Special issue on evidence-based government: secure, transparent and responsible digital governance

2.1Data-driven value transformation chain

The digital society has created an abundance of data, so called Big Data, and the data-driven discoveries, innovations and transformations enabled by Big Data have given rise to the data-driven economy. The idea of the value chain (Porter 1985) is defined as a chain of activities that gives the products more “added value” in a manufacturing (or service) organization. The data-driven economy can be viewed as a value transformation chain, as illustrated in Fig. 1.

Figure 1.

Data-driven value transformation chain.

The raw data sets are generated by a myriad of sources, including government public and administrative records (so-called Open Government Data), citizens’ social media data, movement/locational data from mobile devices, transaction data, surveillance data, etc. The digital economy and digital society are relentlessly generating diverse sources and types of data. Disruptive, smart and innovative technologies (e.g. Fan et al. 2015) utilize this data to create value-added data products, that reveal insights, patterns, association rules and other forms of transferable knowledge or prediction models.

These value-added data products help citizens, governments and other stakeholders to gain insights and to make new discoveries to allow more accurate predictions, better decision making processes and efficient and effective administrative actions. These, in turn, will generate transformed behaviors, improved processes (e.g., backend operations or front end services), regular patterns of actions, and fact-based decision making strategies. The participations and collaborations amplify the effect of individual citizens’ or small groups’ transformations, leading to sustained and systemic changes in society’s value systems, accepted norms, expectations, governing rules and policies, collectively shifting the paradigms of governing, decision making, institutional frameworks and societal behaviors.

At each stage of the data transformation chain, the behaviors and actions generated can serve as feedback to earlier stages, to evaluate the actions or transformations, and to serve as new input to the value chain, resulting in an improvement of the effectiveness of the overall system.

This data-driven value transformation chain (data value chain, in short) is different from the life cycle of data transformation which is a process of cleaning data for higher analytics (Curry 2016). It can apply to any vertical domain, e.g. businesses, healthcare institutions, educational institutions, branches of government, etc., and when applied to the government domain, this value transformation chain can create novel citizen services, new smart city products, new ways to identify and implement critical policies, new governance models where citizen engagement and collaborations are key components, as well as new paradigms and up-to-date norms to govern organizations and people (Osborne 2010).

In order for the data-driven value chain to work effectively, researchers need to address some of the risks in creating values in the data value chain. They should ensure the data and computational technologies are secure, protective of private information, and be in compliance with all relevant standards and regulations. If needed, the data and innovative approaches need to be auditable. Overcoming these risks will ensure that the data-driven value transformation chain works efficiently and responsibly.

2.2Article summaries

In this special issue, selected articles cover various topics on the data transformation value chain that creates evidence-based government, including:

In relation to Value Creation Models, Antonio Cordella and Andrea Paletti compare four different modes of public service production according to their operational capabilities and their related logics of value creation. First, the bureaucracy and manufacturing logic of value creation includes in-house value creation models and joined-up value creation models. Second, the co-production and the service logic of production considers crowdsourced production models and open-sourced production models. For each mode of public service production, they identify the associated benefits, risks and possible solutions that can be deployed to mitigate the risks. The authors suggest that there are no “good or bad” ways to produce services, but different ways to produce value using different configurations of ICT and other resources, and the need to decide about the most suitable way to produce a service should take into consideration the necessary operational capabilities and the need to control the final outcome.

In Smart Governance Models: Authors, Peter Perycheck and Gabriela Viale Pereira conducted a literature review that focuses on smart governance. Smart governance involves the intelligent use of ICT, such as social media, to improve decision-making through better collaboration among different stakeholders, i.e. governments and citizens. It also involves openness as a factor that increases citizen engagement and support for the development of new governance models for smart government. Smart governance may also have an important role in smart city initiatives, which require complex interactions between governments, citizens and other stakeholders. Based on the literature review, this paper coins a definition of ‘smart city governance’ and contributes to developing a framework for building new, smart governance models addressing the challenges of the digital society, collaborative governance, information sharing, citizen engagement, transparency and openness.

The article Policy Changes focusses on using text mining analytics, and Lee and Kim have performed a longitudinal study on institutional changes of economic policies. The concept changes over two decades showing that policy changes occur not necessarily through any external, exogenic factors and regardless of the stability of internal institutions and external environments. The analytical results show that policy elites constantly compete with their past equivalents to influence the “policy package” required to acquire and maintain legitimate authority, while responding to the government’s needs for creative innovation and change. Thus, this data-driven case study of Korean governments shows that value creation and transformation are continuous and dynamic, caused by internal policy competition and the pressure to be more innovative.

In Crowdsourcing for Cybersecurity Policy Innovation, Chatfield and Reddick present the Pentagon’s vulnerability reward program (VRP) or” bug bounty program”, which crowdsources software bug detection to the cybersecurity community and which fits with the US national cybersecurity policy. Their case study results show the feasibility of the government adoption and implementation of the innovative concept of VRP, to enhance the government cybersecurity posture, as well as to address the need to develop sophisticated cybersecurity policies and enhanced cybersecurity capabilities. Public engagement with the vulnerability identification is a way of transforming the national cybersecurity policy making, as shown in the data-driven value creation and transformation chain.

The article Privacy risks and risk distribution models assesses the the data-driven value chain in relation to the protection of personally identifiable data. Joo et al. present a comparative study on the de-identification policies in the European Union, the United States, Japan, and Korea, all of which are now actively pursuing de-identification policies. The major principles used in de-identification policies are used to compare the policies from different regions. Since there is no perfect de-identification, there are always risks of re-identification. The authors identify the constituencies of the de-identification policies and analyze the roles and responsibilities of each of these constituencies. This study may serve as a step toward providing the theoretical basis for distributing burdens and responsibilities arising from the de-identification policies.

In ICT Governance for Compliance Verification, it is argued that in any evidence-based government copious numbers of Government ICT assets are used and innovated daily. Based on a survey of the literature, international and national regulations, and best practices bodies, Clara et al. present and discuss common elements that are considered important to guide regulatory compliance verification of ICT management practices. They provide a methodology of identifying and extracting elements from compliance guidelines, regulations, standards, and academic documents, and use them to audit the ICT compliance. They show a case study of a Brazilian Federal public organization to which they apply these compliance elements for measuring the organization’s compliance and the audit levels for the used software.