Power perceptions and modes of complaining in higher education

This study examines the relationship between student perceptions of different types of educator power and different modes of student complaining behaviour in the case of university education. A large sample of marketing students in the business school responded to the study from a state university in Northeastern United States. Factor analysis and canonical correlation analysis are used to explore the relationships between five bases of power perceptions (referent, expert, reward, legitimate, and punishment) and four modes of complaining behaviour (voice, negative word of mouth, third party, and exit). The results indicate that students engage in different modes of complaining as they perceive different types of educator power. The predominant complaining mode is found to be voice under referent or expert power, third party under legitimate power, and exit under reward or punishment power. Our findings offer important implications for student satisfaction, retention, and completion rates in higher education.

Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty

Modern advances in technology have led to more complex manufacturing processes whose success centres on the ability to control these processes with a very high level of accuracy. Plant complexity inevitably leads to poor models that exhibit a high degree of parametric or functional uncertainty. The situation becomes even more complex if the plant to be controlled is characterised by a multivalued function or even if it exhibits a number of modes of behaviour during its operation. Since an intelligent controller is expected to operate and guarantee the best performance where complexity and uncertainty coexist and interact, control engineers and theorists have recently developed new control techniques under the framework of intelligent control to enhance the performance of the controller for more complex and uncertain plants. These techniques are based on incorporating model uncertainty. The newly developed control algorithms for incorporating model uncertainty are proven to give more accurate control results under uncertain conditions. In this paper, we survey some approaches that appear to be promising for enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty.