Understanding the nature of tasks in crowdwork platforms

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. http://library.fes.de/pdf-files/wiso/13164.pdf

Vakharia, D, & Lease, M. (2015). An analysis of paid crowd work platforms.




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