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This chapter applies three of the most prominent theories in vocational and career psychology to further illuminate the turnover process. Prevailing theories about attrition have rarely integrated explanatory constructs from vocational research, though career (and job) choices clearly have implications for employee affect and loyalty to a chosen job in a career field. Despite remarkable inroads by new perspectives for explaining turnover, career, and vocational formulations can nonetheless enrich these – and conventional – formulations about why incumbents stay or leave their jobs. To illustrate, vocational theories can help clarify why certain shocks (critical events precipitating thoughts of leaving) drive attrition and what embeds incumbents. In particular, this chapter reviews Super's life-span career theory, Holland's career model, and social cognitive career theory and describes how they can fill in theoretical gaps in the understanding of organizational withdrawal.

Using lattices, presents Boolean Algebra, which is usually introduced using its postulate‐oriented approach. Relationships among sets, relations, lattices and Boolean algebra are shown to form a distributive but not complemented lattice. Provides examples together with corresponding Hasse diagrams. References useful application areas.

Defines and investigates fuzzy symmetric functions with don't‐care conditions and most‐unsymmetric functions. Represents and illustrates by examples algorithms for finding the grade of membership function and the number of most unsymmetric functions. Also presents applications to function representation, data reduction and error correction. The results may have useful applications to fuzzy logics, finding most‐unsymmetric images, fuzzy neural networks and related areas.

The set of fuzzy threshold functions is defined to be a fuzzy set over the set of functions. All threshold functions have full memberships in this fuzzy set. Defines and investigates a distance measure between a non‐linearly separable function and the set of all threshold functions. Defines an explicit expression for the membership function of a fuzzy threshold function through the use of this distance measure and finds three upper bounds for this measure. Presents a general method to compute the distance, an algorithm to generate the representation automatically, and a procedure to determine the proper weights and thresholds automatically. Presents the relationships among threshold gate networks, artificial neural networks and fuzzy neural networks. The results may have useful applications in logic design, pattern recognition, fuzzy logic, multi‐objective fuzzy optimization and related areas.