Advanced Research in Technology and Information Systems

1. Introduction

The fields of information systems and technology continue to evolve at a rapid pace, offering new research directions and expanding career opportunities. The digital transformation of organizations from the business and public sectors has accelerated the integration of emerging technologies, leading to significant advancements in automation, data-driven decision-making, intelligent systems, and innovative service delivery models. The interconnected nature of modern technology emphasizes the role of information systems as the central element that bridges technical advancements with practical applications, fostering collaboration between academia, industry, and policymakers.

As the nexus of technology, processes, and people, information systems drive innovation across multiple domains, including artificial intelligence, machine learning, Internet of Things, cybersecurity, smart cities, and electronic commerce. These advancements enable the creation of intelligent and adaptive solutions that optimize efficiency, security, and sustainability across various sectors. From enhancing cybersecurity frameworks to developing predictive analytics in electronic commerce, information systems play a critical role in shaping the digital future of companies and public administration.

This Special Issue, titled “Advanced Research in Technology and Information Systems”, brings together original research, reviews, and systematic review articles that contribute to the discourse on these evolving topics. The 13 papers included in this collection provide critical insights into applications from business and government, as well as the challenges and opportunities in integrating technology into academic curricula. These studies explore cutting-edge solutions that address persistent technological challenges while emphasizing interdisciplinary approaches that merge theoretical foundations with practical implementations across a wide array of application domains, including higher education and sustainable information technology practices, cybersecurity governance, privacy-aware electronic signatures, and Internet of Things security, electronic commerce and tourism, aviation industry procurement, autonomous systems and driver safety, telecommunications, public transit optimization within smart cities, and risk assessment in healthcare planning.

The published articles align with the majority of the key themes outlined for this Special Issue, including, but not limited to, the following:

• Information systems: analyses, designs, and developments;
• Artificial intelligence applications;
• Natural language processing;
• Human–computer interaction;
• Electronic commerce;
• Machine learning techniques for intelligent software development;
• Artificial neural network applications;
• Big data applications;
• Intelligent electronic solutions for future applications;
• IoT implementations;
• Smart government, smart education, smart cities, smart electronics, and smart offices;
• Advanced features of power systems.

2. Review of Contributions

The manuscripts included in this Special Issue span a diverse range of critical subdomains within technology and information systems. They approach both theoretical and practical aspects, tackling information systems design and implementation, as well as their impact on a few domains, primarily focusing on educational integration and sustainability concerns. Furthermore, the articles also explore the transformative role of artificial intelligence, machine learning, and big data across various sectors, from enhancing autonomous systems to improving customer experiences. Crucial aspects of cybersecurity, privacy, and digital governance are addressed, alongside innovations in smart cities, the Internet of Things, and other intelligent systems designed to improve efficiency and safety in modern environments.

To provide a clear overview of these contributions, the papers have been grouped into four thematic subsections. Consequently, Section 2.1 (Information Systems in Education and Sustainability) focuses on the application and analysis of information systems within academia and the growing theme of green IT. Section 2.2 (Artificial Intelligence, Machine Learning, and Big Data Applications) covers diverse applications of algorithms and data analysis techniques specific to artificial intelligence applied in areas such as higher education, autonomous vehicles, telecommunications, and e-commerce while Section 2.3 (Cybersecurity, Privacy, and Digital Governance) addresses security challenges, privacy-enhancing technologies, and governance frameworks in the digital realm. Finally, Section 2.4 (Smart Cities, Internet of Things, and Intelligent Systems) highlights the research on optimizing urban systems, leveraging the Internet of Things, and developing intelligent solutions for risk assessment and human–computer interaction for vehicles.

2.1. Information Systems in Education and Sustainability

Several articles in this Special Issue focus on the design, development, and analysis of information systems. Banța et al. [1] explore the integration of SAP into higher education, analyzing its effectiveness in improving data processing learning through a technological, organizational, and environmental (TOE) framework. Their study introduces an additional dimension, the learning context (L), and reveals how reconfiguring SAP course content based on the TOE-L framework enhances digital competencies among students, ultimately improving their career readiness. The study’s conclusions underscore that enhancing SAP course effectiveness requires an integrated development of technological, organizational, and environmental competencies alongside a robust learning context. The research demonstrates that students’ perceptions strongly correlate key learning elements—such as the impact of SAP on business processes, best practices in its implementation, and the alignment of SAP functionalities with sustainability requirements—with improved learning outcomes, while isolated knowledge in any single dimension does not show the same effect. This original evaluation using the TOE_L framework not only offers critical insights for curriculum improvement and strategic decision-making in digital education but also highlights limitations such as a non-representative sample, a modest response rate, and a sole focus on student perceptions, suggesting the need for broader studies that include faculty and employer feedback.

Altundag and Wynn [2] present a case study on advanced analytics and data management in procurement, emphasizing the role of digital maturity in strategic decision-making within the aviation industry. Their research examines how a multinational aerospace corporation leverages digital tools to enhance procurement efficiency while identifying the regulatory and organizational challenges that limit the full exploitation of procurement analytics. The article provides a novel digital maturity model for the deployment of strategic procurement analytics (SPA) in the aviation industry—a sector that faces significant challenges in meeting the requirements to become both green and innovative. The model highlights the importance of data as a strategic asset and offers potential for benchmarking with suppliers. However, the study’s single-case design and small sample size (15 interviewees) limit generalizability, suggesting that further research is needed to validate the model across different industries and explore the relationship between digital maturity, business performance, and organizational culture.

Radu and Popescul [3] provide a bibliometric analysis of green information systems, including a time span analysis between 2000 and 2023, highlighting the increasing focus on sustainable IT practices across industries. Their study maps the evolution of green information systems research, identifying key trends, interdisciplinary hotspots, and emerging areas, such as CO₂ reduction, energy efficiency, and digital transformation in smart cities. The analysis underscores the role of green information systems in supporting environmental sustainability while offering practical insights for businesses, policymakers, and technology developers. The findings reveal strong research collaboration and a growing body of impactful publications, reflecting a sustained interest in eco-friendly technological advancements. However, limitations include dataset constraints and potential inconsistencies in author name variations. Future research should expand bibliometric studies using multiple databases and explore deeper connections between green information systems and policy implementation for environmental sustainability.

2.2. Artificial Intelligence, Machine Learning, and Big Data

The impact of artificial intelligence in higher education is reviewed by Alshahrani et al. [4], who discuss the societal and pedagogical implications of this technology in education. Their semi-systematic literature review evaluates artificial intelligence’s influence on pedagogical strategies, governance, and institutional readiness. Authors highlight that while artificial intelligence can significantly enhance pedagogical strategies, governance, and institutional readiness, current studies lack a holistic view—particularly regarding artificial intelligence’s impact on academic research—and clear ethical frameworks. The review calls for more comprehensive research that includes all stakeholders and develops standardized ethical guidelines to ensure the responsible integration of artificial intelligence in academia.

Anthony et al. [5] introduce a novel intrusion detection system for autonomous vehicles using non-tree-based machine learning algorithms. Their study demonstrates how K-nearest neighbors and ensemble learning techniques enhance cybersecurity and reliability in autonomous driving. Extensive evaluation on real-world datasets (CICIDS2017, CAN-BUS, and NSL-KDD) confirms that K-nearest neighbors achieves superior accuracy, F1-score, precision, recall, and sensitivity, outperforming traditional algorithms like naïve Bayes and logistic regression. While ensemble methods, such as stacking, also perform well, K-nearest neighbors consistently delivers the highest detection rates across datasets. The study emphasizes the importance of selecting robust algorithms to handle imbalanced data and prevent cyberattacks in autonomous vehicles. Future research should explore deep learning techniques and real-time adaptability to evolving cyber threats.

Zdziebko et al. [6] tackle the issue of customer retention in the telecom industry by proposing a fuzzy-based churn modeling approach. Their study demonstrates that by selecting key indicators such as invoice variations and call history, the model effectively predicts customer churn. The Mamdani model, designed for interpretability, generates concise rules with high accuracy (0.983 on the training set), while the Sugeno model achieves near-perfect prediction capabilities (0.98 accuracy with 10-fold cross-validation). Despite lower precision for churn cases (0.803) and sensitivity (0.629), the approach offers a robust and interpretable method for identifying potential churners. As the authors declare, future research will explore hybrid models incorporating neural fuzzy networks and categorical demographic data to enhance predictive accuracy and applicability across more diverse customer groups.

Zhou et al. [7] contribute to the domain of electronic commerce with their study on hotel accommodation recommendation systems. Their approach combines spatial accessibility and travel route costs to optimize recommendations, significantly reducing travel expenses and improving customer satisfaction. Through experiments conducted in the tourism city of Zhengzhou, their algorithm demonstrated a 29.23% reduction in travel costs compared to the least optimal options and outperformed traditional recommendation methods (UCFR and ICFR) in accuracy and recall rates. The study also highlights how clustering tourist attractions enhances the efficiency of hotel recommendations by grouping destinations with similar features. Future research endeavors aim to expand adaptability by incorporating cultural contexts, seasonal travel variations, and traffic congestion factors to refine recommendations across diverse global destinations.

2.3. Cybersecurity, Privacy, and Digital Governance

Metin et al. [8] review the intersection of digitalization and cybersecurity, proposing an operational framework that categorizes cybersecurity governance processes. Their study emphasizes the inadequacy of existing information security standards for small- and medium-size enterprises (SMEs), emphasizing the need for scalable and adaptable cybersecurity solutions. They introduce a plan–do–check–act (PDCA) governance model, advocating a bottom-up approach tailored to SMEs’ resource constraints. The framework provides structured guidelines for implementing, assessing, and improving cybersecurity measures, integrating best practices from international standards such as ISO/IEC and NIST frameworks. Their findings prove the importance of employee training, organizational accountability, and continuous monitoring as critical success factors. However, the framework remains theoretical and requires empirical validation in real-world business environments. Future research would focus on practitioner feedback, field testing in SMEs, and the development of cybersecurity guidelines specifically tailored for emerging technologies, like the Internet of Things and artificial intelligence.

Aciobăniței et al. [9] discuss privacy-aware remote qualified electronic signatures, introducing a comprehensive framework that integrates Ethereum smart contracts to ensure document integrity and privacy in digital transactions. Their approach ensures interoperability with existing electronic signature solutions while balancing security, legal compliance, and user accessibility. The proposed system enables secure remote document signing, validation, and long-term preservation with minimal integration effort, streamlining digital transactions and reducing reliance on physical documentation. The study highlights the flexibility of the on-premises module, making it adaptable to diverse user requirements. Future research will focus on formal security validation using tools like AVISPA or AVANTSSAR, identifying potential vulnerabilities, and enhancing system resilience against cyber threats.

Tian and Vassilakis [10] address Internet of Things security by examining vulnerabilities in the message queuing telemetry transport (MQTT) protocol and proposing an improved authentication and privacy preservation scheme. Their approach leverages a lightweight improved ciphertext-policy attribute-based encryption (ICP-ABE) mechanism, which enhances security while reducing computational complexity. By incorporating attribute separation and blind keys, the scheme optimizes encryption efficiency, ensuring minimal communication overhead without compromising data integrity. Comparative analysis with existing schemes (PRESENT, KSA-PRESENT, RSA-ECC, and SMQTT) demonstrates that ICP-ABE significantly improves MQTT security in both standard and attack scenarios. Future research paths will focus on refining user revocation mechanisms, tracking malicious users with similar attribute sets, and extending the solution to other Internet of Things communication protocols, including applications in healthcare Internet of Things platforms.

2.4. Smart Cities, Internet of Things, and Intelligent Systems

Kourepinis et al. [11] present an improved particle swarm optimization (PSO) algorithm to enhance urban transit routing efficiency, contributing to smart city solutions by optimizing public transport networks. Their study introduces a refined initialization method that generates high-quality initial solutions, improves objective function selection through extensive parameter testing, and significantly reduces computational costs by 90% using dynamic programming. Comparative analysis with existing metaheuristics demonstrates that the proposed PSO variant achieves superior direct coverage and lower average trip times across all tested scenarios. Future research will explore hybrid metaheuristic designs, parallel PSO implementations to further reduce computational overhead, and reinforcement learning techniques to optimize routing decisions dynamically. Additionally, multi-objective approaches incorporating environmental impact, social factors, and transfer efficiency will be investigated to enhance sustainability and passenger experience in urban transit systems.

Chang et al. [12] introduce a flexible risk priority number (RPN) method to enhance decision-making in uncertain environments. Their approach integrates interval-valued two-tuple weighted averaging techniques, improving risk assessment accuracy by incorporating both subjective and objective weights. The study demonstrates the method’s effectiveness in proton beam radiation therapy planning, where traditional RPN models fail to handle incomplete or hesitant information. Compared to standard RPN techniques, the flexible RPN method produces a more refined ranking of failure modes, ensuring more precise risk management. Additionally, the method is adaptable to different risk factors beyond severity, occurrence, and detectability, making it applicable to diverse domains. Future research aims to expand the model’s capabilities by integrating more complex fuzzy cognitive information types, such as intuitionistic and Pythagorean fuzzy sets, and exploring applications in talent selection, resource allocation, and supplier evaluation.

Krstačić et al. [13] conduct a systematic review of in-vehicle infotainment systems (IVISs) and their safety implications, emphasizing the need to balance usability with driver safety. The findings underscore that head-down displays and touchscreens, while innovative, contribute to driver distraction by requiring visual attention shifts, whereas speech-based interfaces and Bluetooth integration reduce manual interactions, improving safety. However, the study notes a significant gap in real-world testing, as most evaluations are conducted in simulated environments, limiting their applicability to actual driving conditions. The review calls for improved interface designs, including augmented reality head-up displays that project essential information within the driver’s field of vision, reducing cognitive overload. Additionally, the lack of standardized manufacturing protocols results in inconsistencies across different IVIS implementations, underscoring the need for regulatory reforms. The authors also stress the importance of driver education programs to ensure responsible IVIS usage. Future research should focus on extensive real-world testing, enhanced interface safety features, and the establishment of comprehensive industry standards to mitigate technology-induced driving distractions.

3. Conclusions

The 13 contributions in this Special Issue demonstrate the transformative potential of technology and information systems across various sectors. From cybersecurity to artificial intelligence applications, from smart cities to electronic governance, these studies collectively advance our understanding of how technology can be leveraged for business, education, government, cybersecurity, transportation, and public service improvements. As research in this field progresses, it is essential to continue exploring new methodologies and innovations that will shape the future of digital transformation.

This Special Issue provides a valuable collection of knowledge for researchers, practitioners, students, and policymakers interested in the latest developments in technology and information systems. We strongly believe that these contributions will inspire further research and foster interdisciplinary collaboration in this ever-evolving domain.