Objectives:While organizations are increasingly adopting artificial intelligence(AI),its effects on employees’well-being remain poorly understood.Drawing on social cognitive theory,this study aimed to examine the und...Objectives:While organizations are increasingly adopting artificial intelligence(AI),its effects on employees’well-being remain poorly understood.Drawing on social cognitive theory,this study aimed to examine the underlying mechanism through which organizational AI adoption influences employees’well-being.Methods:A two-wave time-lagged research design was conducted with 262 Chinese employees employing a voluntary and anonymous survey.The survey included measures of organizational AI adoption,AI use anxiety,job insecurity,subjective well-being,and psychological well-being.The data were analyzed using SPSS 26.0 software and macro PROCESS.Results:The moderation analysis revealed that AI use anxiety moderated the association between organizational AI adoption and job insecurity(b=0.19,standard error[SE]=0.04,p<0.001),indicating that organizational AI adoption was positively related to job insecurity when AI use anxiety was higher.The moderating mediation analysis further revealed that the indirect effect of organizational AI adoption on employees’well-being via job insecurity was negative(for subjective well-being,moderated mediation index=−0.05,SE=0.03,95%CI=[−0.103,−0.005];for psychological well-being,moderated mediation index=−0.04,SE=0.02,95%CI=[−0.089,−0.007]),indicating that organizational AI adoption would impair employees’well-being by increasing job insecurity for employees with a higher level of AI use anxiety.Conclusions:AI use anxiety acts as a critical moderator in the link between organizational AI adoption and employee well-being.The finding supports the notion that a wide variety of boundary conditions may influence how individuals react to AI filling roles typically held by humans.展开更多
<div style="text-align:justify;"> In the fast-moving world, it is noticed that every industry is developing gradually, but recently it is identified that the use of AI has become the talk of own. There...<div style="text-align:justify;"> In the fast-moving world, it is noticed that every industry is developing gradually, but recently it is identified that the use of AI has become the talk of own. Therefore, this study is focused on gathering data regarding the AI on how it has transformed the entire world’s corporate sector. The essential application AI in the business world helps the business to perform better in the corporate sector. In this paper, the critical role of artificial intelligence is to grow business in different sectors and also address its ethical and unethical issues. The paper has all the initial background and comprehensive literature regarding AI and machine learning. It is discovered how the technological world has been striving to take their business on to new heights, which requires updated technological changes in internal business activities. Companies can now effortlessly interact with their customers in making their application accessible for the end-users through implementing AI and machine learning. Companies are getting higher profitability and enhancing their performance and achieving economic advantages by integrated AI. Moreover, their technological developments will take human jobs in the future, so, it is suggested that humans should work on their skills and competencies so that they can deal with unemployment. </div>展开更多
Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are a...Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.展开更多
文摘Objectives:While organizations are increasingly adopting artificial intelligence(AI),its effects on employees’well-being remain poorly understood.Drawing on social cognitive theory,this study aimed to examine the underlying mechanism through which organizational AI adoption influences employees’well-being.Methods:A two-wave time-lagged research design was conducted with 262 Chinese employees employing a voluntary and anonymous survey.The survey included measures of organizational AI adoption,AI use anxiety,job insecurity,subjective well-being,and psychological well-being.The data were analyzed using SPSS 26.0 software and macro PROCESS.Results:The moderation analysis revealed that AI use anxiety moderated the association between organizational AI adoption and job insecurity(b=0.19,standard error[SE]=0.04,p<0.001),indicating that organizational AI adoption was positively related to job insecurity when AI use anxiety was higher.The moderating mediation analysis further revealed that the indirect effect of organizational AI adoption on employees’well-being via job insecurity was negative(for subjective well-being,moderated mediation index=−0.05,SE=0.03,95%CI=[−0.103,−0.005];for psychological well-being,moderated mediation index=−0.04,SE=0.02,95%CI=[−0.089,−0.007]),indicating that organizational AI adoption would impair employees’well-being by increasing job insecurity for employees with a higher level of AI use anxiety.Conclusions:AI use anxiety acts as a critical moderator in the link between organizational AI adoption and employee well-being.The finding supports the notion that a wide variety of boundary conditions may influence how individuals react to AI filling roles typically held by humans.
文摘<div style="text-align:justify;"> In the fast-moving world, it is noticed that every industry is developing gradually, but recently it is identified that the use of AI has become the talk of own. Therefore, this study is focused on gathering data regarding the AI on how it has transformed the entire world’s corporate sector. The essential application AI in the business world helps the business to perform better in the corporate sector. In this paper, the critical role of artificial intelligence is to grow business in different sectors and also address its ethical and unethical issues. The paper has all the initial background and comprehensive literature regarding AI and machine learning. It is discovered how the technological world has been striving to take their business on to new heights, which requires updated technological changes in internal business activities. Companies can now effortlessly interact with their customers in making their application accessible for the end-users through implementing AI and machine learning. Companies are getting higher profitability and enhancing their performance and achieving economic advantages by integrated AI. Moreover, their technological developments will take human jobs in the future, so, it is suggested that humans should work on their skills and competencies so that they can deal with unemployment. </div>
文摘Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.
