Bringing light into the dark side of digitalization : consequences, antecedents, and mitigation mechanisms

As digital technologies permeate all aspects of our professional and private lives, digitalization causes profound changes for individuals, organizations, and societies. The use of digital technologies makes many activities easier, safer, faster, or more comfortable. In addition to many positive changes, digital technologies are also associated with numerous risks and side effects. The use of digital technologies might come along with severe negative consequences for individuals, organizations, and societies. The negative consequences can be triggered by various antecedents. In addition to identifying the negative consequences of digitalization and their antecedents, it is particularly important to develop appropriate mitigation mechanisms. This dissertation provides novel insights for IS researchers to better understand the negative consequences of using digital technologies. It contains a broad overview of the risks and side effects of digitalization and investigates related antecedents and mitigation mechanisms. To reach this goal, regarding research methods, this dissertation relies on the structured analysis of (scientific) literature and (expert) interviews as well as the analysis and interpretation of empirical data. Chapter 2 contributes to the research on the negative consequences of digitalization. Section 2.1 provides a comprehensive multi-level taxonomy of the risks and side effects of digitalization (RSEDs). Section 2.2 builds on Section 2.1 and is a substantial expansion and improvement of Section 2.1. The iterative taxonomy development process was complemented by four additional cycles. The final taxonomy comprises 11 RSEDs and their 39 subtypes. Both articles show that there is a wide range of risks and side effects of digitalization that need to be explored in more detail in the future. Chapter 3 focuses on the antecedents of digitalizations negative consequences. Section 3.1 sheds light on individuals concerns towards automated decision-making. The concerns are derived from academic literature and semi-structured interviews with potential users of algorithm-based technologies. Section 3.2 focuses on the evaluation of specific mHealth app features by potential users in Germany and Denmark. The study draws on survey data from both countries analyzed using the Kano method. Further, it comprises a quartile-based sample split approach to identify the underlying relationships between users characteristics and their perceptions of the mHealth app features. The results show significant differences between Germans and Danes in the evaluation of the app features and demonstrate which of the user characteristics best explain these differences. Both articles shed light on possible antecedents of negative consequences (i.e., user dissatisfaction, non-use) and thus contribute to a better understanding of the occurrence of negative consequences. Chapter 4 shows exemplary mitigation mechanisms to cope with the negative consequences of digitalization. Section 4.1 takes an organizational perspective and identifies data privacy measures that can be implemented by organizations to protect the personal data of their customers and address their privacy concerns. These measures were evaluated by analyzing data from two independent online surveys with the help of the Kano method. Section 4.2 focuses on an individual perspective by presenting the concept of a privacy bot that contributes to strengthening the digital sovereignty of internet users. With the help of the privacy bot, page-long privacy statements can be checked against previously stored individual data protection preferences. Both articles provide appropriate mitigation mechanisms to cope with users privacy concerns. These two examples show that there are a variety of ways to counter the risks and side effects of digitalization. The research articles included in this dissertation identify various risks and side effects of digitalization that need to be explored in more detail in future research. The two articles on antecedents help to better understand the occurrence of negative consequences of digitalization. The development of appropriate countermeasures, two of which are exemplified in this dissertation, should result in the benefits of digital technologies outweighing their risks.Da digitale Technologien alle Bereiche unseres beruflichen und privaten Lebens durchdringen, bewirkt die Digitalisierung tiefgreifende Veränderungen für Individuen, Organisationen und Gesellschaften. Viele Aktivitäten werden durch den Einsatz digitaler Technologien einfacher, sicherer, schneller oder bequemer. Neben vielen positiven Veränderungen sind digitale Technologien aber auch mit zahlreichen Risiken und Nebenwirkungen verbunden. Der Einsatz digitaler Technologien kann mit schwerwiegenden negativen Folgen für Individuen, Organisationen und Gesellschaften einhergehen. Diese negativen Folgen können durch verschiedene Einflussfaktoren ausgelöst werden. Zusätzlich zur Identifizierung der negativen Folgen der Digitalisierung und ihrer Ursachen ist es besonders wichtig, geeignete Schutzmaßnahmen zu entwickeln. Diese Dissertation liefert neue Erkenntnisse für IS-Forscher:innen, um die negativen Folgen der Nutzung digitaler Technologien besser zu verstehen. Sie enthält einen breiten Überblick über die Risiken und Nebenwirkungen der Digitalisierung und untersucht die damit verbundenen Ursachen und Schutzmaßnahmen. Um dieses Ziel zu erreichen, stützt sich die Dissertation forschungsmethodisch auf die strukturierte Analyse von (wissenschaftlicher) Literatur und (Expert:innen-)Interviews sowie auf die Auswertung und Interpretation empirischer Daten. Kapitel 2 leistet einen Beitrag zur Forschung über die negativen Folgen der Digitalisierung. Abschnitt 2.1 liefert eine umfassende mehrstufige Taxonomie der Risiken und Nebenwirkungen der Digitalisierung (RSEDs). Abschnitt 2.2 baut auf Abschnitt 2.1 auf und ist eine wesentliche Erweiterung und Verbesserung von Abschnitt 2.1. Der iterative Taxonomieentwicklungsprozess wurde durch vier weitere Zyklen ergänzt. Die endgültige Taxonomie umfasst 11 RSED und 39 Untertypen. Beide Artikel zeigen, dass es ein breites Spektrum an Risiken und Nebenwirkungen der Digitalisierung gibt, das in Zukunft noch genauer erforscht werden muss. Kapitel 3 befasst sich mit den Ursachen der negativen Folgen der Digitalisierung. Abschnitt 3.1 beleuchtet die Bedenken von Individuen gegenüber automatisierten Entscheidungen. Die Bedenken wurden aus wissenschaftlicher Literatur und halbstrukturierten Interviews mit potenziellen Nutzer:innen algorithmusbasierter Technologien abgeleitet. Abschnitt 3.2 konzentriert sich auf die Bewertung spezifischer Funktionen von mHealth-Apps durch potenzielle Nutzer in Deutschland und Dänemark. Die Studie basiert auf Umfragedaten aus beiden Ländern, die mit der Kano-Methode analysiert wurden. Darüber hinaus umfasst sie einen quartil-basierten Stichproben-Split-Ansatz, um die zugrundeliegenden Beziehungen zwischen den Merkmalen der Nutzer (z.B. Bedenken hinsichtlich des Datenschutzes) und ihrer Wahrnehmung der Funktionen von mHealth-Apps zu ermitteln. Die Ergebnisse zeigen signifikante Unterschiede zwischen Deutschen und Dänen bei der Bewertung der App-Funktionen und zeigen, welche der Nutzermerkmale diese Unterschiede am besten erklären. Beide Artikel beleuchten mögliche Ursachen negativer Folgen (z.B. Unzufriedenheit der Nutzer, Nichtnutzung) und tragen so zu einem besseren Verständnis des Auftretens negativer Folgen bei. Kapitel 4 zeigt beispielhafte Schutzmaßnahmen zur Bewältigung der negativen Folgen der Digitalisierung. Abschnitt 4.1 nimmt eine organisationale Perspektive ein und identifiziert Datenschutzmaßnahmen, die von Unternehmen umgesetzt werden können, um die personenbezogenen Daten ihrer Kund:innen zu schützen und deren Datenschutzbedenken zu berücksichtigen. Diese Maßnahmen wurden durch die Analyse von Daten aus zwei unabhängigen Online-Umfragen mit Hilfe der Kano-Methode evaluiert. In Abschnitt 4.2 wird eine individuelle Perspektive eingenommen, indem das Konzept eines Privacy Bots vorgestellt wird, der zur Stärkung der digitalen Souveränität von Internetnutzer:innen beiträgt. Mithilfe des Privacy Bots können seitenlange Datenschutzerklärungen mit zuvor gespeicherten individuellen Datenschutzpräferenzen abgeglichen werden. Beide Artikel beschreiben geeignete Schutzmaßnahmen, um den Datenschutzbedenken der Nutzer:innen gerecht zu werden. Diese beiden Beispiele zeigen, dass es eine Vielzahl von Möglichkeiten gibt, den Risiken und Nebenwirkungen der Digitalisierung zu begegnen. Die in dieser Dissertation enthaltenen Forschungsartikel zeigen verschiedene Risiken und Nebenwirkungen der Digitalisierung auf, die in der zukünftigen Forschung noch genauer untersucht werden müssen. Die beiden Artikel zu den Ursachen helfen, das Auftreten von negativen Konsequenzen der Digitalisierung besser zu verstehen. Die Entwicklung geeigneter Schutzmaßnahmen, von denen zwei in dieser Dissertation beispielhaft vorgestellt werden, sollte dazu führen, dass die Vorteile der digitalen Technologien ihre Risiken überwiegen

Beyond Mere Compliance — Delighting Customers by Implementing Data Privacy Measures?

The importance of customer data for business models is increasing, as is the relevance of customers’ concerns regarding privacy aspects. To prevent data privacy incidents and to mitigate the associated risks, companies need to implement appropriate measures. Furthermore, it is unclear whether their implementation – beyond mere compliance – has the potential to actually delight customers and yields competitive advantages. In this paper, we derive specific measures to deal with customers’ data privacy concerns based on the literature, legislative texts, and expert interviews. Next, we leverage the Kano model via an Internet-based survey to analyze the measures’ evaluation by customers. As a result, most measures are considered basic needs of must-be quality. Their implementation is obligatory and is not rewarded by customers. However, delighters of attractive quality do exist and have the potential to create a competitive advantage

Towards an Autonomous Compost Turner: Current State of Research

This preprint presents the current status of research into the development and application of an autonomous, self-driving compost turner. The aim is to overcome challenges in the composting industry, such as adverse working conditions, by automating the composting process. The preprint provides a comprehensive overview of the overall concept of the self-driving compost turner, including the hardware architecture with sensors, navigation module and control module. In addition, the methodical development of the architecture of concepts, models and their subsequent software integration in ROS using model-based systems engineering is described. The validation and verification of the overall system is carried out in an industrial environment using three scenarios. The capabilities of the compost turner are demonstrated by autonomously following predefined trajectories in the composting plant and performing the required composting tasks. The results show that the autonomous compost turner is capable of performing the required activities. In addition, the compost turner has intelligent processing capabilities for compost data as well as its transmission, visualization and storage in a cloud server. It is important to note that this work is a preprint that represents the current state of research. The authors aim to publish the full paper in a peer-reviewed journal in the near future

Learning to Modulate pre-trained Models in RL

Reinforcement Learning (RL) has been successful in various domains like robotics, game playing, and simulation. While RL agents have shown impressive capabilities in their specific tasks, they insufficiently adapt to new tasks. In supervised learning, this adaptation problem is addressed by large-scale pre-training followed by fine-tuning to new down-stream tasks. Recently, pre-training on multiple tasks has been gaining traction in RL. However, fine-tuning a pre-trained model often suffers from catastrophic forgetting. That is, the performance on the pre-training tasks deteriorates when fine-tuning on new tasks. To investigate the catastrophic forgetting phenomenon, we first jointly pre-train a model on datasets from two benchmark suites, namely Meta-World and DMControl. Then, we evaluate and compare a variety of fine-tuning methods prevalent in natural language processing, both in terms of performance on new tasks, and how well performance on pre-training tasks is retained. Our study shows that with most fine-tuning approaches, the performance on pre-training tasks deteriorates significantly. Therefore, we propose a novel method, Learning-to-Modulate (L2M), that avoids the degradation of learned skills by modulating the information flow of the frozen pre-trained model via a learnable modulation pool. Our method achieves state-of-the-art performance on the Continual-World benchmark, while retaining performance on the pre-training tasks. Finally, to aid future research in this area, we release a dataset encompassing 50 Meta-World and 16 DMControl tasks.Comment: 10 pages (+ references and appendix), Code: https://github.com/ml-jku/L2

Whitepaper: The Value of Improving the Separation of Concerns

Microsoft's enterprise customers are demanding better ways to modularize their software systems. They look to the Java community, where these needs are being met with language enhancements, improved developer tools and middleware, and better runtime support. We present a business case for why Microsoft should give priority to supporting better modularization techniques, also known as advanced separation of concerns (ASOC), for the .NET platform, and we provide a roadmap for how to do so

Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning

In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. Consequently, lifelong learning comes with a plethora of research problems such as continual domain shifts, which result in non-stationary rewards and environment dynamics. These non-stationarities are difficult to detect and cope with due to their continuous nature. Therefore, exploration strategies and learning methods are required that are capable of tracking the steady domain shifts, and adapting to them. We propose Reactive Exploration to track and react to continual domain shifts in lifelong reinforcement learning, and to update the policy correspondingly. To this end, we conduct experiments in order to investigate different exploration strategies. We empirically show that representatives of the policy-gradient family are better suited for lifelong learning, as they adapt more quickly to distribution shifts than Q-learning. Thereby, policy-gradient methods profit the most from Reactive Exploration and show good results in lifelong learning with continual domain shifts. Our code is available at: https://github.com/ml-jku/reactive-exploration.Comment: CoLLAs 202

Replication and single-cycle delivery of SARS-CoV-2 replicons

Molecular virology tools are critical for basic studies of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and for developing new therapeutics. There remains a need for experimental systems that do not rely on viruses capable of spread that could potentially be used in lower containment settings. Here, we develop spike-deleted SARS-CoV-2 self-replicating RNAs using a yeast-based reverse genetics system. These non-infectious self-replicating RNAs, or replicons, can be trans-complemented with viral glycoproteins to generate Replicon Delivery Particles (RDPs) for single-cycle delivery into a range of cell types. This SARS-CoV-2 replicon system represents a convenient and versatile platform for antiviral drug screening, neutralization assays, host factor validation, and characterizing viral variants

Language endangerment and language documentation in Africa

Non peer reviewe

Effectiveness and safety of opicapone in Parkinson’s disease patients with motor fluctuations: the OPTIPARK open-label study

Background The efficacy and safety of opicapone, a once-daily catechol-O-methyltransferase inhibitor, have been established in two large randomized, placebo-controlled, multinational pivotal trials. Still, clinical evidence from routine practice is needed to complement the data from the pivotal trials. Methods OPTIPARK (NCT02847442) was a prospective, open-label, single-arm trial conducted in Germany and the UK under clinical practice conditions. Patients with Parkinson’s disease and motor fluctuations were treated with opicapone 50 mg for 3 (Germany) or 6 (UK) months in addition to their current levodopa and other antiparkinsonian treatments. The primary endpoint was the Clinician’s Global Impression of Change (CGI-C) after 3 months. Secondary assessments included Patient Global Impressions of Change (PGI-C), the Unified Parkinson’s Disease Rating Scale (UPDRS), Parkinson’s Disease Questionnaire (PDQ-8), and the Non-Motor Symptoms Scale (NMSS). Safety assessments included evaluation of treatment-emergent adverse events (TEAEs) and serious adverse events (SAEs). Results Of the 506 patients enrolled, 495 (97.8%) took at least one dose of opicapone. Of these, 393 (79.4%) patients completed 3 months of treatment. Overall, 71.3 and 76.9% of patients experienced any improvement on CGI-C and PGI-C after 3 months, respectively (full analysis set). At 6 months, for UK subgroup only (n = 95), 85.3% of patients were judged by investigators as improved since commencing treatment. UPDRS scores at 3 months showed statistically significant improvements in activities of daily living during OFF (mean ± SD change from baseline: − 3.0 ± 4.6, p < 0.0001) and motor scores during ON (− 4.6 ± 8.1, p < 0.0001). The mean ± SD improvements of − 3.4 ± 12.8 points for PDQ-8 and -6.8 ± 19.7 points for NMSS were statistically significant versus baseline (both p < 0.0001). Most of TEAEs (94.8% of events) were of mild or moderate intensity. TEAEs considered to be at least possibly related to opicapone were reported for 45.1% of patients, with dyskinesia (11.5%) and dry mouth (6.5%) being the most frequently reported. Serious TEAEs considered at least possibly related to opicapone were reported for 1.4% of patients. Conclusions Opicapone 50 mg was effective and generally well-tolerated in PD patients with motor fluctuations treated in clinical practice. Trial registration Registered in July 2016 at clinicaltrials.gov (NCT02847442)