The Influence of Social Networks on the Digital Recruitment of Human Resources: An Empirical Study in the Tourism Sector

Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model

Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.

The Practical Impact of Bias against Minority Group Applicants in Resume Screening on Personnel Selection Outcomes

Research has consistently shown that resume screening decisions, despite their practical importance and frequent use in practice, are prone to biases that disadvantage applicants in demographic minority groups. Using a two-stage multiple-hurdle selection simulation as an example (initial selection on resume scores, then selection on a composite of cognitive ability and conscientiousness test scores), the current study illustrates the practical impact that bias against ethnic minority group applicants in resume evaluation can have on the outcomes of selection. Results show that if the bias against minority group applicants creates even a modest level of deflation in the observed resume evaluation scores for minority group applicants, the selection rate for minority group applicants is expected to be meaningfully lower compared to the selection rate for majority group applicants, increasing the likelihood of adverse impact. These findings demonstrate in clear practical terms the critical importance of fair resume evaluations for improving the legal defensibility of selection. Going beyond the simple understanding that bias against minority group applicants in resume screening leads to lower diversity, the current study contributes to the previous literature by clearly outlining the expected effect that varying levels of discriminatory resume evaluation have on the practical outcomes of selection. Moreover, we illustrate these results under a realistic set of conditions implied from the personnel selection literature and meta-analyses of variables relevant to personnel selection.