Algorithmic rewards have been used by sharing economy platforms to maintain platform-supplier relationships by providing relationship benefits to suppliers (e.g., Aribnb hosts). However, anecdotal evidence shows concerns regarding the effectiveness and unintended consequences of current algorithmic reward designs. Drawing insights from commitment-trust theory, algorithmic control literature and reward literature, this study proposes that relationship commitment and trust mediate the effect of different algorithmic reward designs on suppliers’ intention to maintain exchange relationships with sharing economy platforms, manifested by continuance intention in this study. An experimental design will be used to manipulate different characteristics of algorithmic rewards and examine the proposed theoretical framework. This study aims to enrich algorithmic management and relational marketing literature with this examination. This study seeks to offer practical implications for sharing economy platforms to address their high turnover rate issue by optimizing reward system designs.