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With the emergence of the Industrial Internet of Things, a growing number of manufacturing firms has started to adopt non-ownership business models (NOBMs). NOBM providers maintain ownership of offered machinery and sell only the machine use and/or performance as a service to their clients. While the adoption of NOBMs is found to be associated with novel business opportunities related to client-side uncertainties, it is also found to result in a considerable increase in provider-side uncertainties. Drawing on a multiple-case study with three leading manufacturers, we find notable differences in terms of NOBM designs, ranging from a primary focus on exploiting client-side uncertainties to a primary focus on mitigating provider-side uncertainties. Moreover, our study uncovers four context factors that help explain key differences in NOBM designs. In particular, we identify two machine attributes (human dependency and energy efficiency) and two market attributes (average client size and antitrust regulations) that “push” providers toward either uncertainty-exploiting or uncertainty-mitigating NOBM designs. Theoretical and practical implications are discussed.

Annual phytoplankton blooms are key events in marine ecosystems and interannual variability in bloom timing has important implications for carbon export and the marine food web. The degree of match or mismatch between the timing of phytoplankton and zooplankton annual cycles may impact larval survival with knock‐on effects at higher trophic levels. Interannual variability in phytoplankton bloom timing may also be used to monitor changes in the pelagic ecosystem that are either naturally or anthropogenically forced. Seasonality metrics that use satellite ocean color data have been developed to quantify the timing of phenological events which allow for objective comparisons between different regions and over long periods of time. However, satellite data sets are subject to frequent gaps due to clouds and atmospheric aerosols, or persistent data gaps in winter due to low sun angle. Here we quantify the impact of these gaps on determining the start and peak timing of phytoplankton blooms. We use the NASA Ocean Biogeochemical Model that assimilates SeaWiFS data as a gap‐free time series and derive an empirical relationship between the percentage of missing data and error in the phenology metric. Applied globally, we find that the majority of subpolar regions have typical errors of 30 days for the bloom initiation date and 15 days for the peak date. The errors introduced by intermittent data must be taken into account in phenological studies. Key Points Global maps of seasonality metrics and the associated uncertainty are presented Bloom start and peak date errors are 30 and 15 days respectively in most regions The error in bloom start date has a directional bias that changes with latitude