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Understanding, Demystifying and Challenging Perceptions of Gig Worker Vulnerabilities

Sander de Jong, Jane Hsieh, Tzu-Sheng Kuo, Rune Møberg Jacobsen, Niels van Berkel, Haiyi Zhu

TL;DR

This study investigates gig workers' perceptions of hidden vulnerabilities and why they continue in gig labor. Using a two-phase design, Phase I gauges belief in five myths (N = 236), and Phase II tests various persuasive rationales (expert and LLM-generated) with counterarguments (N = 204). Results show widespread, platform-influenced myths and a measurable shift away from myths when counterarguments are provided, especially regarding labor rights and safety. The work demonstrates the potential for scalable, evidence-based interventions to raise awareness, inform policy, and support collective action for better transparency and protections in the gig economy.

Abstract

Gig workers face several vulnerabilities, which are rarely discussed among peers due to the absence of infrastructure for mutual support. To understand how individual gig workers perceive such vulnerabilities and why they continue to pursue such labor, we conducted a scalable two-phase study to probe their rationales. In Phase I, participants (N = 236) rated their agreement with five commonly misconstrued vulnerabilities. In Phase II, we challenged participants who held one or more myth(s) (N = 204) to defend their views, after which we presented an expert- or LLM-generated counterargument to their rationale. Our findings show how workers are underexposed to the personal and shared vulnerabilities of gig work, revealing a knowledge gap where persuasive interventions may help workers recognize such hidden conditions. We discuss the implications of our results to support collective bargaining of workers' rights and reflect on the effectiveness of different persuasion strategies.

Understanding, Demystifying and Challenging Perceptions of Gig Worker Vulnerabilities

TL;DR

This study investigates gig workers' perceptions of hidden vulnerabilities and why they continue in gig labor. Using a two-phase design, Phase I gauges belief in five myths (N = 236), and Phase II tests various persuasive rationales (expert and LLM-generated) with counterarguments (N = 204). Results show widespread, platform-influenced myths and a measurable shift away from myths when counterarguments are provided, especially regarding labor rights and safety. The work demonstrates the potential for scalable, evidence-based interventions to raise awareness, inform policy, and support collective action for better transparency and protections in the gig economy.

Abstract

Gig workers face several vulnerabilities, which are rarely discussed among peers due to the absence of infrastructure for mutual support. To understand how individual gig workers perceive such vulnerabilities and why they continue to pursue such labor, we conducted a scalable two-phase study to probe their rationales. In Phase I, participants (N = 236) rated their agreement with five commonly misconstrued vulnerabilities. In Phase II, we challenged participants who held one or more myth(s) (N = 204) to defend their views, after which we presented an expert- or LLM-generated counterargument to their rationale. Our findings show how workers are underexposed to the personal and shared vulnerabilities of gig work, revealing a knowledge gap where persuasive interventions may help workers recognize such hidden conditions. We discuss the implications of our results to support collective bargaining of workers' rights and reflect on the effectiveness of different persuasion strategies.

Paper Structure

This paper contains 52 sections, 1 equation, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Mystified Perceptions of Gig & Crowd Work Vulnerabilities
  4. Persuasion Techniques
  5. Method Overview
  6. Positionality
  7. Myths
  8. Study Context
  9. Measures
  10. Phase I: Vulnerability Ratings
  11. Method
  12. Results
  13. Phase II: Understanding People's Initial Stances and Persuading People
  14. Method
  15. Rationale Types
  16. ...and 37 more sections

Figures (3)

  • Figure 1: A flowchart of the layered condition design, adding LLM selection of arguments to best match the user's rationale and instructions to be particularly persuasive. The combined rationale type instructs the LLM to combine expert knowledge with additional arguments, and additionally incorporates both the LLM selection and persuasive layers. All rationales except for those in the Expert Knowledge rationale type are LLM-generated.
  • Figure 2:
  • Figure 3: Switch rate by rationale type. Switched to disagree means that people agreed in Phase I but disagreed in Phase II. Lowered agreement means they lowered their final agreement in Phase I but did not switch to a negative agreement.