By Steve W.J. Kozlowski, Eduardo Salas
This scholarly publication in SIOP’s Organizational Frontier sequence seems at study on bettering wisdom acquisition and its software in firms. It concentrates on education, layout and supply given the altering nature of labor and enterprises. Now that paintings is more and more advanced, there's higher emphasis on services and cognitive talents. Advances in expertise reminiscent of desktop simulations and web-based education are necessitating a extra energetic function for the learner within the education approach. within the huge context of the association platforms, this book promotes studying and improvement as a continuing lifelong pastime.
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Additional info for Learning, Training, and Development in Organizations (SIOP Organizational Frontiers Series)
It also appears that some or all of these mechanisms may be equally relevant in team-training contexts (Chen, Thomas, & Wallace, 2005). Although not comprehensive, this serves as a useful starting framework for understanding when, how, and why various individual differences are likely to influence training outcomes. The next section will begin by reviewing the main effects of individual differences on training outcomes. When relevant, intervening mechanisms will be described or included. Main Effects of Individual Differences General Cognitive Ability General cognitive ability, or g, is the capacity to rapidly and fluidly acquire, process, and apply information.
2000) demonstrated that cognitive ability has influences on learning performance both directly and indirectly through motivationally related variables such as self-efficacy and goals. Clearly, cognitive ability matters in training contexts. Trainees who have high cognitive ability tend to learn more, acquire more skills, and transfer training. They also tend to be more efficacious regarding the ability to perform, and therefore have greater motivation. Kanfer and Ackerman (1989) argued that g provides a pool of resources that can be allocated to on- and off-task cognitions, and suggested that trainees with more g can handle more off-task cognitions and demands because they have a greater pool from which to draw.
Schmidt et al. created and tested a model for predicting job performance that included cognitive ability, job knowledge, and work sample performance. They found that the effect of cognitive ability on job performance was almost completely mediated by job knowledge. Similarly, Schmidt and Hunter (1998) found that measures of job knowledge significantly increased the prediction of performance beyond measures of cognitive ability. Schmidt and Hunter also noted that too few studies investigating the relationship between job knowledge and training performance were available to include in their meta-analysis.