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]
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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.

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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

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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

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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.

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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.

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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-

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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.

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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.

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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

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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)

    388 H. J. M. Kooij-de Bode, D. van Knippenberg and W. P. van Ginkel

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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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