"A tiny cog in a large machine": Digital Taylorism in the Translation Industry

Abstract

Translators have worked with the assistance of computers for many years, usually translating whole texts, divided into segments but in sequential order. In order to maximise efficiency and inspired by similar moves in the tech industry and predictions for Industry 4.0, large translation companies have begun to break tasks down into smaller chunks and to rigidly define and monitor translation processes. This is particularly true of platform-mediated work, highly collaborative workflows, and multimedia work that requires near-live turnaround times. This article considers such workflows in the context of measures of job satisfaction and discussion of sustainable work systems, proposing that companies prioritise long-term returns and attempt to balance the needs of all stakeholders in a translation process. Translators and translator trainers also have a role to play in achieving this balance.

Full text

In Translation Spaces 9(1) 2020, 12-34.
“A tiny cog in a large machine”: Digital Taylorism in the Translation Industry
Abstract
Translators have worked with the assistance of computers for many years, usually
translating whole texts, divided into segments but in sequential order. In order to maximise
efficiency and inspired by similar moves in the tech industry and predictions for Industry 4.0,
large translation companies have begun to break tasks down into smaller chunks and to
rigidly define and monitor translation processes. This is particularly true of platform-mediated
work, highly collaborative workflows, and multimedia work that requires near-live turnaround
times. This article considers such workflows in the context of measures of job satisfaction
and discussion of sustainable work systems, proposing that companies prioritise long-term
returns and attempt to balance the needs of all stakeholders in a translation process.
Translators and translator trainers also have a role to play in achieving this balance.
Keywords
Translation industry, fair MT, digital Taylorism, scientific management, business ethics,
labour sociology, sustainable work systems.
Introduction
The continuing growth in digital content means that there is more material to be translated
than ever before (Moorkens 2017). The complexity of processes by which this translation
takes place varies massively. At one extreme of the translation continuum we may have a
highly-paid specialist managing and translating a project from start to finish for a direct client;
at the other extreme, crowd workers accepting a portion of a project that has been
decomposed to many human intelligence tasks involving translation of decontextualized
strings of text anonymously via an online platform in return for a micropayment, with many
other processes in between. In order to mass-produce translated material while integrating
technological innovation, many translation and localisation workflows are becoming
increasingly complex. This is particularly true of large language and localisation service
providers and crowdsourced translation companies that follow the trend among big
technology firms to break large tasks down into small chunks in order to maximise efficiency
(Carr 2010: 150).
This harks back to the system of ‘scientific management’, proposed in 1911 by Frederick
Winslow Taylor, that revolutionised industrial work processes so that workers, rather than
choosing their own work and pace, are monitored and have their tasks arranged so as to
increase overall productivity. Taylor’s ‘work system’, sometimes known as Taylorism, has
experienced a resurgence in recent years, particularly in the tech industry, where the largest
companies appear to believe that, in order to maximise efficiency and to eliminate subjective
judgement, outputs must be quantified and workers continually audited. This
repopularisation of Taylorism has been variously called neo-Taylorism (Conti and Warner
1994), new Taylorism, or Digital Taylorism (Parenti 2001). The dictums of both Taylorism
and Digital Taylorism (DT) advocate workplace surveillance so as to avoid what Taylor
(1911) calls ‘soldiering’ (working at a slow pace or procrastinating), but contemporary
methods link to widespread discussion of data dispossession and surveillance capitalism
(Zuboff 2019).

In Translation Spaces 9(1) 2020, 12-34.
The adoption of DT in translation and localisation differs to its use in the tech industry. The
main difference is that most translators work on a freelance basis, and as such are likely to
have less power and agency than direct employees (Abdallah 2010; Moorkens 2017). The
employer can save on the costs associated with direct employees such as training, holidays
and sick leave, along with office costs such as real estate, light, heat, hardware, software,
desks, and seating (Campbell, Watson, and Buchanan 2004). Freelance translators are
“reliant on ethical behaviour on the part of their employers” (Moorkens & Rocchi,
forthcoming) and are particularly vulnerable to unilateral changes to working conditions when
they work exclusively with a single language service provider. By working as a small and
interchangeable part within a system of DT, they can be swapped out without any
detrimental effect to the system as a whole. This is especially true of crowdsourcing, when
no trust relationship has been built between the entity who commissions the task and the
person who carries it out. On the other hand, not every worker wants to commit to a single
employer or role, and they may prefer to work on small tasks as part of a portfolio career
(Mallon 1998). The indications from surveys published previously suggest that the
implications of DT from the workers’ perspective are more negative than positive. One
respondent to Courtney and Phelan (2019: 108) said that translation is “now more like
working on an assembly line”.
This article considers DT in translation in the context of measures of job satisfaction and
sustainable work systems. Sustainable work systems focus on long-term sustainability of
human, social, and natural resources rather than maximising short-term gains. The aim is for
balanced concern for all stakeholders rather than prioritising the needs of those in positions
of power (Docherty et al. 2008). Sustainability has long been promoted by the United
Nations (UN General Assembly 2015), and the notion of sustainable work systems links to
UN Sustainable Development Goal 8 (“sustained, inclusive and sustainable economic
growth, full and productive employment and decent work for all”), with many commentators
advocating zero growth on the grounds that sustainable growth in 2019 is no longer possible
(Cronin 2017, Heinberg 2011, Smil 2019).
The following section describes Taylor’s work system proposal in some detail, and some
criticisms that arose after it was widely adopted, moving on to DT as practiced currently.
Thereafter, I examine applications of DT in the translation industry, in large conglomerates,
crowdsourcing scenarios, audiovisual translation, and localisation. The next sections look at
measures of job satisfaction, considering the effects of DT on positive and negative
indicators of satisfaction, including some qualitative results from recent surveys of translation
job satisfaction, after which I introduce work system sustainability. There may be reasons
why both employers and translators find the system of DT beneficial, and some external
factors, I suggest, affect how the system is imposed and the disparities of power between
employer and (usually outsourced) translator. The article ends with some recommendations
for achieving a balance of efficiency and sustainability in the translation industry.
From Taylorism to Digital Taylorism
In an influential essay from 1967, Thompson (73) described a time before the clock dictated
working hours, when the “work pattern was one of alternate bouts of intense labour and of
idleness, wherever men were in control of their own working lives”, adding that the “pattern
persists among some self-employed… and provokes the question whether it is not a

In Translation Spaces 9(1) 2020, 12-34.
"natural" human work-rhythm.” The Industrial Revolution changed this work pattern, but it
wasn’t until Taylor began to publicise his work on scientific management, the main principles
of which were published in 1911, that workers began to be organised to maximise efficiency.
Taylor suggested that his principles could be applied in organisations of many types and
sizes, demonstrating their effectiveness with a number of case studies, and that the
application would result in prosperity for both employer and employee.
Taylor was opposed to ‘underworking’, and union-agreed limitations to individual
productivity. He felt that increased overall productivity would lead to lower prices, more
demand, and therefore more work. By monitoring each worker, it would be possible to both
quantify (and with, bonuses, maximise) productivity, and to identify the “one method and one
implement which is quicker and better than any of the rest” (Taylor 1911: 9). Organisation,
training, development, and oversight of work would move from worker to management, with
the worker having little say in the choice of the single best method for carrying out their work.
In practice, financial benefits did not necessarily accrue to both employers and workers. In
addition, by decomposing and documenting work processes so that they were rigidly
defined, the work could be carried out by less-skilled workers for less pay, and, potentially,
automated. Price cuts that give one company a competitive edge will also tend to propagate
throughout an industry, leading to job losses and/or changes of employment practices. For
this reason, and due to workers’ unhappiness with their reduced role and simplified tasks,
the imposition of scientific management met with opposition from labour organisations.
Drury (1915) discusses some contemporary criticisms of scientific management, such as
that employees are overworked or turned into automatons, and dismisses them summarily.
He does, however, note that it disincentivises collective behaviour, and claims that company
owners preferred not to hand control to management experts, and that if they did so, once
the initial changes had been implemented, there was a tendency to “drift back into old
habits” (Drury 1915: 214). Walker and Guest (1952) offered a longer-term perspective,
reporting on interviews with 180 assembly line workers for whom work is repetitive, requiring
minimum skill and attention, and the pace of work is mechanically controlled. Their work has
been subdivided minutely into tasks for which tools and techniques are rigidly defined. These
workers are generally satisfied with pay rates but are otherwise reportedly alienated and
uncommitted. Work processes most typical of mass production appear to correlate with
employee absenteeism and resignations.
Scientific management (and related Fordist mass production (Jessop 2007) from the 1920s
on
1
) assumes that workers are primarily motivated by money and that improved rates of pay
will compensate for reduced autonomy and repetitive work. Over time, however, it became
clear that workers’ motivation was more complex than Taylor had believed. Based on his
own research and several replicating studies, Herzberg (originally published in 1967, but
reprinted in 1987: 8-9) proposed a “motivation-hygiene theory of job attitudes”, identifying
intrinsic motivating factors that “were the primary cause for satisfaction” at work, and
demotivating or ‘hygiene’ factors that “were the primary cause of unhappiness on the job”.
He writes that the “growth or motivator factors that are intrinsic to the job are: achievement,
1
Scientific Management led to Fordist methods of production in subsequent years, which placed less focus on
monitoring, instead making use of moulding and automation to replace skilled with non-skilled jobs, along with
working more broadly with worker organisations and government in order to turbo-charge capitalism and
consumption (Jessop 2007).

In Translation Spaces 9(1) 2020, 12-34.
recognition for achievement, the work itself, responsibility, and growth or advancement”,
whereas the demotivating or “hygiene factors that are extrinsic to the job include: company
policy and administration, supervision, interpersonal relationships, working conditions,
salary, status, and security” (Herzberg 1987: 9). These extrinsic factors may be effective in
producing ‘movement’, if not motivation, due to fear of repercussions. Herzberg’s insights
challenge the benefits assumed from Taylorism, although in his 1987 postscript he lamented
that the focus on motivating factors in US companies was being lost due to the “pragmatics
of worldwide competition” (16). I consider translation work in the context of Herzberg’s theory
in a section to follow.
Postman (1992: 90) saw the influence of Taylor appearing in the digital era and explicitly
linked deterministic thinking to Taylorism, critiquing “beliefs that the primary, if not the only,
goal of human labour and thought is efficiency; that technical calculation is in all respects
superior to human judgement”. The company Carr (2012: 150) most identifies with an
algorithmic striving for efficiency is Google, believing that what “Taylor did for the work of the
hand, Google is doing for the work of the mind”. Parenti (2001: np) provides examples of the
application of surveillance in the workplace to achieve this efficiency, opining that the
internet has created “both the reasons and the means for surveillance”. This is the key
difference between Taylorism and DT: jobs are standardised, methods documented, but now
new technologies enable more varied and invasive monitoring and surveillance of workers to
ensure that their role is carried out as expected, using devices that can “control and extract
value from creative and knowledge work as well as physical labour in more precise,
quantified ways” (Moore and Robinson 2016: 2781). For example, ‘nudge management’ is
the practice of making small changes based on behavioural and psychological studies to
optimise efficient behaviours (Ebert and Frebichler 2017). Frischmann and Selinger (2018:
79) discuss contract creep, nudge creep, and surveillance creep, where limits are stretched
iteratively so that control and extractivism gradually but imperceptibly become more extreme,
and describe these as “fundamental parts of modern Taylorism”.
A well-performing employee in a scientifically managed factory should, in theory, receive a
bonus or premium in salary, and this may be the case for valuable employees working for
tech giants who see only the positive side of teamwork. However, the bonus for the
micromanaged lower-tier or temp employee is to keep their job.
2
Non-performing parts can
easily be swapped out, particularly when not directly employed. Technology workers in DT
often lack visibility of the technology they are contributing to and its eventual use, meaning
that they cannot exercise their own ethical judgement or what Popper (1970) called their
moral responsibility. Weizenbaum (1986: np), in response to military use of artificial
intelligence, highlighted the importance of asking the questions “What is the final application
and use of the products of my work?” and “Am I content or ashamed to have contributed to
this use?”. Workers who are unable to answer these questions risk contributing to projects to
which they are ideologically opposed. This was one of the main complaints of the 20,000
employees involved in the Google walkout in 2018 and continues to cause employee unrest
that has spread beyond the company to Amazon and Microsoft (Scheiber and Conger 2020).
This concern could equally apply to researchers contributing to machine translation (MT)
2
Wakabayashi (2019) reports that most workers for Google worldwide are temps and contractors and subject
to different working conditions to direct employees.

In Translation Spaces 9(1) 2020, 12-34.
systems or translators working on a decomposed section of a large project. The following
section looks at applications of DT in the translation industry.
Digital Taylorism and the translation industry
Most translators worldwide work on a freelance basis. This was noted as early as 1976 by
Tinsley (for the USA) and more recently by Pym et al. (2012). Jemielity (2018) and others
have highlighted the isolation of translators, even within a company. As a result, it has been
difficult for them to organise collectively, and thus it is easy for a company that has
decomposed projects and restricted the scope of work to swap one translator for another
without any real repercussions. This is particularly true of remote workers contracted via a
crowd translation platform, such as Lionbridge GeoWorkz (as mentioned in Ghost Work by
Gray and Suri (2019)), Unbabel, Stepes, or one of the many general-purpose crowd portals.
The tools used for translation and localisation processes are increasingly cloud-based,
permitting monitoring of work in real time and in logs. Garcia (2017) explains that this may
be used to profile translators by speed, quality, and translation type, and that TAUS (the
Translation Automation User Society) has proposed creating an ‘efficiency score’, using
words and edits per hour. Góis and Martins (2019) attempt this, using keystroke data from
332 post-editors to predict future post-editing time.
A series of acquisitions within the translation and interpreting industry has meant that the
largest companies have cemented their positions, with many becoming publicly-traded
conglomerates. Graham (2019) tracked 82 acquisitions of translation and interpreting
companies between 2016 and September of 2019. Kronenberg (2018) created a timeline of
acquisitions by the company RWS, with notes of unilateral changes to payment conditions
and rates imposed concurrently with strong stock market performance. The imposition of
new conditions and rates by RWS and Lionbridge (Llorens 2010) demonstrates the
commodification of translation and a lack of care about motivation and satisfaction for
translators. Bauman (2000: 151) notes the likelihood of stock exchanges and boards of
management “to reward all steps 'in the right direction' of disengagement, like 'slimming
down', 'downsizing' and 'hiving off', while punishing just as promptly any news of staff
expansion, increased employment and the company being 'bogged down' in costly long-term
projects”.
As such, it is little surprise to see translation jobs subdivided into many small tasks and an
increase in translator monitoring. This combination of subdivision of tasks and the integration
of technology built on previously gathered data and serviced via the cloud (as is sometimes
called Industry 4.0; Caruso 2018) massively complicates translation workflows, necessitating
a host of new roles. A check on the Lionbridge website on October 18th 2019 shows 18 roles
that operate within a translation production network including QA, checking, rating, content
preparation; on the same date Transperfect advertised openings for 34 different roles
(excluding accounts, management and marketing) including various types of linguists,
project managers and coordinators, language data analysts and revisers.
3
A LinkedIn search
by Bond (2018a) identified over 600 different job titles in language service providers. These
roles offer translators opportunities to do highly-specified tasks within a translation
production network. On the other hand, as more people become involved in projects, often
as freelancers with a single point of contact within the translation company, this can erode
3
These are (by far) the two largest Language Service Providers in 2019 according to CSA Research (2019) and
Nimdzi (Dranch, Beninatto and Johnson 2019).

In Translation Spaces 9(1) 2020, 12-34.
trust and also lead to the ‘problem of many hands’, when a single person cannot be
identified or held responsible when a problem occurs (Timmermans et al. 2010).
Translators have reported that translation jobs are becoming smaller. According to O’Brien
(2012: 114), “in some domains, the notion of a text, with a beginning, middle and end, has
changed radically”, due in part to “the way in which information is now produced”. This has
been corroborated by translation companies such as Alpha CRC, who reported that “the
average size of translation tasks has fallen dramatically, even though overall volumes are
rising”, which, in combination with technology and agile workflows, is leading to a “degraded
environment for the translator” (Bond 2018b). Decomposition of multilingual projects is
becoming increasingly common for multimedia content due to the expectation of fast
turnaround times. In order to make the programme Chelsea available to “an audience in over
190 countries simultaneously less than 34 hours after each taping”, Netflix “slice each and
every video source file into small chunks” to aid processing, before sending the slices to over
200 translators (Roettgers 2016). Iyuno Media (who received venture capital funding from
Softbank Ventures in 2018 (Bond 2019)) offer an astonishing two-hour turnaround for
translation of 45-minute premium Korean dramas into English, breaking content down into
15-minute chunks and translating with the aid of wikis and neural MT on their own
proprietary cloud platform (Puttock 2018). Pidchamook (forthcoming) has documented
similar work processes for fast turnaround in the Thai over-the-top (OTT) media industry,
where subtitlers await 15-minute clips by email, spot and translate quickly, then send them to
a project manager to reassemble and make consistent. Georgakopolou (2019: np)
associates projections of doubling subscriber revenue for OTT media over the next five
years with increasing requests for “near-live turnaround times”, collaborative workflows, and
crowdsourcing in localisation processes.
The nature of software localisation, which involves “translating text embedded in various
parts of the software interface” (Bass 2006: 78), means that understanding of context can be
a problem. As more software is now made available ‘as a service’, online or via apps, a
constant update cycle has further decontextualized content for translation. More and more
software and games localisation involves high-pressure work on decomposed materials
distributed among a global team, following an agile software development cycle, in order to
turn around regular updates. Agile methods involve prioritisation of features, which are then
developed over ‘sprints’, usually of one or two weeks. For localisation teams, these “rapid
iterations” create more incremental updates, requiring them to “remain flexible throughout in
order to deal with a heavily fragmented supply of assets, and to be ready to absorb text
updates by enlarging teams and working two or three shifts a day during crunch time”
(Bernal-Merino 2013: 285).
Again, the overall purpose of the project may be obscured within such a fragmented
workflow, requiring trust on the part of the translator, whereas increased monitoring evinces
a lack of trust on the part of the translation buyer or employer. Pym (2012) believes that a
job that requires a high-quality publishable text means that the buyer must put more trust in
the translator, whereas a lower-quality, fit-for-purpose translation requires less trust.
Geographic distance and the small number of intermediaries between translator and buyer
make it difficult to build a trust relationship within a translation production network (Abdallah
and Koskinen 2007). This is especially true for networks in which project management is
automated (Sakamoto 2018) and in crowdsourcing scenarios. Building an empathetic,
‘identification-based’ trust relationship requires long personal interaction (Olohan and Davitti

In Translation Spaces 9(1) 2020, 12-34.
2015). Without trust, Chesterman (1997: 182) believes “the profession would collapse, and
so would its practice”.
Garcia (2017: 60) discusses Taylorism in translation crowdsourcing, using the internet as a
“virtual facility that allows independent contractors to perform piece work at a venue of their
choosing, whilst remaining under perfect, big-data encoded supervision”. He continues that,
in order to get paid an average wage, crowdworkers will have to maintain a perfect
reputation and accept most work offered to them. As a result, recent research has found
successful crowd workers (on general crowd platforms) subcontracting work to others for a
lower rate (Wood et al. 2019). The difficulties of establishing trust relationships, allied with
the benefits of translation and user activity data gathering, means that crowd translation
providers who want to maximise their resources are likely to have highly complex workflows.
To take a high-profile, Translation Industry 4.0 example, Unbabel’s platform uses MT for
initial translation, followed by automatic quality estimation (QE). Depending on QE results,
sections of text are distributed to the crowd for MT post-editing on a piecemeal basis. These
are often time-dependent real-time customer support interactions. They also use internal
human terminologists, ‘translation analysts’, ‘linguistic annotators’, and external Natural
Language Processing steps for spellcheck, syntax parsing, and word alignment (Moniz
2019, Silva 2019). Unbabel are unusual in that they are very well resourced, having received
US$91m in external funding, although this brings its own pressures, as venture capital
investors typically expect a high return of investment over a small number of years (Zider
1998), with Series C funding expected to help a successful company to scale.
According to Caruso (2018: 387), companies that use digital crowds have had “limited
success” with increasing employee commitment in the past, particularly as a focus on short-
term returns and shareholder value have put “pressure on employment and working
conditions, downsizing staffing levels, intensifying work, cutting cost and reducing
employment security, in particular in higher wage countries”. The use of reputation scores
and automated tests on a crowd platform in place of interpersonal trust may successfully
weed out workers who try to game the system by contributing work that is malicious, shoddy,
or random, but particularly when reputational scores have not been established, workers on
many platforms may have work rejected or their profile terminated without explanation or
compensation. When a client demonstrates an absence of trust for the worker, particularly
within a short-term relationship mediated solely via a platform, the worker is made aware
that they are disposable, and “so they see little point in developing attachment or
commitment to their jobs” (Bauman 2000: 152).
Working via an online platform or portal has also become commonplace for many freelance
translators, with 89% of 6925 respondents to Pielmeier and O’Mara (2020) saying that they
use them. 69% of respondents said that these portals streamline their work, but 56% said
that they depersonalise the client relationship. The use of such portals may not present the
same level of risk as the general crowd platforms, but nonetheless lessen the translator’s
agency. Cloud-based tools may be discontinued with little notice (like Google Translator
Toolkit was in December of 2019), have terms changed following shareholder pressure or
acquisition (as happened with Lilt in 2018) or made unavailable in one region for reasons
beyond an individual’s control (as happened to Adobe product users in Venezuela in
October of 2019; Lee 2019). For management, cloud-based tools allow monitoring and
surveillance, but they also enable data gathering to address the weak spot in Taylorism:
human resource management. As “subjective judgment is not rigorous or trustworthy as a

In Translation Spaces 9(1) 2020, 12-34.
way to assess talent or create human resources policies”, “large pools of objective, generally
quantitative data” may be accumulated to guide decision-making (Bodie et al. 2017: 964).
Ajunwa (forthcoming) highlights the risk that this data may be sold and notes how automated
hiring decisions based on such data is likely to amplify rather than remove existing bias.
This does not remove the problem of motivating workers who do not see the full picture of
what they are contributing to. Even the most worthy or beautiful text, when decomposed, is
likely to seem meaningless. This is doubtless particularly so for the types of text translated
via crowd platforms. Where games localisation teams previously received a physical copy of
the release that they had contributed to, repetitive work on minor game updates has no
obvious endpoint. In the following section, we consider the effect of DT on measures of job
satisfaction.
Motivation and job satisfaction
Herzberg’s motivation-hygiene model of job satisfaction from 1959 identified factors that
produce motivation or grudging movement at work. Despite this, Kovach still found a
mismatch between what management and workers felt were motivating factors in his 1987
meta-analysis. Management and supervisors believed that wages were most important,
whereas workers said that interesting work and appreciation of work done were most
important (Kovach 1987). DT in the translation industry, coupled with the prevalence of
freelance work, may exacerbate several demotivating factors such as interpersonal
relationships, working conditions, salary, status, and security. Kovach (1987) likens these to
basic needs rather than self-fulfilment needs. Macey et al. (2011) consider these subordinate
to positive motivation factors, suggesting that a worker who likes their job is unlikely to move
to a job they dislike for better pay.
DT in translation offers limited opportunities for Herzberg’s motivating factors, particularly for
freelance workers. Working with decomposed sections of text or portions of game updates
brings little sense of achievement and thus no recognition for achievement. Removing
segments of text from their context limits the abilities of translators to use their key linguistic
and intratextual skills. Some companies have tried to build in motivating factors by creating a
hierarchy for career advancement for in-house workers and celebrating the success of a
randomly-chosen number of product updates. For freelance workers the lack of a defined
career path is not a direct result of DT, but DT mitigates against gaining expertise or
assuming authority by strictly limiting the scope of work and dictating how the work is to be
done. Rodríguez-Castro (2016) developed an instrument for measuring translator
satisfaction based on Herzberg’s two-factor model of extrinsic and intrinsic motivation. The
elements that she found ‘crucial’ for task satisfaction for translators were “successful
completion of projects, ability to perform a wide variety of tasks, and intrinsic pride in their
work”, the first two of which in particular are negatively affected by DT in translation
(Rodríguez-Castro 2016: 223).
Krifa and the Happiness Institute propose seven factors that contribute to job satisfaction.
The effects of these are not independent of one another. For example, the most important
factor, particularly for women, is meaning, “spending your working life on something
meaningful for yourself and others and being able to see that your contribution in particular is
of value” (Krifa/Happiness Institute 2019: 12). The importance of meaning is increased when
surrounded by a number of supportive colleagues. Next-most important is mastery – Shreve
(2009: 265) related translation expertise to “an increased capacity to recognize and

In Translation Spaces 9(1) 2020, 12-34.
represent the problems of translation and an increased ability to effectively resolve those
problems”. However, mastery is difficult to achieve when carrying out varied decomposed
and non-challenging tasks. Furthermore, Sennett (1998: 72) observed that “people identify
with tasks which challenge them, tasks which are difficult”. The other satisfaction factors
proposed by Krifa/Happiness Institute (2019) are, in order of weighting, balance, leadership,
influence, achievements, and colleagues. If we consider how each of these are affected by
DT in translation, it is difficult to see how work on decomposed portions of a project could
contribute to job satisfaction, other than perhaps for balance. The Krifa/Happiness Institute
Report (2019: 22) discusses balance “between the number of tasks you have and the
amount of time you have to perform them”, along with work-life balance. Pym et al. (2012)
highlight the prevalence of paraprofessional translators, who translate as part of their role, as
well as the largely female translation workforce globally. Respondents to Courtney and
Phelan’s survey (2019: 109) felt that “being self-employed has allowed them to spend more
time raising children or relocate to another country with their partner”, thus increasing their
job satisfaction. Rubery (2013), however, discusses how stereotypical notions of female
carers may contribute to women’s lower likelihood to have job security, and the result that
women tend to suffer more in an economic downturn. The Krifa/Happiness Institute (2019)
also report that, based on their Danish studies, women tend to suffer disproportionate work-
related stress.
Courtney and Phelan (2019: 108) report the precarity of freelance translation work as a
major stress-inducing factor, along with poor treatment from agencies and clients that,
according to one respondent, “treat them like robots”. Looking at the results of a survey of
Irish translators carried out in 2016, we can see comments relating to DT in responses to
questions about job satisfaction. The survey had 99 participants, although many did not
complete all sections. These were a mix of public servants, freelance and self-employed
translators, and company owners, all working in Irish-English translation. A portion of these
results and the full methodology are presented in Moorkens (2020). When asked how the
role of a translator in 2016 differed from previous years, one full-time freelance translator
participant responded that “if working for large agencies, we are now just a tiny cog in a
large machine”. Most answers focused on technology and rates, but seven (of 57) responses
mentioned the increasing number of tasks translators were being asked to do as part of
complex workflows, such as “page setting, terminology work, QA etc.”. 22 of 54 participants
who responded to a question about the main threat to the industry wrote about the behaviour
of ‘large translation companies’. One part-time freelancer complained that “more and more
work is going through large translation companies and this has caused a lowering of rates
paid and standards implemented. Another full-time freelancer feared “agencies aggressively
lowering rates and winning huge contracts so they can corner the market and pressure
translators into accepting ridiculous rates”. Comparative job satisfaction analyses in
Moorkens (2020) report that freelance translators appear to have a lower sense of purpose
from their work and feel more isolated than directly employed company and public service
translators. Although English-Irish is not a major, nor a highly-technologized language pair,
these survey results do not seem to be outliers.
In a large-scale survey carried out by Pielmeier and O’Mara (2020), freelance translators
and interpreters responded to questions about what they liked and disliked about their
profession. The positive aspects bear little relation to factors that contribute to motivation
and satisfaction as discussed previously. 91% of 6003 freelancers said that they like the
flexibility of hours offered by their profession, but only 43% said that it gave them a sense of

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purpose. The negative aspects are, however, similar to the extrinsic factors that Herzberg
suggests are likely to cause dissatisfaction: fluctuating income, irregular work, lack of
benefits, and stress (Pielmeier and O’Mara 2020). There are opinions both positive and
negative about the state of the translation industry to be found in translator surveys, but the
fact that job satisfaction and motivation factors are not perceived by many translators across
several studies is a little alarming and raises questions about the sustainability of the
translation industry.
Work system sustainability
Research on translator status, agency, and job satisfaction (such as Dam and Zethsen 2008;
Abdallah 2010; Ruokonen 2013) point to an unbalanced system with disparities of power.
Docherty et al. (2008: 4) believe that a work system must satisfy the needs of many rather
than few stakeholders, and that instead of focusing exclusively on “short-term, static
efficiencies such as productivity and profitability; we must also focus on long-term, dynamic
efficiencies such as learning and innovation”. For a work system to be sustainable, it needs
to “attain a just balance in development” between all stakeholders and long- and short-term
needs (Docherty et al. 2008: 4). This will require continual rebalancing and adjustment
following certain principles, such as regeneration of human, social, material, and natural
resources utilised and non-exploitation of one resource over another, including external
resources. The system should aim to give back to society rather than exploit it as a resource.
A sustainable work system does not prioritise one stakeholder over another.
This is not a particularly controversial assertion, as business and business ethics literature
has moved on from the focus solely on profit from the 1970s (e.g. Friedman 1970) to
Freeman’s (1984) stakeholder approach, whereby the needs of all parties connected to a
company should be considered, although the perception remains that societal concerns
exert a drag on business (Hart 2007). Elkington (1997) popularised the idea of the ‘Triple
Bottom Line’ of people, planet, and profit, promoting a sustainable balance between human
and social resources and with ecological and economic resources. Similarly, De George
(2014: 502) highlighted the need to “respect the rights of employees, consumers, and
society in general”. The additional aspect in a sustainable work system is consideration of
future generations, restricting activities that will delimit their agency or satisfaction of their
needs. In practice, the balance may be tilted towards the profit motive in many businesses,
but there is little sign of the consideration of corporate-social responsibility or sustainability in
the translation industry, particularly in larger companies that are acquisition-hungry and must
prioritise short-term returns to shareholders and investors. Zink (2014: 127), for example,
suggests that outsourcing, particularly to “countries with less pay and without “restrictions” of
work and environmental laws”, as may happen in unrestricted scenarios such as
crowdsourcing, is not compatible with sustainable work.
The concept of sustainability, as used by the UN, was initially introduced with regard to the
environment, but has more recently broadened to include Sustainable Development Goal 8,
which is to promote “sustained, inclusive and sustainable economic growth, full and
productive employment and decent work for all” (UN General Assembly 2015). The
International Labour Organisation’s related definition of Decent Work highlights the need for

In Translation Spaces 9(1) 2020, 12-34.
‘productive’ work, but otherwise focuses on hygiene rather than motivating factors such as
adequate earnings and social security, stable and secure work, and social dialogue between
representatives of workers and employers (Ferraro et al. 2015). Docherty et al. (2008),
however, are more concerned with motivating factors and stress that the individual worker
should have the opportunity to “develop as a person, a professional, and a member of a
society through work experiences”. Moving to a sustainable work system does not imply
stasis, but rather the establishment of “a dynamic flexibility” to maximise sustainability;
according to Docherty et al (2008: 8), economic and human resource sustainability is
foundational for “social development and the sustainability of whole societies” and is
indivisible from environmental sustainability. Cronin (2017: 105) has critiqued this “dual form
of extractivism” of human and non-human resources with “little concern for those who find
themselves in the ‘sacrifice zones’ of depletion.”
For knowledge workers such as translators, Brödner (2009: 56) remarks that “increased
work demands may cause less stress if a working person has control over the working
process”. His suggestions for making knowledge work more sustainable are unlikely to be
part of freelance translators’ current reality, especially those working in DT processes at the
‘bulk’ end of the market for large agencies, and will definitely not be familiar to
crowdworkers. For example, he suggests that workers participate in a “cyclic and
evolutionary procedure of continuous reflection and work redesign during which
improvements are regularly explored, reflected upon, and evaluated” (Brödner 2009: 66).
Based on these reflections plus health and stress monitoring, workers may choose to
moderate workloads to avoid a mismatch of demands and resources, building in recreational
time after stressful periods to avoid burnout. The problem here is that when working life is
“saturated with uncertainty” (Bauman 2000: 147), long-term thinking becomes less likely.
Thus it becomes difficult for the freelancer to turn down work offered via the agency or
platform, as project-oriented work tends towards “insecurity and periods of involuntary
unemployment” (Dachs 2018: 5), even if accepting such work perpetuates an unsustainable
ecosystem.
In summary: Aiming for balance
The success of neoliberal discourse has been such that financialisation – maximisation of
shareholder value – and striving for efficiency have become normalised. This culture will be
difficult to change, as consumers demand ever-more speed in production and fast
turnaround times, especially for multimedia content. A company that weans itself away from
an army of unpaid workers-in-waiting, ready to produce translation at speed and low cost, is
liable to make itself uncompetitive in the sector of the market that prioritises cost and speed
over quality. On the other hand, prioritisation of efficiency above all else is likely to result in
unbalanced levels of satisfaction among all stakeholders, leading to a less sustainable
organisation. As work globally becomes increasingly casual and project-based as part of
what is sometimes termed Post-Fordism (Jessop 2007), workers too have become less
committal as “there is little chance for mutual loyalty and commitment to sprout and take
root” (Bauman 2000: 148).
Efficiency is also internalised as the basis for everyday personal decisions, as we try to
“multitask better and faster in a Taylor-inspired hunt for ever-greater heights of efficiency”
(Jackson 2018: 98). Many authors have noted our constant technologized distractedness,
along with a lowering of capacity to take on tasks that require deep concentration, such as a

In Translation Spaces 9(1) 2020, 12-34.
long, uninterrupted translation task (Carr 2011, Jackson 2018). Gazzaley and Rosen (2016:
108), for example, found that the average smartphone user picks up their device 27 times a
day. DT or project-based work may suit those who prefer variety of work as part of a portfolio
career or work remotely for second job or around carer commitments (Mallon 1998). It may
also be argued that microtasks are part of everyday life for many of us due to ubiquitous
technology. A study of Sydney office workers by Wajcman (2015: 100) found that they had
an average of 88 ‘work episodes’ per day, of an average duration of three minutes, and with
most changes or interruptions (65%) initiated by the workers themselves.
For many freelance translators, flexibility is a benefit rather than a problem. Courtney and
Phelan (2019) and Pielmeier and O’Mara (2020) report flexibility as a source of job
satisfaction, especially for those that enjoy high levels of autonomy. This autonomy is
important for well-being, as not all texts can be meaningful and important. Graeber (2018:
150) reports that most information workers appear to feel that, were their jobs to disappear, it
would make “very little difference to the world”. The problem is that DT removes that
autonomy, potentially leaving a repetitive role of relatively meaningless, rigidly defined and
unsatisfactory tasks. This process is happening in many fields of work other than translation,
as technology for monitoring and automated decision-making becomes more powerful.
In addition, Jessop (2007) and Dachs (2018: 4) report a trend of job polarisation, in which
“demand for middle-skilled people has decreased, while demand for both high-skilled and
low-skilled (paid accordingly) ones has risen”. The demand for translation worldwide is
reportedly growing (CSA Research 2019, Dranch, Beninatto and Johnson 2019), but a good
deal of translation work is at the lower end of the market, where automation will be used
where possible and cost is a major selling point. In their work defining levels of automation,
Parasuraman et al. (2000: 291) propose that poorly-designed automation could result in
reduced system awareness, placing too much trust in the machine, and skill degradation,
whereby if a function is “consistently performed by automation, there will come a time when
the human operator will not be as skilled in performing that function.”
Durban (2011), Jemielity (2018) and others recommend specialisation at the high end of the
translation market, where, for those with the ability and network, they report consistently
strong prices and job satisfaction. This is one obvious solution for some translators, although
the hollowing out of the middle section of the market may make it more difficult to climb to
the high end. There are also plenty of agencies who do not compete solely on cost and build
long-term trust relationships with translators and clients. At the very least, it makes sense for
translators to diversify their agencies or clients so as to protect themselves from unilateral
action or work practices that are demotivating and unsatisfactory. Working together where
possible is also important, even if project-based work mitigates against it, as you “cannot
have solidarity between fragments of time” (Berardi 2012: np). Even with collective action,
one stakeholder group will struggle to change the ecosystem.
Policymakers are aware of the issues with contingent, freelance, and crowd workers in
general, their lack of support from unions, and antitrust problems with collective action (De
Stefano and Aloisi 2018). A large-scale move towards empowering workers should help to
tip the balance towards more sustainable work systems for translators at the lower end of
the market. Even without such a change, however, translation agencies, and particularly
large language service providers need to consider sustainability broadly in their work
systems, to build it into their relationships with translators, and to contribute to national and
international translator organisations who can advocate for translators, increase their
visibility, offer counselling, advice, and training opportunities for self-actualisation and
professional development.

In Translation Spaces 9(1) 2020, 12-34.
There is also an onus on translator trainers to introduce business ethics and to highlight
contemporary work practices in order to prepare students for future roles as both translators
and translation industry workers. Those that work within large companies may sooner or
later become gatekeepers and makers of decisions about work practices and data
harvesting that will impact many other stakeholders within the translation industry. The wider
context of environmental sustainability and climate change (as discussed by Cronin (2017))
needs to be kept in mind in any discussion of work system sustainability, and decision-
making requires cognisance of the consumption cost of translation technology and
automation.
In closing, a broad conceptualisation of sustainability needs to be at the forefront of thinking
at all levels within the translation ecosystem. Responsible and ethical use of Taylor-
influenced methods will require a balancing of efficiency with the need to move away from
extractivism for all resources and to consider long-term value. This necessitates translation
employers bearing some responsibility for the satisfaction and motivation of workers, as a
sustainable industry will ultimately benefit all stakeholders.
Acknowledgements
This work was supported by the ADAPT Centre for Digital Content Technology at Dublin City
University, funded under the Science Foundation Ireland Research Centres Programme
(Grant 13/RC/2106) and is cofunded under the European Regional Development Fund.
I would like to extend my thanks to the editors and anonymous reviewers of this article for
their positive comments and helpful suggestions.
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