Search results

Online gig labor platforms bring together a global and fast-growing workforce to complete highly granular, remote and decontextualized tasks. While these environments might be empowering to some workers, many others feel disenfranchised and removed from the final product of their labor. To better understand the antecedents of continued participation in forms of crowdsourced digital labor, the purpose of this paper is to explore the relationship between worker’s ability to create a narrative of their work mattering regardless, and their continued work engagement (WE) in these work setups.

The authors approach the relationship between individual mattering and digital WE through a longitudinal study among workers on the crowdworking platform Amazon Mechanical Turk. The authors further provide qualitative insight into individual perceptions of mattering based on essay data.

The authors develop a measure of mattering in crowdworking with four dimensions: reliance, social recognition, importance and interaction. Reliance is the most pronounced dimension, followed by interaction, importance and social recognition. In the final longitudinal model, only importance affects WE positively, while the other three mattering dimensions do not have a significant effect.

The findings indicate that individuals who feel that they themselves and their work “count” and “make a difference” will be more engaged in their digital labor. By clarifying the dimensionality of mattering in crowdwork and studying its differentiated effect on WE, the paper makes a contribution to research on crowdwork and the future of work. Beyond the theoretical contributions, the finding that perceived importance fosters WE has important implications for task and platform design.

Independent actors operating through peer-to-peer sharing economy platforms co-create service experiences, such as shared car-rides or home-stays. Emotional labor among both parties, manifested in the mutual enactment of socially desirable behavior, is essential in ensuring that these experiences are successful. However, little is known about emotional labor practices and about how sharing economy platforms enforce emotional labor practices among independent actors, such as guests, hosts, drivers, or passengers. To address this research gap, we follow a mixed methods approach. We combine survey research among Airbnb and Uber users with content analysis of seven leading sharing economy platforms. The findings show that (1) users perform emotional labor despite not seeing is as necessarily desirable and (2) platforms actively encourage the performance of emotional labor practices even in the absence of direct formal control. Emotional labor practices are encouraged through (hard) design features such as mutual ratings, reward systems, and gamification, as well as through more subtle (soft) normative framing of desirable practices via platform and app guidelines, tips, community sites, or blogs. Taken together, these findings expand our understanding of the limitations of peer-to-peer sharing platforms, where control over the service experience and quality can only be enforced indirectly.