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This research aims to explore the research data ethics governance framework and collaborative network to optimize research data ethics governance practices, to balance the relationship between data development and utilization, open sharing, data security and to reduce the ethical risks that may arise from data sharing and utilization.

This study explores the framework and collaborative network of research data ethics policies by using the UK as an example. 78 policies from the UK government, university, research institution, funding agency, publisher, database, library and third-party organization are obtained. Adopting grounded theory (GT) and social network analysis (SNA), Nvivo12 is used to analyze these samples and summarize the research data ethics governance framework. Ucinet and Netdraw are used to reveal collaborative networks in policy.

Results indicate that the framework covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. The collaborative network indicates that research institution plays a central role in ethics governance. The core of the governance content are ethics governance initiatives, governance guidance and governance context description.

This research provides new insights for policy analysis by combining GT and SNA methods. Research data ethics and its governance are conceptualized to complete data governance and research ethics theory.

A research data ethics governance framework and collaborative network are revealed, and actionable guidance for addressing essential aspects of research data ethics and multiple subjects to confer their functions in collaborative governance is provided.

This study analyzes policy text using qualitative and quantitative methods, ensuring fine-grained content profiling and improving policy research. A typical research data ethics governance framework is revealed. Various stakeholders' roles and priorities in collaborative governance are explored. These contribute to improving governance policies and governance levels in both theory and practice.

Upon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.

To develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.

Legislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.

Although this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.

This study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.

The findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.

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.

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.

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.

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.

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.

Blockchain’s impact on legal and ethical facets necessitates further exploration to align with its core principles while addressing governance in e-government settings.

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.

The purpose of the study is to explore the integration of facets of information technology (IT) governance at a professional council in South Africa with the view to develop a framework.

This critical emancipatory study used the Information Governance Initiative pinwheel to explore the architecture facet of information governance at the professional council, with a view to developing a framework for entrenching a culture of good corporate governance. Qualitative data was collected through interviews and document analysis. The study was a participatory action research project that involved collaboration between the researcher and study participants in defining and solving the problem through a needs assessment exercise.

The key findings report on the processes taken by a professional council in identifying and implementing the facets of information governance, that is, records management, IT, content management, data governance, information security, data privacy, risk management, regulatory compliance, long-term digital preservation and, even, business intelligence.

The study was a participatory action research project that involved collaboration between the researcher and study participants in defining and solving the problem through a needs assessment exercise.

The study’s findings suggest that, with the right information governance policy in place, adopting the facets of information governance can be used to address concerns related to information integrity in the short and medium terms. As a long-term option for retaining data and information, it would have various drawbacks and would not, however, ensure the initial dependability of the information.

A framework for information governance to ensure that the professional organisation and board members adopt a tailored governance system is suggested.

Through the study, we identified four effective paths to improve governance performance and also found the key direction for future research on digital twin urban implementation of public crisis governance, i.e. how to find a balance between the cost and the effectiveness of governance.

A total of 22 urban public emergencies were selected based on key influencing factors, and four action paths to improve the performance of public crisis governance in digital twin cities were obtained using a fuzzy set qualitative comparative analysis model.

This paper identified digital twin technologies in urban public crisis governance, analyzed the key factors of public crisis governance in the digital twin city and proposed a path of action to improve the performance of public crisis governance in digital twin cities.

This study focuses on the influencing factors of public crisis governance in digital twin cities and the action paths to promote improved governance performance.

Personal data is a powerful tool. The more someone know about us, the more power they got over us. But who will control the most of our personal data? Does the government and the big tech really care about our personal data? This paper aims to look at data practices, data-related policy making as well as its economic consequences in the context of emerging economies.

Using qualitative methods such as literature review and analysis of numerous government documents, this paper inquires into the dynamics in the use of data by the business sectors, explains how data governance can add value to the business sectors while ensuring customers’ data privacy protection based on the data governance mechanism framework and details what it takes.

Using the case of Indonesian recent development on data privacy regulation, this paper describes the problems and threats to personal data protection. The advent of latest computing and mobile technology is shifting power relations between the governments, the big tech, as well as the end users. To conclude, the strategy and policy recommendations for implementing data privacy protection are also presented.

This paper provides a timely synthesis of data practices in the context of developing countries, particularly in relation to policy making and economic consequences. This paper also identifies and shares several promising future research ideas.

This paper aims to develop a digital transformation model in hospitals. This study proposes a digital transformation model in hospitals by formulating dimensions and sub-dimensions. The proposed hospital digital transformation model outlines why and how each of dimensions and sub-dimensions are important in the hospital digital transformation model.

This study chose the type of qualitative research using a phenomenology approach. This study used observation techniques and in-depth interviews with 11 informants and conducted group discussion forums with information technology governance experts, a hospital information technology department manager who has an information technology background, and doctor representatives. The data were documented and analyzed using triangulation techniques.

This research provides empirical insights into the dimensions and sub-dimensions of hospital digital transformation models. The findings of the digital transformation dimension in hospitals are 7 dimensions and 37 sub-dimensions, namely, the governance and management dimension which has 8 sub-dimensions; the person has 9 sub-dimensions; strategy dimension which has 5 sub-dimensions; information technology capability has 3 sub-dimensions; the data interoperability dimension has 3 sub-dimensions; the data analytics dimension has 5 sub-dimensions; patient dimensions have 4 sub-dimensions; the findings of the sub-dimensions involved in the digital transformation dimension of the hospital can provide input on the accuracy of the indicators measuring the hospital’s digital transformation.

This research is limited to the qualitative type of phenomenology approach so that future research can test empirically with quantitative methods with techniques through surveys of dimensional and sub-dimensional relationships to hospital digital transformation. The researchers also recommend further assessing the findings of this paper which can develop as a model for measuring the maturity of hospital digital transformation.

This paper covers the implications of developing a hospital digital transformation model that can be used to organize and manage hospital digital transformation.

This paper can be used as a guideline for hospital stakeholders when carrying out digital transformation. This paper can be used as a reference for further research to find, study and develop dimensions and sub-dimensions of digital transformation models.

This paper aims to examine the relationship between corporate governance mechanisms and firm performance in Qatari non-financial firms over a nine-year period, including the period of high uncertainty caused by the COVID-19 pandemic.

The study uses data from Refinitiv and employs panel data econometric techniques, namely generalized least squares (GLS), to analyze the impact of board characteristics (board size, board meetings, board gender diversity, board-specific skills, board independence), audit committee features (existence of audit committee, audit committee independence), CEO duality and management scores on both accounting and market performance of Qatari firms. Control variables include firm size, age, leverage and industry classifications.

The findings suggest that board-specific skills positively influence firm performance, while board size and gender diversity exhibit a non-significant impact. Audit committee independence enhances accounting performance but does not significantly affect market performance. Surprisingly, management scores show a significant yet negative impact on certain financial measures, indicating the need for further investigation.

These insights provide valuable guidance for policymakers, investors and corporate leaders, emphasizing the importance of tailored governance practices in Qatar's unique business landscape.

This study provides unique insights into the governance-performance relationship in the context of Qatar, a region with limited existing research. The inclusion of the COVID-19 period adds a contemporary dimension to the analysis, highlighting the resilience and adaptability of corporate governance practices during times of crisis.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data.

Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis.

Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission.

By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.