L-glufosinate-ammonium (L-GLA), a widely used herbicide, exerts detrimental non-target effects on crops, soil microorganisms, and ecosystems. However, its impacts on soil microbial communities and metabolic functions remain poorly understood. In this study, we applied L-GLA at two concentrations—600 g a.i. hm−2 (low, L) and 3,000 g a.i. hm−2 (high, H)—to yellow-brown soil and investigated its effects on microbial community composition, carbon cycle-related metabolic functions, and soil metabolites using integrated metagenomics and soil environmental pseudotargeted metabolomics at 30 and 60 days post-application. The degradation rate of L-GLA was concentration-dependent, with half-lives of 18.8 days (L) and 29.7 days (H). Both doses significantly reduced soil organic matter (SOM) and available potassium (AK) content, and markedly altered microbial community richness, structure, and composition. L-GLA exposure also disrupted the complexity of soil microbial co-occurrence networks and the activities of carbon-cycle-related enzymes. Metabolomic analysis further revealed significant (p < 0.05) and dose-dependent alterations in the soil metabolite profile. Correlation analysis indicated strong associations between characteristic microbial taxa and differential metabolites. Our findings provided critical insights into how L-GLA influences the soil microecological environment and contributed to a deeper understanding of soil microbial ecology in the context of modern agricultural practices.
Yuan-Feng Dai, Han Li, Han-Cheng Wang et al.· Frontiers in Microbiology· 0 citations
This study aims to investigate how artificial intelligence technology integration (AITI) affects employees’ task performance through two distinct role-stress mechanisms – role conflict and role overload – and whether these relationships vary according to employees’ chronic regulatory focus (CRF).
An explanatory sequential mixed-methods design was adopted. First, a two-wave survey was conducted among hotel employees and their supervisors to test the hypothesized model quantitatively. Second, a follow-up qualitative case study based on semi-structured interviews was used to further interpret the quantitative findings in real service settings.
AITI was found to enhance task performance by increasing role overload, while simultaneously impairing task performance through increased role conflict. Moreover, CRF moderated the effects of AITI on both forms of role stress. Compared with promotion-focused employees, prevention-focused employees experienced stronger effects of AITI on role conflict and role overload, resulting in stronger indirect effects on task performance.
Managers should move beyond a purely efficiency-driven approach to AI adoption and pay closer attention to employees’ role perceptions and motivational orientations. Organizations should reduce AI-related role conflict through clearer role design, training and support, while channeling manageable role overload into learning, adaptation and performance improvement. Tailored management practices for prevention- and promotion-focused employees may further enhance AI implementation outcomes.
This study offers a novel explanation of how AITI simultaneously improves and impairs task performance by uncovering two contrasting role-stress mechanisms. By integrating role theory and regulatory focus theory in a mixed-methods design, it provides a richer understanding of employee–AI interaction in service work.
Xiaojun Wu, Yingji Zhang, Chiyin Chen· International Journal of Con...· 0 citations
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