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Drawing on signaling theory and the “signal transmission–interpretation–feedback” framework, this study explores the effects of perceived distributive justice and respect from managers on nurses' work meaningfulness and work effort in public hospitals in China and examines the moderating role of work self-efficacy.

We collected 341 paired questionnaires for nurses and managers from four public hospitals in China. The data were analyzed by structural equation modeling and hierarchical regression analysis.

Distributive justice and managers' respect for employees are positively related to work meaningfulness. Additionally, work self-efficacy negatively moderates this relationship. Work meaningfulness is positively related to work effort and fully mediates the relationships between perceived distributive justice and respect from the manager and work effort.

This study provides useful insights for healthcare organizations to improve nurses' work meaningfulness from the perspectives of their material and emotional needs, according to their work self-efficacy characteristics, thus promoting their work effort. The findings offer important guidance for improving the effectiveness of grass-roots human resources to cope with unpredictable situations such as the COVID-19 pandemic.

This study focuses on the organization's environmental factors that affect the primary staff's work meaningfulness. Further, it analyzes the differences in signal interpretation among nurses with different work self-efficacy characteristics, thus providing new insights into work meaningfulness. Through manager–nurse pairing data, it reveals the important role of work meaningfulness in motivating work effort.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

This research aimed to develop an automatic 3D body measurement line generation method that reduces errors induced by diverse body shapes and incomplete scan areas.

Three-dimensional body scan data from the 5th Size Korea database were used. Measurement extraction methods were developed for each measurement; chest girth, underbust girth, armscye girth and neck base girth.

The research showed that the method adopted in this study enhanced the accuracy of the scan measurements for various body shapes and incomplete scan data. The authors verified the accuracy of the developed methods for various body shapes by comparing the scan measurement against manual measurement.

The automatic 3D body measurement line generation algorithms developed for various human body shapes will improve the reliability and accuracy of 3D body scan measurement program. Also. it will be of practical use in human-size-related production processes.