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Commercial drones are among the technologies that are capable of changing business drastically. This dissertation aims to study the adoption and operations of commercial drones to provide methodologies and insights for scholars and practitioners into different stages of the commercial drones’ lifecycle. The first essay reviews the current body of knowledge towards a broader scope. It provides a comprehensive literature review targeting published articles in top business journals. By using the Technology-Organization-Environment (TOE) framework and Diffusion Innovation theory and synthesizing the reviewed literature, the first essay identifies the influential factors on the rate of commercial drones’ adoption. The second essay focuses on the post-adoption operational issues of commercial drones in a deterministic environment in which the customers’ locations are known and fixed. It analytically studies the logistics planning of hybrid commercial drones and aims to maximize the sustainability of the logistics by evaluating social, environmental, economic costs. Two mathematical models are proposed to maximize the sustainability of logistics planning in a deterministic environment. The third essay incorporates the uncertainty in the customers’ locations and proposes a two-step methodology to address such an uncertainty. The proposed methodology consists of scenario-based mathematical modeling to help with strategic decision-making, and a simulation-optimization approach to aid in tactical and operational decision-making. Overall, this dissertation studies the rapid adoption and sustainable operations of commercial drones in deterministic and stochastic environments. It identifies influential factors in the rate of adoption of commercial drones, develops two mathematical models to aid in sustainable logistics planning of commercial drones considering the deterministic customer locations, and proposes a two-step analytical method to assist in strategic, tactical, and operational decision-making in the presence of the uncertainty in customer locations.