Uber utilizes a technology platform that enables independent drivers to share rides with customers and has become popular over the past few years (Min et al., 2021). Willis & Tranos (2020) highlights the impact of Rogers’ innovation-diffusion characteristics on Uber’s adoption and competitiveness during COVID-19. Relative advantage came in the form of priority for safety.
Uber included an information in-app and no-contact delivery, provided sanitizer and personal protective equipment to couriers and drivers (Scheepers & Bogie, 2020). Safety was a top priority, and Uber made it compulsory for drivers and riders to wear a face covering. These steps have made Uber remain on top of competitors during and after the COVID-19 pandemic.
Compatibility and complexity characteristics are evident in Uber’s focus on support businesses and communities during the pandemic. Uber developed Uber Direct to move goods between locations and to customers for businesses overwhelmed with excess demand (Uber, 2021).
Uber Eats launched a new feature, giving restaurants the option to receive daily payouts during uncertain times caused by the COVID-19 pandemic. Uber partnered with stores to offer customers essential items such as toiletries and Over-the-Counter medicines. With these and other innovative steps, Uber has shown the potential to adapt its business model to unexpected circumstances while remaining committed to providing reliable transportation.
References
Min, S., Fung So, K. K., & Jeong, M. (2021). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Future of Tourism Marketing, 2-15. https://doi.org/10.4324/9781003176039-2
Scheepers, C. B., & Bogie, J. (2020). Uber sub-Saharan Africa: Contextual leadership for sustainable business model innovation during COVID-19. Emerald Emerging Markets Case Studies, 10(3), 1-18. https://doi.org/10.1108/eemcs-05-2020-0165
Uber. (2021, July 18). Introducing Uber direct. Uber Blog. https://www.uber.com/en-ZA/blog/introducing-uber-direct/
Willis, G., & Tranos, E. (2020). Using ‘Big data’ to understand the impacts of Uber on taxis in New York City. https://doi.org/10.31235/osf.io/25fxs
