Neu Ulm University of Applied Sciences

Abstract The use of video interviews in personnel selection is increasing, yet less favorable applicant reactions to these interviews remain a concern among organizations. Alongside replicating prior findings, this study examines whether explaining the environmental benefits of video interviews can improve applicant perceptions. We investigate differences between synchronous (SVIs) and asynchronous video interviews (AVIs), focusing on fairness perceptions, organizational attractiveness, and interview motivation. Findings replicate that SVIs are generally perceived more favorably than AVIs, while explanations of environmental benefits can improve certain fairness perceptions and perceived organizational attractiveness. Additionally, pro‐environmental attitudes do not significantly moderate the effect of explanations. These results highlight the potential of environmental explanations in recruitment regardless of applicants' pro‐environmental attitudes.

The rapid advancement of generative artificial intelligence (AI) has made AI-driven content curation a dominant force in shaping public discourse on social media. Platforms, including Facebook, YouTube, and TikTok employ recommendation algorithms to personalise content and increase user engagement. However, these systems also intensify concerns over media pluralism, algorithmic bias, and misinformation. By prioritising user preferences, they reinforce filter bubbles and restrict exposure to diverse viewpoints. As a result, democratic dialogue weakens, and public opinion formation becomes distorted. This study examines how AI-assisted content curation affects media diversity in European social networks, focusing on platform accountability and regulatory challenges. Special attention is given to recent policy interventions, including DSA (2022), Germany’s Action Plan (2024), and the EU AI Act (2025) initiatives on AI-generated political content. However, they also expose significant gaps in enforcement and oversight. To evaluate regulatory impact, this study analyses platform policies, legal frameworks, and AI content selection mechanisms. Despite transparency being a main objective, findings reveal current regulations unable to reduce algorithmic bias or achieve balanced content representation. In response, the study advocates for improved explainable AI (XAI) models, demands stronger regulatory oversight, and supports increased user control over content selection. By addressing these shortcomings, this research contributes to the wider debate on AI ethics, media governance, and digital policy in Europe.

Artificial Intelligence (AI), a term coined by Emeritus Stanford Professor John McCarthy in 1955 [10], was defined by him as “the science and engineering of making intelligent machines”. Much research has created human programmed machines to behave in a clever way, such as playing chess, but, today, machines can learn, at least partly like human beings do [9]. Machine Learning (ML) is the part of AI studying how computer agents can improve their perception, knowledge, thinking, or actions based on experience or data. For this, ML draws from computer science, statistics, psychology, neuroscience, economics, and control theory [9]. ML learns through examples and benefits from human data contributions, while the process and outcome of the machine remain partially opaque. No wonder users without experts in this field hesitate to trust such a machine.

Digital technologies have radically altered the nature of innovation and its development process. Innovations in the digital age are characterized by a reliance on a network of diverse actors who champion innovation. Despite the important role of these innovation actors' social networks in enabling interactions and collaboration for developing innovation, extant research lacks a synthesis of knowledge on innovation actors' social networks in the digital age. We provide a multi-disciplinary literature review on the social network of actors championing innovation and its interplay with digital technology based on a theoretical framework derived from social network research. Our analysis gives insights into the flows and structure of innovation actors' social networks. We find that digital technology has predominantly led to gradual changes in innovation actors' activities by functioning as a catalyst. Concerning the characteristics of innovation actors' networks, we find more fundamental changes in the agency of digital innovation, as innovation actors form communities and perform activities collectively. Building on the literature on digital technology and digital innovation, we identify promising avenues for future research. Overall, our literature review contributes by proposing a new conceptual model of innovation actors' social networks in the digital age and providing an agenda for future research.

Open access Table of Contents Introduction by Margunn Aanestad, Atreyi Kankanhalli, Likoebe M. Maruping, Min-Seok Pang, and Sudha Ram(1836-1837) Why and How Does a Machine Learning Algorithm Coexist with Alternative Methods? The Case of the Social Welfare Blind Spot Identification System by Gwanhoo Lee and Woo Sik Lee(1838-1841) Small-Scale Farmers as Experts: Mitigating Secondary Injustices By Co-Designing Technology-Enhanced Learning Through Critical Digital Pedagogyby Arlette Roman Almanzar, Rebecca C. Ruehle, Alyssa-Jade McDonald-Bärtl, Carla D. Martin, Laura Marie Edinger-Schons, and Aniruddh Agarwal(1842-1847) Digital Platforms for Social Inclusion: The Case of An Information Platform for Refugeesby Maximilian Schreieck, Manuel Wiesche, Olga Usachova, and Helmut Krcmar(1848-1852) Pursuing Social Justice Through Doxing on Social Media: Implications of the 2019 Hong Kong Protestsby Christy M. K. Cheung, Marten Risius, and Yuan Fang(1853-1857) Environmental Injustice and the Forgotten Community: A Case Study of the Largest E-Waste Dumpsite in Nigeriaby Wenyu (Derek) Du and Delin Zeng(1858-1862) https://doi.org/10.25300/MISQ/2024/19277 This work is licensed under a Creative Commons Attribution 4.0 International License.

Rapid advances in computer vision (CV) and artificial intelligence have opened new avenues for digital pathology, including the diagnosis and treatment of central nervous system (CNS) tumors. In addition to reviewing the state-of-the-art in CV-based digital pathology and highlighting its potential to revolutionize the field, this chapter also provides a general introduction to digital pathology and Machine Learning (ML) for neuropathologists. Although currently limited to research, the integration of CV tools into digital pathology already offers significant advantages, such as automating tissue analysis and providing quantitative assessments. The transition from research to clinical application is slowly gaining momentum. To provide neuropathologists with the necessary skills to succeed in digital pathology and ML, the chapter also discusses how physicians and researchers can create custom models and tools tailored to specific needs using tools such as nnU-Net, deepflash2, and PathML. Emphasis is placed on the importance of interdisciplinary collaboration and continued research to fully realize the potential of CV in digital pathology for CNS tumors, to address the challenges of workforce shortages and increased workloads in neuropathology.

Long hospitalisation has been recognized as an independent risk factor for poor neurodevelopmental outcomes of preterm infants. Systematic training and early inclusion of parents in their preterm infant's care is a strategy to shorten the length of hospital stay. We implemented an enhanced stepwise family-centred care program and assessed its effects on postmenstrual age (PMA) at discharge and parental satisfaction. This prospective single-centre longitudinal cohort study was carried out in a German level III neonatal unit from October 2020 to May 2023. Five consecutive 6-month cohorts (1 baseline and 4 intervention cohorts, 169 infants and their caregivers) were analysed. Mean PMA at discharge did not change in the total cohort but declined significantly in patients without neonatal morbidities from baseline to cohort 4 (37.2 ± 1.4 to 36.1 ± 1.6 weeks; p = 0.036). Concomitantly, discharge with tube feeding raised from 2.4% to 74.1% (p < 0.001) and discharge with home monitoring raised from 9.8% to 74.1% (p < 0.001), while unplanned readmissions remained unchanged (p = 0.44). Parental satisfaction with time point of discharge increased non-significantly from baseline to cohort 4 (75.8% vs. 95.7%; Chi2 0.22). Discharge of preterm infants at a significantly lower PMA is feasible through enhancement of family-centred care and is very well accepted by parents.

BackgroundRevealing the full potential of digital public health (DiPH) systems necessitates a wide-ranging tool to assess their maturity. Essential domains that need to be considered include the literacy and interest in DiPH tool application by society and the workforce and the legal and ICT maturity of DiPH systems. No review has investigated indicators on these national DiPH system maturity (DiPHSM) domains yet. Our narrative review and qualitative analysis aimed to map the landscape of indicators related to DiPHSM measurement and rank them based on their importance for such assessments.MethodsAs original indicators were not published in scientific databases but as grey literature, we used DuckDuckGo to apply a pre-defined search strategy for 11 countries from all continents classified as having reached level 4 of 5 in the Global Digital Health Monitor. Of the 1484 identified references, 137 were included which named 15806 indicators. Consensus on importance was defined as at least 3 or 4 authors rating an indicator as important.ResultsWe recognized 180 indicators on different constructs with importance for DiPHSM analysis, including the availability and use of Smartphones, computers, the Internet or the DiPH intervention, the existence of skilled workforce, infrastructure (investment), and interoperability between interventions, the secondary use of health data, a DiPH strategy and controlling agency, or the application of big data and artificial intelligence for health data collection, analysis, and sharing.ConclusionsOur study holds the potential to develop more comprehensive tools for DiPHSM assessments. Further examination is required to analyze the suitability and applicability of all identified indicators for diverse healthcare settings. By working towards a uniform evaluation of DiPHSM, we foster informed decision-making among healthcare planners and practitioners, improve resource distribution, and continue to drive innovation in healthcare delivery.Key messages• Maturity assessment tools need to consider the complexity of DiPH systems. Thereby, DiPH system evaluations need to be accompanied by analyses of the legal, ICT, and literacy perspective maturities.• New methods are needed to systematically assess and use multidisciplinary grey literature for research.