Research indicates that negative online reviews can detrimentally impact platform workers by decreasing their appeal. However, there is an existing gap in our understanding of how platform workers prevent such reviews from averting algorithmic challenges. Based on impression management theory, the purpose of this paper is to investigate how platform workers use impression management strategies to prevent negative online reviews during the delivery process.
This study adopted a multi-method approach and employed two data sources: online communities and interviews. Our analysis is grounded in a comprehensive content review of 68,727 comments sourced from an online worker community and enriched by detailed interviews with 20 workers.
Our findings revealed that platform workers employ three main impression management strategies to prevent negative online reviews: cultivating relationships with restaurants, enhancing customer experience and strategically using the platform’s algorithmic rules.
Given the critical importance of impression management in the app-based work environment, platforms should actively support workers in managing impressions with various stakeholders. This involves adjusting evaluation systems to account for uncontrollable factors and enhancing customer perceptions through real-time updates. Additionally, improving algorithm accuracy and flexibility is crucial for reducing tensions between workers and platforms. Furthermore, stronger government oversight is necessary to ensure fair working conditions and protect workers’ rights in this evolving landscape.
This study sheds light on how platform workers prevent negative online reviews, making significant contributions to the existing literature on platform workers’ responses to algorithmic reputation. Our insights not only enhance existing impression management theory but also reveal how platform workers manage their interactions with restaurants, extending the focus beyond customer-platform dynamics. By highlighting worker autonomy and strategic behavior, the study deepens insights into their adaptive resilience within digital labor ecosystems.
