Affective Climate 1
Running head: AFFECTIVE CLIMATE AND TEAM PERFORMANCE
Affective climate’s influence on workteam performance: The role of
affective climate strength.
Nuria Gamero Vázquez
University of Valencia
Abstract
Affective Climate 2
Several studies have shown that workteam members experience similar affects related
to their work. These collective affects influence team processes and outcomes. Recently,
the concept of affective climate has been proposed to define shared affective responses
by workteam members. The aim of this study was to validate the affective climate
concept and to examine its possible effects on team performance. With that purpose, we
have considered the intensity dimension (the level of shared affective states) as well as
the strength dimension (the degree of within-unit agreement among team members`
affective experiences). The sample consisted of 133 workteams from several financial
companies. The total number of participants was 581. Hierarchical regression analyses
show that affective climate intensity and strength influence team performance. Further,
our data suggest that affective climate strength may be an important factor to understand
the link between team affective climate intensity and team performance.
Key words: affective climate intensity, affective climate strength, team performance
Affective Climate 3
Introduction
Affects are an integral and inseparable part of organizational life (Ashforth &
Humphrey, 1995). Workteam members` experiences and affective states have been an
area of growing interest in the organizational studies and they are implicit in numerous
organizational and psychological theories, because of their implication in the group and
organizational processes and outcomes (Ashkanasy, Härtel & Zerbe, 2000; Brief &
Weiss, 2002; Domagalski, 1999; Fisher & Ashkanasy, 2000; Muchinski, 2000;).
Most of the research about the role of affect has been carried out at the individual
level. Researchers have described a variety of different kinds of affective experiences
(mood, emotion and dispositional affect, among others and they have defined all of
them as “affect”) showing their important role on work units` processes and outcomes.
However, recent developments in the study of affect have derived to series of
promising research areas with regard to their presence at the group level (Bartel &
Saavedra, 2000; Barsade, 2001; Barsade, Ward, Turner & Sonnenfeld, 2000).
The present study aims to contribute to the study of affect as a group
phenomenon. Our goal is to test the validity of the affective climate concept as a
current and emergent construct and to study its effects on team performance. For it, we
review the main contributions to the affective climate concept and, considering its
dimensions of intensity (or level of shared affective states) and strength (or team
members` affective homogeneity), we provide a theoretical rationale for the hypotheses
tested.
Affective Climate 4
1. Affective climate: a new concept
1.1. The study of collective affect.
Until recently, affect as an area of study has received relatively little attention
from researchers. However, during the last decade, researchers have turned its attention
to the employees` affect dimensions (Domagalski, 1999) and a large number of studies
on causes and consequences of affect in the workplace has been carried out. As a
consequence of the proliferation of literature on individual affect, a new approach to this
topic is emerging, which considers affect as a collective property of work groups.
Several authors have pointed out that group members could develop shared affect.
George (1990) proposed the concept of “group affective tone” and she defined it as
“consistent or homogeneous affective reactions within a group” (p.77). In her study
with a sales teams` sample, she showed that work groups could develop affective tones
when a degree of consistency or homogeneity in the affective reactions among members
was demonstrated. As George (1990, 1995) argued, “when members of a group
experience similar levels of positive/negative mood at work, then the group has a
positive/negative affective tone…. Conversely, if members experience dissimilar levels
of positive/negative mood at work then the group does not have an affective tone”
(p.781). George concluded that when most of group members experience positive (or
negative) affective states, then the affective tone of the group as a whole becomes
positive (or negative) as well. Sessa (1996), in her study on group emotion and conflict,
demonstrated the existence of affective tone in 30 nurse teams. Results showed that
shared affect is exhibited by team members through a series of vocal cues, facial
expression and body movements and, therefore, that it could be observed. Other
contributions come from Totterdell and colleagues` studies. They have asserted that
Affective Climate 5
work group members may share group affect and they found a significant concurrent
mood associated in several professional samples. Totterdell et al. (1998), in two studies
with different samples, nurses and accountants, found evidence showing that the moods
of team members were related to each other in teams over time. Their results showed
that individual mood could be influenced by collective mood of the teammates at work,
such that people’s mood could become linked to the mood of their teammates.
Totterdell (2000) confirmed those data in a sample of professional cricket teams and
also showed that the influence was the same, independently of shared events by the
team. Across short-term meetings of 70 very diverse work group teams, Bartel and
Saavedra (2000) also found convergence of mood. These authors showed that work
group mood is something that can be recognized and reliably measured by members
within the work group and they also showed that group mood was also rated by
observers external to the group. Likewise, Barsade (2001), in a study which examined
the influence of emotional contagion on team dynamics, found that contagion happened
and promoted a strong convergence of group members` mood. As Barsade (2001)
indicated, work group members come to develop mutually shared moods and emotions
in the course of executing their tasks and that these affects may result for a subtle but
continuous transfer of affective states among members.
Thus, group affect studies offer “excellent external validity that shared emotions
occur in organizational workteams and that these emotions can be recognized and
measured” (Barsade, 2001, p. 5).
Different models to explain the processes that contribute to the convergence of
collective affect have been formulated. Among those studies, we highlight George’s
(1990, 1996) research. According to her, there are at least four complementary
Affective Climate 6
theoretical rationales supporting the existence of group affective tone: social
interaction, Schneider’s (1987) attraction-selection-attrition (ASA) framework,
socialization processes and social influence (Fisher, 1986) and, finally, the similarity in
group tasks and outcomes for team members. On the other hand, Barsade and Gibson
(1998) pointed out to a series of fundamental factors to understand and explain group
emotion formation. Those elements would be developed and structured later by Kelly
and Barsade (2001) in a more complete organizational model to understand affective
influences on groups. In this model, these authors noted that group emotions result from
the group’s affective composition and the affective context in which the group is
behaving. It is through a variety of explicit and implicit processes (or bottom-up
components in the model), how affective experiences that group members bring with
them to the group are communicated to other group members and form the affective
compositional group effects. There would be also a series of factors in the group’s
affective context (or top-down components) that may amplify or constrain the group
members` affective experiences. Despite theoretical production, empirical evidence
points out structural elements and team processes which foster the convergence of
affect at the group level. Among them, emotional contagion (Barsade, 2001; Gump &
Kulik, 1997; Hatfield, Caccioppo & Brapson, 1994; Totterdell et al., 1998; Totterdell,
2000), mood regulation norms (Bartel & Saavedra, 2000), emotional comparison
(Sullins, 1991; Totterdell et al., 1998), task and social interdependence and members
stability (Bartel & Saavedra, 2000), member tenure and a set of factors related to being
interdependent in the team and satisfied with it: more committed to the team,
perceiving a better team climate, being happier and engaging in collective activity
(Totterdell, 2000).
Affective Climate 7
1.2. The Affective Climate Construct.
The concept of climate has a wide tradition in Organizational Psychology and its
important role in the different organizational experiences and outcomes has been
demonstrated. Organizational research has usually considered climate as shared
perceptions of unit members (i.e. perceptive climate). Nonetheless, climate can also
represent other shared experiences or responses. Thus, other kind of climates can be
identified. In this sense, Kirton and McCarthy (1988) proposed the concept of cognitive
climate to refer to the cognitive styles that team’s members use to solve their problems.
On the other hand, De Rivera (1992) proposed the concept of emotional climate to refer
to shared emotional responses by a collective. According to him, the emotional climate
reflects the emotional relationship between group members (i.e. how group members
are related to one another). This construct has been the objective of some studies in
social psychology, which have emphasized its validity and usefulness (Fernández-Dols,
1998; Hurley, 1997; Kavanagh, O’Halloran, Manicavasagar, Clark, Piatkowska,
Tennant & Rosen, 1997; Páez, Ruiz, Gailly, Kornblit, Wiesenfeld & Vidal, 1997). Paez
et al. (1997) define emotional climate as a collective “state of mind” which is
characterized by certain predominant emotions. These predominant emotions are
associated to certain predominant actions` tendency. As Paez et al. (1997) indicate,
emotional climate is a subjective construct (emotions are in the mind of the individuals)
as well as an objective one (they are shared and manifested in collective ways of
behaving). They also point out that emotional climate is “an emergent affective
phenomenon which aggregate new elements and it is distributed within group” (p.82).
More recently, González-Romá, Peiró, Subirats and Mañas (2000),
reconceptualized De Rivera’s concept as affective climate. They have described it as
“shared affective responses by workteam’s members” (p.98). They used the concept of
Affective Climate 8
“affect” because it is a wide term which traditionally includes other concepts as mood,
emotion, sentiment, dispositional affect, etc. In this study, we considered two
dimensions of affect: tension and optimist (negative and positive affect, respectively).
These dimensions are extracted starting from a more general conceptual framework,
circumplex model of affect, which represents the principal dimensions of affect and
how they are interrelated (Bartel & Saavedra, 2000; Warr, 1990; Weiss & Copranzano,
1996; Yik, Russell & Barret, 1999). Therefore, we will refer to affective climate in term
of tension and optimism affective climate.
Although the research on this kind of climate is scarce, several studies have
confirmed its existence. Peiró, González-Romá, Tordera, and Belmonte`s (2000) study
with a sample of 250 public health service`s units, highlighted the importance of
concept of emotional climate as a relevant phenomenon which should be empirically
demostrated. On the other hand, Gonzalez-Romá, Peiró, Subirats and Mañas (2000),
using a two-wave panel data design, tested the validity of the concept of affective team
climate in a sample of 33 health care workteams. Their results also showed that shared
perceptions of team climate influence on collective affective responses. Thus, it points
out that workteams` cognitive climate is a significant predictor of workteams’ affective
climate. Their research supported longitudinal studies that showed a unidirectional
relationship between perceptive climate and teams` affective outcomes (GonzálezRomá, Peiró, Lloret, Mañas & Muñoz, 1996).
However, despite existing studies that confirm the validity of this kind of climate,
research is not enough and it is necessary a greater empirical validation of this
construct. Thus, our first objective is to test the existence of affective climates within
workteams. Thus, we propose the following hypothesis:
Affective Climate 9
Hypothesis 1. Team members will have similar affective work experiences in
tension and optimism.
2. Affective climate dimensions and their role on team performance
Affective climate, as other kinds of social climate, is a construct at a collective
level that is operationalized as integration or combination of similar affective
experiences at individual-level. To establish the validity of a construct operationalized
at a collective level, we need composition models. These models specify the functional
relationship among constructs operationalized at different levels of analysis (Chan,
1998).
In the typology of composition models proposed by Chan (1998), the direct
consensus model is likely the most familiar within multilevel researches and the most
frequently used in climate studies (González-Romá, Peiró & Tordera., 2002). In direct
consensus models, the isomorphic relationship between the specified constructs at
different levels (e.g. individual and unit) is based, in the case of the affective climate, on
the within-unit agreement among the individuals` affective responses. Once this
agreement has been demonstrated (internal consistency criterion) and differences have
been observed among units (differentiation criterion), it will be justified to obtain a
measure of central tendency (e.g. average) to represent the unit’s affective climate.
Thus, in this kind of model, within-unit agreement is considered a prerequisite to
indicate that a construct exists at a higher level and that it can be operationalized.
However, in another kind of model, dispersion models, within-unit dispersion is
used as the operationalization of a unit-level construct. In this model, “the degree of
within-group agreement of scores from the lower-level units of attributes… [is] a focal
construct as opposed to merely a statistical prerequisite for aggregation” (Chan, 1998,
239). As Chan (1998) noted “dispersion is by definition a group-level characteristic….
Affective Climate 10
because it refers to the variability within a group and variance statistic in indexing an
attribute of a group as opposed to an attribute of any individual-level response” (p.239).
Dispersion models suggest that the groups vary in their level of homogeneity, in
contrast with the direct consensus models which propose that the groups are
homogeneous with respect to the construct of interest and different in the absolute level
of construct (Klein, Conn, Smith & Sorra, 2001). An example of dispersion construct is
the affective climate strength, conceptualized as the degree of within-unit agreement in
the members` affective responses.
Within-unit agreement (or its opposite, within-unit dispersion) is also a focal
construct in Dispersion Theory (Brown & Kozlowski, 1999). This recent theory
recognizes that higher-level constructs are originated in individual-level processes and
they are developed through social interaction. In Dispersion Theory, unit-level construct
would be operationalized as the degree of within-unit agreement. Within-unit agreement
would indicate the degree of emergence of higher-level constructs (Brown &
Kozlowski, 1999) and it is assumed that "units can be characterized by the extent to
which a phenomenon has emerged as a meaningful unit characteristic" (Brown &
Kozlowski, 1999, p. 6).
Thus, workteams` affective climate can be characterized according to its intensity
(high or low affective climate level) and its strength (high or low similarity among
team members` affective experiences). To consider both affective climate dimensions is
congruent with those authors who state that “group emotion can be examined through a
variety of compositional perspective, including the mean emotions of the group
members or the degree of emotional variance, or homogeneity, within the group”
(Barsade & Gibson, 1998, p.89). Because conceptually both dimensions are different,
we expect that they play different roles on team outcomes. To study the consequences
Affective Climate 11
of these affective dimensions would provide us with a more complete vision of group
affect.
2.1. Affective climate intensity as predictor of team performance
Individual affective states, as fundamental components of human experience, have
shown to have wide-ranging effects on cognitive processes, attitudes, and behaviors
(Brief & Weiss, 2002; Pugh, 2001; Saavedra & Khun, 2000; Staw & Barsade, 1993;
Weiss & Cropanzano, 1996). Thus, several authors have argued that worker behavior
and productivity are directly affected by employee affect (Ashforth & Humphrey, 1995;
Ashkanasy, Härtel & Zerbe, 2000; Fisher &Ashkanasy, 2000; Weiss & Compranzano,
1996).
Nevertheless, until recently, little attention has been paid to the consequences of
shared affects at work (Barsade, 2001). There is some research examining the
relationship between the mean level of affect and various group processes and outcomes
(Barsade & Gibson, 1998; Kelly & Barsade, 2001) as organizational spontaneity
(George & Brief, 1992) and absenteeism and prosocial behaviour (George, 1990). Some
authors have also paid attention to team performance (Barsade et al., 2000; George,
1995; Kelly, 2003; Kelly & Barsade, 2001). George (1995) noted that shared affect may
affect to the helping behaviour or prosocial behaviour within the team and the team
members` conceptions of their capabilities and their expectations about performance,
and, therefore, to team efficacy. Bartel & Saavedra (2000) have asserted that collective
affect may help to produce a normative affective aptitude for social situations and may
affect members` motivation to attain collective goals (Hackman, 1992). Thus, certain
group affects may help to promote congruence in members` attention effort and
persistence which may stimulate well-coordinated patterns of behaviour toward
collective goals. On the other hand, Barsade et al. (2000) argued that inducing positive
Affective Climate 12
mood leads to greater creativity, more efficient cognitive-processing and better use of
heuristics in complex decision-making tasks, as well as broadened categories for
information sorting and greater flexibility in categorization.
The relationship between collective affect and team performance has also been
empirically supported by George (1995). She studied whether positive affective tone,
operationalized as team mood in 65 sales teams, was related to team performance. Team
performance was evaluated by branch managers who received a rating form for each of
the teams in their branch included in the study. Her results showed that team positive
affective tone was significantly and positively associated with perceived group
performance. On the other hand, Totterdell (1999; 2000), examined the moodperformance relationship in cricketer teams. His studies showed that there was a
significant association between team’s average happy mood and team’s subjective and
objective performance. Thus, team performance was greater when team’s members
were happier. Finally, Duffy and Shaw (2000) tested a model of impact of envy in
groups in a longitudinal study with 143 student work-teams. They found a negative
relationship between intra-team envy and several team outcomes as team satisfaction,
absenteeism and team performance ratings by external observers. They also found that
group envy led to greater social loafing and less cohesiveness and group potency, which
was related to lessened team performance.
Considering these theoretical arguments and empirical results, we hypothesize
that:
Hypothesis 2. Affective climate intensity will have direct effects on team
performance. These effects will show a negative sign for group tension and a
positive sign for group optimism.
Affective Climate 13
2.2. The role of affective climate strength
Scholars have shown their interest to examine collective affect through direct
consensus models mainly, using the average to represent the group affect (Barsade &
Gibson, 1998; Kelly, 2003; Kelly & Barsade, 2001). As Kelly & Barsade (2001) argue,
“with regard to future work, it is important to move beyond an investigation of mean
levels and to examine indices based on variance and dispersion as well” (p.113).
Dispersion constructs, such as affective climate strength, are very scarce within
organizational literature (Brown & Kozlowski, 1999; Klein et al., 2001). Studies on the
role of personality differences (Barsade et al., 2000), demography diversity (Chatman,
Polzer, Barsade & Neale, 1999; Klein et al., 2001; O´Really, Williams & Barsade,
1998; Shaw & Barret-Power, 1998; Sargent & Sue-Chan, 2001; Simons, Pelled &
Smith, 1999; Timmerman, 2000; Williams & O´Really, 1998), leader’s charisma
homogeneity (Klein & House, 1995), and organizational culture strength (Waldman &
Yammarino, 1999) show the important role that similarities and differences among
team members have in the different group process and organizational outcomes.
Although climate strength has received little attention in studies of work units` climate
(González-Romá et al., 2002), recently researchers have started to argue that climate
strength could be considered as a valuable theoretical construct to understand work
units` outcomes.
Direct influence of affective climate strength. There is no clear consensus about
how dispersion (or its opposite, homogeneity) influences on performance outcomes,
because different dimensions of diversity may have different impacts (Bartel &
Saavedra, 2000). Several researchers have argued that diversity within team has positive
effects on team outcomes. These studies are based on the idea that the diversity in the
Affective Climate 14
abilities, knowledge and aptitudes within the team encourages their outcomes. As
Barsade et al. (2000) pointed out, team diversity “enhances the breadth of perspective
viewpoints, cognitive resources, experiences, and general problem-solving ability of the
team and that diversity can, therefore, help enhance performance” (p. 809). Regarding
affective diversity, Barsade and Gibson (1998) point out that low levels of affective
diversity, in particular circumstances, may also lead to negative consequences. Thus,
those teams composed of members with high negative or positive affective
homogeneity, especially when dealing with specific emotions such as anger or euphoria
may be unproductive because they may need to be tempered in order for progress to be
made. As Barsade & Gibson (1998) argue, “a team comprised of homogeneously
positive people…. may contribute to unrealistic euphoria, optimism and groupthink.
Conversely, a team comprised of all similarly low energy, pessimistic members could
lead to lethargy, lack of productivity, and unrealistic cautions” (p.93).
However, there are theoretical arguments and empirical results that show the
negative effect of diversity on performance. Thus, as Barsade & Gibson (1998) argue,
dispersion creates distance between group members, which influence negatively on
various group processes and outcomes as trust, rapport, social integration and
communication which in turn have a negative impact on team performance. The
similarity (and attraction) created among group members is the most common
theoretical rationale for predicting positive effects of group homogeneity. The
similarity-attraction paradigm posits that individuals tend to be attracted to those who
are similar to them (Green, Anderson & Shivers, 1996). As Barsade et al. (2000)
highlighted, similarity causes attraction; so, people prefer to interact with other
individuals or groups who have (or are perceived to have) attitudes and values similar to
their own. Most of attraction research has been centred on personality variables,
Affective Climate 15
attitudes and values. However, several authors argue that affect is a dimension on which
people judge similarity to each other as well (Barsade & Gibson, 1998; George, 1996).
They pointed out that affective similarity within a group should lead to higher levels of
member attraction and cohesion. Thus, team members should feel more trust and
rapport to each other, the communication and coordination within the team should be
more fluid and they should engage in more cooperative behavior. This should lead to
more positive outcomes (Barsade & Gibson, 1998).
Empirical evidence has shown the negative effect of team affective dispersion on
team processes and performance. Barsade et al. (2000), with a sample of 62 U.S. top
management teams, found that positive affective diversity in these teams was negatively
associated with team processes such as perceived conflict and cooperativeness. They
also found that homogeneous teams in positive affect had higher company financial
performance.
Thus, we could expect that in high affective homogeneity conditions, team
members feel more comfortable with each others` interpersonal interactions, generating
more cooperation, communication, trust, social integration, and cohesion. This, in turn,
is expected to positively influence group outcomes, so that the greater the affective
homogeneity in a team, the greater the team performance. So, we hypothesize that:
Hypothesis 3. Affective climate strength in tension and optimism will have
positive effects on team performance.
Moderator role of affective climate strength. Most of the climate strength studies
have stressed that climate strength should be also considered as moderator of the
relationship between work units` climate and units` processes and outcomes (Brown &
Kowslowski, 1999; González-Romá et al., 2002; Ostroff & Bowen, 2000). As
González-Romá et al. (2002) pointed out, “considering that even work units with the
Affective Climate 16
same aggregate score on a climate facet can differ in extent within-unit agreement in
climate perceptions, … climate strength must be accounted for in studying the
relationship between work units` climate and work units` outcomes” (p. 466).
The moderator influence of perceptual climate strength has been empirically supported.
Scheneider, Salvaggio & Subirats (2002), in a sample of bank branches, found that
perceptive climate strength moderated the relationship between unit’s climate and
aggregated customer perceptions of service quality. They argued that, when climate for
service is high and there is high climate strength, customer perceptions of service
quality should be higher than when climate strength is low, because under strong
climate conditions, they experience more consistent service. Likewise, in the study
developed by González-Romá et al. (2002), results showed that homogeneity in
climate’s perceptions (innovation climate and goals orientation climate) enhances the
influence of team climate on units` satisfaction and commitment, so that this influence
is only observed in teams whose members have homogeneous perceptions of the team.
All these studies support that high climate strength would foster the impact of units`
climate on units` outcomes. When the within-unit agreement is high, the influence of
the climate on criterion variables would be more consistent and uniform, so that those
outcomes become more predictable in strong climate conditions. In the same way, we
argue that affective climates with high within-unit agreement in which individuals
experience similar affective states at work (strong affective climate conditions)
diminish the variability of associated performance appraisals. Conversely, in affective
climates with low within-unit agreement in members` experiences, the variability of
performance appraisals is larger. So, team performance will be more predictable in
strong affective climate conditions than in weak ones. Barsade et al. (2000) tested, in an
exploratory way, the interaction between mean level trait positive affect (calculated as
Affective Climate 17
the average of the team members` trait positive affect scores), and positive affective
diversity (degree of heterogeneity in trait positive affect at team level). They found that
affective diversity moderated the relationship between teams trait positive affect and
cooperation and conflict as team outcomes, so that “for group conflict and
cooperativeness, being homogeneous compensated for low trait positive affect, and
being high in trait positive affect compensated for being affectively diverse” (p.825).
However, the interaction between affective diversity and mean level affective showed
no relation to company financial performance.
On the basis of this empirical evidence and theoretical rationales, we hypothesize
the following:
Hypothesis 4. Affective climate strength will moderate the relationship between
team’s affective climate intensity and team performance, so that, when climate
strength is high (i.e. affective climates with high within-group agreement in
members` affective states) the influence of affective climate intensity on team
performance will be enhanced and, conversely, when climate strenght is low (low
homogeneity in members` affective experiences) the aforementioned influence
will be weakened.
3. Sources of performance appraisal
Regarding the sources of team performance appraisal, studies reviewed have used
only one source of information. However, changes in organizational structures,
processes and cultures “have combined to create the conditions where several sources of
information have become not only more acceptable but more necessary” (Fletcher,
2001, p.477). Sources of information refer to who provides the information on team
Affective Climate 18
processes and outcomes (Tesluk, Mathieu, Zaccaro & Marks, 1997). Usually, the source
of performance appraisal is the manager (De Quijano, 1992). However, now,
performance appraisal tend to be carried out by several sources: team members
(Roberts, 2003), peers (Fletcher, 1997; Valle & Davis, 1999), subordinates (i.e. the
appraisal of bosses by their subordinates) (Fletcher, 1997), external observers of the
team such as customers or area managers (Hallam & Campbell, 1997; Tesluk et al.,
1997) or even the combination of all them as it happens in the “360º (multirater)
feedback” (Bozeman, 1997; Fletcher, 2001; Facteau & Craig, 2001; Scott & Einstein,
2001). In this case, the use of multiple sources provides multiple perspectives on team
performance in a systematic way from those in good position to observe it (including
members, subordinates, customers, as well as supervisors). As Tesluk et al. (1997)
argue, “because different sources of information are more qualified to provide
assessments on particular aspects of behavior and performance, several sources may be
used in combination to provide a more holistic view of team functioning and
effectiveness” (p. 205). Each source of information provides relevant and meaningful
information. Who appraises will depend of the conception of performance appraisal, the
structure of the organization and the quality of relationships within it (Fletcher, 1997).
In our study, we use as sources of appraisal the team members (including the
manager) and an external evaluator (the area manager). To use both sources has several
advantages. Team members are best positioned to provide certain kinds of information.
Thus, team members would be used to collect information on team processes and
outcomes that are not obvious to outside observers (Tesluk et al., 1997). Team members
possess valid, unique and relevant performance information and insight that is
unavailable or unobservable by the external rater (Dickinson & McIntyre, 1997). Thus,
the quality and quantity of performance appraisal increases leading to a more accurate
Affective Climate 19
and valid rating (Roberts, 2003). However, members have a stake in team’s outcomes
and might provide biased assessments for variables such as team performance (Tesluk et
al., 1997). For it, other type of rater who differs according to their functional
relationship to the team could serve as complementary source of information. Thus, we
included in this study the appraisal of the area manager. To use only the area manager
would have limitations as well, because, although the area manager knows the results of
each team in his/her area, he/she may have several teams to deal with, or see them too
infrequently to know how they are doing. Consequently, the area manager would not
have certain type of information which would be relevant to performance appraisal.
Thus, the limitations of an appraiser are compensated for the other appraiser and we can
prevent the disadvantages of each source of appraisal.
Method
Sample
The sample consisted of 581 employees, members of 133 workteams from several
financial companies of Valencia (Spain). All companies had formally designated work
units (teams). A work unit is defined here as a group in which all personnel report
directly to the same supervisor and interact to complete unit tasks. Team size ranged
from 2 to 14, and the average number of members per teams was 5.6 (s.d. 2.05). The
response rate was high (93.3% percent).
54,4% percent of the sample were men and 45,6% women. Regarding subjects`
age, 9,5% were below 25 years, 41,1% were between 25 and 35, 25,6% were between
36 and 45, 21,3% between 46 and 55, and, finally, 2,5% were above 55. Concerning
academic level, 1,8% percent have primary level, 2,5% percent professional
Affective Climate 20
qualification, 34,7% percent High School, 48,8% percent were University Graduates
and Doctors.
Procedure
The human resources departments of several financial firms were contacted to ask
for permission to administer the questionnaire in their teams. Once the permission was
obtained, supervisors and employees were informed about the study and asked to
voluntarily answer the questionnaires. A battery of questionnaires was applied in order
to collect data from each work team. Questionnaires application was developed in three
different ways. First, a meeting with the workteam was scheduled. During this meeting,
which took place in the participants` office, a researcher explained the aims of the study
and the instructions to fill the questionnaires. When it was possible, the researcher
stayed in the office till participants finished answering the questionnaires; otherwise the
researcher came back some days later and picked the questionnaires up. When
scheduling a meeting was not possible, questionnaires were sent by mail with a letter
that explained the aim of the study and the instructions to fill the questionnaires, and a
pre-stamped envelope to return them. The first procedure was the most desirable on,
because it allowed the explanation of doubts. However, confidentiality and anonymity
of the responses were guaranteed in the three cases.
Measures
Affective Climate Intensity. It was measured by aggregation of individual scores of
the Affective Well-being Scale constructed by Lloret and González-Romá (in press).
This scale is composed by two affective climate dimensions, Tension and Optimism.
We asked respondents to check “how much of the time in the past few weeks their job
Affective Climate 21
had made them feel …”. Participants responded on a 5-point scale which ranged from 1
(not at all) to 5 (very much).
- Tension. This dimension (tension climate) was measured through three couples
of opposed adjectives: Tense vs. Relaxed; Nervous vs. Quiet and Anxious vs. Calm.
Subjects should respond to each of these six adjectives. The scores of the items
relaxed, quiet and calm were reversed. The alpha coefficient was .90.
- Optimism. This dimension was measured through three couples of opposed
adjectives: Gloom vs. Cheerful; Pessimist vs. Optimist and Discouraged vs.
Animated. The scores of the items gloom, pessimist and discouraged were reversed.
The alpha coefficient was .91.
Details of scale development, reliability and validity are presented by Lloret and
González-Romá (in press).
Climate Strength. Two indicators of climate strength were used: one for tension
and the other for optimism. Climate strength was operationalized as the degree of
within-team agreement on these measures. Within-team agreement was measured by
means of the Average Deviation index (ADM(J)). Since this index is a direct measure of
within-team variability, the values provided by the ADM(J) index regarding each climate
scale were multiplied by –1 so that higher scores represented higher within-team
agreement and higher climate strength (González-Romá et al., 2002).
Team Performance. Team performance was measured as the responses to the
following questions on a 5-point Likert scale: “How do you think your work unit
performs?” and “which is the quality of the work that your team achieves?”. The first
question is taken from Jehn’s et al. (1999) Members´ Perceived Group Performance
Affective Climate 22
Questionnaire. The second one was own elaborated. Team performance was evaluated
by team members (members and manager) and by an external rater (area manager).
Alpha coefficient for teams’ perceived team performance was .80 and .78 for external
evaluators’ perceived team performance.
Control Variables. Group size and Team tenure were control variables in this
study because the literature on groups has noted that size and tenure are key variables
influencing affective reactions and group homogeneity (Jehn, 1995; Kelly, 2003).
George (1996) suggested that groups working together over time should come to
display similar levels of positive or negative affects. Thus, as this author indicates, in
those teams whose members are changing constantly, an affective tone can not develop
because the Attraction-Selection-Attrition process would not operate and it could not
produce similarity in group. As Barsade et al. (2000) indicate “team tenure becomes an
important component or moderator of the similarity-attraction process” (p. 827). In
addition, Kelly (2003) indicated that as team size increases, other processes as
cohesiveness, norm enforcement decrease and member participation become more
disproportionate. As a consequence, one would expect less homogeneity of affect within
these teams. Past research also showed that group size influence group performance
(Gladstein, 1984; Jehn, 1995).
We measured those control variables by asking team managers to obtain
information about “Team size" (“How many people are in the team that you manage
now, including yourself?”) and “Team Tenure” (“How long does your team take
working with the current members”).
Affective Climate 23
Data Analysis
Descriptive statistics (mean and standard deviation) were obtained and correlation
matrix was carried out as previous step to hypothesis contrast. In order to test the
hypothesis 1, the Average Deviation Index (Burke, Finkelstein and Dusin, 1999) and
Analysis of the Variance (ANOVA) were calculated.
The impact of affective climates on perceived performance (Hypothesis 2 to 4)
was estimated by a series of hierarchical multiple regression analyses (Cohen & Cohen,
1983). Hierarchical regression analyses were performed to identify the main effects of
climate intensity and main and moderator effects of climate strength of the two affective
climate dimensions. The independent variables and interaction terms were entered into
the regression equation in four successive steps. In step 1, the control variables were
entered as a set. In step 2, the predictor variable was entered into the regression
equation. Significant effects here would indicate that affective climate intensity had a
direct influence on the perceived team performance. In step 3, the moderator variable
was entered into the equation. Significant effects would indicate that affective climate
strength had a direct influence on perceived team performance. Finally, in step 4, the
interaction term was entered into the equation. Significant effects would indicate that
affective climate strength moderate the relationship between affective climate intensity
and team performance.
The usual procedure is to compare the increase of the explained variance (R²) in
each step. Each block of steps was made for each one of the two dimensions of affective
climate on each criterion variable. In total, four hierarchical regression analyses were
performed. We used Z scores as the standardized measure of affective climate, thus
avoiding problems of multicolinearity arising from correlations between product terms
and their component parts (Sivasubramaniam, Murry, Avolio & Jung, 2002). When
Affective Climate 24
evaluating the significance of predicted effects we used one-tailed tests, which are
suitable for directional hypotheses (Pelled, Eixenhardt & Xin, 1999).
To interpreter each significant interaction, we took an additional analytical step.
In this analysis, we examined the functional form of the interactions between
independent variable and moderator variable. As indicated Pelled, Eixenhardt & Xin
(1999), this method “is appropriate for interactions involving two continuous variables
and avoids the information loss associated with median split procedures” (p. 18). First
we took a partial derivate from the regression equation to determine mathematically
whether the moderated relationship was monotonic or nonmonotonic (for revision, see
Schoonhoven, 1981). A nonmonotonic effect is when, in function of portion of the
observed range of the moderator variable, the relationship between independent and
dependent variable changes its sign, that is, the relationship is negative over a portion
of the observed range of the moderator variable and positive over the remainder of its
range. A monotonic effect is when the relationship between both variables does not
change its sign over the range of the moderator variable. To determine it, we estimate
the point² on the range of moderator variable, at which the independent variable has no
effect on the dependent variable (i.e. the point of inflection of the partial relation
dy/dx³). If this point falls within the observed range of moderator variable, it indicates
that the effect of independent variable on dependent variable changes its signs.
We then carried out the graphing of the partial derivate from the regression
equations (relation dy/dx). Each graph expresses the effect of the independent variable
on the criterion variable over the range of the moderator variable, that is, the change in
perceived team performance, given a change in affective climate intensity, over the
range of affective climate strength.
Affective Climate 25
Results
All the statistical analyses conducted for testing the study hypotheses were
performed at the team level. All variables were assessed at the individual level. In order
to meaningfully aggregate individual responses to the group level, differentiation and
internal consistency criterion must be demonstrated (James, 1982). Within-team
agreement was estimated by means of the Average Deviation index (ADM(J)) of Burke,
Finkelstein and Dusin (1999). This index is based on the calculation of the average
deviation for each scale item and has several advantages compared to other indexes as
interrater agreement index (rwg), developed by James, Demaree and Wolf (1984), which
is the most frequently used index, or the intraclass correlation coefficient (ICC) (for
revision see Burke et al., 1999). In order to interpret it, a null response range must be
specified. Burke and colleagues (1999) recommend using a null response range equal to
or less than 1 when the response scale is a Likert-type 5-point scale. Accordingly, we
concluded there was within-unit agreement when the AD M(J) values were equal to or
less than 1.
This index was calculated for each team on each of the individual level variables
(affective work experiences and teams´ perceived group performance). The obtained
values were then averaged across the 133 teams. The mean AD M(J) for tension climate
was 0.63 (sd=0.21), for optimism climate 0.62 (sd=0.25) and for teams´ perceived group
performance was 0.24 (sd=0.16). Thus, we concluded that the level of within-team
agreement was sufficient to aggregate scores to the work-unit level
Discrimination among teams' scores on each affective climate facet and perceived
group performance was investigated by means of a number of one-way ANOVAs. If the
variation between workteams' scores is greater than the variation within workteams,
then it can be concluded that studied variables relate to the particular teamwork and not
Affective Climate 26
to a higher unit level. The observed F values were significant (tension climate: F (132,
447) = 3.07, p<0.01; optimism climate: F (132, 447) = 2.01, p<0.01 and teams´
perceived team performance: F (132, 579) = 2.10, p<0.01) for all three variables they
were assessed and they showed that there was significant between-team differentiation
(Chan, 1998).
Given sufficient within-group agreement and between group differences,
participants´ individual responses regarding members´ affective states and perceived
performance were aggregated to the group level by calculating the mean value within
each group.
Descriptive Statistics and Correlations
Means, standard deviations, correlations, and reliability estimates are provided in
Table 1. The alpha coefficients show sufficient internal consistency in every variable since,
without exception, Cronbach´s α meets the criterion of .70 (Nunnaly, 1978).
PLEASE INSERT TABLE 1 ABOUT HERE
Both affective climate intensity measures (tension and optimism) were significantly
and negatively associated (r = -.61, p<.01). Tension climate strength was positively
correlated with optimism climate strength (r = .41 p<.01).
Examining the relationship between affective climate strength and intensity, optimism
climate intensity was positively associated with optimism climate strength (r= .35, p<.01).
However, there is not a significantly correlation between tension climate intensity and
strength within the team (r=.06, n.s.).
The optimism climate intensity was positively correlated with the two perceived
performance measures (team members: r = .45, p<.01; and external evaluator: r = .19,
Affective Climate 27
p<.05). However, the tension climate intensity was only correlated with members perceived
team performance (r = -.36, p< .01). Regarding affective climate strength, a correlation
between optimism climate strength and teams` perceived performance was observed (r=.30,
p<.01).
Positive correlations between the two performance measures could be expected. This
would indicate a good internal consistency level of performance measures. However, the
external evaluators` perceived performance was not related with the other team
performance measure (r=.11, n.s.).
Hypotheses Testing
1. Within-team agreement in affective responses.
As it indicated above, in order to test Hypothesis 1, the Average Deviation index
was computed for each workteam and each affective variable. The average and median
ADM(J) values, below the critical point of 1 (Burke et al., 1999) for each affective
variable were as follows: tension climate: .63 and .64 and optimist climate: .62 and .60.
Discrimination among teams´ scores on each affective variable was investigated by
means of one-way ANOVAs. The F ratios obtained for each affective variable were the
following: tension climate: F (132, 447) = 3.07, p<0.01; optimism climate: F (132, 447)
= 2.01, p<0.01. These results confirmed that shared affective states relate to the
particular teamwork and not to a higher unit level.
2. Influence of work teams´ affective climate intensity and strength on team
performance
In order to test the effects of affective climate intensity and strength on
perceived team performance, we performed a hierarchical multiple regression analysis
(Cohen & Cohen, 1983). Hypothesis 2 suggested that tension climate intensity would
Affective Climate 28
have a negative influence on team performance and optimist climate intensity would
have a positive influence on performance. As tables 2 and 3 show, this influence was
supported.
PLEASE INSERT TABLE 2 ABOUT HERE
The results of the regression analyses showed that tension climate intensity
negatively predicted the teams` perceived performance (table 2, B= -.33, p< .01), and
external evaluators` perceived performance (table 2, B= -.18, p< .05). So, in those teams
in which tension affective climate is high, the level of perceived team performance is
lower.
PLEASE INSERT TABLE 3 ABOUT HERE
On the other hand (see table 3) optimism climate intensity had a positive influence
on teams` perceived performance (B= .35, p< .01) and external evaluators` perceived
performance (B= .24, p< .01). So, the greater optimism climate within the team, the
greater will be the perception of team performance.
Hypotheses 3 indicated that affective climate strength would have a positive effect
on team performance. The results of hierarchical regression analysis showed that
optimism climate strength (table 3) predicted the teams` perceived performance (B= .14,
p< .05). Thus, the greater degree of homogeneity in the optimism’s affective
experiences within the team, the greater will be the teams` perceived performance.
Tension climate strength (table 2) did not have significant effects on teams` perceived
performance (B=.-.02, n.s.) and external evaluators` perceived performance (B=-.01,
n.s.).
Affective Climate 29
3. Moderator influence of affective climate strength
In order to test the moderator influence of tension and optimism climate strength
on the relationship between affective climate intensity and perceived team performance,
we carried out moderator regression analyses.
Hypothesis 4 proposed that climate strength in tension and in optimism would
increase the relationship between climate intensity in tension and optimism,
respectively, on team performance. The results of the hierarchical regression analyses
showed that the influence of affective climate intensity on team performance was
significantly moderated by affective climate strength. The interaction predicted the team
performance in two of the four hierarchical regression analyses carried out. Tables 2 and
3 show moderator effect of climate strength on the relationship between optimism
climate intensity and teams` perceived team performance (table 3, B= -.12, p< .05), and
tension climate intensity and external evaluators` perceived team performance (table 2,
B= .16, p< .05).
The interactions are displayed in figure 1 and 2. Figures show how the relationship
between independent and criterion variable changes (vertical axis) when the value of
moderator variable changes (horizontal axis). Partial derivate analyses revealed that the
effect of optimism climate intensity on team performance was monotonic over the range
of climate strength observed in our sample (i.e. the relationship between independent
and criterion variable has a single sign over the observed range of the moderator
variable) and the effect of tension climate intensity on team performance was
nonmonotonic (i.e. the negative relationship between tension climate and external
evaluators` perceived performance changed its sign in a portion of range of the
Affective Climate 30
moderator variable). That is, when the homogeneity in tension was large, the
relationship had positive sign although its magnitude was low and it was near to zero.
PLEASE INSERT FIGURE 1 ABOUT HERE
PLEASE INSERT FIGURE 2 ABOUT HERE
Graphical displays showed that the effects were weaker for higher levels of
tension and optimism climate strength. Hypothesis 4 proposed when climate strength is
high, the influence of climate intensity on team performance variable would be high.
Contrarily, our results showed that the relationship between the independent variable
and the criteria was weaker for the high climate strength.
Discussion
The aim of the present study was to examine the existence of affective climate
within the workteams. Likewise, it analyzed the influence of affective climate, intensity
and strength, on team performance as well as the moderator role of affective climate
strength in the relationship between workteams affective climate intensity and their
performance.
Hypothesis 1 indicated that team members have similar affective experiences.
Results provide substantial support for it. It was observed that bank professionals
belonging to the same team had similar levels of tension and optimism. Further, there
were significant differences among workteams` tension and optimism. This
discrimination among teams supported that shared affective states could be relate to the
particular teamwork and not to a higher unit level. Thus, results confirm the existence of
affective climates in workteams. These results are congruent with those obtained by
other researchers (González-Romá et al., 2000; Peiró, et al., 2000). As González-Romá
Affective Climate 31
et al., (2000) argue, when new concepts appear, “it is necessary to study their validity
and usefulness in improving our understanding of organizational behavior” (p.107).
Empirical research on the validity of this kind of climate is still scarce and, because of
it, we believe our study makes an important contribution to the establishment of this
new climate type in the environment of organizational psychology.
This study also shows that team affective climate may have important
consequences for organizations. Results showed direct effects of affective climate
intensity on team performance (hypothesis 2): tension climate intensity had negative
effects on external evaluators` perceived performance and teams´ perceived
performance. Furthermore, optimism climate intensity had positive effects on external
evaluators` perceived performance and teams` perceived performance.
These results show that negative and positive affective climate within the team
influences, negatively and positively respectively, on performance appraisal carried out
by the area manager and team itself. Thus, when affective climate is positive and
members feel more cheerful, optimist and animated, external evaluators` and teams`
perceived performance will be higher that when affective climate is negative and
members feel more tension, nervousness and anxiety.
Our results are congruent with others theoretical works which states that team
affect has a significant effect on group performance (Barsade & Gibson, 1998; George,
1996; Kelly & Barsade, 2001). Thus, as indicated by Kelly (2003), affective tone is
often cited as important factor in many successful groups. The discovered effects show
also the same direction as do the effects reported by George (1995), Totterdell (1999;
2000) and Duffy and Shaw (2000). George (1995) found that team positive affective
tone would be significantly associated with perceived team performance by external
evaluators (branch managers). Similarly, Totterdell`s (1999; 2000) results showed that
Affective Climate 32
team’s positive affect positively influences on team’s subjective and objective
performance. Finally, Duffy and Shaw (2000) found that negative group affect (envy)
was directly and negatively related to group performance. In conclusion, our study
offers additional direct evidence about the influence of group affect on team behavior.
Hypotheses 3 stated that homogeneity in shared affective experiences within the
team would influence on team performance positively. Our results showed that
optimism climate strength had a positive influence on teams` perceived performance, so
that the greater the affective homogeneity in optimism within the team, the greater
teams` perceived performance. Several authors (Barsade & Gibson, 1998; George,
1996) obtained similar results and they argued that members` positive affective
similarity would lead positive outcomes for the team, among them a better performance.
Moreover, our results are congruent with scarce empirical studies on affective diversity
within organizational literature. Barsade et al. (2000) found that homogeneity in
positive affect within the team affect positively on company financial performance, so
that, the greater homogeneous in teams` positive affect, the greater financial
performance.
Homogeneity in negative affect did not have any effect on financial performance
in Barsade`s et al. (2000) study. We neither found a relationship between negative
affectivity diversity and outcome variables. One possible explanation for this may be
the fact that, as George (1996) indicated, both dimensions of affect, positive and
negative, “are caused by different factors, have differential relationships with
behaviors.., and have different consequences for individuals and organizations” (pp. 7879).
In conclusion, our results highlight the relevant role affective climate strength
plays in workteam performance. As pointed out in the dispersion literature (Brown &
Affective Climate 33
Kozlowski, 1999; Klein et al., 2001; Kozlowski & Klein, 2000) to get a complete
picture of group dynamics one must take into account dispersion measures as well as
mean levels of group variables. Future studies should investigate the impact of diversity
in shared affective responses on team functioning and effectiveness.
The moderator role of affective climate strength was partially supported
(Hypothesis 4). Tension climate strength moderated the impact of tension climate
intensity on external evaluators` perceived performance but not on teams` perceived
performance. On the other hand, optimism climate strength moderated the influence of
optimism climate intensity on teams` perceived performance, but not on external
evaluators` perceived performance. These results are congruent with the idea that
climate strength affects the predictability of affective climate intensity on team
outcomes (Lindell & Brandt, 2000). However, in both cases, an unexpected sign was
observed. Tension climate strength weakened the influence of tension climate intensity
on external evaluators` perceived performance and optimism climate strength weakened
the influence of optimism climate intensity on teams` perceived performance.
These moderator effects show the contrary direction that those reported by other
authors. In fact, Scheneider et al. (2002) found that high perceptive climate strength
fostered the impact of units` climate on customer perceptions of service quality, and the
relationship disappeared when climate strength was low. On the other hand, GonzálezRomá`s et al. (2002) results show that units` perceptive climate strength moderated the
relationships between the goals orientation and innovation climate and two criterion
variables, work satisfaction and organizational commitment, so that these relationships
were stronger in strong climate conditions than in weak climate conditions. Finally,
Barsade`s et al. (2000) study suggested that teams with low positive affect and high
affective homogeneity had levels of cooperativeness and conflict similar to those teams
Affective Climate 34
with high positive affect. But teams with low levels of homogeneity and low positive
affect were significantly lower in cooperation and higher in conflict.
As we mentioned before, there is no clear consensus about how the heterogeneity,
or its opposite, influences team’s outcomes. Given the ambiguity in the literature which
shows arguments in favour of benefits of heterogeneity and arguments against them, it
is possible that homogeneity within the team play a different moderator role for
different team processes and outcomes. It may be that climate strength would not
always have the same role on the relationships between teams` climate and certain
team’s outcomes. As Barsade and Gibson (1998) pointed out, although there is a
relationship between team heterogeneity and team processes and outcomes, the nature
and direction of that relationship may vary depending on the type of diversity and type
of outcomes, and even, as in this case, depending on each type of affect. Thus, our
study, partially, supports the influence of affective homogeneity within the team on the
magnitude of the relationship between the affective climate and the team’s outcomes.
However, it is necessary to continue researching on moderator role of affective climate
strength because its role seems to be different for different types of conditions and team
outcomes.
Regarding different results among both sources of performance appraisal, as we
observed before, the two performance measures did not correlate. It could be that
external evaluator (area manager) applied different criteria and standard in his/her
evaluation than team members did, what would cause those inter-observers differences.
As De Quijano (1992) argues, the low correlation among two appraisers` scores does
not necessarily demonstrate low reliability of the performance measure. The lack of
convergence between different sources is a consistent finding in the empirical literature
on performance appraisal systems (Facteau & Craig, 2001; Zammuto, London &
Affective Climate 35
Rowland, 1982), which has shown that different rater groups (e.g. team members and
area manager) frequently do not agree concerning job performance (Bozeman, 1997).
As the most desirable situation, we would hope that the ratings obtained from various
sources would display inter-rater agreement. However, as Valle and Davis (1999) point
out, there are a series of reasons for that multiple raters may not come to the same
conclusions concerning team performance. Among them, these authors suggest the
following: organizational level, job knowledge, information processing tendencies or
rating styles, rater motivations, and perceptual biases (Harris, 1994). On the other hand,
Facteau and Craig (2001) suggest that the failure of observed ratings from different
sources to converge may be less a function of the measurement system than of
substantive differences between the rater groups (e.g. different rater groups may have
different conceptualizations of what constitutes effective performance in a particular
job). Bozeman (1997) pointed out that the performance ratings provided by different
rater groups are role-related, so that, various rater groups likely evaluate the aspects of
the performance that are most relevant to the raters themselves. Baruch and Harel
(1993) suggested that different rating sources have different opportunities to observe
rated work behavior and, therefore, they have different perspectives. Similarly, Lance,
Teachout and Donnelly (1992), noted that the low convergence among different types of
rates may be due to the fact that different rater groups could be exposed to only a part of
rate behavior. For it, some scholars have asserted that ratings obtained from different
rater groups may be valid, even if they do not exhibit high levels of agreement with one
another (Bozeman, 1997). As De Quijano (1992) argued, this gives support to the thesis
that the really important factor is the evaluator and not the instrument, because there are
not such powerful evaluation instruments that can neutralize the huge variance provided
by evaluators. However, it is necessary to continue to look for the reliability and
Affective Climate 36
validity in inter-rater measures (Valle & Davis, 1999). To provide clear and objective
information to the appraisers or to offer them objective standards of performance could
help to reduce such variance (Schrader & Steiner, 1996).
One concern here might be that the observed relationships may be the result of
common method variance due to exclusive use of self-report measures. Thus, a potential
limitation of the finding that showed the role of affective climate (intensity and
strength) on teams` perceived performance is the fact that the source for all of these
variables was the members themselves and this makes the identification of direct and
moderator effects more difficult because same source data tend to be correlated.
However, as we use data which come from different sources for team performance
measure, the potential limitation concerning of these observed relations would be
partially mitigated by examining the findings that showed as affective climate, in
tension and optimism, has direct effects on external evaluators` perceived performance
and affective climate strength moderates those effects. As Schmitt (1994) points out, the
use of measures from distinct sources can avoid the effects of this bias.
However, this study is not without limitations. First, considering the crosssectional nature of study, we cannot provide conclusions about the causal ordering
between the variables. Thus, the results obtained should be interpreted with caution.
Further, affective climate is a dynamic construct. In future research, therefore, it is
necessary panel designs with various data collections. Thus, we could approach to its
dynamic nature. Consideration of different temporal intervals will allow to test causal
relationships and to detect changes in dynamic constructs which could have periods of
latency and effects with different duration.
Second, units sampled for this study met the within-unit agreement criterion for
aggregation of unit members` affective states. This means that if individuals within
Affective Climate 37
teams have similar affective reactions, there will be relatively low variance in these
reactions. To the extent that affective reactions are similar within teams, this reduction
in variance or restriction of range will attenuate their consequences (Cohen & Cohen,
1983) within teams. So, our results suffer from restriction of range problems, which
have probably attenuated the hypotheses test. However, and although a specific withinunit agreement criterion is met, climate strength had significant direct effects on team
performance and moderator effects on the relationship between affective climate
intensity and team performance.
Third, the sample of teams used in the present study is relatively homogeneous (it
only includes bank professionals). This restricts the generalization of our results.
Finally, a last limitation would be the use of subjective measures of team
performance. Replications with a different kind and more objective measure of team
performance may be useful. Furthermore, the appropriate measures of performance
could vary across organizations. However, subjective performance measures have been
used by studies which have shown the important role of the groups’ emotions in the
individual and organizational outcomes (George, 1995; Health & Jourden, 1997;
Totterdell, 2000).
In conclusion, and these limitations notwithstanding, the results of this study have
shown that the team members share affective experiences and that they could be
empirically diagnosed. Moreover, our study suggests that team affective climate, in its
dimensions of intensity and strength, influences on team performance and affective
climate strength is a significant factor to understand the link between team affective
climate intensity and team performance. Thus, due to the affective climate’s influence
on team outcomes, its nature, antecedents, and consequences at work are important
areas for future research.
Affective Climate 38
Our study would also show that when examining affects, one needs to take into
account not only the mean affective level of each group but also the group's affective
diversity. Our data support the importance of the diversity in current researches within
work and organizational psychology. Further, it provides some empirical evidence
supporting Dispersion Theory of Brown and Kozlowski (1999) and demonstrates that
“climate strength” is a meaningful and valid construct and it may have an important role
in the understanding of the consequences of work-units` climate. Moreover, our study
shows that dispersion constructs should be examined in future research if we want to
understand better the factors that are related to team performance (González-Romá et
al., 2002).
With respect to the more general topic of multilevel research in organizations,
affective climate is a multilevel construct and it could be conceptualized and
operationalized at different analysis levels within organizations (González-Romá &
Peiró, 1999). It would be interesting to examine the relationships between affective
climate, at different analysis levels, and performance at different levels as well. As
González-Romá and Peiró (1999) indicate, so we could contribute to the realization of
organizational behavior as an integrated science.
Beyond theoretical interest, results of this study have practical implications. They
confirm that affective climate within the team is an important factor which impact on
performance. It could help managers make decisions about the factors to consider when
deciding how to design their teams. So, positive effects of beneficial affective climate
on team performance would be enhanced.
Affective Climate 39
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Affective Climate 50
Footnotes
1
In this study we have considered as team members the employees without
including the manager (leader). Leader was only included in the team performance
appraisal.
² This point is calculated with the following formula: x = -b1 / b3, being b1 the
predictor coefficient in the regression equation and b3 the interaction coefficient.
³ The relationship between independent and dependent variable over the range of
the moderator variable. It is calculated in the following way: dy/dx = b1 + (b3*
moderator variable)
Affective Climate 51
Table 1. Work-Unit Means, Standard Deviations, and Correlations among team level observed variables
Mean
SD
1
2
3
4
5
1.
Optimism climate intensity
3.62
.56
(.91)
2.
Tension climate intensity
3.25
.64
-.61**
(.90)
3.
Optimism climate strengthª
-.62
.25
.35**
-.38**
-
4.
Tension climate strengthª
-.63
,21
-.13
.06
.41**
-
5.
Teams´ perceived performance
4.09
.29
.45**
-.36**
.30**
-.03
(.80)
6.
External Evaluators´ perceived performance
3.68
.57
.19*
.17
.11
.01
.11
*p< .05 two-tailed, ** p< 0.01 two-tailed
Cronbach's alpha coefficients are in the correlation matrix diagonal
Note. a The Mean shown is for the corresponding Average Deviation index (AD M(J))
6
(.78)
Affective Climate 52
Table 2. Hierarchical Regression Analysis Results: Tension Affective Climate as predictor
Dependent Variables
Predictor
Teams` perceived
performance
External evaluators`
perceived performance
Team Size
n.s.
n.s.
Team Tenure
n.s.
n.s.
R2
.01
.01
Tension Climate Intensity
-.34**
-.18*
Step 1
R2
.13
.04
Δ R2
.12**
.03*
Tension Climate Strenght
-.02
-.01
R2
.13
.04
Δ R2
.00 n.s.
.00 n.s.
Tension Climate Intensity x Strenght
.02
.16*
R2
.13
.07
R2
.00 n.s.
.02*
Step 2
Step 3
Step 4
* p≤ .05
Δ
** p≤ .01 One-tailed tests were used for effects predicted in directional hypotheses
B are the unstandarized regression coefficients from the significant final stage of the regression analysis.
Affective Climate 53
Table 3. Hierarchical Regression Analysis Results: Optimism Affective Climate as Predictor
Dependent Variables
Predictor
Teams` perceived
performance
External evaluators`
perceived performance
Team Size
n.s.
n.s.
Team Tenure
n.s.
n.s.
R2
.02
.01
Optimism Climate Intensity
.40**
.24**
R2
.20
.07
R2
.19**
.06**
Optimism Climate Strenght
.14*
.05
R2
.22
.07
R2
.02*
.00 n.s.
Optimism Climate Intensity x Strenght
-.12*
.02
R2
.24
.07
R2
.02*
.00 n.s.
Step 1
Step 2
Δ
Step 3
Δ
Step 4
* p≤ 0.05
Δ
** p≤ 0.01
One-tailed tests were used for effects predicted in directional hypotheses
B are the unstandarized regression coefficients from the significant final stage of the regression analysis.
Affective Climate 54
Figure Captions
Figure 1. Moderating effect of climate strength in tension on the relationship between
tension climate intensity and external evaluators` perceived performance.
Figure 2. Moderating effect of climate strength in optimism on the relationship between
optimism climate intensity and teams` perceived performance.
d Ext. Evaluators` Perceived Performance / d Tension C. Intensity
Affective Climate 55
,4
,2
0,0
-,2
-,4
-,6
-,8
-3
-2
-1
0
1
Tension Climate Strength
2
3
Affective Climate 56
d Teams` Perceived Performance / d Optimism C. Intensity
1,0
,8
,6
,4
,2
0,0
-3
-2
-1
0
1
Optimism Climate Strength
2
3