Modelling and analysis of network information data for product purchasing decisions

Technology has enabled consumers to gain product information from different online platforms such as social networks, online product reviews and other digital media. Large manufacturers and retailers can make use of this network information to forecast accurately, to manage the demand and thereby to improve profit margin, efficiency, etc. This paper proposes a novel framework to model and analyses consumers' purchase decision for product choices based on information obtained from two different information networks. The model has also taken into account variables such as socio-economic, and demographic characteristics. We develop a utility-based discrete choice model (DCM) to quantify the effect of consumers' attitudinal factors from two different information networks, namely, social network and product information network. The network information modelling and analysis are discussed in detail taking into account the model complexity, heterogeneity and asymmetry due to the dimension, layer and scale of information in each type of network. The likelihood function, parameter estimation and inference procedures of the full model are also derived for the model. Finally, extensive numeric investigations were carried out to establish the model framework.