Numericalizing Performance: on the Role of Metrics in the Gig Economy

Research output: Contribution to conferenceAbstractpeer-review


In 1987, Richard Hyman posed the question as to how far microprocessor-based systems have been integrated with a widespread strategy for the control of labour (Hyman 1987). This issue remains just as relevant today, as crowdwork poses interesting challenges for labour-capital relations. Online recommendation systems, which have become ubiquitous for grading films (Netflix), holidays (TripAdvisor), and books (Amazon), are now being applied to evaluate workers. Given that the terms and conditions of crowdsourcing effectively absolve platform-owners of any responsibility for transactions, interactions require a semblance of quality assurance. Consequently, platforms comprise complex algorithms which monitor and assess workers performance. Some authors view this positively and suggest that the evolving ‘digital trust infrastructure’ (Sundararajan 2016) represents a paradigm shift (Botsman 2015), operating as an ‘invisible hand’ that rewards good workers while punishing poor ones (Goldman 2011).

Algorithmic objectivity is seen to be fundamental to the operation of platforms, and they are bestowed with legitimacy, accuracy and a technologically-inflicted promise of neutrality (Gillespie 2014). They play an important role in the viability of crowdwork since they enable effective and efficient searching, matching, scheduling, and levels of remuneration: in the absence of management they are non-negotiable. Algorithmic outcomes are seen to embody meritocratic ideals and assumed to capture the essence of a workers’ performance, yet in the absence of human intervention and interpretation, they display inadequate levels of accountability and lack transparency (Diakopoulos 2016). Furthermore, the growth of inbuilt performance management techniques for relentless technological filtering of good from bad work represents an extreme example of surveillance and control, as each worker has an ‘invisible supervisor’ (Elliott and Long 2016) monitoring every keystroke. Workers become functionaries in an ‘algorithmically-mediated work environment’ (Iperiotis 2012) of ruthless objectification (Ekbia and Nardi 2014). The presence of algorithmic computation means that continuous appraisal and evaluation generates a level of pressure about job performance that is of such magnitude, it is completely out of synch with the activity or task. Managing one’s online history becomes critical for crowdworkers, even though ratings are not necessarily impartial or free from collusion or retaliation. These systems are only as effective as the testimonies, and negative reviews have been associated with race and gender discrimination (Slee 2015). Performance management in the gig economy epitomises a faceless, centralised and unaccountable form of labour control, which is clearly at odds with the rhetoric of peer-to-peer relationships that are supposedly valued in the sharing economy.
Original languageEnglish
Publication statusPublished - 2017
EventILO Regulating for Decent Work - Geneva
Duration: 1 Jan 1824 → …


ConferenceILO Regulating for Decent Work
Period1/01/24 → …


  • crowdwork, gig economy, performance metrics, platform, algorithmic management


Dive into the research topics of 'Numericalizing Performance: on the Role of Metrics in the Gig Economy'. Together they form a unique fingerprint.

Cite this