Many collaborative online projects such as Wikipedia and OpenStreetMap organize collaboration among their contributors sequentially. In sequential collaboration, one contributor creates an entry which is then consecutively encountered by other contributors who decide whether to adjust or maintain the presented entry. For numeric and geographical judgments, sequential collaboration yields improved judgments over the course of a sequential chain and results in accurate final estimates. We hypothesize that these benefits emerge since contributors adjust entries according to their expertise, implying that judgments of experts have a larger impact compared with those of novices. In three preregistered studies, we measured and manipulated expertise to investigate whether expertise leads to higher change probabilities and larger improvements in judgment accuracy. Moreover, we tested whether expertise results in an increase in accuracy over the course of a sequential chain. As expected, experts adjusted entries more frequently, made larger improvements, and contributed more to the final estimates of sequential chains. Overall, our findings suggest that the high accuracy of sequential collaboration is due to an implicit weighting of judgments by expertise.

Co-evolution accounts have generally been used to describe how problems and solutions both change during the design process. More generally, problems and solutions can be considered as analytic categories, where change is seen to occur within categories or across categories. There are more categories of interest than just problems and solutions, for example, the participants in a design process (such as members of a design team or different design teams) and categories defined by design ontologies (such as function-behaviour-structure or concept-knowledge). In this paper, we consider the co-evolution of different analytic categories (not just problems and solutions), by focussing on how changes to a category originate either from inside or outside that category. We then illustrate this approach by applying it to data from a single design session using three different systems of categorisation (problems and solutions, different designers and function, behaviour and structure). This allows us to represent the reciprocal influence of change within and between these different categories, while using a common notation and common approach to graphing quantitative data. Our approach demonstrates how research traditions that are currently distinct from each other (such as co-evolution, collaboration and function-behaviour-structure) can be connected by a single analytic approach.

In spite of their good intentions, Emergency Medical Teams (EMTs) were relatively disorganized for many years. To enhance the efficient provision of EMT’s field team work, the Training for Emergency Medical Teams and European Medical Corps (TEAMS) project was established. The purpose of this study was to assess the effectiveness and quality of the TEAMS training package in 2 pilot training programs in Germany and Turkey. A total of 19 German and 29 Turkish participants completed the TEAMS training package. Participants were asked to complete a set of questionnaires designed to assess self-efficacy, team work, and quality of training. The results suggest an improvement for both teams’ self-efficacy and team work. The self-efficacy scale improved from 3.912 (± 0.655 SD) prior to training to 4.580 (± 0.369 SD) after training (out of 5). Team work improved from 3.085 (± 0.591 SD) to 3.556 (± 0.339 SD) (out of 4). The overall mean score of the quality of the training scale was 4.443 (± 0.671 SD) (out of 5). In conclusion, The TEAMS Training Package for Emergency Medical Teams has been demonstrated to be effective in promoting EMT team work capacities, and it is considered by its users to be a useful and appropriate tool for addressing their perceived needs.

The present study explores the usefulness of dyadic quantification of group characteristics to predict team work performance. After reviewing the literature regarding team member characteristics predicting group performance, percentages of explained variance between 3% and 18% were found. These studies have followed an individualistic approach to measure group characteristics (e. g., mean and variance), based on aggregation. The aim of the present work was testing whether by means of dyadic measures group output prediction percentage could be increased. The basis of dyadic measures is data obtained from an interdependent pairs of individuals. Specifically, the present research was intended to develop a new dyadic index to measure personality dissimilarity in groups and to explore whether dyadic measurements allow improving groups' outcome predictions compared to individualistic methods. By means of linear regression, 49.5 % of group performance variance was explained using the skewsymmetry and the proposed dissimilarity index in personality as predictors. These results support the usefulness of the dyadic approach for predicting group outcomes.