Introduction

Consider the following two scenarios: (1) A mobile phone service provider launches a new service and wishes to implement a seeding program that will stimulate referrals and attract new adopters. The firm needs to decide how many customers to “seed”, what types of customers to seed, and the monetary rewards each customer will receive once successfully bringing in a new customer (Hinz, Skiera, Barrot, & Becker, 2011). (2) A distributor is asked by a manufacturer to introduce an innovation into a market in which customers are organized in a certain social network structure. The distributor wishes to know whether to demand exclusivity as part of the distribution contract (Peres and Van den Bulte, 2014). These two narratives exemplify managerial decisions regularly faced by marketing executives introducing new products and services. One may wonder whether and how the underlying social network in the market influences such decisions. Should seeding in a network with social hubs differ from that in a network with a flatter distribution of social ties? Should exclusivity decisions depend on the level of clustering among potential adopters? Studying growth of innovations from a social network perspective is of growing importance due to three main catalysts: 1) social ties have become broader, with wider reach, and are easier to activate and maintain; 2) social ties are much more extensively documented, and firms have better capacities to monitor and analyze them; and 3) the increasing coexistence of consumption and social interactions in online spaces may provide firms with more means of manipulating such interactions and potentially influencing the penetration process. To what extent are these increasing market changes reflected in academic research? Research on new product growth has traditionally focused on the aggregate level. The Bass model and its extensions (Bass, 1969; Dekimpe, Parker, & Sarvary, 2000; Jain, Mahajan, & Muller, 1991; Krishnan, Bass, & Jain, 1999; Norton & Bass, 1987) focus on the change in the overall number of adopters, and are agnostic with respect to connectivity structure (Goldenberg, Libai, & Muller, 2002; Peres, Muller, & Mahajan, 2010). While aggregate methods proved effective for forecasting purposes, many managerial questions are of a normative nature: In both examples above, the managers need to choose between paths of actions, and need to know which of them will be optimal. In such cases, the aggregate approach is limited: The underlying social network’s structure is important and should be incorporated into the decision making. Therefore research efforts have gradually shifted their focus to an individual-level perspective, and specifically, to exploring the role of the social network’s structural characteristics in various performance metrics of the innovation’s growth. These efforts have been enhanced by intensive research in computer science and information management. Borrowing from the field of industrial organization in economics, which defines itself as the effect of market structure on market performance (Tirole, 1988, p. 1), the new wave of research on growth of innovations can be described as the effect of social network structure on innovation performance. In other words this branch of research addresses the following question: Given a social network into which an innovation has been introduced, what are the effects of the social network’s structure on the performance of the market penetration of this innovation? This paper seeks to provide a critical review of current knowledge regarding social networks’ roles in new product growth.