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The authors aim to broaden the understanding of incivility through the lens of bystanders who witness incivility toward women. Integrating attributional ambiguity and emotional contagion theories with the literature on workplace mistreatment, the authors propose that witnessing incivility toward women may negatively impact bystanders.

The authors collected multi-wave data from 324 employees to assess the consequences of witnessing incivility toward women at work for bystanders.

Utilizing a serial mediation model, the authors found evidence that witnessing incivility toward women indirectly increased turnover intentions six weeks later, first through elevated negative affect and then through increased cognitive burnout.

Taken together, this study's findings suggest that the negative effects of incivility toward women can spread to bystanders and highlight the importance of considering individuals who are not directly involved, but simply bear witness to incivility at work.

People develop general stereotypes and beliefs about how people look and behave, and discrepancies in perceptions of warmth and competence can lead to incongruence in expected behaviors during service interactions. These perceptions can contribute to negative outcomes for employees; therefore, this study aims to examine how perceptions of warmth affect both customer reactions to employees who are Asian and the work stress, strain and attitudes of employees who are Asian toward their jobs.

To investigate the phenomenon, the authors conducted three studies. The first study examined whether occupational-racial stereotypes influence customers’ evaluation of employees’ service recovery performance. The second study temporally examined whether employees who are Asian engage in more emotional labor strategies to counter these occupational-racial stereotypes and as a result experience more negative outcomes. And a third study investigated the cumulative effects of negative perceptions on employee outcomes.

The results suggest employees in the hospitality industry who are Asian are perceived as less warm during service failure interactions and, thus, must work harder to regulate their emotions. As a result, employees who are Asian reported experiencing more negative outcomes, supporting the notion that occupational-racial stereotypes contribute to racial disparity and act as challenges and barriers for employees in the service industry.

These findings emphasize the need to understand and address occupational-racial stereotypes for employees who are Asian to mitigate racial disparities in the service industry. Organizations can promote diverse work climates, provide emotional support and foster high-quality relationships to support employee well-being and satisfaction.

The results provide insight into how stereotypical warmth perceptions of employees who are Asian may affect the demands they face when engaging in service recovery, and how these increased demands may reduce the quality of their work experiences.

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.