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This study relied on the job demands and resource model to understand employees’ turnover intentions. Recent studies have consistently lent support for the significant association between role ambiguity and turnover intentions; however, only a handful of studies focused on examining the potential mediators in this association. The authors argued that role ambiguity positively influences turnover intentions through affective mechanisms: job involvement and satisfaction.

To test the model, a large sample of working adults participated (N = 505).

Structural equation modeling results showed that role ambiguity, job involvement and job satisfaction were significantly associated with turnover intentions. Moreover, a serial mediation was found among the variables: employees with low levels of role ambiguity tended to report higher job involvement, which further increased their satisfaction with the job and subsequently decreased their turnover intentions.

The cross-sectional design is a limitation.

Practical suggestions regarding how organizations can reduce employee turnover are discussed.

The findings provide support for theory-driven interventions to address developing the intention to stay at work among working adults.

This paper presents the particle swarm optimization (PSO) algorithm in conjuction with the fuzzy logic method in order to achieve an optimized tuning of a proportional integral derivative controller (PID) in the DTC control loops of dual star induction motor (DSIM). The fuzzy controller is insensitive to parametric variations, however, with the PSO-based optimization approach we obtain a judicious choice of the gains to make the system more robust. According to Matlab simulation, the results demonstrate that the hybrid DTC of DSIM improves the speed loop response, ensures the system stability, reduces the steady state error and enhances the rising time. Moreover, with this controller, the disturbances do not affect the motor performances.