Virtually Extended Coworking Spaces? – The Reinforcement of Social Proximity, Motivation and Knowledge Sharing Through ICT

Coworking is characterized by different people sharing a workspace to benefit from the inspiring working atmosphere. Even before Covid-19, many positive effects and dynamics were not fully exploited by their users. One reason is a lack of trust among the users that leads to social isolation, although a coworking space should increase knowledge and idea exchange. As most people in coworking spaces use information and communication technologies (ICT) for their collaboration with their clients or employers, we examined if and how ICT can be used to support the positive effects and dynamics of coworking spaces. For this, we conducted eight interviews with freelancers and entrepreneurs who have already worked in coworking spaces in order to identify requirements for a complementary virtual coworking platform. We found that social proximity, motivation and knowledge sharing could be increased by such a platform. Based on the process virtualization theory, we derived six design principles

Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research

Algorithmic fairness in Information Systems (IS) is a concept that aims to mitigate systematic discrimination and bias in automated decision making. However, previous research argued that different fairness criteria are often incompatible. In hiring, AI is used to assess and rank applicants according to their fit for vacant positions. However, various types of bias also exist for AI-based algorithms (e.g., using biased historical data). To reduce AI’s bias and thereby unfair treatment, we conducted a systematic literature review to identify suitable strategies for the context of hiring. We identified nine fundamental articles in this context and extracted four types of approaches to address unfairness in AI, namely pre-process, in-process, post-process, and feature selection. Based on our findings, we (a) derived a research agenda for future studies and (b) proposed strategies for practitioners who design and develop AIs for hiring purposes

Working with ELSA – How an Emotional Support Agent Builds Trust in Virtual Teams

Virtual collaboration is an increasing part of daily life for many employees. Despite many advantages, however, virtual collaborative work can lead to a lack of trust among virtual team members, e.g., due to spatial separation and little social interaction. Previous findings indicated that emotional support provided by a conversational agent (CA) can impact human-agent trust and the perceived social presence. We developed an emotional support agent called ELSA and conducted a between-subject online experiment to examine how CAs can provide emotional support in order to increase the level of trust among colleagues in virtual teams. We found that human-agent trust positively influences the level of calculus-based trust among team members and increases team cohesion, whereas perceived anthropomorphism and social presence towards a CA seems to be less important for trust among team members

Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research

Algorithmic fairness in Information Systems (IS) is a concept that aims to mitigate systematic discrimination and bias in automated decision making. However, previous research argued that different fairness criteria are often incompatible. In hiring, AI is used to assess and rank applicants according to their fit for vacant positions. However, various types of bias also exist for AI-based algorithms (e.g., using biased historical data). To reduce AI’s bias and thereby unfair treatment, we conducted a systematic literature review to identify suitable strategies for the context of hiring. We identified nine fundamental articles in this context and extracted four types of approaches to address unfairness in AI, namely pre-process, in-process, post-process, and feature selection. Based on our findings, we (a) derived a research agenda for future studies and (b) proposed strategies for practitioners who design and develop AIs for hiring purposes

Mobile intraoperative CT-assisted frameless stereotactic biopsies achieved single-millimeter trajectory accuracy for deep-seated brain lesions in a sample of 7 patients

BACKGROUND Brain biopsies are crucial diagnostic interventions, providing valuable information for treatment and prognosis, but largely depend on a high accuracy and precision. We hypothesized that through the combination of neuronavigation-based frameless stereotaxy and MRI-guided trajectory planning with intraoperative CT examination using a mobile unit, one can achieve a seamlessly integrated approach yielding optimal target accuracy. METHODS We analyzed a total of 7 stereotactic biopsy trajectories for a variety of deep-seated locations and different patient positions. After rigid head fixation, an intraoperative pre-procedural scan using a mobile CT unit was performed for automatic image fusion with the planning MRI images and a peri-procedural scan with the biopsy cannula in situ for verification of the definite target position. We then evaluated the radial trajectory error. RESULTS Intraoperative scanning, surgery, computerized merging of MRI and CT images as well as trajectory planning were feasible without difficulties and safe in all cases. We achieved a radial trajectory deviation of 0.97 ± 0.39 mm at a trajectory length of 60 ± 12.3 mm (mean ± standard deviation). Repositioning of the biopsy cannula due to inaccurate targeting was not required. CONCLUSION Intraoperative verification using a mobile CT unit in combination with frameless neuronavigation-guided stereotaxy and pre-operative MRI-based trajectory planning was feasible, safe and highly accurate. The setting enabled single-millimeter accuracy for deep-seated brain lesions and direct detection of intraoperative complications, did not depend on a dedicated operating room and was seamlessly integrated into common stereotactic procedures

Breaking Down Barriers: How Conversational Agents Facilitate Open Science and Data Sharing

Many researchers hesitate to provide full access to their datasets due to a lack of knowledge about research data management (RDM) tools and perceived fears, such as losing the value of one\u27s own data. Existing tools and approaches often do not take into account these fears and missing knowledge. In this study, we examined how conversational agents (CAs) can provide a natural way of guidance through RDM processes and nudge researchers towards more data sharing. This work offers an online experiment in which researchers interacted with a CA on a self-developed RDM platform and a survey on participants’ data sharing behavior. Our findings indicate that the presence of a guiding and enlightening CA on an RDM platform has a constructive influence on both the intention to share data and the actual behavior of data sharing. Notably, individual factors do not appear to impede or hinder this effect

‘We’re all in this toGather’ – A Virtual World for Improving Knowledge Exchange and Social Interaction for Digital Work

One drastic change that has been established in many organizations is the possibility of location-independent work. However, working remotely also creates distinct challenges that organizations must face. Thus, remote work could lead to a decrease in social interactions and therefore less implicit knowledge exchange in teams. However, informal conversations are crucial for building and maintaining team cohesion as well as experience transfer among employees. To address this problem, we apply a design science research approach to examine how a virtual world as a work environment could help to overcome those challenges within our research group. We designed a prototype of a virtual world that is based on knowledge gained from three design thinking workshops and tested it over four weeks in a real-world work case. Furthermore, we conducted 16 interviews with employees and present our initial findings of the effects on group awareness, social identity, IT identity, trust, and acceptance

Strategies and Influence of Social Bots in a 2017 German state election

This study aims to examine the influence of environmental and personal factors on knowledge-sharing behaviour (KSB) and whether more leads to superior innovative work behaviour (IWB) at tertiary level in Vietnam. Our case is Hanoi University (HANU), one of the Leading Public Universities in Vietnam. This study applies the structural equation modelling (SEM) to investigate the research model based on social cognitive theory. Based on a survey of 320 academic staff at HANU, the results show that two environmental factors (subjective norm, trust) and three personal factors (knowledge self-efficacy, enjoyment in helping others, and reciprocity) significantly influence KSB. The results also indicate that employee willingness to share knowledge enable the organisation to promote IWB. It is hoped that academic staff and university leaders from other countries may find the case study useful for deeper understanding of the effects of social influences, personal perceptions and KSB on IBW in the future