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This study aims to explore the influence of extra-workplace factors, specifically work−family interpersonal capitalization, on employee green behavior. Based on the conservation of resources theory, the research sought to understand how resources gained from positive family interactions spill over into the workplace, enhancing green behavior. In addition, the study investigated the mediating role of relational energy and the moderating effects of work green climate and environmental self-accountability, providing a nuanced understanding of the mechanisms involved.

This study used a multiwave field study combined with an experimental study to investigate the impact of work−family interpersonal capitalization on employee green behavior. Data were collected in several phases to capture changes over time and to understand causal relationships. The multiwave design allowed for observing the dynamic interplay between family and work domains, while the experimental component provided controlled conditions to validate the findings. This approach ensured robust and comprehensive analysis, integrating both real-world and experimental data.

The study revealed that work−family interpersonal capitalization significantly enhances employee green behavior. Relational energy emerged as a crucial mediator in this relationship. Furthermore, the study found that both work green climate and environmental self-accountability positively moderated the relationship between relational energy and green behavior. Notably, the interaction of work green climate and environmental self-accountability further strengthened this relationship, ultimately influencing the indirect effect of relational energy on employee green behavior. These findings highlight the complex interplay between personal and organizational factors in promoting sustainable practices at work.

This study provides valuable insights into the spillover effects from family to work, emphasizing the importance of considering “nongreen” factors in understanding employee green behavior. By identifying relational energy as a key mediator and uncovering the moderating roles of work green climate and environmental self-accountability, the research contributes to the broader literature on environmental sustainability and organizational behavior. The findings suggest practical implications for organizations aiming to foster green behavior, highlighting the potential of enhancing family−work interactions and cultivating a supportive green work environment.

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.

Managerial accounting has traditionally played an important role in analyzing data, estimating performance, and offering suggestions. Modern management accountants face evolving expectations, such as contributing strategically to long-term goals and communicating information using visualizations. We specifically focus on how managerial accounting courses and textbooks should integrate data analytics to better prepare accounting students for the current working requirements. This study presents survey findings encompassing perspectives from 23 accounting professors and 46 practitioners. The survey revealed a prevalent endorsement for data analytics integration, with 91% of practitioners and 78% of professors advocating for inclusion. Specifically, 64% of professors support substantial integration compared to 36% of practitioners. About 25% of both groups believe in discussing data analytics in every management accounting topic if not deeply integrated. This study significantly contributes to accounting education literature by combining insights from educators and practitioners regarding the inclusion of data analytics in management accounting. While professors offer guidance on essential materials and practices, practitioners enrich the discussion with practical, workplace-relevant techniques.

Assisted training using upper limb rehabilitation robots is beneficial for flaccid paralysis patients in recovering their functional abilities. In the assisted training mode, the patient’s motor ability is limited by factors such as limb muscle tension, and it is prone for the rehabilitation robot to deviate from the prescribed training trajectory. A sliding mode control method based on a fixed time observer is proposed to address the problem of delayed trajectory tracking response of upper limb rehabilitation robots caused by external disturbances such as patient limbs.

First, aiming at the problem of estimating and compensating for external disturbances in the upper limb rehabilitation robot system, a fixed time observer was designed based on the robot’s dynamic model. Second, the composite sliding mode reaching law combining the smooth function and the power-exponential function is proposed to shorten the convergence time of system states in the startup phase, thereby reducing chattering in the control process and realizing the real-time tracking of the training trajectory by the control system.

The proposed method provides a solution for the trajectory tracking speed of upper limb rehabilitation robot controllers. In the circular trajectory tracking control, compared to the sliding-mode control method combined with the variable-exponential composite reaching law based on the fixed-time observer, the method in this paper reduces the time for the system state to reach the sliding surface by 0.89 s and improves the response speed by 0.66%.

The composite sliding mode approach law based on smooth function and power exponent function can reduce the time it takes for the system state to reach and remain on the sliding surface and improve the trajectory tracking speed of upper limb rehabilitation robots. This controller improves the accuracy of trajectory control and ensures the robustness of auxiliary rehabilitation training.