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Toll-like receptor (TLR) can trigger an immune response against virus including SARS-CoV-2. TLR expression/distribution is varying in mesenchymal stromal cells (MSCs) depending on their culture environments. Here, to explore the effect of periodic thermomechanical cues on TLRs, thermally controlled shape-memory polymer sheets with programmable actuation capacity were created. The proportion of MSCs expressing SARS-CoV-2-associated TLRs was increased upon stimulation. The TLR4/7 colocalization was promoted and retained in the endoplasmic reticula. The TLR redistribution was driven by myosin-mediated F-actin assembly. These results highlight the potential of boosting the immunity for combating COVID-19 via thermomechanical preconditioning of MSCs.
Urban air pollution is a substantial threat to human health. Traffic emissions remain a large contributor to air pollution in urban areas. The mobility restrictions put in place in response to the COVID-19 pandemic provided a large-scale real-world experiment that allows for the evaluation of changes in traffic emissions and the corresponding changes in air quality. Here we use observational data, as well as modelling, to analyse changes in nitrogen dioxide, ozone, and particulate matter resulting from the COVID-19 restrictions at the height of the lockdown period in Spring of 2020. Accounting for the influence of meteorology on air quality, we found that reduction of ca. 30-50 % in traffic counts, dominated by changes in passenger cars, corresponded to reductions in median observed nitrogen dioxide concentrations of ca. 40 % (traffic and urban background locations) and a ca. 22 % increase in ozone (urban background locations) during weekdays. Lesser reductions in nitrogen dioxide concentrations were observed at urban background stations at weekends, and no change in ozone was observed. The modelled reductions in median nitrogen dioxide at urban background locations were smaller than the observed reductions and the change was not significant. The model results showed no significant change in ozone on weekdays or weekends. The lack of a simulated weekday/weekend effect is consistent with previous work suggesting that NOx emissions from traffic could be significantly underestimated in European cities by models. These results indicate the potential for improvements in air quality due to policies for reducing traffic, along with the scale of reductions that would be needed to result in meaningful changes in air quality if a transition to sustainable mobility is to be seriously considered. They also confirm once more the highly relevant role of traffic for air quality in urban areas.
COVID-19
(2021)
We investigate how the economic consequences of the pandemic and the government-mandated measures to contain its spread affect the self-employed — particularly women — in Germany. For our analysis, we use representative, real-time survey data in which respondents were asked about their situation during the COVID-19 pandemic. Our findings indicate that among the self-employed, who generally face a higher likelihood of income losses due to COVID-19 than employees, women are about one-third more likely to experience income losses than their male counterparts. We do not find a comparable gender gap among employees. Our results further suggest that the gender gap among the self-employed is largely explained by the fact that women disproportionately work in industries that are more severely affected by the COVID-19 pandemic. Our analysis of potential mechanisms reveals that women are significantly more likely to be impacted by government-imposed restrictions, e.g., the regulation of opening hours. We conclude that future policy measures intending to mitigate the consequences of such shocks should account for this considerable variation in economic hardship.
From learners to educators
(2020)
The rapid growth of technology and its evolving potential to support the transformation of teaching and learning in post-secondary institutions is a major challenge to the basic understanding of both the university and the communities it serves. In higher education, the standard forms of learning and teaching are increasingly being challenged and a more comprehensive process of differentiation is taking place. Student-centered teaching methods are becoming increasingly important in course design and the role of the lecturer is changing from the knowledge mediator to moderator and learning companion. However, this is accelerating the need for strategically planned faculty support and a reassessment of the role of teaching and learning. Even though the benefits of experience-based learning approaches for the development of life skills are well known, most knowledge transfer is still realized through lectures in higher education. Teachers have the goal to design the curriculum, new assignments, and share insights into evolving pedagogy. Student engagement could be the most important factor in the learning success of university students, regardless of the university program or teaching format. Against this background, this article presents the development, application, and initial findings of an innovative learning concept. In this concept, students are allowed to deal with a scientific topic, but instead of a presentation and a written elaboration, their examination consists of developing an online course in terms of content, didactics, and concept to implement it in a learning environment, which is state of the art. The online courses include both self-created teaching material and interactive tasks. The courses are created to be available to other students as learning material after a review process and are thus incorporated into the curriculum.
Due to the COVID-19 pandemic, all schools in Germany were locked down for several months in 2020. How schools realized teaching during the school lockdown greatly varied from school to school. N = 2,647 parents participated in an online survey and rated the following activities of teachers in mathematics, language arts (German), English, and science / biology during the school lockdown: frequency of sending task assignments, task solutions and requesting for solutions, giving task-related feedback, grading tasks, providing lessons per videoconference, and communicating via telecommunication tools with students and / or parents. Parents also reported student academic outcomes during the school lockdown (child's learning motivation, competent and independent learning, learning progress). Parents further reported student characteristics and social background variables: child's negative emotionality, school engagement, mathematical and language competencies, and child's social and cultural capital. Data were separately analyzed for elementary and secondary schools. In both samples, frequency of student-teacher communication was associated with all academic outcomes, except for learning progress in elementary school. Frequency of parent-teacher communication was associated with motivation and learning progress, but not with competent and independent learning, in both samples. Other distant teaching activities were differentially related to students' academic outcomes in elementary vs. secondary school. School engagement explained most additional variance in all students' outcomes during the school lockdown. Parent's highest school leaving certificate incrementally predicted students' motivation, and competent and independent learning in secondary school, as well as learning progress in elementary school. The variable "child has own bedroom" additionally explained variance in students' competent and independent learning during the school lockdown in both samples. Thus, both teaching activities during the school lockdown as well as children's characteristics and social background were independently important for students' motivation, competent and independent learning, and learning progress. Results are discussed with regard to their practical implications for realizing distant teaching.
Background:
Corona contact tracing apps are a novel and promising measure to reduce the spread of COVID-19. They can help to balance the need to maintain normal life and economic activities as much as possible while still avoiding exponentially growing case numbers. However, a majority of citizens need to be willing to install such an app for it to be effective. Hence, knowledge about drivers for app uptake is crucial.
Objective:
This study aimed to add to our understanding of underlying psychological factors motivating app uptake. More specifically, we investigated the role of concern for one's own health and concern to unknowingly infect others.
Methods:
A two-wave survey with 346 German-speaking participants from Switzerland and Germany was conducted. We measured the uptake of two decentralized contact tracing apps officially launched by governments (Corona-Warn-App, Germany; SwissCovid, Switzerland), as well as concerns regarding COVID-19 and control variables.
Results:
Controlling for demographic variables and general attitudes toward the government and the pandemic, logistic regression analysis showed a significant effect of self-focused concerns (odds ratio [OR] 1.64, P=.002). Meanwhile, concern of unknowingly infecting others did not contribute significantly to the prediction of app uptake over and above concern for one's own health (OR 1.01, P=.92). Longitudinal analyses replicated this pattern and showed no support for the possibility that app uptake provokes changes in levels of concern. Testing for a curvilinear relationship, there was no evidence that "too much" concern leads to defensive reactions and reduces app uptake.
Conclusions:
As one of the first studies to assess the installation of already launched corona tracing apps, this study extends our knowledge of the motivational landscape of app uptake. Based on this, practical implications for communication strategies and app design are discussed.
Who suffered most?
(2022)
Objective:
This study examines gender and socioeconomic inequalities in parental psychological wellbeing (parenting stress and psychological distress) during the COVID-19 pandemic in Germany.
Background:
The dramatic shift of childcare and schooling responsibility from formal institutions to private households during the pandemic has put families under enormous stress and raised concerns about caregivers' health and wellbeing. Despite the overwhelming media attention to families’ wellbeing, to date limited research has examined parenting stress and parental psychological distress during the COVID-19 pandemic, particularly in Germany.
Method:
We analyzed four waves of panel data (N= 1,771) from an opt-in online survey, which was conducted between March 2020 and April 2021. Multivariable OLS regressions were used to estimate variations in the pandemic's effects on parenting stress and psychological distress by various demographic and socioeconomic characteristics.
Results:
Overall, levels of parenting stress and psychological distress increased during the pandemic. During the first and third wave of the COVID-19 pandemic, mothers, parents with children younger than 11 years, parents with two or more children, parents working from home as well as parents with financial insecurity experienced higher parenting stress than other sociodemographic groups. Moreover, women, respondents with lower incomes, single parents, and parents with younger children experienced higher levels of psychological distress than other groups.
Conclusion:
Gender and socioeconomic inequalities in parents' psychological wellbeing increased among the study participants during the pandemic.
The degree of detrimental effects inflicted on mankind by the COVID-19 pandemic increased the need to develop ASSURED (Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable) POCT (point of care testing) to overcome the current and any future pandemics. Much effort in research and development is currently advancing the progress to overcome the diagnostic pressure built up by emerging new pathogens. LAMP (loop-mediated isothermal amplification) is a well-researched isothermal technique for specific nucleic acid amplification which can be combined with a highly sensitive immunochromatographic readout via lateral flow assays (LFA). Here we discuss LAMP-LFA robustness, sensitivity, and specificity for SARS-CoV-2 N-gene detection in cDNA and clinical swab-extracted RNA samples. The LFA readout is designed to produce highly specific results by incorporation of biotin and FITC labels to 11-dUTP and LF (loop forming forward) primer, respectively. The LAMP-LFA assay was established using cDNA for N-gene with an accuracy of 95.65%. To validate the study, 82 SARS-CoV-2-positive RNA samples were tested. Reverse transcriptase (RT)-LAMP-LFA was positive for the RNA samples with an accuracy of 81.66%; SARS-CoV-2 viral RNA was detected by RT-LAMP-LFA for as low as CT-33. Our method reduced the detection time to 15 min and indicates therefore that RT-LAMP in combination with LFA represents a promising nucleic acid biosensing POCT platform that combines with smartphone based semi-quantitative data analysis.
Background:
There is evidence that fully recovered COVID-19 patients usually resume physical exercise, but do not perform at the same intensity level performed prior to infection. The aim of this study was to evaluate the impact of COVID-19 infection and recovery as well as muscle fatigue on cardiorespiratory fitness and running biomechanics in female recreational runners.
Methods:
Twenty-eight females were divided into a group of hospitalized and recovered COVID-19 patients (COV, n = 14, at least 14 days following recovery) and a group of healthy age-matched controls (CTR, n = 14). Ground reaction forces from stepping on a force plate while barefoot overground running at 3.3 m/s was measured before and after a fatiguing protocol. The fatigue protocol consisted of incrementally increasing running speed until reaching a score of 13 on the 6-20 Borg scale, followed by steady-state running until exhaustion. The effects of group and fatigue were assessed for steady-state running duration, steady-state running speed, ground contact time, vertical instantaneous loading rate and peak propulsion force.
Results:
COV runners completed only 56% of the running time achieved by the CTR (p < 0.0001), and at a 26% slower steady-state running speed (p < 0.0001). There were fatigue-related reductions in loading rate (p = 0.004) without group differences. Increased ground contact time (p = 0.002) and reduced peak propulsion force (p = 0.005) were found for COV when compared to CTR.
Conclusion:
Our results suggest that female runners who recovered from COVID-19 showed compromised running endurance and altered running kinetics in the form of longer stance periods and weaker propulsion forces. More research is needed in this area using larger sample sizes to confirm our study findings.
The coronavirus disease of 2019 (COVID-19) pandemic has forced most academics to work from home. This sudden venue change can affect academics' productivity and exacerbate the challenges that confront universities as they face an uncertain future. In this paper, we identify factors that influence academics' productivity while working from home during the mandate to self-isolate. From analyzing results from a global survey we conducted, we found that both personal and technology-related factors affect an individual's attitude toward working from home and productivity. Our results should prove valuable to university administrators to better address the work-life challenges that academics face.
This article provides a conceptual framework for the analysis of COVID-19 crisis governance in the first half of 2020 from a cross-country comparative perspective. It focuses on the issue of opportunity management, that is, how the crisis was used by relevant actors of distinctly different administrative cultures as a window of opportunity. We started from an overall interest in the factors that have influenced the national politics of crisis management to answer the question of whether and how political and administrative actors in various countries have used the crisis as an opportunity to facilitate, accelerate or prevent changes in institutional settings. The objective is to study the institutional settings and governance structures, (alleged) solutions and remedies, and constellations of actors and preferences that have influenced the mode of crisis and opportunity management. Finally, the article summarizes some major comparative findings drawn from the country studies of this Special Issue, focusing on similarities and differences in crisis responses and patterns of opportunity management.
The COVID-19 pandemic and related closures of day care centres and schools significantly increased the amount of care work done by parents. There has been much speculation over whether the pandemic increased or decreased gender equality in parental care work. Based on representative data for Germany from spring 2020 and winter 2021 we present an empirical analysis that shows that although gender inequality in the division of care work increased to some extent in the beginning of the pandemic, it returned to the pre-pandemic level in the second lockdown almost nine months later. These results suggest that the COVID-19 pandemic neither aggravated nor lessened inequality in the division of unpaid care work among mothers and fathers in any persistent way in Germany.
We introduce non-Markovian SIR epidemic spreading model inspired by the characteristics of the COVID-19, by considering discrete-and continuous-time versions. The distributions of infection intensity and recovery period may take an arbitrary form. By taking corresponding choice of these functions, it is shown that the model reduces to the classical Markovian case. The epidemic threshold is analytically determined for arbitrary functions of infectivity and recovery and verified numerically. The relevance of the model is shown by modeling the first wave of the epidemic in Italy, Spain and the UK, in the spring, 2020.
Background:
Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associatedwith worse outcomes. However, AKI among hospitalized patients with COVID19 in the United States is not well described.
Methods:
This retrospective, observational study involved a review of data from electronic health records of patients aged >= 18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality.
Results:
Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patientswith AKI required dialysis. The proportionswith stages 1, 2, or 3 AKIwere 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up.
Conclusions:
AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge.
In response to strong revenue and income losses facing a large share of self-employed individuals during the COVID-19 pandemic, the German federal government introduced a €50bn emergency-aid program. Based on real-time online-survey data comprising more than 20,000 observations, we analyze the impact of this program on the confidence to survive the crisis. We investigate how the digitalization level of self-employed individuals influences the program’s effectiveness. Employing propensity score matching, we find that the emergency-aid program had only moderately positive effects on the confidence of self-employed to survive the crisis. However, self-employed whose businesses were highly digitalized, benefitted much more from the state aid than those whose businesses were less digitalized. This only holds true for those self-employed, who started the digitalization processes already before the crisis. Taking a regional perspective, we find suggestive evidence that the quality of the regional broadband infrastructure matters in the sense that it increases the effectiveness of the emergency-aid program. Our findings show the interplay between governmental support programs, the digitalization levels of entrepreneurs, and the regional digital infrastructure. The study helps public policy to improve the impact of crisis-related policy instruments, ultimately increasing the resilience of small firms in times of crises.
Pandemic depression
(2022)
We investigate the effect of the COVID-19 pandemic on self-employed people’s mental health. Using representative longitudinal survey data from Germany, we reveal differential effects by gender: whereas self-employed women experienced a substantial deterioration in their mental health, self-employed men displayed no significant changes up to early 2021. Financial losses are important in explaining these differences. In addition, we find larger mental health responses among self-employed women who were directly affected by government-imposed restrictions and bore an increased childcare burden due to school and daycare closures. We also find that self-employed individuals who are more resilient coped better with the crisis.
Forced to stay at home
(2022)
The effects of COVID-19-related lockdowns on deterioration of mental health and use of exercise to remediate such effects has been well documented in numerous populations. However, it remains unknown how lockdown restrictions impacted individuals differently and who was more likely to change their exercise behavior and experience negative well-being. The current study examined exercise dependence as a risk factor and its impact on exercise behavior and mood during the initial COVID-19 lockdowns on a global scale in 11,898 participants from 17 countries. Mixed effects models revealed that reducing exercise behavior was associated with a stronger decrease in mood for individuals at risk of exercise dependence compared to individuals at low risk of exercise dependence. Participants at high risk and exercising more prior to the pandemic reported the most exercise during lockdown. Effects of lowered mood were most pronounced in participants with high risk of exercise dependence who reported greater reduction in exercise frequency during lockdown. These results support recent etiological evidence for exercise dependence and add to a growing body of literature documenting mental health effects related to COVID-19.
Boredom has been identified as one of the greatest psychological challenges when staying at home during quarantine and isolation. However, this does not mean that the situation necessarily causes boredom. On the basis of 13 explorative interviews with bored and non-bored persons who have been under quarantine or in isolation, we explain why boredom is related to a subjective interpretation process rather than being a direct consequence of the objective situation. Specifically, we show that participants vary significantly in their interpretations of staying at home and, thus, also in their experience of boredom. While the non-bored participants interpret the situation as a relief or as irrelevant, the bored participants interpret it as a major restriction that only some are able to cope with.
1,7 Milliarden Studierende waren von der ad hoc Umstellung der Lehre an Hochschulen durch den Ausbruch der COVID-19-Pandemie im Jahr 2020 betroffen. Innerhalb kürzester Zeit mussten Lehr- und Lernformate digital transformiert werden, um ein Distanzlernen für Studierende überall auf der Welt zu ermöglichen. Etwa zwei Jahre später können die Erfahrungen aus der Entwicklung von digitalen Lehr- und Lernformaten dazu genutzt werden, um Blended Learning Formate zielgerecht weiterzuentwickeln. Die nachfolgende Untersuchung zeigt einerseits einen Prozess der evolutionären Entwicklung am Beispiel eines Inverted Classrooms auf. Andererseits wird das Modell des Student Engagement genutzt, um die Einflussfaktoren, im Speziellen die des Verhaltens, zielgerecht anzupassen und so die Outcomes in Form von besseren Noten und einer erhöhten Zufriedenheit bei den Studierenden zu erzielen. Grundlage für die Untersuchung bildet die Lehrveranstaltung Projektmanagement, die an einer deutschen Hochschule durchgeführt wird.
Yes, we can (?)
(2021)
The COVID-19 crisis has caused an extreme situation for higher education institutions around the world, where exclusively virtual teaching and learning has become obligatory rather than an additional supporting feature. This has created opportunities to explore the potential and limitations of virtual learning formats. This paper presents four theses on virtual classroom teaching and learning that are discussed critically. We use existing theoretical insights extended by empirical evidence from a survey of more than 850 students on acceptance, expectations, and attitudes regarding the positive and negative aspects of virtual teaching. The survey responses were gathered from students at different universities during the first completely digital semester (Spring-Summer 2020) in Germany. We discuss similarities and differences between the subjects being studied and highlight the advantages and disadvantages of virtual teaching and learning. Against the background of existing theory and the gathered data, we emphasize the importance of social interaction, the combination of different learning formats, and thus context-sensitive hybrid learning as the learning form of the future.
Despite new challenges like climate change and digitalization, global and regional organizations recently went through turbulent times due to a lack of support from several of their member states. Next to this crisis of multilateralism, the COVID-19 pandemic now seems to question the added value of international organizations for addressing global governance issues more specifically. This article analyses this double challenge that several organizations are facing and compares their ways of managing the crisis by looking at their institutional and political context, their governance structure, and their behaviour during the pandemic until June 2020. More specifically, it will explain the different and fragmented responses of the World Health Organization, the European Union and the International Monetary Fund/World Bank. With the aim of understanding the old and new problems that these international organizations are trying to solve, this article argues that the level of autonomy vis-a-vis the member states is crucial for understanding the politics of crisis management. <br /> Points for practitioners <br /> As intergovernmental bodies, international organizations require authorization by their member states. Since they also need funding for their operations, different degrees of autonomy also matter for reacting to emerging challenges, such as the COVID-19 pandemic. The potential for international organizations is limited, though through proactive and bold initiatives, they can seize the opportunity of the crisis and partly overcome institutional and political constraints.
During the outbreak of the COVID-19 pandemic, many people shared their symptoms across Online Social Networks (OSNs) like Twitter, hoping for others’ advice or moral support. Prior studies have shown that those who disclose health-related information across OSNs often tend to regret it and delete their publications afterwards. Hence, deleted posts containing sensitive data can be seen as manifestations of online regrets. In this work, we present an analysis of deleted content on Twitter during the outbreak of the COVID-19 pandemic. For this, we collected more than 3.67 million tweets describing COVID-19 symptoms (e.g., fever, cough, and fatigue) posted between January and April 2020. We observed that around 24% of the tweets containing personal pronouns were deleted either by their authors or by the platform after one year.
As a practical application of the resulting dataset, we explored its suitability for the automatic classification of regrettable content on Twitter.
Background and objectives
AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited.
Design, setting, participants, & measurements
Using data from adult patients hospitalized with COVID-19 from five hospitals from theMount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to theMount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission.
Results
A total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93-0.98) and area under the precisionrecall curve (AUPRC; range of 0.78-0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85-0.87, and AUPRC range of 0.27-0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model's prediction.
Conclusions
An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models.
Background:
COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking.
Objective:
The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points.
Methods:
We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions.
Results:
Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction.
Conclusions:
We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.
The hospitality industry worldwide is among the hardest-hit industries from the COVID-19 lockdowns. Initial theoretical and practical observations in the hospitality industry indicate that business model innovation (BMI) might be a solution to recover from and successfully cope with the COVID-19 crisis. Interestingly, some firms in the hospitality industry already started to successfully adapt their business models. This study explores the why and how of these successful recovery attempts through BMI by conducting a multiple case study of six hospitality firms in Austria. We rely on interview data from managers together with one of their main stammgasts for each case, which we triangulate with secondary data for the analysis. Findings show that BMI is applied during and after the crisis to create new revenue streams and secure a higher level of liquidity, with an important role of stammgasts.
The economics of COVID-19
(2020)
Purpose
Within a very short period of time, the worldwide pandemic triggered by the novel coronavirus has not only claimed numerous lives but also caused severe limitations to daily private as well as business life. Just about every company has been affected in one way or another. This first empirical study on the effects of the COVID-19 crisis on family firms allows initial conclusions to be drawn about family firm crisis management.
Design/methodology/approach
Exploratory qualitative research design based on 27 semi-structured interviews with key informants of family firms of all sizes in five Western European countries that are in different stages of the crisis.
Findings
The COVID-19 crisis represents a new type and quality of challenge for companies. These companies are applying measures that can be assigned to three different strategies to adapt to the crisis in the short term and emerge from it stronger in the long run. Our findings show how companies in all industries and of all sizes adapt their business models to changing environmental conditions within a short period of time. Finally, the findings also show that the crisis is bringing about a significant yet unintended cultural change. On the one hand, a stronger solidarity and cohesion within the company was observed, while on the other hand, the crisis has led to a tentative digitalization.
Originality/value
To the knowledge of the authors, this is the first empirical study in the management realm on the impacts of COVID-19 on (family) firms. It provides cross-national evidence of family firms' current reactions to the crisis.
We present the first systematic literature review on stress and burnout in K-12 teachers during the COVID-19 pandemic. Based on a systematic literature search, we identified 17 studies that included 9,874 K-12 teachers from around the world. These studies showed some indication that burnout did increase during the COVID-19 pandemic. There were, however, almost no differences in the levels of stress and burnout experienced by K-12 teachers compared to individuals employed in other occupational fields. School principals' leadership styles emerged as an organizational characteristic that is highly relevant for K-12 teachers' levels of stress and burnout. Individual teacher characteristics associated with burnout were K-12 teachers' personality, self-efficacy in online teaching, and perceived vulnerability to COVID-19. In order to reduce stress, there was an indication that stress-management training in combination with training in technology use for teaching may be superior to stress-management training alone. Future research needs to adopt more longitudinal designs and examine the interplay between individual and organizational characteristics in the development of teacher stress and burnout during the COVID-19 pandemic and beyond.
Reacting, fast and slow
(2021)
The COVID-19 pandemic created extraordinary challenges for governments to safeguard the well-being of their people. To what extent has leaders' reliance on scientific advice shaped government responses to the COVID-19 outbreak? We argue that leaders who tend to orient themselves on expert advice realized the extent of the crisis earlier. Consequently, these governments would adopt containment measures relatively quickly, despite the high uncertainty they faced. Over time, differences in government responses based on the use of science would dissipate due to herding effects. We test our argument on data combining 163 government responses to the pandemic with national- and individual-level characteristics. Consistent with our argument, we find that countries governed by politicians with a stronger technocratic mentality, approximated by holding a PhD, adopted restrictive containment measures faster in the early, but not in the later, stages of the crisis. This importance of expert-based leadership plausibly extends to other large-scale societal crises.
There is an urgent need for screening of patients with a communicable viral disease to cut infection chains. Recently, we demonstrated that ion mobility spectrometry coupled with a multicapillary column (MCC-IMS) is able to identify influenza-A infections in patients' breath. With a decreasing influenza epidemic and upcoming SARS-CoV-2 infections we proceeded further and analyzed patients with suspected SARS-CoV-2 infections. In this study, the nasal breath of 75 patients (34 male, 41 female, aged 64.4 +/- 15.4 years) was investigated by MCC-IMS for viral infections. Fourteen were positively diagnosed with influenza-A infection and sixteen with SARS-CoV-2 by reverse transcription polymerase chain reaction (RT-PCR) of nasopharyngeal swabs. In one patient RT-PCR was highly suspicious of SARS-CoV-2 but initially inconclusive. The remaining 44 patients served as controls. Breath fingerprints for specific infections were assessed by a combination of cluster analysis and multivariate statistics. There were no significant differences in gender or age according to the groups. In the cross validation of the discriminant analysis 72 of the 74 clearly defined patients could be correctly classified to the respective group. Even the inconclusive patient could be mapped to the SARS-CoV-2 group by applying the discrimination functions. Conclusion: SARS-CoV-2 infection and influenza-A infection can be detected with the help of MCC-IMS in breath in this pilot study. As this method provides a fast non-invasive diagnosis it should be further developed in a larger cohort for screening of communicable viral diseases. A validation study is ongoing during the second wave of COVID-19.
Trial registration: ClinicalTrial.gov, NCT04282135 Registered 20 February 2020-Retrospectively registered,
Time for change?
(2022)
Purpose:
This study aims to provide probable future developments in the form of holistic scenarios for business negotiations. In recent years, negotiation research did not put a lot of emphasis on external changes. Consequently, current challenges and trends are scarcely integrated, making it difficult to support negotiation practice perspectively.
Design/methodology/approach:
This paper applies the structured, multi-method approach of scenario analysis. To examine the future space of negotiations, this combines qualitative and quantitative measures to base our analysis on negotiation experts’ assessments, estimations and visions of the negotiation future.
Findings:
The results comprise an overview of five negotiation scenarios in the year 2030 and of their individual drivers. The five revealed scenarios are: digital intelligence, business as usual, powerful network – the route to collaboration, powerful network – the route to predominance and system crash.
Originality/value:
The scenario analysis is a suitable approach that enables to relate various factors of the negotiation environment to negotiations themselves and allows an examination of future changes in buyer–seller negotiations and the creation of possible future scenarios. The identified scenarios provide an orientation for business decisions in the field of negotiation.
At the beginning of 2020, with COVID-19, courts of justice worldwide had to move online to continue providing judicial service. Digital technologies materialized the court practices in ways unthinkable shortly before the pandemic creating resonances with judicial and legal regulation, as well as frictions. A better understanding of the dynamics at play in the digitalization of courts is paramount for designing justice systems that serve their users better, ensure fair and timely dispute resolutions, and foster access to justice. Building on three major bodies of literature —e-justice, digitalization and organization studies, and design research— Designing for Digital Justice takes a nuanced approach to account for human and more-than-human agencies.
Using a qualitative approach, I have studied in depth the digitalization of Chilean courts during the pandemic, specifically between April 2020 and September 2022. Leveraging a comprehensive source of primary and secondary data, I traced back the genealogy of the novel materializations of courts’ practices structured by the possibilities offered by digital technologies. In five (5) cases studies, I show in detail how the courts got to 1) work remotely, 2) host hearings via videoconference, 3) engage with users via social media (i.e., Facebook and Chat Messenger), 4) broadcast a show with judges answering questions from users via Facebook Live, and 5) record, stream, and upload judicial hearings to YouTube to fulfil the publicity requirement of criminal hearings. The digitalization of courts during the pandemic is characterized by a suspended normativity, which makes innovation possible yet presents risks. While digital technologies enabled the judiciary to provide services continuously, they also created the risk of displacing traditional judicial and legal regulation.
Contributing to liminal innovation and digitalization research, Designing for Digital Justice theorizes four phases: 1) the pre-digitalization phase resulting in the development of regulation, 2) the hotspot of digitalization resulting in the extension of regulation, 3) the digital innovation redeveloping regulation (moving to a new, preliminary phase), and 4) the permanence of temporal practices displacing regulation. Contributing to design research Designing for Digital Justice provides new possibilities for innovation in the courts, focusing at different levels to better address tensions generated by digitalization. Fellow researchers will find in these pages a sound theoretical advancement at the intersection of digitalization and justice with novel methodological references. Practitioners will benefit from the actionable governance framework Designing for Digital Justice Model, which provides three fields of possibilities for action to design better justice systems. Only by taking into account digital, legal, and social factors can we design better systems that promote access to justice, the rule of law, and, ultimately social peace.
During COVID-19, various public institutions tried to shape citizens’ behaviour to slow the spread of the pandemic. How did their authority affect citizens’ support of public measures taken to combat the spread of COVID-19? The article makes two contributions. First, it presents a novel conceptualisation of authority as a source heuristic. Second, it analyses the authority of four types of public institutions (health ministries, universities, public health agencies, the WHO) in two countries (Germany and the UK), drawing on novel data from a survey experiment conducted in May 2020. On average, institutional endorsements seem to have mattered little. However, there is an observable polarisation effect where citizens who ascribe much expertise to public institutions support COVID-19 measures more than the control group. Furthermore, those who ascribe little expertise support them less than the control group. Finally, neither perception of biases nor exposure to institutions in public debates seems consistently to affect their authority.
This paper examines and discusses the biases and pitfalls of retrospective survey questions that are currently being used in many medical, epidemiological, and sociological studies on the COVID-19 pandemic. By analyzing the consistency of answers to retrospective questions provided by respondents who participated in the first two waves of a survey on the social consequences of the COVID-19 pandemic, we illustrate the insights generated by a large body of survey research on the use of retrospective questions and recall accuracy.
Drawing on three waves of survey data from a non-probability sample from Germany, this paper examines two opposing expectations about the pandemic's impacts on gender equality: The optimistic view suggests that gender equality has increased, as essential workers in Germany have been predominantly female and as fathers have had more time for childcare. The pessimistic view posits that lockdowns have also negatively affected women's jobs and that mothers had to shoulder the additional care responsibilities. Overall, our exploratory analyses provide more evidence supporting the latter view. Parents were more likely than non-parents to work fewer hours during the pandemic than before, and mothers were more likely than fathers to work fewer hours once lockdowns were lifted. Moreover, even though parents tended to divide childcare more evenly, at least temporarily, mothers still shouldered more childcare work than fathers. The division of housework remained largely unchanged. It is therefore unsurprising that women, in particular mothers, reported lower satisfaction during the observation period. Essential workers experienced fewer changes in their working lives than respondents in other occupations.
Since COVID-19 became a pandemic, many studies are being conducted to get a better understanding of the disease itself and its spread. One crucial indicator is the prevalence of SARS-CoV-2 infections. Since this measure is an important foundation for political decisions, its estimate must be reliable and unbiased. This paper presents reasons for biases in prevalence estimates due to unit nonresponse in typical studies. Since it is difficult to avoid bias in situations with mostly unknown nonresponse mechanisms, we propose the maximum amount of bias as one measure to assess the uncertainty due to nonresponse. An interactive web application is presented that calculates the limits of such a conservative unit nonresponse confidence interval (CUNCI).
Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics
(2021)
Newly emerging pandemics like COVID-19 call for predictive models to implement precisely tuned responses to limit their deep impact on society. Standard epidemic models provide a theoretically well-founded dynamical description of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which creates challenges for containment strategies. However, spatial heterogeneity raises questions about the adequacy of modeling epidemic outbreaks on the level of a whole country. Here, we show that by applying sequential data assimilation to the stochastic SEIR epidemic model, we can capture the dynamic behavior of outbreaks on a regional level. Regional modeling, with relatively low numbers of infected and demographic noise, accounts for both spatial heterogeneity and stochasticity. Based on adapted models, short-term predictions can be achieved. Thus, with the help of these sequential data assimilation methods, more realistic epidemic models are within reach.
Background: There is evidence that fully recovered COVID-19 patients usually resume physical exercise, but do not perform at the same intensity level performed prior to infection. The aim of this study was to evaluate the impact of COVID-19 infection and recovery as well as muscle fatigue on cardiorespiratory fitness and running biomechanics in female recreational runners.
Methods: Twenty-eight females were divided into a group of hospitalized and recovered COVID-19 patients (COV, n = 14, at least 14 days following recovery) and a group of healthy age-matched controls (CTR, n = 14). Ground reaction forces from stepping on a force plate while barefoot overground running at 3.3 m/s was measured before and after a fatiguing protocol. The fatigue protocol consisted of incrementally increasing running speed until reaching a score of 13 on the 6–20 Borg scale, followed by steady-state running until exhaustion. The effects of group and fatigue were assessed for steady-state running duration, steady-state running speed, ground contact time, vertical instantaneous loading rate and peak propulsion force.
Results: COV runners completed only 56% of the running time achieved by the CTR (p < 0.0001), and at a 26% slower steady-state running speed (p < 0.0001). There were fatigue-related reductions in loading rate (p = 0.004) without group differences. Increased ground contact time (p = 0.002) and reduced peak propulsion force (p = 0.005) were found for COV when compared to CTR.
Conclusion: Our results suggest that female runners who recovered from COVID-19 showed compromised running endurance and altered running kinetics in the form of longer stance periods and weaker propulsion forces. More research is needed in this area using larger sample sizes to confirm our study findings.
Background: There is evidence that fully recovered COVID-19 patients usually resume physical exercise, but do not perform at the same intensity level performed prior to infection. The aim of this study was to evaluate the impact of COVID-19 infection and recovery as well as muscle fatigue on cardiorespiratory fitness and running biomechanics in female recreational runners.
Methods: Twenty-eight females were divided into a group of hospitalized and recovered COVID-19 patients (COV, n = 14, at least 14 days following recovery) and a group of healthy age-matched controls (CTR, n = 14). Ground reaction forces from stepping on a force plate while barefoot overground running at 3.3 m/s was measured before and after a fatiguing protocol. The fatigue protocol consisted of incrementally increasing running speed until reaching a score of 13 on the 6–20 Borg scale, followed by steady-state running until exhaustion. The effects of group and fatigue were assessed for steady-state running duration, steady-state running speed, ground contact time, vertical instantaneous loading rate and peak propulsion force.
Results: COV runners completed only 56% of the running time achieved by the CTR (p < 0.0001), and at a 26% slower steady-state running speed (p < 0.0001). There were fatigue-related reductions in loading rate (p = 0.004) without group differences. Increased ground contact time (p = 0.002) and reduced peak propulsion force (p = 0.005) were found for COV when compared to CTR.
Conclusion: Our results suggest that female runners who recovered from COVID-19 showed compromised running endurance and altered running kinetics in the form of longer stance periods and weaker propulsion forces. More research is needed in this area using larger sample sizes to confirm our study findings.
Background: As the COVID-19 pandemic continues to spread across the globe, the search for an effective medication to treat the symptoms of COVID-19 continues as well. It would be desirable to identify a medication that is already in use for another condition and whose side effect profile and safety data are already known and approved.
Objective: The objective of this study was to evaluate the effect of different medications on typical COVID-19 symptoms by using data from an online surveillance survey.
Methods: Between early April and late-July 2020, a total of 3654 individuals in Lower Saxony, Germany, participated in an online symptom-tracking survey conducted through the app covid-nein-danke.de. The questionnaire comprised items on typical COVID-19 symptoms, age range, gender, employment in patient-facing healthcare, housing status, postal code, previous illnesses, permanent medication, vaccination status, results of reverse transcription polymerase chain reaction (RT-PCR) and antibody tests for COVID-19 diagnosis, and consequent COVID-19 treatment if applicable. Odds ratio estimates with corresponding 95% CIs were computed for each medication and symptom by using logistic regression models.
Results: Data analysis suggested a statistically significant inverse relationship between typical COVID-19 symptoms self-reported by the participants and self-reported statin therapy and, to a lesser extent, antihypertensive therapy. When COVID-19 diagnosis was based on restrictive symptom criteria (ie, presence of 4 out of 7 symptoms) or a positive RT-PCR test, a statistically significant association was found solely for statins (odds ratio 0.28, 95% CI 0.1-0.78).
Conclusions: Individuals taking statin medication are more likely to have asymptomatic COVID-19, in which case they may be at an increased risk of transmitting the disease unknowingly. We suggest that the results of this study be incorporated into symptoms-based surveillance and decision-making protocols in regard to COVID-19 management. Whether statin therapy has a beneficial effect in combating COVID-19 cannot be deduced based on our findings and should be investigated by further study.
We present the first systematic literature review on stress and burnout in K−12 teachers during the COVID-19 pandemic. Based on a systematic literature search, we identified 17 studies that included 9,874 K−12 teachers from around the world. These studies showed some indication that burnout did increase during the COVID-19 pandemic. There were, however, almost no differences in the levels of stress and burnout experienced by K−12 teachers compared to individuals employed in other occupational fields. School principals' leadership styles emerged as an organizational characteristic that is highly relevant for K−12 teachers' levels of stress and burnout. Individual teacher characteristics associated with burnout were K−12 teachers' personality, self-efficacy in online teaching, and perceived vulnerability to COVID-19. In order to reduce stress, there was an indication that stress-management training in combination with training in technology use for teaching may be superior to stress-management training alone. Future research needs to adopt more longitudinal designs and examine the interplay between individual and organizational characteristics in the development of teacher stress and burnout during the COVID-19 pandemic and beyond.
We present the first systematic literature review on stress and burnout in K−12 teachers during the COVID-19 pandemic. Based on a systematic literature search, we identified 17 studies that included 9,874 K−12 teachers from around the world. These studies showed some indication that burnout did increase during the COVID-19 pandemic. There were, however, almost no differences in the levels of stress and burnout experienced by K−12 teachers compared to individuals employed in other occupational fields. School principals' leadership styles emerged as an organizational characteristic that is highly relevant for K−12 teachers' levels of stress and burnout. Individual teacher characteristics associated with burnout were K−12 teachers' personality, self-efficacy in online teaching, and perceived vulnerability to COVID-19. In order to reduce stress, there was an indication that stress-management training in combination with training in technology use for teaching may be superior to stress-management training alone. Future research needs to adopt more longitudinal designs and examine the interplay between individual and organizational characteristics in the development of teacher stress and burnout during the COVID-19 pandemic and beyond.
This paper analyzes the effect of new bicycle lanes on traffic volume, congestion, and accidents. Crucially, the new bike lanes replace existing car lanes thereby reducing available space for motorized traffic. In order to obtain causal estimates, I exploit the quasi-random timing and location of the newly built cycle lanes. Using an event study design, a two-way fixed effects model and the synthetic control group method on geo-coded data, I show that the construction of pop-up bike lanes significantly reduced average car speed by 8 to 12 percentage points (p.p.) and up to 16 p.p. in peak traffic hours. In contrast, the results for car volume are modest, while the data does not allow for a conclusive judgment of accidents.
We investigate the effect of the COVID-19 pandemic on self-employed people’s mental health. Using representative longitudinal survey data from Germany, we reveal differential effects by gender: whereas self-employed women experienced a substantial deterioration in their mental health, self-employed men displayed no significant changes up to early 2021. Financial losses are important in explaining these differences. In addition, we find larger mental health responses among self-employed women who were directly affected by government-imposed restrictions and bore an increased childcare burden due to school and daycare closures. We also find that self-employed individuals who are more resilient coped better with the crisis.
The COVID-19 pandemic created the largest experiment in working from home. We study how persistent telework may change energy and transport consumption and costs in Germany to assess the distributional and environmental implications when working from home will stick. Based on data from the German Microcensus and available classifications of working-from-home feasibility for different occupations, we calculate the change in energy consumption and travel to work when 15% of employees work full time from home. Our findings suggest that telework translates into an annual increase in heating energy expenditure of 110 euros per worker and a decrease in transport expenditure of 840 euros per worker. All income groups would gain from telework but high-income workers gain twice as much as low-income workers. The value of time saving is between 1.3 and 6 times greater than the savings from reduced travel costs and almost 9 times higher for high-income workers than low-income workers. The direct effects on CO₂ emissions due to reduced car commuting amount to 4.5 millions tons of CO₂, representing around 3 percent of carbon emissions in the transport sector.
Die vorliegende Studie zeigt, dass Daten in der Krise eine herausragende Bedeutung für die wissenschaftliche Politikberatung, administrative Entscheidungsvorbereitung und politische Entscheidungsfindung haben. In der Krise gab es jedoch gravierende Kommunikationsprobleme und Unsicherheiten in der wechselseitigen Erwartungshaltung von wissenschaftlichen Datengebern und politisch-administrativen Datennutzern. Die Wissensakkumulation und Entscheidungsabwägung wurde außerdem durch eine unsichere und volatile Datenlage zum Pandemiegeschehen, verbunden mit einer dynamischen Lageentwicklung, erschwert. Nach wie vor sind das Bewusstsein und wechselseitige Verständnis für die spezifischen Rollenprofile der am wissenschaftlichen Politikberatungsprozess beteiligten Akteure sowie insbesondere deren Abgrenzung als unzureichend einzuschätzen.
Die Studie hat darüber hinaus vielfältige Defizite hinsichtlich der Verfügbarkeit, Qualität, Zugänglichkeit, Teilbarkeit und Nutzbarkeit von Daten identifiziert, die Datenproduzenten und -verwender vor erhebliche Herausforderungen stellen und einen umfangreichen Reformbedarf aufzeigen, da zum einen wichtige Datenbestände für eine krisenbezogene Politikberatung fehlen. Zum anderen sind die Tiefenschärfe und Differenziertheit des verfügbaren Datenbestandes teilweise unzureichend. Dies gilt z.B. für sozialstrukturelle Daten zur Schwere der Pandemiebetroffenheit verschiedener Gruppen oder für kleinräumige Daten über Belastungs- und Kapazitätsparameter, etwa zur Personalabdeckung auf Intensivstationen, in Gesundheitsämtern und Pflegeeinrichtungen. Datendefizite sind ferner im Hinblick auf eine ganzheitliche Pandemiebeurteilung festzustellen, zum Beispiel bezüglich der Gesundheitseffekte im weiteren Sinne, die aufgrund der ergriffenen Maßnahmen entstanden sind (Verschiebung oder Wegfall von Operationen, Behandlungen und Prävention, aber auch häusliche Gewalt und psychische Belastungen). Mangels systematischer Begleitstudien und evaluativer Untersuchungen, u.a. auch zu lokalen Pilotprojekten und Experimenten, bestehen außerdem Datendefizite im Hinblick auf die Wirkungen von Eindämmungsmaßnahmen oder deren Aufhebung auf der gebietskörperschaftlichen Ebene.
Insgesamt belegt die Studie, dass es zur Optimierung der datenbasierten Politikberatung und politischen Entscheidungsfindung in und außerhalb von Krisen nicht nur darum gehen kann, ein „Mehr“ an Daten zu produzieren sowie deren Qualität, Verknüpfung und Teilung zu verbessern. Vielmehr müssen auch die Anreizstrukturen und Interessenlagen in Politik, Verwaltung und Wissenschaft sowie die Kompetenzen, Handlungsorientierungen und kognitiv-kulturellen Prägungen der verschiedenen Akteure in den Blick genommen werden. Es müssten also Anreize gesetzt und Strukturen geschaffen werden, um das Interesse, den Willen und das Können (will and skill) zur Datennutzung auf Seiten politisch-administrativer Entscheider und zur Dateneinspeisung auf Seiten von Wissenschaftlern zu stärken. Neben adressatengerechter Informationsaufbereitung geht es dabei auch um die Gestaltung eines normativen und institutionellen Rahmens, innerhalb dessen die Nutzung von Daten für Entscheidungen effektiver, qualifizierter, aber auch transparenter, nachvollziehbarer und damit demokratisch legitimer erfolgen kann.
Vor dem Hintergrund dieser empirischen Befunde werden acht Cluster von Optimierungsmaßnahmen vorgeschlagen:
(1) Etablierung von Datenstrecken und Datenteams,
(2) Schaffung regionaler Datenkompetenzzentren,
(3) Stärkung von Data Literacy und Beschleunigung des Kulturwandels in der öffentlichen Verwaltung,
(4) Datenstandardisierung, Interoperabilität und Registermodernisierung,
(5) Ausbau von Public Data Pools und Open Data Nutzung,
(6) Effektivere Verbindung von Datenschutz und Datennutzung,
(7) Entwicklung eines hochfrequenten, repräsentativen Datensatzes,
(8) Förderung der europäischen Daten-Zusammenarbeit.
In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring–summer 2020 semester. Our study focused on (1) the students’ acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study’s results and derive short- and long-term implications for science and practice.
In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring–summer 2020 semester. Our study focused on (1) the students’ acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study’s results and derive short- and long-term implications for science and practice.