Theories Of Team Decision-Making

Two well-known contributors in the team decision making field recently published a book on best practices (Sunstein & Hastie, 2015). Sunstein and Hastie argued that current techniques in team decision making have neglected research and, as a result, have a tendency to end unfruitfully. This neglect is problematic, as research shows that teams can potentially outperform individuals and there are tasks that only cross-functional teams with complementary skills can perform (Hinz, Tindale, & Vollrath, 1997; Kerr & Tindale, 2004; van Ginkel, Tindale, & van Knippenberg, 2009; Kameda, Tsukasaki, Hastie, & Berg, 2010). The proliferating use of team-based structures has left many in the current workforce with important decisions to make, but potentially
For instance, there is the Teams Lens Model (Brehmer & Hagafors, 1986), the Multilevel Theory of Team Decision Making (Hollenbeck et al., 1995; Phillips, Douthitt, & Hyland, 2001), the Vroom-Yetton Contingency Model (1973), and the Judge-Advisor Systems (Sniezek & Buckley, 1995). These models are diverse, but team decisions are not simple procedures. There are multiple approaches include majority rules, rank voting, plurality, to requirements for unanimity. The Multilevel Theory of Team Decision Making is perhaps the most relevant within the current characteristics of the American workforce. This approach acknowledges the status differences that occur within teams and the unequal distribution of knowledge and expertise between members. Influencing factors include the social environment, role, individual, tasks, behavioral settings, physical/technical environment, dyadic relationships, team information, and hierarchies within the team. The work of Hollenbeck and colleagues was important for team decision making research because it emphasized that team processes are similar to individual decisions and there are numerous factors affecting the decision making
Mojzisch and Schulz-Hardt (2010) showed that the process of encoding and integrating information was one underlying mechanism. They used a team-based experiment focusing on initial discussion. Results showed that dissent deterred the teams from identifying the hidden profile, as members were unlikely to listen to information shared and less likely to encode information. Furthermore, they contended that the teams were more likely to identify the hidden profile when told not to provide initial preferences. Emich (2014) demonstrated that both positive and negative affect influenced information sharing. Teams with higher member level positive affect were more likely to share information when compared to negative affect teams. On the contrary, van Knippenberg, Kooij-de Bode, and van Ginkel (2010) found interactive effects that showed that teams high in positive mood, but low in trait negative affect, made poorer