive social
climate) experience more work engagement than teachers with
low levels of such resources (Hakanen et al., 2006). Longitudinal
research has also supported this relationship, as illustrated by
Schaufeli and colleagues (2009), who examined a Dutch telecom
company and found that social support predicted work engagement
over a period of one year, controlling for baseline-level engagement.
Recent team-level research also revealed that social phenomena,
such as vertical trust (Acosta, Salanova, & Llorens, 2011) or
healthy organizational practices that include team social resources
(Salanova, Llorens, Cifre, & Martínez, 2011), have a positive
relationship with work engagement at the team level.
Although previous research suggests that a relationship exists
among social resources and work engagement, two issues remain
problematic: (1) social resources have been tested together with
employee and organizational level variables, i.e., including
variables from different levels of analysis in the same structural
model, and (2) to date the relationship between social resources
and work engagement has only been tested at the individual level,
and not at the team level. Therefore, in the current study social
resources are considered at the team level in order to explore their
relationship with team work engagement and team performance as
rated by the supervisor.
Work engagement has traditionally been described as «a
positive, fullling, work-related state of mind that is characterized
by vigor, dedication, and absorption» (Schaufeli et al., 2002, p. 72).
Vigor suggests a willingness to invest high levels of energy and
mental resilience while working. Dedication refers to a particularly
strong work involvement and identication with one’s job. Finally,
absorption denotes being fully concentrated and engrossed in one’s
work.
To date work engagement has been studied mainly at the
individual level (e.g., Llorens et al., 2006, 2007), but it may also
exist as a collective psychosocial construct. The fact that people
who work together experience collective emotions (Barsade, 2002)
may also be applied to work engagement. For instance, Bakker,
van Emmerik and Euwema (2006) identied emotional contagion
as the main crossover mechanism behind the emergence of a
shared psychological state such as team work engagement. Thus,
we conceptualize team work engagement as a positive, fullling,
work-related and shared psychological state characterized by team
work vigor, dedication and absorption which emerges from the
interaction and shared experiences of the members of a work team
(Salanova et al., 2003).
Previous research has shown that collective work engagement
increases: (1) task performance of students working in groups
(Salanova et al., 2003); (2) service climate in service employees
(Salanova, Agut, & Peiró, 2005); (3) collective positive affect and
collective efcacy beliefs (Salanova, Llorens, & Schaufeli, 2011);
and (4) individual-level work engagement (Bakker et al., 2006).
However, as far as we know, no study has explored the relationship
between team work engagement and team-level performance, with
the team as a referent and not the individual employee. One of
the innovations of the present study is that team performance is
not reported by individuals but is assessed by their immediate
supervisor.
According to Goodman and Svyantek (1999), in-role and extra-
role performance are related to task and contextual performance,
respectively. Specically, task performance includes activities
that are related to the formal job. On the other hand, contextual
performance refers to actions that exceed what the employee is
prescribed to do, e.g., helping others or voluntary overtime.
Hence, considering both complementary types of job performance
provides a comprehensive picture of employees’ performance.
Different scholars have conrmed the positive relationship
between employees’ well-being and job performance at the
individual level. For instance, Schaufeli, Taris and Bakker (2006)
concluded that engaged employees show more in-role and extra-
role performance in a broad range of companies and occupations.
Furthermore, in another recent study in a fast-food restaurant
(Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009) engaged
employees managed to accomplish higher objective nancial
returns for the business. This relationship has also been found at
the team level. For example, Salanova et al., (2011) showed that
a set of indicators for healthy employees (i.e., collective efcacy,
work engagement and resilience) had a positive association with
various outcomes (i.e., performance and commitment).
Based on the JD-R Model (Demerouti et al., 2001), our
hypothesis is that team work engagement (i.e., team work vigor,
team work dedication, and team work absorption) fully mediates
the relationship between social resources (i.e., supportive team
climate, coordination and teamwork) and the supervisor’s rating
of performance in teams (i.e., in-role and extra-role performance;
see Figure 1).
Method
Sample and procedure
A convenience sample consisting of 533 employees (average
response rate 58%) nested within 62 teams (with 62 team
supervisors; average response rate 76%) from 13 enterprises was
used in the study. Of the total number, 82% worked in the service
sector, 10% in industry, and 8% in construction. Moreover, 54%
were women, 70% had a tenured contract, 16% were self-employed,
and 14% had a temporary contract. The average job tenure was
4.39 years (SD= 3.47) and the average tenure in the company was
6.6 years (SD= 5.54). Regarding the supervisors, 52% were male,
82% had a tenured contract, 13% were self-employed, and 5%
had a temporary contract. The average job tenure was 6.25 years
(SD= 4.95) and the average tenure in the company was 13.94 years
(SD= 5.88). Finally, teams had an average of almost nine members
(M= 8.6, SD= 8.7).
After reaching an agreement about the company’s participation
in the study, questionnaires were administered to the participants,
who were asked to take part voluntarily. Teams with more than
one supervisor were not included in the data analysis. To lead
respondents’ attention away from the individual level to the team
level, all items focused on team perceptions as stipulated in the
HERO (HEalthy and Resilient Organizations) questionnaire
(Salanova et al., 2011). The condentiality of the answers was
guaranteed.

PEDRO TORRENTE, MARISA SALANOVA, SUSANA LLORENS AND WILMAR B. SCHAUFELI
108
Measures from employees
Team social resources were assessed by nine items in three
different scales: supportive team climate (three items; e.g., ‘In my
team, constructive criticism is rewarded’; alpha= .76), coordination
(three items; e.g., ‘My team is well-coordinated’; alpha= .79),
and teamwork (three items; e.g., ‘My team has well-dened work
goals’; alpha= .75). Respondents answered using a 7-point Likert-
type scale ranging from 0 (never) to 6 (always).
Team work engagement was assessed by nine items validated
for aggregated data at the team level by Torrente, Salanova, Llorens
and Schaufeli (in press). Specically, we tested three dimensions:
team work vigor (three items; e.g., ‘While working, my team feels
full of energy’; alpha= .76), team work dedication (three items; e.g.,
‘My team is enthusiastic about the task’; alpha= .84), and team work
absorption (three items; e.g., ‘While working, we forget everything
else around us’; alpha= .75). Respondents answered using a 7-point
Likert-type scale ranging from 0 (never) to 6 (always).
Measures from supervisors
Team performance was assessed by six items adapted from the
Goodman and Svyantek scale (1999). Two different scales were
considered: in-role performance (three items; e.g., ‘The team
that I supervise achieves its work goals’; alpha= .82) and extra-
role performance (three items; e.g., ‘In the team that I supervise
employees help each other when somebody is overloaded’; alpha=
.72). The supervisors answered using a 7-point Likert-type scale
ranging from 0 (totally disagree) to 6 (totally agree).
Data analyses
Firstly, the Harman’s single factor test (e.g., Podsakoff,
MacKenzie, Lee, & Podsakoff, 2003) was carried out using AMOS
18.0 (Arbuckle, 2009) for the variables assessed by the employees.
Secondly, the agreement of employee perceptions in teams was
checked using various indices: following a consistency-based
approach, both ICC1 and ICC2 indices were calculated. Values
greater than .12 for ICC1 indicate an adequate level of within-
unit agreement (James, 1982). For the ICC2, values greater than
.60 support aggregation (Glick, 1985). From a consensus-based
approach, the Average Deviation Index was computed (ADM(J);
Burke, Finkelstein, & Dusig, 1999), whereby team agreement was
concluded when ADM(J) was equal to or less than 1 (Burke et al.,
1999). Finally, Analyses of Variance (ANOVA) were computed
in order to ascertain whether there was signicant between-group
discrimination for the measures. Thirdly, we computed descriptive
statistics and intercorrelations among the variables at the individual
and the aggregated levels. Finally, AMOS 18.0 (Arbuckle, 2009)
was used to implement Structural Equation Modeling (SEM) using
the maximum likelihood estimation method. Three competitive
models were compared: M0, the independence model; M1, the
fully mediated model; and M2, the partially mediated model.
Two absolute goodness-of-t indices were assessed: (1) the χ2
goodness-of-t statistic; and (2) the Root Mean Square Error of
Approximation (RMSEA). The χ2 goodness-of-t index is sensitive
to sample size and so the use of relative goodness-of-t measures
is recommended (Bentler, 1990). Accordingly, three relative
goodness-of-t indices were used: (1) the Normed Fit Index (NFI);
(2) the Tucker-Lewis Index (TLI); and (3) the Incremental Fit
Index (IFI). Values smaller than .05 are indicative of an excellent
t for RMSEA (Brown & Cudeck, 1993) and values higher than
.95 are indicative of an excellent t for the relative indices (Hoyle,
1995). Finally, we computed the Akaike Information Criterion
(AIC; Akaike, 1987) to compare competing non-nested models;
the lower the AIC index, the better the t is.
Results
Descriptives and aggregation analyses
Firstly, the results of the Harman’s single factor test (e.g.,
Podsakoff et al., 2003) revealed a poor t to the data: χ2(9)=
Team social
resources
Team work
engagement
Team
performance
Supportive
team
climate
Teamwork Vigor Dedication
Coordination Absorption
In-role Extra-role
+ +
Reported by EMPLOYEES SUPERVISORS
Figure 1. Proposed fully mediated model

TEAMS MAKE IT WORK: HOW TEAM WORK ENGAGEMENT MEDIATES BETWEEN SOCIAL RESOURCES AND PERFORMANCE IN TEAMS
109
46.398, RMSEA= .261, NFI= .820, TLI= .744, IFI= .850. Results
also showed that the model considering two latent factors (i.e.,
team social resources and team work engagement) t the data well:
χ2(8)= 5.499, RMSEA= .000, NFI= .979, TLI= 1.019, and IFI=
1.010. The difference between both models is also signicant in
favor of the model with two latent factors, Delta χ2 (1) = 40.899,
p<.001. Consequently, common method variance is not a serious
deciency in these data.
Table 1 shows means, standard deviations, intercorrelations,
and aggregation indices of all the study variables. ICC1, ICC2 and
ADM(J) indices ranged from .12 to .28, from .54 to .77, and from
.64 to 1.13, respectively. Results for these indices were modest
in the case of ADM(J) for supportive team climate (ADM(J)= 1.13)
and of ICC2 for team work vigor (ICC2= .54). However, one-way
ANOVA results showed statistically signicant between-group
discrimination for supportive team climate, F(61, 465)= 3.66,
p<.001; coordination, F(58, 461)= 3.02, p<.001; teamwork, F(61,
468)= 4.30, p<.001; team work vigor, F(61, 471)= 2.19, p<.001;
team work dedication, F(61, 471)= 2.68, p<.001; and team work
absorption, F(61, 471)= 2.96, p<.001. By implication, there was a
signicant degree of between-group discrimination, and therefore
the validity of team social resources and the three dimensions
of team work engagement was supported. In conclusion, overall
aggregation results indicated within-group agreement in the teams
so that unit members’ perceptions can be aggregated.
Further analyses were conducted in order to control for the
inuence of interorganizational variability in the study variables.
Intraclass Correlation Coefcients (ICCs) were calculated by
testing an intercept-only model using a multilevel methodology
(Hox, 2010). ICCs for the study variables ranged from .002 to .14.
Thus, it was concluded that there were no extreme differences
between organizations that could be biasing the results.
Finally, as expected the work engagement dimensions were
positively interrelated (mean r= .74) and positively related to
team social resources (mean r= .54) at the team level. Regarding
the intercorrelations between employee and supervisor variables,
teamwork, coordination, team work vigor, and team work
absorption were signicantly related to in-role performance (mean
r= .27). In-role and extra-role performance were also signicantly
interrelated (r= .68).
Model Fit: Structural Equation Modeling
To compute SEM, we used the aggregated database that
included team social resources and team work engagement
as well as the supervisor’s team performance rating (N= 62).
Table 2 shows the results of the SEM analysis indicating that
the proposed fully mediated model ts the data well, with all t
indices satisfying their corresponding criteria. The chi-square
difference test between M1 (the Fully Mediated model) and M0
(the Independence Model) shows a signicant difference between
the two models in favor of M1, Delta χ2(10)= 297.24, p<.001.
The chi-square difference test between M1 (the Fully Mediated
Model) and M2 (the Partially Mediated Model) shows a non-
Table 1
Means, standard deviations, intercorrelations, and aggregation indices for the study variables
Variables Mean SD ICC1ICC2ADM(J) 12345678
1. Supportive team climate 3.10 .99 .24 .73 1.13 – .69*** .53*** .40***.44*** .43***.11*.06***
2. Teamwork 4.63 .76 .28 .77 .77 .58*** – .78*** .61*** .64*** .62*** .31* .22***
3. Coordination 4.75 .76 .19 .67 .78 .47*** .68*** – .59*** .57*** .55*** .26* .20***
4. Team work vigor 4.42 .57 .12 .54 .64 .29*** .40*** .35*** – .80*** .65*** .26* .16***
5. Team work dedication 4.65 .71 .16 .62 .65 .32*** .46*** .39*** .66*** – .78*** .24*.12***
6. Team work absorption 4.17 .73 .18 .66 .82 .31*** .37*** .28*** .54*** .67*** – .26* .09***
7. In-role performancea4.68 .82 – – – – – – – – – – .68***
8. Extra-role performancea4.55.96–––––––––––
Notes: Intercorrelations are presented at the individual-level (below the diagonal) and at the team-level (above the diagonal)
a Reported by the supervisors
* p<.05; ** p<.01; *** p<.001
Table 2
Goodness-of-t indices for the SEM models
Models χ2df RMSEA NFI TLI IFI AIC ∆χ2∆df ∆AIC
M0 307.07 28 .40 .00 0.00 0.00 323.07
M1 011.66 19 .00 .96 1.04 1.03 045.66
∆M0-M1 295.41*** 9 277.42
M2 009.83 18 .00 .97 1.05 1.03 045.83
∆M2-M1 001.83 ns 1000.17
Notes: χ2= Chi-square; df= degrees of freedom; RMSEA= Root Mean Square Error of Approximation; NFI= Normed Fit Index; TLI= Tucker-Lewis Index; IFI= Incremental Fit Index; AIC=
Akaike Information Criterion
*** p<.001; ns= non-signicant

PEDRO TORRENTE, MARISA SALANOVA, SUSANA LLORENS AND WILMAR B. SCHAUFELI
110
signicant difference between the two models, Delta χ2(1)= 1.83,
ns, which is to be interpreted in favor of the most parsimonious
one, namely M1. On comparing all models, M1 was the model
that showed the lowest AIC value.
To assess the mediation effect, the Sobel test (Sobel, 1988) was
conducted, which showed non-signicant results (Sobel t= 0.36, p=
.72). However, further analyses were conducted using the approach
developed by Baron and Kenny (1986): (1) team social resources
were positively and signicantly related to the supervisor’s
perception of team performance (β= .33, p<.05); (2) team work
engagement was positively and signicantly related to the
supervisor’s perception of team performance (β= .29, p<.05); and
nally, (3) the relationship between team social resources and team
performance became non-signicant (β= .28, p= .117) when team
work engagement was introduced. The fact that the relationship
between team social resources and team performance became
non-signicant suggests that team work engagement mediated the
relationship between team social resources and team performance.
Mediation was also tested by comparing the chi-square statistic of
the partially mediated model (M2) with a third model constraining
the path from team work engagement to team performance (M3) to
the unstandardized coefcient of this path in M1 (for an application
see Salanova et al., 2005). M3 t the data with all goodness-of-t
indices meeting the criteria but the chi-square difference between
M2 and M3 was not signicant. Therefore, the inuence of team
social resources on team performance was mediated by team work
engagement.
In conclusion, previous results using SEM and mediation
analyses provide some evidence for M1, that is, the fully mediated
model. The nal model is depicted in Figure 2. As expected, team
social resources have a positive and signicant inuence on team
work engagement (β= .73, p<.001), which in turn is positively and
signicantly associated with team performance (β= .29, p<.05). It
is interesting to note that team social resources explain 53% of the
variance in team work engagement (R2= .53), and that this in turn
accounts for 8.4% of the variance in team performance (R2= .08).
Discussion
Based on the JD-R Model (Demerouti et al., 2001), we
hypothesized that team work engagement mediates the relationship
between social resources of the team and performance, as measured
by the supervisor’s rating. Results suggest that team social resources
are positively related to a commonly shared psychological state,
namely team work engagement, which is in turn related to team
performance.
At the theoretical level, the present study extends current
knowledge about the key role of team work engagement in the
process by linking team social resources and the supervisor’s view
of team performance. The JD-R Model receives support from the
ndings since they provide evidence of its theoretical validity to
explain team-level processes. The underlying motivational process
is also reinforced as team work engagement is observed to be a
meaningful team well-being construct that mediates the impact
of social resources on performance in teams. At the same time,
the three inner components of team work engagement have been
replicated at the team level, which enhances the validity of the
three-factor model of work engagement.
As suggested by previous research, emotional contagion could
be considered the fundamental underpinning process explaining
how team members share a common idea about a team property
such as team work engagement. This rationale could be applied
to team social resources and team work engagement, since these
constructs were aggregated from individual perceptions of team
properties. Although the underlying crossover mechanism has
not been revealed by our ndings, we assume that emotional
contagion could be the explanatory mechanism that is responsible
for employee agreement – a prerequisite to be aggregated. Team
social resources may trigger emotional contagion of team work
engagement among employees through offering a pool of shared
experiences. Embedded within the organizational environment,
this common background (e.g., a supportive team climate, need for
coordination and task interdependence within team working) can
Team social
resources
Team work
engagement
Team
performance
Supportive
team
climate
Teamwork Vigor Dedication
Coordination Absorption
In-role Extra-role
.73 .29
.70
.80
.98 .84 .94
.67
.90 .75
Reported by EMPLOYEES SUPERVISORS
Figure 2. The nal model with standardized coefcients (N= 62). All coefcients are signicant at p<.001, except for the path between team work engagement
and team performance, which is signicant at p<.05

TEAMS MAKE IT WORK: HOW TEAM WORK ENGAGEMENT MEDIATES BETWEEN SOCIAL RESOURCES AND PERFORMANCE IN TEAMS
111
elicit the functioning of interactive processes between individuals
at work. At this point, employees dispose of a shared scenario to
interact both consciously and unconsciously in order to inuence
each other reciprocally and trigger the emergence of a positive
shared state, as is the case of team work engagement (Bakker et
al., 2006).
With regard to practical implications, results can be used as
recommendations following the advice offered previously from
the individual perspective of work engagement (Schaufeli &
Bakker, 2004), but going deeper into the idea of fostering team-
based resources. When teams are the main work structure in a
given organization, promoting team-oriented policies will be the
most efcient management behavior. Thus, the ndings in the
present study warn organizations of the need to take care of team
social resources if positive consequences regarding employees
and outcomes are desired. Therefore, engaged teams will provide
enterprises with a unique competitive advantage (Macey &
Schneider, 2008).
Specically, results show the relevance of promoting a
supportive team climate, coordination and team working in order
to build more vigorous, dedicated and absorbed teams, which in
turn will enhance their performance at work. Promoting a climate
of psychological safety and rewarding constructive criticism as
well as dealing with interpersonal problems in such a way that
the supervisor is perceived as caring for his/her subordinates
are approaches that are capable of fostering a supportive team
climate. Coordination can be fostered by ensuring the existence of
appropriate channels of communication among the team members.
This will make it easier for the team to accomplish its goals while
avoiding an additional source of stress that would lead to poor
team performance. Lastly, recruiting and selecting applicants who
complement team skills and considering the introduction of team-
based retribution according to performance would help to boost
team working. In general, conclusions derived from the results
provide empirical evidence of previous recommendations on how
to intervene so as to increase work engagement by focusing on
social interactions (Schaufeli & Salanova, 2010).
Another practical implication is related to the relevant voice
of supervisors. Obviously, the team leader plays a key role in
increasing social team resources so that the team not only feels
engaged, but also performs better. Our research shows that in doing
so, good team leaders should be both considerate (i.e., improve the
psychological team climate) and task-oriented (i.e., set clear goals
and coordinate the efforts of team-members).
The present study has several limitations. The rst one is that
a convenience sample was used, which might compromise the
generalizability of the results. However, it is a rather heterogeneous
sample, including different teams from different enterprises.
Secondly, the data was obtained by self-report measures, which
might have caused common method bias. However, data were used
from different sources, employees and supervisors. Furthermore,
the Harman’s single factor test suggested that common method
bias is not very likely. Thirdly, two aggregation indices (i.e., ICC2
for team work vigor, and ADM(J) for supportive team climate),
although close to their cut-off values, did not reach the criteria to
support aggregation. Although indices of this kind are based on
arbitrary rules-of-thumb, these results could be compromising the
validity of the team-level measures for these variables in some
way. Conducting multilevel conrmatory factor analyses is also
encouraged, as this methodology would enhance the multilevel
validation of the work engagement measure at different levels of
analysis. Finally, the present study is cross-sectional in nature.
Although team performance was rated by the immediate supervisor,
who is an independent informant, it is not possible to reach decisive
conclusions about the causation between the variables included
in the model. To deal with this limitation, further research might
use longitudinal techniques that would uncover causal paths. The
knowledge that emerged using two or more data waves would
enhance the validity of the JD-R Model as a useful model of
intervention also at the collective, team level, as well as offering
a thorough comprehension of the crossover processes involved.
Furthermore, reversed and reciprocal relationships could be tested
to explore the existence of positive cycles and spirals between
the variables analyzed and other key variables such as collective
efcacy beliefs. The use of a multilevel methodology would also
be highly recommended to explore cross-level relationships with
enterprise-level variables that could be inuencing and promoting
work engagement within teams, as is the case of Human Resources
Management practices. By so doing, we really will be ensuring
that teams make it work.
Acknowledgements
This study was supported by a grant from the
Spanish Ministry of Work and Social Affairs (#411/UJI/
SALUD), the Spanish Ministry of Science and Innovation
(#PSI2008-01376/PSIC), Universitat Jaume I & Bancaixa
(#P11B2008-06), and Generalitat Valenciana (Programa VALi+d).
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