Understanding the evolution of open government data research: towards open data sustainability and smartness

Abstract

The past decade has witnessed a rapid development of open government data practices and academic research. However, there is no systematic survey of existing research to understand the evolution of open government data. Such research can facilitate knowledge transfer within and across domains, and foster learning for countries in the early stages of open government data development. This study quantitively extracted the evolution trajectory of open government data based on the main path analysis method and then analysed the underlying motivations. The results show that open government data research went through four main phases and that the open government data movement has spread towards developing countries and smart cities. Different challenges and issues faced by the researchers in each phase drove the evolution of open government data research. Finally, we discuss future directions of open government data research based on our findings and recent development. There is a tendency to create sustainable open government data and smartness by employing artificial intelligence and creating data marketplaces. Points for practitioners Open government data efforts have evolved over the years into a global phenomenon. Countries have learned from each other and more and more efforts are focused on innovating with open government data by stimulating co-creation and using other incentives. The way that data are opened should focus on achieving goals like innovation, participation, transparency and accountability. There is a tendency to create sustainable open government data and smartness by employing artificial intelligence and creating data marketplaces.

Factors in the adoption of open government initiatives in Spanish local governments

“…For example, governments can take advantage of the potential that artificial intelligence (AI) offers to analyse data which could help to make decision-making processes more efficient, creating public value (Harrison & Luna-Reyes, 2022). The Internet of Things (IoT) is also creating a large amount of data which is collected by sensors (Gao, Janssen, and Zhang, 2021), given that these technologies could open data in real time and provide IoT-based tools for citizens to analyse them (Haibe-Kains, Adam, Hosny, and a., 2020). The implementation of these emerging technologies could thus allow citizens to access both a greater amount of information and the tools for its management and manipulation, favouring citizen participation in public affairs.…”

Factors influencing open government data post-adoption in the public sector: The perspective of data providers

“…The OGD infusion is intended to ensure that OGD publishing occurs on a continual basis rather than as a one-time activity for data providers. This is because OGD must be sustained over an extended period of time in order to gain benefits and generate more value for the citizen [20,21]. Moreover, Zuiderwijk and de Reuver [17] found that lack of sustainability appears as the new concern to achieving OGD's intended objective.…”

“…Hassandoust, et al [19] suggested that one of the main reasons why organizations fail to fully reap the advantages of their investments in IS/IT is the under-utilization of the technology. This is due to, in many cases, opening up data began as part of a politically motivated effort rather than focusing on the main objective of OGD implementation [20,21]. In a similar case, Zhu [22] revealed that one of the earliest open data initiatives in the United States, JURIS, failed to achieve its goals due to a lack of social-political support.…”

“…These situations raise concerns about the sustainability of OGD implementation in the future. OGD sustainability denotes that government data continue to be routinely supplied and utilized at the same level as before [20].…”

Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review

“…SLR Scope/Focus [4] "A systematic literature review on maturity models that assess the level of maturity for smart city projects" [11] "A systematic literature survey on software architectures for big data systems", with a few connections with SC [12] "IoT challenges in smart cities and provide the gap between the existing state-of-the-art IoT application on S" [13] "A comprehensive analysis of the literature on interoperability of SC data platforms" [10] "Analyze the link between the concepts of smart cities, machine learning techniques and their applicability" [14] "Systematically investigated the evolution of OGD research" [15] "the relationship between big and open data and how they relate to the broad concept of open government" [16] "Covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities" and concludes that "open data movement has increased the number of research works in the field of machine learning, especially in the prediction of air quality". [17] "Comprehensive survey that explores the application of graph neural networks for traffic forecasting problems", presenting also "a comprehensive list of open data and source resources for each problem" [18] "The challenges faced by smart cities and the key role data mining, machine learning and statistical methods can play to enable intelligent solutions for different applications" [19] "Systematically reviews the top 200 Google Scholar publications in the area of smart city with the aid of data-driven methods from the fields natural language processing and time series forecasting" [7] "Generates insights into how AI can contribute to the development of smarter cities".…”