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The opening of government data is high on the policy agenda of governments worldwide. However, data release faces barriers due to limited support of civil servants, whereas the literature neglects civil servants' role in opening data. This paper aims at understanding why civil servants can be reluctant to support the disclosure of data. The authors developed a model to explain civil servants' behavioral intention to open data.

The authors test a series of hypotheses by collecting and analyzing survey data from 387 civil servants and by applying multivariate hierarchical regression.

The results indicate the factors influencing the behavior of civil servants. Social influences, performance expectancy, data management knowledge and risks have a significant influence. Personal characteristics control these effects.

Caution is needed to generalize the findings towards the support to open data provision by civil servants. Though the analyzed sample was limited to Brazil, other countries and cultures might yield different outcomes. Larger and more diversified samples might indicate significant effects on variables not found in this research.

The insights can be used to develop policies for increasing the support of civil servants towards governmental data disclosure.

This study suggests factors of influence to civil servants' behavior intentions to disclose governmental data. It results in a model of factors, specifically for their behavioral intention at the individual level.

The objective of this research is to methodically categorize the various types of Open Government Data (OGD) stakeholders, and to elucidate the intricate network relationships among OGD stakeholders, along with the underlying mechanisms that shape their formation.

To comprehend the collaboration mechanism of stakeholders in the OGD ecosystem, the authors constructed an OGD multi-stakeholder relationship network by using data from the Shandong Province Data Application Innovation and Entrepreneurship Competition. Based on the structural social capital theory and exponential random graph model (ERGM), an analytical framework was established to explore the formation mechanism of the collaborative network of OGD multi-stakeholder.

The results indicate that multi-stakeholder collaboration among government, enterprises and the public is crucial for achieving OGD goals. Organizing OGD competitions serves as an effective mechanism for solidifying and maintaining relationships among OGD stakeholder groups. Degree centrality and structural parameters reveal a Matthew effect within the connection process of the OGD ecosystem's collaborative network. Additionally, there is evidence of agglomeration and transferability within the network's structure.

This study contributes to the understanding regarding the formation mechanism of OGD stakeholders. The findings have implications for developing multi-stakeholder relationship networks of OGD and driving OGD initiatives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2023-0284

Despite the current attention toward the concept of data culture, a commonly accepted scope and definition is currently lacking. Addressing this conceptual fuzziness would be beneficial to pursue the development of knowledge on data culture in the public sector. The research aims at advancing theory by building a novel conceptualization of the constituent elements of data culture in local governments and their relationships.

For this purpose, the authors used a multi-method research design. More precisely, the authors conducted 12 semi-structured interviews with mayors and heads of administration from local governments, and a document analysis. The authors inductively mapped the findings to an existing heuristic featuring seven levels of data culture and extracted relationships between these levels.

The authors find several elements belonging to the data culture of local governments for each level of the existing generic heuristic and identify 24 influence relationships between these levels. The authors integrate these findings into the data culture model, which conceptualizes data culture in local governments.

The data culture model provides a strong theoretical basis for researchers to position their research and further advances knowledge on this still elusive concept. Practitioners can use the data culture model as a reflective tool to understand which elements impacted their current data behavior.

To the best of the authors’ knowledge, this is the first work to provide a conceptualization of data culture in local governments at this level of depth, and to conceptualize relationships between constituent elements of data culture.

This study investigates the individual and binary (i.e. combined) effects of institutional dimensions of open government data (which include instructional, structural and accessible rules) on scientific research innovation, as well as the mediating roles that researchers' perceived data usefulness and data capability play in between.

Based on a sample of 1,092 respondents, this study uses partial least squares structural equation modeling (PLS-SEM) and polynomial regression with response surface analysis to evaluate the direct and indirect effects of individual and binary institutional dimensions on scientific research innovation.

The findings demonstrate that instructional, structural and restricted access data have a positive effect on scientific research innovation in the individual effect. While the binary effect of institutional dimensions produces varying degrees of scientific research innovation. Furthermore, this study discovers that the perceived usefulness and data capability of researchers differ in the mediating effect of institutional dimensions on scientific research innovation.

Theoretically, this study contributes new knowledge on the causal links between data publication institutions and innovation. Practically, the research findings offer government data managers timely suggestions on how to build up institutions to foster greater data usage.

The rising volume of open government data (OGD) contrasts with the limited acceptance and utilization of OGD among citizens. This study investigates the reasons for citizens’ not using available OGD by comparing citizens’ attitudes towards OGD with the development of OGD portals. The comparison includes four OGD utilization processes derived from the literature, namely OGD awareness, needs, access and consumption.

A case study in China has been carried out. A sociological questionnaire was designed to collect data from Chinese citizens (demand), and personal visits were carried out to collect data from OGD portals (supply).

Results show that Chinese citizens have low awareness of OGD and OGD portals. Significant differences were recognized between citizens’ expectations and OGD portals development in OGD categories and features, data access services and support functions. Correlations were found between citizens’ OGD awareness, needs, access and consumption.

By linking the supply of OGD from the governments with each process of citizens’ OGD utilization, this paper proposes a framework for citizens’ OGD utilization lifecycle and provides a new tool to investigate reasons for citizens’ not making use of OGD.

This paper aims to present the results of a project deployment on voluntary data exchange between a municipality and commercial entities built on the Commitment-Trust theory. The research also discovers whether data sharing can be a vital instrument to foster city socio-economic development.

Inspired by the engaged scholarship approach, this research explores the economic and operational results of data integration from two different information sources, the legislation impact and the effects on citizens as final beneficiaries. Over a period of 27 months, the authors observed changes the voluntary data sharing brings in traditional interaction between multiple ecosystems. The impact of positive and negative factors was validated via 12 exit interviews with key stakeholders.

Voluntary data sharing is driven by parties’ goodwill to improve public services for residents, the organisations gain tremendously much more benefits than they can do separately on their own. When the parties commit to voluntarily share data, this increases trust in each other and the responsibility of each data contributor. This research proposes that the Commitment-Trust theory is well-replicable for the government-to-business relationship.

This research contributes to the current body of knowledge of voluntary data exchange between different ecosystems, especially between a government and its environment. This paper presents a relevant project deployment from an emerging economy and its consequences for a city municipality, businesses and residents.

There is a widely held belief that open government data (OGD) have the potential to generate both economic and social value. This study aims to empirically unpack the relationship between OGD, diversification activities and innovation in the pursuit of economic value creation by firms.

Using a matched sample comparison method and difference-in-differences analyses, the authors study the impact of OGD on innovation over time in the USA. The authors considered the open government directive in the end of 2009 in the USA as a policy intervention and collected 10 years of financial data of 79 firms that use OGD and 79 matched control firms in the USA. The authors compare US firms using OGD, with matched control firms, regarding the firms’ level of product diversification as a measure of innovative use of OGD.

The authors provide empirical evidence that OGD policy contributes toward innovation, and hence economic value creation, through product diversification. Firms that leverage OGD show superior product diversification in comparison to the matching control firms. The results suggest that OGD contribute to firms’ innovation and pursuit of economic value, as evidenced by their increased product diversification.

Although the extant literature concerning OGD has underscored the impact of OGD on innovation and economic value generation, there is a lack of empirical evidence in the literature. This study seeks to add to the extant literature by providing empirical evidence that contributes to the understanding of the relationship between OGD, diversification and innovation in the pursuit of economic value creation.

This study aims to look at challenges that hinder small- and medium-sized enterprises (SMEs) from using open data (OD). The research gaps identified are then used to propose a next generation of OD platform (ODP+).

This study proposes a more effective platform for SMEs called ODP+. A proof of concept was implemented by using modern techniques and technologies, with a pilot conducted among selected SMEs and government employees to test the approach’s viability.

The findings identify current OD platforms generally, and in Gulf Cooperation Council (GCC) countries, they encounter several difficulties, including that the data sets are complex to understand and determine their potential for reuse. The application of big data analytics in mitigating the identified challenges is demonstrated through the artefacts that have been developed.

This paper discusses several challenges that must be addressed to ensure that OD is accessible, helpful and of high quality in the future when planning and implementing OD initiatives.

The proposed ODP+ integrates social network data, SME data sets and government databases. It will give SMEs a platform for combining data from government agencies, third parties and social networks to carry out complex analytical scenarios or build the needed application using artificial intelligence.

The findings promote the potential future utilisation of OD and suggest ways to give users access to knowledge and features.

To the best of the authors’ knowledge, no study provides extensive research about OD in Qatar or GCC. Further, the proposed ODP+ is a new platform that allows SMEs to run natural language data analytics queries.

With the continuous development of the global COVID-19 epidemic, mobile learning has become one of the most significant learning approaches. The mobile learning resource is the basis of mobile learning; it may directly affect the effectiveness of mobile learning. However, the current learning resources cannot meet users' needs. This study aims to analyze the influencing factors of accepting open data as learning resources among users.

Based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), this study proposed a comprehensive theoretical research model. Data were obtained from 398 postgraduates from several universities in central China. Confirmatory factor analysis was used to determine the reliability and validity of the measurement model. Data has been analyzed using SPSS and AMOS software.

The results suggested that perceived usefulness, performance expectancy, social influence and facilitating conditions have a positive influence on accepting open data as learning resources. Perceived ease of use was not found significant. Moreover, it was further shown in the study that behavioural intention significantly influenced the acceptance of open data as learning resources.

There is a lack of research on open data as learning resources in developing countries, especially in China. This study addresses the gap and helps us understand the acceptance of open data as learning resources in higher education. This study also pays attention to postgraduates' choice of learning resources, which has been little noticed before. Additionally, this study offers opportunities for further studies on the continuous usage of open data in higher education.

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.