Enhancing Hybrid OSS Development Through Agile Methods and High Media Synchronicity

Open source software (OSS) is increasingly being developed by hybrid teams that consist of a mix of company employees and volunteer developers. While hybrid OSS teams are becoming more prevalent, they also face unique challenges due to the involvement of different constituents. To address those challenges, this paper develops and validates a new organizing model. Specifically, the paper draws on media synchronicity theory (MST) to theorize that hybrid OSS teams would benefit from adopting an organizing model that involves practicing agile methods and using communication tools with multiple symbols sets and high transmission velocity. The paper also extends MST by conceptualizing the theory's key concept of communication convergence as consisting of two distinct dimensions: affective and cognitive convergence. Using primary survey data from hybrid OSS teams, the paper presents empirical evidence that such an organizing model can enhance those teams' affective convergence and cognitive convergence and, in turn, their development productivity and the quality of the software. In addition, the results show that affective convergence has a stronger impact on hybrid OSS teams' performance than cognitive convergence.

A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teams

Online, social media bots have been accused to spread misinformation and support extreme or minority-held opinions. However, bots in hybrid human-machine teams can also be designed to improve team performance. In this paper, we study the effect of a single minority-supporting bot in hybrid teams in a carefully controlled experiment. People working in teams of 10 were asked to solve a hidden-profile prediction task, where task-relevant information was scattered unequally across team members. To do well in this task, pieces of information shared by the minority and the majority of players should be integrated. Simple majority-based decisions are not enough to perform well as information held by minority players is also valuable. We used a variational auto-encoder to train a bot to learn people's information distribution by observing how people's judgements correlated over time. After training, a bot was designed to increase team performance by selectively supporting opinions proportionally to their under-representation in the team. We show that the presence of a single bot (representing 10\% of team members) can significantly increase the polarization between minority and majority opinions by making minority opinions less prone to social influence. Although the effects on hybrid team performance were negligible, the bot presence significantly influenced team opinion dynamics. These findings show that unsupervised learning can be used to program bots that can improve team performance.

COMMUNICATION IN AI-ASSISTED TEAMS DURING AN INTERDISCIPLINARY DRONE DESIGN PROBLEM

Human-artificial intelligent (AI) - assisted teaming is becoming a strategy for coalescing the complementary strengths of humans and computers to solve difficult tasks. Yet, there is still much to learn regarding how the integration of humans with AI agents into a team affects human behavior. Accordingly, this work begins to inform this research gap by focusing specifically on how the communication structure and interaction changes within AI-assisted human teams. The underlying discourse data for this work originates from a prior research study in which teams solve an interdisciplinary drone design and path-planning problem. Several metrics are employed in this work to study team discourse, including count, diversity, content richness, and semantic coherence. Results show significant differences in communication behavior in AI-assisted teams including more diversity and frequency in communication, more exchange of information regarding principal design parameters and problem-solving strategies, and more cohesion. Overall, this work takes meaningful steps towards understanding the effects of AI agents on human behavior in teams, critical for fully building effective human-AI hybrid teams in the future.

From dissemination to engagement: learning over time from a national research intermediary centre (Four Fs)

Background: There has been little applied learning from organisations engaged in making evidence useful for decision makers. More focus has been given either to the work of individuals as knowledge brokers or to theoretical frameworks on embedding evidence. More intelligence is needed on the practice of knowledge intermediation.Aims and objectives: This paper describes the evolution of approaches by one UK Centre to promote and embed evidence in health and care services. This is not a formal evaluation, given the lack of critical distance by authors who led work at the Centre, but a reflective analysis which may be helpful for other evidence intermediary bodies.Conclusions: We analyse the founding conditions and theoretical context at the start of our activity and describe four activities we developed over time. These were filter (screening research for relevance and quality); forge (engaging stakeholders in interpreting evidence); fuse (knowledge brokering with hybrid teams); and fulfil (sustained interaction with implementation partners). We reflect on the tensions between rigour and relevance in the evidence we shared and the way in which our approaches evolved from a programme of evidence outputs to greater focus on sustained engagement and deliberative activities to make sense of evidence and reach wider audiences. Over the lifetime of the Centre, we moved from linear and relational modes towards systems type approaches to embed and mobilise evidence.<br />Key messages<br /><ul><li>There is little shared learning on the practice of evidence use by knowledge intermediaries.</li><br /><li>Our account of a national evidence centre for health decision makers shows the shift towards more engaged and embedded approaches.</li><br /><li>We identify four central activities – filter, forge, fuse and fulfil – and how they evolved over time.</li><br /><li>We note the value of sustained engagement with stakeholders in shaping new evidence narratives relevant to practice.</li></ul>

Toward Hybrid Teams: A Platform To Understand Human-Computer Collaboration During The Design of Complex Engineered Systems

Human-computer hybrid teams can meet challenges in designing complex engineered systems. However, the understanding of interaction in the hybrid teams is lacking. We review the literature and identify four key attributes to construct design research platforms that support multi-phase design, hybrid teams, multiple design scenarios, and data logging. Then, we introduce a platform for unmanned aerial vehicle (UAV) design embodying these attributes. With the platform, experiments can be conducted to study how designers and intelligent computational agents interact, support, and impact each other.

TOWARD HYBRID TEAMS: A PLATFORM TO UNDERSTAND HUMAN-COMPUTER COLLABORATION DURING THE DESIGN OF COMPLEX ENGINEERED SYSTEMS

Human-computer hybrid teams can meet challenges in designing complex engineered systems. However, the understanding of interaction in the hybrid teams is lacking. We review the literature and identify four key attributes to construct design research platforms that support multi-phase design, hybrid teams, multiple design scenarios, and data logging. Then, we introduce a platform for unmanned aerial vehicle (UAV) design embodying these attributes. With the platform, experiments can be conducted to study how designers and intelligent computational agents interact, support, and impact each other.