Suchergebnisse

Amazon Mechanical Turk (AMT) is an online labor market that defines itself as "a marketplace for work that requires human intelligence." Early advocates and developers of crowdsourcing platforms argued that crowdsourcing tasks are designed so people of any skill level can do this labor online. However, as the popularity of crowdsourcing work has grown, the crowdsourcing literature has identified a peculiar issue: that work quality of workers is not responsive to changes in price. This means that unlike what economic theory would predict, paying crowdworkers higher wages does not lead to higher quality work. This has led some to believe that platforms, like AMT, attract poor quality workers. This article examines different market dynamics that might, unwittingly, contribute to the inefficiencies in the market that generate poor work quality. We argue that the cultural logics and socioeconomic values embedded in AMT's platform design generate a greater amount of market power for requesters (those posting tasks) than for individuals doing tasks for pay (crowdworkers). We attribute the uneven distribution of market power among participants to labor market frictions, primarily characterized by uncompetitive wage posting and incomplete information. Finally, recommendations are made for how to tackle these frictions when contemplating the design of an online labor market.

Based on a comprehensive set of studies collected via five academic databases, this scoping review examines how inequality and discrimination have been studied in the context of paid online labor. We identify three approaches in the literature that aim to (1) identify participation patterns in (national) survey data, (2) examine background characteristics of online contractors based on survey or digital trace data, and (3) reveal social biases in the hiring process using experimental data. Building on Shaw and Hargittai's pipeline of participation, we present a multi-stage model of engagement in online labor. When we map the studies across the stages, it becomes clear that the literature focuses on later stages (i.e. having been hired and received payment). Based on this analysis, future research should examine barriers to participation in earlier stages. Furthermore, we advocate for research that examines participation across multiple pipeline stages as well as for analysis of platform-level biases.

We examine the trade-offs associated with usingAmazon.com's Mechanical Turk (MTurk) interface for subject recruitment. We first describe MTurk and its promise as a vehicle for performing low-cost and easy-to-field experiments. We then assess the internal and external validity of experiments performed using MTurk, employing a framework that can be used to evaluate other subject pools. We first investigate the characteristics of samples drawn from the MTurk population. We show that respondents recruited in this manner are often more representative of the U.S. population than in-person convenience samples—the modal sample in published experimental political science—but less representative than subjects in Internet-based panels or national probability samples. Finally, we replicate important published experimental work using MTurk samples.