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Business, Enterprise & Management

Permanent URI for this collectionhttps://eresearch.qmu.ac.uk/handle/20.500.12289/5

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    The Securitisation of Space Mining. Space Resources Acquisition in Between Geopolitics, Supply-Chain Challenges, and Environmental Risks
    (Springer Nature Singapore, 2026-02-16) Paladini, Stefania
    The acquisition and industrial exploitation of resources in/from outer space (i.e., ‘space mining’) is at present more an ambitious plan than an actual thing, and enormous challenges will have to be met before it becomes reality. And yet, the power politics on Earth is already shaping the sector-to-be, with nation-states’ sourcing plans that clash in their trajectories and an increasingly hostile narrative. This is leading to a securitisation of the sector that can transfer dangerous dynamics from Earth to space and threaten the development of the sector itself, which should instead focus on addressing feasibility challenges first and foremost. Moreover, outer space is a fragile environment, and the use of advanced technology could be used in an offensive capacity, leading to cyberattacks and disruptions of space operation. This article investigates the ongoing securitisation and its risks, highlighting risks and challenges, from legal to technical to logistical, and the possible solutions to ensure space sustainability is given the centrality it deserves for a peaceful and fruitful development of the sector.
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    Queen Margaret University and Metropolitan College, Greece
    (British Council, 2026) Makellaraki, Vicky
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    Why not use personal norms in message framing?: Understanding the importance of self-consciousness and green preference when promoting pro-environmental behaviour
    (Elsevier BV, 2026-01-31) Zhou, Yuanyuan; Wilson, Juliette; Karampela, Maria; de Groot, Judith
    Normative messages are an established way to promote pro-environmental behaviour. However, research examining the conditions under which such messages are effective predominantly focus on social rather than personal norms. As personal norms have been identified as a much stronger predictor of pro-environmental behaviour, the present study examined the mechanisms through which personal rather than social normative messages enable people to act in line with these norms. In two experimental studies (N=200 and N=249), in which normative messages and self-consciousness were manipulated, findings reveal that personal normative messages positively impact intentions to re-use hotel towels indirectly through one’s green preference on both studies, and directly as well in Study 2. Furthermore, these (in)direct effects are negatively moderated through one’s self-consciousness. These mechanisms through which personal normative messages vary in their effectiveness in promoting re-using towels, advances our understanding of how personal rather than social normative messages can be used to promote pro-environmental behaviour change.
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    Artificial Intelligence (AI) &; Decision-Making: Will AI Trustworthiness Facilitate Progress Towards Sustainability?
    (Springer Nature Switzerland, 2026-01-23) Ottogalli, Marta
    Decision-makers, from governments to the private sector, are pressured to tackle global sustainability challenges. Applications of artificial intelligence (AI) aim to help decision-makers by simplifying access to a large amount of information, addressing data gaps, improving decisions’ efficiency, automating burdensome processes and more. Humans have been reluctant to adopt AI to take decisions, even when, in some cases, it has proven to outperform humans’ judgement. Hence, the research aims to investigate the influencing factors that generate algorithm aversion among decision-makers during decision-making processes aimed at addressing sustainability challenges. Through a series of mixed research methods, participants including governments, the private sector and intergovernmental organisations (IGOs) took part in the preliminary research by completing a scenario-based survey and semi-structured interviews to further investigate algorithm aversion during sustainability decision-making. It must be noted that a multi-stakeholder approach is necessary to coordinate groups of stakeholders that are required to drive sustainable progress. This preliminary study showed that there is algorithm aversion and as a result: (i) Decision-makers are not willing to use AI cost-benefit analysis models that they do not understand to evaluate decisions; (ii) Decision-makers are more interested in using AI technologies that are based on factors they have helped co-design; (iii) Decision-makers are mostly interested in automating secondary tasks that can help identify the correct data and information to take a decision effectively; (iv) Decision-makers are interested in using AI that helps them identify knowledge that can directly support their decisions.
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    Governing the amorphous: AI, social justice, and the challenge of future-proof regulation
    (Elsevier BV, 2026-01-10) Liang, Guangda
    The rapid integration of artificial intelligence (AI) into the core functions of society presents a profound governance challenge, forcing nations to reconsider the very nature of their social contracts. This paper examines the global effort to regulate AI through a deep and critical analysis of Canada's proposed Artificial Intelligence and Data Act (AIDA). While Canada's adoption of a risk-based framework aligns with emerging international norms, this paper argues that the legislation in its current form is procedurally flawed and substantively hollow, failing to adequately address the deep societal risks posed by AI, particularly concerning social justice, fundamental human rights, and democratic accountability. Through a multi-layered critical analysis of the legislative text, contextualized by a detailed comparison with the European Union's more mature AI Act, this paper deconstructs three core weaknesses that hold urgent lessons for policymakers worldwide: (1) a profound reliance on ambiguous definitions and unfettered executive discretion, which creates a dangerous democratic deficit and denies legal certainty to citizens and innovators alike; (2) the conspicuous absence of a strong, independent, and technically proficient regulator, a decision that undermines public trust and neuters enforcement capacity; and (3) a systemic failure to ground the legislation in a rights-based framework that provides effective, accessible, and meaningful redress for individuals harmed by increasingly powerful algorithmic systems. Ultimately, this paper uses the Canadian example to argue that effective AI governance requires far more than technical rules or industry-led standards; it demands a robust and reimagined social contract for the algorithmic age. It concludes by proposing a comprehensive, “Multi-Pillar Governance Model” centered on legislative clarity, independent oversight, mandatory accountability mechanisms, and, most critically, the substantive empowerment of citizens to understand, challenge, and seek justice from the automated decisions that impact their lives.
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    Love Wars: Television romantic comedy [Book review]
    (Informa UK Limited, 2026-01-05) Geddes, Kevin
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    Enhancing K-means Clustering in B2B Customer Segmentation: A Comparative and Hybrid Approach of Recursive Feature Elimination, Correlation Analysis, and Lasso Regularization
    (Springer Nature Switzerland, 2025-11-21) Bello, Daisy Ipatzi; Tahir, Sabeen; Paladini, Stefania
    This paper evaluates the effectiveness of three feature selection techniques—Recursive Feature Elimination (RFE), Correlation Analysis, and Lasso Regularisation—in enhancing K-means clustering for B2B customer segmentation. Using a quantitative case study approach, the research assesses the individual and combined impact of these methods on clustering performance. The dataset, comprising anonymised B2B interactions from a wholesale distribution company, presented a high-dimensional and complex environment in which to test these techniques. Findings indicate that a hybrid approach—applying Lasso Regularisation, RFE, and Correlation Analysis in sequence—outperforms the individual methods. This integrated strategy improves silhouette scores and cluster cohesion, resulting in more accurate and interpretable segmentation. The study demonstrates that combining these techniques produces a robust framework that yields actionable insights for targeted marketing, resource allocation, and customer engagement within B2B contexts.
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    A deep learning pipeline for age prediction from vocalisations of the domestic feline
    (Nature Research, 2025-10-03) van Toor, Astrid; Qazi, Nadeem; Paladini, Stefania
    Accurate age estimation is essential for advancing interspecies communication but remains a challenge across non-human species. This study presents the first dataset of domestic feline vocalisations specifically designed for age prediction and introduces a novel deep learning pipeline for this purpose. By applying transfer learning with models like VGGish, YAMNet, and Perch, we demonstrate the potential for automated age classification, with VGGish achieving the best results. Our findings hold significant potential for applications in veterinary care and wildlife conservation, building on existing research and pushing forward the boundaries of automated age classification within digital bioacoustics. Future work could explore improving model generalisability and robustness, potentially expanding its application across species.