This paper proposes a new model for studying the new product development process in an artificial environment. We show how connectionist models can be used to simulate the adaptive nature of agents' learning exhibiting similar behavior as practically experienced learning curves. We study the impact of incentive schemes (local, hybrid, and global) on the new product development process for different types of organizations. Sequential organizational structures are compared to two different types of team-based organizations, incorporating methods of quality function deployment such as the house of quality. A key finding of this analysis is that the firms' organizational structure and agents' incentive system significantly interact. We show that the house of quality is less affected by the incentive scheme than firms using a trial and error approach. This becomes an important factor for new product success when the agents' performance measures are conflicting.