Impacts of the COVID-19 pandemic on grocery retail operations: An analytical model.

The COVID-19 pandemic has had profound effects on grocery retailers, forcing them to make many operational changes in response to public health concerns and the shift in customers' shopping behavior. Grocery retailers need to understand the impact of pandemic conditions on their operations, but the literature has not modeled and analyzed this issue. We bridge this gap through economic models that consider the documented changes in the customers' shopping behavior during the COVID-19 pandemic, including less frequent in-store shopping and bulk-shopping tendency. We capture the impact of occupancy limitation guidelines on grocery retailers' service capacity, customers' shopping behavior, and, consequently, on the retailers' store traffic and profit. We find that though store occupancy limitations reduce the in-store foot traffic (which helps with curbing the disease spread), interestingly, they do not necessarily result in a profit decline. Under occupancy limitations and when the retailer offers the delivery or curbside pickup service, our analyses highlight the externality impact of online customers on the shopping behavior of in-store customers. When the retailer adds the delivery service, such externalities may increase the store traffic (higher infection risk inside the grocery store) and reduce the retailer's profit. When the retailer adds the curbside pickup instead, it has more control over the impact of externalities, which helps in lowering the store traffic and increasing the profit. Our results offer valuable insights into how retailers should regard occupancy limitations and health safety measures. Our results also highlight conditions under which various operating modes may help retailers reduce infection risk and achieve higher profit.

The value of mHealth for managing chronic conditions.

Chronic conditions place a high cost burden on the healthcare system and deplete the quality of life for millions of Americans. Digital innovations such as mobile health (mHealth) technology can be used to provide efficient and effective healthcare. In this research we explore the use of mobile technology to manage chronic conditions such as diabetes and hypertension. There is ample empirical evidence in the healthcare literature showing that patients who use mHealth observe improvement in their health. However, an analytical study that quantifies the benefit of using mHealth is lacking. The benefit of using mHealth depends on many factors such as the current health condition of the patient, pattern of disease progression, frequency of measurement and intervention, the effectiveness of intervention, and the cost of measuring. Stochastic modeling is a suitable approach to take these factors into consideration to evaluate the benefit of mHealth. In this paper, we model the disease progression with the help of a Markov chain and quantify the benefits of measuring and intervention taking into consideration the above-mentioned factors. We compare two different modes for measuring and intervention, mHealth mode and conventional office visit mode, and evaluate the impact of these factors on health outcome.