Classroom 3

Understanding the nature of tasks in crowdwork platforms

Understanding the nature of tasks in crowdwork platforms
Anoush Margaryan
University of West London, UK
Conference domain: of the three domains indicated, the paper is most closely aligned with
Purpose and background: The paper analyses the nature of tasks in crowdwork platforms
(Schmidt, 2017). Despite the rapid uptake of crowdwork, the nature of crowdwork tasks –
their complexity and interdependence, the opportunities for discretion, creativity, and
application of workers’ expertise they provide – is poorly understood. In particular, there is
paucity of empirical research examining the nature of crowdwork tasks from the perspective
of crowdworkers. This paper identifies how crowdworkers characterise the tasks they
undertake on the platforms. Furthermore, we compare the perspectives of crowdworkers
and ‘conventional’ employees to identify the similarities and differences in the perceived
nature of tasks within these different work settings. Finally, we identify an indicative
typology of crowdwork tasks.
Prior research has shown that the design of work tasks is crucial for both individual and
organisational outcomes: motivation, job satisfaction, well-being, professional development
and productivity (Parker and Ohly, 2008). Also, previous research has highlighted the
importance of social characteristics in work task design showing that interdependences built
into tasks – collaboration, feedback from others and contact with beneficiaries of work –
enhances workers’ motivation and performance (Morgeson and Campion, 2003). Yet a
review found that many of the leading platforms lack basic task design features, for example
adequate support for complex tasks or workflows, infrastructure tools to support
collaboration and focus on workers’ conditions and motivational dimensions, among other
limitations (Vakharia and Lease, 2013).
Methodology: Using a validated scale (Margaryan et al, 2011) derived from an extant
typology of knowledge work (Davenport, 2005), we surveyed 295 crowdworkers, including
260 (80%) from CrowdFlower and 35 (20%) from Upwork, to scope the tasks they undertake
on the platforms. Using chi-square tests, we compared crowdworkers’ characterisations of
tasks with similar survey data from 459 ‘conventional’ employees from a global company in
the energy sector. We carried out a principal component analysis to identify a typology of
crowdwork tasks emerging from the survey data.
Findings: The most frequently selected crowdwork characteristics were ‘mostly routine’
(58%), ‘systematically repeatable’ (45%) and ‘highly reliant on my own individual experience’
(40%). Other key characterisations of crowdwork tasks included: ‘highly reliant on my
personal expertise/judgement’ (37%); ‘highly reliant on formal standards’ (29%); ‘highly
reliant on formal rules/procedures’ (24%); ‘improvisational and creative’ (21%); ‘lacking discretion’ (17%); ‘dependent on integration across functional/disciplinary boundaries’
(14%); and ‘dependent on collaboration’ (12%)
Comparing crowdworkers’ and conventional workers’ characterisations of their work tasks
we found that crowdwork is more likely to be characterised as being mostly routine (X 2 (1,
N=256) = 128.16, p <.00001), repeatable (X 2 (1, N=235) = 45.91, p<.00001) and lacking in
discretion (X 2 (1, N=86) = 12.99, p=.000313); but less likely to be perceived as being reliant
on formal rules and standards (X 2 (1, N=277) = 33.47, p <.00001), less dependent on
collaboration (X 2 (1, N=242) = 87.98, p<.00001) and interdisciplinary integration (X 2 (1,
N=357) = 213.10, p<.00001), less improvisational and creative (X 2 (1, N=219) = 15.15,
p=.000099), and less reliant on workers’ experience and expertise (X 2 (1, N=434) = 63.55,
The PCA carried out on crowdworkers’ survey responses produced a four-factor solution
that accounted for 53.99% of the variance. Due to low variance, the item ‘my work tasks are
mostly routine’ was excluded. The following indicative typology of crowdwork tasks
Cluster 1. High-agency crowdwork
 Dependent on integration across functional/disciplinary boundaries
 Improvisational/creative
 Dependent on collaboration
 Highly reliant on workers’ individual experience
Cluster 2. Rule-based crowdwork
 Highly reliant on formal rules/procedures
 Highly reliant on formal standards
Cluster 3. Low-agency crowdwork
 No freedom to decide what should be done in any particular situation
 Mostly systematically repeatable
Cluster 4. Expert crowdwork
 Highly reliant on workers’ deep expertise/personal judgment
Although these findings corroborate the extant accounts characterising crowdwork as
routine, systematically-repeatable and low-complexity, they also suggest that crowdwork is
more nuanced incorporating elements of collaborative, high-agency and expert work. This
typology should be further explored, refined and extended with a larger sample of
Originality/value: This research contributes new empirical data in an emergent and under-
researched domain. Improved understanding of work tasks would help crowdwork platform
providers and clients shape the design of tasks in ways that could be beneficial to all
stakeholders improving crowd workplaces for current and future workers.
Davenport, T. (2005). Thinking for a living. Boston, MA: Harvard Business School Press.
Margaryan, A., Milligan, C., & Littlejohn, A. (2011). Validation of Davenport’s classification
structure of knowledge-intensive processes. Journal of Knowledge Management, 15(4),
Morgeson, F., & Campion, M. (2003). Work design. In Borman, W., et al (Eds.), Handbook of
psychology, volume 12 (pp. 423-452). Hoboken, NJ: Wiley.
Parker, S., & Ohly, S. (2008). Designing motivating jobs. In Kanfer, R., et al (Eds.), Work
motivation (pp. 233-284). London: Routledge.
Schmidt, F. (2017). Digital labour markets in the platform economy. Friedrich-Ebert
Foundation, Germany.
Vakharia, D, & Lease, M. (2015). An analysis of paid crowd work platforms.