ACM CHI 2023

Mirjam Augstein, Thomas Neumayr, Johannes Schönböck, and Carrie Kovacs. 2023. Remote Persons Are Closer Than They Appear: Home, Team and a Lockdown. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 615, 1–25.

Since 2020, worldwide COVID-19-related lockdowns have led to a rapid increase of remote collaboration, particularly in the domain of knowledge work. This has undoubtedly brought challenges (e.g., work-life boundary management, social isolation), but also opportunities. Practices that have proven successful (e.g., through increased task performance, efficiency or satisfaction) are worth retaining in future. In this qualitative empirical study, we analyzed four teams’ (14 participants in total) mandatory remote collaboration over a period of several days to several months during a nationally imposed lockdown. We report results derived from questionnaires, logbooks, group interviews, and meeting recordings. We identify possible factors influencing quality of task outcome as well as subjective aspects like satisfaction, motivation, and team atmosphere. As a basis for our conclusions, we provide a scheme for categorizing effects of remote collaboration based on an exhaustive literature review on pandemic-induced mandatory remote work and collaboration.


Thomas Neumayr, Mirjam Augstein, and Bettina Kubicek. 2022. Territoriality in Hybrid Collaboration. Proc. ACM Hum.-Comput. Interact. 6, CSCW2, Article 332 (November 2022), 37 pages.

Abstract: Hybrid Collaboration, where remote and co-located team members work together using different devices and tools, has already been trending in recent years (e.g., through globalization and international cooperation) but experienced a further boost since the outbreak of the COVID-19 pandemic. The reason behind this surge in hybrid practices is probably that the crisis revealed aspects of remote collaboration which proved functional and which many decision makers (in industry as well as academia) plan to retain for the future. Thus, hybrid collaboration is an extremely timely topic which should be further studied in the context of CSCW. One major CSCW-anchored concept that has most intensively been researched in co-located collaboration settings where it is usually inherently related to spatial aspects and proximity, is territoriality. Already work on territoriality in fully distributed, remote settings has shown that there are significant differences due to the characteristics of the scenario. In this paper, we focus on territoriality in hybrid settings where we identified a significant research gap, and present the results of a user study with 22 teams consisting of four people each (distributed across two locations at two different universities), collaborating on a problem-solving task. Our findings reveal that more dimensions and communication channels, in addition to space, might strongly impact territoriality and territorial behavior in hybrid collaboration. Besides classical spatial territories also auditory territories frequently emerged. In addition, visibility of and accessibility to certain territories need to be rethought. We discuss these novel findings also regarding their interplay with earlier ones and derive design implications for CSCW systems supporting hybrid collaboration.

ACM ISS 2022

Thomas Neumayr, Mirjam Augstein, Johannes Schöböck, Sean Rintel, Helmut Leeb, and Thomas Teichmeister. 2022. Semi-automated Analysis of Collaborative Interaction: Are We There Yet?. Proc. ACM Hum.-Comput. Interact. 6, ISS, Article 571 (December 2022), 27 pages.

Abstract: In recent years, research on collaborative interaction has relied on manual coding of rich audio/video recordings. The fine-grained analysis of such material is extremely time-consuming and labor-intensive. This is not only difficult to scale, but, as a result, might also limit the quality and completeness of coding due to fatigue, inherent human biases, (accidental or intentional), and inter-rater inconsistencies. In this paper, we explore how recent advances in machine learning may reduce manual effort and loss of information while retaining the value of human intelligence in the coding process. We present ACACIA (AI Chain for Augmented Collaborative Interaction Analysis), an AI video data analysis application which combines a range of advances in machine perception of video material for the analysis of collaborative interaction. We evaluate ACACIA’s abilities, show how far we can already get, and which challenges remain. Our contribution lies in establishing a combined machine and human analysis pipeline that may be generalized to different collaborative settings and guide future research.