摘要：
This paper analyzes the static resilience of global wood forest products trade networks across upstream, midstream, downstream, and recycling sectors using a complex directed weighted network approach. By examining topological features and resilience from 2002 to 2021, this study reveals significant structural evolution and scale expansion in these networks. It finds improvements in network efficiency and resilience, alongside an increase in weighted hierarchy highlighting the prominent roles of core countries like China, the US, and Germany. While these countries bolster network resilience, they also introduce certain vulnerabilities. This study finds notable disassortative mixing without trade volume weights and diversified trends with weights, offering new insights into network dynamics. Core nodes must address disruption risks, enhance diversity, and establish emergency response mechanisms. In the recycling sector, this paper highlights weak trade connections and low resilience, with the US maintaining dominance, China’s influence waning, and India’s rapid ascent. This paper concludes by emphasizing the need for refined indicator systems and deeper explorations into resilience enhancement strategies for operational and targeted suggestions.
期刊：
FRONTIERS IN PSYCHOLOGY,2023年14:1073301 ISSN：1664-1078
通讯作者：
Pang, Y.
作者机构：
[Jia, Xiaoyun] Shandong Univ, Inst Governance, Sch Polit & Publ Adm, Qingdao, Peoples R China.;[Jia, Xiaoyun; Hou, Feng] Massey Univ, Sch Math & Computat Sci, Auckland, New Zealand.;[Pang, Yan] Cent South Univ Forestry & Technol, Sch Logist & Transportat, Changsha, Peoples R China.;[Huang, Bingqi] Peking Univ, Guanghua Sch Management, Beijing, Peoples R China.
通讯机构：
[Pang, Y.] S;School of Logistics and Transportation, China
关键词：
Continuance intention of watching;Behavioral Intention;Live streaming;ECT;V-ECM;Post-adoption behavior
摘要：
<jats:sec><jats:title>Introduction</jats:title><jats:p>Live stream-watching has become increasingly popular worldwide. Consumers are found to watch streams in a continuous manner. Despite its popularity, there has been limited research investigating why consumers continue to watch streams. Previously, the expectation-confirmation theory (ECT) has been widely adopted to explain users’ continuance intention. However, most current ECT-based models are theoretically incomplete, since they only consider the importance of perceived benefits without considering users’ costs and sacrifices. In this paper, we propose a <jats:italic>value-based continuance</jats:italic> intention model (called V-ECM), and use it to investigate factors influencing consumers’ continuance intention to watch streams.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Our hypotheses were tested using an online survey of 1,220 consumers with continuance stream-watching experiences.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Results indicate that perceived value, a process of an overall assessment between users’ perceived benefits and perceived sacrifices, is proved to be a better variable than perceived benefits in determining consumers’ continuance watching intention. Also, compared with other ECT-based models, V-ECM is a more comprehensive model to explain and predict consumers’ continuance intention.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>V-ECM theoretically extends ECT-based studies, and it has potential to explain and predict other continuance intentions in online or technology-related contexts. In addition, this paper also discusses practical implications for live streaming platforms with regards to their design, functions and marketing.</jats:p></jats:sec>
通讯机构：
[Hou, F] M;[Wang, RL ] C;Cent South Univ Forestry & Technol, Sch Logist & Transportat, Changsha, Hunan, Peoples R China.;Massey Univ, Sch Math & Computat Sci, Auckland, New Zealand.
关键词：
Multivariate time series forecasting;Global graph;Local graph;Graph structure learning;Information fusion
摘要：
Recent status-of-the-art methods for multivariate time series forecasting can be categorized into graph-based approach and global-local approach. The former approach uses graphs to represent the dependencies among variables and apply graph neural networks to the forecasting problem. The latter approach decomposes the matrix of multivariate time series into global components and local components to capture the shared information across variables. However, both approaches cannot capture the propagation delay of the dependencies among individual variables of a multivariate time series, for example, the congestion at intersection A has delayed effects on the neighboring intersection B. In addition, graph-based forecasting methods cannot capture the shared global tendency across the variables of a multivariate time series; and global -local forecasting methods cannot reflect the nonlinear inter-dependencies among variables of a multivariate time series. In this paper, we propose to combine the advantages of both approaches by integrating Adaptive Global -Local Graph Structure Learning with Gated Recurrent Units (AGLG-GRU). We learn a global graph to represent the shared information across variables. And we learn dynamic local graphs to capture the local randomness and nonlinear dependencies among variables. We apply diffusion convolution and graph convolution operations to global and dynamic local graphs to integrate the information of graphs and update gated recurrent unit for multivariate time series forecasting. The experimental results on seven representative real-world datasets demonstrate that our approach outperforms various existing methods.
