Predicting the number of rented and returned bikes at each station is crucial for operators to proactively manage shared bike relocation. Although existing research has proposed spatial-temporal prediction models that significantly advance traffic prediction, these models often neglect the unique characteristics of shared bike systems (BSS). Spatially, the entire bike-sharing system (BSS) experiences peak activity during morning and evening rush hours, whereas, during other periods, activity is localized to local stations, with some recording no rides, highlighting the need to distinguish between global and local spatial information across different times. Temporally, the historical riding records for each station exhibit non-stationary patterns, necessitating the analysis of both global trends and local fluctuations. Existing Graph Neural Network (GNN) approaches to predicting shared bike demand primarily capture static spatial-temporal data and fail to account for the dynamic nature of bike flows. Moreover, these studies focus on global spatial-temporal information without considering local nuances, making it challenging to capture spatiotemporal dynamics in fluctuating BSS. To address these challenges, we introduce the Spatial-Temporal Aggregated Graph Neural Network (STAGNN). Our model first constructs a dynamic adjacent matrix to describe the evolving connections between stations, followed by local and global information layers to capture spatial-temporal information from large-scale shared bike networks accurately. Our methodology has been validated through experiments on four real-world datasets, comparing it against benchmark models to demonstrate superior prediction accuracy. Additionally, we conduct extended experiments on four datasets during the morning and evening rush hours, and the results also affirm the efficacy of the STAGNN in enhancing prediction performance.

This study examines the effects of social capital in the context of e-business and investigates how each of the three dimensions of social capital (structural, relational and cognitive) differentially influences a firm's substantive and symbolic performance. The study explores how structural capital and cognitive capital indirectly affect firm performance through relational capital. The research model is generally supported by data collected from a survey of 205 firms in China. The results suggest that structural and relational capital positively influence substantive and symbolic performance, respectively. However, cognitive capital does not have significant effects on substantive performance, though it positively affects symbolic performance. Also, the study found that structural capital and relational capital have stronger effects on substantive performance than symbolic performance. In contrast, cognitive capital has stronger effects on symbolic performance than substantive performance. Further, both structural capital and cognitive capital positively affect relational capital.

Integrating supply chain processes in e-business in emerging and transitional economies has attracted considerable attention from practitioners and researchers. Based on the theory of market orientation (MO) and the institution-based perspective, this paper investigates the effect of MO on supply chain integration (SCI) in e-business and examines the moderating role of ownership type in the relationship between MO and SCI in China. The results of a survey of 229 firms suggest that the MO dimensions have different effects on the supply and the demand process integration in e-business. Moreover, ownership type moderates the relationship between MO and SCI. Implications and suggestions for future research are likewise discussed.

Knowledge management (KM) is a dominant theme in the behavior of contemporary organizations. While KM has been extensively studied in developed economies, it is much less well understood in developing economies—notably, those that are characterized by different social and cultural traditions to the mainstream of Western societies; this is the case in China. In this article, we develop and test a theoretical model that explains the impact of leadership style and interpersonal trust on the intention of information and knowledge workers in China to share their knowledge with their peers. All the hypotheses are supported, showing that both initiating structure and consideration have a significant effect on employees’ intention to share knowledge through trust building: 28.2% of the variance in employees’ intention to share knowledge is explained. We discuss the theoretical contributions of the article, identify future research opportunities and highlight the implications for practicing managers.