A great deal of literature has examined the factors involved in single goal pursuit. However, there is a burgeoning realization that employees hold multiple goals at any one point in time and that findings from the single goal literature do not necessarily apply to multiple goal situations.Research is now being conducted on multiple goals, but it is being conducted across a broad range of disciplines, examining different levels of the goal hierarchy. Consequently, researchers are using the same label to refer to distinct concepts (the "jangle" fallacy) or different labels to refer to similar concepts (the "jingle" fallacy), and some aspects of the multiple goal space are yet to be examined. We derive seven general principles of the multiple goal process from a broad review of the literature. In doing so, we provide a common architecture and an overarching perspective of the theory for ongoing research as well as highlighting a number of areas for future research.

In a recent article, O'Boyle and Aguinis (2012) argued that job performance is not distributed normally but instead is nonnormal and highly skewed. However, we believe the extreme departures from normality observed by these authors may have been due to characteristics of performance measures used. To address this issue, we identify 7 measurement criteria that we argue must be present for inferences to be made about the distribution of job performance. Specifically, performance measures must: (a) reflect behavior, (b) include an aggregation of multiple behaviors, (c) include the full range of performers, (d) include the full range of performance, (e) be time bounded, (f) focus on comparable jobs, and (g) not be distorted by motivational forces. Next, we present data from a wide range of sources-including the workplace, laboratory, athletics, and computer simulations-that illustrate settings in which failing to meet one or more of these criteria led to a highly skewed distribution providing a better fit to the data than a normal distribution. However, measurement approaches that better align with the 7 criteria listed above resulted in a normal distribution providing a better fit. We conclude that large departures from normality are in many cases an artifact of measurement.

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This research speaks to the ongoing debate regarding the role of self-efficacy in self-regulation. Specifically, we argue that both positive and negative relationships between self-efficacy and resource allocation are part of an adaptive process. We present the results of two empirical studies demonstrating that a negative relationship between self-efficacy and resource allocation is not always maladaptive and, in fact, can lead to positive indirect effects on performance. In Study 1, we observed natural fluctuations in self-efficacy as individuals completed a mathematics test, finding that the tendency to reduce resource allocation with high self-efficacy is most clearly observed when time is scarce. In turn, an inverted-U relationship between resource allocation and overall performance under high time scarcity emerged such that moderate levels of resource allocation resulted in the highest levels of performance. Study 2 used an experimental design in which self-efficacy was manipulated. Replicating core findings from Study 1, individuals drew upon self-efficacy to balance resource allocation across competing demands. We conclude with a discussion of the theoretical and practical implications of our results.