This paper describes a process that integrates behavioral and decision science methods to design and evaluate interventions to disrupt illicit behaviors. We developed this process by extending a framework used to study systems with uncertain outcomes, where only partial information is observable, and wherein there are multiple participating parties with competing goals. The extended framework that we propose builds from artefactual data collection, thematic analysis, and descriptive analysis, toward predictive modeling and agent-based modeling. We use agent-based modeling to characterize and predict interactions between system participants for the purpose of improving our understanding of interventional targets in a virtual environment before piloting them in the field. We apply our extended framework to an exploratory case study that examines the potential of worker centers as a venue for deploying interventions to address labor exploitation and human trafficking. This case study focuses on reducing wage theft, the most prevalent form of exploitation experienced by day laborers and applies the first three steps of the extended framework. Specifically, the case study makes a preliminary assessment of two types of social interventions designed to disrupt exploitative processes and improve the experiences of day laborers, namely: (1) advocates training day laborers about their workers’ rights and options that they have for addressing wage theft and (2) media campaigns designed to disseminate similar educational messages about workers’ rights and options to address wage theft through broadcast channels. Applying the extended framework to this case study of day laborers at a worker center demonstrates how digital technology could be used to monitor, evaluate, and support collaborations between worker center staff and day laborers. Ideally, these collaborations could be improved to mitigate the risks and costs of wage theft, build trust between worker center stakeholders, and address communication challenges between day laborers and employers, in the context of temporary work. Based on the application of the extended framework to this case study of worker center day laborers, we discuss how next steps in the research framework should prioritize understanding how and why employers make decisions to participate in wage theft and the potential for restorative justice and equity matching as a relationship model for employers and laborers in a well-being economy.
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Uncovering the Promises and Challenges of Social Media Use in the Low-Wage Labor Market: Insights from Employers
Social media has become an effective recruitment tool for higher-waged and white-collar professionals. Yet, past studies have questioned its effectiveness for the recruitment of lower-waged workers. It is also unclear whether or how employers leverage social media in their recruitment of low-wage job seekers, and how social media could better support the needs of both stakeholders. Therefore, we conducted 15 semi-structured interviews with employers of low-wage workers in the U.S. We found that employers: use social media, primarily Facebook, to access large pools of active low-wage job seekers; and recognize indirect signals about low-wage job seekers’ commitment and job readiness. Our work suggests that there remains a visible, yet unaddressed power imbalance between low-wage workers and employers in the use of social media, which risks further destabilizing the precarious labor market.
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- NSF-PAR ID:
- 10283349
- Date Published:
- Journal Name:
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems.
- Page Range / eLocation ID:
- 1 to 13
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3411764.3445774
