Categorization by groups and individuals

Organizational Behavior and Human Decision Processes 112 (2010) 70–81

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Categorization by groups and individuals Rebecca W. Hamilton a,*, Stefano Puntoni b, Nader T. Tavassoli c a b c

Department of Marketing, Robert H. Smith School of Business, University of Maryland, College Park, MD 20742, USA Department of Marketing Management, Rotterdam School of Management, Erasmus University, Burgemeester Oudlaan 50, 3062 PA Rotterdam, The Netherlands Department of Marketing, London Business School, Regent’s Park, London NW1 4SA, UK

a r t i c l e

i n f o

Article history: Received 21 August 2007 Accepted 20 January 2010 Available online 18 February 2010 Accepted by Linn Van Dyne Keywords: Categorization Group processes Prior knowledge Task conflict Category breadth

a b s t r a c t Categorization is a core psychological process that is central to decision making. While a substantial amount of research has been conducted to examine individual categorization behavior, little is known about how the outputs of individual and group categorization may differ. Four experiments demonstrate that group categorization differs systematically from individual categorization in the structural dimension of category breadth: categorizing the same set of items, groups tend to create a larger number of smaller categories than individuals. This effect of social context is a function of both taskwork and teamwork. In terms of taskwork, groups’ greater available knowledge mediates differences in category breadth between individuals and groups by increasing utilized knowledge (study 2). In terms of teamwork, task conflict moderates the effect of social context on category breadth (study 3). Moreover, the experience of categorizing individually or in a group influences individuals’ subsequent judgments (study 4). Ó 2010 Elsevier Inc. All rights reserved.

Introduction Consider a product development team tasked with designing a new product. The team has conducted exhaustive interviews with potential customers about their needs. Their next step is to categorize the customer needs so that they can better understand the voice of the customer and decide which functions and features to provide in the product. Structured categorization techniques are widely used in the product development process (e.g., Burchill & Brodie, 1997; Griffin & Hauser, 1993; Shiba, Graham, & Walden, 1993) but the team is undecided about which method to use. Methods such as the customer sort technique (Griffin & Hauser, 1993) and Applied Marketing Science Inc.’s Vocalyst technique rely on individuals to sort customer needs, whereas others, such as the Language Processing method (part of the Total Quality Management toolbox; see Burchill & Brodie, 1997), instruct the product development team to categorize customer needs as a group. Will categorizing individually or in a group affect task outputs and subsequent judgments? While a substantial amount of research has been conducted to examine individuals’ categorization behavior, much less is known about how groups categorize items (Olsson, Juslin, & Olsson, 2006). For example, a team tasked with designing a product cata* Corresponding author. E-mail addresses: [email protected] (R.W. Hamilton), spuntoni@ rsm.nl (S. Puntoni), [email protected] (N.T. Tavassoli). 0749-5978/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.obhdp.2010.01.002

log for an outdoor sports outfitter may group items into several categories: things to take on a camping trip, sailing equipment, fishing-related products, etc. Categorization decisions like these are influenced by exemplar-based reasoning (e.g., evaluating the overall similarity among items) which differs from the multi-attribute judgments we typically associate with decision making (e.g., deciding which alternative to choose by weighting multiple attributes according to their perceived importance; Juslin, Olsson, & Olsson, 2003). Due to the different kinds of cognitive processing involved, it is not clear that we can generalize earlier research comparing individual and group decision making to categorization tasks. Notably, in contrast to the large body of research showing that compared to an equivalent number of individuals groups often do not perform as well as they should (Kerr & Tindale, 2004), there is some evidence that when exemplar-based reasoning is used for a category-based learning task, the performance of dyads is better than the combined performance of individuals (Olsson et al., 2006). In line with calls for organizational researchers to uncover parallels and discontinuities in behavioral phenomena across individuals and groups (e.g., Rousseau & House, 1994), the primary goal of this paper is to understand how and when the outputs of group categorization processes—specifically, the breadth of the categories created—differ from those of individuals. A second goal of the paper is to understand the effect of participating in a group vs. an individual categorization process on categorizers’ subsequent judgments. Past research suggests that categorization shapes consumers’ product evaluations (Meyers-

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Levy & Tybout, 1989; Sujan, 1985), particularly for new products (Moreau, Markman, & Lehmann, 2001) and brand extensions (Boush & Loken, 1991; Meyvis & Janiszewski, 2004). Whether the product development team categorizes individually or as a group may have implications beyond the physical output of the initial categorization task. In the next section, we review previous research on categorization as well as research on individual and group decision making to make predictions about how group and individual categorization behavior might differ. We propose that by pooling the knowledge of their members, groups will have more knowledge available to them than individuals. To the degree that group processes encourage the leveraging of this available knowledge, groups should create smaller categories than individuals. We then report the results of four studies comparing group and individual categorization of the same items. In study 1, we find that groups and individuals systematically generate different category structures: given the same number of items, groups tend to create narrower categories that contain fewer items. In study 2, we show that this effect is mediated by available knowledge and utilized knowledge. Specifically, groups’ greater available knowledge leads to greater utilized knowledge, which in turn leads groups to create smaller categories than individuals. In study 3, we show that group processes moderate the effect of social context on categorization. In particular, task conflict exacerbates differences between individual and group categorization because group processes that involve higher task conflict and more equal participation by group members lead groups to utilize more knowledge. Finally, in study 4, we demonstrate that categorizing individually or in a group has subsequent effects on individuals’ judgments. We conclude with a discussion of theoretical and practical implications.

Comparing group and individual categorization There are often multiple ways to categorize a set of items, for example, using the taxonomic approach of categorizing an apple with other fruits or using the thematic approach of categorizing it with other things to eat for lunch (Ross & Murphy, 1999). The breadth of the categories created can also differ. Categorizers make tradeoffs between identifying relationships among items (increasing category breadth by categorizing apples with non-tree fruits such as strawberries and pineapples) and differentiating among items (decreasing category breadth by categorizing apples with other tree fruits; Rosch, 1978). Although research has shown that there are individual differences in categorization, such that some people categorize more broadly than others (i.e., creating fewer larger categories) regardless of the domain (Gardner, 1953), categorization behavior is quite malleable for any individual. Even small differences in the way individuals interact with the stimuli they are categorizing can have important effects on how they learn and remember category information (Love, 2005). We examine the malleability of category breadth based on whether categories are created by groups or individuals. To the best of our knowledge, no prior research has examined (1) group categorization in sorting tasks like the one described in the opening example and (2) how social context influences category breadth. Earlier research suggests that category breadth can gauge differences in information processing. For example, category breadth has been used to measure cognitive flexibility (Murray, Sujan, Hirt, & Sujan, 1990), complexity (Ulkumen, Morwitz, & Chakravarti, in press), and abstractness (Liberman, Sagristano, & Trope, 2002). Analogously, we believe that category breadth will reflect differences in categorization processes by individuals and groups. Category breadth is also an important variable because it can influence downstream processes such as learning and memory

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(Love, 2005). For example, in consumption-related settings, category breadth influences product-attribute categorization in new product development and consumers’ evaluations of brand extensions (Loken, 2006). There are two important differences between group and individual decision making that may generalize to categorization. First, groups have access to a larger pool of knowledge than individuals (e.g., Bonner, Baumann, & Dalal, 2002; Hinsz, 1990; Olsson et al., 2006). Second, group work involves interpersonal dynamics that individual work does not (McGrath & Kravitz, 1982). These two differences relate closely to the distinction that has been made between taskwork—the skills involved in the execution of a task— and teamwork—the social processes that regulate interaction among group members (Cannon-Bowers, Salas, & Converse, 1993; Thompson & Fine, 1999). These two differences are integrally related because group processes influence the degree to which the pool of knowledge available to the group (available knowledge) is actually used by the group in the execution of a task, becoming utilized knowledge. To understand how the output of categorization tasks varies depending on whether they are performed by individuals or groups, our research focuses on the relationship between taskwork and teamwork. Specifically, we examine the effects of available knowledge, group processes, and utilized knowledge on category breadth.

Taskwork Knowledge in a particular domain is an important moderator of individual-level categorization behavior. While novices tend to categorize at the basic level, experts tend to identify objects at more specific, subordinate levels (Johnson & Mervis, 1997). For example, experts are more likely to organize product information by product subcategories relative to novices (Cowley & Mitchell, 2003). Experts also use a larger number of attributes to discriminate among individual products within a category than novices (Alba & Hutchinson, 1987; Mitchell & Dacin, 1996). For both of these reasons, experts with greater domain knowledge tend to create smaller, more restrictive categories than novices. Thus, more restrictive category membership criteria can stem from more fine-grained distinctions along a principal dimension (Johnson & Mervis, 1997), the consideration of additional independent dimensions (Tversky, 1977), or both. For example, one can categorize soft drinks based on broad (e.g., fruit-flavored soft drinks) vs. narrow (e.g., orange-flavored vs. cranberry-flavored soft drinks) distinctions along one dimension. Alternatively, one can categorize soft drinks using a small (e.g., flavor) vs. a large (e.g., flavor, carbonated vs. noncarbonated, glass vs. plastic container) number of separate attributes. Regardless of the nature of the constraints used in categorization, the use of more restrictive criteria in category membership decisions leads to a larger number of smaller categories (Love, Medin, & Gureckis, 2004; Tversky, 1977). When individuals join together to form a group, the group collectively has access to more knowledge than each individual has (Hinsz, 1990; Olsson et al., 2006). Indeed, cross-functional teams within organizations are often created to bring together divergent perspectives. This does not necessarily make a group more ‘‘expert,” however. Group members may collectively have access to a wider range of knowledge or to more numerous perspectives than any one individual even if none of them is an expert. For example, landscapers and landscape architects working individually categorize the same trees differently because of their different interests, even though they are categorizing the same physical objects (Medin, Lynch, Coley, & Atran, 1997). Thus, a landscaper and a landscape architect working together might generate a larger number of distinct categorization criteria reflecting the dimensions that they

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each consider important (e.g., conditions under which a plant grows best as well as taxonomic relationships). Although groups have more knowledge available to them than individuals, it is not clear that greater knowledge availability will lead to greater knowledge utilization by groups (Stasser & Titus, 1985) and, therefore, more restrictive categorization criteria. Indeed, in the limit, if group members focus only on shared knowledge and do not consider group members’ idiosyncratic knowledge (Stasser & Titus, 1985), they may leverage less knowledge than individuals. It is difficult to measure how much knowledge is actually used by groups in a categorization task, but one consequence of group work should be cognitive consensus (Mohammed & Ringseis, 2001), or similarity in the way group members conceptualize the content that was categorized. To the extent that categorization encourages group members to develop a common vocabulary for discussing the items during their interaction, members’ descriptions of the categorization criteria may converge. The amount of knowledge used in a categorization task can be inferred by the number of different considerations to which category membership decisions are subjected (Love et al., 2004). In particular, when category membership decisions are informed by a larger number of viewpoints or by deeper knowledge about the items, one should expect group members to report using a larger number of criteria. If group members share their knowledge and come to consensus on the categorization criteria used for the task, group members should therefore report a larger number of categorization criteria after the task than individuals who categorized alone. Thus, we will measure the categorization criteria used by the group as a proxy for the degree to which knowledge is utilized by the group. If groups leverage more knowledge and apply more stringent categorization criteria than individuals to the task, groups should form less inclusive, more differentiated categories than individuals. We therefore predict a three-path mediation model (group or individual social context ? available knowledge ? utilized knowledge ? category breadth), formally expressed in the following hypotheses: H1: Groups will create smaller categories than individuals when categorizing the same items. H2: Differences in available knowledge between groups and individuals will mediate the effect of social context on category breadth by increasing utilized knowledge. Teamwork Some group processes are more effective at eliciting knowledge than others. Teamwork dynamics that foster more intense discussion and information elaboration among members improve information sharing (Henri 1995; Mesmer-Magnus & De Church, 2009; Schweiger, Sandberg, & Rechner, 1989; van Ginkel & van Knippenberg, 2008). An important determinant of the intensity of information exchange in a group is the degree to which teamwork processes foster consensus or conflict among group members (Kerr & Tindale, 2004). A large body of research supports the idea that task conflict—taskwork-related disagreement among group members—enables groups to use information more effectively (e.g., Jehn, 1995; Jehn & Bendersky, 2003; Schulz-Hardt, Brodbeck, Mojzisch, Kerschreiter, & Frey, 2006; Schweiger et al., 1989; Van der Vliert & De Dreu, 1994), as long as task conflict does not result in relationship conflict (Jehn, 1997; Simons & Peterson, 2000). For example, prediscussion dissent in a hidden profile problem like the one used by Stasser and Titus (1985) increases discussion intensity and significantly improves the chances of solving the problem, even when none of the members’ prediscussion preferences is correct (Schulz-Hardt et al., 2006). Applying these findings to categorization, if task conflict generates discussion of more cat-

egorization criteria, moderate task conflict should increase the degree to which available knowledge becomes utilized knowledge, reducing category breadth. Moderate levels of task conflict should therefore result in the group creating a larger number of smaller categories. H3a: Task conflict will moderate the effect of social context on category breadth. The difference between group and individual category breadth will be larger when groups experience moderate task conflict than when groups experience low task conflict. One way to estimate knowledge sharing in groups is to examine the degree to which individual team members influence the final decision (e.g., De Dreu & West, 2001; Taggar, 2002; Zarnoth & Sniezek, 1997). Moderate levels of task conflict should increase the degree to which individual mental representations influence the group’s output (DeChurch & Marks, 2001; Jehn & Bendersky, 2003). In other words, task conflict should reduce the likelihood that any one individual dominates the task, resulting, on average, in greater overlap between individual mental representations and the group’s output. We propose that under moderate levels of task conflict, team members will perceive themselves to be contributing more equally to the task and a larger number of individual viewpoints will be represented in the categorization output.1 H3b: Groups experiencing moderate task conflict will perceive a more equal contribution among team members than groups experiencing low task conflict. Effects of categorization on subsequent judgments Thinking back to the product development team choosing a method for categorizing customer needs, another important question is whether categorizing as part of a group or individually will have carry-over effects on individual memory and judgments. In other words, is category breadth exclusively a physical task output, or does it also affect people’s subsequent cognitions? Earlier research shows that experimental manipulations of category breadth can have consequences for subsequent judgments of unrelated targets (e.g., Mullen, Pizzuto, & Foels, 2002). For example, an earlier experience answering a series of questions using a larger number of response points (nine- vs. three-point scales) led participants to create a larger number of categories in a subsequent unrelated sorting task (Ulkumen et al., in press). Moreover, research on group decision making shows significant knowledge transfer from interacting groups to individuals (Hackman, 1992), demonstrating the consequences of group decision making for the subsequent decisions of individual group members (Laughlin & Adamopoulos, 1980). Combining these findings from individual and group settings, we predict that if groups categorize more narrowly than individuals, categorizing as part of a group should lead individuals to internalize a larger number of categorization criteria than categorizing individually. We propose that individuals who categorize in a group (vs. individually) will internalize stricter categorization criteria and that they will then apply these stricter criteria to a subsequent task, such as evaluating new products. Research has characterized evaluations of brand extensions as judgments about category membership, and suggests that consumers evaluate brand extensions by evaluating the degree of similarity or ‘‘fit” between the brand’s original product category and the extension’s product category (Aaker & Keller, 1990). It has been also been shown that individuals who categorize narrowly tend

1 In our conceptualization of task conflict, we limit our focus to the low-tointermediate range of the task conflict continuum because very high levels of task conflict may have negative repercussions for information sharing (Jehn, 1997; Simons & Peterson, 2000). References to high levels of task conflict in the text should therefore be understood in this context and interpreted as ‘‘moderately high”.

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to be less willing to try a new product than individuals who categorize broadly (Donnelly & Etzel, 1973), and that previous experience with a narrow vs. a broad category tends to decrease individuals’ evaluations of new products (Ulkumen et al., in press). Thus, previous experience categorizing in a group should reduce the perceived fit between the parent brand and the extension category and lead to less favorable evaluations of brand extensions, compared to previous experience categorizing individually. Formally, H4: After categorizing in a group rather than individually, participants should (a) report lower perceived fit between a brand extension into a new product category and the parent brand, and (b) evaluate brand extensions into new product categories less favorably. In addition to providing theoretical insight about subsequent effects of social context on judgments, this is an important substantive issue because brand extension is a common strategy for the entry of new markets, accounting for over 95% of all new product introductions in fast moving consumer goods (Information Resources Inc., 2007). Fig. 1 summarizes the conceptual model emerging from the research hypotheses. Study 1: first test of differences (H1) Study 1 was designed to test the prediction that given the same set of items to categorize, individuals will create larger categories than groups (H1). The study was described to participants as a product development task aimed at understanding customer requirements for food containers. It was modeled after the customer sort technique, a categorization task in which customer needs for a product are divided into logically related categories (Griffin & Hauser, 1993). Notably, the procedure is almost identical to tests used in cognitive psychology to study individual differences in categorization behavior (Gardner, 1953). Thus, the experimental task represents both a test of practical differences organizations might expect to realize and a theoretical test of categorization behavior. Method Sixty-one paid undergraduate students (24 were females) at a US university were randomly assigned to conditions, with 44 participants assigned to eleven groups of four participants each and 17 participants participating individually. Following procedures developed by Griffin (1989; Griffin & Hauser, 1993), participants were given instructions for the task and an envelope of cards pre-printed with the customer needs (see also Murray et al.,

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1990). The 48 customer needs used were a subset of the needs for food storage containers (i.e., picnic baskets) identified by Griffin (1989); e.g., ‘‘big enough to carry food for four people,” ‘‘container is waterproof,” and ‘‘protection for glass/breakables”. Participants in both conditions were instructed to create as many categories as they felt were necessary and were allowed to exclude items that they felt did not go with any of the other items. Next, they selected the customer need they felt was most representative of the category and placed it on top of the pile of cards. This minimizes the tendency to create a miscellaneous pile composed of requirements only related by the fact that they do not relate to other categories (Gardner & Schoen, 1962). Participants paper clipped each pile of cards together before putting the piles into an envelope. Participants recorded how long it took to complete the categorization task by noting their start and finish times. After they had finished sorting the cards, all participants individually answered questions about task and outcome satisfaction. Results For the means and standard deviations in this and the following studies, see Table 1. Across the two experimental conditions, there were no differences in participants’ familiarity with the product category being evaluated: 94% of those in the individual condition and 91% of those in the group condition had been on a picnic within the last 12 months (F(1, 59) = .18, p > .70). There was no difference in their satisfaction with the process (Mindiv = 5.47 and Mgroup = 5.73, F(1, 59) = .62, p > .43), their satisfaction with the outcome (Mindiv = 5.53 and Mgroup = 5.59, F(1, 59) = .62, p > .82), or the number of needs they excluded from the categorization task (Mindiv = 1.71 and Mgroup = 2.18, F(1, 26) = .27, p > .60). While previous research has typically found that groups spend more time on tasks than individuals (Hill, 1982), participants did not report spending significantly more time on the task in the group condition (M = 20:27 min) than in the individual condition (M = 19:38, F(1, 26) = .16, p > .69). Next, we computed the average size of categories created by groups and individuals. As predicted by H1, groups categorized more narrowly: groups included a smaller number of customer needs in each category (M = 4.49) than individuals (M = 5.96; F(1, 26) = 7.04, p < .05). Study 2: taskwork (H2) Study 2 was designed to examine whether the effect of social context on category breadth is mediated by available knowledge and utilized knowledge. Specifically, H2 predicts that the effect

Fig. 1. Conceptual model.

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of social context on category breadth results from a three-path mediation process, according to which social context (individuals working alone vs. in a group) influences the amount of available knowledge brought to the task, which in turn influences the number of criteria used in categorization (utilized knowledge), which then determines the size of the categories created. In this study, we assessed available knowledge by measuring participants’ familiarity with each of the items being categorized. We also examined whether categorizing in a group increases the knowledge actually utilized for the categorization task relative to categorizing individually. To measure knowledge utilized during the task, we asked participants to report the categorization criteria they used to organize the items during the task. Another goal of study 2 was to replicate the results of study 1 in a different domain, movies, that provided greater variance in prior knowledge among participants. From a substantive point of view, the use of products rather than customer needs makes the experimental task more similar to categorization situations encountered by consumers in the marketplace. Moreover, the results of similar product sorting tasks are often used to inform decisions such as manufacturers’ brand-extension decisions and retailers’ assortment layout decisions (Morales, Kahn, McAlister, & Broniarczyk, 2005). Method Design and participants Participants were 211 paid undergraduate and graduate students at a Dutch university (96 were females) who were randomly assigned to work either individually or as part of a three-person group. Sixty-one individuals and 50 groups of three participants completed the categorization task. All participants were fluent in English, the language used in all experimental materials. One participant in the individual condition was excluded from the analyses, leaving 110 observations for the analyses below. In addition to other indicators of poor data quality, the excluded respondent completed the categorization task in just 3 min, less than a quarter of the time taken by the average student in the individual condition. Procedure and measures The experiment was introduced as research on students’ opinions of movies. The 50 popular movies used in the study (e.g., ‘‘Finding Nemo,” ‘‘The Matrix,” and ‘‘Titanic”) were selected based on the rated familiarity of 61 movies in a pretest (N = 16) using participants from the same population as the main study. Participants in the main study first individually read the list of 50 movies and rated their familiarity with each movie using a three-point scale (labels were ‘‘Know little or nothing about it,” ‘‘Not seen it but know about it,” and ‘‘Seen it”). At the group level, we operationalized available knowledge as the highest of the three group members’ familiarity scores for each movie, averaged across the 50 movies. Participants then completed the categorization task (self-timed). The instructions and procedures followed those used in study 1. Respondents were provided with a deck of 50 cards, each of them featuring the name of a movie and the year of its release. Participants in the individual condition completed the task alone, whereas those in the group conditions worked together with two other students. As in study 1, our measure of category breadth was average category size. When participants indicated that they were ready, the experimenter collected the cards, and then each individual participant was asked a series of questions. We defined utilized knowledge as the knowledge actually used to complete the task, and this was operationalized by asking each participant to list the categorization criteria they had used during the categorization task (Love

et al., 2004). Participants were provided with a list of seven movie descriptors (‘‘animation,” ‘‘comedy,” etc.) that had been identified as the most common potential categorization criteria during the pretest and they could check off these descriptors as criteria as well as add their own criteria in a series of blank spaces. In the case of groups, each individual’s list of criteria provides a separate measure of the number of categorization criteria used by the group, so we averaged the number of criteria across the three members to create a group-level measure of categorization criteria. Confirming the appropriateness of this procedure, we find a high interrater agreement in the number of categorization criteria reported by members of the same group (for rWG, M = .80, rWG > .70 in 82% of the groups), as compared with a random sample of those not in the same group (for rWG, M = .66, t(98) = 2.37, p < .05). Results For descriptive statistics and correlations see Tables 1 and 2. As in study 1, groups and individuals did not differ in the amount of time they took to complete the categorization task (Mgroup = 13:55 and Mindiv = 12:45 min, t(1 0 8) = .78, p > .43). However, groups created significantly smaller categories on average than individuals (M = 5.64 vs. 6.55 items, t(108) = 2.19, p < .05). This finding replicates that of study 1 and further supports H1.2 H2 predicts a three-path mediation process with available knowledge and utilized knowledge as successive mediators of the effect of social context on category breadth. Following recent organizational literature (Fragale, Rosen, Xu, & Merideth, 2009), we tested this model using the standard causal steps approach (Baron & Kenny, 1986), recently adapted to the case of three-path mediation by Taylor, MacKinnon, and Tein (2008). This procedure requires estimating a regression for each of the dependent variables (in our case, available knowledge, utilized knowledge and category breadth) that features as predictors all the preceding variables in the mediation chain. Accordingly, mediation can be assessed by estimating the following models: (a) the regression of available knowledge on social context; (b) the regression of utilized knowledge on social context and available knowledge; and (c) the regression of category breadth on social context, available knowledge, and utilized knowledge.3 Below, we report these and other analyses that corroborate H2. Available knowledge was higher across groups (M = 2.91) than individuals (M = 2.37, t(1 0 8) = 9.81, p < .001), confirming step (a) of the three-path mediation procedure. As our measure of utilized knowledge, we used participants’ self-reported categorization criteria. Groups (M = 5.61) had higher utilized knowledge than individuals (M = 4.48, t(1 0 8) = 3.80, p < .001, Regression 1 in Table 3). A regression including both social context and available knowledge as predictors confirms that available knowledge mediated the effect of social context on utilized knowledge. In this model (Regression 2 in Table 3, equivalent to step (b) in the causal step approach), greater available knowledge 2 Unlike in study 1, individuals left more of the 50 movies uncategorized than did groups (M = 3.58 vs. 1.02, t(1 0 8) = 3.43, p < .001). Adding the number of unused items to the regression above of category size on social context, a larger number of unused items was associated with a smaller average category size (b = 0.62, t(1 0 7) = 2.48, p < .05). In this model, social context remains significant (t(1 0 7) = 2.91, p < .01). As a robustness check, the analyses reported below were repeated adding unused items as a covariate. The results for key coefficients were unchanged and these models will not be further discussed (the findings of these additional regressions are presented for completeness in Table 3). 3 The procedure proposed by Taylor et al. (2008) differs in one respect from the approach of Baron and Kenny (1986), in that it does not require the overall relation between the predictor and the outcome (in our case, the effect of social context on category breadth) to be significant (Shrout & Bolger, 2002). Note that, because the effect of social context on category breadth is significant in our study, the inclusion of this additional step is inconsequential for inferences of mediation.

R.W. Hamilton et al. / Organizational Behavior and Human Decision Processes 112 (2010) 70–81 Table 1 Means and standard deviations for key constructs in studies 1–4. Variable

Individuals

Groups

Mean

SD

Mean

Study 1 Category breadth

SD

5.96

1.75

4.49

.67

Study 2 Category breadth Number of unused items Available knowledge Utilized knowledge

6.55 3.54 2.37 4.48

2.55 5.05 .38 1.80

5.64 1.02 2.91 5.61

1.61 1.32 .09 1.88

Study 3 Category breadth Low task conflict condition High task conflict condition

6.10 6.57

2.15 2.66

5.00 4.18

1.91 .65

Study 4 Category breadth Brand extension fit Line extension fit Brand extension evaluations Line extension evaluations

7.31 3.98 5.96 3.97 4.95

1.15 1.73 .81 .20 1.21

5.45 3.19 6.11 3.21 4.98

1.49 .63 .40 .20 .84

Note: Category breadth = average number of items contained in each category. Social context is manipulated between-subjects in all studies, except in study 3 where it is manipulated within-subjects (the task conflict manipulation took place after the individual categorization).

Table 2 Correlations in study 2.

Social context Available knowledge Utilized knowledge Category breadth

effect, we used the bias-corrected bootstrap test for three-path mediation developed by Taylor et al. (2008), implemented by drawing 1000 random samples with replacement from the full sample (cf., Fragale et al., 2009). We constructed a bias-corrected confidence interval using this bootstrap sample to test whether the indirect effect is significantly different from 0 (MacKinnon, Lockwood, & Williams, 2004). The indirect effect from the original sample is the product of the effect of social context on available knowledge in step (a), of the effect of available knowledge on utilized knowledge in step (b), and of that of utilized knowledge on category breadth in step (c): 0.27  1.25  0.64 = 0.21. The 95% confidence interval excluded 0 ( 0.53, 0.02), indicating a significant indirect effect. In sum, the results provide support for the three-path mediation model predicted in H2. Discussion Study 2 provides support for our predictions about the influence of social context on category breadth (H1) and about the role of available knowledge and utilized knowledge (H2). As in study 1, groups created smaller categories on average. Social context influenced available knowledge, which in turn influenced utilized knowledge (the number of categorization criteria used in the task), which then resulted in smaller categories on average. Study 3: teamwork (H3)

1 1. 2. 3. 4.

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2

3

– .39*** .26**

–

– .69*** .34*** .21*

Note: Social context is a binary variable (1 = group, * p < .05. ** p < .01. *** p < .001.

.50***

1 = individual).

is associated with greater utilized knowledge (b = 1.25, t(1 0 7) = 2.44, p < .02). Social context in this model becomes nonsignificant (t(1 0 7) = 1.15, p > .25). The final step of the procedure proposed by Taylor et al. (2008) for testing three-path mediation is to estimate a model of category breadth with social context, available knowledge, and utilized knowledge as predictors. In addition to a significant effect of social context in (a) and a significant effect of prior knowledge in (b), three-path mediation requires a significant coefficient for utilized knowledge in (c). In this model (Regression 5 in Table 3), utilized knowledge was the only significant predictor. We observed a negative association between utilized knowledge and category breadth (b = .64, t(1 0 6) = 5.19, p < .001), confirming that a larger number of categorization criteria is associated with smaller categories on average. In this model, the effects of social context (p > .89) and available knowledge (p > .47) are not significant.4 The results of the regressions presented so far are consistent with the contention that the effect of social context on category breadth is mediated by available knowledge and utilized knowledge. To estimate the magnitude of the indirect (i.e., mediated)

4 For completeness, Table 3 also presents the findings of two additional regressions, the one discussed earlier of category breadth on social context (Regression 3), and that of category breadth on social context and available knowledge (Regression 4), which is not discussed here because not required by Taylor et al.’s (2008) procedure for three-path mediation. Regressions 6–8 in Table 3 are analogous to Regressions 3–5 but with unused items as an additional predictor.

Given earlier work suggesting that group members tend to focus on shared information to the exclusion of unshared information (Stasser & Titus, 1985), the findings of study 2 suggesting that available knowledge is a good predictor of utilized knowledge are notable. One possibility is that the nature of the categorization task elicits unshared information better than less structured discussion. Indeed, previous research suggests that the visual nature of categorization tasks used in product development may be a factor that improves communication among team members (Griffin & Hauser, 1992). Another possibility, which is less consistent with our proposed underlying process but consistent with the lack of difference between individual and group task completion times, is that control was ceded to the most expert member of the group rather than group members jointly completing the categorization task. To address these competing explanations, study 3 was designed to explore the teamwork processes involved in group categorization in more detail. Teamwork processes fostering task conflict should encourage groups to create a larger number of smaller categories, hence exacerbating the difference between individual and group categorization. Study 3 was designed to study how task conflict affects the relative influence of team members on group categorization. For this study, we used retail stores as stimuli. This domain is both substantively and theoretically interesting. At a macro level, product market structure represents a social construction based on consensual categorical knowledge between consumers and producers (Rosa, Porac, Runser-Spanjol, & Saxon, 1999). Category breadth plays a crucial role in defining perceived competitive boundaries between firms operating in adjacent markets (Porac & Thomas, 1994). As result, sorting tasks like the one in this study are frequently used to define competitive space (e.g., Day, Shocker, & Srivastava, 1979). We manipulated the degree of conflict experienced by the group by asking some groups to focus on areas of agreement with other group members and asking other groups to focus on areas of disagreement. We also expanded our dependent measures in this study to examine the underlying process in more detail. In addition to measuring category breadth, we examined the specific items

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Table 3 Study 2: OLS regressions to predict utilized knowledge and category breadth (average category size).

Social context Available knowledge Utilized knowledge Unused items R2

Utilized knowledge

Category breadth

(1)

(2)

(3)

0.56***

0.23 1.25**

0.45**

0.11 1.27*

0.03 0.48 0.64***

.12

.16

.04

.07

.26

(4)

(5)

(6)

(7)

0.62***

0.18 1.80**

0.13** .09

0.16*** .14

(8) 0.14 0.99 0.62*** 0.15*** .32

Note: Social context is a binary variable (1 = group, 1 = individual). Regression models are presented in separate columns. The first row of the table indicates the dependent variable. Numbers at the top of the table in parenthesis indicate different regression models (as referred to in the text). Predictors are listed in the first column. The table reports unstandardized regression coefficients. * p < .1. ** p < .05. *** p < .01.

that were categorized together. We created a matrix of the items categorized together by each individual and by the group to compare the outputs across individual group members and between each individual and the group. If task conflict has negative consequences for participants’ mood, the effect of task conflict on category breadth could be a consequence of affective processes. Earlier research (Murray et al., 1990) has shown that individuals in a positive mood create a smaller number of categories during a categorization task than those in a neutral or negative mood. We measured participants’ self-reported positive and negative affect to examine whether the effect of teamwork may be a function of affect as well as cognition. Method Design, participants, and stimuli Participants were 150 undergraduate students (71 were female) at a US university who participated in exchange for course credit. The stimuli for the categorization tasks were 36 men’s and women’s clothing stores located in a local mall (e.g., Nordstrom, Banana Republic, Forever 21), the names of which were pre-printed on 36 cards. The study used a 2 (task conflict: high, low)  2 (social context: individual, three-person group) mixed design. We manipulated task conflict between groups by varying the instructions group members received. This allows us to examine the degree to which differences in categorization can be attributed to differences in teamwork. We manipulated social context within-subjects. Each participant completed a categorization task individually prior to working on a group categorization task using the same stimuli. This within-subjects design helps us measure the influence of each individual on the group’s output. Procedure and measures Upon their arrival in the lab, participants were greeted by an experimenter who formed mixed gender groups of three members each, none of whom knew each other prior to the study. Groups were then randomly assigned to either the high or the low task conflict condition. The experiment was introduced to the participants as a study about categorization. First, each participant was given a set of 36 cards and categorized the stores individually. Participants recorded the time they spent on the task using the stopwatch provided. The procedure was similar to that of the previous studies, except for the fact that respondents were asked to use every item during the card sorting task so that we could rule out differences in groups’ and individuals’ use of the cards as a potential explanation for the effect. As in studies 1 and 2, the dependent variable used to measure category breadth was the average size of the categories created

by respondents. In addition, to gain more insight into the process through which individuals combine their knowledge in group categorization, we also examined the linkages among specific items that were categorized together. A linkage is formed between items when they are put into the same category. For example, if Banana Republic and Ann Taylor are put into the same category, there is a linkage between them; if they are put into different categories, there is no linkage. If Kenneth Cole is also included in this category, three linkages are defined: one between Banana Republic and Ann Taylor, a second between Kenneth Cole and Banana Republic, and a third between Kenneth Cole and Ann Taylor. Thus, the larger the number of items included in a category, the larger the number of linkages defined. After participants completed the individual categorization task, materials from this task were removed, and new cards were given to each group for the group categorization task. Participants worked with the other two members of their group to categorize the same stores. Each group worked in a separate room and timed themselves using the stopwatch provided. Groups in the low task conflict condition were instructed to focus on agreement (‘‘Your team’s goal is to come to consensus, and for all of you to agree on how to categorize these stores. Therefore, each of you should focus on areas of agreement with others rather than on potential differences”), whereas groups in the high task conflict condition were instructed to focus on areas in which their opinions differed from those of others (‘‘Your team’s goal is to identify differences in the way each of you categorized these stores, and to represent each person’s different point of view when the team categorizes these stores. Therefore, each of you should focus on making sure your own views are represented in the team’s category structure”). Following the group task, individual participants responded to the 20item PANAS scale (Watson, Clark, & Tellegen, 1988) to measure their positive and negative affect. Finally, we measured teamwork processes. As a manipulation check, participants answered two task conflict questions based on Jehn’s (1995) scale of task conflict (‘‘We focused a lot on differences in team members’ opinions” and ‘‘We focused a lot on areas where team members agreed;” these items used seven-point scales). Participants were also asked to rate, on a seven-point scale, the degree to which ‘‘One member played a dominant role during the sorting task.” As an additional measure of each participant’s influence during the group task, we measured the consistency between individuals’ categorization and their group’s categorization by counting the number of times the individual and group agreed on linkages among items. For example, if both the individual’s categorization and the group’s categorization of the stores specified a linkage between Ann Taylor and Nordstrom, this was counted as an agreement. Cases where neither the individual’s nor the group’s

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categorization specified a linkage were also counted as agreements. If either the individual’s or the group’s categorization specified a linkage but the other did not, this was not counted as an agreement. Out of a possible 630 agreements, the number of agreements between an individual’s categorization and their group’s categorization ranged from 292 (46%) to 602 (96%). This ‘‘overlap” between individual and group categorization outputs provides a more detailed measure of similarity between the individual and group category structures than category breadth because it takes into consideration category content, that is, which items were grouped with which other items. Results In contrast to the previous two studies, group members had previously performed the categorization task alone and entered the group task with well-defined ideas about category structure and appropriate categorization criteria. As a consequence, it is likely that compared to the previous study, the group negotiation process in this study required more elaborate and lengthy discussion to combine individual mental representations (Walsh, Henderson, & Deighton, 1988). Consistent with this contention, groups spent longer on the task (M = 7:01 min) than individuals (M = 4:36, t(48) = 4.97, p < .001). The difference in time spent in the low conflict (M = 4:22) and high conflict conditions (M = 4:49) was not significant, t(48) = .62, p > .24), nor was the interaction between social context and condition (p > .10). Replicating the between-subjects results of the previous two studies, a 2 (social context)  2 (task conflict) repeated-measures ANOVA on average category size showed a significant within-subjects main effect of social context (F(1, 48) = 28.88, p < .001). The average category size was 4.59 for groups and 5.82 for individuals, again supporting H1. Teamwork Our manipulation checks for task conflict showed that, as expected, participants in the low conflict condition focused more on agreement (M = 5.64 vs. M = 5.20, F(1, 148) = 4.05, p < .05) and less on disagreement (M = 3.37 vs. 3.95, F(1, 148) = 4.05, p < .05) than those in the high conflict condition. In H3a, we predicted that task conflict would moderate the difference in category breadth between groups and individuals. Our repeated-measures ANOVA on category size showed a significant interaction between conflict and social context (F(1, 48) = 7.18, p < .05). Consistent with our prediction, groups in the high conflict condition (M = 4.18) created smaller categories than groups in the low conflict condition (M = 5.00, F(1, 48) = 4.28, p < .05). The individual categorization task was completed prior to the task conflict manipulation, and there was no difference between individuals in the low and high conflict conditions (p > .27). To test H3b, which predicted that task conflict would result in greater average influence of individual group members on categorization output, we examined (1) participants’ perceptions that one member had played a dominant role during the categorization task, and (2) linkage data showing the similarity between each individual’s category structure and their group’s category structure. Participants were significantly more likely to report that one member played a dominant role in the categorization task in the low conflict (M = 4.63) than in the high conflict condition (M = 3.73, F(1, 148) = 8.93, p < .01; see Table 4). Another consistent piece of evidence is provided by our linkage data, which show that the average overlap between individual and group category structures was significantly higher in the high conflict (M = .81) than in the low conflict condition (M = .77; F(1, 148) = 5.10, p < .05). Lower overlap suggests that fewer team members’ views were represented in the low conflict than in the

Table 4 Correlations in study 3. 1 1. 2. 3. 4.

Task conflict Member dominance Category breadth Overlap with group

2

3

– .30*** .29** .26*

Note: Task conflict is a binary variable (1 = high conflict, * p < .10. ** p < .05. *** p < .01.

– .13 .26*

– .69***

1 = low conflict).

high conflict condition. The results provide additional support for H3b. Affect There were no differences across the high and low conflict conditions in either positive affect (M = 2.76 vs. 2.76, p > .98) or negative affect (M = 1.29 vs. 1.30, p > .89). Because the PANAS scale was administered after the group task, this rules out a mood account for the effect of task conflict on categorization (Murray et al., 1990). Discussion Study 3 replicated the key result of studies 1 and 2 in a different context and within-subjects. When categorizing retail stores, groups again created smaller categories on average than did individuals. In addition, study 3 provides support for our hypotheses about the role of teamwork—and, in particular, task conflict. Task conflict moderated the effect of social context on category breadth: higher levels of task conflict exacerbated the difference between individual and group category breadth. Consistent with previous research suggesting that task conflict may increase the degree to which available knowledge is utilized by groups (Schweiger et al., 1989), we find converging evidence for the role of teamwork in group categorization from our measure of the extent to which group categorization was dominated by one individual. When groups were instructed to focus on disagreement, one group member was less likely to dominate the task, and our linkage data also suggest that multiple group members influenced the group’s category structure. Moreover, the group’s smaller category breadth relative to the three individual diagrams in this condition suggests that knowledge was being shared among members during the group task. Study 4: carry-over effects (H4) Study 4 was designed to test whether previous experience categorizing as part of a group or individually affects subsequent judgments of new items. We assess this possibility in a brand extension setting by examining perceived fit with the parent brand and attitudes towards the brand extension. Compared with those who categorized alone, we predicted that individuals who categorized in a group would report lower perceptions of fit between the extension and the parent brand (H4a) and lower evaluations of the brand extensions (H4b). Method Design and participants Participants were 140 undergraduate students at a US university (58 were female) who participated in exchange for course credit. Participants were randomly assigned to either the individual condition or the group condition. Thirty-five participants in

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the individual condition completed the categorization task individually, while 105 participants in the group condition completed the categorization task in three-person groups.

Procedure The experiment was introduced as a study of students’ opinions about bottled beverages. Participants in the individual and group conditions were given an envelope containing 48 cards with the name of a beverage printed on each card (e.g., Diet Coke, Aquafina Purified Drinking Water, Minute Maid Premium Orange Juice, Aquafina Sparkling Citrus Fruit, Sunkist Orange Soda) and instructions similar to those used in studies 1–3. As in study 3, participants were instructed to categorize all of the cards rather than leaving some cards unused. The dependent variable used to measure category breadth was average category size. After participants completed the categorization task, the experimenter collected the cards and administered a follow-up questionnaire to each individual participant. As in study 3, participants completed the 20-item PANAS scale (Watson et al., 1988) to assess their emotional state. Because mood in the within-subjects design of experiment 3 was measured only after the group task, it was not possible to examine the potential confound of mood with social context. Next, participants evaluated the appeal of six products that had not been included in the categorization task. They rated three brand extensions into a new product category from the parent brand (Minute Maid Cranberry Soda, Nestea Raspberry Juice Drink, Purewater Lemon Soda). We also included three flavor-based line extensions (Minute Maid Cranberry Juice, Nestea Raspberry Iced Tea, Purewater Water with Lemon) that do not require participants to re-categorize or ‘‘stretch” the parent brand into a new product category. These serve as a baseline condition, to ensure that individuals did not simply evaluate any product as less attractive following the group vs. individual categorization task. Perceived fit between the new product (either a brand extension or a line extension) and the parent brand was rated using a seven-point scale. Appeal was rated using two seven-point scales (unappealing/appealing and dislike/like).

Results Replicating the results of the previous studies and supporting H1, groups (M = 5.45) created smaller categories on average than individuals (M = 7.31, F(1, 68) = 56.26, p < .001).

Evaluation of brand extensions The reliability of the two-item appeal scale ranged from .85 to .95. We averaged the two items across the three brand extensions to form a brand extension evaluation index and across the three line extensions to form a line extension index. A repeated-measures ANOVA with social context as a between-subjects factor and product type (brand- vs. line extension) as a within-subjects factor showed several significant effects. The baseline line extensions (M = 4.96) were evaluated more favorably than brand extensions (M = 3.59, F(1, 138) = 81.84, p < .001). The between-subjects effect of social context was marginally significant (F(1, 138) = 3.63, p < .10), indicating that those who categorized individually (M = 4.46) rated both types of new products more favorably than those who had categorized in a group (M = 4.09). More importantly, the interaction between product type and social context was also significant (F(1, 138) = 6.74, p < .01). Supporting H4b, brand extensions were evaluated more favorably by those who had categorized individually (M = 3.97) than by those who had categorized in a group (M = 3.21, F(1, 138) = 9.24, p < .01), whereas line extensions were evaluated similarly by individuals who categorized alone (M = 4.95) and individuals who categorized in a group (M = 4.98, F(1, 138) = .02, p > .89). Affect There were no differences between the group and individual conditions in either positive affect (M = 2.81 vs. 2.62, F(1, 138) = 1.80, p > .18) or negative affect (M = 1.27 vs. 1.41, F(1, 138) = 2.61, p > .10), ruling out a mood account for the effect of social context on categorization or brand extension evaluations. Discussion Study 4 replicated our key result in a new context. When categorizing bottled beverages, groups created smaller categories on average than individuals. Study 4 also extends the results of our earlier studies by showing that categorizing concepts individually or as part of a group influences subsequent product evaluations. In other words, social context impacts individuals’ judgments beyond the physical output of a formal categorization task. Participants who had categorized beverages as part of a group evaluated brand extensions into new categories less favorably than participants who had categorized the beverages individually, even though they did not evaluate the baseline line extensions less favorably. General discussion

Brand extension fit We averaged the perceived fit measures across the baseline line extensions and across the brand extensions to form two indices. A repeated-measures ANOVA with product type (brand- vs. line extension) as a within-subjects factor and social context as a between-subjects factor showed that the perceived fit of the line extensions (M = 6.03) was higher than the fit of the brand extensions (M = 3.58, F(1, 138) = 253.87, p < .001). The effect of social context was also significant, with individuals who categorized alone rating the perceived fit of both kinds of products (M = 4.97) more favorably than individuals who categorized in a group (M = 4.65, F(1, 138) = 5.37, p < .05). More importantly, the expected interaction between product type and social context was significant (F(1, 138) = 9.28, p < .01). Supporting H4a, we observed greater perceived fit of the brand extensions for individuals who had categorized alone (M = 3.98) rather than as part of a group (M = 3.19, F(1, 138) = 9.73, p < .01). No differences were observed between social context conditions for the perceived fit of the baseline line extensions (M = 5.96 vs. 6.11, F(1, 138) = .93, p > .33).

The four studies in this paper demonstrate that whether individuals work alone or in groups systematically affects the way they categorize the same set of items. In a setting typical of product development research, study 1 demonstrates that groups tend to create a larger number of smaller categories than individuals when categorizing the same set of items. Studies 2 and 3 link this effect of social context to two distinctive features of group work: first, groups have access to a larger pool of available knowledge than individuals (taskwork), and second, the degree to which available knowledge is utilized by the group is a function of group processes (teamwork). Study 2 focuses on taskwork, and the data support a three-path mediation model where available knowledge differences between groups and individuals mediate the effect of social context on category breadth by increasing utilized knowledge. Study 3 focuses on teamwork, showing that task conflict moderates the effect of social context on category breadth. Among groups, higher levels of task conflict led to more equal input across group members and smaller categories, hence increasing the

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difference between individual and group category breadth. Finally, study 4 provides evidence that the effects of social context on categorization are not limited to the categorization task itself, but may affect subsequent judgments of new items. While both the effect of category knowledge on individual categorization behavior (Johnson & Mervis, 1997) and the greater available knowledge of groups (Hinsz, 1990) have been documented in previous research, our paper is the first to show the relationship between these two streams of research. Our demonstration in study 2 that available knowledge mediates the effect of the social context on category breadth by increasing the knowledge that is actually utilized by the group is consistent with cognitive models of category learning (e.g., Love et al., 2004). This mediation process also provides a clear demonstration of the role of constraints in category formation (McGarty, 1999) because utilized knowledge was operationalized by asking participants to list the criteria they had used to form their categories. We also explored the role of teamwork in group categorization. In study 3, we found that task conflict affected the way group members interacted. Relative to a condition in which they were asked to focus on agreement, one group member was less likely to dominate the task when groups were instructed to focus on disagreement and make sure that each group member’s views were represented in the team’s category structure. More equal engagement in the process also had implications for the group output: higher task conflict groups created a larger number of smaller categories using the same set of items. In recent years, cognitive psychologists have stressed the importance of contextual factors in categorization (e.g., Barsalou, 1991; Love, 2005; Medin et al., 1997). However, the dependent variables in previous research have focused on the content of categories rather than on the structure of categories. For example, the finding that landscape architects and landscapers categorize trees differently relates to the content of the categories they create rather than to the category structure (Medin et al., 1997). Our emphasis on category structure adds to the existing literature by highlighting contextual influences that are independent of the specific experimental stimuli being categorized. From a social cognitive point of view, evidence that teamwork processes related to task conflict affect category structure is especially informative because it suggests that the mechanism responsible for the contextual influence on categorization goes beyond stimulus applicability (Higgins, 1996). At a more general level, our studies answer calls for research into how individual-level phenomena play out in a group setting (e.g., Rousseau & House, 1994). We found that the effects of available knowledge and utilized knowledge on category breadth explain differences between individual and group categorization (taskwork) and that social dynamics within a group (teamwork) can exacerbate or reduce these differences (for a similar approach in a different context, see Taggar, 2002). Limitations and future research Although the available knowledge of groups makes them seem more like experts than novices when we measure category breadth, we do not expect groups to mimic all of the characteristics of expert decision makers. Expertise is itself a multi-dimensional construct. While groups share one aspect of expertise, namely that they collectively bring more knowledge of the items to be grouped, experts also differ from novices in other ways. For example, research has shown that experts are more flexible in their reliance on causal relations (Shafto & Coley, 2003) and more flexible in their use of attributes to describe category members (Mitchell & Dacin, 1996). Moreover, in addition to category breadth, there might be other qualitative differences in categorization behavior between

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individuals and groups, which do not necessarily reflect those of experts. The similarities and differences between experts and interacting groups in decision making and between individuals and groups in categorization behavior therefore need further investigation. While we have examined decision making in which categorization is an explicit part of the task, in many cases categorization is implicit rather than explicit. The effectiveness of information sharing we have observed when categorization is explicit may not generalize to tasks in which categorization is less explicit. Thus, additional research should be conducted using decision making tasks in which categorization is more implicit. Related to this, it is important to note that our studies were conducted with undergraduate students and with relatively small samples. Although the participants showed substantial variation in their knowledge of the product categories we chose, there may be even more variation among individuals of different age groups and backgrounds. Practical implications Revisiting the product development team we considered at the beginning of the paper, we now know that whether they choose to categorize the customer needs individually or in a group affects the output of the categorization task as well as the team’s cognitions and subsequent evaluations. Managers rely on consumers’ categorization of customer needs for products as important inputs into new product development (Griffin & Hauser, 1993). Voice-of-thecustomer techniques provide information precisely because there are multiple ways to categorize the same set of items. As customers categorize customer needs, they make judgments about which needs to group together and how many needs to combine into a single category. Consider, for example, customer needs for a car such as ‘‘I don’t want to hear the engine running,” ‘‘very little noise from the road,” ‘‘I can play my CDs,” and ‘‘music should have a full, rich sound.” The first two needs might be combined into a category described as ‘‘interior of the car should be quiet” while the second two might be combined into a category described as ‘‘car should have a high-quality audio system.” Alternatively, all four could be combined into a single broad category described as ‘‘I want music to sound great in my car.” Our findings suggest that individuals are more likely to create the broader category structure, while groups are more likely to converge on the narrower category structure. While information about both distinctions and similarities among customer needs is likely to be valued by the product development effort, categorization forces a tradeoff between these two kinds of information. Making fine distinctions among customer needs (narrow categories) requires ignoring similarities among them, but focusing on broader similarities among the needs (broad categories) requires ignoring distinctions among them. More narrowly defined categories can provide a better basis for linking customer needs to specific product features. If categories are defined too broadly, the product development effort can miss out on important distinctions. On the other hand, if categories are defined too narrowly, the product development effort may become focused on specific and potentially disjointed needs. As a result, product engineers might not think about exterior noise in conjunction with the car’s sound system. If customer needs are grouped into broader categories, the product development effort might become more integrative. The performance of the sound system, for example, might be viewed as partially dependent on the reduction of exterior noise. Product development teams should think carefully about these tradeoffs when deciding whether to have individuals or groups categorize customer needs. Our studies also provide evidence that categorizing individually or in a group has subsequent effects on team members’ cognitions and judgments. Study 4 suggests, for example, that individuals

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who participate in a group categorization exercise are less likely to favor launching brand extensions into new categories than those who participate in the same exercise individually. Study 2’s measurement of categorization criteria after the categorization task had been completed suggests that categorizing individually and then aggregating the results may lead to less agreement when discussing categorization criteria later. Because the trend toward virtual teams within organizations suggests that it may become more common to aggregate inputs from individual members in the future (Hambley, O’Neill, & Kline, 2007), future research should examine the implications of conducting tasks like categorization in a distributed way rather than engaging in a face-to-face exercise. In sum, by making a connection between the greater available knowledge of groups relative to individuals (Hinsz, 1990) and the effect of category knowledge on individual categorization behavior (Johnson & Mervis, 1997), our research forms a theoretical bridge between one of the most important research areas in cognitive psychology (categorization) and one of the main areas in social and organizational psychology (group decision making). We show that both taskwork and teamwork processes explain differences between individual and group categorization. In terms of taskwork, greater available knowledge in a group context increases the knowledge actually utilized by the group, predicting the narrower categories created by groups relative to individuals. In terms of teamwork, group interaction variables such as the degree of conflict experienced by the group moderate differences between individual and group categorization outcomes. Acknowledgments This work was partially supported by the MIT Center for Innovation in Product Development under NSF Cooperative Agreement Number EEC-9529140, by the Centre for Marketing at London Business School and by the Erasmus Research Institute of Management. The authors would like to thank seminar participants at Georgetown University’s McDonough School of Business 2007 Marketing Camp, session attendees at the 2007 Association for Consumer Research Conference in Memphis, TN, David Bell at Xerox PARC, John Carroll, Abbie Griffin, John Hauser, Robert Klein at AMS, Drazen Prelec and JoAnne Yates for their helpful comments and Xinxin Dai for her help with data entry. References Aaker, D. A., & Keller, K. L. (1990). Consumer evaluations of brand extensions. Journal of Marketing, 54(January), 27–41. Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal of Consumer Research, 13(March), 411–454. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. Barsalou, L. W. (1991). Deriving categories to achieve goals. In G. H. Bower (Ed.). The psychology of learning and motivation: Advances in research and theory (Vol. 27, pp. 1–64). San Diego, CA: Academic Press. Bonner, B. L., Baumann, M. R., & Dalal, R. S. (2002). The effects of member expertise on group decision-making and performance. Organizational Behavior and Human Decision Processes, 88, 719–736. Boush, D. M., & Loken, B. (1991). A process-tracing study of brand extension evaluation. Journal of Marketing Research, 28(February), 16–28. Burchill, G., & Brodie, C. H. (1997). Voices into choices: Acting on the voice of the customer. Madison, WI: Joiner Associates, Inc. Cannon-Bowers, J. A., Salas, E., & Converse, S. (1993). Shared mental models in expert team decision making. In N. J. Castellan (Ed.), Individual and group decision making (pp. 21–46). Hillsdale, NJ: Erlbaum. Cowley, E., & Mitchell, A. A. (2003). The moderating effect of product knowledge on the learning and organization of product information. Journal of Consumer Research, 30(December), 443–454. Day, G. S., Shocker, A. D., & Srivastava, V. (1979). Customer-oriented approaches to identifying product markets. Journal of Marketing, 43(July), 8–19. De Dreu, C. K. W., & West, M. A. (2001). Minority dissent and team innovation: The importance of participation in decision making. Journal of Personality and Social Psychology, 86(6), 1191–1201.

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