This study aimed to measure the impact of implementing mechanisms of accounting data governance, represented by International Accounting Standards, internal auditing, external auditing, audit committees, disclosure and transparency, and performance evaluation, on the quality of financial reporting data for the commercial banks listed on the Amman Stock Exchange, totaling (15) banks. To achieve the objectives of this study, a descriptive-analytical approach was adopted by developing a questionnaire to collect the primary data measuring the study variables. The questionnaire was distributed to employees in the financial and control departments of these banks, with a total of (375) respondents from the total study population of (733) individuals. Appropriate statistical methods were used to analyze the data, test hypotheses, and the results of this study revealed a strong positive impact of five variables of accounting data governance mechanisms on achieving the quality of financial reporting data. These variables are ranked from highest to lowest in terms of the strength of impact and correlation with the quality of financial reports: disclosure and transparency, external auditing, International Accounting Standards, internal auditing, and audit committees. However, there was no impact of the performance evaluation governance variable on achieving the quality of financial reporting data. These results call on the management of commercial banks in the study to commit to the objective implementation of the requirements of accounting data governance mechanisms as stipulated by international professional assemblies.

Health data governance is essential for optimal processing of data collection, sharing, and reuse. Although the World Health Organization (WHO) has proposed practical guidelines for managing health data during the pandemic, the Organization for Economic Cooperation and Development (OECD) found that many countries still lack the use of health data for decision-making. Therefore, this research aimed to identify and assess the challenges faced by health organization in implementing health data governance from various countries based on research articles. The challenges were assessed based on key components of health data governance from practitioner and scientist perspectives. These components include stakeholder, policy, data management, organization, data governance maturity assessment, and goals. The method used followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for collecting and reporting. Data were collected from several databases online with large repositories of academic studies, including IEEE Xplore, ScienceDirect, National Library of Medicine, ProQuest, Taylor and Francis Group, Scopus, and Wiley Online libraries. Based on the 41 papers reviewed, the results showed that policy was found to be the biggest challenge for health data governance. This was followed by data management such as quality, ownership, and access, as well as stakeholders and data governance organization. However, there were no challenges regarding maturity assessment and data governance goals, as the majority of research focused on implementation. Policy and policymaker awareness were identified as major components for the implementation of health data governance. To address challenges in data management and governance organization, creating committees focused on these components proved to be an effective solution. These results provided valuable recommendations for regulators and leaders in a healthcare organization to optimally implement health data governance.

Massive open online courses (MOOCs) are intentionally designed to be easily accessible to many learners, regardless of their academic level or age. MOOCs leverage internet-based technology, allowing anybody with an internet connection to have unrestricted access, regardless of their location or time limitations. MOOCs provide a versatile and easy opportunity for acquiring top-notch education, enabling anyone to learn at their preferred speed, free from limitations of time, cost, or geographical location. Given the advantages they offer, MOOCs are a valuable method for improving the quality and availability of education in Indonesia. Following the outbreak of the COVID-19 pandemic, colleges and institutions have implemented the establishment of digital campuses. One important characteristic of these digital campuses is that they prioritize processes but overlook data and lack standardized standards. The problems and fundamental causes include challenges related to the comprehensive information architecture. The main factor contributing to this challenge is the absence of uniform and well-defined information standards. The existing connectivity and data exchange mechanisms in several schools are poor, leading to substantial data discrepancy among various departments due to the limited content of the fundamental data utilized. Moreover, the absence of clear information about the reliable source of data exacerbates the problem. The main objectives of data governance are to improve data quality, eliminate data inconsistencies, promote extensive data sharing, utilize data aggregation for competitive benefits, supervise data modifications based on data usage patterns, and comply with internal and external regulations and agreed-upon data usage standards. The aim of this project is to create a data governance framework that is customized to the specific conditions in Indonesia, with a specific emphasis on MOOC providers. The researcher chose design science research (DSR) as the research paradigm as it can successfully tackle relevant issues linked to the topic by creating innovative artefacts about the data governance framework for MOOC providers in Indonesia. This research highlights the necessity and significance of implementing a data governance framework for MOOC providers in Indonesia, hence increasing their awareness of this requirement. The researchers incorporated components from the data management body of knowledge (DMBOK) into their data governance framework. This framework includes ten components related to data governance, which are further divided into sub-components within the MOOC providers’ framework.