Challenge or hindrance? The impact of platform algorithmic stressor on digital gig workers’ proactive service behavior

Abstract: The gig economy, a novel form relying on online service platforms, has swiftly emerged as a significant driver for creating employment opportunities and enhancing overall economic efficiency. These platforms not only harness big data algorithms to improve operational efficiency but also dynamically track the labor process of digital gig workers in a comprehensive manner, thereby inducing complex and diverse new stress experiences in their interaction with algorithm systems and platforms. However, existing research has yet clearly defined the concept of digital gig algorithmic stressors and lacks reliable measurement tools. These research gaps have hindered the exploration of the responses to digital gig stressors and their impact on platform service quality. Therefore, centering on the core research topic of "the connotation of platform algorithmic stressors and their differentiated impact on active service behavior of digital gig workers," this study creatively proposes a new definition of algorithmic stressors in the digital gig context, based on the interaction process between algorithmic management functions and gig algorithms. Additionally, the structural elements of algorithm-related stress are identified through the development of scientific measurement tools. Furthermore, integrating the theory of stress cognitive evaluation and the challenge-hinderance stress cognitive evaluation framework, this study unveils the gain and loss pathways through which algorithmic stressors affect the proactive service behavior of gig workers, as well as the boundary conditions under which these dual pathways operate. This research not only expands the theoretical framework of platform algorithm studies within the gig economy context but also provides theoretical guidance for effectively harnessing the positive functions of online service platform algorithms.