Edward Cartwright

The increasing frequency of cyber threats poses substantial challenges for organizations in both the private and public sectors. This systematic literature review evaluates and categorizes current cyber risk assessment methodologies and frameworks, supporting the selection of suitable approaches for practical and academic applications. Utilizing the PRISMA framework, 712 relevant studies were filtered from an initial pool of 1900 academic publications and subsequently analyzed and organized into a structured database, providing an overview of the advantages and limitations of widely cited approaches in this domain. The findings indicate a strong preference for established risk management frameworks, including the ISO 27000 family, OCTAVE, and NIST Special Publications, as well as mathematical approaches such as Bayesian networks, fuzzy logic, and multi-criteria decision-making techniques. The 217 approaches identified were grouped into two primary categories: Standards, Frameworks, and Guidelines and Risk Assessment Methods , with further classification by the application sectors addressed in the literature. Analysis suggests that no single approach offers universal applicability. The choice of methodology should therefore be informed by an organization’s specific resources, size, and sectoral requirements. A cross-analysis of methods and sectors reveals gaps in sector-specific coverage, particularly for healthcare, finance, and small and medium-sized enterprises. The review identifies a trend toward hybrid approaches that combine organizational frameworks with quantitative methods and documents persistent barriers to adoption, including cost, data scarcity, and insufficient management engagement. Based on these findings, a conceptual framework is developed to evaluate approaches across five dimensions and to derive a typology of governance-oriented, quantitative, and hybrid methods. Implications for practitioners, regulators, and researchers are discussed in relation to current regulatory frameworks, including the NIS-2 Directive and the Digital Operational Resilience Act (DORA).

Do individuals strategically optimize AI adoption, or do they exhibit uniform algorithm aversion or appreciation? We conduct a pre-registered experiment (N = 320) varying task creativity, incentive framing, and AI characteristics in educational writing tasks. Students substantially reduce AI reliance for creative tasks compared to routine tasks—a creativity effect that accounts for the majority of explained variance in adoption decisions. Personal issue salience further amplifies this effect asymmetrically: students resist AI most strongly for creative work on personally important topics while increasing AI reliance for routine work on those same topics. This crossing pattern reveals strategic cognitive autonomy—context-dependent preservation of human agency where developmental returns are highest, coupled with algorithmic delegation elsewhere. Performance analyses validate this interpretation: the quality of AI integration drives creative task performance while the adoption decision itself drives routine task performance, confirming that differentiated strategies are rationally calibrated to task demands. Our findings reframe algorithm aversion as potentially rational optimization rather than behavioral bias, with implications for educational technology design, organizational AI deployment, and human capital formation under technological change.

We explore whether information on one's own social value orientation (SVO) impacts contributions in a public good game with leadership by example. In doing so, we compare the predictions of a model of belief‐based preferences, where payoffs depend on first‐ and second‐order beliefs on the contributions of others, and a model of internalized descriptive norms, where payoffs depend on deviation from an empirical norm. We argue that if pro‐social behavior is driven by belief‐based preferences, then private information on SVO should not impact contributions, but if the behavior is driven by internalized descriptive norms, then information on its own SVO should impact contributions. We report an experiment with three treatments: no information on SVO, binary information whether pro‐self or pro‐social, and SVO indicated on a scale from very pro‐social to very pro‐self. We observe no effect of information on contributions. This finding is inconsistent with internalized descriptive norms. We find that contributions are highest with a pro‐social leader.

This study examines the relative effectiveness of the UK government’s public health messages used during the first wave of the COVID-19 pandemic. We focus on the use of a loss versus gain frame. We look at the effect of framing on behavioural inclination to follow COVID-19 guidance, as well as affective mechanisms and individual characteristic moderators that might explain said willingness. We ran two studies with a voluntary sample of the UK adult population (total n = 300). Across both studies, we only find a significant impact of message framing on the level of negative affect triggered, with the loss frame triggering a higher negative affect. Instead, attitude to public health communication had a direct and indirect effect on behavioural inclination. Our results suggest that threat minimisation and satisfaction with authorities handling a health crisis might be key to consider when developing effective public health communications.

Previous studies have shown that pro‐social leaders cooperate, on average, more than pro‐self leaders in social dilemmas. It can, thus, be beneficial for the group to have a pro‐social leader. In this paper we analyze the consequences of a leader informing followers that they are pro‐social (or pro‐self). In doing so, we compare a setting in which the leader's type is truthfully revealed to settings where the leader can ‘hide’ or ‘lie’ about their pro‐sociality. We find that a leader saying they are pro‐social boosts efficiency, even if the signal is not fully credible. Cooperation is highest in a truth setting with a pro‐social leader. We demonstrate that these results are consistent with a belief‐based model of social preference in which the stated type of the leader changes the frame of reference for followers.