Periodic Reporting for period 3 - GHOSTWORK (The ghostworker's well-being: An integrative framework)
Reporting period: 2024-09-01 to 2025-01-31
Today, AI is increasingly demanding low-tech human support in coding, verification and cleaning of software material, such as training data. The so-called ghost workers perform such short-term tasks on demand, anonymously, through automated platforms, without any feedback from colleagues or managers. Despite the growth of this particular phenomenon, the influence of such an occupation on work-related well-being remains unresearched. To address this urgent gap, the EU-funded GHOSTWORK project conducts an in-depth study of ghost workers’ working conditions and how these impact their overall well-being
Subsequently, in Subproject 2, we address the short-term dynamics of microwork for workers' work conditions and well-being by investigating intraindividual processes and the importance of interindividual differences. We measured stable individual differences in motivation for platform work, daily indicators of well-being, daily activities (platform, job, household, social, physical, and low-effort activities), and playful work design (i.e. proactive play during activities). 122 individuals filled out 457 days and 3582 activities. We found that the intrinsic and extrinsic motivation to enter microwork has important implications for well-being in terms of exhaustion, work engagement, and detachment, as well as how the daily hours expended impact their well-being. In addition, we found that designing fun and designing competition primarily attenuate how extrinsically motivated activities are experienced. In contrast, playful work design mainly had main effects during intrinsically motivated activities. Taken together, the findings elucidate how inter- and intraindividual differences determine well-being of microworkers. This study enables us to discern the unique effects of platform activities and time spent on these activities on well-being while correcting for the effects of other activities and time spent on them. The study represents a significant methodological development in the field of platform work. We have disseminated this knowledge at two conferences: The SISEC conference in Italy (2024) and the Work Design Conference in Australia (2024).
In Subproject 2 we developed a comprehensive measurement instrument to assess microworkers' work conditions. We integrated both established measures and theories as well as recently discovered working conditions in microwork (during the interviews of the first Subproject). This is the first measurement instrument to assess microworkers' work conditions, which is helpful for the subsequent studies in this project and for future research on microwork in general. Valuable established insights into working conditions in microwork and platform work rely on existing theoretical frameworks such as the job characteristics model (Deng et al. 2016; Orhan et al. 2022) or Marx’s theory of alienation (Bucher et al. 2021), or the decent work criteria (Wood et al. 2019). Other studies focus on describing working conditions that are unique to this working context (Deng et al. 2016; Howson et al. 2023; Irani 2015; McInnis et al. 2016; Strunk et al. 2022; Wood 2021). To our knowledge there are no studies on microwork combining both existing theories of work with the working conditions specific to microwork. Providing a validated measurement scale, will facilitate more comprehensive and nuanced research unravelling the effects of microwork on well-being and other related work outcomes.
We have also prepared and received ethical approval for Subproject 3, investigating the effects of the advantages and challenges associated with microwork on workers’ well- being over a long-term period. We will collect data from various online platforms throughout Europe and assess the implications of novel microworking conditions, using the measurement scale developed in Subproject 2. To our assumptions, we conduct a four-wave longitudinal study with a time lag of 2 months. We analyze the results using the recently introduced Bayesian Multilevel Latent CLPM (cross-lagged panel model).