Abstract
Malicious activity detection in honeypot environments remains challenging due to the volume and heterogeneity of captured data, as well as the sequential nature of attacker behavior. This study proposes an integrated framework combining a Cowrie-based honeypot with a locally deployed Large Language Model (LLM) to enable automated detection and near-real-time behavioral analysis. Attacker interactions are captured and processed through a structured preprocessing stage that reconstructs session-level activity. These representations are analyzed using an LLM, allowing contextual interpretation of authentication patterns, command execution sequences, and post-compromise behavior. Structured analytical outputs are generated, including severity classification, reasoning, and recommended actions. Evaluation was conducted using isolated and concurrent attack scenarios. Results indicate effective identification of brute-force attacks, reconnaissance activity, persistence staging, and download-and-execute patterns, achieving a 94.23% Accuracy (95% CI: 84.05–98.79%), 100.0% Precision, 87.50% Recall, and an F1-score of 93.33% across an expanded evaluation of 52 independent observations, with zero false positives (FPs). These figures are derived from a single evaluation run and are reported as preliminary, proof-of-concept estimates rather than as stable, production-grade performance. Analytical output remained stable for sessions governed by deterministic severity overrides. However, a low-intensity multi-stage session produced inconsistent severity classifications under concurrent conditions, indicating that analyst review is still required for borderline cases. Integration of deterministic preprocessing with LLM-based reasoning enables the transformation of raw honeypot logs into structured and actionable cybersecurity intelligence, reducing analyst workload while improving the explainability and reliability of intrusion analysis in near-real-time environments.

Abstract
Purpose-The primary objective of this study is to investigate the role of entrepreneurial team stability in the formation, consolidation, and evolution of collective entrepreneurial identity within the context of incubation programs. Specifically, the research aims to examine how long-term relationships, complementary skill sets, shared vision and adaptive capabilities contribute to team cohesion and resilience, and how these factors collectively influence the development of a collective entrepreneurial identity. Design/methodology/approach-It examines five entrepreneurial teams that completed the CEU iLab Incubation Program in Budapest, each associated with a distinct startup across diverse sectors and industries. Data collection was predominantly retrospective, focusing on reconstructing and interpreting the past experiences of team members that contributed to their current entrepreneurial trajectories. Findings-Findings underscore the importance of stability, cohesion and adaptability in shaping entrepreneurial team identity. Teams that cultivate long-term relationships, complementary skill sets and a shared vision demonstrate higher trust, psychological safety and mutual understanding, which facilitate collaboration, conflict resolution and effective decision-making. Open communication and adaptability to market changes further reinforce team cohesion and resilience. Originality/value-The results suggest that stable, skill-diverse teams are better positioned to navigate uncertainties while maintaining a strong, collective entrepreneurial identity. These insights offer practical implications for incubators and policymakers, emphasizing the need to support team continuity alongside venture growth metrics to foster sustainable startup development.

Abstract
Recent technological advancements have increased computer crime, requiring public authorities to implement structured mitigation strategies. While initiatives exist to improve digital literacy on device security, they must also address the complexities of computer crime. Using Design Science Research, this study investigated the applicability of chatbots to raise awareness of computer crime in a public administration setting. A systematic literature review highlighted the issue's relevance and identified knowledge gaps. A scoping review gathered concepts, methodologies, technologies, architectures, and tools for developing and evaluating an effective chatbot. The design and development phase included a detailed proposal for a sophisticated chatbot architecture. During the demonstration and evaluation phases, the utility of the chatbot was tested in the domain of conversational flow efficiency and usability. The study's primary results and contributions are to assess the chatbot's effectiveness in raising awareness of computer crime on public websites. Future work should focus on implementing the chatbot in the actual context of public administration, proposing a network of specialized conversational assistants, and improving public service interoperability to enhance computer crime awareness.

Abstract
Purpose-This study aims to examine how lessons learned from implementing a project management office (PMO) in a defence research, development and innovation (R&D&I) institution generate tangible benefits (efficiency, standardisation) and intangible gains (legitimacy, trust, organisational learning) in complex military environments characterised by strict hierarchies and technological uncertainty. Design/methodology/approach-An ethnographic approach was conducted over three years of participant observation at Brazil's Department of Aerospace Science and Technology. Data included field notes, documents and dialogical engagement. A logical-analytical framework was used to derive lessons from ethnographic descriptions, synthesising them into six analytical categories. Findings-The PMO functions as an institutional translator and cultural mediator, balancing regulatory discipline with adaptive responsiveness. Its effectiveness depends on adaptive governance, institutionalised knowledge management and cultural mediation rather than mere standardisation. PMOs integrate divergent rationalities, reconciling formal processes with situated practices to enable innovation governance under conditions of systemic interdependence and uncertainty. Research limitations/implications-The single-case study limits generalisability. However, the ethnographic depth provides insights for comparative research in defence and complex R&D&I contexts. The framework is applicable to similar organisational settings. Practical implications-PMO effectiveness requires adaptive governance that balances formalisation with flexibility, institutionalised knowledge management and cultural mediation that bridges military, scientific and technical rationalities.

Abstract
Digital influencers have become increasingly relevant in tourism communication, particularly in the dissemination of destination-related information and experiences through social media. This study examines the association between selected digital influencer attributes and respondents’ perceived influence of travel influencers. A quantitative survey of social media users who followed digital influencers was conducted, yielding 147 valid responses. The questionnaire measured perceptions of influencer-generated content, credibility, authenticity, influencer experience, interactivity, relationship-related characteristics, and other relevant attributes using seven-point Likert scales. An exploratory principal component analysis (PCA) with Oblimin rotation was conducted to identify the empirical structure of the observed dimensions, followed by binary logistic regression to examine their associations with respondents’ perceived influence by travel influencers. The PCA yielded eight empirical components accounting for 81.188% of the total variance. Among the dimensions included in the estimated regression model, content essence was the only predictor that reached conventional statistical significance (ß = 0.556, p = 0.035; Exp(B) = 1.743). The remaining predictors did not reach statistical significance at the 5% level. The findings provide exploratory evidence that content-related characteristics may have particular statistical relevance for respondents’ perceived influence by travel influencers when several influencer-related components are considered simultaneously. However, the results should be interpreted as statistical associations rather than causal effects. The study contributes to the tourism influencer marketing literature by highlighting the relevance of content-related characteristics while emphasizing the need for further research using larger samples, validated measurement models, and clearly specified behavioral outcomes. © 2026 by the authors.

Abstract
The transition from centralized to distributed control architectures in mobile robotics introduces the challenge of "Temporal Coherence"- the requirement that physically separated actuators execute commands simultaneously. This paper presents a validation of standard Ethernet Multicast to achieve Soft Real-Time synchronization suitable for dynamic locomotion. An "Inertial Filtering Condition"is proposed, positing that if network jitter (sc) is negligible relative to the robot's mechanical time constant (tmech), the system becomes functionally deterministic. Using a Dual-Core SMP architecture on RP2350 microcontrollers, this study demonstrates that standard Layer 2 Ethernet switches serve as effective hardware synchronizers. Experimental results show a packet-arrival reliability of 99.92% for a 1 kHz control loop and a network jitter standard deviation of s ˜65 µs. Furthermore, a Virtual Hardware-in-the-Loop (HIL) stress test confirmed that inter-leg actuation skew is reduced to ˜ 275 µs, representing a 32x improvement over the 9 ms latency bottleneck observed in previous USB-Serial architectures. Finally, it is concluded that standard Ethernet transport provides sufficient statistical determinism for coordinated gait stability without requiring complex hard real-time protocols.