Organizational learning research is based on the idea that individuals can learn more together than alone. Network connections between members of an organization allow them to engage in a mutual learning process whereby they share what they discover and potentially learn and improve their performance at a faster rate. Existing research highlights the importance of network structure in facilitating mutual learning, with a centralized network expected to improve learning and performance when the unfamiliar assignment is complex. An example of an unfamiliar complex assignment is new product development. The features of a new product and how those features should be combined are unknown. In a centralized network, members are connected to a focal individual but disconnected from each other. The disconnects facilitate individual search and experimentation, increasing the odds that a superior solution will be discovered. To understand how network centralization affects mutual learning and performance when an unfamiliar assignment is complex, this chapter offers a theory-building exercise. The exercise is motivated by a conflicting empirical result. Recent research indicates that for a complex task, individuals learning in a decentralized network, a network that contains a relatively large number of direct and indirect relationships, can outperform individuals learning in a centralized network. The exercise amounts to puzzling through the empirical results. Putting the pieces together suggests that the requirements for mutual learning can be met in either a centralized or decentralized network. Moreover, communication timing appears to be critical. When communication across relationships is delayed, individuals working in a decentralized network can explore a diverse set of ideas while maintaining the ability to exploit a proven idea. When communication across connections is not delayed, a centralized network produces better outcomes. The results of the theory-building exercise suggest a contingency: the ideal network for a complex assignment could depend on the rate of communication across network connections.

组织学习研究基于这样的想法，即个人可以一起学习比单独学习更多。组织成员之间的网络连接使他们能够参与相互学习的过程，从而分享他们发现和潜在学习的内容，并以更快的速度提高他们的绩效。现有研究强调了网络结构在促进相互学习方面的重要性，当不熟悉的任务很复杂时，集中式网络有望提高学习和绩效。一个不为人知的复杂任务的例子是新产品开发。新产品的特性以及这些特性应该如何组合是未知的。在集中式网络中，成员与一个焦点个体相连，但彼此断开连接。这些脱节促进了个人搜索和实验，增加了发现更好解决方案的几率。为了理解在不熟悉的任务很复杂时网络中心化如何影响相互学习和绩效，本章提供了一个理论构建练习。该练习的动机是相互矛盾的实证结果。最近的研究表明，对于一项复杂的任务，在分散网络中学习的个体，一个包含相对大量直接和间接关系的网络，可以胜过在中心化网络中学习的个体。这个练习相当于对实证结果感到困惑。将各个部分放在一起表明可以在集中式或分散式网络中满足相互学习的要求。而且，沟通时机似乎很关键。当跨关系的沟通延迟时，在去中心化网络中工作的个人可以探索各种不同的想法，同时保持利用经过验证的想法的能力。当跨连接的通信没有延迟时，集中式网络会产生更好的结果。理论构建练习的结果表明了一种偶然性：复杂任务的理想网络可能取决于网络连接之间的通信速率。