Collaborative logistics network: a new business mode in the platform economy

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing

In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users' perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett's sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870.

Sustainability Perspectives of the Sharing Economy: Process of Creating a Library of Things in Finland

The transition from a linear economy to a circular economy requires a new way of thinking. In a circular economy, products are used more intensively, for example, by sharing them with others. To understand the possibilities of the sharing economy, environmental, social and economic impacts all need to be considered. The purpose of this paper is to shed light on the importance of the sharing economy as well as to increase understanding of how public sharing-economy services can be launched. The research methods used include a case-study approach and assessment of greenhouse gas emissions. In this paper, an implemented cooperation process of creating a tool and device library (the Library of Things) in a small Finnish municipality is described. Furthermore, the library’s impact on greenhouse gas emissions during the first 14 months of operation is assessed. The results indicate that approximately 5752 kg CO2eq was avoided during the 14-month period, assuming that with each loan, manufacturing of a new good was avoided. In addition, strong implications of local positive effects on social sustainability were found.