Using trait activation theory as a framework, this study developed and tested a cross-level model of individual innovative behavior. Data from a sample of 334 employees within 75 work teams were used to examine the hypothesized model. Results showed that employee learning goal orientation was positively related to innovative behavior only when the team structure was more organic. Additionally, the relationship between employee learning goal orientation and innovative behavior would be strongest when both the team structure was more organic and team mean learning goal orientation was higher.

This paper discusses the effects of direct and indirect communications on social learning and task coordination in design teams. The findings reported in this paper are based on a computational model that simulates the formation of transactive memory (TM) through social learning from direct and indirect communications. Direct communications are explicit information exchanged between team members whereas indirect communication may be opportunistic and coincidental, resulting in learning and information gained through observations of the actions of others. However, team structure mediates opportunities for communication. Three types of team structures are studied, which are differentiated on the basis of their constraints on and opportunities for direct and indirect communications across the team. The differences across the team structures are investigated through a series of simulations in which team member retention, cognitive busyness of team members, and task complexity are additional moderating variables, and task coordination and formation of TM are the dependent variables. Fewer communications to coordinate the same tasks are taken as the measure of efficient task coordination. Findings suggest that reduction in communication and learning opportunities are more detrimental to the task coordination in flat teams as compared to functional teams. Indirect communications contribute more to the formation of TM than to task coordination. Flat teams facilitate the formation of TM, whereas functional teams are more appropriate for efficient task coordination, indicating that the role of TM in mediating task coordination varies with team structure.

This paper reports on a computational model developed to study the effects of various modes of social learning on task coordination in teams through the mapping of distributed team competence, a significant aspect of efficient teamwork. The computational model emphasizes and operationalizes distinct modes of social learning, differentiated in terms of socialization opportunities. Simulation results demonstrate that computational models based on fundamental principles of social learning provide a robust approach to study task coordination in teams and can be used to explore ways to organize opportunities for social learning depending upon member retention, team structure, and the complexity of the design task.