While algorithmic management generates several benefits for platform companies, it emanates several issues for workers, which they perceive as threats triggering different forms of resistance behaviors. Although recent studies identify these issues and resistance behaviors, the perspective of the actual subject of resistance, i.e., the gig worker or group of gig workers with resistant behaviors, is yet not well understood. By adopting a Q-methodology mixed-method approach this study tries to identify resistance types of gig workers, explore their characteristics and similarities, and therefore give a voice to the subject of resistance. Based on 21 threats and 14 resistance behaviors, identified in a literature review, we develop a Q-set containing 35 statements, which will be used for data collection with the goal of revealing the richness of the resistance phenomenon in the context of work in the gig economy.

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Track 9: Human Computer Interaction & Social Online Behavior