Abstract:

The burgeoning gig economy, underpinned by online service platforms, has become a critical source of employment and a catalyst for economic efficiency. Despite the benefits, the algorithmic management inherent in these platforms introduces a new type of stressor for digital gig workers, impacting their service behavior. However, there is a shortage of clarity and measurement tools regarding the concept of digital gigs' algorithmic stressors.

This study seeks to fill an important research gap by examining the implications of platform algorithmic stressors on the proactive service behavior of digital gig workers. Drawing from the gig economy context and existing research on algorithmic management, this study introduces the concept of "algorithmic stressors on digital gig workers." It first delves into the conceptual meanings and structural aspects of this concept, and then introduces the dual pathway of cognitive evaluation of challenge-hindrance stress to explore its impact and boundary conditions on proactive service behavior. The ultimate goal is to address fundamental scientific inquiries such as "What are the effects of algorithmic stressors on digital gig workers?" and "How do these stressors influence gig workers' proactive service behavior?"

This study proposes a research framework based on a mixed-methods approach, combining qualitative exploration and quantitative verification, resulting in three logically connected sub-studies. (1) Starting from the functional characteristics of algorithmic management in online service platforms and combining the interaction process between gig workers and algorithmic systems, this study employs both qualitative and quantitative research methods to define the concept of algorithmic stressors on digital gig workers, extract its subdimensions, analyze its connotations, and subsequently develop measurement tools. (2) Based on the theory of stress cognitive appraisal, this study explores the differentiated mechanisms through which the challenging-hindrance cognitive appraisal of algorithmic stressors on digital gig workers influences gig workers' proactive service behavior, providing a theoretical foundation for clarifying the impact of algorithmic stressors on gig workers' proactive service behavior. (3) Building on the dual pathways of gain and loss of algorithmic stressors on gig workers' proactive service behavior, this study further explores the cross-level moderating effects of platform algorithmic fairness and platform algorithmic support, aiming to elucidate how algorithmic characteristics at the organizational level function as boundary conditions in differentiated pressure impact pathways.

This study defines algorithmic stresses on digital gig workers as the stress experiences generated by digital gig workers in the process of interacting with algorithms under platform algorithm management, and the types of these stress experiences are related to the different functions of algorithm management. The findings indicate that algorithmic stressors on digital gig workers, when perceived as challenging, can positively influence problem-solving pondering and attentiveness, thereby promoting proactive service behavior. Conversely, when these stressors are appraised as hindrances, they can lead to work-related rumination and job anxiety, negatively affecting the propensity for proactive service behavior. When algorithms are perceived as fair, digital gig workers tend to challenge the evaluation of algorithm stressors; At the same time, the higher the level of platform algorithm support perceived by gig workers, the weaker the negative impact of hinderance cognitive appraisal.

This study constructs a theoretical model of the impact of algorithmic stressors on digital gig workers’ individual proactive service behavior, with the following three theoretical innovations. Firstly, based on the characteristics of algorithmic management in online service platforms and the human-computer interaction process between gig workers and algorithmic systems, this study proposes the concept of algorithmic stressors on digital gig workers. It effectively bridges the current research on algorithmic management work characteristics and gig worker stress experiences, providing a reliable measurement tool for subsequent related research. Second, based on the theory of stress cognitive appraisal, this study explores the dual pathways of challenging and hindrance stress cognitive appraisal through which algorithmic stressors influences gig workers' proactive service behavior, addressing the current research gap in neglecting the potential positive work stress brought by algorithmic management to digital gig workers. Third, this study clarifies the cross-level moderating effects of algorithmic management characteristics in online service platforms on the dual cognitive appraisal pathways of algorithmic pressure influencing gig workers' proactive service behavior. This not only supplements more theoretical details for revealing the interactive mechanisms between gig work environment characteristics and stress responses in online service platforms, but also provides more targeted references and insights for optimizing work resource allocation in platform organizations from a "humanistic" perspective in terms of management strategies and tools.

Key words: algorithmic stressor, proactive service behavior, gig economy, cognitive appraisal of stress theory, digital gig workers