Operationalizing team composition has in general been carried out using three different methods (Halfhill, Sundstrom, Lahner, Calderone & Nielsen, 2005). The most usual method is to calculate the mean or summed score for a variety of individual characteristics such as measures of team performance (Ancona & Caldwell, 1992), social cohesiveness, team conflict, member flexibility, workload sharing, team viability and team performance (Barrick, Stewart, Newbert & Mount, 1998), personality, task focus, group cohesion and performance (Barry & Stewart, 1997). This method can have problems as not only will an individual’s characteristics in a small group influence the mean more than those of an individual in a larger group, but also one individual with an extreme outlier score can alter the mean disproportionately. Another method used is to look at how diverse the characteristics are in terms of variance: age, tenure, ethnicity (Tsui, Egan & OReilly, 1992) tenure, age (Ancona & Caldwell, 1992) and consensus of peer ratings of group members (Anderson & Kilduff, 2009). This is a useful method to use when the differences in the team make-up are masked by the mean. It is particularly beneficial when examining the effects of homogeneity of a specific trait on the team process. A third technique looks just at the highest and/or the lowest score for a particular trait within the team. This method is most often used
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when looking at team outcomes that are particularly affected by one member of the group, for example: task performance in which participants were given either a conjunctive task or a disjunctive task to complete (Kerr & Bruun, 1983) and agreeableness and extraversion to see if one particularly disagreeable or extravert person within the group could make a difference to performance (Bell, 2007).
Team composition performance is affected by the nature of the task to be performed by the group. Steiner (1972) classified tasks into five types: additive, where the productivity of the team is dependent on the sum of the members’ abilities such as a tug of war; conjunctive, where team performance is dependent on the weakest member of the group, such as a relay race; disjunctive, where the productivity is dependent on the strongest member, such as a quiz in which only one person needs know the correct answer to achieve the team’s goals; compensatory, where the team mean is used to enhance performance in, for example, a guess the weight of an object where all team members’ guesses are added together and averaged to enhance the accuracy of the guess; and complementary, in which each member of the team adds his or her specialism to the group. Each of these task types requires different aspects from the team. Disjunctive and conjunctive tasks are dependent on the efforts of the best (for disjunctive) or worst (for conjunctive) member of the team. For an additive task (in which all the individuals are required to input to the task) or a compensatory task (which needs every member of the team to perform at least at some level), the mean is probably the most useful technique. The variance technique is most useful for compensatory tasks which require diverse inputs e.g. forecasting which needs a variety of approaches to get a sensible estimate. In the case of a disjunctive task or conjunctive task, the maximum/minimum operationalizing technique is the most useful method as it is the best or the worst member that influences the end performance.
These groupings were supported by Hill (1982) who carried out a literature review of 139 experimental studies which shared outcomes consistent with Steiner’s classifications. She compared studies in which individuals working together on group products were contrasted with individuals working separately on individual products. She studied whether groups were more effective than individuals in a variety of task
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demands in six separate categories: learning concept attainment, concept mastery, creativity, abstract problem solving, brainstorming and complex problem solving. She found that levels of productivity varied as a function of task demands, member resources and group processes. In learning tasks, groups usually outperformed individuals using their abilities to pool resources, to correct errors and to use differing learning strategies. In creative tasks, the success of the group, as opposed to the individual, depended partly on the make-up of the group. If there were high achieving group members competing with high achieving individuals, the groups were more successful. However groups with only low achieving members or with one high achieving member performed worse than expected. Nevertheless, in general, the groups outperformed the individuals. In problem solving tasks, groups usually took longer to reach an answer but generally outperformed individuals, with incorrect answers being rejected by other team members, thereby arriving at a higher level of correct answers. With difficult problems, there was evidence of pooling of resources when no one member could answer the complex problem alone. Group outcome appeared to have been determined by the most competent group member, aided by an ‘assembly bonus effect’ 2 (p525). In brainstorming tasks, pooling of individual’s ideas produced greater numbers of ideas than group interaction. With complex problems group success was superior to individuals’ but not as great as was expected using statistical pooling models leading to some process loss.
Barrick, Stewart, Neubert and Mount (1998) used the input-process-output model as a basis for examining actual teams within bona fide companies. They studied 652 employees from within 51 work teams from four different organizations to see if the team composition was related to the team process and effectiveness. They used the three operationalizing techniques already discussed (mean score; variance diversity; minimum and maximum scores) and a variety of team outcomes as dependent variables (e.g. team performance as assessed by the supervisors; team conflict; viability; flexibility; cohesiveness and ability). They found that the scores from each of the operationalizing methods for some of the team/composition traits were
2 Collins & Guetzkow (1964): Effective interaction allows group members to produce higher quality outcomes than those of the individual.
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only moderately correlated, suggesting that each technique picked up different aspects of team composition. An example of this is that agreeableness was significantly correlated with team performance when measured by mean (r=.34) and minimum score (r=.32) but not significantly correlated using the maximum score (r=- .06) or the variance method (r=-.08). Choosing the appropriate method of operationalizing diversity characteristics seems to be a key decision based on the task being undertaken by the team.