Search results

Reiter-Palmon, Herman, and Yammarino (this volume) put forward a series of useful propositions about the nature of team creativity, its connection with individual creativity and cognitive processes, and its antecedents. This commentary highlights some issues raised by these propositions, and explores the emergence of team creativity in greater depth. In particular, it discusses existing principles of multi-level theory and measurement, and considers how they might be applied to team creativity. We conclude that there is no single unified way to treat the concept of team creativity, but just as the antecedents of creativity may change in different situations, so may the way in which the construct is defined.

Mark D. Agars is an associate professor of psychology at California State University, San Bernardino. He received his Ph.D. from the Pennsylvania State University in industrial and organizational psychology, where he worked with James L. Farr.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.

Recently, there has been an increase in the number and type of studies in the organizational sciences that examine curvilinear relationships. These studies are important because some relationships have context-specific inflection points that alter their magnitude and/or direction. Although some scholars have utilized basic techniques to make meta-analytic inferences about curvilinear effects with the limited information available about them, there is still a tremendous opportunity to advance our knowledge by utilizing rigorous techniques to meta-analytically examine curvilinear effects. In a recent study, we used a novel meta-analytic approach in an effort to comprehensively examine curvilinear relationships between destructive leadership and followers' workplace outcomes. The purpose of this chapter is to provide an actionable guide for conducting curvilinear meta-analyses by describing the meta-analytic techniques we used in our recent study. Our contributions include a detailed guide for conducting curvilinear meta-analyses, the useful context we provide to facilitate its implementation, and our identification of opportunities for scholars to leverage our technique in future studies to generate nuanced knowledge that can advance their fields.

Research has not yet examined how paid labor performed at nontraditional hours may factor into women’s perceptions of the fairness of the division of household labor. Here we specifically examine how being employed during nonstandard hours alters the relationship between the division of household labor and wives’ perceptions of the fairness of this division of labor.

We analyze data from the National Survey of Families and Households using multinomial logistic regression.

We find that over-work in household labor has a weaker effect on perceptions of unfairness for wives who work nonstandard hours than for wives who work standard hours. This interaction effect, however, is partially mediated by husbands’ time in feminine-type chores.

The cross-sectional design does not allow us to draw causal conclusions. Future research would benefit by considering how movement in and out and nonstandard work affects perceptions of fairness of household labor.

Our findings suggest that one way that the gender revolution has stalled is through women’s participation in the service economy since this participation is positively associated with their husbands’ hours in routine chores, which women particularly value. Thus, women may continue to perceive fairness in the home, despite objective inequality, because their husbands are spending more time in feminine-type chores, as necessitated by women’s participation in work at nonstandard times.