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The World Health Organization declared the emergence of the novel coronavirus (SARS-CoV-2) in January 2020. To trace infection chains, Germany launched its smartphone contact tracing app, the “Corona-Warn-App” (CWA), in June 2020. In order to be successful as a tool for fighting the pandemic, a high adoption rate is required in the population. We analyse the respective factors influencing app adoption based on the health belief model (HBM) with a cross-sectional online study including 1752 participants from Germany. The study was conducted with a certified panel provider from the end of December 2020 to January 2021. This model is primarily known from evaluations of medical treatments, such as breast cancer screenings, but it was rarely applied in prior work for a health-related information system such as the CWA. Our results indicate that intrinsic and extrinsic motivation to use the CWA are the strongest drivers of app use. In contrast, technical barriers, privacy concerns and lower income are the main inhibitors. Our findings contribute to the literature on the adoption of contact tracing apps by questioning actual users and non-users of the CWA, and we provide valuable insights for policymakers regarding influences of adoption and potential user groups of disease prevention technologies in times of pandemics.
Background: The German Corona-Warn-App (CWA) is a contact tracing app to mitigate the spread of SARS-CoV-2. As of today, it has been downloaded approximately 45 million times.
Objective: In this study, we investigate the influence of (non-)users' social environments on the usage of the CWA during two time periods with relatively lower death rates and higher death rates caused by SARS-CoV-2.
Methods: We conducted a longitudinal survey study in Germany with 833 participants in two waves to investigate how participants perceive their peer-groups opinion about making use of the German CWA to mitigate the risk of SARS-CoV-2. In addition, we asked whether this perceived opinion, in turn, influences the participants with respect to their own decision to use the CWA. We analyzed these questions with Generalized Estimating Equations (GEE). And with two-related-samples tests to test for differences between users of the CWA and non-users and between the two points in time (wave 1 with the highest death rates observable during the pandemic in Germany versus wave 2 with significantly lower death rates). Results: Participants perceive that peer-groups have a positive opinion towards using the CWA, with more positive opinions by the media, family doctors, politicians and virologists/RKI and a lower, only slightly negative opinion originating from social
media. Users of the CWA perceive their peer groups’ opinions about using the app as more positive than non-users do. Furthermore, the perceived positive opinion of the media and politicians is significantly lower in wave 2 compared to wave 1. The perceived opinion of friends and family as well as their perceived influence towards using the CWA is significantly higher in the latter period compared to wave 1. The influence of virologists (in Germany primarily communicated via the Robert Koch Institute) has the highest positive effect on using the CWA. We only find one decreasing effect of the influence of politicians.
Conclusions: Opinions of peer groups play an important role when it comes to the adoption of the CWA. Our results show that the influence of Virologists / Robert Koch Institute and family/friends exerts the strongest effect on participants decision to use the CWA while politicians had a slightly negative influence. Our results indicate that it is crucial to accompany the introduction of such a contact tracing app with explanations and a media campaign to support its adoption which is backed up by political decision-makers subject-matter experts.
Background: The German Corona-Warn-App (CWA) is a contact tracing app to mitigate the spread of SARS-CoV-2. As of today, it has been downloaded approximately 45 million times.
Objective: This study aims to investigate the influence of (non)users’ social environments on the usage of the CWA during 2 periods with relatively lower death rates and higher death rates caused by SARS-CoV-2.
Methods: We conducted a longitudinal survey study in Germany with 833 participants in 2 waves to investigate how participants perceive their peer groups’ opinion about making use of the German CWA to mitigate the risk of SARS-CoV-2. In addition, we asked whether this perceived opinion, in turn, influences the participants with respect to their own decision to use the CWA. We analyzed these questions with generalized estimating equations. Further, 2 related sample tests were performed to test for differences between users of the CWA and nonusers and between the 2 points in time (wave 1 with the highest death rates observable during the pandemic in Germany versus wave 2 with significantly lower death rates).
Results: Participants perceived that peer groups have a positive opinion toward using the CWA, with more positive opinions by the media, family doctors, politicians, and virologists/Robert Koch Institute and a lower, only slightly negative opinion originating from social media. Users of the CWA perceived their peer groups’ opinions about using the app as more positive than nonusers do. Furthermore, the perceived positive opinion of the media (P=.001) and politicians (P<.001) was significantly lower in wave 2 compared with that in wave 1. The perceived opinion of friends and family (P<.001) as well as their perceived influence (P=.02) among nonusers toward using the CWA was significantly higher in the latter period compared with that in wave 1. The influence of virologists (in Germany primarily communicated via the Robert Koch Institute) had the highest positive effect on using the CWA (B=0.363, P<.001). We only found 1 decreasing effect of the influence of politicians (B=–0.098, P=.04).
Conclusions: Opinions of peer groups play an important role when it comes to the adoption of the CWA. Our results show that the influence of virologists/Robert Koch Institute and family/friends exerts the strongest effect on participants’ decisions to use the CWA while politicians had a slightly negative influence. Our results also indicate that it is crucial to accompany the introduction of such a contact tracing app with explanations and a media campaign to support its adoption that is backed up by political decision makers and subject matter experts.
The rise of shale gas and tight oil development has triggered a major debate about hydraulic fracturing (HF). In an effort to bring light to HF practices and their potential risks to water quality, many U.S. states have mandated disclosure for HF wells and the fluids used. We employ this setting to study whether targeting corporate activities that have dispersed externalities with transparency reduces their environmental impact. Examining salt concentrations that are considered signatures for HF impact, we find significant and lasting improvements in surface water quality between 9-14% after the mandates. Most of the improvement comes from the intensive margin. We document that operators pollute less per unit of production, cause fewer spills of HF fluids and wastewater and use fewer hazardous chemicals. Turning to how transparency regulation works, we show that it increases public pressure and enables social movements, which facilitates internalization.
This study compares the performance of various machine learning methods in predicting the outcomes of mergers and acquisitions (M&A), with application in merger arbitrage. Merger arbitrage capitalizes on price inefficiencies around merger announcements, empirically offering consistent, near-market-neutral returns with Sharpe ratios around 1.20 and a beta of 0.14. Leveraging logistic regression, random forest, gradient boosting machine, and neural network, I analyse 21,020 M&A deals with up to 522 predictors from 1999 to 2023. I examine two datasets: one with all features available prior to deal resolution, serving as an upper bound for predictability, and another with only features available on the announcement. Among the applied methods, XGBoost outperforms in predicting deal closure probabilities, with pseudo-out-of-sample receiver operating characteristic area under the curve (ROC-AUC) scores of 0.99 and 0.81 for the full-feature and announcement-date-only sets, respectively.
I apply these predictions to cash-only merger arbitrage from 2021 to 2023, using a classification method and testing a promising fair value investment criterion. I find that equal-weighted portfolios perform best, driven by diversification and small-size premia, achieving annualized alphas of 10 to 20% against the Fama-French five-factor model. XGBoost’s superior predictive power translates into the best merger arbitrage performance, delivering Sharpe ratios of up to 1.57 for long-only portfolios and 0.60 for zero-net-investment long-short strategies, with the latter maintaining market neutrality. I confirm these results during a second trading period from 2018 to 2020, revealing different market dynamics and similar or better model performance, with Sharpe ratios as high as 2.15.
These findings establish new benchmarks for M&A deal closure prediction, highlight the value of machine learning-driven strategies in enhancing merger arbitrage performance, and offer valuable insights for both researchers and practitioners.
This study investigates the socio-economic characteristics, behavioral preferences, and consumption of individuals who own crypto-assets. Our empirical analysis utilizes data from a German personal finance management app where users connect their bank accounts and depots. We conducted a survey and elicited behavioral factors for financial decision-making. By combining survey with account and security account data, we identify crypto investors’ preferences for financial decision-making and financial advice. Our results suggest that, in particular, students or self-employed, young, and male individuals who are risk-seeking and impatient are more likely to have invested in crypto-assets. Most crypto owners have less experience with financial advisory. They see it as too time-consuming and qualitatively poor, and instead, they prefer to decide on their own as they have self-reported high financial literacy. Investigating their consumption in more detail we conclude that crypto investors more often spend on travelling, electronics, and food delivery and less on health. Our findings suggest policymakers in identifying high-risk consumers and investors, and help financial institutions develop appropriate products.
With adequate support for the learner, errors can have high learning potential. This study investigates rather unsuitable action patterns of teachers in dealing with errors. Teachers rarely investigate the causes that evoke the occurrence of individual students’ errors, but instead often change addressees immediately after an error occurs. Such behavior is frequent in the classroom, leaving unexploited, yet important potential to learn from errors. It has remained unexplained why teachers act the way they do in error situations. Using video-stimulated recalls, I investigate the reasons for teachers’ behavior in students’ error situations by confronting them with recorded episodes from their own teaching. Error situations are analyzed (within-case) and teachers’ beliefs are classified in an explanatory model (cross-case) to illustrate patterns across teachers. Results show that teachers refer to an interaction of student attributes, their own attributes, and error attributes when reasoning their own behavior. I find that reference to specific attributes varies depending on the situation, and so do the described reasons that led to a particular behavior as a spontaneous or more reflective decision.
The crowdfunding of altruism
(2022)
This paper introduces a machine learning approach to quantify altruism from the linguistic style of textual documents. We apply our method to a central question in (social) entrepreneurship: How does altruism impact entrepreneurial success? Specifically, we examine the effects of altruism on crowdfunding outcomes in Initial Coin Offerings (ICOs). The main result suggests that altruism and ICO firm valuation are negatively related. We, then, explore several channels to shed some light on whether the negative altruism-valuation relation is causal. Our findings suggest that it is not altruism that causes lower firm valuation; rather, low-quality entrepreneurs select into altruistic projects, while the marginal effect of altruism on high-quality entrepreneurs is actually positive. Altruism increases the funding amount in ICOs in the presence of high-quality projects, low asymmetric information, and strong corporate governance.
Does political conflict with another country influence domestic consumers' daily consumption choices? We exploit the volatile US-China relations in 2018 and 2019 to analyze whether US consumers reduce their visits to Chinese restaurants when bilateral relations deteriorate. We measure the degree of political conflict through negativity in media reports and rely on smartphone location data to measure daily visits to over 190,000 US restaurants. A deterioration in US-China relations induces a significant decline in visits not only to Chinese but also to other foreign ethnic restaurants, while visits to typical American restaurants increase. We identify consumers' age, race, and cultural openness to moderate the strength of this ethnocentric effect.
External linkages allow nascent ventures to access crucial resources during the process of new product development. Forming external linkages can substantially contribute to a venture’s performance. However, little is known about the paths of external linkage formation, as well as the circumstances that drive the choice to pursue one rather than another path. This gap deserves further investigation, because we do not know whether insights developed for incumbent firms also apply to nascent ventures: To address this gap, we explore a novel dataset of 370 venture creation processes. Using sequence analyses based on optimal matching techniques and cluster analyses, we reveal that nascent ventures pursue one of overall four distinct paths of linkage formation activities during new product development. Contrary to the findings of the strategy literature, we find that if nascent ventures engage in external linkages at all, they do not combine exploration- and exploitation-oriented linkages but form either exploration- or exploitation-oriented linkages. Additional regression analyses highlight the circumstances that lead nascent ventures to pursue one rather than the other pathways. Taken together, our analyses point out that resource scarcity constitutes an important factor shaping the linkage formation activities of nascent ventures. Accordingly, we show that nascent ventures tend not to optimize by adding complementary knowledge to the firm’s knowledge base but rather to extend the existing knowledge base—a strategy which we call bricolage.