Making gigs work: digital platforms, job quality and worker motivations

• Published date: 01 July 2020
• DOI: http://doi.org/10.1111/ntwe.12167
• Date: 01 July 2020

232 New Technology, Work and Employment © 2020 Brian Towers (BRITOW) and

John Wiley & Sons Ltd.

New Technology, Work and Employment 35:2

ISSN 1468-005X

Making gigs work: digital platforms, job

quality and worker motivations

Michael Dunn

Technology has driven new organisations of work and employ-

ment relationships, rendering changes that would have been

unimaginable just a decade ago. The rise of digital platforms

has not only enabled new forms of work activity but also

transformed the way workers nd new opportunities. This de-

velopment, referred to as gig work, is distinct from traditional

employment in that it is mediated through online platforms.

While we can somewhat objectively designate traditional job

characteristics as ‘good’ or ‘bad’, designating gig work itself as

‘good’ or ‘bad’ overlooks the fact that workers are inclined to

evaluate the quality of their jobs according to their own indi-

vidual needs, priorities, backgrounds and other circumstanc-

es—even if those jobs are objectively the same. Unlike previous

scholarship on gig work, which has viewed job quality largely

from a platform-focused perspective, this article takes a work-

er-centric approach and provides a typology of gig workers.

The typology demarcates how gig work is used and indicates

key attributes that differentiate how workers approach such

jobs. Moreover, the typology reveals heterogeneity in gig work-

ers’ motivations, characteristics and intentions. Consequently,

platforms with ‘bad’ job quality characteristics can still offer

work that some workers will see as ‘good’ and vice versa.

Keywords: work, motivation, platforms, job quality, gig economy,

good jobs, bad jobs.

Introduction

The early 21st century has witnessed both rapid technological advancement and a

transformation in the nature of the employment relationship. Flexible workforce strat-

egies have been adopted (Kalleberg, 2003); internal labour markets have disappeared

(Cappelli, 2001); large, vertically integrated rms have vanished (Davis, 2016); and we

have witnessed a shift in the focus of employment, from the career, to the job, to the

task (Davis, 2015).

These trends have led to a dramatic rise in the use of independent contractors. One

report shows that independent contractors grew from <7 per cent of the workforce to

almost 10 per cent from 2005 to 2015 in the United States (Katz and Krueger, 2019).

However, according to the ‘Freelancing in America 2017’ study commissioned by

Upwork and Freelancers Union, this number is signicantly higher: 57.3 million or

about 37 per cent of the workforce. Among these independent contractors, a large

Michael Dunn is a assistant Professor in the Management and Business, Skidmore College, Saratoga

Springs, USA. Dunn’s research broadly explores the consequences of contingent work. His current

research examines the social issues at the conuence of new technologies and work.

Digital platforms, job quality and motivation 233

© 2020 Brian Towers (BRITOW) and

John Wiley & Sons Ltd.

subset is completing work in the gig economy, a growing ecosystem that is managed

by online platforms. Heeks (2017) estimates the number of platform workers to be

around 70 million, while Kässi and Lehdonvirta (2018) estimate an annual growth rate

of 18 per cent.

Given the high number of gig economy workers and the sector’s projected growth,

it is important to understand how and why workers are using this new work arrange-

ment. To that end, this article makes two important contributions. First, it identies

key motivations for pursuing gig work and demarcates the specic attributes (of plat-

forms and workers) that inuence workers’ responses to gig work. Second, it identies

heterogeneity in the motivations, characteristics and intentions of workers within the

same platform, thus indicating that a worker perspective is needed for understanding

job quality in the gig economy.

Gig work and control

The new work ecosystem, in which digital platforms broker work, introduces a

structure and organisation that emphasise technical control, in part through algo-

rithms (Wood et al., 2019). Platforms act as intermediaries between workers and cus-

tomers and have a strong, vested interest ensuring that work is completed correctly

and efciently. This has both economic and managerial implications. Economically,

algorithms keep both marginal and labour costs relatively lower (Schmidt, 2017) and

create and coordinate an efcient matching environment, wherein supply and de-

mand are technologically integrated and managed (Lehdonvirta, 2018; Prassl, 2018).

In theory, workers can choose when and where to work, but many platform workers

are encouraged to maintain high gig acceptance rates (Lee et al., 2015). Furthermore,

using real-time and predictive analytics, platforms produce economic incentives in-

tended to allocate workers to high-demand areas (Van Doorn, 2017). Platforms often

entice workers to earn more money by encouraging them to stay active on the plat-

form at times when customer demand is high (Prassl, 2018). Thus, workers must

work long hours and at peak times in order to garner high earnings and maintain

good ratings.

Such practices have led to the criticism that platforms are commodifying work and

increasing its precariousness (Bergvall-Kåreborn and Howcroft, 2014; De Stefano,

2016). Felstead and Henseke (2017) found that platform-mediated work resulted in

longer working hours, higher work intensity and work–home spillover. Similar to the

technical contractors studied by Barley and Kunda (2006) who were free to set their

own working hours, but in reality many worked evenings and holidays in order to

decrease their chances of being laid off and increase their chances of securing future

contracts (see also Gold and Mustafa, 2013).

Without direct authority over the workers (because workers are independent

contractors), platforms have turned to semi-automated processes, algorithmic sys-

tems and customers themselves to execute de facto management functions so as to

ensure cooperation (Rosenblat and Stark, 2016; Wood, 2018). Namely, a platform

sets the bar for what a customer can expect when they hire a worker. Then, the

platform asks the customer to rate the worker, based on expectations. This instan-

taneous and recurring performance evaluation allows the platform to track its

workers’ performance. Through these mechanisms, platforms aim to provide a

fairly homogeneous experience for the customer without needing to exercise (or

invest in) direct managerial oversight (Steinberger, 2018). In essence, the most suc-

cessful platforms are those that can coerce their workers without having direct

control.

While these platforms exert signicant control, the growth of platforms and plat-

form workers continues to increase. So then why are workers participating in and con-

senting in various ways to the very system that constrains them? To begin to understand

this process, it is essential to understand worker motivation.