Stereotypes about generational differences in the workplace abound, and interventions for helping organizations and managers to deal with these supposed differences are increasing. In addition to popular press articles describing the differences and extolling the practices and strategies to deal with them, there are a growing number of researchers who are attempting to establish that there are such differences. Overall, however, there is little solid empirical evidence supporting generationally based differences and almost no theory behind why such differences should even exist. The purposes of this focal article are to explore the myths surrounding generations, to review the risks in using generations in organizational decisions, and to make recommendations for practitioners and researchers on how to proceed in this area.
We propose an integrated model of leadership potential, the highpotential designation process, and leader success that is intended to clarify the theoretical and practical relationships among these concepts. Drawing on research in the areas of social judgment and cognition, cognitive abilities, personality, leadership development, and motivation and on practice-oriented observations and writings, we
Background data measures are one of the best predictors of job performance. Nonetheless, questions have been raised about their content and construct validity. The present effort describes a set of procedures for developing construct and content valid background data items. Data gathered in seven field studies and six laboratory studies are presented bearing on the reliability and validity of the measures constructed using these item generation procedures. Findings in these studies indicate that construct‐based item generation procedures yield reliable scales evidencing both content and construct validity. Furthermore, these scales are capable of predicting performance in a variety of settings. Theoretical and practical implications of these findings for the development and validation of background data measures are discussed.
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