Burnout vs Bias: The Psychological and Business Impact of Algorithmic Management Systems in the Gig Economy

Abstract

The research studies how perceived bias in Algorithmic Management Systems (AMS) affects the well-being and how well-being affects the coping/unethical behaviours exhibited by gig economy drivers in urban India, and how it affects the consumer experience too. The drivers rely on algorithmics on how to allocate rides, incentives/bonuses and later evaluate driver performance. Perceived AMS bias refers to the perceived unfairness in the algorithmic management systems to the drivers due to lack of transparency and information asymmetry between the platform and the driver. The study adopted a mixed-methods approach for gathering primary data through structured surveys and interviews from consumers and drivers. The study will apply Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze correlations between the three key constructs: perceived AMS bias, driver well-being, and the driver's unethical or coping behavior. The results show that, whereas transparency had no substantial impact on well-being, rating fairness had a considerable negative influence. As the driver's well-being declines, burnout increases, and the driver engages in behaviors such as canceling rides, selectively accepting rides, and bending the rules to improve their well-being. The study also confirms that there are several moderating factors such as cancellation penalties, employment status, and financial dependence on driving, with full-time drivers being impacted the most negatively. Furthermore, the research identifies a clear linkage between the behaviours of drivers and their customer experiences, which has further implications for the sustainability of the platform. The provided research suggests that a toolkit for drivers will help with transparency, decrease the overall amount of stress and allow them to make informed decisions about an algorithmic employment model. Thus, the research contributes to the development of a driver-centric toolkit to build a bridge between algorithmic management and the psychology of workers in the gig labour segment of the Indian economy