Organizations in the artificial intelligence age rely heavily on algorithm control to align the working behavior of their remote workers with organizational goals. The reliance on algorithmic advice to influence the decision-making processes of their workers may have differential weights of various factors that influence the effectiveness and acceptance of advice. This research work utilizes users’ interactions that trust in these algorithmic systems, which can vary significantly based on multiple contextual and individual factors. The platform companies aim to enhance positive engagement with the algorithm system. This research aims to explore the challenges regarding algorithm aversion. This study works on the behavioral concepts to apply them on trust, leading to the impact on algorithm acceptance. Advanced natural language processing techniques will be applied on trace data and to figure out the contextual and individual factors. The outcome will be helpful for companies to make adjustments in the design of their algorithm advice to be better accepted by their workers. This research work required skills in data analytics and a basic understanding of theoretical knowledge related to behavioral science. Interns will have the opportunity to apply and enhance their skills in the working behavior of employees interacting with algorithm bosses. Understanding these factors is critical to designing systems that are both effective and user-centric.