The Effects Of Direction Of Electronic Word-Of-Mouth And Tie Strength On Purchase Decisions: Self-Construal As The Moderator

Electronic Word-of-Mouth (eWOM) has become an important communication method and has received considerable interest. This study examined how direction of eWOM (positive vs. negative) and tie strength influence consumers’ online purchase decision making and how the relationships are moderated by self-construal of online consumers. The empirical results showed that the effects of eWOM direction on intention to click, attitude toward the product ad, product attitude, and purchase intention were stronger for consumers with interdependent self-construal than for those with independent self-construal. Besides, the results also showed that the effects of tie strength between reviewer and consumer on intention to click, attitude toward the product ad, product attitude and purchase intention were stronger for interdependent consumers than for independent consumers when receiving eWOM from their strong ties; whereas, the effects were stronger for independent consumers than for interdependent consumers when receiving eWOM from their weak ties. The findings of this study offer insights for advertiser to develop effective online marketing strategy on social networking sites

Learning the black hole metric from holographic conductivity

We construct a neural network to learn the RN-AdS black hole metric based on the data of optical conductivity by holography. The linear perturbative equation for the Maxwell field is rewritten in terms of the optical conductivity such that the neural network is constructed based on the discretization of this differential equation. In contrast to all previous models in AdS/DL (deep learning) duality, the derivative of the metric function appears in the equation of motion and we propose distinct finite difference methods to discretize this function. The notion of the reduced conductivity is also proposed to avoid the divergence of the optical conductivity near the horizon.The dependence of the training outcomes on the location of the cutoff, the temperature as well as the frequency range is investigated in detail. This work provides a concrete example for the reconstruction of the bulk geometry with the given data on the boundary by deep learning