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Background: Blockchain is increasingly recognized as a transformative enabler of e-governance, offering capabilities to enhance transparency, efficiency, security, and citizen trust in public-sector administration. However, widespread adoption remains constrained by persistent technical, regulatory, and socio-institutional challenges, particularly the absence of coherent governance frameworks across jurisdictions.Objective: This study aims to systematically map the evolution of blockchain applications in e-governance, identify dominant research themes and collaboration patterns, and highlight governance-related challenges that inform future policy and research agenda.Methods: A systematic analysis was performed on 196 publications indexed in Scopus, covering the period 2019–2025, selected to capture the rapid growth of blockchain-related e-governance research. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol guided the screening process, while a bibliometric analysis using Biblioshiny examined publication trends, country contributions, collaboration networks, and thematic clusters.Results: The analysis shows that research output peaked in 2022 and subsequently shifted toward more domain-specific and applied applications, including e-voting, land administration, taxation, intellectual property, and academic credentialing. China, India, and the United States emerged as the most active contributors, though with markedly different collaboration patterns and governance approaches. Key challenges identified across the literature include interoperability limitations, scalability and cybersecurity risks, fragmented regulatory environments, and uneven institutional capacity. These findings highlight the critical role of governance frameworks in enabling sustainable and interoperable blockchain adoption in public-sector contexts.Conclusion: Blockchain represents both a technological and institutional innovation with significant potential to reshape e-governance. Its sustainable adoption requires interdisciplinary collaboration, coherent governance frameworks, and alignment between technological capabilities and regulatory-institutional readiness. Ultimately, blockchain’s transformative value lies in bridging innovation with practical governance to enhance transparency, accountability, and citizen confidence in public services.

Blockchain is transforming the banking sector and offering opportunities for significant cost reduction and efficient banking services. However, implementing blockchain is a challenge due to lack of adequate knowledge and skills on how to implement the technology. As a result, there are very few market-ready blockchain banking products and organisations are unable to realise the promised value. This paper presents an overview of the banking sector’s blockchain use cases, design and implementation considerations and techniques. The aim is to offer an evidence-based primer to guide researchers and practitioners. The study relies on the systematic literature review method and reviews a total of 45 papers comprising 26 peer-reviewed scholarly articles and 19 technical reports from the banking industry. Leximancer software is used to support the thematic data analysis. The results show for the banking sector an increase in experimentation efforts geared towards the development of payment systems. The results also indicate key considerations from a technological, organisational and environmental perspective. The study highlights that platform selection, scalability and resilience are some of the critical technical considerations for implementing blockchain banking systems. Organisational considerations include collaboration and governance-related challenges. From an environmental perspective, the study notes several legal and regulatory considerations. This study contributes to the existing literature on blockchain adoption in banking, which is still in the nascent stage. The study also offers a research agenda for further understanding of blockchain implementation in the banking sector. Opportunities for further research are noted in the areas of interoperability, governance, security and privacy.

Background: Currently, there is a megatrend towards digitalisation and servitisation using digital technologies and digital twins to support the digital transformation of the economy. In the literature, new digital technologies are seen as creating added value, strengthening customer relationships and accelerating the process of servitisation from manufacturing. The implementation of such a complex of technologies and business solutions can lead to the adaptation of the product and service life cycle, as well as the entire business model, to full servitisation.Objective: This study reveals the role of digital twins in the context of entrepreneurship in compliance with the Sustainable Development Goals (SDGs). By constructing a thematic map of scientific clusters and SDGs, the relationship between science and practical aspects is established.Methods: Research into digital twins has led to the use of research methods such as scientific abstraction and synthesis, historical, grouping, analogy, structural-logical modelling, tabular and logical generalisation methods, as well as the bibliometric analysis method based on VOSviewer software.Results: The study analyses the evolution of the latest technology, which demonstrates the relevance of digital twins as one of the key technologies for digitalisation in many business processes. Special attention is paid to the role of digital twins in the implementation of the SDGs. The results of the bibliometric review indicate scientific interest in researching digital twins in the fields of modelling, information technology, operational management, automation and robotics. The thematic map combining scientific clusters and SDGs highlights the importance of digital twins in entrepreneurship and ensuring sustainable industrial development.Conclusion: This study provides valuable information for managers as it proves the need to implement digital twins, which enable intelligent manufacturing, serve as the main technology supporting Industry 4.0, can reflect physical information in cyberspace and manipulate physical objects by studying and researching information models in manufacturing. Therefore, future research should focus on developing reliable mechanisms for applying digital twins in the context of the SDGs in areas such as the economy, social aspects and the biosphere. This will ensure the competitiveness of the industrial sector and the country.

Background: Prediction of financial distress has been made more accurate and reliable through machine learning methods. Financial stress affects the business corporate entity, society and the general economy. Analysing such nonlinear events is essential for preventing the dangers and supporting a favourable economic climate. Objective: This paper seeks to develop a robust predictive model for identifying firms in the Indian context other than the financial service sector that may face financial distress and also to check the impact of one essential predictor, i.e., future cash flow, on financial distress prediction. Besides, the study also aims at making research that can inform public policy and provide recommendations. Methods: The study employs financial information from the Prowess Database but is confined to non-financial service sector firms in India. Logistic regression, linear discriminant analysis (LDA), and artificial neural networks (ANNs) are applied to predict financial distress and their ability to foretell future cash flows. Other methods adopted in evaluating the models include accuracy, sensitivity, and specificity. Results: ANNs outperform the other models based on accuracy and predictability, which are higher than the rates given by the other two models, namely logistic regression and LDA. The ANN model performs well in identifying financially distressed firms; thus, it is informative in evaluating their financial position. Also, results suggest that future cash flow substantially affects financial distress prediction, an essential new variable that needs to be considered in future research. Conclusion: This predictive model of financial distress further gives a sound platform for the corresponding sector in India. In general, ANNs offer profound opportunities for managers, investors, policymakers, regulators and shareholders as an effective tool for preventive decision-making to reinforce the corporate world. This research demonstrates that high-level machine-learning approaches are still crucial in financial analysis and policymaking.

Background: Algorithmic trading systems (ATS) are defined by the use of computational algorithms for automating financial transactions. They have become a critical part of modern financial markets because of their efficiency and ability to carry out complex strategies.Objective: This research involves a systematic review that assesses the market impact, technological advancements, strategic approaches and regulatory challenges related to algorithmic trading.Methods: Following PRISMA 2020 guidelines, this study conducts a systematic literature review by screening 1,567 articles across five academic databases, namely IEEE Xplore, ACM Digital Library, SpringerLink, Web of Science and SSRN. After applying predefined inclusion and exclusion criteria, 208 peer-reviewed journal and conference papers published between 2015 and 2024 are selected. The PICOC framework is used to define the review scope. Data are extracted using structured templates capturing study details, research objectives, artificial intelligence (AI) integration, profitability analysis and limitations. Tools such as Rayyan, NVivo, MS Excel and Zotero support the screening, coding and qualitative synthesis of findings.Results: AI methods, especially machine learning (used in 50% of the studies) and sentiment analysis (20%), significantly improve predictive accuracy and profitability. Most studies focus on equities (35%) and forex (30%), with high-frequency trading being the most examined strategy (30%). Challenges include latency (30%), scalability (25%) and regulatory issues (25%).Conclusion: Future research should prioritize ethical frameworks, regulatory clarity and wider access to AI-driven ATS components. This review provides a robust foundation for academics and practitioners to innovate and optimize algorithmic trading strategies.

In the present times, the healthcare sector has seen an enormous growth in the usage of technology ranging from EHRs (electronic health records) to personal health trackers. Currently, there is a need for managing EHRs effectively with respect to storage, privacy and security measures. State-of-art technologies such as blockchain and artificial intelligence (AI) are applied in the healthcare domain. Innovation in AI is steadily advancing and is finding its place in different industries. The integration of blockchain and AI looks promising as there are several benefits. Blockchain can make the AI more secure and autonomous whereas AI can drive the blockchain with intelligence. The objective of this article is to explore the uses of blockchain as well as AI technology in the field of healthcare. We aim to survey the advantages, issues and challenges of integrating blockchain with AI technology, including future research directions in the healthcare domain. In this study, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) rules and an efficient searching protocol were used to examine several scientific databases to recognize and investigate every important publication. A solid systematic review was carried out on integration of blockchain and AI in the healthcare domain to identify existing challenges and benefits of integrating these two technologies in healthcare. Our study found that the integration of AI and blockchain technology has a potential to provide several benefits in terms of performance and security which conventional EHRs lack. The inherent benefits of blockchain and AI together are limitless, but the bare outcomes based on blockchain powered by AI technology are yet to be obtained. In addition, the outcome of our detailed study may aid researchers to carry out further research.

Background: Artificial intelligence (AI) is increasingly used both to test software (T1) and to assure AI-based systems (T2), with adjacent software-engineering work that shapes testing practice (T3). Prior reviews are mostly descriptive and rarely report comparable maturity or replicability signals.Objective: To provide a PRISMA-style systematic review (2015–2025, Web of Science) that maps T1–T2–T3 within a testing-centric frame, audits evidence maturity, threats reporting, and artefact openness per paper, and adds an explicit lens of large language models or generative AI (LLMs/GenAI).Methods: We queried the Web of Science Core Collection (2015–2025), screened via a predefined protocol, and extracted ten items (D1–D10) per study to normalize comparisons. Seventy-two papers met the criteria. Findings are organized into three themes: (T1) AI-based software testing, (T2) testing/validation of AI systems, and (T3) AI-related software engineering topics with implications for testing—T3 corresponding to the “beyond” in the paper’s title.Results: The corpus is limited in practice-oriented evidence: 31 laboratory/simulation, 3 industrial, 10 hybrid, 6 conceptual/guideline and 22 secondary studies. Only 18/72 provide public artefacts; 33/72 report no empirical metrics. By theme, T1=32, T2=15, T3=25; the LLMs/GenAI subset totals 10 papers. Openness strongly co-occurs with measurable outcomes (88.9% of artefact-sharing papers report metrics vs 42.6% without), yet “all-three credible” studies (industrial/hybrid + open artefacts + metrics) are rare (4/72 overall; 1/10 for LLMs/GenAI).Conclusion: AI shows promise for testing, but evidence remains thin on industrial adoption and reproducibility. We recommend prioritizing hybrid/industrial validations, releasing artefacts by default, and using standardized task–metric bundles. The review presents T1 and T2 results, separates T3 for scope clarity, and provides actionable maturity and replicability signals to guide responsible, empirical adoption.

Czech government institutions commissioned a research on extracting usual unit prices from public IT contracts to aid future public tender sizing. The goal of the project is to obtain millions of contracts from the public register, convert them to full text, extract unit prices from the text and publish a pricelist of IT industry manday prices. This paper designs the process and method of price extraction, demonstrates and evaluates the result on five iterations of extraction and discusses the experience of two years of project performance. The process is designed as a set of repeatable workflows and specified activity and role description. The method is designed as a combination of automated and manual actions. Due to the format and content variability of involved documents and the low mistake tolerance, the possibility of automated extraction of unit prices from full text contract is limited, and human workforce for validation is crucial.

Background: The vast amounts of data generated by consumers require new forms of processing, in which artificial intelligence stands out for its ability to analyse them more quickly and deeply. However, although there is abundant literature on artificial intelligence (AI) and consumption, most of it focuses on its impact on consumer behaviour rather than its usefulness in enhancing understanding.Objective: The aim of this study is to conduct a thorough review of the existing literature on the use of AI to understand consumer behaviour.Methods: This study uses the PRISMA protocol for the selection of the studies. Then, it combines bibliometric methods with a TCM-ADO framework to review articles. The Scopus database was used to gather peer-reviewed articles from 2014 to 2024. VOS Viewer and R-Studio were utilised for the analysis and visualisation of data.Results: The study provides insights into publication trends, dominant theories, methods, antecedents, decisions and results in the literature about the use of AI to understand consumer behaviour. Furthermore, it identifies potential avenues for future research to advance the development of theory and methodology.Conclusion: Research into the use of AI to understand consumers is still in its infancy. However, everything points to the application of AI in consumer behaviour continuing to expand, and its use for analysing attitudes and behaviour becoming more sophisticated and widespread.

Background: Rapid technological advancements have revolutionized research into unmanned aerial vehicles (UAVs), commonly known as drones, particularly in delivery applications. However, despite numerous related publications, there remains a lack of systematic reviews that synthesize challenges, trends and recent advances in drone delivery. To address this gap, the present study conducts a bibliometric analysis to examine evolutionary trends and emerging applications of UAVs between 2015 and 2024.Objective: This study aims to identify established and emerging trends in drone delivery research by analysing articles, journals, authors, institutions, countries and thematic areas.Methods: Previous studies are selected using a systematic approach, followed by bibliometric analysis with tools including VOSviewer, Bibliometrix and ScientoPy, which emphasizes key authors, top journals and countries, collaboration patterns and recurring author keywords.Results: The bibliometric analysis of 1,438 articles from 583 sources authored by 4,333 scholars (2015–2024) reveals a strong interdisciplinary focus in drone delivery research. Military applications largely drove early studies, but recent breakthroughs highlight the integration of artificial intelligence (AI) for autonomous navigation and energy optimization. Emerging themes include the development of drone swarms for scalable applications such as disaster response and agricultural mapping. Geographically, China, the United States and Australia dominate contributions, with extensive international collaborations fostering global innovation. Across journals and authors, the literature reflects a steady evolution from conceptual and technical foundations to applied studies addressing logistics, smart cities and environmental monitoring. Overall, the results suggest that drone delivery research is transitioning from exploratory phases towards AI-enabled autonomy deployment.Conclusion: Drone delivery research has evolved from military origins into a global, interdisciplinary field driven by AI. China, the USA and Australia are the leading contributors. Its future hinges on balancing technological innovation—such as autonomous navigation and swarm applications—with ethical, regulatory and societal considerations for sustainable integration.

Background: Recently, e-learning has emerged as a prominent tool for professional development in the education sector.Objective: The aim of this study was to examine teachers′ perceptions of improving their professional skills through e-learning in the Béni Mellal-Khénifra region of Morocco.Methods: The research utilized a questionnaire survey method to collect primary data from 518 teachers. The variables in the proposed model were assessed using a self-tailored five-point Likert scale. Data analysis was performed through confirmatory factor analysis and structural equation modelling using analysis of moment structures (AMOS).Results: The results indicate the impact of teachers′ satisfaction with their use of e-learning on the development of their professional competencies, without any direct effect of the actual use of e-learning on these skills. This suggests that satisfaction plays a mediating role between actual use and the development of teachers′ professional competencies. The study also found that overall quality significantly affects both user satisfaction and actual use by teachers within an online learning context.Conclusion: As a practical implication, stakeholders should focus on the technical, semantic and efficient features of e-learning to maximize the opportunity for better results among teachers in terms of developing both technical and non-technical skills.

Background: Some of the world’s most valuable platform businesses rely on products and services provided by small and medium-sized enterprises (SMEs). Though, the modern digital platform economy is increasingly shaped by uncertainties and power asymmetries benefitting dominant platform owners and threatening smaller players participating as complementors in those ecosystems. Negative consequences include lock-in effects and platform dependency, exploitative participation terms and eroded entrepreneurial autonomy on the SMEs’ side, which altogether harm the digital platforms’ long-term viability, too. Addressing these issues, this paper investigates design principles for digital platforms taking SME complementors’ needs into account.Objective: This study investigates design principles for digital platforms that enhance suitability for SMEs as complementors, focusing on stakeholder-centric platform design approaches that better accommodate SME-specific needs and requirements. In this, this study aims to address current negative developments concerning imbalanced power dynamics and uncertainties emerging from platform owner-SME-partnerships in dominant digital platform ecosystems.Methods: A qualitative reflective meta-analysis approach was employed, combining explorative expert interviews with SME specialists and a thematic literature review of SME platform design research. The methodology synthesized findings to identify meta-requirements and derive design principles through interpretive analysis.Results: Eleven meta-requirements were identified and synthesized into four design principles: the principle of SME empowerment, the principle of open boundaries, the principle of transparent and fair participation terms, and the principle of reflection of individuality. In combination, these principles aim to inform future digital platform design that takes SME complementor needs into account.Conclusion: The conceptual proposal of four design principles guides researchers and practitioners in creating SME-suitable digital platforms with stakeholder-centric design approaches. The principles enable digital platform models that accommodate diverse SME requirements, enhance participation experiences, and foster collaborative ecosystems tailored to SME characteristics and their operational contexts.

Background: Artificial intelligence (AI) and machine learning (ML) have become a revolutionary force in the development of smart cities and are changing the way cities are built, run and managed. With the rapid acceleration of the degree of urbanization and technological convergence, the state of research in this interdisciplinary area is a question that is very important for both researchers and policy makers.Objective: This study aims to present an all-round analysis of the smart city applications of ML, in terms of both bibliometric and thematic analysis. The focus is on identifying trends in publication, major contributors, emerging topics of research and methodological trends that define advances in this area.Methods: A total of 1960 peer-reviewed journal articles indexed in Scopus (excluding MDPI and Frontiers) from 2015 to April 2025 were analysed according to the PRISMA protocol in order to guarantee data accuracy and transparency. Python and VOSviewer tools were used to summarize and map the publication trends, institutional productivity, thematic clusters and the development of different levels of paradigms. To improve reliability, the same search criteria were used to extract a parallel dataset of 720 records from the Web of Science (WoS) Core Collection (excluding ESCI, MDPI and Frontiers). A cross-validation of publication patterns, thematic and disciplinary representation was conducted separately on the WoS dataset.Results: The analysis reveals a significant rise in research output after 2020, with China, India and Saudi Arabia leading the research output, which is consistent with national AI and urban digitization initiatives. The most-cited articles show a thematic shift from infrastructure-focused works such as IoT, smart grids and mobility systems towards more specifically ethically oriented works such as federated learning, climate resilience and algorithmic governance. A number of different methodologies such as deep learning, reinforcement learning and privacy-preserving AI models have been developed. Two of the four areas studies show a high level of agreement between Scopus and WoS but WoS is more prevalent in urban studies and planning and Scopus additionally encompasses engineering and computer science.Conclusion: In this regard, our findings demonstrate a clear convergence of ML with the domains of urban policy, sustainability and sociotechnical governance and a paradigm shift from technology-led to value-led innovation. This development corresponds to the increased transparency, inclusiveness and responsible use of AI in the field. The paper offers a validated and data-driven point of reference to researchers, practitioners and policy-makers so as to guide and affect the emerging frontiers of AI-enabled smart city transformation.

Background: Artificial intelligence (AI) has become a fundamental part of everyday life, making it crucial to integrate AI into the information society in ways that protect individual rights.Objective: This study explores the perspectives of different stakeholders on the ethical use of AI. The aim of this research is to identify practical measures that can help address ethical challenges associated with AI deployment.Methods: A scoping literature review approach was adopted, focusing on the most relevant articles addressing the ethical aspects of AI usage from Web of Science Core Collection and Scopus databases. The analysis was performed with focus on the perspectives of four key stakeholders: policymakers, AI innovators, business leaders, and individuals.Results: Findings highlight key measures to promote ethical AI usage: technical, organisational, regulatory, and individual measures. In this context: (1) policymakers are responsible for establishing governance and regulations; (2) AI innovators must embed ethics into AI systems; (3) business leaders should establish ethical policies and guidelines; and (4) individuals need to think critically and use AI responsibly.Conclusion: The responsible deployment of AI requires a comprehensive approach that involves the collaboration of all relevant stakeholders. The future development of AI relies on the adoption of ethical guidelines and the assurance of responsible AI system design.

Background: System Development Life Cycles (SDLC) are proposed through development methodologies (e.g. Rational Unified Process) and international standards (e.g. ISO/IEC 12207) to guide the systematic development of software products to meet expected time, budget, and functional quality. The ISO/IEC 29110-5-1-2: Software engineering guidelines for the generic Basic profile standard provides a disciplined-systematic lightweight SDLC alternative to agile approaches for businesses interested in ISO/IEC certifications rather than agile ones. However, its utilization in the domain of Big Data Analytics Systems has not been investigated.Objective: Big Data Analytics Systems (BDAS) have been proposed using rigorous SDLCs such as CRISP-DM, Team Data Science Process, and Domino Data Science Lifecycle, but their utilization for small business – or small teams called Very Small Entities (VSEs) – is scarcely reported given the requirements demanding human, technological and financial resources not available in small business.  Given this problematic situation, new agile SDLCs for BDAS have been proposed such as Data-Driven Scrum, but some small organizations still require the utilization of a more systematic development process, and thus SDLCs based on ISO/IEC standards are expected.Methods: In this research, the design of Light Data Science – Analytics Methodology (LDSAM) and its pilot usability evaluation from a sample of 27 international practitioners are reported. LDSAM is a lightweight development methodology aligned to the ISO/IEC 29110 – Basic Profile – created especially for VSEs.Results: LDSAM was elaborated using Design Science Research Methodology. Initial usability evaluation results of LDSAM are satisfactory, but further empirical research is encouraged to advance to more mature and stable lightweight SDLCs for BDAS.Conclusion: The LDSAM was successfully developed, and its usability was evaluated by a pilot sample of international academics and professionals. Favorable results were obtained on the usability metrics for the proposed LDSAM SDLC for BDAS.

Accounting information systems (AIS) are closely connected with using automated accounting data processing technologies, which increase reliability and prompt information delivery to stakeholders for management decision-making. The purpose of the article is to provide the AIS research domain with an additional impetus for further development based on a comprehensive characterization of quantitative parameters and systematic rethinking of trends in the evolution of the scientific themes. The article contains the methods of bibliometric analysis and chronological literature review based on clustering of keywords from a sample of AIS research indexed in Scopus in 1973–2023. The key findings indicate the nicheness of the AIS research problems, due to which the evaluation of the scientific output requires a multifaceted approach. It is found out which countries, journals, articles and authors play a decisive role in the formation of trends in the AIS research domain. Author keywords are used to assess the content orientation of the AIS research themes and to identify patterns of its evolution. We conclude that there is a content exhaustion in AIS scientific problems and the need to find new objects of research that correspond to the trends of Industry 4.0.

Background: The rapid evolution of software engineering has positioned DevOps practices and Continuous Testing (CT) as critical approaches for achieving speed, quality, and reliability in software delivery. Test automation is central to CT, yet its adoption remains inconsistent due to a complex interplay of technological, organizational, and environmental conditions.Objective: This study employs a systematic literature review guided by the Technology–Organization–Environment (TOE) framework to identify, categorize, and synthesize the antecedents that influence the test automation adoption in DevOps continuous testing.Methods: Using the PRISMA protocol, 49 peer-reviewed studies published between 2015 and 2025 were systematically analyzed, yielding 61 distinct factors comprising 29 technological, 19 organizational, and 13 environmental antecedents. These factors were further consolidated into thematic clusters to enhance analytical clarity and reduce fragmentation.Results: The findings demonstrate that technological and organizational drivers, including relative advantage, compatibility, top management support, and employee competence, dominate the literature, while environmental influences such as competitive pressure, regulatory requirements, and vendor ecosystems are comparatively underexplored. This imbalance indicates that although the TOE framework is widely applied in technology adoption studies, empirical research has given greater attention to internal adoption enablers than to external pressures. By simplifying and synthesizing the factors into coherent sub-themes, this study contributes to both theory and practice by offering a structured lens through which test automation adoption can be examined in the DevOps CT context. Theoretically, it validates the relevance of TOE for analysing multidimensional adoption dynamics, while practically, it provides managers with evidence-based insights to prioritize critical factors when planning automation initiatives. Methodologically, it demonstrates the importance of transparent and replicable review processes for advancing cumulative knowledge.Conclusion: Overall, the study bridges fragmented findings into a coherent framework and strengthens understanding of adoption strategies in continuous testing environments.

Background: The introduction of retail investors to AI-powered trading platforms and especially on emerging markets, has resulted in a new set of risks linked to algorithmic bias and financial forecasting fairness. Social media sentiment and structured data multimodal strategies have demonstrated a potential, but frequently do not have ethical considerations.Objective: This work proposes a multimodal model predictive control (MPC) framework grounded in fairness-based forecasting of next-day returns on stock in stock market settings, particularly ethical behaviour and transparency of the model on retail markets.Methods: We combine BERT-based sentiment analysis of Reddit discussions and organized stock market indicators and use XGBoost as the fundamental model. Bias is measured using fairness metrics, including demographic parity difference and equal opportunity difference. Debiasing measures such as reweighting and stratified calibration were used to curb the differences in stock categories.Results: The first model has an overall accuracy of 72.3 with the highest accuracy of 83.1 in the case of Tesla – representing bias in the model. Fairness assessment shows some significant differences (DPD=0.23, EOD=0.31), but the mitigation decreases to 0.07. However, the massive performance improvement after adjustment brings up the issue of overfitting or fairness overcorrection.Conclusion: While the proposed debiased framework successfully reduces algorithmic bias, the trade-off between fairness and generalizability underscores the need for caution. These results hold significant implications for digital trading systems and regulatory frameworks of emerging economies such as India, where explainability and fairness of AI models are significant for ethical financial engagement.

Background: The internet of things (IoT) has transformed healthcare by integrating various devices and systems, fostering innovation in data management and operational efficiency. However, ensuring data integrity, security and trust within IoT networks remains a pressing challenge, particularly in critical sectors such as healthcare. Objective: This study aims to explore the integration of blockchain technology with IoT systems, focusing on addressing scalability and real-time applicability issues in healthcare data management. By proposing novel solutions, the research seeks to enhance the security and reliability of IoT systems in healthcare environments. Methods: The proposed framework incorporates a lightweight raft-based consensus protocol and enhanced cryptographic measures, such as Schnorr signatures and hashes, to address existing limitations in scalability and latency. The architecture and algorithms for signature generation, encryption, emergency state actions and consensus are developed and evaluated through extensive simulations using the NS3 toolkit. Results: The simulation results validate the effectiveness of the proposed approach in improving healthcare IoT systems. The findings demonstrate enhancements in energy efficiency, throughput and network usage, establishing the potential of the framework for revolutionizing healthcare data management by providing secure, scalable and efficient solutions. Conclusion: The study contributes to advancing secure and reliable decentralized data management systems for IoT in healthcare by making use of blockchain technology. The proposed architecture addresses critical challenges and offers practical benefits such as resource efficiency and system stability. While promising, the framework requires real-world testing and further optimization to overcome potential scalability bottlenecks in large-scale healthcare deployments.

Background: General population surveys now increasingly use nonprobability samples from access panels instead of probability-based methods, which often leads to lower-quality estimates. In response, many official and academic surveys have adopted probability-based online panels (PBOPs), which use probability sampling and retain participants for follow-up surveys. While these panels reduce costs compared to one-time surveys, they still face low response rates and other challenges that may affect data quality.Objective: This study aimed to assess the accuracy of PBOPs by synthesising evidence on relative bias (RB), and to examine how RB varies by country, domain, measurement level, and item sensitivity.Methods: A systematic review yielded 44 eligible studies from 12 countries, and 1,897 effect sizes of absolute RB from studies that compared PBOP estimates to benchmarks. A three-level random effects meta-analytic model accounted for variance across studies, within studies and sampling variance. Moderator analyses evaluated the influence of country, item topic, measurement level and sensitivity on RB. Sensitivity analyses excluded the top 5% of RB outliers to test robustness.Results: The pooled RB was 23.14% (95% CI: 18.38%–27.91%) and heterogeneous. Most variance was attributed to within-study item-level differences. Country and topic did not significantly moderate RB. Items with high topic sensitivity had significantly higher RB (+19.33%) than items with no sensitivity.  Ordinal items had significantly lower RB than nominal (–14.90%). However, when sensitivity and measurement level were modelled together, substantial residual heterogeneity remained.Conclusion: While PBOPs offer cost and logistical advantages, they require careful design considerations to lower substantial bias, especially regarding item sensitivity and measurement scale. PBOPs may not be suitable for certain question types, like sensitive or low-prevalence behaviours, especially when high accuracy is needed. Improved methodological planning and innovations are needed to improve PBOP data quality.

Background: The growth of high-throughput sequencing and multi-omics research has intensified the need for secure, interoperable and transparent data management infrastructures. Blockchain technology has been widely proposed as a potential solution; however, its feasibility, empirical maturity and comparative performance in bioinformatics remain unclear.Objective: This systematic review analyses blockchain applications in bioinformatics, highlighting claimed security and governance benefits, comparing them with traditional data security approaches, discussing implementation challenges and assessing the empirical rigor of existing studies using a structured quality assessment framework.Methods: This overview was conducted using Scopus, ScienceDirect, IEEE Xplore, ACM Digital Library and SpringerLink for publications from 2014 to 2024. Search strings combined blockchain, bioinformatics and security-related terms. Sixty-five studies met the inclusion criteria. Each study was evaluated using five equally weighted quality dimensions: application specificity, clarity of benefits, empirical evaluation, challenge articulation and reproducibility.Results: Most studies focused on blockchain use cases in genomic data sharing, provenance tracking and access control, with a strong emphasis on conceptual benefits such as immutability and auditability. Fewer studies provided empirical evaluations or direct comparisons with traditional security mechanisms. Quality assessment results revealed a predominance of conceptual and prototype-level contributions; over half of the studies lacked empirical benchmarking and reproducibility was frequently limited. Heterogeneity in blockchain architectures and the absence of standardized genomic benchmarking environments hindered cross-study comparison. No study demonstrated deployment within a production-scale genomic pipeline.Conclusion: Blockchain demonstrates conceptual potential for enhancing provenance, decentralized governance and tamper-resistant auditing in bioinformatics data management. However, empirical validation remains limited and significant technical, regulatory and organizational challenges persist. The current evidence base is insufficient to support large-scale adoption. Future research should prioritize benchmarking using realistic genomic workloads, hybrid architectures that integrate off-chain storage, consent-aware governance models and alignment with regulatory frameworks such as GDPR and HIPAA.

Background: Telemedicine, though decades old, saw only limited use until the COVID-19 pandemic, but despite increasing adoption, barriers to using telemedicine remain pervasive.Objective: We sought to explore the acceptability of telemedicine among nurses caring for older adults by extending the Technology Acceptance Model (TAM).Methods: A descriptive, non-experimental, quantitative research design was used. An online survey was administered to Slovenian registered nurses experienced in caring for older adults (n = 244), of whom 111 with no prior telemedicine experience were included in the analysis. Data were collected between September and December 2023 and analysed using descriptive statistics, Pearson correlations and ordered logistic regression models.Results: Willingness to use telemedicine was positively associated with perceived usefulness for nursing practice and patient empowerment (both p < .05) and negatively influenced by technology anxiety and technology-based ageism (both p < .05). Technology anxiety was particularly associated with stronger resistance among the nurses least willing to use telemedicine. Higher self-rated telemedicine knowledge correlated positively with willingness to use telemedicine (p < .05), but this relationship ceased to be significant after perceived usefulness and technology anxiety were included in the model. Age was also negatively associated with nurses’ willingness to use telemedicine (p < .05), suggesting generational barriers.Conclusion: Extending the TAM with domain-specific measures related to nursing practice and patient empowerment and with affective components – technology anxiety and technology-based ageism – improved its power to explain willingness to use telemedicine among nurses. Telemedicine knowledge is an important external factor, so professional development should help to improve digital skills and address ageist assumptions to support a socially inclusive and confident nursing workforce.

Background: The use of blockchain is increasing daily and has become a transformative technology across various sectors. The competent use of blockchain is becoming a fundamental skill. Although numerous studies have attempted to measure digital competencies, the absence of research specifically focused on blockchain literacy has left a significant gap. Objective: The aim of this study is to provide a systematic review of digital literacy and blockchain measurement frameworks, comparing them with existing theoretical digital competence models. Methods: Furthermore, we introduce a newly developed Blockchain Literacy Scale (BLS). The factorial structure of the BLS is assessed to explore its multidimensional nature. A sample of 591 Turkish college students completed a questionnaire to empirically test its reliability, validity and general applicability. Results: Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) supported a five-factor structure for the BLS, comprising: information access, information evaluation, economic literacy, legal literacy and risk literacy. The scale demonstrated strong internal consistency and reliability. Conclusion: Directions for future research and the practical implications of the newly developed BLS are discussed in the conclusion.

Background: Traditional expert systems in education rely on static knowledge bases and rule-based logic, limiting their ability to adapt to the diverse and evolving needs of students. Recent advancements in artificial intelligence and psychological profiling offer new pathways for building personalized support systems.Objective: This study presents a data science framework for developing adaptive expert systems that personalize support delivery using dynamic psychological profiling, with a focus on the higher education domain.Methods: The proposed system integrates five core components: MIND (a multimodal information orchestrator), UEX (expert system knowledge base), ULM (domain-specific large language model), PAGE (a personality-adaptive generative engine), and SYNAPSE (a dynamic profiling module). Psychological personalization is achieved using a multimodel Myers-Briggs Type Indicator (MBTI) classifier developed and validated in a separate study. In this paper, the classifier is operationalized within the system to enable real-time profiling. System behavior is driven by multimodal data fusion and continuously updated based on user interactions. The evaluation focuses on the impact of MBTI-based personalization on user satisfaction, assessed through a controlled survey comparing generic and personalized system responses.Results: In an experiment involving 70 participants with identified MBTI profiles, 79% preferred responses generated by PAGE (psychologically personalized) over generic outputs. This preference was consistent across most MBTI types, indicating the broad applicability of personalization. Minor deviations were observed for types with a preference for concise communication, suggesting variability in personalization effectiveness.Conclusion: The findings demonstrate that embedding psychological profiling into expert system workflows enhances perceived relevance and engagement of system responses. This adaptive framework enables real-time personalization through data-driven profiling, and its modular design allows for deployment across multiple domains. The system’s architecture establishes a scalable and behaviorally grounded foundation for next-generation educational support systems.

Background: Artificial intelligence (AI) is rapidly transforming human resource management (HRM) by automating essential functions such as recruitment, employee performance evaluation and workforce planning. Despite the growing adoption of AI-driven tools, organizations face numerous challenges, including resistance from HR professionals, ethical concerns and data privacy issues. This transformation has sparked significant academic interest, yet several gaps remain in understanding how AI affects HRM practices and organizational outcomes.Objective: This study aims to explore the integration of AI in HRM by analysing its potential opportunities and challenges. Additionally, it investigates how human-AI collaboration can enhance HR functions and drive organizational performance.Methods: A systematic literature review approach was adopted, focusing on 141 recent peer-reviewed articles from Scopus-indexed journals. This review focused on studies published between 2019 and 2025. The analysis was structured around three key dimensions: AI-driven opportunities, challenges associated with AI adoption and its transformative impact on HRM practices.Results: The findings reveal that AI offers significant benefits in HRM, such as improving efficiency, reducing bias and enhancing employee engagement. However, challenges remain regarding ethical decision-making, data security and maintaining human interaction in HR processes. The study also highlights the importance of augmenting, rather than replacing, human roles with AI tools to achieve optimal outcomes.Conclusion: AI has the potential to reshape HRM by streamlining processes and enhancing decision-making. Nevertheless, its successful implementation requires addressing critical challenges such as resistance to change, ethical concerns and legal risks. Organizations should focus on fostering human-AI collaboration to unlock the full potential of AI-driven HRM.

Background: Blockchain technologies have come a long way, and integration of blockchain technologies into different fields is flourishing; however, there is a lack of blockchain platforms to manage the high network loads and more sophisticated security threats. These limitations impede the mass adoption of blockchain applications. One of the main reasons blockchain needs artificial intelligence (AI) is to integrate it for the widespread adoption of blockchain technology, as AI addresses scalability and security problems. Objective: The article proposes a pattern analysis model to overcome scalability and security limitations in blockchain systems by applying advanced AI techniques. Methods: To make the model scalable, the proposed model uses deep learning methods such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. Furthermore, random forest and convolutional neural networks (CNNs) are applied to augment security operations as an effective classier and anomaly detector on transaction data and a real-time threat detection on transaction patterns using the CNNs. By analysing time series data and dealing with long-term dependencies, the model uses RNNs and LSTMs to enable the strategic introduction of the model to predict and control network loads. Results: When the proposed model is tested against a curated cloud dataset, it significantly outperforms the state-of-the-art approach in all the performance parameters. More specifically, it has exhibited a 5.05% increase in processing speed, 8.05% improvement in energy efficiency, and 5.27%, 5.8%, 10.24% and 11.62% better attack analysis precision, accuracy, recall and AUC, respectively. Conclusion: The synergistic interaction of the applied AI techniques results in a blockchain paradigm that is both scalable and resilient to new security threats. This significant improvement in performance parameters demonstrates the effectiveness of integrating AI with blockchain technology to overcome scalability and security limitations, thereby enabling the widespread adoption of blockchain applications.

Background: Osteoporosis is a condition characterized by bones that are porous and brittle, increasing the risk of fractures. It is often asymptomatic until substantial harm develops, making it crucial to treat at the onset of the disorder.Objective: This study aims to develop a practical, in-depth and adaptable framework utilizing constructed clinical and demographic datasets for the early detection of osteoporosis.Methods: We design a cascade convolutional neural network with adaptive weight fusion and fine-tune it with a real-coded genetic algorithm. An anonymized clinical and demographic record publicly available bone mineral density dataset was used. Missing data identification, normalization and encoding of categorical variables were key diagnostic steps. The training subset consisted of 70% of the dataset, while the remaining 30% was used for testing.Results: The predictive capability of the proposed model is demonstrated by utilizing two datasets. Dataset 1 is used for training and testing, achieving a classification accuracy of 99.5%, precision of 98.7%, recall of 99.0% and AUC-ROC of 0.99. Dataset 2 is used to test the model generalizability, achieving a classification accuracy of 97.0%.Conclusion: The model integrates well into primary care settings, as it relies on structured clinical data rather than imaging. Its low costs relative to value and high scalability make it suitable for population-level screening and treatment of osteoporosis. The limitation of the research is that we utilize only a clinical dataset to train the model without image analysis.

Background: Learning programming is often difficult for beginners, primarily because of the challenge of providing timely and personalized feedback in large educational environments. While automated assessment systems have improved efficiency in grading and feedback, they typically focus on correctness and often lack personalized guidance concerning code quality, readability, and maintainability.Objective: This study aims to investigate whether integrating static code analysis into automated assessment systems to provide personalized feedback can effectively enhance students code quality, learning process, and engagement in programming courses.Methods: We designed a personalized feedback system integrated with static analysis tools (Cppcheck and Clang-format), deployed within an existing automated assessment platform used by undergraduate programming students. The system was evaluated in a controlled experiment involving 60 students randomly divided into control and treatment groups. The effectiveness of personalized feedback was measured through quantitative metrics (style violations, potential bugs, and design issues), qualitative surveys, and submission behaviours over multiple assignments.Results: Results demonstrated that students receiving personalized feedback improved their code quality, reducing the number of style violations by 76%, potential bugs by 52%, and structural issues by 32% compared to the control group. Students also expressed higher satisfaction, increased motivation, and greater willingness to iteratively refine their code based on personalized feedback.Conclusion: The integration of static code analysis for personalized feedback not only enhances code quality but also helps a deeper understanding of good programming practices among students. Future research should focus on making feedback systems more adaptive, incorporating intelligent tutoring techniques, and exploring long-term impacts on programming habits and skill retention.

Background: The convergence of large language models (LLMs) and gamification has emerged as a promising direction for enhancing user engagement, personalisation and interaction quality across digital systems. While prior studies have largely examined LLMs or gamification in isolation, limited work has systematically explored their combined role in redefining engagement paradigms beyond static reward-based mechanisms.Objective: This review systematically examines how LLMs are integrated into gamified systems, identifies their application contexts and functional roles and synthesises reported benefits, challenges and methodological trends across existing empirical studies.Methods: This systematic literature review examines peer-reviewed studies published between 2020 and 2025, following established systematic review protocols and the PRISMA 2020 framework. A total of 718 articles were retrieved from Scopus, IEEE Xplore, ScienceDirect and the ACM Digital Library. After applying predefined inclusion and exclusion criteria, 27 studies were selected for in-depth analysis. A quality assessment process was conducted to evaluate methodological rigour, consistency and reporting clarity, ensuring the reliability of the synthesis.Results: The review identifies key application domains where LLMs are integrated into gamified systems, including intelligent non-player characters, adaptive educational feedback, persuasive gamified environments and embodied digital avatars for social and behavioural simulations. Across these contexts, LLMs were found to substantially enhance personalisation, narrative immersion and learner motivation, complementing the motivational structures of gamification. However, recurring challenges were also reported, including hallucination risks, ethical concerns in social and extended reality contexts and misalignment between LLM-driven system behaviour and user expectations.Conclusion: This review provides a comprehensive synthesis of the opportunities, limitations and methodological trends associated with integrating LLMs into gamified environments. By consolidating evidence across diverse application domains, it offers practical and theoretical guidance for researchers, educators and system designers on responsible and effective use of LLMs to support adaptive, engaging and ethically grounded gamified systems.

Background: The phenomenon of artificial intelligence (AI) has been studied for decades. However, only the ascent of tools such as ChatGPT brought AI into a broader public consciousness, as people started using it for a broad spectrum of tasks and questions.Objective: The goal of this overview article is to present a new perspective on AI issues in the context of the social sciences and, more specifically, political science. Indeed, AI tools play an important role in the political process—a fact reflected by governments and other political actors, including political parties.Methods: The qualitatively and interpretively oriented paper seeks to demonstrate existing connotations of the relationship between AI and politics in the Czech context. The text is designed as an overview based on secondary sources. We first focus on AI popularity and use in the general public and public institutions. Then the article focuses on government strategies with implications for international organizations. The final part outlines the relationship between generative AI and Czech political parties.Results: The results indicate that the popularity of AI grew substantially after OpenAI launched its model. Nowadays, generative AI-based tools are commonly used by various public institutions. To date, the Government of the Czech Republic has issued two national strategies on AI issues. Political parties are among the actors using generative AI on a daily basis.Conclusion: The analysis seeks to fill in the blanks in this under-researched area and to demonstrate what kind of interdisciplinary implications of the AI–politics relationship can be examined. Moreover, we view the gradual adoption of AI tools as the next step in the process of adaptation to new digital tools that started years ago.