The Effect of Interactivity on SNS Users\u27 Loyalty: Flow and Presence as Mediators

According to the stimulus–organism–response (S–O–R) paradigm, this study aims to understand how interactivity affects SNS users’ loyalty through user experience of presence and flow. Data collected from 242 respondents was analyzed with structural equation modeling (SEM). The results show that machine interactivity affects telepresence and flow, person interactivity affects social presence and flow, social presence affects flow and flow further affects SNS users’ loyalty. In addition, telepresence has a significant effect on social presence

Asymmetric effects of exchange rate pass-through: an empirical analysis among China, the United States and Japan

Abstract From the perspective of exchange rate direction fluctuations, this paper comparatively studied the asymmetric effect of movements in the nominal exchange rate on consumer prices among China, the United States, and Japan. To this end, the paper used the error correction model (ECM) to conduct an empirical analysis from the first season of 1994 to the last season of 2010 period. The results showed that: (1) the pass-through of exchange rate movements to consumer prices was incomplete; (2) exchange rates fluctuated in different directions, meaning that when the exchange rate appreciated and depreciated, the pass-through of exchange rate movements to consumer prices was asymmetric. However, the direction varied among the three countries. The influence of depreciation on consumer prices was higher in both China and the United States, while Japan was the opposite; (3) exchange rate pass-through was different in the three countries. The level of exchange rate pass-through in China was higher than the other two countries; (4) when short-term fluctuations deviated from long-term equilibrium, the adjustment was higher in the United States, followed by Japan, and China was relatively lower. These results had important implications for current monetary policies and practices

Purchase Prediction Based on a Non-parametric Bayesian Method

Predicting customer’s next purchase is of paramount importance for online retailers. In this paper, we present a new purchase prediction method to predict customer behavior based on non-parametric Bayesian framework. The proposed method is inspired by topic modeling for text mining. Unlike the conventional methods, we regard customer’s purchase as the result of motivations and automatically determine the number of user purchase motivations. Given customer’s purchase history, we show that customer’s next purchase can be predicted by non-parametric Bayesian model. We apply the model to real-world dataset from Amazon.com and prove it outperforms the traditional methods. Besides that, the proposed method can also determine the number of the motivations owned by users automatically, rendering it a promising approach with a good scalability

Information Entropy-based Social Capital Measure Method of Online Influential Users

Measuring online user influence is a major research topic in social marketing performance maximization. In this study, we comprehensively investigate how online influential users gain, accumulate, and use their social capital from the perspective of information resource management and social capital measurement. First, we define the social capital of online influential users and the attribute characters and relationships reflected fully by personality and sociality index data. We then construct a social capital measurement indicator system and information entropy model of online users. After the calculations of this model, we finally forma social capital measure method of online influential users. The rationality and validity of proposed model are tested by experimental study on real datasets

Entropy Weight Measure Model of Online Influential Users’ Relative Social Capital

Based on the perspectives of information resource management and social capital measurement, this paper studies how influential users acquire, accumulate, and use their social capital in social networks to explore the general rules, which enterprises use influential users’ relative competitiveness in their topic areas of expertise to advertise precisely. The paper describes the social capital differences among influential users by introducing and calculating users’ relative social capital. Results show that user’s social capital values in different fields are dissimilar, and the scope and intensity of social capital among different users are relative. The proposed method is proved to be effective and reasonable

Identifying social influence in complex networks: a novel conductance eigenvector centrality model

Identifying influential peers is an important issue for business to promote commercial strategies in social networks. This paper proposes a conductance eigenvector centrality (CEC) model to measure peer influence in the complex social network. The CEC model considers the social network as a conductance network and constructs methods to calculate the conductance matrix of the network. By a novel random walk mechanism, the CEC model obtains stable CEC values which measure the peer influence in the network. The experiments show that the CEC model can achieve robust performance in identifying peer influence. It outperforms the benchmark algorithms and obtains excellent outcomes when the network has high clustering coefficient