An Examination of the Work Practices of Crowdfarms

Title of the Paper

An Examination of the Work Practices of Crowdfarms

Bibliographic Information

• Subject Area: Crowdsourcing economy, particularly the work practices of Chinese Crowdfarms
• Keywords: Crowdsourcing, Crowdfarms, work practices, China, macrotasks, microtasks, reputation management, crowdsourcing platforms, task subcontracting, management methods

Research Background and Issues

• Identified Problems or Challenges:
  • There is limited research on Chinese crowdsourcing, with most studies focusing on the practices of individual crowdworkers while neglecting an emerging subcategory—“Crowdfarms” (small enterprises centered on crowdsourcing work).
  • On crowdsourcing platforms, the demand for complex tasks and high-end skills is increasing, which individual crowdworkers often struggle to meet.
• Significance:
  • The Chinese crowdsourcing market is enormous, with approximately 30 million crowdworkers in 2017 generating a revenue of 500 million RMB.
  • The emergence of Crowdfarms (e.g., "crowdsourcing factories" supported by ZBJ) indicates a shift in the crowdsourcing economy from individual-based to organization-based models.
• Research Motivation:
  • To explore how the emergence of Crowdfarms impacts the crowdsourcing model.
  • To study their operational methods, task management, and interactions with individual workers, as well as their overall influence on the crowdsourcing field.

Proposed Solutions

• Methodology:
  • Using Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW) frameworks to conduct an in-depth analysis of this new organizational form.
  • The research focuses on organizational characteristics, task types, task acquisition and execution processes, subcontracting practices, and reputation management strategies.
• Innovations:
  • This study is the first to comprehensively examine Crowdfarms as a novel form of crowdsourcing participation, offering a perspective distinct from that of individual crowdworkers.
  • It proposes several recommendations for the development of crowdsourcing platforms, including increasing transparency in bidding mechanisms, standardizing task subcontracting, and improving rating systems.
• Implementation Steps:
  1. Data Collection:
     • Recruited 53 Crowdfarms (53 respondents, including 29 crowdworkers and 24 managers) from China's largest crowdsourcing platform, ZBJ.
     • Conducted semi-structured telephone interviews.
     • Collected both quantitative data (e.g., company size, revenue) and qualitative data (e.g., management practices and operational experiences).
  2. Analysis Methods:
     • Used thematic frameworks for coding and conducted cross-coder consistency evaluations.
     • Identified key patterns and innovative practices within emergent themes.

Research Findings

• Specific Findings:
  1. Organizational Characteristics:
     • Crowdfarms are small companies with hierarchical structures, comprising technical teams and sales teams.
     • The complexity of tasks drives these companies to transition from offline businesses to online crowdsourcing.
  2. Task Characteristics:
     • Primarily handle high-value, complex "macrotasks" that require multi-skill collaboration and offer higher compensation.
     • Subcontracting of subtasks is common, with some tasks being reintroduced to crowdsourcing platforms.
  3. Reputation Management:
     • Crowdfarms prioritize customer satisfaction by maintaining good reviews through follow-up communication, task remediation, and incentivizing feedback.
     • They expand market reputation through advertising and customer relationship networks.
• Comparison with Existing Solutions and Advantages:
  • Compared to individual crowdworkers, Crowdfarms exhibit stronger team collaboration capabilities and more specialized task execution.
  • Their ability to decompose and subcontract complex tasks creates new possibilities for platform development.
• Experimental and Evaluation Results:
  • Developed unique application strategies regarding platform task rules, task transparency, and subcontracting practices.
• Limitations and Future Directions:
  • Limitations:
    • Data collection was limited to the ZBJ platform, which may introduce sample representativeness bias.
    • Interviews relied on self-reported data, requiring further validation of objectivity.
  • Future Directions:
    • Expand research to other Chinese crowdsourcing platforms (e.g., EPWK).
    • Employ mixed research methods, such as field observations and large-scale surveys, to obtain more comprehensive workflow data.

In conclusion, this study fills a critical gap in understanding Crowdfarms, offering significant academic and practical insights for platform design, the development of the crowdsourcing ecosystem, and policy formulation.