Abstract: The new form of gig economy has rapidly emerged relying on online service platforms, becoming an important engine for creating employment opportunities and promoting overall economic efficiency. The platform not only utilizes big data algorithms to improve operational efficiency, but also dynamically tracks the labor process of digital gigs in a panoramic manner, resulting in complex and diverse new stress experiences in its interaction with algorithm systems and platforms. However, existing research has not clearly defined the concept of digital gig algorithmic stressor and cannot provide reliable measurement tools. The above-mentioned research gaps have become obstacles to exploring the response of digital gig stressor and its impact on platform service quality. Therefore, focusing on the core research topic of "the connotation of platform algorithmic stressors and its differentiated impact on active service behavior of digital gig workers", based on the interaction process between algorithmic management function and gig algorithm, a new definition of algorithmic stressors on digital gig is creatively proposed, and the structural elements of algorithm pressure are identified through the development of scientific scale tools. On this basis, combined with the theory of stress cognitive evaluation, based on the challenging blocking stress cognitive evaluation framework, this study reveals the gain and loss paths of algorithmic stressor affecting the active service behavior of gig workers, as well as the boundary conditions under which the dual path plays a role. This study not only expands the theoretical framework of platform algorithm research under the background of gig economy, but also provides theoretical guidance for effectively leveraging the positive functions of online service platform algorithms.

Key words: work stress, algorithmic stress, proactive service behavior, gig economy, cognitive appraisal of stress theory