In this paper, we analyze a novel information-sharing policy to reduce stockouts and improve performance on online grocery retail platforms. Stockouts are particularly relevant to our setting, as online platforms lack control over retailers' inventory management, and consequently when/whether items are in stock. Our policy involves leveraging the digital interface of online platforms to disseminate information about delivery windows when retailer stock levels are at their peak. Ex-ante, it is unclear if such a policy would positively influence customers' purchasing patterns, particularly in terms of its impact on platforms' fundamentals. On the one hand, this information might not be informative to customers, having already been factored into their optimal delivery window decision. Alternatively, customers may be limited in their cognitive abilities and ignore stock information when choosing delivery windows. In addition, sharing information about high-stock delivery windows might raise customer awareness (or aversion) regarding stockouts, potentially decreasing the conversion rate. We conducted a large-scale field experiment on over 1M users on Instacart, an online grocery delivery platform. In this experiment, customers in the treatment group were prompted "Higher stock at this time" under delivery windows corresponding to peak supermarket stock levels. We find our cost-free information-sharing policy significantly affected customer behavior, as well as order fulfillment rates. Our main results can be summarized as follows:

The arrival of the gig economy has led to an unprecedented explosion of person-to-person task outsourcing: driving, food pickup, and shopping can all be done by someone other than the consumer. Such outsourcing potentially creates new challenges for gig workers: knowing the most efficient route, determining the entrance to the customer's home, or knowing where to find the product they are shopping for. To better understand the extent to which technological innovations can help mitigate these challenges, we conducted field experiments on a grocery shopping platform that uses an AI-enabled guidance system to help shoppers find products on store shelves. We find that, as one would expect, such technology helps by reducing the number of refunds (due to the item being hard-to-find, e.g. located at a pop-up display, etc.) and that less experienced shoppers tend to use this guidance the most. However, counter-intuitively, we also find that the usage of more complex routing algorithms is not free: it takes a longer time to consult AI guidance and picking times increase as a result. Overall, we find that AI improves the effectiveness of gig workers by helping less experienced workers achieve order outcomes that are more comparable to those of more experienced workers, thus increasing both customer satisfaction and revenue per order. However, there are boundary conditions for technology adoption and overuse of technology can even lead to lower productivity. Our main results can be summarized as follows: