Competition and Change 2022

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Competition & Change

2022, Vol. 0(0) 1–25
Digital technologies shaping the © The Author(s) 2022
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DOI: 10.1177/10245294221107489
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shopfloor work
Andrea Szalavetz 
Institute of World Economics, Centre for Economic and Regional Studies, Budapest, Hungary
Abstract
Drawing on data from Hungarian manufacturing companies, this paper aims to explore the ways in
which digital technologies affect the nature and routineness of work. It concludes that digital
technologies impinge on some defining features of occupations, such as workload and intensity of
work; the degree to which the tasks can be explicitly defined, measured and codified; composition
and amount of skills required for task execution; and the importance of experience or tacit
knowledge for task execution. Non-technological factors, such as managerial approach to tech-
nology and work design moderate outcomes. Evidence indicates that a reduction in the routine
content of job tasks applies only to relatively skilled employees. For low-skilled employees, certain
digital assistance solutions increase routine and engender deskilling. We conclude that a qualitative
enrichment of shopfloor work becomes apparent only if (1) employees are skilled enough to
become upskilled – and thus engaged not only in digitally enabled but also in digitally augmented,
high-value activities; (2) employees’ work tasks are reorganised, work design and work practices
modified, and employees upskilled. Without appropriate managerial interventions envisaging the
augmentation of work, digital technology implementation entails deskilling and/or technological
unemployment, rather than providing richer dimensions to shopfloor work.
Keywords
digitalisation, routine-intensity, nature of work, deskilling, work practices, Hungary
Introduction
Emerging digital technologies portend further contraction in the share of routine tasks and
changes in the kinds of skills jobs require. Several scholars assume that occupational transitions
will bring about substantial improvement in the nature of work (Autor and Dorn, 2013;
Corresponding author:
Andrea Szalavetz, Institute of World Economics, Centre for Economic and Regional Studies, KRTK, Tóth Kálmán utca 4,
Budapest H-1097, Hungary.
Email: szalavetz.andrea@krtk.hu
2 Competition & Change 0(0)
Brynjolffson and McAfee, 2014; Handel, 2020). For example, in manufacturing units, digital
technologies relieve operators of physically demanding, repetitive and dangerous tasks. Sup-
ported by smart digital solutions and working alongside smart machines, shopfloor workers carry
out fewer manual tasks than previously. They are expected, rather, to monitor and interpret the
signals of production equipment, supervise production, solve problems and make decisions if
troubleshooting is required (Leyer et al., 2019; Pfeiffer, 2016). Since tasks involving problem
solving require greater autonomy and decision authority than tasks performed according to work
instructions, digital technologies will entail ‘employee empowerment’ (Kaasinen et al., 2019;
Leyer et al., 2019; Martišková, 2020).
Altogether, there are many scholars who expect that digital technologies convey new work
practices, engender changed forms of control, allow for more self-determined work activities, and
involve higher value and more diversified tasks than previously.
By contrast, other scholars predict an expansion of precarious work practices enabled by digital
technologies. Instead of an alleged smart machine-enabled reduction of workload, Gaddi (2020)
documents a machine-dictated intensification of work processes. Indeed, if software drives
manufacturing processes, human idle time is dramatically reduced. Additionally, digitally enabled
workplace surveillance enables a continuous monitoring and real-time traceability of workers’
actions (Delfanti and Frey, 2020; Pfeiffer, 2017).
Moreover, in line with the classical labour process theory relating technological progress to
deskilling and the progressive routinisation of the workforce and considering this mechanism a
general tendency of capitalist development (Bravermann, 1974), some labour economists contend
that instead of skill-biased and routine-replacing technological change, digitalisation leads to
deskilling. Production work (and work in production support functions) becomes increasingly
standardised and engenders a further routinisation of tasks (Dörrenbächer et al., 2018). In certain
work tasks and at certain hierarchical levels, the deployment of digital technologies is autonomy-
reducing – employees would passively carry out the system’s directives (Gerten et al., 2019; Jarrahi,
2019).
Other, more nuanced studies argue that the specific impact of new technologies on the nature of
work depends on the tasks undertaken by the given employees. Technology is a substitute for
routine, codifiable tasks but it complements activities requiring problem solving and creativity
(Autor, 2015). Moreover, the impact of technology is moderated by several factors. Hirsch-
Kreinsen (2016) considers that industrial relations, cultural factors and management choices can
moderate the impact of technology on work. Krzywdzinski (2017) complements this list,
highlighting that the role of individual business units within the value chain – as reflected by the
labour-use strategies of lead companies – is also an important moderating factor. Consequently,
there is little evidence of one single direction of change in terms of the nature of work (Gallie,
2017; Kopp et al., 2019).
Whilst most of the papers predicting any of the aforementioned developments focus on the
experiences of advanced economies, there might be non-negligible differences across countries of
different development levels and factor endowments in the nature of work and the routine task
content of jobs having the same occupational title (Hardy et al., 2018; Krzywdzinski, 2017). For
example, Dörrenbächer et al. (2018) and Krzywdzinski et al. (2018) argue that instead of upskilling
and empowering workers, digital technology implementation in Central and Eastern Europe (CEE)
might lead to a kind of digital Taylorism, that is, to an increasing standardisation of processes and
deskilling. Keister and Lewandowski (2017) highlight that in contrast to advanced economies
experiencing routine-replacing technical change, routine-intensive employment kept growing in
CEE in the 2010s, particularly in the manufacturing sector.
Szalavetz 3
These latter insightful studies notwithstanding, there is a dearth of studies exploring digital
technologies-induced changes the nature of work in ‘factory economies’ specialised in labour-
intensive activities.1 Additionally, although it is safe to acknowledge the existence of a strong
relation between digitalisation2 and changes in the nature and routine intensity of work, little is
known about the mechanisms involved in this relation. Analyses of digitalisation-induced
changes in the routine intensity of work are concerned mainly with the direction of change,
whether the routine intensity of occupations is reduced or enhanced as a consequence of digital
technology implementation. How the impact of digital technologies on the nature and the
routine intensity of work unfolds, remains unexplored: this process is regarded a black box.
This paper addresses these knowledge gaps by exploring the impact of digitalisation on the
nature and the routine intensity of shopfloor work at a sample of Hungarian manufacturing
companies. Specifically, this paper seeks to answer the following research questions:
Q1 How does the nature and the routine intensity of shopfloor work change as a result of digital
technology implementation?
Q2 How do digital technologies exert their effect on the nature and routineness of work? Which
occupational features are affected?
Q3 Under what conditions can digital technologies exert a beneficial effect on the nature of
shopfloor work?
The case of Hungary is considered a model setting for the investigation. Hungary is a factory
economy, highly specialised in industries, such as automotive, industrial machinery and electronics
that were the pioneers of and are still leading the way in adopting digital technologies.3 These
industries are dominated by foreign-controlled, export-oriented manufacturing units (see e.g.
Pavlı́nek, 2017 for the automotive and Sass, 2015 for the electronics industries). Whilst on one
hand, this indicates a dependent market economy status, that is the exposure of Hungary’s economic
performance to efficiency-seeking FDI in manufacturing (Farkas, 2011), on the other hand, local
subsidiaries can harness their global owners’ investments in digitalising their local manufacturing
facilities.
Hungary is an interesting example also in terms of the low performance of local vocational
institutions and weakness of employee representation bodies. The low and declining investments in
education and training (Neumann and Mészáros, 2019), coupled with race-to-the-bottom type
labour market regulations (Artner, 2020; Köll}o, 2019) and increasingly pressing labour shortages
affect global companies’ local labour-use strategies. These institutional factors influence the
motivations and shape the outcomes of firms’ investments in digital technologies (Krzywdzinski,
2017). Taken together, analysis of the Hungarian setting promises to yield insights of great rel-
evance to the research questions of this study.
This paper differs from prior research in two respects. Firstly, in contrast to the quantitative
approach characterising the majority of analyses discussing routine-biased technical change, we
apply qualitative techniques. We explore ‘the subtleties of human experience’ (Zuboff, 1988:
p. xi) regarding the impact of digital technologies on the nature of work by drawing on
interview-based investigation (Eisenhardt and Graebner, 2007). Secondly, by capturing the
perspectives of middle and lower-level managers and some operators, this paper is intended to
open the black box regarding the impact of digital technologies on the nature and the routine
intensity of shopfloor work. We study within-occupation change both in terms of automation
and augmentation. Extant literature tends to consider these two effects of advanced
manufacturing technologies separately: automation and substitution on the shopfloor, and
augmentation in occupations requiring high qualifications (Jarrahi, 2019; Moniz and Krings,
2016). Our focus is uniquely on the shopfloor: we provide evidence for the multifaceted nature
of digitalisation-induced changes and show that both automation and augmentation are present.
The study proceeds as follows. We begin with a brief review of the related interdisciplinary
literature including approaches by labour economists, scientists specialised in operations research
and technology assessment, and industrial sociologists. Based on this review, we present a number
of propositions on how the nature and routine intensity of shopfloor work might change following
the implementation of digital technologies. Next, we describe the research design, the sample of our
interviewees and the data analysis, and present the results of our empirical investigations. This is
followed by a discussion of the empirical results. Finally, we provide some concluding remarks and
elaborate on the implications and limitations of our findings.
Related work
Labour economists on the impact of digitalisation
Investigating the impact of digital technologies on the routine intensity of shopfloor work, our
research is closely related to labour economics studies analysing the expected net impact of these
technologies on employment (Acemoglu and Restrepo, 2018; Brynjolffson and McAfee, 2014; Frey
and Osborne, 2017).
Prior research on the impact of ICT adoption (e.g. Autor et al., 1998; Bresnahan et al., 2002)
has shown that technology is not neutral to skills. A widespread consensus has emerged among
academics that overall demand for low-skilled, routine task inputs – that is for repetitive and
well-codified tasks – decreases, whereas demand for non-routine tasks increases because of ICT-
related technologies (Arntz et al., 2016; Autor et al., 2003). Accordingly, technological progress
induces changes in countries’ occupational structure: it creates and destroys jobs and drives
employment reallocation both across and within firms (Acemoglu and Restrepo, 2019; Autor
and Dorn, 2013).
Coined as routine-biased technological change (Autor et al., 2003), these developments kept
intensifying with progress in digital technologies (Brynjolffson and McAfee, 2014; Frey and
Osborne, 2017; Manyika et al., 2017). Digital technologies have been evolving and pro-
gressively carrying out tasks that previously were considered professional and tacit knowledge-
intensive.
This raised the question how job content is restructured once part of the tasks that constitute a
given occupation is automated. Accordingly, scholarly interest has increasingly shifted to the
within-component of the structural change in employment (Acemoglu and Restrepo, 2019; Bisello
et al., 2019; Nedelkoska and Quintini, 2018). Atalay et al. (2020) analysed the texts of job ad-
vertisements over a period of six decades (1940–2000) to identify changes in tasks, technologies
and skill requirements. They show that most of the individual occupations have undergone sub-
stantial change, specifically, the share of non-routine analytic and non-routine interactive tasks
increased at the expense of routine ones. Similarly, Nedelkoska and Quintini (2018) found that in the
2010s, jobs across countries have become more intensive in less automatable tasks – both within and
between occupations.
These observations and findings were questioned by Eurofound’s recent survey of European
working conditions. Based on survey evidence, Bisello et al. (2019) report a marked increase in the
degree of repetitiveness and standardisation of work tasks, across EU15 countries, between
1995 and 2015. Since these variables measure routine intensity, the cited authors concluded that
Szalavetz 5
within-occupation developments (increased repetitiveness and standardisation) have more than

offset compositional developments in overall employment. This is in accordance with the widely
documented fact (Dörrenbächer et al., 2018; Goos et al., 2019) that digital technologies require the
standardisation of work procedures.
In summary, apart from transforming the composition of employment, digital technologies have
a meaningful impact on the bundles of tasks associated with individual occupations and the skills
required to perform specific work tasks. However, neither the magnitude nor the direction of change
in the routine content of work tasks is straightforward in the literature.
Operations research and industrial sociology on the impact of digitalisation on

shopfloor work
Apart from labour economics scholars, the impact of technology on work practices and the nature of
work has received ample attention in industrial sociology studies. A classical study is Zuboff’s
(1988) ‘Smart Machine’. Her theory, centred around the distinction between technology ‘auto-
mating’ or ‘informating’ work, is still relevant today, 30 years after the first publication of her book
(Kallinikos, 2011). Automation, in Zuboff’s conceptualisation, is about streamlining, simplifying,
speeding up and increasing the efficiency of work. By contrast, information technology can also be
used to enrich work and give rise to ‘better jobs’. Smart systems enable workers to gain a more
informed perspective of their own activities. This latter effect of information technology is referred
to by Zuboff as technology ‘informating’ workers.
Thirty years later, scholars discussing the implications of digital technology implementation for
work, conduct their analyses along the same lines, and conclude that digital technologies augment or
deskill their users at the workplace. Augmentation takes place if technologies amplify users’ skills
and improve their existing competencies. By contrast, certain digital technologies make users’
skills, competencies and tacit knowledge redundant. These twin effects of technology often prompt
sharp differences in scholars’ predictions and conclusions concerning the outcome of digitalisation-
induced changes in the content and quality of work.
For example operations research and technology assessment studies focus on the ways digital
technologies support production workers. Digitalisation gave birth to a new concept called
‘operator 4.0’ (Romero et al., 2016; Ruppert et al., 2018). Supported by collaborative robots
undertaking physically demanding, ergonomically challenging, and/or repetitive tasks that
require high precision, operators 4.0 are relieved of an increasing number of routine tasks. More
importantly, they receive instructions that support their work tasks in a user-friendly manner, for
example through smart visualisation. Assembly workers are supported by augmented reality
solutions that project contextualised information to operators’ visual field, that is to the point in
space where the tasks requiring this kind of information are performed (Romero et al., 2016).
Warehouse picking is facilitated by indoor positioning systems and/or mixed-reality glasses.
Digital technologies engineer human error out of the production system and enable shopfloor
employees to perform their respective tasks in an improved and more efficient manner
(Lazarevic et al., 2019). Embedded applications provide continuous feedback about successful
task execution (Longo et al., 2017), and/or about the status of the production process.
Employees in production support functions are also assisted by digital solutions that au-
tomate non-value adding activities, such as filling out time sheets or reporting. Digital tech-
nologies support decision-making in shopfloor activities by making the right information
available by the right time. The real-time status of overall equipment effectiveness and order
fulfilment is displayed on dashboards, together with a variety of other performance indicators.
Decision-making for production planners and schedulers is supported by smart algorithms

integrated in the cyber-physical production systems (Colledani et al., 2014) that perform real-
time optimisation.
Whilst operations research is concerned with describing the augmentation effect of technology, a
well-known strand of industrial sociology literature elaborates on the nature of digitalised work in
the context of agility and precarity (Moore, 2019). In this context, worker monitoring and tracking
tools empower advanced control mechanisms. Another adverse effect of digital technology im-
plementation is the intensification of work. Moore maintains that smart technologies ’accelerate the
labour process to the cliff edge of what is possible to endure’ (p. 140). Work intensification is the
outcome of ‘digitally reinvigorated lean’, since digital technologies both require and allow for
enhancing lean practices, optimising and standardising work (Buer et al., 2018; Dörrenbächer et al.,
2018).
Investigating operators’ perceptions of changes in the nature of their work following the
deployment of new automation solutions, Wurhofer et al. (2018) argue that automated systems
such as digital work instructions are associated with a decrease in mental effort. Digital as-
sistance systems have a deskilling effect and convey a loss of know-how. Furthermore, routine
cognitive work involving a passive monitoring of robots that perform the work tasks causes
boredom and has a negative impact on job satisfaction. In a similar vein, Jarrahi (2019) draws
attention to the threat of cognitive complacency, when workers mindlessly follow the in-
structions of the system.
In sharp contrast to this view, Pfeiffer (2016) stresses that the qualitative role of assembly
operators has even increased with automation. These workers do not passively monitor machines
but are expected to intervene to prevent technical incidents and/or address unexpected malfunctions.
In line with this latter assessment, Holm (2018) conjectures that operators’ tasks will be less re-
petitive and more diverse as a result of digital technology implementation. They will have to learn
new workflows, become comfortable with new tools and applications, interact with smart machines,
and work in a more information-intensive and technology-rich environment than previously.
Operators’ responsibility thus grows: in addition to performing technologically enhanced pro-
duction tasks, they are required to take process-related decisions and are encouraged to generate
ideas for process improvement.
In summary, whilst this review confirms the relevance of the twin role of technologies in
transforming work (Zuboff, 1988), it highlights that neither the magnitude nor the direction of
change in the routine content of work tasks is straightforward.
A related stream of research underscores the importance of managerial and organisational
practices, the level and composition of employees’ skills, corporate strategy, and industrial relations,
that is, non-technological factors shaping the impact of digital technologies on work (Hirsch-
Kreinsen, 2016; Krzywdzinski 2017). Several contributions in this stream stress the importance
human-centric work design – associated with a good use of employees’ skills and operators’
autonomy/responsibility to control automated systems and intervene in emergent complex situa-
tions (Fantini et al., 2020; Pinzone et al., 2020). Since evidence (surveyed by Parker and Grote,
2020) has been accumulating to suggest that a human-centric work design is likely to entail better
organisational performance than a pure technocentric approach, managers’ proactive efforts to put
human-in-the-loop principles into practice are of cardinal importance (Waschull et al., 2020). A
quote by Zysman and Kenney (2017, p. 331) is illuminating in this respect: [The specifics of
technology] ‘Deployment will depend on whether firms […] view workers as assets to be aug-
mented or simple costs’. (Italics added)
Szalavetz 7
However, as demonstrated persuasively in Krzywdzinski (2017) managerial liberty in shaping

various dimensions of work is constrained by multiple factors, such as the availability of adequately
skilled workforce, owners’ strategies, the power of trade unions and labour market regulations.
The relation between digital technologies on the shop floor and the routine intensity of
employees’ work
From a shopfloor perspective, the routine intensity of work at manufacturing facilities has been
shaped by multiple developments working in opposite directions. On one hand, shorter product
life cycles, rapidly changing and highly varied demand, and short production runs increase the
non-routine content of work both in production and in production support functions. In line with
the requirements of mass customisation, manufacturing plants are frequently reconfigured and
production lines redesigned (Váncza et al., 2011). Digital technologies and advanced
manufacturing solutions embedded in firms’ production systems are paramount for meeting
these requirements. At the same time, they transform the mix of manual and/or cognitive
activities within individual occupations and exert a significant impact on task-related skill
requirements. In a nutshell, the increased complexity of operations and diversity of work tasks
in all shopfloor functions may increase the diversity of the required skills and reduce the routine
content of work.
On the other hand, an array of smart user assistance solutions is at hand, tailored to the skills of
users. Deployed to prevent errors, offer guidance and make the right information available at the
right time to the right people, these assistive solutions rationalise and simplify complex work tasks,
and … increase the routineness of activities. Developing new routines is often indispensable since
digital technologies not only assist but also intensify work, allowing for inefficiencies to be
systematically eliminated.
Taken together, the impact of digital technologies on the routine intensity of work cannot be
limited to a dichotomy of increasing versus decreasing degrees of routine. Routine may become
completely transformed with or without a meaningful change in the share of routine activities in
total. Some routine and non-routine tasks may be eliminated, replaced by other routine and non-
routine ones, and complemented with new tasks. Technologically enhanced employees may develop
new routines, aligned with the specifics and the requirements of the newly deployed digital so-
lutions. The overall outcome of change might differ even within individual occupational categories.
We propose, therefore, four basic scenarios of digital technologies-induced change in the routine
content of activities: (a) no change in routine; (b) increased routine; (c) transformed routine; and (d)
reduced routine.
As for the mechanisms that induce these scenarios, we propose that – in a direct or indirect
manner – digital technologies affect several variables used as proxies for measuring the nature and
routineness of work. These include (i) the intensity of work; (ii) the degree to which the tasks can be
explicitly defined, measured and codified; (iii) the composition and amount of skills required for
task execution; (iv) the importance of experience and tacit knowledge for task execution and (v)
other features, such as task complexity and importance of independent decision-making and in-
teractions with peers for task execution.
Besides the direct and indirect effects of technology, these features of work are significantly
influenced also by managerial practices, in terms of choice of technology, skill use strategy and
work design (Parker and Grote, 2020; Waschull et al., 2020). Managers may reorganise work by
grouping the remaining, non-automated work tasks into jobs in a way that jobs become more diverse
and engaging. Positive outcomes are particularly strong if managers allocate as much
decision-making authority to lower-level functions as possible. In contrast, managers may favour

task simplification, standardisation and even substitution wherever possible. Advanced digital
technologies may be used for supervisory control over the production and support processes.
Managers’ choices are influenced by task-specific features of work and other factors, such as
corporate culture and competitive strategy (Hirsch-Kreinsen, 2016; Krzywdzinski, 2017). Addi-
tionally, these choices are enabled – albeit often rather constrained – by the level and composition of
current employees’ skills. Consequently, the overall outcome of change might differ even within
individual occupational categories.
These arguments are used as guidelines for our empirical research of digitalisation-induced
changes in the nature and routineness of shopfloor work. We limited our scrutiny to the first four
variables and touched upon the issues listed in (v) only superficially (cf. Appendix B). Since the
surveyed literature makes it clear that technology itself does not determine outcomes, we also
explored the moderating role of managers by way of scrutinising technology-induced new work
practices.
Method
Research design and data
To investigate the specifics of digital technologies-triggered changes in the nature and the routine
intensity of work in the context of Hungarian manufacturing companies, we developed an ex-
ploratory research design. Research involved qualitative data collection from semi-structured
interviews (Patton, 2002) with a sample of key informants: those behind digital technology im-
plementation on the shopfloor of manufacturing companies and informed observers (Westney and
Van Maanen, 2011). Data have been collected on the ‘everyday realities’ of work life: on ‘the
subtleties of human experience’ (Zuboff, 1988: p. xi) regarding the impact of digital technologies on
the nature of work.
Striving to obtain rich details of context-specific changes in work practices (Doz, 2011), we used
insights from the field, gained from interviews with operators and managers, as well as workplace
observations and analysis of corporate videos uploaded by sample companies on YouTube for
employee attraction and marketing purposes.
In order to reinforce the trustworthiness of our qualitative research, we devised research variables
that are indirectly related to routine intensity. Rather than asking our informants to evaluate the
impact of digital technologies on the routine intensity of their work, we asked them about
technology-related changes in various features of work. The research variables we employed to
guide our interviews (Table 1) are ‘suggestive’, evoking the phenomenon investigated in this study
only indirectly (Burgelman, 2011).
To set the context, we started our interviews with inquiries about the particularities of the digital
technologies recently deployed at the given companies. Next, we asked some related, open, ‘how is
it to work with’-type questions, tailored to the solutions mentioned by the firms. The subsequent
group of questions addressed the resulting changes in the features of work and working conditions:
workload, codifiablity (measurement and standardisation), changes in skill requirements and the
role of tacit knowledge and experience. Another bundle of questions addressed changes in work
practices.
As summary questions, utilised to provide opportunity for the interviewees to return to aspects
deemed crucially important, we asked our informants to summarise the overall impact of digital
technologies on work. We also asked them to identify the most important complementary
Szalavetz 9
Table 1. Research variables.
Topic Keywords mentioned during the interviews
Work intensity Changes in work intensity, speed of work and idle time
Standardisation Measurement and standardisation of work tasks and procedures, explicit work
instructions
Skill requirements New skills required and skills becoming redundant
Experience and tacit Changes in the importance of experience and tacit knowledge
knowledge
Work practices Use of teamwork, multiskilling of operators, multitasking, job rotation, personal
feedback, involvement of employees in continuous improvement
investment(s) accompanying digital technology implementation that were deemed necessary to

capture the expected benefits, for example the productivity potential of the newly deployed so-
lutions. In interviews with operators, this summary question was replaced by a question inquiring
about operators’ overall perceptions regarding digital technologies-triggered changes in working
conditions.
Our initial aim was to make the sample of interviewees as heterogeneous as possible regarding
industries, level of digital maturity, and interviewees’ positions and work tasks. Before engaging in
the collection of field data and observations, we conducted expert interviews to gain orientation
about the most recent advances in digital technologies, the characteristics of the Hungarian market
for advanced manufacturing technologies, and the solutions with which some leading companies in
Hungary are currently experimenting. We interviewed an expert representing a robotics technology
provider and researchers engaged in digital solutions provision for business companies. Addi-
tionally, we conducted an interview with a representative of a recruitment and temporary personnel
agency, who proved to be a source of valuable information about recent changes in manufacturing
companies’ demand for skills.
The experts interviewed pointed out that industrial robots are concentrated in specific industries.4
They also stressed that besides industry, firm size and foreign ownership are important determinants
of digital technology adoption.
Accordingly, we decided to focus on three industries instead of adopting a maximum variation
sampling approach. Our sample consists of six automotive, two electronics and five machinery
companies. Sample firms are large, export-oriented, and with the exception of two companies,
foreign-owned.5 The basic data of the companies in the sample are summarised in Table A in the
Appendix.
When selecting the companies, we have followed the principle of purposeful sampling, and
chose companies representing illuminative cases from the point of view of implementing digital
manufacturing technologies (Patton, 2002). Accordingly, the companies in the sample have been
recommended by the experts and/or have been selected on the basis of the author’s previous
experience, gained in the course of earlier investigations.
Apart from conducting interviews with executives in the C-suite (managing directors, plant
managers), we gained access to managers directly involved in the digitalisation of the shopfloor
(responsible for operations, process improvement, IT and digitalisation). Managing directors
provided a broad overview of how the nature of work was changing at their companies, assessed the
potential of digital technologies to drive performance in different arenas, and evaluated the nec-
essary organisational complementarities supporting the introduction of digital technologies. Since
they were the ones deciding about investments in smart worker assistance solutions, they were
remarkable knowledgeable also about shopfloor-level developments and problems. The lower-level
managers interviewed provided rich descriptive insights about changes in the nature of work for
frontline workers, the ways they transformed job designs, and the particularities of human-machine
interaction.
In order to capture diverse perspectives and reduce the single-respondent bias (Eisenhardt and
Graebner, 2007), we conducted interviews with three shopfloor operators and interviewed a
representative of the Metalworkers’ Federation representing several companies in the automotive
and electronics sectors. The accounts of shopfloor workers were centred around the intensification
of work, and social problems on the shopfloor, such as line managers’ authoritarian behaviour and
unequal treatment of certain colleagues.
Interviews with operators, workplace observations and the analysis of videos have to some
extent challenged the overall picture obtained from the interviews with managers. Whilst these
latter laid emphasis on the augmentation effects of digital technologies and argued that work has
become more varied, more interesting, or at least easier, operators’ accounts, videos and
workplace observations indicated that shopfloor work tasks involved a high level of routine and
repetition, irrespective of a smart work environment and that operators were using smart devices
and tools for work. Having considered also these perspectives, we attempted to control for the
social desirability bias (Stockemer, 2019) and increase the trustworthiness of our conclusions.
Altogether, the data used in this study consist of 20 interviews (13 managers, three operators,
a trade union representative and three expert interviews), conducted in the first half of 2020.
Interviews lasted 60 to 90 minutes. In order to triangulate the findings, we have supplemented
interview information with data from multiple sources, including press releases, corporate
websites, business press articles, company reports, notes to the financial statements, and
corporate videos.
Data analysis
For data analysis, we applied the interpretivist methods of analysis, the point of departure of
which is the paramount importance of context (Stake, 1995; Welch et al., 2011). Although an
ethnographic method, involving observations to reveal actual practices, would have been better
suited for studying the nature of work, after the first three interviews the COVID-19 pandemic
prevented us from visiting the other firms and taking factory tours to observe work practices.
Accordingly, we carried on our research interviews by conducting interviews by phone or other
remote communication platforms and relied on corporate videos to obtain an insight about the
nature of work at the given companies. This data collection method allowed for an interpretivist
approach involving a detailed representation of informants’ perceptions and experience.
Our data analysis has also employed certain constituents of grounded theorising (Glaser and
Strauss, 1967) since we applied the methods of constant comparison. The comparison of the
narratives helped us detect a number of inconsistencies in outcomes, which called for caution in
establishing causality. This made us decide for applying process analysis (Hall, 2006), starting
from observations, comparing them, and identifying the context-specific mechanisms
behind them.
The first draft of this paper was sent to all our informants, asking for comments, corrections or
approval. Their focused feedback helped us enhance the cross-sectional validity of our
arguments.
Szalavetz 11
Results
Our initial interview questions were aimed at collecting data about the specifics of digital tech-
nologies implemented in the companies in the sample. These data helped us put the changes in the
nature of work described by our informants into context. Examples of technology-related changes in
the research variables are summarised in Appendix B.
The impact of digital technologies on workload, measurement and standardisation

of work
The first observation crystallised from the interviews is that, compared to our previous investi-
gations (Szalavetz, 2019a, 2020b), robots, relieving humans from tasks involving pure physical
strength, have become more prevalent. Notwithstanding, workload and work intensity have in-
creased in practically all companies in the sample. The reason is that the implementation of digital
technologies requires a reorganisation of work processes, the optimisation of material flows, and the
standardisation of work tasks.
Accordingly, investments in digital technologies were both preceded and accompanied by
projects addressing the design of work. These latter projects can be described with four keywords:
measure, analyse, improve and standardise – as illustrated with two interview excerpts:
“Before installing robots, our process engineers performed a thorough analysis of the given tasks. They
sliced operators’ activities into motions and analysed every motion to determine which one is
superfluous – to be eliminated – and which one can be performed better and quicker. Accordingly, the
process has become more simplified and suitable for being automated.” (head of the industrial engi-
neering team, No. 7)
“To optimise task accomplishment, we first defined the way in which tasks must be performed. In the
case of welding, for example, we specified the temperature, the position, which side to weld from, and
determined the related time standards. Consequently, our operators stopped performing tasks according
to their intuition and experience: they used the prescribed methods. We improved the delivery of raw
material, parts, and tools to workstations and upgraded the organisation of work so that operators do not
wait for input material or for the line manager to tell them what to do next. We initiated a digitalisation
project and implemented a manufacturing execution system only when these processes had been
standardised and were running smoothly. (plant manager, No. 4)
These reorganisation initiatives have significantly increased workers’ productivity. The subsequent
implementation of digital technologies has further improved the efficiency of work processes, resulting
in increased throughput and reduced variations in cycle times. The obvious consequence for operators –
as mentioned in interviews – is reduced idle time and intensification of work. Workers were quick to
recognise that the better organisation of work leads to its added intensity.
Regarding the impact of investments in digitalisation on work efficiency, the managers
interviewed were unanimous in placing the highest emphasis not on robots but on tools and
techniques enabling data acquisition, processing and analytics. Investments in developing
cyber-physical production systems, for example laid the foundation for introducing basic use
cases such as the measurement of the idle time of the machinery. Data allowed for a granular-
level analysis of the production cycle and ensured reliable knowledge of processing times and
idle time, which is the foundation for any process optimisation exercise.
Changes in skill requirements and importance of experience and tacit knowledge

In this section, interviewees’ accounts are analysed from the perspective of digitalisation-
induced changes in the skill-intensity and routine content of work. Relatedly, some illustrative
examples of the influence of digital technologies on the value of experience and tacit knowledge
are presented.
Interviewees unanimously stated that although some of the simplest manual tasks had been
replaced by automation technologies, a number of other tasks remained that require similar
repetitive movements and elementary skills. Since companies face pressing labour shortages,
operators, displaced in tasks that had been automated, were usually not fired but deployed to
perform other manual activities.6 Consequently, as several managers interviewed (No. 1, 3, 8,
12 and 13) claimed, their cases elicited changes neither in the required skills nor in the rou-
tineness of work.
In other instances, the automation of specific work tasks was accompanied by a thorough re-
organisation of work and training provision to manual workers. This enabled operators to engage in
multitasking. Although the skills required for the execution of individual tasks remained the same,
task diversity increased, since operators executed different tasks at several – partially automated –
workstations. Whilst the managers of No. 4, 8 and 9 stressed that this kind of multitasking involves a
reduction in the routineness of work, two operators interviewed (2 and 7) were rather of the opinion
that reduced routine characterises only the initial weeks that follow the reorganised work activities.
Once they had developed new routines, the overall degree of routine was perceived to be the same as
previously.
Regarding the impact of smart visualisation solutions, there was a general agreement among
interviewees that although, indeed they enable faster execution of work tasks and reduce
assembly-related errors, they have a deskilling effect. As an explanation, both the digital
solution providers interviewed and several other informants (No. 3, 8 and 9) pointed out that
today’s assembly processes would change much more frequently than previously. High product
variety coupled with reduced cycle time requirements makes current assembly work hard to
compare with that of previous eras. Changes are so fast that it makes no sense for production
workers to learn the specifics of new products. Easy-to grasp visual work instructions and
integrated digital error-proofing devices prevent operators from assembling wrong parts or
omitting assembly steps. In this way, operators work faster and more precisely than before.
Managers (No. 1 and 13) and experts acknowledged, however, that certain digital assistance
technologies may turn assembly operators into ‘extended workbenches’: they perform tasks
according to simple and precisely defined instructions. The following quotes illustrate these
arguments:
“Visual work instructions simply help operators figure out what has to be assembled in the next moment
and how this needs to be done.” (expert interview, digital solution provider)
“The processes need to be adapted to operators’ capabilities. Operators do not have time to
read lengthy instructions or check the manuals or the printed material. Moreover, if information is
not easy to understand, they just ignore it and rely on their intuition.” (operations manager, No.
8)
“Some testing tasks have predetermined time standards: operators mustn’t finish the given task earlier
than prescribed. Operators should count how long they inspect a given part, for example, for 40 seconds.
Since fatigue is unavoidable, after a couple of hours it becomes increasingly difficult to keep the required
Szalavetz 13
time. Operators would try to finish earlier and move on to the next piece. We have installed a digital
control that provides feedback to operators showing how much time has elapsed.” (process engineering
manager, No. 13)
At operator level, the impact of advanced digital assistance solutions was mainly perceived in
terms of work intensification and occasional multitasking, and not necessarily as a transformation of
the required skill mix. Nevertheless some ‘intellectification of work’ (Jarrahi, 2019) does occur – at
least, this is how the transformation was interpreted by the plant manager of No. 4:
“Operators have to check the pieces that have been deemed defective by the robotic system. This is an
intellectual task, involving ‘problem solving’, since they have to find out the primary reason for the
defects. Is it because of a wrong setup of the machine? Are the defects caused by inadequately placed
pieces? Of course, not only the operators are required to identify the causes: we have line managers,
technicians, and quality inspection engineers to contribute to solving quality problems. However,
working directly with the pieces in question, operators often have good ideas.”
The managers interviewed were not unanimous in stating that monitoring the machinery instead
of – or rather along with – performing physical tasks refers to a reduction in the routineness of work
or just to a changing content of occupational tasks. Nevertheless, experts and managers in No. 4, 6,
7 and 9 acknowledged that this kind of activity requires relatively higher information processing
abilities, and operators’ technical literacy, for example the ability to use the tools and devices
developed for advanced manufacturing applications and become quickly familiar with the logic of
the supporting applications, has become paramount. Operators work in a much more information-
intensive environment than before. Relatedly, advanced manufacturing technologies demand fewer,
albeit more qualified employees.
“As a rule of thumb, newer generations of production lines require ten to fifteen per cent fewer em-
ployees. Since our processes have become highly automated, our operators’ tasks involve observing the
equipment and taking actions in case of errors. This kind of incident management requires higher skills
than what a simple blue-collar operator would have. Accordingly, our operators cannot be labelled as real
blue-collar workers: they are ‘specialists’, often with tertiary educational attainment.” (chief information
officer, automotive company, No. 6)
“Interfaces have become more complex, containing not only two or three buttons or switches as in
traditional machine control units. Being familiar with all the buttons on the control panels or being able
to navigate through the menu of a touch screen are competencies developed through several months of
work experience.” (expert interview: trade union representative)
Digitalisation-induced upskilling and the transformation of the required skill mix were more
straightforward for employees in production supporting functions. Digitally augmented employees
may experience a significant transformation of the composition of their tasks, involving, for instance
increased diversity, interaction-intensity, and mental demand: these are proxies of reduced routine
and improved nature of work. The interview excerpt below (No. 4) illustrates the case of increased
interaction-intensity.
“With digital work shift management and automatic shift handover reports, our line managers have been
relieved from immensely time-consuming and boring administrative tasks. The duties they perform now
correspond more to what one would imagine that a line manager does: they direct and coordinate the activities
of operators, interpret job orders, explain procedures to workers and resolve workers’ problems and
complaints. Line managers have thus genuinely become ‘managers’, engaged mainly in management tasks.”
A digitalisation-induced transformation of work involving increased mental demand is exemplified

by the case of maintenance workers. As reported by No. 2, 9 and 11, retrieving information from
manuals previously accounted for a significant share of maintenance workers’ working hours. Carrying
out regular and often unnecessary checks and inspections of the tools and the machinery was an
additional time-consuming exercise in which the value added was low. Whilst sensor-based continuous
monitoring and algorithms-based analysis of asset conditions have reduced the need for and the time
requirement of this latter exercise, smart supportive solutions (maintenance databases and augmented
reality solutions) have addressed the former type of ‘waste’. Consequently, the share of ‘maintenance’
increased within the activity mix of maintenance employees, and as argued by the business unit leader of
No. 9, by taking up new and relatively higher value activities instead of the ones they had disposed of,
their work has become more engaging and interesting.
The flipside of the coin is that smart technologies reduced the value of maintenance workers’
previously accumulated experience. Equipment maintenance databases made it possible for
maintenance workers, who had no experience with a given machine, to obtain immediate infor-
mation about its past problems (previously recorded defects) and weak points (machine-specific
functionalities that need to be double-checked when inspecting or repairing it). Reduction in the
value of experience was reported also with respect to other activities, such as quality control or
welding, as illustrated by the following quotes.
“Visual work instructions are used to assist quality checking. Since the pieces arriving on the conveyor
are heterogeneous, every time the inspector has to check different parameters. The arriving pieces are
equipped with a radio frequency identification tag (RFID) which is one of the key components of the
dynamic visual work instruction system. Sensing the arrival and the specifics of the new work piece, the
points to be checked will be automatically displayed on the screen, placed in front of the inspector.”
(industrial strategy manager, automotive company, No. 11)
“How is it, to work with a cobot? Well, you know, we classify our welders into four categories according to
their capabilities and experience. Workers who have just left vocational school belong to category one,
whereas highly skilled workers with more than twenty years of experience belong to category four. By using a
cobot for welding, a category 1 welder can perform tasks that require higher skills and experience than what
he has: tasks that were previously allowed to be performed only by category 2 or category 3 welders.”
Conditions moderating the outcome of digitalisation for work

Interview findings have confirmed the consensus view of industrial sociology and labour economics
scholars (Hirsch-Kreinsen, 2016; Krzywdzinski, 2017; Parker and Grote, 2020; Waschull et al., 2020)
that the impact of digital technology deployment is non-deterministic. If digitalisation engendered the
allocation of new tasks to individual employees, these are, in some instances, indeed more diverse, more
complex, and higher value adding. In other contexts, new tasks are as elementary and routine-intensive
as previously. Positive outcomes, in terms of reduced routine and enriched work, are contingent on
employees’ skill level and on the direction and effectiveness of managerial interventions redesigning
work, introducing advanced work practices, and enacting some necessary organisational
transformations.
Szalavetz 15
At the time of our research, components of high-performance work practices (Makó, 2005; Pil
and MacDuffie, 1996; Posthuma et al., 2013), such as advanced performance management,
teamwork, involvement of employees in continuous improvement were already prevalent across the
surveyed companies. The advanced functionalities of digital solutions allowed for enhancing some
of these practices.
One third of the managers interviewed emphasised the importance of employee involvement
with respect to digital technology implementation. These companies started small, usually with pilot
digitalisation projects, and have systematically requested employee feedback about the individual
solutions before rolling it out to other production lines within the plant. Employee involvement did
not affect the eventual impact of technology on the nature of work, but it definitely promoted the
acceptance of the given solutions and thus, indirectly, it enabled these firms to achieve the expected
performance improvements.
Teamwork involving cross-functional collaboration was also a recurrent topic (cases No. 2–12)
in the accounts detailing general changes in the nature of work. Team-based organisational setups
led to a reshaping of traditional authority levels.
“Digitalisation was necessary but insufficient: it turned out that our traditional hierarchical structure with
well delineated responsibilities was not appropriate anymore. We tried to leverage digital intercon-
nection for a project-based configuration of teams. However, interconnection alone failed to induce a
change in employees’ mindset. Previously they used to execute the tasks assigned to them by a couple of
colleagues at higher hierarchical levels in the same department. It was difficult for them to get ac-
customed to a practice in which requests can arrive from any colleague.” (plant manager of an au-
tomotive firm, No. 4)
Accordingly, apart from working out a new division of labour through redistributing work tasks
between humans and machines, managers also had to devise new forms of integrating work. They
were experimenting with new organisational setups that have eventually become effective alter-
natives to hierarchical organising.
In summary, empirical evidence indicates that managerial interventions envisaging the aug-
mentation of work and an increase in company-level (subsidiary-level) value added is a strong
condition of positive outcomes for work. Managers’ conscious approach to technology has un-
questionably improved the nature of work by increasing task diversity, even in those cases where
digital support reduced the importance of experience and tacit knowledge. Obviously, the purpose of
managerial interventions was to ensure that the deployment of advanced manufacturing tech-
nologies delivers upon expectations. Nevertheless, the emergence of more desirable scenarios,
involving a (somewhat) reduced routine was in some instances more than a simple beneficial side
effect. As pointed out by two informants (No. 4 and 9), in an era marked by formidable labour
shortages, offering interesting, digitally assisted work opportunities is a means of employee at-
traction and retention. On the other hand, for higher skilled employees, digitally augmented work
enables a more effective use of their skills.
Discussion and conclusions

This paper investigated the impact of digital technologies on the nature and routine intensity of
shop-floor work, the ways in which digital technologies exert their effects, and the conditions
moderating the outcome of digitalisation for work.
Drawing on qualitative data, we found that in a within-occupation context, digitalisation does not
necessarily involve routine-replacing change. Observational and interview data provided ample
evidence for scenarios involving no change in routine, or, definitely, increased routine. A common
feature of the surveyed context-specific changes in the nature of work was that employees develop
new routines aligned with the specifics and the requirements of the digitally enhanced work
environment.
In other instances, instead of changes in the degree of routine, we rather found a trans-
formation of routine, specifically in cases when advanced automation solutions reduce the
amount of manual labour input on the shopfloor. Instead of performing direct production
activities, production operators embark on monitoring the control panels of the equipment and
adjusting the machinery if necessary. Although these tasks require less routine manual and more
routine cognitive labour input than previously, the routineness of work has not necessarily
changed.
Obviously, we also came across cases characterised by a digital technology-induced reduction in
the routine content of work. The reduction of routine was driven by multitasking or was manifested
in a reduced share of routine tasks within the overall task bundle.
As a definite commonality of the observed heterogeneous developments, we found that the higher the
level of employees’ initial skills, the more likely a scenario involving a reduction in the routine intensity
of their activities. We found that a digital technology-induced reduction in the routine content of work
applies only to relatively skilled employees, albeit not exclusively in high-level shop-floor functions. For
relatively low-skilled employees, the routine content of work activities did not diminish. On the contrary,
routine increased in a number of instances, when digital technology implementation engendered
deskilling.
One of the most conspicuous ways in which digital technologies exert their effect on the
nature and routineness of work is by enabling a precise measurement of a number of work
parameters. Our empirical data highlight a strong association between digital technology
deployment and the measurement, codification, and standardisation of work tasks. Measure-
ment allows for the optimisation of both the individual tasks and the work processes. As set
out in section 5.1, optimisation is followed by standardisation and results in the intensification
of shop-floor work, which, in turn, requires new routines to cope with the increased pace
of work.
The second essential mechanism conveys technology-induced changes in the composition and
amount of skills required for task execution. The direction of change is, however, far from
straightforward, as is demonstrated by examples of digital technologies contributing to the des-
killing of manual workers and/or reducing the importance of experience and tacit knowledge in
several functions.
A conspicuous commonality of digitalisation-driven changes in occupational features was their
context-specificity, or otherwise, the lack of commonalities. The observed heterogeneity of changes
in the nature and routine intensity of work tasks in various shopfloor functions suggests two non-
trivial conclusions.
Firstly, we conclude that all else being equal, digital technology implementation simplifies work
and increases routine on the shop floor. Plant managers, however, can to some extent ‘direct the
impact’ of digital technology implementation towards enrichment by redesigning workflows so that
employees could perform more varied, higher value, and more interesting work tasks. Without
intentional managerial interventions envisaging the augmentation of work, the automation and
deskilling effects of digital technologies will prevail over augmentation. Augmentation requires that
employees’ work tasks be reorganised, work design and work practices modified, and employees
Szalavetz 17
upskilled. Positive developments are thus contingent on conscious organisational and human re-
sources management. Without these managerial interventions, digital technology implementation
will – in line with Braverman (1974) – contribute to deskilling and/or technology unemployment,
rather than provide richer dimensions to shopfloor work.
The second conclusion is that the widely hailed beneficial effects of digital technologies on the
nature of work become apparent only if employees are skilled enough to be upskilled and become
engaged not only in digitally assisted but also in digitally augmented, high-value activities. This is
particularly important in the Hungarian context, characterised by low and declining investments in
education, a lower-than average prevalence of lifelong learning (EC, 2020), and a lower-than-
average performance of the vocational education system in terms of keeping up with the re-
quirements posed by technological progress in manufacturing.7
Over and beyond the common policy recommendations about addressing skill gaps and im-
proving the efficiency of vocational education, our findings indicate that local managers need to be
aware of their roles and responsibilities in configuring socially sustainable work designs. Managers
should act consciously when reorganising the task bundles of employees, to turn digitalisation into
collective benefit. As is well known at least since Zuboff (1988), technology can both enable and
enslave workers. Digital technologies can be used as a means of controlling, instigating, and
disciplining ‘imperfect humans’ – if not completely removing them from the production process.
Positive outcomes require ‘catalysts for progress’ in the form of managerial vision and conscious
approach to technology.
We believe that this study contributes to existing research by offering a more fine-grained
understanding of the ways in which the impact of digital technologies on the nature and the routine
intensity of work unfolds. It integrates three key approaches (labour economics, technology as-
sessment, and industrial sociology) to studying the opportunities, challenges, and risks of digital
technologies for work, and offers insights from the workplace in an under-researched context: that
of a ‘factory economy’ specialised in labour-intensive activities.
The study is, however, not without limitations. On one hand, the usual limitations apply, in terms
of a small number of interviews, industries, and shop-floor functions. Another concern is the bias of
the sample towards the managerial view: frontline workers and trade unions are underrepresented.
Furthermore, the sample is biased towards high-performing flagship manufacturing subsidiaries that
have been present and undergoing continuous expansion and upgrading for more than a decade
(more than half of the sample companies). These subsidiaries are among the largest ones of their
owners’ production facilities, which again, strongly influences their owners’ investment decisions.
Two other factors need to be acknowledged here. Firstly, the study captures a snapshot view of
the impact of digital technologies on the nature and routine intensity of shop-floor work, whilst
developments in this field are dynamically changing. This calls for longitudinal research and
extension of the scope of issues investigated. Secondly, the complexity and multifaceted character
of the topic also calls for further research to gain more evidence regarding the effects across distinct
types of technologies and adoption contexts.
Acknowledgement
Research support by the European Trade Union Institute is gratefully acknowledged.
Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or
publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or pub-
lication of this article: This study is supported by European Trade Union Institute.
ORCID iD
Andrea Szalavetz  https://orcid.org/0000-0001-7655-6994
Notes
1. Baldwin (2013) distinguished two types of countries according to the activities performed by local eco-
nomic actors. Accordingly, there are ‘headquarter economies’ and ‘factory economies’ in international
production networks. Economic actors in headquarter economies are specialised in headquarter-specific
activities: coordination and governance of the production network, business development and other high
value-added, intangible business functions and activities. Actors in factory economies ‘provide the labour’,
performing predominantly labour-intensive activities.
2. This paper uses digitalisation in a broad sense, referring to digital technologies transforming business
processes. In specific contexts, however, the term will simply refer to the automation of work processes.
3. The flipside of the coin is that apart from a few high-flying foreign-owned manufacturing companies, digital
technology adoption leaves a lot to be desired in Hungary. According to the data of the Digital Economy and
Society Index (DESI, 2020) Hungary scores the second lowest in terms of business digitisation in EU28,
trailed only by Bulgaria.
4. According to World Robotics data, the density of industrial robots in Hungary is lower than the European
average: 84 vs 114, in 2018. Industrial robots are concentrated in the automotive industry. Here, robot
density is 369 (per 10,000 employees), whereas the average in all other manufacturing sectors is 46.
5. The average number of employees was 2224 in 2019 (or the latest year available). The average turnover
amounted to EUR 912.6 million, and the average share of exports in total sales was 87.1% (one firm was
predominantly domestic market-oriented, with a share of exports of a mere 11%). These data, as well as the
data in the Appendix apply to single plants in Hungary or in two cases, consolidate the turnover and
employment of multiple plants in Hungary.
6. Instead of firing employees, the companies in the sample have rather relied on employee churn to improve
the average quality of the workforce.
7. In 2016, the majority of vocational training school graduates was employed in unskilled jobs, and only 43%
were employed in activities that required skilled labour (Köll} o, 2017). This is no surprise since the in-
struction time of Hungarian students participating in vocational education comprises far fewer classes
dedicated to general education subjects such as foreign languages, mathematics, sciences, or literature, than
that of their German peers (Hermann et al., 2020). Over and beyond the particularly serious problems in
terms of the availability and quality of the workforce, the local race-to-the-bottom type labour market
regulations and the weakness of trade unions (Artner, 2020) may also influence managers’ technology
choice and skill use approaches.
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Szalavetz 23
Appendix A
Basic data of the companies in the sample 2019 or the latest year available.
Turnover Share of
No. Employment (EUR million) exports (%) Industry Interviewee
1 358 18.2 11.0 Automotive Managing director

2 13,096 8561.1 99.5 Automotive Digital officer; operator performing a variety
of assembly tasks, rotated between several
different types of tasks
3 976 489.8 100.0 Electronics Manager responsible for corporate planning
and IT
4 808 59.3 99.8 Automotive Plant manager responsible for both strategy
and operations
5 1058 125.0 99.6 Machinery Managing director
6 1016 580.1 98.9 Automotive Chief information officer
7 1890 781.4 80.0 Automotive Head of industrial engineering; operator
working in assembly
8 581 89.4 92.4 Machinery Managing director of operations; operator
performing assembly and quality control
tasks
9 890 92.1 92.1 Electronics Business unit manager responsible for lean
development
10 1121 171.8 98.7 Machinery Process improvement leader
11 832 106.0 99.0 Automotive Industrial strategy manager
12 614 73.1 84.9 Machinery Director of operations responsible for
overall equipment effectiveness and quality
13 2807 716.3 76.1 Machinery Process engineering manager responsible for
the optimisation of shopfloor processes
Appendix B
Examples of digitalisation-induced changes in the nature of work.
Investments in digital technologies Changes in the nature of work No(s)
Industrial automation, industrial robots Increased process standardisation; workers 1–13

relieved from performing specific physical
tasks: Instead, employees become
responsible for monitoring the machines,
checking the quality of the output, and
correcting any problems that may arise
Collaborative robots Increased process standardisation, reduced 2, 5, 7, 9, 11*
skill requirements
Real-time OEE calculation through IoT Increased work intensity, reduced idle time, 2, 4, 5, 6, 7, 9,
solutions increased transparency for line and plant 12, 13*
managers
(continued)
(continued)
Visualisation of production status Increased transparency for line and plant 2–7, 9, 11, 13*
managers, feedback for frontline workers
Digital error-proof solutions Increased speed and efficiency of task 2, 4–9, 11, 13*
execution, increased work intensity,
reduced error ratio, reduced mental efforts,
reduced importance of experience and tacit
knowledge
Visualisation of assembly tasks Increased speed and efficiency of task Expert
execution, increased work intensity, interviews
reduced error ratio, reduced skill
requirements
Automation of in-plant logistics, AGVs Increased standardisation of processes, 2, 6, 7, 9, 11*
increased speed and efficiency of task
disappearance of specific work tasks
Digital solutions supporting quality control Increased speed and efficiency of task 1–13•
reduced error ratio, reduced importance of
experience and tacit knowledge, reduced
mental efforts, data-driven decision-making
Digital production monitoring, track and Increased transparency for line and plant 1–13•
trace solutions managers, reduced importance of
experience and tacit knowledge in certain
tasks, data-driven decision-making
Digital shift handover Reduction of time spent on reporting, reduced 2, 6, 7, 9, 11*
interaction-intensity of this task, increased
share of line managers’ working time
dedicated to managing people
Automation of production scheduling Increased speed of task execution, reduced 2, 7, 11*
interaction-intensity of this task, changes in
the related skill requirements, reduced
importance of experience and tacit
knowledge, new tasks and increased task
diversity for schedulers
MES deployment Increased speed and efficiency of task 2, 6, 7, 9, 11*
execution at all levels, reduced idle time,
increased transparency, data-driven
decision-making
Worker augmenting technologies (tablets, Increased speed and efficiency of task 2, 6, 7, 9, 11*
maintenance databases) execution, improved quality, reduced
importance of experience and tacit
knowledge compensated by multitasking
Digital interconnection of workstations and Increased speed and efficiency of task 2–13•
the warehousing, maintenance, or execution, improved quality, support
engineering staff arrives earlier, reduced interaction-
intensity of work
(continued)
Szalavetz 25
(continued)
Support to employee training through digital Increased speed and efficiency of novices’ 2, 4, 9*
solutions learning the necessary tasks, increased ratio
of ‘do it right the first time’
Digital interconnection of some interrelated Increased process standardisation, reduced 2–13•
support processes, such as procurement, communication and interaction, increased
order management, controlling transparency, improved quality and
efficiency of task execution, fewer errors
AGV = automated guided vehicles; IoT = Internet of Things; MES = manufacturing execution system; OEE = overall
equipment effectiveness; No(s) = Companies mentioning this type of change; * = may not be exhaustive; • = the breadth,
maturity, and sophistication of the given solutions are highly heterogeneous across the listed companies.

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Competition & Change

within-occupation developments (increased repetitiveness and standardisation) have more than

Operations research and industrial sociology on the impact of digitalisation on

Decision-making for production planners and schedulers is supported by smart algorithms

However, as demonstrated persuasively in Krzywdzinski (2017) managerial liberty in shaping

decision-making authority to lower-level functions as possible. In contrast, managers may favour

Table 1. Research variables.

Topic Keywords mentioned during the interviews

investment(s) accompanying digital technology implementation that were deemed necessary to

The impact of digital technologies on workload, measurement and standardisation

Changes in skill requirements and importance of experience and tacit knowledge

A digitalisation-induced transformation of work involving increased mental demand is exempliﬁed

Conditions moderating the outcome of digitalisation for work

Discussion and conclusions

Declaration of conﬂicting interests

1 358 18.2 11.0 Automotive Managing director

Industrial automation, industrial robots Increased process standardisation; workers 1–13

Investments in digital technologies Changes in the nature of work No(s)

Investments in digital technologies Changes in the nature of work No(s)

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