Understanding Continuance Intention to Use Mobile Fitness Services: The Roles of Technological Characteristics and Network Effects

Mobile fitness platforms are effective in promoting healthy behaviors but these platforms generally suffer from low retention rates. It is necessary to study how to retain users of mobile fitness platforms. Based on customer value theory and Socio-technical approach, this study proposed a theoretical model to study the factors that affect users’ continuance intention to use mobile fitness platforms from a holistic perspective. A total of 320 valid questionnaires were collected to verify the model. The results indicate that utilitarian value and hedonic value are positively related to continuance intention. Social ties are negatively related to continuance intention. Meanwhile, it is found that technological characteristics have significant positive influences on utilitarian value, hedonic value and social ties. Network effects have significant positive influences on hedonic value and social ties. These findings extend our understanding of users’ continued usage of mobile fitness platforms and provide practical implications for mobile fitness service providers

Multi-category Comparative Analysis of Factors Affecting E-commerce Sales

With the continuous development of e-commerce, more and more types of goods are sold online, so merchants should develop different sales strategies for different types of goods. This paper firstly selects 15 variables to build a stepwise regression model. In the analysis of influencing factors on sales of products in different categories, we find that there are significant differences in the impact of the number of appended reviews and pictures reviews on the sales of utilitarian and hedonic products. In the analysis of influencing factors on sales of products in the same category, we find that the factors influencing the sales of different clothing products are also different to some extent. At last, we put forward some suggestions on adjusting price and title length, and writing product details. This paper is more detailed in variable selection and product classification than some previous studies. It is meaningful for merchants to optimize sales plans and improve product sales

The Role of Internet Search Index for Tourist Volume Prediction Based on GDFM Model

Tourist volume is increasing with the expansion of the scale of tourism, and improving the prediction of tourist volume is helpful for tourism managers to make decisions. Internet search index can be applied to predict the behavior of users, which is widely used in the study of tourist volume prediction and infectious disease prediction. However, the high dimension and correlation of Internet search index tends to reduce the accuracy of the models, which increases the average prediction error of common time-series models. The dynamic factor model (DFM) proposed in our study can be used to solve the problem. This study selects 23 variables and introduces the generalized dynamic factor model (GDFM) to predict tourist volume. The model cannot only reduce the dimensionality of high-dimensional Internet search index data, but also reflects the dynamic correlation between Internet search index data. The results show that the prediction accuracy is improved in our method, and the prediction accuracy of tourist volume is improved by over 10%, with an average error of only 4.3% when compared with the neural network (NN) model. Our study not only provides implications for decision-makers to predict tourist volume timely and accurately, but also helps companies understand tourist’ behavior and make the best strategic decisions