Employees are important stakeholders of firms, and employee responsibility is a crucial dimension of corporate social responsibility. This study employed a multivariable linear regression model to analyze the impact of AI technology on the variation in employee responsibility. We also utilized multiple methods, such as propensity score matching and alternative indicator analysis, to ensure the robustness of the research results. We theorized and found that the application of AI technology has a negative effect on employee responsibility, with supervision cost partially mediating the relationship between AI technology application and employee responsibility. Moreover, the negative relationship between AI technology application and employee responsibility decreases as the level of product market competition in which the firm operates increases, and it is stronger in government-controlled firms than in privately controlled firms. We also found that AI technology application and employee responsibility can improve firm productivity, and employee responsibility has a significant positive impact on innovation output and innovation efficiency, while the application of AI technology does not significantly impact innovation output and innovation efficiency. Our study contributes to research on the impact of AI technology in the workplace and has important implications for organizational practices regarding the application of AI technology and employee responsibility.

Digital technology coupled with the quarantines caused by the COVID-19 pandemic has made working from anywhere (WFA)—a modern form of remote working—a widespread phenomenon. Given that WFA brings new career challenges to and engenders paradoxes of knowledge exchange among employees, this research aims to examine how the interactions of remote work time (RWT), knowledge sharing (KS), and knowledge hiding (KH) affect career development (CD) from a culturally grounded paradoxical framing of yin–yang harmonizing. The data were collected from Chinese manufacturing employees, and a moderated hierarchical regression analysis was used to examine the hypotheses. The results show an inverted U-shaped relationship between RWT and CD. The interaction of KS and KH is significantly related to CD, and the inverted U-shaped RWT–CD relationship is moderated by the interaction term, in which RWT exerts the most substantial positive impact on CD when KS is high and KH is low. This study offers valuable implications for coping with perplexing employment relationships and increasing career challenges in volatile work environments. The primary originality is to adopt a novel cognitive frame of yin–yang harmonizing to examine the nonlinear effect of remote working and the symbiotic impact of KS and KH on CD, which not only enriches the understanding of flexible work arrangements in the digital economy but also provides novel insights into the interconnectedness of KS and KH and their interacting effects on HRM-related outcomes.