Government Data Research Articles

Reduction in all-cause medical and caregiving costs through innovative health awareness projects in a rural area in Japan: a retrospective cohort study

BackgroundThis study evaluates cost reduction in participants of a health awareness program (the Center of Healthy Aging Program, CHAP) in a Japanese rural area, characterized by an annual check-up and personalized interview on health issues and related risks immediately after the check-up.MethodsThis is a cross-sectional study using medical and caregiving costs and Japan-specific health check-up results in Hirosaki residents stored by the local government, which were individually-based linked to the CHAP information collected by Hirosaki University. This is the first study that used anonymized data with individually-based linkages to both a research institute and a local government in Japan under a strict limitation regarding linking to third-party data. We included residents who had been continuously enrolled for > 6 months as of 1 July 2015. We compared 5-year all-cause costs between three groups (with CHAP, with Japan-specific health check-up, and no check-up) using a multivariate negative binomial regression model considering risk factors including lifestyle habits and an inverse probability weight to adjust for baseline characteristics: age, sex, Charlson comorbidity index, baseline care level, and risk score of coronary heart diseases.ResultsA total of 384, 9805, and 32,630 residents aged 40–74 years were included for the CHAP, Japan-specific health check-up, and no check-up groups, respectively. The Japan-specific health check-up group showed older and higher Charlson comorbidity index than the others. After inverse probability weight adjusting, the amount of all-cause medical costs was significantly lower only in the CHAP group. Faster walking speed and exercise habits were independently associated with lower all-cause medical and caregiving costs.ConclusionsWe demonstrated a 5-year all-cause cost reduction in residents who participated in the CHAP and also suggested the effect of exercise habits in Hirosaki, which indicated the significance of individually-based data linkages to external third-party data for all local governments to improve the health condition of residents.

Did People Really “Leave It Blank”? A Tale of What Became of the Census Citizenship Question and Allocation Trends Through Time

Abstract The 2020 Decennial Census garnered unprecedented attention due to the proposed reintroduction of the citizenship question by the Trump Administration. The legal imperative to respond to the Census and the sensitive nature of the citizenship question led to tensions and public outrage. This paper discusses the controversy surrounding the addition of the citizenship question and its implications on vulnerable communities, particularly undocumented immigrants and communities of color. Although the citizenship question was ultimately not added to the 2020 Decennial Census, it continued to be asked on the American Community Survey (ACS). We examine how negative public sentiment during this time, in tandem with historical mistrust of government data collection, may have manifested as item non-response within the ACS, specifically on questions relating to citizenship status and, more broadly, the Origin and Language subgroup of questions. The analyses in this paper explore trends in non-response rates from the American Community Survey from 2014 to 2022. Item allocation rates are used as a proxy for non-response to questions relating to citizenship. A variety of data visualization tools, supported by statistical methods, are employed to weave connections between allocation trends and the proposed addition of the citizenship question to the Decennial Census. We aim to bring transparency to this issue to educate and encourage response within vulnerable communities by emphasizing the importance of accurate data collection and positive perceptions of government-issued surveys, which are used to make key legislative decisions and create a more democratic nation.

Lean resource management and reliable interaction for a low-carbon-oriented new grid

The lean resource management and reliable interaction of massive data are important components of a low-carbon-oriented new grid. However, with a high proportion of distributed low-carbon resources connected to a new grid, issues such as data anomalies, data redundancy, and missing data lead to inefficient resource management and unreliable interaction, affecting the accuracy of power grid decision-making, as well as the effectiveness of emission reduction and carbon reduction. Therefore, this paper proposes a lean resource management and reliable interaction framework of a middle platform based on distributed data governance. On this basis, a distributed data governance approach for the lean resource management method of the middle platform in the low-carbon new grid is proposed, which realizes anomalous data cleaning and missing data filling. Then, a data storage and traceability method for reliable interaction is proposed, which prevents important data from being illegally tampered with in the interaction process. The simulation results demonstrate that the proposed algorithm significantly enhances efficiency, reliability, and accuracy in anomalous data cleaning and filling, as well as data traceability.

Exploring dimensions of metadata quality assessment: A scoping review

Assessing metadata is of paramount importance for several critical reasons. Metadata plays a pivotal role in various aspects, including data retrieval and search, data organization, interoperability, data preservation, and the overall user experience. The purpose of this scoping review is to identify the most commonly measured dimensions of metadata quality in existing studies on metadata quality assessment. The study also investigates the types of data sources and countries contributing most to the literature on metadata quality assessment and the types of documents used to communicate their findings. The methodology involves the application of PRISMA model for qualitatively evaluating 55 studies on metadata quality assessment. The co-occurrence analysis is made on the title and abstract of selected articles using VOSviewer 1.6.18 version, visualization software. The review found that completeness, accuracy, consistency, accessibility, conformance, provenance, and timeliness are commonly used dimensions in metadata quality assessment. However, there is no consensus on their exact definition and measurement, indicating a need for further investigation into less commonly assessed quality dimensions. Digital repositories and open government data are the most commonly studied data sources, with the United States being the leading contributor and journal articles being the most commonly used document type. The cluster analysis based on co-occurrence of terms in title and abstract found three research areas, “Metadata Quality Assessment,” “Metadata Quality Dimensions,” and “Metadata Quality Applications, Frameworks, and Approaches” as prominent areas of research. The originality of the study lies in its methodology that involves rigorous screening of articles on metadata quality. It is a first attempt to qualitatively synthesize literature on metadata quality. The article emphasizes the importance of metadata quality research and the need to improve the flexibility of metadata quality assessment tools to facilitate better metadata quality assurance measures.

Open government data and resource allocation efficiency: evidence from China

ABSTRACT Data are among the most valuable resources. This paper, utilizing the China Tax Survey Database from 2009 to 2016, investigates the impact of Open Government Data (hereafter, OGD) on the efficiency of resource allocation among firms within the Chinese context. The findings indicate that OGD significantly reduces the deviation in firms’ factor inputs, thereby enhancing the efficiency of resource allocation. This is primarily achieved through the optimization of government-business relationships, the reduction of rent-seeking costs, and the decrease in information asymmetry. Given the inclusive nature of OGD, it has a more pronounced positive impact on disadvantaged firms and cities. Furthermore, while OGD directly improves representative firm’s TFP, its greater economic impact lies in elevating aggregate TFP by enhancing resource allocation efficiency. In the current global economy, highly dependent on data-driven processes, the conclusions of this paper provide a novel perspective on the role of OGD in enhancing productivity. This has significant implications for accelerating the implementation of open data policies and enhancing economic efficiency.

Онлайн-ризики в умовах сучасності: проблеми та шляхи подолання

It was emphasized that ensuring the safety of children is one of the priority tasks of the state. Today, in the conditions of rapid digital development, new challenges and threats to the well-being of children appear, which require the search for effective tools to overcome them and provide a comprehensive approach to ensuring the safety of children in cyberspace. Attention is focused on the fact that a comprehensive approach includes, among other things, the appropriate level of training of specialists of entities whose competence includes prevention and response to threats to the safety of children in cyberspace. Timely detection of threats to the safety of children in cyberspace depends on their professionalism, care, and impartiality. It has been proven that information security is more relevant than ever. Growing cyber attacks on public and private enterprises only increase this trend. A separate category of threats concerns citizens, who are increasingly becoming the object of close attention of criminals, therefore, a relatively new concept of the need for users to independently observe basic safety rules is gradually spreading. This approach makes it possible to significantly strengthen the system of collective security of society and the state as a whole. It is noted that cyber hygiene should be treated in the same way as personal hygiene, and when properly integrated into the organization, become a simple daily procedure that will ensure the state of cyber health of the organization in an optimal state. It became clear that violation of the rules of cyber hygiene can lead to harmful consequences not only for an individual person. Quite often, the victim's employer also suffers from the actions of criminals. Even great powers can suffer enormous damage from a careless attitude towards the security requirements of one person. Violators often use an individual to penetrate critical infrastructure facilities, steal government data, and create conditions for coordinated full-scale attacks.

IMPLEMENTING AI IN BUSINESS MODELS: STRATEGIES FOR EFFICIENCY AND INNOVATION

This review delves into the profound impact of artificial intelligence (AI) integration on contemporary business paradigms. The paper meticulously explores diverse AI applications, including machine learning, natural language processing, and predictive analytics, illustrating how these technologies can revolutionize operational processes, augment decision-making capabilities, and foster unparalleled innovation within organizations. Drawing from case studies and industry examples across various sectors such as finance, healthcare, retail, and manufacturing, the study elucidates successful AI implementation strategies. It examines the importance of robust data governance frameworks to ensure quality and integrity, the acquisition of AI talent, and the imperative of fostering a culture of innovation and adaptability within organizations undergoing AI transformation. Furthermore, the paper addresses the nuanced challenges and risks inherent in AI adoption, spanning ethical considerations surrounding data privacy and bias mitigation, cybersecurity vulnerabilities, and the potential impact on the workforce. By providing a comprehensive overview of the opportunities and challenges associated with AI integration in business models, the study equips organizational leaders, policymakers, and stakeholders with invaluable insights to navigate the evolving landscape of AI-driven innovation. It underscores the significance of strategic foresight, cross-functional collaboration, and continuous learning in harnessing the full potential of AI technologies to drive sustainable growth and competitive advantage in the digital era.
 Keywords: AI, Business, Models, Strategies, Efficiency, Innovation.

THE ROLE OF PREDICTIVE ANALYTICS IN OPTIMIZING SUPPLY CHAIN RESILIENCE: A REVIEW OF TECHNIQUES AND CASE STUDIES

This study investigates the transformative impact of predictive analytics on enhancing supply chain resilience (SCR). Employing a systematic literature review and content analysis, the research aims to explore the integration, challenges, and strategic implications of predictive analytics within the supply chain ecosystem. Focusing on literature from 2014 to 2023, the study synthesizes insights from peer-reviewed articles and conference papers, adhering to stringent inclusion and exclusion criteria to ensure relevance and recency. The findings reveal that predictive analytics significantly contributes to supply chain agility, flexibility, and responsiveness, thereby bolstering SCR against disruptions. Key challenges identified include data privacy concerns, the need for skilled personnel, and the integration of predictive analytics into existing supply chain frameworks. Despite these challenges, the future outlook for predictive analytics in SCM is promising, with potential for unprecedented efficiency, sustainability, and competitive advantage. Strategic recommendations for practitioners emphasize the importance of developing predictive analytics capabilities, prioritizing data governance, and continuous staff training. For policymakers, the study suggests the need for standards and regulations that encourage innovation while ensuring the ethical use of predictive analytics. Finally, predictive analytics is pivotal in revolutionizing SCM, offering pathways for innovation, efficiency, and enhanced resilience. Future research should focus on the integration of emerging technologies, ethical data use frameworks, and overcoming adoption barriers, particularly in SMEs and developing economies. This study underscores the critical role of predictive analytics in the future of SCM, driving forward the agenda for research and practice in this evolving field.
 Keywords: Predictive Analytics, Supply Chain Resilience, Systematic Literature Review, Strategic Implications.

Blockchain-based governance models in e-government: a comprehensive framework for legal, technical, ethical and security considerations

Purpose This study aims to evaluate blockchain as an e-government governance model. It assesses its alignment with legal frameworks, emphasizing robustness against disruptions and adherence to existing laws. Design/methodology/approach The paper explores blockchain’s potential in e-government, focusing on legal, ethical and governance aspects. It conducts an in-depth analysis of blockchain’s integration into data governance, emphasizing legal compliance and resilient security protocols. Findings The study comprehensively evaluates blockchain’s implementation, covering privacy, interoperability, consensus mechanisms, scalability and regulatory alignment. It highlights governance’s critical role in ensuring legal compliance within blockchain paradigms. Research limitations/implications Ethical and legal concerns arising from blockchain adoption remain unresolved. The study underscores how blockchain challenges its core principles of anonymity and decentralization in e-government settings. Practical implications The framework outlined offers potential for diverse technological environments, albeit raising ethical and legal queries. It emphasizes governance’s pivotal role in achieving legal compliance in blockchain adoption. Social implications Blockchain’s impact on legal and ethical facets necessitates further exploration to align with its core principles while addressing governance in e-government settings. Originality/value This study presents a robust framework for assessing blockchain’s viability in e-government, emphasizing legal compliance, despite ethical and legal intricacies that challenge its fundamental principles.

Optimizing Big Data Insights with Serverless Architecture

Big data is the huge amount of data, which can be structured, semi-structured, or unstructured, that is required for current commercial processes. Big Data efforts and technologies are used to analyze large amounts of data in order to gain insights critical for strategic decision-making. Data size is constantly rising, reaching petabytes, exabytes, zettabytes, and even yottabytes, offering substantial management and processing issues. In practice, managing massive amounts of data involves several obstacles, such as server management, storage, clustering, and algorithm deployment. Manual intervention hampers the creation of successful Cloud-based data analysis platforms. Serverless computing provides a solution by offering clients pay-per-use backend services, reducing the need for users to manage server operations. This article describes a serverless architecture for large data analytics, including implementation, maintenance, and governance on Amazon Web Services (AWS). Furthermore, it investigates the differences between traditional and big data analytics in a serverless system

Abstract 1303: Harness the power of data to improve cancer care - Understanding the complex landscape of health policies and regulations

Abstract In the era of big data and artificial intelligence, biomedical research and healthcare have been presented with unprecedented opportunities for improving the understanding of cancer biology. In addition, the Open Science Initiative aims to shift the current paradigm of research practice towards broad sharing and public access to research findings as the norm. Big data in oncology usually includes a combination of genomic sequencing, proteomics, medical imaging, electronic health records, payor records, and other data sources from pharmaceutical research, wearables, and medical devices. While combining, linking, and analyzing data from different sources across research and healthcare enterprises is key to realizing the promise of big data and a health learning system, it poses tremendous challenges for researchers and implementers to understand the nuances of relevant policies, laws, and regulations while striving for broad, equitable, timely and responsible data sharing, as well as tackling issues in data quality, harmonization, interoperability, and security. Herein, we aim to shine a light on the ever-evolving landscape of data privacy regulations, laws, and policies that govern different touch points of a patient's journey where data are collected for research and healthcare purposes throughout the lifecycle that would impact data-sharing outcomes. We hope the illumination of a complex landscape of data governance framework to consider for many data sources will facilitate broad community engagement including data scientists and technologists, patients and advocacy groups, and policymakers to maximize open data and science while protecting patient privacy and confidentiality. Citation Format: Ying Huang, Heather Basehore, Emily S. Boja, Jaime M. Guidry Auvil. Harness the power of data to improve cancer care - Understanding the complex landscape of health policies and regulations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1303.

Abstract 3568: Managing large-scale cancer research data programs

Abstract Purpose: The National Cancer Institute’s (NCI’s) Cancer Research Data Commons (CRDC) is a data ecosystem for the cancer research community that provides cloud-based, secure storage and analytic tools for many cancer data types, including genomic, proteomic, imaging, and clinical data. CRDC requires a carefully designed, federated data governance framework to enable CRDC’s goals to sustain a collaborative data environment where researchers can access timely, accurate, and relevant cancer data. Methods: In 2023, CRDC chartered a Data Governance Framework of Committees and Working Groups across all CRDC components to: Enable diverse data type sharing; provide secure data access; optimize common infrastructure components and functions; and adhere to FAIR data principles (Findable, Accessible, Interoperable, and Reusable). We considered many designs for the CRDC Data Governance Framework, including fully centralizing or decentralizing decision-making bodies. Implementing a fully centralized data governance framework for a diverse data ecosystem like CRDC would risk top-down decisions that do not meet the unique needs of the CRDC and cancer research community. A fully decentralized data governance framework risks inconsistencies and compliance challenges. Additional governance design considerations included a federated approach that emphasizes clear roles and responsibilities; prioritizing cross-component policies to ensure consistency and promote operational efficiency; defining measurable performance standards; and keeping data owners, stewards, and users informed, trained, and supported. Results: We have determined that long term sustainment of CRDC requires a federated governance approach with broad stakeholder participation through Committees and Working Groups. Currently established CRDC governance committees include the Enterprise Architecture Review Team (EART), Submission Review Team, Data Standards Working Group, and Data Advisory Board consisting of cancer research domain experts, researchers, and skilled cloud engineers and developers. In 2023, the CRDC EART defined data lifecycle stages and identified technical optimization opportunities for submission coordination and indexing. The established governance groups are working to identify optimization opportunities for data quality checks, submission review workflows, study acceptance criteria, data submission reviews, and cross-CRDC emerging data standards requirements. Conclusions: Sustaining large, complex, cancer research data requires a federated governance approach with participation from all relevant stakeholder groups and cannot simply be top-down imposition of policies, standards, and procedures. The newly established CRDC federated governance framework will contribute to improving the long-term sustainability of the CRDC infrastructure and ensuring CRDC data drive meaningful research outcomes. Citation Format: Juergen Klenk, Dina Mikdadi, Chelsea Owens, Angela Maggio, Bhavani Singh, Eric Barner, Tanja Davidsen, Erika Kim. Managing large-scale cancer research data programs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3568.

Navigating data governance challenges in healthcare

This teaching case study delves into the journey of a public sector healthcare organisation in Australia, Vitality Alliance Queensland (VAQ), as they strive to future-proof the organisation’s data governance framework. Drawing from a real-world scenario, the case describes three key governance challenges faced by the organisation: data accessibility, data sensitivity, and data democratisation and empowerment. The rich narrative allows students to gain a deep understanding of the tensions that arise within and across these challenges and insights into how diverse stakeholders must navigate complexities to create and realise value from organisational data.

Competing digital capacities: between state-led digital governance and local data center tradeoffs

ABSTRACT Governments worldwide are embracing digital transformation, envisioning limitless data capacities for public policy. However, this paper uncovers a crucial oversight: the neglect of material resources sustaining digital ecosystems. This paper introduces contested ‘digital capacities’ in public policy, to show how the aspiration for unlimited data processing strains local resources. Through a comparative analysis of national digitization strategies in the United States and the United Kingdom, this paper reveals a disconnect between the sociotechnical imaginaries and infrastructural demands underpinning these strategies. The infrastructural demands are leading to social and environmental tradeoffs in regional areas strained by digital infrastructure development. I argue that these competing digital capacities – the capacity to govern through digitization and related resource capacities – provide productive data friction for governments to pause and assess the broader social and ecological costs of their national digitization policies. As such, this article emphasizes the need for policymakers to align national digital governance strategies with regional planning for equitable outcomes. By reevaluating competing digital capacities encompassed in national sociotechnical imaginaries, this paper highlights the urgent need for a balanced approach to digitization.

Digital market and its adequacy of merger assessment in Indonesian business competition law

Purpose This study aims to analyze the development of digital market characteristics particularly focusing on how the strategic choices of platforms are not fully reflected in pricing. In addition, the implications for the development of theories of harm are investigated to explore the necessity of a relevant market definition in assessing infringement and evaluating the adequacy of Indonesian competition law. Design/methodology/approach This study is a legal analysis that uses statutory approaches, cases, comparative law and the development of theories of harm in digital mergers. The case approach is conducted by analyzing three cases decided by the Indonesia Business Competition Supervisory Commission. This approach provides insight into the response of Komisi Pengawas Persaingan Usaha concerning the merger and acquisition cases in the digital era as well as the provision of different analyses in conventional markets. However, competition can be potentially damaged in digital markets and a comparative law approach is taken by analyzing digital merger cases decided by authorities in other countries. Findings Results reveal that the digital market has created a “relevant market” that is challenging and blurred due to multi-sided network effects and consumer data usage characteristics. Platform-based enterprises’ prices fluctuate due to the digital market’s network effect and consumer data statistics. Smartphone prices depend on the number of apps and consumer data. Neoclassical theory focusing on product markets and location applied in Indonesia must be revised to establish a relevant digital economy market. To evaluate digital mergers, new harm theories are needed. The merger should also protect consumer data. Law Number 27 of 2022 on Personal Data Protection and Government Regulation on the Implementation of Electronic Systems and Transactions protects online consumers, a basic step in due diligence for digital mergers. The Indonesian Government should promptly strengthen the notion of “relevant markets” in the digital economy, which could lead to fair business competition violations like big data control. Notify partners or digital merger participants of the accessibility of sensitive data like transaction history and user location. Originality/value The development of digital market characteristics has implications for developing theories of harm in digital markets. Indonesian competition law needs to develop such theories of harm to analyze the potential for anticompetitive digital mergers in the digital economy era.

Investigating the Roles and Responsibilities of Institutional Signing Officials After Data Sharing Policy Reform for Federally Funded Research in the United States: National Survey.

New federal policies along with rapid growth in data generation, storage, and analysis tools are together driving scientific data sharing in the United States. At the same, triangulating human research data from diverse sources can also create situations where data are used for future research in ways that individuals and communities may consider objectionable. Institutional gatekeepers, namely, signing officials (SOs), are therefore at the helm of compliant management and sharing of human data for research. Of those with data governance responsibilities, SOs most often serve as signatories for investigators who deposit, access, and share research data between institutions. Although SOs play important leadership roles in compliant data sharing, we know surprisingly little about their scope of work, roles, and oversight responsibilities. The purpose of this study was to describe existing institutional policies and practices of US SOs who manage human genomic data access, as well as how these may change in the wake of new Data Management and Sharing requirements for National Institutes of Health-funded research in the United States. We administered an anonymous survey to institutional SOs recruited from biomedical research institutions across the United States. Survey items probed where data generated from extramurally funded research are deposited, how researchers outside the institution access these data, and what happens to these data after extramural funding ends. In total, 56 institutional SOs participated in the survey. We found that SOs frequently approve duplicate data deposits and impose stricter access controls when data use limitations are unclear or unspecified. In addition, 21% (n=12) of SOs knew where data from federally funded projects are deposited after project funding sunsets. As a consequence, most investigators deposit their scientific data into "a National Institutes of Health-funded repository" to meet the Data Management and Sharing requirements but also within the "institution's own repository" or a third-party repository. Our findings inform 5 policy recommendations and best practices for US SOs to improve coordination and develop comprehensive and consistent data governance policies that balance the need for scientific progress with effective human data protections.

Research of open government data in China: A systematic literature study in public administration (2013–2023)

By building structures on open government data and illuminating the area of China with a systematic assessment of 220 papers published in major journals of public administration from 2013 to 2023, this article aims to bridge the research gap of open government data. It presents an overview of the landscape of Chinese OGD research and exposes the current gaps with the paths of unresolved research in the future by thoroughly examining the overall progress, theoretical topics, theoretical underpinnings, and methodologies of these studies. Combining the findings of this analysis with the overall trends in Chinese OGD development and the current international research frontiers on OGD, the article suggests that Chinese scholars should critically examine international theories and construct a development framework tailored to the specific needs of Chinese OGD. It also proposes the coordinate mechanism and the synergistic effect at the micro-level need to be further explored and use more advanced quantitative research methodologies to confirm theoretical and qualitative empirical findings.

Research Directions of the Energy Transformation Impact on the Economy in the Aspect of Asset Analysis

The challenges of climate change encourage immediate solutions by creating the conditions for energy transformation. This process is significantly related to capital investments and the conversion of existing assets to clean energy sources. Due to the need to save resources, workplaces, and public favor, specific studies involving different data groups are necessary. This article presents key data sources for analyzing the impact of energy transformation on Lithuanian regions—governance, potential, and existing power indicators. The economic, social, and governance data combined in the article will allow us to determine the perspectives of sustainable energy transformation in the regions. The article showed that in certain regions there may be major, complex problems that hinder the breakthrough of the energy system. Cluster analysis and a generic system diagram were used for the research, which showed that municipalities have a wide potential for energy transformation. The research emphasizes the governance component as a key success factor. After reorganizing the management of municipal enterprises, further actions can be initiated, since emerging competences allow progress to be made efficiently and with less time spent. The asset analysis methodology presented in the article allows for the identification of the main groups of assets that can be converted in the short term for the purpose of energy transformation, according to sustainable development principles. These decisions provide an opportunity for decision-makers to consistently implement energy transformation decisions.

The unresolved need for dependable guarantees on security, sovereignty, and trust in data ecosystems

Data ecosystems emerged as a new paradigm to facilitate the automated and massive exchange of data from heterogeneous information sources between different stakeholders. However, the corresponding benefits come with unforeseen risks as sensitive information is potentially exposed, questioning data ecosystem reliability. Consequently, data security is of utmost importance and, thus, a central requirement for successfully realizing data ecosystems. Academia has recognized this requirement, and current initiatives foster sovereign participation via a federated infrastructure where participants retain local control over what data they offer to whom. However, recent proposals place significant trust in remote infrastructure by implementing organizational security measures such as certification processes before the admission of a participant. At the same time, the data sensitivity incentivizes participants to bypass the organizational security measures to maximize their benefit. This issue significantly weakens security, sovereignty, and trust guarantees and highlights that organizational security measures are insufficient in this context. In this paper, we argue that data ecosystems must be extended with technical means to (re)establish dependable guarantees. We underpin this need with three representative use cases for data ecosystems, which cover personal, economic, and governmental data, and systematically map the lack of dependable guarantees in related work. To this end, we identify three enablers of dependable guarantees, namely trusted remote policy enforcement, verifiable data tracking, and integration of resource-constrained participants. These enablers are critical for securely implementing data ecosystems in data-sensitive contexts.

How can communities better prepare for future disasters? Learning from the tourism community resilience model from Bali, Indonesia

PurposeThe impact of COVID-19 on tourism destinations has been severe, but a future crisis is never far away. How communities can better prepare for disasters to come in the near future continues to be researched. This research aims to understand the tourism community’s responses to the COVID-19 pandemic and present the Tourism Community Resilience Model as a useful instrument to help communities better respond to disasters in the future.Design/methodology/approachThis research uses a qualitative research approach which seeks to understand phenomena, events, social activities, attitudes, beliefs, perceptions and individual and group opinions that are dynamic in character in accordance with the situation in the field. Research primary data is in the form of Kuta Traditional Village local community responses in enduring the COVID-19 pandemic conducted between January and May 2022. These data were obtained through in-depth observations and interviews involving informants based on purposive sampling, including traditional community leaders, village officials, tourism actors (i.e. street vendors, tourist local guides, taxi drivers and art workers) and tourism community members. We selected the informants who are not only directly impacted by the pandemic, but also some of them have to survive during the pandemic because they do not have other job options. The results of previous research and government data concerning the pandemic and community resilience were needed as secondary data, which were obtained through a study of the literature. The data which had been obtained were further analysed based on the Interpretative Phenomenological Analysis (IPA) technique, which seeks to make meaning of something from the participants’ perspective and the researchers’ perspective as a result there occurs a cognition of a central position.FindingsBased on findings from Bali, Indonesia, this resilience model for the tourism community was created in response to the difficulties and fortitude shown by the community during the COVID-19 pandemic. It comprises four key elements, namely the Local Wisdom Foundation, Resource Management, Government Contributions and External Community Support. These elements are all rooted in the concepts of niskala (spirituality) and sekala (real response); it is these elements that give the tourism community in the Kuta Traditional Village a unique approach, which can inspire other tourism destinations in other countries around the world.Research limitations/implicationsA tourism community resilience model based on local community responses has implications for the process of enriching academic research and community management practices in facing future crisis, particularly by involving local wisdom foundation.Practical implicationsA tourism community resilience model based on local community responses has implications for the process of enriching academic research and community management practices in facing future crisis, particularly by involving local wisdom foundation.Social implicationsThe existence of the resilience model strengthens local community social cohesion, which has been made stronger by the bonds of culture and shared faith in facing disaster. This social cohesion then stimulates the strength of sustainable and long-term community collaboration in the post-pandemic period. For tourism businesses, having strong connections with the local communities is an important condition to thrive.Originality/valueThe value of this research is the Tourism Resilience Community Model, which is a helpful tool to optimise and improve future strategies for dealing with disasters. Illustrated by this Balinese example, this paper emphasises the importance of adding social factors such as niskala and sekala to existing community resilience models. Addressing these local characteristics is the innovative aspect of this paper and will help inspire communities around the world to prepare for future disasters better and build more sustainable and resilient tourism destinations elsewhere.