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Four types of calculations are currently being used to identify discrepancies between intellectual ability and achievement. These include deviation from grade level, expectancy formulas, simple standard score differences, and regression-based differences (Fletcher et al., 1994; Gresham, 2001; Sattler, 1992; Van den Broeck, 2002).

In the field of global leadership, much of the research has focused on uncovering competencies and methods for assessing competencies. The process of developing global leaders has been researched less frequently; however, it is widely accepted that this process involves learning on the part of the leader. Mezirow’s (1978, 1991) transformative learning is a special type of learning in the domain of adult education and a useful lens to better understand the disorienting triggers that are thought to induce global leadership development (GLD). In simple terms, a disorienting experience occurs when we discover that something we thought was certain is now uncertain. Conducting business in another country or merely navigating to a grocery store or restaurant there can be a disorienting experience. In these situations, people are exposed to new information that does not fit their current meaning structures or thought paradigms, and at this juncture, people have a choice: to transform their perspective or remain unchanged. When individuals transform their perspective, they are experiencing transformative learning. The first section of this chapter reviews the concept of disorienting experiences across disciplines and within the domain of learning and education. The second section explains three GLD process models with a special focus on the role played by disorienting trigger events in each one. The final section explains the Disorientation Index (Ensign, 2019), which articulates dimensions of trigger events. The chapter concludes with future research directions and practical implications.

To fully accommodate the correlations between semiconductor product demands and external information such as the end market trends or regional economy growth, a linear dynamic system is introduced in this chapter to improve forecasting performance in supply chain operations. In conjunction with the generic Gaussian noise assumptions, the proposed state-space model leads to an expectation-maximization (EM) algorithm to estimate model parameters and predict production demands. Since the set of external indicators is of high dimensionality, principal component analysis (PCA) is applied to reduce the model order and corresponding computational complexity without loss of substantial statistical information. Experimental study on certain real electronic products demonstrates that this forecasting methodology produces more accurate predictions than other conventional approaches, which thereby helps improve the production planning and the quality of semiconductor supply chain management.