With the growing popularity of online social networks, it is becoming more important for marketing researchers to understand and measure social intercorrelations among consumers. The authors show that the estimation of consumers' social intercorrelations can be significantly affected by the sampling method used in the study and the topology of the social network. Through a series of simulation studies using a spatial model, the authors find that the magnitude of social intercorrelations in consumer networks tends to be underestimated if samples of the networks are used (rather than using the entire population of the network). The authors further demonstrate that sampling methods that better preserve the network structure perform best in recovering the social intercorrelations. However, this advantage decreases in networks characterized by the scale-free power-law distribution for the number of connections of each member. The authors discuss the insights they glean from these findings and propose a method to obtain unbiased estimation of the magnitude of social intercorrelations.

In this chapter, we fi rst describe how structural pricing models are different from reduced-form models and what the advantages of using structural pricing models might be. Specifi cally, we discuss how structural models are based on behavioral assumptions of consumer and fi rm behavior, and how these behavioral assumptions translate to market outcomes. Specifying the model from these fi rst principles of behavior makes these models useful for understanding the conditions under which observed market outcomes are generated. Based on the results, managers can conduct simulations to determine the optimal pricing policy should the underlying market conditions (customer tastes, competitive behavior, production costs etc.) change.

This paper quantitatively analyzes the poverty reduction effect in hunan province from three aspects of economic growth, financial support and industrial development. First, use of factor analysis on the independent variables for dimension reduction, extracting a factor for regional development level factor, the region's economic growth is expressed by the regional development level factors, financial support and industry development. Secondly, poverty incidence rate was selected as poverty reduction effect measurement to build an exponential regression model of regional development level and poverty incidence rate. Finally, it is concluded that regional development level has certain effect on poverty reduction in hunan province.