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The volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.

The synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.

Utilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.

The construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.

The global popularity of short video platforms has surged with the rapid development of mobile internet and 5G technology. DOUYIN, among other platforms, has amassed a massive user base in China. This study presents a theoretical framework based on media dependency theory and user stickiness perspectives. It identifies three key factors that affect user stickiness: platform algorithms, content resources and user interaction. An interpretive philosophy and inductive qualitative approach were adopted to conduct an in-depth case study of DOUYIN. Thematic analysis of secondary data from various sources was used. The findings demonstrate DOUYIN’s innovative approach to utilising advanced algorithms, diverse content and social interactions to enhance user engagement. DOUYIN utilises machine learning techniques to create user profiles and comprehend video content. It subsequently provides real-time personalised recommendations and optimises the algorithms based on user feedback. DOUYIN also incorporates PGC-, UGC- and PUGC-generated content, supported by a creator incentive system. Moreover, DOUYIN enables interactions between users, creators and the platform through commenting, sharing and live streaming features.

This study aims to construct a “contractual–relational–governmental” 3D governance framework for new infrastructure projects (NIPs) within China’s distinct institutional context. The primary objective is to explore the impact of multiple governance mechanisms on the NIP performance, thus identifying the key governance mechanisms and proposing targeted performance improvement strategies.

The research design follows a sequential mixed methodology of integrating qualitative and quantitative data collection and analysis. Firstly, project governance and performance indicators were collected from relevant literature and expert interviews. Secondly, a questionnaire was developed, and data were collected through on-site and online means. Finally, the partial least square structural equation model (PLS-SEM) was utilized to examine and analyze the relationships between governance mechanisms and NIP performance.

Contractual, relational and governmental governance all have a certain role in promoting the NIP performance. Specifically, contract stringency, trust and governmental decision are the core elements of contractual, relational and governmental governance, respectively, while commitment does not significantly affect NIP performance. Generally, relational and governmental governance exert a more substantial influence compared to contractual governance, with governmental decision and trust being the most effective.

This paper contributes to the field by introducing PLS-SEM as a measurement tool for exploring the impact of multiple governance mechanisms on governance performance in NIPs. The results offer valuable insights for project managers, enabling them to concentrate on core factors while refining and optimizing governance mechanisms and strategies.

The purpose of this study was to investigate the impact of artificial intelligence (AI) adoption on knowledge workers' innovative work behaviors (IWB), as well as the mediating role of stress appraisal and the moderating role of individual learning abilities.

This study analyzed the questionnaire results of 313 knowledge workers, and data analysis was conducted by using SPSS 25.0, SPSS 25.0 macro-PROCESS and AMOS 28.0.

This study found that AI adoption has a double-edged sword effect on knowledge workers' IWB. Specifically, AI adoption can promote IWB by enhancing knowledge workers' challenging stress appraisal, while inhibiting IWB by fostering their hindering stress appraisal. Moreover, individual learning ability significantly moderated the relationship between AI adoption and stress appraisal, which further influenced IWB.

This study integrates the conflicting findings of previous studies and proposes a comprehensive theoretical model based on the theory of cognitive appraisal of stress. This study enriches the research on AI in the field of knowledge management, especially extending the understanding of the relationship between AI adoption and knowledge workers’ IWB by unraveling the psychological mechanisms and behavior outcomes of users' technology usage. Additionally, we provide new insights and suggestions for organizations to seek the cooperation and support of employees in introducing new technologies or driving intelligent transformation.