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The purpose of this mapping study has been performed to identify, critically analyze and synthesize research performed in the area of software engineering teams. Teams, in a general sense, have been studied extensively. But the distinctive processes that need to be executed effectively and efficiently in software engineering require a better understanding of current software engineering team research.

In this work, software engineering team publications were analyzed and the key findings of each paper that met our search inclusion criteria were synthesized. In addition, a keyword content analysis was performed to create a taxonomy to categorize each paper and evaluate the state of software engineering team research.

In software engineering team research, the resulting areas that are the most active are teamwork/collaboration, process/design and coordination. Clear themes of analysis have been determined to help understand how team members collaborate, factors affecting their success and interactions among all project stakeholders. In addition, themes related to tools to support team collaboration, improve the effectiveness of software engineering processes and support team coordination have been found. However, the research gaps determined from the content analysis point toward a need for more research in the area of communication and tools.

The goal of this work is to define the span of previous research in this area, create a taxonomy to categorize such research and identify open research areas to provide a clear road map for future research in the area of software engineering teams. These results, along with the key finding themes presented, will help guide future research in an area that touches all parts of the software engineering and development processes.

Often, it is assumed that teams are better at solving problems than individuals working independently. However, recent work in engineering, design and psychology contradicts this assumption. This study aims to examine the behavior of teams engaged in data science competitions. Crowdsourced competitions have seen increased use for software development and data science, and platforms often encourage teamwork between participants.

We specifically examine the teams participating in data science competitions hosted by Kaggle. We analyze the data provided by Kaggle to compare the effect of team size and interaction frequency on team performance. We also contextualize these results through a semantic analysis.

This work demonstrates that groups of individuals working independently may outperform interacting teams on average, but that small, interacting teams are more likely to win competitions. The semantic analysis revealed differences in forum participation, verb usage and pronoun usage when comparing top- and bottom-performing teams.

These results reveal a perplexing tension that must be explored further: true teams may experience better performance with higher cohesion, but nominal teams may perform even better on average with essentially no cohesion. Limitations of this research include not factoring in team member experience level and reliance on extant data.

These results are potentially of use to designers of crowdsourced data science competitions as well as managers and contributors to distributed software development projects.

This study examines the effect of design quality (i.e. appearance, navigation, information and interactivity) on cognitive and affective involvement leading to continued intention to use the online learning application.

We assume that design quality potentially contributes to enhance the individual's involvement and excitement. An experimental prototype is developed for collecting data used to verify and validate the proposed research model and hypotheses. A partial-least-squares approach is used to analyze the data collected from the participants (n = 662).

Communication, aesthetic and information quality revealed to be strong determinants of both cognitive and affective involvement. However, font quality and user control positively influence cognitive involvement, while navigation quality and responsiveness were observed as significant indicators of affective involvement. Lastly, cognitive and affective involvement equally contribute to determining the continued intention to use.

This study will draw the attention of designers and practitioners towards the perception of users for providing appropriate and engaging learning resources.

Prevalent research in the online context is focused primarily on cognitive and utilization behavior. However, these works overlook the implication of design quality on cognitive and affective involvement.