"At the end of the day, I am accountable": Gig Workers' Self-Tracking for Multi-Dimensional Accountability Management

Title of the Paper

"At the end of the day, I am accountable": Gig Workers’ Self-Tracking for Multi-Dimensional Accountability Management

Paper Information

• Domain: Personal informatics and workplace algorithmic management in HCI
• Keywords: Gig workers, gig economy, accountability, personal informatics, self-tracking

Research Background and Problem

• Identified Issues or Challenges:

  1. Gig economy platforms monitor work through algorithms, but research primarily focuses on platform-driven tracking.
  2. Understanding of gig workers' self-tracking behaviors remains limited, including why they track, what they track, and how they use the data.
  3. There is a lack of systematic research on how self-tracking as a phenomenon mitigates information and power asymmetries.

• Significance: Against the backdrop of the growing gig economy (e.g., approximately 58 million people in the U.S. engaged in gig work), understanding workers' self-tracking behaviors not only sheds light on current labor relations but also provides guidance for optimizing platform design.

• Research Motivation and Related Work: Self-tracking is closely tied to personal informatics in HCI and shares similarities with workplace tracking. To address the aforementioned research gaps, this study aims to explore why and how gig workers use self-tracking tools.

Solution

• Proposed Approach: This study investigates gig workers' self-tracking behaviors and their purposes through 25 semi-structured interviews combined with inductive thematic analysis.

• Innovations:

  1. Connect workers' tracking behaviors to multi-dimensional accountability management across three identities: holistic self, entrepreneurial self, and platformized self.
  2. Propose design implications to optimize tools that support gig workers in multi-dimensional tracking.

• Implementation Steps and Techniques:

  1. Interview questions cover the selection of tracked information, tool usage experiences, and the meaning and analysis of tracking data.
  2. Utilize a six-phase reflexive thematic analysis method to generate and organize themes representing workers' behaviors and perspectives.

Research Findings

• Specific Outcomes:

  1. Gig workers' self-tracking practices reveal complex accountability management, including personal accountability (holistic self), financial and resource management (entrepreneurial self), and performance management (platformized self).
  2. Preliminary findings highlight the shortcomings of gig platform tracking, such as incomplete records of mileage, income, and working hours.
  3. Discuss the impact on workers' invisible labor (e.g., data recording and analysis) and the transfer of accountability.

• Advantages: This study enriches discussions on algorithmic management and information asymmetry while revealing the potential of self-tracking to mitigate power imbalances between platforms and workers.

• Experimental or Evaluation Results: Experiments show that gig workers' use of self-tracking tools generates actionable insights into work and finances, enabling optimized work decisions and achieving certain levels of autonomy.

• Limitations and Future Directions:

  1. The study focuses solely on U.S.-based gig drivers; tracking behaviors of other types of gig workers may differ.
  2. Legal accountability (e.g., taxation) impacts workers differently across countries, requiring further investigation.
  3. Future research could explore the use of data collectives and collective tracking tools to further empower workers.