Investigating Coordinative Work Practices in Distributed Workgroups Using Narrative Networks

The use of collaboration software to coordinate collaborative work has become essential for the organisation of joint work in distributed settings. However, the analysis of distinct Coordinative Work Practices (CWP) is challenging as flexible work arrangements, digital workplace initiatives, and new types of collaboration software have increased the complexity of coordinating. Research studies mainly provide textual descriptions and there are few rich visualisations of CWP. To address these shortcomings, this research investigates CWP within distributed workgroups by using Narrative Networks (NN) as a novel and promising application for this context. NN is a research approach to describe, visualise and analyse software in use as sequences of actions. While NN have primarily been used to study structured work processes with repetitive/prescriptive tasks (e.g., invoice processing) and software designed to support these kinds of processes (e.g., ERP systems), this research argues that it is also suitable to investigate CWP, which involves less formalized/less well-defined tasks and that occur as a variety of nested activities. More comprehensive understanding of CWP can be gained using NN in the context of distributed workgroups in the digital workplace

“All the data you need” – striving for “overview” in cross-sectoral chronic disease management

Chronic-care-management relies on the availability of clinical data on a patient’s current health status, as well as data about treatment activities taking place in other sectors and at other points in the patient’s trajectory. In the Danish data-intensive health system, such data are, in principle, already available across sectors through a common national digital health data infrastructure. Nevertheless, the collective opinion among stakeholders is that the ‘overview’ of the patient’s case is lacking. In this paper, we explore the striving for ‘overview’ among healthcare professionals in cross-sectoral management of type 2 diabetes, with a digital data retrieval system called SAMBLIK. SAMBLIK echoes a well-known story of effortless data flow, hoping to enable digital integration through technological innovation. Based on ethnographical fieldwork and concepts of data work and data experience, we outline a diverse set of ‘uses’ of SAMBLIK that enact different ‘overviews’. We preliminary delineate four enactments: 1) expedient overview, 2) glancing overview, 3) care-continuum overview, and 4) a non-overview. Outlining the multiplicity of SAMBLIK is the first step toward critically evaluating the role of technology in helping data users deal with the paradoxes of experiencing a need for more data along with raising amounts of data work

The Ripple Effect of Information Infrastructures

This paper explores how the nature of work is impacted by the information infrastructure within the work exists. Drawing on an empirical case of a global organization replacing the local area network (LAN), we examine the work required for (re)designing, implementing, maintaining, and managing the sociotechnical aspects of the LAN. We identify breakdowns related to cooperative, technical, and organizational work, revealing faultlines in boundary-crossing activities. By exploring the characteristics of these faultlines, our study highlights how work and infrastructure co-evolve. Work may appear to take place within a local context, yet in practice, it transforms the global infrastructure, with interdependent entities located elsewhere in the infrastructural setup, such as people, artifacts, and policies that only have peripheral (or invisible) relations to the work. This interplay impacts not only the characteristics of the work itself but also the inherent characteristics and legacy of multiple work contexts beyond immediate boundaries. We argue that viewing work from an infrastructural perspective is crucial for identifying who and what is needed to accomplish work tasks. The ripple effect of information infrastructures impacts local work contexts in unanticipated ways, extending beyond visible work practices. Transforming infrastructures thus requires an extended peripheral perception in shaping and scoping the work at multiple scales

Uncovering Non-native Speakers’ Experiences in Global Software Development Teams - a Bourdieusian Perspective

Globally distributed software development has been a mainstream paradigm in developing modern software systems. We have witnessed a fast-growing population of software developers from areas where English is not a native language in the last several decades. Given that English is still the de facto working language in most global software engineering teams, we need to gain more knowledge about the experiences of developers who are non-native English speakers. We conducted an empirical study to fill this research gap. In this study, we interviewed 27 Chinese developers in commercial software development and open source global software development teams and applied Bourdieu’s capital-field-habitus framework in an abductive data analysis process. Our study reveals four types of capital (language, social, symbolic, and economic) involved in their experiences and examines the interrelations among them. We found that non-native speakers’ insufficient language capital played an essential role in prohibiting them from accessing and accumulating other capital, thus reproducing the sustained and systematic disadvantaged positions of non-native English speakers in GSD teams. We further discussed the theoretical and practical implications of the study

Bridging EU Climate Policy and AI Development: Toward Design Implications for Collaborative Sustainable AI Communities

European Union (EU) climate and AI policies articulate ambitious sustainability goals. Yet, a notable gap persists between high-level directives and everyday practices in AI development communities. This paper examines how EU governance frameworks can be operationalized through design criteria that support sustainable AI development in HCI contexts. Drawing on current practices within online platforms such as Hugging Face, we propose eight design implications organized into policy-driven, community-driven, and hybrid strategies. These include standardized metadata reporting, real-time environmental impact visualization, incentive frameworks, and integrated community benchmarks. Our approach bridges the policy-practice divide by aligning regulatory requirements with community innovation. It fosters a human-centric, transparent, and sustainable AI ecosystem. Such integrated strategies can promote digital sovereignty and environmental accountability while supporting a transition toward sustainable AI practices

Trust as Affects. Conceptualizing trust for digital public services to foster social inclusion for migrants

The digitalization of public services poses a risk of exacerbating existing social inequalities with migrant communities facing various problems from access to ease of use. Within the Nordics, this poses a particular risk as migration is increasingly necessary for the continuation of the welfare state, but the exclusion risks the foundations of trust and equality that are at their core. By reviewing empirical studies on trust, migrants and public services, we conceptualize trust as an affect that emerges from interactions between individual histories, societal discourses and public institutions. Our work bridges social sciences and HCI together to bring a unique contribution that builds a theoretical framework for trust in public services, particularly digital services, which have not been studied extensively among migrant populations. Our framework supports emerging understandings of trust and indicates shifts necessary in creating and implementing digital public services in ways that support an affect of trust

Workplace Aspects of Knowledge and Expertise Sharing Practices Supported by Augmented Reality Systems: Findings from a Design Case Study

In this article, we present findings concerning how environment can both impact upon and be impacted by knowledge and expertise sharing practices (KES) supported by augmented reality (AR) systems. We draw on findings from a Design Case Study (DCS) carried out for the design and evaluation of an AR system to support KES in complex production contexts. Our results suggest that the proposed system not only changes the perception of the technical environment but is itself gradually perceived as an element of it. The results also reveal changes in the employees’ focus of attention when working with the AR-aid in question and how they individually adapt to it. Moreover, our findings suggest that the proposed system facilitates a change in the proximity between experts and non-experts, bridging spatial and temporal distances, fostering cooperation between those two categories of workers, at the same time that enhance their autonomy. Overall, the results highlight how changes in the social environment of digitised production cannot be separated from changes in the technical environment

Cooperating in Academia with underspecified protocols: A case study of students and researchers practitioners of Grounded theory methodology (GTM).

As scientific projects become increasingly cooperative, we can identify two approaches: the first one tends to distribute datawork among participants performing the same task while the other brings together different experts to work on the same data. Grounded Theory Methodology (GTM) is frequently used for academic cooperative projects but does not clearly specify how cooperation should be organized. This paper aims to identify the different cooperative work practices of actors who use this method. We conducted a qualitative study combining semi-structured interviews with participant and non-participant observations involving social sciences researchers and students. We find that cooperation in GTM projects relies on continuous adjustments, informal task distribution, and digital artifacts not explicitly designed to support this methodology. Qualitative analysis tools incorporating coordination protocols are often perceived as too constraining, requiring actors to engage in additional articulation work. Based on these findings, we propose recommendations to improve the design of existing tools and design artifacts better suited to cooperative work in GTM. These artifacts would accommodate more adaptable and customizable protocols, facilitating task coordination and enhancing collaboration within research teams

Cultivating a Space for Learning: A Study of Note-Taking and Sharing on a Video-Sharing Platform Bilibili

Video-sharing platforms such as YouTube are popular today, not only for entertainment but also for learning, e.g., for understanding lecture content or acquiring everyday skills. Some platforms, like Bilibili, a popular video-sharing platform in China, even introduced note-taking and sharing features to further foster such learning practices. While note-taking and sharing have been extensively explored in environments specifically for learning, their use around these social media platforms such as YouTube and Bilibili remains under-studied. In this paper, we present a qualitative study with 15 participants who have used the note-taking and sharing feature called BNote launched on Bilibili. Our study reveals how note-taking and sharing are used as a way to cultivate a space for learning on such a general video-sharing platform by structuring, supplementing, and substituting user-generated videos and by leveraging social forces for motivation, self-discipline, and organization for learning. We end by discussing our findings and providing design recommendations to better support a learning space on social media platforms

Identification of latent biomarkers in brain imaging of Parkinson’s disease using explainable artificial intelligence

Parkinson’s disease (PD) is the second most common neurodegenerative disorder worldwide, characterized by the progressive degeneration of dopaminergic neurons. Early diagnosis remains challenging due to the lack of specific clinical tests. Although imaging techniques such as SPECT and MRI are commonly used to support diagnosis, their analysis is most often limited to striatal regions. In this study, we introduce a deep learning-based method for PD detection, while also exploring the role of non-striatal brain regions, which are often overlooked. Using DaTSCAN volumes, we trained a three-dimensional convolutional neural network (3D CNN) to distinguish control subjects from PD patients at different stages (1 to 3). To interpret the predictions, we applied the Grad-CAM technique to localize the regions influencing the model’s decision. Our network achieved remarkable accuracy (>97%) across all stages of the disease, and the Grad-CAM maps revealed a significant involvement of cortical and subcortical regions beyond the striatum. These findings suggest the existence of early or complementary biomarkers, highlighting the value of explainable artificial intelligence in brain imaging to refine PD diagnosis and broaden our understanding of how the disease develops and affects the brai