Personality across working life:the longitudinal and reciprocal influences of personality on work

Summary: This paper focuses on the role of personality at different stages of people's working lives. We begin by reviewing the research in industrial, work, and organizational (IWO) psychology regarding the longitudinal and dynamic influences of personality as an independent variable at different career stages, structuring our review around a framework of people's working lives and careers over time. Next, we review recent studies in the personality and developmental psychology domain regarding the influence of changing life roles on personality. In this domain, personality also serves as a dependent variable. By blending these two domains, it becomes clear that the study of reciprocal effects of work and personality might open a new angle in IWO psychology's long-standing tradition of personality research. To this end, we outline various implications for conceptual development (e.g., trait stability) and empirical research (e.g., personality and work incongruence). Finally, we discuss some methodological and statistical considerations for research in this new research domain. In the end, our review should enrich the way that IWO psychologists understand personality at work, focusing away from its unidirectional predictivist influence on job performance toward a more complex longitudinal reciprocal interplay of personality and working life. © 2013 John Wiley & Sons, Ltd.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.