Team reflexivity and innovation:the moderating role of team context

We developed and tested a team level contingency model of innovation, integrating theories regarding work demands, team reflexivity - the extent to which teams collectively reflect upon their working methods and functioning -, and team innovation. We argued that highly reflexive teams will be more innovative than teams low in reflexivity when facing a demanding work environment. The relationships between team reflexivity, a demanding work environment (i.e. quality of the physical work environment and work load) and team innovation was examined among 98 primary health care teams (PHCTs) in the UK, comprised of 1137 individuals. Results showed that team reflexivity is positively related to team innovation, and that there is an interaction between team reflexivity, team level workload, and team innovation, such that when team level workload is high, combined with a high level of team reflexivity, team innovation is also higher. The complementary interaction between team reflexivity, quality of physical work environment, and team innovation, showed that when the quality of the work environment is low, combined with a high level of team reflexivity, team innovation was also higher. These results are discussed in the context of the need for team reflexivity and team innovation among teams at work facing high work demands.

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.