Search Results (65)

The primary objective of this research is to identify, analyse, and compare the development of risk management approaches adopted by Slovak industrial enterprises in two distinct economic periods: during a phase of economic growth (2019) and during a period of global crises and regional crises with significant global implications, which have had substantial global economic, energy, and security impacts, as well as the increasing intensity of cyber threats affecting enterprises in Slovakia (2022–2023). Emphasis is placed on identifying key factors influencing the effectiveness of risk management implementation, as well as on assessing the use of individual stages of the risk management process in business practice. The research has a quantitative character and consists of two empirical surveys conducted through questionnaire-based data collection. The first survey was carried out in 2019 under conditions of economic growth, while the second was conducted in 2022–2023 in the context of multiple global crises and regional crises, particularly the impacts of the COVID-19 pandemic, the global energy crisis, the military conflict in Ukraine, and increasing cyber threats. The first study obtained 450 valid responses, and the second obtained 390 responses from enterprises operating across various sectors of the private economy in Slovakia. The results of the study confirmed the existence of significant differences in companies’ approaches to risk management depending on the economic context. During the period of economic growth, the main reason for insufficient attention to risks was low staff motivation, with enterprises focusing primarily on risk identification, analysis, and assessment, and less on designing specific mitigation measures. In contrast, during the period of global crises and regional crises, companies’ attitudes shifted, with stronger resistance to implemented measures but, at the same time, increased attention to the development of risk-reduction actions. Neglecting systematic preventive steps increases companies’ vulnerability to crises, which may result in operational, financial, and reputational losses, delayed responses, and a decline in competitiveness. The two-phase nature of the research made it possible to capture the dynamics of managerial behaviour under different economic conditions and to formulate practical recommendations for integrating risk management into both strategic and operational levels of management. Full article

Supply chain disruptions during the COVID-19 pandemic exposed structural vulnerabilities of centrally controlled manufacturing systems, motivating renewed interest in organizational resilience within the context of Industry 5.0 human–cyber–physical systems. This study investigates how organizational decision-making paradigms and crisis-aware artificial intelligence (AI) jointly influence performance, crisis response, and recovery. An agent-based modeling (ABM) framework is developed to compare centralized, distributed, and self-organized organizational structures across 650 simulation runs under a controlled supply side disruption. A crisis-aware Q-learning architecture enables AI agents to shift from efficiency-oriented to stability-oriented strategies when resource scarcity is detected. To avoid baseline-dependent bias, resilience is evaluated using an absolute, capacity-normalized metric. Results indicate that self-organized systems consistently outperform centralized and distributed structures in baseline performance, crisis throughput, and recovery speed. The integration of crisis-aware AI further increases absolute resilience by approximately 10.7% and enables substantially higher throughput during disruption compared to hierarchical control. Enhanced performance is primarily driven by adaptive coalition formation, proactive resource conservation, and rapid post-crisis recovery supported by preserved coordination structures. These findings provide quantitative support for Industry 5.0’s human-centric principles and show that decentralized decision-making augmented by context-adaptive AI offers a robust organizational design strategy for volatile manufacturing environments. Full article

In response to the challenges of urbanization, digitalization, and the e-commerce surge intensified by the COVID-19 pandemic, Smart Urban Logistics (SUL) has become a key framework for addressing last-mile delivery issues, congestion, and environmental impacts. This study introduces a System Dynamics (SD)-based approach to investigate how urban logistics and access management policies may interact. At the center, there is a Causal Loop Diagram (CLD) that illustrates dynamic interdependencies among fleet composition, access regulations, logistics productivity, and environmental externalities. The CLD is a conceptual basis for future stock-and-flow simulations to support data-driven decision-making. The approach highlights the importance of route optimization, dynamic access control, and smart parking management systems as strategic tools, increasingly enabled by Industry 4.0 technologies, such as IoT, big data analytics, AI, and cyber-physical systems, which support real-time monitoring and adaptive planning. In alignment with the Industry 5.0 paradigm, this technological integration is paired with social and environmental sustainability goals. The study also emphasizes public–private collaboration in designing access policies and promoting alternative fuel vehicle adoption, supported by specific incentives. These coordinated efforts contribute to achieving the objectives of the 2030 Agenda, fostering a cleaner, more efficient, and inclusive urban logistics ecosystem. Full article

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge and cloud computing frameworks, and a suite of DL tools. The framework uses a composite attention-driven framework incorporating various DL pre-trained models (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies. Full article

In recent decades, sustainable tourism has emerged as a central paradigm, attracting growing scholarly interest. External factors, such as the SDGs, climate change agendas, smart and digitalized tourism, cyber and astronaut travel, pandemics, and shifting trends in economic competitiveness, mass tourism, and overtourism, are shaping the 21st-century paradigmatic landscape, challenging both the theoretical “what” and practical “how” of the sustainable tourism paradigm. Using Kuhn’s paradigmatic framework and the Web of Science bibliometric database from 1991 to 2022, this analysis traces trends in sustainable tourism research, advances in academic communication through influential co-citation networks and interdisciplinarity, and the emergence of alternative and quasi-paradigms. The findings suggest, first, a positive trend in tourism scholarly research production; second, weak and diverse communication and interdisciplinarity, as scholars do not sufficiently collaborate in co-citations; and third, the coexistence of the sustainable tourism paradigm with numerous alternative, rival, and quasi-paradigms. The lack of influential knowledge communication highlights the need for the academic tourism community to reconsider its knowledge generation practices. Enhanced collaboration through co-citation and interdisciplinary cooperation is crucial for fostering a deeper and shared understanding of multiple tourism-related concepts. Further thematic and interactive research is needed on the resilience and adaptability of the sustainable tourism paradigm. This article contributes to advancing sustainable tourism scholarship by advocating for a more influential and adaptable paradigm to ensure its relevance amidst emerging challenges. Full article

The global rise in cybercrime is fueled by the pervasive digitization of work and personal life, compounded by the shift to online formats during the COVID-19 pandemic. As digital channels flourish, so too do the opportunities for cyberattacks, particularly those exposing small and medium-sized enterprises (SMEs) to potential economic devastation. These businesses often lack comprehensive defense strategies and/or the necessary resources to implement effective cybersecurity measures. The authors have addressed this issue by developing an Educational Escape Room (EER) that supports scenario-based learning to enhance cybersecurity awareness among SME employees, enabling them to handle cyber threats more effectively. By integrating hands-on scenarios based on real-life examples, the authors aimed to improve the knowledge retention and the operational performance of SME staff in terms of cybersafe practices. The results achieved during pilot testing with more than 200 participants suggest that the EER approach engaged the trainees and boosted their cybersecurity awareness, marking a step forward in cybersecurity education. Full article

In the wake of the COVID-19 pandemic, there has been a significant digital transformation. The widespread use of wireless communication in IoT has posed security challenges due to its vulnerability to cybercrime. The Indonesian National Police’s Directorate of Cyber Crime is expected to play a preventive role in supervising these attacks, despite lacking a specific cyber-attack prevention function. An Intrusion Detection System (IDS), employing artificial intelligence, can differentiate between cyber-attacks and non-attacks. This study focuses on developing a machine learning-based policing model to detect cyber-attacks on Wi-Fi networks. The model analyzes network data, enabling quick identification of attack indications in the command room. The research involves simulations and analyses of various feature selection methods and classification models using a public dataset of cyber-attacks on Wi-Fi networks. The study identifies mutual information with 20 features such as the optimal feature reduction method and the Neural Network as the best classification method, achieving a 94% F1-Score within 95 s. These results demonstrate the proposed IDS’s ability to swiftly detect attacks, aligning with previous research findings. Full article

Large, powerful corporations were formerly solely and exclusively responsible for supplies, manufacturing, and distribution; however, the supply chain has undergone significant transformations over the last half-century. Almost all supply chain processes are currently outsourced, owing to the initiatives of cutting-edge, contemporary businesses. According to a compilation of studies, analysts, and news sources, the level of risk associated with modern supply chains is considerably higher than the majority of supply chain managers believe. Supply chain vulnerabilities continue to pose a substantial obstacle for a great number of organizations. Neglecting to adequately address these risks—encompassing natural disasters, cyber assaults, acts of terrorism, the credit crisis, pandemic scenarios, and war—could result in substantial reductions in metrics such as profitability, productivity, revenue, and competitive advantage. Unresolved concerns persist with respect to the risk assessment of the supply chain. The purpose of this article is to propose a framework for risk evaluation that can be efficiently applied to the evaluation of hazards within the supply chain. This research study significantly enhances the existing knowledge base by offering supply chain managers a pragmatic tool to evaluate their processes, regardless of the mathematical foundations or the variety of variables utilized in risk assessment. The outcomes of multiple aggregation methods are compared using a case study from an automotive EMS production; the conclusions are validated by risk and FMEA specialists from the same factory. Full article

Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS). The fact that SDN has centralized management and programmability features causes attackers to carry out attacks that specifically target the SDN controller and data plane. If DDoS attacks against the SDN-based SCADA network are not detected and precautions are not taken, they can cause chaos and have terrible consequences. By detecting a possible DDoS attack at an early stage, security measures that can reduce the impact of the attack can be taken immediately, and the likelihood of being a direct victim of the attack decreases. This study proposes a multi-stage learning model using a 1-dimensional convolutional neural network (1D-CNN) and decision tree-based classification to detect DDoS attacks in SDN-based SCADA systems effectively. A new dataset containing various attack scenarios on a specific experimental network topology was created to be used in the training and testing phases of this model. According to the experimental results of this study, the proposed model achieved a 97.8% accuracy rate in DDoS-attack detection. The proposed multi-stage learning model shows that high-performance results can be achieved in detecting DDoS attacks against SDN-based SCADA systems. Full article

The COVID-19 pandemic made all organizations and enterprises work on cloud platforms from home, which greatly facilitates cyberattacks. Employees who work remotely and use cloud-based platforms are chosen as targets for cyberattacks. For that reason, cyber security is a more concerning issue and is now incorporated into almost every smart gadget and has become a prerequisite in every software product and service. There are various mitigations for external cyber security attacks, but hardly any for insider security threats, as they are difficult to detect and mitigate. Thus, insider cyber security threat detection has become a serious concern in recent years. Hence, this paper proposes an unsupervised deep learning approach that employs an artificial neural network (ANN)-based autoencoder to detect anomalies in an insider cyber security attack scenario. The proposed approach analyzes the behavior of the patterns of users and machines for anomalies and sends an alert based on a set security threshold. The threshold value set for security detection is calculated based on reconstruction errors that are obtained through testing the normal data. When the proposed model reconstructs the user behavior without generating sufficient reconstruction errors, i.e., no more than the threshold, the user is flagged as normal; otherwise, it is flagged as a security intruder. The proposed approach performed well, with an accuracy of 94.3% for security threat detection, a false positive rate of 11.1%, and a precision of 89.1%. From the obtained experimental results, it was found that the proposed method for insider security threat detection outperforms the existing methods in terms of performance reliability, due to implementation of ANN-based autoencoder which uses a larger number of features in the process of security threat detection. Full article

The primary aim of this research is to thoroughly examine the determinants that influence customers’ intention towards embracing FinTech products and services, thereby enriching our understanding of the adoption and utilization trends within the FinTech industry in the aftermath of the COVID-19 pandemic. This is quantitative research in the context of India covering five major tech-savvy cities—Mumbai, Bengaluru, New Delhi, Pune, and Chennai. Using structural equation modeling (SEM), the mediation effect of Perceived Trust was examined in order to see the relationship between the retrieved constructs and their attributes. Predominantly, the data delve into the utilization of financial technology and the prevailing embrace of this transformative innovation by contemporary Indian society. From the findings, it has emerged that the three factors influencing Customer Intention to Adopt FinTech products are “Perceived Security”, “Perceived Risks”, and “Perceived Trust”. The significance of Perceived Security in the realm of defending against cyber risks and safeguarding personal information has been discovered to have a profound effect on individuals’ inclination to embrace FinTech. Likewise, acknowledging the potential risks and uncertainties that come with FinTech has proven to have a favorable impact on the intention to adopt. Notably, the perception of trust, which encompasses aspects such as the credibility of the company and the user-friendly nature of the technology, has been identified as a significant driver towards adoption. Full article

The COVID-19 pandemic significantly increased e-commerce growth, adding more than 218 billion US dollars to the United States e-commerce sales. With this significant growth, various operational challenges have appeared, including logistic difficulties and customer satisfaction. Businesses that strive to take advantage of increased e-commerce growth must understand data and rely on e-commerce analytics. The large scale of e-commerce data requires sophisticated information technology techniques and cyber-infrastructure to leverage and analyze. This study presents a big e-commerce data platform to address several challenges in e-commerce. The presented platform’s design is based on a distributed system architecture that supports e-commerce analytics applications using historical and real-time data and features a continuous feedback loop to observe the decision-making and evaluation processes to achieve the desired objectives. The platform was validated using two analytical applications. The first application was to identify the periods in which customers prefer to place orders, while the second was used to verify the big e-commerce data platform. The resulting insights and findings promote informed e-commerce decisions. Furthermore, viewing and acting on insight results and findings promote informed decisions that potentially benefit the e-commerce industry. The proposed platform can perform numerous e-commerce applications that potentially benefit the e-commerce industry. Full article

Nowadays, young students spend a lot of time playing video games and browsing on the Internet. Using the Internet has become even more widespread for young students due to the COVID-19 pandemic lockdown, which resulted in transferring several educational activities online. The Internet and generally the digital world that we live in offers many possibilities in our everyday lives, but it also entails dangers such as cyber threats and unethical use of personal data. It is widely accepted that everyone, especially young students, should be educated on safe Internet use and should be supported on acquiring other Digital Intelligence (DI) competencies as well. Towards this goal, we present the design and evaluation of the game “Follow the Paws” that aims to educate primary school students on safe Internet use and support them in acquiring relevant DI competencies. The game was designed taking into account relevant literature and was evaluated by 213 prospective Information and Communication Technology (ICT) teachers. The participants playtested the game and evaluated it through an online questionnaire that was based on validated instruments proposed in the literature. The participants evaluated positively to the didactic utility of the game and the anticipated player experience, while they highlighted several improvements to be taken into consideration in a future revision of the game. Based on the results, proposals for further research are presented, including DI competencies detection through the game and evaluating its actual effectiveness in the classroom. Full article

The onset of the COVID-19 pandemic prompted educational institutions to swiftly integrate e-learning software systems, including learning management systems (LMSs), as essential tools for online education. This study aims to probe the inherent security vulnerabilities of three widely utilized e-learning platforms, namely, Moodle, Chamilo, and Ilias, spanning the pre-pandemic, pandemic, and post-pandemic periods. The rapid adoption of these platforms during the pandemic revolutionized online education but also unveiled security risks. This paper delves into these security vulnerabilities, offering insights before, during, and after the pandemic. Through an analysis of existing patches and security measures, areas for improvement are identified. Furthermore, the paper considers emerging cybersecurity technologies and trends, providing comprehensive recommendations to enhance system resilience against evolving cyber threats. The results obtained here can provide educational institutions with a guide for action to enable effective mitigation of e-learning software security vulnerabilities and ensure the continued security and sustainability of online education systems. Full article

Cyberattacks capitalize on human behaviors. The prevalence of cyberattacks surged during the COVID-19 pandemic, fueled by the increased interconnectivity of individuals on online platforms and shifts in their psychological dynamics due to the pandemic’s context. The enhancement of human factors becomes imperative in formulating a robust cybersecurity strategy against social engineering in the post-COVID-19 era and in anticipation of analogous pandemics. This study aims to propose a model for delineating strategies across various phases of cyberattacks, grounded in the cyber kill chain model, while also encompassing cognitive mechanisms for adaptive responses. This approach aims to cultivate defensive cognitive factors like resilience and self-efficacy. To achieve this objective, we conducted an exploratory study adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Subsequently, we pursued a descriptive and correlational study based on prevalent attacks during the pandemic. The intention was to pinpoint proactive factors conducive to the development of cognitive capabilities to counter cyberattacks. These insights could pave the way for the creation of training programs and technological solutions aimed at mitigating the impact of such cyberattacks. Full article