A theoretical framework to evaluate ICT disparities and digital divides: Challenges and implications for e-government development

Abstract

Understanding and using ICT is critical for increasing effectiveness of the public sector, improving access to public services, enhancing transparency and engagement of various stakeholders in public decision-making processes. At the same time, utilization of new emerging technologies such as cloud computing, big data analytics, open data, social media, or Internet of Things has become more commonplace in recent years. This study aims to understand what determines ICT disparities and digital divides in the context of new technologies and digital world shifts. For this purpose, a theoretical framework of ICT-related indicators elucidating differences between the traditional and new approach is proposed. These ICT indicators were identified through the decomposition of related ICT and e-government indices together with explanatory factors derived from the literature. The European Union Member States were chosen as a sample for the exploratory analysis because of the availability of relevant indicators. More specifically, correlation, factor, regression, and cluster analyses were employed as empirical strategies to carry out this study. The Human Development Index together with research and development expenditure are the most important explanatory factors for the new approach, while the traditional set of indicators is still mostly correlated with the gross domestic product per capita. Finally, five clusters of Member States were identified and their implications for improving the use of ICT in the public sector are described.

Identifying patterns and recommendations of and for sustainable open data initiatives: A benchmarking-driven analysis of open government data initiatives among European countries

“…They grouped the indices into three groups: (1) benchmarks that consider the publication of government as one of the most important characteristics of open data progress looking exclusively at open data publication (GODI, Open Data Economy, OURdata Index, ODIN); (2) benchmarks that exclusively focus on the use or potential use of OGD (WJP Index and EIU); (3) benchmarks that look into both aspects (ODB, ODMR, Open Data Readiness Assessment). Lnenicka et al (2022) identified six popular and widely rankings (independently or forming an input to other OGD systems) used -GODI, ODB, OURdata Index, ODIN, ODMR, and the Open Government Development Index (OGDI), which were rigorously inspected by analyzing their underlying methodologies and indicators, and how they have changed over time, and, more importantly, whether the results of different editions of the same index can be comparable and used as the basis for decision-making on the development of specific aspects and input data to determine further actions for the OGD initiative. They grouped the indices into three groups depending on their focus, i.e., what aspect(s) of the ODE they measure: (1) openness of selected data categories (GODI, ODIN); (2) various aspects of the ODE through a (large) number of variables (OURdata Index, ODMR); (3) those that try to combine both of the above approaches (ODB, OGDI).…”

“…Users of the OGD, policymakers, civil servants, and government officials may struggle to decide which benchmark model to apply. At the same time, rarely are existing indices' underlying methodologies fully disclosed in detail, including how data for the index were collected and the specific calculations as reports (Lnenicka et al, 2022). Moreover, in many cases, the methodology used by these benchmarks changes over time to be more aligned with the current state-of-the-art.…”

“…and "what needs to be done to achieve it?". Answering these questions has triggered the development of many benchmarks, indices, ranks, and other types of comparisons over the past decade, e.g., see Lnenicka et al (2022), Meuleman et al (2022), Susha et al (2015), Zheng et al (2020), Zuiderwijk et al (2021). Also, many study-specific benchmarks proposed a combination of the above or a completely new benchmarking framework or index, e.g., de Juana-Espinosa and Luján-Mora (2019), Kawashita et al (2020), Kubler et al (2018), Machova and Lnenicka (2017), Neumaier et al (2016), Quarati et al, (2021, Vancauwenberghe et al (2018), Zheng et al (2020).…”

“…Benchmarking tracks and monitors the progress of the OGD initiative (or a specific part of it), comparing it to competitors to drive improvements and the continuous development of the public data ecosystem at different levels of such as OGD portal and its specific feature, data release and maintenance, created impact, etc. (Dawes et al, 2016;Lnenicka et al, 2022;Meuleman et al, 2022;Welle Donker and van Loenen, 2017;Zuiderwijk et al 2021). Measuring open government (data) progress provides insight into the strengths and weaknesses of a particular aspect, serving as a powerful incentive/stimulus for scoping improvements, or to accelerate its development.…”

“…While benchmarks can provide insights, help comparisons with competitors, or serve as a source of inspiration, choosing from the wide variety which ones to consider when setting an agenda for changes or improvements can be difficult. This problem increases due to the rankings and their individual results across different categories of OGD initiatives or artifacts, if they overlap across different indices, may vary significantly (Kawashita et al, 2020;Lnenicka et al, 2022;Meuleman et al, 2022;Susha et al, 2015;Zuiderwijk et al, 2021). Users of the OGD, policymakers, civil servants, and government officials may struggle to decide which benchmark model to apply.…”