Centre for Digital Governance
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To unlock the full potential of ICT-related public sector innovation and digital transformation, governments must embrace collaborative working structures and leadership, is commonly argued. However, little is known about the dynamics of such collaborations in contexts of hierarchy, silo cultures, and procedural accountability. A widely voiced but empirically insufficiently substantiated claim is that bringing cross-cutting digital endeavours forward requires more lateral, network-based approaches to governance beyond traditional Weberian ideals. We test this claim by shedding light on three distinct challenges (complexity, risk, and power imbalance) encountered when implementing the specific collaborative case of the German Online Access Act (OAA) and by examining how they have been addressed in institutional design and leadership. Our analysis, which combines desk research and semi-structured expert interviews, reveals that flexible, horizontal approaches are on the rise. Taking a closer look, however, vertical coordination continues to serve as complementary means to problem-solving capability.
This paper seeks to better understand the paradigm shift towards ‘networked governance’ in digitalisation discourse. Little is known about the link between digitalisation reforms and the main reform paradigms in public management studies. By analysing French and German national digitalisation strategies over time, we find that neo-Weberian, new public management, and networked governance discourses co-exist within the digital era, although networked governance rhetoric is increasingly influential. However, a closer examination reveals that this shift in discourse is unrelated to the increased integration of nonstate actors in actual decision-making and service delivery.
Why do local governments create and reform public service companies, given their uncertain economic benefits and potential damage to accountability and service transparency? Taking an extended transaction cost perspective, we argue that corporatization—the provision of public services by publicly owned companies—is a function of fiscal hardship, the decision maker’s economic orientation and the level of operator transparency. Using a two-way fixed effects regression, we test this expectation on 680 investment reports of 34 German cities from 1998 to 2017, representing 11,062 year-corporatized entity combinations. We show that the drivers of corporatization are sensitive to the depth of local ownership analyzed. In doing so, we highlight the theoretical need and potential for conceptual differentiation between ownership levels along a corporation’s lineage. Exploiting the data’s panel structure, we also find that the intensity of corporatization has heightened since the late 1990s, largely due to increasingly complex corporate structures of indirect ownership.
KI und datengesteuerte Kampagnen: Eine Diskussion der Rolle generativer KI im politischen Wahlkampf
(2023)
This book focuses on the implementation of digital strategies in the public sectors in the US, Mexico, Brazil, India and Germany. The case studies presented examine different digital projects by looking at their impact as well as their alignment with their national governments’ digital strategies. The contributors assess the current state of digital government, analyze the contribution of digital technologies in achieving outcomes for citizens, discuss ways to measure digitalization and address the question of how governments oversee the legal and regulatory obligations of information technology. The book argues that most countries formulate good strategies for digital government, but do not effectively prescribe and implement corresponding policies and programs. Showing specific programs that deliver results can help policy makers, knowledge specialists and public-sector researchers to develop best practices for future national strategies.
In today's world, disasters, both natural and manmade, are becoming increasingly frequent, and new solutions are of a compelling need to provide and disseminate information about these disasters to the public and concerned authorities in an effective and efficient manner. One of the most frequently used ways for information dissemination today is through social media, and when it comes to real-time information, Twitter is often the channel of choice. Thus, this paper discusses how Big Data Analytics (BDA) can take advantage of information streaming from Twitter to generate alerts and provide information in real-time on ongoing disasters. The paper proposes TAGS (Twitter Alert Generation System), a novel solution for collecting and analyzing social media streaming data in realtime and subsequently issue warnings related to ongoing disasters using a combination of Hadoop and Spark frameworks. The paper tests and evaluates the proposed solution using Twitter data from the 2018 earthquake in Palu City, Sulawesi, Indonesia. The proposed architecture was able to issue alert messages on various disaster scenarios and identify critical information that can be utilized for further analysis. Moreover, the performance of the proposed solution is assessed with respect to processing time and throughput that shows reliable system efficiency.
The global spread of Covid-19 has caused major economic disruptions. Governments around the world provide considerable financial support to mitigate the economic downturn. However, effective policy responses require reliable data on the economic consequences of the corona pandemic. We propose the CoRisk-Index: a real-time economic indicator of corporate risk perceptions related to Covid-19. Using data mining, we analyse all reports from US companies filed since January 2020, representing more than a third of the US workforce. We construct two measures—the number of ‘corona’ words in each report and the average text negativity of the sentences mentioning corona in each industry—that are aggregated in the CoRisk-Index. The index correlates with U.S. unemployment rates across industries and with an established market volatility measure, and it preempts stock market losses of February 2020. Moreover, thanks to topic modelling and natural language processing techniques, the CoRisk data provides highly granular data on different dimensions of the crisis and the concerns of individual industries. The index presented here helps researchers and decision makers to measure risk perceptions of industries with regard to Covid-19, bridging the quantification gap between highly volatile stock market dynamics and long-term macroeconomic figures. For immediate access to the data, we provide all findings and raw data on an interactive online dashboard.
While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such policy measures, it is key to identify the industries that are most in need of support. In this study, we introduce a data-mining approach to measure industry-specific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries. The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is updated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis.
Tech companies and the public interest: the role of the state in governing social media platforms
(2023)
In the early days of the internet, it was hoped that digital technology would bring about democracy and positive outcomes for society. Recently, the debate has shifted towards tech lash with many critics pointing towards technology companies undermining democracy, stability, and sustainability. As a result, a new consensus seems to be emerging among policymakers, companies, and civil societal actors that self-regulation has to move towards co-regulation. This Special Issue of Information, Communication and Society draws together cutting-edge contributions on three core themes in scholarly and policy discourse on platform regulation: First, the papers in this special issue enhance empirical understandings of the role of the state in governing social media platforms developing in the United States, Europe, and Sub-Saharan Africa. Second, they provide a holistic framework to understand policy problems that need to be addressed, which helps to develop and evaluate new policy initiatives. Finally, papers point towards three approaches in governing social media platforms and discuss the advantages and disadvantages of content moderation, process-based co-regulation, as well as competition regulation and alternative business models.
Race to the Bottom: Media Marketization and Increasing Negativity Toward the United States in China
(2012)
This article examines how Chinese newspapers respond to opposing demands by audiences and Propaganda Department authorities about news regarding the United States when competition poses pressure on marketized media to make a profit. To examine the tone of news reporting about the U.S., I rely on a computer-aided text analysis of news stories published in the People's Daily and the Beijing Evening News, comparing the years 1999 and 2003 before and after the rise of commercialized newspapers in the Beijing newspaper market. Results show that the emergence of news competitors may exert pressure on less marketized papers to change news content, resulting in an increase of negative news about the United States. Evidence is provided to show that the rise of negative news is unlikely to result from an intended strategy by Propaganda authorities, actions undertaken by the American government, or journalists' own attitudes.
This article investigates how and why measures developed in the American context yield different results in China. Research in the United States has shown that a person's level of political knowledge is a stronger and more consistent predictor of news reception compared to alternative measures, such as media consumption or education. Yet a case study of news reception of pension reform in Beijing demonstrates that attentiveness and education constitute more valid indicators than knowledge. These differences in the empirical findings may result from translation from English into Chinese as well as specifics of the Chinese education system. However, when using valid measures the relationship between attentiveness and news reception is strong among Beijing residents, revealing that information-processing works as anticipated based on American media research.
While the Internet was created without much governmental oversight, states have gradually drawn territorial borders via Internet governance. China stands out as a promoter of such a territorial‐based approach. China's separate Web infrastructure shapes data when information technologies capture traces of human behavior. As a result, area expertise can contribute to the substantive, methodological, and ethical debates surrounding big data. This article discusses how a number of critical questions that have been raised about big data more generally apply to the Chinese context: How does big data change our understanding of China? What are the limitations of big data from China? What is the context in which big data is generated in China? Who has access to big data and who knows the tools? How can big data from China be used in an ethical way? These questions are intended to spark conversations about best practices for collaboration between data scientists and China experts.
This study examines the role of the media in sustaining regime stability in an authoritarian context. The article engages the recent work on authoritarian resilience in comparative politics but goes beyond the standard focus on elections to other important institutions, such as the media and courts, that are used by authoritarian leaders to bolster legitimacy. The authors find that the Chinese media contribute to regime legitimacy and effective rule by propagandizing citizens’ experiences in the legal system. However, unlike the “mouthpieces” of earlier communist regimes, the marketized Chinese media provide more convincing and sophisticated messages that continue to accord with state censorship demands while satisfying readers’ interest in real-life stories and problems. The “positive propaganda” and the relative uniformity of information sources because of state censorship lead aggrieved citizens to the law as a realm for dispute resolution and rights protection. Statistical analysis of a randomly sampled survey conducted in four Chinese cities in 2005 demonstrates that exposure to media reporting about labor-law-related issues successfully promotes the image of a proworker bias in the law among citizens, thus encouraging them to participate in the legal system. The state is able to achieve its political goal because of the lack of conflicting sources of information and the lack of previous experience with the reformed legal system among citizens.