When the Known Well May Sell

While Streaming Video on Demand (SVOD) consumers may express a desire for unlimited new and novel choices, excess choice can cause anxiety and choice overload. Further, familiarity can also influence perceptions of available choices. Addressing a gap in the literature on familiarity’s interaction with choice numeracy in an SVOD recommendation environment, this 2x2 experiment examines effects of choice-set size of TV sitcoms on potential use/perceptions of SVOD platforms and to what extent familiarity with the choices interacts with those perceptions through examining differences and interactions between small vs large choice sets, and familiar vs non-familiar shows. Familiarity tends to override potential choice overload effects and non-familiarity tends to exacerbate negative effects associated with too much choice, which has implications for purveyors of not only SVOD systems but other digital applications with similar recommendation systems, such as music streaming and podcasts, to emphasise choices that are likely to have familiarity to consumers, especially when interfaces themselves may be unfamiliar.

Integration of Non-Terrestrial Network for 5G IoT and Future 6G

The exponential growth of the Internet of Things (IoT) has spurred the need for robust and extensive connectivity solutions. In response to the market and use-case requirements, the Third Generation Partnership Project (3GPP) introduced Narrowband IoT (NB-IoT). The latest NB-IoT integration with Non-Terrestrial Network (NTN) aims to bridge the coverage gaps in challenging terrains and address capacity issues in dense urban environments through the utilisation of satellites and Unmanned Aerial Vehicles (UAVs). This research paper explores the potential of NTN systems to complement and enhance the capabilities of 5G NB-IoT and 6G technologies. Through a combination of academic research and simulation, it seeks to evaluate the advantages of NTN, such as extended coverage and improved signal quality, for massive IoT device connectivity. Our simulation results demonstrate a trade-off between coverage and network capacity: while higher UAV altitudes provide extensive coverage by reaching more receivers, they also suffer from reduced signal strength and capacity due to increased path loss. This paper provides an overview of technical specifications and challenges related to NB-IoT and NTN, an assessment of NB-IoT NTN performance in a real-world scenario, and an analytical framework with simplified graphical representations for evaluating NTN integration into NB-IoT.

Corporate Governance at the Crossroads of AI

Artificial intelligence (AI) is changing corporate governance (CG), creating a lack of consensus among studies regarding its impact. The purpose of this article is to critically explore scholars’ perspectives on AI’s transformative role in CG, from one side, and suggest the necessary strategies to ensure its responsible and effective integration from another side. The authors have conducted a systematic literature review following the PRISMA flowcharts, analysed 24 indexed journal articles and conference papers using Zotero for reference management, and VOSviewer for bibliometric analysis. The findings start by a double analysis of the AI implementation in CG - ‘nice-to-have’ or ‘must-have’ versus ‘disruption’ or ‘integration’ - categorising the literature into three groups drawing for scholars: ‘enhancers’, ‘integrators’, ‘pioneers’ and a fourth category perceived by the authors, ‘catalysts’. In addition, a set of five strategies for optimising AI use is proposed: ethical frameworks, strengthened governance, skills development, transparency and control mechanisms, and aligning AI with strategic objectives. This study aims to fill two inter-correlated research gaps: exploring how AI is perceived in governance contexts and its potential to integrate with or disrupt traditional structures and establishing a theoretical intersection between the ‘governance of AI’ and the ‘governance by AI’.

Empowering Data Spaces for Future Mobility

Recognising the growing demand for sovereign and interoperable data exchange in advanced mobility services, this paper presents a conceptual framework for developing data spaces within the data economy. Drawing on the Gaia-X initiative, we address the challenges of ensuring data sovereignty and cross-organisational collaboration in complex, diverse data ecosystems. By applying the Institutional Role Model (IRM), we systematically identify and classify 32 distinct organisational roles, organised into eight overarching meta-roles, that are crucial for the successful operation of a data space. Based on expert interviews and the in-depth analysis of two practical use cases, our approach uncovers how technological and economic roles interact to enable the seamless and sovereign exchange of mobility data. The findings enhance theoretical understanding of role-based data space structures and provide insights into data ecosystems’ design, governance, and implementation. Stakeholders looking to leverage data space for efficient, sovereign, and innovative mobility services will find these insights highly valuable.

How to Successfully Implement Telework?

Teleworking in the Covid-19 pandemic has become a widespread practice adopted by companies worldwide, providing a unique opportunity to experiment with it on a large scale. This study aimed to elucidate the organisational factors facilitating the successful implementation of telework. The primary objective was to identify the organisational and individual prerequisites, as well as the mediating mechanisms, necessary for the successful establishment of telework and measured by the telework outcomes. To do so, we developed a conceptual model to analyse the factors influencing the success of this transition and we tested it on a sample of 171 employees from the banking, IT, and telecommunication sectors. The results indicate that perceived autonomy, management of work-family conflict, and organisational support are key factors in ensuring employee satisfaction, performance, and engagement within this new work framework. However, telework does not have a direct impact on these outcomes without mediating mechanisms.

Modelling Inherent Risk of Data Intensive Technologies

This study introduces a systematic methodology for risk management in data-intensive systems inside regulated environments, with a special emphasis on European Union scenarios. The framework tackles the distinct issues of reconciling regulatory compliance with the necessity for technical innovation. It delineates a risk trajectory throughout multiple phases of the data pipeline: collection, intake, processing, modelling, and application. Each stage corresponds to certain risk controls, ranging from fundamental validations at lower risk tiers to stringent security and accountability protocols for elevated risks. Organisations can mitigate any negative effects and successfully utilise data-driven insights by implementing appropriate controls at each phase. The suggested approach incorporates a quantitative risk formula that considers data volume, parameter complexity, and sensitive data items to yield a comprehensive risk score. Risk levels are assigned through Monte Carlo simulations, ensuring probabilistic accuracy in risk assessment. To enhance applicability, the framework defines risk thresholds and proposes differentiated controls, enabling organisations to simulate risk scenarios before implementation. This flexible framework seeks to promote the secure and responsible development of data-intensive applications, allowing European companies to enhance their competitiveness globally while upholding ethical and legal standards, such as the EU AI Act, or the EU Digital Services Act.