In spite of the increasing demand for virtual cooperation, still relatively little is known about the knowledge, skills, abilities, and other characteristics (KSAOs) individuals need for virtual teamwork. Thus, the current paper aims at synthesizing the existing literature into a comprehensive model of virtual teamwork KSAOs. To this end, we review (a) existing frameworks of KSAO requirements for virtual teamwork, (b) challenges posed by different facets of virtuality, and (c) KSAOs particularly relevant for meeting the identified challenges. The results of this review are integrated into a holistic model of virtual teamwork KSAOs with distal characteristics (personality, experience) and more proximal qualities (knowledge, skills, and motivation). Research gaps as well as avenues for future research will be outlined and applications for virtual team staffing and training will be discussed.

This study seeks to explain the interplay between chronological age and technology-related strain through techno-stressors and coping strategy choices in organizational settings. Grounded in Lazarus´ stress theory, theories of cognitive aging, the life span theory of control and socioemotional selectivity theory, this study argues that even though older workers are more prone to techno-stressors, aging is connected to gaining coping skills, which in turn reduce technology-related strain over time. Understanding these processes enables modifying employees’ coping strategy choices and mitigating negative outcomes of technostress at the workplace. Longitudinal data from 1,216 employees over a time period of 8 months were used to perform multilevel mediation modeling. The findings reveal that age was negatively related to technology-related strain. The link between age and technology-related strain was explained through behavioral disengagement, which older workers used less than younger workers. Active coping and social coping did not act as mediators of this relationship across time points. These relationships were stable after controlling for dependency on technology.

Being able to work on several tasks at the same time (multitasking) is an important performance aspect of many jobs. Recent research findings pointed out the important role of working memory for multitasking performance in general. To understand more about the role of working memory in predicting the speed and the error aspect of multitasking performance, this research was based on a newly developed and well-elaborated multidimensional model of working memory (Oberauer, Süß, Wilhelm, & Wittmann, 2003). Its 3 dimensions are storage in the context of processing, coordination, and supervision. In addition, attention and reasoning were controlled when predicting multitasking speed and error. A multitasking scenario, a battery of working memory tests, a battery of reasoning tests, and 2 attention tests were administered to 135 participants. As expected working memory was the best predictor of multitasking performance, followed by reasoning and attention. Working memory components showed a differential validity...

Purpose – The ongoing demographic changes in many industrialized countries affect managerial decisions in many ways, and require sound knowledge of systematic age differences in central work-related variables. The current paper aims to address age differences in the experience of work-related stress. Based on life-span approaches, the authors focus on age differences in different components of the work-related stress process and meta-analyze existing empirical studies on the relationship between age and short-term indicators of work-related stress (i.e. irritation). Design/methodology/approach – The authors conduct both a literature review and a meta-analysis of age and indicators of work-related stress. Findings – The literature review revealed that age might affect several components of the stress process at work. However, as these effects are partly conflicting, they might nullify each other in the overall relation between age and stress. Indeed, the conducted meta-analysis showed no general correlatio...

The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general and specific factors on criterion variables can be scrutinized in latent multiple regression models that are built on bifactor measurement models. This study employs an extended bifactor model to predict mathematics and English grades by three facets of intelligence (number series, verbal analogies, and unfolding). We show that, if the observed variables do not differ in their loadings, extended bifactor models are not identified and not applicable. Moreover, we reveal that standard errors of regression weights in extended bifactor models can be very large and, thus, lead to invalid conclusions. A formal proof of the nonidentification is presented. Subsequently, we suggest alternative approaches for predicting criterion variables by general and specific factors. In particular, we illustrate how (1) composite ability factors can be defined in extended first-order factor models and (2) how bifactor(S-1) models can be applied. The differences between first-order factor models and bifactor(S-1) models for predicting criterion variables are discussed in detail and illustrated with the empirical example.