Search Results (1,518)

How does artificial intelligence (AI) adoption shape administrative efficiency and e-citizen integration in local governments, and what role does professional development play in mediating these relationships? Drawing on a survey of 500 municipal employees across Spanish municipalities, this study employs partial least squares structural equation modelling (PLS-SEM), with formal non-linearity testing via Warp3 algorithms, to test a theoretically grounded model. The conceptual framework integrates Digital Transformation Theory and Public Value Theory as primary explanatory lenses, while drawing on the Technology Acceptance Model (TAM) and Total Factor Productivity (TFP) logic as complementary background perspectives that contextualise rather than directly operationalise the micro-level findings. Structural results reveal that AI adoption exerts a strong direct (and statistically linear) effect on perceived administrative efficiency (β = 1.04, p < 0.001; the standardised coefficient exceeding 1.0 and R² > 1 are a legitimate WarpPLS warp-model fit index rather than evidence of model misspecification: the Warp3 warp functions inflate the variance of predicted efficiency and break the additive identity SST = SSM + SSE, with the high AI–PD collinearity (r ≈ 0.84) as the contributing mechanism (RSCR = 1.000, SSR = 1.000); a comparative re-estimation without the moderation term yields β = 0.87 and R² = 0.76; we adopt this parsimonious specification (β ≈ 0.87, R² = 0.76) as the substantively interpretable estimate, with predictive relevance confirmed by a high Stone–Geisser Q² = 0.685, indicating that the model fits and predicts well rather than overfitting, while simultaneously stimulating professional development (β = 0.84, p < 0.001, R² = 0.70). Professional development positively predicted both efficiency (β = 0.27, p < 0.001) and e-citizen integration (β = 0.26, p < 0.01). Efficiency is the primary driver of e-citizen integration (β = 0.54, p < 0.001, R² = 0.53). The proposed moderation of AI adoption by professional development on efficiency was not supported (β = −0.01, p = 0.44), suggesting additive rather than synergistic effects. Model fit was robust (GoF = 0.701; ARS = 0.749; APC = 0.495); convergent and discriminant validity were confirmed by composite reliability, average variance extracted, Fornell–Larcker, and HTMT criteria; and common method bias diagnostics (Harman’s single-factor test, full-collinearity AFVIF, and marker-variable analysis) indicated that systematic method variance was not a material threat. These findings offer micro-empirical evidence of the mechanisms linking AI adoption to citizen service outcomes via a professional development pathway and provide actionable recommendations for Spanish and European municipalities navigating AI-driven governance reform. Full article

Phishing email remains a persistent and operationally critical cybersecurity threat, yet existing detection approaches, including traditional machine learning and single-pass large language model systems, either lack native interpretability or provide explanations that are difficult to standardize and audit. This paper introduces an explainable multi-agent LLM framework that decomposes phishing evidence across three role-specialized agents focused on linguistic patterns, psychological manipulation, and sender identity consistency. The framework then aggregates specialist outputs through schema-governed synthesis, enabling each intermediate and final decision to be structured, comparable, and auditable. The central contribution is the treatment of role-specialized evidence decomposition and explanation structure as first-class design constraints rather than post hoc additions. The framework is evaluated on a fixed 1000-email subset drawn from a unified TREC/Nazario corpus of 56,212 emails under controlled zero-shot conditions. The full multi-agent Meta-Judge system achieves Macro-F1 = 98.28% and phishing recall = 99.45%, improving Macro-F1 by 6.3 percentage points over a zero-shot single-model GPT-4o-mini baseline. Paired statistical testing confirms that this improvement is significant and is driven primarily by reduced false positives on legitimate emails while preserving high phishing recall. Additional evaluation on an independent LLM-attributed email benchmark shows a consistent Macro-F1 improvement of 0.0773 over the zero-shot baseline under distribution shift. Ablation results show that role-specialized decomposition is the primary performance driver, while deterministic voting provides a competitive raw-classification aggregator and Meta-Judge synthesis provides structured, analyst-facing explanations. These results indicate that role-specialized evidence decomposition combined with schema-governed explanation can improve both detection reliability and auditability in phishing classification workflows. Full article

This study critically investigates the scientific literature on the intersection of management accounting and intellectual capital using a bibliometric performance analysis and science-mapping approach. Drawing on a sample of 59 publications from the Scopus and Web of Science databases, the paper maps the intellectual structure, key contributors, and thematic evolution of the field. This study conceptualizes management accounting not merely as a neutral technical system but as a socio-political mechanism that shapes how intellectual capital is rendered visible, measurable, and governable within organizations. The findings identify five dominant research clusters (intellectual capital and corporate strategy, management accounting and performance, green intellectual capital, digitalization and value creation, and management control and intangibles), revealing how accounting practices actively participate in constructing organizational realities and legitimizing particular forms of value and knowledge. The analysis highlights that measurement and reporting practices privilege certain dimensions of intellectual capital while potentially obscuring others, raising critical questions about power, visibility, and accountability in knowledge-based economies. In particular, the growing emphasis on digitalization and sustainability reflects shifting governance regimes in which accounting systems extend their influence over organizational conduct and strategic decision-making. By integrating bibliometric techniques with a critical interpretive lens, this study contributes to the literature by reframing management accounting as a key site where knowledge, control, and organizational value are negotiated. It also identifies gaps for future research, particularly regarding the ethical and political implications of accounting for intangible resources in increasingly digital and transparency-driven environments. Full article

We, the reviewers, explore Alex Baratta’s The Societal Codification of Korean English, highlighting Korean English (KE), since expanding circle English varieties are often overlooked despite their significant global role. Baratta argues that codification should be reconceptualized as a societal process driven by users themselves, where socially-used innovations become legitimate conventions, rather than having to be officially recognized as per tradition. Building on and moving beyond other works, he insists the field cannot wait for formal codification, even while acknowledging that some may find his framing of KE unconvincing or premature. We summarize such arguments around the legitimization of KE, offer insights into what Baratta’s work effectively addresses and leaves less explored. We then offer a conceptual matrix and metaphor to depict the complexity of KE and its codification. Finally, we introduce a new term, “K-English(es)”. The review aims to help readers better grasp the nuanced dynamics of KE that Baratta and the field engage with and to situate readers’ own interests (e.g., as English language teachers or Hallyu fans) around KE, while supporting the expanding scholarship on the topic. Full article

Affine frequency division multiplexing (AFDM) is regarded as a promising waveform for high-mobility wireless systems. However, the public codebook used in AFDM raises security concerns when the link is observed by an eavesdropper, and meanwhile AFDM communication suffers from a high peak-to-average power ratio (PAPR). This paper proposes a security-aware codebook design for low-PAPR AFDM systems. Specifically, the codebook is designed to minimize an eavesdropper-oriented cross-alignment metric while maintaining the legitimate user’s decoding reliability and keeping the PAPR low. Since the resulting design problem is non-convex, we develop a dedicated alternating discrete coordinate descent algorithm to solve it. Simulation results show that the proposed codebook design significantly degrades the eavesdropper’s decoding performance without degrading that of the legitimate receiver while maintaining the low-PAPR. Full article

As the internet and complex network infrastructures continue to expand, so does the threat of sophisticated cyberattacks, compelling organizations to adopt advanced proactive defenses. A cornerstone of these defensive strategies is the honeypot. However, existing dynamic solutions often rely on reactive deployment or centroid-based clustering (e.g., K-Means), which mathematically yields invalid, unrealistic host profiles. Because intelligent threat detection increasingly relies on high-fidelity honeypot data to analyze adversary tactics, deploying easily fingerprinted decoys fundamentally undermines downstream AI-driven defense mechanisms. To overcome this limitation, we propose DYNAMIT, an intelligent honeynet deployment system that resolves the centroid validity problem by utilizing the unsupervised K-Medoids algorithm. By combining K-Medoids with a novel hybrid Manhattan-Jaccard distance metric, DYNAMIT selects valid, existing hosts as templates based on categorical hardware and binary software similarities. The system then leverages containerization and network virtualization to simulate multiple realistic, internet-facing honeypot profiles from a single physical host, ensuring the decoys remain indistinguishable from legitimate targets. Our evaluation demonstrates that DYNAMIT accurately captures the intended number of clusters with a low relative error ($18.75\%$ for 40 hosts and $6.625\%$ for 1000 hosts) while maintaining minimal resource overhead, establishing it as a highly scalable and robust data-generation prerequisite for modern intelligent network security. Full article

Java middleware platforms expose powerful management functions through HTTP-accessible interfaces such as Jolokia. This article discusses the analysis of CVE-2023-50780 in Apache ActiveMQ Artemis by framing the vulnerability as a management-plane state-transition problem rather than as a set of isolated exploit recipes. We analyze three remote-code-execution paths that combine Jolokia-accessible MBeans with Log4J2 configuration mutability, Artemis filesystem and deployment semantics, broker or web-server restart behavior, and, in one vector, the Java DiagnosticCommand interface. The study defines a formal attacker model; separates demonstrated preconditions from deployment-dependent assumptions; compares the three vectors across required privileges, network dependencies, writable artifacts, execution triggers, reliability, detection opportunities, and mitigations; and evaluates defensive controls at the level of the exploit stage they interrupt. The paper also clarifies the responsible-disclosure context and reduces operational payload detail in favor of defender-oriented evidence, validation tables, and architectural analysis. The resulting contribution is a reproducible but bounded case study of how legitimate administrative operations can compose into code execution when management interfaces are exposed without sufficient privilege separation, MBean restriction, filesystem hardening, and upgrade controls. Full article

The First Balkan War was not merely a military defeat but also a crisis of knowledge production. Although there is a vast body of academic literature in Turkey focusing on the causes, consequences, and military failures of the war, the discursive dimension of Western correspondents’ narratives has not yet been sufficiently analyzed. This research examines correspondent narratives within an integrated religious-civilizational framework that combines Edward Said’s concept of “Orientalism,” Stuart Hall’s concept of “Representation,” and Maria Todorova’s concept of “Balkanism.” Employing Critical Discourse Analysis (CDA) based on Norman Fairclough’s three-dimensional model, the article investigates how reporter texts—often accepted as “transparent” primary sources in Turkish historiography—function as symbolic instruments of construction. By analyzing recurring representations of Turks as “fatalistic,” “pre-modern,” and “alien to European values,” the study reveals how these narratives legitimize a civilization hierarchy by exploiting the “Cross and Crescent” dichotomy. By revealing how these boundary-producing discourses transform military events into evidence of barbarism, the article challenges the claim of neutrality in archival records and contributes to the literature in this regard. By distinguishing between Orientalist representations of the Ottoman Turks and Balkanist representations of the Balkan nations, this study contributes to a more nuanced understanding of Western discursive hierarchies during the geopolitical crises of the early 20th century. Full article

This study examines how employee experience (EX) in professional kitchens is constituted through the interplay of embodied strain, temporal constraints, care responsibilities, and the meanings attached to work. Drawing on an embedded single-case study conducted in the main kitchen of a five-star city hotel in Istanbul, the study is based on in-depth interviews with workers across hierarchical levels. The findings show that physical strain, long and irregular working hours, and non-work care burdens are not experienced as separate challenges, but as mutually reinforcing pressures that extend into everyday life. Based on these findings, the study reconceptualizes EX as a life-embedded psychosocial process constituted across bodily, temporal, and extra-organizational domains. Within this framework, the study introduces two conceptual mechanisms: embodied time poverty, which captures how time pressure becomes intertwined with bodily exhaustion and limited recovery, and the hidden social burden of culinary labour, which explains how care responsibilities and everyday life demands become constitutive of EX. The findings further show that the discourse of passion operates as a meaning-making and normalizing mechanism that legitimizes demanding working conditions while obscuring their cumulative psychosocial costs. Overall, the study contributes to the EX literature by offering a more integrated understanding of work, well-being, and everyday life in labour-intensive settings. Full article

Identifying insider threats in modern enterprise environments presents a unique cybersecurity challenge. Although malicious activity may often appear to be legitimate user activity, it is difficult to recognize the distinction. This study presents an innovative approach to insider threat detection by analyzing enterprise activity logs for session-level behavioural risk monitoring with behavioural biometrics. Behavioural patterns are modelled as temporal sequences across consecutive monitoring windows to capture both short-term behavioural intensity and long-term behavioural drift. The proposed system utilizes a hybrid deep learning architecture that includes a Long Short-Term Memory (LSTM) network and an autoencoder model to model temporal dependence of a user’s behaviour and to identify anomalies through reconstruction error analysis. The LSTM network captures user’s sequential activity and autoencoder determines variance from the user’s typical behavioural profile. The outputs of both models are aggregated using a unified behavioural risk scoring mechanism for session-level risk monitoring and ongoing insider threat assessment. The experimental results from Insider Threat Dataset for Corporate Environments demonstrate that proposed approach is effective in classifying normal versus malicious behaviours of users. The proposed framework achieves an accuracy of 97.65%, a precision of 96.35%, a recall of 99.05%, an F1-score of 97.68%, and a ROC-AUC of 99.20% on a near-balanced benchmark split. Under realistic class imbalance conditions, the framework achieves a PR-AUC of 0.842 and MCC of 0.781, representing the more operationally conservative performance estimate. These findings confirm that the proposed framework constitutes a viable solution for integrating behavioural modelling and anomaly detection within continuous enterprise authentication systems. Full article

Intrusion detection on in-vehicle networks requires high accuracy, which is reported by many papers so far, but also computational efficiency to make it suitable for real-world scenarios. The achievement of both requirements at the same time becomes harder to achieve, especially as the number of attacks diversifies. An approach to leverage computational costs is the use of sliding windows, i.e., batch processing, which extends the detection over multiple frames, but the use of multitask learning is also advantageous because a number of layers are shared between classes to extract common relevant features. While indeed the greatest computational gains are from the use of a sliding window, multitask learning has benefits too and is in fact necessary as multiple attack types can coexist in the same window. We explore the benefits of this approach on three existing attack datasets and we also build our own dataset that garners more attack complexity so that we can concretely measure the benefits of multitask learning both in terms of detection rate and computational savings. Our approach considers the feature-level similarity between attack types and legitimate frames, extracted from the mutual information between the two, and extends detection over windows of multiple frames, which justify multitask learning as frames belonging to different classes can co-exist in the same window. Full article

Mechanical locks have not been fully replaced by electrical locks and are still being researched and improved, along with advanced electronic methods of attack. Moreover, reading pin lengths by detecting their natural frequencies (lock decoding) to forge copies of a legitimate key can be done quickly using active or passive ultrasonic detectors. One possible method of defence against them is manufacturing lock pins using functionally graded materials (FGMs). A pin’s natural frequency (in the range 100 kHz–1 MHz) and hence its ultrasonic pulse transit/reflection time can be correlated to its length if it is made of a homogeneous material. The idea is to design pins made of functionally graded alloys to achieve equal natural frequencies, but also desired positions of standing wave nodes regardless of pin length. To calculate the composition of the FGM alloy, we must first develop mathematical models of a pin’s vibrations. Two simple and fast mathematical models are first derived from the finite-element model (FEM) of a pin. These models are used in an optimization procedure based on the Nelder–Mead simplex method to calculate optimal profiles of Young’s modulus and density along a pin’s longitudinal axis. A successful optimization procedure for 10 key pin lengths is performed to make a pin-tumbler lock resistant to ultrasonic attacks. Full article

Electronic voting requires the simultaneous admission of only legitimate participants, ballot uniqueness, vote confidentiality, storage integrity, and result verifiability. Blockchain alone does not solve these problems, since ledger immutability does not guarantee anonymity, ballot correctness, or reduced trust concentration. The purpose of this work is to develop a parameterizable research framework for electronic voting scenarios with enhanced cryptographic protection, allowing the security level to be varied according to the requirements of a voting scenario. The main contribution of the work is a parameterizable research architecture for composing and experimentally comparing electronic voting configurations with different security and computational profiles. The cryptographic and audit mechanisms integrated into this architecture include blind-signature-based anonymous authorization, encrypted ballot submission, blockchain-style audit, receipt verification, homomorphic tally publication, and threshold-supported tally artifacts. These mechanisms are not proposed as new cryptographic primitives; rather, they are integrated into a reproducible prototype to study how their combination affects verifiability, privacy support, auditability, and computational cost. Compared with basic blockchain-based voting prototypes, this architecture explicitly separates security, privacy, and verifiability profiles and makes their computational cost observable. The implemented prototype is used as an experimental platform for analyzing supported security properties, threat modeling, and computational cost estimation. The results show that authentication, anonymous token issuance, and receipt verification maintain an almost constant cost at the studied scale, while the main cryptographic burden is associated with encrypted ballot submission and threshold-supported tally publication. The scientific novelty of the work lies in constructing a parameterizable architecture that integrates several cryptographic mechanisms and a blockchain audit layer into one reproducible research prototype. At the same time, the proposed approach retains prototype-level limitations associated with the absence of a full zero-knowledge proof stack, independently deployed threshold authorities, and coercion-resistance mechanisms. Full article

Family firms are central to global economic stability and employment. Generational transitions, however, involve not only the transfer of leadership but also changes in organizational structures and culture. As digitalization becomes increasingly important for competitiveness, successors are introducing digital marketing practices that may influence organizational culture during leadership transitions. While previous research has examined digital transformation in family businesses, limited attention has been given to the role of digital marketing as a mechanism of cultural change during generational succession. This article addresses the question: How do second-generation successors use digital marketing practices to shape organizational culture during generational transitions in family firms? Drawing on practice theory and Schein’s model of organizational culture, the study explores how cultural change unfolds through everyday practices within organizations. The research employs a qualitative multiple-case study approach based on semi-structured interviews with representatives from 35 family firms in Latvia. The findings identify key digital marketing practices implemented by second-generation successors and illustrate how these practices influence organizational culture during the transition process. The results suggest that digital marketing can both reinforce existing organizational values and selectively reshape organizational identity and legitimacy. The study highlights digital marketing as a culturally legitimate tool through which successors can influence decision-making processes, coordination mechanisms, and authority structures during generational succession. Full article

This article revisits the debate on whether the Brussels Agreement and the Ohrid Agreement, including its Implementation Annex, are legally binding. It develops a three-test framework that separates international-law binding force from EU-law legal effects. Tests A and B adapt the International Court of Justice (ICJ) indicators of animus contrahendi and acceptance through subsequent conduct, acquiescence and silence. Test C examines whether the agreements produce legal effects through EU enlargement conditionality, monitoring and reporting. The analysis finds that the treaty status of both instruments remains contestable. The Brussels Agreement is textually specific, yet intent signals are mixed, practice remains reversible and treaty-type obligation structures are weak. The Ohrid Agreement is drafted in a more treaty-like register, but references to a future “legally binding agreement” and the politics of non-signature leave inter se binding force unsettled. Nonetheless, both agreements can produce EU legal effects. They operate as enlargement benchmarks that shape assessments of Serbia’s and Kosovo’s progress, while Commission reporting and standardised compliance indicators may indirectly bind EU institutions through consistency, equal treatment and legitimate expectations. Full article