Breaking the chain with individual gain? Investigating the moral intensity of COVID-19 digital contact tracing.

During the COVID-19 pandemic, contact tracing apps such as the German Corona-Warning-App (CWA) were introduced to facilitate contact tracing of infected individuals with the aim of breaking chains of infection. Therefore, using a contact tracing app is beneficial to society as a whole. Even though this is a good cause, the rather reluctant use of the CWA in the beginning indicated that the pains (e.g., privacy concerns) obviously outweighed the gains (helping others) at the level of the individual user. Thus, in order to identify what lies behind the gain of this app and how it can be promoted, we were interested in the individual's moral perspective (helping others) on the app. We expected a positive relation between CWA download and moral intensity derived from (i) the magnitude or seriousness of consequences, (ii) social norms about app use, (iii) the individual proximity to COVID-19 cases, and (iv) the probability of the app's positive effect. Using a heterogeneous German sample of N = 1,454, we found a strong influence of moral intensity on app download. Furthermore, a manipulation of moral intensity among non-users led to a higher number of downloads in a follow-up study (N = 662) as compared to the population. Our results show possibilities to enhance the adoption of contact tracing apps and potentially other apps for the common good in the population.

Crisis-driven innovation and fundamental human needs: A typological framework of rapid-response COVID-19 innovations.

As a microcosm for future challenges, the COVID-19 pandemic exhibits increasingly transboundary dynamics, causing interconnected problems across multiple societal systems. To examine the role of innovations as a social mechanism to reconcile these arising challenges, we view the unfolding of the pandemic through the lens of a content analysis of 707 innovation projects that address the fundamental human needs of consumers and businesses. This study proposes a novel procedure to characterize large-scale innovative activities via text mining and employs a theoretical framework for identifying the pressing societal needs amidst crises. Our typology of rapid-response COVID-19 innovations exhibits a diverse set of domains ranging from technological innovations to what may be described as frugal and social innovations. We provide evidence for the growing prevalence of social needs beyond the basic notion of safety during the early months of the crisis. Our contributions show that a structural model of innovation activities and their latent drivers may help policy makers and innovators to move toward achieving a systemic reaction to such crises.

It's a match! Simulating compatibility-based learning in a network of networks.

In this article, we develop a new way to capture knowledge diffusion and assimilation in innovation networks by means of an agent-based simulation model. The model incorporates three essential characteristics of knowledge that have not been covered entirely by previous diffusion models: the network character of knowledge, compatibility of new knowledge with already existing knowledge, and the fact that transmission of knowledge requires some form of attention. We employ a network-of- networks approach, where agents are located within an innovation network and each agent itself contains another network composed of knowledge units (KUs). Since social learning is a path-dependent process, in our model, KUs are exchanged among agents and integrated into their respective knowledge networks depending on the received KUs' compatibility with the currently focused ones. Thereby, we are also able to endogenize attributes such as absorptive capacity that have been treated as an exogenous parameter in some of the previous diffusion models. We use our model to simulate and analyze various scenarios, including cases for different degrees of knowledge diversity and cognitive distance among agents as well as knowledge exploitation vs. exploration strategies. Here, the model is able to distinguish between two levels of knowledge diversity: heterogeneity within and between agents. Additionally, our simulation results give fresh impetus to debates about the interplay of innovation network structure and knowledge diffusion. In summary, our article proposes a novel way of modeling knowledge diffusion, thereby contributing to an advancement of the economics of innovation and knowledge.