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8/10/2019 Afectivitate Negativa Si Luarea Deciziilor in Grup
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Good Effects of Bad Feelings: NegativeAffectivity and Group Decision-making
Hanneke J. M. Kooij-de Bode, Daan van Knippenberg1 andWendy P. van Ginkel1
TNO Kwaliteit van Leven, PO Box 718, 2130 AS Hoofddorp, The Netherlands, and 1Rotterdam School
of Management, Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands
Corresponding author email: [email protected]
Extending the growing interest in the relationship between affect and workgroup processes,we propose that groups make better use of their distributed information and there-fore make better decisions when group members are higher in negative affectivity. In anexperiment, we studied the influence of negative affectivity when information wasdistributed among group members and when group members had fully shared information.Results indicated that negative affectivity indeed stimulates group information processingand decision quality when information is distributed among group members.
Introduction
Organizations often rely on groups for decision-
making based on the assumption that groups
possess a broader range of informational resourcesand more diversity of insights than individuals
(Ilgenet al., 2005; Jackson, 1991; Tindale, Kameda
and Hinsz, 2001). This is expected to enhancedecision quality when groups exchange and inte-
grate the task-relevant information and perspec-
tives that may be distributed over their members
(De Dreu, Nijstad and van Knippenberg, 2008;
Hinsz, Tindale and Vollrath, 1997; van Knippen-
berg, De Dreu and Homan, 2004). However,
group decision-making studies show that groups
typically are poor users of their distributed
informational resources. Groups with distributed
information often fail to discuss individual group
members unique information and focus more on
information known to all members before groupdiscussion (Stasser and Titus, 1985; Wittenbaum
and Stasser, 1996). Even when unique informa-
tion and perspectives are entered into group
discussion, groups often fail to recognize the
relevance of unique information and base decisions
on information that was already known to all
members before discussion (Gigone and Hastie,1993; Scholten et al., 2007; Winquist and Larson,
1998). A core issue for research in groups effective
use of distributed information therefore is to
identify factors that are conducive to the elabora-tion the exchange, discussion and integration of
task-relevant information and perspectives (van
Knippenberg, De Dreu and Homan, 2004).
Over the course of more than 20 years, research
in group decision-making with distributed infor-
mation has made substantial progress in uncover-
ing factors that affect a groups use of distributed
information (e.g. Kerr and Tindale, 2004). Con-
sistent with the conclusion that research in
organizational behaviour has a longer tradition
in the study of cognitive processes than in the
study of affective processes (Brief and Weiss,2002), research in group decision-making has so
far neglected the influence of group member
affective dispositions. There is a consistent body
of evidence, however, suggesting that negative
affect is associated with more careful and extensive
information processing. Building on this work we
develop and test the hypothesis that when group
This research was financially supported by Grant 402-01-043 of the Netherlands Foundations for ScientificResearch (NWO) to Daan van Knippenberg.
British Journal of Management, Vol. 21, 375392 (2010)
DOI: 10.1111/j.1467-8551.2009.00675.x
r 2009 British Academy of Management. Published by Blackwell Publishing Ltd, 9600 Garsington Road, OxfordOX4 2DQ, UK and 350 Main Street, Malden, MA, 02148, USA.
mailto:[email protected]:[email protected]8/10/2019 Afectivitate Negativa Si Luarea Deciziilor in Grup
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members are more strongly disposed to experience
negative affect (i.e. are higher on negative affectiv-
ity; Watson and Clark, 1984), the group engages in
a more careful and extensive process of informa-tion elaboration and reaches higher-quality deci-
sions. This focus on group member negative
affectivity is in line with a more general trend inresearch in organizational behaviour to become
increasingly attuned to the role that affect moods
and emotions and affective dispositions play in
organizational behaviour (e.g. Brief and Weiss,
2002; Elfenbein, 2007; van Knippenberg et al.,2008; Staw and Barsade, 1993; Weiss and Cro-
panzano, 1996), including in groups and teams
(Barsadeet al., 2000; George, 1990).
The problem of distributed information in group
decision-making: hidden profiles
A key rationale for the team-based organizationof work and for making groups rather than
individuals responsible for decisions is the prover-
bial notion that two heads know more than one.
Different group members may know different
things and have different perspectives on the task
at hand. Combining this distributed information
(i.e. information that is only known to one group
member or a subset of group members) should thus
allow groups to reach high-quality decisions (Ilgen
et al., 2005). Research in distributed informationhas described this as the hidden profile to be
uncovered by the group (e.g. Wittenbaum and
Stasser, 1996). While the information available to
individual group members before group interaction
may suggest one decision alternative, full use of the
information available to the group i.e. integrating
the information distributed over group members
would suggest another, superior, decision alter-
native. Viewed in this way, the core task of
decision-making groups with distributed informa-
tion (cf. informational diversity; van Knippenberg
and Schippers, 2007) is to exchange, discuss andintegrate their distributed information to come to a
high-quality decision (van Ginkel and van Knip-
penberg, 2008) a process called group informa-
tion elaboration (van Knippenberg, De Dreu and
Homan, 2004). Recent research in group decision-
making has consistently supported this proposition
regarding the core role of group information
elaboration (van Ginkel and van Knippenberg,
2008, 2009; van Ginkel, Tindale and van Knippen-
berg, in press; Homan et al., 2007, 2008; Kooij-de
Bode, van Knippenberg and van Ginkel, 2008).
Research in distributed information, however,
shows that groups are poor users of their distributedinformation (Stasser and Titus, 1985; Wittenbaum
and Stasser, 1996). Group members tend to focus
on discussion of the information that is alreadyavailable to all before group discussion, and to the
extent that distributed information does enter group
discussion its impact on the final group decision
tends to be much smaller than that of information
available to all. One reason for this is hard tochange: information that is known to all has a
higher chance of entering group discussion because
it can be brought up by all individuals in the group
(Stasser, 1999). Other reasons are directly related to
the cognitive-motivational processes involved in
group information elaboration and are therefore
more interesting from an applied perspective.As a somewhat obvious consequence of the
need to reach agreement, group members are
often primarily focused on a search for common
ground in an attempt to reach consensus about a
decision (van Ginkel and van Knippenberg,
2008). Such common ground is often found in
the information available to all group members
before discussion, which will typically argue in
favour of a certain decision preference. Because
group members have a tendency to discuss infor-
mation in a preference-driven way (i.e. focusing
on information that supports a certain prefer-ence) rather than in an evidence-driven way (i.e.
focusing on all decision-relevant information;
Hastie and Pennington, 1991), such preferences
tend to bias discussion away from distributed
information and render it more likely that group
members zoom in on an emerging group con-
sensus. Faced with such emerging consensus,
group members may take this consensus as a sign
of the validity of the groups preferred decision
option, and move to finalize the decision without
further exploring the information available to the
group (van Ginkel and van Knippenberg, 2008).Factors that render group members more open to
new information, less easily satisfied with an emerg-
ing consensus, and less hesitant to move against this
emerging consensus, may thus motivate more
extensive information elaboration and result in
higher-quality decisions (cf. Scholten et al., 2007).
Interestingly and importantly, research in indivi-
dual judgement and decision-making suggests that
negative affectivity may accomplish just that.
376 H. J. M. Kooij-de Bode, D. van Knippenberg and W. P. van Ginkel
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Negative affect and information processing
The concept of affect captures the disposition to
experience positive and negative feelings (alsoknown as positive affectivity and negative affectiv-
ity) as well as positive and negative transient mood
states (Elfenbein, 2007; Forgas, 1995; Russelland Barrett, 1999; Watson and Tellegen 1985).
Whether as disposition or as transient mood,
research in affect supports the conclusion that
negative affect has important consequences for
information processing that are directly relevant toa groups use of distributed information.
To capture the influence of negative affect on
group information elaboration and decision-
making in the present study, we focus on group
member dispositional (trait) negative affectivity.
The interest value of this focus on negative
affectivity lies in the fact that while state negativeaffect (i.e. negative mood) may be relatively
unpredictable unless it is specifically tied to
circumstances that would directly feed into the
group decision-making context (an interesting
but different issue altogether), negative affectivity
may be seen as a group composition variable that
exerts a predictable influence (Barsadeet al., 2000;
Kelly and Barsade, 2001). In that sense, findings
for negative affectivity may have important
implications for team staffing and design. Even
so, given that negative affective states are a core
reason why negative affectivity influences groupinformation processing, we build on theory and
research in both negative affectivity and negative
moods to outline how group member negative
affectivity influences group information elabora-
tion and decision quality.
Negative affectivity is the disposition to ex-
perience subjective distress (Watson and Clark,
1984). People score higher on negative affectivity
the more they are disposed to experience negative
feelings like sadness, guilt, nervousness and so
on, while they score lower on negative affectivity
the less they are disposed to experience suchfeelings (Watson, Clark and Tellegen, 1988). While
clearly the core of negative affectivity is the disposi-
tion to experience negative affect, this disposition is
associated with a specific pattern of cognitions, and
these cognitions and affect mutually influence each
other to create and maintain the disposition of
negative affectivity (Watson and Clark, 1984).
Note that in healthy populations high negative
affectivity refers to relatively mild levels of negative
affectivity and not to the higher levels of negative
affectivity that may be observed in clinical samples
(Watson and Clark, 1984; Watson, Clark and
Tellegen, 1988), just as moods reflect low-intensityaffective states that do not have the intensity of
emotions (Forgas, 1995). It is these mild levels of
negative affect that are associated with moreextensive information processing rather than with
some of the dysfunctional consequences of clinical
levels of negative affectivity.
Key to understanding the influence of negative
affectivity on information processing is the proposi-tion that affect has an important signalling function
affective states are informative to the individual.
Affect signals whether the state of things requires
attention and potentially action or is satisfactory
and does not require vigilant monitoring. Negative
affect signals that the state of things is problematic
and therefore requires attention and potentialaction. As a consequence, negative affect is associ-
ated with more extensive information processing
and greater openness and attention to new informa-
tion (Bless and Schwarz, 1999; Clore, Schwarz and
Conway, 1994; Forgas, 1995; Forgas and Bower,
1987; Forgas and George, 2001; Frijda, 1988;
Schwarz, 1990; Schwarz and Bless, 1991). Negative
affect does not simply influence cognitive effort or
processing capacity, but rather induces a particular
style of processing (Bless and Fiedler, 2006). Nega-
tive affect supports a bottom-up processing style (i.e.
evidence-driven rather than preference-driven) fo-cused on external/situational information relevant
to the issue (e.g. task) at hand. Negative affect thus
may motivate adapting the internal state (e.g. atti-
tudes, beliefs, preferences) to new information (e.g.
the requirements of a problematic external state)
(Bless, 2001; Bless and Fiedler, 2006; Fiedler, 2001;
Forgas, 1995, 2002; Forgas and George, 2001).
At the individual level of analysis, these infor-
mation processing benefits of negative affect/
affectivity (i.e. of mild levels of negative affect/
affectivity compared with lower levels) have been
demonstrated for a variety of issues. Research inpersuasive communication for instance shows
that negative mood compared with positive mood
results in more careful information processing
(Bohner et al., 1992; Mackie and Worth, 1989)
and work by Forgas suggests that negative com-
pared with neutral moods lead to more accurate
perceptions and attributions of external stimuli
(Forgas, 1998; Forgas, Laham and Vargas, 2005).
Negative affectivity is likewise associated with
Negative Affectivity and Group Decision-making 377
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more vigilant attention to new and potentially
worrisome information and with less closing of
the mind based on decision preferences and greater
openness to decision-disconfirming information(Olsen and Zanna, 1979; Watson and Clark,
1984). Also of relevance to the benefits of negative
affectivity for group processing of distributedinformation, Tong et al. (2008) show that indivi-
duals conformed less to others opinions when in a
negative mood compared with a neutral or positive
mood, and Watson and Clark (1984) conclude in
similar vein that negative affectivity is associatedwith lower conformity to others opinions.
The group information elaboration process is
not identical to individual-level information
processing, but there are obvious linkages between
the conclusions of these conceptual and empirical
analyses and the proposition that a groups use of
distributed information is critically contingent onopenness to and elaborate processing of new
decision-relevant information and perspectives.
Accordingly, based on the evidence that negative
affectivity is associated with greater attention and
openness to new information, we propose that
group member negative affectivity is associated
with more elaboration of distributed information
and therefore with higher-quality decisions in
groups with distributed information.
Negative affectivity and distributed information:
the present study
Research in group decision-making with distrib-
uted information has identified insufficient infor-
mation elaboration inspired by a focus on common
ground and emerging group consensus as the key
problem standing in the way of high-quality group
decisions. Negative affectivity may be an important
factor in this respect, because it inspires greater
attention and openness to new decision-relevant
information and motivates more evidence-driven
and less preference-driven information processing.
It is exactly this bottom-up processing style fo-cused on new information associated with negative
affectivity that may help decision-making groups
break away from their tendency to limit group
discussion largely to information that is already
known to all group members before discussion.
The lower tendency to conform to others opinions
associated with negative affectivity further con-
tributes to the processing benefits of negative
affectivity by making group members less likely
to zoom in on emerging consensus without
extensive consideration of the available informa-
tion. Applying these individual-level insights to the
group decision-making context, we may thereforeexpect that decision-making groups dealing with
distributed information engage in more informa-
tion elaboration and reach higher-quality decisionsthe higher their members are in negative affectivity
(i.e. again noting that this implies a comparison of
moderate levels of negative affectivity with lower
levels of negative affectivity).
Clearly, group information elaboration is agroup process shaped by the interaction and
exchange between group members and not an
individual-level process, and when applying in-
sights from the individual study of negative affect
and information processing to group processes an
important question is what the most appropriate
model is to relate group composition in terms ofindividual negative affectivity to group process
(elaboration) and performance (decision quality).
Indeed, when studying the influence of disposi-
tional factors in groups and teams, it is good to
realize that different composition models are
possible: mean trait, variance in trait, and mini-
mum or maximum level of the trait probably are
the more obvious ones (Barricket al., 1998). While
all these models may be legitimately studied,
conceptual analysis should typically be able to
identify the more appropriate model. To determine
the most appropriate composition model a sensiblestrategy is to follow the notion that the composi-
tion model used should match the nature of the
task, and to use Steiners (1972) taxonomy, which
distinguishes disjunctive, conjunctive and additive
tasks, to classify the type of task used in the study
(Beersmaet al., 2003; Homanet al., 2008; Neuman
and Wright, 1999). If the task is essentially additive
in nature, a mean (average) model is for instance
the more appropriate model: because group mem-
bers contributions additively combine to create
the group product, the underlying dispositional
influences that influence group members beha-viour are most appropriately modelled as additive
(Beersmaet al., 2003; Homanet al., 2008). Follow-
ing this reasoning, in the present study we focused
on a mean negative affectivity (i.e. disposition
averaged over group members) model to test our
predictions (cf. Barsade et al., 2000; Kelly and
Barsade, 2001), because the task of exchanging,
discussing and integrating information to reach a
group decision is primarily an additive task. The
378 H. J. M. Kooij-de Bode, D. van Knippenberg and W. P. van Ginkel
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group relies equally on all members to contribute
their unique knowledge, and the discussion of this
information is also an additive process that suffers
if not all members contribute.Individual-level research in the role of affect
suggests that negative affectivity is not related to
cognitive effort per se but rather expresses itsinfluence through differences in processing style,
specifically in greater openness to new information
and less preference-driven information processing,
as well as in a lower tendency to conform to the
opinion of others. Accordingly, we expect that theinfluence of negative affectivity is contingent on the
distribution of information in the group. From the
perspective of the distributed information problem,
distribution of information can range from a
situation in which all information is available to
all group members before discussion (fully shared
information) to a situation in which certain piecesof information are available to only one of the
members (distributed information; Gruenfeldet al.,
1996; Stasser and Titus, 1985). In view of its
influence on processing style, the influence of
negative affectivity should be especially apparent
in the treatment of distributed information (which
should come up as new information in group
discussion) and not necessarily evident in the
treatment of information that is already known to
all (which could also reflect differences in pre-
discussion information processing). To substantiate
this point, in the present study we include acomparison of a situation in which groups deal
with distributed information with a situation in
which all information is already fully shared (i.e.
known to all members) before group discussion (cf.
Kooij-de Bode, van Knippenberg and van Ginkel,
2008). Based on the notion that negative affectivity
is particularly relevant to the elaboration of new
information that may run counter to pre-discussion
preferences and emerging group consensus, we
predict that negative affectivity only shows a
positive relationship with information elaboration
and decision quality in groups with distributedinformation. This leads to the following hypotheses.
H1: Group member negative affectivity is
positively related to group information elabora-
tion in groups with distributed information, but
not in groups with fully shared information.
H2: Group member negative affectivity is
positively related to group decision quality in
groups with distributed information, but not in
groups with fully shared information.
Based on analyses of decision-making with distri-
buted information that put information elaboration
centre-stage in mobilizing the groups informational
resources and reaching high-quality decisions (van
Ginkel and van Knippenberg, 2008; van Knippen-berg, De Dreu and Homan, 2004), we expected that
information elaboration mediates the predicted
relationship with decision quality. That is, negative
affectivity is expected to influence decision quality
through its relationship with group information
elaboration.
H3: Elaboration of task-relevant information me-
diates the interaction of negative affectivity and
distribution of information on decision quality.
We tested these hypotheses in an experimentalstudy of decision-making groups. This allowed us
to manipulate the distribution of information and
reach conclusions about causality in this respect,
and enabled us to assess the group processes
leading to the final decision through behavioural
coding of group interaction, which is a more
objective and more valid way of assessing group
process than the team self-reports typical of field
research (Weingart, 1997; Wittenbaum, Hollings-
head and Botero, 2004). This experimental set-up
also allowed us to define a relatively objective
measure of group decision quality. Moreover, testsof interactions in survey research often suffer from
low power because same-source biases and correla-
tions between independent variables lead to the
underestimation of interaction effects (Evans, 1985;
McClelland and Judd, 1993), and an additional
advantage of our experimental set-up was that we
could partly address these problems and thus
increase the statistical power of our interaction tests.
Method
Sample and designTwo hundred and seventy students (175 male and
95 female) from a university in The Netherlands
participated in the study for monetary compen-
sation (10 euro, approximately 13 US dollars or
9 pounds sterling). The majority of the partici-
pants were management students (70%). Their
mean age was 20 (SD51.89). The experimental
design included distribution of information as an
experimental manipulation (distributed versus
Negative Affectivity and Group Decision-making 379
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fully shared) and negative affectivity as a quasi-
experimental factor. Participants were randomly
assigned to 90 groups of three, and groups were
randomly assigned to the experimental condi-tions. Dependent variables were group informa-
tion elaboration and decision quality.
Data of some groups could not be included be-cause of missing values. For one group the decisions
were not available, five groups were not videotaped
due to technical problems, in one group a partici-
pant did not fill out the questionnaire that measured
negative affectivity, and in two groups participantsdid not fill out the items that measured the mani-
pulation check for distributed information.1 Finally,
residual analysis identified two groups as outliers on
the information elaboration measures. When re-
examining these groups using the audio-video data,
one of the groups seemed to have incorrectly
understood the instructions, while no irregularitieswere found in the other group. Subsequently, this
one group as well as the nine other groups described
above were excluded from further analyses.
Measurement of negative affectivity
Negative affectivity was measured before the actual
experiment by the full ten-item measure developed
as part of the Positive and Negative Affect Schedule
(PANAS) to measure negative affectivity (Watson,
Clark and Tellegen, 1988). For this measure,
individuals, following the instruction Indicate to
what extent you generally feel this way, rate theextent to which they tend to experience such
negative affective states as distressed, nervous
and jittery. Responses were assessed on five-point
scales (15disagree and 55agree; a50.83). This
PANAS measure has been shown to be internally
consistent and exhibit trait-like stability.2
As discussed in the introduction, the average of
group members scores was used to represent
negative affectivity at the group level (M51.77,
SD50.28, Md51.73, range51.13). Note thatthe observed levels of negative affectivity were
indeed quite low as would be expected from a
mentally healthy population, and that as outlinedin the introduction our analysis pertains to the
range of very low to moderate levels of negative
affectivity.
Also note that because negative affectivity is an
individual differences measure and individualsare randomly assigned to groups, there is no
reason to expect agreement between group
members levels of negative affectivity nor is such
agreement a precondition for computing group
members mean level of negative affect (cf.
Beersma et al., 2003; Homan et al., 2008). (In
contrast, such agreement would be required whenindividual ratings refer to a shared group experi-
ence such as group conflict or group cohesion,
but this is not the issue we are dealing with here;
cf. Klein and Kozlowski, 2000).3
Decision task
The experimental task was a three-person decision
task that was an altered version of Architectural
Design Firm (Palmer and Thompson, 1998).
Although the original task was a negotiation task,
the task was changed to make it a purely cooper-ative decision task. Participants received a case in
which they had to design a house, and in which a
client specified required features and a limited
budget. Participants were told that they were a
team of experts who had to work together to (a)
make a design that met the requirements and
budget of the client and (b) earn maximum profit
for the architectural firm. All participants were
given information about pricing for various
options they could include in the design plan, a
profit schedule (indicating the amount of profit for
the firm if an option would be included in the
1Preliminary analyses including these two groupsshowed that conclusions were unaffected by whether ornot these groups were included. However, because weconsidered the most appropriate test of our hypotheses
to be one in which all analyses were based on data fromthe exact same groups, we decided to leave these twogroups out of the analyses.2Our analysis links information processing benefits tonegative affectivity and not to the absence of positiveaffectivity, which as a trait is independent of negativeaffectivity (Watson, Clark and Tellegen, 1988). Even so,in anticipation of questions from interested readers, wealso assessed positive affectivity (with the full ten-itemPANAS measure) and analysed its relationship withinformation elaboration and decision quality. As antici-pated, no significant relationships were obtained.
3To further validate our conclusion that the mean modelis the more appropriate model to predict groupinformation elaboration and group decision quality,we also explored other models focusing on variance innegative affectivity, minimum level of negative affectiv-ity and maximum level of negative affectivity. Corro-borating our conceptual analysis, these analyses showedthat the mean model was the only model able to predictgroups use of distributed information.
380 H. J. M. Kooij-de Bode, D. van Knippenberg and W. P. van Ginkel
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design, with some options being more profitable
than others) and special extra profit information
involving certain (combined) options.
The highest possible joint profit was h73,250.While in theory the lowest possible profit was
zero, this would in fact only be achieved if groups
did not spend any of the clients budget in effectwhen they did not execute the task. As soon as
groups start investing the budget, they make a
profit. Because groups are not required to spend
the entire budget of the client, and indeed not all
groups did, it is not possible to put a concretenumber to the lowest possible score for groups
following task instructions, as any value we
would quote would be arbitrary. Note that this
also means that differences between conditions
should not be interpreted with reference to the
theoretical scale low of zero, but rather with
reference to this more realistic but impossible tospecify low. We therefore suggest relying more on
indicators of effect size to assess the magnitude of
differences between conditions than on reference
to theoretical scale range.
Manipulation of distribution of information
All groups received the same information, but the
way in which the information was distributed
among group members differed between the two
conditions (Gruenfeld et al ., 1996; Kooij-de
Bode, van Knippenberg and van Ginkel, 2008;Stasser and Titus, 1985). General information
(e.g. information about the purpose of the task)
and task-irrelevant information (e.g. the children
of the customers love the zoo) were shared in
both conditions. In addition, part of the informa-
tion about the profit associated with different
decision options was also shared in both condi-
tions. In the fully shared condition, the full set of
decision-relevant information was given to all
members. However, in the distributed informa-
tion condition, several items of information that
were necessary to reach an optimal decision weredistributed over group members in such a way
that each group member received some informa-
tion that no other member had. Because of this,
group members individual information argued
for a lower-quality decision than the information
of all group members combined (i.e. a hidden
profile was created; Stasser and Titus, 1985).
Specifically, in addition to the profit associated
with each design option, certain combinations of
design options yielded a higher profit than could
be expected on the basis of the associated profits
of the options in isolation. There were six such
combinations of options that would yield extraprofit. In the distributed information condition,
information about the extra profit associated
with these combinations was distributed amongmembers i.e. available to only one of the
members, with each member receiving informa-
tion about two different combinations (i.e. no
member was favoured in terms of receiving
unique information). In addition, informationabout the availability and associated profit of
some of the design options was distributed
among group members.
Importantly, at the group level groups in both
conditions possessed the exact same pool of
information (as they should; otherwise we would
experimentally confound distribution of informa-tion and amount of information available to the
group). Yet, group members in the distributed
information condition possessed some informa-
tion not known to their fellow group members,
whereas all group members possessed the same
(full) set of information in the fully shared
condition. Following standard procedures for
research in distributed information (e.g. Gruen-
feld et al., 1996; Stasser and Titus, 1985), in the
distributed information condition group mem-
bers were informed that the information they
received might slightly differ between members,but they were not informed about the exact
nature of the differences.
Procedure
Groups were seated in a small room, where
participants were asked for permission to record
the group interaction on audio-video tapes.
Participants first filled out the negative affectivity
questionnaire. After completion, they received
a folder containing general information about
the decision task and specific decision-relevantinformation that varied as a function of the
distribution of information manipulation (see
above). Groups were then given 20 minutes to
complete the decision task and to write down
which options they had chosen with the asso-
ciated prices and profits. After that, they filled
out a questionnaire to check the experimental
manipulation and assess demographics. Finally,
participants were debriefed and paid.
Negative Affectivity and Group Decision-making 381
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Dependent measures
Manipulation check. To assess the success of the
distribution of information manipulation we used
four items (responses on five-point scales: 15dis-
agree, 55agree). Examples of items are The other
two group members had partly other information
than I and The other two group members hadexactly the same information as I (reverse coded).To assess interrater agreement we used the awg(1)value (instead of the more frequently used rwg(1)index), following the recommendations of R. D.
Brown and Hauenstein (2005). We did not rely on
ICC(1) because indices of agreement in this context
seem more important than indices of consistency in
scores over group members (cf. Kozlowski and
Hattrup, 1992). The awg(1)value for the manipula-tion check was 0.79, indicating strong agreement,
so this variable was aggregated to the group level
(a50.98).
Information elaboration. Group information ela-
boration was assessed through behavioural observa-
tion using audio-video recordings of the group
discussions. Coding followed the logic of the cod-
ing scheme developed by van Ginkel and van
Knippenberg (2008), which is rooted in van
Knippenberg, De Dreu and Homans (2004) anal-
ysis and definition of group information elaboration
(also see van Ginkel and van Knippenberg, 2009;
van Ginkel, Tindale and van Knippenberg, in press;Homan et al., 2007; Kooij-de Bode, van Knippen-
berg and van Ginkel, 2008). The rating scheme used
in the present study resulted in scores on a five-point
scale, where each scale point is operationalized in
terms of specific behavioural standards observable
from the audio-video recordings. In line with earlier
research in distributed information these standards
include such behaviour as the exchange and repe-
tition of information (e.g. Larson, Foster-Fishman
and Keys, 1994; Stasser, Taylor and Hanna, 1989).
Based on the conceptualization of information
elaboration these standards also include behaviour-al indicators of the actual use and integration of
distributed information such as asking questions
about information introduced in group discussion
or drawing conclusions from the combination of
different pieces of information (note that these
behaviours cannot exist independently from the
exchange of information).
A score of 5 was given when the group elaborated
thoroughly on the information, i.e. when the three
group members actively discussed all task-relevant
options and information, considered these facts at
length, asked each other for task-relevant informa-
tion, and discussed it in detail. A score of 1 wasgiven when a group hardly elaborated on the
information, i.e. when group members mainly gave
their opinion about certain options, discussed theiropinions instead of task-relevant information, and
agreed with each other without much discussion
(M52.59, SD51.20). Information elaboration
was coded on the group level by two judges
(k50.75, indicating good interrater reliability).
Decision quality. Decision quality was opera-
tionalized as the amount of profit the groups
earned. Groups had to write down their chosen
design options on a form, with corresponding
prices and profits and (by summing up) their totaljoint profit. For simplicity, we divided this joint
profit associated with the group decision by 1000
before analysing (M569.48, SD53.14).
Results
Treatment of the data
Regression analyses were conducted for the mani-
pulation check for informational diversity and the
three hypotheses. We dummy-coded distribution
of information (
0.5 for distributed informationand 0.5 for fully shared information). We centred
negative affectivity and computed the cross-pro-duct of negative affectivity and the dummy for
distribution of information following the recom-
mendations of Aiken and West (1991).
Manipulation check
Hierarchical regression of the manipulation
check on negative affectivity, distribution of in-
formation and their cross-product only showed a
main effect of distribution of information,b5 0.93, po0.001. Groups with fully shared
information indicated less diversity of informa-
tion (M51.81, SD50.70) than groups in
which information was distributed among group
members (M54.58, SD50.29). No influence of
negative affectivity was observed, b5 0.02, ns,
nor was there an interaction effect of distribu-
tion of information and negative affectivity,b5 0.01, ns. We concluded that the mani-
382 H. J. M. Kooij-de Bode, D. van Knippenberg and W. P. van Ginkel
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pulation of distribution of information was
successful.
Information elaboration
To test whether negative affectivity was more
strongly related to elaboration in groups withdistributed information than in groups with fully
shared information, as predicted in Hypothesis 1,
we entered negative affectivity and distribution
of information in the regression equation in step 1
and added their cross-product in step 2. Asexpected, the interaction between negative affec-
tivity and distribution of information was signifi-
cant,DR250.05, po0.05,b5 0.22, po0.05. In
line with Hypothesis 1, groups with distributed
information engaged in more information elabora-
tion when they were higher on negative affectivity.
Simple slope analysis to determine the relationshipbetween negative affectivity and information ela-
boration in the fully shared and distributed
information conditions showed that this relation-
ship was significant in the distributed information
condition, b50.52, po0.001. Information ela-
boration in groups with fully shared information
was not affected by negative affectivity, b50.07,
ns (see Figure 1).
Main effects of negative affectivity and distribu-
tion of information on information elabora-
tion were also found. Groups higher in negative
affectivity elaborated more on information, DR250.09, po0.01, b50.29, po0.01. Groups with distri-
buted information elaborated less on information
than groups with fully shared information, DR250.11, po0.01, b50.34, po0.01. As the pattern of
results for the interaction shows, however, these
findings were qualified by the interaction.
Decision quality
To test Hypothesis 2 we entered negative affectivity
and distribution of information into the regressionequation in step 1 and added their cross-product in
step 2. The predicted interaction between negative
affectivity and distribution of information
was significant, DR250.06, po0.05, b5 0.24,
po0.05. As expected, groups with distributed
information reached higher-quality decisions when
they were higher in negative affectivity, as was
evident in a significant simple slope, b50.39,
po0.01. Decision quality in groups with fully
shared information was not related to negative
affectivity,b5 0.09, ns (see Figure 2).
A main effect of distribution of information ondecision quality was also found. Groups with
distributed information reached lower-quality
decisions than groups with fully shared infor-
mation, DR250.22, po0.01, b50.47, po0.01.
As the pattern of findings for the interaction shows,
however, this effect was qualified by the interaction.
Information elaboration and decision quality:
mediation analysis
Information elaboration was positively corre-
lated with decision quality (r50.65, po0.01).To test whether elaboration mediated the inter-
action of negative affectivity and distribution of
information on decision quality (Hypothesis 3),
we relied on a method recently proposed to test
1
2
3
4
+1SD1SD
Negative Affectivity
Distributed
Fully Shared
InformationElaboration
Distribution of information
Figure 1. Interaction effect of negative affectivity and distribution
of information on information elaboration.
65
66
67
68
69
70
71
72
+1SD1SD
Negative Affectivity
Distributed
Fully Shared
DecisionQuality
Distribution of information
Figure 2. Interaction effect of negative affectivity and distribution
of information on decision quality.
Negative Affectivity and Group Decision-making 383
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mediated moderation (Preacher, Rucker and
Hayes, 2007). This method is often seen as
superior to models relying solely on hierarchical
regression analyses (e.g. Baron and Kenny, 1986;Yzerbyt, Muller and Judd, 2004), because it
provides more information about the nature of
mediated moderation (cf. Edwards and Lambert,2007) and because a bootstrapping procedure is
applied which makes the method more robust.
An SPSS macro provided by Preacher, Rucker
and Hayes (2007) was used to test the model.
For the present study, this involved three steps.In the first step, regression analysis is used to test
the extent to which the mediator (information
elaboration) is predicted by both independent
variables (negative affectivity and distribution of
information) and their interaction. In the second
step, the dependent variable (group decision
quality) is regressed on both independent vari-ables, the interaction between the independent
variables, and the mediator. In the third step, a
bootstrapping procedure is used to verify the
conditional indirect effects in groups with dis-
tributed information versus groups with fully
shared information. As can be seen in Table 1,
the interaction between negative affectivity and
distribution of information predicted information
elaboration (as also reported above). Further-
more, when performance was regressed on nega-
tive affectivity, distribution of information, their
interaction, and information elaboration, the
effect of information elaboration was significant,
while the interaction was not. Step 3 shows that
the conditional indirect effect of negative affec-
tivity on group decision quality through informa-tion elaboration is only significant for groups
with distributed information and not for groups
in which information is fully shared. In sum, theanalysis provides support for Hypothesis 3 that
predicts that information elaboration mediates
the interaction of negative affectivity and dis-
tribution of information on decision quality (see
Figure 3).
Discussion
Groups often make suboptimal use of their
distributed information (Wittenbaum and Stas-
ser, 1996). Research has identified group mem-bers tendency to search for common ground and
to process information in a preference-driven way
(as opposed to a bottom-up, evidence-driven
way) as key influences in this respect. This leadsgroup members to follow emerging group con-
sensus based on the information already available
to all prior to group discussion, while ignoring
distributed information that may argue against
emerging group consensus. The current analysis
points to group member negative affectivity as an
important influence to the good here, because
negative affectivity is associated with a processing
Table1. Test of the conditional indirect effects
b SE b t p
Step 1 Regressing information elaboration on
Negative affectivity 1.26 0.42 3.14 0.002
Distribution of information 0.88 0.23 3.89 o0.001
Negative affectivity distribution of information 1.92 0.80 2.39 0.019
Step 2 Regressing decision quality on
Negative affectivity 0.02 0.97 0.02 0.98
Distribution of information 1.90 0.56 3.37 0.001
Negative affectivity distribution of information 2.85 1.90 1.50 0.14Information elaboration 1.30 0.26 4.95 o0.001
Step 3 Conditional indirect effects Boot con. ind. effects Boot SE Boot z Boot p
Distributed information 2.91 0.92 3.17 0.002
Fully shared information 0.45 0.94 0.48 0.63
Note. Step 1 consists of regressing information elaboration on negative affectivity, distribution of information and their interaction.
Step 2 consists of regressing group decision quality on negative affectivity, distribution of information, their interaction, and
information elaboration. Step 3 consists of testing the conditional indirect effects of negative affectivity on group decision quality
through information elaboration for groups with distributed information and groups with fully shared information using
bootstrapping.
384 H. J. M. Kooij-de Bode, D. van Knippenberg and W. P. van Ginkel
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style that is less bound by preferences and parti-
cularly attuned to new information. In support of
this analysis, groups with distributed informationengaged in more information elaboration (Hy-
pothesis 1) and reached higher-quality decisions
(Hypothesis 2) the higher their members were in
negative affectivity. Further corroborating our
proposition that this influence of negative affec-
tivity is tied in particular to the processing of
new (i.e. distributed) information, group membernegative affectivity did not affect information
elaboration or decision quality in groups with
fully shared information. Supporting our propo-
sition regarding the key role of information
elaboration in mobilizing distributed informa-
tion, the interactive effect of negative affectivity
and distribution of information was mediated by
information elaboration (Hypothesis 3).
Implications for theory and practice
Perhaps somewhat counter-intuitively, the pre-sent study thus suggests that negative affectivity
the moderate as compared with lower levels
studied here may have positive effects. For group
tasks requiring the careful elaboration of new
information it may pay off to compose teams with
members with moderately high negative affectivity.
While we emphasize that the current findings
concern trait affect and not state affect and that
conclusions regarding the role of state negative
affect require future research, the implication of
these findings for negative mood states is interest-
ing and potentially important. Our findingswould suggest that just as negative affectivity
may have good effects, a negative affective state
is not necessarily a bad thing. Indeed, it may be
very functional that situational influences that are
typically associated with negative affect and
distress such as crisis and uncertainty elicit negative
affect (cf. Frijda, 1988). Negative affect may
motivate appropriate responses to the situation if
it leads group members to be more vigilant and to
better process new information that is potentially
relevant to resolving the problems at hand. Rather
than only focusing on alleviating negative affect
states in times of crisis and uncertainty, it maytherefore be valuable to try to mobilize these states
in situation-appropriate responses. This, however,
is a hypothesis to be tested in future research andnot a conclusion that can be based on the current
findings. In that sense, the present findings invite
future research to extend the current research to
the influence of negative mood states to more
comprehensively cover the influence of negativeaffect in groups.
An interesting implication of the finding that
negative affectivity is especially beneficial in groups
with distributed information lies in the clear link
between distributed information and diversity
(van Knippenberg and van Ginkel, in press; van
Knippenberg and Schippers, 2007; Williams andOReilly, 1998). As van Knippenberg, De Dreu
and Homan (2004) argue, the potential benefits of
work group diversity for group performance lie in
diversity as an informational resource in the pool
of task-relevant knowledge, expertise and perspec-
tives available to the group. Viewed from this
perspective, diversity is a distributed informational
resource, and it requires group information elabo-
ration to mobilize this informational resource.
Integrating these observations with the present
analysis, we may thus propose that group member
negative affectivity may be instrumental in harvest-ing the benefits in diversity.
An important point to note here is that negative
affectivity does not imply negative relationships
between group members. Good relationships may
be conducive to the effective use of distributed
information (Gruenfeld et al ., 1996; Kooij-
de Bode, van Knippenberg and van Ginkel, 2008;
Phillips, Northcraft and Neale, 2006) and to group
performance more generally (De Dreu and Wein-
gart, 2003; Mullen and Copper, 1994). If group
member negative affectivity were to disrupt rela-
tionships between group members, we would notexpect to obtain the current findings. Negative
affectivity need not lead to dysfunctional social
behaviour, however, nor does negative affect in
a group necessarily lead to problematic outcomes.
Sy, Cote and Saavedra (2005) for instance
observed that groups in a negative mood were
more persistent in task performance than groups in
a positive or in a neutral mood. In a related
vein, both Damen, van Knippenberg and van
Information
Elaboration Decision Quality
Distribution of
Information
Negative Affectivity
Figure 3. Interaction effect of negative affectivity and distribu-
tion of information on decision quality: mediation by information
elaboration.
Negative Affectivity and Group Decision-making 385
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Knippenberg (2008) and Van Kleef et al. (2009)
observed that under some circumstances team
leader negative affect can lead to higher perfor-
mance than team leader positive affect. Whileclearly poor relationships between group mem-
bers may be associated with negative affect,
negative affect is more likely to be consequencethan cause here, and the current findings are not in
opposition with findings of better team perfor-
mance in groups with better social relationships
among their membership.
We should be careful not to over-generalize theimplications of the present findings, however.
Negative affectivity should not always be bene-
ficial to group performance. Research in affect in
groups is limited, but it has also documented
instances in which positive affect is associated
with desirable outcomes and negative affect is
not. Barsade et al. (2000) and Sy, Cote andSaavedra (2005) found that group member posi-
tive affect predicted cooperation and coordina-
tion within the group, whereas negative affect
was unrelated to cooperation and coordination.
In addition, individual-level research suggests
that positive affect may be more conducive to
creativity than negative affect (Baas, De Dreu
and Nijstad, 2008; but see George and Zhou,
2002), and evidence suggests that negative affect
may render individuals more risk-avoidant in
decision-making (Williams, Zainuba and Jack-
son, 2003). Both these influences suggest thatnegative affect may be less conducive to the
performance of for instance research and devel-
opment teams that rely heavily on creativity and
a willingness to take risks in trying out new
avenues in product development (cf. Hirst, van
Knippenberg and Zhou, 2009). The conclusion
thus seems warranted that negative affect
and positive affect each have their own beneficial
influence, though each on different processes
and depending on contextual factors (George
and Zhou, 2002; Isen and Baron, 1991). While
negative affect may not influence social processeslike cooperation and coordination (cf. McIntyre
et al., 1991; Watson et al., 1992), it may exert
more influence on task persistence and informa-
tion processing.
Consistent with the general trend in research in
affect (Watson and Clark, 1997) and with the
analyses linking negative affect to information
processing on which our analysis builds (Bless,
2001; Forgas, 1995), we focused on general
negative affectivity. Negative affect/affectivity is
a higher-order factor that groups together more
specific instances of negative affect such as
distress, hostility and sadness, and one may raisethe question of whether our findings hold equally
for more specific conceptualizations of negative
affect. As Watson and Clark (1997) point out, theempirical evidence consistently shows that the
intercorrelations of different subsets of negative
affect are very high too high in fact to empi-
rically distinguish them and that the different
subdimensions typically do not differ in theirrelationship with other variables, thus favouring
a focus on the higher-order factor of general
negative affect. The more appropriate question
may in that sense be whether specific negative
emotions would have similar effects as we
observed for negative affect in the present study.
Emotions in contrast to moods (i.e. which arecloser to the general notion of affective states,
and the disposition to experience certain affective
states) are more intense, more short-lived and in
contrast to diffuse moods have a distinct cause.
From the perspective of the present analysis, this
greater intensity may be problematic as it might
be associated with impaired cognitive capacity
for information processing (cf. Ellis and Ash-
brook, 1988). The current data cannot speak to
this, however, and it remains a question for
future research whether the beneficial effects of
negative affect are indeed limited to mild levels ofnegative affect (cf. Forgas, 1995).
While the present findings clearly support the
conclusion that groups with distributed informa-
tion benefit from negative affectivity, a recent
study by Bramesfeld and Gasper (2008) warrants
discussion for yielding different results for mood
states. Although they also included an argument
leading to the prediction that negative affect is
more conducive to the use of distributed infor-
mation as we do in the current study, Bramesfeld
and Gasper observed that groups with members
in a positive mood made better use of theirdistributed information in group decision-making
than groups with members in a negative mood.
They attribute this finding to the greater cogni-
tive flexibility of happy as compared with sad
individuals (Lyubomirsky, King and Diener,
2005) a factor often associated with greater
creativity of people in a positive mood (Baas, De
Dreu and Nijstad, 2008). While both the current
study and the Bramesfeld and Gasper study focus
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on decision-making with distributed information,
there are many differences between the studies
that could potentially account for the differences
in findings (from differences in the decision-making task to cross-national differences), and
we can of course only speculate about the factors
underlying these differential findings.One factor standing out as a theoretically
interesting lead in this respect is the difference
between the structure of the decision options.
Whereas group members in our study had to
make a series of interrelated decisions to arrive atan overall group decision, group members in the
Bramesfeld and Gasper study faced a far simpler
decision structure they had to pick one of three
decision options. The latter set-up may create
clear opposition between group member decision
preferences and thus potentially opposition be-
tween group members (De Dreu, Nijstad and vanKnippenberg, 2008) in which the more coopera-
tive tendencies associated with positive affect as
opposed to the competitive tendencies associated
with negative affect (Forgas and George, 2001)
would help reach superior outcomes through
greater willingness to also consider the others
perspective (cf. De Dreu, Weingart and Kwon,
2000). In the current task, in contrast, the
decision structure is more complex with multiple
interrelated decision issues, and decision prefer-
ences are less easily construed as being in direct
opposition. This may make the development ofgroup discussion less contingent on cooperative
versus competitive tendencies than presumably in
the Bramesfeld and Gasper study, and thus allow
groups to benefit from the information proces-
sing advantage of negative affectivity (i.e. rather
than from the cooperative advantage of positive
affect). Clearly, currently this can only be
speculation, but future research might fruitfully
investigate the role of cooperative versus compe-
titive motives in the effects of affect on informa-
tion elaboration.
One other factor that deserves mention in thisrespect is that we focused on trait affect while
Bramesfeld and Gasper focused on state affect.
This may give rise to the question of whether
traitstate differences might explain the differ-
ences in findings. Trait and state indeed should
not be equated. Trait affect captures the disposi-
tion (i.e. likelihood) to experience certain affec-
tive states, which should not be equated with
being in that state (i.e. state affect) at any given
moment in time. Moreover, trait affect is
characterized by the habitual interplay of cogni-
tions and affect that presumably mutually influ-
ence each other (Watson and Clark, 1984),whereas state affect in principle refers to nothing
more than the experience of a certain affective
state. Accordingly, the influence of trait and stateaffect need not be identical (George, 1991). But
this is not to say that they would have the
opposite effect. Our analysis is rooted in a mix of
theory and research in trait and state negative
affect, and there is nothing in this literature tosuggest that trait negative affectivity and state
negative affect would have opposing influences.
We therefore deem it highly unlikely that this
would explain the difference between our findings
and the Bramesfeld and Gasper findings.
Limitations and future directions
Even though experiments are not conducted toestablish external validity (Brown, D. J., and
Lord, 1999; Dipboye, 1990; Mook, 1983), the
experimental nature of the current study may
raise questions about the generalizability of our
findings to work groups and teams in organiza-
tions. In this respect, it is important to note that
evidence from research in organizational beha-
viour suggests that many findings from labora-
tory experiments generalize to the field (Dipboye,
1990; van Knippenberg and van Knippenberg,2005; Locke, 1986) and recent research suggests
that this also holds for findings concerning
information elaboration in groups (Kearney and
Gebert, 2009; Kearney, Gebert and Voelpel,
2009; cf. van Dick et al ., 2008). Obviously,
however, the proof of the pudding is in the
eating, and it would be valuable if future research
established that the present relationships may
also be observed in work groups in organizations.
In reference to the experimental nature of the
task, we should also note that we should not
generalize conclusions regarding effect size fromexperiments (cf. Locke, 1986; Mook, 1983). The
interaction between negative affectivity and dis-
tribution of information explained 6% of var-
iance in decision quality, which is a decent-sized
effect in a test of an interaction relying on natural
variations in negative affectivity within the
sample (cf. Evans, 1985; McClelland and Judd,
1993), but we should not assume that similar-
sized effects would be observed in organizational
Negative Affectivity and Group Decision-making 387
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settings they could be larger as well as smaller. In
this respect, it is important to note that, in line with
the experimental character of our study, partici-
pants were randomly assigned to groups andconditions. As a result, variations in mean group
member negative affectivity between groups are
likely to be more modest than would be observedin organizations where attraction, selection and
attrition processes may create a wider range of
group mean negative affectivity (George, 1991).
Accordingly, due to restriction of range our
findings may underestimate the influence ofnegative affectivity as it might be observed in work
groups and teams in organizations.
A focus on trait negative affectivity may raise the
question of the relationship between negative
affectivity and other personality or individual
difference variables. As Watson and Clark (1984)
argue, negative affectivity should be related to thepersonality factor neuroticism as well as to other
individual difference variables with clear affective
components. However, for the study of affective
experiences and influences negative affectivity is the
more appropriate focus as it most directly and
purely captures the issue at stake: the disposition
to experience negative affect. Because our analysis
builds on research specifically concerning negative
affect, we therefore focused on negative affectivity
rather than on other personality or individual
differences indicators that may be more distal to
negative affect and more broadly defined. We donote the interesting possibility, however, that other
individual difference variables may have effects
similar to the ones observed in the present study
to the extent to which they are associated with
negative affect (e.g. neuroticism). Viewed from a
different perspective, we should acknowledge in this
respect that a limitation of our study is that we did
not include measures of other individual difference
variables to determine whether negative affectivity
indeed is the key driver of the observed relationships
rather than another individual difference variable
with which negative affectivity is correlated.Key to our analysis is the notion that negative
affectivity motivates a more evidence-driven pro-
cessing style that is more open to new decision-
relevant information. This prediction is firmly
rooted in individual-level research and our ela-
boration data suggest that at the group level it is
exactly this influence that obtains. Even so, our
study does not contain measures of group member
motivations that could have further substantiated
our propositions regarding the influence of nega-
tive affect. Future research that would also include
such motivational measures might in that sense be
valuable.The outcome of interest in the present study
was group decision quality, a group level vari-
able, and the group process predicting decisionquality, information elaboration, clearly is a
group-level variable too. Negative affectivity,
even though aggregated to the group level for
obvious reasons in the present study (i.e. we study
group-level process and outcome), is inherently anindividual-level factor and members within the
group may differ in their level of negative
affectivity. Individual performance at work is often
also enacted in the context of a work group or
team (e.g. Hirst, van Knippenberg and Zhou,
2009), and an interesting question for future
research would therefore be whether in a groupcontext there are individual-level outcomes that
would be contingent on group member negative
affectivity. The more elaborate information pro-
cessing of group members higher in negative
affectivity could for instance mean that group
members with higher negative affectivity learn
more (e.g. acquire new knowledge) in the course
of group interaction, or alternatively that all group
members learn more as a consequence of more
extensive group information elaboration in a group
where the average level of negative affectivity is
higher. Exploring such multilevel issues (i.e. groupcomposition and group process as predictor of
individual-level outcomes) in future research would
further advance our understanding of the role of
negative affectivity in work groups and teams.
While groups with distributed information
higher in negative affectivity engaged in more
information elaboration, they did not outperform
groups with fully shared information. This
finding should be seen in the context of this
current study, where methodological considera-
tions require that groups with distributed infor-
mation and groups with fully shared informationhave access to the exact same pool of information
at the group level (i.e. information distribution
and information available to the group should
not be confounded). In organizational practice,
however, groups with distributed information
(e.g. cross-functional teams) will typically have
access to a larger pool of information than
groups in which information is fully shared (cf.
van Knippenberg, De Dreu and Homans (2004)
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discussion of informational diversity). Accord-
ingly, in such situations negative affectivity may
actually help groups with distributed information
to outperform groups that are more homoge-neous in terms of their information. An impor-
tant next step would therefore be to study the
influence of negative affectivity on team decision-making and performance in organizational con-
texts where groups may differ in the (distributed)
informational resources available to them.
In conclusion
There seems to be an implicit assumption in
research and practice in organizational behaviour
that positive affect(ivity) is preferable over
negative affect(ivity) when it comes to desirable
organizational outcomes. The present findings
provide an important caveat in this respect,suggesting that positive affect, while no doubt
more enjoyable than negative affect, is not always
preferable to negative affect when it comes to the
quality of group performance. The influence of
negative affect in group performance may extend
beyond groups use of distributed information,
and our understanding of group decision-making
and group performance in general may stand
much to gain by the systematic investigation of
these potential influences of negative affect on
group decision-making and performance. In that
sense, the present study also provides furtherevidence for the importance of the growing
attention to affect and emotions in organizational
behaviour.
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