Information Systems

The Metaverse holds the promise of profoundly transforming education by reshaping how courses are delivered and how students learn and engage, thanks to the novel possibilities it offers for immersive and interactive learning experiences. This study seeks to pinpoint both the challenges and advantages of integrating the metaverse into education, based on a systematic review of existing literature from both a technological and human viewpoint. It provides an in-depth analysis of not only the benefits of employing the Metaverse in education, but also the obstacles that educational institutions, designers, and implementers encounter when creating or utilizing these digital realms. The study’s findings underscore the need to develop and deploy metaverse systems to improve educational methods and maximize learner satisfaction. Furthermore, they will provide researchers with insights to tackle the multifaceted challenges associated with the use of metaverse tools and environments in education.

AI-powered Voice Assistants (VAs) emerge as attractive facilitators of the increasing interactions between people and machines in the Metaverse. VAs are still at early stages of adoption, so this systematic literature review charts the landscape of existing VA research with a focus on their use in different context. This helps us identify important aspects that require further examination. The findings indicate that while academic interest in this novel subject is increasing, the literature regarding the factors that drive continuous use does not take into account VAs’ intelligence. Indeed, users perceive such devices as collaborative sentient actors rather than as tools. Taking these perceptions into consideration can help to increase the engagement of users with VAs.

The regulatory landscape for cryptocurrencies and blockchain tokens is a critical factor shaping business decisions and opportunities. This study proposes a computational model that leverages Web mining through search engines to quantitatively estimate the regulatory readiness of countries in relation to cryptocurrencies. The model’s performance is validated through experimental trials supplemented with un- supervised clustering techniques for deeper analysis of the derived estimations. The findings demonstrate the effectiveness of the model in assessing regulatory tendencies and offer valuable insights for policymakers and industry stakeholders. This algorithmic approach presents an algorithmic methodology over manual regulatory assessments and subjective methods, showcasing its potential to guide regulatory frameworks in the rapidly evolving space of cryptocurrencies.

Since e-Cash and electronic transactions consist of digital data, there are risks that this data can be easily falsified, compromising the security and efficiency of the entire electronic payment protocol. To ensure the security of the protocol, these data must be protected. This is why researchers have focused on several cryptographic studies including secret sharing. Secret sharing techniques are used to divide a secret into multiple shares and to distribute them among a group of participants. Only when a sufficient number of shares are combined together, the original secret can be reconstructed. Moreover, no information about the secret can be deducted from the shares by a smaller group.

In this paper, we investigate the different phases of an electronic payment protocol where secret sharing can be used and more particularly distributed key generation and distributed multikey generation protocols. This paper also highlights the importance of using secret sharing in enhancing the security and confidentiality of electronic payment systems, as well as the ability to prevent unauthorized access to sensitive information. The trust is shared among multiple parties.

This paper addresses a significant gap in the current understanding of decentralization within Decentralized Autonomous Organizations (DAOs). While many theoretical discussions generalize governance models, there is a lack of in-depth analysis of the unique governance structures and their inherent decentralization mechanisms in individual DAOs. To bridge this gap, the paper presents two primary contributions. First, it offers a foundational analysis of potential points of failure in decentralization within DAOs. Second, the study introduces a “decentralization layer system”, a new framework aimed at guiding future quantitative analyses of decentralization. This system is based on two pillars: a critical evaluation of governance structures and a stratification approach to identify distinct layers within these structures. The paper further demonstrates the application of this framework by analyzing the governance models of several DAOs, including Uniswap, Compound, and ApeCoin. Through these analyses, the paper provides insights into the strengths and vulnerabilities of each DAO’s decentralization mechanisms, offering a comprehensive perspective on DAO governance.

The adoption of carbon credit systems has emerged as a pivotal strategy in addressing climate change and promoting sustainable environmental practices. This research paper delves into the multifaceted landscape of carbon credit systems, specifically focusing on the challenges inherent in their design and implementation. We investigate the innovative use of Non-Fungible Tokens (NFTs) as a transformative approach to carbon credits, exploring the intricacies of NFT structures tailored for carbon credit trading. Furthermore, the paper presents a comprehensive examination of the system architecture underpinning a blockchain-enabled NFT trading platform for carbon credits. The architectural framework’s design and components are meticulously scrutinized to ensure transparency, security, and efficiency in the trading ecosystem. The research also dissects the intricate functionalities that empower the blockchain-NFT trading system, facilitating seamless transactions and traceability. A pivotal facet of this study is a compelling case study spotlighting the application of a Blockchain-NFT trading system for Agricultural Carbon Credits within a rural farming community. The case study highlights the tangible benefits reaped by farmers through the innovative system, shedding light on how the technology incentivizes sustainable farming practices and offers an additional revenue stream.

The accumulation of wealth and assets through inheritance forms the basis for future generations’ prosperity, encompassing financial assets, physical possessions, and intangible riches like knowledge and skills. However, inheriting such multidimensional wealth presents complex technological, social, and legal challenges. To address these issues, the authors present a comprehensive exploration of technology-driven inheritance methods and the role of blockchain in asset management. Current research in this area is fragmented, lacking a unified conceptual framework, making it challenging to grasp the broader implications of blockchain in inheritance. This study aims to fill this gap by taking an exploratory approach to investigate the integration of blockchain and digital asset ownership and transfer in the context of Succession and Inheritance. The authors provide a conceptual framework to guide all stakeholders involved in the inheritance process. The study seeks to bridge existing knowledge gaps and offer a cohesive perspective on how blockchain technology can revolutionize inheritance practices through a combination of case studies, theoretical research, and practical implementation insights. The article is structured into four sections: an introductory section providing background context, an extensive review of relevant literature, including key principles and practical examples, an innovative conceptual framework, and a thorough analysis and synthesis of the research findings.

In the last decade, Bitcoin has transformed from an experimental asset into a global store of value, emerging as a viable alternative to traditional finance. This study explores Bitcoin adoption among merchants in Panajachel (Guatemala), El Zonte (El Salvador), and Uvita (Costa Rica). Quantitative research gathered insights from 64 respondents, revealing that 64% of merchants believed they attracted more customers due to Bitcoin acceptance. Usage of Bitcoin earnings varied, with 44% holding it as an asset and 27% converting it to fiat. Additionally, 67% found Bitcoin easier than credit cards. The analysis was guided by the Technology Acceptance Model and the Diffusion of Innovations theory, focusing on perceived utility, ease of use, market advantage, and compatibility. This research highlights the value of localized studies in understanding Bitcoin’s adoption, offering valuable insights for policymakers and businesses. It sheds light on Bitcoin’s dual role as both a currency and a payment system, enabling users to transact outside of traditional finance.

Chatbots, the pioneering conversational artificial intelligence (AI) agents, have experienced remarkable growth and integration in various domains. In modern societies, chatbots have emerged as transformative digital entities, revolutionizing the way humans interact with technology. These conversational AI agents have transcended their initial applications to become integral parts of various industries and daily life. One of the most prominent roles of chatbots is in customer service, where they offer round-the-clock assistance, swift issue resolution, and personalized interactions. By handling routine queries and tasks, chatbots free up human agents to focus on complex and specialized issues, thus optimizing overall efficiency and customer satisfaction. To this end, this paper aims to present and describe the architecture of a novel chatbot generator with improved functionality in terms of quality of communication with end users and level of provided services, with a specialized infrastructure understanding the Greek language. The chatbot generator was developed in the framework of a research project and will be pilot tested by two end-users, the National Bank of Greece (NBG) and the General Secretariat for Information Systems & Digital Governance (GSIS-DG).

The concept of linking data, in its core, is one of the cornerstones of the Semantic Web, and the design principles of Linked Data aim to establish standardization for knowledge representation. The rationale behind this research is to analyze Linked Open Vocabularies (LOV) to elicit useful insights regarding the status and potential of interoperability of existing vocabularies, organized in thematic areas (domains). The goal behind the latter is to identify how semantic interoperability of vocabularies (and datasets which are described by the relevant knowledge schema) could be improved, while the expected outcome is an enhanced overview of the existing knowledge representation in the LOV ecosystem, with emphasis put on domain-specific vocabularies and their reuse. In order to gain a general perspective of the domain-specific vocabularies that currently exist, a mapping between the 13 European Data Portal (EDP) thematic categories and the categorization of LOV was initially performed. Afterwards, an analysis framework to capture the information from this desk-based research was included. The analysis framework included: i) the connections of the identified (from the initial mapping) domain-specific vocabularies to core vocabularies in the LOV ecosystem, ii) the connections of domain-specific vocabularies to vocabularies within the same domain, and iii) the connections of domain-specific vocabularies to the other domains. The previous three dimensions were then combined to create an initial assessment on how reusable and thus, interoperable (and beyond) the vocabularies under analysis were, acting as an indicator for domain interoperability readiness.

In market-based economies often appear significant decreases of economic activity, which lead to recessionary economic crises. These economic crises have quite negative consequences for firms, as they lead to significant decrease of their sales revenues; firms respond by decreasing on one hand their production and in general operational activities and expenses, personnel employment and materials’ procurement, and on the other hand their investments in production equipment, digital technologies, etc., which leads to technological obsolescence. This reduction of investments, and especially of the ones in digital technologies, due to their importance for firms’ efficiency, effectiveness, and innovation, can have quite negative impact on their future competitiveness, and even put at risk their survival. However, these negative consequences of economic crises differ significantly among firms: some of them exhibit a lower vulnerability to the crisis, so they have less negative consequences, while some other firms exhibit a higher vulnerability, and have more negative consequences; so the competitive position of the former is significantly strengthened with respect to the latter, and finally the former are the ‘winners’ of the crisis, while the latter are the ‘losers’. This paper proposes a methodology for predicting the winner and loser firms of future economic crises with respect to a highly important class of technologies: the digital technologies. In particular, the proposed methodology enables the prediction of the multi-dimensional ‘pattern of digital vulnerability’ of an individual firm to a future economic crisis, which consists of the degrees of reduction of the main types of ‘digital investments’ as well as ‘digital operating expenses’ in a future economic crisis. For this purpose, we are using Machine Learning algorithms, in combination with the Synthetic Minority Oversampling Technique (SMOTE), in order to increase their performance, which are trained using open government data from Statistical Authorities. Furthermore, a first application/validation of the proposed methodology is presented, using open data from the Greek Statistical Authority for 363 firms for the severe Greek economic crisis period 2009–2014, which gave satisfactory results concerning the prediction of nine different aspects of digital vulnerability to economic crisis (five of them concerned the main types of digital investment, and the other four concerned the main types of digital operating expenses).

In recent years, the field of Artificial Intelligence has seen significant progress, particularly in the development of chatbots via Natural Language Processing (NLP) technology. Recently, however, there has been a real race in this sector with major technology companies constantly presenting new improved solutions. However, the Greek reality presents several peculiarities and difficulties in adopting modern solutions, both due to the idiosyncrasies and rarity of the language and the limited funding capabilities of the Greek economy. The purpose of this research is to evaluate the performance of chatbots in terms of the quality of their responses regarding relevance, naturalness, cohesion, accuracy, vocabulary, as well as to assess the user experience and satisfaction. Another goal is to gain a comprehensive comparative picture of chatbot operation in Greece, both per question and in comparison, between relevant questions. A guided interview with closed-type questions was chosen as the method of evaluation. The aim is to obtain structured and quantified data in an area where the average internet user is not fully familiarized and does not have previous relevant evaluation experience. Conclusions were drawn per question in order to evaluate the level of solutions in a focused and comparative way to identify possible trends and to confirm the consistency of the responses.

Economic crises repeatedly appear in market-based economies and have serious consequences on them. They are causing serious decreases of the sales revenue and therefor the financial resources of organizations, as well as of their operations and investments. However, they can have some positive effects as well, leading to processes’ rationalizations in important functions of them, improvements of their efficiency and better exploitation of their resources. In this paper we investigate the effects of the strong economic crisis that hit Greece between 2010–2018 on the digitization of one of the most important and costly domains of government activity: the social security. It has been concluded that during the economic crisis period there has been a large decrease of the ICT-related investment and operating expenses; however due to some improvement and rationalization of ICT-related processes and practices, as well as a better utilization of knowledgeable ICT personnel (mainly of the central ‘Electronic Government Center for Social Security’), a significant increase in the digitalization of the Greek Social Security has been achieved during this difficult period.

Data journalism is a valuable emergent and growing trend, which can lead to higher quality journalism, based on real-life data and not on prejudice and pre-existing stereotypes. However, the effective realization (i.e., the ‘real-life’ implementation) of this concept requires the development of high-quality software tools that enable journalists to access useful data sources, are easy to use, and provide clear, understandable and intuitive presentations of the data, as well as practices and processes for using them. In this direction our paper is making a contribution. It describes the design, implementation and evaluation of a software tool that utilizes open data sources concerning food prices in Greece, in order to support data journalism on this topic, which constitutes one of the most critical topics that the societies of many countries face. It is a fully automated solution that can generate new visual reports whenever the data provider updates the food prices data and requires minimal intervention from the journalists; this makes the above data usable for journalistic purposes. The results of the evaluation were positive, indicating a high degree of journalists-users satisfaction, and at the same time revealed difficulties in using these open data for journalism purpose due to quality problems, and also revealing directions for future development of the tool.

Over one in ten babies are born preterm annually, presenting challenges beyond the neonatology intensive care unit (NICU), including depression and post-traumatic stress for parents. Research has demonstrated that tailored interventions supporting parents transitioning from NICU to home can decrease these adverse outcomes. Parents often seek online answers for children’s health before consulting medical professionals. Smartphone applications (apps) supporting parents are being increasingly developed, however, the literature suggests that current apps lack quality and credibility. This study offers a methodical assessment of the quality, level of evidence-based content, and data protection of apps aiming to support parents of premature infants in their transition from NICU to home. The web-based application aggregator Appagg was used to list free and paid Android and iOS apps using keywords such as NICU, preterm, discharge and parenting support in English and in French. The apps were evaluated between March and July 2023. Quality was evaluated using the Mobile Application Rating Scale (MARS) to measure engagement, functionality, aesthetics and information. Then, this study suggests an evidence-based content (EBC) assessment based on the most recent recommendations, guidelines, and scientific literature regarding NICU discharge for premature infants. Finally, the apps’ data protection was evaluated in regards to compliance with the European General Data Protection Regulation (GDPR) or GDPR-like regulation. The search yielded a total of 896 unique apps. Screening for title and abstract selected 22 apps. 12 remained for final analysis as 9 were not accessible to Switzerland or needed patient ID for access and one did not work (video content was not working at all). The results showed that three apps (3/12, 25%) received a good MARS score on overall quality (>4.0 out of 5.0), five apps (5/12, 42%) presented good levels of EBC assessment (≥4 out of 5) and four apps (4/12, 33%) were explicitly compliant to at least one data protection standard (≥4 out of 5).

Online health services provision is the future of heath sector globally. Hospitals that do not make the transition from paper-based systems to electronic may swiftly undermine their chances of sustaining competitiveness in the market and efficiency towards their patients. This paper applies Hospital Online Service Index (HOSPI) assessment methodology to analyse progress in Portuguese hospitals’ migration to a digital state. It employs data from an assessment conducted in 2021 to examine online health services provision. The article compares the results of that assessment to a previous one in 2019 and finds that Portuguese hospitals’ websites remain in a stable status with slight progress in administration procedures, navigability, usability and readability aspects. The paper also suggests possible steps that could be considered, by Portuguese hospitals, towards covering the existing gaps.

Data spaces are one of the key technology pillars of the European Strategy for Data, which intends to establish a single market inside the European Union (EU) for the efficient and secure sharing and interchange of data across industries. A data space that could combine and correlate cross-sector knowledge from the healthcare and environmental sectors could play a crucial role in determining the future of healthcare. Given that climate change is the single greatest health threat facing humanity and that health professionals worldwide are already responding to the health harms caused by this developing crisis, such a solution would enable the identification and correlation of environmental influences on human health as well as the extraction of novel biomarkers. However, the difficulties in creating such infrastructure necessitate cutting-edge, multidisciplinary research in numerous fields. This manuscript contributes into providing a visionary approach toward a single-entry point ecosystem to access, share, and trade cross-sector data assets originating from the environmental and healthcare domains through a Cross-sector Data Space (CDS), thereby effectively promoting European technological autonomy in data sharing. This CDS will consider a variety of analytics as ready-to-use solutions to facilitate analysis, prediction, and monitoring of the causality, correlation, reasoning, and practical visualization of real-time environmental settings, as well as to identify the effects of climate change to human health.

This research explores the role of the District Health Information System (DHIS2) in global health decision-making. Although DHIS2 is widely used in healthcare systems around the world, there is a significant shortage of in-depth scholarly research assessing its effectiveness and scope. This gap is critical, as understanding DHIS2’s full potential is central in today’s global health context, where data-driven decision-making is fundamental to both managing public health crises and improving healthcare delivery. To address this, our research employs the Latent Dirichlet Allocation (LDA) methodology to analyze a corpus of DHIS2-centric research. By doing so, we aim to uncover the main themes and applications of DHIS2 for informed action, thereby providing a more structured view of its impact in the health data management field. In addition, the study yielded a word cloud which depicted a variety of diseases connected to DHIS2 research, outlining its fundamental value in disease surveillance and management. The results emphasize DHIS2’s enabling role within the broader HMIS ecosystem in developing a data-centric approach for effective public health interventions and management of health emergencies. This study develops a blueprint for exploiting HMIS to its maximum potential for informed public health activities by gaining sophisticated knowledge of DHIS2 through the LDA analysis. It guides researchers in understanding DHIS2’s applications, facilitating further exploration in areas like disease trend analysis. Practitioners can use these insights to improve health surveillance, for instance, by tracking and managing outbreaks, thereby making more informed decisions in public health scenarios.

In today’s data-driven business environment, the ethical management of knowledge and data utilization for decision-making in supply chain management has become increasingly vital. This study explores how artificial ethics principles can guide businesses in managing knowledge ethically and enable data-driven decision-making in supply chain management. The study specifically looks into two key areas: establishing moral standards for handling data and knowledge throughout the supply chain and incorporating artificial ethics principles into data analytics systems to support fairness and impartiality. The study follows a semi-systematic review approach. The findings show the importance of ethical considerations and their contributions to knowledge management and data-driven decision-making in supply chain management. By integrating artificial ethics principles, organizations can uphold ethical values such as accountability, fairness, and transparency in their decision-making procedures. Moreover, integrating these principles into data analytics systems ensures unbiased and equitable decision-making. This study emphasizes the value of integrating ethics into supply chain operations and provides advice for businesses looking to use data ethically and efficiently.

This paper explores the integration of artificial intelligence (AI) into project management, proposing a decision support system that optimizes project timelines and resources. The pilot study focuses on the Port of Agios Konstantinos in Greece. The methodology section introduces dual annealing as a stochastic optimization method and explains the use of a customizable cost function with overlap calculation to prioritize project aspects. An objective function is defined to maximize task alignment with optimal scheduling periods. The experimental results section presents three optimization cases, adjusting schedules for critical tasks in the pilot project based on different weightings of budget and weather considerations.

Despite the concerns that recent developments in Large Language Models (LLMs) have raised, they undoubtedly revealed a novel potential of Artificial Intelligence (AI) algorithms in educational environments. Whether they are used for tutoring, in a manner similar to that of Intelligent Tutoring Systems (ITS), or to support assessment design and delivery, their impact in a learning setting is remarkable. In this paper, we propose an interactive tutoring approach, utilizing ChatGPT’s API. By exploiting ChatGPT’s programming interface, we can develop customized interactive problem-solving and tutoring sessions on specific topics of interest. The API’s versatility allows for dynamic interactions, fostering a deeper understanding of subjects taught and effective problem-solving skills. We demonstrate the application of the developed code in an applied educational setting with specific use cases.