5. Directions for future research
The first scenario in the introduction to this paper describes a new service launched by a mobile provider that wishes to implement a referral program. How and in what fashion do we need to extend our current cumulative knowledge in order to suggest an answer to such issues that marketing managers face? Research so far has provided many insights into the relationship between structural characteristics and innovation performance. However, most papers reviewed here focus on a single structural characteristic, use a single type of network, and measure a single performance metric. We argue that in order to provide meaningful generalizations, research needs to move toward standardization and integration. Standardization means reaching agreement on an accepted performance measure and set of structural characteristics to be measured. Integration means testing the impact of multiple factors, measuring their relative effects and synergies, and moving toward a broader range of marketing decisions. In what follows, we propose a roadmap for future research in order to achieve these goals. Our proposed roadmap is comprised of seven stages:
1. A unified performance metric – As indicated in Table 2, current research uses a variety of metrics to describe growth performance, yet generalization across scenarios and papers requires standardization of the performance metric. As discussed above, we suggest using the NPV of either the number of adopters, or the adoption profits. NPV's value stems from capturing the number of adopters, the speed of growth, and the cost effectiveness of the process. Hence, we view it as the most appropriate performance measure of an innovation's growth.
2. A unified set of structural characteristics – Network research has proposed numerous characteristics through which a social network's structure can be described. In the aforementioned, we suggested a set of structural characteristics that have been shown to be important for innovation growth, and that are fairly independent of each other. These characteristics include: (1) global characteristics: average degree, degree distribution, clustering, and degree assortativity; (2) dyadic characteristics: tie strength and embeddedness; (3) individual characteristics including personal characteristics: opinion leadership and susceptibility; and location characteristics, namely degree centrality, closeness centrality, and betweenness centrality. We propose refining this core set of structural characteristics for the innovation at hand, and determining their relative importance to innovation growth. To do so, we propose using simulations to run large-scale full-factorial experiments on networks, varying independently the various structural characteristics and determining the relative impact of each on growth performance.
