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Although the level of power held by the marketing department can determine key organizational outcomes, including firm performance, this power often is modest and, in many firms, diminishing. To address this apparent disconnect, the authors propose that the board of directors is a critical but overlooked driver of marketing department power. In particular, directors’ marketing exposure through board service at other firms (i.e., board-interlocked firms) may affect the marketing department’s power in the firms on whose boards they also serve. With a sample of 4,422 firms, spanning 2007–2013, this study reveals that marketing department power in board-interlocked firms significantly and positively drives marketing department power in the focal firms. Consistent with an information sharing view, the magnitude of this effect varies with the focal firm’s network position in the board-interlocked network, such that it strengthens as the focal firm’s network centrality (information amount) and network brokerage (information quality) increase. These robust results suggest that board members and their social networks significantly influence marketing department power; if marketing wants to increase its power, it should get the board “on board.”

The trajectories of social processes (e.g., peer pressure, imitation, and assimilation) that take place on social networks depend on the structure of those networks. Thus, to understand a social process or to predict the associated outcomes accurately, marketers would need good knowledge of the social network structure. However, many social networks of relevance to marketers are large, complex, or hidden, making it prohibitively expensive to map out an entire social network. Instead, marketers often need to work with a sample (i.e., a subgraph) of a social network. In this paper we evaluate the efficacy of nine different sampling methods for generating subgraphs that recover four structural characteristics of importance to marketers, namely, the distributions of degree, clustering coefficient, betweenness centrality, and closeness centrality, which are important for understanding how social network structure influences outcomes of processes that take place on the network. Via extensive simulations, we find that sampling methods differ substantially in their ability to recover network characteristics. Traditional sampling procedures, such as random node sampling, result in poor subgraphs. When the focus is on understanding local network effects (e.g., peer influence) then forest fire sampling with a medium burn rate performs the best, i.e., it is most effective for recovering the distributions of degree and clustering coefficient. When the focus is on global network effects (e.g., speed of diffusion, identifying influential nodes, or the “multiplier” effects of network seeding), then random-walk sampling (i.e., forest-fire sampling with a low burn rate) performs the best, and it is most effective for recovering the distributions of betweenness and closeness centrality. Further, we show that accurate recovery of social network structure in a sample is important for inferring the properties of a network process, when one observes only the process in the sampled network. We validate our findings on four different real-world networks, including a Facebook network and a co-authorship network, and conclude with recommendations for practice.