Employee turnover(ET)can cause severe consequences to a company,which are hard to be replaced or rebuilt.It is thus crucial to develop an intelligent system that can accurately predict the likelihood of ET,allowing th...Employee turnover(ET)can cause severe consequences to a company,which are hard to be replaced or rebuilt.It is thus crucial to develop an intelligent system that can accurately predict the likelihood of ET,allowing the human resource management team to take pro-active action for retention or plan for succession.However,building such a system faces challenges due to the variety of influential human factors,the lack of training data,and the large pool of candidate models to choose from.Solutions offered by existing studies only adopt essential learning strategies.To fill this methodological gap,we propose a machine learning-based analytical framework that adopts a streamlined approach to feature engineering,model training and validation,and ensemble learning towards building an accurate and robust predictive model.The proposed framework is evaluated on two representative datasets with different sizes and feature settings.Results demonstrate the superior performance of the final model produced by our framework.展开更多
It is very important for organizations to develop a competitive advantage for long-term survival in the market. For this purpose, the main objective of the study was to assess the role of data mining and employee trai...It is very important for organizations to develop a competitive advantage for long-term survival in the market. For this purpose, the main objective of the study was to assess the role of data mining and employee training & Development to gain a competitive advantage. Moreover, the mediating role of personnel role and knowledge management is also assessed in the present study. The data in the present study were collected from the employees of SMEs in KSA using convenient sampling. The response rate of the study was 58.36%. For the analysis of the collected data, the study used PLS 3.2.9. The findings of the study reveal that data mining and training and development plays an important role for organizations to gain a competitive advantage through Knowledge management and personnel role. The findings of the study fill the gap of limited studies conducted regarding SMEs of KSA to gain a competitive advantage. The findings of the study are helpful for the policymakers of SMEs around the globe.展开更多
文摘Employee turnover(ET)can cause severe consequences to a company,which are hard to be replaced or rebuilt.It is thus crucial to develop an intelligent system that can accurately predict the likelihood of ET,allowing the human resource management team to take pro-active action for retention or plan for succession.However,building such a system faces challenges due to the variety of influential human factors,the lack of training data,and the large pool of candidate models to choose from.Solutions offered by existing studies only adopt essential learning strategies.To fill this methodological gap,we propose a machine learning-based analytical framework that adopts a streamlined approach to feature engineering,model training and validation,and ensemble learning towards building an accurate and robust predictive model.The proposed framework is evaluated on two representative datasets with different sizes and feature settings.Results demonstrate the superior performance of the final model produced by our framework.
文摘It is very important for organizations to develop a competitive advantage for long-term survival in the market. For this purpose, the main objective of the study was to assess the role of data mining and employee training & Development to gain a competitive advantage. Moreover, the mediating role of personnel role and knowledge management is also assessed in the present study. The data in the present study were collected from the employees of SMEs in KSA using convenient sampling. The response rate of the study was 58.36%. For the analysis of the collected data, the study used PLS 3.2.9. The findings of the study reveal that data mining and training and development plays an important role for organizations to gain a competitive advantage through Knowledge management and personnel role. The findings of the study fill the gap of limited studies conducted regarding SMEs of KSA to gain a competitive advantage. The findings of the study are helpful for the policymakers of SMEs around the globe.
