Startups often demonstrate a unique agility in crisis response, allowing them to pivot strategies and reallocate resources faster than larger, more bureaucratic organizations. Their inherent lean structure means decisions are made quickly, enabling a swifter adaptation to unforeseen market shifts or operational disruptions. However, this agility doesn't always translate to *better* forecasting results, as they often lack the extensive historical data, dedicated forecasting teams, or robust financial buffers that larger companies possess. While they excel at rapid iteration and experimentation during a crisis, their predictions can be less reliable due to limited resources for in-depth market analysis and risk modeling. Instead, startups often rely on nimble adjustments and continuous feedback loops to navigate uncertainty, which is a different approach than traditional, data-heavy forecasting. Therefore, while their response is often faster and more adaptive, labeling it as "better" for *forecasting results* specifically might be an oversimplification, as their strength lies more in adaptive execution rather than predictive accuracy. More details: https://primesgeneva.ch/front/traduction?lang=1&backto=https://abcname.com.ua/