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Since the public release of Chat Generative Pre-Trained Transformer (ChatGPT), extensive discourse has emerged concerning the potential advantages and challenges of integrating Generative Artificial Intelligence (GenAI) into education. In the realm of information systems, research on technology adoption is crucial for understanding the diverse factors influencing the uptake of specific technologies. Theoretical frameworks, refined and validated over decades, serve as guiding tools to elucidate the individual and organizational dynamics, obstacles, and perceptions surrounding technology adoption. However, while several models have been proposed, they often prioritize elucidating the factors that facilitate acceptance over those that impede it, typically focusing on the student perspective and leaving a gap in empirical evidence regarding educators viewpoints. Given the pivotal role educators play in higher education, this study aims to develop a theoretical model to empirically predict the barriers preventing educators from adopting GenAI in their classrooms. Acknowledging the lack of theoretical models tailored to identifying such barriers, our approach is grounded in the Innovation Resistance Theory (IRT) framework and augmented with constructs from the Technology-Organization-Environment (TOE) framework. This model is transformed into a measurement instrument employing a quantitative approach, complemented by a qualitative approach to enrich the analysis and uncover concerns related to GenAI adoption in the higher education domain.

Efforts directed towards promoting Open Government Data (OGD) have gained significant traction across various governmental tiers since the mid-2000s. As more datasets are published on OGD portals, finding specific data becomes harder, leading to information overload. Complete and accurate documentation of datasets, including association of proper tags with datasets is key to improving dataset findability and accessibility. Analysis conducted on the Estonian Open Data Portal, revealed that 11% datasets have no associated tags, while 26% had only one tag assigned to them, which underscores challenges in data findability and accessibility within the portal, which, according to the recent Open Data Maturity Report, is considered trend-setter. The aim of this study is to propose an automated solution to tagging datasets to improve data findability on OGD portals. This paper presents Tagify - a prototype of tagging interface that employs large language models (LLM) such as GPT-3.5-turbo and GPT-4 to automate dataset tagging, generating tags for datasets in English and Estonian, thereby augmenting metadata preparation by data publishers and improving data findability on OGD portals by data users. The developed solution was evaluated by users and their feedback was collected to define an agenda for future prototype improvements.

In the contemporary data-driven landscape, ensuring data quality (DQ) is crucial for deriving actionable insights from vast data repositories. The objective of this study is to explore the potential for automating data quality management within data warehouses as data repository commonly used by large organizations. By conducting a systematic review of existing DQ tools available in the market and academic literature, the study assesses their capability to automatically detect and enforce data quality rules. The review encompassed 151 tools from various sources, revealing that most current tools focus on data cleansing and fixing in domain-specific databases rather than data warehouses. Only a limited number of tools, specifically ten, demonstrated the capability to detect DQ rules, not to mention implementing this in data warehouses. The findings underscore a significant gap in the market and academic research regarding AI-augmented DQ rule detection in data warehouses. This paper advocates for further development in this area to enhance the efficiency of DQ management processes, reduce human workload, and lower costs. The study highlights the necessity of advanced tools for automated DQ rule detection, paving the way for improved practices in data quality management tailored to data warehouse environments. The study can guide organizations in selecting data quality tool that would meet their requirements most.

Recognized for fostering innovation and transparency, driving economic growth, enhancing public services, supporting research, empowering citizens, and promoting environmental sustainability, High-Value Datasets (HVD) play a crucial role in the broader Open Government Data (OGD) movement. However, identifying HVD presents a resource-intensive and complex challenge due to the nuanced nature of data value. Our proposal aims to automate the identification of HVDs on OGD portals using a quantitative approach based on a detailed analysis of user interest derived from data usage statistics, thereby minimizing the need for human intervention. The proposed method involves extracting download data, analyzing metrics to identify high-value categories, and comparing HVD datasets across different portals. This automated process provides valuable insights into trends in dataset usage, reflecting citizens' needs and preferences. The effectiveness of our approach is demonstrated through its application to a sample of US OGD city portals. The practical implications of this study include contributing to the understanding of HVD at both local and national levels. By providing a systematic and efficient means of identifying HVD, our approach aims to inform open governance initiatives and practices, aiding OGD portal managers and public authorities in their efforts to optimize data dissemination and utilization.

Open Government Data (OGD) initiatives aim to enhance public participation and collaboration by making government data accessible to diverse stakeholders, fostering social, environmental, and economic benefits through public value generation. However, challenges such as declining popularity, lack of OGD portal usability, and private interests overshadowing public accessibility persist. This study proposes an integrated usability framework for evaluating OGD portals, focusing on inclusivity, user collaboration, and data exploration. Employing Design Science Research (DSR), the framework is developed and applied to 33 OGD portals from the European Union (EU) and Gulf Cooperation Council (GCC) countries. The quantitative analysis is complemented by qualitative analysis and clustering, enabling assessment of portal performance, identification of best practices, and common weaknesses. This results in 19 high-level recommendations for improving the open data ecosystem. Key findings highlight the competitive nature of EU portals and the innovative features of GCC portals, emphasizing the need for multilingual support, better communication mechanisms, and improved dataset usability. The study stresses trends towards exposing data quality indicators and incorporating advanced functionalities such as AI systems. This framework serves as a baseline for OGD portal requirements elicitation, offering practical implications for developing sustainable, collaborative, and robust OGD portals, ultimately contributing to a more transparent and equitable world.

Open Government Data (OGD) plays a crucial role in transforming smart cities into sustainable and intelligent entities by providing data for analytics, real-time monitoring, and informed decision-making. This data is increasingly used in urban digital twins, enhancing city management through stakeholder collaboration. However, local administrative data remains underutilized even in digitally advanced countries like Estonia. This study explores barriers preventing Estonian municipalities from sharing OGD, using a qualitative approach through interviews with Estonian municipalities and drawing on the OGD-adapted Innovation Resistance Theory model (IRT). Interviews with local government officials highlight ongoing is-sues in data provision and quality. By addressing overlooked weaknesses in the Estonian open data ecosystem and providing actionable recommendations, this research contributes to a more resilient and sustainable open data ecosystem. Additionally, by validating the OGD-adapted Innovation Resistance Theory model and proposing a revised version tailored for local government contexts, the study advances theoretical frameworks for understanding data sharing resistance. Ultimately, this study serves as a call to action for policymakers and practitioners to prioritize local OGD initiatives, ensuring the full utilization of OGD in smart city development.

Estonia has a global reputation of a digital state or e-country. However, despite the success in digital governance, the country has faced challenges in the realm of Open Government Data (OGD) area, with significant advancements in its OGD ecosystem, as reflected in various open data rankings from 2020 and onwards, in the recent years being recognized among trend-setters. This paper aims to explore the evolution and positioning of Estonia's OGD development, encompassing national and local levels, through an integrated analysis of various indices, primary data from the Estonian OGD portal, and a thorough literature review. The research shows that Estonia has made progress in the national level open data ecosystem, primarily due to improvements in the OGD portal usability and legislation amendments. However, the local level is not as developed, with local governments lagging behind in OGD provision. The literature review highlights the lack of previous research focusing on Estonian and European local open data, emphasizing the need for future studies to explore the barriers and enablers of municipal OGD. This study contributes to a nuanced understanding of Estonia's dynamic journey in the OGD landscape, shedding light on both achievements and areas warranting further attention for establishing a sustainable open data ecosystem., Comment: This paper has been accepted for publication in Proceedings of the 25th Annual International Conference on Digital Government Research and this is a pre-print version of the manuscript. It is posted here for your personal use. Not for redistribution

Today, data has an unprecedented value as it forms the basis for data-driven decision-making, including serving as an input for AI models, where the latter is highly dependent on the availability of the data. However, availability of data in an open data format creates a little added value, where the value of these data, i.e., their relevance to the real needs of the end user, is key. This is where the concept of high-value dataset (HVD) comes into play, which has become popular in recent years. Defining and opening HVD is an ongoing process consisting of a set of interrelated steps, the implementation of which may vary from one country or region to another. Therefore, there has recently been a call to conduct research in a country or region setting considered to be of greatest national value. So far, only a few studies have been conducted at the regional or national level, most of which consider only one step of the process, such as identifying HVD or measuring their impact. With this study, we answer this call and examine the national case of Taiwan by exploring the entire lifecycle of HVD opening. The aim of the paper is to understand and evaluate the lifecycle of high-value dataset publishing in one of the world's leading producers of information and communication technology (ICT) products - Taiwan. To do this, we conduct a qualitative study with exploratory interviews with representatives from government agencies in Taiwan responsible for HVD opening, exploring HVD opening lifecycle. As such, we examine (1) strategic aspects related to the HVD determination process, (2) technical aspects, and (3) application aspects., Comment: This paper has been accepted for publication in Proceedings of the 25th Annual International Conference on Digital Government Research and this is a pre-print version of the manuscript. It is posted here for your personal use. Not for redistribution

This study explores the critical role of open government data (OGD) portals in fostering transparency and collaboration between diverse stakeholders. Recognizing the challenges of usability, communication with diverse populations, and strategic value creation, this paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. The framework is validated by applying it to 33 national portals across European Union and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings. Notably, the study unveils the competitive and innovative nature of GCC OGD portals, pinpointing specific improvement areas such as multilingual support and data understandability. The findings underscore the growing trend of exposing data quality metrics and advocate for enhanced two-way communication channels between users and portal representatives. Overall, the study contributes to accelerating the development of user-friendly, collaborative, and sustainable OGD portals while addressing gaps identified in previous research., Comment: This paper has been accepted for publication in Proceedings of the 25th Annual International Conference on Digital Government Research and this is a preprint version of the manuscript

As artificial intelligence continues its unprecedented global expansion, accompanied by a proliferation of benefits, an increasing apprehension about the privacy and security implications of AI-enabled systems emerges. The pivotal question of effectively controlling AI development at both jurisdictional and organizational levels has become a prominent theme in contemporary discourse. While the European Parliament and Council have taken a decisive step by reaching a political agreement on the EU AI Act, the first comprehensive AI law, organizations still find it challenging to adapt to the fast-evolving AI landscape, lacking a universal tool for evaluating the privacy and security dimensions of their AI models and systems. In response to this critical challenge, this study conducts a systematic literature review spanning the years 2020 to 2023, with a primary focus on establishing a unified definition of key concepts in AI Ethics, particularly emphasizing the domains of privacy and security. Through the synthesis of knowledge extracted from the SLR, this study presents a conceptual framework tailored for privacy- and security-aware AI systems. This framework is designed to assist diverse stakeholders, including organizations, academic institutions, and governmental bodies, in both the development and critical assessment of AI systems. Essentially, the proposed framework serves as a guide for ethical decision-making, fostering an environment wherein AI is developed and utilized with a strong commitment to ethical principles. In addition, the study unravels the key issues and challenges surrounding the privacy and security dimensions, delineating promising avenues for future research, thereby contributing to the ongoing dialogue on the globalization and democratization of AI ethics., Comment: \c{opyright} Korobenko, D., Nikiforova, A., Rajesh, S. | ACM 2024. This paper has been accepted for publication in In Proceedings of the 25th Annual International Conference on Digital Government Research. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record will be published in ACM DL

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset.

Open government and open (government) data are seen as tools to create new opportunities, eliminate or at least reduce information inequalities and improve public services. More than a decade of these efforts has provided much experience, practices, and perspectives to learn how to better deal with them. This paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. To do this, we studied benchmarks and indices published over the last years (57 editions of 8 artifacts) and conducted a comparative case study of eight European countries, identifying patterns among them considering different potentially relevant contexts such as e-government, open government data, open data indices and rankings, and others relevant for the country under consideration. Using a Delphi method, we reached a consensus within a panel of experts and validated a final list of 94 patterns, including their frequency of occurrence among studied countries and their effects on the respective countries. Finally, we took a closer look at the developments in identified contexts over the years and defined 21 recommendations for more resilient and sustainable open government data initiatives and ecosystems and future steps in this area., Comment: 46 pages

Recently, there has been increasing awareness of the tremendous opportunities inherent in quantum computing (QC). Specifically, the speed and efficiency of QC will significantly impact the Internet of Things, cryptography, finance, and marketing. Accordingly, there has been increased QC research funding from national and regional governments and private firms. However, critical concerns regarding legal, political, and business-related policies germane to QC adoption exist. Since this is an emerging and highly technical domain, most of the existing studies focus heavily on the technical aspects of QC, but our study highlights its practical and social uses cases, which are needed for the increased interest of governments. Thus, this study offers a multidisciplinary review of QC, drawing on the expertise of scholars from a wide range of disciplines whose insights coalesce into a framework that simplifies the understanding of QC, identifies possible areas of market disruption and offer empirically based recommendations that are critical for forecasting, planning, and strategically positioning QCs for accelerated diffusion.