Purpose: This study examines which vaccination organization system performed best in the COVID-19 pandemic to improve future vaccination organization. Study design: The vaccination organization of every federal state is categorized as decentralized or centralized and analysed based on their daily vaccination rates and the vaccination time series of the federal state with the highest vaccination rate is analysed by using the Event Study Methodology [ESM]. Findings: In Germany’s federal state with the highest vaccination rate (i.e., Saarland), the change from a system of availability-based offerings to a pre-registration with preferences and automatic appointment allocation system was a significant performance factor. Originality: A quasi-experimental study with a different vaccination organization is setup and the Event Study Methodology [ESM] is applied to the vaccination context. Research limitations: This study is limited on the vaccination organization of high-developped countries with a comprehensive health system such as Germany. Practical implications: A pre-registration and automatic appointment allocation system is recommended as best practice to policy makers and pandemic managers for their vaccination organization given the first half-year experience in the COVID-19 pandemic. Social implications: A cumulative additional vaccination rate of 8.44 per 100 inhabitants and an 14% overperformance is found. The implementation of this system for whole Germany would have resulted in 4% higher protection, estimated 26’596 less infections, US$ 7 million less hospitalization costs, and earlier relaxation of lockdown of two months.
This research paper examines the adoption of digital services for the vaccination in the COVID-19 pandemic in Germany. Based on a survey in Germany's federal state with the highest vaccination rate, which used digital vaccination services, its platform configuration and adoption barriers are analyzed to understand existing and future levers for optimizing vaccination success. Though technological adoption and resistance models have been originally developed for consumer goods markets, this study gives empirical evidence for the applicability of an adjusted model explaining platform adoption for vaccination services in special and for digital health services in general. In this model, the configuration areas personalization, communication, and data management have a remarkable effect to lower adoption barriers, but only functional and psychological factors affect the adoption intention. Above all, the usability barrier stands out with the strongest effect while the often-cited value barrier is not significant at all. Personalization is found to be the most important factor for managing the usability barrier and thus for addressing the needs, preferences, situation, and ultimately the adoption of the citizens as users. Implications are given for policy makers and managers in such pandemic crisis to focus on the click flow and server-to-human interaction rather than emphasizing value messages or touching traditional factors.