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Inter-organisational governance is an important enabler for information processing, particularly in relationships undergoing digital transformation (DT) where partners depend on each other for information in decision-making. Based on information processing theory (IPT), the authors theoretically and empirically investigate how governance mechanisms address information asymmetry (uncertainty and equivocality) arising in capturing, sharing and interpreting information generated by digital technologies.

IPT is applied to four cases of public–private relationships in the Dutch infrastructure sector that aim to enhance the quantity and quality of information-based decision-making by implementing digital technologies. The investigated relationships are characterised by differing degrees and types of information uncertainty and equivocality. The authors build on rich data sets including archival data, observations, contract documents and interviews.

Addressing information uncertainty requires invoking contractual control and coordination. Contract clauses should be precise and incentive schemes functional in terms of information requirements. Information equivocality is best addressed by using relational governance. Identifying information requirements and reducing information uncertainty are a prerequisite for the transformation activities that organisations perform to reduce information equivocality.

The study offers insights into the roles of both governance mechanisms in managing information asymmetry in public–private relationships. The study uncovers key activities for gathering, sharing and transforming information when using digital technologies.

This study draws on IPT to study public–private relationships undergoing DT. The study links contractual control and coordination as well as relational governance mechanisms to information-processing activities that organisations deploy to reduce information uncertainty and equivocality.

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.

Access to personal data is key to many of the most successful recent business models. These models rely on individuals outside of traditional organizational boundaries as their product, content providers, and customers. The topic of organizational boundaries is central to organizational research, and these models raise questions about the permeability of these new forms’ boundaries. Herein I elaborate on data-based business models, the organizational field that has emerged around data governance issues, and the institutions that have formed around it at different stages, by various actors. I also explore the interplay of institutional field and organizational boundaries, to identify how field-level issues influence the permeability of organizational boundaries.

Disasters continue to be most prevalent and severe for marginalised communities. To reach those furthest behind first, as the global community pledges in the 2030 Agenda, a critical assessment of equity in disaster risk governance is necessary. Yet, the understanding of factors that mediate the capacity of the governance processes to achieve equity ambitions is limited. This paper addresses this gap by proposing and testing a conceptual framework to assess equity in disaster risk governance.

The framework analyses the extent to which institutional relationships and data in risk governance support inclusion and diversity of voice and enable the equitable engagement of communities. The study applied the framework to key risk policies across governance levels in Brazil and Colombia.

The study finds that institutional awareness of cross-sectoral and -scalar coordination clearly exists. Yet, the engagement of actors further down the governance scale is framed reactively at all scales in both countries. The analysis of the risk data practices indicates that although data integration and sharing are key policy priorities, the policies frame the relations of disaster risk data actors as hierarchical, with data needs determined from the top down.

A key contribution of this framework is that its equity view results in a nuanced analysis, thus pointing to the differences between the two countries concerning the factors that mediate these challenges and providing specific entry points for strengthening equity in risk governance policies.

This paper introduces a new set of equity and rights-based principles for health data governance (HDG) and makes the case for their adoption into global, regional and national policy and practice.

This paper discusses the need for a unified approach to HDG that maximises the value of data for whole populations. It describes the unique process employed to develop a set of HDG principles. The paper highlights lessons learned from the principle development process and proposes steps to incorporate them into data governance policies and practice.

More than 200 individuals from 130 organisations contributed to the development of the HDG principles, which are clustered around three interconnected objectives of protecting people, promoting health value and prioritising equity. The principles build on existing norms and guidelines by bringing a human rights and equity lens to HDG.

The principles offer a strong vision for HDG that reaps the public good benefits of health data whilst safeguarding individual rights. They can be used by governments and other actors as a guide for the equitable collection and use of health data. The inclusive model used to develop the principles can be replicated to strengthen future data governance approaches.

The article describes the first bottom-up effort to develop a set of principles for HDG.

In the past 25 years, and as recent as 2011, all external evaluations of the Quality Improvement Organization (QIO) Program have found its impact to be small or difficult to discern. The QIO program costs about $200 million on average to administer each year to improve quality of healthcare for people of 65 years or older. The program was created to address questionable quality of care. QIOs review how care is provided based on performance measures. The paper aims to discuss these issues.

In 2012, the author supported the production of quarterly reports and reviewed internal monitoring and evaluation protocols of the program. The task also required reviewing all previous program evaluations. The task involved many conversations about the complexities of the program, why impact is difficult to discern and possible ways for eventual improvement. Process flow charts were created to simulate the data life cycle and discrete event models were created based on the sequence of data collection and reporting to identify gaps in data flow.

The internal evaluation uncovered data gaps within the program. The need for a system of specification rules for data conceptualization, collection, distribution, discovery, analysis and repurposing is clear. There were data inconsistencies and difficulty of integrating data from one instance of measurement to the next. The lack of good and reliable data makes it difficult to discern true impact.

The prescription is for a formal data policy or data governance structure to integrate and document all aspects of the data life cycle. The specification rules for governance are exemplified by the Data Documentation Initiative and the requirements published by the Data Governance Institute. The elements are all in place for a solid foundation of the data governance structure. These recommendations will increase the value of program data.

The model specifies which agency units must be included in the governance authority and the data team. The model prescribes in detail a data governance model to address gaps in the life cycle. These prescriptive measures will allow the program to integrate all of its data. Without this formal data governance structure, the QIO program will be undetermined by the persistent lack of good data for monitoring and evaluation.

The purpose of this paper is to examine and provide insights into one of the most influential technologies impacting the tourism and hospitality industry over the next five years, i.e. big data and analytics. It reflects on both opportunities and risks that such technological advances create for both consumers and tourism organisations, highlighting the importance of data governance and processes for effective and ethical data management in both tourism and hospitality.

This paper is based on a review of academic and industry literature and access to trends data and information from a series of academic and industry databases and reports to examine how big data and analytics shape the future of the industry and the associated risks and opportunities.

This paper identifies and examines key opportunities and risks posed by the rising technological trend of big data and analytics in tourism and hospitality. While big data is generally regarded as beneficial to tourism and hospitality organisations, there are extensively held ethical, privacy and security concerns about it. Therefore, the paper is making the case for more research on data governance and data ethics in tourism and hospitality and posits that to successfully use data for competitive advantage, tourism and hospitality organisations need to solely expand compliance-based data governance frameworks to frameworks that include more effective privacy and ethics data solutions.

This paper provides useful insights into the use of big data and analytics for both researchers and practitioners and offers new perspectives on the debate on data governance and ethical data management in both tourism and hospitality. Because forecasts from the UNWTO indicate a significant increase in international tourist arrivals (1.8 billion tourist arrivals by 2030), the ways tourism and hospitality organisations manage customers’ data become important.

Most local governance assessment tools are entirely or partially based on stakeholders’ surveys, focus groups and benchmarks of different local governments in the world. These tools remain a subjective way of local governance evaluation. To measure the performance of local good-governance using an unbiased assessment technique, the authors have developed a framework to help automate the design process of a data warehouse (DW), which provides local and central decision-makers with factual, measurable and accurate local government data to help assess the performance of local government. The purpose of this paper is to propose the extraction of the DW schema based on a mixed approach that adopts both i* framework for requirements-based representation and domain ontologies for data source representation, to extract the multi-dimensional (MD) elements. The data was collected from various sources and information systems (ISs) deployed in different municipalities.

The authors present a framework for the design and implementation of a DW for local good-governance assessment. The extraction of facts and dimensions of the DW’s MD schema is done using a hybrid approach, where the extraction of requirement-based DW schema and source-based DW schema are done in parallel followed by the reconciliation of the obtained schemas to obtain the good-governance assessment DW final design.

The authors developed a novel framework to design and implement a DW for local good-governance assessment. The framework enables the extraction of the DW MD schema by using domain ontologies to help capture semantic artifacts and minimize misconceptions and misunderstandings between different stakeholders. The introduction and use of domain ontologies during the design process serves the generalization and automation purpose of the framework.

The presently conducted research faced two main limitations as follows: the first is the full automation of the design process of the DW and the second, and most important, is access to local government data as it remains limited because of the lack of digitally stored data in municipalities, especially in developing countries in addition to the difficulty of accessing the data because of regulatory aspects and bureaucracy.

The local government environment is among the public administrations most subject to change-adverse cultures and where the authors can face high levels of resistance and significant difficulties during the implementation of decision support systems, despite the commitment/engagement of decision-makers. Access to data sources stored by different ISs might be challenging. While approaching the municipalities for data access, it was done in the framework of a research project within one of the most notorious universities in the country, which gave more credibility and trust to the research team. There is also a need for further testing of the framework to reveal its scalability and performance characteristics.

Compared to other local government assessment ad hoc tools that are partially or entirely based on subjectively collected data, the framework provides a basis for automated design of a comprehensive local government DW using e-government domain ontologies for data source representation coupled with the goal, rationale and business process diagrams for user requirements representations, thus enabling the extraction of the final DW MD schema.