Hierarchies of factor solutions in the intelligence domain : applying methodology from personality psychology to gain insights into the nature of intelligence

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Specific abilities in the workplace : more important than g?

A frequently reported finding is that general mental ability (GMA) is the best single psychological predictor of job performance. Furthermore, specific abilities often add little incremental validity beyond GMA, suggesting that they are not useful for predicting job performance criteria once general intelligence is accounted for. We review these findings and their historical background, along with different approaches to studying the relative influence of g and narrower abilities. Then, we discuss several recent studies that used relative importance analysis to study this relative influence and that found that specific abilities are equally good, and sometimes better, predictors of work performance than GMA. We conclude by discussing the implications of these findings and sketching future areas for research

Employee Work Ethic in Nine Nonindustrialized Contexts: Some Surprising Non-POSH Findings

Gross, Carr, Reichman, Abdul-Nasiru, and Oestereich's (2017) article argues that industrial and organizational (I-O) psychology has a limited perspective that rarely goes beyond the specific professional populations in formal economies of high-income countriesa perspective they refer to as a POSH perspective. This valuable criticism should also eschew the notion that workers in nonindustrialized countries are necessarily different

The great debate : general ability and specific abilities in the prediction of important outcomes

The relative value of specific versus general cognitive abilities for the prediction of practical outcomes has been debated since the inception of modern intelligence theorizing and testing. This editorial introduces a special issue dedicated to exploring this ongoing “great debate”. It provides an overview of the debate, explains the motivation for the special issue and two types of submissions solicited, and briefly illustrates how differing conceptualizations of cognitive abilities demand different analytic strategies for predicting criteria, and that these different strategies can yield conflicting findings about the real-world importance of general versus specific abilities

General mental ability and specific abilities : their relative importance for extrinsic career success

Recent research on the role of general mental ability (GMA) and specific abilities in work-related outcomes has shown that the results differ depending on the theoretical and conceptual approach that researchers use. While earlier research has typically assumed that GMA causes the specific abilities and has thus used incremental validity analysis, more recent research has explored the implications of treating GMA and specific abilities as equals (differing only in breadth and not subordination) and has used relative importance analysis. In this article, we extend this work to the prediction of extrinsic career success operationalized as pay, income, and the attainment of jobs with high prestige. Results, based on a large national sample, revealed that GMA and specific abilities measured in school were good predictors of job prestige measured after 11 years, pay measured after 11 years, and income 51 years later toward the end of the participants' work lives. With 1 exception, GMA was a dominant predictor in incremental validity analyses. However, in relative importance analyses, the majority of the explained variance was explained by specific abilities, and GMA was not more important than single specific abilities in relative importance analyses. Visuospatial, verbal, and mathematical abilities all had substantial variance shares and were also more important than GMA in some of the analyses. Implications for the interpretation of cognitive ability data and facilitating people's success in their careers are discussed

A multilevel approach for assessing business strategies on climate change

The need for an interdisciplinary and integrative approach for doing research on business strategies and climate change is gaining increasing recognition. However, there is a consensus that such crossfertilization is currently missing. Multilevel research methods by virtue of being interdisciplinary in nature may address this need. This paper proposes to advance the adoption of multilevel research approach in the context of business strategies and climate change. As a guide for conducting multilevel assessment, a flexible analytical framework is presented. The framework is developed through a process of structured literature review. The framework consists of thirteen contextual factors spread across five levels and identifies the key multilevel relationships that moderate organisational level climate change related strategy formulation. Level specificities of several theories across these five levels are also identified to facilitate application of the framework in building multilevel hypotheses for business strategies on climate change. In addition, a concise summary of the fundamental concepts of multilevel modelling techniques is provided to help researchers in selecting suitable multilevel models during the operationalization of the framework. The operationalization of the framework is demonstrated by building and testing a three level hypotheses on corporate lobbying activities on climate change issues. It is observed that irrespective of their locations, financially underperforming companies with a larger workforce and belonging to sectors with higher Green House Gas emission intensities particularly lobby intensely on climate change issues. In conclusion, the potential challenges and opportunities in applying the framework for building multilevel theories in the context of business strategies and climate change are discussed. (C) 2017 The Authors. Published by Elsevier Ltd

Detecting consensus emergence in organizational multilevel data : power simulations

Theories suggest that groups within organizations often develop shared values, beliefs, affect, behaviors, or agreed-on routines; however, researchers rarely study predictors of consensus emergence over time. Recently, a multilevel-methods approach for detecting and studying emergence in organizational field data has been described. This approach-the consensus emergence model-builds on an extended three-level multilevel model. Researchers planning future studies based on the consensus emergence model need to consider (a) sample size characteristics required to detect emergence effects with satisfactory statistical power and (b) how the distribution of the overall sample size across the levels of the multilevel model influences power. We systematically address both issues by conducting a power simulation for detecting main and moderating effects involving consensus emergence under a variety of typical research scenarios and provide an R-based tool that readers can use to estimate power. Our discussion focuses on the future use and development of multilevel methods for studying emergence in organizational research