Research summary: We consider conditions in which incumbent firms are particularly poised to benefit from knowledge spilling in from new ventures that employ individuals previously employed by the focal incumbent firm. We distinguish between inventors who leave their incumbent employers to found spin‐outs and those who become non‐founding employees of existing new ventures. Using a sample of new ventures and incumbent firms in the U.S. information technology (IT) sector, we find that incumbents are more likely to benefit from patented knowledge that spills in from their spin‐outs than from new ventures that employ non‐founding inventors formerly employed by the respective incumbent. Any advantage that parent firms have in reaping such knowledge quickly dissipates, however, when these parents have a history of misappropriating the intellectual property of others. Managerial summary: It has long been acknowledged that new ventures can acquire valuable knowledge from their larger and more established counterparts by hiring away their talented employees. We consider the possibility of a reverse flow of knowledge where established firms learn from those new ventures that have poached employees from them. We find that established information technology (IT) firms are more likely to learn and build on the technology of their spin‐outs (i.e., new ventures founded by their former inventors) than from new ventures that simply employ non‐founding inventors formerly employed by the respective IT firm. Any advantage that these IT firms had in reaping technical know‐how from their spin‐outs quickly dissipated, however, when they had a history of misappropriating the intellectual property of others. Copyright © 2016 John Wiley & Sons, Ltd.

Research summary: This article uses Exponential Random Graph Models (ERGMs) to advance strategic management research, focusing on an application to board interlock network tie formation. Networks form as the result of actor attributes as well as through the influence of existing ties. Conventional regression models require assumptions of independence between observations, and fail to incorporate endogenous structural effects of the observed network. ERGMs represent a methodological innovation for network formation research given their ability to model actor attributes along with endogenous structural processes. We illustrate these advantages by modeling board interlock formation among Fortune 100 firms. We also demonstrate how ERGMs offer significant opportunities to extend existing strategy research and open new pathways in multiparty alliances, microfoundations of interorganizational network formation, and multiplexity of ties among actors. Managerial summary: Social networks are increasingly important in the business world, not only between individuals but also between organizations. Firms can obtain information, resources, and status through their external network connections, and understanding how these outside ties form is an important goal of strategy research. Our paper helps advance this effort by introducing a new tool for social network analysis, Exponential Random Graph Models (ERGMs) to the management and strategy fields. We provide an example of this method, demonstrating how social network ties form between companies when they hire common directors to their boards. Executives can benefit from this research through a greater understanding of how corporate relationships are built with allies as well as among competitors. Copyright © 2015 John Wiley & Sons, Ltd.