How ‘Who You Know’ Affects What You Decide
Over the past several decades, research has shown how both cognitive biases and small group dynamics can undermine effective decision making in organizations. However, there has been little work on the ways that informal networks impact framing and execution of decisions. In this article, the authors examine the roles decision networks play, both within teams and throughout organizations, in the way decisions are framed and carried out.
Although company leaders frequently recognize the importance of such decision networks, they fail to leverage their potential and focus instead on the organization’s formal structure. The authors present two indepth case studies to show how network analysis applied to decision-making interactions within organizations can help improve the effectiveness and efficiency of decision making. The first case demonstrates how a rapidly growing pharmaceuticals company used process mapping and network analysis to streamline decision-making interactions. A team found that decision-making inefficiencies permeated the organization. Decision rights were not clearly delineated or allocated, and even mundane approvals had high collaborative costs.
The second case, based on a larger, more established company, shows how network analysis can improve top-team decision making and execution in organizations slowed by bureaucracy and a culture of consensus. The company had sought a technological fix in an effort to rescue itself from organizational gridlock, but the problems persisted. A network analysis helped senior managers identify the underlying network drivers of gridlock, thereby enabling them to take targeted steps to speed up and improve decision making.
In both cases, the authors highlight the insights and performance impact that can result when decisions are viewed through a network perspective. Although it is still early, the benefits of understanding how decision-making networks affect the top team appear to be compelling. The number of collaborations required to execute decisions at key points in the network was significantly reduced. This had a positive effect on both company performance and morale.