This study aims to identify important predictors of turnover intention and to characterize subgroups of U.S. federal employees at high risk for turnover intention. Data were drawn from the 2018 Federal Employee Viewpoint Survey (FEVS, unweighted N = 598,003), a nationally representative sample of U.S. federal employees. Machine learning Classification and Regression Tree (CART) analyses were conducted to predict turnover intention and accounted for sample weights. CART analyses identified six at-risk subgroups. Predictor importance scores showed job satisfaction was the strongest predictor of turnover intention, followed by satisfaction with organization, loyalty, accomplishment, involvement in decisions, likeness to job, satisfaction with promotion opportunities, skill development opportunities, organizational tenure, and pay satisfaction. Consequently, Human Resource (HR) departments should seek to implement comprehensive HR practices to enhance employees’ perceptions on job satisfaction, workplace environments and systems, and favorable organizational policies and supports and make tailored interventions for the at-risk subgroups.

Perceived organizational performance (POP) is an important factor that influences employees’ attitudes and behaviors such as retention and turnover, which in turn improve or impede organizational sustainability. The current study aims to identify interaction patterns of risk factors that differentiate public health and human services employees who perceived their agency performance as low. The 2018 Federal Employee Viewpoint Survey (FEVS), a nationally representative sample of U.S. federal government employees, was used for this study. The study included 43,029 federal employees (weighted n = 75,706) among 10 sub-agencies in the public health and human services sector. The machine-learning classification decision-tree modeling identified several tree-splitting variables and classified 33 subgroups of employees with 2 high-risk, 6 moderate-risk and 25 low-risk subgroups of POP. The important variables predicting POP included performance-oriented culture, organizational satisfaction, organizational procedural justice, task-oriented leadership, work security and safety, and employees’ commitment to their agency, and important variables interacted with one another in predicting risks of POP. Complex interaction patterns in high- and moderate-risk subgroups, the importance of a machine-learning approach to sustainable human resource management in industry 4.0, and the limitations and future research are discussed.

Massive open online courses (MOOCs) have been touted as an effective way to make higher education accessible for free or for only a small fee, thus addressing the problem of unequal access and providing new opportunities to young people in middle and low income groups. However, many critiques of MOOCs have indicated that low completion rates are a major concern. Using a latent class analysis (LCA), a more advanced methodology to identify latent subgroups, this study examined the heterogeneity of learners’ behavioral patterns in a MOOC, categorized them into distinctive subgroups, and ultimately determined the optimal number of latent subgroups in a MOOC. The five subgroups identified in this study were: completing (6.6%); disengaging (4.8%); auditing (4.6%); sampling (21.1%); and enrolling (62.8%). Results indicated this was the optimal number of subgroups. Given the characteristics of the three at-risk subgroups (disengaging, sampling, and enrolling), tailored instructional strategies and interventions to improve behavioral engagement are discussed.

This study identified personal and organizational environmental factors associated with perceived organizational performance (POP) and moderation of race/ethnicity in these relationships with POP in the U.S. federal government employees among 10 health agencies. Cross-sectional, nationally representative data were obtained from the 2017, 2018, and 2019 Federal Employee Viewpoint Surveys, completed by 117,353 (weighed 190,736) employees among 10 health agencies. We employed multivariable linear models of POP to examine associations between job satisfaction and organizational environmental factors and POP and to assess the effects of racial/ethnic minority as a modifier on these relationships after controlling for sampling weights. Job satisfaction, trust in leadership, organizational procedural justice, and diversity management were positively associated with POP. Significant moderating effects of race/ethnicity were observed in the relationships of POP with job satisfaction and diversity management. In the conclusion, we discuss how federal public health agencies strategically develop management practices, particularly for employees of color.

This study, the first part of a series of research projects on advancing the knowledge base toward a comprehensive performance model, examines structural associations among performance‐support systems, human behaviors, and performance and empirically tests the proposed performance model, using national government‐wide representative data (2012 Federal Employee Viewpoint Survey, N = 687,687, 82 federal agencies). How the results and findings of this study contribute to theory, research, and practices supporting human behavior and successful organization performance is discussed.

This study, the second part of a series of research on advancing the knowledge base toward a comprehensive performance model, discusses the implications as to how a theoretically and empirically tested model may be put into practice in a more specific way in the light of the three aspects of human resource cognate fields: human performance technology, human resource management, and human resource development.