Viral product design for social network effects
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Recent advances in social media have profound technical and economic implications for innovative design. This research is motivated to investigate social network effects on product design with a focus on the interface of engineering design, viral marketing, and social computing. This dissertation envisions a new paradigm of design, called viral product design for social network effects. The research problem is formulated as identification of both an optimal set of product configurations and an optimal set of seed customers so as to maximize product adoption via online social networks through equilibrium solutions to marketing-engineering coordination. Fundamental issues are investigated and a technical framework is proposed with integrated decision-based design methods. Results of case studies demonstrate that the proposed research is able to bridge the gaps between the domains of engineering design and viral marketing by incorporating social network effects. The proposed work is geared towards new design theory and decision models by integrating peer influence of social networks, which shed light on understanding the social aspect of design. The dissertation reveals the fundamental issues underlying viral product design, including the identification of viral attributes, customer preference modeling incorporating subjective experiences, the dynamics of the diffusion mechanism of online social networks, formulation of adoption maximization, and coordination between the marketing and engineering domains. In order to tackle the fundamental issues, a technical framework of viral product design for social network effects is proposed. Accordingly, mathematical and computational models are developed within the framework to support 1) latent customer needs elicitation for viral product attributes extraction, 2) customer preference modeling and quantification for product choice decision making, 3) social network modeling for product adoption prediction, and 4) viral product design evaluation by adoption maximization. These coherent models along the technical framework lay the theoretical foundation of this research, as described below. First, in order to extract potential viral product attributes, latent customer needs elicitation is emphasized. This is because latent customer needs can delight customers unexpectedly, and thus lead to potential product adoption to a large extent. We propose to elicit latent customer needs by use case analogical reasoning from sentiment analysis of online product reviews. A case study of Kindle Fire HD tablets shows the potential and feasibility of the proposed method. The extracted product attributes and attribute levels provide the choice set of viral product attributes. Second, based on the extracted product attributes, a customer preference model based on cumulative prospect theory is presented, accommodating subjective experiences in the product choice decision making process. Moreover, a hierarchical Bayesian model with Markov chain Monte Carlo is used to estimate parameters involved in the model. Based on the case study of aircraft cabin interior design, the model parameters under different experimental conditions show systematic influence of subjective experiences in choice decision making. Furthermore, a copula structure is used to construct a holistic product utility, showing customers' overall preferences to a product. This measure is crucial to product choice decision making in the context of social networks. Third, in order to predict product adoption incorporating peer influence of social networks, a linear threshold-hurdle model is proposed. It overcomes multiple drawbacks of traditional diffusion models by modeling activation thresholds, influence probability, adoption spread, holistic utility of the product, and hurdle utility of a customer in a holistic fashion. A case study of Kindle Fire HD tablets demonstrates both the predictive power of the proposed model and interesting results about customers' adoption behavior. This model paves the way for product adoption maximization in large social networks. Fourth, in order to coordinate between marketing-engineering concerns, I formulate a bi-level game theoretic optimization model 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, the expected number of product adopters and the expected shared surplus, resulting from the identified product configurations and seed customers, are proved to be larger than those obtained from existing practice of viral marketing and product line design respectively, based on the case study of Kindle Fire HD tablets. Thus, the proposed paradigm of design extends the traditional boundaries among domains of engineering design, viral marketing, and social computing.