The Cyber Kill Chain (CKC) is a prevalent concept in cyber defense; nevertheless, its emphasis on post-reconnaissance phases limits the capacity to foresee attacker activities outside the organizational boundary. This study introduces and empirically substantiates a pre-chain phase, referred to as contextual anticipation, which broadens the temporal framework of the CKC by methodically identifying subtle yet actionable signals prior to reconnaissance. The methodology combines the STEMPLES+ framework for socio-technical scanning with General Morphological Analysis (GMA), generating internally coherent scenarios that are translated into Indicators of Threats (IOT). These indicators connect contextual triggers to threshold-based monitoring activities and established courses of action, forming a reproducible and auditable relationship between foresight analysis and operational defense. The application of three illustrative cases—a banking merger, the distribution of a phishing kit in underground marketplaces, and wartime contribution scams—illustrated that contextual anticipation consistently provided quantifiable lead-time benefits varying from several days to six weeks. This proactive stance enabled measures such as registrar takedowns, targeted awareness campaigns, and anticipatory monitoring before distribution and exploitation. By formalizing CKC-0 as an integrated socio-technical phase, the research enhances cybersecurity practice by demonstrating how diffuse contextual drivers can be converted into organized, actionable mechanisms for proactive resilience.

Cyberattacks increasingly generate systemic economic and geopolitical effects in an era of dense global interdependence. While prior research emphasises geopolitical rivalry, institutional capacity, and technological sophistication as determinants of national cyber risk, less attention has been given to structural vulnerabilities arising from countries’ positions within global economic networks. This study advances a relational theory of national cyber risk, arguing that structurally central countries provide greater systemic leverage to attackers because disruptions to highly accessible nodes can propagate widely across interconnected trade systems. Using annual bilateral trade data from 2010 to 2020, we construct directed, weighted global trade networks and derive centrality measures capturing accessibility, brokerage, and embeddedness. These indicators are linked to country-level cyber incident data to evaluate both the probability and intensity of cyberattacks. Logistic and negative binomial models with lagged network metrics show that countries occupying more accessible positions face significantly higher cyberattack risk. The findings demonstrate that national cyber vulnerability emerges from relational exposure within interconnected economic systems, underscoring the importance of systems-based cybersecurity risk assessment.

International academic exchange is widely regarded as an important mechanism of knowledge transfer and international collaboration, yet relatively little is known about how countries’ structural positions within academic mobility networks relate to national innovation performance. This study examines the relationship between Erasmus academic mobility and innovation using a multidimensional quantitative framework integrating Social Network Analysis (SNA), econometric modelling, and inferential statistical analysis. Innovation performance is operationalised through the European Innovation Scoreboard (EIS), while Erasmus mobility data from 2008–2013 are analysed at both country and institutional levels. The empirical design combines regression analyses of mobility characteristics with two complementary network models: a classical model based on conventional centrality measures and an extended model incorporating weighted, influence-based, and temporal network metrics. The results indicate that overall mobility volume is only weakly associated with innovation performance, whereas mobility directed towards highly innovative countries exhibits substantially stronger associations. Network analyses further demonstrate that countries occupying more influential structural positions within Erasmus mobility networks consistently achieve higher innovation performance across multiple EIS dimensions. The comparison of the two analytical models shows that the extended multidimensional network representation captures complementary aspects of knowledge diffusion that remain only partially visible through conventional centrality measures. The study contributes conceptually by framing academic mobility as a knowledge network and methodologically by integrating econometric and network-based approaches within a unified analytical framework for examining international academic exchange and innovation.