The digital transformation has a profound impact on how we live, learn, and work while information, knowledge, and learning become increasingly important (Castells, 2017). In this context, learning is not only characterised by an acquisition of knowledge but rather by the continuous creation and recreation of knowing and learning in practice (Gherardi, 2008). To meet the associated demands employees often rely on workplace learning, a concept that has attracted practical and scientific attention in recent years. However, there remains a scarcity of empirical evidence in digital contexts (Ifenthaler, 2017). This study explores how learning and knowing are enacted in digital work practices in crowdwork. Crowdwork describes “paid crowdsourced work [mediated through platforms] where the delivery of service occurs entirely online” (Margaryan & Hofmeister, 2021, p. 44). It combines various trends in digital work like flexibilization, marketisation, individualisation, and the dissolution of boundaries (Ashford et al., 2018; Frey et al., 2004; Kleemann et al., 1999; Rump & Eilers, 2017).

Learning in the workplace is based on the “holistic nature of performance […] [and] requires several different types of knowledge and skill” (Eraut, 2004, pp. 256–257). Unlike formal education learning in the workplace is often unintentional, contextual, and collaborative. It produces situation-specific explicit and tacit knowing through cognitive and physical activities (Eraut, 2004; Tynjälä, 2008). The separation between performance and learning is much less evident (Billett, 2010). To consider the holistic and complex character of workplace learning, this study builds on a practice-based approach.  

Summarizing different praxeological perspectives, Schatzki (2001, p. 2) defines practices as “arrays of human activity centrally organized around shared practical understandings”. Most practice theories agree that practices are human mental and physical activities, mediated through artefacts and objects. However, practice theories disagree on the relationship between knowing, learning, and practice (Gherardi, 2008; Schatzki, 2001). Building on the concept of knowing-in-practice by Gherardi (2019), knowing and learning cannot be separated as they are constantly produced and reproduced in practice. This makes knowing and learning part of everyday routines which are socially shared as “something people do together” (Gherardi, 2008, p. 517). These everyday routines are not mindless automatic acts. Rather they combine intentionality, cultural norms, and unpredictability (Billett, 2010; Reckwitz, 2003). A praxeological analysis of workplace learning, therefore, comprises carefully examining working practices and how learning and knowing are enacted (Gherardi, 2010).

In 2020, there were more than 500 digital labour platforms in Europe, comprising online crowdwork and offline services (Groen et al., 2021). Crowdworkers mainly work part-time and self-employed (Mrass & Peters, 2017; Piasna et al., 2022). Crowdwork tasks require various skill levels. Microwork asks for low to medium-level skills, online freelancing requires specialised professional skills (Margaryan & Hofmeister, 2021). Findings on learning are ambivalent. On the one hand, opportunities for personal and professional development seem limited, as most tasks can be carried out with low to medium-level skills (Groen et al., 2021) and Altenried (2017, p. 176) even defines crowdwork as “digital taylorism” (Altenried, 2017, p. 176). On the other hand, crowdwork offers informal workplace learning opportunities (Margaryan, 2017) and supports crowdworkers with learning skills for the “new world of work” (Ashford et al., 2018, p. 23).

This study aims to contribute to a better understanding of how learning and knowing are enacted in crowdwork practices that reflect digital workplace learning. Therefore, this study’s research question is:

• How is knowing and learning in digital work practices enacted in crowdwork?

This study will explore the finding’s implications for future educational research and policies in crowdwork and digital workplace settings.

This study employs a Netnographic approach. Netnography describes a methodology which encompasses ethnographic and qualitative research methods and is deployed in online environments (Kozinets, 2020). Going beyond its original use for social media research, Netnography has become an accepted approach to many digital contexts (Kozinets & Gambetti, 2021). Like other ethnographic approaches it implies an iterative research process in which data generation and evaluation alternate and researchers are in close contact with the research field (Breidenstein et al., 2020; Kozinets, 2020). Netnography is characterized by investigative, interactive and immersive data operations, meaning exploration of the research field, interacting with research participants, and keeping a research diary (Kozinets, 2020). It can also encompass autoethnography, in which the researchers “immerse themselves in an interpretive exploration of their own participation”(Howard, 2021, p. 218) in a certain research field. Ethnographic approaches like Netnography are particularly useful for workplace learning studies (Eraut, 2004, p. 248). This study employs qualitative methods to examine the work and learning practices of crowdworkers on two platforms from a holistic, explorative point of view. One platform offers hardware and software testing tasks requiring low to medium level skills and one is a freelancing platform offering more complex tasks. The fieldwork combines three phases. First, an autoethnographic study was conducted from June until August 2022 to gain first-hand insights into crowdwork practices. Then, a diary study comprising 24 crowdworkers was conducted consisting of a pilot and a main study phase. The pilot was carried out from February until April and the main study from Mai until June 2023. Over a period of up to six weeks participants submitted voice messages in which they described crowdwork tasks. Furthermore, they answered questions about learning practices and the recreation of knowing in practice. Following the diary study, they participated in online interviews. Among the 24 participants, 12 were testers and 12 were freelancers who work on the platform between a few weeks and more than eleven years. The crowdworkers have been contacted through the work platforms and voluntarily participated based on the ethical principles of Netnography including informed consent and data protection agreements. The resulting screenshots, documents, transcripts and research diary entries were analysed following the recommendations of Netnography combining coding and interpreting data analysis (Kozinets, 2020). In addition to netnographic principles the coding follows Kuckartz's (2016) qualitative content analysis. The interpretation of data was based on the Bohnsack et al.'s (2013) documentary method.

The results suggest that crowdwork practices comprise more than “digital taylorism” (Altenried, 2017, p. 176). Crowdworkers do not only process paid orders. Beyond that, they interpret digitally mediated social indices, integrate tasks into complex work and life arrangements, practice self-guidance, deal with uncertainty, and navigate the digital, market-based sphere. Crowdworkers categorise their digital practices in between work and leisure. The knowing incorporated includes, for instance, an understanding of the digital interconnectedness of the world, the navigation of contradicting life spheres and self-governance. Unlike most work and learning practices in permanent employment, crowdwork practices comprise more self-regulated learning techniques and the use of digital artefacts. Activities typically associated with workplace learning, like interactive activities (Eraut, 2004) are less evident. Crowdworkers often are amateurs who learn even basic skills through practice. Their learning has an implicit and reactive character which can imply the risk of making false assumptions. Despite this risk, some crowdworkers overcome limitations and even manage to pursue personal learning goals. This study shows that digital work contexts, such as crowdwork, are more contextualised and connected to other areas of life than in permanent employment. Altough further research is required, the findings also imply that workplace learning theory could benefit from a more holistic and inclusive perspective on professional learning. This comprises, for instance, targeting people who are not fully trained before engaging in a task and considering their work and life contexts. It also implies educational interventions in crowdwork such as supporting reflection on learning and offering opportunities for social exchange. In the long term, this could facilitate the utilisation of the advantages of the “new world of work” (Ashford et al., 2018, p. 23) and mitigate its disadvantages such as a shortage of skilled labour.

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