SUMMARY

Recent advances in social media that allow better accesses to social networks of customers have profound technical and economic implications for design and innovation research. This research is motivated to investigate social network effects on product design with a focus on the interface of engineering, marketing, and social computing. In the context of online social networks, a customer’s choice of a product is substantially influenced by the rest of his or her social network, i.e., peer influence of social networks effects. However, on the one hand, traditional product portfolio planning (i.e., product line design) is mainly concerned with customer preferences with its focus on optimal product line performance. It ignores the social context in the decision making process of product adoption. On the other hand, peer influence of social networks effects is only limited to marketing related literature, such as influence maximization in viral marketing. One prominent drawback is that customer preferences to a product is considered homogeneous and thus are not incorporated in the diffusion process of product adoption. Hence, product design needs to explore the intersection and interaction of customers, products, and social contexts by integrating fundamental principles from multiple disciplines across domains of engineering, marketing, and social science. In this research, we propose a new paradigm of design, i.e., viral product design for social network effects. Fundamental issues are identified under this paradigm along with a technical framework, which drives viral product design for social network effects with rigorous engineering methods. The objective is to identity a set of optimal product configurations as an equilibrium solution between the engineering objective and the marketing objective. Specifically, from the perspective of engineering design, we have 1) explored product innovation to mine customer preferred product attributes and latent customer needs through analogical use case reasoning from sentiment analysis of online product reviews, and 2) investigated customer preference modeling and quantification, incorporating subjective experiences, especially affective influence, for product choice decision making. From the perspective of marketing, we have 1) accounted for important underlying factors and attacked multiple limitations of traditional diffusion models in the product diffusion process for product adoption prediction in the social network, and 2) formulated a bi-level game theoretic optimization strategy for viral product design evaluation, in which the leader maximizes product adoption while the follower optimizes product line performance. Through social network effects in terms of viral product attributes and viral influence attributes, four case studies in this research demonstrate that the obtained results are advantageous over those obtained from existing viral marketing and product design practice.